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Remove unnecessary type dispatches from Variable::Impl ctor (#13630)

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Summary:
This should improve the performance of wrapping a tensor in a Variable
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13630

Reviewed By: ezyang

Differential Revision: D12944960

Pulled By: zou3519

fbshipit-source-id: 89fa78a563e46a747d851a90ffd1b5cf3cd2d0d7
This commit is contained in:
Richard Zou
2018-11-07 07:23:12 -08:00
committed by Facebook Github Bot
parent 2ae8e46105
commit e70321ed9e
2 changed files with 2 additions and 2 deletions
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2
torch/csrc/autograd/variable.cpp
View File

@ -22,7 +22,7 @@
namespace torch {
namespace autograd {
Variable::Impl::Impl(at::Tensor data, bool requires_grad, Edge gradient_edge)
: TensorImpl(data.type().type_id(), data.type().typeMeta(), data.type().allocator(), /* is variable */ true),
: TensorImpl(data.type_id(), data.dtype(), /*allocator=*/nullptr, /* is variable */ true),
data_(std::move(data)),
grad_fn_(std::move(gradient_edge.function)),
requires_grad_(false),

2
torch/csrc/autograd/variable.h
View File

@ -319,7 +319,7 @@ struct TORCH_API Variable::Impl : public at::TensorImpl {
/// variables.
void set_requires_grad(bool requires_grad) override {
AT_CHECK(
!requires_grad || at::isFloatingType(type().scalarType()),
!requires_grad || at::isFloatingType(at::typeMetaToScalarType(dtype())),
"Only Tensors of floating point dtype can require gradients");
requires_grad_ = requires_grad;
}