jit: enable conv_relu fusion #15
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If the code is invalid, could we remove it directly? |
EikanWang
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May 19, 2020
XiaobingSuper
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pinzhenx
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May 25, 2020
pinzhenx
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pinzhenx
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pinzhenx
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@EikanWang , please help merge it, thanks! |
This was referenced May 29, 2020
pinzhenx
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Jun 9, 2020
| def linear(input, weight, bias: Optional[torch.Tensor] = None): | ||
| if bias is None: | ||
| bias = torch.zeros(weight.size(0)) | ||
| return torch.ops.torch_ipex.linear(input, weight, bias) |
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@XiaobingSuper there's no way to fallback to aten linear if there's any exception happens. I suggest we should add try-catch here? or in the NewLinearOp?
pinzhenx
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Jun 9, 2020
| @@ -65,6 +39,11 @@ def max_pool3d(input, kernel_size, stride, padding, dilation, ceil_mode): | |||
| pass | |||
| return torch_max_pool3d(input, kernel_size, stride, padding, dilation, ceil_mode) | |||
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| def max_pool2d(input, kernel_size: Vector, stride: Vector, padding: Vector, dilation: Vector, ceil_mode: bool): | |||
pinzhenx
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Jun 9, 2020
| pass | ||
| return torch_max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) | ||
| def adaptive_avg_pool2d(input, output_size: Vector): | ||
| return torch.ops.torch_ipex.adaptive_avg_pool2d(input, _pair(output_size)) |
zhuhaozhe
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optimize sparse adagrad compute and update
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Now, only enable conv_relu fusion for jit path.