add inference api:exp_specify_tensorrt_subgraph_precision

paddle/fluid/inference/analysis/argument.h

@@ -256,6 +256,15 @@ struct Argument {
   DECL_ARGUMENT_FIELD(tensorrt_disabled_ops,
                       TensorRtDisabledOPs,
                       std::vector<std::string>);
+  DECL_ARGUMENT_FIELD(trt_parameter_run_fp16,
+                      TRTParameterRunFp16,
+                      std::vector<std::string>);
+  DECL_ARGUMENT_FIELD(trt_parameter_run_int8,
+                      TRTParameterRunInt8,
+                      std::vector<std::string>);
+  DECL_ARGUMENT_FIELD(trt_parameter_run_bfp16,
+                      TRTParameterRunBfp16,
+                      std::vector<std::string>);
   DECL_ARGUMENT_FIELD(tensorrt_precision_mode, TensorRtPrecisionMode, int);
   DECL_ARGUMENT_FIELD(tensorrt_use_static_engine,
                       TensorRtUseStaticEngine,

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -173,6 +173,15 @@ void IRPassManager::CreatePasses(Argument *argument,
       pass->Set(
           "trt_exclude_var_names",
           new std::vector<std::string>(argument->trt_exclude_var_names()));
+      pass->Set(
+          "trt_parameter_run_fp16",
+          new std::vector<std::string>(argument->trt_parameter_run_fp16()));
+      pass->Set(
+          "trt_parameter_run_int8",
+          new std::vector<std::string>(argument->trt_parameter_run_int8()));
+      pass->Set(
+          "trt_parameter_run_bfp16",
+          new std::vector<std::string>(argument->trt_parameter_run_bfp16()));
       pass->Set("forbid_dynamic_op",
                 new bool(argument->trt_forbid_dynamic_op()));
 

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

@@ -14,7 +14,6 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
-
 #include <fcntl.h>
 #include <cstddef>
 #include <memory>
@@ -476,9 +475,47 @@ std::string TensorRtSubgraphPass::CreateTensorRTOp(
   }
   auto precision_mode =
       static_cast<phi::DataType>(Get<int>("trt_precision_mode"));
+  auto trt_params_run_fp16 =
+      Get<std::vector<std::string>>("trt_parameter_run_fp16");
+  auto trt_params_run_int8 =
+      Get<std::vector<std::string>>("trt_parameter_run_int8");
+  auto trt_params_run_bfp16 =
+      Get<std::vector<std::string>>("trt_parameter_run_bfp16");
+
+  for (auto para : parameters) {
+    if (std::find(trt_params_run_fp16.begin(),
+                  trt_params_run_fp16.end(),
+                  para) != trt_params_run_fp16.end()) {
+      precision_mode = phi::DataType::FLOAT16;
+      break;
+    }
+  }
+
   bool enable_fp16 = false;
   if (precision_mode == phi::DataType::FLOAT16) enable_fp16 = true;
   auto enable_int8 = Get<bool>("enable_int8");
+
+  for (auto para : parameters) {
+    if (std::find(trt_params_run_int8.begin(),
+                  trt_params_run_int8.end(),
+                  para) != trt_params_run_int8.end()) {
+      enable_int8 = true;
+      precision_mode = phi::DataType::INT8;
+      break;
+    }
+  }
+
+  for (auto para : parameters) {
+    if (std::find(trt_params_run_bfp16.begin(),
+                  trt_params_run_bfp16.end(),
+                  para) != trt_params_run_bfp16.end()) {
+      precision_mode = phi::DataType::BFLOAT16;
+      break;
+    }
+  }
+  bool enable_bfp16 = false;
+  if (precision_mode == phi::DataType::BFLOAT16) enable_bfp16 = true;
+
   auto use_calib_mode = Get<bool>("use_calib_mode");
   auto &subgraph_nodes = *framework::ir::Agent(node).subgraph();
   auto min_input_shape =
@@ -724,6 +761,7 @@ std::string TensorRtSubgraphPass::CreateTensorRTOp(
   op_desc->SetAttr("calibration_data", calibration_data);
   op_desc->SetAttr("enable_int8", enable_int8);
   op_desc->SetAttr("enable_fp16", enable_fp16);
+  op_desc->SetAttr("enbale_bfp16", enable_bfp16);
   op_desc->SetAttr("use_calib_mode", use_calib_mode);
   op_desc->SetAttr("engine_key", engine_key);
   op_desc->SetAttr("calibration_engine_key", calibration_engine_key);

paddle/fluid/inference/api/analysis_config.cc

@@ -462,7 +462,13 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
   CP_MEMBER(tensorrt_min_subgraph_size_);
   CP_MEMBER(tensorrt_precision_mode_);
   CP_MEMBER(trt_mark_output_);
+<<<<<<< HEAD
+  CP_MEMBER(trt_parameters_run_fp16_);
+  CP_MEMBER(trt_parameters_run_int8_);
+  CP_MEMBER(trt_parameters_run_bfp16_);
+=======
   CP_MEMBER(trt_forbid_dynamic_op_)
+>>>>>>> 2ca34a759a255660844914004f2b8b59057ce0fe
   CP_MEMBER(trt_output_tensor_names_);
   CP_MEMBER(trt_disabled_ops_);
   CP_MEMBER(trt_use_dla_);
@@ -880,6 +886,21 @@ void AnalysisConfig::Exp_DisableTensorRtSubgraph(
                                 var_name_not_trt.end());
 }
 
+void AnalysisConfig::Exp_Specify_TensorRT_Subgraph_Precision(
+    const std::vector<std::string> &trt_parameters_run_fp16,
+    const std::vector<std::string> &trt_parameters_run_int8,
+    const std::vector<std::string> &trt_parameters_run_bfp16) {
+  trt_parameters_run_fp16_.insert(trt_parameters_run_fp16_.end(),
+                                  trt_parameters_run_fp16.begin(),
+                                  trt_parameters_run_fp16.end());
+  trt_parameters_run_int8_.insert(trt_parameters_run_int8_.end(),
+                                  trt_parameters_run_int8.begin(),
+                                  trt_parameters_run_int8.end());
+  trt_parameters_run_bfp16_.insert(trt_parameters_run_bfp16_.end(),
+                                   trt_parameters_run_bfp16.begin(),
+                                   trt_parameters_run_bfp16.end());
+}
+
 void AnalysisConfig::EnableVarseqlen() { trt_use_varseqlen_ = true; }
 
 void AnalysisConfig::SetTensorRtOptimizationLevel(int level) {
@@ -1135,6 +1156,12 @@ std::string AnalysisConfig::SerializeInfoCache() {
   ss << tensorrt_max_batchsize_;
   ss << tensorrt_min_subgraph_size_;
   ss << trt_mark_output_;
+  for (auto &name : trt_parameters_run_fp16_) ss << name.c_str();
+  ss << ";";
+  for (auto &name : trt_parameters_run_int8_) ss << name.c_str();
+  ss << ";";
+  for (auto &name : trt_parameters_run_bfp16_) ss << name.c_str();
+  ss << ";";
   ss << trt_forbid_dynamic_op_;
 
   ss << use_dlnne_;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -1755,6 +1755,9 @@ void AnalysisPredictor::PrepareArgument() {
     argument_->SetTensorRtMinSubgraphSize(config_.tensorrt_min_subgraph_size_);
     argument_->SetTRTMarkOutput(config_.trt_mark_output_);
     argument_->SetTRTOutputTensorNames(config_.trt_output_tensor_names_);
+    argument_->SetTRTParameterRunFp16(config_.trt_parameters_run_fp16_);
+    argument_->SetTRTParameterRunInt8(config_.trt_parameters_run_int8_);
+    argument_->SetTRTParameterRunBfp16(config_.trt_parameters_run_bfp16_);
     argument_->SetTensorRtDisabledOPs(config_.trt_disabled_ops_);
     argument_->SetTRTExcludeVarNames(config_.trt_exclude_var_names_);
     argument_->SetTRTForbidDynamicOp(config_.trt_forbid_dynamic_op_);

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -813,6 +813,10 @@ struct PD_INFER_DECL AnalysisConfig {
   void Exp_DisableTensorRtSubgraph(
       const std::vector<std::string>& var_name_not_trt);
 
+  void Exp_Specify_TensorRT_Subgraph_Precision(
+      const std::vector<std::string>& trt_parameters_fp16,
+      const std::vector<std::string>& trt_parameters_int8,
+      const std::vector<std::string>& trt_parameters_bfp16);
   void Exp_DisableTensorRTDynamicShapeOPs(bool trt_forbid_dynamic_op);
 
   ///
@@ -1289,6 +1293,10 @@ struct PD_INFER_DECL AnalysisConfig {
 
   std::vector<std::string> trt_output_tensor_names_{};
   std::vector<std::string> trt_exclude_var_names_{};
+  std::vector<std::string> trt_parameters_run_fp16_{};
+  std::vector<std::string> trt_parameters_run_int8_{};
+  std::vector<std::string> trt_parameters_run_bfp16_{};
+
   std::string tensorrt_transformer_posid_{""};
   std::string tensorrt_transformer_maskid_{""};
   bool trt_use_dla_{false};

paddle/fluid/pybind/inference_api.cc

@@ -937,6 +937,8 @@ void BindAnalysisConfig(py::module *m) {
       .def("exp_disable_tensorrt_ops", &AnalysisConfig::Exp_DisableTensorRtOPs)
       .def("exp_disable_tensorrt_subgraph",
            &AnalysisConfig::Exp_DisableTensorRtSubgraph)
+      .def("exp_specify_tensorrt_subgraph_precision",
+           &AnalysisConfig::Exp_Specify_TensorRT_Subgraph_Precision)
       .def("exp_disable_tensorrt_dynamic_shape_ops",
            &AnalysisConfig::Exp_DisableTensorRTDynamicShapeOPs)
       .def("enable_tensorrt_dla",

test/ir/inference/test_trt_ops_fp16_mix_precision.py

@@ -0,0 +1,144 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle import nn, static
+from paddle.inference import Config, PrecisionType, create_predictor
+
+paddle.enable_static()
+
+
+class SimpleNet(nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2D(
+            in_channels=4,
+            out_channels=4,
+            kernel_size=3,
+            stride=2,
+            padding=0,
+        )
+        self.relu1 = nn.ReLU()
+        self.conv2 = nn.Conv2D(
+            in_channels=4,
+            out_channels=2,
+            kernel_size=3,
+            stride=2,
+            padding=0,
+        )
+        self.relu2 = nn.ReLU()
+        self.conv3 = nn.Conv2D(
+            in_channels=2,
+            out_channels=1,
+            kernel_size=3,
+            stride=2,
+            padding=0,
+        )
+        self.relu3 = nn.ReLU()
+        self.flatten = nn.Flatten()
+        self.fc = nn.Linear(729, 10)
+        self.softmax = nn.Softmax()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.relu1(x)
+        x = self.conv2(x)
+        x = self.relu2(x)
+        x = self.conv3(x)
+        x = self.relu3(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+        x = self.softmax(x)
+        return x
+
+
+class TestTRTOptimizationLevel(unittest.TestCase):
+    def setUp(self):
+        self.place = paddle.CUDAPlace(0)
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.path = os.path.join(self.temp_dir.name, 'optimization_level', '')
+        self.model_prefix = self.path + 'infer_model'
+
+    def tearDown(self):
+        shutil.rmtree(self.path)
+
+    def build_model(self):
+        image = static.data(
+            name='img', shape=[None, 4, 224, 224], dtype='float32'
+        )
+        predict = SimpleNet()(image)
+        exe = paddle.static.Executor(self.place)
+        exe.run(paddle.static.default_startup_program())
+        paddle.static.save_inference_model(
+            self.model_prefix, [image], [predict], exe
+        )
+
+    def init_predictor(self):
+        config = Config(
+            self.model_prefix + '.pdmodel', self.model_prefix + '.pdiparams'
+        )
+        config.enable_use_gpu(256, 0, PrecisionType.Float32)
+        config.exp_disable_tensorrt_ops(["relu_1.tmp_0"])
+        config.enable_tensorrt_engine(
+            workspace_size=1 << 30,
+            max_batch_size=1,
+            min_subgraph_size=3,
+            precision_mode=PrecisionType.Float32,
+            use_static=False,
+            use_calib_mode=False,
+        )
+
+        config.exp_specify_tensorrt_subgraph_precision(
+            ["conv2d_1.w_0"], [""], ["conv2d_2.w_0"]
+        )
+
+        config.enable_memory_optim()
+        # config.disable_glog_info()
+        config.set_tensorrt_optimization_level(0)
+        self.assertEqual(config.tensorrt_optimization_level(), 0)
+        predictor = create_predictor(config)
+        return predictor
+
+    def infer(self, predictor, img):
+        input_names = predictor.get_input_names()
+        for i, name in enumerate(input_names):
+            input_tensor = predictor.get_input_handle(name)
+            input_tensor.reshape(img[i].shape)
+            input_tensor.copy_from_cpu(img[i].copy())
+
+        predictor.run()
+        results = []
+        output_names = predictor.get_output_names()
+        for i, name in enumerate(output_names):
+            output_tensor = predictor.get_output_handle(name)
+            output_data = output_tensor.copy_to_cpu()
+            results.append(output_data)
+        return results
+
+    def test_optimization_level(self):
+        self.build_model()
+        predictor = self.init_predictor()
+        img = np.ones((1, 4, 224, 224), dtype=np.float32)
+        results = self.infer(predictor, img=[img])
+
+
+if __name__ == '__main__':
+    unittest.main()