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https://github.com/zebrajr/pytorch.git
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6384c2d81b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115 This commit runs the newly added tools/clang_format.py on the JIT codebase and includes all of the formatting changes thus produced. Testing: Ran the script, CI. Test Plan: Imported from OSS Reviewed By: eellison Differential Revision: D20568523 Pulled By: SplitInfinity fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
53 lines
1.5 KiB
C++
53 lines
1.5 KiB
C++
#pragma once
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#include <torch/csrc/jit/testing/file_check.h>
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#include "test/cpp/jit/test_base.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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