[fx] add test for meta tensor. (#1527)

* [fx] add test for meta tensor.

* [fx] add test for meta tensor.

* [fx] add test for meta tensor.

* [fx] add test for meta tensor.

* [fx] fix error.

tests/test_fx/test_meta/test_aten.py

+from typing import Any, Callable, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from colossalai.fx.profiler import MetaTensor
+
+import pytest
+
+try:
+    meta_lib = torch.library.Library("aten", "IMPL", "Meta")
+    INCOMPATIBLE = False    # version > 1.12.0
+except:
+    INCOMPATIBLE = True
+
+aten = torch.ops.aten
+
+registered_meta = {
+    ('aten.convolution.default', True): [    # (aten ops, requires_backward)
+        (nn.Conv1d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4)),
+        (nn.Conv2d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4, 4)),
+        (nn.Conv3d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4, 4, 4)),
+        (nn.ConvTranspose1d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4)),
+        (nn.ConvTranspose2d(in_channels=3, out_channels=4, kernel_size=2, padding=1,
+                            dilation=2), torch.rand(2, 3, 4, 4)),
+        (nn.ConvTranspose3d(in_channels=3, out_channels=4, kernel_size=2, padding=1,
+                            dilation=2), torch.rand(2, 3, 4, 4, 4)),
+    ],
+    ('aten.native_batch_norm.default', True): [
+        (nn.BatchNorm1d(4), torch.rand(2, 4)),
+        (nn.BatchNorm2d(4), torch.rand(1, 4, 4, 4)),
+        (nn.BatchNorm3d(4), torch.rand(1, 4, 4, 4, 4)),
+    ],
+    ('aten.native_layer_norm.default', True): [(nn.LayerNorm(4), torch.rand(1, 2, 3, 4)),],
+    ('aten.avg_pool1d.default', True): [
+        (nn.MaxPool1d(3, stride=2), torch.rand(4, 5, 5)),
+        (nn.AvgPool1d(3, stride=2), torch.rand(4, 5, 5)),
+        (nn.AdaptiveMaxPool1d(3), torch.rand(4, 5, 5)),
+        (nn.AdaptiveAvgPool1d(3), torch.rand(4, 5, 5)),
+    ],
+    ('aten.avg_pool2d.default', True): [
+        (nn.MaxPool2d((3, 2), stride=(2, 1)), torch.rand(2, 4, 5, 5)),
+        (nn.AvgPool2d((3, 2), stride=(2, 1)), torch.rand(2, 4, 5, 5)),
+        (nn.AdaptiveMaxPool2d((3, 2)), torch.rand(2, 4, 5, 5)),
+        (nn.AdaptiveAvgPool2d((3, 2)), torch.rand(2, 4, 5, 5)),
+    ],
+    ('aten.relu.default', True): [
+        (nn.ReLU(), torch.rand(4, 3, 1, 2)),
+        (nn.LeakyReLU(), torch.rand(4, 3, 1, 2)),
+        (nn.SiLU(), torch.rand(4, 3, 1, 2)),
+        (nn.GELU(), torch.rand(4, 3, 1, 2)),
+        (nn.ELU(), torch.rand(4, 3, 1, 2)),
+        (nn.Sigmoid(), torch.rand(4, 3, 1, 2)),
+        (nn.Tanh(), torch.rand(4, 3, 1, 2)),
+        (nn.Hardswish(), torch.rand(4, 3, 1, 2)),
+    ]
+}
+
+
+def compare_all(tensor: torch.Tensor, meta_tensor: MetaTensor) -> Any:
+    assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
+    assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
+    assert tensor.stride() == meta_tensor.stride(
+    ), f'the stride of tensor ({tensor.stride()}) and meta tensor ({meta_tensor.stride()}) does not match.'
+
+
+def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_backward=False) -> Any:
+    x.requires_grad = requires_backward
+    meta_x = MetaTensor(x.to('meta'))
+    if isinstance(f, nn.Module):
+        x_out, meta_out = f(x), f.to('meta')(meta_x)
+    else:
+        x_out, meta_out = f(x), f(meta_x)
+    compare_all(x_out, meta_out)
+    if requires_backward:
+        x_out.sum().backward()
+        meta_out.sum().backward()
+        compare_all(x.grad, meta_x.grad)
+
+
+@pytest.mark.skipif(INCOMPATIBLE, reason='torch version is lower than 1.12.0')
+def test_meta_aten():
+    for (aten_op, requires_backward), v in registered_meta.items():
+        for f, x in v:
+            run_and_compare(f, x, requires_backward)
+
+
+if __name__ == '__main__':
+    test_meta_aten()

tests/test_fx/test_meta/test_backward.py

+import torchvision.models as tm
+import timm.models as tmm
+import torch
+from colossalai.fx.profiler import MetaTensor
+
+import pytest
+
+try:
+    meta_lib = torch.library.Library("aten", "IMPL", "Meta")
+    incompatible = False  # version > 1.12.0
+except:
+    incompatible = True
+
+
+tm_models = [
+    tm.vgg11, 
+    tm.resnet18, 
+    tm.densenet121, 
+    tm.mobilenet_v3_small, 
+    tm.resnext50_32x4d, 
+    tm.wide_resnet50_2,
+    tm.regnet_x_16gf, 
+    tm.mnasnet0_5, 
+    tm.efficientnet_b0,
+]
+
+
+tmm_models = [
+    tmm.resnest.resnest50d,
+    tmm.beit.beit_base_patch16_224,
+    tmm.cait.cait_s24_224,
+    tmm.efficientnet.efficientnetv2_m,
+    tmm.resmlp_12_224,
+    tmm.vision_transformer.vit_base_patch16_224,
+    tmm.deit_base_distilled_patch16_224,
+    tmm.convnext.convnext_base, 
+    tmm.vgg.vgg11, 
+    tmm.dpn.dpn68, 
+    tmm.densenet.densenet121, 
+    tmm.rexnet.rexnet_100,
+    tmm.swin_transformer.swin_base_patch4_window7_224
+]
+
+
+@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
+def test_torchvision_models():
+    for m in tm_models:
+        model = m().to('meta')
+        data = torch.rand(1000, 3, 224, 224, device='meta')
+        model(MetaTensor(data)).sum().backward()
+
+
+@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
+def test_timm_models():
+    for m in tmm_models:
+        model = m().to('meta')
+        data = torch.rand(1000, 3, 224, 224, device='meta')
+        model(MetaTensor(data)).sum().backward()
+
+
+if __name__ == '__main__':
+    test_torchvision_models()
+    test_timm_models()