未验证 提交 2eb3a7a9 编写于 作者: Z Zhaolong Xing 提交者: GitHub

[Cherry-pick] [Fix BUG]: Core when multi thread + clone + paddle-tr #22442 (#22471)

test=release/1.7
上级 6892deb1
......@@ -138,7 +138,8 @@ void ZeroCopyTensor::copy_to_cpu(T *data) {
static_cast<const platform::CUDADeviceContext *>(pool.Get(gpu_place));
memory::Copy(platform::CPUPlace(), static_cast<void *>(data), gpu_place,
t_data, ele_num * sizeof(T), dev_ctx->stream());
cudaDeviceSynchronize();
cudaStreamSynchronize(dev_ctx->stream());
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
......
......@@ -38,13 +38,13 @@ void TensorRTEngine::Execute(int batch_size, std::vector<void *> *buffers,
const std::thread::id tid = std::this_thread::get_id();
batch_size_ = batch_size;
if (infer_context_.find(tid) == infer_context_.end()) {
std::unique_lock<std::mutex> lock(mutex_);
PADDLE_ENFORCE_NOT_NULL(
infer_engine_,
"You should build engine first and then set the context.");
infer_context_[tid].reset(infer_engine_->createExecutionContext());
}
infer_context_[tid]->enqueue(batch_size, buffers->data(), stream, nullptr);
cudaStreamSynchronize(stream);
SetRuntimeBatch(batch_size);
}
......
......@@ -82,7 +82,7 @@ class TensorRTEngine {
void Build(const DescType& paddle_model);
void Execute(int batch_size, std::vector<void*>* buffers,
cudaStream_t stream);
cudaStream_t stream = nullptr);
// Initialize the inference network, so that TensorRT layers can add to this
// network.
......@@ -216,6 +216,7 @@ class TensorRTEngine {
infer_context_;
infer_ptr<nvinfer1::IHostMemory> ihost_memory_;
std::unordered_map<nvinfer1::ITensor*, float> quant_dynamic_range_;
std::mutex mutex_;
}; // class TensorRTEngine
#define IS_TRT_VERSION_GE(version) \
......
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <numeric>
#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
......@@ -44,6 +45,15 @@ TEST(quant_int8, resnet50) {
input_t->copy_from_cpu(input);
ASSERT_TRUE(predictor->ZeroCopyRun());
std::vector<float> out_data;
auto output_names = predictor->GetOutputNames();
auto output_t = predictor->GetOutputTensor(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->copy_to_cpu(out_data.data());
}
} // namespace inference
......
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