mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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f73a538f61
* [backend] support bechmark mode for runtime and backend * [backend] support bechmark mode for runtime and backend * [pybind11] add benchmark methods pybind * [pybind11] add benchmark methods pybind * [Other] Update build scripts * [Other] Update cmake/summary.cmake * [Other] update build scripts * [Other] add ENABLE_BENCHMARK option -> setup.py * optimize backend time recording * optimize backend time recording * optimize trt backend time record * [backend] optimze backend_time recording for trt * [benchmark] remove redundant logs * fixed ov_backend confilct * [benchmark] fixed paddle_backend conflicts * [benchmark] fixed paddle_backend conflicts * [benchmark] fixed paddle_backend conflicts * [benchmark] remove use_gpu option from ort backend option * [benchmark] update benchmark_ppdet.py * [benchmark] update benchmark_ppcls.py * fixed lite backend conflicts * [Lite] fixed lite xpu * add benchmark macro * add RUNTIME_PROFILE_LOOP macros * add comments for RUNTIME_PROFILE macros * add comments for new apis * add comments for new apis * update benchmark_ppdet.py * afixed bugs * remove unused codes * optimize RUNTIME_PROFILE_LOOP macros * optimize RUNTIME_PROFILE_LOOP macros * add comments for benchmark option and result * add docs for benchmark namespace
84 lines
3.1 KiB
C++
Executable File
84 lines
3.1 KiB
C++
Executable File
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "fastdeploy/core/fd_type.h"
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// https://github.com/PaddlePaddle/Paddle-Lite/issues/8290
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#if (defined(WITH_LITE_STATIC) && defined(WITH_STATIC_LIB))
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// Whether to output some warning messages when using the
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// FastDepoy static library, default OFF. These messages
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// are only reserve for debugging.
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#if defined(WITH_STATIC_WARNING)
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#warning You are using the FastDeploy static library. \
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We will automatically add some registration codes for \
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ops, kernels and passes for Paddle Lite.
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#endif
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#if !defined(WITH_STATIC_LIB_AT_COMPILING)
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#include "paddle_use_ops.h" // NOLINT
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#include "paddle_use_kernels.h" // NOLINT
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#include "paddle_use_passes.h" // NOLINT
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#endif
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#endif
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#include <iostream>
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#include <memory>
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#include <string>
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#include <vector>
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#include <map>
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namespace fastdeploy {
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/*! Paddle Lite power mode for mobile device. */
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enum LitePowerMode {
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LITE_POWER_HIGH = 0, ///< Use Lite Backend with high power mode
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LITE_POWER_LOW = 1, ///< Use Lite Backend with low power mode
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LITE_POWER_FULL = 2, ///< Use Lite Backend with full power mode
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LITE_POWER_NO_BIND = 3, ///< Use Lite Backend with no bind power mode
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LITE_POWER_RAND_HIGH = 4, ///< Use Lite Backend with rand high mode
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LITE_POWER_RAND_LOW = 5 ///< Use Lite Backend with rand low power mode
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};
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struct LiteBackendOption {
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/// Paddle Lite power mode for mobile device.
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LitePowerMode power_mode = LITE_POWER_NO_BIND;
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/// Number of threads while use CPU
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int cpu_threads = 1;
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/// Enable use half precision
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bool enable_fp16 = false;
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/// Enable use int8 precision for quantized model
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bool enable_int8 = false;
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Device device = Device::CPU;
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// optimized model dir for CxxConfig
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std::string optimized_model_dir = "";
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std::string nnadapter_subgraph_partition_config_path = "";
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std::string nnadapter_subgraph_partition_config_buffer = "";
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std::string nnadapter_context_properties = "";
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std::string nnadapter_model_cache_dir = "";
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std::string nnadapter_mixed_precision_quantization_config_path = "";
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std::map<std::string, std::vector<std::vector<int64_t>>>
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nnadapter_dynamic_shape_info = {{"", {{0}}}};
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std::vector<std::string> nnadapter_device_names = {};
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int device_id = 0;
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int kunlunxin_l3_workspace_size = 0xfffc00;
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bool kunlunxin_locked = false;
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bool kunlunxin_autotune = true;
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std::string kunlunxin_autotune_file = "";
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std::string kunlunxin_precision = "int16";
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bool kunlunxin_adaptive_seqlen = false;
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bool kunlunxin_enable_multi_stream = false;
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};
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} // namespace fastdeploy
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