[Relay] [PyTorch] Add aten::broadcast_tensors #11863
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masahi
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Jun 24, 2022
| import torch | ||
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| res_shape = list(torch.broadcast_shapes(*[self.infer_shape(t) for t in tensor_list])) | ||
| return [_op.broadcast_to(tensor, res_shape) for tensor in tensor_list] |
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oh interesting. Have you verified that this is what broadcast_tensors does? It's interesting that we are actually invoking pytorch function for conversion purpose.
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Thanks for the review! This is definition of broadcast_shapes in pytorch's doc:
This is equivalent to torch.broadcast_tensors(*map(torch.empty, shapes))[0].shape but avoids the need create to intermediate tensors. This is useful for broadcasting tensors of common batch shape but different rightmost shape, e.g. to broadcast mean vectors with covariance matrices.
This workaround avoid the need to create intermediate tensors on our end. broadcast_tensors works like the following:
a.shape = [1,1,2]
b.shape = [1,3,1]
c.shape = [4,1,1]
#x, y, z = broadcast_tensors(a,b,c)
x = a.broadcast_to(shape = [4,3,2])
y = b.broadcast_to(shape = [4,3,2])
y = b.broadcast_to(shape = [4,3,2])
meaning the resulting tensors of broadcast_tensors will all have the same shape [4,3,2]. This is a bit different from what we discussed yesterday but seems to be the right broadcasting semantics.
reference: https://numpy.org/doc/stable/user/basics.broadcasting.html
masahi
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Jun 24, 2022
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June 24, 2022 12:13
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zxybazh
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* add aten::broadcast_tensors * add entry * fix test
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* add aten::broadcast_tensors * add entry * fix test
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* add aten::broadcast_tensors * add entry * fix test
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This PR intends to add
aten::broadcast_tensorsops in the PyTorch frontend.@masahi @junrushao1994 @zxybazh