[Paddle Inference] Add float_to_half_pass to support inference with mixed precision (#47993)

paddle/fluid/framework/ir/CMakeLists.txt

@@ -104,6 +104,7 @@ pass_library(delete_c_identity_op_pass inference)
 pass_library(preln_residual_bias_fuse_pass inference)
 pass_library(delete_fill_constant_op_pass inference)
 pass_library(constant_folding_pass inference)
+pass_library(float_to_half_pass inference)
 pass_library(conv2d_fusion_layout_transfer_pass inference)
 pass_library(simplify_with_basic_ops_pass base)
 pass_library(fc_elementwise_layernorm_fuse_pass base)

paddle/fluid/framework/ir/float_to_half_pass.h

+// Copyright (c) 2022 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.
+
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/node.h"
+#include "paddle/phi/common/backend.h"
+#include "paddle/phi/common/data_type.h"
+#include "paddle/phi/common/float16.h"
+#include "paddle/phi/common/layout.h"
+#include "paddle/phi/common/place.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class FloatToHalfPass : public FusePassBase {
+ public:
+  using VarType = framework::proto::VarType;
+
+ public:
+  FloatToHalfPass() = default;
+  ~FloatToHalfPass() = default;
+
+ protected:
+  void ApplyImpl(Graph* graph) const override;
+
+ private:
+  void Init(Graph* graph) const;
+
+  void SetDefaultBlacklist() const;
+
+  bool OpSupportPrecision(const std::string& op_type,
+                          phi::DataType precision,
+                          phi::Backend backend = phi::Backend::GPU) const;
+
+  void SetOpUniqueType() const;
+
+  void RestoreOpOriginType() const;
+
+  inline std::string GetOpOriginalType(const std::string& op_type) const;
+
+  void GetOpPrecision() const;
+
+  void UpdateOpPrecision() const;
+
+  void InsertCastOp() const;
+
+  void ProcessOpWithDtypeAttr() const;
+
+  bool InputVarsNotConvert(Node* op_node, const std::string& var_name) const;
+
+  bool OutputVarsNotConvert(Node* op_node, const std::string& var_name) const;
+
+  void SetVarPrecision() const;
+
+  void ConvertWeightsData() const;
+
+ private:
+  mutable bool keep_io_types_;
+  // float16 or bfloat16 now
+  mutable phi::DataType half_precision_;
+
+  mutable std::unordered_set<std::string> black_list_;
+
+  // subgraph id -> pointer to subgraph
+  mutable std::vector<Graph*> subgraphes_;
+  // var name -> real var node
+  mutable std::unordered_map<std::string, Node*> real_vars_;
+  // subgraph id -> all op nodes in subgraph
+  mutable std::vector<std::vector<Node*>> all_op_nodes_;
+  // op's unique type -> the op's origin type
+  mutable std::unordered_map<std::string, std::string> op_original_type_;
+  // op's unique type -> whether the op run at half precision
+  mutable std::unordered_set<std::string> op_run_half_;
+
+  mutable std::unordered_set<std::string> vars_convert_to_half_;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/inference/analysis/argument.h

@@ -365,6 +365,8 @@ struct Argument {
   DECL_ARGUMENT_FIELD(mixed_black_list,
                       MixedBlackList,
                       std::unordered_set<std::string>);
+  DECL_ARGUMENT_FIELD(enable_gpu_half, EnableGPUHalf, bool);
+  DECL_ARGUMENT_FIELD(mixed_precision_mode, MixedPrecisionMode, int);
 
  private:
   std::unordered_set<std::string> valid_fields_;

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -86,10 +86,14 @@ void IRPassManager::CreatePasses(Argument *argument,
                               argument->tensorrt_tuned_dynamic_shape();
     pass->Set("with_dynamic_shape", new bool(with_dynamic_shape));
 
+    // mixed precision related
     pass->Set("model_precision", new int(argument->model_precision()));
     pass->Set(
         "mixed_black_list",
         new std::unordered_set<std::string>(argument->mixed_black_list()));
+    pass->Set("enable_gpu_half", new bool(argument->enable_gpu_half()));
+    pass->Set("mixed_precision_mode",
+              new int(argument->mixed_precision_mode()));
 
     if (pass_name == "graph_viz_pass") {
       std::string optim_cache_dir = argument->optim_cache_dir();

paddle/fluid/inference/api/analysis_config.cc

@@ -85,16 +85,29 @@ void AnalysisConfig::SetModel(const std::string &prog_file_path,
 
   Update();
 }
+
 void AnalysisConfig::EnableUseGpu(uint64_t memory_pool_init_size_mb,
-                                  int device_id) {
+                                  int device_id,
+                                  Precision precision_mode) {
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   use_gpu_ = true;
   memory_pool_init_size_mb_ = memory_pool_init_size_mb;
   FLAGS_initial_gpu_memory_in_mb = memory_pool_init_size_mb_;
   gpu_device_id_ = device_id;
+  mixed_precision_mode_ = precision_mode;
+  if (precision_mode == Precision::kFloat32) {
+    // default
+  } else if (precision_mode == Precision::kHalf ||
+             precision_mode == Precision::kBf16) {
+    enable_gpu_half_ = true;
+  } else {
+    LOG(ERROR)
+        << "The Paddle-GPU inference currently only supports "
+           "float32/float16/bfloat16 precision. Please check the parameters "
+           "you specified in EnableUseGpu or enable_use_gpu function.";
+  }
 #else
-  LOG(ERROR) << "Please compile with gpu to EnableGpu()";
-  use_gpu_ = false;
+  LOG(ERROR) << "Please use PaddlePaddle with GPU version.";
 #endif
 
   Update();
@@ -381,8 +394,10 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
   CP_MEMBER(gpu_device_id_);
   CP_MEMBER(memory_pool_init_size_mb_);
 
-  // Mixed related.
+  // Mixed precision related.
   CP_MEMBER(mixed_black_list_);
+  CP_MEMBER(enable_gpu_half_);
+  CP_MEMBER(mixed_precision_mode_);
 
   CP_MEMBER(enable_memory_optim_);
   // TensorRT related.
@@ -996,6 +1011,7 @@ std::string AnalysisConfig::SerializeInfoCache() {
   ss << params_file_;
 
   ss << use_gpu_;
+  ss << enable_gpu_half_;
   ss << use_external_stream_;
   ss << exec_stream_;
   ss << use_fc_padding_;
@@ -1212,6 +1228,7 @@ std::string AnalysisConfig::Summary() {
   os.InsertRow({"use_gpu", use_gpu_ ? "true" : "false"});
   if (use_gpu_) {
     os.InsertRow({"gpu_device_id", std::to_string(gpu_device_id_)});
+    os.InsertRow({"enable_gpu_half_", std::to_string(enable_gpu_half_)});
     os.InsertRow({"memory_pool_init_size",
                   std::to_string(memory_pool_init_size_mb_) + "MB"});
     os.InsertRow(
@@ -1407,7 +1424,7 @@ bool AnalysisConfig::trt_allow_build_at_runtime() const {
   return trt_allow_build_at_runtime_;
 }
 
-void AnalysisConfig::Exp_SetBlackListOpsForMixedModel(
+void AnalysisConfig::Exp_DisableMixedInferOps(
     const std::unordered_set<std::string> &black_list) {
   mixed_black_list_ = black_list;
 }

paddle/fluid/inference/api/analysis_predictor.cc

@@ -1257,12 +1257,26 @@ void AnalysisPredictor::PrepareArgument() {
       }
     }
   }
+
+  if (!config_.ir_optim()) {
+    argument_.SetEnableIrOptim(false);
+    if (config_.enable_gpu_half_) {
+      argument_.SetEnableIrOptim(true);
+      pass_builder->ClearPasses();
+      pass_builder->AppendPass("float_to_half_pass");
+      LOG(INFO)
+          << "This model run in Paddle-GPU mixed precision mode with no ir "
+             "optimization.";
+    } else {
+      LOG(INFO) << "ir_optim is turned off, no IR pass will be executed.";
+    }
+  } else {
     if (config_.ir_debug_) {
       pass_builder->TurnOnDebug();
     }
-  if (!config_.ir_optim()) {
-    argument_.SetEnableIrOptim(false);
-    LOG(INFO) << "ir_optim is turned off, no IR pass will be executed";
+    if (config_.enable_gpu_half_) {
+      LOG(INFO) << "This model run in Paddle-GPU mixed precision mode.";
+    }
   }
   argument_.SetDisableLogs(config_.glog_info_disabled());
   argument_.SetIrAnalysisPasses(pass_builder->AllPasses());
@@ -1272,6 +1286,9 @@ void AnalysisPredictor::PrepareArgument() {
   // mixed precison.
   argument_.SetModelPrecision(static_cast<int>(model_precision_));
   argument_.SetMixedBlackList(config_.mixed_black_list_);
+  argument_.SetEnableGPUHalf(config_.enable_gpu_half_);
+  argument_.SetMixedPrecisionMode(static_cast<int>(
+      paddle::ConvertPrecision(config_.mixed_precision_mode_)));
 }
 
 // NOTE All the members in AnalysisConfig should be copied to Argument.

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -247,8 +247,12 @@ struct PD_INFER_DECL AnalysisConfig {
   ///
   /// \param memory_pool_init_size_mb initial size of the GPU memory pool in MB.
   /// \param device_id device_id the GPU card to use (default is 0).
+  /// \param precision the precision used in Paddle-GPU inference.
   ///
-  void EnableUseGpu(uint64_t memory_pool_init_size_mb, int device_id = 0);
+  void EnableUseGpu(uint64_t memory_pool_init_size_mb,
+                    int device_id = 0,
+                    Precision precision_mode = Precision::kFloat32);
+
   ///
   /// \brief Turn off GPU.
   ///
@@ -1005,7 +1009,7 @@ struct PD_INFER_DECL AnalysisConfig {
   /// interface is in the experimental stage and may change in the future. Note
   /// that the blacklist must be the same as the model conversion blacklist.
   ///
-  void Exp_SetBlackListOpsForMixedModel(
+  void Exp_DisableMixedInferOps(
       const std::unordered_set<std::string>& black_list);
 
   void SetApplyOptim(bool value) { apply_optim_ = value; }
@@ -1024,13 +1028,15 @@ struct PD_INFER_DECL AnalysisConfig {
   mutable std::string prog_file_;
   mutable std::string params_file_;
 
-  // Mixed precision.
+  // Mixed precision related.
+  Precision mixed_precision_mode_{Precision::kFloat32};
   std::unordered_set<std::string> mixed_black_list_;
 
   // GPU related.
   bool use_gpu_{false};
   int gpu_device_id_{0};
   uint64_t memory_pool_init_size_mb_{100};  // initial size is 100MB.
+  bool enable_gpu_half_{false};
   bool thread_local_stream_{false};
 
   bool use_cudnn_{false};

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -246,9 +246,10 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
         "conv_elementwise_add_fuse_pass",      //
 #endif                                         //
         "transpose_flatten_concat_fuse_pass",  //
-        "constant_folding_pass",
+        "constant_folding_pass",               //
         // following pass should be located in the last, since it will
         // work on all fused ops.
+        "float_to_half_pass",  //
         "runtime_context_cache_pass"
   });
 

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -416,6 +416,9 @@ download_result(${ERNIE_INSTALL_DIR} "Ernie_result.txt.tar.gz"
 if(WITH_GPU)
   inference_analysis_api_test(test_analyzer_ernie ${ERNIE_INSTALL_DIR}
                               analyzer_ernie_tester.cc)
+  inference_analysis_api_test(gpu_ernie_half_test ${ERNIE_INSTALL_DIR}
+                              gpu_ernie_half_test.cc)
+  set_tests_properties(gpu_ernie_half_test PROPERTIES TIMEOUT 40)
 endif()
 inference_analysis_api_int8_test(test_analyzer_ernie_int8 ${ERNIE_INSTALL_DIR}
                                  analyzer_ernie_int8_tester.cc)

paddle/fluid/inference/tests/api/gpu_ernie_half_test.cc

+// Copyright (c) 2022 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.
+
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/tests/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+
+using paddle::PaddleTensor;
+
+template <typename T>
+void GetValueFromStream(std::stringstream *ss, T *t) {
+  (*ss) >> (*t);
+}
+
+template <>
+void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) {
+  *t = ss->str();
+}
+
+// Split string to vector
+template <typename T>
+void Split(const std::string &line, char sep, std::vector<T> *v) {
+  std::stringstream ss;
+  T t;
+  for (auto c : line) {
+    if (c != sep) {
+      ss << c;
+    } else {
+      GetValueFromStream<T>(&ss, &t);
+      v->push_back(std::move(t));
+      ss.str({});
+      ss.clear();
+    }
+  }
+
+  if (!ss.str().empty()) {
+    GetValueFromStream<T>(&ss, &t);
+    v->push_back(std::move(t));
+    ss.str({});
+    ss.clear();
+  }
+}
+
+// Parse tensor from string
+template <typename T>
+bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) {
+  std::vector<std::string> data;
+  Split(field, ':', &data);
+  if (data.size() < 2) return false;
+
+  std::string shape_str = data[0];
+
+  std::vector<int> shape;
+  Split(shape_str, ' ', &shape);
+
+  std::string mat_str = data[1];
+
+  std::vector<T> mat;
+  Split(mat_str, ' ', &mat);
+
+  tensor->shape = shape;
+  auto size =
+      std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()) *
+      sizeof(T);
+  tensor->data.Resize(size);
+  std::copy(mat.begin(), mat.end(), static_cast<T *>(tensor->data.data()));
+  tensor->dtype = GetPaddleDType<T>();
+
+  return true;
+}
+
+// Parse input tensors from string
+bool ParseLine(const std::string &line,
+               std::vector<paddle::PaddleTensor> *tensors) {
+  std::vector<std::string> fields;
+  Split(line, ';', &fields);
+
+  tensors->clear();
+  tensors->reserve(4);
+
+  int i = 0;
+  auto input_name = FLAGS_ernie_large ? "eval_placeholder_" : "placeholder_";
+  for (; i < 3; i++) {
+    paddle::PaddleTensor temp;
+    ParseTensor<int64_t>(fields[i], &temp);
+    temp.name = input_name + std::to_string(i);
+    tensors->push_back(temp);
+  }
+
+  // input_mask
+  paddle::PaddleTensor input_mask;
+  ParseTensor<float>(fields[i], &input_mask);
+  input_mask.name = input_name + std::to_string(i);
+  tensors->push_back(input_mask);
+
+  return true;
+}
+
+bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs,
+                   int batch_size = 1) {
+  if (FLAGS_infer_data.empty()) {
+    LOG(ERROR) << "please set input data path";
+    return false;
+  }
+
+  std::ifstream fin(FLAGS_infer_data);
+  std::string line;
+  int sample = 0;
+
+  // The unit-test dataset only have 10 samples, each sample have 5 feeds.
+  while (std::getline(fin, line)) {
+    std::vector<paddle::PaddleTensor> feed_data;
+    ParseLine(line, &feed_data);
+    inputs->push_back(std::move(feed_data));
+    sample++;
+    if (!FLAGS_test_all_data && sample == batch_size) break;
+  }
+  LOG(INFO) << "number of samples: " << sample;
+  return true;
+}
+
+// Compare results
+TEST(Ernie_gpu_fp16_no_ir, compare_results) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_infer_model);
+  config.EnableUseGpu(512, 0, paddle_infer::PrecisionType::kHalf);
+  config.SwitchIrOptim(false);
+
+  auto predictor = CreatePaddlePredictor(config);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  LoadInputData(&input_slots_all);
+
+  std::ifstream fin(FLAGS_refer_result);
+  std::string line;
+  std::vector<float> ref;
+
+  while (std::getline(fin, line)) {
+    Split(line, ' ', &ref);
+  }
+
+  std::vector<PaddleTensor> outputs;
+  for (size_t i = 0; i < input_slots_all.size(); i++) {
+    outputs.clear();
+    predictor->Run(input_slots_all[i], &outputs);
+
+    auto output = outputs.front();
+    size_t outputs_size = 1;
+    for (auto dim : output.shape) {
+      outputs_size *= dim;
+    }
+    float *result = reinterpret_cast<float *>(output.data.data());
+    for (size_t j = 0; j < outputs_size; ++j) {
+      EXPECT_NEAR(ref[i * outputs_size + j], result[j], 5e-2);
+    }
+  }
+}
+
+// Compare results
+TEST(Ernie_gpu_fp16_with_ir, compare_results) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_infer_model);
+  config.EnableUseGpu(512, 0, paddle_infer::PrecisionType::kHalf);
+  config.SwitchIrOptim(true);
+  // The fc_fuse_pass has diff, which will be repaired later.
+  config.pass_builder()->DeletePass("fc_fuse_pass");
+  // There is a problem with the model itself, which has nothing to do with
+  // constant_folding_pass.
+  config.pass_builder()->DeletePass("constant_folding_pass");
+
+  auto predictor = CreatePaddlePredictor(config);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  LoadInputData(&input_slots_all);
+
+  std::ifstream fin(FLAGS_refer_result);
+  std::string line;
+  std::vector<float> ref;
+
+  while (std::getline(fin, line)) {
+    Split(line, ' ', &ref);
+  }
+
+  std::vector<PaddleTensor> outputs;
+  for (size_t i = 0; i < input_slots_all.size(); i++) {
+    outputs.clear();
+    predictor->Run(input_slots_all[i], &outputs);
+
+    auto output = outputs.front();
+    size_t outputs_size = 1;
+    for (auto dim : output.shape) {
+      outputs_size *= dim;
+    }
+    float *result = reinterpret_cast<float *>(output.data.data());
+    for (size_t j = 0; j < outputs_size; ++j) {
+      EXPECT_NEAR(ref[i * outputs_size + j], result[j], 5e-2);
+    }
+  }
+}
+
+// Compare results
+TEST(Ernie_gpu_bf16_no_ir, compare_results) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_infer_model);
+  config.EnableUseGpu(512, 0, paddle_infer::PrecisionType::kBf16);
+  config.SwitchIrOptim(false);
+
+  auto predictor = CreatePaddlePredictor(config);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  LoadInputData(&input_slots_all);
+
+  std::ifstream fin(FLAGS_refer_result);
+  std::string line;
+  std::vector<float> ref;
+
+  while (std::getline(fin, line)) {
+    Split(line, ' ', &ref);
+  }
+
+  std::vector<PaddleTensor> outputs;
+  for (size_t i = 0; i < input_slots_all.size(); i++) {
+    outputs.clear();
+    predictor->Run(input_slots_all[i], &outputs);
+
+    auto output = outputs.front();
+    size_t outputs_size = 1;
+    for (auto dim : output.shape) {
+      outputs_size *= dim;
+    }
+    float *result = reinterpret_cast<float *>(output.data.data());
+    for (size_t j = 0; j < outputs_size; ++j) {
+      EXPECT_NEAR(ref[i * outputs_size + j], result[j], 7e-2);
+    }
+  }
+}
+
+// Compare results
+TEST(Ernie_gpu_bf16_with_ir, compare_results) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_infer_model);
+  config.EnableUseGpu(512, 0, paddle_infer::PrecisionType::kBf16);
+  config.SwitchIrOptim(true);
+  // The fc_fuse_pass has diff, which will be repaired later.
+  config.pass_builder()->DeletePass("fc_fuse_pass");
+  // There is a problem with the model itself, which has nothing to do with
+  // constant_folding_pass.
+  config.pass_builder()->DeletePass("constant_folding_pass");
+
+  auto predictor = CreatePaddlePredictor(config);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  LoadInputData(&input_slots_all);
+
+  std::ifstream fin(FLAGS_refer_result);
+  std::string line;
+  std::vector<float> ref;
+
+  while (std::getline(fin, line)) {
+    Split(line, ' ', &ref);
+  }
+
+  std::vector<PaddleTensor> outputs;
+  for (size_t i = 0; i < input_slots_all.size(); i++) {
+    outputs.clear();
+    predictor->Run(input_slots_all[i], &outputs);
+
+    auto output = outputs.front();
+    size_t outputs_size = 1;
+    for (auto dim : output.shape) {
+      outputs_size *= dim;
+    }
+    float *result = reinterpret_cast<float *>(output.data.data());
+    for (size_t j = 0; j < outputs_size; ++j) {
+      EXPECT_NEAR(ref[i * outputs_size + j], result[j], 7e-2);
+    }
+  }
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tests/api/paddle_infer_api_test.cc

-/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2022 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.
@@ -12,15 +12,11 @@ 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. */
 
-#include <cuda_runtime.h>
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 
-#include <cstring>
-#include <numeric>
-
 #include "gflags/gflags.h"
-#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
+#include "paddle/fluid/inference/tests/api/tester_helper.h"
 
 namespace paddle_infer {
 

paddle/fluid/pybind/inference_api.cc

@@ -644,7 +644,8 @@ void BindAnalysisConfig(py::module *m) {
       .def("enable_use_gpu",
            &AnalysisConfig::EnableUseGpu,
            py::arg("memory_pool_init_size_mb"),
-           py::arg("device_id") = 0)
+           py::arg("device_id") = 0,
+           py::arg("precision_mode") = AnalysisConfig::Precision::kFloat32)
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
       .def("set_exec_stream",
            [](AnalysisConfig &self, phi::CUDAStream &stream) {