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https://github.com/pytorch/pytorch.git
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b5d8844bbe
Summary: Tries to fix C++ API's usage of MAGMA-based functions. Attempts to Fix https://github.com/pytorch/pytorch/issues/18074 Pull Request resolved: https://github.com/pytorch/pytorch/pull/18527 Differential Revision: D14691694 Pulled By: soumith fbshipit-source-id: dd04e74418e486d73ea4a92193ddf79352ed71ba
124 lines
4.8 KiB
C++
124 lines
4.8 KiB
C++
#include <gtest/gtest.h>
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#include <ATen/ATen.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({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<float>(), tensor.data<float>());
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hopefully_not_copy = tensor.to(at::kFloat);
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ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<float>());
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hopefully_not_copy = tensor.to("cuda");
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ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<float>());
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hopefully_not_copy = tensor.to(at::TensorOptions("cuda"));
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ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<float>());
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hopefully_not_copy = tensor.to(at::TensorOptions(at::kFloat));
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ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<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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auto tensor = at::arange(1, 17, at::TensorOptions(at::kFloat).device(at::Device("cuda")));
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tensor = tensor.view({4, 4});
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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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