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https://github.com/zebrajr/pytorch.git
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a9e6a673ae
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11876 Modern C++ api instead of macros, item() is aligned with Python frontend. caffe2::Tensor::capacity_nbytes is effecitvely unused and confusing w.r.t. caffe2::Tensor::nbytes(). codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte "item<uint8_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong "item<int64_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt "item<int32_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData "data<uint8_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData "data<int64_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData "data<int32_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData "data<float>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte "item<uint8_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong "item<int64_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt "item<int32_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData "data<uint8_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData "data<int64_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData "data<int32_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData "data<float>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCComplexDouble "item<std::complex<double>>" codemod -d tc --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" Reviewed By: ezyang Differential Revision: D9948572 fbshipit-source-id: 70c9f5390d92b82c85fdd5f8a5aebca338ab413c
54 lines
1.4 KiB
C++
54 lines
1.4 KiB
C++
#include <gtest/gtest.h>
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#include <torch/nn/init.h>
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#include <torch/nn/modules/linear.h>
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#include <torch/tensor.h>
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#include <torch/utils.h>
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#include <test/cpp/api/support.h>
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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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s.backward();
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ASSERT_FALSE(model->parameters()["weight"].grad().defined());
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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();
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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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TEST(NNInitTest, CanInitializeTensorThatRequiresGrad) {
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auto tensor = torch::empty({3, 4}, torch::requires_grad());
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ASSERT_THROWS_WITH(
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tensor.fill_(1),
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"a leaf Variable that requires grad "
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"has been used in an in-place operation");
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ASSERT_EQ(torch::nn::init::ones_(tensor).sum().item<int32_t>(), 12);
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}
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