(简体中文|English)
TensorRT是一个高性能的深度学习推理（Inference）优化器，可以为深度学习应用提供低延迟、高吞吐率的部署推理。 以下将分别从 Pipeline Serving 和 C++ Serving 介绍 Tensorrt 开启方式以及配置动态 shape(Dynamic Shape)。
void SetTRTDynamicShapeInfo(
std::map<std::string, std::vector<int>> min_input_shape,
std::map<std::string, std::vector<int>> max_input_shape,
std::map<std::string, std::vector<int>> optim_input_shape,
bool disable_trt_plugin_fp16 = false);
一. C++ Serving Tensorrt 开启方式
在 C++ Serving 启动命令加上--use_trt
python -m paddle_serving_server.serve \
--model serving_server \
--thread 2 --port 9000 \
--gpu_ids 0 \
--use_trt \
--precision FP16
二. C++ Serving 设置动态 shape
在**/paddle_inference/paddle/include/paddle_engine.h 修改如下代码
if (engine_conf.has_use_trt() && engine_conf.use_trt()) {
config.SwitchIrOptim(true);
if (!engine_conf.has_use_gpu() || !engine_conf.use_gpu()) {
config.EnableUseGpu(50, gpu_id);
if (engine_conf.has_gpu_multi_stream() &&
engine_conf.gpu_multi_stream()) {
config.EnableGpuMultiStream();
}
}
config.EnableTensorRtEngine((1 << 30) + (1 << 29),
max_batch,
min_subgraph_size,
precision_type,
true,
FLAGS_use_calib);
// set trt dynamic shape
{
int bsz = 1;
int max_seq_len = 512;
std::map<std::string, std::vector<int>> min_input_shape;
std::map<std::string, std::vector<int>> max_input_shape;
std::map<std::string, std::vector<int>> optim_input_shape;
int hidden_size = 0;
min_input_shape["stack_0.tmp_0"] = {1, 16, 1, 1};
min_input_shape["stack_1.tmp_0"] = {1, 2, 1, 1};
min_input_shape["input_mask"] = {1, 1, 1};
min_input_shape["_generated_var_64"] = {1, 1, 768};
min_input_shape["fc_0.tmp_0"] = {1, 1, 768};
min_input_shape["_generated_var_87"] = {1, 1, 768};
min_input_shape["tmp_175"] = {1, 1, 768};
min_input_shape["c_allreduce_sum_0.tmp_0"] = {1,1, 12288};
min_input_shape["embedding_1.tmp_0"] = {1, 1, 12288};
max_input_shape["stack_0.tmp_0"] = {bsz, 16, max_seq_len, max_seq_len};
max_input_shape["stack_1.tmp_0"] = {bsz, 2, max_seq_len, max_seq_len};
max_input_shape["input_mask"] = {bsz, max_seq_len, max_seq_len};
max_input_shape["_generated_var_64"] = {bsz, max_seq_len, 768};
max_input_shape["fc_0.tmp_0"] = {bsz, max_seq_len, 768};
max_input_shape["_generated_var_87"] = {bsz, max_seq_len, 768};
max_input_shape["tmp_175"] = {bsz, max_seq_len, 768};
max_input_shape["c_allreduce_sum_0.tmp_0"] = {bsz,max_seq_len, 12288};
max_input_shape["embedding_1.tmp_0"] = {bsz, max_seq_len, 12288};
int g1 = 0;
int g2 = 0;
int t1 = 0;
int t2 = 0;
std::string var_name = "_generated_var_";
std::string tmp_name = "tmp_";
for (int i = 0; i < 44; ++i) {
if (i > 32) {
hidden_size = 768;
g1 = 2*i-1;
g2 = 2*i;
t1 = 4*i-1;
t2 = 4*i;
min_input_shape[var_name+std::to_string(g1)] = {1, 1, hidden_size};
min_input_shape[var_name+std::to_string(g2)] = {1, 1, hidden_size};
min_input_shape[tmp_name+std::to_string(t1)] = {1, 1, hidden_size};
min_input_shape[tmp_name+std::to_string(t2)] = {1, 1, hidden_size};
max_input_shape[var_name+std::to_string(g1)] = {bsz, max_seq_len, hidden_size};
max_input_shape[var_name+std::to_string(g2)] = {bsz, max_seq_len, hidden_size};
max_input_shape[tmp_name+std::to_string(t1)] = {bsz, max_seq_len, hidden_size};
max_input_shape[tmp_name+std::to_string(t2)] = {bsz, max_seq_len, hidden_size};
}
if (i <32) {
hidden_size = 12288;
g1 = 2*i;
g2 = 2*i+1;
t1 = 4*i;
t2 = 4*i+3;
min_input_shape[var_name+std::to_string(g1)] = {1, 1, hidden_size};
min_input_shape[var_name+std::to_string(g2)] = {1, 1, hidden_size};
min_input_shape[tmp_name+std::to_string(t1)] = {1, 1, hidden_size};
min_input_shape[tmp_name+std::to_string(t2)] = {1, 1, hidden_size};
max_input_shape[var_name+std::to_string(g1)] = {bsz, max_seq_len, hidden_size};
max_input_shape[var_name+std::to_string(g2)] = {bsz, max_seq_len, hidden_size};
max_input_shape[tmp_name+std::to_string(t1)] = {bsz, max_seq_len, hidden_size};
max_input_shape[tmp_name+std::to_string(t2)] = {bsz, max_seq_len, hidden_size};
}
}
optim_input_shape = max_input_shape;
config.SetTRTDynamicShapeInfo(
min_input_shape, max_input_shape, optim_input_shape);
}
LOG(INFO) << "create TensorRT predictor";
}
一. Pipeline Serving Tensorrt 开启方式
在示例目录下的 config.yml 文件, 修改device_type: 2, 配置 GPU 使用的核心 devices: "0,1,2,3"
注意: Tensorrt 需要配合 GPU 使用
二. Pipeline Serving 设置动态 shape
在示例目录下的 web_service.py, 在每个 op 下可以通过 def set_dynamic_shape_info(self): 添加动态 shape 相关的配置
示例如下
def set_dynamic_shape_info(self):
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_182.tmp_0": [1, 1, 20, 20],
"nearest_interp_v2_2.tmp_0": [1, 1, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 1, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 1, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 1, 20, 20]
}
max_input_shape = {
"x": [1, 3, 1536, 1536],
"conv2d_182.tmp_0": [20, 200, 960, 960],
"nearest_interp_v2_2.tmp_0": [20, 200, 960, 960],
"nearest_interp_v2_3.tmp_0": [20, 200, 960, 960],
"nearest_interp_v2_4.tmp_0": [20, 200, 960, 960],
"nearest_interp_v2_5.tmp_0": [20, 200, 960, 960],
}
opt_input_shape = {
"x": [1, 3, 960, 960],
"conv2d_182.tmp_0": [3, 96, 240, 240],
"nearest_interp_v2_2.tmp_0": [3, 96, 240, 240],
"nearest_interp_v2_3.tmp_0": [3, 24, 240, 240],
"nearest_interp_v2_4.tmp_0": [3, 24, 240, 240],
"nearest_interp_v2_5.tmp_0": [3, 24, 240, 240],
}
self.dynamic_shape_info = {
"min_input_shape": min_input_shape,
"max_input_shape": max_input_shape,
"opt_input_shape": opt_input_shape,
}
具体可以参考Pipeline OCR
注意: 由于不同的模型具有不同的动态 shape 配置，因此不存在通用的动态 shape 配置方法。当运行 Pipeline Serving 出现报错信息时，应该使用netron 加载模型，查看各个 op 的输入输出 shape。之后，结合报错信息，在 web_service.py 添加相应的动态 shape 配置代码。