test: Fix RNN testing

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 RNN, BatchNorm2d, InstanceNorm2d, Linear
+from torch.nn import BatchNorm2d, InstanceNorm2d, Linear
 from torch.optim import SGD
 from torch.testing import assert_close
 from utils.architectures import (
@@ -56,6 +56,7 @@
     WithModuleWithStringOutput,
     WithMultiHeadAttention,
     WithNoTensorOutput,
+    WithRNN,
     WithSideEffect,
     WithSomeFrozenModule,
     WithTransformer,
@@ -179,10 +180,7 @@ def test_compute_gramian(factory: ModuleFactory, batch_size: int, batch_dim: int
         ModuleFactory(WithSideEffect),
         ModuleFactory(Randomness),
         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,
-        ),
+        param(ModuleFactory(WithRNN), marks=mark.xfail_if_cuda),
     ],
 )
 @mark.parametrize("batch_size", [1, 3, 32])
@@ -398,7 +396,7 @@ def test_autograd_while_modules_are_hooked(
     ["factory", "batch_dim"],
     [
         (ModuleFactory(InstanceNorm2d, num_features=3, affine=True, track_running_stats=True), 0),
-        (ModuleFactory(RNN, input_size=8, hidden_size=5, batch_first=True), 0),
+        param(ModuleFactory(WithRNN), 0),
         (ModuleFactory(BatchNorm2d, num_features=3, affine=True, track_running_stats=False), 0),
     ],
 )

tests/utils/architectures.py

@@ -45,11 +45,6 @@ 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
@@ -737,6 +732,21 @@ 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:
+        output, _ = self.rnn(input)
+        return output
+
+
 class WithDropout(ShapedModule):
     """Simple model containing Dropout layers."""