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f01548e5a4
Summary: This PR removes SymbolicVariable from all tests as well as the specialize_autogradzero and canonicalize_ops passes. These passes used SymbolicVariable in a relatively simple way compared to its few remaining uses. Removing SymbolicVariable means graphs must be constructed by other methods. IRParser was preferred for tests, but tests requiring pointers to graph internals or differentiation use direct construction instead. See https://github.com/pytorch/pytorch/issues/23989, which was discovered during this process, for why IRParser cannot be used when differentiation is required. Direct construction was also used in the updated passes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/24007 Test Plan: Only refactors existing tests and preserves current checks; no additional testing needed. Differential Revision: D16906045 Pulled By: mruberry fbshipit-source-id: b67df4611562cd7618f969890e2b6840750c7266
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/autodiff.h"
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#include "torch/csrc/jit/interpreter.h"
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#include "torch/csrc/jit/irparser.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 upto 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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