From 7128ed36fd8188e4c4ca09d9bbe99a87e174506b Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 22:20:33 +0900
Subject: [PATCH 01/23] glm/cuda: tell each projection's stored format from its
MIXED_PRECISION recipe, and decode csf_32x2_v1 expert scales
nvidia/GLM-5.3-Flash-FP4-Local stores each module in the format its MIXED_PRECISION recipe names. cuda_formats reads a
projection's format from its module's recipe (core/modelopt's per-module table) checked against its tensors' dtypes and
shapes, and names a mismatch. The loader checks each projection's stored format before reading it as bf16 or NVFP4 and
refuses another by name. The entry admits MIXED_PRECISION in the recipes this engine reads and refuses another by its
module, so FP4-Local reaches the loader's named refusal instead of the entry's. Routed experts whose block scales are
csf_32x2_v1 decode on the host (core/nvfp4_csf32) into the e4m3 scales the packers take, each checked against its recipe
first.
---
zig/src/families/glm/cuda_engine.zig | 4 +-
zig/src/families/glm/cuda_formats.zig | 209 ++++++++++++++++++++++++++
zig/src/families/glm/cuda_native.zig | 5 +
zig/src/families/glm/cuda_weights.zig | 66 +++++++-
zig/tests/cuda/glm_synth.zig | 2 +-
5 files changed, 282 insertions(+), 4 deletions(-)
create mode 100644 zig/src/families/glm/cuda_formats.zig
diff --git a/zig/src/families/glm/cuda_engine.zig b/zig/src/families/glm/cuda_engine.zig
index 3e9da65961..919f0f54c4 100644
--- a/zig/src/families/glm/cuda_engine.zig
+++ b/zig/src/families/glm/cuda_engine.zig
@@ -103,8 +103,8 @@ pub const Engine = struct {
const cfg_path = try std.fs.path.join(a, &.{ checkpoint, "config.json" });
const text = try std.Io.Dir.cwd().readFileAlloc(io, cfg_path, a, .limited(1 << 24));
var why: modelopt.Why = .{};
- const admitted = if (exl3.isExl3(gpa, text)) exl3.admit(gpa, text, &why) else modelopt.admit(gpa, text, &why);
- admitted catch |err| { // anything but ModelOpt NVFP4 or EXL3 (4-bit mcg routed experts) is refused before loading
+ const admitted = if (exl3.isExl3(gpa, text)) exl3.admit(gpa, text, &why) else @import("cuda_formats.zig").admit(gpa, text, &why);
+ admitted catch |err| { // anything but NVFP4, MIXED_PRECISION in recipes read here or EXL3 is refused by name before loading
std.log.err("{s}: {s}", .{ cfg_path, why.text() });
return err;
};
diff --git a/zig/src/families/glm/cuda_formats.zig b/zig/src/families/glm/cuda_formats.zig
new file mode 100644
index 0000000000..0ddff2188b
--- /dev/null
+++ b/zig/src/families/glm/cuda_formats.zig
@@ -0,0 +1,209 @@
+//! Which stored format a GLM CUDA projection is read in (its recipe, checked against its tensors); csf scales decoded.
+
+const std = @import("std");
+const core = @import("core");
+
+const Meta = core.nvfp4.Meta;
+const Recipe = core.modelopt.Recipe;
+
+/// A projection's stored format, as the loader reads it.
+pub const Format = enum { bf16, nvfp4, nvfp4_csf, mxfp8, fp8_pb };
+
+/// A projection's tensors as stored: its weight, its scale when it has one, and whether csf_32x2_v1 parts replace it.
+pub const Stored = struct {
+ weight: Meta,
+ scale: ?Meta = null,
+ csf: bool = false,
+};
+
+/// How a checkpoint names its formats: one NVFP4 for every quantized linear, or MIXED_PRECISION's recipe per module.
+pub const Declared = union(enum) {
+ nvfp4,
+ mixed: ?Recipe,
+};
+
+/// The format of `stored`, or error.FormatMismatch when its tensors are not what `declared` says they are.
+pub fn of(declared: Declared, stored: Stored) error{FormatMismatch}!Format {
+ const f = try fromTensors(stored);
+ const ok = switch (declared) {
+ .nvfp4 => f == .bf16 or f == .nvfp4,
+ .mixed => |r| if (r) |recipe| switch (recipe) {
+ .nvfp4, .w4a16_nvfp4 => f == .nvfp4,
+ .nvfp4_csf => f == .nvfp4_csf,
+ .mxfp8 => f == .mxfp8,
+ .fp8_pb_wo => f == .fp8_pb,
+ } else f == .bf16,
+ };
+ return if (ok) f else error.FormatMismatch;
+}
+
+fn fromTensors(s: Stored) error{FormatMismatch}!Format {
+ const w = s.weight;
+ if (w.shape.len != 2) return error.FormatMismatch;
+ const n = w.shape[0];
+ switch (w.dtype) {
+ .bf16 => return if (s.scale == null and !s.csf) .bf16 else error.FormatMismatch,
+ .u8 => {
+ if (s.csf) return if (s.scale == null) .nvfp4_csf else error.FormatMismatch;
+ const sc = s.scale orelse return error.FormatMismatch;
+ const k = 2 * w.shape[1];
+ const fits = sc.dtype == .f8_e4m3 and sc.shape.len == 2 and sc.shape[0] == n and sc.shape[1] * core.nvfp4.group == k;
+ return if (fits) .nvfp4 else error.FormatMismatch;
+ },
+ .f8_e4m3 => {
+ const sc = s.scale orelse return error.FormatMismatch;
+ const k = w.shape[1];
+ if (sc.dtype == .u8) return if (k % 32 == 0 and sc.shape.len == 2 and sc.shape[0] == n and sc.shape[1] == k / 32) .mxfp8 else error.FormatMismatch;
+ if (sc.dtype != .f32) return error.FormatMismatch;
+ const rb = (n + 127) / 128;
+ const cb = (k + 127) / 128;
+ const blocks = switch (sc.shape.len) {
+ 2 => sc.shape[0] == rb and sc.shape[1] == cb,
+ 4 => sc.shape[0] == rb and sc.shape[1] == 1 and sc.shape[2] == cb and sc.shape[3] == 1,
+ else => false,
+ };
+ return if (blocks) .fp8_pb else error.FormatMismatch;
+ },
+ else => return error.FormatMismatch,
+ }
+}
+
+/// `module`'s NVFP4 linear whose block scales are csf_32x2_v1 parts, decoded into `scales` ([n, k/16] e4m3).
+pub fn csfLinear(ck: *core.Checkpoint, module: []const u8, scales: []u8) !core.nvfp4.Linear {
+ var buf: [256]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
+ if (w.dtype != .u8 or w.rank != 2) return error.BadNvfp4;
+ const n = w.shape[0];
+ const k = 2 * w.shape[1];
+ if (k % core.nvfp4.group != 0 or scales.len != n * k / core.nvfp4.group) return error.BadNvfp4;
+ var p: core.nvfp4_csf32.Parts = undefined;
+ inline for (.{ "bases", "codes", "page_bases", "offsets", "exceptions" }, .{ .u8, .u8, .u32, .u16, .u16 }) |part, dtype| {
+ const t = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_" ++ part, .{module}));
+ if (t.dtype != dtype or t.rank != 1) return error.BadNvfp4;
+ @field(p, part) = t.bytes;
+ }
+ try core.nvfp4_csf32.decode(p, n, k / core.nvfp4.group, scales);
+ const g = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale_2", .{module}));
+ if (g.dtype != .f32 or g.numel() != 1) return error.BadNvfp4;
+ _ = try ck.get(try std.fmt.bufPrint(&buf, "{s}.input_scale", .{module})); // the W4A4 math's, unread by W4A16
+ return .{ .n = n, .k = k, .codes = w.bytes, .scales = scales, .global = std.mem.bytesToValue(f32, g.bytes[0..4]) };
+}
+
+/// The refusal's text: the module, what the checkpoint declares and what its weight holds.
+pub fn describe(buf: []u8, module: []const u8, declared: Declared, stored: Stored) []const u8 {
+ const said = switch (declared) {
+ .nvfp4 => "NVFP4",
+ .mixed => |r| if (r) |recipe| @tagName(recipe) else "no recipe (bf16)",
+ };
+ return std.fmt.bufPrint(buf, "{s}: declared {s}, stored {s} {any}", .{ module, said, @tagName(stored.weight.dtype), stored.weight.shape }) catch buf;
+}
+
+/// The entry's check (modelopt.admitRecipes), its table dropped: weights.load reads it again beside the tensors.
+pub fn admit(gpa: std.mem.Allocator, config_json: []const u8, why: *core.modelopt.Why) !void {
+ var recipes = try core.modelopt.admitRecipes(gpa, config_json, why);
+ recipes.deinit();
+}
+
+const testing = std.testing;
+
+fn meta(dtype: core.checkpoint.DType, shape: []const usize) Meta {
+ return .{ .dtype = dtype, .shape = shape };
+}
+
+test "a recipe-less module of a MIXED_PRECISION checkpoint is bf16, and a plain NVFP4 one may be either" {
+ const w = meta(.bf16, &.{ 8192, 4096 });
+ try testing.expectEqual(Format.bf16, try of(.{ .mixed = null }, .{ .weight = w }));
+ try testing.expectEqual(Format.bf16, try of(.nvfp4, .{ .weight = w }));
+ const q = Stored{ .weight = meta(.u8, &.{ 2048, 2048 }), .scale = meta(.f8_e4m3, &.{ 2048, 256 }) };
+ try testing.expectEqual(Format.nvfp4, try of(.nvfp4, q));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = null }, q));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = null }, .{ .weight = meta(.f8_e4m3, &.{ 64, 64 }), .scale = meta(.u8, &.{ 64, 2 }) }));
+}
+
+test "nvidia/GLM-5.3-Flash-FP4-Local's projections, by their recipes and their shapes" {
+ const kda_q = Stored{ .weight = meta(.f8_e4m3, &.{ 8192, 4096 }), .scale = meta(.u8, &.{ 8192, 128 }) };
+ try testing.expectEqual(Format.mxfp8, try of(.{ .mixed = .mxfp8 }, kda_q));
+ const kv_a = Stored{ .weight = meta(.f8_e4m3, &.{ 512, 4096 }), .scale = meta(.f32, &.{ 4, 1, 32, 1 }) };
+ try testing.expectEqual(Format.fp8_pb, try of(.{ .mixed = .fp8_pb_wo }, kv_a));
+ const mla_o = Stored{ .weight = meta(.f8_e4m3, &.{ 4096, 16384 }), .scale = meta(.f32, &.{ 32, 1, 128, 1 }) };
+ try testing.expectEqual(Format.fp8_pb, try of(.{ .mixed = .fp8_pb_wo }, mla_o));
+ const expert = Stored{ .weight = meta(.u8, &.{ 2048, 2048 }), .csf = true };
+ try testing.expectEqual(Format.nvfp4_csf, try of(.{ .mixed = .nvfp4_csf }, expert));
+ const mtp_expert = Stored{ .weight = meta(.u8, &.{ 4096, 1024 }), .scale = meta(.f8_e4m3, &.{ 4096, 128 }) };
+ try testing.expectEqual(Format.nvfp4, try of(.{ .mixed = .w4a16_nvfp4 }, mtp_expert));
+ const kv_b = Stored{ .weight = meta(.bf16, &.{ 32768, 512 }) };
+ try testing.expectEqual(Format.bf16, try of(.{ .mixed = null }, kv_b));
+}
+
+test "a recipe the tensors do not hold is refused, and the refusal names the module" {
+ const bf = Stored{ .weight = meta(.bf16, &.{ 8192, 4096 }) };
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .mxfp8 }, bf));
+ const pb = Stored{ .weight = meta(.f8_e4m3, &.{ 512, 4096 }), .scale = meta(.f32, &.{ 4, 1, 32, 1 }) };
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .mxfp8 }, pb));
+ try testing.expectError(error.FormatMismatch, of(.nvfp4, pb));
+ var buf: [160]u8 = undefined;
+ const why = describe(&buf, "model.language_model.layers.0.self_attn.q_proj", .{ .mixed = .mxfp8 }, bf);
+ try testing.expect(std.mem.startsWith(u8, why, "model.language_model.layers.0.self_attn.q_proj: declared mxfp8, stored bf16"));
+}
+
+test "scales of the wrong shape or kind are refused" {
+ const wide = meta(.f8_e4m3, &.{ 64, 96 });
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .mxfp8 }, .{ .weight = wide, .scale = meta(.u8, &.{ 64, 2 }) }));
+ try testing.expectEqual(Format.mxfp8, try of(.{ .mixed = .mxfp8 }, .{ .weight = wide, .scale = meta(.u8, &.{ 64, 3 }) }));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .mxfp8 }, .{ .weight = meta(.f8_e4m3, &.{ 64, 80 }), .scale = meta(.u8, &.{ 64, 2 }) }));
+ const partial = meta(.f8_e4m3, &.{ 130, 260 });
+ try testing.expectEqual(Format.fp8_pb, try of(.{ .mixed = .fp8_pb_wo }, .{ .weight = partial, .scale = meta(.f32, &.{ 2, 3 }) }));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .fp8_pb_wo }, .{ .weight = partial, .scale = meta(.f32, &.{ 1, 1, 3, 1 }) }));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .fp8_pb_wo }, .{ .weight = partial, .scale = meta(.bf16, &.{ 2, 3 }) }));
+ try testing.expectError(error.FormatMismatch, of(.nvfp4, .{ .weight = meta(.u8, &.{ 64, 32 }), .scale = meta(.f8_e4m3, &.{ 64, 2 }) }));
+ try testing.expectError(error.FormatMismatch, of(.{ .mixed = .nvfp4_csf }, .{ .weight = meta(.u8, &.{ 64, 32 }), .scale = meta(.f8_e4m3, &.{ 64, 4 }), .csf = true }));
+}
+
+test "csf_32x2_v1 scales read as the same NVFP4 linear as the scales stored plain, every part used" {
+ const gpa = testing.allocator;
+ const n = 64;
+ const k = 96; // 6 scale columns: three blocks wide
+ var prng: std.Random.DefaultPrng = .init(5);
+ const rng = prng.random();
+ var codes: [n * k / 2]u8 = undefined;
+ rng.bytes(&codes);
+ var scale: [n * k / 16]u8 = undefined;
+ for (&scale, 0..) |*s, j| s.* = if (j % 37 == 0) 0x7e else 0x30 + rng.uintLessThan(u8, 16); // a band and exceptions
+ const enc = try core.nvfp4_csf32.encode(gpa, &scale, n, k / 16);
+ defer enc.deinit(gpa);
+ const one = std.mem.toBytes(@as(f32, 0.25));
+ const lens = [_]usize{ enc.bases.len, enc.codes.len, enc.page_bases.len / 4, enc.offsets.len / 2, enc.exceptions.len / 2 };
+ const tensors = [_]core.shard_edit.New{
+ .{ .name = "a.weight", .dtype = .u8, .shape = &.{ n, k / 2 }, .bytes = &codes },
+ .{ .name = "a.weight_scale", .dtype = .f8_e4m3, .shape = &.{ n, k / 16 }, .bytes = &scale },
+ .{ .name = "a.weight_scale_2", .dtype = .f32, .shape = &.{}, .bytes = &one },
+ .{ .name = "a.input_scale", .dtype = .f32, .shape = &.{}, .bytes = &one },
+ .{ .name = "b.weight", .dtype = .u8, .shape = &.{ n, k / 2 }, .bytes = &codes },
+ .{ .name = "b.weight_scale.csf32_bases", .dtype = .u8, .shape = lens[0..1], .bytes = enc.bases },
+ .{ .name = "b.weight_scale.csf32_codes", .dtype = .u8, .shape = lens[1..2], .bytes = enc.codes },
+ .{ .name = "b.weight_scale.csf32_page_bases", .dtype = .u32, .shape = lens[2..3], .bytes = enc.page_bases },
+ .{ .name = "b.weight_scale.csf32_offsets", .dtype = .u16, .shape = lens[3..4], .bytes = enc.offsets },
+ .{ .name = "b.weight_scale.csf32_exceptions", .dtype = .u16, .shape = lens[4..5], .bytes = enc.exceptions },
+ .{ .name = "b.weight_scale_2", .dtype = .f32, .shape = &.{}, .bytes = &one },
+ .{ .name = "b.input_scale", .dtype = .f32, .shape = &.{}, .bytes = &one },
+ };
+ var tmp = testing.tmpDir(.{});
+ defer tmp.cleanup();
+ var dir: [64]u8 = undefined;
+ const at = try std.fmt.bufPrint(&dir, ".zig-cache/tmp/{s}", .{tmp.sub_path});
+ var path: [96]u8 = undefined;
+ try core.shard_edit.create(gpa, try std.fmt.bufPrint(&path, "{s}/model.safetensors", .{at}), &.{}, &tensors);
+ var ck = try core.Checkpoint.openModel(gpa, testing.io, at);
+ defer ck.close();
+ const want = try core.nvfp4.Linear.read(&ck, "a");
+ var decoded: [n * k / 16]u8 = undefined;
+ const got = try csfLinear(&ck, "b", &decoded);
+ try testing.expectEqual(want.n, got.n);
+ try testing.expectEqual(want.k, got.k);
+ try testing.expectEqual(want.global, got.global);
+ try testing.expectEqualSlices(u8, want.codes, got.codes);
+ try testing.expectEqualSlices(u8, want.scales, got.scales);
+ try testing.expect(enc.exceptions.len > 0);
+ try testing.expectEqual(@as(usize, 0), ck.unused());
+ try testing.expectError(error.BadNvfp4, csfLinear(&ck, "b", decoded[1..]));
+}
diff --git a/zig/src/families/glm/cuda_native.zig b/zig/src/families/glm/cuda_native.zig
index c55edd63c0..b7c46c17c2 100644
--- a/zig/src/families/glm/cuda_native.zig
+++ b/zig/src/families/glm/cuda_native.zig
@@ -320,6 +320,7 @@ pub fn explain(_: ?*anyopaque, err: anyerror) ?[]const u8 {
.{ ranks.max_world, ranks.max_world },
),
error.NotModelOpt, error.UnsupportedQuant => "GLM-5.3-Flash on CUDA reads ModelOpt NVFP4 checkpoints (the log names what config.json has)",
+ error.UnreadableFormat => "a projection is stored in a format GLM-5.3-Flash on CUDA does not read (the log names its module, recipe and tensors)",
else => null,
};
}
@@ -331,6 +332,10 @@ test "a place inside its world of at most 16 ranks serves; a larger world is ref
try std.testing.expect(explain(null, error.TooManyRanks) != null);
}
+test "a checkpoint refused for its format, at the entry or by the loader, is explained" {
+ for ([_]anyerror{ error.NotModelOpt, error.UnsupportedQuant, error.UnreadableFormat }) |e| try std.testing.expect(explain(null, e) != null);
+}
+
test "capacity keeps a verify window past the context and a multiple of 64" {
try std.testing.expectEqual(@as(usize, 8256), capacity(8192));
try std.testing.expectEqual(@as(usize, 330560), capacity(330529));
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index 0ab39ac1d7..3eee550540 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -14,6 +14,7 @@ const quant = @import("cuda_quant.zig");
const q4 = @import("cuda_q4.zig");
const exl3 = @import("cuda_exl3.zig");
const mm_mod = @import("cuda_mm.zig");
+const formats = @import("cuda_formats.zig");
const Tensor = core.checkpoint.Tensor;
const prefix = "model.language_model.";
@@ -81,12 +82,49 @@ const Loader = struct {
ck: *core.Checkpoint,
host: std.ArrayList(u8) = .empty,
name_buf: [192]u8 = undefined,
+ recipes: ?*const core.modelopt.Recipes = null, // MIXED_PRECISION's table; null: the tensors decide (NVFP4)
pub fn get(L: *Loader, comptime fmt: []const u8, args: anytype) !Tensor {
const name = try std.fmt.bufPrint(&L.name_buf, prefix ++ fmt, args);
return L.ck.get(name);
}
+ /// The projection `fmt` (a module under the prefix) is stored as `want`, or it is refused by name (formats.of).
+ fn format(L: *Loader, comptime fmt: []const u8, args: anytype, want: formats.Format) !void {
+ var buf: [192]u8 = undefined;
+ return L.formatIn(L.ck, try std.fmt.bufPrint(&buf, prefix ++ fmt, args), want);
+ }
+
+ fn formatIn(L: *Loader, ck: *core.Checkpoint, module: []const u8, want: formats.Format) !void {
+ var buf: [224]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
+ const scale_name = try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module});
+ const s: ?Tensor = if (ck.has(scale_name)) try ck.get(scale_name) else null;
+ const stored: formats.Stored = .{
+ .weight = .{ .dtype = w.dtype, .shape = w.shape[0..w.rank] },
+ .scale = if (s) |*t| .{ .dtype = t.dtype, .shape = t.shape[0..t.rank] } else null,
+ .csf = ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module})),
+ };
+ const declared: formats.Declared = if (L.recipes) |r| .{ .mixed = r.of(module) } else .nvfp4;
+ const got: ?formats.Format = formats.of(declared, stored) catch null;
+ if (got != null and got.? == want) return;
+ var why: [320]u8 = undefined;
+ std.log.err("{s}; this loader reads it as {t}", .{ formats.describe(&why, module, declared, stored), want });
+ return error.UnreadableFormat;
+ }
+
+ /// A routed expert's NVFP4 linear: scales as stored, or csf_32x2_v1's decoded into `scales`; else refused by name.
+ fn expert(L: *Loader, module: []const u8, scales: []u8) !core.nvfp4.Linear {
+ var buf: [224]u8 = undefined;
+ const csf = L.ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module}));
+ try L.formatIn(L.ck, module, if (csf) .nvfp4_csf else .nvfp4);
+ if (!csf) return core.nvfp4.Linear.read(L.ck, module);
+ return formats.csfLinear(L.ck, module, scales) catch |e| {
+ std.log.err("{s}: its csf_32x2_v1 scales do not read as docs/csf-scale-blocks.md has them ({t})", .{ module, e });
+ return e;
+ };
+ }
+
/// The host bytes gathered so far uploaded as one tensor named `fmt`.
pub fn put(L: *Loader, comptime fmt: []const u8, args: anytype) !u64 {
const b = try cuda.DeviceBuffer.fromHost(L.d, L.host.items);
@@ -149,6 +187,7 @@ const Loader = struct {
const h = L.w.share.kda;
const dk = c.kda_dim;
const all = c.kda_heads * dk;
+ inline for (.{ "q", "k", "v", "o" }) |x| try L.format("layers.{d}.self_attn." ++ x ++ "_proj", .{i}, .bf16);
inline for (.{ "q", "k", "v" }) |x| try L.rows(try L.get("layers.{d}.self_attn." ++ x ++ "_proj.weight", .{i}), h.lo * dk, h.hi * dk, all, .bf16);
inline for (.{ "f_a", "g_a" }) |x| try L.whole(try L.get("layers.{d}.self_attn." ++ x ++ "_proj.weight", .{i}), .bf16);
try L.rows(try L.get("layers.{d}.self_attn.b_proj.weight", .{i}), h.lo, h.hi, c.kda_heads, .bf16);
@@ -175,6 +214,7 @@ const Loader = struct {
const c = &L.w.config;
const h = L.w.share.mla;
const qk = c.nope;
+ inline for (.{ "q_a_proj", "kv_a_proj_with_mqa", "q_b_proj", "kv_b_proj", "o_proj" }) |x| try L.format("layers.{d}.self_attn." ++ x, .{i}, .bf16);
inline for (.{ "q_a_proj", "kv_a_proj_with_mqa" }) |x| try L.whole(try L.get("layers.{d}.self_attn." ++ x ++ ".weight", .{i}), .bf16);
const proj = try L.put("layers.{d}.dsa.proj", .{i});
try L.whole(try L.get("layers.{d}.self_attn.q_a_layernorm.weight", .{i}), .bf16);
@@ -261,6 +301,8 @@ const Loader = struct {
const c = &L.w.config;
const s = L.w.share.dense;
const d = c.hidden;
+ const want: formats.Format = if (c.exl3) .bf16 else .nvfp4;
+ inline for (.{ "gate_proj", "up_proj", "down_proj" }) |x| try L.format("layers.{d}.mlp." ++ x, .{i}, want);
const w: mlp.Weights = if (c.exl3) bf16: { // an EXL3 checkpoint keeps its dense layers bf16
inline for (.{ "gate_proj", "up_proj" }) |x| try L.rows(try L.get("layers.{d}.mlp." ++ x ++ ".weight", .{i}), s.lo, s.hi, c.dense_inter, .bf16);
const gu = try L.put("layers.{d}.mlp.gu", .{i});
@@ -362,6 +404,7 @@ const Loader = struct {
if (!x3.?.shx) L.w.exl3_shx = false;
} else routed = try L.nvfp4Experts(i);
const sh = L.w.share.shared;
+ inline for (.{ "gate_proj", "up_proj", "down_proj" }) |x| try L.format("layers.{d}.mlp.shared_experts." ++ x, .{i}, .bf16);
inline for (.{ "gate_proj", "up_proj" }) |x| try L.rows(try L.get("layers.{d}.mlp.shared_experts." ++ x ++ ".weight", .{i}), sh.lo, sh.hi, c.shared_inter, .bf16);
const gu = try L.put("layers.{d}.moe.shared.gu", .{i});
try L.cols(try L.get("layers.{d}.mlp.shared_experts.down_proj.weight", .{i}), sh.lo, sh.hi, .bf16);
@@ -402,11 +445,14 @@ const Loader = struct {
defer L.gpa.free(codes);
const scales = try L.gpa.alloc(u8, d * ni / 16);
defer L.gpa.free(scales);
+ const per = c.moe_inter * d / 16; // a whole gate, up or down's e4m3 scales
+ const decoded = try L.gpa.alloc(u8, 3 * per); // csf_32x2_v1's, decoded before the rank's cut
+ defer L.gpa.free(decoded);
if (i == c.layers) try L.quantizedExperts(i, up, down, up_scale, down_scale) else for (0..count) |e| {
var ls: [3]core.nvfp4.Linear = undefined;
inline for (.{ "gate_proj", "up_proj", "down_proj" }, 0..) |proj, m| {
const name = try std.fmt.bufPrint(&L.name_buf, prefix ++ "layers.{d}.mlp.experts.{d}." ++ proj, .{ i, e });
- ls[m] = try core.nvfp4.Linear.read(L.ck, name);
+ ls[m] = try L.expert(name, decoded[m * per ..][0..per]);
}
// gate and up: the rank's rows; down: every row, the rank's columns
const gate = .{ ls[0].codes[cut.lo * d / 2 ..][0 .. ni * d / 2], ls[0].scales[cut.lo * d / 16 ..][0 .. ni * d / 16] };
@@ -503,6 +549,9 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
defer head_ck.close();
var L: Loader = .{ .gpa = gpa, .d = d, .stream = stream, .pack_dense = tools.pack_dense, .tools = tools, .w = &w, .ck = &ck };
defer L.host.deinit(gpa);
+ var recipes = try readRecipes(gpa, io, dir);
+ defer if (recipes) |*r| r.deinit();
+ if (recipes) |*r| L.recipes = if (r.modules.count() > 0) r else null;
w.layers = try gpa.alloc(Layer, layers.len);
for (layers, w.layers) |i, *l| {
if (i >= c.layers) return error.BadLayer;
@@ -513,15 +562,30 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
w.embed = try L.put("embed", .{});
try L.whole(try L.get("norm.weight", .{}), .bf16);
w.norm = try L.put("norm", .{});
+ try L.formatIn(&head_ck, "lm_head", .bf16);
try L.rows(try head_ck.get("lm_head.weight"), share.vocab.lo, share.vocab.hi, c.vocab, .bf16);
w.head = try L.put("head", .{});
if (with_mtp and c.mtp > 0) w.mtp = try L.mtp();
return w;
}
+/// config.json's MIXED_PRECISION recipes, or null when it declares none this reader takes (the entry refuses those).
+fn readRecipes(gpa: std.mem.Allocator, io: std.Io, dir: []const u8) !?core.modelopt.Recipes {
+ const path = try std.fs.path.join(gpa, &.{ dir, "config.json" });
+ defer gpa.free(path);
+ const text = std.Io.Dir.cwd().readFileAlloc(io, path, gpa, .limited(1 << 24)) catch return null;
+ defer gpa.free(text);
+ var why: core.modelopt.Why = .{};
+ return core.modelopt.admitRecipes(gpa, text, &why) catch null;
+}
+
test "bf16 rounding of fp32 is to nearest even" {
try std.testing.expectEqual(@as(u16, 0x3f80), toBf16(1.0));
try std.testing.expectEqual(@as(u16, 0x3f80), toBf16(@bitCast(@as(u32, 0x3f808000)))); // a tie to even (down)
try std.testing.expectEqual(@as(u16, 0x3f82), toBf16(@bitCast(@as(u32, 0x3f818000)))); // a tie to even (up)
try std.testing.expectEqual(@as(u16, 0x3f81), toBf16(@bitCast(@as(u32, 0x3f808001))));
}
+
+test {
+ _ = @import("cuda_formats.zig");
+}
diff --git a/zig/tests/cuda/glm_synth.zig b/zig/tests/cuda/glm_synth.zig
index 2f38f1fbd3..3470e31de4 100644
--- a/zig/tests/cuda/glm_synth.zig
+++ b/zig/tests/cuda/glm_synth.zig
@@ -11,7 +11,7 @@ const layers = 2;
const experts = 16;
const vocab = 8192;
const hidden = 4096;
-/// The ModelOpt block nvidia/GLM-5.3-Flash-NVFP4 writes, which the CUDA family admits (modelopt.admit).
+/// The ModelOpt block nvidia/GLM-5.3-Flash-NVFP4 writes, which the CUDA family admits (modelopt.admitRecipes).
const modelopt_block = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"NVFP4\", " ++
"\"config_groups\": {\"group_0\": {\"weights\": {\"num_bits\": 4, \"type\": \"float\", \"group_size\": 16}}}}";
const P = "model.language_model.";
From ec922db43912b347589cf0b7cbb8b478656f7cba Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 22:21:07 +0900
Subject: [PATCH 02/23] glm/cuda: read the MTP layer's W4A16_NVFP4 experts as
stored; glm-loader-gate checks every rank's uploads on the host
tf-cuda-test glm-loader-gate compares every rank's uploads at TP 1, 2 and 3 with the checkpoint cut again on the host,
byte for byte, and names the packed ones it does not compare yet; TF_GATE_TAMPER flips one bit of the upload it names
after each download, so a run shows the gate refusing that upload by name. The MTP layer's routed experts stored as
W4A16_NVFP4 (FP4-Local's) are read as stored, without an input_scale; bf16 ones are still quantized on the GPU, each
checked against its recipe, and the gate compares the stored scales. A routed expert's NVFP4 read and config.json's
recipes move to cuda_formats, so cuda_weights.zig stays within the lean rule's 600 lines.
---
zig/src/families/glm/cuda_formats.zig | 89 +++++++-
zig/src/families/glm/cuda_weights.zig | 36 +---
zig/tests/cuda/glm_loader_gate.zig | 300 ++++++++++++++++++++++++++
zig/tests/cuda/main.zig | 3 +
4 files changed, 401 insertions(+), 27 deletions(-)
create mode 100644 zig/tests/cuda/glm_loader_gate.zig
diff --git a/zig/src/families/glm/cuda_formats.zig b/zig/src/families/glm/cuda_formats.zig
index 0ddff2188b..7e3172862a 100644
--- a/zig/src/families/glm/cuda_formats.zig
+++ b/zig/src/families/glm/cuda_formats.zig
@@ -89,6 +89,26 @@ pub fn csfLinear(ck: *core.Checkpoint, module: []const u8, scales: []u8) !core.n
return .{ .n = n, .k = k, .codes = w.bytes, .scales = scales, .global = std.mem.bytesToValue(f32, g.bytes[0..4]) };
}
+/// `module`'s NVFP4 linear, scales stored plain; only W4A16_NVFP4 may lack the input_scale (FP4-Local's MTP experts).
+pub fn plainLinear(ck: *core.Checkpoint, module: []const u8, declared: Declared) !core.nvfp4.Linear {
+ var buf: [256]u8 = undefined;
+ if (ck.has(try std.fmt.bufPrint(&buf, "{s}.input_scale", .{module}))) return core.nvfp4.Linear.read(ck, module);
+ const w4a16 = switch (declared) {
+ .nvfp4 => false,
+ .mixed => |r| if (r) |recipe| recipe == .w4a16_nvfp4 else false,
+ };
+ if (!w4a16) return error.NoInputScale;
+ const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
+ const s = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module}));
+ const g = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale_2", .{module}));
+ const d = try core.nvfp4.dims(metaOf(&w), metaOf(&s), metaOf(&g), null);
+ return .{ .n = d.n, .k = d.k, .codes = w.bytes, .scales = s.bytes, .global = std.mem.bytesToValue(f32, g.bytes[0..4]) };
+}
+
+fn metaOf(t: *const core.checkpoint.Tensor) Meta {
+ return .{ .dtype = t.dtype, .shape = t.shape[0..t.rank] };
+}
+
/// The refusal's text: the module, what the checkpoint declares and what its weight holds.
pub fn describe(buf: []u8, module: []const u8, declared: Declared, stored: Stored) []const u8 {
const said = switch (declared) {
@@ -104,6 +124,34 @@ pub fn admit(gpa: std.mem.Allocator, config_json: []const u8, why: *core.modelop
recipes.deinit();
}
+/// A routed expert's NVFP4 linear: scales as stored, or csf_32x2_v1's decoded into `scales`; else refused by name.
+pub fn expert(L: anytype, module: []const u8, scales: []u8) !core.nvfp4.Linear {
+ var buf: [224]u8 = undefined;
+ const csf = L.ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module}));
+ try L.formatIn(L.ck, module, if (csf) .nvfp4_csf else .nvfp4);
+ if (!csf) return plainLinear(L.ck, module, L.declaredAs(module)) catch |e| switch (e) {
+ error.NoInputScale => {
+ std.log.err("{s}: declared NVFP4 (W4A4) but stores no input_scale", .{module});
+ return error.UnreadableFormat;
+ },
+ else => return e,
+ };
+ return csfLinear(L.ck, module, scales) catch |e| {
+ std.log.err("{s}: its csf_32x2_v1 scales do not read as docs/csf-scale-blocks.md has them ({t})", .{ module, e });
+ return e;
+ };
+}
+
+/// config.json's MIXED_PRECISION recipes, or null when it declares none this reader takes (the entry refuses those).
+pub fn readRecipes(gpa: std.mem.Allocator, io: std.Io, dir: []const u8) !?core.modelopt.Recipes {
+ const path = try std.fs.path.join(gpa, &.{ dir, "config.json" });
+ defer gpa.free(path);
+ const text = std.Io.Dir.cwd().readFileAlloc(io, path, gpa, .limited(1 << 24)) catch return null;
+ defer gpa.free(text);
+ var why: core.modelopt.Why = .{};
+ return core.modelopt.admitRecipes(gpa, text, &why) catch null;
+}
+
const testing = std.testing;
fn meta(dtype: core.checkpoint.DType, shape: []const usize) Meta {
@@ -127,8 +175,8 @@ test "nvidia/GLM-5.3-Flash-FP4-Local's projections, by their recipes and their s
try testing.expectEqual(Format.fp8_pb, try of(.{ .mixed = .fp8_pb_wo }, kv_a));
const mla_o = Stored{ .weight = meta(.f8_e4m3, &.{ 4096, 16384 }), .scale = meta(.f32, &.{ 32, 1, 128, 1 }) };
try testing.expectEqual(Format.fp8_pb, try of(.{ .mixed = .fp8_pb_wo }, mla_o));
- const expert = Stored{ .weight = meta(.u8, &.{ 2048, 2048 }), .csf = true };
- try testing.expectEqual(Format.nvfp4_csf, try of(.{ .mixed = .nvfp4_csf }, expert));
+ const routed = Stored{ .weight = meta(.u8, &.{ 2048, 2048 }), .csf = true };
+ try testing.expectEqual(Format.nvfp4_csf, try of(.{ .mixed = .nvfp4_csf }, routed));
const mtp_expert = Stored{ .weight = meta(.u8, &.{ 4096, 1024 }), .scale = meta(.f8_e4m3, &.{ 4096, 128 }) };
try testing.expectEqual(Format.nvfp4, try of(.{ .mixed = .w4a16_nvfp4 }, mtp_expert));
const kv_b = Stored{ .weight = meta(.bf16, &.{ 32768, 512 }) };
@@ -207,3 +255,40 @@ test "csf_32x2_v1 scales read as the same NVFP4 linear as the scales stored plai
try testing.expectEqual(@as(usize, 0), ck.unused());
try testing.expectError(error.BadNvfp4, csfLinear(&ck, "b", decoded[1..]));
}
+
+test "a W4A16_NVFP4 linear reads without an input_scale, which NVFP4's W4A4 recipe requires" {
+ const gpa = testing.allocator;
+ var codes: [4 * 16]u8 = undefined; // n = 4, k = 32
+ for (&codes, 0..) |*c, j| c.* = @truncate(j * 7);
+ const scale = [_]u8{ 0x30, 0x31, 0x32, 0x33, 0x34, 0x35, 0x36, 0x37 };
+ const half = std.mem.toBytes(@as(f32, 0.5));
+ const tensors = [_]core.shard_edit.New{
+ .{ .name = "m.weight", .dtype = .u8, .shape = &.{ 4, 16 }, .bytes = &codes },
+ .{ .name = "m.weight_scale", .dtype = .f8_e4m3, .shape = &.{ 4, 2 }, .bytes = &scale },
+ .{ .name = "m.weight_scale_2", .dtype = .f32, .shape = &.{}, .bytes = &half },
+ .{ .name = "a.weight", .dtype = .u8, .shape = &.{ 4, 16 }, .bytes = &codes },
+ .{ .name = "a.weight_scale", .dtype = .f8_e4m3, .shape = &.{ 4, 2 }, .bytes = &scale },
+ .{ .name = "a.weight_scale_2", .dtype = .f32, .shape = &.{}, .bytes = &half },
+ .{ .name = "a.input_scale", .dtype = .f32, .shape = &.{}, .bytes = &half },
+ };
+ var tmp = testing.tmpDir(.{});
+ defer tmp.cleanup();
+ var dir: [64]u8 = undefined;
+ const at = try std.fmt.bufPrint(&dir, ".zig-cache/tmp/{s}", .{tmp.sub_path});
+ var path: [96]u8 = undefined;
+ try core.shard_edit.create(gpa, try std.fmt.bufPrint(&path, "{s}/model.safetensors", .{at}), &.{}, &tensors);
+ var ck = try core.Checkpoint.openModel(gpa, testing.io, at);
+ defer ck.close();
+ try testing.expectError(error.NoInputScale, plainLinear(&ck, "m", .nvfp4));
+ try testing.expectError(error.NoInputScale, plainLinear(&ck, "m", .{ .mixed = .nvfp4 }));
+ const got = try plainLinear(&ck, "m", .{ .mixed = .w4a16_nvfp4 });
+ try testing.expectEqual(@as(usize, 4), got.n);
+ try testing.expectEqual(@as(usize, 32), got.k);
+ try testing.expectEqual(@as(f32, 0.5), got.global);
+ try testing.expectEqualSlices(u8, &codes, got.codes);
+ try testing.expectEqualSlices(u8, &scale, got.scales);
+ const stored = try plainLinear(&ck, "a", .{ .mixed = .w4a16_nvfp4 }); // an input_scale it does store is read too
+ try testing.expectEqualSlices(u8, got.scales, stored.scales);
+ _ = try plainLinear(&ck, "a", .nvfp4);
+ try testing.expectEqual(@as(usize, 0), ck.unused());
+}
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index 3eee550540..9569aecb30 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -95,7 +95,7 @@ const Loader = struct {
return L.formatIn(L.ck, try std.fmt.bufPrint(&buf, prefix ++ fmt, args), want);
}
- fn formatIn(L: *Loader, ck: *core.Checkpoint, module: []const u8, want: formats.Format) !void {
+ pub fn formatIn(L: *Loader, ck: *core.Checkpoint, module: []const u8, want: formats.Format) !void {
var buf: [224]u8 = undefined;
const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
const scale_name = try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module});
@@ -105,7 +105,7 @@ const Loader = struct {
.scale = if (s) |*t| .{ .dtype = t.dtype, .shape = t.shape[0..t.rank] } else null,
.csf = ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module})),
};
- const declared: formats.Declared = if (L.recipes) |r| .{ .mixed = r.of(module) } else .nvfp4;
+ const declared = L.declaredAs(module);
const got: ?formats.Format = formats.of(declared, stored) catch null;
if (got != null and got.? == want) return;
var why: [320]u8 = undefined;
@@ -113,16 +113,9 @@ const Loader = struct {
return error.UnreadableFormat;
}
- /// A routed expert's NVFP4 linear: scales as stored, or csf_32x2_v1's decoded into `scales`; else refused by name.
- fn expert(L: *Loader, module: []const u8, scales: []u8) !core.nvfp4.Linear {
- var buf: [224]u8 = undefined;
- const csf = L.ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module}));
- try L.formatIn(L.ck, module, if (csf) .nvfp4_csf else .nvfp4);
- if (!csf) return core.nvfp4.Linear.read(L.ck, module);
- return formats.csfLinear(L.ck, module, scales) catch |e| {
- std.log.err("{s}: its csf_32x2_v1 scales do not read as docs/csf-scale-blocks.md has them ({t})", .{ module, e });
- return e;
- };
+ /// What config.json declares `module` to be: its MIXED_PRECISION recipe, or NVFP4 for every quantized linear.
+ pub fn declaredAs(L: *const Loader, module: []const u8) formats.Declared {
+ return if (L.recipes) |r| .{ .mixed = r.of(module) } else .nvfp4;
}
/// The host bytes gathered so far uploaded as one tensor named `fmt`.
@@ -356,6 +349,7 @@ const Loader = struct {
const n = if (m < 2) ni else d; // gate and up: the rank's rows; down: every row, the rank's columns
const k = if (m < 2) d else ni;
for (e0..e0 + ec) |e| {
+ try L.format("layers.{d}.mlp.experts.{d}." ++ proj, .{ i, e }, .bf16);
const t = try L.get("layers.{d}.mlp.experts.{d}." ++ proj ++ ".weight", .{ i, e });
if (m < 2) try L.rows(t, cut.lo, cut.hi, c.moe_inter, .bf16) else try L.cols(t, cut.lo, cut.hi, .bf16);
}
@@ -448,11 +442,13 @@ const Loader = struct {
const per = c.moe_inter * d / 16; // a whole gate, up or down's e4m3 scales
const decoded = try L.gpa.alloc(u8, 3 * per); // csf_32x2_v1's, decoded before the rank's cut
defer L.gpa.free(decoded);
- if (i == c.layers) try L.quantizedExperts(i, up, down, up_scale, down_scale) else for (0..count) |e| {
+ // the MTP layer's experts: bf16 (nvidia's), quantized here, or W4A16_NVFP4 (FP4-Local's), read as stored
+ const bf16 = i == c.layers and (try L.get("layers.{d}.mlp.experts.0.gate_proj.weight", .{i})).dtype == .bf16;
+ if (bf16) try L.quantizedExperts(i, up, down, up_scale, down_scale) else for (0..count) |e| {
var ls: [3]core.nvfp4.Linear = undefined;
inline for (.{ "gate_proj", "up_proj", "down_proj" }, 0..) |proj, m| {
const name = try std.fmt.bufPrint(&L.name_buf, prefix ++ "layers.{d}.mlp.experts.{d}." ++ proj, .{ i, e });
- ls[m] = try L.expert(name, decoded[m * per ..][0..per]);
+ ls[m] = try formats.expert(L, name, decoded[m * per ..][0..per]);
}
// gate and up: the rank's rows; down: every row, the rank's columns
const gate = .{ ls[0].codes[cut.lo * d / 2 ..][0 .. ni * d / 2], ls[0].scales[cut.lo * d / 16 ..][0 .. ni * d / 16] };
@@ -549,7 +545,7 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
defer head_ck.close();
var L: Loader = .{ .gpa = gpa, .d = d, .stream = stream, .pack_dense = tools.pack_dense, .tools = tools, .w = &w, .ck = &ck };
defer L.host.deinit(gpa);
- var recipes = try readRecipes(gpa, io, dir);
+ var recipes = try formats.readRecipes(gpa, io, dir);
defer if (recipes) |*r| r.deinit();
if (recipes) |*r| L.recipes = if (r.modules.count() > 0) r else null;
w.layers = try gpa.alloc(Layer, layers.len);
@@ -569,16 +565,6 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
return w;
}
-/// config.json's MIXED_PRECISION recipes, or null when it declares none this reader takes (the entry refuses those).
-fn readRecipes(gpa: std.mem.Allocator, io: std.Io, dir: []const u8) !?core.modelopt.Recipes {
- const path = try std.fs.path.join(gpa, &.{ dir, "config.json" });
- defer gpa.free(path);
- const text = std.Io.Dir.cwd().readFileAlloc(io, path, gpa, .limited(1 << 24)) catch return null;
- defer gpa.free(text);
- var why: core.modelopt.Why = .{};
- return core.modelopt.admitRecipes(gpa, text, &why) catch null;
-}
-
test "bf16 rounding of fp32 is to nearest even" {
try std.testing.expectEqual(@as(u16, 0x3f80), toBf16(1.0));
try std.testing.expectEqual(@as(u16, 0x3f80), toBf16(@bitCast(@as(u32, 0x3f808000)))); // a tie to even (down)
diff --git a/zig/tests/cuda/glm_loader_gate.zig b/zig/tests/cuda/glm_loader_gate.zig
new file mode 100644
index 0000000000..a145468115
--- /dev/null
+++ b/zig/tests/cuda/glm_loader_gate.zig
@@ -0,0 +1,300 @@
+//! `glm-loader-gate DIR`: each rank's uploads at TP 1, 2 and 3, byte for byte against the checkpoint cut on the host.
+const std = @import("std");
+const cuda = @import("cuda");
+const core = @import("core");
+const glm = @import("glm_cuda");
+const check = @import("check.zig");
+
+const DType = core.checkpoint.DType;
+const Tensor = core.checkpoint.Tensor;
+const Config = glm.config.Config;
+const P = "model.language_model.";
+
+/// A piece of an upload: tensor `name`'s outer rows [lo, hi) (a vector's elements), or a matrix's columns [lo, hi).
+const Cut = struct { name: []const u8, cols: bool = false, lo: usize, hi: usize, to: DType = .bf16 };
+
+/// One upload's pieces as the Python family's weights.load cuts them, read from a checkpoint apart from the loader's.
+const Plan = struct {
+ a: std.mem.Allocator,
+ ck: *core.Checkpoint,
+ cuts: std.ArrayList(Cut) = .empty,
+
+ fn rows(p: *Plan, comptime fmt: []const u8, args: anytype, lo: usize, hi: usize, to: DType) !void {
+ try p.cuts.append(p.a, .{ .name = try std.fmt.allocPrint(p.a, fmt, args), .lo = lo, .hi = hi, .to = to });
+ }
+
+ fn whole(p: *Plan, comptime fmt: []const u8, args: anytype, to: DType) !void {
+ const name = try std.fmt.allocPrint(p.a, fmt, args);
+ try p.cuts.append(p.a, .{ .name = name, .lo = 0, .hi = (try p.ck.get(name)).dim(0), .to = to });
+ }
+
+ fn cols(p: *Plan, comptime fmt: []const u8, args: anytype, lo: usize, hi: usize) !void {
+ try p.cuts.append(p.a, .{ .name = try std.fmt.allocPrint(p.a, fmt, args), .cols = true, .lo = lo, .hi = hi });
+ }
+
+ /// The outer rows of tensor `fmt`, for a cut by heads.
+ fn outer(p: *Plan, comptime fmt: []const u8, args: anytype) !usize {
+ return (try p.ck.get(try std.fmt.allocPrint(p.a, fmt, args))).dim(0);
+ }
+};
+
+fn eql(a: []const u8, b: []const u8) bool {
+ return std.mem.eql(u8, a, b);
+}
+
+/// The MTP head's uploads (`mtp.{what}`) and the tensors under layer `layers` they come from.
+const mtp_sources = [_][2][]const u8{ .{ "enorm", "enorm" }, .{ "hnorm", "hnorm" }, .{ "eh", "eh_proj" }, .{ "norm", "shared_head.norm" } };
+
+/// Upload `name`'s pieces at share `s` into `p`; false when it is packed (no host reference here yet).
+fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !bool {
+ if (eql(name, "embed")) {
+ try p.whole(P ++ "embed_tokens.weight", .{}, .bf16);
+ } else if (eql(name, "norm")) {
+ try p.whole(P ++ "norm.weight", .{}, .bf16);
+ } else if (eql(name, "head")) {
+ try p.rows("lm_head.weight", .{}, s.vocab.lo, s.vocab.hi, .bf16);
+ } else if (std.mem.startsWith(u8, name, "draft_head.")) {
+ return false; // the GPU's 4-bit tiles of the head's rows
+ } else if (std.mem.startsWith(u8, name, "mtp.")) {
+ for (mtp_sources) |m| if (eql(name[4..], m[0])) {
+ try p.whole(P ++ "layers.{d}.{s}.weight", .{ c.layers, m[1] }, .bf16);
+ return true;
+ };
+ return error.UnknownUpload;
+ } else if (std.mem.startsWith(u8, name, "layers.")) {
+ const dot = std.mem.indexOfScalarPos(u8, name, "layers.".len, '.') orelse return error.UnknownUpload;
+ const i = std.fmt.parseInt(u32, name["layers.".len..dot], 10) catch return error.UnknownUpload;
+ return layer(p, c, s, i, name[dot + 1 ..]);
+ } else return error.UnknownUpload;
+ return true;
+}
+
+/// Layer `i`'s upload `what`, as `plan`.
+fn layer(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+ const L = P ++ "layers.{d}.";
+ if (eql(what, "in_norm")) {
+ try p.whole(L ++ "input_layernorm.weight", .{i}, .bf16);
+ return true;
+ }
+ if (eql(what, "post_norm")) {
+ try p.whole(L ++ "post_attention_layernorm.weight", .{i}, .bf16);
+ return true;
+ }
+ inline for (.{ "attn", "ffn" }) |site| {
+ if (eql(what, site ++ "_hc.fn")) {
+ try p.whole(L ++ "hc_" ++ site ++ "_fn", .{i}, .bf16);
+ return true;
+ }
+ inline for (.{ "base", "scale" }) |x| if (eql(what, site ++ "_hc." ++ x)) {
+ try p.whole(L ++ "hc_" ++ site ++ "_" ++ x, .{i}, .f32);
+ return true;
+ };
+ }
+ if (std.mem.startsWith(u8, what, "kda.")) return kda(p, c, s, i, what[4..]);
+ if (std.mem.startsWith(u8, what, "dsa.")) return dsa(p, c, s, i, what[4..]);
+ if (std.mem.startsWith(u8, what, "moe.")) return moe(p, c, s, i, what[4..]);
+ if (std.mem.startsWith(u8, what, "mlp.") and (std.mem.endsWith(u8, what, ".words") or std.mem.endsWith(u8, what, ".scales"))) {
+ return false; // a dense NVFP4 linear: tf_pack_dense's words, packScales' tiles
+ }
+ return error.UnknownUpload;
+}
+
+/// KDA: [q | k | v | f_a | g_a | b], q k v b by the rank's heads; f_b, g_b, taps, dt_bias by heads; o by columns.
+fn kda(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+ const at = P ++ "layers.{d}.self_attn.";
+ const h = s.kda;
+ const dk: usize = c.kda_dim;
+ if (eql(what, "proj")) {
+ inline for (.{ "q", "k", "v" }) |x| try p.rows(at ++ x ++ "_proj.weight", .{i}, h.lo * dk, h.hi * dk, .bf16);
+ inline for (.{ "f_a", "g_a" }) |x| try p.whole(at ++ x ++ "_proj.weight", .{i}, .bf16);
+ try p.rows(at ++ "b_proj.weight", .{i}, h.lo, h.hi, .bf16);
+ } else if (eql(what, "fb") or eql(what, "gb")) {
+ try p.rows(at ++ "{s}_b_proj.weight", .{ i, what[0..1] }, h.lo * dk, h.hi * dk, .bf16);
+ } else if (eql(what, "conv")) {
+ inline for (.{ "q", "k", "v" }) |x| try p.rows(at ++ x ++ "_conv1d.weight", .{i}, h.lo * dk, h.hi * dk, .f32);
+ } else if (eql(what, "a_log")) {
+ try p.rows(at ++ "A_log", .{i}, h.lo, h.hi, .f32);
+ } else if (eql(what, "dt_bias")) {
+ try p.rows(at ++ "dt_bias", .{i}, h.lo * dk, h.hi * dk, .f32);
+ } else if (eql(what, "norm")) {
+ try p.whole(at ++ "o_norm.weight", .{i}, .bf16);
+ } else if (eql(what, "o")) {
+ try p.cols(at ++ "o_proj.weight", .{i}, h.lo * dk, h.hi * dk);
+ } else return error.UnknownUpload;
+ return true;
+}
+
+/// Latent attention: [q_a | kv_a] whole; q_b, kv_b's keys and values by heads (rows from the tensors); o by columns.
+fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+ const at = P ++ "layers.{d}.self_attn.";
+ const h = s.mla;
+ const dv: usize = c.v_dim;
+ if (eql(what, "proj")) {
+ try p.whole(at ++ "q_a_proj.weight", .{i}, .bf16);
+ try p.whole(at ++ "kv_a_proj_with_mqa.weight", .{i}, .bf16);
+ } else if (eql(what, "q_norm")) {
+ try p.whole(at ++ "q_a_layernorm.weight", .{i}, .bf16);
+ } else if (eql(what, "kv_norm")) {
+ try p.whole(at ++ "kv_a_layernorm.weight", .{i}, .bf16);
+ } else if (eql(what, "q_b")) {
+ const per = try p.outer(at ++ "q_b_proj.weight", .{i}) / c.mla_heads;
+ try p.rows(at ++ "q_b_proj.weight", .{i}, h.lo * per, h.hi * per, .bf16);
+ } else if (eql(what, "absorb.wk") or eql(what, "absorb.wv")) {
+ const per = try p.outer(at ++ "kv_b_proj.weight", .{i}) / c.mla_heads; // a head's keys, then its values
+ const keys = per - dv;
+ for (h.lo..h.hi) |head| {
+ const lo = if (eql(what, "absorb.wk")) head * per else head * per + keys;
+ try p.rows(at ++ "kv_b_proj.weight", .{i}, lo, lo + (if (eql(what, "absorb.wk")) keys else dv), .bf16);
+ }
+ } else if (eql(what, "o")) {
+ try p.cols(at ++ "o_proj.weight", .{i}, h.lo * dv, h.hi * dv);
+ } else if (eql(what, "index.kw")) {
+ try p.whole(at ++ "indexer.wk.weight", .{i}, .bf16);
+ try p.whole(at ++ "indexer.weights_proj.weight", .{i}, .bf16);
+ } else {
+ const index = [_][2][]const u8{ .{ "index.qb", "wq_b.weight" }, .{ "index.ln_w", "k_norm.weight" }, .{ "index.ln_b", "k_norm.bias" }, .{ "index.gate", "index_kpool_compress_gate" }, .{ "index.ape", "index_kpool_compress_ape" } };
+ for (index) |x| if (eql(what, x[0])) {
+ try p.whole(at ++ "indexer.{s}", .{ i, x[1] }, .bf16);
+ return true;
+ };
+ return error.UnknownUpload;
+ }
+ return true;
+}
+
+/// MoE: router and bias whole, the experts' global scales, the shared expert's gate and up by rows, down by columns.
+fn moe(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+ const at = P ++ "layers.{d}.mlp.";
+ if (eql(what, "router")) {
+ try p.whole(at ++ "gate.weight", .{i}, .bf16);
+ } else if (eql(what, "bias")) {
+ try p.whole(at ++ "gate.e_score_correction_bias", .{i}, .f32);
+ } else if (eql(what, "experts.up") or eql(what, "experts.down")) {
+ return false; // packGateUp's and packOne's tiles
+ } else if (eql(what, "experts.up_scale") or eql(what, "experts.down_scale")) {
+ // the MTP layer's bf16 experts take the GPU quantizer's scales; W4A16_NVFP4 ones (FP4-Local's) keep their own
+ if (i == c.layers and !p.ck.has(try std.fmt.allocPrint(p.a, at ++ "experts.0.gate_proj.weight_scale_2", .{i}))) return false;
+ for (0..c.experts) |e| {
+ const projs: []const []const u8 = if (eql(what, "experts.up_scale")) &.{ "gate_proj", "up_proj" } else &.{"down_proj"};
+ for (projs) |x| try p.whole(at ++ "experts.{d}.{s}.weight_scale_2", .{ i, e, x }, .f32);
+ }
+ } else if (eql(what, "shared.gu")) {
+ inline for (.{ "gate", "up" }) |x| try p.rows(at ++ "shared_experts." ++ x ++ "_proj.weight", .{i}, s.shared.lo, s.shared.hi, .bf16);
+ } else if (eql(what, "shared.down")) {
+ try p.cols(at ++ "shared_experts.down_proj.weight", .{i}, s.shared.lo, s.shared.hi);
+ } else return error.UnknownUpload;
+ return true;
+}
+
+/// The bytes `cuts` make: each piece's elements in order, written as its dtype.
+fn bytesOf(gpa: std.mem.Allocator, ck: *core.Checkpoint, cuts: []const Cut) ![]u8 {
+ var out: std.ArrayList(u8) = .empty;
+ errdefer out.deinit(gpa);
+ for (cuts) |c| {
+ const t = try ck.get(c.name);
+ const outer = t.dim(0);
+ const width = t.numel() / outer;
+ if (c.cols) {
+ if (t.rank != 2 or c.lo > c.hi or c.hi > width) return error.CutOutside;
+ for (0..outer) |r| try put(&out, gpa, t, r * width + c.lo, r * width + c.hi, c.to);
+ } else {
+ if (c.lo > c.hi or c.hi > outer) return error.CutOutside;
+ try put(&out, gpa, t, c.lo * width, c.hi * width, c.to);
+ }
+ }
+ return out.toOwnedSlice(gpa);
+}
+
+/// Elements [lo, hi) of `t` appended as `to`.
+fn put(out: *std.ArrayList(u8), gpa: std.mem.Allocator, t: Tensor, lo: usize, hi: usize, to: DType) !void {
+ if (t.dtype == to) return out.appendSlice(gpa, t.bytes[lo * to.size() .. hi * to.size()]);
+ for (lo..hi) |i| {
+ const v: f32 = switch (t.dtype) {
+ .bf16 => @bitCast(@as(u32, std.mem.readInt(u16, t.bytes[2 * i ..][0..2], .little)) << 16),
+ .f32 => @bitCast(std.mem.readInt(u32, t.bytes[4 * i ..][0..4], .little)),
+ else => return error.UnexpectedDType,
+ };
+ switch (to) {
+ .f32 => try out.appendSlice(gpa, &std.mem.toBytes(v)),
+ .bf16 => try out.appendSlice(gpa, &std.mem.toBytes(bf16(v))),
+ else => return error.UnexpectedDType,
+ }
+ }
+}
+
+/// fp32 to bf16 to nearest even (torch's), NaN kept quiet.
+fn bf16(v: f32) u16 {
+ const b: u32 = @bitCast(v);
+ if (std.math.isNan(v)) return @intCast((b >> 16) | 0x40);
+ return @intCast((b + 0x7fff + ((b >> 16) & 1)) >> 16);
+}
+
+/// What a run changes on purpose: one bit of upload `name`'s downloaded bytes, and how many copies it changed.
+const Tamper = struct { name: ?[]const u8, done: usize = 0 };
+
+/// Rank `rank` of `world` loaded, and each upload compared: how many differ or are not known.
+fn rankGate(gpu: check.Gpu, a: std.mem.Allocator, ck: *core.Checkpoint, c: *const Config, m: *const glm.forward.Modules, stream: cuda.Stream, dir: []const u8, layers: []const u32, rank: usize, world: usize, tamper: *Tamper) !usize {
+ const s = try glm.shard.Share.of(c, rank, world);
+ var w = try glm.weights.load(gpu.gpa, gpu.io, gpu.d, stream, m.tools(), dir, c.*, s, layers, true);
+ defer w.deinit();
+ var bad: usize = 0;
+ var same: usize = 0;
+ var bytes: usize = 0;
+ var skipped: std.ArrayList([]const u8) = .empty;
+ for (w.named.items) |n| {
+ var p: Plan = .{ .a = a, .ck = ck };
+ const known = plan(&p, c, s, n.name) catch |e| {
+ std.debug.print("FAIL TP={d} rank {d} {s}: {t}\n", .{ world, rank, n.name, e });
+ bad += 1;
+ continue;
+ };
+ if (!known) {
+ try skipped.append(a, n.name);
+ continue;
+ }
+ const want = try bytesOf(gpu.gpa, ck, p.cuts.items);
+ defer gpu.gpa.free(want);
+ const got = try gpu.gpa.alloc(u8, n.len);
+ defer gpu.gpa.free(got);
+ const b: cuda.DeviceBuffer = .{ .d = gpu.d, .ptr = n.ptr, .len = n.len, .borrowed = true };
+ try b.download(0, got);
+ if (tamper.name) |t| if (eql(t, n.name)) {
+ got[got.len / 2] ^= 1;
+ tamper.done += 1;
+ };
+ var label: [160]u8 = undefined;
+ check.sameBytes(try std.fmt.bufPrint(&label, "TP={d} rank {d} {s}", .{ world, rank, n.name }), got, want) catch {
+ bad += 1;
+ continue;
+ };
+ same += 1;
+ bytes += n.len;
+ }
+ check.pass("glm-loader-gate TP={d} rank {d}: {d} uploads ({d} MiB) as the checkpoint cut on the host, {d} packed not compared", .{ world, rank, same, bytes >> 20, skipped.items.len });
+ if (world == 1) for (skipped.items) |name| std.debug.print(" packed, not compared: {s}\n", .{name});
+ return bad;
+}
+
+/// TF_GATE_TAMPER=NAME flips a bit of upload NAME after each download, so a run shows the gate refusing it by name.
+pub fn run(gpu: check.Gpu, dir: []const u8) !void {
+ var arena = std.heap.ArenaAllocator.init(gpu.gpa);
+ defer arena.deinit();
+ const a = arena.allocator();
+ const text = try std.Io.Dir.cwd().readFileAlloc(gpu.io, try std.fs.path.join(a, &.{ dir, "config.json" }), a, .limited(1 << 24));
+ const c = try glm.config.parse(a, text);
+ var m = try glm.forward.Modules.load(gpu.d, gpu.ctx);
+ defer m.deinit();
+ var stream = try cuda.Stream.init(gpu.d, false);
+ defer stream.deinit();
+ var ck = try core.Checkpoint.openModel(gpu.gpa, gpu.io, dir);
+ defer ck.close();
+ const layers = try a.alloc(u32, c.layers);
+ for (layers, 0..) |*l, i| l.* = @intCast(i);
+ var tamper: Tamper = .{ .name = glm.ranks.environment("TF_GATE_TAMPER") };
+ var bad: usize = 0;
+ for (1..4) |world| {
+ for (0..world) |rank| bad += try rankGate(gpu, a, &ck, &c, &m, stream, dir, layers, rank, world, &tamper);
+ }
+ if (tamper.name) |t| try check.expect(tamper.done > 0, "glm-loader-gate: TF_GATE_TAMPER={s} names no upload it compares", .{t});
+ try check.expect(bad == 0, "glm-loader-gate: {d} uploads differ from the checkpoint or are not known", .{bad});
+}
diff --git a/zig/tests/cuda/main.zig b/zig/tests/cuda/main.zig
index d7dd86fc20..6c85575eb2 100644
--- a/zig/tests/cuda/main.zig
+++ b/zig/tests/cuda/main.zig
@@ -82,6 +82,7 @@ const flashnext_hc_tests = @import("flashnext_hc_tests.zig");
const flashnext_attn_tests = @import("flashnext_attn_tests.zig");
const ggml_tests = @import("ggml_tests.zig");
const paged_tests = @import("paged_tests.zig");
+const glm_loader_gate = @import("glm_loader_gate.zig");
const gdn_back_tests = @import("gdn_back_tests.zig");
const q4_back_tests = @import("q4_back_tests.zig");
@@ -211,6 +212,7 @@ const usage =
\\ kolibri-greedy MODEL PROMPTS N OUT Kolibri-1 greedy from each prompt (JSON id lists): ids and top-2 margins
\\ kolibri-logits MODEL IDS OUT [PROMPTS] Kolibri-1's teacher-forced fp32 logits of 2,048 prompt rows; each prompt's last
\\ glm-synth
a synthetic GLM-5.3-Flash checkpoint (2 layers, 16 experts) and its dc.json for the lane checks
+ \\ glm-loader-gate every rank's uploads at TP 1, 2 and 3 against the checkpoint cut on the host, byte for byte
\\ aot-fallback a launch's 1 from the runtime-parameter variant: the folded variant's bits (#1003)
\\
;
@@ -385,6 +387,7 @@ fn run(gpu: check.Gpu, cmd: []const u8, rest: []const [:0]const u8) !void {
if (std.mem.eql(u8, cmd, "glm-lanes-cover")) return glm_lanes_tests.coverCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
if (std.mem.eql(u8, cmd, "glm-shared-check")) return glm_lanes_tests.sharedCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
if (std.mem.eql(u8, cmd, "glm-synth")) return glm_synth.run(gpu, try arg(rest, 0));
+ if (std.mem.eql(u8, cmd, "glm-loader-gate")) return glm_loader_gate.run(gpu, try arg(rest, 0));
if (std.mem.eql(u8, cmd, "kolibri-reference")) return kolibri_layer_tests.reference(gpu, try arg(rest, 0), try arg(rest, 1));
if (std.mem.eql(u8, cmd, "kolibri-layers")) return kolibri_layer_tests.layers(gpu, try arg(rest, 0), try arg(rest, 1));
if (std.mem.eql(u8, cmd, "kolibri-full")) return kolibri_tests.full(gpu, try arg(rest, 0), try arg(rest, 1), try arg(rest, 2));
From 96a95e5b34489cee098e88f3b7255ab131b21e85 Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 22:22:35 +0900
Subject: [PATCH 03/23] glm/cuda: MLA's projections and the shared expert
stored FP8_PB_WO run as stored on qlinear's fp8g lane
qlinear's decodeF32 and promptF32 take block FP8 (fp8g) on the lane, whose FP32 instances #908 builds, so a
tensor-parallel rank's fp8g partials stay unrounded. MLA's [q_a | kv_a], q_b and o and the shared expert are read as
stored, bf16 or FP8_PB_WO packed on the host for the lane, a TP share starting inside a 128 block included (the scale
pack from global rank-share origins). tileLinears tiles a projection's bf16 rows for glm_mm and leaves a block-FP8 one
to the lane's own tiles, which read as bf16 would be overwritten in place. FP8_PB_WO's read, sizes and pack live in
cuda_fp8pb.zig. glm-synth fp8pb writes layer 1's MLA and shared expert as FP8_PB_WO under a MIXED_PRECISION config,
glm-fp8g-shares checks the loader's TP shares on the lane against FP64 of the cut, and glm-loader-gate compares the
packed uploads too (NVFP4 dense words and scales, routed experts, block-FP8 codes and scale tiles) against the host
packers fed the gate's own cut; only the GPU quantizer's are left out, by name.
---
zig/src/cuda/qlinear.zig | 6 +-
zig/src/cuda/qlinear_test.zig | 4 +-
zig/src/families/glm/cuda_dsa_block.zig | 13 +-
zig/src/families/glm/cuda_formats.zig | 65 ++++++-
zig/src/families/glm/cuda_forward.zig | 2 +-
zig/src/families/glm/cuda_fp8pb.zig | 217 ++++++++++++++++++++++++
zig/src/families/glm/cuda_glue.zig | 4 +-
zig/src/families/glm/cuda_kv8_test.zig | 14 +-
zig/src/families/glm/cuda_moe.zig | 10 +-
zig/src/families/glm/cuda_proj.zig | 37 ++++
zig/src/families/glm/cuda_tile.zig | 19 ++-
zig/src/families/glm/cuda_weights.zig | 38 +++--
zig/tests/cuda/glm_fp8g_shares.zig | 90 ++++++++++
zig/tests/cuda/glm_loader_gate.zig | 214 +++++++++++++++++++----
zig/tests/cuda/glm_synth.zig | 68 ++++++--
zig/tests/cuda/main.zig | 7 +-
16 files changed, 712 insertions(+), 96 deletions(-)
create mode 100644 zig/src/families/glm/cuda_fp8pb.zig
create mode 100644 zig/src/families/glm/cuda_proj.zig
create mode 100644 zig/tests/cuda/glm_fp8g_shares.zig
diff --git a/zig/src/cuda/qlinear.zig b/zig/src/cuda/qlinear.zig
index b801c535da..7b5539341b 100644
--- a/zig/src/cuda/qlinear.zig
+++ b/zig/src/cuda/qlinear.zig
@@ -155,7 +155,7 @@ pub const Linear = struct {
}
}
- /// Decode rows to FP32 without the output BF16 cast (NVFP4 and EXL3), including TP partial rows.
+ /// Decode rows to FP32 without the output BF16 cast (NVFP4, block FP8 and EXL3), including TP partial rows.
pub fn decodeF32(l: Linear, s: Stream, w: Weight, in: Rows, out: u64, rows: usize) !void {
const q = switch (w) {
.exl3 => |q| {
@@ -163,13 +163,14 @@ pub const Linear = struct {
if (in.ldx != 0 and in.ldx != q.k) return error.RowStride;
return e.decode(s, q, in.x, .bf16, rows, out, .f32);
},
+ .fp8g => |q| return l.onLaneAs(s, q, .fp8g, in, out, .f32, rows),
.nvfp4 => |q| q,
else => return error.EpilogueNotBuilt,
};
return l.onLaneAs(s, q, .fp4, in, out, .f32, rows);
}
- /// Prompt rows to FP32 without the output BF16 cast (NVFP4 and EXL3).
+ /// Prompt rows to FP32 without the output BF16 cast (NVFP4, EXL3, and block FP8 on the lane as prompt() runs it).
pub fn promptF32(l: Linear, s: Stream, w: Weight, in: Rows, out: u64, rows: usize) !void {
const q = switch (w) {
.exl3 => |q| {
@@ -177,6 +178,7 @@ pub const Linear = struct {
if (in.ldx != 0 and in.ldx != q.k) return error.RowStride;
return e.prompt(s, q, in.x, .bf16, rows, out, .f32);
},
+ .fp8g => |q| return l.onLaneAs(s, q, .fp8g, in, out, .f32, rows),
.nvfp4 => |q| q,
else => return error.EpilogueNotBuilt,
};
diff --git a/zig/src/cuda/qlinear_test.zig b/zig/src/cuda/qlinear_test.zig
index 1515a3b140..2a41b8f523 100644
--- a/zig/src/cuda/qlinear_test.zig
+++ b/zig/src/cuda/qlinear_test.zig
@@ -39,8 +39,10 @@ test "a lane weight whose mode is not its format's is refused before any launch"
try std.testing.expectError(error.FormatMismatch, l.prompt(s, .{ .mxfp8 = fp4 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.FormatNotLoaded, l.prompt(s, .{ .nvfp4 = fp4 }, .{ .x = 0 }, 0, 1));
const f8: qmmf.Weight = .{ .mode = .fp8g, .codes = 0, .scales = 0, .n = 64, .k = 64, .npad = 128 };
- try std.testing.expectError(error.EpilogueNotBuilt, l.decodeF32(s, .{ .fp8g = f8 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.EpilogueNotBuilt, l.decodeF32(s, .{ .mxfp8 = f8 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.EpilogueNotBuilt, l.promptF32(s, .{ .bf16 = f8 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.FormatMismatch, l.decodeF32(s, .{ .fp8g = fp4 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.FormatMismatch, l.promptF32(s, .{ .fp8g = fp4 }, .{ .x = 0 }, 0, 1));
}
test "EXL3 FP32 entries check loading and stride before launching" {
diff --git a/zig/src/families/glm/cuda_dsa_block.zig b/zig/src/families/glm/cuda_dsa_block.zig
index 1d5779cf29..9ca84b5fcb 100644
--- a/zig/src/families/glm/cuda_dsa_block.zig
+++ b/zig/src/families/glm/cuda_dsa_block.zig
@@ -4,6 +4,7 @@ const std = @import("std");
const cuda = @import("cuda");
const glue = @import("cuda_glue.zig");
const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
const latent = @import("cuda_latent.zig");
const sparse = @import("cuda_sparse.zig");
const prof = @import("cuda_prof.zig");
@@ -16,13 +17,13 @@ pub const Index = struct { kw: u64, gate: u64, ln_w: u64, ln_b: u64, ape: u64, q
/// One DSA layer on a rank of `heads` heads: proj, norms, q_b, kv_b per head as wk/wv, o, indexer; shapes from config.
pub const Layer = struct {
- proj: u64,
+ proj: projection.Proj, // [q_a | kv_a], q_b and o as the checkpoint stores them: bf16 or block FP8
q_norm: u64,
kv_norm: u64,
- q_b: u64,
+ q_b: projection.Proj,
wk: u64,
wv: u64,
- o: u64,
+ o: projection.Proj,
index: Index,
heads: usize,
qk: usize,
@@ -73,7 +74,7 @@ pub fn block(k: Kernels, stream: cuda.Stream, l: Layer, c: Caches, s: Scratch, x
/// forward.out_proj's fill for the DSA block: o_proj of rows lo..hi (s.vn) into out [hi - lo][dims] fp32.
pub fn fill(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch, lo: usize, hi: usize, out: u64) !void {
const w = l.heads * l.dv;
- try b16.matmul(set, stream, s.vn + lo * w * 2, w, l.o, out, s.mm_part, hi - lo, l.dims, w, true);
+ try projection.matmul(set, stream, l.o, s.vn + lo * w * 2, w, out, s.mm_part, hi - lo, l.dims, w, true);
}
/// The block up to its out_proj (s.vn holds the expanded rows).
@@ -98,13 +99,13 @@ pub fn prepare(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch, x: u64,
pub fn prepareProfile(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch, x: u64, x_stride: usize, rows: usize, qi: bool, profile: ?*prof.Prof) !void {
var stamp = prof.span(profile, stream, .replicated_qakva);
const width = l.q_lora + l.lw;
- try b16.matmul(set, stream, x, x_stride, l.proj, s.dp, s.mm_part, rows, width, l.dims, false);
+ try projection.matmul(set, stream, l.proj, x, x_stride, s.dp, s.mm_part, rows, width, l.dims, false);
try glue.rmsnorm(set, stream, s.dp, width, l.q_norm, s.qr, l.q_lora, s.xs_qr, l.eps, rows, l.q_lora);
try glue.rmsnorm(set, stream, s.dp + l.q_lora * 2, width, l.kv_norm, s.lat, l.lw, s.xs_lat, l.eps, rows, l.lw);
stamp.end();
stamp = prof.span(profile, stream, .projections);
defer stamp.end();
- try b16.matmul(set, stream, s.qr, l.q_lora, l.q_b, s.q, s.mm_part, rows, l.heads * l.qk, l.q_lora, false);
+ try projection.matmul(set, stream, l.q_b, s.qr, l.q_lora, s.q, s.mm_part, rows, l.heads * l.qk, l.q_lora, false);
const ix = l.index;
try b16.matmul(set, stream, x, x_stride, ix.kw, s.ikr, s.mm_part, rows, ix.d + ix.heads, l.dims, false);
try glue.router(set, stream, x, x_stride, ix.gate, s.igr, s.router_part, rows, l.dims, ix.d);
diff --git a/zig/src/families/glm/cuda_formats.zig b/zig/src/families/glm/cuda_formats.zig
index 7e3172862a..129c2ddb26 100644
--- a/zig/src/families/glm/cuda_formats.zig
+++ b/zig/src/families/glm/cuda_formats.zig
@@ -1,7 +1,11 @@
-//! Which stored format a GLM CUDA projection is read in (its recipe, checked against its tensors); csf scales decoded.
+//! Which stored format a GLM CUDA projection is read in (its recipe against its tensors), and how: csf, block-FP8 cuts.
const std = @import("std");
const core = @import("core");
+const cuda = @import("cuda");
+const fp8 = cuda.fp8;
+const projection = @import("cuda_proj.zig");
+pub const fp8pb = @import("cuda_fp8pb.zig");
const Meta = core.nvfp4.Meta;
const Recipe = core.modelopt.Recipe;
@@ -105,10 +109,67 @@ pub fn plainLinear(ck: *core.Checkpoint, module: []const u8, declared: Declared)
return .{ .n = d.n, .k = d.k, .codes = w.bytes, .scales = s.bytes, .global = std.mem.bytesToValue(f32, g.bytes[0..4]) };
}
-fn metaOf(t: *const core.checkpoint.Tensor) Meta {
+pub fn metaOf(t: *const core.checkpoint.Tensor) Meta {
return .{ .dtype = t.dtype, .shape = t.shape[0..t.rank] };
}
+/// A projection's part as a rank takes it: tensor `rel` under the layer, its rows or its inputs, or the whole of it.
+pub const Part = struct { rel: []const u8, rows: ?[2]usize = null, cols: ?[2]usize = null };
+
+/// Projection `name` of `parts` stacked: bf16 cut as split.py cuts it, or FP8_PB_WO packed; else refused by name.
+pub fn cutProj(L: anytype, comptime prefix: []const u8, i: u32, parts: []const Part, comptime name: []const u8) !projection.Proj {
+ var bufs: [2][192]u8 = undefined;
+ var mods: [2][]const u8 = undefined;
+ if (parts.len == 0 or parts.len > bufs.len) return error.BadFp8Share; // one tensor, or [q_a | kv_a] and [gate | up]
+ var got: ?Format = null;
+ for (parts, 0..) |q, j| {
+ mods[j] = try std.fmt.bufPrint(&bufs[j], prefix ++ "layers.{d}.{s}", .{ i, q.rel });
+ const f = try L.formatOne(L.ck, mods[j], &.{ .bf16, .fp8_pb });
+ if (got != null and got.? != f) {
+ std.log.err("{s}: stored as {t}, stacked with a part stored as {t}", .{ mods[j], f, got.? });
+ return error.UnreadableFormat;
+ }
+ got = f;
+ }
+ var wb: [224]u8 = undefined;
+ if (got.? == .bf16) {
+ for (parts, mods[0..parts.len]) |q, m| {
+ const t = try L.ck.get(try std.fmt.bufPrint(&wb, "{s}.weight", .{m}));
+ if (q.cols) |c| try L.cols(t, c[0], c[1], .bf16) else if (q.rows) |r| try L.rows(t, r[0], r[1], t.dim(0), .bf16) else try L.whole(t, .bf16);
+ }
+ return .{ .bf16 = try L.put(name, .{i}) };
+ }
+ var stored: [2]fp8pb.Fp8pb = undefined;
+ var made: usize = 0;
+ defer for (stored[0..made]) |t| t.deinit(L.gpa);
+ var shares: [2]fp8pb.Share = undefined;
+ for (parts, mods[0..parts.len], 0..) |q, m, j| {
+ stored[j] = try fp8pb.Fp8pb.read(L.gpa, L.ck, m);
+ made += 1;
+ const r = q.rows orelse [2]usize{ 0, stored[j].n };
+ const c = q.cols orelse [2]usize{ 0, stored[j].k };
+ shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = c[0], .khi = c[1] };
+ }
+ return .{ .fp8g = try fp8g(L, shares[0..parts.len], name, .{i}) };
+}
+
+/// `shares` packed for the lane as one weight (fp8pb.pack), uploaded as `name`'s codes and scale tiles.
+fn fp8g(L: anytype, shares: []const fp8pb.Share, comptime name: []const u8, args: anytype) !cuda.qmmf.Weight {
+ const z = fp8pb.sizes(shares) catch |e| {
+ std.log.err(name ++ ": a rank's cut leaves the lane's 64-row tiles or 64-input groups", args);
+ return e;
+ };
+ const codes = try L.gpa.alloc(u8, z.codes);
+ defer L.gpa.free(codes);
+ const scales = try L.gpa.alloc(f32, z.scales);
+ defer L.gpa.free(scales);
+ try fp8pb.pack(L.gpa, shares, codes, scales); // packed apart from L.host, then moved in for each put
+ try L.host.appendSlice(L.gpa, codes);
+ const c = try L.put(name ++ ".codes", args);
+ try L.host.appendSlice(L.gpa, std.mem.sliceAsBytes(scales));
+ return fp8.weight(c, try L.put(name ++ ".scales", args), z.n, z.k);
+}
+
/// The refusal's text: the module, what the checkpoint declares and what its weight holds.
pub fn describe(buf: []u8, module: []const u8, declared: Declared, stored: Stored) []const u8 {
const said = switch (declared) {
diff --git a/zig/src/families/glm/cuda_forward.zig b/zig/src/families/glm/cuda_forward.zig
index aa87369737..4b4364fa58 100644
--- a/zig/src/families/glm/cuda_forward.zig
+++ b/zig/src/families/glm/cuda_forward.zig
@@ -183,7 +183,7 @@ pub const Forward = struct {
taps: ?taps_mod.Taps = null, // DFlash2 (--drafter): the tapped layers' rows, after each such layer
pub fn set(f: *const Forward) glue.Set {
- return .{ .glue = &f.m.glue, .mm = if (f.m.tiled) &f.m.mm else null, .packs = f.m.packs, .prompt = f.ranks[0].b.prefill };
+ return .{ .glue = &f.m.glue, .mm = if (f.m.tiled) &f.m.mm else null, .packs = f.m.packs, .prompt = f.ranks[0].b.prefill, .linear = .{ .lane = &f.m.lane } };
}
/// reduce.sums' gather: every rank's partial of `rows` rows into every rank's gathered buffer, rank order.
diff --git a/zig/src/families/glm/cuda_fp8pb.zig b/zig/src/families/glm/cuda_fp8pb.zig
new file mode 100644
index 0000000000..7878eade60
--- /dev/null
+++ b/zig/src/families/glm/cuda_fp8pb.zig
@@ -0,0 +1,217 @@
+//! FP8_PB_WO (e4m3 codes, fp32 128 x 128 block scales) as a rank's lane weight: read, cut and packed on the host.
+
+const std = @import("std");
+const core = @import("core");
+const cuda = @import("cuda");
+const fp8 = cuda.fp8;
+const formats = @import("cuda_formats.zig");
+
+/// An FP8_PB_WO projection as stored: e4m3 codes [n, k] and its fp32 128 x 128 block scales, copied out of the file.
+pub const Fp8pb = struct {
+ n: usize,
+ k: usize,
+ codes: []const u8,
+ scales: []f32,
+
+ /// `module`'s weight and weight_scale ([Rb, 1, Cb, 1] or [Rb, Cb]), checked as formats.of checks them.
+ pub fn read(gpa: std.mem.Allocator, ck: *core.Checkpoint, module: []const u8) !Fp8pb {
+ var buf: [256]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
+ const s = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module}));
+ _ = formats.of(.{ .mixed = .fp8_pb_wo }, .{ .weight = formats.metaOf(&w), .scale = formats.metaOf(&s) }) catch return error.BadFp8pb;
+ const scales = try gpa.alloc(f32, s.numel());
+ for (scales, 0..) |*v, j| v.* = @bitCast(std.mem.readInt(u32, s.bytes[4 * j ..][0..4], .little));
+ return .{ .n = w.dim(0), .k = w.dim(1), .codes = w.bytes, .scales = scales };
+ }
+
+ pub fn deinit(p: Fp8pb, gpa: std.mem.Allocator) void {
+ gpa.free(p.scales);
+ }
+};
+
+/// Rows [lo, hi) and inputs [klo, khi) of a stored projection: one part of a rank's lane weight.
+pub const Share = struct { p: *const Fp8pb, lo: usize, hi: usize, klo: usize, khi: usize };
+
+/// The rows and inputs of `parts` stacked, and the bytes and floats its packed codes and scale tiles take.
+pub fn sizes(parts: []const Share) error{BadFp8Share}!struct { n: usize, k: usize, codes: usize, scales: usize } {
+ if (parts.len == 0) return error.BadFp8Share;
+ const k = parts[0].khi -| parts[0].klo;
+ var n: usize = 0;
+ for (parts) |s| {
+ const inside = s.lo < s.hi and s.hi <= s.p.n and s.klo < s.khi and s.khi <= s.p.k;
+ // the lane's 64-input groups and 64-row tiles: a cut off them would mix two groups' scales or rows
+ if (!inside or s.khi - s.klo != k or s.klo % fp8.group != 0 or k % fp8.group != 0 or (s.hi - s.lo) % 64 != 0) return error.BadFp8Share;
+ n += s.hi - s.lo;
+ }
+ const npad = fp8.padded(n);
+ return .{ .n = n, .k = k, .codes = npad * k, .scales = npad * (k / fp8.group) };
+}
+
+/// `parts` packed one after another in 64-row tiles as the lane's one weight (cuda.fp8's order); padding 0 and 1.
+pub fn pack(gpa: std.mem.Allocator, parts: []const Share, codes: []u8, scales: []f32) !void {
+ const z = try sizes(parts);
+ if (codes.len != z.codes or scales.len != z.scales) return error.BadFp8Share;
+ @memset(codes, 0);
+ @memset(scales, 1.0);
+ const kg = z.k / fp8.group;
+ var t0: usize = 0; // tiles written
+ for (parts) |s| {
+ const n = s.hi - s.lo;
+ const npad = fp8.padded(n);
+ const cut = try gpa.alloc(u8, n * z.k);
+ defer gpa.free(cut);
+ for (0..n) |r| @memcpy(cut[r * z.k ..][0..z.k], s.p.codes[(s.lo + r) * s.p.k + s.klo ..][0..z.k]);
+ const pc = try gpa.alloc(u8, npad * z.k);
+ defer gpa.free(pc);
+ fp8.packCodes(pc, cut, n, z.k);
+ const ps = try gpa.alloc(f32, npad * kg);
+ defer gpa.free(ps);
+ fp8.packScalesAt(ps, s.p.scales, s.p.n, s.p.k, s.lo, s.klo, n, z.k);
+ const tiles = n / 64;
+ @memcpy(codes[t0 * 64 * z.k ..][0 .. tiles * 64 * z.k], pc[0 .. tiles * 64 * z.k]);
+ @memcpy(scales[t0 * kg * 64 ..][0 .. tiles * kg * 64], ps[0 .. tiles * kg * 64]);
+ t0 += tiles;
+ }
+}
+
+const testing = std.testing;
+
+/// A packed weight's scale for row `r`, input group `g` (cuda.fp8's tiles [npad/64][kg][64]).
+fn tileScale(tiles: []const f32, kg: usize, r: usize, g: usize) f32 {
+ return tiles[(r / 64 * kg + g) * 64 + r % 64];
+}
+
+fn fp8pbOf(codes: []const u8, scales: []f32, n: usize, k: usize) Fp8pb {
+ return .{ .n = n, .k = k, .codes = codes, .scales = scales };
+}
+
+test "an FP8_PB_WO projection reads its block scales as stored, in either shape" {
+ const gpa = testing.allocator;
+ var codes: [192 * 256]u8 = undefined;
+ for (&codes, 0..) |*c, j| c.* = @truncate(j * 13 + 5);
+ const inv = [_]f32{ 1.5, 2.5, 3.5, 4.5 };
+ const tensors = [_]core.shard_edit.New{
+ .{ .name = "a.weight", .dtype = .f8_e4m3, .shape = &.{ 192, 256 }, .bytes = &codes },
+ .{ .name = "a.weight_scale", .dtype = .f32, .shape = &.{ 2, 1, 2, 1 }, .bytes = std.mem.sliceAsBytes(&inv) },
+ .{ .name = "b.weight", .dtype = .f8_e4m3, .shape = &.{ 192, 256 }, .bytes = &codes },
+ .{ .name = "b.weight_scale", .dtype = .f32, .shape = &.{ 2, 2 }, .bytes = std.mem.sliceAsBytes(&inv) },
+ .{ .name = "c.weight", .dtype = .f8_e4m3, .shape = &.{ 192, 256 }, .bytes = &codes },
+ .{ .name = "c.weight_scale", .dtype = .f32, .shape = &.{ 1, 2 }, .bytes = std.mem.sliceAsBytes(inv[0..2]) },
+ };
+ var tmp = testing.tmpDir(.{});
+ defer tmp.cleanup();
+ var dir: [64]u8 = undefined;
+ const at = try std.fmt.bufPrint(&dir, ".zig-cache/tmp/{s}", .{tmp.sub_path});
+ var path: [96]u8 = undefined;
+ try core.shard_edit.create(gpa, try std.fmt.bufPrint(&path, "{s}/model.safetensors", .{at}), &.{}, &tensors);
+ var ck = try core.Checkpoint.openModel(gpa, testing.io, at);
+ defer ck.close();
+ inline for (.{ "a", "b" }) |m| {
+ const p = try Fp8pb.read(gpa, &ck, m);
+ defer p.deinit(gpa);
+ try testing.expectEqual(@as(usize, 192), p.n);
+ try testing.expectEqual(@as(usize, 256), p.k);
+ try testing.expectEqualSlices(u8, &codes, p.codes);
+ try testing.expectEqualSlices(f32, &inv, p.scales);
+ }
+ try testing.expectError(error.BadFp8pb, Fp8pb.read(gpa, &ck, "c")); // one block row for 192 rows
+}
+
+test "a rank's FP8_PB_WO share packs as cuda.fp8 packs the rows and inputs it holds, a cut inside a block too" {
+ const gpa = testing.allocator;
+ const n = 192;
+ const k = 256;
+ const codes = try gpa.alloc(u8, n * k);
+ defer gpa.free(codes);
+ for (codes, 0..) |*c, j| c.* = @truncate(j * 29 + 3);
+ var inv = [_]f32{ 1.5, 2.5, 3.5, 4.5 };
+ const p = fp8pbOf(codes, &inv, n, k);
+ // rows [64, 192): a q_b or gate/up share starting inside the first 128-row block
+ {
+ const z = try sizes(&.{.{ .p = &p, .lo = 64, .hi = 192, .klo = 0, .khi = k }});
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(f32, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &.{.{ .p = &p, .lo = 64, .hi = 192, .klo = 0, .khi = k }}, got_c, got_s);
+ const want_c = try gpa.alloc(u8, fp8.padded(128) * k);
+ defer gpa.free(want_c);
+ fp8.packCodes(want_c, codes[64 * k ..], 128, k);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ for (0..128) |r| for (0..k / 64) |g| {
+ try testing.expectEqual(inv[((64 + r) / 128) * 2 + g * 64 / 128], tileScale(got_s, k / 64, r, g));
+ };
+ }
+ // inputs [128, 256) of every row: an o_proj or down share, its 192 rows padded to 256
+ {
+ const share = [_]Share{.{ .p = &p, .lo = 0, .hi = n, .klo = 128, .khi = k }};
+ const z = try sizes(&share);
+ try testing.expectEqual(@as(usize, 256 * 128), z.codes);
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(f32, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &share, got_c, got_s);
+ const cut = try gpa.alloc(u8, n * 128);
+ defer gpa.free(cut);
+ for (0..n) |r| @memcpy(cut[r * 128 ..][0..128], codes[r * k + 128 ..][0..128]);
+ const want_c = try gpa.alloc(u8, 256 * 128);
+ defer gpa.free(want_c);
+ fp8.packCodes(want_c, cut, n, 128);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ for (0..256) |r| for (0..2) |g| {
+ const want: f32 = if (r < n) inv[(r / 128) * 2 + 1] else 1.0;
+ try testing.expectEqual(want, tileScale(got_s, 2, r, g));
+ };
+ }
+}
+
+test "stacked shares follow each other in 64-row tiles, the stack's padding rows zero with scale one" {
+ const gpa = testing.allocator;
+ const k = 128;
+ const a_codes = try gpa.alloc(u8, 192 * k);
+ defer gpa.free(a_codes);
+ for (a_codes, 0..) |*c, j| c.* = @truncate(j * 7 + 1);
+ const b_codes = try gpa.alloc(u8, 128 * k);
+ defer gpa.free(b_codes);
+ for (b_codes, 0..) |*c, j| c.* = @truncate(j * 11 + 2);
+ var a_inv = [_]f32{ 2, 3 };
+ var b_inv = [_]f32{5};
+ const a = fp8pbOf(a_codes, &a_inv, 192, k);
+ const b = fp8pbOf(b_codes, &b_inv, 128, k);
+ // [gate | up]-like: 64 rows of a from inside its second block, then 128 rows of b: 192 rows padded to 256
+ const parts = [_]Share{ .{ .p = &a, .lo = 128, .hi = 192, .klo = 0, .khi = k }, .{ .p = &b, .lo = 0, .hi = 128, .klo = 0, .khi = k } };
+ const z = try sizes(&parts);
+ try testing.expectEqual(@as(usize, 192), z.n);
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(f32, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &parts, got_c, got_s);
+ const both = try gpa.alloc(u8, 192 * k);
+ defer gpa.free(both);
+ @memcpy(both[0 .. 64 * k], a_codes[128 * k ..][0 .. 64 * k]);
+ @memcpy(both[64 * k ..], b_codes);
+ const want_c = try gpa.alloc(u8, 256 * k);
+ defer gpa.free(want_c);
+ fp8.packCodes(want_c, both, 192, k);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ for (0..256) |r| for (0..2) |g| {
+ const want: f32 = if (r < 64) 3 else if (r < 192) 5 else 1;
+ try testing.expectEqual(want, tileScale(got_s, 2, r, g));
+ };
+}
+
+test "a share off the lane's 64-input groups or 64-row tiles, or past its tensor, is refused" {
+ const codes: [128 * 128]u8 = @splat(0);
+ var inv = [_]f32{1};
+ const p = fp8pbOf(&codes, &inv, 128, 128);
+ try testing.expectError(error.BadFp8Share, sizes(&.{.{ .p = &p, .lo = 0, .hi = 128, .klo = 32, .khi = 128 }}));
+ try testing.expectError(error.BadFp8Share, sizes(&.{.{ .p = &p, .lo = 0, .hi = 100, .klo = 0, .khi = 128 }}));
+ try testing.expectError(error.BadFp8Share, sizes(&.{.{ .p = &p, .lo = 64, .hi = 192, .klo = 0, .khi = 128 }}));
+ try testing.expectError(error.BadFp8Share, sizes(&.{ .{ .p = &p, .lo = 0, .hi = 64, .klo = 0, .khi = 128 }, .{ .p = &p, .lo = 0, .hi = 64, .klo = 0, .khi = 64 } }));
+ try testing.expectError(error.BadFp8Share, sizes(&.{}));
+ var small: [8]u8 = undefined;
+ var tiles: [2]f32 = undefined;
+ try testing.expectError(error.BadFp8Share, pack(testing.allocator, &.{.{ .p = &p, .lo = 0, .hi = 64, .klo = 0, .khi = 128 }}, &small, &tiles));
+}
diff --git a/zig/src/families/glm/cuda_glue.zig b/zig/src/families/glm/cuda_glue.zig
index e091a93394..4ead02cb10 100644
--- a/zig/src/families/glm/cuda_glue.zig
+++ b/zig/src/families/glm/cuda_glue.zig
@@ -4,8 +4,8 @@ const std = @import("std");
const cuda = @import("cuda");
const mm_mod = @import("cuda_mm.zig");
-/// What the forward's kernels launch from: our glue's functions.
-pub const Set = struct { glue: *const Kernels, mm: ?*const mm_mod.Kernels = null, packs: ?*const mm_mod.Packed = null, prompt: bool = false }; // mm: linears tiled at load
+/// What the forward's kernels launch from: our glue's functions, glm_mm's when tiled, and qlinear's lane for block FP8.
+pub const Set = struct { glue: *const Kernels, mm: ?*const mm_mod.Kernels = null, packs: ?*const mm_mod.Packed = null, prompt: bool = false, linear: cuda.qlinear.Linear = .{} };
/// Our glm_glue.cu's functions.
pub const Kernels = struct {
diff --git a/zig/src/families/glm/cuda_kv8_test.zig b/zig/src/families/glm/cuda_kv8_test.zig
index 01d7210c01..e99b34ddb6 100644
--- a/zig/src/families/glm/cuda_kv8_test.zig
+++ b/zig/src/families/glm/cuda_kv8_test.zig
@@ -134,6 +134,18 @@ const Ops = struct {
return 0;
}
};
+/// std.mem.zeroes, a projection as stored (a union) taken as a zero bf16 address.
+fn zeroed(comptime T: type) T {
+ if (T == @import("cuda_proj.zig").Proj) return .{ .bf16 = 0 };
+ switch (@typeInfo(T)) {
+ .@"struct" => |info| {
+ var v: T = undefined;
+ inline for (info.field_names, info.field_types) |name, F| @field(v, name) = zeroed(F);
+ return v;
+ },
+ else => return std.mem.zeroes(T),
+ }
+}
test "GLM FP8 KV: actual DSA dense and sparse passes retain the sequence-owned scale pointer" {
const glue = @import("cuda_glue.zig");
const dsa = @import("cuda_dsa_block.zig");
@@ -147,7 +159,7 @@ test "GLM FP8 KV: actual DSA dense and sparse passes retain the sequence-owned s
ks.merge.handle = @ptrFromInt(7);
const k: dsa.Kernels = .{ .set = .{ .glue = &ks }, .ops = .{ .d = &d, .handle = null } };
const stream: cuda.Stream = .{ .d = &d, .handle = null };
- var l = std.mem.zeroes(dsa.Layer);
+ var l = zeroed(dsa.Layer);
l.lw = 512;
l.heads = 32;
l.qk = 256;
diff --git a/zig/src/families/glm/cuda_moe.zig b/zig/src/families/glm/cuda_moe.zig
index 9bcd84b144..e200c92241 100644
--- a/zig/src/families/glm/cuda_moe.zig
+++ b/zig/src/families/glm/cuda_moe.zig
@@ -4,9 +4,9 @@ const std = @import("std");
const cuda = @import("cuda");
const glue = @import("cuda_glue.zig");
const experts = @import("cuda_experts.zig");
-const b16 = @import("cuda_b16.zig");
const exl3 = @import("cuda_exl3.zig");
const prof_mod = @import("cuda_prof.zig");
+const projection = @import("cuda_proj.zig");
/// One MoE layer on a rank: router (bf16 [count][dims]) and fp32 bias, routed experts, shared [gate | up] and down.
pub const Layer = struct {
@@ -15,8 +15,8 @@ pub const Layer = struct {
routed: cuda.experts.Layer, // NVFP4 experts, or only the shape (width, dims, limit) when `exl3` holds them
exl3: ?exl3.Experts = null,
count: usize,
- shared_gu: u64,
- shared_down: u64,
+ shared_gu: projection.Proj, // as the checkpoint stores them: bf16 or block FP8
+ shared_down: projection.Proj,
shared: usize,
top_k: usize,
scale: f32,
@@ -78,9 +78,9 @@ pub fn fill(k: Kernels, stream: cuda.Stream, l: Layer, s: Scratch, x: u64, x_str
const n = hi - lo;
const bf16_slots = prompt and l.exl3 == null; // the slot rows: bf16 in an NVFP4 prompt chunk, else fp32
const eb: usize = if (bf16_slots) 2 else 4;
- try b16.matmul(k.set, stream, x + lo * x_stride * 2, x_stride, l.shared_gu, s.sgu + lo * 2 * l.shared * 2, s.mm_part, n, 2 * l.shared, d, false);
+ try projection.matmul(k.set, stream, l.shared_gu, x + lo * x_stride * 2, x_stride, s.sgu + lo * 2 * l.shared * 2, s.mm_part, n, 2 * l.shared, d, false);
try glue.swiglu(k.set, stream, s.sgu + lo * 2 * l.shared * 2, s.sact + lo * l.shared * 2, s.sxs + lo * (l.shared / 64) * 4, l.routed.limit, n, l.shared);
- try b16.matmul(k.set, stream, s.sact + lo * l.shared * 2, l.shared, l.shared_down, s.sy + lo * d * 4, s.mm_part, n, d, l.shared, true);
+ try projection.matmul(k.set, stream, l.shared_down, s.sact + lo * l.shared * 2, l.shared, s.sy + lo * d * 4, s.mm_part, n, d, l.shared, true);
var a: cuda.Args = .{};
a.add(s.sy + lo * d * 4);
a.add(s.ey + lo * slots * d * eb);
diff --git a/zig/src/families/glm/cuda_proj.zig b/zig/src/families/glm/cuda_proj.zig
new file mode 100644
index 0000000000..4c5efb0d54
--- /dev/null
+++ b/zig/src/families/glm/cuda_proj.zig
@@ -0,0 +1,37 @@
+//! A GLM-5.3-Flash projection as its checkpoint stores it: bf16 through the glue's matmuls, block FP8 through qlinear.
+
+const std = @import("std");
+const cuda = @import("cuda");
+const glue = @import("cuda_glue.zig");
+const b16 = @import("cuda_b16.zig");
+
+/// A rank's projection: bf16 rows for the glue, or an FP8_PB_WO checkpoint's block FP8 packed for the lane.
+pub const Proj = union(enum) { bf16: u64, fp8g: cuda.qmmf.Weight };
+
+/// x [m][k] (rows `x_stride` apart) by `w` into out [m][n]: bf16, or fp32 with `wide`; `part` the bf16 path's slices.
+pub fn matmul(set: glue.Set, stream: cuda.Stream, w: Proj, x: u64, x_stride: usize, out: u64, part: u64, m: usize, n: usize, k: usize, wide: bool) !void {
+ switch (w) {
+ .bf16 => |p| return b16.matmul(set, stream, x, x_stride, p, out, part, m, n, k, wide),
+ .fp8g => |q| {
+ if (q.n != n or q.k != k) return error.ShapeMismatch;
+ const lane = set.linear.lane orelse return error.FormatNotLoaded;
+ // GB10's lane meets its K slices in a cluster; a GPU that sums them in memory would need partials held here
+ if (lane.partBytes(m, q) != 0) return error.SlicePartialsNotHeld;
+ const in: cuda.qlinear.Rows = .{ .x = x, .ldx = x_stride };
+ const f: cuda.qlinear.Weight = .{ .fp8g = q };
+ return if (wide) set.linear.decodeF32(stream, f, in, out, m) else set.linear.decode(stream, f, in, out, m);
+ },
+ }
+}
+
+test "a block-FP8 projection is refused before any launch when the call's shape or the loaded kernels do not fit" {
+ const q = cuda.fp8.weight(0, 0, 64, 4096);
+ const s: cuda.Stream = undefined; // never reached: the refusals come first
+ var set: glue.Set = .{ .glue = undefined };
+ try std.testing.expectError(error.ShapeMismatch, matmul(set, s, .{ .fp8g = q }, 0, 4096, 0, 0, 1, 128, 4096, false));
+ try std.testing.expectError(error.FormatNotLoaded, matmul(set, s, .{ .fp8g = q }, 0, 4096, 0, 0, 1, 64, 4096, true));
+ var lane: cuda.qmmf.Lane = undefined;
+ lane.clusters = false; // a GPU before sm_90: 64 outputs from 4,096 inputs take K slices summed in memory
+ set.linear = .{ .lane = &lane };
+ try std.testing.expectError(error.SlicePartialsNotHeld, matmul(set, s, .{ .fp8g = q }, 0, 4096, 0, 0, 1, 64, 4096, true));
+}
diff --git a/zig/src/families/glm/cuda_tile.zig b/zig/src/families/glm/cuda_tile.zig
index 882ef34c0d..4a2a6a24de 100644
--- a/zig/src/families/glm/cuda_tile.zig
+++ b/zig/src/families/glm/cuda_tile.zig
@@ -4,6 +4,7 @@ const cuda = @import("cuda");
const weights = @import("cuda_weights.zig");
const mm_mod = @import("cuda_mm.zig");
const qpack = @import("cuda_qpack.zig");
+const projection = @import("cuda_proj.zig");
const Weights = weights.Weights;
const Layer = weights.Layer;
@@ -14,6 +15,14 @@ fn add(l: *std.ArrayList(Lin), gpa: std.mem.Allocator, field: *u64, n: usize, k:
if (field.* != 0) try l.append(gpa, .{ .field = field, .n = n, .k = k });
}
+/// A projection as stored: its bf16 rows like any linear's; block FP8 stays the lane's tiles, never read as bf16 here.
+fn addProj(l: *std.ArrayList(Lin), gpa: std.mem.Allocator, p: *projection.Proj, n: usize, k: usize) !void {
+ switch (p.*) {
+ .bf16 => |*w| try add(l, gpa, w, n, k),
+ .fp8g => {},
+ }
+}
+
fn layerLinears(gpa: std.mem.Allocator, l: *Layer, list: *std.ArrayList(Lin)) !void {
const kd = @import("cuda_kda.zig").dk;
switch (l.mixer) {
@@ -27,11 +36,11 @@ fn layerLinears(gpa: std.mem.Allocator, l: *Layer, list: *std.ArrayList(Lin)) !v
try add(list, gpa, &x.o, x.dims, n);
},
.mla => |*x| {
- try add(list, gpa, &x.proj, x.q_lora + x.lw, x.dims);
- try add(list, gpa, &x.q_b, x.heads * x.qk, x.q_lora);
+ try addProj(list, gpa, &x.proj, x.q_lora + x.lw, x.dims);
+ try addProj(list, gpa, &x.q_b, x.heads * x.qk, x.q_lora);
try add(list, gpa, &x.index.kw, x.index.d + x.index.heads, x.dims);
try add(list, gpa, &x.index.qb, x.index.heads * x.index.d, x.q_lora);
- try add(list, gpa, &x.o, x.dims, x.heads * x.dv);
+ try addProj(list, gpa, &x.o, x.dims, x.heads * x.dv);
},
}
switch (l.ffn) {
@@ -43,8 +52,8 @@ fn layerLinears(gpa: std.mem.Allocator, l: *Layer, list: *std.ArrayList(Lin)) !v
.nvfp4 => {},
},
.moe => |*x| {
- try add(list, gpa, &x.shared_gu, 2 * x.shared, x.routed.dims);
- try add(list, gpa, &x.shared_down, x.routed.dims, x.shared);
+ try addProj(list, gpa, &x.shared_gu, 2 * x.shared, x.routed.dims);
+ try addProj(list, gpa, &x.shared_down, x.routed.dims, x.shared);
},
}
}
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index 9569aecb30..0d7787adab 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -14,7 +14,7 @@ const quant = @import("cuda_quant.zig");
const q4 = @import("cuda_q4.zig");
const exl3 = @import("cuda_exl3.zig");
const mm_mod = @import("cuda_mm.zig");
-const formats = @import("cuda_formats.zig");
+pub const formats = @import("cuda_formats.zig");
const Tensor = core.checkpoint.Tensor;
const prefix = "model.language_model.";
@@ -96,6 +96,11 @@ const Loader = struct {
}
pub fn formatIn(L: *Loader, ck: *core.Checkpoint, module: []const u8, want: formats.Format) !void {
+ _ = try L.formatOne(ck, module, &.{want});
+ }
+
+ /// `module`'s stored format (formats.of) when it is one of `wants`; any other is refused by name.
+ pub fn formatOne(L: *Loader, ck: *core.Checkpoint, module: []const u8, wants: []const formats.Format) !formats.Format {
var buf: [224]u8 = undefined;
const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
const scale_name = try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module});
@@ -107,9 +112,9 @@ const Loader = struct {
};
const declared = L.declaredAs(module);
const got: ?formats.Format = formats.of(declared, stored) catch null;
- if (got != null and got.? == want) return;
+ if (got) |f| for (wants) |want| if (f == want) return f;
var why: [320]u8 = undefined;
- std.log.err("{s}; this loader reads it as {t}", .{ formats.describe(&why, module, declared, stored), want });
+ std.log.err("{s}; this loader reads it as {any}", .{ formats.describe(&why, module, declared, stored), wants });
return error.UnreadableFormat;
}
@@ -148,18 +153,18 @@ const Loader = struct {
}
/// Rows [lo, hi) of `t`'s outermost axis (`total` long; a vector's elements): split.py's "row" cut.
- fn rows(L: *Loader, t: Tensor, lo: usize, hi: usize, total: usize, to: core.checkpoint.DType) !void {
+ pub fn rows(L: *Loader, t: Tensor, lo: usize, hi: usize, total: usize, to: core.checkpoint.DType) !void {
const per = t.numel() / total;
if (per * total != t.numel()) return error.UnexpectedTensor;
try L.add(t, lo * per, hi * per, to);
}
- fn whole(L: *Loader, t: Tensor, to: core.checkpoint.DType) !void {
+ pub fn whole(L: *Loader, t: Tensor, to: core.checkpoint.DType) !void {
try L.add(t, 0, t.numel(), to);
}
/// Columns [lo, hi) of every row of a matrix: split.py's "col" cut.
- fn cols(L: *Loader, t: Tensor, lo: usize, hi: usize, to: core.checkpoint.DType) !void {
+ pub fn cols(L: *Loader, t: Tensor, lo: usize, hi: usize, to: core.checkpoint.DType) !void {
if (t.rank != 2) return error.UnexpectedTensor;
const k = t.dim(1);
for (0..t.dim(0)) |r| try L.add(t, r * k + lo, r * k + hi, to);
@@ -207,15 +212,13 @@ const Loader = struct {
const c = &L.w.config;
const h = L.w.share.mla;
const qk = c.nope;
- inline for (.{ "q_a_proj", "kv_a_proj_with_mqa", "q_b_proj", "kv_b_proj", "o_proj" }) |x| try L.format("layers.{d}.self_attn." ++ x, .{i}, .bf16);
- inline for (.{ "q_a_proj", "kv_a_proj_with_mqa" }) |x| try L.whole(try L.get("layers.{d}.self_attn." ++ x ++ ".weight", .{i}), .bf16);
- const proj = try L.put("layers.{d}.dsa.proj", .{i});
+ try L.format("layers.{d}.self_attn.kv_b_proj", .{i}, .bf16);
+ const proj = try formats.cutProj(L, prefix, i, &.{ .{ .rel = "self_attn.q_a_proj" }, .{ .rel = "self_attn.kv_a_proj_with_mqa" } }, "layers.{d}.dsa.proj");
try L.whole(try L.get("layers.{d}.self_attn.q_a_layernorm.weight", .{i}), .bf16);
const q_norm = try L.put("layers.{d}.dsa.q_norm", .{i});
try L.whole(try L.get("layers.{d}.self_attn.kv_a_layernorm.weight", .{i}), .bf16);
const kv_norm = try L.put("layers.{d}.dsa.kv_norm", .{i});
- try L.rows(try L.get("layers.{d}.self_attn.q_b_proj.weight", .{i}), h.lo * qk, h.hi * qk, c.mla_heads * qk, .bf16);
- const q_b = try L.put("layers.{d}.dsa.q_b", .{i});
+ const q_b = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.q_b_proj", .rows = .{ h.lo * qk, h.hi * qk } }}, "layers.{d}.dsa.q_b");
const kv_b = try L.get("layers.{d}.self_attn.kv_b_proj.weight", .{i});
const per = qk + c.v_dim; // a head's kv_b rows: its keys, then its values
const total = c.mla_heads * per;
@@ -223,8 +226,7 @@ const Loader = struct {
const wk = try L.put("layers.{d}.dsa.absorb.wk", .{i});
for (h.lo..h.hi) |head| try L.rows(kv_b, head * per + qk, (head + 1) * per, total, .bf16);
const wv = try L.put("layers.{d}.dsa.absorb.wv", .{i});
- try L.cols(try L.get("layers.{d}.self_attn.o_proj.weight", .{i}), h.lo * c.v_dim, h.hi * c.v_dim, .bf16);
- const o = try L.put("layers.{d}.dsa.o", .{i});
+ const o = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.o_proj", .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } }}, "layers.{d}.dsa.o");
inline for (.{ "wk", "weights_proj" }) |x| try L.whole(try L.get("layers.{d}.self_attn.indexer." ++ x ++ ".weight", .{i}), .bf16);
const kw = try L.put("layers.{d}.dsa.index.kw", .{i});
try L.whole(try L.get("layers.{d}.self_attn.indexer.wq_b.weight", .{i}), .bf16);
@@ -398,11 +400,9 @@ const Loader = struct {
if (!x3.?.shx) L.w.exl3_shx = false;
} else routed = try L.nvfp4Experts(i);
const sh = L.w.share.shared;
- inline for (.{ "gate_proj", "up_proj", "down_proj" }) |x| try L.format("layers.{d}.mlp.shared_experts." ++ x, .{i}, .bf16);
- inline for (.{ "gate_proj", "up_proj" }) |x| try L.rows(try L.get("layers.{d}.mlp.shared_experts." ++ x ++ ".weight", .{i}), sh.lo, sh.hi, c.shared_inter, .bf16);
- const gu = try L.put("layers.{d}.moe.shared.gu", .{i});
- try L.cols(try L.get("layers.{d}.mlp.shared_experts.down_proj.weight", .{i}), sh.lo, sh.hi, .bf16);
- const sd = try L.put("layers.{d}.moe.shared.down", .{i});
+ const gu_parts = [_]formats.Part{ .{ .rel = "mlp.shared_experts.gate_proj", .rows = .{ sh.lo, sh.hi } }, .{ .rel = "mlp.shared_experts.up_proj", .rows = .{ sh.lo, sh.hi } } };
+ const gu = try formats.cutProj(L, prefix, i, &gu_parts, "layers.{d}.moe.shared.gu");
+ const sd = try formats.cutProj(L, prefix, i, &.{.{ .rel = "mlp.shared_experts.down_proj", .cols = .{ sh.lo, sh.hi } }}, "layers.{d}.moe.shared.down");
return .{
.router = router,
.bias = bias,
@@ -574,4 +574,6 @@ test "bf16 rounding of fp32 is to nearest even" {
test {
_ = @import("cuda_formats.zig");
+ _ = @import("cuda_proj.zig");
+ _ = @import("cuda_fp8pb.zig");
}
diff --git a/zig/tests/cuda/glm_fp8g_shares.zig b/zig/tests/cuda/glm_fp8g_shares.zig
new file mode 100644
index 0000000000..5877ef9762
--- /dev/null
+++ b/zig/tests/cuda/glm_fp8g_shares.zig
@@ -0,0 +1,90 @@
+//! `glm-fp8g-shares`: block-FP8 TP shares packed as GLM's loader packs them, on the lane, against FP64 of the cut.
+const std = @import("std");
+const cuda = @import("cuda");
+const core = @import("core");
+const glm = @import("glm_cuda");
+const check = @import("check.zig");
+const reference = @import("quant_ref_tests.zig");
+const Rig = reference.RigT;
+const bf = reference.helpers.bfOf;
+const fp8pb = glm.weights.formats.fp8pb;
+const e4m3 = core.quant.fp8.e4m3;
+
+/// A random FP8_PB_WO matrix [n, k]: finite e4m3 codes and its 128 x 128 block scales.
+fn matrix(r: *Rig, n: usize, k: usize) !fp8pb.Fp8pb {
+ const codes = try r.a.alloc(u8, n * k);
+ for (codes) |*v| v.* = reference.helpers.fp8CodeOf(r);
+ const inv = try r.a.alloc(f32, ((n + 127) / 128) * ((k + 127) / 128));
+ for (inv) |*v| v.* = r.rng.float(f32) * 0.01 + 0.001;
+ return .{ .n = n, .k = k, .codes = codes, .scales = inv };
+}
+
+/// Weight (row, col) of the stored matrix, read without the packing.
+fn at(p: fp8pb.Fp8pb, row: usize, col: usize) f64 {
+ return e4m3(p.codes[row * p.k + col]) * p.scales[(row / 128) * ((p.k + 127) / 128) + col / 128];
+}
+
+/// `parts` packed for the lane, x [m][k] times them in fp32, against FP64 of the parts' rows and inputs as stored.
+fn share(r: *Rig, lane: *const cuda.qmmf.Lane, label: []const u8, parts: []const fp8pb.Share) !void {
+ const z = try fp8pb.sizes(parts);
+ const codes = try r.a.alloc(u8, z.codes);
+ const tiles = try r.a.alloc(f32, z.scales);
+ try fp8pb.pack(r.a, parts, codes, tiles);
+ const w = cuda.fp8.weight(try r.dev(codes), try r.dev(std.mem.sliceAsBytes(tiles)), z.n, z.k);
+ const m = 17; // two 16-row tiles, the second partial
+ const x = try r.bfs(m * z.k, 1.0);
+ const dx = try r.dev(std.mem.sliceAsBytes(x));
+ const want = try r.a.alloc(f64, m * z.n);
+ const tol = try r.a.alloc(f64, m * z.n);
+ const wrow = try r.a.alloc(f64, z.k);
+ var col: usize = 0;
+ for (parts) |q| for (q.lo..q.hi) |src| {
+ for (wrow, 0..) |*v, i| v.* = at(q.p.*, src, q.klo + i); // output col's weights, decoded once
+ for (0..m) |row| {
+ var sum: f64 = 0;
+ var magnitude: f64 = 0;
+ for (wrow, 0..) |wv, i| {
+ const v = bf(x[row * z.k + i]) * wv;
+ sum += v;
+ magnitude += @abs(v);
+ }
+ want[row * z.n + col] = sum;
+ tol[row * z.n + col] = magnitude * 2e-6 + @abs(sum) * 1e-6 + 1e-30;
+ }
+ col += 1;
+ };
+ var out = try cuda.DeviceBuffer.alloc(r.gpu.d, m * z.n * 4);
+ defer out.free();
+ try out.fill8(0xff, r.s.handle);
+ if (lane.partBytes(m, w) != 0) return error.SlicePartialsNotHeld;
+ try lane.matmulOut(r.s, dx, z.k, m, w, out.ptr, .f32, 0);
+ try r.close(label, out.ptr, true, want, tol);
+}
+
+pub fn run(gpu: check.Gpu) !void {
+ var arena = std.heap.ArenaAllocator.init(gpu.gpa);
+ defer arena.deinit();
+ var s = try cuda.Stream.init(gpu.d, false);
+ defer s.deinit();
+ var prng = std.Random.DefaultPrng.init(0xf8_5ba7e);
+ var r: Rig = .{ .gpu = gpu, .a = arena.allocator(), .s = s, .rng = prng.random() };
+ defer {
+ for (r.bufs.items) |*b| b.free();
+ r.bufs.deinit(gpu.gpa);
+ }
+ var lane = try cuda.qmmf.Lane.load(gpu.d, (try gpu.ctx.capability()) / 10);
+ defer lane.unload();
+ // the shared expert as the loader cuts it (shard.Share's spans of 2,048 rows): [gate | up] by rows, down by inputs
+ const gate = try matrix(&r, 2048, 4096);
+ const up = try matrix(&r, 2048, 4096);
+ const down = try matrix(&r, 4096, 2048);
+ const spans = [_][2]usize{ .{ 0, 2048 }, .{ 0, 1024 }, .{ 1024, 2048 }, .{ 0, 704 }, .{ 704, 1408 }, .{ 1408, 2048 } };
+ var label: [96]u8 = undefined;
+ for (spans) |sp| {
+ const gu = [_]fp8pb.Share{ .{ .p = &gate, .lo = sp[0], .hi = sp[1], .klo = 0, .khi = 4096 }, .{ .p = &up, .lo = sp[0], .hi = sp[1], .klo = 0, .khi = 4096 } };
+ try share(&r, &lane, try std.fmt.bufPrint(&label, "[gate | up] rows {d}..{d}", .{ sp[0], sp[1] }), &gu);
+ const d = [_]fp8pb.Share{.{ .p = &down, .lo = 0, .hi = 4096, .klo = sp[0], .khi = sp[1] }};
+ try share(&r, &lane, try std.fmt.bufPrint(&label, "down inputs {d}..{d}", .{ sp[0], sp[1] }), &d);
+ }
+ check.pass("glm-fp8g-shares: TP 1, 2 and 3 shares of block-FP8 [gate | up] and down within {d:.3} of FP64", .{r.worst});
+}
diff --git a/zig/tests/cuda/glm_loader_gate.zig b/zig/tests/cuda/glm_loader_gate.zig
index a145468115..0c7169f097 100644
--- a/zig/tests/cuda/glm_loader_gate.zig
+++ b/zig/tests/cuda/glm_loader_gate.zig
@@ -13,9 +13,13 @@ const P = "model.language_model.";
/// A piece of an upload: tensor `name`'s outer rows [lo, hi) (a vector's elements), or a matrix's columns [lo, hi).
const Cut = struct { name: []const u8, cols: bool = false, lo: usize, hi: usize, to: DType = .bf16 };
+/// What an upload is held against: its cut as stored, a host packer's bytes from the gate's own cut, or nothing.
+const Want = union(enum) { cut, made: []u8, skip: []const u8 };
+
/// One upload's pieces as the Python family's weights.load cuts them, read from a checkpoint apart from the loader's.
const Plan = struct {
a: std.mem.Allocator,
+ gpa: std.mem.Allocator, // a packed upload's expected bytes, freed once compared
ck: *core.Checkpoint,
cuts: std.ArrayList(Cut) = .empty,
@@ -45,8 +49,8 @@ fn eql(a: []const u8, b: []const u8) bool {
/// The MTP head's uploads (`mtp.{what}`) and the tensors under layer `layers` they come from.
const mtp_sources = [_][2][]const u8{ .{ "enorm", "enorm" }, .{ "hnorm", "hnorm" }, .{ "eh", "eh_proj" }, .{ "norm", "shared_head.norm" } };
-/// Upload `name`'s pieces at share `s` into `p`; false when it is packed (no host reference here yet).
-fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !bool {
+/// Upload `name`'s pieces at share `s` into `p`, or the bytes a host packer makes of them, or why it is left out.
+fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !Want {
if (eql(name, "embed")) {
try p.whole(P ++ "embed_tokens.weight", .{}, .bf16);
} else if (eql(name, "norm")) {
@@ -54,11 +58,11 @@ fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !bool
} else if (eql(name, "head")) {
try p.rows("lm_head.weight", .{}, s.vocab.lo, s.vocab.hi, .bf16);
} else if (std.mem.startsWith(u8, name, "draft_head.")) {
- return false; // the GPU's 4-bit tiles of the head's rows
+ return .{ .skip = "the GPU quantizer's 4-bit tiles of the head's rows" };
} else if (std.mem.startsWith(u8, name, "mtp.")) {
for (mtp_sources) |m| if (eql(name[4..], m[0])) {
try p.whole(P ++ "layers.{d}.{s}.weight", .{ c.layers, m[1] }, .bf16);
- return true;
+ return .cut;
};
return error.UnknownUpload;
} else if (std.mem.startsWith(u8, name, "layers.")) {
@@ -66,41 +70,43 @@ fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !bool
const i = std.fmt.parseInt(u32, name["layers.".len..dot], 10) catch return error.UnknownUpload;
return layer(p, c, s, i, name[dot + 1 ..]);
} else return error.UnknownUpload;
- return true;
+ return .cut;
}
/// Layer `i`'s upload `what`, as `plan`.
-fn layer(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+fn layer(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const L = P ++ "layers.{d}.";
if (eql(what, "in_norm")) {
try p.whole(L ++ "input_layernorm.weight", .{i}, .bf16);
- return true;
+ return .cut;
}
if (eql(what, "post_norm")) {
try p.whole(L ++ "post_attention_layernorm.weight", .{i}, .bf16);
- return true;
+ return .cut;
}
inline for (.{ "attn", "ffn" }) |site| {
if (eql(what, site ++ "_hc.fn")) {
try p.whole(L ++ "hc_" ++ site ++ "_fn", .{i}, .bf16);
- return true;
+ return .cut;
}
inline for (.{ "base", "scale" }) |x| if (eql(what, site ++ "_hc." ++ x)) {
try p.whole(L ++ "hc_" ++ site ++ "_" ++ x, .{i}, .f32);
- return true;
+ return .cut;
};
}
+ const fp8g = std.mem.endsWith(u8, what, ".codes") or std.mem.endsWith(u8, what, ".scales");
+ if (fp8g and (std.mem.startsWith(u8, what, "dsa.") or std.mem.startsWith(u8, what, "moe.shared."))) return fp8Made(p, c, s, i, what);
if (std.mem.startsWith(u8, what, "kda.")) return kda(p, c, s, i, what[4..]);
if (std.mem.startsWith(u8, what, "dsa.")) return dsa(p, c, s, i, what[4..]);
if (std.mem.startsWith(u8, what, "moe.")) return moe(p, c, s, i, what[4..]);
if (std.mem.startsWith(u8, what, "mlp.") and (std.mem.endsWith(u8, what, ".words") or std.mem.endsWith(u8, what, ".scales"))) {
- return false; // a dense NVFP4 linear: tf_pack_dense's words, packScales' tiles
+ return denseMade(p, c, s, i, what);
}
return error.UnknownUpload;
}
/// KDA: [q | k | v | f_a | g_a | b], q k v b by the rank's heads; f_b, g_b, taps, dt_bias by heads; o by columns.
-fn kda(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+fn kda(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const at = P ++ "layers.{d}.self_attn.";
const h = s.kda;
const dk: usize = c.kda_dim;
@@ -121,11 +127,11 @@ fn kda(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
} else if (eql(what, "o")) {
try p.cols(at ++ "o_proj.weight", .{i}, h.lo * dk, h.hi * dk);
} else return error.UnknownUpload;
- return true;
+ return .cut;
}
/// Latent attention: [q_a | kv_a] whole; q_b, kv_b's keys and values by heads (rows from the tensors); o by columns.
-fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const at = P ++ "layers.{d}.self_attn.";
const h = s.mla;
const dv: usize = c.v_dim;
@@ -155,25 +161,27 @@ fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
const index = [_][2][]const u8{ .{ "index.qb", "wq_b.weight" }, .{ "index.ln_w", "k_norm.weight" }, .{ "index.ln_b", "k_norm.bias" }, .{ "index.gate", "index_kpool_compress_gate" }, .{ "index.ape", "index_kpool_compress_ape" } };
for (index) |x| if (eql(what, x[0])) {
try p.whole(at ++ "indexer.{s}", .{ i, x[1] }, .bf16);
- return true;
+ return .cut;
};
return error.UnknownUpload;
}
- return true;
+ return .cut;
}
-/// MoE: router and bias whole, the experts' global scales, the shared expert's gate and up by rows, down by columns.
-fn moe(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !bool {
+/// MoE: router and bias whole, the experts' tiles and global scales, shared gate and up by rows, down by columns.
+fn moe(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const at = P ++ "layers.{d}.mlp.";
if (eql(what, "router")) {
try p.whole(at ++ "gate.weight", .{i}, .bf16);
} else if (eql(what, "bias")) {
try p.whole(at ++ "gate.e_score_correction_bias", .{i}, .f32);
} else if (eql(what, "experts.up") or eql(what, "experts.down")) {
- return false; // packGateUp's and packOne's tiles
+ return expertsMade(p, c, s, i, eql(what, "experts.up"));
} else if (eql(what, "experts.up_scale") or eql(what, "experts.down_scale")) {
// the MTP layer's bf16 experts take the GPU quantizer's scales; W4A16_NVFP4 ones (FP4-Local's) keep their own
- if (i == c.layers and !p.ck.has(try std.fmt.allocPrint(p.a, at ++ "experts.0.gate_proj.weight_scale_2", .{i}))) return false;
+ if (i == c.layers and !p.ck.has(try std.fmt.allocPrint(p.a, at ++ "experts.0.gate_proj.weight_scale_2", .{i}))) {
+ return .{ .skip = "the GPU quantizer's, from the MTP layer's bf16 experts" };
+ }
for (0..c.experts) |e| {
const projs: []const []const u8 = if (eql(what, "experts.up_scale")) &.{ "gate_proj", "up_proj" } else &.{"down_proj"};
for (projs) |x| try p.whole(at ++ "experts.{d}.{s}.weight_scale_2", .{ i, e, x }, .f32);
@@ -183,7 +191,140 @@ fn moe(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
} else if (eql(what, "shared.down")) {
try p.cols(at ++ "shared_experts.down_proj.weight", .{i}, s.shared.lo, s.shared.hi);
} else return error.UnknownUpload;
- return true;
+ return .cut;
+}
+
+/// `module`'s e4m3 block scales [n, k/16]: as stored, or its csf_32x2_v1 parts decoded here.
+fn scalesOf(p: *Plan, module: []const u8, n: usize, k: usize) ![]u8 {
+ const out = try p.gpa.alloc(u8, n * k / 16);
+ errdefer p.gpa.free(out);
+ if (!p.ck.has(try std.fmt.allocPrint(p.a, "{s}.weight_scale.csf32_bases", .{module}))) {
+ const t = try p.ck.get(try std.fmt.allocPrint(p.a, "{s}.weight_scale", .{module}));
+ if (t.dtype != .f8_e4m3 or t.bytes.len != out.len) return error.UnexpectedDType;
+ @memcpy(out, t.bytes);
+ return out;
+ }
+ var parts: core.nvfp4_csf32.Parts = undefined;
+ inline for (.{ "bases", "codes", "page_bases", "offsets", "exceptions" }) |x| {
+ @field(parts, x) = (try p.ck.get(try std.fmt.allocPrint(p.a, "{s}.weight_scale.csf32_" ++ x, .{module}))).bytes;
+ }
+ try core.nvfp4_csf32.decode(parts, n, k / 16, out);
+ return out;
+}
+
+/// NVFP4 matrix `module`'s rows [lo, hi) and inputs [klo, khi): its codes and its e4m3 scales, each its own copy.
+fn cut4(p: *Plan, module: []const u8, lo: usize, hi: usize, klo: usize, khi: usize) ![2][]u8 {
+ const w = try p.ck.get(try std.fmt.allocPrint(p.a, "{s}.weight", .{module}));
+ const kk = 2 * w.dim(1);
+ if (w.dtype != .u8 or w.rank != 2 or lo > hi or hi > w.dim(0) or klo > khi or khi > kk or klo % 16 != 0 or (khi - klo) % 64 != 0) return error.CutOutside;
+ const all = try scalesOf(p, module, w.dim(0), kk);
+ defer p.gpa.free(all);
+ const n = hi - lo;
+ const k = khi - klo;
+ const codes = try p.gpa.alloc(u8, n * k / 2);
+ errdefer p.gpa.free(codes);
+ const scales = try p.gpa.alloc(u8, n * k / 16);
+ for (0..n) |r| {
+ @memcpy(codes[r * k / 2 ..][0 .. k / 2], w.bytes[(lo + r) * kk / 2 + klo / 2 ..][0 .. k / 2]);
+ @memcpy(scales[r * k / 16 ..][0 .. k / 16], all[(lo + r) * kk / 16 + klo / 16 ..][0 .. k / 16]);
+ }
+ return .{ codes, scales };
+}
+
+/// A dense NVFP4 linear's `.words` or `.scales`: cuda.nvfp4's host packers (the loader packs words on the GPU).
+fn denseMade(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
+ const proj = what["mlp.".len..std.mem.lastIndexOfScalar(u8, what, '.').?];
+ const d: usize = c.hidden;
+ const down = eql(proj, "down_proj"); // down: every row, the rank's inputs; gate and up: the rank's rows
+ const at: [4]usize = if (down) .{ 0, d, s.dense.lo, s.dense.hi } else .{ s.dense.lo, s.dense.hi, 0, d };
+ const q = try cut4(p, try std.fmt.allocPrint(p.a, P ++ "layers.{d}.mlp.{s}", .{ i, proj }), at[0], at[1], at[2], at[3]);
+ defer for (q) |b| p.gpa.free(b);
+ const n = at[1] - at[0];
+ const k = at[3] - at[2];
+ const npad = cuda.nvfp4.padded(n);
+ if (std.mem.endsWith(u8, what, ".scales")) {
+ const tiles = try p.gpa.alloc(u8, npad * k / 16);
+ cuda.nvfp4.packScales(tiles, q[1], n, k);
+ return .{ .made = tiles };
+ }
+ const words = try p.gpa.alloc(u32, npad / 64 * (k / cuda.nvfp4.group) * 512);
+ defer p.gpa.free(words);
+ cuda.nvfp4.packWords(words, q[0], n, k);
+ return .{ .made = try p.gpa.dupe(u8, std.mem.sliceAsBytes(words)) };
+}
+
+/// The routed experts' tiles: gate and up by the rank's rows (packGateUp), down by its inputs (packOne).
+fn expertsMade(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, up: bool) !Want {
+ const at = P ++ "layers.{d}.mlp.experts.{d}.";
+ if (i == c.layers and (try p.ck.get(try std.fmt.allocPrint(p.a, at ++ "gate_proj.weight", .{ i, 0 }))).dtype == .bf16) {
+ return .{ .skip = "the GPU quantizer's, from the MTP layer's bf16 experts" };
+ }
+ const X = cuda.experts.Nvfp4Experts;
+ const d: usize = c.hidden;
+ const cut = s.moe;
+ const ni = cut.len();
+ const per = if (up) X.bytes(1, ni, d, 2) / 4 else X.bytes(1, d, ni, 1) / 4;
+ const out = try p.gpa.alloc(u32, c.experts * per);
+ defer p.gpa.free(out);
+ for (0..c.experts) |e| {
+ const block = out[e * per ..][0..per];
+ if (!up) {
+ const q = try cut4(p, try std.fmt.allocPrint(p.a, at ++ "down_proj", .{ i, e }), 0, d, cut.lo, cut.hi);
+ defer for (q) |b| p.gpa.free(b);
+ X.packOne(block, q[0], q[1], d, ni);
+ continue;
+ }
+ const g = try cut4(p, try std.fmt.allocPrint(p.a, at ++ "gate_proj", .{ i, e }), cut.lo, cut.hi, 0, d);
+ defer for (g) |b| p.gpa.free(b);
+ const u = try cut4(p, try std.fmt.allocPrint(p.a, at ++ "up_proj", .{ i, e }), cut.lo, cut.hi, 0, d);
+ defer for (u) |b| p.gpa.free(b);
+ X.packGateUp(block, .{ g[0], g[1] }, .{ u[0], u[1] }, ni, d);
+ }
+ return .{ .made = try p.gpa.dupe(u8, std.mem.sliceAsBytes(out)) };
+}
+
+/// A block-FP8 projection's lane codes or scale tiles: the rank's cut of each stored part, packed as cuda_fp8pb packs.
+fn fp8Made(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
+ const FP = glm.weights.formats.fp8pb;
+ const base = what[0..std.mem.lastIndexOfScalar(u8, what, '.').?];
+ const Part = struct { rel: []const u8, rows: ?[2]usize = null, cols: ?[2]usize = null };
+ const h = s.mla;
+ const sh = s.shared;
+ const at = P ++ "layers.{d}.";
+ var parts: [2]Part = undefined;
+ const n_parts: usize = if (eql(base, "dsa.proj") or eql(base, "moe.shared.gu")) 2 else 1;
+ if (eql(base, "dsa.proj")) {
+ parts = .{ .{ .rel = "self_attn.q_a_proj" }, .{ .rel = "self_attn.kv_a_proj_with_mqa" } };
+ } else if (eql(base, "dsa.q_b")) {
+ const per = try p.outer(at ++ "self_attn.q_b_proj.weight", .{i}) / c.mla_heads;
+ parts[0] = .{ .rel = "self_attn.q_b_proj", .rows = .{ h.lo * per, h.hi * per } };
+ } else if (eql(base, "dsa.o")) {
+ parts[0] = .{ .rel = "self_attn.o_proj", .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } };
+ } else if (eql(base, "moe.shared.gu")) {
+ parts = .{ .{ .rel = "mlp.shared_experts.gate_proj", .rows = .{ sh.lo, sh.hi } }, .{ .rel = "mlp.shared_experts.up_proj", .rows = .{ sh.lo, sh.hi } } };
+ } else if (eql(base, "moe.shared.down")) {
+ parts[0] = .{ .rel = "mlp.shared_experts.down_proj", .cols = .{ sh.lo, sh.hi } };
+ } else return error.UnknownUpload;
+ var stored: [2]FP.Fp8pb = undefined;
+ var made: usize = 0;
+ defer for (stored[0..made]) |t| t.deinit(p.gpa);
+ var shares: [2]FP.Share = undefined;
+ for (parts[0..n_parts], 0..) |q, j| {
+ stored[j] = try FP.Fp8pb.read(p.gpa, p.ck, try std.fmt.allocPrint(p.a, at ++ "{s}", .{ i, q.rel }));
+ made += 1;
+ const r = q.rows orelse [2]usize{ 0, stored[j].n };
+ const k = q.cols orelse [2]usize{ 0, stored[j].k };
+ shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = k[0], .khi = k[1] };
+ }
+ const z = try FP.sizes(shares[0..n_parts]);
+ const codes = try p.gpa.alloc(u8, z.codes);
+ errdefer p.gpa.free(codes);
+ const tiles = try p.gpa.alloc(f32, z.scales);
+ defer p.gpa.free(tiles);
+ try FP.pack(p.gpa, shares[0..n_parts], codes, tiles);
+ if (std.mem.endsWith(u8, what, ".codes")) return .{ .made = codes };
+ p.gpa.free(codes);
+ return .{ .made = try p.gpa.dupe(u8, std.mem.sliceAsBytes(tiles)) };
}
/// The bytes `cuts` make: each piece's elements in order, written as its dtype.
@@ -238,21 +379,25 @@ fn rankGate(gpu: check.Gpu, a: std.mem.Allocator, ck: *core.Checkpoint, c: *cons
var w = try glm.weights.load(gpu.gpa, gpu.io, gpu.d, stream, m.tools(), dir, c.*, s, layers, true);
defer w.deinit();
var bad: usize = 0;
- var same: usize = 0;
+ var cut: usize = 0;
+ var made: usize = 0;
var bytes: usize = 0;
- var skipped: std.ArrayList([]const u8) = .empty;
+ var left: std.ArrayList([2][]const u8) = .empty;
for (w.named.items) |n| {
- var p: Plan = .{ .a = a, .ck = ck };
- const known = plan(&p, c, s, n.name) catch |e| {
+ var p: Plan = .{ .a = a, .gpa = gpu.gpa, .ck = ck };
+ const how = plan(&p, c, s, n.name) catch |e| {
std.debug.print("FAIL TP={d} rank {d} {s}: {t}\n", .{ world, rank, n.name, e });
bad += 1;
continue;
};
- if (!known) {
- try skipped.append(a, n.name);
- continue;
- }
- const want = try bytesOf(gpu.gpa, ck, p.cuts.items);
+ const want = switch (how) {
+ .skip => |why| {
+ try left.append(a, .{ n.name, why });
+ continue;
+ },
+ .cut => try bytesOf(gpu.gpa, ck, p.cuts.items),
+ .made => |b| b,
+ };
defer gpu.gpa.free(want);
const got = try gpu.gpa.alloc(u8, n.len);
defer gpu.gpa.free(got);
@@ -267,11 +412,14 @@ fn rankGate(gpu: check.Gpu, a: std.mem.Allocator, ck: *core.Checkpoint, c: *cons
bad += 1;
continue;
};
- same += 1;
+ if (how == .made) made += 1 else cut += 1;
bytes += n.len;
}
- check.pass("glm-loader-gate TP={d} rank {d}: {d} uploads ({d} MiB) as the checkpoint cut on the host, {d} packed not compared", .{ world, rank, same, bytes >> 20, skipped.items.len });
- if (world == 1) for (skipped.items) |name| std.debug.print(" packed, not compared: {s}\n", .{name});
+ const line = "glm-loader-gate TP={d} rank {d}: {d} uploads as the checkpoint cut, {d} as the host packers make them " ++
+ "({d} MiB), {d} left out, {d} differ or are not known";
+ const args = .{ world, rank, cut, made, bytes >> 20, left.items.len, bad };
+ if (bad == 0) check.pass(line, args) else std.debug.print("FAIL " ++ line ++ "\n", args);
+ if (world == 1) for (left.items) |x| std.debug.print(" left out: {s} ({s})\n", .{ x[0], x[1] });
return bad;
}
diff --git a/zig/tests/cuda/glm_synth.zig b/zig/tests/cuda/glm_synth.zig
index 3470e31de4..1c2d9bf27e 100644
--- a/zig/tests/cuda/glm_synth.zig
+++ b/zig/tests/cuda/glm_synth.zig
@@ -1,4 +1,4 @@
-//! `glm-synth OUT_DIR`: a 2-layer + MTP GLM-5.3-Flash checkpoint, dc.json; run TF_GLM_LAYERS=2.
+//! `glm-synth OUT_DIR [fp8pb]`: a 2-layer + MTP GLM-5.3-Flash checkpoint, dc.json; run TF_GLM_LAYERS=2.
const std = @import("std");
const core = @import("core");
const glm = @import("glm_cuda");
@@ -14,8 +14,21 @@ const hidden = 4096;
/// The ModelOpt block nvidia/GLM-5.3-Flash-NVFP4 writes, which the CUDA family admits (modelopt.admitRecipes).
const modelopt_block = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"NVFP4\", " ++
"\"config_groups\": {\"group_0\": {\"weights\": {\"num_bits\": 4, \"type\": \"float\", \"group_size\": 16}}}}";
+/// fp8pb: FP4-Local's MIXED_PRECISION form, layer 1's MLA and shared expert in block FP8, the NVFP4 ones as before.
+const mixed_block = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"MIXED_PRECISION\", " ++
+ "\"quantized_layers\": {" ++ nv4("layers.0.mlp") ++ "," ++ nv4("layers.1.mlp.experts") ++ "," ++ pb("layers.1.mlp.shared_experts") ++ "," ++
+ pb("layers.1.self_attn.q_a_proj") ++ "," ++ pb("layers.1.self_attn.kv_a_proj_with_mqa") ++ "," ++ pb("layers.1.self_attn.q_b_proj") ++ "," ++
+ pb("layers.1.self_attn.o_proj") ++ "}}";
const P = "model.language_model.";
+fn nv4(comptime module: []const u8) []const u8 {
+ return "\"" ++ P ++ module ++ "\": {\"quant_algo\": \"NVFP4\", \"group_size\": 16}";
+}
+
+fn pb(comptime module: []const u8) []const u8 {
+ return "\"" ++ P ++ module ++ "\": {\"quant_algo\": \"FP8_PB_WO\", \"weight_block_size\": [128, 128]}";
+}
+
const Out = struct {
a: std.mem.Allocator,
rng: std.Random,
@@ -62,6 +75,24 @@ const Out = struct {
try o.scalar(try cat(o.a, base, ".input_scale"), 1.0);
}
+ /// FP8_PB_WO: e4m3 codes of magnitude 2^-4 to 1.875 (no NaN), fp32 scales [Rb, 1, Cb, 1] near 1/(2 sqrt(k)).
+ fn fp8pb(o: *Out, base: []const u8, n: usize, k: usize) !void {
+ const codes = try o.a.alloc(u8, n * k);
+ for (codes) |*x| x.* = (o.rng.uintLessThan(u8, 2) << 7) | ((3 + o.rng.uintLessThan(u8, 5)) << 3) | o.rng.uintLessThan(u8, 8);
+ try o.put(try cat(o.a, base, ".weight"), .f8_e4m3, &.{ n, k }, codes);
+ const rb = (n + 127) / 128;
+ const cb = (k + 127) / 128;
+ const v = try o.a.alloc(f32, rb * cb);
+ for (v) |*x| x.* = (0.5 + o.rng.float(f32)) / (2.0 * @sqrt(@as(f32, @floatFromInt(k))));
+ try o.put(try cat(o.a, base, ".weight_scale"), .f32, &.{ rb, 1, cb, 1 }, std.mem.sliceAsBytes(v));
+ }
+
+ /// A linear as bf16, or as FP8_PB_WO with `fp8`.
+ fn either(o: *Out, base: []const u8, n: usize, k: usize, fp8: bool) !void {
+ if (fp8) return o.fp8pb(base, n, k);
+ try o.linear(try cat(o.a, base, ".weight"), n, k);
+ }
+
fn scalar(o: *Out, name: []const u8, value: f32) !void {
const v = try o.a.alloc(f32, 1);
v[0] = value;
@@ -118,15 +149,15 @@ fn kda(o: *Out, i: usize) !void {
try o.linear(try cat(o.a, s, "o_proj.weight"), hidden, 8192);
}
-fn mla(o: *Out, i: usize) !void {
+fn mla(o: *Out, i: usize, fp8: bool) !void {
const s = try tensor(o.a, "layers.{d}.self_attn.", .{i});
- try o.linear(try cat(o.a, s, "q_a_proj.weight"), 1536, hidden);
- try o.linear(try cat(o.a, s, "kv_a_proj_with_mqa.weight"), 512, hidden);
+ try o.either(try cat(o.a, s, "q_a_proj"), 1536, hidden, fp8);
+ try o.either(try cat(o.a, s, "kv_a_proj_with_mqa"), 512, hidden, fp8);
try o.bf16Const(try cat(o.a, s, "q_a_layernorm.weight"), &.{1536}, 1.0);
try o.bf16Const(try cat(o.a, s, "kv_a_layernorm.weight"), &.{512}, 1.0);
- try o.linear(try cat(o.a, s, "q_b_proj.weight"), 16384, 1536);
+ try o.either(try cat(o.a, s, "q_b_proj"), 16384, 1536, fp8);
try o.linear(try cat(o.a, s, "kv_b_proj.weight"), 32768, 512);
- try o.linear(try cat(o.a, s, "o_proj.weight"), hidden, 16384);
+ try o.either(try cat(o.a, s, "o_proj"), hidden, 16384, fp8);
try o.linear(try cat(o.a, s, "indexer.wk.weight"), 128, hidden);
try o.linear(try cat(o.a, s, "indexer.weights_proj.weight"), 32, hidden);
try o.linear(try cat(o.a, s, "indexer.wq_b.weight"), 4096, 1536);
@@ -141,13 +172,13 @@ fn dense(o: *Out, i: usize) !void {
try o.nvfp4(try tensor(o.a, "layers.{d}.mlp.down_proj", .{i}), hidden, 12288);
}
-/// The router, the shared expert, and the routed ones in NVFP4 (backbone) or bf16 (the MTP layer).
-fn moe(o: *Out, i: usize, bf: bool) !void {
+/// The router, the shared expert (bf16, or block FP8 with `fp8`), the routed ones in NVFP4 or bf16 (the MTP layer).
+fn moe(o: *Out, i: usize, bf: bool, fp8: bool) !void {
try o.linear(try tensor(o.a, "layers.{d}.mlp.gate.weight", .{i}), experts, hidden);
try o.f32s(try tensor(o.a, "layers.{d}.mlp.gate.e_score_correction_bias", .{i}), &.{experts}, 0.0, 0.0);
- try o.linear(try tensor(o.a, "layers.{d}.mlp.shared_experts.gate_proj.weight", .{i}), 2048, hidden);
- try o.linear(try tensor(o.a, "layers.{d}.mlp.shared_experts.up_proj.weight", .{i}), 2048, hidden);
- try o.linear(try tensor(o.a, "layers.{d}.mlp.shared_experts.down_proj.weight", .{i}), hidden, 2048);
+ try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.gate_proj", .{i}), 2048, hidden, fp8);
+ try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.up_proj", .{i}), 2048, hidden, fp8);
+ try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.down_proj", .{i}), hidden, 2048, fp8);
for (0..experts) |e| {
const s = try tensor(o.a, "layers.{d}.mlp.experts.{d}.", .{ i, e });
if (bf) {
@@ -162,7 +193,7 @@ fn moe(o: *Out, i: usize, bf: bool) !void {
}
}
-pub fn run(gpu: check.Gpu, out_dir: []const u8) !void {
+pub fn run(gpu: check.Gpu, out_dir: []const u8, fp8: bool) !void {
var arena = std.heap.ArenaAllocator.init(gpu.gpa);
defer arena.deinit();
const a = arena.allocator();
@@ -175,20 +206,21 @@ pub fn run(gpu: check.Gpu, out_dir: []const u8) !void {
try hc(&o, 0);
try norms(&o, 0);
try dense(&o, 0);
- try mla(&o, 1);
+ try mla(&o, 1, fp8);
try hc(&o, 1);
try norms(&o, 1);
- try moe(&o, 1, false);
- try mla(&o, layers); // the MTP layer: no hyper-connections, bf16 routed experts, the draft head's norms and eh_proj
+ try moe(&o, 1, false, fp8);
+ // the MTP layer: no hyper-connections, bf16 attention and routed experts, the draft head's norms and eh_proj
+ try mla(&o, layers, false);
try norms(&o, layers);
- try moe(&o, layers, true);
+ try moe(&o, layers, true, false);
for ([_][]const u8{ "enorm", "hnorm", "shared_head.norm" }) |m| try o.bf16Const(try tensor(a, "layers.{d}.{s}.weight", .{ layers, m }), &.{hidden}, 1.0);
try o.linear(try tensor(a, "layers.{d}.eh_proj.weight", .{layers}), hidden, 2 * hidden);
try std.Io.Dir.cwd().createDirPath(gpu.io, out_dir);
const text = try glm.config.textConfig(a, layers, 1, &.{1}, experts, vocab, hidden);
const end = std.mem.lastIndexOfScalar(u8, text, '}') orelse return error.BadConfig;
- const cfg = try std.mem.concat(a, u8, &.{ text[0..end], modelopt_block, text[end..] }); // its linears are NVFP4
+ const cfg = try std.mem.concat(a, u8, &.{ text[0..end], if (fp8) mixed_block else modelopt_block, text[end..] });
_ = try glm.config.parse(a, cfg);
try std.Io.Dir.cwd().writeFile(gpu.io, .{ .sub_path = try std.fs.path.join(a, &.{ out_dir, "config.json" }), .data = cfg });
try core.shard_edit.create(gpu.gpa, try std.fs.path.join(a, &.{ out_dir, "model.safetensors" }), &.{.{ "format", "pt" }}, o.list.items);
@@ -207,5 +239,5 @@ pub fn run(gpu: check.Gpu, out_dir: []const u8) !void {
try std.Io.Dir.cwd().writeFile(gpu.io, .{ .sub_path = try std.fs.path.join(a, &.{ out_dir, "dc.json" }), .data = dc.written() });
var bytes: usize = 0;
for (o.list.items) |t| bytes += t.bytes.len;
- check.pass("glm-synth: {d} tensors, {d} MiB, config {d} layers {d} experts vocabulary {d}, dc.json in {s}", .{ o.list.items.len, bytes >> 20, layers, experts, vocab, out_dir });
+ check.pass("glm-synth: {d} tensors, {d} MiB, config {d} layers {d} experts vocabulary {d}{s}, dc.json in {s}", .{ o.list.items.len, bytes >> 20, layers, experts, vocab, if (fp8) ", layer 1's MLA and shared in FP8_PB_WO" else "", out_dir });
}
diff --git a/zig/tests/cuda/main.zig b/zig/tests/cuda/main.zig
index 6c85575eb2..71a7dca050 100644
--- a/zig/tests/cuda/main.zig
+++ b/zig/tests/cuda/main.zig
@@ -83,6 +83,7 @@ const flashnext_attn_tests = @import("flashnext_attn_tests.zig");
const ggml_tests = @import("ggml_tests.zig");
const paged_tests = @import("paged_tests.zig");
const glm_loader_gate = @import("glm_loader_gate.zig");
+const glm_fp8g_shares = @import("glm_fp8g_shares.zig");
const gdn_back_tests = @import("gdn_back_tests.zig");
const q4_back_tests = @import("q4_back_tests.zig");
@@ -211,8 +212,9 @@ const usage =
\\ kolibri-full MODEL IDS N Kolibri-1: eager rows, steps, drafted, sampled, shared rounds, cuts and kept states alike
\\ kolibri-greedy MODEL PROMPTS N OUT Kolibri-1 greedy from each prompt (JSON id lists): ids and top-2 margins
\\ kolibri-logits MODEL IDS OUT [PROMPTS] Kolibri-1's teacher-forced fp32 logits of 2,048 prompt rows; each prompt's last
- \\ glm-synth a synthetic GLM-5.3-Flash checkpoint (2 layers, 16 experts) and its dc.json for the lane checks
+ \\ glm-synth [fp8pb] a synthetic GLM-5.3-Flash checkpoint (2 layers, 16 experts) and its dc.json for the lane checks
\\ glm-loader-gate every rank's uploads at TP 1, 2 and 3 against the checkpoint cut on the host, byte for byte
+ \\ glm-fp8g-shares block-FP8 TP shares packed as GLM's loader packs them, on the lane, against FP64 of the cut
\\ aot-fallback a launch's 1 from the runtime-parameter variant: the folded variant's bits (#1003)
\\
;
@@ -386,8 +388,9 @@ fn run(gpu: check.Gpu, cmd: []const u8, rest: []const [:0]const u8) !void {
if (std.mem.eql(u8, cmd, "train-pack")) return train_pack.run(gpu, try arg(rest, 0), try arg(rest, 1), if (rest.len > 2) try std.fmt.parseInt(usize, rest[2], 10) else 24);
if (std.mem.eql(u8, cmd, "glm-lanes-cover")) return glm_lanes_tests.coverCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
if (std.mem.eql(u8, cmd, "glm-shared-check")) return glm_lanes_tests.sharedCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
- if (std.mem.eql(u8, cmd, "glm-synth")) return glm_synth.run(gpu, try arg(rest, 0));
+ if (std.mem.eql(u8, cmd, "glm-synth")) return glm_synth.run(gpu, try arg(rest, 0), rest.len > 1 and std.mem.eql(u8, rest[1], "fp8pb"));
if (std.mem.eql(u8, cmd, "glm-loader-gate")) return glm_loader_gate.run(gpu, try arg(rest, 0));
+ if (std.mem.eql(u8, cmd, "glm-fp8g-shares")) return glm_fp8g_shares.run(gpu);
if (std.mem.eql(u8, cmd, "kolibri-reference")) return kolibri_layer_tests.reference(gpu, try arg(rest, 0), try arg(rest, 1));
if (std.mem.eql(u8, cmd, "kolibri-layers")) return kolibri_layer_tests.layers(gpu, try arg(rest, 0), try arg(rest, 1));
if (std.mem.eql(u8, cmd, "kolibri-full")) return kolibri_tests.full(gpu, try arg(rest, 0), try arg(rest, 1), try arg(rest, 2));
From 3b72fe8d240f8231c0f73c0538eada943e314f8e Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 22:23:26 +0900
Subject: [PATCH 04/23] glm/cuda: FP4-Local's MXFP8 projections run as stored
on the lane: dense layers, KDA, lm_head and the MTP layer
qlinear's decodeF32 and promptF32 take MXFP8 on the lane too, so a TP rank's MXFP8 partials stay unrounded. An MXFP8
projection is read, cut and packed for the lane on the host (cuda_mxfp8.zig). A dense layer (layers 0-2) runs [gate | up]
stacked into one weight that writes [rows][2 width] straight into the SwiGLU's input, and down as fp32 partials. The
lm_head runs on the lane and stays out of glm_mm's tiles and TF_GLM_HEAD_BITS; the draft head quantizes its rows widened
to bf16, exactly or refused by name. KDA's q, k and v run as one [q | k | v] weight and f_a, g_a and b as a bf16 rest,
each into its columns of the stack's rows; o runs as stored, bf16 or MXFP8. The MTP layer's attention, indexer and shared
expert run on the lane, its kv_b widened to bf16 exactly or refused by name; weights.load's MTP head lives in
cuda_mtp_load.zig. glm-synth local writes layer 0's KDA, dense MLP and lm_head and the MTP layer in MXFP8 under
FP4-Local's per-module recipes, with scale bytes about linear()'s RMS for their k, so its random model drafts copied rows
as the bf16 one does. glm-loader-gate packs every lane upload from its own cut, block FP8 or MXFP8 as stored, and takes
TF_GATE_WORLDS, TF_GATE_RANKS and TF_GATE_LAYERS, so a real checkpoint's gate runs the widths that fit a GB10.
1.1.1 runs the head on prompt rows in three new places: prompt_logprobs scores a chunk's rows
(cuda_prompt_logprobs.zig), a grammar redraws the first token from the prompt's last row (cuda_structured.zig), and a
batched prompt pass finishes each segment's last row (cuda_shared.zig). Each called b16.matmul on the head, which
FP4-Local stores in MXFP8. They now go through cuda_proj's matmul as Forward.finish does: a bf16 head as before, with
the same glm_mm prompt policy, and an MXFP8 one on qlinear's lane, whose rows' bits do not depend on the row count, so
a prompt row scores as the forward's last-row head does. projectionSet carries the lane. glm_prompt_logprobs_host's
head pass now counts its launches by function: an MXFP8 head runs the lane's kernel once and no bmm.
---
zig/glm_prompt_logprobs_host.zig | 30 +-
zig/src/cuda/qlinear.zig | 6 +-
zig/src/cuda/qlinear_test.zig | 4 +-
zig/src/families/glm/cuda_dsa_block.zig | 9 +-
zig/src/families/glm/cuda_formats.zig | 97 ++++--
zig/src/families/glm/cuda_forward.zig | 5 +-
zig/src/families/glm/cuda_glue.zig | 4 +-
zig/src/families/glm/cuda_kda_block.zig | 45 ++-
zig/src/families/glm/cuda_kda_tp3_test.zig | 4 +-
zig/src/families/glm/cuda_mlp.zig | 9 +-
zig/src/families/glm/cuda_mtp_load.zig | 62 ++++
zig/src/families/glm/cuda_mxfp8.zig | 295 ++++++++++++++++++
zig/src/families/glm/cuda_proj.zig | 11 +-
zig/src/families/glm/cuda_prompt_logprobs.zig | 7 +-
zig/src/families/glm/cuda_shared.zig | 4 +-
zig/src/families/glm/cuda_state.zig | 4 +
zig/src/families/glm/cuda_structured.zig | 4 +-
zig/src/families/glm/cuda_tile.zig | 46 +--
zig/src/families/glm/cuda_weights.zig | 123 ++++----
zig/tests/cuda/glm_loader_gate.zig | 146 ++++++---
zig/tests/cuda/glm_synth.zig | 168 +++++++---
zig/tests/cuda/main.zig | 7 +-
22 files changed, 850 insertions(+), 240 deletions(-)
create mode 100644 zig/src/families/glm/cuda_mtp_load.zig
create mode 100644 zig/src/families/glm/cuda_mxfp8.zig
diff --git a/zig/glm_prompt_logprobs_host.zig b/zig/glm_prompt_logprobs_host.zig
index 361a01e28d..305b5369c0 100644
--- a/zig/glm_prompt_logprobs_host.zig
+++ b/zig/glm_prompt_logprobs_host.zig
@@ -89,7 +89,11 @@ fn driver() cuda.Driver {
const Heads = struct {
var broadcasts: [2]usize = .{ 0, 0 };
- fn launch(_: cuda.abi.Function, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: cuda.abi.Stream, _: ?[*]?*anyopaque, _: ?[*]?*anyopaque) callconv(.c) cuda.abi.Result {
+ var launches: [3]usize = .{ 0, 0, 0 }; // by function handle: [1] the glue's bmm_decode, [2] the lane's
+ var mxfp8 = false; // score's head: bf16, or MXFP8 (FP4-Local's)
+ fn launch(f: cuda.abi.Function, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: c_uint, _: cuda.abi.Stream, _: ?[*]?*anyopaque, _: ?[*]?*anyopaque) callconv(.c) cuda.abi.Result {
+ const h = @intFromPtr(f);
+ if (h < launches.len) launches[h] += 1;
return 0;
}
fn copy(_: u64, _: u64, _: usize, _: cuda.abi.Stream) callconv(.c) cuda.abi.Result {
@@ -127,10 +131,14 @@ const Heads = struct {
e.m.tiled = false;
e.m.packs = null;
e.m.glue.bmm_decode = .{ .d = d, .handle = @ptrFromInt(1) };
+ e.m.lane.clusters = true;
+ for (&e.m.lane.fns) |*fns| for (fns) |*f| {
+ f.* = .{ .d = d, .handle = @ptrFromInt(2) };
+ };
e.bp.fnormed = 0x10000;
e.bp.kda.mm_part = 0x20000;
e.bd.logits = 0x30000;
- e.w.head = 0x40000;
+ e.w.head = if (mxfp8) .{ .mxfp8 = cuda.mxfp8.weight(0x40000, 0x48000, 8, 64) } else .{ .bf16 = 0x40000 };
e.smp.d = d;
e.smp.vocab = 16;
e.smp.full = .{ .d = d, .ptr = 0x50000, .len = 1024 };
@@ -192,3 +200,21 @@ test "GLM prompt rows: both cached addresses survive later driver mapping refusa
try std.testing.expectEqualSlices(usize, &.{ 1, 1 }, &Mapping.maps);
try std.testing.expectEqualSlices(usize, &.{ 2, 2 }, &Heads.broadcasts);
}
+
+test "GLM prompt rows: an MXFP8 head (FP4-Local's) is scored on the lane, as the forward runs it" {
+ var d = [_]cuda.Driver{ driver(), driver() };
+ var outputs: [2]?Output = .{ null, null };
+ defer for (&outputs) |*o| if (o.*) |*p| p.free();
+ Mapping.refuse = .{ false, false };
+ for (0..2) |r| {
+ Mapping.allocating = r;
+ outputs[r] = try Output.init(&d[r]);
+ }
+ Heads.mxfp8 = true;
+ defer Heads.mxfp8 = false;
+ for (0..2) |r| {
+ Heads.launches = .{ 0, 0, 0 };
+ try Heads.score(&d[r], outputs[r].?, r);
+ try std.testing.expectEqualSlices(usize, &.{ 0, 0, 1 }, &Heads.launches);
+ }
+}
diff --git a/zig/src/cuda/qlinear.zig b/zig/src/cuda/qlinear.zig
index 7b5539341b..a45dc1e85a 100644
--- a/zig/src/cuda/qlinear.zig
+++ b/zig/src/cuda/qlinear.zig
@@ -155,7 +155,7 @@ pub const Linear = struct {
}
}
- /// Decode rows to FP32 without the output BF16 cast (NVFP4, block FP8 and EXL3), including TP partial rows.
+ /// Decode rows to FP32 without the output BF16 cast (NVFP4, block FP8, MXFP8 and EXL3), including TP partial rows.
pub fn decodeF32(l: Linear, s: Stream, w: Weight, in: Rows, out: u64, rows: usize) !void {
const q = switch (w) {
.exl3 => |q| {
@@ -164,13 +164,14 @@ pub const Linear = struct {
return e.decode(s, q, in.x, .bf16, rows, out, .f32);
},
.fp8g => |q| return l.onLaneAs(s, q, .fp8g, in, out, .f32, rows),
+ .mxfp8 => |q| return l.onLaneAs(s, q, .mxfp8, in, out, .f32, rows),
.nvfp4 => |q| q,
else => return error.EpilogueNotBuilt,
};
return l.onLaneAs(s, q, .fp4, in, out, .f32, rows);
}
- /// Prompt rows to FP32 without the output BF16 cast (NVFP4, EXL3, and block FP8 on the lane as prompt() runs it).
+ /// Prompt rows to FP32 without the output BF16 cast (NVFP4, EXL3; block FP8 and MXFP8 on the lane, as prompt()).
pub fn promptF32(l: Linear, s: Stream, w: Weight, in: Rows, out: u64, rows: usize) !void {
const q = switch (w) {
.exl3 => |q| {
@@ -179,6 +180,7 @@ pub const Linear = struct {
return e.prompt(s, q, in.x, .bf16, rows, out, .f32);
},
.fp8g => |q| return l.onLaneAs(s, q, .fp8g, in, out, .f32, rows),
+ .mxfp8 => |q| return l.onLaneAs(s, q, .mxfp8, in, out, .f32, rows),
.nvfp4 => |q| q,
else => return error.EpilogueNotBuilt,
};
diff --git a/zig/src/cuda/qlinear_test.zig b/zig/src/cuda/qlinear_test.zig
index 2a41b8f523..d11737b37d 100644
--- a/zig/src/cuda/qlinear_test.zig
+++ b/zig/src/cuda/qlinear_test.zig
@@ -39,10 +39,12 @@ test "a lane weight whose mode is not its format's is refused before any launch"
try std.testing.expectError(error.FormatMismatch, l.prompt(s, .{ .mxfp8 = fp4 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.FormatNotLoaded, l.prompt(s, .{ .nvfp4 = fp4 }, .{ .x = 0 }, 0, 1));
const f8: qmmf.Weight = .{ .mode = .fp8g, .codes = 0, .scales = 0, .n = 64, .k = 64, .npad = 128 };
- try std.testing.expectError(error.EpilogueNotBuilt, l.decodeF32(s, .{ .mxfp8 = f8 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.EpilogueNotBuilt, l.decodeF32(s, .{ .bf16 = f8 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.EpilogueNotBuilt, l.promptF32(s, .{ .bf16 = f8 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.FormatMismatch, l.decodeF32(s, .{ .fp8g = fp4 }, .{ .x = 0 }, 0, 1));
try std.testing.expectError(error.FormatMismatch, l.promptF32(s, .{ .fp8g = fp4 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.FormatMismatch, l.decodeF32(s, .{ .mxfp8 = fp4 }, .{ .x = 0 }, 0, 1));
+ try std.testing.expectError(error.FormatMismatch, l.promptF32(s, .{ .mxfp8 = fp4 }, .{ .x = 0 }, 0, 1));
}
test "EXL3 FP32 entries check loading and stride before launching" {
diff --git a/zig/src/families/glm/cuda_dsa_block.zig b/zig/src/families/glm/cuda_dsa_block.zig
index 9ca84b5fcb..8d82343aeb 100644
--- a/zig/src/families/glm/cuda_dsa_block.zig
+++ b/zig/src/families/glm/cuda_dsa_block.zig
@@ -3,7 +3,6 @@
const std = @import("std");
const cuda = @import("cuda");
const glue = @import("cuda_glue.zig");
-const b16 = @import("cuda_b16.zig");
const projection = @import("cuda_proj.zig");
const latent = @import("cuda_latent.zig");
const sparse = @import("cuda_sparse.zig");
@@ -12,8 +11,8 @@ const prof = @import("cuda_prof.zig");
/// A prompt chunk's rows one dense latent attention call takes (geometry.MLA_PROMPT_ATT_ROWS).
pub const prompt_att_rows = 512;
-/// The indexer's weights, replicated: [wk | weights_proj] [d + heads][dims], gate, key LayerNorm, pool bias, wq_b.
-pub const Index = struct { kw: u64, gate: u64, ln_w: u64, ln_b: u64, ape: u64, qb: u64, heads: usize, d: usize };
+/// The indexer's weights, replicated: [wk | weights_proj] [d + heads][dims], wq_b (as stored), gate, LayerNorm, bias.
+pub const Index = struct { kw: projection.Proj, gate: u64, ln_w: u64, ln_b: u64, ape: u64, qb: projection.Proj, heads: usize, d: usize };
/// One DSA layer on a rank of `heads` heads: proj, norms, q_b, kv_b per head as wk/wv, o, indexer; shapes from config.
pub const Layer = struct {
@@ -107,10 +106,10 @@ pub fn prepareProfile(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch,
defer stamp.end();
try projection.matmul(set, stream, l.q_b, s.qr, l.q_lora, s.q, s.mm_part, rows, l.heads * l.qk, l.q_lora, false);
const ix = l.index;
- try b16.matmul(set, stream, x, x_stride, ix.kw, s.ikr, s.mm_part, rows, ix.d + ix.heads, l.dims, false);
+ try projection.matmul(set, stream, ix.kw, x, x_stride, s.ikr, s.mm_part, rows, ix.d + ix.heads, l.dims, false);
try glue.router(set, stream, x, x_stride, ix.gate, s.igr, s.router_part, rows, l.dims, ix.d);
try latent.absorbQ(set, stream, s.q, l.wk, s.qa, rows, l.heads, l.qk, l.lw);
- if (qi) try b16.matmul(set, stream, s.qr, l.q_lora, ix.qb, s.qi, s.mm_part, rows, ix.heads * ix.d, l.q_lora, false);
+ if (qi) try projection.matmul(set, stream, ix.qb, s.qr, l.q_lora, s.qi, s.mm_part, rows, ix.heads * ix.d, l.q_lora, false);
}
/// One sequence's rows r0..r0 + rows at pos: latent and index keys written, dense then sparse attention into s.ol.
diff --git a/zig/src/families/glm/cuda_formats.zig b/zig/src/families/glm/cuda_formats.zig
index 129c2ddb26..cb51c10749 100644
--- a/zig/src/families/glm/cuda_formats.zig
+++ b/zig/src/families/glm/cuda_formats.zig
@@ -1,4 +1,4 @@
-//! Which stored format a GLM CUDA projection is read in (its recipe against its tensors), and how: csf, block-FP8 cuts.
+//! Which stored format a GLM CUDA projection is read in (its recipe against its tensors), and how: csf, FP8 cuts.
const std = @import("std");
const core = @import("core");
@@ -6,6 +6,7 @@ const cuda = @import("cuda");
const fp8 = cuda.fp8;
const projection = @import("cuda_proj.zig");
pub const fp8pb = @import("cuda_fp8pb.zig");
+pub const mxfp8 = @import("cuda_mxfp8.zig");
const Meta = core.nvfp4.Meta;
const Recipe = core.modelopt.Recipe;
@@ -113,44 +114,65 @@ pub fn metaOf(t: *const core.checkpoint.Tensor) Meta {
return .{ .dtype = t.dtype, .shape = t.shape[0..t.rank] };
}
-/// A projection's part as a rank takes it: tensor `rel` under the layer, its rows or its inputs, or the whole of it.
+/// A projection's part as a rank takes it: tensor `rel` (under the layer, for cutProj), its rows or inputs, or all.
pub const Part = struct { rel: []const u8, rows: ?[2]usize = null, cols: ?[2]usize = null };
-/// Projection `name` of `parts` stacked: bf16 cut as split.py cuts it, or FP8_PB_WO packed; else refused by name.
-pub fn cutProj(L: anytype, comptime prefix: []const u8, i: u32, parts: []const Part, comptime name: []const u8) !projection.Proj {
- var bufs: [2][192]u8 = undefined;
- var mods: [2][]const u8 = undefined;
- if (parts.len == 0 or parts.len > bufs.len) return error.BadFp8Share; // one tensor, or [q_a | kv_a] and [gate | up]
+/// Layer `i`'s projection `name` of `parts` stacked, as `cut` reads them.
+pub fn cutProj(L: anytype, comptime prefix: []const u8, i: u32, parts: []const Part, wants: []const Format, comptime name: []const u8) !projection.Proj {
+ var bufs: [3][192]u8 = undefined;
+ var mods: [3][]const u8 = undefined;
+ if (parts.len == 0 or parts.len > bufs.len) return error.UnreadableFormat; // a tensor, [q_a | kv_a], [q | k | v]
+ for (parts, 0..) |q, j| mods[j] = try std.fmt.bufPrint(&bufs[j], prefix ++ "layers.{d}.{s}", .{ i, q.rel });
+ return cut(L, L.ck, mods[0..parts.len], parts, wants, name, .{i});
+}
+
+/// Modules `mods` of `ck` cut as `parts`, stacked as `name`: bf16, or FP8 kinds packed for the lane; else refused.
+pub fn cut(L: anytype, ck: *core.Checkpoint, mods: []const []const u8, parts: []const Part, wants: []const Format, comptime name: []const u8, args: anytype) !projection.Proj {
var got: ?Format = null;
- for (parts, 0..) |q, j| {
- mods[j] = try std.fmt.bufPrint(&bufs[j], prefix ++ "layers.{d}.{s}", .{ i, q.rel });
- const f = try L.formatOne(L.ck, mods[j], &.{ .bf16, .fp8_pb });
+ for (mods) |m| {
+ const f = try L.formatOne(ck, m, wants);
if (got != null and got.? != f) {
- std.log.err("{s}: stored as {t}, stacked with a part stored as {t}", .{ mods[j], f, got.? });
+ std.log.err("{s}: stored as {t}, stacked with a part stored as {t}", .{ m, f, got.? });
return error.UnreadableFormat;
}
got = f;
}
var wb: [224]u8 = undefined;
- if (got.? == .bf16) {
- for (parts, mods[0..parts.len]) |q, m| {
- const t = try L.ck.get(try std.fmt.bufPrint(&wb, "{s}.weight", .{m}));
- if (q.cols) |c| try L.cols(t, c[0], c[1], .bf16) else if (q.rows) |r| try L.rows(t, r[0], r[1], t.dim(0), .bf16) else try L.whole(t, .bf16);
- }
- return .{ .bf16 = try L.put(name, .{i}) };
- }
- var stored: [2]fp8pb.Fp8pb = undefined;
- var made: usize = 0;
- defer for (stored[0..made]) |t| t.deinit(L.gpa);
- var shares: [2]fp8pb.Share = undefined;
- for (parts, mods[0..parts.len], 0..) |q, m, j| {
- stored[j] = try fp8pb.Fp8pb.read(L.gpa, L.ck, m);
- made += 1;
- const r = q.rows orelse [2]usize{ 0, stored[j].n };
- const c = q.cols orelse [2]usize{ 0, stored[j].k };
- shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = c[0], .khi = c[1] };
+ switch (got.?) {
+ .bf16 => {
+ for (parts, mods) |q, m| {
+ const t = try ck.get(try std.fmt.bufPrint(&wb, "{s}.weight", .{m}));
+ if (q.cols) |c| try L.cols(t, c[0], c[1], .bf16) else if (q.rows) |r| try L.rows(t, r[0], r[1], t.dim(0), .bf16) else try L.whole(t, .bf16);
+ }
+ return .{ .bf16 = try L.put(name, args) };
+ },
+ .fp8_pb => {
+ var stored: [3]fp8pb.Fp8pb = undefined;
+ var made: usize = 0;
+ defer for (stored[0..made]) |t| t.deinit(L.gpa);
+ var shares: [3]fp8pb.Share = undefined;
+ for (parts, mods, 0..) |q, m, j| {
+ stored[j] = try fp8pb.Fp8pb.read(L.gpa, ck, m);
+ made += 1;
+ const r = q.rows orelse [2]usize{ 0, stored[j].n };
+ const c = q.cols orelse [2]usize{ 0, stored[j].k };
+ shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = c[0], .khi = c[1] };
+ }
+ return .{ .fp8g = try fp8g(L, shares[0..parts.len], name, args) };
+ },
+ .mxfp8 => {
+ var stored: [3]mxfp8.Mxfp8 = undefined;
+ var shares: [3]mxfp8.Share = undefined;
+ for (parts, mods, 0..) |q, m, j| {
+ stored[j] = try mxfp8.Mxfp8.read(ck, m);
+ const r = q.rows orelse [2]usize{ 0, stored[j].n };
+ const c = q.cols orelse [2]usize{ 0, stored[j].k };
+ shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = c[0], .khi = c[1] };
+ }
+ return .{ .mxfp8 = try mxLane(L, shares[0..parts.len], name, args) };
+ },
+ else => return error.UnreadableFormat, // no lane weight here: NVFP4 and csf are read where they are packed
}
- return .{ .fp8g = try fp8g(L, shares[0..parts.len], name, .{i}) };
}
/// `shares` packed for the lane as one weight (fp8pb.pack), uploaded as `name`'s codes and scale tiles.
@@ -170,6 +192,23 @@ fn fp8g(L: anytype, shares: []const fp8pb.Share, comptime name: []const u8, args
return fp8.weight(c, try L.put(name ++ ".scales", args), z.n, z.k);
}
+/// `shares` packed for the lane as one weight (mxfp8.pack), uploaded as `name`'s codes and scale tiles.
+fn mxLane(L: anytype, shares: []const mxfp8.Share, comptime name: []const u8, args: anytype) !cuda.qmmf.Weight {
+ const z = mxfp8.sizes(shares) catch |e| {
+ std.log.err(name ++ ": a rank's cut leaves a scale byte's 32 inputs or the lane's 64-row tiles", args);
+ return e;
+ };
+ const codes = try L.gpa.alloc(u8, z.codes);
+ defer L.gpa.free(codes);
+ const scales = try L.gpa.alloc(u8, z.scales);
+ defer L.gpa.free(scales);
+ try mxfp8.pack(L.gpa, shares, codes, scales);
+ try L.host.appendSlice(L.gpa, codes);
+ const c = try L.put(name ++ ".codes", args);
+ try L.host.appendSlice(L.gpa, scales);
+ return cuda.mxfp8.weight(c, try L.put(name ++ ".scales", args), z.n, z.k);
+}
+
/// The refusal's text: the module, what the checkpoint declares and what its weight holds.
pub fn describe(buf: []u8, module: []const u8, declared: Declared, stored: Stored) []const u8 {
const said = switch (declared) {
diff --git a/zig/src/families/glm/cuda_forward.zig b/zig/src/families/glm/cuda_forward.zig
index 4b4364fa58..a15215fe17 100644
--- a/zig/src/families/glm/cuda_forward.zig
+++ b/zig/src/families/glm/cuda_forward.zig
@@ -7,6 +7,7 @@ const weights = @import("cuda_weights.zig");
const glue = @import("cuda_glue.zig");
const kda = @import("cuda_kda.zig");
const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
const latent = @import("cuda_latent.zig");
const sparse = @import("cuda_sparse.zig");
const experts = @import("cuda_experts.zig");
@@ -183,7 +184,7 @@ pub const Forward = struct {
taps: ?taps_mod.Taps = null, // DFlash2 (--drafter): the tapped layers' rows, after each such layer
pub fn set(f: *const Forward) glue.Set {
- return .{ .glue = &f.m.glue, .mm = if (f.m.tiled) &f.m.mm else null, .packs = f.m.packs, .prompt = f.ranks[0].b.prefill, .linear = .{ .lane = &f.m.lane } };
+ return .{ .glue = &f.m.glue, .mm = if (f.m.tiled) &f.m.mm else null, .packs = f.m.packs, .prompt = f.ranks[0].b.prefill, .linear = .{ .lane = &f.m.lane }, .copy_rows = f.m.copy_rows };
}
/// reduce.sums' gather: every rank's partial of `rows` rows into every rank's gathered buffer, rank order.
@@ -456,7 +457,7 @@ pub const Forward = struct {
}
const head_rows: usize = if (b.prefill) 1 else R;
const first = if (b.prefill) R - 1 else 0;
- try b16.matmul(f.set(), f.stream, b.fnormed + first * c.hidden * 2, c.hidden, r.w.head, b.logits, b.kda.mm_part, head_rows, r.w.share.vocab.len(), c.hidden, false);
+ try projection.matmul(f.set(), f.stream, r.w.head, b.fnormed + first * c.hidden * 2, c.hidden, b.logits, b.kda.mm_part, head_rows, r.w.share.vocab.len(), c.hidden, false);
}
}
diff --git a/zig/src/families/glm/cuda_glue.zig b/zig/src/families/glm/cuda_glue.zig
index 4ead02cb10..b3d76cac86 100644
--- a/zig/src/families/glm/cuda_glue.zig
+++ b/zig/src/families/glm/cuda_glue.zig
@@ -4,8 +4,8 @@ const std = @import("std");
const cuda = @import("cuda");
const mm_mod = @import("cuda_mm.zig");
-/// What the forward's kernels launch from: our glue's functions, glm_mm's when tiled, and qlinear's lane for block FP8.
-pub const Set = struct { glue: *const Kernels, mm: ?*const mm_mod.Kernels = null, packs: ?*const mm_mod.Packed = null, prompt: bool = false, linear: cuda.qlinear.Linear = .{} };
+/// What the forward's kernels launch from: our glue's, glm_mm's when tiled, qlinear's lane for FP8 kinds, copy_rows.
+pub const Set = struct { glue: *const Kernels, mm: ?*const mm_mod.Kernels = null, packs: ?*const mm_mod.Packed = null, prompt: bool = false, linear: cuda.qlinear.Linear = .{}, copy_rows: ?cuda.Function = null };
/// Our glm_glue.cu's functions.
pub const Kernels = struct {
diff --git a/zig/src/families/glm/cuda_kda_block.zig b/zig/src/families/glm/cuda_kda_block.zig
index 8388a9bec1..ee95c3b3c3 100644
--- a/zig/src/families/glm/cuda_kda_block.zig
+++ b/zig/src/families/glm/cuda_kda_block.zig
@@ -6,13 +6,20 @@ const glue = @import("cuda_glue.zig");
const kda = @import("cuda_kda.zig");
const b16 = @import("cuda_b16.zig");
const prof = @import("cuda_prof.zig");
+const projection = @import("cuda_proj.zig");
-/// One KDA layer, `heads` heads: proj bf16 [3 heads 128 + 256 + heads][dims], f_b, g_b, o, the chain's taps and norm.
+/// The input projection [q | k | v | f_a | g_a | b] [3 heads 128 + 256 + heads][dims]: one bf16 stack, or two parts.
+pub const In = union(enum) {
+ bf16: u64,
+ split: struct { qkv: cuda.qmmf.Weight, rest: u64 }, // MXFP8 [q | k | v] for the lane (FP4-Local's), bf16 the rest
+};
+
+/// One KDA layer, `heads` heads: its input projection, f_b, g_b, o (bf16 or MXFP8), the chain's taps and norm.
pub const Layer = struct {
- proj: u64,
+ proj: In,
fb: u64,
gb: u64,
- o: u64,
+ o: projection.Proj,
conv_w: u64,
a_log: u64,
dt_bias: u64,
@@ -32,8 +39,8 @@ pub const Layer = struct {
}
};
-/// The block's scratch for `rows` rows: projection, decay and gate rows, chain saves and scratch, B16 split-K partials.
-pub const Scratch = struct { p: u64, ka: u64, kg: u64, saves: kda.Saves, wide: kda.Wide, mm_part: u64 };
+/// The block's scratch for `rows` rows: projection, decay and gate rows, chain saves, B16 partials, a split's tmp.
+pub const Scratch = struct { p: u64, ka: u64, kg: u64, saves: kda.Saves, wide: kda.Wide, mm_part: u64, tmp: u64 = 0 };
/// forward.kda_block: out [rows][dims] fp32 partial; `wide`: three-kernel chain; `prompt`: commits the conv window.
pub fn block(set: glue.Set, ks: *const kda.Kernels, stream: cuda.Stream, l: Layer, s: Scratch, x: u64, x_stride: usize, rows: usize, conv_state: u64, state_in: u64, wide: bool, prompt: bool, out: u64) !void {
@@ -44,7 +51,7 @@ pub fn block(set: glue.Set, ks: *const kda.Kernels, stream: cuda.Stream, l: Laye
/// forward.out_proj's fill for the KDA block: o_proj of rows lo..hi (s.saves.out) into out [hi - lo][dims] fp32.
pub fn fill(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch, lo: usize, hi: usize, out: u64) !void {
const n = l.heads * kda.dk;
- try b16.matmul(set, stream, s.saves.out + lo * n * 2, n, l.o, out, s.mm_part, hi - lo, l.dims, n, true);
+ try projection.matmul(set, stream, l.o, s.saves.out + lo * n * 2, n, out, s.mm_part, hi - lo, l.dims, n, true);
}
/// The block up to its out_proj: the projections, the chain, and a prompt chunk's conv window commit.
@@ -69,11 +76,35 @@ pub fn project(set: glue.Set, stream: cuda.Stream, l: Layer, s: Scratch, x: u64,
const n = l.heads * kda.dk;
const w = l.width();
const fa = 3 * n;
- try b16.matmul(selected, stream, x, x_stride, l.proj, s.p, s.mm_part, rows, w, l.dims, false);
+ switch (l.proj) {
+ .bf16 => |p| try b16.matmul(selected, stream, x, x_stride, p, s.p, s.mm_part, rows, w, l.dims, false),
+ .split => |sp| { // each part on its own kernel into its columns of s.p: one row straight there, more through tmp
+ // cc-defer: two row copies a window; an output row stride on the lane and glm_mm when 03 times them
+ const one = rows == 1;
+ try projection.matmul(set, stream, .{ .mxfp8 = sp.qkv }, x, x_stride, if (one) s.p else s.tmp, s.mm_part, rows, fa, l.dims, false);
+ if (!one) try columns(set, stream, s.tmp, fa, s.p, w, rows);
+ // the bf16 rest (2 * dk + heads wide) tiles with the stack, or stays row-major with it when uneven
+ try b16.matmul(selected, stream, x, x_stride, sp.rest, if (one) s.p + fa * 2 else s.tmp, s.mm_part, rows, w - fa, l.dims, false);
+ if (!one) try columns(set, stream, s.tmp, w - fa, s.p + fa * 2, w, rows);
+ },
+ }
try b16.matmul(selected, stream, s.p + fa * 2, w, l.fb, s.ka, s.mm_part, rows, n, kda.dk, false);
try b16.matmul(selected, stream, s.p + (fa + kda.dk) * 2, w, l.gb, s.kg, s.mm_part, rows, n, kda.dk, false);
}
+/// `rows` rows of `cols` bf16 packed at `src` into `dst`, rows `stride` apart (tf_glm_copy_rows).
+fn columns(set: glue.Set, stream: cuda.Stream, src: u64, cols: usize, dst: u64, stride: usize, rows: usize) !void {
+ const f = set.copy_rows orelse return error.FormatNotLoaded;
+ var a: cuda.Args = .{};
+ a.add(src);
+ a.add(@as(c_longlong, @intCast(cols * 2)));
+ a.add(dst);
+ a.add(@as(c_longlong, @intCast(stride * 2)));
+ a.add(@as(c_int, @intCast(rows)));
+ a.add(@as(c_int, @intCast(cols * 2)));
+ try cuda.launch.launch(f, .{ .grid = .{ .x = @intCast((rows * cols * 2 + 255) / 256) }, .block = .{ .x = 256 } }, stream, &a);
+}
+
/// The chain over `rows` projected rows (s.p, s.ka, s.kg) on one sequence's conv window and recurrent state.
pub fn chainRows(ks: *const kda.Kernels, stream: cuda.Stream, l: Layer, s: Scratch, rows: usize, conv_state: u64, state_in: u64, wide: bool) !void {
const n = l.heads * kda.dk;
diff --git a/zig/src/families/glm/cuda_kda_tp3_test.zig b/zig/src/families/glm/cuda_kda_tp3_test.zig
index b99c9b34cf..252757ef65 100644
--- a/zig/src/families/glm/cuda_kda_tp3_test.zig
+++ b/zig/src/families/glm/cuda_kda_tp3_test.zig
@@ -36,7 +36,7 @@ fn function(d: *const cuda.Driver, id: usize) cuda.Function {
return .{ .d = d, .handle = @ptrFromInt(id) };
}
fn layer(heads: usize, dims: usize) kda.Layer {
- return .{ .proj = 0x100000000, .fb = 0x200000000, .gb = 0x300000000, .o = 0x400000000, .conv_w = 0, .a_log = 0, .dt_bias = 0, .norm_w = 0, .eps = 1e-6, .lower = -5, .heads = heads, .dims = dims };
+ return .{ .proj = .{ .bf16 = 0x100000000 }, .fb = 0x200000000, .gb = 0x300000000, .o = .{ .bf16 = 0x400000000 }, .conv_w = 0, .a_log = 0, .dt_bias = 0, .norm_w = 0, .eps = 1e-6, .lower = -5, .heads = heads, .dims = dims };
}
test "GLM TP3: real KDA projections accept uneven shares and retain aligned tiled dispatch" {
const c = Config{};
@@ -58,7 +58,7 @@ test "GLM TP3: real KDA projections accept uneven shares and retain aligned tile
for ([_]usize{ 1, 17, 64, 65, 127, 128, 2066 }) |rows| for ([_]bool{ false, true }) |prompt| {
Capture.count = 0;
try kda.project(.{ .glue = &raw, .mm = &kernels, .prompt = prompt }, stream, l, scratch, 0x900000000, c.hidden, rows);
- const weights = [_]u64{ l.proj, l.fb, l.gb };
+ const weights = [_]u64{ l.proj.bf16, l.fb, l.gb };
const xs = [_]u64{ 0x900000000, scratch.p + 3 * l.heads * 128 * 2, scratch.p + (3 * l.heads * 128 + 128) * 2 };
const strides = [_]usize{ c.hidden, width, width };
const ns = [_]usize{ width, l.heads * 128, l.heads * 128 };
diff --git a/zig/src/families/glm/cuda_mlp.zig b/zig/src/families/glm/cuda_mlp.zig
index a9e55254b9..68a94468fe 100644
--- a/zig/src/families/glm/cuda_mlp.zig
+++ b/zig/src/families/glm/cuda_mlp.zig
@@ -4,11 +4,13 @@ const std = @import("std");
const cuda = @import("cuda");
const glue = @import("cuda_glue.zig");
const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
-/// A dense layer's weights: NVFP4 as Fp4Linear holds them, or the checkpoint's bf16 (an EXL3 checkpoint's).
+/// A dense layer's weights: NVFP4 as Fp4Linear holds them, an EXL3 checkpoint's bf16, or FP4-Local's MXFP8.
pub const Weights = union(enum) {
nvfp4: struct { gate: cuda.qmmf.Weight, up: cuda.qmmf.Weight, down: cuda.qmmf.Weight },
bf16: struct { gu: u64, down: u64 }, // [gate | up] rows [2 width][dims], down [dims][width]
+ mxfp8: struct { gu: cuda.qmmf.Weight, down: cuda.qmmf.Weight }, // the same two, packed for the lane
};
/// One dense layer on a rank: gate and up [width][dims] and down [dims][width].
@@ -41,6 +43,7 @@ pub fn fill(k: Kernels, stream: cuda.Stream, l: Layer, s: Scratch, lo: usize, hi
switch (l.w) {
.nvfp4 => |w| try mm(k, stream, w.down, s.act + lo * l.width * 2, l.width, hi - lo, out, true, prompt),
.bf16 => |w| try b16.matmul(k.set, stream, s.act + lo * l.width * 2, l.width, w.down, out, s.mm_part, hi - lo, l.dims, l.width, true),
+ .mxfp8 => |w| try projection.matmul(k.set, stream, .{ .mxfp8 = w.down }, s.act + lo * l.width * 2, l.width, out, s.mm_part, hi - lo, l.dims, l.width, true),
}
}
@@ -53,6 +56,10 @@ pub fn body(k: Kernels, stream: cuda.Stream, l: Layer, s: Scratch, x: u64, x_str
try b16.matmul(k.set, stream, x, x_stride, b.gu, s.gu, s.mm_part, rows, 2 * w, l.dims, false);
return glue.swiglu(k.set, stream, s.gu, s.act, s.xs_act, l.limit, rows, w);
},
+ .mxfp8 => |m| { // [gate | up] stacked on the lane, its rows straight into s.gu
+ try projection.matmul(k.set, stream, .{ .mxfp8 = m.gu }, x, x_stride, s.gu, s.mm_part, rows, 2 * w, l.dims, false);
+ return glue.swiglu(k.set, stream, s.gu, s.act, s.xs_act, l.limit, rows, w);
+ },
};
for ([_]cuda.qmmf.Weight{ q.gate, q.up }, 0..) |proj, j| {
// Fp4Linear(out=a column slice): the rows computed contiguous, then copied into their half of each row
diff --git a/zig/src/families/glm/cuda_mtp_load.zig b/zig/src/families/glm/cuda_mtp_load.zig
new file mode 100644
index 0000000000..fb05721a66
--- /dev/null
+++ b/zig/src/families/glm/cuda_mtp_load.zig
@@ -0,0 +1,62 @@
+//! The MTP head's load: its layer as plain DSA + MoE, norms and eh_proj, and the draft head from the head's rows.
+const std = @import("std");
+const core = @import("core");
+const cuda = @import("cuda");
+const formats = @import("cuda_formats.zig");
+const weights = @import("cuda_weights.zig");
+const quant = @import("cuda_quant.zig");
+const q4 = @import("cuda_q4.zig");
+
+const prefix = "model.language_model.";
+
+/// weights.load's MTPW: layer `layers` as plain DSA + MoE, norms, eh_proj, draft head from the rank's head rows.
+pub fn load(L: anytype, head_ck: *core.Checkpoint) !weights.Mtp {
+ const c = &L.w.config;
+ const i = c.layers;
+ try L.whole(try L.get("layers.{d}.input_layernorm.weight", .{i}), .bf16);
+ const in_norm = try L.put("layers.{d}.in_norm", .{i});
+ try L.whole(try L.get("layers.{d}.post_attention_layernorm.weight", .{i}), .bf16);
+ const post_norm = try L.put("layers.{d}.post_norm", .{i});
+ const none: weights.Hc = .{ .fn_w = 0, .base = 0, .scale = 0 };
+ const layer_w: weights.Layer = .{ .index = i, .attn_hc = none, .ffn_hc = none, .in_norm = in_norm, .post_norm = post_norm, .mixer = .{ .mla = try L.dsa(i) }, .ffn = .{ .moe = try L.moeLayer(i) } };
+ L.ck.release();
+ var out: [4]u64 = undefined;
+ for (&out, [_][]const u8{ "enorm.weight", "hnorm.weight", "eh_proj.weight", "shared_head.norm.weight" }, [_][]const u8{ "enorm", "hnorm", "eh", "norm" }) |*p, name, what| {
+ try L.whole(try L.ck.get(try std.fmt.bufPrint(&L.name_buf, prefix ++ "layers.{d}.{s}", .{ i, name })), .bf16);
+ p.* = try L.put("mtp.{s}", .{what});
+ }
+ // the draft head: quantize4 of the head's bf16 rows (an MXFP8 head's widened exactly), then make_q4's tiling
+ const qk = L.tools.quant orelse return error.NoQuantizer;
+ const tk = L.tools.q4 orelse return error.NoQuantizer;
+ const n = L.w.share.vocab.len();
+ const k: usize = c.hidden;
+ var raw: [3]cuda.DeviceBuffer = undefined;
+ var made: usize = 0;
+ defer for (raw[0..made]) |*b| b.free();
+ for (&raw, [_]usize{ n * k / 2, n * k / 32, n * k / 32 }) |*b, size| {
+ b.* = try L.scratch(size);
+ made += 1;
+ }
+ var wide: ?cuda.DeviceBuffer = null;
+ defer if (wide) |*b| b.free();
+ const rows: u64 = switch (L.w.head) {
+ .bf16 => |p| p,
+ .mxfp8 => r: {
+ wide = try formats.mxfp8.wideRows(L.d, L.gpa, head_ck, "lm_head", L.w.share.vocab.lo, L.w.share.vocab.hi);
+ break :r wide.?.ptr;
+ },
+ .fp8g => return error.UnreadableFormat, // weights.load reads a head as bf16 or MXFP8 only
+ };
+ try quant.q4(qk, L.stream, rows, n, k, raw[0].ptr, raw[1].ptr, raw[2].ptr);
+ var tiled: [3]u64 = undefined;
+ for (&tiled, q4.bytes(n, k), [_][]const u8{ "words", "scales", "biases" }) |*p, size, what| {
+ const b = try cuda.DeviceBuffer.alloc(L.d, size);
+ try L.w.buffers.append(L.gpa, b);
+ try L.w.named.append(L.gpa, .{ .name = try std.fmt.allocPrint(L.gpa, "draft_head.{s}", .{what}), .ptr = b.ptr, .len = b.len });
+ p.* = b.ptr;
+ }
+ const draft: q4.Q4 = .{ .w = tiled[0], .s = tiled[1], .b = tiled[2], .n = n, .k = k, .npad = q4.npadOf(n) };
+ try q4.pack(tk, L.stream, raw[0].ptr, raw[1].ptr, raw[2].ptr, draft);
+ try L.stream.synchronize();
+ return .{ .enorm = out[0], .hnorm = out[1], .eh = out[2], .norm = out[3], .layer = layer_w, .draft = draft };
+}
diff --git a/zig/src/families/glm/cuda_mxfp8.zig b/zig/src/families/glm/cuda_mxfp8.zig
new file mode 100644
index 0000000000..9c4ad607a9
--- /dev/null
+++ b/zig/src/families/glm/cuda_mxfp8.zig
@@ -0,0 +1,295 @@
+//! MXFP8 (e4m3 codes, a UE8M0 power of two per 32 inputs) as a rank's lane weight: read, cut and packed on the host.
+
+const std = @import("std");
+const core = @import("core");
+const cuda = @import("cuda");
+const mx = cuda.mxfp8;
+const formats = @import("cuda_formats.zig");
+
+/// An MXFP8 projection as stored: e4m3 codes [n, k] and its UE8M0 scale bytes [n, k/32], the file's own bytes.
+pub const Mxfp8 = struct {
+ n: usize,
+ k: usize,
+ codes: []const u8,
+ scales: []const u8,
+
+ /// `module`'s weight and weight_scale, checked as formats.of checks them.
+ pub fn read(ck: *core.Checkpoint, module: []const u8) !Mxfp8 {
+ var buf: [256]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
+ const s = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale", .{module}));
+ _ = formats.of(.{ .mixed = .mxfp8 }, .{ .weight = formats.metaOf(&w), .scale = formats.metaOf(&s) }) catch return error.BadMxfp8;
+ return .{ .n = w.dim(0), .k = w.dim(1), .codes = w.bytes, .scales = s.bytes };
+ }
+};
+
+/// Rows [lo, hi) and inputs [klo, khi) of a stored projection: one part of a rank's lane weight.
+pub const Share = struct { p: *const Mxfp8, lo: usize, hi: usize, klo: usize, khi: usize };
+
+/// The rows and inputs of `parts` stacked, and the bytes its packed codes and scale tiles take.
+pub fn sizes(parts: []const Share) error{BadMxfp8Share}!struct { n: usize, k: usize, codes: usize, scales: usize } {
+ if (parts.len == 0) return error.BadMxfp8Share;
+ const k = parts[0].khi -| parts[0].klo;
+ var n: usize = 0;
+ for (parts, 1..) |s, j| {
+ const inside = s.lo < s.hi and s.hi <= s.p.n and s.klo < s.khi and s.khi <= s.p.k;
+ // a cut splits no scale byte's 32 inputs, 64-input stage or 64-row tile; the last part may end inside its tile
+ const tiled = (s.hi - s.lo) % 64 == 0 or j == parts.len;
+ if (!inside or s.khi - s.klo != k or s.klo % mx.group != 0 or k % 64 != 0 or !tiled) return error.BadMxfp8Share;
+ n += s.hi - s.lo;
+ }
+ const npad = mx.padded(n);
+ return .{ .n = n, .k = k, .codes = npad * k, .scales = npad * (k / mx.group) };
+}
+
+/// `parts` packed one after another in 64-row tiles as the lane's one weight (cuda.mxfp8's order); padding 0 and 127.
+pub fn pack(gpa: std.mem.Allocator, parts: []const Share, codes: []u8, scales: []u8) !void {
+ const z = try sizes(parts);
+ if (codes.len != z.codes or scales.len != z.scales) return error.BadMxfp8Share;
+ @memset(codes, 0);
+ @memset(scales, 127);
+ const sg = z.k / mx.group; // a row's scale bytes
+ var t0: usize = 0; // tiles written
+ for (parts) |s| {
+ const n = s.hi - s.lo;
+ const npad = mx.padded(n);
+ const row_sg = s.p.k / mx.group;
+ const cut = try gpa.alloc(u8, n * z.k);
+ defer gpa.free(cut);
+ const sc = try gpa.alloc(u8, n * sg);
+ defer gpa.free(sc);
+ for (0..n) |r| {
+ @memcpy(cut[r * z.k ..][0..z.k], s.p.codes[(s.lo + r) * s.p.k + s.klo ..][0..z.k]);
+ @memcpy(sc[r * sg ..][0..sg], s.p.scales[(s.lo + r) * row_sg + s.klo / mx.group ..][0..sg]);
+ }
+ const pc = try gpa.alloc(u8, npad * z.k);
+ defer gpa.free(pc);
+ mx.packCodes(pc, cut, n, z.k);
+ const ps = try gpa.alloc(u8, npad * sg);
+ defer gpa.free(ps);
+ mx.packScales(ps, sc, n, z.k);
+ const tiles = (n + 63) / 64; // the last part's partial tile with its padding rows
+ @memcpy(codes[t0 * 64 * z.k ..][0 .. tiles * 64 * z.k], pc[0 .. tiles * 64 * z.k]);
+ @memcpy(scales[t0 * 64 * sg ..][0 .. tiles * 64 * sg], ps[0 .. tiles * 64 * sg]);
+ t0 += tiles;
+ }
+}
+
+/// Rows [lo, hi) of `p` as bf16, each value e4m3 times its power of two; error.NotExact when one is not a bf16.
+pub fn bf16Rows(p: Mxfp8, lo: usize, hi: usize, out: []u16) error{ NotExact, BadMxfp8Share }!void {
+ if (lo > hi or hi > p.n or out.len != (hi - lo) * p.k) return error.BadMxfp8Share;
+ const sg = p.k / mx.group;
+ for (lo..hi) |r| for (0..p.k) |c| {
+ const e = p.scales[r * sg + c / mx.group];
+ if (e == 0xff) return error.NotExact; // UE8M0's NaN
+ const v = core.quant.fp8.e4m3(p.codes[r * p.k + c]) * std.math.ldexp(@as(f64, 1), @as(i32, e) - 127);
+ const b: u32 = @bitCast(@as(f32, @floatCast(v)));
+ if (@as(f64, @as(f32, @bitCast(b & 0xffff0000))) != v) return error.NotExact;
+ out[(r - lo) * p.k + c] = @intCast(b >> 16);
+ };
+}
+
+/// Rows [lo, hi) of `p` widened to bf16 onto the loader's host bytes (an MTP kv_b); inexact: refused by `module`.
+pub fn appendWide(L: anytype, p: Mxfp8, module: []const u8, lo: usize, hi: usize) !void {
+ const out = try L.gpa.alloc(u16, (hi -| lo) * p.k);
+ defer L.gpa.free(out);
+ bf16Rows(p, lo, hi, out) catch |e| {
+ std.log.err("{s}: its MXFP8 rows hold a value no bf16 does ({t}); the absorbed attention reads bf16", .{ module, e });
+ return e;
+ };
+ try L.host.appendSlice(L.gpa, std.mem.sliceAsBytes(out));
+}
+
+/// Rows [lo, hi) of MXFP8 `module` widened to bf16 on the device, for a draft head's quantizer; inexact: refused.
+pub fn wideRows(d: *const cuda.Driver, gpa: std.mem.Allocator, ck: *core.Checkpoint, module: []const u8, lo: usize, hi: usize) !cuda.DeviceBuffer {
+ const p = try Mxfp8.read(ck, module);
+ const out = try gpa.alloc(u16, (hi -| lo) * p.k);
+ defer gpa.free(out);
+ bf16Rows(p, lo, hi, out) catch |e| {
+ std.log.err("{s}: its MXFP8 rows hold a value no bf16 does ({t}); the draft head quantizes bf16 rows", .{ module, e });
+ return e;
+ };
+ return cuda.DeviceBuffer.fromHost(d, std.mem.sliceAsBytes(out));
+}
+
+const testing = std.testing;
+
+/// e4m3 codes without NaN (0x7f, 0xff), from `seed`.
+fn codesOf(out: []u8, seed: u8) void {
+ for (out, 0..) |*c, j| {
+ const v: u8 = @truncate(j * 29 + seed);
+ c.* = if (v & 0x7f == 0x7f) v ^ 1 else v;
+ }
+}
+
+test "an MXFP8 projection reads its codes and scale bytes as stored, and a wrong scale shape is refused" {
+ const gpa = testing.allocator;
+ var codes: [128 * 64]u8 = undefined;
+ codesOf(&codes, 3);
+ var sc: [128 * 2]u8 = undefined;
+ for (&sc, 0..) |*s, j| s.* = @truncate(120 + j % 8);
+ const tensors = [_]core.shard_edit.New{
+ .{ .name = "a.weight", .dtype = .f8_e4m3, .shape = &.{ 128, 64 }, .bytes = &codes },
+ .{ .name = "a.weight_scale", .dtype = .u8, .shape = &.{ 128, 2 }, .bytes = &sc },
+ .{ .name = "b.weight", .dtype = .f8_e4m3, .shape = &.{ 128, 64 }, .bytes = &codes },
+ .{ .name = "b.weight_scale", .dtype = .u8, .shape = &.{ 128, 1 }, .bytes = sc[0..128] },
+ };
+ var tmp = testing.tmpDir(.{});
+ defer tmp.cleanup();
+ var dir: [64]u8 = undefined;
+ const at = try std.fmt.bufPrint(&dir, ".zig-cache/tmp/{s}", .{tmp.sub_path});
+ var path: [96]u8 = undefined;
+ try core.shard_edit.create(gpa, try std.fmt.bufPrint(&path, "{s}/model.safetensors", .{at}), &.{}, &tensors);
+ var ck = try core.Checkpoint.openModel(gpa, testing.io, at);
+ defer ck.close();
+ const p = try Mxfp8.read(&ck, "a");
+ try testing.expectEqual(@as(usize, 128), p.n);
+ try testing.expectEqual(@as(usize, 64), p.k);
+ try testing.expectEqualSlices(u8, &codes, p.codes);
+ try testing.expectEqualSlices(u8, &sc, p.scales);
+ try testing.expectError(error.BadMxfp8, Mxfp8.read(&ck, "b"));
+}
+
+test "a rank's MXFP8 share packs as cuda.mxfp8 packs the rows and inputs it holds, inputs from inside a 64-input stage" {
+ const gpa = testing.allocator;
+ const n = 192;
+ const k = 256;
+ const codes = try gpa.alloc(u8, n * k);
+ defer gpa.free(codes);
+ codesOf(codes, 5);
+ const sc = try gpa.alloc(u8, n * k / 32);
+ defer gpa.free(sc);
+ for (sc, 0..) |*s, j| s.* = @truncate(110 + j % 17);
+ const p: Mxfp8 = .{ .n = n, .k = k, .codes = codes, .scales = sc };
+ // rows [64, 192) and inputs [32, 160): an o_proj or down share whose origin is a scale byte, not a 64-input stage
+ const share = [_]Share{.{ .p = &p, .lo = 64, .hi = 192, .klo = 32, .khi = 160 }};
+ const z = try sizes(&share);
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(u8, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &share, got_c, got_s);
+ const cut = try gpa.alloc(u8, 128 * 128);
+ defer gpa.free(cut);
+ const cut_s = try gpa.alloc(u8, 128 * 4);
+ defer gpa.free(cut_s);
+ for (0..128) |r| {
+ @memcpy(cut[r * 128 ..][0..128], codes[(64 + r) * k + 32 ..][0..128]);
+ @memcpy(cut_s[r * 4 ..][0..4], sc[(64 + r) * 8 + 1 ..][0..4]);
+ }
+ const want_c = try gpa.alloc(u8, mx.padded(128) * 128);
+ defer gpa.free(want_c);
+ mx.packCodes(want_c, cut, 128, 128);
+ const want_s = try gpa.alloc(u8, mx.padded(128) * 4);
+ defer gpa.free(want_s);
+ mx.packScales(want_s, cut_s, 128, 128);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ try testing.expectEqualSlices(u8, want_s, got_s);
+}
+
+test "stacked MXFP8 parts follow each other in 64-row tiles, the stack's padding rows zero with scale 127" {
+ const gpa = testing.allocator;
+ const k = 64;
+ var a_codes: [128 * k]u8 = undefined;
+ codesOf(&a_codes, 7);
+ var b_codes: [64 * k]u8 = undefined;
+ codesOf(&b_codes, 9);
+ var a_sc: [128 * 2]u8 = @splat(121);
+ const b_sc: [64 * 2]u8 = @splat(124);
+ a_sc[2 * 100] = 119;
+ const a: Mxfp8 = .{ .n = 128, .k = k, .codes = &a_codes, .scales = &a_sc };
+ const b: Mxfp8 = .{ .n = 64, .k = k, .codes = &b_codes, .scales = &b_sc };
+ // [q | k]-like: rows [64, 128) of a, then all of b: 128 rows, no padding; then a third part pads to 256
+ const parts = [_]Share{ .{ .p = &a, .lo = 64, .hi = 128, .klo = 0, .khi = k }, .{ .p = &b, .lo = 0, .hi = 64, .klo = 0, .khi = k }, .{ .p = &a, .lo = 0, .hi = 64, .klo = 0, .khi = k } };
+ const z = try sizes(&parts);
+ try testing.expectEqual(@as(usize, 192), z.n);
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(u8, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &parts, got_c, got_s);
+ var all: [192 * k]u8 = undefined;
+ @memcpy(all[0 .. 64 * k], a_codes[64 * k ..][0 .. 64 * k]);
+ @memcpy(all[64 * k ..][0 .. 64 * k], &b_codes);
+ @memcpy(all[128 * k ..][0 .. 64 * k], a_codes[0 .. 64 * k]);
+ var all_s: [192 * 2]u8 = undefined;
+ @memcpy(all_s[0..128], a_sc[128..256]);
+ @memcpy(all_s[128..256], &b_sc);
+ @memcpy(all_s[256..384], a_sc[0..128]);
+ const want_c = try gpa.alloc(u8, 256 * k);
+ defer gpa.free(want_c);
+ mx.packCodes(want_c, &all, 192, k);
+ const want_s = try gpa.alloc(u8, 256 * 2);
+ defer gpa.free(want_s);
+ mx.packScales(want_s, &all_s, 192, k);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ try testing.expectEqualSlices(u8, want_s, got_s);
+}
+
+test "a last MXFP8 part may end inside its 64-row tile: [64 | 32] rows pack as the 96 rows packed whole" {
+ const gpa = testing.allocator;
+ const k = 128;
+ var a_codes: [64 * k]u8 = undefined;
+ codesOf(&a_codes, 11);
+ var b_codes: [32 * k]u8 = undefined;
+ codesOf(&b_codes, 13);
+ var a_sc: [64 * 4]u8 = undefined;
+ for (&a_sc, 0..) |*s, j| s.* = @truncate(118 + j % 9);
+ var b_sc: [32 * 4]u8 = undefined;
+ for (&b_sc, 0..) |*s, j| s.* = @truncate(121 + j % 5);
+ const a: Mxfp8 = .{ .n = 64, .k = k, .codes = &a_codes, .scales = &a_sc };
+ const b: Mxfp8 = .{ .n = 32, .k = k, .codes = &b_codes, .scales = &b_sc };
+ const parts = [_]Share{ .{ .p = &a, .lo = 0, .hi = 64, .klo = 0, .khi = k }, .{ .p = &b, .lo = 0, .hi = 32, .klo = 0, .khi = k } };
+ const z = try sizes(&parts);
+ try testing.expectEqual(@as(usize, 96), z.n);
+ const got_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(got_c);
+ const got_s = try gpa.alloc(u8, z.scales);
+ defer gpa.free(got_s);
+ try pack(gpa, &parts, got_c, got_s);
+ var all_c: [96 * k]u8 = undefined;
+ @memcpy(all_c[0 .. 64 * k], &a_codes);
+ @memcpy(all_c[64 * k ..], &b_codes);
+ var all_s: [96 * 4]u8 = undefined;
+ @memcpy(all_s[0 .. 64 * 4], &a_sc);
+ @memcpy(all_s[64 * 4 ..], &b_sc);
+ const want_c = try gpa.alloc(u8, z.codes);
+ defer gpa.free(want_c);
+ mx.packCodes(want_c, &all_c, 96, k);
+ const want_s = try gpa.alloc(u8, z.scales);
+ defer gpa.free(want_s);
+ mx.packScales(want_s, &all_s, 96, k);
+ try testing.expectEqualSlices(u8, want_c, got_c);
+ try testing.expectEqualSlices(u8, want_s, got_s);
+}
+
+test "an MXFP8 share off a scale byte, a 64-input stage or a 64-row tile, or past its tensor, is refused" {
+ const codes: [128 * 128]u8 = @splat(0);
+ const sc: [128 * 4]u8 = @splat(127);
+ const p: Mxfp8 = .{ .n = 128, .k = 128, .codes = &codes, .scales = &sc };
+ try testing.expectError(error.BadMxfp8Share, sizes(&.{.{ .p = &p, .lo = 0, .hi = 128, .klo = 16, .khi = 80 }}));
+ try testing.expectError(error.BadMxfp8Share, sizes(&.{.{ .p = &p, .lo = 0, .hi = 128, .klo = 32, .khi = 128 }}));
+ try testing.expectError(error.BadMxfp8Share, sizes(&.{ .{ .p = &p, .lo = 0, .hi = 100, .klo = 0, .khi = 128 }, .{ .p = &p, .lo = 0, .hi = 64, .klo = 0, .khi = 128 } }));
+ try testing.expectError(error.BadMxfp8Share, sizes(&.{.{ .p = &p, .lo = 64, .hi = 192, .klo = 0, .khi = 128 }}));
+ try testing.expectError(error.BadMxfp8Share, sizes(&.{}));
+ var small: [8]u8 = undefined;
+ var tiles: [2]u8 = undefined;
+ try testing.expectError(error.BadMxfp8Share, pack(testing.allocator, &.{.{ .p = &p, .lo = 0, .hi = 64, .klo = 0, .khi = 128 }}, &small, &tiles));
+}
+
+test "MXFP8 rows widen to bf16 exactly, and a value bf16 cannot hold is refused" {
+ const codes = [_]u8{ 0x38, 0xb8, 0x3f, 0x01, 0x00, 0x7e } ++ @as([26]u8, @splat(0x30));
+ var sc = [_]u8{127};
+ const p: Mxfp8 = .{ .n = 1, .k = 32, .codes = &codes, .scales = &sc };
+ var out: [32]u16 = undefined;
+ try bf16Rows(p, 0, 1, &out);
+ try testing.expectEqual(@as(u16, 0x3f80), out[0]); // 1.0
+ try testing.expectEqual(@as(u16, 0xbf80), out[1]); // -1.0
+ try testing.expectEqual(@as(u16, 0x3ff0), out[2]); // 1.875
+ try testing.expectEqual(@as(u16, 0x3b00), out[3]); // 2^-9, e4m3's least subnormal
+ try testing.expectEqual(@as(u16, 0), out[4]);
+ sc[0] = 0; // 2^-127: e4m3's subnormals fall below bf16's
+ try testing.expectError(error.NotExact, bf16Rows(p, 0, 1, &out));
+ sc[0] = 0xff;
+ try testing.expectError(error.NotExact, bf16Rows(p, 0, 1, &out));
+}
diff --git a/zig/src/families/glm/cuda_proj.zig b/zig/src/families/glm/cuda_proj.zig
index 4c5efb0d54..6a10b341c3 100644
--- a/zig/src/families/glm/cuda_proj.zig
+++ b/zig/src/families/glm/cuda_proj.zig
@@ -1,24 +1,24 @@
-//! A GLM-5.3-Flash projection as its checkpoint stores it: bf16 through the glue's matmuls, block FP8 through qlinear.
+//! A GLM-5.3-Flash projection as its checkpoint stores it: bf16 through the glue's matmuls, FP8 kinds through qlinear.
const std = @import("std");
const cuda = @import("cuda");
const glue = @import("cuda_glue.zig");
const b16 = @import("cuda_b16.zig");
-/// A rank's projection: bf16 rows for the glue, or an FP8_PB_WO checkpoint's block FP8 packed for the lane.
-pub const Proj = union(enum) { bf16: u64, fp8g: cuda.qmmf.Weight };
+/// A rank's projection: bf16 rows for the glue, or FP8_PB_WO's block FP8 or MXFP8, packed for the lane as stored.
+pub const Proj = union(enum) { bf16: u64, fp8g: cuda.qmmf.Weight, mxfp8: cuda.qmmf.Weight };
/// x [m][k] (rows `x_stride` apart) by `w` into out [m][n]: bf16, or fp32 with `wide`; `part` the bf16 path's slices.
pub fn matmul(set: glue.Set, stream: cuda.Stream, w: Proj, x: u64, x_stride: usize, out: u64, part: u64, m: usize, n: usize, k: usize, wide: bool) !void {
switch (w) {
.bf16 => |p| return b16.matmul(set, stream, x, x_stride, p, out, part, m, n, k, wide),
- .fp8g => |q| {
+ .fp8g, .mxfp8 => |q| {
if (q.n != n or q.k != k) return error.ShapeMismatch;
const lane = set.linear.lane orelse return error.FormatNotLoaded;
// GB10's lane meets its K slices in a cluster; a GPU that sums them in memory would need partials held here
if (lane.partBytes(m, q) != 0) return error.SlicePartialsNotHeld;
const in: cuda.qlinear.Rows = .{ .x = x, .ldx = x_stride };
- const f: cuda.qlinear.Weight = .{ .fp8g = q };
+ const f: cuda.qlinear.Weight = if (w == .fp8g) .{ .fp8g = q } else .{ .mxfp8 = q };
return if (wide) set.linear.decodeF32(stream, f, in, out, m) else set.linear.decode(stream, f, in, out, m);
},
}
@@ -34,4 +34,5 @@ test "a block-FP8 projection is refused before any launch when the call's shape
lane.clusters = false; // a GPU before sm_90: 64 outputs from 4,096 inputs take K slices summed in memory
set.linear = .{ .lane = &lane };
try std.testing.expectError(error.SlicePartialsNotHeld, matmul(set, s, .{ .fp8g = q }, 0, 4096, 0, 0, 1, 64, 4096, true));
+ try std.testing.expectError(error.SlicePartialsNotHeld, matmul(set, s, .{ .mxfp8 = cuda.mxfp8.weight(0, 0, 64, 4096) }, 0, 4096, 0, 0, 1, 64, 4096, true));
}
diff --git a/zig/src/families/glm/cuda_prompt_logprobs.zig b/zig/src/families/glm/cuda_prompt_logprobs.zig
index ffff5adedc..c1bcecd738 100644
--- a/zig/src/families/glm/cuda_prompt_logprobs.zig
+++ b/zig/src/families/glm/cuda_prompt_logprobs.zig
@@ -3,6 +3,7 @@ const std = @import("std");
const cuda = @import("cuda");
const lanes = @import("lanes");
const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
const fw = @import("cuda_forward.zig");
const config = @import("config.zig");
const shard = @import("cuda_shard.zig");
@@ -76,7 +77,7 @@ pub fn run(e: *Engine, want: Want, next: []const u32) !void {
while (r < next.len) : (r += rows) {
const n = @min(rows, next.len - r);
// as the head's last-row pass (Forward.finish); its column count takes no K slices (the test below)
- try b16.matmul(projectionSet(e), e.stream, e.bp.fnormed + r * D * 2, D, e.w.head, columns, e.bp.kda.mm_part, n, e.share.vocab.len(), D, false);
+ try projection.matmul(projectionSet(e), e.stream, e.w.head, e.bp.fnormed + r * D * 2, D, columns, e.bp.kda.mm_part, n, e.share.vocab.len(), D, false);
try e.smp.gather(e.stream, e.exchange(), e.spans, &.{.{ .rank = e.share.rank, .logits = columns }}, n, full);
const reader = want.reader orelse continue;
@memcpy(picks[0..n], next[r..][0..n]);
@@ -112,7 +113,7 @@ test "the head's columns at TP=2 and TP=3 take no K slices: a pass of any rows n
};
}
-/// The projection policy used by the prompt-row head passes.
+/// The projection policy used by the prompt-row head passes; an MXFP8 head (FP4-Local's) runs on the lane.
pub fn projectionSet(e: anytype) @import("cuda_glue.zig").Set {
- return .{ .glue = &e.m.glue, .mm = if (e.m.tiled) &e.m.mm else null, .packs = e.m.packs, .prompt = true };
+ return .{ .glue = &e.m.glue, .mm = if (e.m.tiled) &e.m.mm else null, .packs = e.m.packs, .prompt = true, .linear = .{ .lane = &e.m.lane } };
}
diff --git a/zig/src/families/glm/cuda_shared.zig b/zig/src/families/glm/cuda_shared.zig
index 1899ed31b2..68d32f36d4 100644
--- a/zig/src/families/glm/cuda_shared.zig
+++ b/zig/src/families/glm/cuda_shared.zig
@@ -9,7 +9,7 @@ const latent = @import("cuda_latent.zig");
const kda_block = @import("cuda_kda_block.zig");
const dsa_block = @import("cuda_dsa_block.zig");
const glue = @import("cuda_glue.zig");
-const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
/// Checks a shared window's segments (a prompt buffer's: whole prompts) against its `rows` rows; each holds its own.
pub fn begin(r: fw.Rank, rows: usize, stream: cuda.Stream) !void {
@@ -33,7 +33,7 @@ pub fn heads(f: *const fw.Forward, r: fw.Rank) !void {
const D = r.w.config.hidden;
const b = r.b;
for (r.segs, 0..) |g, k| try f.d.check(f.d.api.cuMemcpyDtoDAsync_v2(b.normed + k * D * 2, b.fnormed + (g.row0 + g.rows - 1) * D * 2, D * 2, f.stream.handle), "cuMemcpyDtoDAsync");
- try b16.matmul(f.set(), f.stream, b.normed, D, r.w.head, b.logits, b.kda.mm_part, r.segs.len, r.w.share.vocab.len(), D, false);
+ try projection.matmul(f.set(), f.stream, r.w.head, b.normed, D, b.logits, b.kda.mm_part, r.segs.len, r.w.share.vocab.len(), D, false);
}
/// Forward.mixer on a shared window: all rows' projections, each segment's chain or attention, o_proj (null: pieced).
diff --git a/zig/src/families/glm/cuda_state.zig b/zig/src/families/glm/cuda_state.zig
index f039b6bb45..0cee25d086 100644
--- a/zig/src/families/glm/cuda_state.zig
+++ b/zig/src/families/glm/cuda_state.zig
@@ -145,6 +145,8 @@ pub const Buffers = struct {
// windows under rows_from rows keep their K slices apart (a prompt's tail chunk on the prefill buffers too)
for (b16Shapes(w)) |sh_| mm = @max(mm, b16.partBytes(@min(rows, b16.rows_from - 1), sh_[0], sh_[1]));
const x3: [4]usize = if (c.exl3) exl3.scratchBytes(rows, D, ew, prefill, w.exl3_shx) else @splat(4);
+ var split = false; // a KDA stack in two parts moves each part's rows through one more buffer
+ for (w.layers) |l| split = split or (l.mixer == .kda and l.mixer.kda.proj == .split);
const hist_len = if (pairs > cuda.grouped.small) (pairs + cuda.grouped.small - 1) / cuda.grouped.small * (count + 1) else 1;
const sizes = [_]usize{
rows * 4, rows * S * D * 2, rows * D * 2, rows * (D / 64) * 4,
@@ -183,6 +185,7 @@ pub const Buffers = struct {
rows * D * 2, rows * D * 2, rows * 2 * D * 2, rows * D * 2, // hin, me, mcat, mx
rows * D * 4, // part: this rank's fp32 partial rows a split exchange sends from
x3[0], x3[1], x3[2], x3[3], // EXL3 experts: 8-slot picks, rotated inputs, a decode window's partials
+ if (split) rows * @max(3 * n, kw - 3 * n) * 2 else 4, // a split KDA stack's part rows
};
var p: [sizes.len]u64 = undefined;
const a = try arena(d, &sizes, &p);
@@ -237,6 +240,7 @@ pub const Buffers = struct {
.part = p[70],
};
out.moe.x3 = .{ .picks = p[71], .xg = p[72], .xu = p[73], .z = p[74] };
+ out.kda.tmp = p[75];
return out;
}
diff --git a/zig/src/families/glm/cuda_structured.zig b/zig/src/families/glm/cuda_structured.zig
index c450427054..b155320732 100644
--- a/zig/src/families/glm/cuda_structured.zig
+++ b/zig/src/families/glm/cuda_structured.zig
@@ -1,7 +1,7 @@
//! Whole-vocabulary grammar draws and prompt redraws.
const cuda = @import("cuda");
const sampler = @import("cuda_sampler.zig");
-const b16 = @import("cuda_b16.zig");
+const projection = @import("cuda_proj.zig");
const prompt_lp = @import("cuda_prompt_logprobs.zig");
const Engine = @import("cuda_engine.zig").Engine;
@@ -43,5 +43,5 @@ pub fn redrawFirst(e: anytype, len: usize, bits: u64) !u32 {
/// Recompute only the bound sequence's prompt head, using the same projection policy as prompt finishing.
pub fn promptHead(e: *Engine) !void {
const D = e.c.hidden;
- try b16.matmul(prompt_lp.projectionSet(e), e.stream, e.cur.st.prompt_hidden, D, e.w.head, e.bd.logits, e.bd.kda.mm_part, 1, e.share.vocab.len(), D, false);
+ try projection.matmul(prompt_lp.projectionSet(e), e.stream, e.w.head, e.cur.st.prompt_hidden, D, e.bd.logits, e.bd.kda.mm_part, 1, e.share.vocab.len(), D, false);
}
diff --git a/zig/src/families/glm/cuda_tile.zig b/zig/src/families/glm/cuda_tile.zig
index 4a2a6a24de..0cdcc049b3 100644
--- a/zig/src/families/glm/cuda_tile.zig
+++ b/zig/src/families/glm/cuda_tile.zig
@@ -15,11 +15,11 @@ fn add(l: *std.ArrayList(Lin), gpa: std.mem.Allocator, field: *u64, n: usize, k:
if (field.* != 0) try l.append(gpa, .{ .field = field, .n = n, .k = k });
}
-/// A projection as stored: its bf16 rows like any linear's; block FP8 stays the lane's tiles, never read as bf16 here.
+/// A projection as stored: its bf16 rows like any linear's; FP8 kinds stay the lane's tiles, never read as bf16 here.
fn addProj(l: *std.ArrayList(Lin), gpa: std.mem.Allocator, p: *projection.Proj, n: usize, k: usize) !void {
switch (p.*) {
.bf16 => |*w| try add(l, gpa, w, n, k),
- .fp8g => {},
+ .fp8g, .mxfp8 => {},
}
}
@@ -29,17 +29,20 @@ fn layerLinears(gpa: std.mem.Allocator, l: *Layer, list: *std.ArrayList(Lin)) !v
.kda => |*x| {
const n = x.heads * kd;
if (x.tiledInput()) {
- try add(list, gpa, &x.proj, x.width(), x.dims);
+ switch (x.proj) { // a split stack's MXFP8 [q | k | v] is the lane's; its bf16 [f_a | g_a | b] tiles
+ .bf16 => |*p| try add(list, gpa, p, x.width(), x.dims),
+ .split => |*sp| try add(list, gpa, &sp.rest, x.width() - 3 * n, x.dims),
+ }
try add(list, gpa, &x.fb, n, kd);
try add(list, gpa, &x.gb, n, kd);
}
- try add(list, gpa, &x.o, x.dims, n);
+ try addProj(list, gpa, &x.o, x.dims, n);
},
.mla => |*x| {
try addProj(list, gpa, &x.proj, x.q_lora + x.lw, x.dims);
try addProj(list, gpa, &x.q_b, x.heads * x.qk, x.q_lora);
- try add(list, gpa, &x.index.kw, x.index.d + x.index.heads, x.dims);
- try add(list, gpa, &x.index.qb, x.index.heads * x.index.d, x.q_lora);
+ try addProj(list, gpa, &x.index.kw, x.index.d + x.index.heads, x.dims);
+ try addProj(list, gpa, &x.index.qb, x.index.heads * x.index.d, x.q_lora);
try addProj(list, gpa, &x.o, x.dims, x.heads * x.dv);
},
}
@@ -49,7 +52,7 @@ fn layerLinears(gpa: std.mem.Allocator, l: *Layer, list: *std.ArrayList(Lin)) !v
try add(list, gpa, &b.gu, 2 * x.width, x.dims);
try add(list, gpa, &b.down, x.dims, x.width);
},
- .nvfp4 => {},
+ .nvfp4, .mxfp8 => {},
},
.moe => |*x| {
try addProj(list, gpa, &x.shared_gu, 2 * x.shared, x.routed.dims);
@@ -65,7 +68,7 @@ fn linears(w: *Weights, list: *std.ArrayList(Lin)) !void {
try layerLinears(w.gpa, &m.layer, list);
try add(list, w.gpa, &m.eh, w.config.hidden, 2 * w.config.hidden);
}
- try add(list, w.gpa, &w.head, w.share.vocab.len(), w.config.hidden);
+ try addProj(list, w.gpa, &w.head, w.share.vocab.len(), w.config.hidden);
}
/// A packed linear moved into a buffer its own size, its bf16 one freed; the bytes freed (0: it shares a buffer).
@@ -114,7 +117,7 @@ pub fn tileLinears(gpa: std.mem.Allocator, w: *Weights, mk: *const mm_mod.Kernel
const n = e.n;
const k = e.k;
const ptr = e.field.*;
- const bits = if (i + 1 == list.items.len) head_bits else dense_bits;
+ const bits = if (i + 1 == list.items.len and w.head == .bf16) head_bits else dense_bits; // an MXFP8 head stays
if (bits == 16) {
try mm_mod.tile(mk, s, ptr, tmp.ptr, n, k);
try d.check(d.api.cuMemcpyDtoDAsync_v2(ptr, tmp.ptr, n * k * 2, s.handle), "cuMemcpyDtoDAsync");
@@ -162,17 +165,20 @@ test "GLM TP3: the tiler keeps unsupported KDA input and view weights in row-maj
const a = std.testing.allocator;
for ([_]usize{ 1, 2, 3, 4, 5, 8 }) |world| for (0..world) |rank| {
const share = try Share.of(&c, rank, world);
- var l: Layer = undefined;
- l.mixer = .{ .kda = .{ .proj = 1, .fb = 2, .gb = 3, .o = 4, .conv_w = 0, .a_log = 0, .dt_bias = 0, .norm_w = 0, .eps = 1e-6, .lower = -5, .heads = share.kda.len(), .dims = c.hidden } };
- l.ffn = .{ .dense = .{ .w = .{ .nvfp4 = undefined }, .width = share.dense.len(), .dims = c.hidden, .limit = 0 } };
- var list: std.ArrayList(Lin) = .empty;
- defer list.deinit(a);
- try layerLinears(a, &l, &list);
- const aligned = share.kda.len() % 8 == 0;
- try std.testing.expectEqual(@as(usize, if (aligned) 4 else 1), list.items.len);
- try std.testing.expectEqual(&l.mixer.kda.o, list.items[list.items.len - 1].field);
- if (aligned) {
- for (list.items, [_]u64{ 1, 2, 3, 4 }) |linear, ptr| try std.testing.expectEqual(ptr, linear.field.*);
+ for ([_]bool{ false, true }) |split| { // a split stack's bf16 rest tiles or stays row-major with the stack
+ const in: @import("cuda_kda_block.zig").In = if (split) .{ .split = .{ .qkv = undefined, .rest = 1 } } else .{ .bf16 = 1 };
+ var l: Layer = undefined;
+ l.mixer = .{ .kda = .{ .proj = in, .fb = 2, .gb = 3, .o = .{ .bf16 = 4 }, .conv_w = 0, .a_log = 0, .dt_bias = 0, .norm_w = 0, .eps = 1e-6, .lower = -5, .heads = share.kda.len(), .dims = c.hidden } };
+ l.ffn = .{ .dense = .{ .w = .{ .nvfp4 = undefined }, .width = share.dense.len(), .dims = c.hidden, .limit = 0 } };
+ var list: std.ArrayList(Lin) = .empty;
+ defer list.deinit(a);
+ try layerLinears(a, &l, &list);
+ const aligned = share.kda.len() % 8 == 0;
+ try std.testing.expectEqual(@as(usize, if (aligned) 4 else 1), list.items.len);
+ try std.testing.expectEqual(&l.mixer.kda.o.bf16, list.items[list.items.len - 1].field);
+ if (aligned) {
+ for (list.items, [_]u64{ 1, 2, 3, 4 }) |linear, ptr| try std.testing.expectEqual(ptr, linear.field.*);
+ }
}
};
}
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index 0d7787adab..de69483e03 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -15,6 +15,7 @@ const q4 = @import("cuda_q4.zig");
const exl3 = @import("cuda_exl3.zig");
const mm_mod = @import("cuda_mm.zig");
pub const formats = @import("cuda_formats.zig");
+const projection = @import("cuda_proj.zig");
const Tensor = core.checkpoint.Tensor;
const prefix = "model.language_model.";
@@ -43,7 +44,7 @@ pub const Weights = struct {
embed: u64 = 0, // bf16 [vocab][dims]
layers: []Layer = &.{},
norm: u64 = 0,
- head: u64 = 0, // bf16 [share.vocab][dims]
+ head: projection.Proj = .{ .bf16 = 0 }, // [share.vocab][dims]: bf16, or MXFP8 for the lane
mtp: ?Mtp = null,
exl3_shx: bool = true, // EXL3: every layer's gate and up share one input rotation (a prompt rotates each row once)
buffers: std.ArrayList(cuda.DeviceBuffer) = .empty,
@@ -185,11 +186,17 @@ const Loader = struct {
const h = L.w.share.kda;
const dk = c.kda_dim;
const all = c.kda_heads * dk;
- inline for (.{ "q", "k", "v", "o" }) |x| try L.format("layers.{d}.self_attn." ++ x ++ "_proj", .{i}, .bf16);
- inline for (.{ "q", "k", "v" }) |x| try L.rows(try L.get("layers.{d}.self_attn." ++ x ++ "_proj.weight", .{i}), h.lo * dk, h.hi * dk, all, .bf16);
+ const mx = (try L.get("layers.{d}.self_attn.q_proj.weight", .{i})).dtype == .f8_e4m3; // FP4-Local's q, k, v and o
+ inline for (.{ "q", "k", "v" }) |x| try L.format("layers.{d}.self_attn." ++ x ++ "_proj", .{i}, if (mx) .mxfp8 else .bf16);
+ const hr: [2]usize = .{ h.lo * dk, h.hi * dk }; // the rank's heads' rows
+ var qkv: ?cuda.qmmf.Weight = null; // [q | k | v] as one lane weight, then the stack's bf16 rest
+ if (mx) {
+ const parts = [_]formats.Part{ .{ .rel = "self_attn.q_proj", .rows = hr }, .{ .rel = "self_attn.k_proj", .rows = hr }, .{ .rel = "self_attn.v_proj", .rows = hr } };
+ qkv = (try formats.cutProj(L, prefix, i, &parts, &.{.mxfp8}, "layers.{d}.kda.qkv")).mxfp8;
+ } else inline for (.{ "q", "k", "v" }) |x| try L.rows(try L.get("layers.{d}.self_attn." ++ x ++ "_proj.weight", .{i}), hr[0], hr[1], all, .bf16);
inline for (.{ "f_a", "g_a" }) |x| try L.whole(try L.get("layers.{d}.self_attn." ++ x ++ "_proj.weight", .{i}), .bf16);
try L.rows(try L.get("layers.{d}.self_attn.b_proj.weight", .{i}), h.lo, h.hi, c.kda_heads, .bf16);
- const proj = try L.put("layers.{d}.kda.proj", .{i});
+ const proj: kda_block.In = if (qkv) |q| .{ .split = .{ .qkv = q, .rest = try L.put("layers.{d}.kda.rest", .{i}) } } else .{ .bf16 = try L.put("layers.{d}.kda.proj", .{i}) };
try L.rows(try L.get("layers.{d}.self_attn.f_b_proj.weight", .{i}), h.lo * dk, h.hi * dk, all, .bf16);
const fb = try L.put("layers.{d}.kda.fb", .{i});
try L.rows(try L.get("layers.{d}.self_attn.g_b_proj.weight", .{i}), h.lo * dk, h.hi * dk, all, .bf16);
@@ -202,35 +209,40 @@ const Loader = struct {
const dt = try L.put("layers.{d}.kda.dt_bias", .{i});
try L.whole(try L.get("layers.{d}.self_attn.o_norm.weight", .{i}), .bf16);
const norm = try L.put("layers.{d}.kda.norm", .{i});
- try L.cols(try L.get("layers.{d}.self_attn.o_proj.weight", .{i}), h.lo * dk, h.hi * dk, .bf16);
- const o = try L.put("layers.{d}.kda.o", .{i});
+ const o = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.o_proj", .cols = hr }}, &.{ .bf16, .mxfp8 }, "layers.{d}.kda.o");
return .{ .proj = proj, .fb = fb, .gb = gb, .o = o, .conv_w = conv, .a_log = a_log, .dt_bias = dt, .norm_w = norm, .eps = c.eps, .lower = c.lower_bound, .heads = h.len(), .dims = c.hidden };
}
/// weights.load's dsa(i) (latent): [q_a | kv_a], norms, q_b by heads, kv_b as wk/wv (AbsorbW), o by cols, indexer.
- fn dsa(L: *Loader, i: u32) !dsa_block.Layer {
+ pub fn dsa(L: *Loader, i: u32) !dsa_block.Layer {
const c = &L.w.config;
const h = L.w.share.mla;
const qk = c.nope;
- try L.format("layers.{d}.self_attn.kv_b_proj", .{i}, .bf16);
- const proj = try formats.cutProj(L, prefix, i, &.{ .{ .rel = "self_attn.q_a_proj" }, .{ .rel = "self_attn.kv_a_proj_with_mqa" } }, "layers.{d}.dsa.proj");
+ // the MTP layer's may be MXFP8 (FP4-Local's): on the lane as stored, kv_b widened exactly for its halves
+ const mtp = i == c.layers;
+ const wants: []const formats.Format = if (mtp) &.{ .bf16, .fp8_pb, .mxfp8 } else &.{ .bf16, .fp8_pb };
+ var kb: [96]u8 = undefined;
+ const kv_name = try std.fmt.bufPrint(&kb, prefix ++ "layers.{d}.self_attn.kv_b_proj", .{i});
+ const kv_mx = mtp and (try L.get("layers.{d}.self_attn.kv_b_proj.weight", .{i})).dtype == .f8_e4m3;
+ try L.format("layers.{d}.self_attn.kv_b_proj", .{i}, if (kv_mx) .mxfp8 else .bf16);
+ const kv_wide: ?formats.mxfp8.Mxfp8 = if (kv_mx) try formats.mxfp8.Mxfp8.read(L.ck, kv_name) else null;
+ const proj = try formats.cutProj(L, prefix, i, &.{ .{ .rel = "self_attn.q_a_proj" }, .{ .rel = "self_attn.kv_a_proj_with_mqa" } }, wants, "layers.{d}.dsa.proj");
try L.whole(try L.get("layers.{d}.self_attn.q_a_layernorm.weight", .{i}), .bf16);
const q_norm = try L.put("layers.{d}.dsa.q_norm", .{i});
try L.whole(try L.get("layers.{d}.self_attn.kv_a_layernorm.weight", .{i}), .bf16);
const kv_norm = try L.put("layers.{d}.dsa.kv_norm", .{i});
- const q_b = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.q_b_proj", .rows = .{ h.lo * qk, h.hi * qk } }}, "layers.{d}.dsa.q_b");
+ const q_b = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.q_b_proj", .rows = .{ h.lo * qk, h.hi * qk } }}, wants, "layers.{d}.dsa.q_b");
const kv_b = try L.get("layers.{d}.self_attn.kv_b_proj.weight", .{i});
const per = qk + c.v_dim; // a head's kv_b rows: its keys, then its values
const total = c.mla_heads * per;
- for (h.lo..h.hi) |head| try L.rows(kv_b, head * per, head * per + qk, total, .bf16);
+ for (h.lo..h.hi) |head| try absorbRows(L, kv_wide, kv_b, kv_name, head * per, head * per + qk, total);
const wk = try L.put("layers.{d}.dsa.absorb.wk", .{i});
- for (h.lo..h.hi) |head| try L.rows(kv_b, head * per + qk, (head + 1) * per, total, .bf16);
+ for (h.lo..h.hi) |head| try absorbRows(L, kv_wide, kv_b, kv_name, head * per + qk, (head + 1) * per, total);
const wv = try L.put("layers.{d}.dsa.absorb.wv", .{i});
- const o = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.o_proj", .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } }}, "layers.{d}.dsa.o");
- inline for (.{ "wk", "weights_proj" }) |x| try L.whole(try L.get("layers.{d}.self_attn.indexer." ++ x ++ ".weight", .{i}), .bf16);
- const kw = try L.put("layers.{d}.dsa.index.kw", .{i});
- try L.whole(try L.get("layers.{d}.self_attn.indexer.wq_b.weight", .{i}), .bf16);
- const qb = try L.put("layers.{d}.dsa.index.qb", .{i});
+ const o = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.o_proj", .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } }}, wants, "layers.{d}.dsa.o");
+ const iw: []const formats.Format = if (mtp) &.{ .bf16, .mxfp8 } else &.{.bf16};
+ const kw = try formats.cutProj(L, prefix, i, &.{ .{ .rel = "self_attn.indexer.wk" }, .{ .rel = "self_attn.indexer.weights_proj" } }, iw, "layers.{d}.dsa.index.kw");
+ const qb = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.indexer.wq_b" }}, iw, "layers.{d}.dsa.index.qb");
try L.whole(try L.get("layers.{d}.self_attn.indexer.k_norm.weight", .{i}), .bf16);
const ln_w = try L.put("layers.{d}.dsa.index.ln_w", .{i});
try L.whole(try L.get("layers.{d}.self_attn.indexer.k_norm.bias", .{i}), .bf16);
@@ -259,6 +271,12 @@ const Loader = struct {
};
}
+ /// kv_b's rows [lo, hi) onto the host bytes: bf16 as stored, or MXFP8 (`mx`, the MTP layer's) widened exactly.
+ fn absorbRows(L: *Loader, mx: ?formats.mxfp8.Mxfp8, t: Tensor, name: []const u8, lo: usize, hi: usize, total: usize) !void {
+ if (mx) |p| return formats.mxfp8.appendWide(L, p, name, lo, hi);
+ try L.rows(t, lo, hi, total, .bf16);
+ }
+
/// An NVFP4 linear's rows [lo, hi), inputs [klo, khi) as Fp4Linear.from_checkpoint (tf_pack_dense, packScales).
fn fp4(L: *Loader, comptime fmt: []const u8, i: u32, lo: usize, hi: usize, klo: usize, khi: usize) !cuda.qmmf.Weight {
const name = try std.fmt.bufPrint(&L.name_buf, prefix ++ fmt, .{i});
@@ -291,18 +309,24 @@ const Loader = struct {
return cuda.nvfp4.weight(words.ptr, bs, l.global, n, k);
}
- /// weights.load's mlp for a dense layer: gate and up by the rank's rows, down by its columns (NVFP4).
+ /// weights.load's mlp for a dense layer: gate and up by the rank's rows, down by its columns (NVFP4 or MXFP8).
fn dense(L: *Loader, i: u32) !mlp.Layer {
const c = &L.w.config;
const s = L.w.share.dense;
const d = c.hidden;
- const want: formats.Format = if (c.exl3) .bf16 else .nvfp4;
+ const mx = !c.exl3 and (try L.get("layers.{d}.mlp.gate_proj.weight", .{i})).dtype == .f8_e4m3; // FP4-Local's
+ const want: formats.Format = if (c.exl3) .bf16 else if (mx) .mxfp8 else .nvfp4;
inline for (.{ "gate_proj", "up_proj", "down_proj" }) |x| try L.format("layers.{d}.mlp." ++ x, .{i}, want);
const w: mlp.Weights = if (c.exl3) bf16: { // an EXL3 checkpoint keeps its dense layers bf16
inline for (.{ "gate_proj", "up_proj" }) |x| try L.rows(try L.get("layers.{d}.mlp." ++ x ++ ".weight", .{i}), s.lo, s.hi, c.dense_inter, .bf16);
const gu = try L.put("layers.{d}.mlp.gu", .{i});
try L.cols(try L.get("layers.{d}.mlp.down_proj.weight", .{i}), s.lo, s.hi, .bf16);
break :bf16 .{ .bf16 = .{ .gu = gu, .down = try L.put("layers.{d}.mlp.down", .{i}) } };
+ } else if (mx) mx8: {
+ const parts = [_]formats.Part{ .{ .rel = "mlp.gate_proj", .rows = .{ s.lo, s.hi } }, .{ .rel = "mlp.up_proj", .rows = .{ s.lo, s.hi } } };
+ const gu = try formats.cutProj(L, prefix, i, &parts, &.{.mxfp8}, "layers.{d}.mlp.gu");
+ const down = try formats.cutProj(L, prefix, i, &.{.{ .rel = "mlp.down_proj", .cols = .{ s.lo, s.hi } }}, &.{.mxfp8}, "layers.{d}.mlp.down");
+ break :mx8 .{ .mxfp8 = .{ .gu = gu.mxfp8, .down = down.mxfp8 } };
} else .{ .nvfp4 = .{
.gate = try L.fp4("layers.{d}.mlp.gate_proj", i, s.lo, s.hi, 0, d),
.up = try L.fp4("layers.{d}.mlp.up_proj", i, s.lo, s.hi, 0, d),
@@ -317,7 +341,7 @@ const Loader = struct {
}
/// One device allocation the loader frees itself (staging for the GPU's quantizers).
- fn scratch(L: *Loader, n: usize) !cuda.DeviceBuffer {
+ pub fn scratch(L: *Loader, n: usize) !cuda.DeviceBuffer {
return cuda.DeviceBuffer.alloc(L.d, n);
}
@@ -383,7 +407,7 @@ const Loader = struct {
}
/// weights.load's moe(i) on NVFP4: router and bias, routed experts as Experts4, the BF16 shared expert's share.
- fn moeLayer(L: *Loader, i: u32) !moe.Layer {
+ pub fn moeLayer(L: *Loader, i: u32) !moe.Layer {
const c = &L.w.config;
const d: usize = c.hidden;
const cut = L.w.share.moe;
@@ -400,9 +424,10 @@ const Loader = struct {
if (!x3.?.shx) L.w.exl3_shx = false;
} else routed = try L.nvfp4Experts(i);
const sh = L.w.share.shared;
+ const sw: []const formats.Format = if (i == c.layers) &.{ .bf16, .fp8_pb, .mxfp8 } else &.{ .bf16, .fp8_pb }; // MTP's MXFP8
const gu_parts = [_]formats.Part{ .{ .rel = "mlp.shared_experts.gate_proj", .rows = .{ sh.lo, sh.hi } }, .{ .rel = "mlp.shared_experts.up_proj", .rows = .{ sh.lo, sh.hi } } };
- const gu = try formats.cutProj(L, prefix, i, &gu_parts, "layers.{d}.moe.shared.gu");
- const sd = try formats.cutProj(L, prefix, i, &.{.{ .rel = "mlp.shared_experts.down_proj", .cols = .{ sh.lo, sh.hi } }}, "layers.{d}.moe.shared.down");
+ const gu = try formats.cutProj(L, prefix, i, &gu_parts, sw, "layers.{d}.moe.shared.gu");
+ const sd = try formats.cutProj(L, prefix, i, &.{.{ .rel = "mlp.shared_experts.down_proj", .cols = .{ sh.lo, sh.hi } }}, sw, "layers.{d}.moe.shared.down");
return .{
.router = router,
.bias = bias,
@@ -472,48 +497,6 @@ const Loader = struct {
return .{ .format = .nvfp4, .up = ptrs[0], .down = ptrs[1], .up_scale = ptrs[2], .down_scale = ptrs[3], .width = ni, .dims = d, .experts = count, .limit = c.swiglu_limit };
}
- /// weights.load's MTPW: layer `layers` as plain DSA + MoE, norms, eh_proj, draft head from the rank's head rows.
- fn mtp(L: *Loader) !Mtp {
- const c = &L.w.config;
- const i = c.layers;
- try L.whole(try L.get("layers.{d}.input_layernorm.weight", .{i}), .bf16);
- const in_norm = try L.put("layers.{d}.in_norm", .{i});
- try L.whole(try L.get("layers.{d}.post_attention_layernorm.weight", .{i}), .bf16);
- const post_norm = try L.put("layers.{d}.post_norm", .{i});
- const none: Hc = .{ .fn_w = 0, .base = 0, .scale = 0 };
- const layer_w: Layer = .{ .index = i, .attn_hc = none, .ffn_hc = none, .in_norm = in_norm, .post_norm = post_norm, .mixer = .{ .mla = try L.dsa(i) }, .ffn = .{ .moe = try L.moeLayer(i) } };
- L.ck.release();
- var out: [4]u64 = undefined;
- for (&out, [_][]const u8{ "enorm.weight", "hnorm.weight", "eh_proj.weight", "shared_head.norm.weight" }, [_][]const u8{ "enorm", "hnorm", "eh", "norm" }) |*p, name, what| {
- try L.whole(try L.ck.get(try std.fmt.bufPrint(&L.name_buf, prefix ++ "layers.{d}.{s}", .{ i, name })), .bf16);
- p.* = try L.put("mtp.{s}", .{what});
- }
- // the draft head: quantize4 of the BF16 head's rows, then make_q4's tiling
- const qk = L.tools.quant orelse return error.NoQuantizer;
- const tk = L.tools.q4 orelse return error.NoQuantizer;
- const n = L.w.share.vocab.len();
- const k: usize = c.hidden;
- var raw: [3]cuda.DeviceBuffer = undefined;
- var made: usize = 0;
- defer for (raw[0..made]) |*b| b.free();
- for (&raw, [_]usize{ n * k / 2, n * k / 32, n * k / 32 }) |*b, size| {
- b.* = try L.scratch(size);
- made += 1;
- }
- try quant.q4(qk, L.stream, L.w.head, n, k, raw[0].ptr, raw[1].ptr, raw[2].ptr);
- var tiled: [3]u64 = undefined;
- for (&tiled, q4.bytes(n, k), [_][]const u8{ "words", "scales", "biases" }) |*p, size, what| {
- const b = try cuda.DeviceBuffer.alloc(L.d, size);
- try L.w.buffers.append(L.gpa, b);
- try L.w.named.append(L.gpa, .{ .name = try std.fmt.allocPrint(L.gpa, "draft_head.{s}", .{what}), .ptr = b.ptr, .len = b.len });
- p.* = b.ptr;
- }
- const draft: q4.Q4 = .{ .w = tiled[0], .s = tiled[1], .b = tiled[2], .n = n, .k = k, .npad = q4.npadOf(n) };
- try q4.pack(tk, L.stream, raw[0].ptr, raw[1].ptr, raw[2].ptr, draft);
- try L.stream.synchronize();
- return .{ .enorm = out[0], .hnorm = out[1], .eh = out[2], .norm = out[3], .layer = layer_w, .draft = draft };
- }
-
fn layer(L: *Loader, i: u32) !Layer {
const c = &L.w.config;
try L.whole(try L.get("layers.{d}.input_layernorm.weight", .{i}), .bf16);
@@ -535,7 +518,7 @@ const Loader = struct {
}
};
-/// weights.load without the MTP head: embedding, layers, final norm, the rank's BF16 head rows; `pack_dense`.
+/// weights.load without the MTP head: embedding, layers, final norm, the rank's head rows (bf16, MXFP8); `pack_dense`.
pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: cuda.Stream, tools: Tools, dir: []const u8, c: cfg.Config, share: shard.Share, layers: []const u32, with_mtp: bool) !Weights {
var w: Weights = .{ .gpa = gpa, .config = c, .share = share };
errdefer w.deinit();
@@ -558,10 +541,9 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
w.embed = try L.put("embed", .{});
try L.whole(try L.get("norm.weight", .{}), .bf16);
w.norm = try L.put("norm", .{});
- try L.formatIn(&head_ck, "lm_head", .bf16);
- try L.rows(try head_ck.get("lm_head.weight"), share.vocab.lo, share.vocab.hi, c.vocab, .bf16);
- w.head = try L.put("head", .{});
- if (with_mtp and c.mtp > 0) w.mtp = try L.mtp();
+ const vocab: formats.Part = .{ .rel = "lm_head", .rows = .{ share.vocab.lo, share.vocab.hi } };
+ w.head = try formats.cut(&L, &head_ck, &.{"lm_head"}, &.{vocab}, &.{ .bf16, .mxfp8 }, "head", .{});
+ if (with_mtp and c.mtp > 0) w.mtp = try @import("cuda_mtp_load.zig").load(&L, &head_ck);
return w;
}
@@ -576,4 +558,5 @@ test {
_ = @import("cuda_formats.zig");
_ = @import("cuda_proj.zig");
_ = @import("cuda_fp8pb.zig");
+ _ = @import("cuda_mxfp8.zig");
}
diff --git a/zig/tests/cuda/glm_loader_gate.zig b/zig/tests/cuda/glm_loader_gate.zig
index 0c7169f097..c4e6e8d1ce 100644
--- a/zig/tests/cuda/glm_loader_gate.zig
+++ b/zig/tests/cuda/glm_loader_gate.zig
@@ -57,6 +57,8 @@ fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !Want
try p.whole(P ++ "norm.weight", .{}, .bf16);
} else if (eql(name, "head")) {
try p.rows("lm_head.weight", .{}, s.vocab.lo, s.vocab.hi, .bf16);
+ } else if (eql(name, "head.codes") or eql(name, "head.scales")) {
+ return laneMade(p, &.{.{ .module = "lm_head", .rows = .{ s.vocab.lo, s.vocab.hi } }}, name);
} else if (std.mem.startsWith(u8, name, "draft_head.")) {
return .{ .skip = "the GPU quantizer's 4-bit tiles of the head's rows" };
} else if (std.mem.startsWith(u8, name, "mtp.")) {
@@ -94,8 +96,11 @@ fn layer(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u
return .cut;
};
}
- const fp8g = std.mem.endsWith(u8, what, ".codes") or std.mem.endsWith(u8, what, ".scales");
- if (fp8g and (std.mem.startsWith(u8, what, "dsa.") or std.mem.startsWith(u8, what, "moe.shared."))) return fp8Made(p, c, s, i, what);
+ const lane = std.mem.endsWith(u8, what, ".codes") or std.mem.endsWith(u8, what, ".scales");
+ // the lane's projections first: a dense MXFP8 MLP's mlp.gu. and mlp.down. before the NVFP4 one's .scales
+ for ([_][]const u8{ "dsa.", "moe.shared.", "kda.", "mlp.gu.", "mlp.down." }) |x| {
+ if (lane and std.mem.startsWith(u8, what, x)) return laneUpload(p, c, s, i, what);
+ }
if (std.mem.startsWith(u8, what, "kda.")) return kda(p, c, s, i, what[4..]);
if (std.mem.startsWith(u8, what, "dsa.")) return dsa(p, c, s, i, what[4..]);
if (std.mem.startsWith(u8, what, "moe.")) return moe(p, c, s, i, what[4..]);
@@ -110,8 +115,8 @@ fn kda(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
const at = P ++ "layers.{d}.self_attn.";
const h = s.kda;
const dk: usize = c.kda_dim;
- if (eql(what, "proj")) {
- inline for (.{ "q", "k", "v" }) |x| try p.rows(at ++ x ++ "_proj.weight", .{i}, h.lo * dk, h.hi * dk, .bf16);
+ if (eql(what, "proj") or eql(what, "rest")) { // rest: a split stack's bf16 [f_a | g_a | b], its q k v on the lane
+ if (eql(what, "proj")) inline for (.{ "q", "k", "v" }) |x| try p.rows(at ++ x ++ "_proj.weight", .{i}, h.lo * dk, h.hi * dk, .bf16);
inline for (.{ "f_a", "g_a" }) |x| try p.whole(at ++ x ++ "_proj.weight", .{i}, .bf16);
try p.rows(at ++ "b_proj.weight", .{i}, h.lo, h.hi, .bf16);
} else if (eql(what, "fb") or eql(what, "gb")) {
@@ -148,10 +153,16 @@ fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
} else if (eql(what, "absorb.wk") or eql(what, "absorb.wv")) {
const per = try p.outer(at ++ "kv_b_proj.weight", .{i}) / c.mla_heads; // a head's keys, then its values
const keys = per - dv;
+ const kv = try std.fmt.allocPrint(p.a, at ++ "kv_b_proj", .{i});
+ const mx = (try p.ck.get(try std.fmt.allocPrint(p.a, "{s}.weight", .{kv}))).dtype == .f8_e4m3; // MTP's MXFP8
+ var wide: std.ArrayList(u8) = .empty;
+ errdefer wide.deinit(p.gpa);
for (h.lo..h.hi) |head| {
const lo = if (eql(what, "absorb.wk")) head * per else head * per + keys;
- try p.rows(at ++ "kv_b_proj.weight", .{i}, lo, lo + (if (eql(what, "absorb.wk")) keys else dv), .bf16);
+ const hi = lo + (if (eql(what, "absorb.wk")) keys else dv);
+ if (mx) try widen(p, kv, lo, hi, &wide) else try p.rows(at ++ "kv_b_proj.weight", .{i}, lo, hi, .bf16);
}
+ if (mx) return .{ .made = try wide.toOwnedSlice(p.gpa) };
} else if (eql(what, "o")) {
try p.cols(at ++ "o_proj.weight", .{i}, h.lo * dv, h.hi * dv);
} else if (eql(what, "index.kw")) {
@@ -168,6 +179,16 @@ fn dsa(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8)
return .cut;
}
+/// MXFP8 `module`'s rows [lo, hi) widened to bf16 bytes onto `out`, as the loader widens an MTP layer's kv_b.
+fn widen(p: *Plan, module: []const u8, lo: usize, hi: usize, out: *std.ArrayList(u8)) !void {
+ const MX = glm.weights.formats.mxfp8;
+ const t = try MX.Mxfp8.read(p.ck, module);
+ const rows = try p.gpa.alloc(u16, (hi - lo) * t.k);
+ defer p.gpa.free(rows);
+ try MX.bf16Rows(t, lo, hi, rows);
+ try out.appendSlice(p.gpa, std.mem.sliceAsBytes(rows));
+}
+
/// MoE: router and bias whole, the experts' tiles and global scales, shared gate and up by rows, down by columns.
fn moe(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const at = P ++ "layers.{d}.mlp.";
@@ -283,46 +304,80 @@ fn expertsMade(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, up: bool)
return .{ .made = try p.gpa.dupe(u8, std.mem.sliceAsBytes(out)) };
}
-/// A block-FP8 projection's lane codes or scale tiles: the rank's cut of each stored part, packed as cuda_fp8pb packs.
-fn fp8Made(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
- const FP = glm.weights.formats.fp8pb;
+/// A stored part of a lane upload: its module and the rank's rows or inputs of it (all of either when null).
+const Part = struct { module: []const u8, rows: ?[2]usize = null, cols: ?[2]usize = null };
+
+/// Module `rel` of layer `i`, by its checkpoint name.
+fn modOf(p: *Plan, i: u32, comptime rel: []const u8) ![]const u8 {
+ return std.fmt.allocPrint(p.a, P ++ "layers.{d}." ++ rel, .{i});
+}
+
+/// Layer `i`'s lane upload `what` (.codes or .scales): its stored parts as the loader stacks and cuts them.
+fn laneUpload(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const base = what[0..std.mem.lastIndexOfScalar(u8, what, '.').?];
- const Part = struct { rel: []const u8, rows: ?[2]usize = null, cols: ?[2]usize = null };
+ const at = P ++ "layers.{d}.";
const h = s.mla;
const sh = s.shared;
- const at = P ++ "layers.{d}.";
- var parts: [2]Part = undefined;
- const n_parts: usize = if (eql(base, "dsa.proj") or eql(base, "moe.shared.gu")) 2 else 1;
- if (eql(base, "dsa.proj")) {
- parts = .{ .{ .rel = "self_attn.q_a_proj" }, .{ .rel = "self_attn.kv_a_proj_with_mqa" } };
- } else if (eql(base, "dsa.q_b")) {
+ const kh: [2]usize = .{ s.kda.lo * c.kda_dim, s.kda.hi * c.kda_dim };
+ const dn: [2]usize = .{ s.dense.lo, s.dense.hi };
+ if (eql(base, "dsa.proj")) return laneMade(p, &.{ .{ .module = try modOf(p, i, "self_attn.q_a_proj") }, .{ .module = try modOf(p, i, "self_attn.kv_a_proj_with_mqa") } }, what);
+ if (eql(base, "dsa.q_b")) {
const per = try p.outer(at ++ "self_attn.q_b_proj.weight", .{i}) / c.mla_heads;
- parts[0] = .{ .rel = "self_attn.q_b_proj", .rows = .{ h.lo * per, h.hi * per } };
- } else if (eql(base, "dsa.o")) {
- parts[0] = .{ .rel = "self_attn.o_proj", .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } };
- } else if (eql(base, "moe.shared.gu")) {
- parts = .{ .{ .rel = "mlp.shared_experts.gate_proj", .rows = .{ sh.lo, sh.hi } }, .{ .rel = "mlp.shared_experts.up_proj", .rows = .{ sh.lo, sh.hi } } };
- } else if (eql(base, "moe.shared.down")) {
- parts[0] = .{ .rel = "mlp.shared_experts.down_proj", .cols = .{ sh.lo, sh.hi } };
- } else return error.UnknownUpload;
- var stored: [2]FP.Fp8pb = undefined;
+ return laneMade(p, &.{.{ .module = try modOf(p, i, "self_attn.q_b_proj"), .rows = .{ h.lo * per, h.hi * per } }}, what);
+ }
+ if (eql(base, "dsa.o")) return laneMade(p, &.{.{ .module = try modOf(p, i, "self_attn.o_proj"), .cols = .{ h.lo * c.v_dim, h.hi * c.v_dim } }}, what);
+ if (eql(base, "dsa.index.kw")) return laneMade(p, &.{ .{ .module = try modOf(p, i, "self_attn.indexer.wk") }, .{ .module = try modOf(p, i, "self_attn.indexer.weights_proj") } }, what);
+ if (eql(base, "dsa.index.qb")) return laneMade(p, &.{.{ .module = try modOf(p, i, "self_attn.indexer.wq_b") }}, what);
+ if (eql(base, "moe.shared.gu")) return laneMade(p, &.{ .{ .module = try modOf(p, i, "mlp.shared_experts.gate_proj"), .rows = .{ sh.lo, sh.hi } }, .{ .module = try modOf(p, i, "mlp.shared_experts.up_proj"), .rows = .{ sh.lo, sh.hi } } }, what);
+ if (eql(base, "moe.shared.down")) return laneMade(p, &.{.{ .module = try modOf(p, i, "mlp.shared_experts.down_proj"), .cols = .{ sh.lo, sh.hi } }}, what);
+ if (eql(base, "kda.qkv")) return laneMade(p, &.{ .{ .module = try modOf(p, i, "self_attn.q_proj"), .rows = kh }, .{ .module = try modOf(p, i, "self_attn.k_proj"), .rows = kh }, .{ .module = try modOf(p, i, "self_attn.v_proj"), .rows = kh } }, what);
+ if (eql(base, "kda.o")) return laneMade(p, &.{.{ .module = try modOf(p, i, "self_attn.o_proj"), .cols = kh }}, what);
+ if (eql(base, "mlp.gu")) return laneMade(p, &.{ .{ .module = try modOf(p, i, "mlp.gate_proj"), .rows = dn }, .{ .module = try modOf(p, i, "mlp.up_proj"), .rows = dn } }, what);
+ if (eql(base, "mlp.down")) return laneMade(p, &.{.{ .module = try modOf(p, i, "mlp.down_proj"), .cols = dn }}, what);
+ return error.UnknownUpload;
+}
+
+/// A lane upload packed from the rank's cut of each part: block FP8 or MXFP8, as its first scale is stored (f32 or u8).
+fn laneMade(p: *Plan, parts: []const Part, what: []const u8) !Want {
+ const want_codes = std.mem.endsWith(u8, what, ".codes");
+ if ((try p.ck.get(try std.fmt.allocPrint(p.a, "{s}.weight_scale", .{parts[0].module}))).dtype == .u8) {
+ const MX = glm.weights.formats.mxfp8;
+ var stored: [3]MX.Mxfp8 = undefined;
+ var shares: [3]MX.Share = undefined;
+ for (parts, 0..) |q, j| {
+ stored[j] = try MX.Mxfp8.read(p.ck, q.module);
+ const r = q.rows orelse [2]usize{ 0, stored[j].n };
+ const k = q.cols orelse [2]usize{ 0, stored[j].k };
+ shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = k[0], .khi = k[1] };
+ }
+ const z = try MX.sizes(shares[0..parts.len]);
+ const codes = try p.gpa.alloc(u8, z.codes);
+ errdefer p.gpa.free(codes);
+ const tiles = try p.gpa.alloc(u8, z.scales);
+ errdefer p.gpa.free(tiles);
+ try MX.pack(p.gpa, shares[0..parts.len], codes, tiles);
+ p.gpa.free(if (want_codes) tiles else codes);
+ return .{ .made = if (want_codes) codes else tiles };
+ }
+ const FP = glm.weights.formats.fp8pb;
+ var stored: [3]FP.Fp8pb = undefined;
var made: usize = 0;
defer for (stored[0..made]) |t| t.deinit(p.gpa);
- var shares: [2]FP.Share = undefined;
- for (parts[0..n_parts], 0..) |q, j| {
- stored[j] = try FP.Fp8pb.read(p.gpa, p.ck, try std.fmt.allocPrint(p.a, at ++ "{s}", .{ i, q.rel }));
+ var shares: [3]FP.Share = undefined;
+ for (parts, 0..) |q, j| {
+ stored[j] = try FP.Fp8pb.read(p.gpa, p.ck, q.module);
made += 1;
const r = q.rows orelse [2]usize{ 0, stored[j].n };
const k = q.cols orelse [2]usize{ 0, stored[j].k };
shares[j] = .{ .p = &stored[j], .lo = r[0], .hi = r[1], .klo = k[0], .khi = k[1] };
}
- const z = try FP.sizes(shares[0..n_parts]);
+ const z = try FP.sizes(shares[0..parts.len]);
const codes = try p.gpa.alloc(u8, z.codes);
errdefer p.gpa.free(codes);
const tiles = try p.gpa.alloc(f32, z.scales);
defer p.gpa.free(tiles);
- try FP.pack(p.gpa, shares[0..n_parts], codes, tiles);
- if (std.mem.endsWith(u8, what, ".codes")) return .{ .made = codes };
+ try FP.pack(p.gpa, shares[0..parts.len], codes, tiles);
+ if (want_codes) return .{ .made = codes };
p.gpa.free(codes);
return .{ .made = try p.gpa.dupe(u8, std.mem.sliceAsBytes(tiles)) };
}
@@ -423,7 +478,22 @@ fn rankGate(gpu: check.Gpu, a: std.mem.Allocator, ck: *core.Checkpoint, c: *cons
return bad;
}
-/// TF_GATE_TAMPER=NAME flips a bit of upload NAME after each download, so a run shows the gate refusing it by name.
+/// Numbers from an environment value like "0-3,41,44", each under `max`; `all` when the variable is unset.
+fn picks(a: std.mem.Allocator, name: [:0]const u8, all: []const u32, max: usize) ![]const u32 {
+ const text = glm.ranks.environment(name) orelse return all;
+ var out: std.ArrayList(u32) = .empty;
+ var it = std.mem.tokenizeScalar(u8, text, ',');
+ while (it.next()) |part| {
+ const dash = std.mem.indexOfScalar(u8, part, '-');
+ const lo = try std.fmt.parseInt(u32, if (dash) |i| part[0..i] else part, 10);
+ const hi = if (dash) |i| try std.fmt.parseInt(u32, part[i + 1 ..], 10) else lo;
+ if (hi < lo or hi >= max) return error.BadGateList;
+ for (lo..hi + 1) |v| try out.append(a, @intCast(v));
+ }
+ return out.toOwnedSlice(a);
+}
+
+/// TF_GATE_TAMPER=NAME flips a bit of upload NAME; TF_GATE_WORLDS, _RANKS and _LAYERS narrow a real checkpoint's run.
pub fn run(gpu: check.Gpu, dir: []const u8) !void {
var arena = std.heap.ArenaAllocator.init(gpu.gpa);
defer arena.deinit();
@@ -436,12 +506,18 @@ pub fn run(gpu: check.Gpu, dir: []const u8) !void {
defer stream.deinit();
var ck = try core.Checkpoint.openModel(gpu.gpa, gpu.io, dir);
defer ck.close();
- const layers = try a.alloc(u32, c.layers);
- for (layers, 0..) |*l, i| l.* = @intCast(i);
+ const every = try a.alloc(u32, c.layers);
+ for (every, 0..) |*l, i| l.* = @intCast(i);
+ const layers = try picks(a, "TF_GATE_LAYERS", every, c.layers);
+ const worlds = try picks(a, "TF_GATE_WORLDS", &.{ 1, 2, 3 }, 4);
+ const ranks = try picks(a, "TF_GATE_RANKS", &.{ 0, 1, 2 }, 3);
var tamper: Tamper = .{ .name = glm.ranks.environment("TF_GATE_TAMPER") };
var bad: usize = 0;
- for (1..4) |world| {
- for (0..world) |rank| bad += try rankGate(gpu, a, &ck, &c, &m, stream, dir, layers, rank, world, &tamper);
+ for (worlds) |world| {
+ if (world == 0) return error.BadGateList;
+ for (ranks) |rank| if (rank < world) {
+ bad += try rankGate(gpu, a, &ck, &c, &m, stream, dir, layers, rank, world, &tamper);
+ };
}
if (tamper.name) |t| try check.expect(tamper.done > 0, "glm-loader-gate: TF_GATE_TAMPER={s} names no upload it compares", .{t});
try check.expect(bad == 0, "glm-loader-gate: {d} uploads differ from the checkpoint or are not known", .{bad});
diff --git a/zig/tests/cuda/glm_synth.zig b/zig/tests/cuda/glm_synth.zig
index 1c2d9bf27e..2fd1dda8ec 100644
--- a/zig/tests/cuda/glm_synth.zig
+++ b/zig/tests/cuda/glm_synth.zig
@@ -1,4 +1,4 @@
-//! `glm-synth OUT_DIR [fp8pb]`: a 2-layer + MTP GLM-5.3-Flash checkpoint, dc.json; run TF_GLM_LAYERS=2.
+//! `glm-synth OUT_DIR [fp8pb|local]`: a 2-layer + MTP GLM-5.3-Flash checkpoint, dc.json; run TF_GLM_LAYERS=2.
const std = @import("std");
const core = @import("core");
const glm = @import("glm_cuda");
@@ -15,12 +15,31 @@ const hidden = 4096;
const modelopt_block = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"NVFP4\", " ++
"\"config_groups\": {\"group_0\": {\"weights\": {\"num_bits\": 4, \"type\": \"float\", \"group_size\": 16}}}}";
/// fp8pb: FP4-Local's MIXED_PRECISION form, layer 1's MLA and shared expert in block FP8, the NVFP4 ones as before.
-const mixed_block = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"MIXED_PRECISION\", " ++
- "\"quantized_layers\": {" ++ nv4("layers.0.mlp") ++ "," ++ nv4("layers.1.mlp.experts") ++ "," ++ pb("layers.1.mlp.shared_experts") ++ "," ++
- pb("layers.1.self_attn.q_a_proj") ++ "," ++ pb("layers.1.self_attn.kv_a_proj_with_mqa") ++ "," ++ pb("layers.1.self_attn.q_b_proj") ++ "," ++
- pb("layers.1.self_attn.o_proj") ++ "}}";
+const mixed_head = ",\"quantization_config\": {\"quant_method\": \"modelopt\", \"quant_algo\": \"MIXED_PRECISION\", \"quantized_layers\": {";
+const layer1 = nv4("layers.1.mlp.experts") ++ "," ++ pb("layers.1.mlp.shared_experts") ++ "," ++ pb("layers.1.self_attn.q_a_proj") ++ "," ++
+ pb("layers.1.self_attn.kv_a_proj_with_mqa") ++ "," ++ pb("layers.1.self_attn.q_b_proj") ++ "," ++ pb("layers.1.self_attn.o_proj");
+const mixed_block = mixed_head ++ nv4("layers.0.mlp") ++ "," ++ layer1 ++ "}}";
+/// local: as fp8pb; layer 0's KDA q k v o, dense MLP, lm_head and the MTP layer (mtp_block) as FP4-Local stores them.
+const local_block = mixed_head ++ mx8("lm_head") ++ "," ++ mx8(P ++ "layers.0.self_attn.q_proj") ++ "," ++ mx8(P ++ "layers.0.self_attn.k_proj") ++ "," ++
+ mx8(P ++ "layers.0.self_attn.v_proj") ++ "," ++ mx8(P ++ "layers.0.self_attn.o_proj") ++ "," ++ mx8(P ++ "layers.0.mlp.gate_proj") ++ "," ++
+ mx8(P ++ "layers.0.mlp.up_proj") ++ "," ++ mx8(P ++ "layers.0.mlp.down_proj") ++ "," ++ layer1 ++ mtp_block ++ "}}";
+const mtp_at = std.fmt.comptimePrint("layers.{d}.", .{layers});
+/// The MTP layer's recipes as FP4-Local's: attention, kv_b, indexer, shared in MXFP8 (group_size), experts W4A16.
+const mtp_block = mtp: {
+ var s: []const u8 = ",\"" ++ P ++ mtp_at ++ "mlp.experts\": {\"group_size\": 16, \"quant_algo\": \"W4A16_NVFP4\"}";
+ for ([_][]const u8{ "q_a_proj", "kv_a_proj_with_mqa", "q_b_proj", "kv_b_proj", "o_proj", "indexer.wk", "indexer.weights_proj", "indexer.wq_b" }) |m| {
+ s = s ++ ",\"" ++ P ++ mtp_at ++ "self_attn." ++ m ++ "\": {\"group_size\": 32, \"quant_algo\": \"MXFP8\"}";
+ }
+ for ([_][]const u8{ "gate_proj", "up_proj", "down_proj" }) |m| {
+ s = s ++ ",\"" ++ P ++ mtp_at ++ "mlp.shared_experts." ++ m ++ "\": {\"group_size\": 32, \"quant_algo\": \"MXFP8\"}";
+ }
+ break :mtp s;
+};
const P = "model.language_model.";
+/// The synthetic checkpoint's forms: NVFP4 as nvidia's, FP4-Local's block FP8 added, or its MXFP8 too.
+pub const Kind = enum { nvfp4, fp8pb, local };
+
fn nv4(comptime module: []const u8) []const u8 {
return "\"" ++ P ++ module ++ "\": {\"quant_algo\": \"NVFP4\", \"group_size\": 16}";
}
@@ -29,6 +48,10 @@ fn pb(comptime module: []const u8) []const u8 {
return "\"" ++ P ++ module ++ "\": {\"quant_algo\": \"FP8_PB_WO\", \"weight_block_size\": [128, 128]}";
}
+fn mx8(comptime name: []const u8) []const u8 {
+ return "\"" ++ name ++ "\": {\"quant_algo\": \"MXFP8\", \"weight_block_size\": [1, 32]}";
+}
+
const Out = struct {
a: std.mem.Allocator,
rng: std.Random,
@@ -65,6 +88,12 @@ const Out = struct {
/// modelopt NVFP4: random e2m1 codes, e4m3 block scales 0.5 to 1.875, a global scale near 1/(6 sqrt(k)).
fn nvfp4(o: *Out, base: []const u8, n: usize, k: usize) !void {
+ try o.w4a16(base, n, k);
+ try o.scalar(try cat(o.a, base, ".input_scale"), 1.0);
+ }
+
+ /// W4A16_NVFP4 (FP4-Local's MTP experts): as nvfp4 without an input_scale.
+ fn w4a16(o: *Out, base: []const u8, n: usize, k: usize) !void {
const codes = try o.a.alloc(u8, n * k / 2);
o.rng.bytes(codes);
try o.put(try cat(o.a, base, ".weight"), .u8, &.{ n, k / 2 }, codes);
@@ -72,14 +101,18 @@ const Out = struct {
for (scales) |*x| x.* = 0x30 + o.rng.uintLessThan(u8, 16);
try o.put(try cat(o.a, base, ".weight_scale"), .f8_e4m3, &.{ n, k / 16 }, scales);
try o.scalar(try cat(o.a, base, ".weight_scale_2"), 1.0 / (6.0 * @sqrt(@as(f32, @floatFromInt(k)))));
- try o.scalar(try cat(o.a, base, ".input_scale"), 1.0);
}
- /// FP8_PB_WO: e4m3 codes of magnitude 2^-4 to 1.875 (no NaN), fp32 scales [Rb, 1, Cb, 1] near 1/(2 sqrt(k)).
- fn fp8pb(o: *Out, base: []const u8, n: usize, k: usize) !void {
+ /// e4m3 codes [n, k] of magnitude 2^-4 to 1.875: never 0x7f or 0xff (NaN).
+ fn e4m3(o: *Out, base: []const u8, n: usize, k: usize) !void {
const codes = try o.a.alloc(u8, n * k);
for (codes) |*x| x.* = (o.rng.uintLessThan(u8, 2) << 7) | ((3 + o.rng.uintLessThan(u8, 5)) << 3) | o.rng.uintLessThan(u8, 8);
try o.put(try cat(o.a, base, ".weight"), .f8_e4m3, &.{ n, k }, codes);
+ }
+
+ /// FP8_PB_WO: e4m3 codes, fp32 scales [Rb, 1, Cb, 1] near 1/(2 sqrt(k)).
+ fn fp8pb(o: *Out, base: []const u8, n: usize, k: usize) !void {
+ try o.e4m3(base, n, k);
const rb = (n + 127) / 128;
const cb = (k + 127) / 128;
const v = try o.a.alloc(f32, rb * cb);
@@ -87,12 +120,34 @@ const Out = struct {
try o.put(try cat(o.a, base, ".weight_scale"), .f32, &.{ rb, 1, cb, 1 }, std.mem.sliceAsBytes(v));
}
+ /// MXFP8: e4m3 codes, a UE8M0 scale byte a 32 inputs within a step of linear()'s RMS 1/sqrt(3k) over the codes'.
+ fn mxfp8(o: *Out, base: []const u8, n: usize, k: usize) !void {
+ try o.e4m3(base, n, k);
+ const codes_rms = 0.7566; // e4m3()'s draw: (1 + m/8) 2^(e - 7), m 0 to 7 and e 3 to 7 alike
+ const e: u8 = @intFromFloat(127 + @round(-std.math.log2(codes_rms * @sqrt(3.0 * @as(f64, @floatFromInt(k))))));
+ const scales = try o.a.alloc(u8, n * k / 32);
+ for (scales) |*x| x.* = e - 1 + o.rng.uintLessThan(u8, 3);
+ try o.put(try cat(o.a, base, ".weight_scale"), .u8, &.{ n, k / 32 }, scales);
+ }
+
/// A linear as bf16, or as FP8_PB_WO with `fp8`.
fn either(o: *Out, base: []const u8, n: usize, k: usize, fp8: bool) !void {
if (fp8) return o.fp8pb(base, n, k);
try o.linear(try cat(o.a, base, ".weight"), n, k);
}
+ /// A linear as bf16, or as MXFP8 with `mx`.
+ fn bfOrMx(o: *Out, base: []const u8, n: usize, k: usize, mx: bool) !void {
+ if (mx) return o.mxfp8(base, n, k);
+ try o.linear(try cat(o.a, base, ".weight"), n, k);
+ }
+
+ /// A linear as bf16, FP8_PB_WO with `fp8`, or MXFP8 with `mx`.
+ fn any(o: *Out, base: []const u8, n: usize, k: usize, fp8: bool, mx: bool) !void {
+ if (mx) return o.mxfp8(base, n, k);
+ try o.either(base, n, k, fp8);
+ }
+
fn scalar(o: *Out, name: []const u8, value: f32) !void {
const v = try o.a.alloc(f32, 1);
v[0] = value;
@@ -132,10 +187,10 @@ fn norms(o: *Out, i: usize) !void {
try o.bf16Const(try tensor(o.a, "layers.{d}.post_attention_layernorm.weight", .{i}), &.{hidden}, 1.0);
}
-fn kda(o: *Out, i: usize) !void {
+fn kda(o: *Out, i: usize, mx: bool) !void {
const s = try tensor(o.a, "layers.{d}.self_attn.", .{i});
for ([_][]const u8{ "q", "k", "v" }) |m| {
- try o.linear(try std.fmt.allocPrint(o.a, "{s}{s}_proj.weight", .{ s, m }), 8192, hidden);
+ try o.bfOrMx(try std.fmt.allocPrint(o.a, "{s}{s}_proj", .{ s, m }), 8192, hidden, mx);
try o.f32s(try std.fmt.allocPrint(o.a, "{s}{s}_conv1d.weight", .{ s, m }), &.{ 8192, 1, 4 }, -0.5, 0.5);
}
try o.linear(try cat(o.a, s, "f_a_proj.weight"), 128, hidden);
@@ -146,81 +201,92 @@ fn kda(o: *Out, i: usize) !void {
try o.f32s(try cat(o.a, s, "A_log"), &.{64}, 0.0, 2.5);
try o.f32s(try cat(o.a, s, "dt_bias"), &.{8192}, -0.5, 0.5);
try o.bf16Const(try cat(o.a, s, "o_norm.weight"), &.{128}, 1.0);
- try o.linear(try cat(o.a, s, "o_proj.weight"), hidden, 8192);
+ try o.bfOrMx(try cat(o.a, s, "o_proj"), hidden, 8192, mx);
}
-fn mla(o: *Out, i: usize, fp8: bool) !void {
+/// Latent attention in bf16, its projections in FP8_PB_WO with `fp8`, or with `mx` all of it but norms in MXFP8.
+fn mla(o: *Out, i: usize, fp8: bool, mx: bool) !void {
const s = try tensor(o.a, "layers.{d}.self_attn.", .{i});
- try o.either(try cat(o.a, s, "q_a_proj"), 1536, hidden, fp8);
- try o.either(try cat(o.a, s, "kv_a_proj_with_mqa"), 512, hidden, fp8);
+ try o.any(try cat(o.a, s, "q_a_proj"), 1536, hidden, fp8, mx);
+ try o.any(try cat(o.a, s, "kv_a_proj_with_mqa"), 512, hidden, fp8, mx);
try o.bf16Const(try cat(o.a, s, "q_a_layernorm.weight"), &.{1536}, 1.0);
try o.bf16Const(try cat(o.a, s, "kv_a_layernorm.weight"), &.{512}, 1.0);
- try o.either(try cat(o.a, s, "q_b_proj"), 16384, 1536, fp8);
- try o.linear(try cat(o.a, s, "kv_b_proj.weight"), 32768, 512);
- try o.either(try cat(o.a, s, "o_proj"), hidden, 16384, fp8);
- try o.linear(try cat(o.a, s, "indexer.wk.weight"), 128, hidden);
- try o.linear(try cat(o.a, s, "indexer.weights_proj.weight"), 32, hidden);
- try o.linear(try cat(o.a, s, "indexer.wq_b.weight"), 4096, 1536);
+ try o.any(try cat(o.a, s, "q_b_proj"), 16384, 1536, fp8, mx);
+ try o.bfOrMx(try cat(o.a, s, "kv_b_proj"), 32768, 512, mx);
+ try o.any(try cat(o.a, s, "o_proj"), hidden, 16384, fp8, mx);
+ try o.bfOrMx(try cat(o.a, s, "indexer.wk"), 128, hidden, mx);
+ try o.bfOrMx(try cat(o.a, s, "indexer.weights_proj"), 32, hidden, mx);
+ try o.bfOrMx(try cat(o.a, s, "indexer.wq_b"), 4096, 1536, mx);
try o.bf16Const(try cat(o.a, s, "indexer.k_norm.weight"), &.{128}, 1.0);
try o.bf16Const(try cat(o.a, s, "indexer.k_norm.bias"), &.{128}, 0.0);
try o.linear(try cat(o.a, s, "indexer.index_kpool_compress_gate"), 128, hidden);
try o.bf16(try cat(o.a, s, "indexer.index_kpool_compress_ape"), &.{ 4, 128 }, 0.02);
}
-fn dense(o: *Out, i: usize) !void {
- for ([_][]const u8{ "gate", "up" }) |m| try o.nvfp4(try tensor(o.a, "layers.{d}.mlp.{s}_proj", .{ i, m }), 12288, hidden);
- try o.nvfp4(try tensor(o.a, "layers.{d}.mlp.down_proj", .{i}), hidden, 12288);
+fn dense(o: *Out, i: usize, mx: bool) !void {
+ const shapes = [_]struct { []const u8, usize, usize }{ .{ "gate", 12288, hidden }, .{ "up", 12288, hidden }, .{ "down", hidden, 12288 } };
+ for (shapes) |x| {
+ const base = try tensor(o.a, "layers.{d}.mlp.{s}_proj", .{ i, x[0] });
+ if (mx) try o.mxfp8(base, x[1], x[2]) else try o.nvfp4(base, x[1], x[2]);
+ }
}
-/// The router, the shared expert (bf16, or block FP8 with `fp8`), the routed ones in NVFP4 or bf16 (the MTP layer).
-fn moe(o: *Out, i: usize, bf: bool, fp8: bool) !void {
+/// The routed experts' form: bf16 (the MTP layer's, nvidia's), NVFP4, or W4A16_NVFP4 (FP4-Local's MTP layer).
+const Experts = enum { bf16, nvfp4, w4a16 };
+
+/// The router, the shared expert (bf16, block FP8 with `fp8`, MXFP8 with `mx`) and the routed ones.
+fn moe(o: *Out, i: usize, routed: Experts, fp8: bool, mx: bool) !void {
try o.linear(try tensor(o.a, "layers.{d}.mlp.gate.weight", .{i}), experts, hidden);
try o.f32s(try tensor(o.a, "layers.{d}.mlp.gate.e_score_correction_bias", .{i}), &.{experts}, 0.0, 0.0);
- try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.gate_proj", .{i}), 2048, hidden, fp8);
- try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.up_proj", .{i}), 2048, hidden, fp8);
- try o.either(try tensor(o.a, "layers.{d}.mlp.shared_experts.down_proj", .{i}), hidden, 2048, fp8);
+ try o.any(try tensor(o.a, "layers.{d}.mlp.shared_experts.gate_proj", .{i}), 2048, hidden, fp8, mx);
+ try o.any(try tensor(o.a, "layers.{d}.mlp.shared_experts.up_proj", .{i}), 2048, hidden, fp8, mx);
+ try o.any(try tensor(o.a, "layers.{d}.mlp.shared_experts.down_proj", .{i}), hidden, 2048, fp8, mx);
for (0..experts) |e| {
const s = try tensor(o.a, "layers.{d}.mlp.experts.{d}.", .{ i, e });
- if (bf) {
- try o.linear(try cat(o.a, s, "gate_proj.weight"), 2048, hidden);
- try o.linear(try cat(o.a, s, "up_proj.weight"), 2048, hidden);
- try o.linear(try cat(o.a, s, "down_proj.weight"), hidden, 2048);
- } else {
- try o.nvfp4(try cat(o.a, s, "gate_proj"), 2048, hidden);
- try o.nvfp4(try cat(o.a, s, "up_proj"), 2048, hidden);
- try o.nvfp4(try cat(o.a, s, "down_proj"), hidden, 2048);
- }
+ const shapes = [_]struct { []const u8, usize, usize }{ .{ "gate_proj", 2048, hidden }, .{ "up_proj", 2048, hidden }, .{ "down_proj", hidden, 2048 } };
+ for (shapes) |x| switch (routed) {
+ .bf16 => try o.linear(try std.fmt.allocPrint(o.a, "{s}{s}.weight", .{ s, x[0] }), x[1], x[2]),
+ .nvfp4 => try o.nvfp4(try cat(o.a, s, x[0]), x[1], x[2]),
+ .w4a16 => try o.w4a16(try cat(o.a, s, x[0]), x[1], x[2]),
+ };
}
}
-pub fn run(gpu: check.Gpu, out_dir: []const u8, fp8: bool) !void {
+pub fn run(gpu: check.Gpu, out_dir: []const u8, form: Kind) !void {
var arena = std.heap.ArenaAllocator.init(gpu.gpa);
defer arena.deinit();
const a = arena.allocator();
var prng = std.Random.DefaultPrng.init(53);
var o: Out = .{ .a = a, .rng = prng.random() };
+ const fp8 = form != .nvfp4;
+ const mx = form == .local;
try o.bf16(P ++ "embed_tokens.weight", &.{ vocab, hidden }, 1.0);
try o.bf16Const(P ++ "norm.weight", &.{hidden}, 1.0);
- try o.linear("lm_head.weight", vocab, hidden);
- try kda(&o, 0);
+ try o.bfOrMx("lm_head", vocab, hidden, mx);
+ try kda(&o, 0, mx);
try hc(&o, 0);
try norms(&o, 0);
- try dense(&o, 0);
- try mla(&o, 1, fp8);
+ try dense(&o, 0, mx);
+ try mla(&o, 1, fp8, false);
try hc(&o, 1);
try norms(&o, 1);
- try moe(&o, 1, false, fp8);
- // the MTP layer: no hyper-connections, bf16 attention and routed experts, the draft head's norms and eh_proj
- try mla(&o, layers, false);
+ try moe(&o, 1, .nvfp4, fp8, false);
+ // the MTP layer: no hyper-connections, the draft head's norms and eh_proj; bf16, or with local FP4-Local's forms
+ try mla(&o, layers, false, mx);
try norms(&o, layers);
- try moe(&o, layers, true, false);
+ try moe(&o, layers, if (mx) .w4a16 else .bf16, false, mx);
for ([_][]const u8{ "enorm", "hnorm", "shared_head.norm" }) |m| try o.bf16Const(try tensor(a, "layers.{d}.{s}.weight", .{ layers, m }), &.{hidden}, 1.0);
try o.linear(try tensor(a, "layers.{d}.eh_proj.weight", .{layers}), hidden, 2 * hidden);
try std.Io.Dir.cwd().createDirPath(gpu.io, out_dir);
const text = try glm.config.textConfig(a, layers, 1, &.{1}, experts, vocab, hidden);
const end = std.mem.lastIndexOfScalar(u8, text, '}') orelse return error.BadConfig;
- const cfg = try std.mem.concat(a, u8, &.{ text[0..end], if (fp8) mixed_block else modelopt_block, text[end..] });
+ const block = switch (form) {
+ .nvfp4 => modelopt_block,
+ .fp8pb => mixed_block,
+ .local => local_block,
+ };
+ const cfg = try std.mem.concat(a, u8, &.{ text[0..end], block, text[end..] });
_ = try glm.config.parse(a, cfg);
try std.Io.Dir.cwd().writeFile(gpu.io, .{ .sub_path = try std.fs.path.join(a, &.{ out_dir, "config.json" }), .data = cfg });
try core.shard_edit.create(gpu.gpa, try std.fs.path.join(a, &.{ out_dir, "model.safetensors" }), &.{.{ "format", "pt" }}, o.list.items);
@@ -239,5 +305,11 @@ pub fn run(gpu: check.Gpu, out_dir: []const u8, fp8: bool) !void {
try std.Io.Dir.cwd().writeFile(gpu.io, .{ .sub_path = try std.fs.path.join(a, &.{ out_dir, "dc.json" }), .data = dc.written() });
var bytes: usize = 0;
for (o.list.items) |t| bytes += t.bytes.len;
- check.pass("glm-synth: {d} tensors, {d} MiB, config {d} layers {d} experts vocabulary {d}{s}, dc.json in {s}", .{ o.list.items.len, bytes >> 20, layers, experts, vocab, if (fp8) ", layer 1's MLA and shared in FP8_PB_WO" else "", out_dir });
+ const forms = switch (form) {
+ .nvfp4 => "",
+ .fp8pb => ", layer 1's MLA and shared in FP8_PB_WO",
+ .local => ", layer 1's MLA and shared in FP8_PB_WO, layer 0's KDA q k v o, dense MLP, lm_head and the MTP layer's " ++
+ "attention, indexer and shared in MXFP8, its experts W4A16_NVFP4",
+ };
+ check.pass("glm-synth: {d} tensors, {d} MiB, config {d} layers {d} experts vocabulary {d}{s}, dc.json in {s}", .{ o.list.items.len, bytes >> 20, layers, experts, vocab, forms, out_dir });
}
diff --git a/zig/tests/cuda/main.zig b/zig/tests/cuda/main.zig
index 71a7dca050..3250f57ea4 100644
--- a/zig/tests/cuda/main.zig
+++ b/zig/tests/cuda/main.zig
@@ -212,7 +212,7 @@ const usage =
\\ kolibri-full MODEL IDS N Kolibri-1: eager rows, steps, drafted, sampled, shared rounds, cuts and kept states alike
\\ kolibri-greedy MODEL PROMPTS N OUT Kolibri-1 greedy from each prompt (JSON id lists): ids and top-2 margins
\\ kolibri-logits MODEL IDS OUT [PROMPTS] Kolibri-1's teacher-forced fp32 logits of 2,048 prompt rows; each prompt's last
- \\ glm-synth [fp8pb] a synthetic GLM-5.3-Flash checkpoint (2 layers, 16 experts) and its dc.json for the lane checks
+ \\ glm-synth [fp8pb|local] a synthetic GLM-5.3-Flash checkpoint (2 layers, 16 experts) and its dc.json for the lane checks
\\ glm-loader-gate every rank's uploads at TP 1, 2 and 3 against the checkpoint cut on the host, byte for byte
\\ glm-fp8g-shares block-FP8 TP shares packed as GLM's loader packs them, on the lane, against FP64 of the cut
\\ aot-fallback a launch's 1 from the runtime-parameter variant: the folded variant's bits (#1003)
@@ -388,7 +388,10 @@ fn run(gpu: check.Gpu, cmd: []const u8, rest: []const [:0]const u8) !void {
if (std.mem.eql(u8, cmd, "train-pack")) return train_pack.run(gpu, try arg(rest, 0), try arg(rest, 1), if (rest.len > 2) try std.fmt.parseInt(usize, rest[2], 10) else 24);
if (std.mem.eql(u8, cmd, "glm-lanes-cover")) return glm_lanes_tests.coverCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
if (std.mem.eql(u8, cmd, "glm-shared-check")) return glm_lanes_tests.sharedCheck(gpu, try arg(rest, 0), try arg(rest, 1), try std.fmt.parseInt(c_int, try arg(rest, 2), 10), try std.fmt.parseInt(c_int, try arg(rest, 3), 10), try arg(rest, 4));
- if (std.mem.eql(u8, cmd, "glm-synth")) return glm_synth.run(gpu, try arg(rest, 0), rest.len > 1 and std.mem.eql(u8, rest[1], "fp8pb"));
+ if (std.mem.eql(u8, cmd, "glm-synth")) {
+ const kind = if (rest.len > 1) std.meta.stringToEnum(glm_synth.Kind, rest[1]) orelse return error.UnknownCommand else .nvfp4;
+ return glm_synth.run(gpu, try arg(rest, 0), kind);
+ }
if (std.mem.eql(u8, cmd, "glm-loader-gate")) return glm_loader_gate.run(gpu, try arg(rest, 0));
if (std.mem.eql(u8, cmd, "glm-fp8g-shares")) return glm_fp8g_shares.run(gpu);
if (std.mem.eql(u8, cmd, "kolibri-reference")) return kolibri_layer_tests.reference(gpu, try arg(rest, 0), try arg(rest, 1));
From f98b6fa5ba7cd2e2a03c8a00c06e695dbb43a0f5 Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 02:51:41 +0900
Subject: [PATCH 05/23] glm/cuda: FP4-Local's float32 norm weights stay
float32, read by rmsnorm_4096_f32w and hc_finish_f32w with the bf16 forms'
arithmetic
FP4-Local stores post_attention_layernorm (layers 0-44) and the final norm in float32, with values bf16 does not hold.
The loader keeps such a hidden-size norm float32 (narrowing a float32 to bf16 only when exact, else refusing it by
name), and glue.Norm carries bf16 or f32. rmsnorm_4096 and hc_finish take the weight's type as a template parameter:
the float32 forms read two 16-byte loads where bf16 reads one and compute bf16(w * bf16(rinv x)) and the 64-sums in the
same order, so a float32 norm adds no launch and the bf16 forms compile to the same SASS. glm-synth local writes the
float32 norms, the gate compares them as stored, and glm-ref checks that a float32 weight holding bf16 values gives the
bf16 weight's bytes (hc pre's four outputs, the final norm's rows and 64-sums, 1, 5 and 70 rows). Both kernels are under
the kernel contract, each output against a float64 reference of the kernel's formula, hc_finish_f32w's post and comb
through Sinkhorn, with their launches recorded from nvcc 13.0.
---
docs/kernel-contract.md | 6 +-
zig/kernels/cuda/glm_glue_hc.cuh | 58 ++++-
zig/kernels/cuda/glm_glue_norm.cuh | 40 +++-
zig/src/core/contract/glm_cuda_records.zig | 4 +-
zig/src/families/glm/cuda_forward.zig | 10 +-
zig/src/families/glm/cuda_glue.zig | 27 ++-
zig/src/families/glm/cuda_mtp_load.zig | 8 +-
zig/src/families/glm/cuda_weights.zig | 50 +++-
zig/tests/cuda/contract_glm_f32w.zig | 156 ++++++++++++
zig/tests/cuda/contract_glm_f32w_ref.zig | 261 +++++++++++++++++++++
zig/tests/cuda/contract_tests.zig | 10 +
zig/tests/cuda/glm_loader_gate.zig | 13 +-
zig/tests/cuda/glm_ref_hc.zig | 45 +++-
zig/tests/cuda/glm_ref_tests.zig | 1 +
zig/tests/cuda/glm_synth.zig | 25 +-
15 files changed, 657 insertions(+), 57 deletions(-)
create mode 100644 zig/tests/cuda/contract_glm_f32w.zig
create mode 100644 zig/tests/cuda/contract_glm_f32w_ref.zig
diff --git a/docs/kernel-contract.md b/docs/kernel-contract.md
index f64fc4bb49..d1a74a2d4d 100644
--- a/docs/kernel-contract.md
+++ b/docs/kernel-contract.md
@@ -115,8 +115,10 @@ bit-equal to it), prompt16's FP4 tile with bf16 and fp32 out, qmmf's FP4 tiles w
cluster, fused, or by `reduce_kernel`), the checkpoint math's FP8 128-row prompt tiles (both slice meetings,
bit-equal to the 64-row tiles) and NVFP4 experts: the decode-sized forms and the prompt tiles (up to down's NVFP4
rows, fp32 down and the down summing two width shares, each bit-equal to the kernels it replaces; sm_120 up), and the
-reduce-scatter's `tf_rank_sum`, and the activation rows in MLX's bf16 steps (GeGLU, and SwiGLU as DFlash drafters
-round it; twelve controls, each dropping one rounding point; their 64-value sums bit-equal to the fp32 tree).
+reduce-scatter's `tf_rank_sum`, GLM-5.3-Flash's float32 norm weight forms `tf_glm_rmsnorm_4096_f32w` (its rows and
+64-sums) and `tf_glm_hc_finish_f32w` (its normed rows, 64-sums, post and Sinkhorn comb), and the activation rows in
+MLX's bf16 steps (GeGLU, and SwiGLU as DFlash drafters round it; twelve controls, each dropping one rounding point;
+their 64-value sums bit-equal to the fp32 tree).
CUDA also checks the MLX affine token rows (4 bits, and 2 to 6 or 8 bits times a bf16 scale, each
output bit-equal to float64 at its rounding points; two controls, one dropping the rounding point, one folding the scale
into the group's scale and bias).
diff --git a/zig/kernels/cuda/glm_glue_hc.cuh b/zig/kernels/cuda/glm_glue_hc.cuh
index 1de6bb3b71..c4e31d3b38 100644
--- a/zig/kernels/cuda/glm_glue_hc.cuh
+++ b/zig/kernels/cuda/glm_glue_hc.cuh
@@ -114,13 +114,36 @@ __device__ __forceinline__ float col4(float v) {
}
__device__ __forceinline__ float sigmoid(float v) { return div_full(1.0f, exp_approx(0.0f - v) + 1.0f); }
-} // namespace glm_hc
+// A thread's 8 norm weights of each half, loaded with its inputs: bf16 in one 16-byte load, float32 in two.
+template
+struct Weights;
+template <>
+struct Weights<__nv_bfloat16> {
+ uint4 u[2];
+ __device__ __forceinline__ void load(const __nv_bfloat16* nw, int rep, int t) {
+ u[rep] = __ldg(reinterpret_cast(nw + rep * 2048 + t * 8));
+ }
+ __device__ __forceinline__ float at(int rep, int j) const {
+ return bf(reinterpret_cast(&u[rep])[j]);
+ }
+};
+template <>
+struct Weights {
+ float4 u[2][2];
+ __device__ __forceinline__ void load(const float* nw, int rep, int t) {
+ u[rep][0] = __ldg(reinterpret_cast(nw + rep * 2048 + t * 8));
+ u[rep][1] = __ldg(reinterpret_cast(nw + rep * 2048 + t * 8 + 4));
+ }
+ __device__ __forceinline__ float at(int rep, int j) const {
+ return reinterpret_cast(&u[rep][0])[j];
+ }
+};
// glue._hc_finish, 256 threads a row (D 4096, 4 streams): warp 0 sums K blocks and Sinkhorn, then collapse, RMS-norm.
-extern "C" __global__ void __launch_bounds__(256)
- tf_glm_hc_finish(const __nv_bfloat16* x, const float* part, const float* base, const float* scale,
- const __nv_bfloat16* nw, __nv_bfloat16* out, float* xs, float* post, float* comb, float eps,
- float hc_eps, int nb, int iters) {
+template
+__device__ __forceinline__ void finish(const __nv_bfloat16* x, const float* part, const float* base, const float* scale,
+ const W* nw, __nv_bfloat16* out, float* xs, float* post, float* comb, float eps,
+ float hc_eps, int nb, int iters) {
constexpr int D = 4096, S = 4;
__shared__ float smem[8];
__shared__ float pre[4];
@@ -128,10 +151,11 @@ extern "C" __global__ void __launch_bounds__(256)
const int t = threadIdx.x, lane = t & 31;
// A thread's 8 inputs of each half are contiguous: one 16-byte load per stream and half, issued before Sinkhorn.
const __nv_bfloat16* row = x + r * S * D;
- uint4 xu[2][S], wu[2];
+ uint4 xu[2][S];
+ Weights wu;
for (int rep = 0; rep < 2; ++rep) {
for (int st = 0; st < S; ++st) xu[rep][st] = *reinterpret_cast(row + st * D + rep * 2048 + t * 8);
- wu[rep] = __ldg(reinterpret_cast(nw + rep * 2048 + t * 8));
+ wu.load(nw, rep, t);
}
if (t < 32) {
const float* p = part + r * nb * 32;
@@ -183,11 +207,10 @@ extern "C" __global__ void __launch_bounds__(256)
const float rinv = div_full(1.0f, sqrt_approx(eps + div_full(total, (float)D)));
for (int rep = 0; rep < 2; ++rep) {
float y[8];
- const __nv_bfloat16* wh = reinterpret_cast(&wu[rep]);
uint4 ou;
__nv_bfloat16* oh = reinterpret_cast<__nv_bfloat16*>(&ou);
for (int j = 0; j < 8; ++j) {
- const __nv_bfloat16 o = __float2bfloat16_rn(__fmul_rn(bf(wh[j]), rbf(__fmul_rn(rinv, c[rep * 8 + j]))));
+ const __nv_bfloat16 o = __float2bfloat16_rn(__fmul_rn(wu.at(rep, j), rbf(__fmul_rn(rinv, c[rep * 8 + j]))));
oh[j] = o;
y[j] = bf(o);
}
@@ -198,3 +221,20 @@ extern "C" __global__ void __launch_bounds__(256)
if ((t & 7) == 0) xs[r * (D / 64) + rep * 32 + t / 8] = g;
}
}
+
+} // namespace glm_hc
+
+extern "C" __global__ void __launch_bounds__(256)
+ tf_glm_hc_finish(const __nv_bfloat16* x, const float* part, const float* base, const float* scale,
+ const __nv_bfloat16* nw, __nv_bfloat16* out, float* xs, float* post, float* comb, float eps,
+ float hc_eps, int nb, int iters) {
+ glm_hc::finish(x, part, base, scale, nw, out, xs, post, comb, eps, hc_eps, nb, iters);
+}
+
+// FP4-Local's float32 post-attention norm weight (D1): hc_finish's arithmetic, the weight read as float32.
+extern "C" __global__ void __launch_bounds__(256)
+ tf_glm_hc_finish_f32w(const __nv_bfloat16* x, const float* part, const float* base, const float* scale,
+ const float* nw, __nv_bfloat16* out, float* xs, float* post, float* comb, float eps,
+ float hc_eps, int nb, int iters) {
+ glm_hc::finish(x, part, base, scale, nw, out, xs, post, comb, eps, hc_eps, nb, iters);
+}
diff --git a/zig/kernels/cuda/glm_glue_norm.cuh b/zig/kernels/cuda/glm_glue_norm.cuh
index c9d74c57f3..436f5bf0d8 100644
--- a/zig/kernels/cuda/glm_glue_norm.cuh
+++ b/zig/kernels/cuda/glm_glue_norm.cuh
@@ -33,6 +33,31 @@ __device__ __forceinline__ void load_s(const __nv_bfloat16* p, float* v, bool ve
for (int j = 0; j < S; ++j) v[j] = bf(p[j]);
}
}
+// A norm's weight as float: bf16 (load_s's packing), or float32 (FP4-Local's hidden norms, D1) in 16-byte loads.
+template
+__device__ __forceinline__ bool wpacked(const __nv_bfloat16* w) {
+ return packed(w, 0);
+}
+template
+__device__ __forceinline__ bool wpacked(const float* w) {
+ return (reinterpret_cast(w) & 15) == 0;
+}
+template
+__device__ __forceinline__ void load_w(const __nv_bfloat16* p, float* v, bool vec) {
+ load_s(p, v, vec);
+}
+template
+__device__ __forceinline__ void load_w(const float* p, float* v, bool vec) {
+ static_assert(S % 4 == 0, "whole float4 loads");
+#pragma unroll
+ for (int j = 0; j < S; j += 4) {
+ const float4 f = vec ? *reinterpret_cast(p + j) : make_float4(p[j], p[j + 1], p[j + 2], p[j + 3]);
+ v[j] = f.x;
+ v[j + 1] = f.y;
+ v[j + 2] = f.z;
+ v[j + 3] = f.w;
+ }
+}
template
__device__ __forceinline__ void store_s(__nv_bfloat16* p, const __nv_bfloat16* o, bool vec) {
if (vec) {
@@ -48,15 +73,15 @@ __device__ __forceinline__ void store_s(__nv_bfloat16* p, const __nv_bfloat16* o
}
// glue._rmsnorm, BLOCK = D to a power of two, NW warps, S values a thread: bf16(w * bf16(x / rms)), `sums` per 64.
-template
-__device__ void rmsnorm(const __nv_bfloat16* x, int64_t x_stride, const __nv_bfloat16* w, __nv_bfloat16* out,
+template
+__device__ void rmsnorm(const __nv_bfloat16* x, int64_t x_stride, const W* w, __nv_bfloat16* out,
int64_t o_stride, float* xs, float eps, int sums) {
constexpr int T = NW * 32, TILE = S * T, REPS = BLOCK / TILE, N = REPS * S, G = 64 / S;
static_assert(D % S == 0, "a thread's values all inside D or all past it");
__shared__ float smem[NW];
const int64_t r = blockIdx.x;
const int t = threadIdx.x;
- const bool xv = packed(x, x_stride), wv = packed(w, 0), ov = packed(out, o_stride);
+ const bool xv = packed(x, x_stride), wv = wpacked(w), ov = packed(out, o_stride);
float v[N];
#pragma unroll
for (int rep = 0; rep < REPS; ++rep) {
@@ -85,7 +110,7 @@ __device__ void rmsnorm(const __nv_bfloat16* x, int64_t x_stride, const __nv_bfl
}
float wf[S];
__nv_bfloat16 o[S];
- load_s(w + d, wf, wv);
+ load_w(w + d, wf, wv);
#pragma unroll
for (int j = 0; j < S; ++j) {
o[j] = __float2bfloat16_rn(__fmul_rn(wf[j], rbf(__fmul_rn(rinv, v[rep * S + j]))));
@@ -120,3 +145,10 @@ __device__ void rmsnorm(const __nv_bfloat16* x, int64_t x_stride, const __nv_bfl
RMSNORM(4096, 4096, 8, 8)
RMSNORM(1536, 2048, 4, 8)
RMSNORM(512, 512, 4, 4)
+
+// FP4-Local's float32 hidden norm weight (D1): rmsnorm_4096's arithmetic, the weight read as float32.
+extern "C" __global__ void __launch_bounds__(256)
+ tf_glm_rmsnorm_4096_f32w(const __nv_bfloat16* x, int64_t x_stride, const float* w, __nv_bfloat16* out,
+ int64_t o_stride, float* xs, float eps, int sums) {
+ glm_glue::rmsnorm<4096, 4096, 8, 8, float>(x, x_stride, w, out, o_stride, xs, eps, sums);
+}
diff --git a/zig/src/core/contract/glm_cuda_records.zig b/zig/src/core/contract/glm_cuda_records.zig
index 025f5f9022..9d13d3ae4f 100644
--- a/zig/src/core/contract/glm_cuda_records.zig
+++ b/zig/src/core/contract/glm_cuda_records.zig
@@ -1,4 +1,4 @@
-//! GLM-5.3-Flash's packed matmul and DFlash2 launches on CUDA, read by kernel-contract-limits from every SM's image.
+//! GLM-5.3-Flash's packed matmul, DFlash2 and float32 norm weight launches, by kernel-contract-limits from every SM.
const registry = @import("registry.zig");
/// nvcc 13.0's usage, the compiler of the release fatbins.
@@ -36,6 +36,8 @@ pub const cuda = [_]registry.CudaRecord{
.{ .launch = .{ .name = "df_top16_part", .symbol = "df_top16_part", .image = "glm_dflash", .threads = 256, .min_sm = 80 }, .usage = .{ .{ .regs = 32, .static = 32864, .bound = 256 }, .{ .regs = 32, .static = 32864, .bound = 256 }, .{ .regs = 32, .static = 32864, .bound = 256 }, .{ .regs = 30, .static = 32864, .bound = 256 }, .{ .regs = 30, .static = 32864, .bound = 256 } } },
.{ .launch = .{ .name = "df_top16_merge", .symbol = "df_top16_merge", .image = "glm_dflash", .threads = 256, .min_sm = 80 }, .usage = .{ .{ .regs = 23, .static = 8416, .bound = 256 }, .{ .regs = 23, .static = 8416, .bound = 256 }, .{ .regs = 23, .static = 8416, .bound = 256 }, .{ .regs = 23, .static = 8416, .bound = 256 }, .{ .regs = 23, .static = 8416, .bound = 256 } } },
.{ .launch = .{ .name = "df_gsum", .symbol = "df_gsum", .image = "glm_dflash", .threads = 128, .min_sm = 80 }, .usage = .{ .{ .regs = 30, .static = 0, .bound = 0 }, .{ .regs = 38, .static = 0, .bound = 0 }, .{ .regs = 38, .static = 0, .bound = 0 }, .{ .regs = 38, .static = 0, .bound = 0 }, .{ .regs = 38, .static = 0, .bound = 0 } } },
+ .{ .launch = .{ .name = "glm_glue tf_glm_rmsnorm_4096_f32w", .symbol = "tf_glm_rmsnorm_4096_f32w", .image = "glm_glue", .threads = 256, .min_sm = 80 }, .usage = .{ .{ .regs = 44, .static = 32, .bound = 256 }, .{ .regs = 44, .static = 32, .bound = 256 }, .{ .regs = 44, .static = 32, .bound = 256 }, .{ .regs = 40, .static = 32, .bound = 256 }, .{ .regs = 40, .static = 32, .bound = 256 } } },
+ .{ .launch = .{ .name = "glm_glue tf_glm_hc_finish_f32w", .symbol = "tf_glm_hc_finish_f32w", .image = "glm_glue", .threads = 256, .min_sm = 80 }, .usage = .{ .{ .regs = 80, .static = 48, .bound = 256 }, .{ .regs = 78, .static = 48, .bound = 256 }, .{ .regs = 78, .static = 48, .bound = 256 }, .{ .regs = 80, .static = 48, .bound = 256 }, .{ .regs = 80, .static = 48, .bound = 256 } } },
};
/// nvcc 13.2's usage where it differs from 13.0's.
diff --git a/zig/src/families/glm/cuda_forward.zig b/zig/src/families/glm/cuda_forward.zig
index a15215fe17..414b8675a5 100644
--- a/zig/src/families/glm/cuda_forward.zig
+++ b/zig/src/families/glm/cuda_forward.zig
@@ -213,7 +213,7 @@ pub const Forward = struct {
}
/// forward._pre: hc_pre of every row at a site into b.normed, xs, post and comb.
- fn pre(f: *const Forward, r: Rank, hc: weights.Hc, norm: u64, rows: usize) !void {
+ fn pre(f: *const Forward, r: Rank, hc: weights.Hc, norm: glue.Norm, rows: usize) !void {
const stamp = prof_mod.span(f.prof, f.stream, .glue);
defer stamp.end();
const c = &r.w.config;
@@ -336,7 +336,7 @@ pub const Forward = struct {
}
/// forward._pre of rows lo..hi.
- fn preRows(f: *const Forward, r: Rank, hc: weights.Hc, norm: u64, lo: usize, hi: usize) !void {
+ fn preRows(f: *const Forward, r: Rank, hc: weights.Hc, norm: glue.Norm, lo: usize, hi: usize) !void {
const c = &r.w.config;
const b = r.b;
const S: usize = c.hc;
@@ -450,7 +450,7 @@ pub const Forward = struct {
const c = &f.ranks[0].w.config;
for (f.ranks) |r| {
const b = r.b;
- try glue.rmsnorm(f.set(), f.stream, b.hidden, c.hidden, r.w.norm, b.fnormed, c.hidden, b.fxs, c.eps, R, c.hidden);
+ try glue.rmsnormBy(f.set(), f.stream, b.hidden, c.hidden, r.w.norm, b.fnormed, c.hidden, b.fxs, c.eps, R, c.hidden);
if (b.prefill and r.segs.len > 0) {
try shared.heads(f, r);
continue;
@@ -481,7 +481,7 @@ pub const Forward = struct {
try glue.rmsnorm(f.set(), f.stream, b.me, D, m.enorm, b.mcat, 2 * D, null, c.eps, n, D);
try glue.rmsnorm(f.set(), f.stream, b.hin, D, m.hnorm, b.mcat + D * 2, 2 * D, null, c.eps, n, D);
try b16.matmul(f.set(), f.stream, b.mcat, 2 * D, m.eh, b.mx, b.kda.mm_part, n, D, 2 * D, false);
- try glue.rmsnorm(f.set(), f.stream, b.mx, D, m.layer.in_norm, b.normed, D, b.xs, c.eps, n, D);
+ try glue.rmsnormBy(f.set(), f.stream, b.mx, D, m.layer.in_norm, b.normed, D, b.xs, c.eps, n, D);
const nch = @min(latent.chunksFor(r.st.mtp_len + n), b.nch);
try dsa_block.block(.{ .set = f.set(), .ops = f.m.ops() }, f.stream, m.layer.mixer.mla, r.st.mtp_caches, b.dsa, b.normed, D, n, r.st.mtp_pos_dev, r.st.mtp_len, nch, b.part_rows, slot(r, n));
}
@@ -490,7 +490,7 @@ pub const Forward = struct {
const m = r.w.mtp.?;
const b = r.b;
try glue.residualAdd(f.set(), f.stream, b.mx, b.mx, b.gathered, n, D, r.w.share.world);
- try glue.rmsnorm(f.set(), f.stream, b.mx, D, m.layer.post_norm, b.normed, D, b.xs, c.eps, n, D);
+ try glue.rmsnormBy(f.set(), f.stream, b.mx, D, m.layer.post_norm, b.normed, D, b.xs, c.eps, n, D);
try f.ffn(r, m.layer, n, slot(r, n));
}
try f.gather(n);
diff --git a/zig/src/families/glm/cuda_glue.zig b/zig/src/families/glm/cuda_glue.zig
index b3d76cac86..9051b64d83 100644
--- a/zig/src/families/glm/cuda_glue.zig
+++ b/zig/src/families/glm/cuda_glue.zig
@@ -7,11 +7,15 @@ const mm_mod = @import("cuda_mm.zig");
/// What the forward's kernels launch from: our glue's, glm_mm's when tiled, qlinear's lane for FP8 kinds, copy_rows.
pub const Set = struct { glue: *const Kernels, mm: ?*const mm_mod.Kernels = null, packs: ?*const mm_mod.Packed = null, prompt: bool = false, linear: cuda.qlinear.Linear = .{}, copy_rows: ?cuda.Function = null };
+/// A hidden-size norm's weight: bf16, or float32 (FP4-Local's, D1) read as float32 by the _f32w kernels.
+pub const Norm = union(enum) { bf16: u64, f32: u64 };
+
/// Our glm_glue.cu's functions.
pub const Kernels = struct {
rmsnorm_4096: cuda.Function,
rmsnorm_1536: cuda.Function,
rmsnorm_512: cuda.Function,
+ rmsnorm_4096_f32w: cuda.Function,
residual_add: cuda.Function,
stream_mean: cuda.Function,
rank_sum: cuda.Function,
@@ -31,6 +35,7 @@ pub const Kernels = struct {
hc_partial: cuda.Function,
hc_partial8: cuda.Function,
hc_finish: cuda.Function,
+ hc_finish_f32w: cuda.Function,
absorb_q: cuda.Function,
expand_v: cuda.Function,
lat_write: cuda.Function,
@@ -110,6 +115,18 @@ pub fn rmsnorm(set: Set, stream: cuda.Stream, x: u64, x_stride: usize, w: u64, o
try cuda.launch.launch(f, .{ .grid = .{ .x = @intCast(rows) }, .block = .{ .x = warps * 32 } }, stream, &a);
}
+/// rmsnorm by a Norm: a float32 weight (the hidden size) through rmsnorm_4096_f32w, rmsnorm_4096's arithmetic.
+pub fn rmsnormBy(set: Set, stream: cuda.Stream, x: u64, x_stride: usize, n: Norm, out: u64, o_stride: usize, xs: ?u64, eps: f32, rows: usize, d: usize) !void {
+ const g = switch (n) {
+ .bf16 => |w| return rmsnorm(set, stream, x, x_stride, w, out, o_stride, xs, eps, rows, d),
+ .f32 => |g| g,
+ };
+ if (d != 4096) return error.NoRmsnormWidth;
+ var a: cuda.Args = .{};
+ inline for (.{ x, i64of(x_stride), g, out, i64of(o_stride), xs orelse @as(u64, 0), eps, @as(c_int, @intFromBool(xs != null)) }) |v| a.add(v);
+ try cuda.launch.launch(set.glue.rmsnorm_4096_f32w, .{ .grid = .{ .x = @intCast(rows) }, .block = .{ .x = 256 } }, stream, &a);
+}
+
fn cdiv(a: usize, b: usize) u32 {
return @intCast((a + b - 1) / b);
}
@@ -178,7 +195,7 @@ pub fn hcPartBytes(rows: usize) usize {
}
/// glue.hc_pre: streams [rows][s d] -> the collapsed RMS-normed row and its 64-sums `xs`; `post`, `comb` (Sinkhorn).
-pub fn hcPre(set: Set, stream: cuda.Stream, x: u64, fn_w: u64, base: u64, scale: u64, norm_w: u64, out: u64, xs: u64, post: u64, comb: u64, part: u64, eps: f32, hc_eps: f32, iters: usize, rows: usize, d: usize, s: usize) !void {
+pub fn hcPre(set: Set, stream: cuda.Stream, x: u64, fn_w: u64, base: u64, scale: u64, norm: Norm, out: u64, xs: u64, post: u64, comb: u64, part: u64, eps: f32, hc_eps: f32, iters: usize, rows: usize, d: usize, s: usize) !void {
if (d != 4096 or s != 4) return error.HcShape;
var a: cuda.Args = .{};
inline for (.{ x, fn_w, part, i64of(s * d), @as(c_int, hc_blocks) }) |v| a.add(v);
@@ -189,9 +206,13 @@ pub fn hcPre(set: Set, stream: cuda.Stream, x: u64, fn_w: u64, base: u64, scale:
a.add(i64of(rows));
try cuda.launch.launch(set.glue.hc_partial, .{ .grid = .{ .x = cdiv(rows, 4), .y = hc_blocks }, .block = .{ .x = 128 } }, stream, &a);
}
+ const f, const w = switch (norm) {
+ .bf16 => |p| .{ set.glue.hc_finish, p },
+ .f32 => |p| .{ set.glue.hc_finish_f32w, p },
+ };
var b: cuda.Args = .{};
- inline for (.{ x, part, base, scale, norm_w, out, xs, post, comb, eps, hc_eps, @as(c_int, hc_blocks), cint(iters) }) |v| b.add(v);
- try cuda.launch.launch(set.glue.hc_finish, .{ .grid = .{ .x = @intCast(rows) }, .block = .{ .x = 256 } }, stream, &b);
+ inline for (.{ x, part, base, scale, w, out, xs, post, comb, eps, hc_eps, @as(c_int, hc_blocks), cint(iters) }) |v| b.add(v);
+ try cuda.launch.launch(f, .{ .grid = .{ .x = @intCast(rows) }, .block = .{ .x = 256 } }, stream, &b);
}
/// glue.hc_post: branch = bf16(`g`, rank 0 first); xout[s] = bf16(post[s] branch + sum_t comb[t][s] x[t]); x may alias.
diff --git a/zig/src/families/glm/cuda_mtp_load.zig b/zig/src/families/glm/cuda_mtp_load.zig
index fb05721a66..ae488de25e 100644
--- a/zig/src/families/glm/cuda_mtp_load.zig
+++ b/zig/src/families/glm/cuda_mtp_load.zig
@@ -13,16 +13,14 @@ const prefix = "model.language_model.";
pub fn load(L: anytype, head_ck: *core.Checkpoint) !weights.Mtp {
const c = &L.w.config;
const i = c.layers;
- try L.whole(try L.get("layers.{d}.input_layernorm.weight", .{i}), .bf16);
- const in_norm = try L.put("layers.{d}.in_norm", .{i});
- try L.whole(try L.get("layers.{d}.post_attention_layernorm.weight", .{i}), .bf16);
- const post_norm = try L.put("layers.{d}.post_norm", .{i});
+ const in_norm = try L.norm("layers.{d}.input_layernorm.weight", .{i}, "layers.{d}.in_norm");
+ const post_norm = try L.norm("layers.{d}.post_attention_layernorm.weight", .{i}, "layers.{d}.post_norm");
const none: weights.Hc = .{ .fn_w = 0, .base = 0, .scale = 0 };
const layer_w: weights.Layer = .{ .index = i, .attn_hc = none, .ffn_hc = none, .in_norm = in_norm, .post_norm = post_norm, .mixer = .{ .mla = try L.dsa(i) }, .ffn = .{ .moe = try L.moeLayer(i) } };
L.ck.release();
var out: [4]u64 = undefined;
for (&out, [_][]const u8{ "enorm.weight", "hnorm.weight", "eh_proj.weight", "shared_head.norm.weight" }, [_][]const u8{ "enorm", "hnorm", "eh", "norm" }) |*p, name, what| {
- try L.whole(try L.ck.get(try std.fmt.bufPrint(&L.name_buf, prefix ++ "layers.{d}.{s}", .{ i, name })), .bf16);
+ try L.whole(try L.get("layers.{d}.{s}", .{ i, name }), .bf16);
p.* = try L.put("mtp.{s}", .{what});
}
// the draft head: quantize4 of the head's bf16 rows (an MXFP8 head's widened exactly), then make_q4's tiling
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index de69483e03..7438ddcb40 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -16,6 +16,7 @@ const exl3 = @import("cuda_exl3.zig");
const mm_mod = @import("cuda_mm.zig");
pub const formats = @import("cuda_formats.zig");
const projection = @import("cuda_proj.zig");
+const glue = @import("cuda_glue.zig");
const Tensor = core.checkpoint.Tensor;
const prefix = "model.language_model.";
@@ -26,7 +27,7 @@ pub const Hc = struct { fn_w: u64, base: u64, scale: u64 };
pub const Mixer = union(cfg.Kind) { kda: kda_block.Layer, mla: dsa_block.Layer };
pub const Ffn = union(enum) { dense: mlp.Layer, moe: moe.Layer };
-pub const Layer = struct { index: u32, attn_hc: Hc, ffn_hc: Hc, in_norm: u64, post_norm: u64, mixer: Mixer, ffn: Ffn };
+pub const Layer = struct { index: u32, attn_hc: Hc, ffn_hc: Hc, in_norm: glue.Norm, post_norm: glue.Norm, mixer: Mixer, ffn: Ffn };
/// The MTP head (weights.MTPW): norms, eh_proj, a DSA + MoE layer (NVFP4 experts), shared_head.norm, 4-bit draft head.
pub const Mtp = struct { enorm: u64, hnorm: u64, eh: u64, norm: u64, layer: Layer, draft: q4.Q4 };
@@ -43,7 +44,7 @@ pub const Weights = struct {
share: shard.Share,
embed: u64 = 0, // bf16 [vocab][dims]
layers: []Layer = &.{},
- norm: u64 = 0,
+ norm: glue.Norm = .{ .bf16 = 0 },
head: projection.Proj = .{ .bf16 = 0 }, // [share.vocab][dims]: bf16, or MXFP8 for the lane
mtp: ?Mtp = null,
exl3_shx: bool = true, // EXL3: every layer's gate and up share one input rotation (a prompt rotates each row once)
@@ -73,6 +74,12 @@ fn toBf16(v: f32) u16 {
return @intCast((b + 0x7fff + ((b >> 16) & 1)) >> 16);
}
+/// A float32 narrowed to bf16 by the loader, or null when bf16 does not hold it exactly (NaN stays NaN).
+fn narrow(v: f32) ?u16 {
+ const h = toBf16(v);
+ return if (std.math.isNan(v) or @as(f32, @bitCast(@as(u32, h) << 16)) == v) h else null;
+}
+
const Loader = struct {
gpa: std.mem.Allocator,
d: *const cuda.Driver,
@@ -83,11 +90,22 @@ const Loader = struct {
ck: *core.Checkpoint,
host: std.ArrayList(u8) = .empty,
name_buf: [192]u8 = undefined,
+ last: []const u8 = "", // the tensor `get` read last, named when its values are refused
recipes: ?*const core.modelopt.Recipes = null, // MIXED_PRECISION's table; null: the tensors decide (NVFP4)
pub fn get(L: *Loader, comptime fmt: []const u8, args: anytype) !Tensor {
- const name = try std.fmt.bufPrint(&L.name_buf, prefix ++ fmt, args);
- return L.ck.get(name);
+ L.last = try std.fmt.bufPrint(&L.name_buf, prefix ++ fmt, args);
+ return L.ck.get(L.last);
+ }
+
+ /// Hidden-size norm `fmt` uploaded as `up`: bf16 as stored, or kept float32 (D1) for the _f32w kernels.
+ pub fn norm(L: *Loader, comptime fmt: []const u8, args: anytype, comptime up: []const u8) !glue.Norm {
+ const t = try L.get(fmt, args);
+ const kept = t.dtype == .f32;
+ if (kept and t.numel() != L.w.config.hidden) return error.UnexpectedTensor;
+ try L.whole(t, if (kept) .f32 else .bf16);
+ const w = try L.put(up, args);
+ return if (kept) .{ .f32 = w } else .{ .bf16 = w };
}
/// The projection `fmt` (a module under the prefix) is stored as `want`, or it is refused by name (formats.of).
@@ -147,7 +165,10 @@ const Loader = struct {
};
switch (to) {
.f32 => try L.host.appendSlice(L.gpa, std.mem.asBytes(&v)),
- .bf16 => try L.host.appendSlice(L.gpa, std.mem.asBytes(&toBf16(v))),
+ .bf16 => try L.host.appendSlice(L.gpa, std.mem.asBytes(&(narrow(v) orelse {
+ std.log.err("{s}: a float32 value bf16 does not hold; only the hidden norms keep float32", .{L.last});
+ return error.InexactBf16;
+ }))),
else => return error.UnexpectedTensor,
}
}
@@ -208,9 +229,9 @@ const Loader = struct {
try L.rows(try L.get("layers.{d}.self_attn.dt_bias", .{i}), h.lo * dk, h.hi * dk, all, .f32);
const dt = try L.put("layers.{d}.kda.dt_bias", .{i});
try L.whole(try L.get("layers.{d}.self_attn.o_norm.weight", .{i}), .bf16);
- const norm = try L.put("layers.{d}.kda.norm", .{i});
+ const o_norm = try L.put("layers.{d}.kda.norm", .{i});
const o = try formats.cutProj(L, prefix, i, &.{.{ .rel = "self_attn.o_proj", .cols = hr }}, &.{ .bf16, .mxfp8 }, "layers.{d}.kda.o");
- return .{ .proj = proj, .fb = fb, .gb = gb, .o = o, .conv_w = conv, .a_log = a_log, .dt_bias = dt, .norm_w = norm, .eps = c.eps, .lower = c.lower_bound, .heads = h.len(), .dims = c.hidden };
+ return .{ .proj = proj, .fb = fb, .gb = gb, .o = o, .conv_w = conv, .a_log = a_log, .dt_bias = dt, .norm_w = o_norm, .eps = c.eps, .lower = c.lower_bound, .heads = h.len(), .dims = c.hidden };
}
/// weights.load's dsa(i) (latent): [q_a | kv_a], norms, q_b by heads, kv_b as wk/wv (AbsorbW), o by cols, indexer.
@@ -499,10 +520,8 @@ const Loader = struct {
fn layer(L: *Loader, i: u32) !Layer {
const c = &L.w.config;
- try L.whole(try L.get("layers.{d}.input_layernorm.weight", .{i}), .bf16);
- const in_norm = try L.put("layers.{d}.in_norm", .{i});
- try L.whole(try L.get("layers.{d}.post_attention_layernorm.weight", .{i}), .bf16);
- const post_norm = try L.put("layers.{d}.post_norm", .{i});
+ const in_norm = try L.norm("layers.{d}.input_layernorm.weight", .{i}, "layers.{d}.in_norm");
+ const post_norm = try L.norm("layers.{d}.post_attention_layernorm.weight", .{i}, "layers.{d}.post_norm");
return .{
.index = i,
.attn_hc = try L.hc(i, "attn"),
@@ -539,8 +558,7 @@ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: c
}
try L.whole(try L.get("embed_tokens.weight", .{}), .bf16);
w.embed = try L.put("embed", .{});
- try L.whole(try L.get("norm.weight", .{}), .bf16);
- w.norm = try L.put("norm", .{});
+ w.norm = try L.norm("norm.weight", .{}, "norm");
const vocab: formats.Part = .{ .rel = "lm_head", .rows = .{ share.vocab.lo, share.vocab.hi } };
w.head = try formats.cut(&L, &head_ck, &.{"lm_head"}, &.{vocab}, &.{ .bf16, .mxfp8 }, "head", .{});
if (with_mtp and c.mtp > 0) w.mtp = try @import("cuda_mtp_load.zig").load(&L, &head_ck);
@@ -554,6 +572,12 @@ test "bf16 rounding of fp32 is to nearest even" {
try std.testing.expectEqual(@as(u16, 0x3f81), toBf16(@bitCast(@as(u32, 0x3f808001))));
}
+test "the loader narrows a float32 to bf16 only when bf16 holds it, unlike FP4-Local's norms" {
+ try std.testing.expectEqual(@as(?u16, 0x3f80), narrow(1.0));
+ try std.testing.expectEqual(@as(?u16, null), narrow(@bitCast(@as(u32, 0x3f808001))));
+ try std.testing.expect(narrow(std.math.nan(f32)) != null);
+}
+
test {
_ = @import("cuda_formats.zig");
_ = @import("cuda_proj.zig");
diff --git a/zig/tests/cuda/contract_glm_f32w.zig b/zig/tests/cuda/contract_glm_f32w.zig
new file mode 100644
index 0000000000..375a08b41d
--- /dev/null
+++ b/zig/tests/cuda/contract_glm_f32w.zig
@@ -0,0 +1,156 @@
+//! FP4-Local's float32 norm weights (D1) under the contract: rmsnorm_4096_f32w and hc_finish_f32w, each output alone.
+const std = @import("std");
+const cuda = @import("cuda");
+const core = @import("core");
+const rig_ = @import("contract_rig.zig");
+const ref = @import("contract_glm_f32w_ref.zig");
+
+const contract = core.contract;
+const c = contract.spec;
+const g = contract.gates;
+const d = ref.d;
+
+/// norm_*: rmsnorm_4096_f32w's rows and their 64-sums; hc_*: hc_finish_f32w's normed rows, 64-sums, post and comb.
+pub const Which = enum { norm_y, norm_sums, hc_y, hc_sums, hc_post, hc_comb };
+
+/// A thread's 16 squares in one fma chain, the warp's butterfly, then 8 warps' partials by butterfly.
+fn normDepth(_: c.Shape) usize {
+ return 16 + 5 + 3;
+}
+
+/// hc_finish's 16 squares in fma and mul-add pairs (23 roundings), then the same butterflies.
+fn hcDepth(_: c.Shape) usize {
+ return 23 + 5 + 3;
+}
+
+/// A thread's 8 outputs in one chain, then 8 threads by xor 4, 2 and 1.
+fn groupDepth(_: c.Shape) usize {
+ return 7 + 3;
+}
+
+/// The 16 K blocks' partials of a logit and of the squares, each one chain.
+fn mixDepth(_: c.Shape) usize {
+ return ref.nb;
+}
+
+const rms_why = "a bf16 output follows the sum of squares as ss^-1/2: a bf16 sum's error is a few output ulps";
+const hc_why = "three bf16 roundings in the bound: fp16 squares overflow to a zero output, 64 bounds; bf16 sums move it ulps";
+const sums_why = "64 terms of at most 8 bits: fp16 holds every partial, so bf16's miss is the one shown";
+const mix_why = "16 integer partials: fp16 holds every one, so bf16's miss is the one shown";
+
+pub fn spec(w: Which) c.Spec {
+ const rows4096: []const c.Shape = &.{ .{ .rows = 1, .n = d, .k = d }, .{ .rows = 70, .n = d, .k = d } };
+ const groups: []const c.Shape = &.{ .{ .rows = 1, .n = 64, .k = 64 }, .{ .rows = 70, .n = 64, .k = 64 } };
+ return switch (w) {
+ .norm_y => .{ .name = "cuda glm tf_glm_rmsnorm_4096_f32w (float32 norm weight)", .accum = .f32, .depth = normDepth, .ops = 8, .mids = 1, .out = .bf16, .served = rows4096, .sums = .{ .bf16 = 0, .why = rms_why } },
+ .norm_sums => .{ .name = "cuda glm tf_glm_rmsnorm_4096_f32w (its 64-sums)", .accum = .f32, .depth = groupDepth, .out = .f32, .served = groups, .sums = .{ .f16 = 0, .why = sums_why } },
+ .hc_y => .{ .name = "cuda glm tf_glm_hc_finish_f32w (normed rows)", .accum = .f32, .depth = hcDepth, .ops = 8, .mids = 3, .out = .bf16, .served = rows4096, .sums = .{ .f16 = 0, .bf16 = 0, .why = hc_why } },
+ .hc_sums => .{ .name = "cuda glm tf_glm_hc_finish_f32w (its 64-sums)", .accum = .f32, .depth = groupDepth, .out = .f32, .served = groups, .sums = .{ .f16 = 0, .why = sums_why } },
+ .hc_post => .{ .name = "cuda glm tf_glm_hc_finish_f32w (post)", .accum = .f32, .depth = mixDepth, .ops = 16, .out = .f32, .served = &.{ .{ .rows = 1, .n = 4, .k = ref.nb }, .{ .rows = 70, .n = 4, .k = ref.nb } }, .sums = .{ .f16 = 0, .why = mix_why } },
+ .hc_comb => .{ .name = "cuda glm tf_glm_hc_finish_f32w (comb, Sinkhorn)", .accum = .f32, .depth = mixDepth, .ops = 256, .out = .f32, .served = &.{ .{ .rows = 1, .n = 16, .k = ref.nb }, .{ .rows = 70, .n = 16, .k = ref.nb } }, .sums = .{ .f16 = 0, .why = mix_why } },
+ };
+}
+
+pub const F32w = struct {
+ rig: rig_.Rig,
+ which: Which,
+ f: cuda.Function,
+ in: ?ref.In = null,
+ arena: std.heap.ArenaAllocator,
+ ptrs: [9]u64 = @splat(0), // norm: x, gamma, y, xs; hc: x, part, base, scale, gamma, y, xs, post, comb
+
+ pub fn init(rig: rig_.Rig, which: Which, f: cuda.Function) F32w {
+ return .{ .rig = rig, .which = which, .f = f, .arena = .init(rig.gpu.gpa) };
+ }
+
+ pub fn deinit(self: *F32w) void {
+ self.arena.deinit();
+ self.rig.deinit();
+ }
+
+ pub fn kernel(self: *F32w) g.Kernel {
+ return .{ .spec = spec(self.which), .ctx = self, .vt = &vt };
+ }
+
+ const vt = g.hooks(F32w);
+
+ fn hc(self: *const F32w) bool {
+ return switch (self.which) {
+ .norm_y, .norm_sums => false,
+ else => true,
+ };
+ }
+
+ /// The inputs (`ref.fill`), the checked output, and the kernel's other outputs as scratch.
+ pub fn prepare(self: *F32w, shape: c.Shape, fill: g.Fill, seed: u64) !bool {
+ const served = spec(self.which).served[0];
+ if (shape.n != served.n or shape.k != served.k) return false;
+ self.rig.reset();
+ _ = self.arena.reset(.retain_capacity);
+ const exact = self.which == .norm_sums or self.which == .hc_sums;
+ const in = try ref.fill(self.arena.allocator(), self.hc(), shape.rows, fill, seed, exact);
+ self.in = in;
+ const r = &self.rig;
+ const n = shape.rows;
+ const y = if (self.which == .norm_y or self.which == .hc_y) try r.output(n * d * 2) else try r.scratch(n * d * 2);
+ const xs = if (exact) try r.output(n * (d / 64) * 4) else if (self.hc()) try r.scratch(n * (d / 64) * 4) else 0;
+ const x = try r.input(std.mem.sliceAsBytes(in.x));
+ const gm = try r.input(std.mem.sliceAsBytes(in.gamma));
+ if (!self.hc()) {
+ self.ptrs = .{ x, gm, y, xs, 0, 0, 0, 0, 0 };
+ return true;
+ }
+ const part = try r.input(std.mem.sliceAsBytes(in.part));
+ const base = try r.input(std.mem.asBytes(&in.base));
+ const sc = try r.input(std.mem.asBytes(&ref.scale));
+ const post = if (self.which == .hc_post) try r.output(n * 4 * 4) else try r.scratch(n * 4 * 4);
+ const comb = if (self.which == .hc_comb) try r.output(n * 16 * 4) else try r.scratch(n * 16 * 4);
+ self.ptrs = .{ x, part, base, sc, gm, y, xs, post, comb };
+ return true;
+ }
+
+ /// One launch, 256 threads a row: the norm's (x, stride, w, out, stride, xs, eps, sums) or hc_finish's arguments.
+ pub fn run(self: *F32w) !void {
+ const p = self.ptrs;
+ const in = self.in.?;
+ var a: cuda.Args = .{};
+ if (self.hc()) {
+ inline for (p) |v| a.add(v);
+ inline for (.{ in.eps, in.hc_eps, @as(c_int, ref.nb), @as(c_int, ref.iters) }) |v| a.add(v);
+ } else {
+ inline for (.{ p[0], @as(i64, d), p[1], p[2], @as(i64, d), p[3], in.eps, @as(c_int, @intFromBool(p[3] != 0)) }) |v| a.add(v);
+ }
+ try cuda.launch.launch(self.f, .{ .grid = .{ .x = @intCast(in.rows) }, .block = .{ .x = 256 } }, self.rig.s, &a);
+ try self.rig.s.synchronize();
+ }
+
+ pub fn count(self: *F32w) usize {
+ const n = self.in.?.rows;
+ return n * @as(usize, switch (self.which) {
+ .norm_y, .hc_y => d,
+ .norm_sums, .hc_sums => d / 64,
+ .hc_post => 4,
+ .hc_comb => 16,
+ });
+ }
+
+ fn outputs(self: *F32w, m: ref.Sum, want: []f64, mag: []f64) !void {
+ const in = self.in.?;
+ const a = self.arena.allocator();
+ switch (self.which) {
+ .norm_y, .hc_y => try ref.normed(a, in, m, want, mag),
+ .norm_sums, .hc_sums => try ref.sums(a, in, m, want, mag),
+ .hc_post => ref.post(in, m, want, mag),
+ .hc_comb => ref.comb(in, m, want, mag),
+ }
+ }
+
+ pub fn reference(self: *F32w, want: []f64, mag: []f64) !void {
+ return self.outputs(.{}, want, mag);
+ }
+
+ pub fn simulate(self: *F32w, acc: c.Accum, order: g.Order, out: []f64) !void {
+ const mag = try self.arena.allocator().alloc(f64, out.len);
+ return self.outputs(.{ .acc = acc, .order = order }, out, mag);
+ }
+};
diff --git a/zig/tests/cuda/contract_glm_f32w_ref.zig b/zig/tests/cuda/contract_glm_f32w_ref.zig
new file mode 100644
index 0000000000..2ffcdb1a29
--- /dev/null
+++ b/zig/tests/cuda/contract_glm_f32w_ref.zig
@@ -0,0 +1,261 @@
+//! FP4-Local's float32 norm weight kernels in float64 (rmsnorm_4096_f32w, hc_finish_f32w): the inputs and references.
+const std = @import("std");
+const core = @import("core");
+
+const contract = core.contract;
+const c = contract.spec;
+const Order = contract.gates.Order;
+const Fill = contract.gates.Fill;
+const sumIn = contract.rows.sumIn;
+
+pub const d = 4096;
+pub const s = 4;
+pub const nb = 16; // glue.hc_blocks
+pub const iters = 20;
+pub const scale = [3]f32{ 0.7, 0.9, 1.1 };
+
+/// What a call reads: x bf16 [rows][d] (norm) or [rows][s][d] (hc), the float32 weight, hc's partials and logits' base.
+pub const In = struct {
+ hc: bool,
+ rows: usize,
+ x: []u16,
+ gamma: []f32,
+ part: []f32, // [rows][nb][32]: 24 dots, then the K block's sum of squares
+ base: [24]f32,
+ eps: f32,
+ hc_eps: f32,
+};
+
+/// A row's sums: exact in float64, or as an accumulator in an order would add them (gate 4's simulation).
+pub const Sum = struct {
+ acc: ?c.Accum = null,
+ order: Order = .sequential,
+
+ pub fn of(m: Sum, t: []const f64) f64 {
+ if (m.acc) |a| return sumIn(a, m.order, t);
+ var v: f64 = 0;
+ for (t) |x| v += x;
+ return v;
+ }
+};
+
+pub fn bf(v: f64) f64 {
+ return c.fromBf16(c.toBf16(@floatCast(v)));
+}
+
+/// fmaxf: a NaN operand gives the other one.
+fn fmax(a: f64, b: f64) f64 {
+ return if (std.math.isNan(a)) b else if (std.math.isNan(b)) a else @max(a, b);
+}
+
+fn sigmoid(v: f64) f64 {
+ return 1 / (@exp(0 - v) + 1);
+}
+
+/// Inputs for a fill (stress: integer terms); `exact`: rows whose norm is x itself, so the 64-sums' reference is exact.
+pub fn fill(a: std.mem.Allocator, hc: bool, rows: usize, mode: Fill, seed: u64, exact: bool) !In {
+ var prng = std.Random.DefaultPrng.init(seed);
+ const rng = prng.random();
+ const x = try a.alloc(u16, rows * d * @as(usize, if (hc) s else 1));
+ const gamma = try a.alloc(f32, d);
+ const part = try a.alloc(f32, if (hc) rows * nb * 32 else 0);
+ var in: In = .{ .hc = hc, .rows = rows, .x = x, .gamma = gamma, .part = part, .base = undefined, .eps = 1e-6, .hc_eps = 1e-6 };
+ const stress = mode == .stress;
+ for (gamma) |*g| g.* = if (exact) 1 else 0.5 + rng.float(f32);
+ for (&in.base) |*b| b.* = (rng.float(f32) * 2 - 1) * 0.5;
+ for (part, 0..) |*p, i| p.* = if (i % 32 == 24) // stress logits under 30, where fp32's exp keeps every entry
+ (if (stress) @floatFromInt(1024 + rng.uintLessThan(u32, 1024)) else 200 + rng.float(f32) * 200)
+ else if (stress) @as(f32, @floatFromInt(rng.uintLessThan(u32, 5))) - 2 else rng.float(f32) * 2 - 1;
+ if (exact or (hc and stress)) { // signed permutations of one row; eps 1 - mean square; hc's c = stream 0 / 2
+ const row = try a.alloc(f64, d);
+ defer a.free(row);
+ for (row, 0..) |*v, i| v.* = if (stress)
+ @as(f64, @floatFromInt(rng.uintLessThan(u32, 64))) - 31
+ else
+ bf((rng.float(f64) * 2 - 1) * (if (i % 64 == 7) @as(f64, 20) else 1));
+ var ss: f64 = 0;
+ for (row) |v| ss += v * v;
+ if (exact) in.eps = @floatCast(1 - ss / d);
+ if (hc) { // pre 0.5: zero dots, base and hc_eps; squares that keep the logits' rms finite under that eps
+ in.hc_eps = 0;
+ for (in.base[0..4]) |*b| b.* = 0;
+ const sq: f32 = @ceil((@abs(in.eps) + 1) * s * d / nb);
+ for (0..rows) |r| for (0..nb) |b| {
+ for (0..4) |k| part[(r * nb + b) * 32 + k] = 0;
+ if (exact) part[(r * nb + b) * 32 + 24] = sq;
+ };
+ }
+ const order = try a.alloc(usize, d);
+ defer a.free(order);
+ for (0..rows) |r| {
+ for (order, 0..) |*o, i| o.* = i;
+ rng.shuffle(usize, order);
+ for (0..d) |i| {
+ const v = row[order[i]] * @as(f64, if (rng.boolean()) -1 else 1);
+ if (hc) {
+ x[(r * s) * d + i] = c.toBf16(@floatCast(2 * v));
+ for (1..s) |k| x[(r * s + k) * d + i] = 0;
+ } else x[r * d + i] = c.toBf16(@floatCast(v));
+ }
+ }
+ } else for (x, 0..) |*v, i| {
+ v.* = c.toBf16(if (stress) @floatFromInt(@as(i32, @intCast(rng.uintLessThan(u32, 65))) - 32) else (rng.float(f32) * 2 - 1) * (if (i % 64 == 7) @as(f32, 20) else 1));
+ }
+ if (mode == .nonfinite) plant(&in);
+ return in;
+}
+
+/// NaN and both infinities in rows 0 to 3: x (the norms' outputs), and hc's dots for post (4, 5) and comb (8, 9).
+fn plant(in: *In) void {
+ const nan = c.toBf16(std.math.nan(f32));
+ const inf = c.toBf16(std.math.inf(f32));
+ const ninf = c.toBf16(-std.math.inf(f32));
+ const w: usize = if (in.hc) s * d else d;
+ const at = [_][2]usize{ .{ 0, d / 3 }, .{ 1, d / 2 }, .{ 2, d - 1 }, .{ 3, 0 } };
+ const put = [_]u16{ nan, inf, ninf, inf };
+ for (at, put) |p, v| if (p[0] < in.rows) {
+ in.x[p[0] * w + p[1]] = v;
+ };
+ if (!in.hc) return;
+ const dots = [_][3]usize{ .{ 0, 3, 4 }, .{ 1, 5, 5 }, .{ 2, 0, 8 }, .{ 3, 15, 9 } };
+ const vals = [_]f32{ std.math.nan(f32), std.math.inf(f32), -std.math.inf(f32), std.math.nan(f32) };
+ for (dots, vals) |p, v| if (p[0] < in.rows) {
+ in.part[(p[0] * nb + p[1]) * 32 + p[2]] = v;
+ };
+}
+
+fn xv(in: In, i: usize) f64 {
+ return c.fromBf16(in.x[i]);
+}
+
+/// A row's hc logits: the 24 mixing sums times rinv of the squares' sum, and each one's |terms| times that rinv.
+fn mixRow(in: In, r: usize, m: Sum, mixn: *[24]f64, mass: *[24]f64) void {
+ var t: [nb]f64 = undefined;
+ for (0..nb) |b| t[b] = in.part[(r * nb + b) * 32 + 24];
+ const rinv = 1 / @sqrt(in.eps + m.of(&t) / (s * d));
+ for (0..24) |k| {
+ var abs: f64 = 0;
+ for (0..nb) |b| {
+ t[b] = in.part[(r * nb + b) * 32 + k];
+ abs += @abs(t[b]);
+ }
+ mixn[k] = m.of(&t) * rinv;
+ mass[k] = abs * rinv;
+ }
+}
+
+/// rmsnorm_4096_f32w's outputs [rows][d] or hc_finish_f32w's normed rows, unrounded; `mag` each one's sums' terms.
+pub fn normed(a: std.mem.Allocator, in: In, m: Sum, want: []f64, mag: []f64) !void {
+ const cv = try a.alloc(f64, d);
+ defer a.free(cv);
+ const cm = try a.alloc(f64, d); // the |terms| of each c: hc's collapse and its logits' sums
+ defer a.free(cm);
+ const sq = try a.alloc(f64, d);
+ defer a.free(sq);
+ for (0..in.rows) |r| {
+ if (in.hc) {
+ var mixn: [24]f64 = undefined;
+ var mass: [24]f64 = undefined;
+ mixRow(in, r, m, &mixn, &mass);
+ var pre: [s]f64 = undefined;
+ var dpre: [s]f64 = undefined; // pre's sensitivity to its logit's sum: sigmoid' <= 1/4, rinv's own error 1/2
+ for (0..s) |k| {
+ pre[k] = in.hc_eps + sigmoid(mixn[k] * scale[0] + in.base[k]);
+ dpre[k] = 0.25 * scale[0] * 1.5 * mass[k];
+ }
+ for (cv, cm, 0..) |*o, *om, i| {
+ var t: [s]f64 = undefined;
+ om.* = 0;
+ for (0..s) |k| {
+ const xk = xv(in, (r * s + k) * d + i);
+ t[k] = pre[k] * xk;
+ om.* += @abs(t[k]) + @abs(xk) * dpre[k];
+ }
+ o.* = m.of(&t);
+ }
+ } else for (cv, cm, 0..) |*o, *om, i| {
+ o.* = xv(in, r * d + i);
+ om.* = 0;
+ }
+ for (sq, cv) |*q, v| q.* = v * v;
+ const rinv = 1 / @sqrt(in.eps + m.of(sq) / d);
+ for (want[r * d ..][0..d], mag[r * d ..][0..d], cv, cm, in.gamma) |*w, *g, v, om, gm| {
+ w.* = gm * rinv * v;
+ g.* = gm * rinv * om + @abs(w.*) / 2; // y ~ ss^-1/2
+ }
+ }
+}
+
+/// Each 64 outputs' sum, the outputs rounded as the kernel does, bf16(gamma bf16(rinv c)), from `fill`'s exact rows.
+pub fn sums(a: std.mem.Allocator, in: In, m: Sum, want: []f64, mag: []f64) !void {
+ const row = try a.alloc(f64, d);
+ defer a.free(row);
+ for (0..in.rows) |r| {
+ var sq: f64 = 0;
+ for (row, 0..) |*v, i| {
+ v.* = if (in.hc) bf(0.5 * xv(in, (r * s) * d + i)) else xv(in, r * d + i);
+ sq += v.* * v.*;
+ }
+ const rinv = 1 / @sqrt(in.eps + sq / d);
+ for (row, in.gamma) |*o, gm| o.* = bf(gm * bf(rinv * o.*));
+ for (0..d / 64) |g| {
+ const t = row[g * 64 ..][0..64];
+ var abs: f64 = 0;
+ for (t) |v| abs += @abs(v);
+ want[r * (d / 64) + g] = m.of(t);
+ mag[r * (d / 64) + g] = abs;
+ }
+ }
+}
+
+/// hc_finish_f32w's post: 2 sigmoid of the logits 4 to 7; `mag` the logits' sums' terms through sigmoid' (1/4).
+pub fn post(in: In, m: Sum, want: []f64, mag: []f64) void {
+ for (0..in.rows) |r| {
+ var mixn: [24]f64 = undefined;
+ var mass: [24]f64 = undefined;
+ mixRow(in, r, m, &mixn, &mass);
+ for (0..4) |k| {
+ want[r * 4 + k] = 2 * sigmoid(mixn[4 + k] * scale[1] + in.base[4 + k]);
+ mag[r * 4 + k] = 0.5 * scale[1] * 1.5 * mass[4 + k] + @abs(want[r * 4 + k]);
+ }
+ }
+}
+
+/// hc_finish_f32w's comb: rows' softmax of the 4x4 logits plus hc_eps, then Sinkhorn's column and row normalizations.
+pub fn comb(in: In, m: Sum, want: []f64, mag: []f64) void {
+ for (0..in.rows) |r| {
+ var mixn: [24]f64 = undefined;
+ var mass: [24]f64 = undefined;
+ mixRow(in, r, m, &mixn, &mass);
+ var cm: [16]f64 = undefined;
+ var most: f64 = 0;
+ for (0..16) |i| most = @max(most, mass[8 + i]);
+ for (0..4) |i| {
+ var cl: [4]f64 = undefined;
+ var top: f64 = std.math.nan(f64);
+ for (0..4) |j| {
+ cl[j] = mixn[8 + 4 * i + j] * scale[2] + in.base[8 + 4 * i + j];
+ top = fmax(top, cl[j]);
+ }
+ var e: [4]f64 = undefined;
+ for (&e, cl) |*o, v| o.* = @exp(v - top);
+ const sum = (e[0] + e[1]) + (e[2] + e[3]);
+ for (0..4) |j| cm[4 * i + j] = in.hc_eps + e[j] / sum;
+ }
+ normalize(&cm, in.hc_eps, .col);
+ for (1..iters) |_| {
+ normalize(&cm, in.hc_eps, .row);
+ normalize(&cm, in.hc_eps, .col);
+ }
+ for (0..16) |i| {
+ want[r * 16 + i] = cm[i];
+ mag[r * 16 + i] = @abs(cm[i]) * 2 * scale[2] * 1.5 * most; // a logit's error moves each entry twice it
+ }
+ }
+}
+
+fn normalize(cm: *[16]f64, eps: f64, by: enum { row, col }) void {
+ var tot: [4]f64 = @splat(0);
+ for (0..16) |idx| tot[if (by == .row) idx / 4 else idx % 4] += cm[idx];
+ for (0..16) |idx| cm[idx] /= eps + tot[if (by == .row) idx / 4 else idx % 4];
+}
diff --git a/zig/tests/cuda/contract_tests.zig b/zig/tests/cuda/contract_tests.zig
index b5dcebd49a..fe0c64a282 100644
--- a/zig/tests/cuda/contract_tests.zig
+++ b/zig/tests/cuda/contract_tests.zig
@@ -25,6 +25,7 @@ const scan_k = @import("contract_scan.zig");
const lane8_k = @import("contract_ck_lane.zig");
const latent_k = @import("contract_latent.zig");
const act_round_k = @import("contract_act_round.zig");
+const f32w_k = @import("contract_glm_f32w.zig");
const round_k = @import("contract_tree_round.zig");
@@ -406,6 +407,15 @@ pub fn run(gpu: check.Gpu) !void {
for (std.enums.values(glue_k.Kind), &glues) |kind, *m| m.* = .{ .rig = .{ .gpu = gpu, .s = s }, .kind = kind, .f = &glue_fns, .glue = &fglue };
defer for (&glues) |*m| m.deinit();
for (&glues) |*m| try kernels.append(a, m.kernel());
+ var glm_glue = try cuda.Module.load(gpu.d, kk.glm_glue);
+ defer glm_glue.unload();
+ var f32ws: [6]f32w_k.F32w = undefined;
+ for (std.enums.values(f32w_k.Which), &f32ws) |w, *x| x.* = .init(.{ .gpu = gpu, .s = s }, w, try glm_glue.function(switch (w) {
+ .norm_y, .norm_sums => "tf_glm_rmsnorm_4096_f32w",
+ else => "tf_glm_hc_finish_f32w",
+ }));
+ defer for (&f32ws) |*x| x.deinit();
+ for (&f32ws) |*x| try kernels.append(a, x.kernel());
var t: g.Tally = .{};
try @import("contract_dump.zig").run(a, kernels.items);
const t0 = check.now(gpu.io);
diff --git a/zig/tests/cuda/glm_loader_gate.zig b/zig/tests/cuda/glm_loader_gate.zig
index c4e6e8d1ce..b0f1f55947 100644
--- a/zig/tests/cuda/glm_loader_gate.zig
+++ b/zig/tests/cuda/glm_loader_gate.zig
@@ -32,6 +32,13 @@ const Plan = struct {
try p.cuts.append(p.a, .{ .name = name, .lo = 0, .hi = (try p.ck.get(name)).dim(0), .to = to });
}
+ /// A hidden-size norm as the loader keeps it: float32 when stored so (FP4-Local's, D1), else bf16.
+ fn norm(p: *Plan, comptime fmt: []const u8, args: anytype) !void {
+ const name = try std.fmt.allocPrint(p.a, fmt, args);
+ const t = try p.ck.get(name);
+ try p.cuts.append(p.a, .{ .name = name, .lo = 0, .hi = t.dim(0), .to = if (t.dtype == .f32) .f32 else .bf16 });
+ }
+
fn cols(p: *Plan, comptime fmt: []const u8, args: anytype, lo: usize, hi: usize) !void {
try p.cuts.append(p.a, .{ .name = try std.fmt.allocPrint(p.a, fmt, args), .cols = true, .lo = lo, .hi = hi });
}
@@ -54,7 +61,7 @@ fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !Want
if (eql(name, "embed")) {
try p.whole(P ++ "embed_tokens.weight", .{}, .bf16);
} else if (eql(name, "norm")) {
- try p.whole(P ++ "norm.weight", .{}, .bf16);
+ try p.norm(P ++ "norm.weight", .{});
} else if (eql(name, "head")) {
try p.rows("lm_head.weight", .{}, s.vocab.lo, s.vocab.hi, .bf16);
} else if (eql(name, "head.codes") or eql(name, "head.scales")) {
@@ -79,11 +86,11 @@ fn plan(p: *Plan, c: *const Config, s: glm.shard.Share, name: []const u8) !Want
fn layer(p: *Plan, c: *const Config, s: glm.shard.Share, i: u32, what: []const u8) !Want {
const L = P ++ "layers.{d}.";
if (eql(what, "in_norm")) {
- try p.whole(L ++ "input_layernorm.weight", .{i}, .bf16);
+ try p.norm(L ++ "input_layernorm.weight", .{i});
return .cut;
}
if (eql(what, "post_norm")) {
- try p.whole(L ++ "post_attention_layernorm.weight", .{i}, .bf16);
+ try p.norm(L ++ "post_attention_layernorm.weight", .{i});
return .cut;
}
inline for (.{ "attn", "ffn" }) |site| {
diff --git a/zig/tests/cuda/glm_ref_hc.zig b/zig/tests/cuda/glm_ref_hc.zig
index bbbd26707d..b6a90137d7 100644
--- a/zig/tests/cuda/glm_ref_hc.zig
+++ b/zig/tests/cuda/glm_ref_hc.zig
@@ -15,12 +15,46 @@ const mixes = 24;
const Outs = struct { out: u64, xs: u64, post: u64, comb: u64, part: u64 };
-fn pre(r: *Rig, set: glue.Set, x: u64, w: [4]u64, rows: usize) !Outs {
+fn pre(r: *Rig, set: glue.Set, x: u64, w: [3]u64, norm: glue.Norm, rows: usize) !Outs {
const o: Outs = .{ .out = try r.zeros(rows * d * 2), .xs = try r.zeros(rows * (d / 64) * 4), .post = try r.zeros(rows * s * 4), .comb = try r.zeros(rows * s * s * 4), .part = try r.zeros(glue.hcPartBytes(rows)) };
- try glue.hcPre(set, r.s, x, w[0], w[1], w[2], w[3], o.out, o.xs, o.post, o.comb, o.part, 1e-6, 1e-6, 20, rows, d, s);
+ try glue.hcPre(set, r.s, x, w[0], w[1], w[2], norm, o.out, o.xs, o.post, o.comb, o.part, 1e-6, 1e-6, 20, rows, d, s);
return o;
}
+/// The mixing weights: fn bf16 [24][s d] in [-0.02, 0.02), base fp32 [24], scale fp32 [3].
+fn mixWeights(r: *Rig) ![3]u64 {
+ const base = try r.a.alloc(f32, mixes);
+ for (base) |*b| b.* = (r.rng.float(f32) * 2 - 1) * 0.5;
+ const scale = [3]f32{ 0.7, 0.9, 1.1 };
+ return .{ try r.dev(std.mem.sliceAsBytes(try r.bfs(mixes * wide, 0.02))), try r.dev(std.mem.sliceAsBytes(base)), try r.dev(std.mem.asBytes(&scale)) };
+}
+
+/// A float32 norm weight (FP4-Local's, D1) holding bf16 values: the _f32w kernels give the bf16 weight's bytes.
+pub fn gammas(r: *Rig, set: glue.Set) !void {
+ const rows = 70;
+ const w = try mixWeights(r);
+ const streams = try r.dev(std.mem.sliceAsBytes(try r.bfs(rows * wide, 1.0)));
+ const x = try r.dev(std.mem.sliceAsBytes(try r.bfs(rows * d, 1.0)));
+ const nw = try r.bfs(d, 1.0);
+ const g = try r.a.alloc(f32, d);
+ for (g, nw) |*o, v| o.* = @floatCast(bf(v));
+ const plain: glue.Norm = .{ .bf16 = try r.dev(std.mem.sliceAsBytes(nw)) };
+ const kept: glue.Norm = .{ .f32 = try r.dev(std.mem.sliceAsBytes(g)) };
+ var name: [128]u8 = undefined;
+ for ([_]usize{ 1, 5, rows }) |m| {
+ const a = try pre(r, set, streams, w, plain, m);
+ const b = try pre(r, set, streams, w, kept, m);
+ for ([4]u64{ a.out, a.xs, a.post, a.comb }, [4]u64{ b.out, b.xs, b.post, b.comb }, [4]usize{ d * 2, (d / 64) * 4, s * 4, s * s * 4 }, [4][]const u8{ "normed rows", "group sums", "post", "comb" }) |want, got, n, label|
+ try ref.sameRows(r, try std.fmt.bufPrint(&name, "hc pre, {d} rows: {s} from the float32 weight equal the bf16 weight's", .{ m, label }), want, got, m * n);
+ const y = [2]u64{ try r.zeros(m * d * 2), try r.zeros(m * d * 2) };
+ const xs = [2]u64{ try r.zeros(m * (d / 64) * 4), try r.zeros(m * (d / 64) * 4) };
+ for ([2]glue.Norm{ plain, kept }, y, xs) |n, yo, xo| try glue.rmsnormBy(set, r.s, x, d, n, yo, d, xo, 1e-6, m, d);
+ try ref.sameRows(r, try std.fmt.bufPrint(&name, "final norm, {d} rows: from the float32 weight equal the bf16 weight's", .{m}), y[0], y[1], m * d * 2);
+ try ref.sameRows(r, try std.fmt.bufPrint(&name, "final norm, {d} rows: group sums likewise", .{m}), xs[0], xs[1], m * (d / 64) * 4);
+ }
+ check.pass("float32 norm weights: hc_finish_f32w and rmsnorm_4096_f32w give the bf16 weight's bytes (1, 5, 70 rows)", .{});
+}
+
/// 70 rows (hc_partial) vs float64 within (1,024 + 8) 2^-24 of sum |terms|; 1, 5, 63 rows (hc_partial8) same bytes.
pub fn mixing(r: *Rig, set: glue.Set) !void {
const rows = 70;
@@ -30,9 +64,10 @@ pub fn mixing(r: *Rig, set: glue.Set) !void {
for (base) |*b| b.* = (r.rng.float(f32) * 2 - 1) * 0.5;
const scale = [3]f32{ 0.7, 0.9, 1.1 };
const nw = try r.bfs(d, 1.0);
- const w = [4]u64{ try r.dev(std.mem.sliceAsBytes(fnw)), try r.dev(std.mem.sliceAsBytes(base)), try r.dev(std.mem.asBytes(&scale)), try r.dev(std.mem.sliceAsBytes(nw)) };
+ const w = [3]u64{ try r.dev(std.mem.sliceAsBytes(fnw)), try r.dev(std.mem.sliceAsBytes(base)), try r.dev(std.mem.asBytes(&scale)) };
+ const norm: glue.Norm = .{ .bf16 = try r.dev(std.mem.sliceAsBytes(nw)) };
const dx = try r.dev(std.mem.sliceAsBytes(x));
- const all = try pre(r, set, dx, w, rows);
+ const all = try pre(r, set, dx, w, norm, rows);
const nb = glue.hc_blocks;
const kb = wide / nb;
@@ -63,7 +98,7 @@ pub fn mixing(r: *Rig, set: glue.Set) !void {
const per = [5]usize{ d * 2, (d / 64) * 4, s * 4, s * s * 4, glue.hcPartBytes(1) };
const what = [5][]const u8{ "normed rows", "group sums", "post", "comb", "partials" };
for ([_]usize{ 1, 5, 63 }) |m| {
- const o = try pre(r, set, dx, w, m);
+ const o = try pre(r, set, dx, w, norm, m);
for ([5]u64{ o.out, o.xs, o.post, o.comb, o.part }, full, per, what) |got, want, n, label|
try ref.sameRows(r, try std.fmt.bufPrint(&name, "hc pre: {d} rows' {s} equal the {d}-row call's", .{ m, label, rows }), want, got, m * n);
}
diff --git a/zig/tests/cuda/glm_ref_tests.zig b/zig/tests/cuda/glm_ref_tests.zig
index 10e8ca40f8..40f89b7d3c 100644
--- a/zig/tests/cuda/glm_ref_tests.zig
+++ b/zig/tests/cuda/glm_ref_tests.zig
@@ -204,5 +204,6 @@ pub fn run(gpu: Gpu) !void {
var ops = try cuda.Module.load(gpu.d, cuda.kernels.glm_ops);
defer ops.unload();
try @import("glm_ref_index.zig").selection(&r, set, ops);
+ try @import("glm_ref_hc.zig").gammas(&r, set); // last: the checks above keep the draws they had
check.pass("glm-ref: every projection within {e:.3} of its float64 tolerance", .{r.worst});
}
diff --git a/zig/tests/cuda/glm_synth.zig b/zig/tests/cuda/glm_synth.zig
index 2fd1dda8ec..d4641ccfc4 100644
--- a/zig/tests/cuda/glm_synth.zig
+++ b/zig/tests/cuda/glm_synth.zig
@@ -74,6 +74,13 @@ const Out = struct {
try o.put(name, .bf16, shape, std.mem.sliceAsBytes(v));
}
+ /// A float32 norm weight 1 + u 2^-9 bf16 does not hold (FP4-Local's, D1), from `g`, not the weights' stream.
+ fn norm32(o: *Out, name: []const u8, g: std.Random) !void {
+ const v = try o.a.alloc(f32, hidden);
+ for (v) |*x| x.* = 1 + g.float(f32) * 0x1p-9;
+ try o.put(name, .f32, &.{hidden}, std.mem.sliceAsBytes(v));
+ }
+
/// f32 values uniform in [lo, hi).
fn f32s(o: *Out, name: []const u8, shape: []const usize, lo: f32, hi: f32) !void {
const v = try o.a.alloc(f32, count(shape));
@@ -182,9 +189,11 @@ fn hc(o: *Out, i: usize) !void {
}
}
-fn norms(o: *Out, i: usize) !void {
+/// Layer i's norms, ones in bf16; with `g` (local) its post-attention norm float32 as FP4-Local stores it.
+fn norms(o: *Out, i: usize, g: ?std.Random) !void {
try o.bf16Const(try tensor(o.a, "layers.{d}.input_layernorm.weight", .{i}), &.{hidden}, 1.0);
- try o.bf16Const(try tensor(o.a, "layers.{d}.post_attention_layernorm.weight", .{i}), &.{hidden}, 1.0);
+ const post = try tensor(o.a, "layers.{d}.post_attention_layernorm.weight", .{i});
+ if (g) |r| try o.norm32(post, r) else try o.bf16Const(post, &.{hidden}, 1.0);
}
fn kda(o: *Out, i: usize, mx: bool) !void {
@@ -258,22 +267,24 @@ pub fn run(gpu: check.Gpu, out_dir: []const u8, form: Kind) !void {
const a = arena.allocator();
var prng = std.Random.DefaultPrng.init(53);
var o: Out = .{ .a = a, .rng = prng.random() };
+ var g32 = std.Random.DefaultPrng.init(32); // local's float32 norms: the other tensors keep the bytes they had
const fp8 = form != .nvfp4;
const mx = form == .local;
+ const g: ?std.Random = if (mx) g32.random() else null;
try o.bf16(P ++ "embed_tokens.weight", &.{ vocab, hidden }, 1.0);
- try o.bf16Const(P ++ "norm.weight", &.{hidden}, 1.0);
+ if (g) |r| try o.norm32(P ++ "norm.weight", r) else try o.bf16Const(P ++ "norm.weight", &.{hidden}, 1.0);
try o.bfOrMx("lm_head", vocab, hidden, mx);
try kda(&o, 0, mx);
try hc(&o, 0);
- try norms(&o, 0);
+ try norms(&o, 0, g);
try dense(&o, 0, mx);
try mla(&o, 1, fp8, false);
try hc(&o, 1);
- try norms(&o, 1);
+ try norms(&o, 1, g);
try moe(&o, 1, .nvfp4, fp8, false);
// the MTP layer: no hyper-connections, the draft head's norms and eh_proj; bf16, or with local FP4-Local's forms
try mla(&o, layers, false, mx);
- try norms(&o, layers);
+ try norms(&o, layers, null);
try moe(&o, layers, if (mx) .w4a16 else .bf16, false, mx);
for ([_][]const u8{ "enorm", "hnorm", "shared_head.norm" }) |m| try o.bf16Const(try tensor(a, "layers.{d}.{s}.weight", .{ layers, m }), &.{hidden}, 1.0);
try o.linear(try tensor(a, "layers.{d}.eh_proj.weight", .{layers}), hidden, 2 * hidden);
@@ -309,7 +320,7 @@ pub fn run(gpu: check.Gpu, out_dir: []const u8, form: Kind) !void {
.nvfp4 => "",
.fp8pb => ", layer 1's MLA and shared in FP8_PB_WO",
.local => ", layer 1's MLA and shared in FP8_PB_WO, layer 0's KDA q k v o, dense MLP, lm_head and the MTP layer's " ++
- "attention, indexer and shared in MXFP8, its experts W4A16_NVFP4",
+ "attention, indexer and shared in MXFP8, its experts W4A16_NVFP4, the post-attention and final norms float32",
};
check.pass("glm-synth: {d} tensors, {d} MiB, config {d} layers {d} experts vocabulary {d}{s}, dc.json in {s}", .{ o.list.items.len, bytes >> 20, layers, experts, vocab, forms, out_dir });
}
From 3903298b0825a7bdf8554b036d0d101620521194 Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 22:23:56 +0900
Subject: [PATCH 06/23] glm/cuda: format classification refuses empty and
overflowing shapes; docs say which float32 tensors stay float32
The classifier computed 2 * cols, scale columns * 16 and (n + 127) / 128 unchecked and let a zero dimension through: an
empty weight read as a zero-width format, and an oversized one overflowed (a panic with safety checks, a wrap into a
scale that fits without them). of() now refuses a zero dimension as EmptyShape, and a width, element count or block
count that overflows as ShapeOverflow (checked arithmetic), before any of them is used; csfLinear checks the same, and
the loader's refusal names the module with the reason. The two new tests fail on the previous classifier (an empty bf16
weight read as bf16, an oversized NVFP4 one panicked) and pass now.
1.1.0 rounded a float32 tensor it uploaded as bf16 to the nearest bf16. The loader now keeps float32 where the kernels
read float32 and refuses, by the tensor's name, one whose values bf16 does not hold, so a checkpoint that loaded rounded
now stops at load. docs/quantization.md says which tensors stay float32 and that. The NVFP4 and FP4-Local checkpoints
load unchanged; the EXL3/TR3 checkpoint's float32 tensors (the hyper-connections' bases and scales, the correction
bias, A_log and dt_bias: the 295,518 float32 values of the Hub's safetensors metadata) are all of the kinds kept
float32.
---
docs/quantization.md | 11 ++++++
zig/src/families/glm/cuda_formats.zig | 56 +++++++++++++++++++++------
zig/src/families/glm/cuda_weights.zig | 11 ++++--
3 files changed, 64 insertions(+), 14 deletions(-)
diff --git a/docs/quantization.md b/docs/quantization.md
index 0efc2fc3df..08ddd0e55e 100644
--- a/docs/quantization.md
+++ b/docs/quantization.md
@@ -132,6 +132,17 @@ cold 8,192-token prompt in 4.41 s against `full`'s 7.96 s (1.81x; 1.82x at 2,048
52.3 ms against 50.1 ms without drafts: decode reads the same weight bytes either way, and `checkpoint` adds the
rows' quantization.
+## GLM-5.3-Flash's float32 tensors on CUDA
+
+The GLM CUDA loader keeps float32 where its kernels read float32: the hyper-connections' bases and scales, KDA's short
+convolutions, `A_log` and `dt_bias`, the router's correction bias, and the hidden-size norm weights that
+`nvidia/GLM-5.3-Flash-FP4-Local` stores in float32 (`post_attention_layernorm` of layers 0-44 and the final norm, which
+bf16 does not hold). Any other float32 tensor it uploads as bf16 loads only when bf16 holds every value, and a value bf16
+would round is refused with the tensor's name. 1.1.0 rounded such a value to the nearest bf16 instead, so a checkpoint
+whose other float32 tensors hold values bf16 does not now stops at load where it loaded rounded before. The NVFP4 and
+FP4-Local checkpoints load unchanged, and the EXL3/TR3 checkpoint's float32 tensors are all of the kinds kept float32.
+A stored MXFP8 tensor widened to bf16 (`kv_b` of FP4-Local's MTP layer) follows the same rule.
+
## Native safetensors metadata
The shared Zig safetensors index retains up to five dimensions, including temporal patch convolutions
diff --git a/zig/src/families/glm/cuda_formats.zig b/zig/src/families/glm/cuda_formats.zig
index cb51c10749..ff63299c01 100644
--- a/zig/src/families/glm/cuda_formats.zig
+++ b/zig/src/families/glm/cuda_formats.zig
@@ -27,8 +27,11 @@ pub const Declared = union(enum) {
mixed: ?Recipe,
};
-/// The format of `stored`, or error.FormatMismatch when its tensors are not what `declared` says they are.
-pub fn of(declared: Declared, stored: Stored) error{FormatMismatch}!Format {
+/// Why `of` refuses: an empty shape, a width or block count that overflows, or tensors other than `declared` says.
+pub const Refusal = error{ EmptyShape, ShapeOverflow, FormatMismatch };
+
+/// The format of `stored`, or its Refusal; the shape is checked before any width or block count is computed from it.
+pub fn of(declared: Declared, stored: Stored) Refusal!Format {
const f = try fromTensors(stored);
const ok = switch (declared) {
.nvfp4 => f == .bf16 or f == .nvfp4,
@@ -42,26 +45,27 @@ pub fn of(declared: Declared, stored: Stored) error{FormatMismatch}!Format {
return if (ok) f else error.FormatMismatch;
}
-fn fromTensors(s: Stored) error{FormatMismatch}!Format {
+fn fromTensors(s: Stored) Refusal!Format {
const w = s.weight;
if (w.shape.len != 2) return error.FormatMismatch;
const n = w.shape[0];
+ if (n == 0 or w.shape[1] == 0) return error.EmptyShape;
+ const k = if (w.dtype == .u8) try checked(std.math.mul(usize, 2, w.shape[1])) else w.shape[1]; // two FP4 codes a byte
+ _ = try checked(std.math.mul(usize, n, k));
switch (w.dtype) {
.bf16 => return if (s.scale == null and !s.csf) .bf16 else error.FormatMismatch,
.u8 => {
if (s.csf) return if (s.scale == null) .nvfp4_csf else error.FormatMismatch;
const sc = s.scale orelse return error.FormatMismatch;
- const k = 2 * w.shape[1];
- const fits = sc.dtype == .f8_e4m3 and sc.shape.len == 2 and sc.shape[0] == n and sc.shape[1] * core.nvfp4.group == k;
- return if (fits) .nvfp4 else error.FormatMismatch;
+ if (sc.dtype != .f8_e4m3 or sc.shape.len != 2 or sc.shape[0] != n) return error.FormatMismatch;
+ return if (try checked(std.math.mul(usize, sc.shape[1], core.nvfp4.group)) == k) .nvfp4 else error.FormatMismatch;
},
.f8_e4m3 => {
const sc = s.scale orelse return error.FormatMismatch;
- const k = w.shape[1];
if (sc.dtype == .u8) return if (k % 32 == 0 and sc.shape.len == 2 and sc.shape[0] == n and sc.shape[1] == k / 32) .mxfp8 else error.FormatMismatch;
if (sc.dtype != .f32) return error.FormatMismatch;
- const rb = (n + 127) / 128;
- const cb = (k + 127) / 128;
+ const rb = try checked(std.math.add(usize, n, 127)) / 128;
+ const cb = try checked(std.math.add(usize, k, 127)) / 128;
const blocks = switch (sc.shape.len) {
2 => sc.shape[0] == rb and sc.shape[1] == cb,
4 => sc.shape[0] == rb and sc.shape[1] == 1 and sc.shape[2] == cb and sc.shape[3] == 1,
@@ -73,14 +77,20 @@ fn fromTensors(s: Stored) error{FormatMismatch}!Format {
}
}
+/// A width or count from checked arithmetic, its overflow refused as error.ShapeOverflow.
+fn checked(v: error{Overflow}!usize) error{ShapeOverflow}!usize {
+ return v catch error.ShapeOverflow;
+}
+
/// `module`'s NVFP4 linear whose block scales are csf_32x2_v1 parts, decoded into `scales` ([n, k/16] e4m3).
pub fn csfLinear(ck: *core.Checkpoint, module: []const u8, scales: []u8) !core.nvfp4.Linear {
var buf: [256]u8 = undefined;
const w = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight", .{module}));
if (w.dtype != .u8 or w.rank != 2) return error.BadNvfp4;
const n = w.shape[0];
- const k = 2 * w.shape[1];
- if (k % core.nvfp4.group != 0 or scales.len != n * k / core.nvfp4.group) return error.BadNvfp4;
+ if (n == 0 or w.shape[1] == 0) return error.EmptyShape;
+ const k = try checked(std.math.mul(usize, 2, w.shape[1]));
+ if (k % core.nvfp4.group != 0 or scales.len != try checked(std.math.mul(usize, n, k)) / core.nvfp4.group) return error.BadNvfp4;
var p: core.nvfp4_csf32.Parts = undefined;
inline for (.{ "bases", "codes", "page_bases", "offsets", "exceptions" }, .{ .u8, .u8, .u32, .u16, .u16 }) |part, dtype| {
const t = try ck.get(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_" ++ part, .{module}));
@@ -307,6 +317,30 @@ test "scales of the wrong shape or kind are refused" {
try testing.expectError(error.FormatMismatch, of(.{ .mixed = .nvfp4_csf }, .{ .weight = meta(.u8, &.{ 64, 32 }), .scale = meta(.f8_e4m3, &.{ 64, 4 }), .csf = true }));
}
+test "an empty shape is refused by name before any width or block count" {
+ try testing.expectError(error.EmptyShape, of(.{ .mixed = null }, .{ .weight = meta(.bf16, &.{ 0, 4096 }) }));
+ try testing.expectError(error.EmptyShape, of(.nvfp4, .{ .weight = meta(.u8, &.{ 0, 0 }), .scale = meta(.f8_e4m3, &.{ 0, 0 }) }));
+ try testing.expectError(error.EmptyShape, of(.{ .mixed = .nvfp4_csf }, .{ .weight = meta(.u8, &.{ 2048, 0 }), .csf = true }));
+ try testing.expectError(error.EmptyShape, of(.{ .mixed = .mxfp8 }, .{ .weight = meta(.f8_e4m3, &.{ 64, 0 }), .scale = meta(.u8, &.{ 64, 0 }) }));
+ try testing.expectError(error.EmptyShape, of(.{ .mixed = .fp8_pb_wo }, .{ .weight = meta(.f8_e4m3, &.{ 0, 0 }), .scale = meta(.f32, &.{ 0, 0 }) }));
+ var buf: [160]u8 = undefined;
+ const why = describe(&buf, "model.language_model.layers.3.mlp.experts.0.up_proj", .nvfp4, .{ .weight = meta(.u8, &.{ 0, 0 }) });
+ try testing.expectEqualStrings("model.language_model.layers.3.mlp.experts.0.up_proj: declared NVFP4, stored u8 { 0, 0 }", why);
+}
+
+test "an oversized shape is refused by checked arithmetic, never wrapped into a scale that fits" {
+ const max = std.math.maxInt(usize);
+ const half = max / 2 + 1; // two FP4 codes a byte: 2 * half wraps to 0
+ try testing.expectError(error.ShapeOverflow, of(.nvfp4, .{ .weight = meta(.u8, &.{ 1, half }), .scale = meta(.f8_e4m3, &.{ 1, 0 }) }));
+ try testing.expectError(error.ShapeOverflow, of(.{ .mixed = .nvfp4_csf }, .{ .weight = meta(.u8, &.{ 1, half }), .csf = true }));
+ const quarter = half / 2; // k = half; 3 * half / 16 scale columns times 16 wrap to it
+ try testing.expectError(error.ShapeOverflow, of(.nvfp4, .{ .weight = meta(.u8, &.{ 1, quarter }), .scale = meta(.f8_e4m3, &.{ 1, 3 * (half / 16) }) }));
+ const side = @as(usize, 1) << (@bitSizeOf(usize) / 2 + 1); // side * side elements overflow
+ try testing.expectError(error.ShapeOverflow, of(.{ .mixed = .mxfp8 }, .{ .weight = meta(.f8_e4m3, &.{ side, side }), .scale = meta(.u8, &.{ side, side / 32 }) }));
+ try testing.expectError(error.ShapeOverflow, of(.{ .mixed = .fp8_pb_wo }, .{ .weight = meta(.f8_e4m3, &.{ max, 1 }), .scale = meta(.f32, &.{ 0, 1 }) }));
+ try testing.expectError(error.ShapeOverflow, of(.{ .mixed = null }, .{ .weight = meta(.bf16, &.{ side, side }) }));
+}
+
test "csf_32x2_v1 scales read as the same NVFP4 linear as the scales stored plain, every part used" {
const gpa = testing.allocator;
const n = 64;
diff --git a/zig/src/families/glm/cuda_weights.zig b/zig/src/families/glm/cuda_weights.zig
index 7438ddcb40..2e4459f7eb 100644
--- a/zig/src/families/glm/cuda_weights.zig
+++ b/zig/src/families/glm/cuda_weights.zig
@@ -130,10 +130,15 @@ const Loader = struct {
.csf = ck.has(try std.fmt.bufPrint(&buf, "{s}.weight_scale.csf32_bases", .{module})),
};
const declared = L.declaredAs(module);
- const got: ?formats.Format = formats.of(declared, stored) catch null;
- if (got) |f| for (wants) |want| if (f == want) return f;
+ const got = formats.of(declared, stored);
+ if (got) |f| {
+ for (wants) |want| if (f == want) return f;
+ } else |_| {}
var why: [320]u8 = undefined;
- std.log.err("{s}; this loader reads it as {any}", .{ formats.describe(&why, module, declared, stored), wants });
+ const said = formats.describe(&why, module, declared, stored);
+ if (got) |_| {
+ std.log.err("{s}; this loader reads it as {any}", .{ said, wants });
+ } else |e| std.log.err("{s} ({t}); this loader reads it as {any}", .{ said, e, wants });
return error.UnreadableFormat;
}
From 784fd99479522ee934633ce0c35f17c764208eaf Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 12:33:26 +0900
Subject: [PATCH 07/23] glm/cuda: FP4-Local's image tower runs its MXFP8 and
W4A16_NVFP4 projections as stored, on the engine's qlinear
nvidia/GLM-5.3-Flash-FP4-Local stores the tower's attention qkv and proj in MXFP8 (48) and its MLPs and merger in
W4A16_NVFP4 (76), and the tower read bf16 only (UnsupportedVisionDType). It now classifies each projection its
MIXED_PRECISION recipe names with formats.of, refusing another format by name, packs it as qlinear reads it
(mxfp8.packCodes and packScales, nvfp4.packWords and packScales with the global scale), and runs it through the engine's
qlinear.Linear: the lane for MXFP8 and the prompt GEMM for NVFP4, fp32 or bf16 out. The bf16 projections and the
attention's products stay on cuBLASLt, and a checkpoint without recipes loads its bf16 tower as before.
glm_vision_ref.py reads the quantized projections as the float32 values their codes and scales hold, so
tf-cuda-test glm-vision checks the tower against it.
---
tools/glm_vision_ref.py | 30 +++-
zig/src/families/glm/cuda_images.zig | 4 +-
zig/src/families/glm/cuda_native.zig | 3 +-
zig/src/families/glm/cuda_vision.zig | 203 +++++++++++++++++++++++----
zig/tests/cuda/glm_vision_tests.zig | 6 +-
5 files changed, 210 insertions(+), 36 deletions(-)
diff --git a/tools/glm_vision_ref.py b/tools/glm_vision_ref.py
index 25c1c4234a..be33aedcd0 100644
--- a/tools/glm_vision_ref.py
+++ b/tools/glm_vision_ref.py
@@ -1,6 +1,7 @@
#!/usr/bin/env python3
"""GLM-5.3-Flash's vision tower as mlx-vlm's glm5_next (vision.py, processing.py) defines it, in torch on the CPU
-(fp32 math on the checkpoint's bf16 weights): the reference the native tower is checked against.
+(fp32 math on the checkpoint's weights: bf16, or FP4-Local's MXFP8 and NVFP4 projections as the values their codes and
+scales hold): the reference the native tower is checked against.
usage: glm_vision_ref.py [--bf16] ...
--bf16: the same tower in bfloat16 (tf-cuda-test glm-vision bounds the native tower's error by this run's)
@@ -90,6 +91,22 @@ def frames_to_patches(frames, min_tokens, max_tokens):
return np.ascontiguousarray(p.reshape(gt * gh * gw, 3 * TEMPORAL * PATCH * PATCH)), [gt, gh, gw]
+DTYPES = {"BF16": torch.bfloat16, "F32": torch.float32, "U8": torch.uint8, "F8_E4M3": torch.uint8}
+E2M1 = torch.tensor([0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, -0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0])
+
+
+def dequant(w, scale, scale2):
+ """A projection stored MXFP8 (e4m3, a UE8M0 byte a 32 inputs) or NVFP4 (two e2m1 codes a byte, the low nibble
+ first, an e4m3 scale a 16 inputs and a global scale) as the float32 values its codes and scales hold."""
+ if scale2 is None:
+ n, k = w.shape
+ return (w.view(torch.float8_e4m3fn).float().reshape(n, k // 32, 32) * torch.exp2(scale.float() - 127)[..., None]).reshape(n, k)
+ n, half = w.shape
+ codes = torch.stack([w & 15, w >> 4], -1).reshape(n, 2 * half).long()
+ s = scale.view(torch.float8_e4m3fn).float()
+ return (E2M1[codes].reshape(n, -1, 16) * s[..., None]).reshape(n, 2 * half) * scale2.float()
+
+
def load_visual(model_dir):
idx = json.loads((model_dir / "model.safetensors.index.json").read_text())["weight_map"]
files = sorted({f for k, f in idx.items() if k.startswith("model.visual.")})
@@ -102,10 +119,13 @@ def load_visual(model_dir):
if not k.startswith("model.visual."):
continue
a, b = v["data_offsets"]
- assert v["dtype"] == "BF16", (k, v["dtype"])
- t = torch.frombuffer(bytearray(raw[8 + n + a:8 + n + b]), dtype=torch.bfloat16).reshape(v["shape"])
- out[k[len("model.visual."):]] = t.float()
- return out
+ t = torch.frombuffer(bytearray(raw[8 + n + a:8 + n + b]), dtype=DTYPES[v["dtype"]]).reshape(v["shape"])
+ out[k[len("model.visual."):]] = t
+ for k in [k for k in out if k.endswith(".weight_scale")]: # FP4-Local's MXFP8 and W4A16_NVFP4 projections
+ m = k[: -len(".weight_scale")]
+ out[m + ".weight"] = dequant(out[m + ".weight"], out.pop(k), out.pop(m + ".weight_scale_2", None))
+ out.pop(m + ".input_scale", None)
+ return {k: v.float() for k, v in out.items()}
def rms(x, w, eps=1e-5):
diff --git a/zig/src/families/glm/cuda_images.zig b/zig/src/families/glm/cuda_images.zig
index 9f593da099..708979d563 100644
--- a/zig/src/families/glm/cuda_images.zig
+++ b/zig/src/families/glm/cuda_images.zig
@@ -48,7 +48,7 @@ pub const Images = struct {
pub const video = video_mod;
/// Rank 0 (`tower`) or another rank: the rows buffer, and rank 0's tower; null with `problem` when no tower.
- pub fn init(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: cuda.Stream, dir: []const u8, limits: Limits, max_rows: usize, tower: bool, problem: *[]const u8) !?*Images {
+ pub fn init(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, stream: cuda.Stream, dir: []const u8, limits: Limits, max_rows: usize, tower: bool, ql: cuda.qlinear.Linear, problem: *[]const u8) !?*Images {
var arena: std.heap.ArenaAllocator = .init(gpa);
defer arena.deinit();
const a = arena.allocator();
@@ -71,7 +71,7 @@ pub const Images = struct {
errdefer v.map.free();
v.host_map = try gpa.alloc(i32, max_rows);
errdefer gpa.free(v.host_map);
- if (tower) v.tower = try vision.Tower.load(gpa, io, d, dir);
+ if (tower) v.tower = try vision.Tower.load(gpa, io, d, dir, ql);
v.marks = .{ .image = cfg.image_token, .open = cfg.image_start, .close = cfg.image_end };
v.mark_ids = .{ cfg.image_start, cfg.image_token, cfg.image_end };
return v;
diff --git a/zig/src/families/glm/cuda_native.zig b/zig/src/families/glm/cuda_native.zig
index b7c46c17c2..cc7bbdd9d1 100644
--- a/zig/src/families/glm/cuda_native.zig
+++ b/zig/src/families/glm/cuda_native.zig
@@ -176,7 +176,8 @@ fn openRank(gpa: std.mem.Allocator, io: std.Io, ctx: *const cuda.Context, dir: [
errdefer own.e.deinit();
if (o.vision) |lim| { // before the rooms: the tower's bytes count against rank 0's
var problem: []const u8 = "";
- own.e.images = (try images_mod.Images.init(gpa, io, ctx.d, own.e.stream, dir, lim, fw.prefill_rows + 1, p.rank == 0, &problem)) orelse {
+ const ql: cuda.qlinear.Linear = .{ .lane = &own.e.m.lane, .nvfp4_prompt = &own.e.m.prompt }; // the tower's MXFP8, NVFP4
+ own.e.images = (try images_mod.Images.init(gpa, io, ctx.d, own.e.stream, dir, lim, fw.prefill_rows + 1, p.rank == 0, ql, &problem)) orelse {
std.log.err("{s}", .{problem});
return error.NoVisionTower;
};
diff --git a/zig/src/families/glm/cuda_vision.zig b/zig/src/families/glm/cuda_vision.zig
index 9a38cda1d5..d965cb2f55 100644
--- a/zig/src/families/glm/cuda_vision.zig
+++ b/zig/src/families/glm/cuda_vision.zig
@@ -3,6 +3,7 @@
const std = @import("std");
const cuda = @import("cuda");
const core = @import("core");
+const formats = @import("cuda_formats.zig");
const H: i64 = 1024; // hidden
const HEADS: i64 = 16;
@@ -18,7 +19,64 @@ const ROWS: i64 = 4096; // rows a linear's fp32 product covers (the scratch's bo
const EPS: f32 = 1e-5;
const LIMIT: f32 = 10.0;
-const Block = struct { n1: u64, n2: u64, qkv_w: u64, qkv_b: u64, qn: u64, kn: u64, proj_w: u64, proj_b: u64, g_w: u64, g_b: u64, u_w: u64, u_b: u64, d_w: u64, d_b: u64 };
+/// A projection's weight: the checkpoint's bf16 for cuBLASLt, or MXFP8 or NVFP4 as stored for qlinear's prompt rows.
+const Lin = union(enum) { bf16: u64, q: cuda.qlinear.Weight };
+
+const Block = struct { n1: u64, n2: u64, qkv_w: Lin, qkv_b: u64, qn: u64, kn: u64, proj_w: Lin, proj_b: u64, g_w: Lin, g_b: u64, u_w: Lin, u_b: u64, d_w: Lin, d_b: u64 };
+
+/// A tensor's module and the MIXED_PRECISION recipe that quantizes it.
+const Quant = struct { module: []const u8, recipe: core.modelopt.Recipe };
+
+/// `name`'s module and recipe when the checkpoint's recipes quantize it: its weight and scales are packed together.
+fn quantOf(recipes: *const ?core.modelopt.Recipes, name: []const u8) ?Quant {
+ const r = if (recipes.*) |*rs| rs else return null;
+ inline for (.{ ".weight", ".weight_scale", ".weight_scale_2", ".input_scale" }) |suffix| {
+ if (std.mem.endsWith(u8, name, suffix)) {
+ const module = name[0 .. name.len - suffix.len];
+ return if (r.of(module)) |recipe| .{ .module = module, .recipe = recipe } else null;
+ }
+ }
+ return null;
+}
+
+/// A quantized projection as packed: its stored format, shape, packed bytes and NVFP4's global scale.
+const Plan = struct { module: []const u8, format: formats.Format, n: usize, k: usize, codes: u64, scales: u64, global: f32 = 0 };
+
+/// `q`'s weight classified against its recipe (formats.of; any other format refused by name) and its packed bytes.
+fn plan(ck: *core.Checkpoint, q: Quant) !Plan {
+ var nb: [160]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&nb, "{s}.weight", .{q.module}));
+ const scale_name = try std.fmt.bufPrint(&nb, "{s}.weight_scale", .{q.module});
+ const s: ?core.checkpoint.Tensor = if (ck.has(scale_name)) try ck.get(scale_name) else null;
+ const declared: formats.Declared = .{ .mixed = q.recipe };
+ const stored: formats.Stored = .{ .weight = formats.metaOf(&w), .scale = if (s) |*x| formats.metaOf(x) else null };
+ const got = formats.of(declared, stored) catch |e| return refuse(q.module, declared, stored, e);
+ const n = w.shape[0];
+ const k_in = if (got == .nvfp4) 2 * w.shape[1] else w.shape[1];
+ if (k_in % 64 != 0) return refuse(q.module, declared, stored, error.BadVisionShape); // the packers' 64-input tiles
+ switch (got) {
+ .mxfp8 => {
+ const k = w.shape[1];
+ const npad = cuda.mxfp8.padded(n);
+ return .{ .module = q.module, .format = got, .n = n, .k = k, .codes = npad * k, .scales = npad * (k / cuda.mxfp8.group) };
+ },
+ .nvfp4 => {
+ const k = 2 * w.shape[1];
+ const npad = cuda.nvfp4.padded(n);
+ const g = try ck.get(try std.fmt.bufPrint(&nb, "{s}.weight_scale_2", .{q.module}));
+ if (g.dtype != .f32 or g.numel() != 1) return refuse(q.module, declared, stored, error.NoGlobalScale);
+ return .{ .module = q.module, .format = got, .n = n, .k = k, .codes = npad / 64 * (k / 64) * 512 * 4, .scales = npad * (k / 16), .global = std.mem.bytesToValue(f32, g.bytes[0..4]) };
+ },
+ else => return refuse(q.module, declared, stored, error.FormatMismatch),
+ }
+}
+
+/// The refusal, by the module's name: what it declares, what it stores and why the tower cannot read it.
+fn refuse(module: []const u8, declared: formats.Declared, stored: formats.Stored, e: anyerror) error{UnreadableFormat} {
+ var why: [320]u8 = undefined;
+ std.log.err("{s} ({t}); the image tower reads MXFP8 and W4A16_NVFP4 projections", .{ formats.describe(&why, module, declared, stored), e });
+ return error.UnreadableFormat;
+}
const LinKey = struct { m: u64, n: u64, k: u64, f32_out: bool };
@@ -62,12 +120,13 @@ pub const Tower = struct {
post: u64 = 0,
down_w: u64 = 0, // [4096, 2, 2, 1024]: the conv's taps in the merge block's (row, col) order, then the channels
down_b: u64 = 0,
- m_proj: u64 = 0,
+ m_proj: Lin = .{ .bf16 = 0 },
m_ln_w: u64 = 0,
m_ln_b: u64 = 0,
- m_gate: u64 = 0,
- m_up: u64 = 0,
- m_down: u64 = 0,
+ m_gate: Lin = .{ .bf16 = 0 },
+ m_up: Lin = .{ .bf16 = 0 },
+ m_down: Lin = .{ .bf16 = 0 },
+ ql: cuda.qlinear.Linear, // the engine's lane and prompt GEMM, for the projections stored MXFP8 or NVFP4
inv_freq: cuda.DeviceBuffer,
linears: std.AutoHashMap(LinKey, cuda.cublaslt.Linear),
ws: cuda.DeviceBuffer,
@@ -75,21 +134,33 @@ pub const Tower = struct {
bytes: u64 = 0,
stages: ?[]f32 = null, // checks: the fp32 stream after the patch embedding and after every block, read back
- /// The tower's tensors (model.visual.*, bf16) from the checkpoint's shards in ``dir``, in one allocation.
- pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, dir: []const u8) !Tower {
+ /// The tower's tensors (model.visual.*) in one allocation: bf16 as stored, MXFP8 and NVFP4 packed for ``ql``.
+ pub fn load(gpa: std.mem.Allocator, io: std.Io, d: *const cuda.Driver, dir: []const u8, ql: cuda.qlinear.Linear) !Tower {
var ck = try core.Checkpoint.openModelPrefix(gpa, io, dir, "model.visual.");
defer ck.close();
+ var recipes = try formats.readRecipes(gpa, io, dir);
+ defer if (recipes) |*r| r.deinit();
+ var plans: std.ArrayList(Plan) = .empty;
+ defer plans.deinit(gpa);
var total: u64 = 0;
for (ck.files.items) |f| for (f.names.keys()) |name| if (std.mem.startsWith(u8, name, "model.visual.")) {
- total += std.mem.alignForward(u64, (try ck.get(name)).bytes.len, 256);
+ if (quantOf(&recipes, name)) |q| {
+ if (!std.mem.endsWith(u8, name, ".weight")) continue; // its scales are packed with its weight
+ const p = try plan(&ck, q);
+ try plans.append(gpa, p);
+ total += std.mem.alignForward(u64, p.codes, 256) + std.mem.alignForward(u64, p.scales, 256);
+ } else total += std.mem.alignForward(u64, (try ck.get(name)).bytes.len, 256);
};
- var t: Tower = .{ .gpa = gpa, .d = d, .lt = try cuda.cublaslt.Library.open(), .mem = try cuda.DeviceBuffer.alloc(d, total), .inv_freq = try cuda.DeviceBuffer.alloc(d, 16 * 4), .linears = .init(gpa), .ws = undefined, .bytes = total };
+ var t: Tower = .{ .gpa = gpa, .d = d, .lt = try cuda.cublaslt.Library.open(), .mem = try cuda.DeviceBuffer.alloc(d, total), .ql = ql, .inv_freq = try cuda.DeviceBuffer.alloc(d, 16 * 4), .linears = .init(gpa), .ws = undefined, .bytes = total };
errdefer t.deinit();
t.ws = try cuda.DeviceBuffer.alloc(d, t.ws_len);
var at: u64 = 0;
var names = std.StringHashMap(u64).init(gpa);
defer names.deinit();
- for (ck.files.items) |f| for (f.names.keys()) |name| if (std.mem.startsWith(u8, name, "model.visual.")) {
+ var quant = std.StringHashMap(cuda.qlinear.Weight).init(gpa);
+ defer quant.deinit();
+ for (plans.items) |p| try quant.put(p.module, try t.pack(&ck, p, &at));
+ for (ck.files.items) |f| for (f.names.keys()) |name| if (std.mem.startsWith(u8, name, "model.visual.") and quantOf(&recipes, name) == null) {
const ten = try ck.get(name);
if (ten.dtype != .bf16) return error.UnsupportedVisionDType;
var bytes = ten.bytes;
@@ -120,32 +191,45 @@ pub const Tower = struct {
};
}
}.f;
+ const lin = struct {
+ fn f(m: *const std.StringHashMap(u64), qm: *const std.StringHashMap(cuda.qlinear.Weight), comptime fmt: []const u8, args: anytype) !Lin {
+ var nb: [128]u8 = undefined;
+ const module = try std.fmt.bufPrint(&nb, "model.visual." ++ fmt, args);
+ if (qm.get(module)) |q| return .{ .q = q };
+ var wb: [136]u8 = undefined;
+ const name = try std.fmt.bufPrint(&wb, "{s}.weight", .{module});
+ return .{ .bf16 = m.get(name) orelse {
+ std.log.err("glm vision: no tensor {s}", .{name});
+ return error.MissingVisionTensor;
+ } };
+ }
+ }.f;
t.patch_w = try get(&names, "patch_embed.proj.weight", .{});
t.patch_b = try get(&names, "patch_embed.proj.bias", .{});
t.post = try get(&names, "post_layernorm.weight", .{});
t.down_w = try get(&names, "downsample.weight", .{});
t.down_b = try get(&names, "downsample.bias", .{});
- t.m_proj = try get(&names, "merger.proj.weight", .{});
+ t.m_proj = try lin(&names, &quant, "merger.proj", .{});
t.m_ln_w = try get(&names, "merger.post_projection_norm.weight", .{});
t.m_ln_b = try get(&names, "merger.post_projection_norm.bias", .{});
- t.m_gate = try get(&names, "merger.gate_proj.weight", .{});
- t.m_up = try get(&names, "merger.up_proj.weight", .{});
- t.m_down = try get(&names, "merger.down_proj.weight", .{});
+ t.m_gate = try lin(&names, &quant, "merger.gate_proj", .{});
+ t.m_up = try lin(&names, &quant, "merger.up_proj", .{});
+ t.m_down = try lin(&names, &quant, "merger.down_proj", .{});
for (&t.blocks, 0..) |*bl, i| {
bl.* = .{
.n1 = try get(&names, "blocks.{d}.norm1.weight", .{i}),
.n2 = try get(&names, "blocks.{d}.norm2.weight", .{i}),
- .qkv_w = try get(&names, "blocks.{d}.attn.qkv.weight", .{i}),
+ .qkv_w = try lin(&names, &quant, "blocks.{d}.attn.qkv", .{i}),
.qkv_b = try get(&names, "blocks.{d}.attn.qkv.bias", .{i}),
.qn = try get(&names, "blocks.{d}.attn.q_norm.weight", .{i}),
.kn = try get(&names, "blocks.{d}.attn.k_norm.weight", .{i}),
- .proj_w = try get(&names, "blocks.{d}.attn.proj.weight", .{i}),
+ .proj_w = try lin(&names, &quant, "blocks.{d}.attn.proj", .{i}),
.proj_b = try get(&names, "blocks.{d}.attn.proj.bias", .{i}),
- .g_w = try get(&names, "blocks.{d}.mlp.gate_proj.weight", .{i}),
+ .g_w = try lin(&names, &quant, "blocks.{d}.mlp.gate_proj", .{i}),
.g_b = try get(&names, "blocks.{d}.mlp.gate_proj.bias", .{i}),
- .u_w = try get(&names, "blocks.{d}.mlp.up_proj.weight", .{i}),
+ .u_w = try lin(&names, &quant, "blocks.{d}.mlp.up_proj", .{i}),
.u_b = try get(&names, "blocks.{d}.mlp.up_proj.bias", .{i}),
- .d_w = try get(&names, "blocks.{d}.mlp.down_proj.weight", .{i}),
+ .d_w = try lin(&names, &quant, "blocks.{d}.mlp.down_proj", .{i}),
.d_b = try get(&names, "blocks.{d}.mlp.down_proj.bias", .{i}),
};
}
@@ -187,16 +271,65 @@ pub const Tower = struct {
try gop.value_ptr.run(x, w, d, t.ws.ptr, t.ws_len, ru.stream.handle);
}
+ /// D[m, n] = X[m, k] . W^T for a projection's weight: cuBLASLt for bf16, qlinear's prompt rows as stored otherwise.
+ fn project(t: *Tower, ru: *const Run, x: u64, w: Lin, d: u64, m: i64, n: i64, kk: i64, f32_out: bool) !void {
+ switch (w) {
+ .bf16 => |p| return t.linear(ru, x, p, d, m, n, kk, f32_out),
+ .q => |q| {
+ if (q.outputs() != n) return error.BadVisionShape;
+ const in: cuda.qlinear.Rows = .{ .x = x, .ldx = @intCast(kk) };
+ const rows: usize = @intCast(m);
+ return if (f32_out) t.ql.promptF32(ru.stream, q, in, d, rows) else t.ql.prompt(ru.stream, q, in, d, rows);
+ },
+ }
+ }
+
+ /// `p`'s weight packed into ``t.mem`` at ``at`` as qlinear reads it: MXFP8 codes and scales, or NVFP4's.
+ fn pack(t: *Tower, ck: *core.Checkpoint, p: Plan, at: *u64) !cuda.qlinear.Weight {
+ var nb: [160]u8 = undefined;
+ const w = try ck.get(try std.fmt.bufPrint(&nb, "{s}.weight", .{p.module}));
+ const s = try ck.get(try std.fmt.bufPrint(&nb, "{s}.weight_scale", .{p.module}));
+ const scales = try t.gpa.alloc(u8, p.scales);
+ defer t.gpa.free(scales);
+ switch (p.format) {
+ .mxfp8 => {
+ const codes = try t.gpa.alloc(u8, p.codes);
+ defer t.gpa.free(codes);
+ cuda.mxfp8.packCodes(codes, w.bytes, p.n, p.k);
+ cuda.mxfp8.packScales(scales, s.bytes, p.n, p.k);
+ const c = try t.put(codes, at);
+ return .{ .mxfp8 = cuda.mxfp8.weight(c, try t.put(scales, at), p.n, p.k) };
+ },
+ .nvfp4 => {
+ const words = try t.gpa.alloc(u32, p.codes / 4);
+ defer t.gpa.free(words);
+ cuda.nvfp4.packWords(words, w.bytes, p.n, p.k);
+ cuda.nvfp4.packScales(scales, s.bytes, p.n, p.k);
+ const c = try t.put(std.mem.sliceAsBytes(words), at);
+ return .{ .nvfp4 = cuda.nvfp4.weight(c, try t.put(scales, at), p.global, p.n, p.k) };
+ },
+ else => unreachable, // plan refused every other format
+ }
+ }
+
+ /// `bytes` copied into ``t.mem`` at ``at``, which moves past them to the next 256-byte boundary.
+ fn put(t: *Tower, bytes: []const u8, at: *u64) !u64 {
+ const ptr = t.mem.ptr + at.*;
+ try cuda.copy_source.synchronousCopy(t.d, ptr, bytes, std.heap.page_allocator);
+ at.* += std.mem.alignForward(u64, bytes.len, 256);
+ return ptr;
+ }
+
fn grid1(n: i64) cuda.Dim3 {
return .{ .x = @intCast(@min(std.math.divCeil(i64, n, 256) catch unreachable, 65535)) };
}
/// out = bf16(x . W^T + bias) (bias 0: none), in row chunks through ``acc``; with ``x32``, x32 += that instead.
- fn dense(t: *Tower, ru: *const Run, x: u64, w: u64, bias: u64, out: u64, x32: u64, acc: u64, m: i64, n: i64, kk: i64) !void {
+ fn dense(t: *Tower, ru: *const Run, x: u64, w: Lin, bias: u64, out: u64, x32: u64, acc: u64, m: i64, n: i64, kk: i64) !void {
var r: i64 = 0;
while (r < m) : (r += ROWS) {
const rows = @min(ROWS, m - r);
- try t.linear(ru, x + @as(u64, @intCast(r * kk * 2)), w, acc, rows, n, kk, true);
+ try t.project(ru, x + @as(u64, @intCast(r * kk * 2)), w, acc, rows, n, kk, true);
var a: cuda.Args = .{};
a.add(acc);
a.add(bias);
@@ -209,14 +342,14 @@ pub const Tower = struct {
}
/// The limited SwiGLU of two linears (biases 0: none) over x [m, kk] -> out [m, n] bf16, in row chunks.
- fn swiglu(t: *Tower, ru: *const Run, x: u64, gw: u64, gb: u64, uw: u64, ub: u64, out: u64, acc: u64, acc2: u64, m: i64, n: i64, kk: i64) !void {
+ fn swiglu(t: *Tower, ru: *const Run, x: u64, gw: Lin, gb: u64, uw: Lin, ub: u64, out: u64, acc: u64, acc2: u64, m: i64, n: i64, kk: i64) !void {
var r: i64 = 0;
const step = @divFloor(ROWS * MLP, n); // the fp32 products' rows within the scratch
while (r < m) : (r += step) {
const rows = @min(step, m - r);
const xr = x + @as(u64, @intCast(r * kk * 2));
- try t.linear(ru, xr, gw, acc, rows, n, kk, true);
- try t.linear(ru, xr, uw, acc2, rows, n, kk, true);
+ try t.project(ru, xr, gw, acc, rows, n, kk, true);
+ try t.project(ru, xr, uw, acc2, rows, n, kk, true);
var a: cuda.Args = .{};
a.add(acc);
a.add(acc2);
@@ -376,12 +509,12 @@ pub const Tower = struct {
// the post-norm, then each merge block's four patches (adjacent rows) through the 2 x 2 conv as one linear
try rmsnorm(ru, x32, t.post, xn, N, H);
const M = @divExact(N, MERGE * MERGE);
- try t.dense(ru, xn, t.down_w, t.down_b, att, 0, acc, M, OUT, 4 * H);
+ try t.dense(ru, xn, .{ .bf16 = t.down_w }, t.down_b, att, 0, acc, M, OUT, 4 * H);
// the merger: projection, LayerNorm + GELU, the limited SwiGLU to 10240 and back
var r: i64 = 0;
while (r < M) : (r += ROWS) {
const rows = @min(ROWS, M - r);
- try t.linear(ru, att + @as(u64, @intCast(r * OUT * 2)), t.m_proj, acc, rows, OUT, OUT, true);
+ try t.project(ru, att + @as(u64, @intCast(r * OUT * 2)), t.m_proj, acc, rows, OUT, OUT, true);
var a: cuda.Args = .{};
a.add(acc);
a.add(t.m_ln_w);
@@ -396,8 +529,24 @@ pub const Tower = struct {
while (r < M) : (r += step) {
const rows = @min(step, M - r);
try t.swiglu(ru, xn + @as(u64, @intCast(r * OUT * 2)), t.m_gate, 0, t.m_up, 0, big, acc, acc2, rows, PROJ, OUT);
- try t.linear(ru, big, t.m_down, out + @as(u64, @intCast(r * OUT * 2)), rows, OUT, PROJ, false);
+ try t.project(ru, big, t.m_down, out + @as(u64, @intCast(r * OUT * 2)), rows, OUT, PROJ, false);
}
try ru.stream.synchronize(); // the scratch is freed on return
}
};
+
+test "a projection its recipe quantizes is packed with its scales; biases, norms and bf16 ones read as stored" {
+ var r: ?core.modelopt.Recipes = .{ .arena = .init(std.testing.allocator) };
+ defer r.?.deinit();
+ try r.?.modules.put(r.?.arena.allocator(), "model.visual.blocks.0.attn.qkv", .mxfp8);
+ try r.?.modules.put(r.?.arena.allocator(), "model.visual.merger.down_proj", .w4a16_nvfp4);
+ const q = quantOf(&r, "model.visual.blocks.0.attn.qkv.weight_scale").?;
+ try std.testing.expectEqualStrings("model.visual.blocks.0.attn.qkv", q.module);
+ try std.testing.expectEqual(core.modelopt.Recipe.mxfp8, q.recipe);
+ try std.testing.expectEqual(core.modelopt.Recipe.w4a16_nvfp4, quantOf(&r, "model.visual.merger.down_proj.weight_scale_2").?.recipe);
+ try std.testing.expect(quantOf(&r, "model.visual.blocks.0.attn.qkv.bias") == null);
+ try std.testing.expect(quantOf(&r, "model.visual.blocks.0.norm1.weight") == null);
+ try std.testing.expect(quantOf(&r, "model.visual.patch_embed.proj.weight") == null);
+ const none: ?core.modelopt.Recipes = null;
+ try std.testing.expect(quantOf(&none, "model.visual.blocks.0.attn.qkv.weight") == null);
+}
diff --git a/zig/tests/cuda/glm_vision_tests.zig b/zig/tests/cuda/glm_vision_tests.zig
index f7d5968b54..31cf67466b 100644
--- a/zig/tests/cuda/glm_vision_tests.zig
+++ b/zig/tests/cuda/glm_vision_tests.zig
@@ -63,7 +63,11 @@ pub fn run(gpu: Gpu, model: []const u8, case: []const u8, bf16_case: []const u8,
var mod = try cuda.Module.load(gpu.d, cuda.kernels.glm_vision);
defer mod.unload();
const ru: glm.vision.Run = .{ .stream = s, .f = try glm.vision.Funcs.load(mod) };
- var t = try glm.vision.Tower.load(a, gpu.io, gpu.d, model);
+ var lane = try cuda.qmmf.Lane.load(gpu.d, (try gpu.ctx.capability()) / 10);
+ defer lane.unload();
+ var prompt = try cuda.nvfp4.Prompt.load(gpu.d);
+ defer prompt.unload();
+ var t = try glm.vision.Tower.load(a, gpu.io, gpu.d, model, .{ .lane = &lane, .nvfp4_prompt = &prompt });
defer t.deinit();
var out = try cuda.DeviceBuffer.alloc(gpu.d, @as(usize, p.tokens) * glm.vision_config.out_hidden * 2);
defer out.free();
From e1d331af68194dd845934e9696c163604a6a66d1 Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Mon, 19 Oct 2026 15:02:21 +0900
Subject: [PATCH 08/23] glm/cuda: every rank prints the load and cleanup
receipts tools/memory_receipts.py reads, and a follower frees what it owns
before it leaves
With TF_CUDA_MEMORY_RECEIPTS=1 (#865) each rank prints load_begin before NCCL and its weights, load_end once every
rank has loaded, and cleanup_begin and cleanup_end around freeing its buffers, so the wrapper records one complete
lifecycle a rank. A follower used to leave without freeing anything; after the end step it now frees its link, image
rows, engine and group as rank 0's close does, so its cleanup receipts measure that. A follower that fails still
leaves with _exit(1).
---
zig/src/families/glm/cuda_native.zig | 15 ++++++++++++++-
1 file changed, 14 insertions(+), 1 deletion(-)
diff --git a/zig/src/families/glm/cuda_native.zig b/zig/src/families/glm/cuda_native.zig
index cc7bbdd9d1..7b95ba343b 100644
--- a/zig/src/families/glm/cuda_native.zig
+++ b/zig/src/families/glm/cuda_native.zig
@@ -154,6 +154,7 @@ pub fn open(gpa: std.mem.Allocator, io: std.Io, ctx: *const cuda.Context, dir: [
/// `open` once the ranks' lines are up: NCCL, the engine and the link, then a follower's loop or rank 0's lanes.
fn openRank(gpa: std.mem.Allocator, io: std.Io, ctx: *const cuda.Context, dir: []const u8, o: Options, p: Place, set: ranks.Settings, roce_bytes: usize) !Loaded {
+ cuda.memory_receipt.emit(@intCast(p.rank), .load_begin, true); // the four phases tools/memory_receipts.py reads
const own = try gpa.create(Owned);
errdefer gpa.destroy(own);
own.gpa = gpa;
@@ -203,7 +204,8 @@ fn openRank(gpa: std.mem.Allocator, io: std.Io, ctx: *const cuda.Context, dir: [
};
// every rank loaded and agreed (cuda_ranks) before the first step; the digest names what they agreed on
if (p.world > 1) try cuda.tp_watch.ready(io, own.e.checkpoint_digest);
- if (p.rank != 0) { // a follower never serves: it runs rank 0's steps, then leaves the process
+ cuda.memory_receipt.emit(@intCast(p.rank), .load_end, false);
+ if (p.rank != 0) { // a follower never serves: it runs rank 0's steps, frees what it owns, then leaves the process
follow.follow(own.e, &own.link) catch |err| {
std.log.err("rank {d}: following rank 0 stopped ({s})", .{ p.rank, @errorName(err) });
cuda.tp_watch.fail(@errorName(err)); // rank 0 and the others stop with it
@@ -211,6 +213,9 @@ fn openRank(gpa: std.mem.Allocator, io: std.Io, ctx: *const cuda.Context, dir: [
};
cuda.tp_watch.ended();
std.log.info("rank {d}: rank 0 ended; leaving", .{p.rank});
+ cuda.memory_receipt.emit(@intCast(p.rank), .cleanup_begin, false);
+ release(own);
+ cuda.memory_receipt.emit(@intCast(p.rank), .cleanup_end, false);
std.process.exit(0);
}
// Rank 0 alone reads these: TF_GLM_PROMPT_STEPS=0 passes prompts whole, TF_GLM_SHARED=0 runs windows alone.
@@ -299,9 +304,17 @@ pub fn snap_drop(ptr: *anyopaque, saved: *anyopaque) void {
fn close(ptr: *anyopaque) void {
const own: *Owned = @ptrCast(@alignCast(ptr));
+ const rank: u32 = @intCast(own.group.rank);
+ cuda.memory_receipt.emit(rank, .cleanup_begin, false);
cuda.tp_watch.ended(); // the other ranks leave on the end step, so their lines close on purpose from here
own.lanes.x.end() catch {};
own.lanes.deinit();
+ release(own);
+ cuda.memory_receipt.emit(rank, .cleanup_end, false);
+}
+
+/// What a rank owns, freed by rank 0's close and by a follower before it leaves.
+fn release(own: *Owned) void {
own.link.deinit();
if (own.e.images) |v| v.deinit();
own.e.deinit();
From c837802b7d9040d4fed26308ad0b15f4cbca74f8 Mon Sep 17 00:00:00 2001
From: Bizuayeu <30091157+Bizuayeu@users.noreply.github.com>
Date: Tue, 20 Oct 2026 10:34:24 +0900
Subject: [PATCH 09/23] docs: the GLM-5.3-Flash FP4-Local receipt, taken on
80517fc3 at TP=2 and TP=3
The loader gate on every layer, the lanes, served exactness, media, structured output and tools, prompt logprobs against a BF16 reference, memory, the kernel contract and glm-ref, the no-GPU CI steps and speed against plain v1.1.1. A receipt-only commit: the tested implementation is 80517fc3.
---
docs/receipts/glm-fp4local-80517fc3/README.md | 91 +
.../receipts/glm-fp4local-80517fc3/SHA256SUMS | 309 +
.../glm-fp4local-80517fc3/binaries.txt | 14 +
.../ci/base111-fresh-test.log | 843 +
.../glm-fp4local-80517fc3/ci/ci-summary.txt | 26 +
.../glm-fp4local-80517fc3/ci/diff-check.txt | 0
.../ci/flashnext-served.log | 5 +
.../receipts/glm-fp4local-80517fc3/ci/fmt.log | 0
.../ci/fresh-test-golden.log | 82 +
.../ci/fresh-test-kernel-contract.log | 12 +
.../glm-fp4local-80517fc3/ci/fresh-test.log | 750 +
.../ci/fuzz-cortheon.log | 4 +
.../glm-fp4local-80517fc3/ci/lean-base111.txt | 445 +
.../ci/lean-head-111.txt | 445 +
.../glm-fp4local-80517fc3/ci/lean.log | 4 +
.../ci/split/1-461d4473.log | 466 +
.../ci/split/2-26894027.log | 467 +
.../ci/split/3-251ef9ea.log | 453 +
.../ci/split/4-0eac5947.log | 467 +
.../ci/split/5-3c341bae.log | 466 +
.../ci/split/6-4c1a57ed.log | 453 +
.../ci/split/7-9e1d20f7.log | 467 +
.../glm-fp4local-80517fc3/ci/test-golden.log | 82 +
.../ci/test-kernel-contract.log | 12 +
.../glm-fp4local-80517fc3/ci/test.log | 441 +
.../receipts/glm-fp4local-80517fc3/commit.txt | 12 +
.../lib/libtfgrammar-source.txt | 2 +
.../glm-fp4local-80517fc3/memory/summary.json | 1901 +
.../memory/tp2-ex-fl-rank0.json | 41634 +++++++++++++++
.../memory/tp2-ex-fl-rank1.json | 43159 ++++++++++++++++
.../redaction-report.json | 314 +
docs/receipts/glm-fp4local-80517fc3/runs.log | 114 +
.../s5/bf16-ref-b2.pt.json | 30 +
.../s5/check-plp-lp-fl.log | 10 +
.../s5/check-plp-lp-nv.log | 10 +
.../glm-fp4local-80517fc3/s5/plp-sha256.txt | 2 +
.../glm-fp4local-80517fc3/s5/s5-compare.json | 50 +
.../glm-fp4local-80517fc3/s9/compare-fl.txt | 24 +
.../glm-fp4local-80517fc3/s9/compare-nv.txt | 24 +
.../single-v1.1.1/fatbins.log | 473 +
.../single-v1.1.1/glm-ref-80517fc3.log | 26 +
.../single-v1.1.1/glm-ref-b10231b9.log | 25 +
.../single-v1.1.1/kernel-contract.log | 2637 +
.../single-v1.1.1/limits.log | 6586 +++
.../single-v1.1.1/rc-glm-ref.txt | 2 +
.../single-v1.1.1/rc-kernel-contract.txt | 3 +
.../glm-fp4local-80517fc3/single/fatbins.log | 473 +
.../single/glm-ref-80517fc3.log | 26 +
.../single/glm-ref-b10231b9.log | 25 +
.../single/kernel-contract.log | 2663 +
.../glm-fp4local-80517fc3/single/limits.log | 6598 +++
.../single/rc-glm-ref.txt | 2 +
.../single/rc-kernel-contract.txt | 3 +
.../glm-fp4local-80517fc3/tools/batch_http.py | 126 +
.../glm-fp4local-80517fc3/tools/dc.json | 1 +
.../tools/golden_http.py | 82 +
.../glm-fp4local-80517fc3/tools/media_http.py | 56 +
.../tools/media_resume_http.py | 92 +
.../glm-fp4local-80517fc3/tools/mkprompts.py | 16 +
.../tools/openai_aligned.py | 71 +
.../tools/resume_http.py | 84 +
.../tools/s5_bf16_ref.py | 80 +
.../glm-fp4local-80517fc3/tools/s5_compare.py | 73 +
.../glm-fp4local-80517fc3/tools/s5_export.py | 29 +
.../tools/s5_prompt_lp.py | 64 +
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diff --git a/docs/receipts/glm-fp4local-80517fc3/README.md b/docs/receipts/glm-fp4local-80517fc3/README.md
new file mode 100644
index 0000000000..9ff6b51139
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/README.md
@@ -0,0 +1,91 @@
+# GLM-5.3-Flash FP4-Local on CUDA: final-source receipt
+
+Tested implementation: `80517fc36ea6ee9183c5237ae92b1abd70619f75` (commit.txt has the tree and the eight commits).
+Base: `v1.1.1` (`b10231b94f8adf3d4132acebdc0a1ac87e53a838`); baseline binaries are plain v1.1.1 built the same way (`bin-b10231b9`).
+Test date: 2026-10-20. Platform: NVIDIA DGX Spark (GB10, sm_121, 128 GB shared memory), two at TP=2 over a direct
+RoCE link (two rails), three at TP=3 over a ring of direct links. Containers pinned to the efficiency cores
+(`--cpuset-cpus=0-4,10-14`, as 1.1.1's runbook in #1033).
+Toolchain: Zig 0.17.0, nvcc 13.0 (V13.0.88) for sm_121; runtime NCCL 2.30.7. binaries.txt has every binary's sha256.
+Checkpoints: `nvidia/GLM-5.3-Flash-FP4-Local` at `76348ddb2be2e35e39ecfc00e75dc07703f4f3d5`,
+`nvidia/GLM-5.3-Flash-NVFP4` at `423acf37583782c51c142d145aef733d72943d93`.
+Grammar: `lib/libtfgrammar.so` from the v1.1.1 release archive (a source build makes no `lib/`); lib/ has its sha256.
+
+## Scope
+
+This qualifies FP4-Local loaded as stored and served at TP=2 and TP=3: the loader gate on every layer, the lane
+invariants, served exactness (drafted = `"draft": false`, resumed = cold, concurrent = alone, prompts admitted
+together = alone), media, structured output and tool calls, prompt logprobs against a BF16 reference, per-rank load
+and peak memory, the kernel contract and glm-ref, the no-GPU CI steps, NVFP4 before and after against plain v1.1.1,
+and FP4-Local against NVFP4 on matched inputs. It does not qualify Metal, or a comparison with vLLM.
+
+## Results
+
+| Check | Result |
+| --- | --- |
+| Loader gate, TP=2, every layer (`tp2/gate/all-layers-*`) | Ranks 0 and 1: 894 uploads as the checkpoint cut, 486 as the host packers make them, 0 differ (3 left out: the draft head's GPU-quantized tiles) |
+| Loader gate, TP=3, every layer (`tp3/gate/all-layers-*`) | Ranks 0, 1 and 2: 894 and 486, 0 differ (TP=3's FP8 shares start inside a 128 block: #1024's repacking from the origin, per rank) |
+| Loader gate, six layers (0, 3, 4, 43, 44, MTP) | TP=2 and TP=3: 115 and 72, 0 differ |
+| `glm-lanes-cover`, `glm-lanes-concurrent`, `glm-shared-check` | TP=2 and TP=3: 40/40, 38/38, and 108 rows of 5 streams over 12 rounds equal to their lone twins byte for byte |
+| Golden ids (`tp2/serve/ex-*`) | NVFP4 on this head equals plain v1.1.1 on all three prompts (count `6faafd98`, prose `b82116bd`, code `ddd42119`), drafts and tokens per round included; FP4-Local drafted = `"draft": false` on all three |
+| Concurrent (`bench_concurrent.py --alone --serial`, levels 1, 2, 4) | 12 cells a checkpoint at TP=2 and TP=3, 0 unequal, 0 failed |
+| Resume (a 6,593-token chat resumed from its kept state, `"draft": false`, two at once) | Every reply equals the cold one, both checkpoints, TP=2 and TP=3 |
+| Prompts sent together beside a decode (`batch`) | Four fresh prompts, each equal to its alone run, both checkpoints, TP=2 and TP=3 (GLM fills them in chunks between rounds by default) |
+| 1.1.1's batched prompt pass (`bt-*`, `TF_GLM_PROMPT_STEPS=0`) | Three fresh prompts shared one pass (first tokens within 0.3 to 0.5 ms, equal `prefill_seconds`), each equal to its alone run: plain v1.1.1 and this head on NVFP4, this head on FP4-Local (its MXFP8 `lm_head` through `cuda_shared.heads`) |
+| Media, media resume (`--vision`, `assets/tensorfold-hero.png`) | Drafted = `"draft": false`, repeated, three at once = alone; resumed past the image = cold: FP4-Local and NVFP4 at TP=2, FP4-Local at TP=3, plain v1.1.1 for resume |
+| Structured output and tools (`s9/`, #1017's and #1018's items) | 26/26 on each checkpoint with drafts: schemas with thinking off and on, a schema plain already meets, `"draft": false`, seeded sampling twice, nested, resume from cache, required, named, parallel, single, nested and empty tool calls each again with `"draft": false`, mixed streams and prompts before and behind a constrained one; `--no-drafts` serves: 22/22; `compare`: all 22 shared greedy replies equal, and the resumed reply equals a cold one |
+| Prompt logprobs against BF16 (`s5/`, 144,285 positions) | Mean NLL: BF16 1.00691, FP4-Local 1.01811 (+1.11%), NVFP4 1.02069 (+1.37%); top-1 equal to BF16's 0.9282 and 0.9268; KL over BF16's top 20 (an approximation) 0.0528 and 0.0537 |
+| Memory (`memory/summary.json`, `memory_receipts.py`) | Every rank's load and cleanup receipts recorded; TP=2 FP4-Local 90.27 GiB at load end, 92.96 GiB peak, 0.00 after cleanup; TP=3 63.01, 62.88 and 57.74 GiB at load end |
+| TP=3 rank loss (`tp3/serve/sp3-fl`) | Rank 2 killed: ranks 0 and 1 left 7 s later |
+| kernel contract (`single/`) | `SUMMARY 91 kernels: 91 pass gates 1-4, 0 fail, 0 known findings; 7 controls: 7 red as planted, 0 missed; 1533 limit problems`; plain v1.1.1 (`single-v1.1.1/`): 85 kernels, 85 pass, the same 1533 |
+| kernel-contract-limits, sm_80 to sm_121 | 877 problems, the same 877 on plain v1.1.1; this branch's two kernels (`tf_glm_rmsnorm_4096_f32w`, `tf_glm_hc_finish_f32w`) pass on all five (3000 PASS lines against 2990) |
+| glm-ref | 12 pass, every RESULT line equal to plain v1.1.1's |
+| No-GPU CI (`ci/`) | See ci/ci-summary.txt: every step passes; with fresh caches upstream's `cuda_cost_identity` mtime test fails here, on plain v1.1.1 too |
+
+Speed, medians (`tp2/serve/sp*`, `tp3/serve/sp3-*`); at TP=2 each binary and checkpoint served twice, alternating
+(both values given), at TP=3 once:
+
+| TP=2 | plain v1.1.1 | this head |
+| --- | --- | --- |
+| NVFP4, drafted decode, fibonacci-raw t=0 / t=1 (tok/s) | 56.2, 56.1 / 36.0, 35.9 | 56.1, 56.0 / 36.0, 35.9 |
+| NVFP4, drafted decode, gpu-chat-no-think t=0 / t=1 (tok/s) | 30.6, 30.7 / 28.3, 28.3 | 30.6, 30.6 / 28.4, 28.4 |
+| NVFP4, cold prefill of 18,789 tokens (s) | 14.19, 14.13 | 14.14, 14.10 |
+
+| Matched inputs, this head | NVFP4 | FP4-Local |
+| --- | --- | --- |
+| TP=2, `"draft": false` decode, fibonacci-raw and gpu-chat-no-think (tok/s) | 18.3, 18.3 | 24.5 to 24.6 |
+| TP=2, cold prefill of 9,600 and 38,400 token ids (s) | 7.22 and 28.46, 7.21 and 28.35 | 7.22 and 28.52, 7.24 and 28.53 |
+| TP=3, `"draft": false` decode (tok/s) | 21.8 to 21.9 | 31.4 to 31.6 |
+| TP=3, cold prefill of 9,600 and 38,400 token ids (s) | 6.33 and 25.06 | 5.93 and 23.44 |
+
+## Reproduction
+
+Every serve: `tensorfold-native serve CKPT --name local-model --parallel 4 --context 40960 --port 8095`, drafts on,
+one rank a host (`TF_GLM_RANK`, `TF_GLM_WORLD`), each host's page cache of both checkpoints dropped first. The checks
+are tools/: `golden_http.py` on dc.json, upstream's `bench_concurrent.py`, `bench_openai.py` and `bench_cache.py`,
+`resume_http.py`, `batch_http.py` (`one-pass` for `bt-*`), `media_http.py`, `media_resume_http.py`, `x9_live.py`
+(and its `compare`), `s5_prompt_lp.py` (prompt_logprobs 20 on B-2's 128 sequences) with `s5_compare.py` against
+`s5_bf16_ref.py`'s reference. `mkprompts.py` makes the matched token-id files. runs.log has one line a check.
+
+## Remaining coverage limits
+
+- TP=3 needs `TF_GLM_MM=0` on every rank: the default glm_mm refuses KDA's input stack there with `BadShape`
+ (#1067; still so on 1.1.1, `tp3/serve/smoke3-mm1`), so TP=3 has no batched prompt pass (it needs glm_mm's tiles).
+- TP=3's ranks meet through a local bootstrap commit that is not part of this branch (`tp3/bootstrap.patch`); the
+ gate and the lanes at TP=3 ran this branch's own binaries.
+- 1.1.1's batched prompt pass runs only with `TF_GLM_PROMPT_STEPS=0` on rank 0; `bt-*` set it, the other serves ran
+ the default.
+- The KL is over BF16's top 20 only, and both sides' logits are bf16, so best tokens tie; `s5-compare.json` also
+ gives top-1 equal within ties.
+- runs.log's FAIL lines: `smoke3-mm1` is the #1067 probe above; the first `x9-fl` ran before `libtfgrammar.so` was
+ in place (HTTP 400 naming it) and its retake is the one published; `single` is the limits line above, on this head
+ and on plain v1.1.1 alike.
+- Metal and MLX were not run (no Mac). No vLLM comparison is claimed.
+
+## Publication handling
+
+Machine names, internal addresses, home paths and working-session names replaced, CRLF line ends made LF and trailing blanks dropped; result
+values, token ids and token hashes kept.
+`redaction-report.json` counts the replacements a file. The memory wrapper's receipts are raw for TP=2 `ex-fl` only;
+`memory/summary.json` summarizes every receipt with its original's sha256. The prompt-logprob rows (87 MB a
+checkpoint) are not included; `s5/plp-sha256.txt` has their sha256. SHA256SUMS pins every published file except
+itself.
diff --git a/docs/receipts/glm-fp4local-80517fc3/SHA256SUMS b/docs/receipts/glm-fp4local-80517fc3/SHA256SUMS
new file mode 100644
index 0000000000..22ca0ebabd
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/SHA256SUMS
@@ -0,0 +1,309 @@
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diff --git a/docs/receipts/glm-fp4local-80517fc3/binaries.txt b/docs/receipts/glm-fp4local-80517fc3/binaries.txt
new file mode 100644
index 0000000000..21aa8b7884
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/binaries.txt
@@ -0,0 +1,14 @@
+bin-b10231b9: b10231b9 built from src-b10231b9 with Zig 0.17.0 and nvcc 13.0 for sm_121
+4acb451d00a1df581fa36139093e9516eff1ad971ff377b1810705e070b6e3d4 tf-cuda-test
+8599f3e6b092403149549e6bf867083c379d348ad96e4cfab0072a9688246277 tensorfold
+2632ac57a0c4ff912c6a640ab8db82c416bdba42cce2f9876273d4e99b7facfc tensorfold-native
+
+bin-80517fc3: 80517fc3 built from src-80517fc3 with Zig 0.17.0 and nvcc 13.0 for sm_121
+68a07d82b7e26c37d41a3ee7c85e1a53775192784f23f555ee5cc5d25624c442 tf-cuda-test
+f61721fbf38e0cf724fa1c3eee678527cbbe61c88fdc48bd01ae8a731cc630d9 tensorfold
+9008f7983ade52021179d07640728b671bd2944a6af8ce841e873adce73abddc tensorfold-native
+
+bin-bf07ac07: bf07ac07 built from src-bf07ac07 with Zig 0.17.0 and nvcc 13.0 for sm_121
+f7ed1e7fb75b18c52bff8b0c4b64a15e61746f16062241801260c72410693630 tf-cuda-test
+5617159f17300e5d0694fca4fb329c41a67e7afc216835b656b12807c5c9479a tensorfold
+2e9c18bcedef2d4250bd25c4e370fb4a0a273de7ee8d3138a546009498bf5721 tensorfold-native
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/base111-fresh-test.log b/docs/receipts/glm-fp4local-80517fc3/ci/base111-fresh-test.log
new file mode 100644
index 0000000000..009603c49d
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/base111-fresh-test.log
@@ -0,0 +1,843 @@
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/995264e9d3cae44baa65c27cb330486b/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/03b9ac00a14ff737f05d030849c0f1b9/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/5ec0f8e5b1ca933b518023f3b609e86b/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/1f41760a27965dd5af5090477ce0a80b/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/fbd59efbbaaf14c1cfb18d69c181078d/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/cc818bedabe6a1bddbe83ebed9830360/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/7121d48644cc44393801c483a23b9817/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/4722d4c3a3570a53922410dba0fe6849/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/b2b9577220438c2df26bc8e9cafd7ad3/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/6a3a515d6048b6f5af1b3afa50753245/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/d3acf2be6c687bc2368dbb817764e4a3/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/a4e80a1ecdd1fb947c6385172dcee05e/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/base111-fresh/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+failed command: ./.zig-cache/o/8b452f41deac4e2c411434c043fafd81/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/93d3cfe56814536516a614c10fc9f564/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/52073982a17aafb419f0955cef4736cb/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/b966a33752f3b46ee2c661998aa0a229/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/241654e7ca5be6ac604bf9499e189e86/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/base111-fresh/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/base111-fresh/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/base111-fresh/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/base111-fresh/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/8a5206918bde986fa8871f17dafc1259/test --cache-dir=./.zig-cache --seed=0x246c4194 --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2954/2973 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-kernel-symbols-host success
+| +- run test 3 pass (3 total) 4s MaxRSS:78M
+| +- compile test debug native success 1s MaxRSS:145M
++- run test 37 pass (37 total) 83ms MaxRSS:13M
+| +- compile test debug native success 923ms MaxRSS:156M
++- run test 7 pass (7 total) 1s MaxRSS:69M
+| +- compile test debug native success 1s MaxRSS:154M
++- run test 9 pass (9 total) 404ms MaxRSS:7M
+| +- compile test debug native success 1s MaxRSS:147M
+| +- options success
+| +- compile exe digest-writer debug native success 1s MaxRSS:140M
++- run test 6 pass (6 total) 20ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:145M
++- test-flashnext-batch-host success
+| +- run test 11 pass (11 total) 4s MaxRSS:34M
+| +- compile test debug native success 5s MaxRSS:195M
+| +- WriteFile flashnext-test-empty.zig success
++- run test 2 pass (2 total) 74ms MaxRSS:6M
+| +- compile test debug native success 5s MaxRSS:146M
++- run test 4 pass (4 total) 35ms MaxRSS:5M
+| +- compile test debug native success 5s MaxRSS:260M
++- run test 4 pass (4 total) 31ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:147M
++- run test 3 pass (3 total) 17ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:145M
++- run test 23 pass (23 total) 52ms MaxRSS:6M
+| +- compile test debug native success 5s MaxRSS:150M
++- run test 27 pass (27 total) 5s MaxRSS:24M
+| +- compile test debug native success 1s MaxRSS:163M
++- run test 56 pass, 1 skip (57 total) 40s MaxRSS:50M
+| +- compile test debug native success 5s MaxRSS:165M
+| | +- options success
+| +- compile obj tp_host_compile debug x86_64-linux-gnu success 536ms MaxRSS:117M
+| +- options cached
++- test-cuda-host transitive failure
+| +- run test 1 pass (1 total) 20ms MaxRSS:12M
+| | +- compile test debug native success 818ms MaxRSS:159M
+| +- run test 1 pass (1 total) 20ms MaxRSS:10M
+| | +- compile test debug native success 925ms MaxRSS:146M
+| +- run test 10 pass (10 total) 40ms MaxRSS:12M
+| | +- compile test debug native success 1s MaxRSS:156M
+| +- run test 2 pass (2 total) 43ms MaxRSS:12M
+| | +- compile test debug native success 918ms MaxRSS:147M
+| +- run exe runtime-discovery-exit success 42ms
+| | +- compile exe runtime-discovery-exit debug native success 1s MaxRSS:141M
+| | +- options success
+| | +- compile lib runtime-discovery-mock debug native success 22s MaxRSS:343M
+| +- run exe rpc-warning-check success 2ms
+| | +- compile exe rpc-warning-check debug native success 1s MaxRSS:140M
+| | +- options success
+| | +- compile lib rpc-mock-complete debug native success 237ms MaxRSS:63M
+| | +- compile lib rpc-mock-abi debug native success 316ms MaxRSS:62M
+| | +- compile lib rpc-mock-wait debug native success 208ms MaxRSS:62M
+| | +- compile lib rpc-mock-store debug native success 280ms MaxRSS:62M
+| | +- compile lib rpc-mock-wrong debug native success 264ms MaxRSS:63M
+| +- run test 11 pass (11 total) 18ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:149M
+| | +- options (+5 more reused dependencies)
+| +- run test 1 pass (1 total) 28ms MaxRSS:12M
+| | +- compile test debug native success 1s MaxRSS:146M
+| +- run test 30 pass (30 total) 74ms MaxRSS:11M
+| | +- compile test debug native success 2s MaxRSS:231M
+| | +- WriteFile h3-compile-only.fatbin success
+| | +- options cached
+| +- run test 3 pass (3 total) 1s MaxRSS:74M
+| | +- compile test debug native success 1s MaxRSS:156M
+| +- run test 3 pass (3 total) 28ms MaxRSS:9M
+| | +- compile test debug native success 8s MaxRSS:165M
+| | +- WriteFile attn_train.fatbin success
+| | +- WriteFile dsv41_exl3_experts.fatbin success
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
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+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
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+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- options success
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- options success
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| +- run test 10 pass (10 total) 18ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:166M
+| | +- WriteFile draft_ids.txt success
+| | +- options (reused)
+| +- run test 10 pass (10 total) 606ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:192M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 9 pass (9 total) 154ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:176M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- compile obj exl3_host_compile debug native success 895ms MaxRSS:118M
+| | +- options (reused)
+| +- run test 6 pass (6 total) 29ms MaxRSS:6M
+| | +- compile test debug native success 1s MaxRSS:168M
+| | +- options (reused)
+| +- run test 6 pass (6 total) 325ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:161M
+| | +- options (reused)
+| +- run test 8 pass (8 total) 36ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:155M
+| +- run test 3 pass (3 total) 215ms MaxRSS:13M
+| | +- compile test debug native success 1s MaxRSS:155M
+| +- run test 8 pass (8 total) 32ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:156M
+| +- run test 4 pass (4 total) 30ms MaxRSS:7M
+| | +- compile test debug native success 1s MaxRSS:159M
+| | +- options (reused)
+| +- run test 1 pass (1 total) 78ms MaxRSS:7M
+| | +- compile test debug native success 1s MaxRSS:159M
+| | +- options (reused)
+| +- run test 4 pass (4 total) 3ms MaxRSS:11M
+| | +- compile test fast native success 34s MaxRSS:434M
+| | +- options (reused)
+| +- run test 1 pass (1 total) 13ms MaxRSS:7M
+| | +- compile test fast native success 24s MaxRSS:440M
+| | +- options (reused)
+| +- compile obj bolt_host_compile debug x86_64-linux-gnu success 1s MaxRSS:140M
+| | +- options cached
+| | +- WriteFile draft_ids.txt (reused)
+| +- run test 26 pass (26 total) 5s MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:178M
+| | +- options (reused)
+| +- run test 4 pass (4 total) 15ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:168M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 3 pass (3 total) 15s MaxRSS:42M
+| | +- compile test debug native success 1s MaxRSS:213M
+| | +- options (reused)
+| | +- WriteFile draft_ids.txt (reused)
+| +- run exe qwen-cost-key-process success 9s
+| | +- compile exe qwen-cost-key-process debug native success 1s MaxRSS:173M
+| | | +- WriteFile draft_ids.txt (reused)
+| | | +- options (reused)
+| | +- WriteFile target/config.json success
+| +- run test 0 pass, 1 fail (1 total)
+| | +- compile test debug native success 1s MaxRSS:161M
+| +- compile obj drama_cuda_native_host debug native success 1s MaxRSS:131M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 21ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:146M
+| +- run test 502 pass, 4 fail (506 total)
+| | +- compile test debug native success 6s MaxRSS:434M
+| +- run test 5 pass (5 total) 41ms MaxRSS:12M
+| | +- compile test debug native success 1s MaxRSS:150M
+| +- run test 208 pass (208 total) 349ms MaxRSS:10M
+| | +- compile test debug native success 2s MaxRSS:181M
+| | +- options (reused)
+| +- run test 319 pass (319 total) 13s MaxRSS:135M
+| | +- compile test debug native success 7s MaxRSS:228M
+| +- run test 178 pass (178 total) 8s MaxRSS:45M
+| | +- compile test debug native success 2s MaxRSS:185M
+| +- run test 74 pass, 1 skip (75 total) 240ms MaxRSS:15M
+| | +- compile test debug native success 2s MaxRSS:217M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 58 pass, 2 skip (60 total) 12s MaxRSS:27M
+| | +- compile test debug native success 1s MaxRSS:184M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 12 pass (12 total) 41ms MaxRSS:6M
+| | +- compile test debug native success 1s MaxRSS:174M
+| | +- options (reused)
+| +- run test 92 pass (92 total) 1s MaxRSS:78M
+| | +- compile test debug native success 3s MaxRSS:206M
+| | +- options (reused)
+| +- run test 13 pass (13 total) 9s MaxRSS:13M
+| | +- compile test debug native success 2s MaxRSS:184M
+| | +- options (reused)
+| +- run test 29 pass (29 total) 11s MaxRSS:1G
+| | +- compile test debug native success 2s MaxRSS:175M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 32ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:150M
+| +- run test 20 pass (20 total) 221ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:164M
+| +- run test 217 pass (217 total) 8s MaxRSS:45M
+| | +- compile test debug native success 2s MaxRSS:207M
+| +- run test 65 pass (65 total) 221ms MaxRSS:33M
+| | +- compile test debug native success 3s MaxRSS:280M
+| | +- options (reused)
+| | +- WriteFile draft_ids.txt (reused)
+| +- run test 3 pass (3 total) 18ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:144M
+| +- run test 2 pass (2 total) 33ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:208M
+| | +- options (reused)
+| +- run test 56 pass (56 total) 128ms MaxRSS:69M
+| | +- compile test debug native success 2s MaxRSS:187M
+| | +- options (reused)
+| +- run test 15 pass, 1 skip (16 total) 64ms MaxRSS:8M
+| | +- compile test debug native success 2s MaxRSS:175M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 21ms MaxRSS:10M
+| | +- compile test debug native success 995ms MaxRSS:146M
+| +- run test 25 pass (25 total) 11s MaxRSS:12M
+| | +- compile test debug native success 1s MaxRSS:168M
+| +- run test 55 pass (55 total) 3s MaxRSS:14M
+| | +- compile test debug native success 2s MaxRSS:178M
+| +- run test 44 pass, 1 skip (45 total) 3s MaxRSS:11M
+| | +- compile test debug native success 2s MaxRSS:172M
+| +- run test 1 pass (1 total) 13ms MaxRSS:10M
+| +- compile test debug native success 1s MaxRSS:179M
+| +- options success
+| +- options (reused)
+| +- WriteFile draft_ids.txt (reused)
+| +- WriteFile exlr8_alias.zig success
++- run test 4 pass (4 total) 19ms MaxRSS:5M
+| +- compile test debug native success 1s MaxRSS:145M
+| +- options cached
++- run test 13 pass, 1 skip (14 total) 141ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:163M
+| +- options (reused)
++- run test 3 pass (3 total) 36ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:156M
+| +- options (reused)
++- run test 1 pass (1 total) 35ms MaxRSS:6M
+| +- compile test debug native success 5s MaxRSS:145M
++- run test 217 pass (217 total) 4s MaxRSS:15M
+| +- compile test fast native success 1m MaxRSS:976M
++- test-kv-pool success
+| +- run test 9 pass (9 total) 23ms MaxRSS:13M
+| | +- compile test debug native success 1s MaxRSS:146M
+| +- run test 6 pass (6 total) 1s MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:174M
++- run test 51 pass (51 total) 65ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:168M
+| +- options success
+| | +- compile lib hip-mock-success debug native success 311ms MaxRSS:62M
+| | +- compile lib hip-mock-failed debug native success 22s MaxRSS:395M
+| | +- compile lib hip-mock-missing debug native success 289ms MaxRSS:62M
+| +- WriteFile kernels.zig cached
++- run test 23 pass, 1 skip (24 total) 83ms MaxRSS:8M
+| +- compile test debug native success 1s MaxRSS:169M
++- run test 49 pass (49 total) 262ms MaxRSS:13M
+| +- compile test debug native success 2s MaxRSS:170M
+| +- WriteFile kernels.zig success
++- run test 30 pass, 1 skip (31 total) 82ms MaxRSS:9M
+| +- compile test debug native success 6s MaxRSS:216M
+| +- WriteFile kernels.zig (reused)
++- run test 18 pass (18 total) 331ms MaxRSS:11M
+| +- compile test debug native success 4s MaxRSS:215M
+| +- WriteFile kernels.zig (reused)
++- run test 4 pass (4 total) 17ms MaxRSS:6M
+| +- compile test debug native success 897ms MaxRSS:157M
+| +- WriteFile kernels.zig (reused)
++- run test 3 pass (3 total) 15ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:165M
+| +- WriteFile kernels.zig (reused)
++- compile exe tf-qwen35-test debug native success 3s MaxRSS:304M
+| +- WriteFile kernels.zig (reused)
++- compile exe tensorfold-native safe native success 6s MaxRSS:251M
+| +- options (reused)
+| +- WriteFile exlr8_alias.zig cached
+| +- WriteFile kernels.zig (reused)
++- run test 26 pass, 5 skip (31 total) 36ms MaxRSS:9M
+| +- compile test safe native success 47s MaxRSS:599M
++- run test 112 pass (112 total) 11s MaxRSS:42M
+| +- compile test debug native success 2s MaxRSS:169M
++- run test 20 pass (20 total) 214ms MaxRSS:14M
+| +- compile test debug native success 1s MaxRSS:169M
++- run exe learned-warning-test success 74ms
+ +- compile exe learned-warning-test debug native success 1s MaxRSS:153M
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/ci-summary.txt b/docs/receipts/glm-fp4local-80517fc3/ci/ci-summary.txt
new file mode 100644
index 0000000000..13e4a7fe65
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/ci-summary.txt
@@ -0,0 +1,26 @@
+No-GPU CI steps (no-gpu.yml) on 80517fc3, Linux x86_64 under WSL2 (kernel 6.6.87), Zig 0.17.0, an LF tree.
+
+Warm caches:
+ zig build test 211/211 steps succeeded test.log
+ zig build test-kernel-contract 11/11 steps, 65/65 tests passed test-kernel-contract.log
+ zig build test-golden 5/5 steps succeeded test-golden.log
+ fuzz-cortheon 11/11 tests passed fuzz-cortheon.log
+ served Flash Next unittest 8 tests OK flashnext-served.log
+ lean 444 problems, 444 on v1.1.1, 0 new lean.log, lean-base111.txt, lean-head-111.txt
+ zig fmt --check the 35 Zig files this branch adds or changes: clean (empty) fmt.log
+ git diff --check v1.1.1 80517fc3 clean (empty; 40 files) diff-check.txt
+
+Fresh caches (every test run again):
+ 80517fc3 zig build test 208/211 steps, 2981/2996 tests passed, 14 skipped, 1 failed fresh-test.log
+ the one failure: cuda_cost_identity.zig:64 (upstream's 9c11d9d9; rewrites a same-size file and expects
+ its (device, inode, size, mtime, ctime) hash to change; under WSL2's coarse timestamps both writes can
+ land in one tick)
+ test-kernel-contract 65/65 passed, test-golden 5/5 steps fresh-test-kernel-contract.log, fresh-test-golden.log
+ v1.1.1 zig build test 207/211 steps, 2954/2973 tests passed, 14 skipped, 5 failed base111-fresh-test.log
+ the same cuda_cost_identity test fails on plain v1.1.1; the other four are benchmark_receipt_test,
+ failing only in this git-archive extraction of v1.1.1 (they pass in the branch's working copy)
+
+Each intermediate commit (git archive, fresh build of each, shared cache) split/
+ 461d4473 2961/2980, 26894027 2962/2981, 251ef9ea 2968/2986, 0eac5947 2973/2992, 3c341bae 2974/2993,
+ 4c1a57ed 2977/2995, 9e1d20f7 2977/2996 tests passed (14 skipped each): no compile errors; the failures are the
+ four benchmark_receipt_test failures of the git-archive extraction and, in five of the seven, cuda_cost_identity.
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/diff-check.txt b/docs/receipts/glm-fp4local-80517fc3/ci/diff-check.txt
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/flashnext-served.log b/docs/receipts/glm-fp4local-80517fc3/ci/flashnext-served.log
new file mode 100644
index 0000000000..945435baae
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/flashnext-served.log
@@ -0,0 +1,5 @@
+........
+----------------------------------------------------------------------
+Ran 8 tests in 0.054s
+
+OK
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/fmt.log b/docs/receipts/glm-fp4local-80517fc3/ci/fmt.log
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-golden.log b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-golden.log
new file mode 100644
index 0000000000..855feee74e
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-golden.log
@@ -0,0 +1,82 @@
+KNOWN-DIFFER tools/two_tools-stream
+ reply 0 body differs at 313: golden [content": "\n"}, "finish_reason": null}]}
+
+data: {"id": "", "type": "function"]
+KNOWN-DIFFER tools/two_tools-single-stream
+ reply 0 body differs at 313: golden [content": "\n"}, "finish_reason": null}]}
+
+data: {"id": "", "type": "function"]
+KNOWN-DIFFER tools/choice-"required"
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "object": "chat.completion", "created": , ] zig [error": {"message": "backend 'lanes' cannot enforce tool_cho]
+ reply 0 headers differ
+KNOWN-DIFFER tools/choice-{"type": "function", "function": {"name": "lookup"}}
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "object": "chat.completion", "created": , ] zig [error": {"message": "backend 'lanes' cannot enforce tool_cho]
+ reply 0 headers differ
+KNOWN-DIFFER errors/format-json
+ reply 0 body differs at 41: golden [needs the checkpoint's config.json vocab_size", "type": "inv] zig [(response_format and the guided_* fields) is not supported b]
+ reply 0 headers differ
+KNOWN-DIFFER errors/stream-structured
+ connection after: golden closed, zig open
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 0: golden [data: {"id": "", "object": "chat.completion.chunk", "cr] zig [{"error": {"message": "structured output (response_format an]
+KNOWN-DIFFER anthropic/tool-use-stream
+ reply 0 body differs at 704: golden [\n"}}
+
+event: content_block_stop
+data: {"type": "content_blo] zig ["}}
+
+event: content_block_stop
+data: {"type": "content_block]
+KNOWN-DIFFER anthropic/tool-result
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "type": "message", "role": "assistant", "model] zig [type": "error", "error": {"type": "invalid_request_error", "]
+ reply 0 headers differ
+KNOWN-DIFFER anthropic/refuse-format
+ reply 0 body differs at 91: golden [needs the checkpoint's config.json vocab_size"}}] zig [(response_format and the guided_* fields) is not supported b]
+ reply 0 headers differ
+KNOWN-DIFFER cancel/metrics-after
+ reply 0 body differs at 307: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER metrics-open/metrics
+ reply 0 body differs at 307: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER metrics-open/v1-metrics
+ reply 0 body differs at 487: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER keys/metrics-counted
+ reply 0 body differs at 887: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+ chat 16 equal 0 differ 0 known
+ thinking 16 equal 0 differ 0 known
+ stops 13 equal 0 differ 0 known
+ usage 10 equal 0 differ 0 known
+ tools 48 equal 0 differ 4 known
+ errors 37 equal 0 differ 2 known
+ completions 15 equal 0 differ 0 known
+ responses 29 equal 0 differ 0 known
+ anthropic 26 equal 0 differ 3 known
+ tokens 19 equal 0 differ 0 known
+ status 11 equal 0 differ 0 known
+ framing 42 equal 0 differ 0 known
+ cancel 1 equal 0 differ 1 known
+ lifecycle 2 equal 0 differ 0 known
+metrics-open 2 equal 0 differ 2 known
+ keys 19 equal 0 differ 1 known
+306/319 equal to server goldens, 0 unexpected differences
+ keys 6 equal 0 differ 0 known
+ origin 20 equal 0 differ 0 known
+26/26 equal to server goldens, 0 unexpected differences
+Build Summary: 5/5 steps succeeded
+test-golden success
++- run exe golden_check success 1m
+| +- compile exe golden_check debug native success 532ms MaxRSS:146M
+| +- compile exe fake_serve debug native success 2s MaxRSS:311M
++- run exe golden_check success 4s
+ +- compile exe golden_check debug native (reused)
+ +- compile exe fake_serve debug native (reused)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-kernel-contract.log b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-kernel-contract.log
new file mode 100644
index 0000000000..165ab0256c
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test-kernel-contract.log
@@ -0,0 +1,12 @@
+Build Summary: 11/11 steps succeeded; 65/65 tests passed
+test-kernel-contract success
++- run test 46 pass (46 total) 742ms MaxRSS:9M
+| +- compile test debug native success 4s MaxRSS:169M
++- test-gemma4-contract-host success
+| +- run test 9 pass (9 total) 25s MaxRSS:14M
+| +- compile test debug native success 1s MaxRSS:225M
+| +- WriteFile gemma4-host-empty.metallib success
++- run test 3 pass (3 total) 25ms MaxRSS:6M
+| +- compile test debug native success 715ms MaxRSS:151M
++- run test 7 pass (7 total) 1s MaxRSS:55M
+ +- compile test debug native success 924ms MaxRSS:148M
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test.log b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test.log
new file mode 100644
index 0000000000..57be3cc3d0
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/fresh-test.log
@@ -0,0 +1,750 @@
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/dev-111/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+failed command: ./.zig-cache/o/14877a188be45c8e09f7b2d1b0f2ba29/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/ee66ecfd3bae61a9eeb3bd70d278bf57/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/d9e45d8fc0b17344351defb4be2f7054/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/2a9696025367cd11a9fd7ce4bb3525cf/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/e478176cc2e4ceea386ea6b7c305b964/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/27d5365a5e27fbb1dba81b18e5abce46/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/eaaca3d2a1eb9a05f54975693d9338ce/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/4e028724100949671e8a7d991ef4f2dc/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/59ef2b622f150974beb1aef10e193853/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/998a652df81f44fd8b27c9f2baa8dfd4/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/7b8452bca717ff30f4bfde4b0c6f82f2/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/f67e0e5da781afdde8021ffa08ef0c4e/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/6f1fe2c7c0057cd920445e3c01d35bca/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/8ec5861930a1480123ba940e659520df/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/8f4139d5d501eabda251a16b95c51617/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/546a08d7c2e1eec091a20d1a6f9d586d/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/9e713de11aa801b598b64c5bcab0063e/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/ea4a4a6c7e8df5b24bab5fc2f09bac60/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/b47892d63ef72e7b19b72bb98df19c75/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/c4054c8ba5e1acde7faea349e8073ec3/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/a2699a23610895fc89d016f5fe0ba563/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/42fb69f317552276a1fbf13af756f669/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/7e99be1922a023b6ed59fe96d5ec871f/test --cache-dir=./.zig-cache --seed=0x471918d0 --listen=-
+
+Build Summary: 208/211 steps succeeded (1 failed); 2981/2996 tests passed (14 skipped, 1 failed)
+test transitive failure
++- test-kernel-symbols-host success
+| +- run test 3 pass (3 total) 9s MaxRSS:77M
+| +- compile test debug native success 1s MaxRSS:146M
++- run test 37 pass (37 total) 118ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:156M
++- run test 7 pass (7 total) 1s MaxRSS:72M
+| +- compile test debug native success 1s MaxRSS:156M
++- run test 9 pass (9 total) 283ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:149M
+| +- options success
+| +- compile exe digest-writer debug native success 1s MaxRSS:141M
++- run test 6 pass (6 total) 14ms MaxRSS:12M
+| +- compile test debug native success 1s MaxRSS:147M
++- test-flashnext-batch-host success
+| +- run test 11 pass (11 total) 5s MaxRSS:34M
+| +- compile test debug native success 5s MaxRSS:293M
+| +- WriteFile flashnext-test-empty.zig success
++- run test 2 pass (2 total) 42ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:145M
++- run test 4 pass (4 total) 35ms MaxRSS:12M
+| +- compile test debug native success 1s MaxRSS:143M
++- run test 4 pass (4 total) 37ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:147M
++- run test 3 pass (3 total) 13ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:145M
++- run test 23 pass (23 total) 44ms MaxRSS:13M
+| +- compile test debug native success 1s MaxRSS:148M
++- run test 27 pass (27 total) 5s MaxRSS:24M
+| +- compile test debug native success 1s MaxRSS:161M
++- run test 56 pass, 1 skip (57 total) 39s MaxRSS:50M
+| +- compile test debug native success 1s MaxRSS:165M
+| | +- options cached
+| +- compile obj tp_host_compile debug x86_64-linux-gnu success 1s MaxRSS:111M
+| +- options cached
++- test-cuda-host transitive failure
+| +- run test 1 pass (1 total) 22ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:159M
+| +- run test 1 pass (1 total) 13ms MaxRSS:11M
+| | +- compile test debug native success 822ms MaxRSS:144M
+| +- run test 10 pass (10 total) 71ms MaxRSS:6M
+| | +- compile test debug native success 1s MaxRSS:154M
+| +- run test 2 pass (2 total) 20ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:145M
+| +- run exe runtime-discovery-exit success 54ms
+| | +- compile exe runtime-discovery-exit debug native success 1s MaxRSS:144M
+| | +- options success
+| | +- compile lib runtime-discovery-mock debug native success 47s MaxRSS:406M
+| +- run exe rpc-warning-check success 4ms
+| | +- compile exe rpc-warning-check debug native success 1s MaxRSS:141M
+| | +- options success
+| | +- compile lib rpc-mock-complete debug native success 300ms MaxRSS:63M
+| | +- compile lib rpc-mock-abi debug native success 449ms MaxRSS:63M
+| | +- compile lib rpc-mock-wait debug native success 357ms MaxRSS:63M
+| | +- compile lib rpc-mock-store debug native success 417ms MaxRSS:63M
+| | +- compile lib rpc-mock-wrong debug native success 359ms MaxRSS:63M
+| +- run test 11 pass (11 total) 28ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:152M
+| | +- options (+5 more reused dependencies)
+| +- run test 1 pass (1 total) 37ms MaxRSS:5M
+| | +- compile test debug native success 4s MaxRSS:144M
+| +- run test 30 pass (30 total) 86ms MaxRSS:8M
+| | +- compile test debug native success 2s MaxRSS:232M
+| | +- WriteFile h3-compile-only.fatbin success
+| | +- options success
+| +- run test 3 pass (3 total) 1s MaxRSS:74M
+| | +- compile test debug native success 1s MaxRSS:156M
+| +- run test 3 pass (3 total) 15ms MaxRSS:10M
+| | +- compile test debug native success 7s MaxRSS:164M
+| | +- WriteFile attn_train.fatbin success
+| | +- WriteFile dsv41_exl3_experts.fatbin success
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- options success
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- options success
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| | +- WriteFile attn_train.fatbin (reused)
+| +- run test 10 pass (10 total) 37ms MaxRSS:6M
+| | +- compile test debug native success 1s MaxRSS:166M
+| | +- WriteFile draft_ids.txt success
+| | +- options (reused)
+| +- run test 10 pass (10 total) 784ms MaxRSS:7M
+| | +- compile test debug native success 2s MaxRSS:194M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 9 pass (9 total) 270ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:171M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- compile obj exl3_host_compile debug native success 557ms MaxRSS:128M
+| | +- options (reused)
+| +- run test 6 pass (6 total) 63ms MaxRSS:7M
+| | +- compile test debug native success 1s MaxRSS:167M
+| | +- options (reused)
+| +- run test 6 pass (6 total) 340ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:161M
+| | +- options (reused)
+| +- run test 8 pass (8 total) 46ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:156M
+| +- run test 3 pass (3 total) 237ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:154M
+| +- run test 8 pass (8 total) 43ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:155M
+| +- run test 4 pass (4 total) 33ms MaxRSS:7M
+| | +- compile test debug native success 1s MaxRSS:159M
+| | +- options (reused)
+| +- run test 1 pass (1 total) 77ms MaxRSS:7M
+| | +- compile test debug native success 1s MaxRSS:160M
+| | +- options (reused)
+| +- run test 4 pass (4 total) 10ms MaxRSS:6M
+| | +- compile test fast native success 28s MaxRSS:441M
+| | +- options (reused)
+| +- run test 1 pass (1 total) 16ms MaxRSS:11M
+| | +- compile test fast native success 38s MaxRSS:453M
+| | +- options (reused)
+| +- compile obj bolt_host_compile debug x86_64-linux-gnu success 1s MaxRSS:141M
+| | +- options cached
+| | +- WriteFile draft_ids.txt (reused)
+| +- run test 26 pass (26 total) 5s MaxRSS:9M
+| | +- compile test debug native success 2s MaxRSS:177M
+| | +- options (reused)
+| +- run test 4 pass (4 total) 18ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:167M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 3 pass (3 total) 18s MaxRSS:42M
+| | +- compile test debug native success 5s MaxRSS:232M
+| | +- options (reused)
+| | +- WriteFile draft_ids.txt (reused)
+| +- run exe qwen-cost-key-process success 5s
+| | +- compile exe qwen-cost-key-process debug native success 1s MaxRSS:173M
+| | | +- WriteFile draft_ids.txt (reused)
+| | | +- options (reused)
+| | +- WriteFile target/config.json success
+| +- run test 0 pass, 1 fail (1 total)
+| | +- compile test debug native success 4s MaxRSS:170M
+| +- compile obj drama_cuda_native_host debug native success 1s MaxRSS:129M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 16ms MaxRSS:10M
+| | +- compile test debug native success 1s MaxRSS:145M
+| +- run test 506 pass (506 total) 56s MaxRSS:54M
+| | +- compile test debug native success 8s MaxRSS:428M
+| +- run test 5 pass (5 total) 22ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:148M
+| +- run test 208 pass (208 total) 331ms MaxRSS:10M
+| | +- compile test debug native success 2s MaxRSS:182M
+| | +- options (reused)
+| +- run test 319 pass (319 total) 18s MaxRSS:135M
+| | +- compile test debug native success 9s MaxRSS:228M
+| +- run test 178 pass (178 total) 9s MaxRSS:45M
+| | +- compile test debug native success 2s MaxRSS:188M
+| +- run test 74 pass, 1 skip (75 total) 232ms MaxRSS:14M
+| | +- compile test debug native success 2s MaxRSS:217M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 58 pass, 2 skip (60 total) 12s MaxRSS:27M
+| | +- compile test debug native success 2s MaxRSS:182M
+| | +- WriteFile draft_ids.txt (reused)
+| | +- options (reused)
+| +- run test 12 pass (12 total) 27ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:172M
+| | +- options (reused)
+| +- run test 92 pass (92 total) 947ms MaxRSS:78M
+| | +- compile test debug native success 1s MaxRSS:201M
+| | +- options (reused)
+| +- run test 13 pass (13 total) 8s MaxRSS:13M
+| | +- compile test debug native success 1s MaxRSS:183M
+| | +- options (reused)
+| +- run test 29 pass (29 total) 10s MaxRSS:1G
+| | +- compile test debug native success 2s MaxRSS:177M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 38ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:148M
+| +- run test 20 pass (20 total) 274ms MaxRSS:8M
+| | +- compile test debug native success 1s MaxRSS:167M
+| +- run test 217 pass (217 total) 11s MaxRSS:45M
+| | +- compile test debug native success 2s MaxRSS:209M
+| +- run test 65 pass (65 total) 484ms MaxRSS:33M
+| | +- compile test debug native success 5s MaxRSS:284M
+| | +- options (reused)
+| | +- WriteFile draft_ids.txt (reused)
+| +- run test 3 pass (3 total) 26ms MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:144M
+| +- run test 2 pass (2 total) 48ms MaxRSS:9M
+| | +- compile test debug native success 2s MaxRSS:204M
+| | +- options (reused)
+| +- run test 79 pass (79 total) 279ms MaxRSS:71M
+| | +- compile test debug native success 2s MaxRSS:200M
+| | +- options (reused)
+| +- run test 15 pass, 1 skip (16 total) 116ms MaxRSS:8M
+| | +- compile test debug native success 2s MaxRSS:175M
+| | +- options (reused)
+| +- run test 5 pass (5 total) 19ms MaxRSS:9M
+| | +- compile test debug native success 1s MaxRSS:145M
+| +- run test 25 pass (25 total) 17s MaxRSS:12M
+| | +- compile test debug native success 1s MaxRSS:169M
+| +- run test 55 pass (55 total) 3s MaxRSS:14M
+| | +- compile test debug native success 2s MaxRSS:176M
+| +- run test 44 pass, 1 skip (45 total) 2s MaxRSS:11M
+| | +- compile test debug native success 1s MaxRSS:172M
+| +- run test 1 pass (1 total) 21ms MaxRSS:9M
+| +- compile test debug native success 1s MaxRSS:178M
+| +- options success
+| +- options (reused)
+| +- WriteFile draft_ids.txt (reused)
+| +- WriteFile exlr8_alias.zig success
++- run test 4 pass (4 total) 19ms MaxRSS:5M
+| +- compile test debug native success 1s MaxRSS:145M
+| +- options cached
++- run test 13 pass, 1 skip (14 total) 77ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:163M
+| +- options (reused)
++- run test 3 pass (3 total) 39ms MaxRSS:6M
+| +- compile test debug native success 4s MaxRSS:155M
+| +- options (reused)
++- run test 1 pass (1 total) 17ms MaxRSS:12M
+| +- compile test debug native success 1s MaxRSS:145M
++- run test 217 pass (217 total) 3s MaxRSS:15M
+| +- compile test fast native success 1m MaxRSS:1G
++- test-kv-pool success
+| +- run test 9 pass (9 total) 58ms MaxRSS:5M
+| | +- compile test debug native success 4s MaxRSS:146M
+| +- run test 6 pass (6 total) 1s MaxRSS:9M
+| +- compile test debug native success 5s MaxRSS:182M
++- run test 51 pass (51 total) 114ms MaxRSS:12M
+| +- compile test debug native success 1s MaxRSS:167M
+| +- options success
+| | +- compile lib hip-mock-success debug native success 494ms MaxRSS:63M
+| | +- compile lib hip-mock-failed debug native success 247ms MaxRSS:63M
+| | +- compile lib hip-mock-missing debug native success 437ms MaxRSS:62M
+| +- WriteFile kernels.zig cached
++- run test 23 pass, 1 skip (24 total) 79ms MaxRSS:12M
+| +- compile test debug native success 1s MaxRSS:172M
++- run test 49 pass (49 total) 181ms MaxRSS:13M
+| +- compile test debug native success 2s MaxRSS:170M
+| +- WriteFile kernels.zig success
++- run test 30 pass, 1 skip (31 total) 85ms MaxRSS:9M
+| +- compile test debug native success 5s MaxRSS:209M
+| +- WriteFile kernels.zig (reused)
++- run test 18 pass (18 total) 428ms MaxRSS:11M
+| +- compile test debug native success 6s MaxRSS:215M
+| +- WriteFile kernels.zig (reused)
++- run test 4 pass (4 total) 52ms MaxRSS:6M
+| +- compile test debug native success 1s MaxRSS:158M
+| +- WriteFile kernels.zig (reused)
++- run test 3 pass (3 total) 35ms MaxRSS:6M
+| +- compile test debug native success 4s MaxRSS:168M
+| +- WriteFile kernels.zig (reused)
++- compile exe tf-qwen35-test debug native success 7s MaxRSS:165M
+| +- WriteFile kernels.zig (reused)
++- compile exe tensorfold-native safe native success 11s MaxRSS:241M
+| +- options (reused)
+| +- WriteFile exlr8_alias.zig cached
+| +- WriteFile kernels.zig (reused)
++- run test 26 pass, 5 skip (31 total) 30ms MaxRSS:13M
+| +- compile test safe native success 34s MaxRSS:590M
++- run test 112 pass (112 total) 13s MaxRSS:41M
+| +- compile test debug native success 2s MaxRSS:172M
++- run test 20 pass (20 total) 362ms MaxRSS:14M
+| +- compile test debug native success 1s MaxRSS:167M
++- run exe learned-warning-test success 67ms
+ +- compile exe learned-warning-test debug native success 1s MaxRSS:155M
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/fuzz-cortheon.log b/docs/receipts/glm-fp4local-80517fc3/ci/fuzz-cortheon.log
new file mode 100644
index 0000000000..47a4b6bb7d
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/fuzz-cortheon.log
@@ -0,0 +1,4 @@
+Build Summary: 3/3 steps succeeded; 11/11 tests passed
+fuzz-cortheon success
++- run test 11 pass (11 total) 12s MaxRSS:7M
+ +- compile test safe native success 13s MaxRSS:479M
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/lean-base111.txt b/docs/receipts/glm-fp4local-80517fc3/ci/lean-base111.txt
new file mode 100644
index 0000000000..7c25d6c3ab
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/lean-base111.txt
@@ -0,0 +1,445 @@
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diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/lean-head-111.txt b/docs/receipts/glm-fp4local-80517fc3/ci/lean-head-111.txt
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index 0000000000..5cff5b556b
--- /dev/null
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+zig/src/families/flashnext/tp.zig:438: comment past 120 columns
+zig/src/families/flashnext/tp.zig:474: multi-line comment
+zig/src/families/flashnext/tp.zig:475: multi-line comment
+zig/src/families/flashnext/tp.zig:496: comment past 120 columns
+zig/src/families/flashnext/tp.zig:526: comment past 120 columns
+zig/src/families/flashnext/tp.zig:547: comment past 120 columns
+zig/src/families/flashnext/tp.zig:562: comment past 120 columns
+zig/src/families/flashnext/tp.zig:604: multi-line comment
+zig/src/families/flashnext/tp.zig:605: multi-line comment
+zig/src/families/flashnext/tp_settings.zig:8: comment past 120 columns
+zig/src/families/flashnext/tp_settings.zig:39: comment past 120 columns
+zig/src/families/glm/attn_check.zig:1: comment past 120 columns
+zig/src/families/glm/attn_check.zig:127: comment past 120 columns
+zig/src/families/glm/backend.zig:178: comment past 120 columns
+zig/src/families/glm/backend.zig:326: comment past 120 columns
+zig/src/families/glm/checks.zig:37: comment past 120 columns
+zig/src/families/glm/checks.zig:72: comment past 120 columns
+zig/src/families/glm/checks.zig:105: comment past 120 columns
+zig/src/families/glm/checks.zig:153: comment past 120 columns
+zig/src/families/glm/checks.zig:190: comment past 120 columns
+zig/src/families/glm/config.zig:222: comment past 120 columns
+zig/src/families/glm/engine.zig:1: comment past 120 columns
+zig/src/families/glm/engine.zig:42: comment past 120 columns
+zig/src/families/glm/engine.zig:105: comment past 120 columns
+zig/src/families/glm/engine.zig:236: comment past 120 columns
+zig/src/families/glm/engine.zig:455: comment past 120 columns
+zig/src/families/glm/ep.zig:1: comment past 120 columns
+zig/src/families/glm/ep.zig:25: comment past 120 columns
+zig/src/families/glm/ep.zig:50: comment past 120 columns
+zig/src/families/glm/ep.zig:204: comment past 120 columns
+zig/src/families/glm/ep.zig:218: comment past 120 columns
+zig/src/families/glm/ep.zig:309: comment past 120 columns
+zig/src/families/glm/ep_control.zig:1: comment past 120 columns
+zig/src/families/glm/ep_control.zig:120: comment past 120 columns
+zig/src/families/glm/forward.zig:1: comment past 120 columns
+zig/src/families/glm/forward.zig:197: comment past 120 columns
+zig/src/families/glm/forward.zig:218: comment past 120 columns
+zig/src/families/glm/forward.zig:286: comment past 120 columns
+zig/src/families/glm/forward.zig:330: comment past 120 columns
+zig/src/families/glm/forward.zig:393: comment past 120 columns
+zig/src/families/glm/forward.zig:406: comment past 120 columns
+zig/src/families/glm/forward.zig:536: comment past 120 columns
+zig/src/families/glm/kept.zig:29: comment past 120 columns
+zig/src/families/glm/kept.zig:80: comment past 120 columns
+zig/src/families/glm/kept.zig:104: comment past 120 columns
+zig/src/families/glm/kept.zig:205: comment past 120 columns
+zig/src/families/glm/kernels.zig:1: comment past 120 columns
+zig/src/families/glm/load_plan.zig:1: comment past 120 columns
+zig/src/families/glm/load_plan.zig:96: comment past 120 columns
+zig/src/families/glm/moe.zig:18: comment past 120 columns
+zig/src/families/glm/moe.zig:150: comment past 120 columns
+zig/src/families/glm/mtp.zig:1: comment past 120 columns
+zig/src/families/glm/mtp.zig:17: comment past 120 columns
+zig/src/families/glm/plain_rounds.zig:14: comment past 120 columns
+zig/src/families/glm/plain_rounds.zig:53: comment past 120 columns
+zig/src/families/glm/prompt.zig:1: comment past 120 columns
+zig/src/families/glm/prompt.zig:21: comment past 120 columns
+zig/src/families/glm/prompt.zig:134: comment past 120 columns
+zig/src/families/glm/prompt.zig:156: comment past 120 columns
+zig/src/families/glm/prompt.zig:218: comment past 120 columns
+zig/src/families/glm/prompt.zig:246: comment past 120 columns
+zig/src/families/glm/prompt.zig:342: comment past 120 columns
+zig/src/families/glm/prompt.zig:393: comment past 120 columns
+zig/src/families/glm/prompt.zig:417: comment past 120 columns
+zig/src/families/glm/prompt.zig:432: comment past 120 columns
+zig/src/families/glm/prompt.zig:553: comment past 120 columns
+zig/src/families/glm/snapshot.zig:1: comment past 120 columns
+zig/src/families/glm/snapshot.zig:58: comment past 120 columns
+zig/src/families/glm/snapshot.zig:90: comment past 120 columns
+zig/src/families/glm/weights.zig:1: comment past 120 columns
+zig/src/families/kimi_k3/kernels.zig:283: comment past 120 columns
+zig/src/families/kimi_k3/prepare.zig:1: comment past 120 columns
+zig/src/families/nemotron/backend.zig:305: comment past 120 columns
+zig/src/families/nemotron/backend.zig:425: comment past 120 columns
+zig/src/families/nemotron/copy_lanes.zig:1: comment past 120 columns
+zig/src/families/nemotron/copy_lanes.zig:66: comment past 120 columns
+zig/src/families/nemotron/cuda_mtp.zig:372: comment past 120 columns
+zig/src/families/nemotron/cuda_segments.zig:1: comment past 120 columns
+zig/src/families/nemotron/cuda_segments.zig:64: comment past 120 columns
+zig/src/families/nemotron/cuda_triton.zig:109: comment past 120 columns
+zig/src/families/nemotron/cuda_triton.zig:116: comment past 120 columns
+zig/src/families/nemotron/forward.zig:35: comment past 120 columns
+zig/src/families/nemotron/head_block.zig:21: comment past 120 columns
+zig/src/families/nemotron/layers.zig:61: comment past 120 columns
+zig/src/families/nemotron/mtp.zig:193: comment past 120 columns
+zig/src/families/nemotron/prefill_launch.zig:59: comment past 120 columns
+zig/src/families/nemotron/prefill_launch.zig:137: comment past 120 columns
+zig/src/families/nemotron/prefill_mixers.zig:1: comment past 120 columns
+zig/src/families/nemotron/round_depth.zig:1: comment past 120 columns
+zig/src/families/nemotron/simd_attention.zig:1: comment past 120 columns
+zig/src/families/nemotron/simd_attention.zig:5: comment past 120 columns
+zig/src/families/nemotron/simd_attention.zig:88: comment past 120 columns
+zig/src/families/qwen3_5/config.zig:1: comment past 120 columns
+zig/src/families/qwen3_5/qualify.zig:1: comment past 120 columns
+zig/src/families/qwen3_5/weights.zig:1: comment past 120 columns
+zig/src/hip/device_arch.c:1: block comment
+zig/src/hip/driver.zig:1: comment past 120 columns
+zig/src/hip/memory.zig:1: comment past 120 columns
+zig/src/main.zig:226: comment past 120 columns
+zig/src/native/flashnext_host.zig:445: comment past 120 columns
+zig/src/native/metal.zig:325: comment past 120 columns
+zig/src/server/chunk_plan.zig:1: comment past 120 columns
+zig/src/server/hf_text.zig:23: comment past 120 columns
+zig/src/server/log.zig:106: comment past 120 columns
+zig/src/server/messages.zig:25: comment past 120 columns
+zig/src/server/messages.zig:151: comment past 120 columns
+zig/src/server/metrics.zig:325: multi-line comment
+zig/src/server/model_text.zig:24: comment past 120 columns
+zig/src/server/tool_params.zig:114: comment past 120 columns
+zig/src/server/tool_parse.zig:1: comment past 120 columns
+zig/src/server/tool_stream.zig:420: comment past 120 columns
+zig/src/server/tool_streaming.zig:1: comment past 120 columns
+zig/tests/cuda/bench.zig:17: comment past 120 columns
+zig/tests/cuda/box/run.sh:2: comment past 120 columns
+zig/tests/cuda/nemotron/box/build.sh:2: comment past 120 columns
+zig/tests/cuda/nemotron/box/capture.sh:2: comment past 120 columns
+zig/tests/cuda/nemotron/box/zrun.sh:2: comment past 120 columns
+zig/tests/cuda/nemotron/capture.py:2: docstring past 120 columns
+zig/tests/cuda/nemotron/test_aot_manifest.py:2: multi-line docstring
+zig/tests/cuda/oracle/oracle.py:1: docstring past 120 columns
+zig/tests/cuda/oracle/oracle.py:82: docstring past 120 columns
+zig/tests/cuda/oracle/sass_compare.py:1: docstring past 120 columns
+zig/tests/cuda/runtime_tests.zig:276: comment past 120 columns
+zig/tests/dispatch_bench.zig:1: comment past 120 columns
+zig/tests/fabric_handoff.zig:1: comment past 120 columns
+zig/tests/fabric_library.zig:1: comment past 120 columns
+zig/tests/fabric_vectors.zig:1: comment past 120 columns
+zig/tests/flashnext_bench.zig:69: comment past 120 columns
+zig/tests/flashnext_bench.zig:119: multi-line comment
+zig/tests/flashnext_bench.zig:169: multi-line comment
+zig/tests/flashnext_reuse.zig:93: comment past 120 columns
+zig/tests/flashnext_reuse.zig:183: comment past 120 columns
+zig/tests/flashnext_run.zig: 1690 lines (split past 600)
+zig/tests/flashnext_run.zig:1: multi-line comment
+zig/tests/flashnext_run.zig:2: multi-line comment
+zig/tests/flashnext_run.zig:762: multi-line comment
+zig/tests/flashnext_run.zig:828: multi-line comment
+zig/tests/flashnext_run.zig:1308: multi-line comment
+zig/tests/flashnext_run.zig:1393: multi-line comment
+zig/tests/flashnext_run.zig:1441: multi-line comment
+zig/tests/flashnext_run.zig:1442: multi-line comment
+zig/tests/glm_run.zig:1: comment past 120 columns
+zig/tests/grid_sync_bench.zig:1: comment past 120 columns
+zig/tests/hip_mock.c:1: block comment
+zig/tests/linktest_suite.zig:1: comment past 120 columns
+zig/tests/mm_bench.zig:1: comment past 120 columns
+zig/tests/moe_bench.zig:1: comment past 120 columns
+zig/tests/nemotron_dense.zig:1: comment past 120 columns
+zig/tests/nemotron_dense.zig:11: comment past 120 columns
+zig/tests/nemotron_dense.zig:100: comment past 120 columns
+zig/tests/nemotron_dense.zig:292: comment past 120 columns
+zig/tests/nemotron_experts.zig:1: comment past 120 columns
+zig/tests/nemotron_experts.zig:133: comment past 120 columns
+zig/tests/nemotron_prefill_check.zig:1: comment past 120 columns
+zig/tests/nemotron_sample_check.zig:1: comment past 120 columns
+zig/tests/nemotron_wide.zig:1: comment past 120 columns
+zig/tests/nemotron_wide.zig:244: comment past 120 columns
+zig/tests/ordering.zig:1: comment past 120 columns
+zig/tests/ordering.zig:29: comment past 120 columns
+zig/tests/row_projection_check.zig:1: comment past 120 columns
+zig/tests/row_projection_check.zig:19: comment past 120 columns
+zig/tests/row_projection_check.zig:163: comment past 120 columns
+zig/tests/server/build.sh:2: comment past 120 columns
+zig/tests/server/freeze_cases.py:1: docstring past 120 columns
+zig/tests/server/freeze_cases.py:150: comment past 120 columns
+zig/tests/server/golden_check.zig:79: comment past 120 columns
+zig/tests/server/golden_check.zig:173: comment past 120 columns
+zig/tests/server/wire.py:52: docstring past 120 columns
+zig/tests/server/zig_test.sh:2: comment past 120 columns
+zig/tests/tp2_bench.zig:1: comment past 120 columns
+zig/tests/wide_heads.zig:46: comment past 120 columns
+zig/tests/wide_probe.zig:1: comment past 120 columns
+zig/tests/wide_probe.zig:263: comment past 120 columns
+zig/tests/wide_trees.zig:1: comment past 120 columns
+zig/tests/wide_trees.zig:126: comment past 120 columns
+zig/tests/wide_trees.zig:156: comment past 120 columns
+tools/zig/check_prefill_layers.py:1: docstring past 120 columns
+tools/zig/check_prefill_model.py:1: docstring past 120 columns
+tools/zig/check_prefill_ops.py:1: docstring past 120 columns
+tools/zig/flashnext_dump.py:1: multi-line docstring
+tools/zig/flashnext_hc_coalesce.py:1: multi-line docstring
+tools/zig/gen_glm_kernels.py:43: comment past 120 columns
+tools/zig/glm_compare.py:1: docstring past 120 columns
+tools/zig/glm_ref.py:1: docstring past 120 columns
+tools/zig/glm_ref.py:96: docstring past 120 columns
+tools/zig/glm_ref.py:122: docstring past 120 columns
+tools/zig/oracle_nemotron_rows.py:38: docstring past 120 columns
+tools/zig/prefill_dump.py:1: docstring past 120 columns
+tools/zig/prefill_glue.py:1: docstring past 120 columns
+tools/zig/prefill_kernels.py:34: docstring past 120 columns
+tools/zig/prefill_launch.py:1: docstring past 120 columns
+tools/zig/prefill_launch.py:173: docstring past 120 columns
+tools/zig/prefill_swap.py:1: docstring past 120 columns
+tools/zig/reuse_served.py:133: docstring past 120 columns
+tools/zig/reuse_served.py:232: comment past 120 columns
+tools/zig/reuse_served.py:254: docstring past 120 columns
+tools/zig/reuse_served.py:290: docstring past 120 columns
+tools/zig/reuse_served.py:375: multi-line docstring
+tools/zig/reuse_served.py:401: docstring past 120 columns
+tools/zig/reuse_served.py:423: docstring past 120 columns
+tools/zig/reuse_served.py:440: docstring past 120 columns
+tools/zig/test_fabric.sh:2: comment past 120 columns
+tools/zig/test_lanes.sh:2: comment past 120 columns
+tools/zig/tokenizer_parity.py:1: docstring past 120 columns
+tools/zig/triton_aot_manifest.py:2: docstring past 120 columns
+tools/zig/unicode_tables.py:1: docstring past 120 columns
+3348 files, 444 problems
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/lean.log b/docs/receipts/glm-fp4local-80517fc3/ci/lean.log
new file mode 100644
index 0000000000..1e13bffcc5
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/lean.log
@@ -0,0 +1,4 @@
+ 445 /zt/lean-base111.txt
+ 445 /zt/lean-head-111.txt
+lean: 444 problems, 444 on the base, 0 new
+lean_new rc=0
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/1-461d4473.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/1-461d4473.log
new file mode 100644
index 0000000000..fadf8ce98c
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/1-461d4473.log
@@ -0,0 +1,466 @@
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/b4129bb1c93af2d836ffd747ad1f5bd4/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/995264e9d3cae44baa65c27cb330486b/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/b04d71d7413f2e8c60e72ed4de95fd90/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/split/1-461d4473/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+failed command: ./.zig-cache/o/181540fd09b2273fc28f97b4fa58accf/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/defc62007143b141f518d170fe669edf/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/703de3c1d862da759499071cd40987eb/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/681acf7a7c4b0c2f132fdaddfc56f80a/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/5ec0f8e5b1ca933b518023f3b609e86b/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/7948860bea77578b1f2d4ab97a175d7d/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/20728c98208eca19a6bc4fe413444d73/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/bfb664677c98d19f493fcbe9ce1ad97e/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/b2b9577220438c2df26bc8e9cafd7ad3/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/2500dd950d44d89769d14cafc7c05146/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/9aa9cfd8c8722a7e820adcf3655717bc/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/3dc524f6a000852fe9373bdf5532bd01/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/4c0f16bbe409b46fff4cd29aaf55c27a/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/81b6a9b7aa6ad7445d409da4cc716230/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/241654e7ca5be6ac604bf9499e189e86/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/1-461d4473/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/1-461d4473/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/1-461d4473/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/1-461d4473/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/8a5206918bde986fa8871f17dafc1259/test --cache-dir=./.zig-cache --seed=0x1d1ce93a --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2961/2980 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 0 pass, 1 fail (1 total)
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/2-26894027.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/2-26894027.log
new file mode 100644
index 0000000000..e056eb1610
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/2-26894027.log
@@ -0,0 +1,467 @@
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/cec5cc3c2f82de9dc8adae1dee69cbc6/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/cc818bedabe6a1bddbe83ebed9830360/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/4cce67b04bac23848b43829b4201a9d9/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/7121d48644cc44393801c483a23b9817/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/681acf7a7c4b0c2f132fdaddfc56f80a/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5a63689c93f20f1439a315f67a5b3ca9/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/23bc55ba18bb68c73ac3a60cc2090a58/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/defc62007143b141f518d170fe669edf/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/39bf6569b3b64beb9683be9579ef6685/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/ca8e9b1ef39a70822a55a6b2aca359a2/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/4265b261cd992cfdea48cb2ab09d353f/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+d.mem.eql(u8, &first, &changed));
+ ^
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/split/2-26894027/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!st
+failed command: ./.zig-cache/o/8b452f41deac4e2c411434c043fafd81/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/241654e7ca5be6ac604bf9499e189e86/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/81b6a9b7aa6ad7445d409da4cc716230/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/e672bf756c631844fe611c06f7939d60/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/5ec0f8e5b1ca933b518023f3b609e86b/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/a48a80ea0c6032de346ed15cb4f0df25/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/a943a959588fcde6bfe4c52fc8ffe8fb/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/b2b9577220438c2df26bc8e9cafd7ad3/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/2-26894027/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/2-26894027/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/2-26894027/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/2-26894027/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/8a5206918bde986fa8871f17dafc1259/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x37243ee3 --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2962/2981 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 0 pass, 1 fail (1 total)
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/3-251ef9ea.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/3-251ef9ea.log
new file mode 100644
index 0000000000..96e80056e1
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/3-251ef9ea.log
@@ -0,0 +1,453 @@
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/b4129bb1c93af2d836ffd747ad1f5bd4/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/a62b47cf8fb4a81c86ba9621ac6e6f31/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/e5c2ce5ec50f8a72585185786fb27ec5/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/4d8c1daf266cce911f6e619368511bdc/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/902564e65f59dd4681131ccdfec94c43/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/4534de06fe931f28b2f57cbf71ccd89d/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/33cbca9f7909acae318d45fb4d175416/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/ff41bd8eaec3527f49f151d2df668674/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/b48640da8a3cfc497c7f999c50172c83/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/b9a00135375680d12d6a350c52bb96d0/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/75f9d6b9ff28e4e39f1a290a5ad28251/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/262b5cfad34ec279ab77a1560c431540/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/f27cb3087b31fb29aaa5cbf9cf2ba70d/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/cb9e3a49f42591d337fdd3a74ff913be/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/60214c802dc155c14bc1bdecbd826e8f/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/241654e7ca5be6ac604bf9499e189e86/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/07a3fdca799a367c5455ff40f74594b6/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/c016307f285b00111a2061b872d5d8ed/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/3-251ef9ea/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/3-251ef9ea/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/3-251ef9ea/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/3-251ef9ea/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/8a5206918bde986fa8871f17dafc1259/test --cache-dir=./.zig-cache --seed=0xed853d37 --listen=-
+
+Build Summary: 208/211 steps succeeded (1 failed); 2968/2986 tests passed (14 skipped, 4 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/4-0eac5947.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/4-0eac5947.log
new file mode 100644
index 0000000000..c402026398
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/4-0eac5947.log
@@ -0,0 +1,467 @@
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/a943a959588fcde6bfe4c52fc8ffe8fb/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/995264e9d3cae44baa65c27cb330486b/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/6f2de2ae3ac073b6a2b21114183c34c6/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/55692598fe6c4fc24cce60f6f2480e27/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/33adbaaa6cb9ccc0710888a5c828e942/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/a05b7af01c576cff6582aff8b836ebe4/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/7a68c42f4e407bd51c3c8a95667f08f5/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/08055c19daea7123a7ffc131dc517291/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/9a15fd8c54dd30bc64201853e4e5857c/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+ectedResult;
+ ^
+/zt/split/4-0eac5947/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexp
+failed command: ./.zig-cache/o/8b452f41deac4e2c411434c043fafd81/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/ae27ba71e2201f4b631313bd1087b09b/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/ea445eedf9005f1a880c6763bcd4f7dd/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/0bd19452f7a85e3ed68b13ab6cde3b61/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/68b6cd194046dee6d134bb77dbe28b40/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/24d66b284b0095ef3eaf26e8a06d9360/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/241654e7ca5be6ac604bf9499e189e86/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/6bb4190d34b446f14bc2ccb47f1b3639/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/4-0eac5947/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/4-0eac5947/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/4-0eac5947/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/4-0eac5947/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/8a5206918bde986fa8871f17dafc1259/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x868003a5 --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2973/2992 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 0 pass, 1 fail (1 total)
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/5-3c341bae.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/5-3c341bae.log
new file mode 100644
index 0000000000..4a1f7e1602
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/5-3c341bae.log
@@ -0,0 +1,466 @@
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/551e9cbc17f16b6195954a6a530991e4/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/a943a959588fcde6bfe4c52fc8ffe8fb/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/ac671476c2c474bd9fc543101b11e938/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/4c9597a78d98f3fb6665c40e5321ea61/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/782b3ff7e70832dbc21b95c1e5d929f0/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/0ed9b9b7ca11ea00b90b8fa93855da8b/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/d7c6a69ca183b46be4aa871d3301b1c2/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/511930af689f3c10ff6506b642e14340/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/7a68c42f4e407bd51c3c8a95667f08f5/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/a0c3fd011abdf097e816d7413fc3e50a/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/4552f57b65e281292cd31dc30cf48f63/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/93f79a717c12ad6ee8f43dd2c2ccf16e/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/dcb8093a4cbb5e2227374d9a64f4346d/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/a0a7d37839b396576d03be01a8b14c63/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/b551cade5bf6f4977f99ee27cec96676/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/f297d020599f56ce743d75f76ec10353/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/split/5-3c341bae/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cuda_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+failed command: ./.zig-cache/o/68d1a82152d373418d897627d792b627/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/c1bffab480fb8c9f93c3b0db9167ae23/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/5-3c341bae/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/5-3c341bae/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/5-3c341bae/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/5-3c341bae/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/d29f7d3c46d14c74fa4d673a4266cb59/test --cache-dir=./.zig-cache --seed=0x91c600b2 --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2974/2993 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 0 pass, 1 fail (1 total)
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/6-4c1a57ed.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/6-4c1a57ed.log
new file mode 100644
index 0000000000..53f3e314ce
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/6-4c1a57ed.log
@@ -0,0 +1,453 @@
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/551e9cbc17f16b6195954a6a530991e4/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/b4129bb1c93af2d836ffd747ad1f5bd4/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/f63ff2456f2d7c3ff04a5e94dcf003ba/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/7a68c42f4e407bd51c3c8a95667f08f5/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/545637059ce142f0f09fc11c291f40c1/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/ae0e9b160ccf08834131f0925dafcf69/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/fc76de995ae0106173ed93c289f2c829/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/0ed9b9b7ca11ea00b90b8fa93855da8b/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/6e30319579da4aa7a8ba6f61faf5a515/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/a0c3fd011abdf097e816d7413fc3e50a/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/0470feed9d93318abe59b367c87dca8f/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/69503c3a32624f71b4559c8a65c85205/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/75e4d8c023b6c1553f791e5cbedc8f55/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/c1bffab480fb8c9f93c3b0db9167ae23/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/bab528e5d00528236b436981769f52fb/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/dc24f7953f4a043581ab711ed146a488/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/6-4c1a57ed/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/6-4c1a57ed/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/6-4c1a57ed/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/6-4c1a57ed/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/d29f7d3c46d14c74fa4d673a4266cb59/test --cache-dir=./.zig-cache --seed=0xabb2dcfc --listen=-
+
+Build Summary: 208/211 steps succeeded (1 failed); 2977/2995 tests passed (14 skipped, 4 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/split/7-9e1d20f7.log b/docs/receipts/glm-fp4local-80517fc3/ci/split/7-9e1d20f7.log
new file mode 100644
index 0000000000..10dc36f7f5
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/split/7-9e1d20f7.log
@@ -0,0 +1,467 @@
+test
++- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/a943a959588fcde6bfe4c52fc8ffe8fb/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+fake target: proposed=68 verified=68 accepted=68
+failed command: ./.zig-cache/o/d1dbc9d0827be661e614bb06f8c4a4bb/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/5767271c59af538187ddb995ff171bd3/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 1 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+[default] (warn): slide pocket: a delete failed (UploadFailed), and its put-back did not reach the device (PutBackNotOnDevice)
+[default] (warn): slide pocket: a deleted fact's 2 layers are put back in memory, but not all on the device (UploadFailed): replies are refused until they are
+failed command: ./.zig-cache/o/5d61929277e62c5ed95608152f9a3987/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/545637059ce142f0f09fc11c291f40c1/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 0 cannot exchange with rank 2; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 1 cannot exchange with rank 3; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 2 cannot exchange with rank 0; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+REFUSE TF_GLM_PREFILL_REDUCE=split: rank 3 cannot exchange with rank 1; split sends every rank's rows to every other, and a fabric where some ranks reach only their neighbours (a ring of four without a switch) needs TF_GLM_PREFILL_REDUCE=ring or gather
+failed command: ./.zig-cache/o/abf1eae1ddf8d2b5922c9bd8640fa527/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- run test w
+[default] (warn): a round could not be recorded: OutOfDeviceMemory
+failed command: ./.zig-cache/o/551e9cbc17f16b6195954a6a530991e4/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+FAIL kernel-contract: unknown image name: unknown_image
+FAIL kernel-contract: unknown image name: glm_glu
+FAIL kernel-contract: unknown image name:
+FAIL kernel-contract: unknown image name: contract_control
+FAIL kernel-contract: unknown image name: GLM_GLUE
+failed command: ./.zig-cache/o/c1bffab480fb8c9f93c3b0db9167ae23/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): libcublasLt.so.13 is required for CUDA vision towers (--vision) and CUDA cuBLASLt projections; install the NVIDIA CUDA 13 cuBLAS runtime; on Linux, add its directory to LD_LIBRARY_PATH; a driver-only host does not include it
+[default] (warn): libnccl.so.2 is required for CUDA ranks (including the GLM pair); install the NVIDIA NCCL 2 runtime compatible with CUDA 13; on Linux, add its directory to LD_LIBRARY_PATH
+[default] (warn): libibverbs.so.1 is required for RoCE/RDMA gathers; install rdma-core's libibverbs runtime and the provider for the network adapter
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+[default] (warn): libmcdma-rpc.so is required for MCDMA mailbox and Metal speed-up communication; build and install MCDMA's RPC helper and set MCDMA_RPC_LIBRARY to its file
+failed command: ./.zig-cache/o/8a07182476cd9bf8eff36a0697a89bf0/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (err): rank 0: another rank's process ended (its line closed); this rank stops instead of waiting on it
+[default] (err): rank 0: rank 1 stopped (peer OOM); this rank stops too
+failed command: ./.zig-cache/o/7a68c42f4e407bd51c3c8a95667f08f5/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+libjpeg-turbo 3.1.4.1 not found: LFM2's JPEG cases skipped
+failed command: ./.zig-cache/o/4c9597a78d98f3fb6665c40e5321ea61/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/29e2eff01116c05024e590aa7ca71198/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+RESULT valid stale prefix: largest difference 0.000 of its tolerance (element 0)
+FAIL no-op projection: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/95b69175216a35ed8d5c4f2b7d539596/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/fbe54065d893545d0f104ecb94c5a353/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+[default] (warn): qwen3_5: a kept state's host copy goes to the copy-source quarantine: a restore's copies did not drain
+[default] (warn): qwen3_5: a failed restore's stream did not drain: its kept state's host copy stays
+failed command: ./.zig-cache/o/f7e3d0be2e81693a5dd21239bc5eb185/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test 0 pass, 1 fail (1 total)
+da_cost_identity.zig)
+ try std.testing.expect(!std.mem.eql(u8, &first, &changed));
+ ^
+error: 'cuda_cost_identity.test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it' failed:
+ /.local/share/zig/0.17.0/lib/std/testing.zig:584:14: 0x123f289 in expect (std.zig)
+ if (!ok) return error.TestUnexpectedResult;
+ ^
+ /zt/split/7-9e1d20f7/zig/src/families/qwen3_5/cuda_cost_identity.zig:64:5: 0x123fd33 in test.cost identity: stable reads reuse a drafter, while a changed payload at the same path invalidates it (cu
+failed command: ./.zig-cache/o/68d1a82152d373418d897627d792b627/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lm_head is 8-bit in groups of 64: CUDA reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+[default] (warn): backbone.embeddings is 8-bit in groups of 64: the Intel GPU engine reads Nemotron MLX checkpoints at 4-bit in groups of 64 only; mixed and 6- or 8-bit (oQ) checkpoints serve on Macs
+failed command: ./.zig-cache/o/75fc6fb66bc2af0472b799403a2ad853/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+libwebp 1.6.0 not found: WebP pixel cases skipped
+libjpeg-turbo 3.1.4.1 not found: JPEG pixel cases skipped
+[default] (warn): model.layers.0.mlp.up_proj.weight: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+[default] (warn): model.layers.0.mlp.up_proj: another spelling of module layers.0.mlp.up_proj declares a different width
+failed command: ./.zig-cache/o/75e4d8c023b6c1553f791e5cbedc8f55/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/652c3f156c7d815dc6725dc3a566539d/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 1.000 of its tolerance (element 1)
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+RESULT CPU reference comparison: largest difference 2.000 of its tolerance (element 0)
+FAIL CPU reference comparison within its float64 tolerance
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 2 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 1 (got -nan, reference 0e0, tolerance 1e0)
+FAIL CPU reference comparison: non-finite error at element 0 (got inf, reference 0e0, tolerance 1e0)
+failed command: ./.zig-cache/o/4f00f67965ed409f0dc44fc073e41dbd/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- test-cuda-host
+ +- run test 502 pass, 4 fail (506 total)
+error: 'benchmark_receipt_test.test.the receipt built from the golden facts equals the golden canonical bytes' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/7-9e1d20f7/zig/src/server/benchmark_receipt_test.zig:73:5: 0x22e7a52 in test.the receipt built from the golden facts equals the golden canonical bytes (root.zig)
+ try std.testing.expectEqualStrings(golden, bytes);
+ ^
+error: 'benchmark_receipt_test.test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2)' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ (end of string)
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/7-9e1d20f7/zig/src/server/benchmark_receipt_test.zig:91:5: 0x22e6a51 in test.the parsed golden writes back byte for byte, and previews as json.dumps(indent=2) (root.zig)
+ try std.testing.expectEqualStrings(golden[0 .. golden.len - 1], try receipt.canonical(a, v));
+ ^
+error: 'benchmark_receipt_test.test.a saved receipt is private, atomic and loads back equal' failed:
+ ====== expected this output: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======== instead found this: =========
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0d')
+ found:
+ {"created_at":"2026-01-01T00:00:00Z","hardware":{"cpu_model":null,"gpu_count":null,"gpus":[],"memory_type":"unknown","source":"unavailable","system_memory_bytes":null},"model":{"config_sha256":null,"drafter_repo_id":null,"family":null,"local":false,"quantization":null,"repo_id":"example-org/test-model","revision":null,"tokenizer_sha256":null},"run_id":"00000000-0000-4000-8000-000000000001","runtime":{"backend":"cuda","dependencies":{},"platform":"linux","python_version":"3.11.0","tensorfold_version":"0.0.0-test"},"samples":[{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"code","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05},{"cached_tokens":0,"completion_tokens":4,"delivery_seconds":0.3,"delivery_tps":10.0,"end_to_end_seconds":0.4,"error_code":null,"finish_reason":"length","fixture_id":"chat","prefill_seconds":0.04,"prompt_tokens":24,"repeat":0,"route":"/v1/chat/completions","seed":1234,"server_decode_seconds":0.25,"server_decode_tps":12.0,"status":"ok","temperature":0.0,"token_sha":"0123456789ab","ttft_seconds":0.05}],"schema_version":1,"settings":{"context_tokens":null,"managed":false,"rank_count":null,"repetitions":1,"serial":false,"temperatures":[0.0],"tokens":4},"suite":{"id":"community-v2","sha256":"51136f5d6775b2ff427834c6b1038b584de27e0a267cfc088ff2694ea069dcdc"}}
+ ^ ('\x0a')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/7-9e1d20f7/zig/src/server/benchmark_receipt_test.zig:119:5: 0x22e5341 in test.a saved receipt is private, atomic and loads back equal (root.zig)
+ try std.testing.expectEqualStrings(golden, try std.Io.Dir.cwd().readFileAlloc(io, path, a, .limited(1 << 20)));
+ ^
+error: 'benchmark_receipt_test.test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words' failed:
+ ====== expected this output: =========
+ Duplicate JSON field␃
+
+ ======== instead found this: =========
+ Invalid benchmark JSON␃
+
+ ======================================
+ First difference occurs on line 1:
+ expected:
+ Duplicate JSON field
+ ^ ('\x44')
+ found:
+ Invalid benchmark JSON
+ ^ ('\x49')
+ /.local/share/zig/0.17.0/lib/std/testing.zig:696:9: 0x14a85f5 in expectEqualStrings (std.zig)
+ return error.TestExpectedEqual;
+ ^
+ /zt/split/7-9e1d20f7/zig/src/server/benchmark_receipt_test.zig:141:9: 0x22e3b39 in test.oversized, duplicate-key, non-finite and deeply nested files are refused with fixed words (root.zig)
+ try std.testing.expectEqualStrings(c[1], problem);
+ ^
+failed command: ./.zig-cache/o/d29f7d3c46d14c74fa4d673a4266cb59/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- run test w
+[default] (warn): XPU: a command list did not drain at close; its memory and context stay until the process ends
+failed command: ./.zig-cache/o/118b9193dec9a3e60accadd0f8f10aca/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- run test w
+[default] (warn): glm: restoring 1500 tokens failed (PoolMoveNotDrained); pool relocation aborted
+failed command: ./.zig-cache/o/6b599836a11a2a971768fa7362d7b429/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+test
++- run test w
+[default] (warn): lane host: stopping at a round boundary: 2 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (server restarting: retry shortly)
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (GpuFailed): every admitted stream fails
+[default] (warn): 4 prompts together: SamplingRefused; each goes alone
+[default] (warn): lane host: a round failed (the planner refused): every admitted stream fails
+[default] (warn): lane host: a round failed (PlannerFull): every admitted stream fails
+[default] (warn): lane host: stopping at a round boundary: 1 running and 1 queued request(s) end (drain deadline)
+[default] (warn): lane host: stopping at a round boundary: 0 running and 1 queued request(s) end (NoContext)
+[default] (warn): lane host: a round failed (OutOfMemory): every admitted stream fails
+[default] (warn): lane host: a round failed (AdmittedFailed): every admitted stream fails
+[default] (warn): lane host: a round failed (FinalsFailed): every admitted stream fails
+failed command: ./.zig-cache/o/c74456518c2943afafde330acec5dd48/test --cache-dir=./.zig-cache --seed=0x2b47b4ae --listen=-
+
+Build Summary: 207/211 steps succeeded (2 failed); 2977/2996 tests passed (14 skipped, 5 failed)
+test transitive failure
++- test-cuda-host transitive failure
+ +- run test 0 pass, 1 fail (1 total)
+ +- run test 502 pass, 4 fail (506 total)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/test-golden.log b/docs/receipts/glm-fp4local-80517fc3/ci/test-golden.log
new file mode 100644
index 0000000000..223ad806e1
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/test-golden.log
@@ -0,0 +1,82 @@
+ keys 6 equal 0 differ 0 known
+ origin 20 equal 0 differ 0 known
+26/26 equal to server goldens, 0 unexpected differences
+KNOWN-DIFFER tools/two_tools-stream
+ reply 0 body differs at 313: golden [content": "\n"}, "finish_reason": null}]}
+
+data: {"id": "", "type": "function"]
+KNOWN-DIFFER tools/two_tools-single-stream
+ reply 0 body differs at 313: golden [content": "\n"}, "finish_reason": null}]}
+
+data: {"id": "", "type": "function"]
+KNOWN-DIFFER tools/choice-"required"
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "object": "chat.completion", "created": , ] zig [error": {"message": "backend 'lanes' cannot enforce tool_cho]
+ reply 0 headers differ
+KNOWN-DIFFER tools/choice-{"type": "function", "function": {"name": "lookup"}}
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "object": "chat.completion", "created": , ] zig [error": {"message": "backend 'lanes' cannot enforce tool_cho]
+ reply 0 headers differ
+KNOWN-DIFFER errors/format-json
+ reply 0 body differs at 41: golden [needs the checkpoint's config.json vocab_size", "type": "inv] zig [(response_format and the guided_* fields) is not supported b]
+ reply 0 headers differ
+KNOWN-DIFFER errors/stream-structured
+ connection after: golden closed, zig open
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 0: golden [data: {"id": "", "object": "chat.completion.chunk", "cr] zig [{"error": {"message": "structured output (response_format an]
+KNOWN-DIFFER anthropic/tool-use-stream
+ reply 0 body differs at 704: golden [\n"}}
+
+event: content_block_stop
+data: {"type": "content_blo] zig ["}}
+
+event: content_block_stop
+data: {"type": "content_block]
+KNOWN-DIFFER anthropic/tool-result
+ reply 0 status: golden HTTP/1.1 200 OK, zig HTTP/1.1 400 Bad Request
+ reply 0 body differs at 2: golden [id": "", "type": "message", "role": "assistant", "model] zig [type": "error", "error": {"type": "invalid_request_error", "]
+ reply 0 headers differ
+KNOWN-DIFFER anthropic/refuse-format
+ reply 0 body differs at 91: golden [needs the checkpoint's config.json vocab_size"}}] zig [(response_format and the guided_* fields) is not supported b]
+ reply 0 headers differ
+KNOWN-DIFFER cancel/metrics-after
+ reply 0 body differs at 307: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER metrics-open/metrics
+ reply 0 body differs at 307: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER metrics-open/v1-metrics
+ reply 0 body differs at 487: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+KNOWN-DIFFER keys/metrics-counted
+ reply 0 body differs at 887: golden [prompt_tokens_total Prompt tokens of finished requests.
+# TY] zig [generation_tokens_running Generated tokens held by live stre]
+ reply 0 headers differ
+ chat 16 equal 0 differ 0 known
+ thinking 16 equal 0 differ 0 known
+ stops 13 equal 0 differ 0 known
+ usage 10 equal 0 differ 0 known
+ tools 48 equal 0 differ 4 known
+ errors 37 equal 0 differ 2 known
+ completions 15 equal 0 differ 0 known
+ responses 29 equal 0 differ 0 known
+ anthropic 26 equal 0 differ 3 known
+ tokens 19 equal 0 differ 0 known
+ status 11 equal 0 differ 0 known
+ framing 42 equal 0 differ 0 known
+ cancel 1 equal 0 differ 1 known
+ lifecycle 2 equal 0 differ 0 known
+metrics-open 2 equal 0 differ 2 known
+ keys 19 equal 0 differ 1 known
+306/319 equal to server goldens, 0 unexpected differences
+Build Summary: 5/5 steps succeeded
+test-golden success
++- run exe golden_check success 1m
+| +- compile exe golden_check debug native success 540ms MaxRSS:149M
+| +- compile exe fake_serve debug native success 2s MaxRSS:314M
++- run exe golden_check success 4s
+ +- compile exe golden_check debug native (reused)
+ +- compile exe fake_serve debug native (reused)
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/test-kernel-contract.log b/docs/receipts/glm-fp4local-80517fc3/ci/test-kernel-contract.log
new file mode 100644
index 0000000000..604a9b699a
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/test-kernel-contract.log
@@ -0,0 +1,12 @@
+Build Summary: 11/11 steps succeeded; 65/65 tests passed
+test-kernel-contract success
++- run test 46 pass (46 total) 592ms MaxRSS:9M
+| +- compile test debug native success 3s MaxRSS:166M
++- test-gemma4-contract-host success
+| +- run test 9 pass (9 total) 23s MaxRSS:14M
+| +- compile test debug native success 908ms MaxRSS:224M
+| +- WriteFile gemma4-host-empty.metallib success
++- run test 3 pass (3 total) 12ms MaxRSS:6M
+| +- compile test debug native success 751ms MaxRSS:147M
++- run test 7 pass (7 total) 787ms MaxRSS:55M
+ +- compile test debug native success 749ms MaxRSS:147M
diff --git a/docs/receipts/glm-fp4local-80517fc3/ci/test.log b/docs/receipts/glm-fp4local-80517fc3/ci/test.log
new file mode 100644
index 0000000000..d7f410cf4e
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/ci/test.log
@@ -0,0 +1,441 @@
+discovery verbs: retained through TLS, atexit and destructor; child exit 0
+discovery grammar: retained through TLS, atexit and destructor; child exit 0
+test
++- test-cuda-host
+ +- run test w
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Dimensions
+tf_h3_forward_host: BadH3Dimensions
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Rotary
+tf_h3_forward_host: BadH3Rotary
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Keep
+tf_h3_forward_host: BadH3Keep
+tf_h3_forward: BadH3Blocks
+tf_h3_forward_host: BadH3Blocks
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Scalar
+tf_h3_forward_host: BadH3Scalar
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Address
+tf_h3_forward_host: BadH3Scalar
+H3 model retained at close: NotReady
+H3 geometry retained: NotReady
+tensor blocks.0.norm1.weight: MissingTensor
+tensor blocks.1.attn.qkv_proj.weight: MissingTensor
+tensor blocks.1.norm1.weight: MissingTensor
+failed command: ./.zig-cache/o/dfb5f26b13d589e8eb9be92dde512a7e/test --cache-dir=./.zig-cache --seed=0x3c290419 --listen=-
+
+test
++- test-cuda-host
+ +- run test w
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+[default] (warn): Drama readback drain failed: retaining its buffers and engine owner
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+codec requested 4, actual sizes { 4, 4, 4, 4, 1 }
+codec requested 16, actual sizes { 16, 1 }
+codec requested 1, actual sizes { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
+Drama replay passed: eager/graphs, poison/reset, codec chunks and interleaved state
+[default] (warn): drama3: config hidden_size = 4096, the kernels need 2048
+[default] (warn): codec.pth pickle names os.system, outside the tensor state-dict allow-list
+failed command: ./.zig-cache/o/dd3045c7c153b28989d056af18783173/test --cache-dir=./.zig-cache --seed=0x3c290419 --listen=-
+
+Build Summary: 211/211 steps succeeded; 99/100 tests passed (1 skipped)
+test success
++- test-kernel-symbols-host success
+| +- run test 3 pass (3 total) 3s MaxRSS:77M
+| +- compile test debug native cached 48ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 101ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 58ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 100ms MaxRSS:38M
+| +- options cached
+| +- compile exe digest-writer debug native cached 49ms MaxRSS:38M
++- run test 6 pass (6 total) 21ms MaxRSS:5M
+| +- compile test debug native cached 51ms MaxRSS:37M
++- test-flashnext-batch-host success
+| +- run test 11 pass (11 total) 1s MaxRSS:34M
+| +- compile test debug native cached 102ms MaxRSS:38M
+| +- WriteFile flashnext-test-empty.zig cached
++- run test cached
+| +- compile test debug native cached 46ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 51ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 66ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 52ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 61ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 60ms MaxRSS:38M
++- run test cached
+| +- compile test debug native cached 51ms MaxRSS:38M
+| | +- options cached
+| +- compile obj tp_host_compile debug x86_64-linux-gnu cached 4ms MaxRSS:38M
+| +- options cached
++- test-cuda-host success
+| +- run test cached
+| | +- compile test debug native cached 73ms MaxRSS:38M
+| +- run test cached
+| | +- compile test debug native cached 51ms MaxRSS:38M
+| +- run test cached
+| | +- compile test debug native cached 57ms MaxRSS:38M
+| +- run test cached
+| | +- compile test debug native cached 42ms MaxRSS:38M
+| +- run exe runtime-discovery-exit success 25ms
+| | +- compile exe runtime-discovery-exit debug native cached 62ms MaxRSS:38M
+| | +- options cached
+| | +- compile lib runtime-discovery-mock debug native cached 38ms MaxRSS:37M
+| +- run exe rpc-warning-check success 5ms
+| | +- compile exe rpc-warning-check debug native cached 66ms MaxRSS:37M
+| | +- options cached
+| | +- compile lib rpc-mock-complete debug native cached 48ms MaxRSS:37M
+| | +- compile lib rpc-mock-abi debug native cached 39ms MaxRSS:36M
+| | +- compile lib rpc-mock-wait debug native cached 35ms MaxRSS:37M
+| | +- compile lib rpc-mock-store debug native cached 41ms MaxRSS:37M
+| | +- compile lib rpc-mock-wrong debug native cached 51ms MaxRSS:37M
+| +- run test cached
+| | +- compile test debug native cached 67ms MaxRSS:36M
+| | +- options (+5 more reused dependencies)
+| +- run test cached
+| | +- compile test debug native cached 63ms MaxRSS:38M
+| +- run test 30 pass (30 total) 49ms MaxRSS:9M
+| | +- compile test debug native cached 48ms MaxRSS:38M
+| | +- WriteFile h3-compile-only.fatbin cached
+| | +- options cached
+| +- run test cached
+| | +- compile test debug native cached 49ms MaxRSS:38M
+| +- run test cached
+| | +- compile test debug native cached 91ms MaxRSS:38M
+| | +- WriteFile attn_train.fatbin cached
+| | +- WriteFile dsv41_exl3_experts.fatbin cached
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
+| | +- WriteFile dsv41_exl3_experts.fatbin (reused)
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diff --git a/docs/receipts/glm-fp4local-80517fc3/commit.txt b/docs/receipts/glm-fp4local-80517fc3/commit.txt
new file mode 100644
index 0000000000..e41d13baef
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/commit.txt
@@ -0,0 +1,12 @@
+tested implementation: 80517fc36ea6ee9183c5237ae92b1abd70619f75
+implementation tree: 65c5530c8c180dc1d1cd94e8d6fdb260feda49a5
+base: v1.1.1 b10231b94f8adf3d4132acebdc0a1ac87e53a838
+
+80517fc36ea6ee9183c5237ae92b1abd70619f75 glm/cuda: every rank prints the load and cleanup receipts tools/memory_receipts.py reads, and a follower frees what it owns before it leaves
+9e1d20f7df3423a0d595c8cbbf9be8f05c33759e glm/cuda: FP4-Local's image tower runs its MXFP8 and W4A16_NVFP4 projections as stored, on the engine's qlinear
+4c1a57eda857e0835f03624c6d6ba5cad94e569b glm/cuda: format classification refuses empty and overflowing shapes; docs say which float32 tensors stay float32
+3c341bae13589042ad1f5dbb0e88d472d645d674 glm/cuda: FP4-Local's float32 norm weights stay float32, read by rmsnorm_4096_f32w and hc_finish_f32w with the bf16 forms' arithmetic
+0eac5947149120a6d8f24a00c39b205ffce33193 glm/cuda: FP4-Local's MXFP8 projections run as stored on the lane: dense layers, KDA, lm_head and the MTP layer
+251ef9eaf459391a3dd219966c5eaf46fea85155 glm/cuda: MLA's projections and the shared expert stored FP8_PB_WO run as stored on qlinear's fp8g lane
+26894027ebf5aa0273b6def8bc3596bc9b083b0b glm/cuda: read the MTP layer's W4A16_NVFP4 experts as stored; glm-loader-gate checks every rank's uploads on the host
+461d44736c996b7c2d4382e0d54c50df9ed2cf48 glm/cuda: tell each projection's stored format from its MIXED_PRECISION recipe, and decode csf_32x2_v1 expert scales
diff --git a/docs/receipts/glm-fp4local-80517fc3/lib/libtfgrammar-source.txt b/docs/receipts/glm-fp4local-80517fc3/lib/libtfgrammar-source.txt
new file mode 100644
index 0000000000..5943dcfd49
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/lib/libtfgrammar-source.txt
@@ -0,0 +1,2 @@
+v1.1.1 release tensorfold-1.1.1-linux-aarch64.tar.gz sha256 2e0103a544e64354bfec65bd8a052ede17aa9be1f6a56d8e37c868af9139cb39, lib/libtfgrammar.so
+801c4c3434251eecc572b753edeb79162866a0063f3850e73b8a21471b6e284e
diff --git a/docs/receipts/glm-fp4local-80517fc3/memory/summary.json b/docs/receipts/glm-fp4local-80517fc3/memory/summary.json
new file mode 100644
index 0000000000..47b80eefc8
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/memory/summary.json
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diff --git a/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank0.json b/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank0.json
new file mode 100644
index 0000000000..8ab839318c
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank0.json
@@ -0,0 +1,41634 @@
+{
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diff --git a/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank1.json b/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank1.json
new file mode 100644
index 0000000000..3bb5fef125
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/memory/tp2-ex-fl-rank1.json
@@ -0,0 +1,43159 @@
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+}
diff --git a/docs/receipts/glm-fp4local-80517fc3/runs.log b/docs/receipts/glm-fp4local-80517fc3/runs.log
new file mode 100644
index 0000000000..c0180dbeb5
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/runs.log
@@ -0,0 +1,114 @@
+2026-10-20 00:48:23 ex-111 golden PASS
+2026-10-20 00:54:28 ex-111 conc PASS
+2026-10-20 00:54:46 ex-111 resume PASS
+2026-10-20 00:55:19 ex-111 batch PASS
+2026-10-20 00:55:32 ex-111 serve END rc=0
+2026-10-20 00:58:29 gate bin-80517fc3 PASS tp2 rank 0 rc=0 82s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=2 rank 0: 115 uploads as the checkpoint cut, 72 as the host packers make them (11922 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 100 GiB, 0 guard stops
+2026-10-20 00:58:29 gate bin-80517fc3 PASS tp2 rank 1 rc=0 80s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=2 rank 1: 115 uploads as the checkpoint cut, 72 as the host packers make them (11922 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 100 GiB, 0 guard stops
+2026-10-20 01:07:52 pair cover-bin-80517fc3-fp4local PASS 469 s
+2026-10-20 01:16:40 pair concurrent-bin-80517fc3-fp4local PASS 436 s
+2026-10-20 01:23:52 pair shared-bin-80517fc3-fp4local PASS 340 s
+2026-10-20 01:33:24 ex-nv golden PASS
+2026-10-20 01:39:29 ex-nv conc PASS
+2026-10-20 01:39:48 ex-nv resume PASS
+2026-10-20 01:40:18 ex-nv batch PASS
+2026-10-20 01:40:32 ex-nv serve END rc=0
+2026-10-20 01:49:43 ex-fl golden PASS
+2026-10-20 01:54:44 ex-fl conc PASS
+2026-10-20 01:55:01 ex-fl resume PASS
+2026-10-20 01:55:25 ex-fl batch PASS
+2026-10-20 01:55:38 ex-fl serve END rc=0
+2026-10-20 02:04:20 bt-111 batch1 PASS
+2026-10-20 02:04:33 bt-111 serve END rc=0
+2026-10-20 02:11:47 bt-nv batch1 PASS
+2026-10-20 02:12:01 bt-nv serve END rc=0
+2026-10-20 02:19:07 bt-fl batch1 PASS
+2026-10-20 02:19:21 bt-fl serve END rc=0
+2026-10-20 02:28:32 smoke3-mm1 golden FAIL response = self.parent.error( File "/usr/lib/python3.12/urllib/request.py", line 559, in error
+2026-10-20 02:28:32 smoke3-mm1 serve STOP no health after golden
+2026-10-20 02:28:45 smoke3-mm1 serve END rc=2
+2026-10-20 02:35:29 smoke3 golden PASS
+2026-10-20 02:35:43 smoke3 serve END rc=0
+2026-10-20 02:38:26 gate3 bin-80517fc3 PASS tp3 rank 0 rc=0 68s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 0: 115 uploads as the checkpoint cut, 72 as the host packers make them (8614 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 104 GiB, 0 guard stops
+2026-10-20 02:38:26 gate3 bin-80517fc3 PASS tp3 rank 1 rc=0 66s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 1: 115 uploads as the checkpoint cut, 72 as the host packers make them (8602 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 105 GiB, 0 guard stops
+2026-10-20 02:38:26 gate3 bin-80517fc3 PASS tp3 rank 2 rc=0 63s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 2: 115 uploads as the checkpoint cut, 72 as the host packers make them (7990 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 105 GiB, 0 guard stops
+2026-10-20 02:48:32 gate3 bin-80517fc3-all PASS tp3 rank 0 rc=0 493s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 0: 894 uploads as the checkpoint cut, 486 as the host packers make them (62094 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 51 GiB, 0 guard stops
+2026-10-20 02:48:32 gate3 bin-80517fc3-all PASS tp3 rank 1 rc=0 509s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 1: 894 uploads as the checkpoint cut, 486 as the host packers make them (61997 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 50 GiB, 0 guard stops
+2026-10-20 02:48:32 gate3 bin-80517fc3-all PASS tp3 rank 2 rc=0 475s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=3 rank 2: 894 uploads as the checkpoint cut, 486 as the host packers make them (56738 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 56 GiB, 0 guard stops
+2026-10-20 02:56:35 pair3 cover-bin-80517fc3-fp4local PASS 385 s
+2026-10-20 03:04:09 pair3 concurrent-bin-80517fc3-fp4local PASS 361 s
+2026-10-20 03:10:27 pair3 shared-bin-80517fc3-fp4local PASS 286 s
+2026-10-20 03:18:28 ex3-fl golden PASS
+2026-10-20 03:22:25 ex3-fl conc PASS
+2026-10-20 03:22:38 ex3-fl resume PASS
+2026-10-20 03:22:58 ex3-fl batch PASS
+2026-10-20 03:23:12 ex3-fl serve END rc=0
+2026-10-20 03:31:21 ex3-nv golden PASS
+2026-10-20 03:36:24 ex3-nv conc PASS
+2026-10-20 03:36:39 ex3-nv resume PASS
+2026-10-20 03:36:59 ex3-nv batch PASS
+2026-10-20 03:37:13 ex3-nv serve END rc=0
+2026-10-20 03:44:19 md3-fl media PASS
+2026-10-20 03:44:38 md3-fl mresume PASS
+2026-10-20 03:44:51 md3-fl serve END rc=0
+2026-10-20 03:53:13 sp3-nv nodraft PASS
+2026-10-20 03:55:23 sp3-nv prefill PASS
+2026-10-20 03:55:37 sp3-nv serve END rc=0
+2026-10-20 04:04:56 sp3-fl nodraft PASS
+2026-10-20 04:06:57 sp3-fl prefill PASS
+2026-10-20 04:07:04 sp3-fl watch PASS rank 0 'exit 1', rank 1 'exit 1' 7 s after rank 2 was killed
+2026-10-20 04:07:10 sp3-fl serve END rc=0
+2026-10-20 04:19:16 lp-fl plp PASS
+2026-10-20 04:19:29 lp-fl serve END rc=0
+2026-10-20 04:28:34 lp-nv plp PASS
+2026-10-20 04:28:48 lp-nv serve END rc=0
+2026-10-20 06:37:49 x9-fl x9 FAIL response = self.parent.error( File "/usr/lib/python3.12/urllib/request.py", line 559, in error
+2026-10-20 06:38:03 x9-fl serve END rc=1
+2026-10-20 06:49:56 gate bin-80517fc3-all PASS tp2 rank 0 rc=0 616s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=2 rank 0: 894 uploads as the checkpoint cut, 486 as the host packers make them (89527 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 23 GiB, 0 guard stops
+2026-10-20 06:49:56 gate bin-80517fc3-all PASS tp2 rank 1 rc=0 617s : 1 PASS 0 FAIL; PASS glm-loader-gate TP=2 rank 1: 894 uploads as the checkpoint cut, 486 as the host packers make them (89527 MiB), 3 left out, 0 differ or are not known MemAvailable lowest 23 GiB, 0 guard stops
+2026-10-20 06:58:02 md-fl media PASS
+2026-10-20 06:58:15 md-fl serve END rc=0
+2026-10-20 07:06:10 md-nv media PASS
+2026-10-20 07:06:23 md-nv serve END rc=0
+2026-10-20 07:13:33 mr-111 mresume PASS
+2026-10-20 07:13:47 mr-111 serve END rc=0
+2026-10-20 07:20:57 mr-nv mresume PASS
+2026-10-20 07:21:11 mr-nv serve END rc=0
+2026-10-20 07:28:19 mr-fl mresume PASS
+2026-10-20 07:28:33 mr-fl serve END rc=0
+2026-10-20 07:38:18 sp1-111 openai PASS
+2026-10-20 07:39:40 sp1-111 cache PASS
+2026-10-20 07:39:53 sp1-111 serve END rc=0
+2026-10-20 07:51:09 sp1-nv openai PASS
+2026-10-20 07:52:30 sp1-nv cache PASS
+2026-10-20 07:55:20 sp1-nv nodraft PASS
+2026-10-20 07:57:47 sp1-nv prefill PASS
+2026-10-20 07:58:01 sp1-nv serve END rc=0
+2026-10-20 08:08:24 sp1-fl nodraft PASS
+2026-10-20 08:10:51 sp1-fl prefill PASS
+2026-10-20 08:11:05 sp1-fl serve END rc=0
+2026-10-20 08:22:20 sp2-111 openai PASS
+2026-10-20 08:23:41 sp2-111 cache PASS
+2026-10-20 08:23:54 sp2-111 serve END rc=0
+2026-10-20 08:35:10 sp2-nv openai PASS
+2026-10-20 08:36:31 sp2-nv cache PASS
+2026-10-20 08:39:21 sp2-nv nodraft PASS
+2026-10-20 08:41:48 sp2-nv prefill PASS
+2026-10-20 08:42:02 sp2-nv serve END rc=0
+2026-10-20 08:52:35 sp2-fl nodraft PASS
+2026-10-20 08:55:03 sp2-fl prefill PASS
+2026-10-20 08:55:17 sp2-fl serve END rc=0
+2026-10-20 09:19:44 single FAIL kernel-contract SUMMARY 91 kernels: 91 pass gates 1-4, 0 fail, 0 known findings; 7 controls: 7 red as planted, 0 missed; 1533 limit problems; gates 567857 ms, limits 99197 ms; kernel-contract rc=1 fatbins rc=0 limits rc=1
+2026-10-20 09:19:44 single FAIL limits FAIL kernel-contract-limits: 877 problems
+2026-10-20 09:19:44 single PASS glm-ref 12 PASS 0 FAIL, RESULT lines 14 (same as b10231b9's); glm-ref 80517fc3 rc=0 glm-ref b10231b9 rc=0
+2026-10-20 09:27:47 x9-nv x9 PASS
+2026-10-20 09:28:01 x9-nv serve END rc=0
+2026-10-20 09:35:25 x9-fl-nd x9 PASS
+2026-10-20 09:35:39 x9-fl-nd serve END rc=0
+2026-10-20 09:43:16 x9-nv-nd x9 PASS
+2026-10-20 09:43:30 x9-nv-nd serve END rc=0
+2026-10-20 10:08:02 single FAIL kernel-contract SUMMARY 85 kernels: 85 pass gates 1-4, 0 fail, 0 known findings; 7 controls: 7 red as planted, 0 missed; 1533 limit problems; gates 568074 ms, limits 100525 ms; kernel-contract rc=1 fatbins rc=0 limits rc=1
+2026-10-20 10:08:02 single FAIL limits FAIL kernel-contract-limits: 877 problems
+2026-10-20 10:08:02 single PASS glm-ref 11 PASS 0 FAIL, RESULT lines 14 (same as b10231b9's); glm-ref b10231b9 rc=0 glm-ref b10231b9 rc=0
+2026-10-20 10:15:59 x9-fl x9 PASS
+2026-10-20 10:16:13 x9-fl serve END rc=0
diff --git a/docs/receipts/glm-fp4local-80517fc3/s5/bf16-ref-b2.pt.json b/docs/receipts/glm-fp4local-80517fc3/s5/bf16-ref-b2.pt.json
new file mode 100644
index 0000000000..d528625249
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s5/bf16-ref-b2.pt.json
@@ -0,0 +1,30 @@
+{
+ "ids": "ids.pt",
+ "ids_file_sha256": "6ec42069590d444f83492545be234e2d288287f055f6c4d3ce9828118fdf0cf1",
+ "ids_sha256": "d857818d0cd8c6d3",
+ "hidden_sha256": "3b9094f8720ab62bed1438b7296e070391a3db048038ebfff4af63ac863cc21b",
+ "hidden_dtype": "torch.bfloat16",
+ "lm_head": {
+ "file": "model-00001-of-00120.safetensors",
+ "shape": [
+ 154880,
+ 4096
+ ],
+ "dtype": "torch.bfloat16",
+ "sha256": "aa21c427970f64edd82669db3a8fb46613084e8bc271a3728784a52eb3f25ab4"
+ },
+ "snapshot": "a5b45eb41df6402735dedc900be14a42e8d5e538",
+ "sequences": 128,
+ "positions": 144285,
+ "top": 20,
+ "chunk": 512,
+ "nll_mean": 1.0069094541699932,
+ "top1_is_next": 0.753169059753418,
+ "check": {
+ "file": "/LLLLM/records/osfp4/eval/b2/fp4local/summary.json",
+ "ids_sha256": "d857818d0cd8c6d3",
+ "nll_ref": 1.0069093704223633,
+ "positions": 144285,
+ "equal": true
+ }
+}
diff --git a/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-fl.log b/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-fl.log
new file mode 100644
index 0000000000..66c7238875
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-fl.log
@@ -0,0 +1,10 @@
+16 sequences, 23 s
+32 sequences, 49 s
+48 sequences, 70 s
+64 sequences, 90 s
+80 sequences, 104 s
+96 sequences, 121 s
+112 sequences, 127 s
+128 sequences, 134 s
+128 sequences in 134 s, 0 bad
+exit 0
diff --git a/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-nv.log b/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-nv.log
new file mode 100644
index 0000000000..30a18484f9
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s5/check-plp-lp-nv.log
@@ -0,0 +1,10 @@
+16 sequences, 24 s
+32 sequences, 50 s
+48 sequences, 71 s
+64 sequences, 92 s
+80 sequences, 106 s
+96 sequences, 123 s
+112 sequences, 130 s
+128 sequences, 137 s
+128 sequences in 137 s, 0 bad
+exit 0
diff --git a/docs/receipts/glm-fp4local-80517fc3/s5/plp-sha256.txt b/docs/receipts/glm-fp4local-80517fc3/s5/plp-sha256.txt
new file mode 100644
index 0000000000..657250654a
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s5/plp-sha256.txt
@@ -0,0 +1,2 @@
+0fd93f6bcbd36eb54d985cfaef662312f35c1e675c84d9431628d9861ac840f0 *plp-lp-fl.jsonl
+6d19d534761b3e25210c3ea770444fc4df3856f2fe8bf62cd559b22b2dfa245d *plp-lp-nv.jsonl
diff --git a/docs/receipts/glm-fp4local-80517fc3/s5/s5-compare.json b/docs/receipts/glm-fp4local-80517fc3/s5/s5-compare.json
new file mode 100644
index 0000000000..b4834ce8e5
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s5/s5-compare.json
@@ -0,0 +1,50 @@
+{
+ "fl": {
+ "positions": 144285,
+ "top1_equal_bf16": 0.928239248709152,
+ "top1_equal_bf16_tied": 0.9347264095366808,
+ "top1_is_given": 0.7508611428769449,
+ "top1_is_given_tied": 0.7551997782167238,
+ "bf16_top1_is_given": 0.7531690750944312,
+ "bf16_top1_is_given_tied": 0.7573483037044738,
+ "nll": 1.0181083014664334,
+ "nll_bf16": 1.0069094541699932,
+ "nll_rise": 0.011198847296440162,
+ "nll_rise_rel": 0.01112200034477926,
+ "given_lp_abs_diff": {
+ "mean": 0.16241118692124937,
+ "p99": 1.7738856315612854,
+ "max": 18.7728271484375
+ },
+ "kl_top20": {
+ "mean": 0.05275108871995653,
+ "p99": 0.7498343022204862
+ },
+ "bf16_top20_mass": 0.9711863556798259,
+ "bf16_top20_found_in_engine_top20": 0.8531839761582978
+ },
+ "nv": {
+ "positions": 144285,
+ "top1_equal_bf16": 0.9268461725058045,
+ "top1_equal_bf16_tied": 0.933562047336868,
+ "top1_is_given": 0.7497245035866514,
+ "top1_is_given_tied": 0.7541047232907093,
+ "bf16_top1_is_given": 0.7531690750944312,
+ "bf16_top1_is_given_tied": 0.7573483037044738,
+ "nll": 1.0206881481656729,
+ "nll_bf16": 1.0069094541699932,
+ "nll_rise": 0.013778693995679658,
+ "nll_rise_rel": 0.013684144029651197,
+ "given_lp_abs_diff": {
+ "mean": 0.16197861962081198,
+ "p99": 1.7685399985313441,
+ "max": 18.303059339523315
+ },
+ "kl_top20": {
+ "mean": 0.05369183298976444,
+ "p99": 0.7640779384290483
+ },
+ "bf16_top20_mass": 0.9711863556798259,
+ "bf16_top20_found_in_engine_top20": 0.8523678830093219
+ }
+}
diff --git a/docs/receipts/glm-fp4local-80517fc3/s9/compare-fl.txt b/docs/receipts/glm-fp4local-80517fc3/s9/compare-fl.txt
new file mode 100644
index 0000000000..55ae038794
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s9/compare-fl.txt
@@ -0,0 +1,24 @@
+SAME draft-false: {"text": "{\"name\": \"Tokyo\", \"population\": 13960000}", "sha": "795208091607", "calls": []}
+SAME nested: {"text": "{\n \"cities\": [\n {\n \"name\": \"Tokyo\",\n \"country\": \"Japan\",\n \"size\": \"large\"\n },\n {\n \"name\": \"Osak
+SAME plain: {"text": " Paris. In French, Paris is pronounced", "sha": "09a15f0582e7", "calls": []}
+SAME resume-a-1: {"text": "{\"entry\": 17, \"boats\": 23}", "sha": "cd608af3c0b2", "calls": []}
+SAME resume-a-2: {"text": "{\"entry\": 17, \"boats\": 23}", "sha": "cd608af3c0b2", "calls": []}
+SAME same-as-plain-plain: {"text": "{\"ok\": true}", "sha": "80d142b4c0db", "calls": []}
+SAME same-as-plain-shaped: {"text": "{\"ok\": true}", "sha": "80d142b4c0db", "calls": []}
+SAME schema: {"text": "{\"name\": \"Tokyo\", \"population\": 13960000}", "sha": "795208091607", "calls": []}
+SAME schema-thinking: {"text": "{\n \"name\": \"Tokyo\",\n \"population\": 14047894\n}", "sha": "94687a0b2aa7", "calls": []}
+SAME tool-empty: {"text": "", "sha": "d1ff6466e51f", "calls": [["get_server_time", "{}"]]}
+SAME tool-empty-draft-false: {"text": "", "sha": "d1ff6466e51f", "calls": [["get_server_time", "{}"]]}
+SAME tool-named: {"text": "", "sha": "909c93fa814c", "calls": [["get_time", "{\"city\":\"Osaka\"}"]]}
+SAME tool-named-draft-false: {"text": "", "sha": "909c93fa814c", "calls": [["get_time", "{\"city\":\"Osaka\"}"]]}
+SAME tool-nested: {"text": "", "sha": "4beafe560502", "calls": [["plan_trip", "{\"travellers\":[{\"name\":\"Aiko\",\"age\":34},{\"name\":\"Ken\",\"age\":36}],\"stops\":[{\"city\"
+SAME tool-nested-draft-false: {"text": "", "sha": "4beafe560502", "calls": [["plan_trip", "{\"travellers\":[{\"name\":\"Aiko\",\"age\":34},{\"name\":\"Ken\",\"age\":36}],\"stops\":[{\"city\"
+SAME tool-parallel: {"text": "", "sha": "2621da3eecc5", "calls": [["get_weather", "{\"city\":\"Osaka\"}"], ["get_weather", "{\"city\":\"Sapporo\"}"]]}
+SAME tool-parallel-draft-false: {"text": "", "sha": "2621da3eecc5", "calls": [["get_weather", "{\"city\":\"Osaka\"}"], ["get_weather", "{\"city\":\"Sapporo\"}"]]}
+SAME tool-required: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-required-3: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-required-3-draft-false: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-single: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-single-draft-false: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME resume-b (resumed against cold): {"entry": 17, "boats": 23} | {"entry": 17, "boats": 23}
+22 shared replies; all the same
diff --git a/docs/receipts/glm-fp4local-80517fc3/s9/compare-nv.txt b/docs/receipts/glm-fp4local-80517fc3/s9/compare-nv.txt
new file mode 100644
index 0000000000..513412cb28
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/s9/compare-nv.txt
@@ -0,0 +1,24 @@
+SAME draft-false: {"text": "{\"name\": \"Tokyo\", \"population\": 13960000}", "sha": "795208091607", "calls": []}
+SAME nested: {"text": "{\n \"cities\": [\n {\n \"name\": \"Tokyo\",\n \"country\": \"Japan\",\n \"size\": \"large\"\n },\n {\n \"name\": \"Osak
+SAME plain: {"text": " Paris. In French, Paris is pronounced", "sha": "09a15f0582e7", "calls": []}
+SAME resume-a-1: {"text": "{\"entry\": 17, \"boats\": 23}", "sha": "cd608af3c0b2", "calls": []}
+SAME resume-a-2: {"text": "{\"entry\": 17, \"boats\": 23}", "sha": "cd608af3c0b2", "calls": []}
+SAME same-as-plain-plain: {"text": "{\"ok\": true}", "sha": "80d142b4c0db", "calls": []}
+SAME same-as-plain-shaped: {"text": "{\"ok\": true}", "sha": "80d142b4c0db", "calls": []}
+SAME schema: {"text": "{\"name\": \"Tokyo\", \"population\": 13960000}", "sha": "795208091607", "calls": []}
+SAME schema-thinking: {"text": "{\n \"name\": \"Tokyo\",\n \"population\": 14000000\n}", "sha": "dcfcc508946b", "calls": []}
+SAME tool-empty: {"text": "", "sha": "d1ff6466e51f", "calls": [["get_server_time", "{}"]]}
+SAME tool-empty-draft-false: {"text": "", "sha": "d1ff6466e51f", "calls": [["get_server_time", "{}"]]}
+SAME tool-named: {"text": "", "sha": "909c93fa814c", "calls": [["get_time", "{\"city\":\"Osaka\"}"]]}
+SAME tool-named-draft-false: {"text": "", "sha": "909c93fa814c", "calls": [["get_time", "{\"city\":\"Osaka\"}"]]}
+SAME tool-nested: {"text": "", "sha": "4beafe560502", "calls": [["plan_trip", "{\"travellers\":[{\"name\":\"Aiko\",\"age\":34},{\"name\":\"Ken\",\"age\":36}],\"stops\":[{\"city\"
+SAME tool-nested-draft-false: {"text": "", "sha": "4beafe560502", "calls": [["plan_trip", "{\"travellers\":[{\"name\":\"Aiko\",\"age\":34},{\"name\":\"Ken\",\"age\":36}],\"stops\":[{\"city\"
+SAME tool-parallel: {"text": "", "sha": "2621da3eecc5", "calls": [["get_weather", "{\"city\":\"Osaka\"}"], ["get_weather", "{\"city\":\"Sapporo\"}"]]}
+SAME tool-parallel-draft-false: {"text": "", "sha": "2621da3eecc5", "calls": [["get_weather", "{\"city\":\"Osaka\"}"], ["get_weather", "{\"city\":\"Sapporo\"}"]]}
+SAME tool-required: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-required-3: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-required-3-draft-false: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-single: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME tool-single-draft-false: {"text": "", "sha": "427c0c4efc04", "calls": [["get_weather", "{\"city\":\"Osaka\"}"]]}
+SAME resume-b (resumed against cold): {"entry": 17, "boats": 23} | {"entry": 17, "boats": 23}
+22 shared replies; all the same
diff --git a/docs/receipts/glm-fp4local-80517fc3/single-v1.1.1/fatbins.log b/docs/receipts/glm-fp4local-80517fc3/single-v1.1.1/fatbins.log
new file mode 100644
index 0000000000..49b6e17478
--- /dev/null
+++ b/docs/receipts/glm-fp4local-80517fc3/single-v1.1.1/fatbins.log
@@ -0,0 +1,473 @@
+kernel-contract-fatbins
++- fatbins
+ +- install generated to fatbin/window_attention.fatbin
+ +- run /usr/local/cuda-13.0/bin/nvcc (window_attention.fatbin) w
+./zig/kernels/cuda/window_attention.cu(174): warning #1556-D: name linkage conflicts with previous declaration of variable "tf_window_prompt::smem" (declared at line 360)
+ extern __attribute__((shared)) uint4 smem[];
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/window_attention.cu(174): warning #1556-D: name linkage conflicts with previous declaration of variable "tf_window_prompt::smem" (declared at line 360)
+ extern __attribute__((shared)) uint4 smem[];
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/window_attention.cu(174): warning #1556-D: name linkage conflicts with previous declaration of variable "tf_window_prompt::smem" (declared at line 360)
+ extern __attribute__((shared)) uint4 smem[];
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/window_attention.cu(174): warning #1556-D: name linkage conflicts with previous declaration of variable "tf_window_prompt::smem" (declared at line 360)
+ extern __attribute__((shared)) uint4 smem[];
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/window_attention.cu(174): warning #1556-D: name linkage conflicts with previous declaration of variable "tf_window_prompt::smem" (declared at line 360)
+ extern __attribute__((shared)) uint4 smem[];
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+
+kernel-contract-fatbins
++- fatbins
+ +- install generated to fatbin/prefill_attention.fatbin
+ +- run /usr/local/cuda-13.0/bin/nvcc (prefill_attention.fatbin) w
+./zig/kernels/cuda/prefill_attention.cu(18): warning #177-D: variable "tf_prefill_attention::SMEM_MAX" was declared but never referenced
+ constexpr int SMEM_MAX = 96 * 1024;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/prefill_attention.cu(18): warning #177-D: variable "tf_prefill_attention::SMEM_MAX" was declared but never referenced
+ constexpr int SMEM_MAX = 96 * 1024;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/prefill_attention.cu(18): warning #177-D: variable "tf_prefill_attention::SMEM_MAX" was declared but never referenced
+ constexpr int SMEM_MAX = 96 * 1024;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/prefill_attention.cu(18): warning #177-D: variable "tf_prefill_attention::SMEM_MAX" was declared but never referenced
+ constexpr int SMEM_MAX = 96 * 1024;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/prefill_attention.cu(18): warning #177-D: variable "tf_prefill_attention::SMEM_MAX" was declared but never referenced
+ constexpr int SMEM_MAX = 96 * 1024;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+
+kernel-contract-fatbins
++- fatbins
+ +- install generated to fatbin/drama3_fp8.fatbin
+ +- run /usr/local/cuda-13.0/bin/nvcc (drama3_fp8.fatbin) w
+./zig/kernels/cuda/drama3_fp8.cu(188): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/drama3_fp8.cu(188): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/drama3_fp8.cu(188): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/drama3_fp8.cu(188): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/drama3_fp8.cu(188): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+
+kernel-contract-fatbins
++- fatbins
+ +- install generated to fatbin/fp8_experts.fatbin
+ +- run /usr/local/cuda-13.0/bin/nvcc (fp8_experts.fatbin) w
+./zig/kernels/cuda/fp8_experts.cu(153): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/fp8_experts.cu(153): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/fp8_experts.cu(153): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/fp8_experts.cu(153): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/fp8_experts.cu(153): warning #177-D: variable "RW" was declared but never referenced
+ constexpr int RW = PromptCfg::RW;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+
+kernel-contract-fatbins
++- fatbins
+ +- install generated to fatbin/glm_mm.fatbin
+ +- run /usr/local/cuda-13.0/bin/nvcc (glm_mm.fatbin) w
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=6, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=4, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+Remark: The warnings can be suppressed with "-diag-suppress "
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=32, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=16, BM=16, WM=1, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=128, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile::SMEM [with BITS=8, BM=64, WM=2, STAGES=3]" was declared but never referenced
+ static constexpr int SMEM = STAGES * STAGE;
+ ^
+
+./zig/kernels/cuda/glm_mm.cu(84): warning #177-D: member "::Tile