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batch_permutation_op_gpu_test.cc
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batch_permutation_op_gpu_test.cc
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#include "caffe2/core/context_gpu.h"
#include "caffe2/core/flags.h"
#include "caffe2/operators/batch_permutation_op.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
#include "gtest/gtest.h"
namespace caffe2 {
namespace {
// Add the vector as an input to a Workspace depending on the context of the
// workspace
template <typename T>
void AddInputCPU(
const vector<int64_t>& shape,
const vector<T>& values,
const string& name,
Workspace* ws) {
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, CPU);
tensor->Resize(shape);
EigenVectorMap<T> tensor_vec(tensor->mutable_data<T>(), tensor->numel());
tensor_vec.array() = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>{
values.data(), static_cast<int>(values.size())};
}
template <typename T>
void AddInputGPU(
const vector<int64_t>& shape,
const vector<T>& values,
const string& name,
Workspace* ws) {
Tensor tmp(shape, CPU);
EigenVectorMap<T> tmp_vec(tmp.mutable_data<T>(), tmp.numel());
tmp_vec.array() = Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>{
values.data(), static_cast<int>(values.size())};
Blob* blob = ws->CreateBlob(name);
auto* tensor = BlobGetMutableTensor(blob, CUDA);
tensor->CopyFrom(tmp);
}
// Overload 4 different signatures for AddInput because clang does not allow
// template <typename T>
// void AddInput<CPUContext>(...) {...}
template <typename T, class Context>
void AddInput(
const vector<int64_t>& shape,
const vector<T>& values,
const string& name,
Workspace* ws);
template <>
void AddInput<int, CPUContext>(
const vector<int64_t>& shape,
const vector<int>& values,
const string& name,
Workspace* ws) {
AddInputCPU<int>(shape, values, name, ws);
}
template <>
void AddInput<float, CPUContext>(
const vector<int64_t>& shape,
const vector<float>& values,
const string& name,
Workspace* ws) {
AddInputCPU<float>(shape, values, name, ws);
}
template <>
void AddInput<int, CUDAContext>(
const vector<int64_t>& shape,
const vector<int>& values,
const string& name,
Workspace* ws) {
AddInputGPU<int>(shape, values, name, ws);
}
template <>
void AddInput<float, CUDAContext>(
const vector<int64_t>& shape,
const vector<float>& values,
const string& name,
Workspace* ws) {
AddInputGPU<float>(shape, values, name, ws);
}
template <class Context>
DeviceTypeProto GetDeviceType() {
return PROTO_CPU;
}
template <>
DeviceTypeProto GetDeviceType<CUDAContext>() {
return PROTO_CUDA;
}
// Create a BatchPermutationOp with the given inputs (actual values are
// generated sequentially) and run it
template <class Context>
void CreateAndRun(
TensorCPU* outResult,
int N,
vector<int64_t>& shape,
vector<float>& features,
vector<int> indices) {
Workspace ws;
AddInput<float, Context>(shape, features, "X", &ws);
AddInput<int, Context>(vector<int64_t>{N}, indices, "indices", &ws);
OperatorDef def;
def.set_name("test");
def.set_type("BatchPermutation");
def.add_input("X");
def.add_input("indices");
def.add_output("Y");
def.mutable_device_option()->set_device_type(GetDeviceType<Context>());
unique_ptr<OperatorBase> op = CreateOperator(def, &ws);
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
Blob* Y_blob = ws.GetBlob("Y");
EXPECT_NE(nullptr, Y_blob);
auto& Y = Y_blob->Get<Tensor>();
outResult->CopyFrom(Y);
}
// Create a BatchPermutationOp with the given inputs (actual values are
// generated sequentially) and run it
template <class Context>
void CreateAndRunGradient(
TensorCPU* outResult,
int N,
vector<int64_t>& shape,
vector<float>& features,
vector<int> indices) {
Workspace ws;
AddInput<float, Context>(shape, features, "dY", &ws);
AddInput<int, Context>(vector<int64_t>{N}, indices, "indices", &ws);
OperatorDef def;
def.set_name("test");
def.set_type("BatchPermutationGradient");
def.add_input("indices");
def.add_input("dY");
def.add_output("dX");
def.mutable_device_option()->set_device_type(GetDeviceType<Context>());
unique_ptr<OperatorBase> op = CreateOperator(def, &ws);
EXPECT_NE(nullptr, op.get());
EXPECT_TRUE(op->Run());
Blob* Y_blob = ws.GetBlob("dX");
EXPECT_NE(nullptr, Y_blob);
auto& Y = Y_blob->Get<Tensor>();
outResult->CopyFrom(Y);
}
// Check that the CPU and GPU implementations provide the exact same results
void CheckCPUGPUEqual(vector<int64_t> shape, vector<int> indices) {
// Prepare input data
EXPECT_GT(shape.size(), 1);
int N = shape[0];
int input_size = 1;
for (auto k : shape) {
input_size *= k;
}
int K = N ? input_size / N : 0;
vector<float> features(input_size);
std::iota(features.begin(), features.end(), 0);
// CPU outputs
Tensor y_cpu{CPU};
Tensor y_cpu_grad{CPU};
// CPU BatchPermutation
CreateAndRun<CPUContext>(&y_cpu, N, shape, features, indices);
// CPU BatchPermutationGradient
CreateAndRunGradient<CPUContext>(&y_cpu_grad, N, shape, features, indices);
// Check CPU output values
for (auto i = 0; i < indices.size(); ++i) {
for (auto k = 0; k < K; ++k) {
EXPECT_NEAR(
y_cpu.data<float>()[indices[i] * K + k], features[i * K + k], 1e4);
EXPECT_NEAR(
y_cpu_grad.data<float>()[i * K + k],
features[indices[i] * K + k],
1e4);
}
}
if (!caffe2::HasCudaGPU()) {
VLOG(2) << "No CudaGPU found. Skip GPU test." << std::endl;
return;
}
// GPU outputs
Tensor y_gpu{CPU};
Tensor y_gpu_grad{CPU};
// GPU BatchPermutation
CreateAndRun<CPUContext>(&y_gpu, N, shape, features, indices);
// Compare CPU and GPU BatchPermutation outputs
EXPECT_EQ(y_cpu.sizes(), y_gpu.sizes());
ConstEigenVectorMap<float> y_cpu_vec(y_cpu.data<float>(), y_cpu.numel());
ConstEigenVectorMap<float> y_gpu_vec(y_gpu.data<float>(), y_gpu.numel());
EXPECT_TRUE(y_cpu_vec.isApprox(y_gpu_vec));
// GPU BatchPermutationGradient
CreateAndRunGradient<CUDAContext>(&y_gpu_grad, N, shape, features, indices);
// Check GPU outputs
for (auto i = 0; i < indices.size(); ++i) {
for (auto k = 0; k < K; ++k) {
EXPECT_NEAR(
y_gpu.data<float>()[indices[i] * K + k], features[i * K + k], 1e4);
EXPECT_NEAR(
y_gpu_grad.data<float>()[i * K + k],
features[indices[i] * K + k],
1e4);
}
}
// Compare CPU and GPU BatchPermutationGradient outputs
EXPECT_EQ(y_cpu_grad.sizes(), y_gpu_grad.sizes());
ConstEigenVectorMap<float> y_cpu_vec_grad(
y_cpu_grad.data<float>(), y_cpu_grad.numel());
ConstEigenVectorMap<float> y_gpu_vec_grad(
y_gpu_grad.data<float>(), y_gpu_grad.numel());
EXPECT_TRUE(y_cpu_vec_grad.isApprox(y_gpu_vec_grad));
}
} // namespace
TEST(BatchPermutationTest, CHECKCPUGPUEqualGenericDimension) {
auto t0 = std::chrono::high_resolution_clock::now();
int batch_size = 8;
int max_dimension = 6;
vector<int64_t> shape = vector<int64_t>{batch_size};
auto seed = std::chrono::system_clock::now().time_since_epoch().count();
std::default_random_engine generator(seed);
for (int i = 2; i < max_dimension; ++i) {
std::uniform_int_distribution<> dis(1, i);
shape.push_back(dis(generator));
CheckCPUGPUEqual(shape, vector<int>{0, 1, 2, 3, 4, 5, 6, 7});
CheckCPUGPUEqual(shape, vector<int>{7, 6, 5, 4, 3, 2, 1, 0});
CheckCPUGPUEqual(shape, vector<int>{1, 3, 5, 7, 0, 2, 4, 6});
CheckCPUGPUEqual(shape, vector<int>{4, 5, 6, 7, 0, 1, 2, 3});
CheckCPUGPUEqual(shape, vector<int>{3, 1, 5, 7, 6, 2, 4, 0});
}
CheckCPUGPUEqual({0, 128}, vector<int>{});
auto t1 = std::chrono::high_resolution_clock::now();
double elapsed =
std::chrono::duration_cast<std::chrono::milliseconds>(t1 - t0).count();
VLOG(2) << "Time elapsed: " << elapsed << " ms" << std::endl;
return;
}
} // namespace caffe2