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https://github.com/zebrajr/pytorch.git
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44a8d4d998
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74309 Since the test file is large, it can be landed on its own and then switched on in the diff that actually builds lazy tensor code. Test Plan: verify CI passes Reviewed By: desertfire Differential Revision: D34928619 fbshipit-source-id: cd556155326f7fb55b3f29031f80bc36c936d565 (cherry picked from commit 60945adbefb6a8d19f89e330f8b344d076b13bfc)
69 lines
2.3 KiB
C++
69 lines
2.3 KiB
C++
#pragma once
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#include <gtest/gtest.h>
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#include <torch/csrc/lazy/backend/backend_device.h>
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#include <torch/csrc/lazy/core/debug_util.h>
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#include <torch/csrc/lazy/core/ir.h>
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#include <torch/csrc/lazy/core/tensor.h>
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#include <torch/torch.h>
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#include <cmath>
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#include <functional>
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#include <string>
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#include <unordered_set>
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namespace torch {
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namespace lazy {
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const std::unordered_set<std::string>* GetIgnoredCounters();
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// Converts an at::Tensor(device=torch::kLazy) to at::Tensor(device=torch::kCPU)
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// This at::Tensor can be torch::Tensor which is a Variable, or at::Tensor which
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// know nothing about autograd. If the input tensor is already a CPU tensor, it
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// will be returned. Needed because EqualValues and AllClose require CPU tensors
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// on both sides.
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at::Tensor ToCpuTensor(const at::Tensor& tensor);
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// Helper function to copy a tensor to device.
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torch::Tensor CopyToDevice(const torch::Tensor& tensor,
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const torch::Device& device);
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bool EqualValues(at::Tensor tensor1, at::Tensor tensor2);
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bool EqualValuesNoElementTypeCheck(at::Tensor tensor1, at::Tensor tensor2);
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bool CloseValues(at::Tensor tensor1, at::Tensor tensor2, double rtol = 1e-5,
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double atol = 1e-8);
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static inline void AllClose(at::Tensor tensor, at::Tensor xla_tensor,
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double rtol = 1e-5, double atol = 1e-8) {
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EXPECT_TRUE(CloseValues(tensor, xla_tensor, rtol, atol));
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}
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static inline void AllClose(at::Tensor tensor, torch::lazy::LazyTensor& xla_tensor,
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double rtol = 1e-5, double atol = 1e-8) {
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EXPECT_TRUE(
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CloseValues(tensor, xla_tensor.ToTensor(/*detached=*/false), rtol, atol));
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}
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static inline void AllEqual(at::Tensor tensor, at::Tensor xla_tensor) {
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EXPECT_TRUE(EqualValues(tensor, xla_tensor));
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}
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void ForEachDevice(const std::function<void(const torch::Device&)>& devfn);
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std::string GetTensorTextGraph(at::Tensor tensor);
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std::string GetTensorDotGraph(at::Tensor tensor);
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std::string GetTensorHloGraph(at::Tensor tensor);
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void TestBackward(
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const std::vector<torch::Tensor>& inputs, const torch::Device& device,
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const std::function<torch::Tensor(const std::vector<torch::Tensor>&)>&
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testfn,
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double rtol = 1e-5, double atol = 1e-8, int derivative_level = 1);
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} // namespace lazy
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} // namespace torch
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