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22401b850b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45264 Context for why we are porting to gtest in: https://github.com/pytorch/pytorch/pull/45018. This PR completes the process of porting and removes unused files/macros. Test Plan: Imported from OSS Reviewed By: ZolotukhinM Differential Revision: D23901392 Pulled By: suo fbshipit-source-id: 89526890e1a49462f3f77718f4ee273c5bc578ba
52 lines
1.5 KiB
C++
52 lines
1.5 KiB
C++
#pragma once
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#include <torch/csrc/jit/testing/file_check.h>
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#include "torch/csrc/jit/ir/irparser.h"
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#include "torch/csrc/jit/runtime/autodiff.h"
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#include "torch/csrc/jit/runtime/interpreter.h"
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namespace torch {
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namespace jit {
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using tensor_list = std::vector<at::Tensor>;
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using namespace torch::autograd;
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// work around the fact that variable_tensor_list doesn't duplicate all
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// of std::vector's constructors.
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// most constructors are never used in the implementation, just in our tests.
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Stack createStack(std::vector<at::Tensor>&& list);
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void assertAllClose(const tensor_list& a, const tensor_list& b);
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std::vector<at::Tensor> run(
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InterpreterState& interp,
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const std::vector<at::Tensor>& inputs);
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std::pair<tensor_list, tensor_list> runGradient(
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Gradient& grad_spec,
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tensor_list& tensors_in,
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tensor_list& tensor_grads_in);
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std::shared_ptr<Graph> build_lstm();
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at::Tensor t_use(at::Tensor x);
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at::Tensor t_def(at::Tensor x);
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// given the difference of output vs expected tensor, check whether the
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// difference is within a relative tolerance range. This is a standard way of
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// matching tensor values up to certain precision
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bool checkRtol(const at::Tensor& diff, const std::vector<at::Tensor> inputs);
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bool almostEqual(const at::Tensor& a, const at::Tensor& b);
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bool exactlyEqual(const at::Tensor& a, const at::Tensor& b);
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std::pair<at::Tensor, at::Tensor> lstm(
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at::Tensor input,
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at::Tensor hx,
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at::Tensor cx,
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at::Tensor w_ih,
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at::Tensor w_hh);
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} // namespace jit
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} // namespace torch
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