[Inference] Support NNAdapter and ascend310

cmake/external/lite.cmake

@@ -35,6 +35,14 @@ if (LITE_WITH_XPU)
   ENDIF()
 endif()
 
+if (LITE_WITH_NNADAPTER)
+  add_definitions(-DLITE_SUBGRAPH_WITH_NNADAPTER) 
+  if (NNADAPTER_WITH_NPU)
+    add_definitions(-DLITE_SUBGRAPH_WITH_NPU)
+    set(NPU_SDK_ROOT "/usr/local/Ascend/ascend-toolkit/latest" CACHE STRING "default NPU SDK ROOT")
+  endif()
+endif()
+
 if (NOT LITE_SOURCE_DIR OR NOT LITE_BINARY_DIR)
   include(ExternalProject)
   set(LITE_PROJECT extern_lite)
@@ -67,6 +75,9 @@ if (NOT LITE_SOURCE_DIR OR NOT LITE_BINARY_DIR)
                            -DLITE_WITH_XPU=${LITE_WITH_XPU}
                            -DXPU_SDK_URL=${XPU_BASE_URL}
                            -DXPU_SDK_ENV=${XPU_SDK_ENV}
+                           -DLITE_WITH_NNADAPTER=${LITE_WITH_NNADAPTER}
+                           -DNNADAPTER_WITH_HUAWEI_ASCEND_NPU=${NNADAPTER_WITH_NPU}
+                           -DNNADAPTER_HUAWEI_ASCEND_NPU_SDK_ROOT=${NPU_SDK_ROOT}
                            -DLITE_WITH_CODE_META_INFO=OFF
                            -DLITE_WITH_ARM=ON)
     ExternalProject_Add(
@@ -77,6 +88,7 @@ if (NOT LITE_SOURCE_DIR OR NOT LITE_BINARY_DIR)
       PREFIX              ${LITE_SOURCES_DIR}
       PATCH_COMMAND       mkdir -p ${LITE_SOURCES_DIR}/src/extern_lite-build/lite/gen_code && touch ${LITE_SOURCES_DIR}/src/extern_lite-build/lite/gen_code/__generated_code__.cc && sed -i "/aarch64-linux-gnu-gcc/d" ${LITE_SOURCES_DIR}/src/extern_lite/cmake/cross_compiling/armlinux.cmake && sed -i "/aarch64-linux-gnu-g++/d" ${LITE_SOURCES_DIR}/src/extern_lite/cmake/cross_compiling/armlinux.cmake
       UPDATE_COMMAND      ""
+      BUILD_ALWAYS         1
       BUILD_COMMAND       ${LITE_BUILD_COMMAND}
       INSTALL_COMMAND     ""
       CMAKE_ARGS          -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
@@ -110,6 +122,9 @@ if (NOT LITE_SOURCE_DIR OR NOT LITE_BINARY_DIR)
                            -DLITE_WITH_XPU=${LITE_WITH_XPU}
                            -DXPU_SDK_URL=${XPU_BASE_URL}
                            -DXPU_SDK_ENV=${XPU_SDK_ENV}
+                           -DLITE_WITH_NNADAPTER=${LITE_WITH_NNADAPTER}
+                           -DNNADAPTER_WITH_HUAWEI_ASCEND_NPU=${NNADAPTER_WITH_NPU}
+                           -DNNADAPTER_HUAWEI_ASCEND_NPU_SDK_ROOT=${NPU_SDK_ROOT}
                            -DLITE_WITH_CODE_META_INFO=OFF
                            -DLITE_WITH_ARM=OFF)
 
@@ -146,6 +161,11 @@ endif()
 if (WITH_ARM)
   if(LITE_WITH_XPU)
     set(LITE_OUTPUT_BIN_DIR inference_lite_lib.armlinux.armv8.xpu)
+  elseif(LITE_WITH_NNADAPTER)
+    message("Enable LITE_WITH_NNADAPTER")
+    if (NNADAPTER_WITH_NPU)
+      set(LITE_OUTPUT_BIN_DIR inference_lite_lib.armlinux.armv8.nnadapter)
+    endif()
   else()
     set(LITE_OUTPUT_BIN_DIR inference_lite_lib.armlinux.armv8)
   endif()
@@ -174,5 +194,17 @@ endfunction()
 external_lite_libs(lite_full_static ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libpaddle_full_api_shared.so)
 set(LITE_SHARED_LIB ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libpaddle_full_api_shared.so)
 
+if (LITE_WITH_NNADAPTER)
+  if (NNADAPTER_WITH_NPU)
+    external_lite_libs(lite_nnadapter ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libnnadapter.so ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libnnadapter_driver_huawei_ascend_npu.so)
+    set(LITE_DEPS lite_full_static lite_nnadapter)
+    set(LITE_SHARED_LIB ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libpaddle_full_api_shared.so 
+      ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libnnadapter.so
+      ${LITE_BINARY_DIR}/${LITE_OUTPUT_BIN_DIR}/cxx/lib/libnnadapter_driver_huawei_ascend_npu.so)
+  endif()
+else()
+  set(LITE_DEPS lite_full_static)
+endif()
+
 add_definitions(-DPADDLE_WITH_LITE)
 add_definitions(-DLITE_WITH_LOG)

paddle/fluid/inference/analysis/argument.h

@@ -233,6 +233,22 @@ struct Argument {
   DECL_ARGUMENT_FIELD(xpu_precision, XpuPrecision, std::string);
   DECL_ARGUMENT_FIELD(xpu_adaptive_seqlen, XpuAdaptiveSeqlen, bool);
 
+  DECL_ARGUMENT_FIELD(use_nnadapter, UseNNAdapter, bool);
+  DECL_ARGUMENT_FIELD(nnadapter_model_cache_dir, NNAdapterModelCacheDir,
+                      std::string);
+  DECL_ARGUMENT_FIELD(nnadapter_device_names, NNAdapterDeviceNames,
+                      std::vector<std::string>);
+  DECL_ARGUMENT_FIELD(nnadapter_context_properties, NNAdapterContextProperties,
+                      std::string);
+  DECL_ARGUMENT_FIELD(nnadapter_subgraph_partition_config_buffer,
+                      NNAdapterSubgraphPartitionConfigBuffer, std::string);
+  DECL_ARGUMENT_FIELD(nnadapter_subgraph_partition_config_path,
+                      NNAdapterSubgraphPartitionConfigPath, std::string);
+  DECL_ARGUMENT_FIELD(nnadapter_model_cache_token, NNAdapterModelCacheToken,
+                      std::vector<std::string>);
+  DECL_ARGUMENT_FIELD(nnadapter_model_cache_buffer, NNAdapterModelCacheBuffer,
+                      std::vector<std::vector<char>>);
+
   // Memory optimized related.
   DECL_ARGUMENT_FIELD(enable_memory_optim, EnableMemoryOptim, bool);
 

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -194,6 +194,26 @@ void IRPassManager::CreatePasses(Argument *argument,
                 new std::string(argument->xpu_autotune_file()));
       pass->Set("precision", new std::string(argument->xpu_precision()));
       pass->Set("adaptive_seqlen", new bool(argument->xpu_adaptive_seqlen()));
+      // NNAdapter Related
+      pass->Set("use_nnadapter", new bool(argument->use_nnadapter()));
+      pass->Set("model_cache_dir",
+                new std::string(argument->nnadapter_model_cache_dir()));
+      pass->Set("device_names", new std::vector<std::string>(
+                                    argument->nnadapter_device_names()));
+      pass->Set("context_properties",
+                new std::string(argument->nnadapter_context_properties()));
+      pass->Set("subgraph_partition_config_buffer",
+                new std::string(
+                    argument->nnadapter_subgraph_partition_config_buffer()));
+      pass->Set("subgraph_partition_config_path",
+                new std::string(
+                    argument->nnadapter_subgraph_partition_config_path()));
+      pass->Set("nnadapter_model_cache_buffer",
+                new std::vector<std::vector<char>>(
+                    argument->nnadapter_model_cache_buffer()));
+      pass->Set("nnadapter_model_cache_token",
+                new std::vector<std::string>(
+                    argument->nnadapter_model_cache_token()));
     }
     disable_logs_ = argument->disable_logs();
     if (pass_name == "fc_fuse_pass") {

paddle/fluid/inference/analysis/ir_passes/lite_subgraph_pass.cc

@@ -250,12 +250,27 @@ void LiteSubgraphPass::SetUpEngine(
   std::string autotune_file = Get<std::string>("autotune_file");
   std::string precision = Get<std::string>("precision");
   bool adaptive_seqlen = Get<bool>("adaptive_seqlen");
+  // NPU Related
+  bool use_nnadapter = Get<bool>("use_nnadapter");
+  std::string model_cache_dir = Get<std::string>("model_cache_dir");
+  auto device_names = Get<std::vector<std::string>>("device_names");
+  std::string context_properties = Get<std::string>("context_properties");
+  std::string subgraph_partition_config_buffer =
+      Get<std::string>("subgraph_partition_config_buffer");
+  std::string nnadapter_subgraph_partition_config_path =
+      Get<std::string>("subgraph_partition_config_path");
+  auto nnadapter_model_cache_buffer =
+      Get<std::vector<std::vector<char>>>("nnadapter_model_cache_buffer");
+  auto nnadapter_model_cache_token =
+      Get<std::vector<std::string>>("nnadapter_model_cache_token");
 
   lite_api::TargetType target_type;
   if (use_gpu) {
     target_type = TARGET(kCUDA);
   } else if (use_xpu) {
     target_type = TARGET(kXPU);
+  } else if (use_nnadapter) {
+    target_type = TARGET(kNNAdapter);
   } else {
 #ifdef PADDLE_WITH_ARM
     target_type = TARGET(kARM);
@@ -292,6 +307,17 @@ void LiteSubgraphPass::SetUpEngine(
   config.autotune_file = autotune_file;
   config.precision = precision;
   config.adaptive_seqlen = adaptive_seqlen;
+  // NPU Related
+  config.nnadapter_model_cache_dir = model_cache_dir;
+  config.nnadapter_device_names = device_names;
+  config.nnadapter_context_properties = context_properties;
+  config.nnadapter_subgraph_partition_config_buffer =
+      subgraph_partition_config_buffer;
+  config.nnadapter_subgraph_partition_config_path =
+      nnadapter_subgraph_partition_config_path;
+  config.nnadapter_model_cache_buffer = nnadapter_model_cache_buffer;
+  config.nnadapter_model_cache_token = nnadapter_model_cache_token;
+
   if (dump_model) {
     lite::StrToBinaryFile("./model.bin", config.model);
     lite::StrToBinaryFile("./param.bin", config.param);

paddle/fluid/inference/api/analysis_config.cc

@@ -195,6 +195,7 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
   // NPU related.
   CP_MEMBER(use_npu_);
   CP_MEMBER(npu_device_id_);
+  CP_MEMBER(nnadapter_config_);
 
   // profile related.
   CP_MEMBER(with_profile_);
@@ -542,7 +543,7 @@ void AnalysisConfig::Update() {
   }
 
   if (use_npu_) {
-#ifdef PADDLE_WITH_ASCEND_CL
+#if defined(PADDLE_WITH_ASCEND_CL) || defined(LITE_SUBGRAPH_WITH_NPU)
     PADDLE_ENFORCE_EQ(use_gpu_, false,
                       platform::errors::Unavailable(
                           "Currently, NPU and GPU cannot be enabled in the "
@@ -816,4 +817,59 @@ std::string AnalysisConfig::Summary() {
   return os.PrintTable();
 }
 
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetDeviceNames(
+    const std::vector<std::string> &names) {
+  device_names = names;
+  return *this;
+}
+
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetContextProperties(
+    const std::string &properties) {
+  context_properties = properties;
+  return *this;
+}
+
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetModelCacheDir(
+    const std::string &dir) {
+  model_cache_dir = dir;
+  return *this;
+}
+
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetModelCacheBuffers(
+    const std::string &model_cache_token,
+    const std::vector<char> &model_cache_buffer) {
+  PADDLE_ENFORCE_EQ(model_cache_token.empty(), false,
+                    platform::errors::InvalidArgument(
+                        "model_cache_token should not be empty."));
+  PADDLE_ENFORCE_EQ(model_cache_buffer.empty(), false,
+                    platform::errors::InvalidArgument(
+                        "model_cache_buffer should not be empty."));
+  PADDLE_ENFORCE_EQ(model_cache_buffers.count(model_cache_token), false,
+                    platform::errors::InvalidArgument(
+                        "model_cache_token has already been set."));
+
+  model_cache_buffers[model_cache_token] = model_cache_buffer;
+  return *this;
+}
+
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetSubgraphPartitionConfigPath(
+    const std::string &path) {
+  subgraph_partition_config_path = path;
+  return *this;
+}
+
+LiteNNAdapterConfig &LiteNNAdapterConfig::SetSubgraphPartitionConfigBuffer(
+    const std::string &buffer) {
+  subgraph_partition_config_buffer = buffer;
+  return *this;
+}
+LiteNNAdapterConfig &LiteNNAdapterConfig::Enable() {
+  use_nnadapter = true;
+  return *this;
+}
+LiteNNAdapterConfig &LiteNNAdapterConfig::Disable() {
+  use_nnadapter = false;
+  return *this;
+}
+
 }  // namespace paddle

paddle/fluid/inference/api/analysis_predictor.cc

@@ -272,6 +272,29 @@ bool AnalysisPredictor::CreateExecutor() {
         "You tried to use NPU forward propagation, but Paddle was not compiled "
         "with WITH_ASCEND_CL."));
 #endif
+  } else if (config_.NNAdapter().use_nnadapter) {
+    if (config_.lite_engine_enabled()) {
+#ifdef LITE_SUBGRAPH_WITH_NNADAPTER
+      // Currently, Paddle-Lite's NNAdapter user interface only supports the
+      // transfer
+      // of Host data pointers. If it is currently used as a subgraph, execution
+      // efficiency will be sacrificed, so it is temporarily set to cpu place.
+      // And, the current lite engine of xpu must execute all parts of the
+      // model.
+      place_ = paddle::platform::CPUPlace();
+#else
+      PADDLE_THROW(
+          platform::errors::Unavailable("You tried to use an NNAdapter lite "
+                                        "engine, but Paddle was not compiled "
+                                        "with it."));
+#endif  // LITE_SUBGRAPH_WITH_NNADAPTER
+    } else {
+      PADDLE_THROW(
+          platform::errors::Unavailable("You tried to use NNadapter forward "
+                                        "propagation (inference without lite "
+                                        "engine), but Paddle was not compiled "
+                                        "with LITE_WITH_NNADAPTER."));
+    }
   } else {
     place_ = paddle::platform::CPUPlace();
   }
@@ -592,6 +615,24 @@ void AnalysisPredictor::PrepareArgument() {
     argument_.SetXpuAutotuneFile(config_.xpu_autotune_file_);
     argument_.SetXpuPrecision(config_.xpu_precision_);
     argument_.SetXpuAdaptiveSeqlen(config_.xpu_adaptive_seqlen_);
+    // NNAdapter related
+    argument_.SetUseNNAdapter(config_.NNAdapter().use_nnadapter);
+    argument_.SetNNAdapterDeviceNames(config_.NNAdapter().device_names);
+    argument_.SetNNAdapterContextProperties(
+        config_.NNAdapter().context_properties);
+    argument_.SetNNAdapterModelCacheDir(config_.NNAdapter().model_cache_dir);
+    argument_.SetNNAdapterSubgraphPartitionConfigBuffer(
+        config_.NNAdapter().subgraph_partition_config_buffer);
+    argument_.SetNNAdapterSubgraphPartitionConfigPath(
+        config_.NNAdapter().subgraph_partition_config_path);
+    std::vector<std::string> buffer_keys;
+    std::vector<std::vector<char>> buffer_vals;
+    for (auto it : config_.NNAdapter().model_cache_buffers) {
+      buffer_keys.emplace_back(it.first);
+      buffer_vals.emplace_back(it.second);
+    }
+    argument_.SetNNAdapterModelCacheToken(buffer_keys);
+    argument_.SetNNAdapterModelCacheBuffer(buffer_vals);
     LOG(INFO) << "Lite subgraph engine is enabled";
   }
 

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -48,6 +48,34 @@ namespace paddle {
 class AnalysisPredictor;
 struct MkldnnQuantizerConfig;
 
+struct LiteNNAdapterConfig {
+  bool use_nnadapter{false};
+  std::string model_cache_dir;
+  std::map<std::string, std::vector<char>> model_cache_buffers;
+  std::vector<std::string> device_names;
+  std::string context_properties;
+  std::string subgraph_partition_config_path;
+  std::string subgraph_partition_config_buffer;
+
+  LiteNNAdapterConfig& SetDeviceNames(const std::vector<std::string>& names);
+
+  LiteNNAdapterConfig& SetContextProperties(const std::string& properties);
+
+  LiteNNAdapterConfig& SetModelCacheDir(const std::string& dir);
+
+  LiteNNAdapterConfig& SetModelCacheBuffers(
+      const std::string& model_cache_token,
+      const std::vector<char>& model_cache_buffer);
+
+  LiteNNAdapterConfig& SetSubgraphPartitionConfigPath(const std::string& path);
+
+  LiteNNAdapterConfig& SetSubgraphPartitionConfigBuffer(
+      const std::string& buffer);
+
+  LiteNNAdapterConfig& Enable();
+  LiteNNAdapterConfig& Disable();
+};
+
 ///
 /// \brief configuration manager for AnalysisPredictor.
 /// \since 1.7.0
@@ -627,6 +655,8 @@ struct PD_INFER_DECL AnalysisConfig {
   ///
   std::string Summary();
 
+  LiteNNAdapterConfig& NNAdapter() { return nnadapter_config_; }
+
  protected:
   // Update the config.
   void Update();
@@ -726,6 +756,9 @@ struct PD_INFER_DECL AnalysisConfig {
   std::string xpu_precision_;
   bool xpu_adaptive_seqlen_;
 
+  // NNAdapter related
+  LiteNNAdapterConfig nnadapter_config_;
+
   // mkldnn related.
   int mkldnn_cache_capacity_{10};
   bool use_mkldnn_quantizer_{false};

paddle/fluid/inference/lite/CMakeLists.txt

@@ -2,8 +2,8 @@ if(XPU_SDK_ROOT)
   set(XPU_DEPS xpuapi xpurt)
 endif()
 
-cc_library(lite_op_teller SRCS op_teller.cc DEPS lite_full_static framework_proto device_context boost xxhash)
-cc_library(lite_engine SRCS engine.cc DEPS lite_full_static framework_proto ${XPU_DEPS})
-cc_library(lite_tensor_utils SRCS tensor_utils.cc DEPS memcpy lite_full_static framework_proto boost device_context ${XPU_DEPS})
+cc_library(lite_op_teller SRCS op_teller.cc DEPS ${LITE_DEPS} framework_proto device_context boost xxhash)
+cc_library(lite_engine SRCS engine.cc DEPS ${LITE_DEPS} framework_proto ${XPU_DEPS})
+cc_library(lite_tensor_utils SRCS tensor_utils.cc DEPS memcpy ${LITE_DEPS} framework_proto boost device_context ${XPU_DEPS})
 cc_test(test_lite_engine SRCS test_engine_lite.cc DEPS lite_engine protobuf framework_proto glog gtest analysis)
 cc_test(test_lite_tensor_utils SRCS test_tensor_utils.cc DEPS lite_engine lite_tensor_utils)