convert support multi block. (#44866)

* convert support multi block.

* update

paddle/fluid/inference/analysis/passes/convert_to_mixed_precision.cc

@@ -14,7 +14,10 @@
 
 #include "paddle/fluid/inference/analysis/passes/convert_to_mixed_precision.h"
 
+#include <algorithm>
+#include <iterator>
 #include <string>
+#include <unordered_map>
 #include <unordered_set>
 
 #include "paddle/fluid/framework/block_desc.h"
@@ -39,7 +42,106 @@ namespace analysis {
 
 namespace {
 
-bool IsKernelSupportPrecision(
+inline std::string SerializeParams(framework::Scope* scope,
+                                   const std::vector<std::string>& params) {
+  std::ostringstream os;
+  phi::CPUContext ctx;
+  for (const auto& param : params) {
+    VLOG(3) << "Serialize param: " << param;
+    PADDLE_ENFORCE_NOT_NULL(
+        scope->FindVar(param),
+        platform::errors::NotFound("Block should already have a '%s' variable",
+                                   param));
+    auto* tensor = scope->FindVar(param)->GetMutable<framework::LoDTensor>();
+    framework::SerializeToStream(os, *tensor, ctx);
+  }
+  return os.str();
+}
+
+inline void StrToBinary(const std::string& path, const std::string& str) {
+  std::ofstream file(path.c_str(), std::ios::binary);
+  file.write(str.c_str(), str.size());
+  file.close();
+}
+inline bool NodeVarHasDtype(framework::ir::Node* node) {
+  if (node->IsCtrlVar()) return false;
+
+  if (node->IsVar() &&
+      (node->Var()->GetType() ==
+           paddle::framework::proto::VarType::SELECTED_ROWS ||
+       node->Var()->GetType() ==
+           paddle::framework::proto::VarType::LOD_TENSOR ||
+       node->Var()->GetType() ==
+           paddle::framework::proto::VarType::LOD_TENSOR_ARRAY ||
+       node->Var()->GetType() == paddle::framework::proto::VarType::STRINGS ||
+       node->Var()->GetType() == paddle::framework::proto::VarType::VOCAB)) {
+    return true;
+  }
+
+  return false;
+}
+void SaveMixedModel(framework::ir::Graph* graph,
+                    framework::Scope* scope,
+                    framework::ProgramDesc* mixed_program_desc,
+                    const std::string& mixed_model_file,
+                    const std::string& mixed_params_file,
+                    phi::DataType mixed_precision) {
+  paddle::CPUPlace place;
+  auto parameters = scope->LocalVarNames();
+  std::sort(parameters.begin(), parameters.end());
+
+  std::unordered_set<std::string> weights_should_be_fp32;
+  for (auto* node : graph->Nodes()) {
+    if (!(node->IsVar() && !node->IsCtrlVar())) continue;
+    if (NodeVarHasDtype(node)) {
+      if (node->Var()->Persistable() &&
+          node->Var()->GetDataType() ==
+              paddle::framework::proto::VarType::FP32) {
+        VLOG(2) << "weights keep to fp32: " << node->Name();
+        weights_should_be_fp32.insert(node->Name());
+      }
+    }
+  }
+
+  for (const auto& param_name : parameters) {
+    auto* var = scope->FindLocalVar(param_name);
+    if (var->IsType<framework::LoDTensor>() ||
+        var->IsType<framework::Tensor>()) {
+      auto* t = var->GetMutable<framework::LoDTensor>();
+      framework::Tensor mixed_tensor;
+      mixed_tensor.Resize(t->dims());
+      auto* data = t->mutable_data<float>(platform::CPUPlace());
+
+      if (mixed_precision == phi::DataType::FLOAT16 &&
+          !weights_should_be_fp32.count(param_name)) {
+        mixed_tensor.set_type(paddle::experimental::DataType::FLOAT16);
+        auto* mixed_data =
+            mixed_tensor.mutable_data<float16>(platform::CPUPlace());
+        for (int i = 0; i < t->numel(); i++) {
+          mixed_data[i] = static_cast<float16>(data[i]);
+        }
+        t->clear();
+        paddle::framework::TensorCopySync(mixed_tensor, place, t);
+      } else if (mixed_precision == phi::DataType::BFLOAT16 &&
+                 !weights_should_be_fp32.count(param_name)) {
+        mixed_tensor.set_type(paddle::experimental::DataType::BFLOAT16);
+        auto* mixed_data =
+            mixed_tensor.mutable_data<bfloat16>(platform::CPUPlace());
+        for (int i = 0; i < t->numel(); i++) {
+          mixed_data[i] = static_cast<bfloat16>(data[i]);
+        }
+        t->clear();
+        paddle::framework::TensorCopySync(mixed_tensor, place, t);
+      }
+    }
+  }
+
+  StrToBinary(mixed_model_file,
+              mixed_program_desc->Proto()->SerializeAsString());
+  StrToBinary(mixed_params_file, SerializeParams(scope, parameters));
+}
+
+bool PhiKernelSupportPrecision(
     const std::string& op_type,
     phi::Backend backend,
     phi::DataType data_type,
@@ -56,10 +158,23 @@ bool GpuKernelSupportPrecision(
     const std::string& op_type,
     phi::DataType data_type,
     phi::DataLayout layout = phi::DataLayout::ALL_LAYOUT) {
-  bool res =
-      IsKernelSupportPrecision(op_type, phi::Backend::GPU, data_type, layout);
-  res |= IsKernelSupportPrecision(
-      op_type, phi::Backend::GPUDNN, data_type, layout);
+  auto phi_op_type = phi::TransToPhiKernelName(op_type);
+  bool res = PhiKernelSupportPrecision(
+      phi_op_type, phi::Backend::GPU, data_type, layout);
+  res |= PhiKernelSupportPrecision(
+      phi_op_type, phi::Backend::GPUDNN, data_type, layout);
+
+  if (!res) {
+    auto& all_kernels = OperatorWithKernel::AllOpKernels();
+    auto it = all_kernels.find(op_type);
+    if (it != all_kernels.end()) {
+      for (auto& kern_pair : it->second) {
+        if (platform::is_gpu_place(kern_pair.first.place_)) {
+          res = true;
+        }
+      }
+    }
+  }
   return res;
 }
 
@@ -90,30 +205,16 @@ bool OutShouldNotConvert(ir::Node* var_node) {
 
   return false;
 }
-
-// Get weight names which appear in multiple block (block 0 and block n).
-std::unordered_set<std::string> GetMultiBlockPersistableNames(
-    framework::ProgramDesc* program_desc) {
-  std::unordered_set<std::string> special_weights;
-  size_t block_size = program_desc->Size();
-
-  std::unordered_set<std::string> block_0_weights;
-  for (auto var : program_desc->Block(0).AllVars()) {
-    if (var->Persistable()) block_0_weights.insert(var->Name());
-  }
-
-  for (size_t i = 1; i < block_size; ++i) {
-    // std::cout << program_desc->MutableBlock(i)->Proto()->DebugString() <<
-    // std::endl;;
-    auto all_ops = program_desc->Block(i).AllOps();
-    for (auto op : all_ops) {
-      for (auto name : op->InputArgumentNames()) {
-        if (block_0_weights.count(name)) special_weights.insert(name);
-      }
-    }
+void ProcessOutputNode(ir::Node* var_node,
+                       framework::proto::VarType::Type to_type) {
+  if (!NodeVarHasDtype(var_node)) return;
+  auto* out_var = var_node->Var();
+  if (out_var->GetDataType() == framework::proto::VarType::FP32) {
+    if (OutShouldNotConvert(var_node)) return;
+    out_var->SetDataType(to_type);
   }
-
-  return special_weights;
+  VLOG(3) << " out_node name " << var_node->Name() << " data_type "
+          << out_var->GetDataType();
 }
 
 // Just process special cases for weights conversion.
@@ -143,21 +244,8 @@ bool WeightsShouldNotConvert(ir::Node* var_node) {
     }
   }
 
-  // If cur_op's next is condition_flow op, then cur op should be fp32. Note, we
-  // now only convert to mixed in block 0.
-  for (auto* op_node : op_nodes) {
-    for (auto var : op_node->outputs) {
-      for (auto next_op : var->outputs) {
-        if (next_op->Op()->HasAttr("sub_block")) {
-          return true;
-        }
-      }
-    }
-  }
-
   return false;
 }
-
 inline bool IsFloatVarType(framework::proto::VarType::Type type) {
   if (type == framework::proto::VarType::FP16 ||
       type == framework::proto::VarType::FP32 ||
@@ -165,6 +253,56 @@ inline bool IsFloatVarType(framework::proto::VarType::Type type) {
     return true;
   return false;
 }
+void ProcessInputNode(
+    bool support_precision,
+    framework::ir::Graph* graph,
+    ir::Node* in_node,
+    ir::Node* op_node,
+    int* suffix,
+    framework::BlockDesc* block_desc,
+    std::unordered_map<framework::ir::Node*, framework::ir::Node*>* cast_map,
+    framework::proto::VarType::Type to_type,
+    bool is_main_block,
+    std::unordered_map<std::string, framework::proto::VarType::Type>*
+        vars_in_multi_block_map) {
+  if (!NodeVarHasDtype(in_node)) return;
+  auto* in_var = in_node->Var();
+  auto in_var_type = in_var->GetDataType();
+  if (!is_main_block && vars_in_multi_block_map->count(in_var->Name())) {
+    in_var_type = vars_in_multi_block_map->at(in_var->Name());
+  }
+  if (support_precision) {
+    if (in_var->Persistable() &&
+        in_var_type == framework::proto::VarType::FP32) {
+      if (WeightsShouldNotConvert(in_node)) return;
+      in_var->SetDataType(to_type);
+    } else if (!in_var->Persistable() && IsFloatVarType(in_var_type) &&
+               in_var_type != to_type) {
+      AddCastOp(graph,
+                in_node,
+                op_node,
+                in_var_type,
+                to_type,
+                suffix,
+                block_desc,
+                cast_map);
+    }
+  } else {
+    if (!in_var->Persistable() && IsFloatVarType(in_var_type) &&
+        in_var_type != to_type) {
+      AddCastOp(graph,
+                in_node,
+                op_node,
+                in_var_type,
+                to_type,
+                suffix,
+                block_desc,
+                cast_map);
+    }
+  }
+  VLOG(3) << " in_node name " << in_var->Name() << " data_type "
+          << in_var->GetDataType();
+}
 
 void ConvertAllFp64ToFp32(framework::ir::Graph* graph) {
   auto op_nodes = framework::ir::TopologySortOperations(*graph);
@@ -239,6 +377,11 @@ void HandleSpecialOps(framework::OpDesc* op_desc) {
         static_cast<int>(framework::proto::VarType::FP32))
       op_desc->SetAttr("dtype",
                        static_cast<int>(framework::proto::VarType::FP16));
+  } else if (op_desc->Type() == "fill_constant_batch_size_like") {
+    if (PADDLE_GET_CONST(int, op_desc->GetAttr("dtype")) ==
+        static_cast<int>(framework::proto::VarType::FP32))
+      op_desc->SetAttr("dtype",
+                       static_cast<int>(framework::proto::VarType::FP16));
   }
 }
 
@@ -260,26 +403,47 @@ void FixCastAttr(framework::ir::Graph* graph) {
   }
 }
 
-// If op's output var is condition flow op's input, then the op must be fp32
-// precision.
-bool NextOpIncludesConditionFlowOp(framework::ir::Node* cur_op_node) {
-  auto cur_op_outs = cur_op_node->outputs;
-  for (auto out_var : cur_op_outs) {
-    for (auto next_op_node : out_var->outputs) {
-      if (next_op_node->Op()->HasAttr("sub_block")) {
-        return true;
+void FindVarsInMultiBlock(
+    framework::ProgramDesc* program_desc,
+    std::unordered_map<std::string, framework::proto::VarType::Type>*
+        vars_in_multi_block_map) {
+  std::set<std::string> vars_in_multi_block;
+  std::set<std::string> main_block_var_names_set;
+  for (auto op : program_desc->Block(0).AllOps()) {
+    auto in_names = op->InputArgumentNames();
+    main_block_var_names_set.insert(in_names.begin(), in_names.end());
+  }
+
+  for (size_t i = 1; i < program_desc->Size(); ++i) {
+    std::set<std::string> block_var_names_set;
+    for (auto op : program_desc->Block(i).AllOps()) {
+      auto in_names = op->InputArgumentNames();
+      block_var_names_set.insert(in_names.begin(), in_names.end());
     }
+
+    std::set_intersection(
+        main_block_var_names_set.begin(),
+        main_block_var_names_set.end(),
+        block_var_names_set.begin(),
+        block_var_names_set.end(),
+        std::inserter(vars_in_multi_block, vars_in_multi_block.begin()));
   }
+
+  for (auto name : vars_in_multi_block) {
+    vars_in_multi_block_map->emplace(name, framework::proto::VarType::FP32);
   }
-  return false;
 }
 
-void ConvertTensorDtype(framework::ProgramDesc* program_desc,
+void ConvertTensorDtype(
+    framework::ProgramDesc* program_desc,
     framework::ir::Graph* graph,
     const std::unordered_set<std::string>& blacklist,
     bool keep_io_types,
     phi::Backend backend,
-                        phi::DataType tensor_dtype) {
+    phi::DataType tensor_dtype,
+    bool is_main_block,
+    std::unordered_map<std::string, framework::proto::VarType::Type>*
+        vars_in_multi_block_map) {
   framework::proto::VarType::Type to_type;
   if (tensor_dtype == phi::DataType::FLOAT16) {
     to_type = framework::proto::VarType::FP16;
@@ -287,25 +451,27 @@ void ConvertTensorDtype(framework::ProgramDesc* program_desc,
     to_type = framework::proto::VarType::BF16;
   } else {
     PADDLE_THROW(paddle::platform::errors::InvalidArgument(
-        "mixed_precision currently not supported dtype %d, we now only support "
+        "mixed_precision currently not supported dtype %d, we now only "
+        "support "
         "fp16 and bf16.",
         static_cast<int>(tensor_dtype)));
   }
 
-  auto weight_name_in_multi_block = GetMultiBlockPersistableNames(program_desc);
+  auto* block_desc =
+      framework::ir::TopologySortOperations(*graph)[0]->Op()->Block();
+
   int num_low_precision = 0;
   int suffix = 0;
-  framework::BlockDesc* block_desc{nullptr};
   std::vector<framework::ir::Node*> output_nodes;
   std::unordered_map<framework::ir::Node*, framework::ir::Node*> cast_map;
   auto op_nodes = framework::ir::TopologySortOperations(*graph);
   for (auto* op_node : op_nodes) {
     if (!op_node->IsOp()) continue;
     auto op_type = op_node->Op()->Type();
-    auto phi_op_type = phi::TransToPhiKernelName(op_type);
+    VLOG(3) << "-------------------- op_type " << op_type << ", phi_type "
+            << phi::TransToPhiKernelName(op_type);
     // 1. set input dtype.
     if (op_type == "feed") {
-      block_desc = op_node->Op()->Block();
       auto feed_var = op_node->outputs[0]->Var();
       if (!keep_io_types &&
           feed_var->GetDataType() == framework::proto::VarType::FP32) {
@@ -319,71 +485,73 @@ void ConvertTensorDtype(framework::ProgramDesc* program_desc,
       continue;
     }
 
+    else if (op_node->Op()->HasAttr("sub_block")) {  // NOLINT
+      // sub_block op's output dtype should be same as input dtype, if have the
+      // same name.
+      std::unordered_map<std::string, framework::ir::Node*> in_name_to_node;
+      for (auto* in : op_node->inputs) {
+        if (NodeVarHasDtype(in)) {
+          in_name_to_node[in->Name()] = in;
+        }
+      }
+
+      for (auto out : op_node->outputs) {
+        if (NodeVarHasDtype(out)) {
+          if (in_name_to_node.count(out->Name()))
+            out->Var()->SetDataType(
+                in_name_to_node[out->Name()]->Var()->GetDataType());
+        }
+      }
+
+      continue;
+    }
+
     // 2. if op support fp16/bf16 and not in blacklist.
     //      - cast weight to fp16/bf16.
     //      - add cast op if the input dtype is not fp16/bf16.
     //      - set output dtype.
-    else if (blacklist.count(phi_op_type) == 0 &&  // NOLINT
-             !NextOpIncludesConditionFlowOp(op_node)) {
+    else if (blacklist.count(op_type) == 0) {  // NOLINT
       bool support_precision =
-          OpSupportPrecision(phi_op_type, backend, tensor_dtype, blacklist);
-      VLOG(2) << "op_type " << op_type << ", phi_op_type " << phi_op_type
-              << " support low precision " << support_precision << ", "
+          OpSupportPrecision(op_type, backend, tensor_dtype, blacklist);
+      VLOG(2) << "op_type " << op_type << ", phi_op_type "
+              << phi::TransToPhiKernelName(op_type) << " support low precision "
+              << support_precision << ", "
               << reinterpret_cast<void*>(op_node->Op()->Block());
 
-      for (auto in_node : op_node->inputs) {
-        if (weight_name_in_multi_block.count(in_node->Name()))
-          support_precision = false;
-      }
-
       if (support_precision) {
         HandleSpecialOps(op_node->Op());
         ++num_low_precision;
         auto inputs = op_node->inputs;
+        // Process inputs.
         for (auto* in_node : inputs) {
-          if (in_node->IsCtrlVar()) continue;
-          auto* in_var = in_node->Var();
-          if (in_var->Persistable() &&
-              in_var->GetDataType() == framework::proto::VarType::FP32) {
-            if (WeightsShouldNotConvert(in_node)) continue;
-            in_var->SetDataType(to_type);
-          } else if (!in_var->Persistable() &&
-                     IsFloatVarType(in_var->GetDataType()) &&
-                     in_var->GetDataType() != to_type) {
-            AddCastOp(graph,
+          ProcessInputNode(true,
+                           graph,
                            in_node,
                            op_node,
-                      in_var->GetDataType(),
-                      to_type,
                            &suffix,
                            block_desc,
-                      &cast_map);
-          }
+                           &cast_map,
+                           to_type,
+                           is_main_block,
+                           vars_in_multi_block_map);
         }
+        // Process outputs.
         for (auto* out_node : op_node->outputs) {
-          if (out_node->IsCtrlVar()) continue;
-          auto* out_var = out_node->Var();
-          if (out_var->GetDataType() == framework::proto::VarType::FP32) {
-            if (OutShouldNotConvert(out_node)) continue;
-            out_var->SetDataType(to_type);
-          }
+          ProcessOutputNode(out_node, to_type);
         }
       } else {
         auto inputs = op_node->inputs;
         for (auto* in_node : inputs) {
-          if (in_node->IsCtrlVar()) continue;
-          auto* in_var = in_node->Var();
-          if (!in_var->Persistable() && IsFloatVarType(in_var->GetDataType()) &&
-              in_var->GetDataType() != framework::proto::VarType::FP32) {
-            AddCastOp(graph,
+          ProcessInputNode(false,
+                           graph,
                            in_node,
                            op_node,
-                      in_var->GetDataType(),
-                      framework::proto::VarType::FP32,
                            &suffix,
                            block_desc,
-                      &cast_map);
-          }
+                           &cast_map,
+                           framework::proto::VarType::FP32,
+                           is_main_block,
+                           vars_in_multi_block_map);
         }
       }
     }
@@ -409,8 +577,8 @@ void ConvertTensorDtype(framework::ProgramDesc* program_desc,
     }
   }
 
-  // 4. if output_op's dtype is not compatible to output dtype, then just insert
-  // cast.
+  // 4. if output_op's dtype is not compatible to output dtype, then just
+  // insert cast.
   for (auto* node : output_nodes) {
     if (node->IsCtrlVar()) continue;
     auto var = node->Var();
@@ -438,22 +606,31 @@ void ConvertTensorDtype(framework::ProgramDesc* program_desc,
     }
   }
 
+  if (is_main_block) {
+    for (auto node : graph->Nodes()) {
+      if (vars_in_multi_block_map->count(node->Name())) {
+        vars_in_multi_block_map->at(node->Name()) = node->Var()->GetDataType();
+      }
+    }
+  }
+
   if (num_low_precision)
     LOG(INFO) << "---  detected " << num_low_precision << " low precision ops";
 }
 }  // namespace
 
-bool OpSupportPrecision(const std::string& phi_op_type,
+bool OpSupportPrecision(const std::string& op_type,
                         phi::Backend backend,
                         phi::DataType precision,
                         const std::unordered_set<std::string>& blacklist) {
+  auto phi_op_type = phi::TransToPhiKernelName(op_type);
   bool support_precision = false;
-  if (blacklist.count(phi_op_type) == 0) {
+  if (blacklist.count(op_type) == 0) {
     if (backend == phi::Backend::GPU)
-      support_precision = GpuKernelSupportPrecision(phi_op_type, precision);
+      support_precision = GpuKernelSupportPrecision(op_type, precision);
     else
       support_precision =
-          IsKernelSupportPrecision(phi_op_type, backend, precision);
+          PhiKernelSupportPrecision(phi_op_type, backend, precision);
   }
   return support_precision;
 }
@@ -521,102 +698,41 @@ void ConvertToMixedPrecision(const std::string& model_file,
   framework::Scope scope;
   auto program_desc =
       inference::Load(&executor, &scope, model_file, params_file);
-  auto graph = std::unique_ptr<framework::ir::Graph>(
+  auto main_graph = std::unique_ptr<framework::ir::Graph>(
       new framework::ir::Graph(*program_desc));
 
-  ConvertAllFp64ToFp32(graph.get());
+  std::unordered_map<std::string, framework::proto::VarType::Type>
+      vars_in_multi_block_map;
+  FindVarsInMultiBlock(program_desc.get(), &vars_in_multi_block_map);
+
+  for (size_t i = 0; i < main_graph->SubGraphsSize(); ++i) {
+    auto graph = main_graph->GetSubGraph(i);
+    VLOG(2) << " --------  handle subgraph " << i << ", has "
+            << graph->Nodes().size() << " nodes";
+
+    program_desc->Block(i).LocalVarNames();
+
+    ConvertAllFp64ToFp32(graph);
     ConvertTensorDtype(program_desc.get(),
-                     graph.get(),
+                       graph,
                        black_list,
                        keep_io_types,
                        backend,
-                     mixed_precision);
-  FixCastAttr(graph.get());
-
-  framework::ProgramDesc mixed_program_desc;
-  framework::ir::GraphToProgram(*graph, &mixed_program_desc);
-
-  auto parameters = scope.LocalVarNames();
-  std::sort(parameters.begin(), parameters.end());
-
-  auto serialize_params =
-      [](framework::Scope* scope,
-         const std::vector<std::string>& params) -> std::string {
-    std::ostringstream os;
-    phi::CPUContext ctx;
-    for (const auto& param : params) {
-      VLOG(3) << "Serialize param: " << param;
-      PADDLE_ENFORCE_NOT_NULL(
-          scope->FindVar(param),
-          platform::errors::NotFound(
-              "Block should already have a '%s' variable", param));
-      auto* tensor = scope->FindVar(param)->GetMutable<framework::LoDTensor>();
-      framework::SerializeToStream(os, *tensor, ctx);
+                       mixed_precision,
+                       i == 0,
+                       &vars_in_multi_block_map);
+    FixCastAttr(graph);
   }
-    return os.str();
-  };
-
-  std::unordered_set<std::string> weights_should_be_fp32;
-  for (auto* node : graph->Nodes()) {
-    if (!(node->IsVar() && !node->IsCtrlVar())) continue;
-    if (node->Var()->GetType() ==
-            paddle::framework::proto::VarType::SELECTED_ROWS ||
-        node->Var()->GetType() ==
-            paddle::framework::proto::VarType::LOD_TENSOR ||
-        node->Var()->GetType() ==
-            paddle::framework::proto::VarType::LOD_TENSOR_ARRAY ||
-        node->Var()->GetType() == paddle::framework::proto::VarType::STRINGS ||
-        node->Var()->GetType() == paddle::framework::proto::VarType::VOCAB) {
-      if (node->Var()->Persistable() &&
-          node->Var()->GetDataType() ==
-              paddle::framework::proto::VarType::FP32) {
-        VLOG(2) << "weights keep to fp32: " << node->Name();
-        weights_should_be_fp32.insert(node->Name());
-      }
-    }
-  }
-
-  for (const auto& param_name : parameters) {
-    auto* var = scope.FindLocalVar(param_name);
-    if (var->IsType<framework::LoDTensor>() ||
-        var->IsType<framework::Tensor>()) {
-      auto* t = var->GetMutable<framework::LoDTensor>();
-      framework::Tensor mixed_tensor;
-      mixed_tensor.Resize(t->dims());
-      auto* data = t->mutable_data<float>(platform::CPUPlace());
 
-      if (mixed_precision == phi::DataType::FLOAT16 &&
-          !weights_should_be_fp32.count(param_name)) {
-        mixed_tensor.set_type(paddle::experimental::DataType::FLOAT16);
-        auto* mixed_data =
-            mixed_tensor.mutable_data<float16>(platform::CPUPlace());
-        for (int i = 0; i < t->numel(); i++) {
-          mixed_data[i] = static_cast<float16>(data[i]);
-        }
-        t->clear();
-        paddle::framework::TensorCopySync(mixed_tensor, place, t);
-      } else if (mixed_precision == phi::DataType::BFLOAT16 &&
-                 !weights_should_be_fp32.count(param_name)) {
-        mixed_tensor.set_type(paddle::experimental::DataType::BFLOAT16);
-        auto* mixed_data =
-            mixed_tensor.mutable_data<bfloat16>(platform::CPUPlace());
-        for (int i = 0; i < t->numel(); i++) {
-          mixed_data[i] = static_cast<bfloat16>(data[i]);
-        }
-        t->clear();
-        paddle::framework::TensorCopySync(mixed_tensor, place, t);
-      }
-    }
-  }
+  framework::ProgramDesc mixed_program_desc;
+  framework::ir::GraphToProgram(*main_graph, &mixed_program_desc);
 
-  auto StrToBinary = [](const std::string& path, const std::string& str) {
-    std::ofstream file(path.c_str(), std::ios::binary);
-    file.write(str.c_str(), str.size());
-    file.close();
-  };
-  StrToBinary(mixed_model_file,
-              mixed_program_desc.Proto()->SerializeAsString());
-  StrToBinary(mixed_params_file, serialize_params(&scope, parameters));
+  SaveMixedModel(main_graph.get(),
+                 &scope,
+                 &mixed_program_desc,
+                 mixed_model_file,
+                 mixed_params_file,
+                 mixed_precision);
 }
 
 }  // namespace analysis

paddle/fluid/inference/api/analysis_config.cc

@@ -410,6 +410,10 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
       pass_builder_->DeletePass(ps);
     }
   }
+
+  for (auto &delete_pass : other.pass_builder()->GetAllDeletedPasses()) {
+    pass_builder_->DeletePass(delete_pass);
+  }
 }
 
 void AnalysisConfig::EnableCUDNN() {