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https://github.com/zebrajr/pytorch.git
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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
from functools import reduce
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def maybe_view(tensor, size, check_same_size=True):
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if check_same_size and tensor.size() == size:
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return tensor
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return tensor.contiguous().view(size)
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def maybe_unexpand(tensor, old_size, check_same_size=True):
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if check_same_size and tensor.size() == old_size:
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return tensor
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num_unsqueezed = tensor.dim() - len(old_size)
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expanded_dims = [dim for dim, (expanded, original)
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in enumerate(zip(tensor.size()[num_unsqueezed:], old_size))
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if expanded != original]
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for _ in range(num_unsqueezed):
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tensor = tensor.sum(0, keepdim=False)
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for dim in expanded_dims:
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tensor = tensor.sum(dim, keepdim=True)
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return tensor
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# Generate paddings in ONNX order based on pad in pytorch.
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# Arguments:
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# dim: the dimension of the tensor.
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# pad: the paddings in pytorch.
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# The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ...
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def prepare_onnx_paddings(dim, pad):
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assert isinstance(dim, int)
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# The desired order of paddings is
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# dim_0_begin, dim_1_begin, ... , dim_0_end, ..., dim_n_end.
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# n is the dimension of input.
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assert len(pad) <= dim * 2
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# assume zero-dimensions in the beginning
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paddings = list(pad[:]) + [0] * (dim * 2 - len(pad))
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# reverse order and collate first beginnings and then ends
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paddings = paddings[-2::-2] + paddings[-1::-2]
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assert len(paddings) == dim * 2
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return paddings
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# Check whether the op enable broadcasting, and whether it is supported by ONNX.
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# If dims1 and dims2 are different, then broadcast is True.
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# We always assume the combination of dims1 and dims2 is broadcastable.
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# The following types of broadcasting are supported in ONNX:
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# 1) Only one element in dims2, such as dims2 = [1, 1]
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# 2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
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# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
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def check_onnx_broadcast(dims1, dims2):
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broadcast = False
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supported = True
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len1 = len(dims1)
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len2 = len(dims2)
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numel1 = reduce(lambda x, y: x * y, dims1)
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numel2 = reduce(lambda x, y: x * y, dims2)
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if len1 < len2:
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broadcast = True
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if numel2 != 1:
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supported = False
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elif len1 > len2:
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broadcast = True
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if numel2 != 1 and dims1[len1 - len2:] != dims2:
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supported = False
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else:
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if dims1 != dims2:
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broadcast = True
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if numel2 != 1:
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supported = False
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if not supported:
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raise ValueError("Numpy style broadcasting is not supported in ONNX. "
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"Input dims are: {}, {}".format(dims1, dims2))
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return broadcast
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