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blob_gpu_test.cc
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blob_gpu_test.cc
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#include <iostream> // NOLINT
#include <gtest/gtest.h>
#include "caffe2/core/blob.h"
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/common_gpu.h"
#include "caffe2/core/context_gpu.h"
#include "caffe2/proto/caffe2_pb.h"
namespace caffe2 {
namespace {
template <typename T> class TensorGPUTest : public ::testing::Test {};
template <typename T> class TensorGPUDeathTest : public ::testing::Test {};
typedef ::testing::Types<char, int, float> TensorTypes;
TYPED_TEST_CASE(TensorGPUTest, TensorTypes);
TYPED_TEST_CASE(TensorGPUDeathTest, TensorTypes);
TYPED_TEST(TensorGPUTest, TensorInitializedEmpty) {
if (!caffe2::HasCudaGPU()) return;
Tensor tensor(CUDA);
EXPECT_EQ(tensor.numel(), 0);
EXPECT_EQ(tensor.dim(), 1);
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
tensor.Resize(dims);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 3);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
}
TYPED_TEST(TensorGPUTest, TensorInitializedNonEmpty) {
if (!HasCudaGPU()) return;
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CUDA);
EXPECT_EQ(tensor.dim(), 3);
EXPECT_EQ(tensor.dim32(0), 2);
EXPECT_EQ(tensor.dim32(1), 3);
EXPECT_EQ(tensor.dim32(2), 5);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
dims[0] = 7;
dims[1] = 11;
dims[2] = 13;
dims.push_back(17);
tensor.Resize(dims);
EXPECT_EQ(tensor.dim(), 4);
EXPECT_EQ(tensor.dim32(0), 7);
EXPECT_EQ(tensor.dim32(1), 11);
EXPECT_EQ(tensor.dim32(2), 13);
EXPECT_EQ(tensor.dim32(3), 17);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
}
TYPED_TEST(TensorGPUTest, TensorAlias) {
if (!HasCudaGPU()) return;
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CUDA);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
EXPECT_TRUE(other_tensor.data<TypeParam>() != nullptr);
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
}
TYPED_TEST(TensorGPUTest, TensorAliasCanUseDifferentShapes) {
if (!HasCudaGPU()) return;
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
vector<int> alternate_dims(1);
alternate_dims[0] = 2 * 3 * 5;
Tensor tensor(dims, CUDA);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
other_tensor.Resize(alternate_dims);
EXPECT_EQ(other_tensor.dim(), 1);
EXPECT_EQ(other_tensor.dim32(0), alternate_dims[0]);
EXPECT_TRUE(tensor.data<TypeParam>() != nullptr);
EXPECT_TRUE(other_tensor.data<TypeParam>() != nullptr);
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
}
TYPED_TEST(TensorGPUTest, NoLongerAliasAfterNumelChanges) {
if (!HasCudaGPU()) return;
vector<int> dims(3);
dims[0] = 2;
dims[1] = 3;
dims[2] = 5;
Tensor tensor(dims, CUDA);
EXPECT_TRUE(tensor.mutable_data<TypeParam>() != nullptr);
Tensor other_tensor = tensor.Alias();
EXPECT_EQ(tensor.data<TypeParam>(), other_tensor.data<TypeParam>());
auto* old_pointer = other_tensor.data<TypeParam>();
dims[0] = 7;
tensor.Resize(dims);
EXPECT_EQ(old_pointer, other_tensor.data<TypeParam>());
EXPECT_NE(old_pointer, tensor.mutable_data<TypeParam>());
}
TYPED_TEST(TensorGPUDeathTest, CannotAccessDataWhenEmpty) {
if (!HasCudaGPU()) return;
::testing::FLAGS_gtest_death_test_style = "threadsafe";
Tensor tensor(CUDA);
EXPECT_EQ(tensor.dim(), 1);
EXPECT_EQ(tensor.numel(), 0);
EXPECT_THROW(tensor.data<TypeParam>(), EnforceNotMet);
}
#define TEST_SERIALIZATION_GPU_WITH_TYPE(TypeParam, field_name) \
TEST(TensorGPUTest, TensorSerialization_##TypeParam) { \
if (!HasCudaGPU()) { \
return; \
} \
Blob blob; \
Tensor cpu_tensor(CPU); \
cpu_tensor.Resize(2, 3); \
for (int i = 0; i < 6; ++i) { \
cpu_tensor.mutable_data<TypeParam>()[i] = static_cast<TypeParam>(i); \
} \
BlobGetMutableTensor(&blob, CUDA)->CopyFrom(cpu_tensor); \
string serialized = SerializeBlob(blob, "test"); \
BlobProto proto; \
CAFFE_ENFORCE(proto.ParseFromString(serialized)); \
EXPECT_EQ(proto.name(), "test"); \
EXPECT_EQ(proto.type(), "Tensor"); \
EXPECT_TRUE(proto.has_tensor()); \
const TensorProto& tensor_proto = proto.tensor(); \
EXPECT_EQ( \
tensor_proto.data_type(), \
TypeMetaToDataType(TypeMeta::Make<TypeParam>())); \
EXPECT_EQ(tensor_proto.field_name##_size(), 6); \
for (int i = 0; i < 6; ++i) { \
EXPECT_EQ(tensor_proto.field_name(i), static_cast<TypeParam>(i)); \
} \
Blob new_blob; \
EXPECT_NO_THROW(DeserializeBlob(serialized, &new_blob)); \
EXPECT_TRUE(BlobIsTensorType(new_blob, CUDA)); \
Tensor new_cpu_tensor(blob.Get<Tensor>(), CPU); \
EXPECT_EQ(new_cpu_tensor.dim(), 2); \
EXPECT_EQ(new_cpu_tensor.size(0), 2); \
EXPECT_EQ(new_cpu_tensor.size(1), 3); \
for (int i = 0; i < 6; ++i) { \
EXPECT_EQ( \
cpu_tensor.data<TypeParam>()[i], \
new_cpu_tensor.data<TypeParam>()[i]); \
} \
}
TEST_SERIALIZATION_GPU_WITH_TYPE(bool, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(double, double_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(float, float_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(int, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(int8_t, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(int16_t, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(uint8_t, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(uint16_t, int32_data)
TEST_SERIALIZATION_GPU_WITH_TYPE(int64_t, int64_data)
TEST(TensorConstruction, ReinitializeTensorTest) {
if (!HasCudaGPU()) return;
Tensor x = caffe2::empty({1}, at::dtype<float>().device(CUDA, 0));
auto* data_before = x.template mutable_data<float>();
// We'll only compare device_type in ReinitializeTensor,
// so no tensor reallocation will happen here
ReinitializeTensor(&x, {1}, at::dtype<float>().device(CUDA));
auto* data_after = x.template mutable_data<float>();
EXPECT_EQ(data_before, data_after);
}
TEST(TensorTest, TensorSerializationMultiDevices) {
Blob blob;
Tensor tensor(CPU);
tensor.Resize(2, 3);
for (int i = 0; i < 6; ++i) {
tensor.mutable_data<float>()[i] = i;
}
for (int gpu_id = 0; gpu_id < NumCudaDevices(); ++gpu_id) {
CUDAGuard guard(gpu_id);
CUDAContext context(gpu_id); // switch to the current gpu
blob.Reset(new Tensor(tensor, CUDA));
string serialized = SerializeBlob(blob, "test");
BlobProto proto;
CAFFE_ENFORCE(proto.ParseFromString(serialized));
EXPECT_EQ(proto.name(), "test");
EXPECT_TRUE(proto.has_tensor());
const TensorProto& tensor_proto = proto.tensor();
EXPECT_EQ(tensor_proto.data_type(), TensorProto::FLOAT);
EXPECT_EQ(tensor_proto.float_data_size(), 6);
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(tensor_proto.float_data(i), i);
}
EXPECT_TRUE(tensor_proto.has_device_detail());
EXPECT_EQ(tensor_proto.device_detail().device_type(), PROTO_CUDA);
EXPECT_EQ(tensor_proto.device_detail().device_id(), gpu_id);
// Test if the restored blob is still of the same device.
blob.Reset();
EXPECT_NO_THROW(DeserializeBlob(serialized, &blob));
EXPECT_TRUE(BlobIsTensorType(blob, CUDA));
EXPECT_EQ(GetGPUIDForPointer(blob.Get<TensorCUDA>().data<float>()),
gpu_id);
// Test if we force the restored blob on a different device, we
// can still get so.
blob.Reset();
proto.mutable_tensor()->mutable_device_detail()->set_device_id(0);
EXPECT_NO_THROW(DeserializeBlob(proto.SerializeAsString(), &blob));
EXPECT_TRUE(BlobIsTensorType(blob, CUDA));
EXPECT_EQ(GetGPUIDForPointer(blob.Get<TensorCUDA>().data<float>()), 0);
}
}
} // namespace
} // namespace caffe2