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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/06/24 00:28:39 UTC
[GitHub] [tvm] shingjan opened a new pull request, #11863: [Relay] [PyTorch] Add aten::broadcast_tensors
shingjan opened a new pull request, #11863:
URL: https://github.com/apache/tvm/pull/11863
This PR intends to add `aten::broadcast_tensors` ops in the PyTorch frontend.
@masahi @junrushao1994 @zxybazh
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[GitHub] [tvm] shingjan commented on a diff in pull request #11863: [Relay] [PyTorch] Add aten::broadcast_tensors
Posted by GitBox <gi...@apache.org>.
shingjan commented on code in PR #11863:
URL: https://github.com/apache/tvm/pull/11863#discussion_r905701026
##########
python/tvm/relay/frontend/pytorch.py:
##########
@@ -1952,6 +1952,13 @@ def expand_as(self, inputs, input_types):
target = _op.cast(target, t0)
return _op.broadcast_to_like(inputs[0], target)
+ def broadcast_tensors(self, inputs, input_types):
+ tensor_list = inputs[0]
+ import torch
+
+ 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]
Review Comment:
Thanks for the review! This is definition of `broadcast_shapes` in pytorch's [doc](https://pytorch.org/docs/stable/generated/torch.broadcast_shapes.html?highlight=broadcast_shapes#torch.broadcast_shapes):
```
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
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[GitHub] [tvm] masahi commented on a diff in pull request #11863: [Relay] [PyTorch] Add aten::broadcast_tensors
Posted by GitBox <gi...@apache.org>.
masahi commented on code in PR #11863:
URL: https://github.com/apache/tvm/pull/11863#discussion_r905654191
##########
python/tvm/relay/frontend/pytorch.py:
##########
@@ -1952,6 +1952,13 @@ def expand_as(self, inputs, input_types):
target = _op.cast(target, t0)
return _op.broadcast_to_like(inputs[0], target)
+ def broadcast_tensors(self, inputs, input_types):
+ tensor_list = inputs[0]
+ import torch
+
+ 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]
Review Comment:
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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[GitHub] [tvm] masahi merged pull request #11863: [Relay] [PyTorch] Add aten::broadcast_tensors
Posted by GitBox <gi...@apache.org>.
masahi merged PR #11863:
URL: https://github.com/apache/tvm/pull/11863
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[GitHub] [tvm] shingjan commented on a diff in pull request #11863: [Relay] [PyTorch] Add aten::broadcast_tensors
Posted by GitBox <gi...@apache.org>.
shingjan commented on code in PR #11863:
URL: https://github.com/apache/tvm/pull/11863#discussion_r905701026
##########
python/tvm/relay/frontend/pytorch.py:
##########
@@ -1952,6 +1952,13 @@ def expand_as(self, inputs, input_types):
target = _op.cast(target, t0)
return _op.broadcast_to_like(inputs[0], target)
+ def broadcast_tensors(self, inputs, input_types):
+ tensor_list = inputs[0]
+ import torch
+
+ 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]
Review Comment:
Thanks for the review! This is definition of `broadcast_shapes` in pytorch's [doc](https://pytorch.org/docs/stable/generated/torch.broadcast_shapes.html?highlight=broadcast_shapes#torch.broadcast_shapes):
```
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
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