From 6e6fd528ca8b3094e620ab2c2ea09e95e134b06d Mon Sep 17 00:00:00 2001 From: nachiketb Date: Mon, 24 Aug 2026 17:10:59 -0700 Subject: [PATCH] feat(prefill-router): run batched prefill inference Signed-off-by: nachiketb --- Cargo.lock | 2 +- crates/prefill-router/Cargo.toml | 4 +- crates/prefill-router/pyproject.toml | 6 +- .../python/transformers_forward.py | 289 +++++---- crates/prefill-router/src/error.rs | 8 +- crates/prefill-router/src/lib.rs | 310 +--------- crates/prefill-router/src/router.rs | 119 ++++ crates/prefill-router/src/transformers.rs | 424 ++++++------- crates/prefill-router/uv.lock | 559 ++---------------- 9 files changed, 550 insertions(+), 1171 deletions(-) create mode 100644 crates/prefill-router/src/router.rs diff --git a/Cargo.lock b/Cargo.lock index 945c6d5f0..99b812aca 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1502,7 +1502,7 @@ version = "0.2.0" dependencies = [ "pyo3", "pyo3-build-config", - "serde_json", + "switchyard-protocol", "thiserror 2.0.18", "tracing", ] diff --git a/crates/prefill-router/Cargo.toml b/crates/prefill-router/Cargo.toml index 5dbdfa527..df4c5a3b9 100644 --- a/crates/prefill-router/Cargo.toml +++ b/crates/prefill-router/Cargo.toml @@ -4,7 +4,7 @@ [package] name = "prefill-router" version.workspace = true -description = "Prefill feature extraction for Switchyard routing" +description = "Learned prefill routing for Switchyard" authors.workspace = true edition.workspace = true license.workspace = true @@ -13,7 +13,7 @@ rust-version.workspace = true [dependencies] pyo3 = { version = "0.28.3", features = ["auto-initialize"] } -serde_json.workspace = true +switchyard-protocol.workspace = true thiserror.workspace = true tracing.workspace = true diff --git a/crates/prefill-router/pyproject.toml b/crates/prefill-router/pyproject.toml index 7fa7c0f65..e291067a3 100644 --- a/crates/prefill-router/pyproject.toml +++ b/crates/prefill-router/pyproject.toml @@ -6,16 +6,12 @@ name = "prefill-router-tests" version = "0.0.0" requires-python = ">=3.10" dependencies = [ + "accelerate>=1.0", "numpy>=1.26", "torch>=2.0", "transformers>=5.0", ] -[dependency-groups] -dev = [ - "model-router-toolkit @ https://github.com/NVIDIA-AI-Blueprints/llm-router/archive/8a9d3509fbde879d9795258081bab9553b458e04.tar.gz", -] - [tool.uv] package = false diff --git a/crates/prefill-router/python/transformers_forward.py b/crates/prefill-router/python/transformers_forward.py index 9ce74bd90..0c6946c88 100644 --- a/crates/prefill-router/python/transformers_forward.py +++ b/crates/prefill-router/python/transformers_forward.py @@ -1,10 +1,11 @@ # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 -"""Hugging Face Transformers prefill forward used by the Rust crate.""" +"""Complete Transformers prefill and checkpoint inference for the Rust crate.""" from __future__ import annotations +from pathlib import Path from typing import Any @@ -37,117 +38,141 @@ def _resolve_device(torch: Any, override: str | None) -> str: class TransformersForward: - """Lazily load a causal LM and return pooled prefill hidden states.""" + """Run encoder extraction and learned confidence inference in one pass.""" def __init__( self, - model: str, + checkpoint_path: str | Path, *, device: str | None = None, cache_dir: str | None = None, ) -> None: - self._model_path = model + import numpy as np + import torch + + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True) + if checkpoint["format_version"] != 1: + raise ValueError("unsupported checkpoint format_version") + + encoder = checkpoint["encoder"] + architecture = checkpoint["architecture"] + pipeline = checkpoint["feature_pipeline"] + if encoder["view"] != "task_prompt_only": + raise ValueError("checkpoint encoder view must be task_prompt_only") + if pipeline["pooling"] != "mean of independently standardized selected layers": + raise ValueError("unsupported checkpoint feature pooling") + if architecture["activation"] != "ReLU": + raise ValueError("checkpoint activation must be ReLU") + if architecture["ensemble_reduction"] != "mean(sigmoid(logits))": + raise ValueError("unsupported checkpoint ensemble reduction") + + self._numpy = np + self._torch = torch + self._model_path = str(encoder["name"]) + self._expected_layers = int(encoder["n_layers"]) + self._hidden_dim = int(encoder["hidden_dim"]) + self._models = [str(model) for model in checkpoint["models"]] + self._selected_layers = [int(layer) for layer in pipeline["selected_layers"]] + self._layer_mean = torch.stack( + [pipeline["layer_mean"][str(layer)].float() for layer in self._selected_layers] + ).numpy() + self._layer_std = torch.stack( + [pipeline["layer_std"][str(layer)].float() for layer in self._selected_layers] + ).numpy() + self._scaler_mean = pipeline["scaler_mean"].numpy() + self._scaler_scale = pipeline["scaler_scale"].numpy() + self._pca_mean = pipeline["pca_mean"].numpy() + self._pca_components = pipeline["pca_components"].numpy() + self._states = checkpoint["model_state_dicts"] self._cache_dir = cache_dir self._device_override = device self._model = None self._tokenizer = None - self._torch = None - self.n_layers = 0 - self.hidden_dim = 0 - def _ensure_loaded(self) -> str: + if not self._selected_layers or len(set(self._selected_layers)) != len( + self._selected_layers + ): + raise ValueError("checkpoint selected layers must be non-empty and unique") + if not self._models or not self._states: + raise ValueError("checkpoint must contain models and ensemble members") + if self._layer_mean.shape != self._layer_std.shape or self._layer_mean.shape != ( + len(self._selected_layers), + self._hidden_dim, + ): + raise ValueError("checkpoint layer normalization shape is inconsistent") + if not bool(np.all(self._layer_std > 0)) or not bool(np.all(self._scaler_scale > 0)): + raise ValueError("checkpoint normalization scales must be positive") + + def metadata(self) -> tuple[str, int]: + """Return the encoder and ordered output count consumed by Rust.""" + return self._model_path, len(self._models) + + def _ensure_loaded(self) -> None: if self._model is not None: - return str(self._model.device) + return - import torch from transformers import AutoModelForCausalLM, AutoTokenizer - self._torch = torch + torch = self._torch device = _resolve_device(torch, self._device_override) - if device == "cpu": - dtype = torch.float32 - elif device == "mps": - dtype = torch.float16 - else: - dtype = ( - torch.bfloat16 - if torch.cuda.get_device_capability(device)[0] >= 8 - else torch.float16 - ) - + dtype = ( + torch.float32 + if device == "cpu" + else torch.float16 + if device == "mps" + else torch.bfloat16 + if torch.cuda.get_device_capability(device)[0] >= 8 + else torch.float16 + ) + model_kwargs: dict[str, Any] = {} + if device == "cuda": + model_kwargs["device_map"] = "auto" + elif device.startswith("cuda:"): + model_kwargs["device_map"] = {"": device} self._tokenizer = AutoTokenizer.from_pretrained( self._model_path, cache_dir=self._cache_dir, ) if self._tokenizer.pad_token is None: self._tokenizer.pad_token = self._tokenizer.eos_token - - load_kwargs: dict[str, Any] = { - "dtype": dtype, - "cache_dir": self._cache_dir, - } - - self._model = AutoModelForCausalLM.from_pretrained( + causal_model = AutoModelForCausalLM.from_pretrained( self._model_path, - **load_kwargs, + dtype=dtype, + cache_dir=self._cache_dir, + **model_kwargs, ) - if device != "cpu": + self._model = causal_model.base_model + del causal_model + if device == "mps": self._model.to(device) self._model.eval() - self.n_layers = self._model.config.num_hidden_layers - self.hidden_dim = self._model.config.hidden_size - return str(self._model.device) + model_config = getattr(self._model.config, "text_config", self._model.config) + if ( + model_config.num_hidden_layers != self._expected_layers + or model_config.hidden_size != self._hidden_dim + ): + raise ValueError("loaded encoder dimensions do not match checkpoint metadata") - def extract_batch( - self, - prompts: list[str], - *, - chat_template_kwargs: dict[str, Any] | None = None, - extract_layers: list[int] | str = "upper_half", - pooling_modes: list[str] | None = None, - batch_size: int = 4, - max_length: int = 2048, - ) -> dict[str, Any]: - """Extract pooled hidden states using the blueprint's direct indexing.""" + def forward(self, prompts: list[str], batch_size: int, max_length: int) -> bytes: + """Return a row-major F32 probability matrix for ordered prompts.""" self._ensure_loaded() + if not prompts or any(not prompt for prompt in prompts): + raise ValueError("prompts must be non-empty") + if batch_size <= 0 or max_length <= 0: + raise ValueError("batch_size and max_length must be positive") - if extract_layers == "all": - layers = list(range(self.n_layers)) - elif extract_layers == "upper_half": - layers = list(range(self.n_layers // 2, self.n_layers)) - elif isinstance(extract_layers, list): - layers = [int(layer) for layer in extract_layers] - else: - raise ValueError(f"Unsupported layer selection: {extract_layers}") - if not layers: - raise ValueError("extract_layers resolved to an empty list") - invalid = [layer for layer in layers if layer < 0 or layer >= self.n_layers] - if invalid: - raise ValueError( - f"Requested indexes {invalid} are outside hidden-state range 0..{self.n_layers - 1}" + formatted = [ + self._tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + tokenize=False, + add_generation_prompt=True, ) - - pools = {"last", "mean"} if pooling_modes is None else set(pooling_modes) - unknown_pools = pools - {"last", "mean"} - if unknown_pools: - raise ValueError(f"Unknown pooling modes: {sorted(unknown_pools)}") - if not pools: - raise ValueError("At least one pooling mode is required") - - template_kwargs = chat_template_kwargs or {} - conversations = [[{"role": "user", "content": prompt}] for prompt in prompts] - formatted = self._tokenizer.apply_chat_template( - conversations, - tokenize=False, - add_generation_prompt=True, - **template_kwargs, - ) - all_last = {layer: [] for layer in layers} if "last" in pools else {} - all_mean = {layer: [] for layer in layers} if "mean" in pools else {} - - for batch_start in range(0, len(formatted), batch_size): + for prompt in prompts + ] + predictions = [] + for start in range(0, len(formatted), batch_size): inputs = self._tokenizer( - formatted[batch_start : batch_start + batch_size], + formatted[start : start + batch_size], return_tensors="pt", padding=True, truncation=True, @@ -155,7 +180,6 @@ def extract_batch( ) input_ids = inputs["input_ids"].to(self._model.device) attention_mask = inputs["attention_mask"].to(self._model.device) - with self._torch.inference_mode(): outputs = self._model( input_ids=input_ids, @@ -164,45 +188,74 @@ def extract_batch( use_cache=False, ) - hidden_states = outputs.hidden_states - token_mask = attention_mask.bool() - token_count = token_mask.sum(dim=1, keepdim=True) - positions = self._torch.arange(token_mask.shape[1], device=token_mask.device).expand_as( - token_mask + layers = [] + for layer in self._selected_layers: + hidden = outputs.hidden_states[layer].float() + # The checkpoint consumes the last real token from each selected layer. + token_mask = attention_mask.to(hidden.device).bool() + positions = self._torch.arange(hidden.shape[1], device=hidden.device) + last_token = ( + positions.expand_as(token_mask).masked_fill(~token_mask, -1).max(dim=1).values + ) + pooled = hidden[ + self._torch.arange(hidden.shape[0], device=hidden.device), last_token + ] + layers.append(pooled.cpu().numpy()) + predictions.append(self._predict(layers)) + + return self._numpy.ascontiguousarray( + self._numpy.concatenate(predictions, axis=0), dtype=self._numpy.float32 + ).tobytes() + + def _predict(self, layers: list[Any]) -> Any: + np = self._numpy + torch = self._torch + stacked = np.stack(layers) + if stacked.ndim != 3 or stacked.shape[0] != len(self._selected_layers): + raise ValueError("encoder returned invalid selected-layer features") + standardized = (stacked - self._layer_mean[:, None, :]) / self._layer_std[:, None, :] + pooled = standardized.mean(axis=0) + scaled = (pooled - self._scaler_mean) / self._scaler_scale + features = torch.from_numpy( + np.ascontiguousarray( + (scaled - self._pca_mean) @ self._pca_components.T, + dtype=np.float32, ) - last_index = positions.masked_fill(~token_mask, -1).max(dim=1).values - batch_index = self._torch.arange(token_mask.shape[0], device=token_mask.device) - - for layer in layers: - hidden = hidden_states[layer].float() - if "last" in pools: - all_last[layer].append(hidden[batch_index, last_index].cpu()) - if "mean" in pools: - masked = hidden.masked_fill(~token_mask.unsqueeze(-1), 0) - all_mean[layer].append((masked.sum(dim=1) / token_count).cpu()) - - del outputs, hidden_states, input_ids, attention_mask - - return { - "hidden_last": { - layer: self._torch.cat(rows).contiguous().numpy().tobytes() - for layer, rows in all_last.items() - }, - "hidden_mean": { - layer: self._torch.cat(rows).contiguous().numpy().tobytes() - for layer, rows in all_mean.items() - }, - "n_layers": self.n_layers, - "hidden_dim": self.hidden_dim, - } + ) + + members = [] + with torch.inference_mode(): + for state in self._states: + logits = [] + for index in range(len(self._models)): + adapter = torch.nn.functional.relu( + torch.nn.functional.linear( + features, + state[f"adapters.{index}.weight"], + state[f"adapters.{index}.bias"], + ) + ) + trunk = torch.nn.functional.relu( + torch.nn.functional.linear( + adapter, + state["trunk.0.weight"], + state["trunk.0.bias"], + ) + ) + logits.append( + torch.nn.functional.linear( + trunk, + state[f"heads.{index}.weight"], + state[f"heads.{index}.bias"], + ) + ) + members.append(torch.sigmoid(torch.cat(logits, dim=1))) + return torch.stack(members).mean(dim=0).contiguous().numpy() def unload(self) -> None: self._model = None self._tokenizer = None - self.n_layers = 0 - self.hidden_dim = 0 - if self._torch is not None: - if self._torch.cuda.is_available(): - self._torch.cuda.empty_cache() - if hasattr(self._torch, "mps") and self._torch.backends.mps.is_available(): - self._torch.mps.empty_cache() + if self._torch.cuda.is_available(): + self._torch.cuda.empty_cache() + if hasattr(self._torch, "mps") and self._torch.backends.mps.is_available(): + self._torch.mps.empty_cache() diff --git a/crates/prefill-router/src/error.rs b/crates/prefill-router/src/error.rs index 90f907ab6..6da9a381e 100644 --- a/crates/prefill-router/src/error.rs +++ b/crates/prefill-router/src/error.rs @@ -7,12 +7,16 @@ use thiserror::Error; /// Errors produced while extracting prefill features. #[derive(Debug, Error)] pub enum PrefillRouterError { + /// The learned router could not be constructed from its configuration. + #[error("invalid prefill-router configuration: {0}")] + InvalidConfiguration(String), + /// The caller supplied an invalid prefill request. #[error("invalid prefill request: {0}")] InvalidRequest(String), - /// The embedded Transformers implementation failed. - #[error("Transformers {operation} failed: {source}")] + /// An embedded Python operation failed. + #[error("embedded Python {operation} failed: {source}")] Python { operation: &'static str, #[source] diff --git a/crates/prefill-router/src/lib.rs b/crates/prefill-router/src/lib.rs index c99a36bc9..04f06a421 100644 --- a/crates/prefill-router/src/lib.rs +++ b/crates/prefill-router/src/lib.rs @@ -1,313 +1,27 @@ // SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // SPDX-License-Identifier: Apache-2.0 -//! Prefill feature extraction behind a backend-neutral Rust contract. -//! -//! [`TransformersForward`] embeds Python and reproduces the Hugging Face -//! Transformers forward path used by the NVIDIA LLM Router blueprint. Router -//! policy and model selection intentionally live outside this crate boundary. +//! Learned prefill routing behind a backend-neutral forward contract. mod error; +mod router; mod transformers; -use std::collections::{BTreeMap, BTreeSet}; - pub use error::{PrefillRouterError, Result}; +pub use router::PrefillRouter; pub use transformers::{TransformersForward, TransformersForwardConfig}; -/// A prefill implementation that extracts pooled hidden states from prompts. +/// A complete prefill forward pass from prompt to per-model probabilities. /// -/// The contract contains no Python-specific types so another implementation, -/// such as Candle, can replace [`TransformersForward`] without changing users. +/// Implementations own feature extraction and learned checkpoint inference, so +/// callers do not depend on intermediate tensor layouts or a specific runtime. pub trait PrefillForward: Send { - /// Runs one batched prefill and returns the requested hidden-state features. - fn forward(&mut self, request: &ForwardRequest) -> Result; - - /// Releases model resources held by the implementation. - fn unload(&mut self) -> Result<()>; -} - -/// Inputs for one batched prefill forward pass. -#[derive(Clone, Debug, PartialEq)] -pub struct ForwardRequest { - /// User prompts to format and tokenize. - pub prompts: Vec, - /// Extra keyword arguments passed to the tokenizer's chat template. - pub chat_template_kwargs: serde_json::Map, - /// Reference-compatible direct hidden-state indexes to extract. - pub layers: LayerSelection, - /// Pooling modes to calculate for every requested hidden-state index. - pub pooling: Vec, - /// Maximum number of prompts in each model forward pass. - pub batch_size: usize, - /// Maximum tokenized length of each prompt. - pub max_length: usize, -} - -impl ForwardRequest { - /// Creates a request using the reference Transformers defaults. - pub fn new(prompts: Vec) -> Self { - Self { - prompts, - chat_template_kwargs: serde_json::Map::new(), - layers: LayerSelection::UpperHalf, - pooling: vec![Pooling::Last, Pooling::Mean], - batch_size: 4, - max_length: 2_048, - } - } - - pub(crate) fn validate(&self) -> Result<()> { - if self.prompts.is_empty() { - return Err(PrefillRouterError::InvalidRequest( - "at least one prompt is required".to_string(), - )); - } - if self.batch_size == 0 { - return Err(PrefillRouterError::InvalidRequest( - "batch_size must be greater than zero".to_string(), - )); - } - if self.max_length == 0 { - return Err(PrefillRouterError::InvalidRequest( - "max_length must be greater than zero".to_string(), - )); - } - if self.pooling.is_empty() { - return Err(PrefillRouterError::InvalidRequest( - "at least one pooling mode is required".to_string(), - )); - } - if matches!(&self.layers, LayerSelection::Selected(layers) if layers.is_empty()) { - return Err(PrefillRouterError::InvalidRequest( - "at least one hidden-state index is required".to_string(), - )); - } - Ok(()) - } -} - -/// Reference-compatible direct indexes into Transformers hidden states. -/// -/// Index zero is the embedding output. To preserve parity with the reference -/// extractor, valid indexes stop before the final transformer-block output. -#[derive(Clone, Debug, Default, Eq, PartialEq)] -pub enum LayerSelection { - /// Extract the upper half of the reference-compatible indexes. - #[default] - UpperHalf, - /// Extract every reference-compatible index. - All, - /// Extract the listed direct hidden-state indexes in order. - Selected(Vec), -} - -/// Pooling applied across each prompt's non-padding tokens. -#[derive(Clone, Copy, Debug, Eq, PartialEq)] -pub enum Pooling { - /// Select the final non-padding token. - Last, - /// Average all non-padding tokens. - Mean, -} - -impl Pooling { - pub(crate) const fn as_str(self) -> &'static str { - match self { - Self::Last => "last", - Self::Mean => "mean", - } - } -} - -/// Pooled hidden states returned by one forward pass. -/// -/// Each layer maps to one hidden vector per input prompt. -#[derive(Clone, Debug, PartialEq)] -pub struct ForwardOutput { - /// Last-token vectors keyed by direct hidden-state index. - hidden_last: BTreeMap>>, - /// Mean-pooled vectors keyed by direct hidden-state index. - hidden_mean: BTreeMap>>, - /// Transformer layer count used as the direct-index upper bound. - n_layers: usize, - /// Width of each hidden-state vector. - hidden_dim: usize, -} + /// Returns the number of ordered model probabilities produced per prompt. + fn output_count(&self) -> usize; -impl ForwardOutput { - pub(crate) fn parse( - request: &ForwardRequest, - hidden_last: BTreeMap>>, - hidden_mean: BTreeMap>>, - n_layers: usize, - hidden_dim: usize, - ) -> Result { - if n_layers == 0 || hidden_dim == 0 { - return Err(PrefillRouterError::InvalidResult( - "model dimensions must be non-zero".to_string(), - )); - } + /// Predicts one ordered probability row per prompt. + fn forward(&mut self, prompts: &[String]) -> Result>>; - let expected_layers: BTreeSet = match &request.layers { - LayerSelection::UpperHalf => (n_layers / 2..n_layers).collect(), - LayerSelection::All => (0..n_layers).collect(), - LayerSelection::Selected(layers) => layers.iter().copied().collect(), - }; - if expected_layers.is_empty() || expected_layers.iter().any(|layer| *layer >= n_layers) { - return Err(PrefillRouterError::InvalidResult( - "requested layers are empty or outside the encoder range".to_string(), - )); - } - - let wants_last = request.pooling.contains(&Pooling::Last); - let wants_mean = request.pooling.contains(&Pooling::Mean); - if !wants_last && !wants_mean { - return Err(PrefillRouterError::InvalidResult( - "at least one pooling mode is required".to_string(), - )); - } - - parse_pooling( - "last", - wants_last, - &hidden_last, - &expected_layers, - request.prompts.len(), - hidden_dim, - )?; - parse_pooling( - "mean", - wants_mean, - &hidden_mean, - &expected_layers, - request.prompts.len(), - hidden_dim, - )?; - - Ok(Self { - hidden_last, - hidden_mean, - n_layers, - hidden_dim, - }) - } - - /// Returns last-token vectors keyed by direct hidden-state index. - pub fn hidden_last(&self) -> &BTreeMap>> { - &self.hidden_last - } - - /// Returns mean-pooled vectors keyed by direct hidden-state index. - pub fn hidden_mean(&self) -> &BTreeMap>> { - &self.hidden_mean - } - - /// Returns the transformer layer count used as the direct-index upper bound. - pub const fn n_layers(&self) -> usize { - self.n_layers - } - - /// Returns the width of each hidden-state vector. - pub const fn hidden_dim(&self) -> usize { - self.hidden_dim - } -} - -fn parse_pooling( - name: &str, - requested: bool, - layers: &BTreeMap>>, - expected_layers: &BTreeSet, - prompt_count: usize, - hidden_dim: usize, -) -> Result<()> { - let actual_layers = layers.keys().copied().collect::>(); - let expected = if requested { - expected_layers.clone() - } else { - BTreeSet::new() - }; - if actual_layers != expected { - return Err(PrefillRouterError::InvalidResult(format!( - "{name} pooling returned layers {actual_layers:?}, expected {expected:?}" - ))); - } - - for (layer, rows) in layers { - if rows.len() != prompt_count { - return Err(PrefillRouterError::InvalidResult(format!( - "{name} layer {layer} returned {} rows for {prompt_count} prompts", - rows.len() - ))); - } - if rows.iter().any(|row| row.len() != hidden_dim) { - return Err(PrefillRouterError::InvalidResult(format!( - "{name} layer {layer} contains a vector with the wrong hidden width" - ))); - } - } - - Ok(()) -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn validates_requests_and_outputs() { - assert!(matches!( - ForwardRequest::new(Vec::new()).validate(), - Err(PrefillRouterError::InvalidRequest(_)) - )); - - let mut request = ForwardRequest::new(vec!["test prompt".to_string()]); - request.batch_size = 0; - assert!(matches!( - request.validate(), - Err(PrefillRouterError::InvalidRequest(_)) - )); - request.batch_size = 1; - request.max_length = 0; - assert!(matches!( - request.validate(), - Err(PrefillRouterError::InvalidRequest(_)) - )); - request.max_length = 1; - request.pooling.clear(); - assert!(matches!( - request.validate(), - Err(PrefillRouterError::InvalidRequest(_)) - )); - request.pooling = vec![Pooling::Last, Pooling::Mean]; - request.layers = LayerSelection::Selected(Vec::new()); - assert!(matches!( - request.validate(), - Err(PrefillRouterError::InvalidRequest(_)) - )); - request.layers = LayerSelection::Selected(vec![0, 1]); - request.validate().expect("request should be valid"); - - let pooled = || BTreeMap::from([(0, vec![vec![0.0, 1.0]]), (1, vec![vec![2.0, 3.0]])]); - let output = ForwardOutput::parse(&request, pooled(), pooled(), 2, 2) - .expect("complete output should parse"); - assert_eq!(output.hidden_last().len(), 2); - assert_eq!(output.hidden_mean().len(), 2); - - assert!(matches!( - ForwardOutput::parse(&request, pooled(), BTreeMap::new(), 2, 2), - Err(PrefillRouterError::InvalidResult(_)) - )); - assert!(matches!( - ForwardOutput::parse( - &request, - BTreeMap::from([(0, vec![vec![0.0, 1.0]])]), - pooled(), - 2, - 2, - ), - Err(PrefillRouterError::InvalidResult(_)) - )); - } + /// Releases resources held by the implementation. + fn unload(&mut self) -> Result<()>; } diff --git a/crates/prefill-router/src/router.rs b/crates/prefill-router/src/router.rs new file mode 100644 index 000000000..2e2c60f65 --- /dev/null +++ b/crates/prefill-router/src/router.rs @@ -0,0 +1,119 @@ +// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// SPDX-License-Identifier: Apache-2.0 + +//! Target binding for checkpoint-backed prefill inference. + +use std::collections::BTreeSet; + +use switchyard_protocol::ModelId; + +use crate::{PrefillForward, PrefillRouterError, Result}; + +/// Maps ordered prefill probabilities to semantic model identifiers. +pub struct PrefillRouter { + forward: F, + targets: Vec, +} + +impl PrefillRouter { + /// Positionally binds checkpoint heads to unique targets. + pub fn new(targets: Vec, forward: F) -> Result { + let output_count = forward.output_count(); + if targets.is_empty() { + return Err(configuration_error("at least one target is required")); + } + if targets.len() != output_count { + return Err(configuration_error(format!( + "received {} targets for {} checkpoint heads", + targets.len(), + output_count + ))); + } + if targets.iter().collect::>().len() != targets.len() { + return Err(configuration_error("targets must be unique")); + } + Ok(Self { forward, targets }) + } + + /// Predicts correctness for each target in checkpoint-head order. + pub fn predict(&mut self, prompt: &str) -> Result> { + let mut predictions = self.predict_batch(&[prompt.to_string()])?; + predictions.pop().ok_or_else(|| { + PrefillRouterError::InvalidResult("forward returned no prediction".to_string()) + }) + } + + /// Predicts correctness for a batch while preserving prompt order. + pub fn predict_batch(&mut self, prompts: &[String]) -> Result>> { + if prompts.is_empty() || prompts.iter().any(String::is_empty) { + return Err(PrefillRouterError::InvalidRequest( + "prompts must be non-empty".to_string(), + )); + } + let probabilities = self.forward.forward(prompts)?; + if probabilities.len() != prompts.len() + || probabilities.iter().any(|row| { + row.len() != self.targets.len() + || row + .iter() + .any(|value| !value.is_finite() || !(0.0..=1.0).contains(value)) + }) + { + return Err(PrefillRouterError::InvalidResult( + "forward returned invalid probabilities".to_string(), + )); + } + Ok(probabilities + .into_iter() + .map(|row| self.targets.iter().cloned().zip(row).collect()) + .collect()) + } + + /// Releases resources held by the configured implementation. + pub fn unload(&mut self) -> Result<()> { + self.forward.unload() + } +} + +fn configuration_error(message: impl Into) -> PrefillRouterError { + PrefillRouterError::InvalidConfiguration(message.into()) +} + +#[cfg(test)] +mod tests { + use super::*; + + struct FixedForward; + + impl PrefillForward for FixedForward { + fn output_count(&self) -> usize { + 2 + } + + fn forward(&mut self, prompts: &[String]) -> Result>> { + Ok(vec![vec![0.25, 0.75]; prompts.len()]) + } + + fn unload(&mut self) -> Result<()> { + Ok(()) + } + } + + #[test] + fn maps_probabilities_to_targets() { + let targets = vec![ModelId::from("small"), ModelId::from("large")]; + let mut router = PrefillRouter::new(targets.clone(), FixedForward).expect("valid router"); + + assert_eq!( + router.predict("route me").expect("valid prediction"), + vec![(targets[0].clone(), 0.25), (targets[1].clone(), 0.75)] + ); + assert_eq!( + router + .predict_batch(&["first".to_string(), "second".to_string()]) + .expect("valid batch") + .len(), + 2 + ); + } +} diff --git a/crates/prefill-router/src/transformers.rs b/crates/prefill-router/src/transformers.rs index f40a50b58..32ae60f32 100644 --- a/crates/prefill-router/src/transformers.rs +++ b/crates/prefill-router/src/transformers.rs @@ -1,9 +1,8 @@ // SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. // SPDX-License-Identifier: Apache-2.0 -//! Embedded Python implementation of the Transformers prefill forward path. +//! Embedded Transformers and checkpoint implementation of the complete forward pass. -use std::collections::BTreeMap; use std::env; use std::mem::size_of; use std::path::PathBuf; @@ -13,42 +12,54 @@ use pyo3::prelude::*; use pyo3::types::{PyDict, PyModule}; use crate::error::python_error; -use crate::{ForwardOutput, ForwardRequest, LayerSelection, PrefillForward, Result}; +use crate::{PrefillForward, PrefillRouterError, Result}; -/// Configuration for the embedded Hugging Face Transformers implementation. +/// Configuration for embedded Transformers prefill inference. #[derive(Clone, Debug, Eq, PartialEq)] pub struct TransformersForwardConfig { - /// Hugging Face model identifier or local model path. - pub model: String, + /// Tensor-only router checkpoint path. + pub checkpoint: PathBuf, /// Torch device override. Auto-detected when omitted. pub device: Option, /// Optional Hugging Face cache directory. pub cache_dir: Option, + /// Maximum tokenized prompt length. + pub max_length: usize, + /// Maximum prompts per encoder forward pass. + pub batch_size: usize, } impl TransformersForwardConfig { /// Creates a configuration using automatic device and cache selection. - pub fn new(model: impl Into) -> Self { + pub fn new(checkpoint: impl Into) -> Self { Self { - model: model.into(), + checkpoint: checkpoint.into(), device: None, cache_dir: None, + max_length: 2_048, + batch_size: 32, } } } -/// Embedded Python Transformers implementation of [`PrefillForward`]. +/// Embedded Python implementation of [`PrefillForward`]. pub struct TransformersForward { - extractor: Py, + inner: Py, model: String, + output_count: usize, + max_length: usize, + batch_size: usize, loaded: bool, } impl TransformersForward { - /// Creates a lazily loaded Transformers forward implementation. - /// - /// `torch` and `transformers` are imported when the first forward runs. + /// Loads checkpoint metadata and defers loading the encoder until first use. pub fn new(config: TransformersForwardConfig) -> Result { + if config.max_length == 0 || config.batch_size == 0 { + return Err(PrefillRouterError::InvalidConfiguration( + "max_length and batch_size must be greater than zero".to_string(), + )); + } Python::attach(|py| { add_venv_site_packages(py) .map_err(|error| python_error("virtual environment setup", error))?; @@ -59,39 +70,81 @@ impl TransformersForward { c"_switchyard_prefill_transformers", ) .map_err(|error| python_error("module initialization", error))?; - let class = module - .getattr("TransformersForward") - .map_err(|error| python_error("class lookup", error))?; let kwargs = PyDict::new(py); kwargs .set_item("device", config.device) + .and_then(|()| { + kwargs.set_item( + "cache_dir", + config.cache_dir.map(|path| path.into_os_string()), + ) + }) .map_err(|error| python_error("configuration", error))?; - kwargs - .set_item( - "cache_dir", - config.cache_dir.map(|path| path.into_os_string()), - ) - .map_err(|error| python_error("configuration", error))?; - let extractor = class - .call((config.model.as_str(),), Some(&kwargs)) + let checkpoint = config.checkpoint.into_os_string(); + let inner = module + .getattr("TransformersForward") + .and_then(|class| class.call((checkpoint,), Some(&kwargs))) .map_err(|error| python_error("construction", error))?; + let (model, output_count) = inner + .call_method0("metadata") + .and_then(|value| value.extract()) + .map_err(|error| python_error("checkpoint metadata", error))?; Ok(Self { - extractor: extractor.unbind(), - model: config.model, + inner: inner.unbind(), + model, + output_count, + max_length: config.max_length, + batch_size: config.batch_size, loaded: false, }) }) } } +impl PrefillForward for TransformersForward { + fn output_count(&self) -> usize { + self.output_count + } + + fn forward(&mut self, prompts: &[String]) -> Result>> { + if prompts.is_empty() || prompts.iter().any(String::is_empty) { + return Err(PrefillRouterError::InvalidRequest( + "prompts must be non-empty".to_string(), + )); + } + Python::attach(|py| { + let bytes = self + .inner + .bind(py) + .call_method1("forward", (prompts, self.batch_size, self.max_length)) + .and_then(|value| value.extract::>()) + .map_err(|error| python_error("forward", error))?; + if !self.loaded { + tracing::info!(model = %self.model, "prefill model loaded"); + self.loaded = true; + } + decode_probabilities(&bytes, prompts.len(), self.output_count) + }) + } + + fn unload(&mut self) -> Result<()> { + Python::attach(|py| { + self.inner + .bind(py) + .call_method0("unload") + .map_err(|error| python_error("unload", error))?; + self.loaded = false; + Ok(()) + }) + } +} + // Embedded Python does not discover an activated virtual environment because -// the host executable is Rust. Add its standard site-packages directory before -// importing optional Python dependencies. +// the host executable is Rust. Add its site-packages before importing dependencies. fn add_venv_site_packages(py: Python<'_>) -> PyResult<()> { let Some(venv) = env::var_os("VIRTUAL_ENV") else { return Ok(()); }; - let site_packages = if cfg!(windows) { PathBuf::from(venv).join("Lib").join("site-packages") } else { @@ -103,226 +156,135 @@ fn add_venv_site_packages(py: Python<'_>) -> PyResult<()> { .join(format!("python{major}.{minor}")) .join("site-packages") }; - PyModule::import(py, "site")?.call_method1("addsitedir", (site_packages,))?; Ok(()) } -impl PrefillForward for TransformersForward { - fn forward(&mut self, request: &ForwardRequest) -> Result { - request.validate()?; - Python::attach(|py| { - if !self.loaded { - let device = self - .extractor - .bind(py) - .call_method0("_ensure_loaded") - .and_then(|value| value.extract::()) - .map_err(|error| python_error("model loading", error))?; - tracing::info!(model = %self.model, %device, "prefill model loaded"); - self.loaded = true; - } - - let kwargs = PyDict::new(py); - let template_json = serde_json::to_string(&request.chat_template_kwargs) - .map_err(|error| crate::PrefillRouterError::InvalidResult(error.to_string()))?; - let template_kwargs = PyModule::import(py, "json") - .and_then(|json| json.call_method1("loads", (template_json,))) - .map_err(|error| python_error("chat template conversion", error))?; - - kwargs - .set_item("chat_template_kwargs", template_kwargs) - .map_err(|error| python_error("forward configuration", error))?; - match &request.layers { - LayerSelection::UpperHalf => kwargs - .set_item("extract_layers", "upper_half") - .map_err(|error| python_error("forward configuration", error))?, - LayerSelection::All => kwargs - .set_item("extract_layers", "all") - .map_err(|error| python_error("forward configuration", error))?, - LayerSelection::Selected(layers) => kwargs - .set_item("extract_layers", layers) - .map_err(|error| python_error("forward configuration", error))?, - } - let pooling = request - .pooling - .iter() - .map(|mode| mode.as_str()) - .collect::>(); - kwargs - .set_item("pooling_modes", pooling) - .and_then(|()| kwargs.set_item("batch_size", request.batch_size)) - .and_then(|()| kwargs.set_item("max_length", request.max_length)) - .map_err(|error| python_error("forward configuration", error))?; - - let result = self - .extractor - .bind(py) - .call_method("extract_batch", (&request.prompts,), Some(&kwargs)) - .map_err(|error| python_error("forward", error))?; - extract_output(&result, request) - }) +fn decode_probabilities( + bytes: &[u8], + prompt_count: usize, + output_count: usize, +) -> Result>> { + let expected = prompt_count + .checked_mul(output_count) + .and_then(|values| values.checked_mul(size_of::())) + .ok_or_else(|| PrefillRouterError::InvalidResult("output is too large".to_string()))?; + if bytes.len() != expected { + return Err(PrefillRouterError::InvalidResult(format!( + "forward returned {} bytes, expected {expected}", + bytes.len() + ))); } - - fn unload(&mut self) -> Result<()> { - Python::attach(|py| { - self.extractor - .bind(py) - .call_method0("unload") - .map_err(|error| python_error("unload", error))?; - self.loaded = false; - Ok(()) + let values = bytes + .chunks_exact(size_of::()) + .map(|bytes| { + let value = f32::from_ne_bytes([bytes[0], bytes[1], bytes[2], bytes[3]]); + if value.is_finite() && (0.0..=1.0).contains(&value) { + Ok(value) + } else { + Err(PrefillRouterError::InvalidResult( + "forward returned an invalid probability".to_string(), + )) + } }) - } + .collect::>>()?; + Ok(values + .chunks_exact(output_count) + .map(<[f32]>::to_vec) + .collect()) } -fn extract_output(result: &Bound<'_, PyAny>, request: &ForwardRequest) -> Result { - let item = |name| { - result - .get_item(name) - .map_err(|error| python_error("result decoding", error)) - }; - let n_layers = item("n_layers")? - .extract::() - .map_err(|error| python_error("result decoding", error))?; - let hidden_dim = item("hidden_dim")? - .extract::() - .map_err(|error| python_error("result decoding", error))?; - if n_layers == 0 || hidden_dim == 0 { - return Err(crate::PrefillRouterError::InvalidResult( - "model dimensions must be non-zero".to_string(), +#[cfg(test)] +fn decode_expected_probabilities( + bytes: &[u8], + prompt_count: usize, + output_count: usize, +) -> Result>> { + let expected = prompt_count + .checked_mul(size_of::()) + .and_then(|row_bytes| row_bytes.checked_mul(output_count)) + .ok_or_else(|| PrefillRouterError::InvalidResult("expected output is too large".into()))?; + if bytes.len() != expected { + return Err(PrefillRouterError::InvalidResult( + "expected probabilities have an invalid shape".into(), )); } - - ForwardOutput::parse( - request, - decode_tensor_map(&item("hidden_last")?, request.prompts.len(), hidden_dim)?, - decode_tensor_map(&item("hidden_mean")?, request.prompts.len(), hidden_dim)?, - n_layers, - hidden_dim, - ) -} - -fn decode_tensor_map( - value: &Bound<'_, PyAny>, - prompt_count: usize, - hidden_dim: usize, -) -> Result>>> { - let tensors = value - .extract::>>() - .map_err(|error| python_error("result decoding", error))?; - let row_bytes = hidden_dim.checked_mul(size_of::()).ok_or_else(|| { - crate::PrefillRouterError::InvalidResult("hidden width is too large".to_string()) - })?; - let expected_bytes = prompt_count.checked_mul(row_bytes).ok_or_else(|| { - crate::PrefillRouterError::InvalidResult("tensor shape is too large".to_string()) - })?; - - tensors - .into_iter() - .map(|(layer, bytes)| { - if bytes.len() != expected_bytes { - return Err(crate::PrefillRouterError::InvalidResult(format!( - "layer {layer} returned {} bytes, expected {expected_bytes}", - bytes.len() - ))); - } - let values = bytes - .chunks_exact(size_of::()) - .map(|encoded| { - let encoded = <[u8; size_of::()]>::try_from(encoded).map_err(|_| { - crate::PrefillRouterError::InvalidResult( - "invalid f32 tensor encoding".to_string(), - ) - })?; - Ok(f32::from_ne_bytes(encoded)) - }) - .collect::>>()?; - let rows = values - .chunks_exact(hidden_dim) - .map(<[f32]>::to_vec) - .collect(); - Ok((layer, rows)) - }) - .collect() + Ok(bytes + .chunks_exact(size_of::()) + .map(|bytes| f32::from_ne_bytes([bytes[0], bytes[1], bytes[2], bytes[3]])) + .collect::>() + .chunks_exact(output_count) + .map(<[f32]>::to_vec) + .collect()) } #[cfg(test)] mod tests { - use super::*; - use crate::{LayerSelection, Pooling}; - use pyo3::exceptions::{PyRuntimeError, PyTypeError}; + use std::env; + use std::path::Path; - const REFERENCE_MODEL: &str = "Qwen/Qwen3.5-0.8B"; + use super::*; #[test] - #[ignore = "downloads and runs the Qwen3.5-0.8B reference model"] - fn matches_the_reference_transformers_tensors_exactly() -> Result<()> { - let config = TransformersForwardConfig { - model: REFERENCE_MODEL.to_string(), - device: Some("cpu".to_string()), - cache_dir: None, - }; - let mut backend = TransformersForward::new(config)?; - let mut request = ForwardRequest::new(vec![ - "Explain routing clearly".into(), - "Write a short proof".into(), - ]); - request - .chat_template_kwargs - .insert("enable_thinking".into(), true.into()); - request.layers = LayerSelection::All; - request.pooling = vec![Pooling::Last, Pooling::Mean]; - request.batch_size = 2; - request.max_length = 32; - - let actual = backend.forward(&request)?; - backend.unload()?; - let expected = Python::attach(|py| { - add_venv_site_packages(py)?; - let reference = PyModule::import(py, "model_router_toolkit.prefill.extract")?; - let constructor_kwargs = PyDict::new(py); - constructor_kwargs.set_item("device", "cpu")?; - let extractor = reference - .getattr("PrefillExtractor")? - .call((REFERENCE_MODEL,), Some(&constructor_kwargs))?; - - let template_kwargs = PyDict::new(py); - template_kwargs.set_item("enable_thinking", true)?; - let kwargs = PyDict::new(py); - kwargs.set_item("chat_template_kwargs", template_kwargs)?; - kwargs.set_item("extract_layers", "all")?; - kwargs.set_item("pooling_modes", vec!["last", "mean"])?; - kwargs.set_item("batch_size", request.batch_size)?; - kwargs.set_item("max_length", request.max_length)?; - kwargs.set_item("show_progress", false)?; - let result = - extractor.call_method("extract_batch", (&request.prompts,), Some(&kwargs))?; - - ForwardOutput::parse( - &request, - extract_tensor_map(&result.getattr("hidden_last")?)?, - extract_tensor_map(&result.getattr("hidden_mean")?)?, - result.getattr("n_layers")?.extract()?, - result.getattr("hidden_dim")?.extract()?, + #[ignore = "requires PREFILL_ROUTER_HANDOFF_DIR"] + fn matches_handoff_from_raw_prompts_end_to_end() -> Result<()> { + let directory = env::var_os("PREFILL_ROUTER_HANDOFF_DIR").ok_or_else(|| { + PrefillRouterError::InvalidConfiguration( + "PREFILL_ROUTER_HANDOFF_DIR must name the handoff directory".to_string(), ) - .map_err(|error| PyRuntimeError::new_err(error.to_string())) - }) - .map_err(|error| python_error("reference forward", error))?; - - assert_eq!(actual, expected); - Ok(()) - } - - fn extract_tensor_map(value: &Bound<'_, PyAny>) -> PyResult>>> { - let tensors = value - .cast::() - .map_err(|error| PyTypeError::new_err(error.to_string()))?; - tensors + })?; + let directory = Path::new(&directory); + let config = TransformersForwardConfig::new( + directory.join("selected_dual_oof_router_checkpoint.pt"), + ); + let mut forward = TransformersForward::new(config)?; + let test_data = directory.join("fold_0_test_data.npz").into_os_string(); + let (prompts, expected) = Python::attach(|py| { + let data = PyModule::import(py, "numpy") + .and_then(|numpy| numpy.call_method1("load", (test_data,))) + .map_err(|error| python_error("handoff data loading", error))?; + let prompts = data + .get_item("prompt") + .and_then(|value| value.call_method0("tolist")) + .and_then(|value| value.extract::>()) + .map_err(|error| python_error("handoff prompt decoding", error))?; + let probabilities = data + .get_item("expected_probabilities") + .map_err(|error| python_error("handoff probability decoding", error))?; + let (_, output_count) = probabilities + .getattr("shape") + .and_then(|shape| shape.extract::<(usize, usize)>()) + .map_err(|error| python_error("handoff probability decoding", error))?; + let bytes = probabilities + .call_method0("tobytes") + .and_then(|value| value.extract::>()) + .map_err(|error| python_error("handoff probability decoding", error))?; + let expected = decode_expected_probabilities(&bytes, prompts.len(), output_count)?; + Ok((prompts, expected)) + })?; + let actual = forward.forward(&prompts)?; + assert_eq!(actual.len(), expected.len()); + // BF16 kernels can perturb confidences; every resulting route must remain exact. + for (actual, expected) in actual.iter().zip(&expected) { + let actual_decision = actual + .iter() + .enumerate() + .max_by(|(_, left), (_, right)| left.total_cmp(right)) + .map(|(index, _)| index); + let expected_decision = expected + .iter() + .enumerate() + .max_by(|(_, left), (_, right)| left.total_cmp(right)) + .map(|(index, _)| index); + assert_eq!(actual_decision, expected_decision); + } + let max_delta = actual .iter() - .map(|(layer, tensor)| { - Ok((layer.extract()?, tensor.call_method0("tolist")?.extract()?)) - }) - .collect() + .flatten() + .zip(expected.iter().flatten()) + .map(|(actual, expected)| (actual - expected).abs()) + .fold(0.0_f32, f32::max); + assert!(max_delta <= 0.04, "probability max delta: {max_delta}"); + Ok(()) } } diff --git a/crates/prefill-router/uv.lock b/crates/prefill-router/uv.lock index f3008badf..1dc80aee9 100644 --- a/crates/prefill-router/uv.lock +++ b/crates/prefill-router/uv.lock @@ -11,21 +11,33 @@ resolution-markers = [ ] [[package]] -name = "annotated-doc" -version = "0.0.5" +name = "accelerate" +version = "1.14.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = 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