[fx] added module patch for pooling layers (#1197)

colossalai/fx/tracer/meta_patch/__init__.py

@@ -1,4 +1,3 @@
-from sys import meta_path
 from .registry import *
 from .patched_function import *
 from .patched_module import *

colossalai/fx/tracer/meta_patch/patched_module.py

@@ -86,3 +86,33 @@ def torch_nn_conv3d(self, input):
         w_out,
     )
     return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_module.register(torch.nn.MaxPool3d)
+def torch_nn_maxpool3d(self, input):
+    num_dim = input.dim()
+    assert num_dim in [4, 5], f'expected the input to have 4 or 5 dimensions, but got {num_dim} dimensions'
+
+    d_in, h_in, w_in = input.shape[-3:]
+
+    def _convert_int_to_list(item):
+        if isinstance(item, int):
+            return [item] * 3
+        else:
+            return item
+
+    padding = _convert_int_to_list(self.padding)
+    dilation = _convert_int_to_list(self.dilation)
+    kernel_size = _convert_int_to_list(self.kernel_size)
+    stride = _convert_int_to_list(self.stride)
+
+    d_out = math.floor((d_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+    h_out = math.floor((h_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
+    w_out = math.floor((w_in + 2 * padding[2] - dilation[2] * (kernel_size[2] - 1) - 1) / stride[2] + 1)
+
+    result_shape = input.shape[:-3] + (
+        d_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')

tests/test_fx/test_tracer/test_non_patched_module.py

@@ -0,0 +1,31 @@
+import torch
+import torch.nn
+
+
+def test_maxpool():
+    layer_to_test = dict(maxpool_1d=dict(layer=torch.nn.MaxPool1d, shape=(4, 3, 4)),
+                         maxpool_2d=dict(layer=torch.nn.MaxPool2d, shape=(4, 3, 4, 4)))
+
+    for name, info in layer_to_test.items():
+        data = torch.rand(*info['shape'])
+        meta_data = data.to('meta')
+        layer = info['layer'](kernel_size=3)
+        out = layer(data)
+        meta_out = layer(meta_data)
+        assert meta_out.is_meta
+        assert out.shape == meta_out.shape
+
+
+def test_avgpool():
+    layer_to_test = dict(maxpool_1d=dict(layer=torch.nn.AvgPool1d, shape=(4, 3, 4)),
+                         maxpool_2d=dict(layer=torch.nn.AvgPool2d, shape=(4, 3, 4, 4)),
+                         maxpool_3d=dict(layer=torch.nn.AvgPool3d, shape=(4, 3, 4, 4, 4)))
+
+    for name, info in layer_to_test.items():
+        data = torch.rand(*info['shape'])
+        meta_data = data.to('meta')
+        layer = info['layer'](kernel_size=3)
+        out = layer(data)
+        meta_out = layer(meta_data)
+        assert meta_out.is_meta
+        assert out.shape == meta_out.shape

tests/test_fx/test_tracer/test_patched_module.py

@@ -225,3 +225,33 @@ def test_conv3d():
                          patch_fn=patched_module.torch_nn_conv3d,
                          expect_exception=False,
                          output_shape=materialized_output.shape)
+
+
+def test_maxpool3d():
+    pooler = torch.nn.MaxPool3d(kernel_size=3)
+
+    # test max pool 3d
+    data = torch.rand(2, 3, 4, 4, 4)
+    materialized_output = pooler(data)
+    _assert_output_shape(data=data,
+                         module=pooler,
+                         patch_fn=patched_module.torch_nn_maxpool3d,
+                         expect_exception=False,
+                         output_shape=materialized_output.shape)
+
+    # test max pool 3d
+    data = torch.rand(2, 3, 4, 4)
+    materialized_output = pooler(data)
+    _assert_output_shape(data=data,
+                         module=pooler,
+                         patch_fn=patched_module.torch_nn_maxpool3d,
+                         expect_exception=False,
+                         output_shape=materialized_output.shape)
+
+    # test max pool 3d
+    data = torch.rand(2, 3, 4)
+    _assert_output_shape(data=data,
+                         module=pooler,
+                         patch_fn=patched_module.torch_nn_maxpool3d,
+                         expect_exception=True,
+                         output_shape=None)