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50a1850d8d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36984 Follow LOG(WARNING) format for c++ side warnings in order to play well with larger services, especially when using glog. I need to hook up into GLOG internals a bit in order to override FILE/LINE without having to change the whole thing to be macros, but it seems to be stable between glog versions. Note, this also changes caffe2_log_level to warning by default - I think it's a much better default when compiling without glog (or maybe even have info). With glog output, stderr capture doesn't work any more in tests. That's why we instead use c10-level warnings capture. Test Plan: Run unittest in both glog and non-glog build mode: glog: ``` W0416 12:06:49.778215 3311666 exception_test.cpp:23] Warning: I'm a warning (function TestBody) ``` no-glog: ``` [W exception_test.cpp:23] Warning: I'm a warning (function TestBody) ``` Reviewed By: ilia-cher Differential Revision: D21151351 fbshipit-source-id: fa926d9e480db5ff696990dad3d80f79ef79f24a
85 lines
1.8 KiB
C++
85 lines
1.8 KiB
C++
#include <gtest/gtest.h>
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#include <torch/torch.h>
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#include <test/cpp/api/support.h>
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#include <functional>
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using namespace torch::test;
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void torch_warn_once_A() {
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TORCH_WARN_ONCE("warn once");
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}
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void torch_warn_once_B() {
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TORCH_WARN_ONCE("warn something else once");
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}
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void torch_warn() {
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TORCH_WARN("warn multiple times");
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}
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TEST(UtilsTest, WarnOnce) {
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{
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WarningCapture warnings;
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torch_warn_once_A();
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torch_warn_once_A();
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torch_warn_once_B();
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torch_warn_once_B();
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ASSERT_EQ(count_substr_occurrences(warnings.str(), "warn once"), 1);
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ASSERT_EQ(
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count_substr_occurrences(warnings.str(), "warn something else once"),
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1);
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}
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{
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WarningCapture warnings;
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torch_warn();
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torch_warn();
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torch_warn();
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ASSERT_EQ(
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count_substr_occurrences(warnings.str(), "warn multiple times"), 3);
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}
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}
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TEST(NoGradTest, SetsGradModeCorrectly) {
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torch::manual_seed(0);
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torch::NoGradGuard guard;
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torch::nn::Linear model(5, 2);
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auto x = torch::randn({10, 5}, torch::requires_grad());
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auto y = model->forward(x);
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torch::Tensor s = y.sum();
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// Mimicking python API behavior:
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ASSERT_THROWS_WITH(s.backward(),
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"element 0 of tensors does not require grad and does not have a grad_fn")
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}
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struct AutogradTest : torch::test::SeedingFixture {
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AutogradTest() {
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x = torch::randn({3, 3}, torch::requires_grad());
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y = torch::randn({3, 3});
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z = x * y;
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}
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torch::Tensor x, y, z;
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};
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TEST_F(AutogradTest, CanTakeDerivatives) {
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z.backward(torch::ones_like(z));
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ASSERT_TRUE(x.grad().allclose(y));
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}
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TEST_F(AutogradTest, CanTakeDerivativesOfZeroDimTensors) {
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z.sum().backward();
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ASSERT_TRUE(x.grad().allclose(y));
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}
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TEST_F(AutogradTest, CanPassCustomGradientInputs) {
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z.sum().backward(torch::ones({}) * 2);
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ASSERT_TRUE(x.grad().allclose(y * 2));
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}
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