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38bc732b2d
* this removes the flag controlling whether the interpreter works on variables. * now the interpreter _always_ works on variables * constants in the IR are still _always_ non-variables, and an assert was added to ensure this. * as_tensor was split into as_variable and as_tensor since it is sometimes used to construct constants in the IR * I tried changing the IR to also always use variables but that change was much more cross cutting and fragile and I never got it working
35 lines
1.3 KiB
C++
35 lines
1.3 KiB
C++
#pragma once
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#include <ATen/ATen.h>
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#include <torch/csrc/THP_export.h>
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namespace torch {
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// NOTE: This API is currently highly experimental and may change drastically
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// in the near future.
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// These functions provide a small wrapper around aten ensuring
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// that we create tensors with type Variable rather than raw tensors
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// when we create new tensors. We also provide a few accessors like requires_grad
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// that make it easier to get to varible information when we have a at::Tensor
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/// Returns a `Type` object for the given backend (e.g. `at::kCPU`) and
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/// `ScalarType` (e.g. `at::kDouble`).
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THP_CLASS at::Type& getType(at::Backend backend, at::ScalarType type);
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/// Returns a `Type` object for the CPU backend and the given `ScalarType`
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/// (e.g. `at::kDouble`). Equivalent to `getType(kCPU, type)`.
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THP_CLASS at::Type& CPU(at::ScalarType type);
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/// Returns a `Type` object for the CUDA backend and the given `ScalarType`
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/// (e.g. `at::kDouble`). Equivalent to `getType(kCUDA, type)`.
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THP_CLASS at::Type& CUDA(at::ScalarType type);
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/// Sets the `requires_grad` property of the given `Tensor`.
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THP_CLASS void set_requires_grad(at::Tensor& tensor, bool requires_grad) noexcept;
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/// Returns the `requires_grad` of the given `Tensor`.
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THP_CLASS bool requires_grad(const at::Tensor& tensor) noexcept;
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} // namespace torch
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