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fd6655a0f5
Fixes #115611 Autogen kernel may cause redundant copy, so we develop the kernel to improve efficiency. Test Case: ```c++ #include <torch/torch.h> #include <iostream> #include <ATen/ATen.h> #include <ATen/cuda/CUDAContext.h> int main() { auto input = torch::rand({2, 3, 4, 4}, torch::device(torch::kCUDA)); auto weight = torch::randn({3}, torch::device(torch::kCUDA)); auto bias = torch::randn({3}, torch::device(torch::kCUDA)); auto running_mean = torch::zeros({3}, torch::device(torch::kCUDA)); auto running_var = torch::ones({3}, torch::device(torch::kCUDA)); bool training = true; double exponential_average_factor = 0.1; double epsilon = 1e-5; auto output = torch::empty_like(input); auto save_mean = torch::empty({3}, torch::device(torch::kCUDA)); auto save_var = torch::empty({3}, torch::device(torch::kCUDA)); auto reserve = torch::empty({0}, torch::device(torch::kCUDA)); // empty place-holder at::native::cudnn_batch_norm_out(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, output, save_mean, save_var, reserve); auto outputs = at::native::cudnn_batch_norm(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); bool is_close_output = torch::allclose(output, std::get<0>(outputs)); bool is_close_save_mean = torch::allclose(save_mean, std::get<1>(outputs)); bool is_close_save_var = torch::allclose(save_var, std::get<2>(outputs)); bool is_close_reserve = torch::allclose(reserve, std::get<3>(outputs)); std::cout << "Is output close: " << is_close_output << std::endl; std::cout << "Is save_mean close: " << is_close_save_mean << std::endl; std::cout << "Is save_var close: " << is_close_save_var << std::endl; std::cout << "Is reserve close: " << is_close_reserve << std::endl; return 0; } ``` Please CC @albanD Pull Request resolved: https://github.com/pytorch/pytorch/pull/123020 Approved by: https://github.com/andrewor14, https://github.com/eqy, https://github.com/albanD
189 lines
6.7 KiB
C++
189 lines
6.7 KiB
C++
#include <gtest/gtest.h>
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#include <ATen/ATen.h>
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#include <ATen/Context.h>
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#include <torch/torch.h>
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#include <cmath>
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#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
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ASSERT_TRUE( \
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tensor.device().type() == at::Device((device_), (index_)).type()); \
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ASSERT_TRUE( \
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tensor.device().index() == at::Device((device_), (index_)).index()); \
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ASSERT_EQ(tensor.dtype(), (type_)); \
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ASSERT_TRUE(tensor.layout() == (layout_))
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TEST(TensorTest, AllocatesTensorOnTheCorrectDevice_MultiCUDA) {
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auto tensor = at::tensor({1, 2, 3}, at::device({at::kCUDA, 1}));
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ASSERT_EQ(tensor.device().type(), at::Device::Type::CUDA);
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ASSERT_EQ(tensor.device().index(), 1);
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}
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TEST(TensorTest, ToDevice_MultiCUDA) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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tensor = tensor.to({at::kCUDA, 1});
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
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tensor = tensor.to({at::kCUDA, 0});
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
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tensor = tensor.to({at::kCUDA, 1});
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
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tensor = tensor.to(at::Device(at::kCPU));
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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tensor = tensor.to(at::kCUDA);
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
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tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
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tensor = tensor.to(at::TensorOptions({at::kCUDA, 0}));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
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tensor = tensor.to(at::TensorOptions(at::kDouble));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kDouble, at::kStrided);
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tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kDouble, at::kStrided);
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tensor = tensor.to(at::TensorOptions(at::kInt));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
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tensor = tensor.to(at::TensorOptions(at::Device(at::kCPU)));
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
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tensor = tensor.to(at::TensorOptions(at::kCUDA));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kInt, at::kStrided);
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}
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TEST(TensorTest, ToTensorAndTensorAttributes_MultiCUDA) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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auto other = at::empty({3, 4}, at::kFloat);
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tensor = tensor.to(other);
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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other = at::empty({3, 4}, at::TensorOptions(at::kCUDA).dtype(at::kDouble));
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tensor = tensor.to(other.dtype());
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
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tensor = tensor.to(other.device());
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kDouble, at::kStrided);
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other = at::empty({3, 4}, at::TensorOptions({at::kCUDA, 1}).dtype(at::kLong));
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tensor = tensor.to(other.device(), other.dtype());
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kLong, at::kStrided);
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other = at::empty({3, 4}, at::kFloat);
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tensor = tensor.to(other.options());
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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}
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TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame_CUDA) {
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auto tensor = at::empty(
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{3, 4}, at::TensorOptions(at::kFloat).device(at::Device("cuda")));
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auto hopefully_not_copy = tensor.to(tensor.options());
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ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
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hopefully_not_copy = tensor.to(at::kFloat);
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ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
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hopefully_not_copy = tensor.to("cuda");
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ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
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hopefully_not_copy = tensor.to(at::TensorOptions("cuda"));
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ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
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hopefully_not_copy = tensor.to(at::TensorOptions(at::kFloat));
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ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
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}
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TEST(TensorTest, ToDeviceAndDtype_MultiCUDA) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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tensor = tensor.to({at::kCUDA, 1}, at::kInt);
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
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tensor = tensor.to(at::TensorOptions({at::kCUDA, 0}).dtype(at::kLong));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kLong, at::kStrided);
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tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}).dtype(at::kDouble));
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REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kDouble, at::kStrided);
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tensor = tensor.to(at::kCPU, at::kInt);
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
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}
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TEST(TensorTest, MagmaInitializesCorrectly_CUDA) {
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// Any tensor will work here as long as it's invertible
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
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float data[] = {1, 1, 1, 0, 0, 3, 1, 2, 2, 3, 1, 0, 1, 0, 2, 1};
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auto tensor =
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at::from_blob(data, {4, 4}, at::TensorOptions(at::kFloat)).cuda();
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if (at::hasMAGMA()) {
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at::inverse(tensor);
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}
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}
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#ifdef USE_CUDA
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#include <ATen/cuda/CUDAConfig.h>
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#if AT_CUDNN_ENABLED()
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TEST(CuDNNBatchNormTest, OutVariantMatchesFunctional) {
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if (!torch::cuda::is_available()) {
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GTEST_SKIP() << "CUDA is not available";
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}
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if (!at::Context::hasCuDNN()) {
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GTEST_SKIP() << "cuDNN is not available";
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}
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auto device = torch::device(torch::kCUDA);
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auto input = torch::rand({2, 3, 4, 4}, device);
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auto weight = torch::randn({3}, device);
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auto bias = torch::randn({3}, device);
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auto running_mean = torch::zeros({3}, device);
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auto running_var = torch::ones({3}, device);
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bool training = true;
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double exponential_average_factor = 0.1;
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double epsilon = 1e-5;
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auto output = torch::empty_like(input);
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auto save_mean = torch::empty({3}, device);
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auto save_var = torch::empty({3}, device);
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auto reserve = torch::empty({0}, device.dtype(torch::kByte));
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at::native::cudnn_batch_norm_out(
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input,
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weight,
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bias,
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running_mean,
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running_var,
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training,
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exponential_average_factor,
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epsilon,
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output,
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save_mean,
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save_var,
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reserve);
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auto ref_outputs = at::native::cudnn_batch_norm(
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input,
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weight,
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bias,
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running_mean,
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running_var,
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training,
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exponential_average_factor,
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epsilon);
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ASSERT_TRUE(torch::allclose(output, std::get<0>(ref_outputs)));
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ASSERT_TRUE(torch::allclose(save_mean, std::get<1>(ref_outputs)));
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ASSERT_TRUE(torch::allclose(save_var, std::get<2>(ref_outputs)));
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ASSERT_TRUE(torch::equal(reserve, std::get<3>(ref_outputs)));
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}
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#endif // AT_CUDNN_ENABLED()
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#endif // USE_CUDA
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