enable DNNL Python OPs(adaptive_avg_pool2d, max_pool2d, max_pool3d) t… #7
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pinzhenx
reviewed
May 13, 2020
| @@ -25,7 +26,15 @@ def backward(ctx, grad_output): | |||
| class MaxPoolingFunction(Function): | |||
| @staticmethod | |||
| def forward(ctx, input, kernel_size, stride, padding, dilation, ceil_mode): | |||
| output = core.max_pooling(input, (kernel_size,), (stride,), (padding,), (dilation,), ceil_mode) | |||
| if type(kernel_size) is int: | |||
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torch.nn.modules.utils._single could serve the same purpose
from torch.nn.modules.utils import _single
kernel_size = _single(kernel_size)
pinzhenx
reviewed
May 13, 2020
| if input.device.type == 'dpcpp' and core.get_auto_dnnl(): | ||
| return AdaptiveAvgPool2dFunction.apply(input, output_size) | ||
| except RuntimeError: | ||
| return torch_adaptive_avg_pool2d(input, output_size) |
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may use pass in except block to avoid duplicate fallback code?
|
Please check if pass all unit test case. |
…o fallback to CPU.
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enable DNNL Python OPs(adaptive_avg_pool2d, max_pool2d, max_pool3d) to fallback to CPU.