mirror of
https://github.com/zebrajr/pytorch.git
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a5df8ec841
* Created DefaultTensorOptions * Fix TensorOptions() call which was interpreted as function decl * Fix empty OptionsGuard * Make options_ and mutex_ in DefaultTensorOptions class static because of dynamic linker issues * Make DefaultOptions thread local
137 lines
4.0 KiB
C++
137 lines
4.0 KiB
C++
#include "catch.hpp"
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#include <torch/tensor.h>
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#include <ATen/Context.h>
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#include <ATen/Functions.h>
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#include <ATen/OptionsGuard.h>
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#include <ATen/TensorOptions.h>
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#include <vector>
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#include <string>
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using namespace at;
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// A macro so we don't lose location information when an assertion fails.
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#define REQUIRE_OPTIONS(device_, index_, type_, layout_) \
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REQUIRE(options.device().type() == Device((device_), (index_)).type()); \
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REQUIRE(options.device().index() == Device((device_), (index_)).index()); \
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REQUIRE(options.dtype() == (type_)); \
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REQUIRE(options.layout() == (layout_))
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#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
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REQUIRE(tensor.device().type() == Device((device_), (index_)).type()); \
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REQUIRE(tensor.device().index() == Device((device_), (index_)).index()); \
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REQUIRE(tensor.type().scalarType() == (type_)); \
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REQUIRE(tensor.type().layout() == (layout_))
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TEST_CASE("TensorOptions/DefaultsToTheRightValues") {
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TensorOptions options;
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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}
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TEST_CASE("TensorOptions/ReturnsTheCorrectType") {
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auto options = TensorOptions().device(kCPU).dtype(kInt).layout(kSparse);
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REQUIRE(options.type() == getType(kSparseCPU, kInt));
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}
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TEST_CASE("TensorOptions/UtilityFunctionsReturnTheRightTensorOptions") {
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auto options = dtype(kInt);
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REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
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options = layout(kSparse);
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
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options = device({kCUDA, 1});
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
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options = device_index(1);
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
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options = dtype(kByte).layout(kSparse).device({kCUDA, 2}).device_index(3);
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REQUIRE_OPTIONS(kCUDA, 3, kByte, kSparse);
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}
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TEST_CASE("TensorOptions/ConstructsWellFromCPUTypes") {
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TensorOptions options;
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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options = TensorOptions({kCPU, 0});
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REQUIRE_OPTIONS(kCPU, 0, kFloat, kStrided);
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options = TensorOptions(kInt);
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REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
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options = TensorOptions(getType(kSparseCPU, kFloat));
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
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options = TensorOptions(getType(kSparseCPU, kByte));
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REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
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}
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TEST_CASE("TensorOptions/ConstructsWellFromCPUTensors") {
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auto options = TensorOptions(empty(5, kDouble));
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REQUIRE_OPTIONS(kCPU, -1, kDouble, kStrided);
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options = TensorOptions(empty(5, getType(kSparseCPU, kByte)));
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REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
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}
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TEST_CASE("TensorOptions/ConstructsWellFromVariables") {
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auto options = TensorOptions(torch::empty(5));
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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REQUIRE(!options.requires_grad());
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options = TensorOptions(torch::empty(5, at::requires_grad()));
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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REQUIRE(!options.requires_grad());
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}
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TEST_CASE("Device/ParsesCorrectlyFromString") {
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Device device("cpu:0");
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REQUIRE(device == Device(kCPU, 0));
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device = Device("cpu");
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REQUIRE(device == Device(kCPU));
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device = Device("cuda:123");
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REQUIRE(device == Device(kCUDA, 123));
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device = Device("cuda");
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REQUIRE(device == Device(kCUDA));
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std::vector<std::string> badnesses = {
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"", "cud:1", "cuda:", "cpu::1", ":1", "3", "tpu:4", "??"};
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for (const auto& badness : badnesses) {
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REQUIRE_THROWS(Device(badness));
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}
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}
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TEST_CASE("OptionsGuard") {
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Tensor tensor;
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{
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OptionsGuard guard(TensorOptions{});
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
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{
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OptionsGuard guard(TensorOptions().dtype(kInt));
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kStrided);
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{
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OptionsGuard guard(TensorOptions().dtype(kInt).layout(kSparse));
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kSparse);
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{
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OptionsGuard guard(requires_grad(true));
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tensor = torch::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
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REQUIRE(tensor.requires_grad());
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}
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