Remove Tensor constructor of Scalar.#10852
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This is along the way of removing Tensor as a member of the tagged union in Scalar. This simplifies ordering dependencies, because currently Scalar and Tensor both depend on each other (so we introduce a TensorBase). Also, this API isn't particularly useful publically: we can't autograd through Scalars, so you still need a Tensor overload basically everywhere anyway. I'm undecided what the final API should be here. We could keep a Tensor constructor on Scalar, but have it generate a local scalar; this is convenient but given this API used to be non-synchronizing, it may not be the best. For now, I'm just using _local_scalar, which is clear, although we should get rid of the prefix _ if that's the API we intend to promote.
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ezyang
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aten/src/ATen/Scalar.cpp
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| return Scalar(-v.i); | ||
| } else { | ||
| return Scalar(-Tensor(t)); | ||
| return Scalar(-Tensor(t)._local_scalar()); |
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ezyang
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zdevito
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Aug 25, 2018
Summary: This is along the way of removing Tensor as a member of the tagged union in Scalar. This simplifies ordering dependencies, because currently Scalar and Tensor both depend on each other (so we introduce a TensorBase). Also, this API isn't particularly useful publicly: we can't autograd through Scalars, so you still need a Tensor overload basically everywhere anyway. I'm undecided what the final API should be here. We could keep a Tensor constructor on Scalar, but have it generate a local scalar; this is convenient but given this API used to be non-synchronizing, it may not be the best. For now, I'm just using _local_scalar, which is clear, although we should get rid of the prefix _ if that's the API we intend to promote. Pull Request resolved: pytorch/pytorch#10852 Reviewed By: ezyang Differential Revision: D9496766 Pulled By: gchanan fbshipit-source-id: 16f39b57536b9707132a5a4d915650c381bb57db
petrex
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Aug 27, 2018
* upstream/master: (89 commits) move HeatmapMaxKeypointOp unittest to oss fix xfails involving literals (pytorch#10905) Bag of Distributions doc fixes (pytorch#10894) Remove FIXME_zerol() from test_jit.py (pytorch#10900) Increase BC for PackedSequence ctor (pytorch#9864) Remove ability of Scalars to hold Tensors. Begin a bestiary of MSVC/NVCC bugs. (pytorch#10883) Prevent JIT from overspecializing to every single size configuration (pytorch#10844) Handling failing test on ROCm. Update mobile predictor caller's interface Cache isContiguous and numel Create class constant for string literal 'blob_names' Conv BN fusion for 3D conv (pytorch#10239) Stop using symbolic override for tracing RNNs (pytorch#10638) Add registry to pybind_state (pytorch#10759) Remove the nanopb submodule Create at::linear (pytorch#10799) Refactor THCNumerics and add common math functions for at::Half (pytorch#10301) Remove Tensor constructor of Scalar. (pytorch#10852) Revert D9492561: [pytorch][PR] Moving the operator argument to the front for kernelPointwiseApply. ...
PenghuiCheng
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Sep 11, 2018
Summary: This is along the way of removing Tensor as a member of the tagged union in Scalar. This simplifies ordering dependencies, because currently Scalar and Tensor both depend on each other (so we introduce a TensorBase). Also, this API isn't particularly useful publicly: we can't autograd through Scalars, so you still need a Tensor overload basically everywhere anyway. I'm undecided what the final API should be here. We could keep a Tensor constructor on Scalar, but have it generate a local scalar; this is convenient but given this API used to be non-synchronizing, it may not be the best. For now, I'm just using _local_scalar, which is clear, although we should get rid of the prefix _ if that's the API we intend to promote. Pull Request resolved: pytorch#10852 Reviewed By: ezyang Differential Revision: D9496766 Pulled By: gchanan fbshipit-source-id: 16f39b57536b9707132a5a4d915650c381bb57db
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This is along the way of removing Tensor as a member of the tagged union in Scalar. This simplifies ordering dependencies, because currently Scalar and Tensor both depend on each other (so we introduce a TensorBase). Also, this API isn't particularly useful publicly: we can't autograd through Scalars, so you still need a Tensor overload basically everywhere anyway.
I'm undecided what the final API should be here. We could keep a Tensor constructor on Scalar, but have it generate a local scalar; this is convenient but given this API used to be non-synchronizing, it may not be the best.
For now, I'm just using _local_scalar, which is clear, although we should get rid of the prefix _ if that's the API we intend to promote.