delldu commented on a change in pull request #8443:
URL: https://github.com/apache/tvm/pull/8443#discussion_r670228541
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File path: tests/python/frontend/pytorch/test_forward.py
##########
@@ -3912,6 +3911,98 @@ def forward(self, x):
verify_model(Flip(axis=-1), input_data=input)
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+def test_forward_im2col():
+ torch.set_grad_enabled(False)
+
+ class Im2col(Module):
+ def __init__(self, kernel_size, dilation, padding, stride):
+ super(Im2col, self).__init__()
+ self.kernel_size = (kernel_size, kernel_size)
+ self.dilation = (dilation, dilation)
+ self.padding = (padding, padding)
+ self.stride = (stride, stride)
+
+ def forward(self, x):
+ #
***********************************************************************************
+ #
+ # !!! DO NOT USE !!!
+ # F.unfold(x, kernel_size=3, dilation=1, padding=1, stride=1)
+ # for it broken TVM "if conditional expression" in torch script
mode
+ #
+ #
***********************************************************************************
+ return torch._C._nn.im2col(
+ x, self.kernel_size, self.dilation, self.padding, self.stride
+ )
+
+ input = torch.randn(2, 3, 32, 32)
+ verify_model(Im2col(5, 1, 1, 2), input_data=input)
+ verify_model(Im2col(3, 1, 2, 1), input_data=input)
+ verify_model(Im2col(5, 1, 2, 2), input_data=input)
+
+
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+def test_forward_grid_sampler():
+ torch.set_grad_enabled(False)
+
+ class GridSampler(Module):
+ def __init__(self, output_h, output_w):
+ super(GridSampler, self).__init__()
+ self.output_h = output_h
+ self.output_w = output_w
+
+ # normalize to [-1.0, 1.0]
+ h = torch.arange(0, output_h) / (output_h - 1.0) * 2.0 - 1.0
+ w = torch.arange(0, output_w) / (output_w - 1.0) * 2.0 - 1.0
+ grid = torch.zeros(output_h, output_w, 2)
+ grid[:, :, 0] = w.unsqueeze(0).repeat(output_h, 1)
+ grid[:, :, 1] = h.unsqueeze(0).repeat(output_w, 1).transpose(0, 1)
+ self.grid = grid.unsqueeze(0)
+
+ def forward(self, input):
+ batch = input.size(0)
+ grid = self.grid.repeat(batch, 1, 1, 1).to(input.device)
+
+ # Torch grid_sample default: mode='bilinear',
padding_mode='zeros', align_corners=False
+ # tvm seems align corners as True
+
+ #
***********************************************************************************
+ #
+ # !!! DO NOT USE !!!
+ # F.grid_sample(input, grid, mode='bilinear',
padding_mode='zeros', align_corners=True)
+ # for it broken TVM "if conditional expression" in torch script
mode
+ #
+ #
***********************************************************************************
+
+ return torch.grid_sampler(input, grid, 0, 0, True)
+
+ model = GridSampler(16, 32)
+ input = torch.randn(2, 3, 32, 32)
+
+ verify_model(model, input_data=input)
+ verify_script_model(model.eval(), [(2, 3, 32, 32)],
_get_default_vm_targets())
+
+
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+def test_forward_float():
+ torch.set_grad_enabled(False)
+
+ def convert_i(i: int) -> float:
+ return float(i)
+
+ class FloatModel(Module):
+ def __init__(self):
+ super(FloatModel, self).__init__()
+
+ def forward(self, x):
+ f = convert_i(10)
Review comment:
@masahi, I have little difference opinion.
Of course, if you just use traced mode, previous aten::Float is OK, NO BUG,
NO NEED Fixed.
if you want script mode, convert_i(10) and float(10) is not same thing. you
can explore them with test.
As a matter of fact, user in some case could not avoid script model.
Should we remove all verify_script_model in test_forward ?
You are TVM expert, feel free give us comments. 1) Remove it? 2) Reserved it
?
Thanks a lot.
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