test: Remove trivial ShapedModules (#461)

* Add support for RNN, BatchNorm2d and InstanceNorm2d in get_in_out_shapes
* Remove WithRNN, WithBatchNorm and WithModuleTrackingRunningStats - use simple factories instead

Author: ValerianRey

tests/unit/autogram/test_engine.py

@@ -6,7 +6,7 @@
 import torch
 from pytest import mark, param
 from torch import Tensor
-from torch.nn import Linear
+from torch.nn import RNN, BatchNorm2d, InstanceNorm2d, Linear
 from torch.optim import SGD
 from torch.testing import assert_close
 from utils.architectures import (
@@ -47,18 +47,15 @@
     SomeUnusedOutput,
     SomeUnusedParam,
     SqueezeNet,
-    WithBatchNorm,
     WithBuffered,
     WithDropout,
-    WithModuleTrackingRunningStats,
     WithModuleWithHybridPyTreeArg,
     WithModuleWithHybridPyTreeKwarg,
     WithModuleWithStringArg,
     WithModuleWithStringKwarg,
     WithModuleWithStringOutput,
     WithMultiHeadAttention,
     WithNoTensorOutput,
-    WithRNN,
     WithSideEffect,
     WithSomeFrozenModule,
     WithTransformer,
@@ -178,11 +175,14 @@ def test_compute_gramian(factory: ModuleFactory, batch_size: int, batch_dim: int
 @mark.parametrize(
     "factory",
     [
-        ModuleFactory(WithBatchNorm),
+        ModuleFactory(BatchNorm2d, num_features=3, affine=True, track_running_stats=False),
         ModuleFactory(WithSideEffect),
         ModuleFactory(Randomness),
-        ModuleFactory(WithModuleTrackingRunningStats),
-        param(ModuleFactory(WithRNN), marks=mark.xfail_if_cuda),
+        ModuleFactory(InstanceNorm2d, num_features=3, affine=True, track_running_stats=True),
+        param(
+            ModuleFactory(RNN, input_size=8, hidden_size=5, batch_first=True),
+            marks=mark.xfail_if_cuda,
+        ),
     ],
 )
 @mark.parametrize("batch_size", [1, 3, 32])
@@ -397,9 +397,9 @@ def test_autograd_while_modules_are_hooked(
 @mark.parametrize(
     ["factory", "batch_dim"],
     [
-        (ModuleFactory(WithModuleTrackingRunningStats), 0),
-        (ModuleFactory(WithRNN), 0),
-        (ModuleFactory(WithBatchNorm), 0),
+        (ModuleFactory(InstanceNorm2d, num_features=3, affine=True, track_running_stats=True), 0),
+        (ModuleFactory(RNN, input_size=8, hidden_size=5, batch_first=True), 0),
+        (ModuleFactory(BatchNorm2d, num_features=3, affine=True, track_running_stats=False), 0),
     ],
 )
 def test_incompatible_modules(factory: ModuleFactory, batch_dim: int | None):

tests/utils/architectures.py

@@ -44,6 +44,18 @@ def __init_subclass__(cls):
 def get_in_out_shapes(module: nn.Module) -> tuple[PyTree, PyTree]:
     if isinstance(module, ShapedModule):
         return module.INPUT_SHAPES, module.OUTPUT_SHAPES
+
+    elif isinstance(module, nn.RNN):
+        assert module.batch_first
+        SEQ_LEN = 20  # Arbitrary choice
+        return (SEQ_LEN, module.input_size), (SEQ_LEN, module.hidden_size)
+
+    elif isinstance(module, (nn.BatchNorm2d, nn.InstanceNorm2d)):
+        HEIGHT = 6  # Arbitrary choice
+        WIDTH = 6  # Arbitrary choice
+        shape = (module.num_features, HEIGHT, WIDTH)
+        return shape, shape
+
     else:
         raise ValueError("Unknown input / output shapes of module", module)
 
@@ -725,48 +737,6 @@ def forward(self, input: Tensor) -> Tensor:
         return torch.einsum("bi,icdef->bcdef", input, self.tensor)
 
 
-class WithRNN(ShapedModule):
-    """Simple model containing an RNN module."""
-
-    INPUT_SHAPES = (20, 8)  # Size 20, dim input_size (8)
-    OUTPUT_SHAPES = (20, 5)  # Size 20, dim hidden_size (5)
-
-    def __init__(self):
-        super().__init__()
-        self.rnn = nn.RNN(input_size=8, hidden_size=5, batch_first=True)
-
-    def forward(self, input: Tensor) -> Tensor:
-        return self.rnn(input)
-
-
-class WithModuleTrackingRunningStats(ShapedModule):
-    """Simple model containing a module that has side-effects and modifies tensors in-place."""
-
-    INPUT_SHAPES = (3, 6, 6)
-    OUTPUT_SHAPES = (3, 6, 6)
-
-    def __init__(self):
-        super().__init__()
-        self.instance_norm = nn.InstanceNorm2d(3, affine=True, track_running_stats=True)
-
-    def forward(self, input: Tensor) -> Tensor:
-        return self.instance_norm(input)
-
-
-class WithBatchNorm(ShapedModule):
-    """Simple model containing a BatchNorm layer."""
-
-    INPUT_SHAPES = (3, 6, 6)
-    OUTPUT_SHAPES = (3, 6, 6)
-
-    def __init__(self):
-        super().__init__()
-        self.batch_norm = nn.BatchNorm2d(3, affine=True, track_running_stats=False)
-
-    def forward(self, input: Tensor) -> Tensor:
-        return self.batch_norm(input)
-
-
 class WithDropout(ShapedModule):
     """Simple model containing Dropout layers."""