[fx] added torchvision model tracing testing (#1216)

* [fx] added torchvision model tracing testing

* remove unused imports

colossalai/fx/tracer/meta_patch/patched_function.py

-from curses import meta
 import operator
 import torch
 from .registry import meta_patched_function
@@ -142,3 +141,40 @@ def torch_bmm(input, mat2, *, out=None):
     batch_size, n, m = input.shape
     _, _, p = mat2.shape
     return torch.empty(batch_size, n, p, device="meta")
+
+
+@meta_patched_function.register(torch.squeeze)
+def torch_squeeze(input, dim=None):
+    shape = list(input.shape)
+    if dim is not None:
+        if dim < 0:
+            dim = input.dim() + dim
+        if shape[dim] == 1:
+            shape.pop(dim)
+    else:
+        new_shape = []
+        for dim_value in shape:
+            if dim_value == 1:
+                continue
+            new_shape.append(dim_value)
+        shape = new_shape
+    return torch.empty(shape, device="meta")
+
+
+@meta_patched_function.register(torch.Tensor.squeeze)
+def torch_tensor_squeeze(self, dim=None):
+    return torch_squeeze(self, dim)
+
+
+@meta_patched_function.register(torch.unsqueeze)
+def torch_unsqueeze(input, dim):
+    shape = list(input.shape)
+    if dim < 0:
+        dim = input.dim() + 1 + dim
+    shape.insert(dim, 1)
+    return torch.empty(shape, device="meta")
+
+
+@meta_patched_function.register(torch.Tensor.unsqueeze)
+def torch_tensor_unsqueeze(self, dim):
+    return torch_unsqueeze(self, dim)

colossalai/fx/tracer/meta_patch/patched_module.py

@@ -88,6 +88,137 @@ def torch_nn_conv3d(self, input):
     return torch.empty(result_shape, device='meta')
 
 
+@meta_patched_module.register(torch.nn.AvgPool1d)
+def torch_nn_avgpool1d(self, input):
+    num_dim = input.dim()
+    assert num_dim in [2, 3], f'expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions'
+
+    l_in = input.shape[-1]
+
+    def _convert_int_to_list(item):
+        if isinstance(item, int):
+            return [item] * 1
+        else:
+            return item
+
+    padding = _convert_int_to_list(self.padding)
+    kernel_size = _convert_int_to_list(self.kernel_size)
+    stride = _convert_int_to_list(self.stride)
+
+    l_out = math.floor((l_in + 2 * padding[0] - kernel_size[0]) / stride[0] + 1)
+
+    result_shape = input.shape[:-1] + (l_out,)
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_module.register(torch.nn.AvgPool2d)
+def torch_nn_avgpool2d(self, input):
+    num_dim = input.dim()
+    assert num_dim in [3, 4], f'expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions'
+
+    h_in, w_in = input.shape[-2:]
+
+    def _convert_int_to_list(item):
+        if isinstance(item, int):
+            return [item] * 2
+        else:
+            return item
+
+    padding = _convert_int_to_list(self.padding)
+    kernel_size = _convert_int_to_list(self.kernel_size)
+    stride = _convert_int_to_list(self.stride)
+
+    h_out = math.floor((h_in + 2 * padding[0] - kernel_size[0]) / stride[0] + 1)
+    w_out = math.floor((w_in + 2 * padding[1] - kernel_size[1]) / stride[1] + 1)
+
+    result_shape = input.shape[:-2] + (
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_module.register(torch.nn.AvgPool3d)
+def torch_nn_avgpool3d(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)
+    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] - kernel_size[0]) / stride[0] + 1)
+    h_out = math.floor((h_in + 2 * padding[1] - kernel_size[1]) / stride[1] + 1)
+    w_out = math.floor((w_in + 2 * padding[2] - kernel_size[2]) / stride[2] + 1)
+
+    result_shape = input.shape[:-3] + (
+        d_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_module.register(torch.nn.MaxPool1d)
+def torch_nn_maxpool1d(self, input):
+    num_dim = input.dim()
+    assert num_dim in [2, 3], f'expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions'
+
+    l_in = input.shape[-1]
+
+    def _convert_int_to_list(item):
+        if isinstance(item, int):
+            return [item] * 1
+        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)
+
+    l_out = math.floor((l_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+
+    result_shape = input.shape[:-1] + (l_out,)
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_module.register(torch.nn.MaxPool2d)
+def torch_nn_maxpool2d(self, input):
+    num_dim = input.dim()
+    assert num_dim in [3, 4], f'expected the input to have 3 or 4 dimensions, but got {num_dim} dimensions'
+
+    h_in, w_in = input.shape[-2:]
+
+    def _convert_int_to_list(item):
+        if isinstance(item, int):
+            return [item] * 2
+        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)
+
+    h_out = math.floor((h_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+    w_out = math.floor((w_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
+
+    result_shape = input.shape[:-2] + (
+        h_out,
+        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()

colossalai/fx/tracer/tracer.py

@@ -4,9 +4,8 @@ tracer.py:
     Implemented a tracer which supports control flow and user-defined meta arguments.
     The implementation is partly inspired HuggingFace's fx tracer
 """
-
+import enum
 import inspect
-import math
 import functools
 import torch
 import torch.nn as nn
@@ -22,6 +21,11 @@ from .meta_patch import meta_patched_function, meta_patched_module
 __all__ = ['ColoTracer']
 
 
+class TracerType(enum.Enum):
+    DEFAULT = 1
+    META = 2
+
+
 class ColoTracer(Tracer):
     """
     ColoTracer is a symbolic tracer designed to support dynamic control flow by using meta tensors for the `colossalai.fx` module.
@@ -48,6 +52,11 @@ class ColoTracer(Tracer):
         graph = tracer.trace(model, concrete_args={'y': torch.rand(4, 10)}, meta_args={'x': torch.rand(4, 10, device='meta')})
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.tracer_type = TracerType.META
+        self.proxy_cls = ColoProxy
+
     # Feature flag for proxying accesses to buffer values
     proxy_buffer_attributes: bool = True
 
@@ -58,6 +67,12 @@ class ColoTracer(Tracer):
         Create a proxy for different kinds of operations.
         """
         proxy = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
+
+        if self.tracer_type == TracerType.DEFAULT:
+            # since meta_args is not given
+            # we just fall back to the original torch.fx.Tracer
+            return proxy
+
         proxy: ColoProxy
 
         if kind == "placeholder" and target in self.meta_args and self.meta_args[target].is_meta:
@@ -168,11 +183,21 @@ class ColoTracer(Tracer):
         self.orig_forward = forward
         return super().call_module(m, forward, args, kwargs)
 
-    def proxy(self, node) -> ColoProxy:
+    def proxy(self, node) -> Proxy:
         """
         Returns a ColoProxy object.
         """
-        return ColoProxy(node, self)
+        return self.proxy_cls(node, self)
+
+    def _configure_tracer_type(self, tracer_type: TracerType):
+        if tracer_type == TracerType.DEFAULT:
+            self.proxy_cls = Proxy
+            self.tracer_type = TracerType.DEFAULT
+        elif tracer_type == TracerType.META:
+            self.proxy_cls = ColoProxy
+            self.tracer_type = TracerType.META
+        else:
+            raise ValueError(f"Unrecognised tracer type {tracer_type}")
 
     def trace(self,
               root: nn.Module,
@@ -193,6 +218,11 @@ class ColoTracer(Tracer):
         if concrete_args is None:
             concrete_args = {}
 
+        if len(meta_args) == 0:
+            self._configure_tracer_type(TracerType.DEFAULT)
+        else:
+            self._configure_tracer_type(TracerType.META)
+
         # check concrete and meta args have valid names
         sig = inspect.signature(root.forward)
         sig_names = set(sig.parameters.keys())
@@ -235,18 +265,21 @@ class ColoTracer(Tracer):
         self.concrete_args = concrete_args
         self.meta_args = meta_args
 
-        # wrap the torch tensor constructing methods so that they are captured in the graph
-        self.patched_torch_tensor_methods = {
-            target: wrap_tensor_constructor_method(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
-        }
+        self.patched_torch_tensor_methods = {}
+        if self.tracer_type == TracerType.META:
+            # wrap the torch tensor constructing methods so that they are captured in the graph
+            self.patched_torch_tensor_methods = {
+                target: wrap_tensor_constructor_method(getattr(torch, target))
+                for target in self._TORCH_METHODS_TO_PATCH
+            }
 
-        # patch these methods to replace their original use
-        for name, (wrapper, orig) in self.patched_torch_tensor_methods.items():
-            setattr(torch, name, wrapper)
+            # patch these methods to replace their original use
+            for name, (wrapper, orig) in self.patched_torch_tensor_methods.items():
+                setattr(torch, name, wrapper)
 
-        # cache these methods so that we can detect whether a method call
-        # should be patched during tracing
-        self.orig_torch_tensor_methods = [val[1] for val in self.patched_torch_tensor_methods.values()]
+            # cache these methods so that we can detect whether a method call
+            # should be patched during tracing
+            self.orig_torch_tensor_methods = [val[1] for val in self.patched_torch_tensor_methods.values()]
 
         try:
             self.graph = super().trace(root, concrete_args=concrete_args)
@@ -255,6 +288,9 @@ class ColoTracer(Tracer):
             for name, (_, orig) in self.patched_torch_tensor_methods.items():
                 setattr(torch, name, orig)
 
+        if self.tracer_type == TracerType.DEFAULT:
+            return self.graph
+
         # This is necessary because concrete args are added as input to the traced module since
         # https://github.com/pytorch/pytorch/pull/55888.
         for node in self.graph.nodes:

tests/test_fx/test_tracer/test_non_patched_module.py

-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

@@ -227,31 +227,88 @@ def test_conv3d():
                          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)
+def test_pool1d():
+    combinations = [[torch.nn.MaxPool1d, patched_module.torch_nn_maxpool1d],
+                    [torch.nn.AvgPool1d, patched_module.torch_nn_avgpool1d]]
+
+    for (layer_cls, patch_func) in combinations:
+        pooler = layer_cls(kernel_size=3)
+
+        data = torch.rand(2, 3, 4)
+        materialized_output = pooler(data)
+        _assert_output_shape(data=data,
+                             module=pooler,
+                             patch_fn=patch_func,
+                             expect_exception=False,
+                             output_shape=materialized_output.shape)
+
+        data = torch.rand(2, 4)
+        materialized_output = pooler(data)
+        _assert_output_shape(data=data,
+                             module=pooler,
+                             patch_fn=patch_func,
+                             expect_exception=False,
+                             output_shape=materialized_output.shape)
+
+        data = torch.rand(2, 3, 4, 4)
+        _assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
+
+
+def test_pool2d():
+    combinations = [[torch.nn.MaxPool2d, patched_module.torch_nn_maxpool2d],
+                    [torch.nn.AvgPool2d, patched_module.torch_nn_avgpool2d]]
+
+    for (layer_cls, patch_func) in combinations:
+        pooler = layer_cls(kernel_size=3)
+
+        # test max pool 3d
+        data = torch.rand(2, 3, 4, 4)
+        materialized_output = pooler(data)
+        _assert_output_shape(data=data,
+                             module=pooler,
+                             patch_fn=patch_func,
+                             expect_exception=False,
+                             output_shape=materialized_output.shape)
+
+        # test max pool 3d
+        data = torch.rand(2, 4, 4)
+        materialized_output = pooler(data)
+        _assert_output_shape(data=data,
+                             module=pooler,
+                             patch_fn=patch_func,
+                             expect_exception=False,
+                             output_shape=materialized_output.shape)
+
+        # test max pool 3d
+        data = torch.rand(2, 3, 4, 4, 4)
+        _assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
+
+
+def test_pool3d():
+    combinations = [[torch.nn.MaxPool3d, patched_module.torch_nn_maxpool3d],
+                    [torch.nn.AvgPool3d, patched_module.torch_nn_avgpool3d]]
+
+    for (layer_cls, patch_func) in combinations:
+        pooler = layer_cls(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=patch_func,
+                             expect_exception=False,
+                             output_shape=materialized_output.shape)
+
+        # test max pool 3d
+        data = torch.rand(2, 4, 4, 4)
+        materialized_output = pooler(data)
+        _assert_output_shape(data=data,
+                             module=pooler,
+                             patch_fn=patch_func,
+                             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=patch_func, expect_exception=True, output_shape=None)

tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py

+import torch
+import pytest
+try:
+    import torchvision.models as tm
+except:
+    pass
+from colossalai.fx import ColoTracer
+from torch.fx import GraphModule
+
+
+@pytest.mark.skip('skip as torchvision is required')
+def test_torchvision_models():
+    MODEL_LIST = [
+        tm.vgg11, tm.resnet18, tm.densenet121, tm.mobilenet_v3_small, tm.resnext50_32x4d, tm.wide_resnet50_2,
+        tm.regnet_x_16gf, tm.vit_b_16, tm.convnext_small, tm.mnasnet0_5, tm.efficientnet_b0
+    ]
+
+    torch.backends.cudnn.deterministic = True
+
+    tracer = ColoTracer()
+    data = torch.rand(2, 3, 224, 224)
+
+    for model_cls in MODEL_LIST:
+        if model_cls in [tm.convnext_small, tm.efficientnet_b0]:
+            # remove the impact of randomicity
+            model = model_cls(stochastic_depth_prob=0)
+        else:
+            model = model_cls()
+
+        graph = tracer.trace(root=model)
+
+        gm = GraphModule(model, graph, model.__class__.__name__)
+        gm.recompile()
+
+        model.eval()
+        gm.eval()
+
+        with torch.no_grad():
+            fx_out = gm(data)
+            non_fx_out = model(data)
+        assert torch.allclose(
+            fx_out, non_fx_out), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}'
+
+
+if __name__ == '__main__':
+    test_torchvision_models()