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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)
#include "ceres/internal/config.h"
#ifndef CERES_NO_CUDA
#include <numeric>
#include "ceres/cuda_streamed_buffer.h"
#include "gtest/gtest.h"
namespace ceres::internal {
TEST(CudaStreamedBufferTest, IntegerCopy) {
// Offsets and sizes of batches supplied to callback
std::vector<std::pair<int, int>> batches;
const int kMaxTemporaryArraySize = 16;
const int kInputSize = kMaxTemporaryArraySize * 7 + 3;
ContextImpl context;
std::string message;
ASSERT_TRUE(context.InitCuda(&message))
<< "InitCuda() failed because: " << message;
std::vector<int> inputs(kInputSize);
std::vector<int> outputs(kInputSize, -1);
std::iota(inputs.begin(), inputs.end(), 0);
CudaStreamedBuffer<int> streamed_buffer(&context, kMaxTemporaryArraySize);
streamed_buffer.CopyToGpu(inputs.data(),
kInputSize,
[&outputs, &batches](const int* device_pointer,
int size,
int offset,
cudaStream_t stream) {
batches.emplace_back(offset, size);
ASSERT_EQ(cudaSuccess,
cudaMemcpyAsync(outputs.data() + offset,
device_pointer,
sizeof(int) * size,
cudaMemcpyDeviceToHost,
stream));
});
// All operations in all streams should be completed when CopyToGpu returns
// control to the callee
for (int i = 0; i < ContextImpl::kNumCudaStreams; ++i) {
ASSERT_EQ(cudaSuccess, cudaStreamQuery(context.streams_[i]));
}
// Check if every element was visited
for (int i = 0; i < kInputSize; ++i) {
ASSERT_EQ(outputs[i], i);
}
// Check if there is no overlap between batches
std::sort(batches.begin(), batches.end());
const int num_batches = batches.size();
for (int i = 0; i < num_batches; ++i) {
const auto [begin, size] = batches[i];
const int end = begin + size;
ASSERT_GE(begin, 0);
ASSERT_LT(begin, kInputSize);
ASSERT_GT(size, 0);
ASSERT_LE(end, kInputSize);
if (i + 1 == num_batches) continue;
ASSERT_EQ(end, batches[i + 1].first);
}
}
TEST(CudaStreamedBufferTest, IntegerNoCopy) {
// Offsets and sizes of batches supplied to callback
std::vector<std::pair<int, int>> batches;
const int kMaxTemporaryArraySize = 16;
const int kInputSize = kMaxTemporaryArraySize * 7 + 3;
ContextImpl context;
std::string message;
ASSERT_TRUE(context.InitCuda(&message))
<< "InitCuda() failed because: " << message;
int* inputs;
int* outputs;
ASSERT_EQ(cudaSuccess,
cudaHostAlloc(
&inputs, sizeof(int) * kInputSize, cudaHostAllocWriteCombined));
ASSERT_EQ(
cudaSuccess,
cudaHostAlloc(&outputs, sizeof(int) * kInputSize, cudaHostAllocDefault));
std::fill(outputs, outputs + kInputSize, -1);
std::iota(inputs, inputs + kInputSize, 0);
CudaStreamedBuffer<int> streamed_buffer(&context, kMaxTemporaryArraySize);
streamed_buffer.CopyToGpu(inputs,
kInputSize,
[outputs, &batches](const int* device_pointer,
int size,
int offset,
cudaStream_t stream) {
batches.emplace_back(offset, size);
ASSERT_EQ(cudaSuccess,
cudaMemcpyAsync(outputs + offset,
device_pointer,
sizeof(int) * size,
cudaMemcpyDeviceToHost,
stream));
});
// All operations in all streams should be completed when CopyToGpu returns
// control to the callee
for (int i = 0; i < ContextImpl::kNumCudaStreams; ++i) {
ASSERT_EQ(cudaSuccess, cudaStreamQuery(context.streams_[i]));
}
// Check if every element was visited
for (int i = 0; i < kInputSize; ++i) {
ASSERT_EQ(outputs[i], i);
}
// Check if there is no overlap between batches
std::sort(batches.begin(), batches.end());
const int num_batches = batches.size();
for (int i = 0; i < num_batches; ++i) {
const auto [begin, size] = batches[i];
const int end = begin + size;
ASSERT_GE(begin, 0);
ASSERT_LT(begin, kInputSize);
ASSERT_GT(size, 0);
ASSERT_LE(end, kInputSize);
if (i + 1 == num_batches) continue;
ASSERT_EQ(end, batches[i + 1].first);
}
ASSERT_EQ(cudaSuccess, cudaFreeHost(inputs));
ASSERT_EQ(cudaSuccess, cudaFreeHost(outputs));
}
} // namespace ceres::internal
#endif // CERES_NO_CUDA