Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into move_temporal_shift_to_phi

.gitignore

@@ -52,12 +52,12 @@ tools/__pycache__
 
 # This file is automatically generated.
 # TODO(zhiqiang) Move this file to build directory.
-paddle/infrt/dialect/pd_ops.td
+paddle/infrt/dialect/pd/ir/pd_ops.td
 paddle/infrt/dialect/phi/ir/phi_cpu_kernels.td
 paddle/infrt/dialect/phi/ir/phi_gpu_kernels.td
 tools/infrt/kernels.json
 tools/infrt/kernel_signature.json
-paddle/infrt/dialect/pd_ops_info.h
+paddle/infrt/dialect/pd/common/pd_ops_info.h
 .lit_test_times.txt
 paddle/infrt/tests/dialect/Output
 paddle/infrt/tests/lit.cfg.py

paddle/fluid/eager/accumulation/accumulation_node.cc

@@ -24,7 +24,7 @@
 #include "paddle/fluid/platform/errors.h"
 
 #include "glog/logging.h"
-
+DECLARE_bool(retain_grad_for_all_tensor);
 namespace egr {
 
 static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
@@ -39,8 +39,8 @@ static void CopyOrAddTensor(paddle::experimental::Tensor* tensor,
 }
 
 std::vector<std::vector<paddle::experimental::Tensor>> GradNodeAccumulation::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   VLOG(3) << "Running Eager Backward Node: GradNodeAccumulation";
   PADDLE_ENFORCE(grads.size() == 1,
                  paddle::platform::errors::Fatal(
@@ -62,7 +62,7 @@ operator()(
     grad_out = grads[0][0];
   }
 
-  if (!weak_grad_.expired()) {
+  if (!weak_grad_.expired() && FLAGS_retain_grad_for_all_tensor) {
     auto grad = weak_grad_.lock();
     CopyOrAddTensor(grad.get(), grad_out);
   }

paddle/fluid/eager/accumulation/accumulation_node.h

@@ -35,8 +35,15 @@ class GradNodeAccumulation : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override;
+
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
 
   std::string name() { return "GradNodeAccumulation"; }
 

paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.cc

@@ -145,8 +145,8 @@ void GradNodeScale::SetTensorWrappers_X(
 void GradNodeScale::SetAttributes_scale(float scale) { scale_ = scale; }
 
 std::vector<std::vector<paddle::experimental::Tensor>> GradNodeScale::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   // 1. Check Output Size
   PADDLE_ENFORCE(
       ((grads.size() == 1) && (grads[0].size() == 1)),

paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.h

@@ -39,8 +39,15 @@ class GradNodeScale : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override;
+
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
 
   void SetTensorWrappers_X(
       const std::vector<paddle::experimental::Tensor>& tensors);

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -2074,7 +2074,8 @@ static std::string GenerateGradNodeCCContents(
   const char* GRAD_FUNCTION_TEMPLATE =
       "std::vector<std::vector<paddle::experimental::Tensor>> "
       "GradNode%s::operator()(const "
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads) {\n%s\n}";
+      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, "
+      "bool create_graph) {\n%s\n}";
   std::string grad_function_str = paddle::string::Sprintf(
       GRAD_FUNCTION_TEMPLATE, fwd_op_type, generated_grad_function_body);
 
@@ -2109,18 +2110,28 @@ static std::string GenerateGradNodeHeaderContents(
       "\n"
       "  virtual std::vector<std::vector<paddle::experimental::Tensor>> "
       "operator()(const "
-      "std::vector<std::vector<paddle::experimental::Tensor>>& grads) "
+      "std::vector<std::vector<paddle::experimental::Tensor>>& grads, const "
+      "bool create_graph = false) "
       "override;\n"
       "\n"
+      "  void ClearTensorWrappers() override { \n"
+      "%s\n"
+      "    is_tensor_wrappers_cleared = true;\n"
+      "  }\n"
       "  std::string name() override { return \" GradNode%s \"; } \n "
       "\n"
       "  // SetX, SetY, ...\n"
       "%s\n"
       "  // SetAttrMap\n"
       "%s\n"
+      "  bool IsTensorWrappersCleared() override { \n"
+      "    return is_tensor_wrappers_cleared;\n"
+      "  }\n"
       " private:\n"
       "   // TensorWrappers\n"
       "%s\n"
+      "   bool is_tensor_wrappers_cleared = false;\n"
+      "\n"
       "   // Attribute Map\n"
       "%s\n"
       "};";
@@ -2154,6 +2165,7 @@ static std::string GenerateGradNodeHeaderContents(
 
   std::string set_tensor_wrappers_str = "";
   std::string tensor_wrapper_members_str = "";
+  std::string clear_tensor_wrappers_str = "";
   for (const auto& iter : op_base_infos) {
     const std::map<std::string, std::string>& grad_ins_fwd_slotname_map =
         iter.GetGradInsFwdSlotnameMap();
@@ -2185,6 +2197,13 @@ static std::string GenerateGradNodeHeaderContents(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, tensor_wrapper_name,
             struct_tensor_wrapper_name);
 
+        const char* CLEAR_TENSOR_WRAPPER_TEMPLATE =
+            "for (auto tw: %s)   {\n"
+            "       tw.clear();\n"
+            "     }\n";
+        clear_tensor_wrappers_str += paddle::string::Sprintf(
+            CLEAR_TENSOR_WRAPPER_TEMPLATE, struct_tensor_wrapper_name);
+
       } else {
         const char* ATTR_TENSOR_WRAPPER_ARG_TEMPLATE =
             "const paddle::experimental::Tensor& %s";
@@ -2197,10 +2216,14 @@ static std::string GenerateGradNodeHeaderContents(
             TENSOR_WRAPPER_MEMBER_TEMPLATE, struct_tensor_wrapper_name);
 
         const char* SET_TENSOR_WRAPPER_BODY_TEMPLATE =
-            "%s = egr::TensorWrapper(%s, %s /*full_reserved*/);";
+            "%s = egr::TensorWrapper(%s, %s /*full_reserved*/);\n";
         tensor_wrapper_body_str = paddle::string::Sprintf(
             SET_TENSOR_WRAPPER_BODY_TEMPLATE, struct_tensor_wrapper_name,
             tensor_wrapper_name, full_reserved_str);
+
+        const char* CLEAR_TENSOR_WRAPPER_TEMPLATE = "   %s.clear();\n";
+        clear_tensor_wrappers_str += paddle::string::Sprintf(
+            CLEAR_TENSOR_WRAPPER_TEMPLATE, struct_tensor_wrapper_name);
       }
       std::string full_reserved_signature_str = "bool full_reserved";
       const char* SET_TENSOR_WRAPPER_TEMPLATE =
@@ -2215,8 +2238,8 @@ static std::string GenerateGradNodeHeaderContents(
 
   std::string grad_node_str = paddle::string::Sprintf(
       GRAD_NODE_TEMPLATE, op_type, op_type, op_type, op_type, op_type, op_type,
-      op_type, op_type, set_tensor_wrappers_str, set_attr_map_str,
-      tensor_wrapper_members_str, attr_members_str);
+      op_type, clear_tensor_wrappers_str, op_type, set_tensor_wrappers_str,
+      set_attr_map_str, tensor_wrapper_members_str, attr_members_str);
 
   return grad_node_str;
 }

paddle/fluid/eager/auto_code_generator/final_state_generator/eager_gen.py

@@ -478,6 +478,7 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
     # SetTensorWrapper Methods & TensorWrapper Members
     set_tensor_wrapper_methods_str = ""
     tensor_wrapper_members_str = ""
+    clear_tensor_wrapper_str = ""
     for tname, (ttype, is_fwd_input, _) in backward_fwd_input_map.items():
         if tname in no_need_buffer_set:
             no_need_buffer = "true"
@@ -499,6 +500,13 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
 """
             tensor_wrapper_members_str += PLAIN_TENSOR_MEMBER_TEMPLATE.format(
                 tensor_wrapper_name)
+
+            CLEAR_TENSOR_WRAPPERS_TEMPLATE = """
+   {}.clear();
+"""
+            clear_tensor_wrapper_str += CLEAR_TENSOR_WRAPPERS_TEMPLATE.format(
+                tensor_wrapper_name)
+
         else:
             assert IsVectorTensorType(ttype)
             SET_VECTOR_TENSOR_WRAPPER_TEMPLATE = """
@@ -516,6 +524,15 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
 """
             tensor_wrapper_members_str += VECTOR_TENSOR_MEMBER_TEMPLATE.format(
                 tensor_wrapper_name)
+
+            CLEAR_TENSOR_WRAPPERS_TEMPLATE = """
+   for (auto tw: {}) {
+     tw.clear();
+   };
+"""
+            clear_tensor_wrapper_str += CLEAR_TENSOR_WRAPPERS_TEMPLATE.format(
+                tensor_wrapper_name)
+
     # End: SetTensorWrapper Methods & TensorWrapper Members
 
     # SetAttributes & Attribute Members
@@ -524,7 +541,7 @@ def GenerateNodeDeclaration(fwd_api_name, backward_fwd_input_map,
     for aname, atype, default_val, _ in backward_attrs_list:
         saved_attr_name = GetSavedName(aname)
         SET_ATTR_METHOD_TEMPLATE = """
-   void SetAttribute{}({} {}) {{     
+   void SetAttribute{}({} {}) {{
      {} = {};
    }}
 """
@@ -555,25 +572,37 @@ class {} : public egr::GradNodeBase {{
   ~{}() override = default;
 
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads) override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph = false) override;
   std::string name() override {{ return \" {} \"; }}
+  
+  void ClearTensorWrappers() override {{
+      {}
+    is_tensor_wrappers_cleared = true;
+  }}
+  
   // SetTensorWrapperX, SetTensorWrapperY, ...
   {}
   // SetAttributes
   {}
+
+  bool IsTensorWrappersCleared() override {{
+      return is_tensor_wrappers_cleared;  
+  }}
  private:
   // TensorWrappers
   {}
 
+  bool is_tensor_wrappers_cleared = false;
+
   // Attributes
   {}
 }};
 """
     node_declaration_str = NODE_DECLARATION_TEMPLATE.format(
         grad_node_name, grad_node_name, grad_node_name, grad_node_name,
-        grad_node_name, set_tensor_wrapper_methods_str,
-        set_attribute_methods_str, tensor_wrapper_members_str,
-        attribute_members_str)
+        grad_node_name, clear_tensor_wrapper_str,
+        set_tensor_wrapper_methods_str, set_attribute_methods_str,
+        tensor_wrapper_members_str, attribute_members_str)
 
     return node_declaration_str
 
@@ -637,7 +666,7 @@ def GenerateNodeDefinition(fwd_api_name, bwd_api_name, backward_fwd_input_map,
         grad_api_namespace = f"paddle::experimental"
 
     FUNCTION_TEMPLATE = """
-std::vector<std::vector<paddle::experimental::Tensor>> {}::operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {{
+std::vector<std::vector<paddle::experimental::Tensor>> {}::operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads, bool create_graph) {{
     // Call grad_api function
     auto grad_api_returns = {}::{}({});
     {}

paddle/fluid/eager/backward.cc

@@ -39,12 +39,21 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
   // Copy nodes
   std::queue<GradNodeBase*> queue = init_queue;
   std::unordered_set<GradNodeBase*> visited;
+  size_t potential_startup_ops_cnt = queue.size();
+  size_t cnt = 0;
 
   // Visit each node exactly once in any order
   while (!queue.empty()) {
     GradNodeBase* node = queue.front();
     queue.pop();
 
+    if (cnt < potential_startup_ops_cnt) {
+      if (!node_in_degree_map.count(node)) {
+        node_in_degree_map[node] = 0;
+      }
+      cnt += 1;
+    }
+
     if (visited.count(node)) {
       continue;
     }
@@ -76,23 +85,248 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
   return node_in_degree_map;
 }
 
-void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
-                 const std::vector<paddle::experimental::Tensor>& grad_tensors,
-                 bool retain_graph) {
-  paddle::platform::RecordEvent backward_record_event(
-      "backward", paddle::platform::TracerEventType::Operator, 1);
+// Remove some nodes those doesn't need to be
+// stored in potential_stop_nodes、potential_startup_nodes
+void UpdateGraphInfo(
+    std::unordered_map<GradNodeBase*, AutogradMeta*>*
+        target_nodes_inputmeta_map,
+    std::unordered_map<GradNodeBase*, std::unordered_set<GradNodeBase*>>*
+        depending_nodes,
+    std::unordered_set<GradNodeBase*>* potential_stop_nodes,
+    std::unordered_set<GradNodeBase*>* potential_startup_nodes) {
+  // Updated potential_sotp_nodes by depending_nodes,
+  // make sure the path from root to target_node is ok
+  std::unordered_set<GradNodeBase*> _startup_ops;
+  VLOG(6) << "Running in UpdateGraphInfo";
+  std::queue<GradNodeBase*> queue;
+  for (auto& target_nodes_inputmeta_pair : *target_nodes_inputmeta_map) {
+    queue.emplace(target_nodes_inputmeta_pair.first);
+  }
+
+  while (!queue.empty()) {
+    auto* target_node = queue.front();
+    queue.pop();
+    if (!(*depending_nodes)[target_node].empty()) {
+      auto precedding_nodes = (*depending_nodes)[target_node];
+      for (auto pre_nodes : precedding_nodes) {
+        queue.emplace(pre_nodes);
+        if (potential_stop_nodes->find(pre_nodes) !=
+            potential_stop_nodes->end()) {
+          potential_stop_nodes->erase(pre_nodes);
+        }
+      }
+    } else {  // startup_ops have no precedding nodes
+      VLOG(6) << "Emplace _startup_ops";
+      _startup_ops.emplace(target_node);
+    }
+  }
+  // Purify potential_startup_nodes again, remove some
+  // potential startup_nodes that unreach to input target nodes
+  if (!_startup_ops.empty()) {
+    std::unordered_set<GradNodeBase*> potential_startup_nodes_to_be_erased;
+    for (auto node : *potential_startup_nodes) {
+      if (_startup_ops.count(node) == 0) {
+        VLOG(6) << "Set up potential_startup_nodes_to_be_erased";
+        potential_startup_nodes_to_be_erased.emplace(node);
+      }
+    }
+    if (!potential_startup_nodes_to_be_erased.empty()) {
+      for (auto node : potential_startup_nodes_to_be_erased) {
+        VLOG(6) << "Erase nodes in potential_startup_nodes_to_be_erased";
+        potential_startup_nodes->erase(node);
+      }
+    }
+  }
+}
+
+// Get Graph Info Betweent input target gradnode and outputs，
+// record depending_nodes、 potential_stop_nodes、potential_startup_nodes
+void GetGraphInfoBetweenTargets(
+    const std::queue<GradNodeBase*>& init_queue,
+    std::unordered_map<GradNodeBase*, AutogradMeta*>*
+        input_target_nodes_inputmeta_map,
+    std::unordered_map</*child node*/ GradNodeBase*,
+                       /*father nodes*/ std::unordered_set<GradNodeBase*>>*
+        depending_nodes,
+    std::unordered_set<GradNodeBase*>* potential_stop_nodes,
+    std::unordered_set<GradNodeBase*>* potential_startup_nodes) {
+  if (input_target_nodes_inputmeta_map->empty()) return;
+
+  VLOG(6) << "Runing In GetGraphInfoBetweenTargets";
+
+  // Calculate in_degree for each node
+  std::unordered_map<GradNodeBase*, int> node_in_degree_map;
+
+  // Copy nodes
+  std::queue<GradNodeBase*> queue = init_queue;
+  std::unordered_set<GradNodeBase*> visited;
+
+  // Visit each node exactly once in any order
+  while (!queue.empty()) {
+    GradNodeBase* node = queue.front();
+    queue.pop();
+
+    if (visited.count(node)) {
+      continue;
+    }
+    visited.insert(node);
+
+    // Check node is target_nodes or not, if node is not target_node,
+    // all the next_node will be marked in potential_stop_nodes
+    bool is_potential_stop_nodes =
+        input_target_nodes_inputmeta_map->count(node);
+
+    // Find and append next nodes
+    const std::vector<std::vector<Edge>>& edges = node->GetEdges();
+    for (const auto& edge_list : edges) {
+      for (const Edge& edge : edge_list) {
+        GradNodeBase* next_node = edge.GetMutableGradNode().get();
+
+        // Next node could be nullptr if it is leaf tensor with no
+        // AccumulationNode attached
+        // Or it could also originated from dispensable inputs
+        if (!next_node) continue;
+
+        // if node not in input_target_nodes,
+        // all the next_nodes of current node will be inserted to
+        // potential_stop_node
+        if (is_potential_stop_nodes) {
+          potential_stop_nodes->emplace(next_node);
+        }
+
+        // Update in_degree
+        if (!node_in_degree_map.count(next_node))
+          node_in_degree_map[next_node] = 0;
+        node_in_degree_map[next_node]++;
 
+        // Record depending relationship
+        (*depending_nodes)[next_node].emplace(node);
+        queue.push(next_node);
+      }
+    }
+  }
+  // Update Graph Info, remove some stop_node in potential_stop_nodes
+  UpdateGraphInfo(input_target_nodes_inputmeta_map, depending_nodes,
+                  potential_stop_nodes, potential_startup_nodes);
+}
+
+void GetTargetNodesInfo(const std::vector<paddle::experimental::Tensor>& inputs,
+                        std::unordered_map<GradNodeBase*, AutogradMeta*>*
+                            target_nodes_inputmeta_map) {
+  VLOG(6) << "Running in GetTargetNodesInfo";
+  if (!inputs.empty()) {
+    VLOG(6) << "Inputs are not empty";
+    size_t num_inputs = inputs.size();
+    for (size_t i = 0; i < num_inputs; i++) {
+      AutogradMeta* auto_grad_meta =
+          EagerUtils::unsafe_autograd_meta(inputs[i]);
+      auto target_node = auto_grad_meta->GetMutableGradNode().get();
+
+      PADDLE_ENFORCE_NOT_NULL(target_node,
+                              paddle::platform::errors::Fatal(
+                                  "There is no grad op for input:%d or it's"
+                                  "stop_gradient=True",
+                                  i));
+      (*target_nodes_inputmeta_map)[target_node] = auto_grad_meta;
+    }
+  }
+}
+
+std::vector<paddle::experimental::Tensor> GetResults(
+    const std::vector<paddle::experimental::Tensor>& inputs,
+    std::unordered_map<GradNodeBase*, paddle::experimental::Tensor>*
+        results_map,
+    bool allow_unused, bool create_graph) {
+  VLOG(6) << "Running in GetResults";
+  if (inputs.empty()) return {};
+
+  std::vector<paddle::experimental::Tensor> results;
+  results.reserve(inputs.size());
+
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    auto& input = inputs[i];
+    AutogradMeta* auto_grad_meta = EagerUtils::unsafe_autograd_meta(input);
+    auto target_node = auto_grad_meta->GetMutableGradNode().get();
+
+    auto iter = results_map->find(target_node);
+    if (iter != results_map->end()) {
+      // set StopGradient = !create_graph
+      AutogradMeta* tensor_auto_grad_meta =
+          EagerUtils::autograd_meta(&(iter->second));
+      tensor_auto_grad_meta->SetStopGradient(!create_graph);
+      results.emplace_back(iter->second);
+    } else {
+      PADDLE_ENFORCE_EQ(allow_unused, true,
+                        paddle::platform::errors::InvalidArgument(
+                            "The %d-th input does not appear in the backward "
+                            "graph. Please check the input variable or set "
+                            "allow_unused=True to get None result.",
+                            i));
+      results.emplace_back();
+    }
+  }
+  return results;
+}
+
+// Enforce GradNode has TensorWrappers as Input
+void EnforceGradNodeHasInput(GradNodeBase* node) {
+  VLOG(6) << "Running in EnforceGradNodeHasInput";
+  PADDLE_ENFORCE_NE(
+      node->IsTensorWrappersCleared(), true,
+      paddle::platform::errors::Fatal(
+          "The TensorWrappers of %s do not exist. This may be because:\n"
+          "You calculate backward twice for the same subgraph without "
+          "setting retain_graph=True. Please set retain_graph=True in the "
+          "first backward/grad call.\n",
+          node->name()));
+}
+
+// Purify potential_startup_nodes, remove nodes those are the same as
+// input_target_nodes
+void PurifyPotentialStartUpNodes(
+    std::unordered_set<GradNodeBase*>* potential_startup_nodes,
+    std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>*
+        input_target_nodes_inputmeta_map) {
+  VLOG(6) << "Running in PurifyPotentialStartUpNodes";
+  if (input_target_nodes_inputmeta_map->empty()) return;
+  std::unordered_set<GradNodeBase*> potential_startup_nodes_to_be_erased;
+  for (auto startup_op : *potential_startup_nodes) {
+    auto iter = input_target_nodes_inputmeta_map->find(startup_op);
+    if (iter != input_target_nodes_inputmeta_map->end()) {
+      potential_startup_nodes_to_be_erased.emplace(iter->first);
+    }
+  }
+  if (!potential_startup_nodes_to_be_erased.empty()) {
+    for (auto nodes : potential_startup_nodes_to_be_erased) {
+      potential_startup_nodes->erase(nodes);
+    }
+  }
+}
+
+std::vector<paddle::experimental::Tensor> RunBackward(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& grad_tensors,
+    bool retain_graph, bool create_graph = false,
+    const std::vector<paddle::experimental::Tensor>& inputs = {},
+    bool allow_unused = false,
+    const std::vector<paddle::experimental::Tensor>& no_grad_vars = {}) {
   VLOG(6) << "Start Backward";
   // *Gradient Hook should happen at node-level
   // *Inplace version check should perform at node-level
   // *Cross-batch accumulation happens at forward pass
 
+  std::unordered_map<GradNodeBase*, AutogradMeta*>
+      no_grad_var_nodes_inputmeta_map;
+  // Get no_grad_vars's GradNodes and InputMeta Info
+  GetTargetNodesInfo(no_grad_vars, &no_grad_var_nodes_inputmeta_map);
+
   /* --- Initialization --- */
   // 1. Init queue with starting nodes
   // 2. Prepare initial input buffers
   std::queue<GradNodeBase*> queue;
   std::unordered_map<GradNodeBase*, std::unique_ptr<GradTensorHolder>>
       node_input_buffers_dict;
+  std::unordered_set<GradNodeBase*> potential_startup_nodes;
   for (size_t i = 0; i < tensors.size(); i++) {
     const paddle::experimental::Tensor& tensor = tensors[i];
 
@@ -132,8 +366,17 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
               "size = 0 or same size as tensors"));
       // Feed given tensor if it's provided
       VLOG(6) << "Fill grad input tensor " << i << "with give grad tensor";
-      node_input_buffers_dict[grad_node]->add(
-          input_info.first, input_info.second, grad_tensors[i]);
+
+      if (grad_tensors[i].is_initialized()) {
+        // Deep copy
+        paddle::experimental::Tensor tmp_tensor;
+        tmp_tensor.copy_(grad_tensors[i], true);
+        node_input_buffers_dict[grad_node]->add(input_info.first,
+                                                input_info.second, tmp_tensor);
+      } else {
+        node_input_buffers_dict[grad_node]->add(
+            input_info.first, input_info.second, grad_tensors[i]);
+      }
 
     } else {
       VLOG(6) << "Fill grad input tensor " << i << " with 1.0";
@@ -146,8 +389,9 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
           input_info.first, input_info.second, tensor, true /*fill_one=true*/);
     }
 
-    // Prepare queue
+    // Prepare queue, potential startup_nodes
     queue.push(grad_node);
+    potential_startup_nodes.emplace(grad_node);
   }
 
   VLOG(6) << "Update In degree Map for backward";
@@ -155,25 +399,74 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
   std::unordered_map<GradNodeBase*, int> node_in_degree_map =
       getInDegreeMap(queue);
 
+  // Get input's GradNodes and InputMeta Info
+  std::unordered_map<GradNodeBase*, AutogradMeta* /* InputMeta */>
+      input_target_nodes_inputmeta_map;
+  GetTargetNodesInfo(inputs, &input_target_nodes_inputmeta_map);
+
+  // Purify potential_startup_ops, remove those nodes that are the same as
+  // input_target_nodes
+  PurifyPotentialStartUpNodes(&potential_startup_nodes,
+                              &input_target_nodes_inputmeta_map);
+
+  // Get Graph Info Betweent input target gradnode and outputs
+  // Record the depending_nodes and potential_stop_nodes
+  std::unordered_map<GradNodeBase* /* child node */,
+                     std::unordered_set<GradNodeBase*> /* father node */>
+      depending_nodes;
+  std::unordered_set<GradNodeBase*> potential_stop_nodes;
+  // std::unordered_set<GradNodeBase*> startup_ops;
+
+  GetGraphInfoBetweenTargets(queue, &input_target_nodes_inputmeta_map,
+                             &depending_nodes, &potential_stop_nodes,
+                             &potential_startup_nodes);
+
+  // ready_queue store all startup nodes
+  std::queue<GradNodeBase*> ready_queue;
+  // startup op's indegree should be 0
+  for (auto node : potential_startup_nodes) {
+    if (node_in_degree_map[node] == 0) {
+      ready_queue.emplace(node);
+    }
+  }
+
+  VLOG(1) << " startup_ops' size is :" << ready_queue.size();
+
+  std::unordered_map<GradNodeBase*, paddle::experimental::Tensor> results_map;
+
+  // read_queue is empty only when 1.input equals to output. 2.input can not
+  // reach to output.
+  if (ready_queue.size() == 0) {
+    for (auto input_target_node : input_target_nodes_inputmeta_map) {
+      // out rank_info of forward op
+      auto rank_info = input_target_node.second->OutRankInfo();
+      if (node_input_buffers_dict[input_target_node.first]) {
+        auto& target_result =
+            node_input_buffers_dict[input_target_node.first]
+                ->Buffers()[rank_info.first][rank_info.second];
+        // save the target result
+        results_map[input_target_node.first] = target_result;
+      }
+    }
+  }
+
   /* --- Topological Visit --- */
   // 1. Pop queue
   // 2. Run node
+  //    |- Check and capture target result
   //    |- node(grads)
   //    |- Prepare for next node
   // 3. Update queue
   VLOG(6) << "Run Backward";
-  while (!queue.empty()) {
-    GradNodeBase* node = queue.front();
+  while (!ready_queue.empty()) {
+    GradNodeBase* node = ready_queue.front();
+    VLOG(6) << "Running GradNode:" << node->name();
+    ready_queue.pop();
 
     paddle::platform::RecordEvent node_record_event(
         std::string(typeid(*node).name()) + " grad_node",
         paddle::platform::TracerEventType::Operator, 1);
 
-    if (queue.size() > 1 && node_in_degree_map[node] != 0) {
-      queue.pop();
-      continue;
-    }
-    queue.pop();
     // Run node: This is where Hook happens
     PADDLE_ENFORCE(
         node_input_buffers_dict.count(node),
@@ -184,10 +477,45 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
     std::unique_ptr<GradTensorHolder> node_input_buffer =
         std::move(node_input_buffers_dict[node]);
 
+    // get target grad_var from node_input_buffer by inputmeta
+    if (input_target_nodes_inputmeta_map.find(node) !=
+        input_target_nodes_inputmeta_map.end()) {
+      VLOG(6) << "Get target result by by inputmeta";
+      // out rank_info of forward op
+      auto rank_info = input_target_nodes_inputmeta_map[node]->OutRankInfo();
+      // rank_info is a pair, first means slot_id, second means rank.
+      auto& target_result =
+          node_input_buffer->Buffers()[rank_info.first][rank_info.second];
+      // save the target result
+      results_map[node] = target_result;
+    }
+
+    // no_grad_vars
+    if (no_grad_var_nodes_inputmeta_map.find(node) !=
+        no_grad_var_nodes_inputmeta_map.end()) {
+      VLOG(6) << "Change the input buffer[slot][rank] by Zeros";
+      auto rank_info = no_grad_var_nodes_inputmeta_map[node]->OutRankInfo();
+      node_input_buffer->SetBufferSlotRankZeros(rank_info.first,
+                                                rank_info.second);
+    }
+
+    VLOG(6) << "Running GradNode:" << node->name();
+
+    // check input
+    EnforceGradNodeHasInput(node);
+
     VLOG(6) << "Run Backward Kernel with GradTensorHolder";
     // Run Pre Backward Node and get outputs
     std::vector<std::vector<paddle::experimental::Tensor>> grad_output_tensors =
-        (*node)(node_input_buffer->Buffers());
+        (*node)(node_input_buffer->Buffers(), create_graph);
+
+    // retain_grad or not
+    if (!retain_graph) {
+      VLOG(6)
+          << "retain_graph is false, need to clear the TensorWrapper of nodes.";
+      node->ClearTensorWrappers();
+    }
+
     // TODO(jiabin): Should we erase it or find a more efficient way.
     node_input_buffers_dict.erase(node);
 
@@ -252,18 +580,44 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
 
         // Update queue
         node_in_degree_map[next_node]--;
+
         PADDLE_ENFORCE(
             node_in_degree_map[next_node] >= 0,
             paddle::platform::errors::Fatal(
                 "Detected in-degree value smaller than zero. For Node: %s"
                 "Node's in-degree cannot be negative",
                 next_node->name()));
-        if (node_in_degree_map[next_node] == 0) {
-          queue.emplace(std::move(next_node));
+
+        bool is_potential_stop_node = potential_stop_nodes.count(next_node);
+
+        if (node_in_degree_map[next_node] == 0 && !is_potential_stop_node) {
+          ready_queue.emplace(std::move(next_node));
         }
       }
     }
   }
+
+  return GetResults(inputs, &results_map, allow_unused, create_graph);
 }
 
+void Backward(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& grad_tensors,
+    bool retain_graph) {
+  VLOG(6) << "Run in Backward";
+  paddle::platform::RecordEvent backward_record_event(
+      "backward", paddle::platform::TracerEventType::Operator, 1);
+  RunBackward(tensors, grad_tensors, retain_graph);
+}
+
+std::vector<paddle::experimental::Tensor> Grad(
+    const std::vector<paddle::experimental::Tensor>& tensors,  // output
+    const std::vector<paddle::experimental::Tensor>& inputs,
+    const std::vector<paddle::experimental::Tensor>& grad_tensors,
+    bool retain_graph, bool create_graph, bool only_inputs, bool allow_unused,
+    const std::vector<paddle::experimental::Tensor>& no_grad_vars) {
+  VLOG(6) << "Run in Grad";
+  return RunBackward(tensors, grad_tensors, retain_graph, create_graph, inputs,
+                     allow_unused, no_grad_vars);
+}
 }  // namespace egr

paddle/fluid/eager/backward.h

@@ -19,12 +19,20 @@
 
 namespace egr {
 
-// run_backward():
+// Backward():
 // tensors corresponds to those lived in the backward graph
 // each grad_tensors[i] keeps the value for its corresponding tensors[i]
-void RunBackward(const std::vector<paddle::experimental::Tensor> &tensors,
-                 const std::vector<paddle::experimental::Tensor> &grad_tensors,
-                 bool retain_graph = false);
+void Backward(const std::vector<paddle::experimental::Tensor>& tensors,
+              const std::vector<paddle::experimental::Tensor>& grad_tensors,
+              bool retain_graph = false);
+
+std::vector<paddle::experimental::Tensor> Grad(
+    const std::vector<paddle::experimental::Tensor>& tensors,
+    const std::vector<paddle::experimental::Tensor>& inputs,
+    const std::vector<paddle::experimental::Tensor>& grad_tensors = {},
+    bool retain_graph = false, bool create_graph = false,
+    bool only_inputs = false, bool allow_unused = false,
+    const std::vector<paddle::experimental::Tensor>& no_grad_vars = {});
 
 // Reserved for gradient()
 

paddle/fluid/eager/custom_operator/custom_operator_node.cc

@@ -20,8 +20,8 @@
 
 namespace egr {
 std::vector<std::vector<paddle::experimental::Tensor>> RunCustomOpNode::
-operator()(
-    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+operator()(const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+           bool create_graph) {
   paddle::CustomOpKernelContext ctx;
   auto grad_inputs_name = paddle::framework::OpMetaInfoHelper::GetInputs(
       egr::Controller::Instance().GetOpMetaInfoMap().at(op_type_)[1]);

paddle/fluid/eager/custom_operator/custom_operator_node.h

@@ -37,8 +37,8 @@ class RunCustomOpNode : public GradNodeBase {
 
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph) override;
 
   std::string name() {
     return paddle::string::Sprintf("RunCustomOpNode: %s_grad", op_type_);
@@ -62,6 +62,12 @@ class RunCustomOpNode : public GradNodeBase {
     return res;
   }
 
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
+
   void SetAttrs(const std::vector<paddle::any>& attr) { attrs_ = attr; }
 
  public:

paddle/fluid/eager/grad_node_info.h

@@ -95,8 +95,12 @@ class GradNodeBase {
    * is better choice to fit this format.
    * **/
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads) = 0;
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) = 0;
 
+  virtual void ClearTensorWrappers() = 0;
+
+  virtual bool IsTensorWrappersCleared() = 0;
   /**
    * AddEdges is designed to set input tensors' backward Node as current
    * node's Edges.

paddle/fluid/eager/grad_tensor_holder.cc

@@ -21,6 +21,11 @@
 
 namespace egr {
 
+void GradTensorHolder::SetBufferSlotRankZeros(size_t slot_id, size_t rank) {
+  buffer_[slot_id][rank] =
+      paddle::experimental::zeros_like(buffer_[slot_id][rank]);
+}
+
 void GradTensorHolder::add(size_t slot_id, size_t rank,
                            const paddle::experimental::Tensor& t,
                            bool fill_one) {

paddle/fluid/eager/grad_tensor_holder.h

@@ -56,6 +56,8 @@ class GradTensorHolder {
     return buffer_;
   }
 
+  void SetBufferSlotRankZeros(size_t slot_id, size_t rank);
+
  private:
   std::vector<std::vector<paddle::experimental::Tensor>> buffer_;
 };

paddle/fluid/eager/tensor_wrapper.h

@@ -98,6 +98,8 @@ class TensorWrapper {
     }
   }
 
+  void clear() { intermidiate_tensor_.reset(); }
+
  private:
   bool full_reserved_ = false;
   std::pair<size_t, size_t> out_rank_info_;

paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc

@@ -17,6 +17,14 @@
 
 #include "paddle/fluid/eager/eager_tensor.h"
 #include "paddle/phi/api/lib/utils/allocator.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy_sr, CPU, ALL_LAYOUT);
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_DECLARE_KERNEL(copy, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy_sr, GPU, ALL_LAYOUT);
+#endif
 
 namespace eager_test {
 using AbstractAutogradMeta = paddle::experimental::AbstractAutogradMeta;
@@ -151,5 +159,50 @@ TEST(EagerVariable, Constructor) {
   CHECK_EQ(dt3_tmp_ptr[1], 10.0f);
   t4.reset();
   CHECK(t4.defined() == false);
+
+  VLOG(6) << "Check Tensor Copy_";
+  std::vector<int64_t> rows = {1, 2};
+  std::vector<int64_t> dims = {2};
+  paddle::experimental::Tensor t7(std::make_shared<phi::SelectedRows>(rows, 2));
+  std::dynamic_pointer_cast<phi::SelectedRows>(t7.impl())
+      ->mutable_value()
+      ->Resize(phi::make_ddim(dims));
+  auto* dt7_tmp_ptr = std::dynamic_pointer_cast<phi::SelectedRows>(t7.impl())
+                          ->mutable_value()
+                          ->mutable_data<float>(paddle::platform::CPUPlace());
+  dt7_tmp_ptr[0] = 6.0f;
+  dt7_tmp_ptr[1] = 11.0f;
+
+  paddle::experimental::Tensor t8;
+  paddle::experimental::Tensor t5;
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  paddle::experimental::Tensor t6;
+  paddle::experimental::Tensor t9;
+  VLOG(6) << "Check Tensor Copy_ Selected Rows";
+  t8.copy_(t7, paddle::platform::CUDAPlace(0), true);
+  t9.copy_(t8, paddle::platform::CPUPlace(), true);
+  auto* dt9_tmp_ptr = std::dynamic_pointer_cast<phi::SelectedRows>(t9.impl())
+                          ->value()
+                          .data<float>();
+  CHECK_EQ(dt9_tmp_ptr[0], 6.0f);
+  CHECK_EQ(dt9_tmp_ptr[1], 11.0f);
+  CHECK_EQ(std::dynamic_pointer_cast<phi::SelectedRows>(t9.impl())->height(),
+           2);
+
+  VLOG(6) << "Check Tensor Copy_ Dense Tensor";
+  t5.copy_(t3, paddle::platform::CUDAPlace(0), true);
+  t6.copy_(t5, paddle::platform::CPUPlace(), true);
+  auto* dt6_tmp_ptr =
+      std::dynamic_pointer_cast<phi::DenseTensor>(t6.impl())->data<float>();
+  CHECK_EQ(dt6_tmp_ptr[0], 5.0f);
+  CHECK_EQ(dt6_tmp_ptr[1], 10.0f);
+#else
+  t5.copy_(t3, paddle::platform::CPUPlace(), true);
+  auto* dt5_tmp_ptr =
+      std::dynamic_pointer_cast<phi::DenseTensor>(t5.impl())->data<float>();
+  CHECK_EQ(dt5_tmp_ptr[0], 5.0f);
+  CHECK_EQ(dt5_tmp_ptr[1], 10.0f);
+#endif
+
   VLOG(6) << "Finish";
 }

paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h

@@ -32,8 +32,8 @@ class GradTestNode : public egr::GradNodeBase {
   GradTestNode() : GradNodeBase() { val_ = 1.0; }
   std::string name() override { return "GradTestNode"; }
   std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
-      override {
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads,
+      bool create_graph = false) override {
     val_ = std::dynamic_pointer_cast<phi::DenseTensor>(grads[0][0].impl())
                ->data<float>()[0];
     phi::DenseTensorMeta meta =
@@ -49,6 +49,11 @@ class GradTestNode : public egr::GradNodeBase {
     std::vector<std::vector<paddle::experimental::Tensor>> res = {{et1}};
     return res;
   }
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
   float val_;
 };
 }  // namespace eager_test

paddle/fluid/eager/tests/performance_tests/benchmark_utils.cc

@@ -58,7 +58,7 @@ void benchmark_eager_scale(const paddle::experimental::Tensor& tensor,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 10)
@@ -80,7 +80,7 @@ void benchmark_eager_matmul(const paddle::experimental::Tensor& X,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor0};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 2)
@@ -106,7 +106,7 @@ void benchmark_eager_intermediate_matmul(const paddle::experimental::Tensor& X,
   }
 
   std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor0};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     // Examine Forward Grad (w.r.t max_num_runs = 2)
@@ -137,7 +137,7 @@ void benchmark_eager_intermediate_mlp(
       reduce_sum_dygraph_function(input0, {{"reduce_all", true}});
 
   std::vector<paddle::experimental::Tensor> target_tensors = {Out};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   if (accuracy_check) {
     std::unordered_map<std::string, float> result =

paddle/fluid/eager/tests/task_tests/CMakeLists.txt

@@ -5,6 +5,7 @@ cc_test(test_egr_task_backward SRCS backward_test.cc DEPS ${eager_deps} ${fluid_
 cc_test(test_egr_task_hook SRCS hook_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 cc_test(test_egr_task_cross_batch SRCS cross_batch_accumulation_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 cc_test(test_egr_task_fwd_bwd_joint SRCS fwd_bwd_joint_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
+cc_test(test_egr_task_grad SRCS grad_test.cc DEPS ${eager_deps} ${fluid_deps} eager_scale scale_node)
 
 if(NOT ((NOT WITH_PYTHON) AND ON_INFER))
     cc_test(test_egr_task_hook_intermidiate SRCS hook_test_intermidiate.cc DEPS ${eager_deps} ${fluid_deps} ${generated_deps} dygraph_node)

paddle/fluid/eager/tests/task_tests/backward_test.cc

@@ -33,6 +33,7 @@
 #include "paddle/phi/core/kernel_registry.h"
 
 PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
 
 namespace egr {
 
@@ -79,7 +80,7 @@ TEST(Backward, SingleNodeEmptyGrad) {
   }
   std::vector<paddle::experimental::Tensor> outs = {target_tensor};
   // Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 5.0);
@@ -138,7 +139,7 @@ TEST(Backward, SingleNodeCustomGrad) {
   }
 
   // Run Backward
-  RunBackward(target_tensors, grad_tensors);
+  Backward(target_tensors, grad_tensors);
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 50.0);
@@ -211,7 +212,7 @@ TEST(Backward, LinearNodes) {
   }
 
   // Use Empty Grad Tensor
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   // Check Output Value
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 50.0);
@@ -315,7 +316,7 @@ TEST(Backward, WithAccumulation) {
     node2_ptr->AddEdges(&res2, 0);
   }
 
-  RunBackward(target_tensors, grad_tensors);
+  Backward(target_tensors, grad_tensors);
 
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 2500.0);
 }

paddle/fluid/eager/tests/task_tests/cross_batch_accumulation_test.cc

@@ -71,12 +71,12 @@ TEST(CrossBatchAccumulation, SingleScaleNode) {
   std::vector<egr::AutogradMeta*> res = {meta};
   scale_node_ptr->AddEdges(&res, 0);
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 5.0);
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 10.0);

paddle/fluid/eager/tests/task_tests/fwd_bwd_joint_test.cc

@@ -86,7 +86,7 @@ TEST(FwdBwdJoint, SingleNode) {
 
   std::vector<paddle::experimental::Tensor> outs = {out};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   VLOG(7) << "Target Grad is: "
           << std::static_pointer_cast<phi::DenseTensor>(
@@ -137,7 +137,7 @@ TEST(FwdBwdJoint, LinearNodes) {
 
   std::vector<paddle::experimental::Tensor> outs = {out1};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 10.0);
@@ -203,7 +203,7 @@ TEST(FwdBwdJoint, BranchedNodes) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);
@@ -260,7 +260,7 @@ TEST(FwdBwdJoint, GradientHook) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   // leaf grad
@@ -318,13 +318,13 @@ TEST(FwdBwdJoint, CrossBatchAccumulation) {
 
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);
 
   // Cross Batch Accumulation
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 60.0);
@@ -356,7 +356,7 @@ TEST(FwdBwdJoint, SingleNodeCUDA) {
 
   std::vector<paddle::experimental::Tensor> outs = {out};
   // 4. Run Backward
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 2.0);
@@ -412,7 +412,7 @@ TEST(FwdBwdJoint, BranchedNodesCUDA) {
   // TODO(jiabin): fix this with add functor
   // 4. Run Backward
   std::vector<paddle::experimental::Tensor> outs = {out1, out2};
-  RunBackward(outs, {});
+  Backward(outs, {});
 
   // Examine Backward Grad
   eager_test::CompareGradTensorWithValue<float>(tensor, 30.0);

paddle/fluid/eager/tests/task_tests/generated_test.cc

@@ -57,7 +57,7 @@ TEST(Generated, Sigmoid) {
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
   VLOG(6) << "Runing Backward";
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   VLOG(6) << "Finish Backward";
   eager_test::CompareGradTensorWithValue<float>(tensor, 0.25);
@@ -89,7 +89,7 @@ TEST(Generated, Matmul_v2) {
   eager_test::CompareTensorWithValue<float>(output_tensor, 96);
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 2.0 * 20);
   eager_test::CompareGradTensorWithValue<float>(Y, 3.0 * 4);
@@ -120,7 +120,7 @@ TEST(Generated, ElementwiseAdd) {
   eager_test::CompareTensorWithValue<float>(output_tensor, 5);
 
   std::vector<paddle::experimental::Tensor> target_tensors = {output_tensor};
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 1.0);
   eager_test::CompareGradTensorWithValue<float>(Y, 1.0);

paddle/fluid/eager/tests/task_tests/grad_test.cc

+// Copyright (c) 2021 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 <sstream>
+
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+#include "paddle/fluid/eager/accumulation/accumulation_node.h"
+#include "paddle/fluid/eager/api/generated/eager_generated/backwards/scale_node.h"
+#include "paddle/fluid/eager/api/utils/tensor_utils.h"
+#include "paddle/fluid/eager/autograd_meta.h"
+#include "paddle/fluid/eager/backward.h"
+#include "paddle/fluid/eager/grad_node_info.h"
+#include "paddle/fluid/eager/tests/test_utils.h"
+
+#include "paddle/fluid/eager/api/all.h"
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_meta.h"
+
+PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
+namespace egr {
+
+TEST(Grad, SingleNodeEmptyGrad) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor (output)
+  paddle::experimental::Tensor output_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+
+  // Create input tensor
+  const paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+
+  {
+    // Create Scale Node
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Output_tensor set GradNode、OutRank、StopGradient propertis
+    AutogradMeta* auto_grad_meta = EagerUtils::autograd_meta(&output_tensor);
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+
+    // Get autograd_meta from input tensor
+    AutogradMeta* auto_grad_meta1 =
+        EagerUtils::unsafe_autograd_meta(leaf_tensor);
+
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    // input tensor set GradNode、OutRank、StopGradient propertis
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+
+    // grad_node Add Edges
+    std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
+    node0_ptr->AddEdges(&res, 0);
+  }
+  std::vector<paddle::experimental::Tensor> outs = {output_tensor};
+
+  // Run Grad
+  auto result = Grad(outs, {leaf_tensor}, {});
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 5.0);
+}
+
+TEST(Grad, SingleNodeCustomGrad) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  paddle::experimental::Tensor tensor = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor));
+
+  std::vector<paddle::experimental::Tensor> grad_tensors;
+  // Create Grad Tensor
+  paddle::experimental::Tensor grad_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 10.0 /*value*/, false /*is_leaf*/);
+  grad_tensors.emplace_back(std::move(grad_tensor));
+
+  paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+
+  {
+    // Create Scale Node
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Connect Tensor and Node via AutoGradMeta
+    AutogradMeta* auto_grad_meta =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+
+    AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res = {auto_grad_meta1};
+    node0_ptr->AddEdges(&res, 0);
+  }
+
+  auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);
+
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 50.0);
+}
+
+/*
+Node1
+  |
+Node0
+  |
+ { } // empty grad tensor
+*/
+TEST(Grad, LinearNodes) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Target Tensor
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  paddle::experimental::Tensor tensor = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor));
+
+  paddle::experimental::Tensor leaf_tensor =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 1.0 /*value*/, true /*is_leaf*/);
+  {
+    // Create Node0
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+
+    // Set grad in/out meta for node0
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Create Node1
+    auto node1_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node1_ptr->SetAttributes_scale(10.0 /*scale*/);
+
+    // Set grad in/out meta for node1
+    node1_ptr->SetDefaultGradInOutMeta();
+
+    // Connect Input Tensor and Node0 via AutoGradMeta
+    AutogradMeta* auto_grad_meta =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta->SetStopGradient(false);
+    // Connect Node0 -> Node1 via Edge
+    auto meta0 = egr::AutogradMeta();
+    meta0.SetStopGradient(false);
+    meta0.SetSingleOutRankWithSlot(0, 0);
+    meta0.SetGradNode(node1_ptr);
+    std::vector<egr::AutogradMeta*> res0 = {&meta0};
+    node0_ptr->AddEdges(&res0, 0);
+
+    AutogradMeta* auto_grad_meta1 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
+
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+
+    auto_grad_meta1->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res1 = {auto_grad_meta1};
+    node1_ptr->AddEdges(&res1, 0);
+  }
+
+  // Use Empty Grad Tensor
+  auto result = Grad(target_tensors, {leaf_tensor}, {});
+
+  // Check Output Value
+  eager_test::CompareTensorWithValue<float>(result[0], 50.0);
+}
+
+/*
+    Node2
+    |   |
+Node0   Node1
+  |      |
+ in0   in1
+*/
+TEST(Grad, WithAccumulation) {
+  // Prepare Device Contexts
+  eager_test::InitEnv(paddle::platform::CPUPlace());
+
+  // Prepare Inputs
+  paddle::framework::DDim ddim = phi::make_ddim({4, 16, 16, 32});
+
+  // Create Target Tensor
+  std::vector<paddle::experimental::Tensor> target_tensors;
+  paddle::experimental::Tensor tensor0 = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  paddle::experimental::Tensor tensor1 = egr_utils_api::CreateTensorWithValue(
+      ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+      phi::DataLayout::NCHW, 1.0 /*value*/, false /*is_leaf*/);
+  target_tensors.emplace_back(std::move(tensor0));
+  target_tensors.emplace_back(std::move(tensor1));
+
+  // Create Grad Tensor
+  std::vector<paddle::experimental::Tensor> grad_tensors;
+  paddle::experimental::Tensor grad_tensor0 =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 5.0 /*value*/, false /*is_leaf*/);
+  paddle::experimental::Tensor grad_tensor1 =
+      egr_utils_api::CreateTensorWithValue(
+          ddim, paddle::platform::CPUPlace(), phi::DataType::FLOAT32,
+          phi::DataLayout::NCHW, 10.0 /*value*/, false /*is_leaf*/);
+  grad_tensors.emplace_back(std::move(grad_tensor0));
+  grad_tensors.emplace_back(std::move(grad_tensor1));
+
+  paddle::experimental::Tensor leaf_tensor;
+  {
+    // Create Node0
+    auto node0_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node0_ptr->SetAttributes_scale(5.0 /*scale*/);
+    node0_ptr->SetDefaultGradInOutMeta();
+
+    // Create Node1
+    auto node1_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node1_ptr->SetAttributes_scale(10.0 /*scale*/);
+    node1_ptr->SetDefaultGradInOutMeta();
+    // Create Node2
+    auto node2_ptr = std::make_shared<GradNodeScale>(1, 1);
+    node2_ptr->SetAttributes_scale(20.0 /*scale*/);
+    node2_ptr->SetDefaultGradInOutMeta();
+    // Connect Inp0 and Node0 via AutoGradMeta
+    AutogradMeta* auto_grad_meta0 =
+        EagerUtils::autograd_meta(&(target_tensors[0]));
+    auto_grad_meta0->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
+    auto_grad_meta0->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta0->SetStopGradient(false);
+    // Connect Inp1 and Node1 via AutoGradMeta
+    AutogradMeta* auto_grad_meta1 =
+        EagerUtils::autograd_meta(&(target_tensors[1]));
+    auto_grad_meta1->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(node1_ptr));
+    auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);
+    auto_grad_meta1->SetStopGradient(false);
+
+    // Connect Node0 -> Node2 via Edge
+    auto meta0 = egr::AutogradMeta();
+    meta0.SetStopGradient(false);
+    meta0.SetSingleOutRankWithSlot(0, 0);
+    meta0.SetGradNode(node2_ptr);
+    std::vector<egr::AutogradMeta*> res0 = {&meta0};
+    node0_ptr->AddEdges(&res0, 0);
+
+    // Connect Node1 -> Node2 via Edge
+    auto meta1 = egr::AutogradMeta();
+    meta1.SetStopGradient(false);
+    meta1.SetSingleOutRankWithSlot(0, 0);
+    meta1.SetGradNode(node2_ptr);
+    std::vector<egr::AutogradMeta*> res1 = {&meta1};
+    node1_ptr->AddEdges(&res1, 0);
+
+    AutogradMeta* auto_grad_meta2 = EagerUtils::autograd_meta(&leaf_tensor);
+    // Connect Tensor and AccumulationNode via AutoGradMeta
+    auto acc_node_ptr =
+        std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta2);
+
+    auto_grad_meta2->SetGradNode(
+        std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
+    auto_grad_meta2->SetSingleOutRankWithSlot(0, 0);
+
+    auto_grad_meta2->SetStopGradient(false);
+    std::vector<egr::AutogradMeta*> res2 = {auto_grad_meta2};
+    node2_ptr->AddEdges(&res2, 0);
+  }
+
+  auto result = Grad(target_tensors, {leaf_tensor}, grad_tensors);
+
+  eager_test::CompareTensorWithValue<float>(result[0], 2500.0);
+}
+
+}  // namespace egr

paddle/fluid/eager/tests/task_tests/hook_test.cc

@@ -132,7 +132,7 @@ TEST(RetainGrad, HookBeforeRetainGrad) {
         leaf_tensor);  // result: 4.0*5.0 + 3.0 = 23.0
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 4.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 23.0);
@@ -199,7 +199,7 @@ TEST(RetainGrad, HookAfterRetainGrad) {
         leaf_tensor, std::make_shared<egr::CppTensorHook>(hook_function));
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
   eager_test::CompareGradTensorWithValue<float>(target_tensor, 1.0);
   eager_test::CompareGradTensorWithValue<float>(leaf_tensor, 23.0);
 }

paddle/fluid/eager/tests/task_tests/hook_test_intermidiate.cc

@@ -108,7 +108,7 @@ void test_sigmoid(bool is_remove_gradient_hook) {
   }
 
   VLOG(6) << "Runing Backward";
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
   VLOG(6) << "Finish Backward";
 
   eager_test::CompareGradTensorWithValue<float>(
@@ -166,7 +166,7 @@ void test_elementwiseAdd(bool is_remove_gradient_hook) {
     grad_node_tmp->RemoveGradientHook(hook_id);
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 1.0);
   eager_test::CompareGradTensorWithValue<float>(
@@ -224,7 +224,7 @@ void test_matmul(bool is_remove_gradient_hook) {
     grad_node_tmp->RemoveGradientHook(hook_id);
   }
 
-  RunBackward(target_tensors, {});
+  Backward(target_tensors, {});
 
   eager_test::CompareGradTensorWithValue<float>(X, 2.0 * 20);
   eager_test::CompareGradTensorWithValue<float>(

paddle/fluid/eager/to_static/run_program_op_node.h

@@ -370,8 +370,8 @@ class GradNodeRunProgram : public egr::GradNodeBase {
   ~GradNodeRunProgram() override = default;
   // Functor: perform backward computations
   virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
-      const std::vector<std::vector<paddle::experimental::Tensor>> &grads)
-      override {
+      const std::vector<std::vector<paddle::experimental::Tensor>> &grads,
+      bool create_graph) override {
     VLOG(3) << "Running Eager Backward Node: GradNodeRunProgram";
     PADDLE_ENFORCE_EQ(
         grads.size(), 1,
@@ -415,6 +415,12 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     // return {x_grad, details::DereferenceTensors(params_grad_ptr)};
   }
 
+  void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
+  bool IsTensorWrappersCleared() override {
+    VLOG(6) << "Do nothing here now";
+    return false;
+  }
+
   // SetAttrMap
   void SetAttrMap(const paddle::framework::AttributeMap &attrs) {
     attrs_ = attrs;

paddle/fluid/framework/ir/CMakeLists.txt

@@ -97,6 +97,7 @@ pass_library(layer_norm_fuse_pass inference)
 pass_library(add_support_int8_pass inference)
 pass_library(matmul_scale_fuse_pass inference)
 pass_library(gpu_cpu_map_matmul_to_mul_pass inference)
+pass_library(mixed_precision_configure_pass inference)
 pass_library(generate_pass DEPS pass_desc_proto)
 target_link_libraries(generate_pass pass_desc_proto)
 

paddle/fluid/framework/ir/mixed_precision_configure_pass.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/framework/ir/mixed_precision_configure_pass.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void MixedPrecisionConfigurePass::InsertCastOps(
+    Graph* graph, const StringSet& blacklist) const {
+  VLOG(3) << "Insert the cast op before and after the kernel that does not "
+             "supports fp16 precision";
+
+  auto update_cast_desc = [&](
+      framework::OpDesc& desc, const std::string& x_name,
+      const std::string& out_name, const int in_dtype, const int out_dtype) {
+    desc.SetType("cast");
+    desc.SetInput("X", {x_name});
+    desc.SetOutput("Out", {out_name});
+    desc.SetAttr("in_dtype", in_dtype);
+    desc.SetAttr("out_dtype", out_dtype);
+    desc.SetAttr("use_mkldnn", false);
+    desc.SetAttr("with_quant_attr", false);
+    desc.Flush();
+  };
+
+  auto cast_input = [&](Graph* graph, Node* op_node,
+                        const StringSet& cast_list) {
+    auto inlinks = op_node->inputs;
+    for (auto* pre_node : inlinks) {
+      if (pre_node->IsVar()) {
+        const auto is_persistable = pre_node->Var()->Persistable();
+        const auto is_float =
+            pre_node->Var()->GetDataType() == proto::VarType::FP16 ||
+            pre_node->Var()->GetDataType() == proto::VarType::FP32 ||
+            pre_node->Var()->GetDataType() == proto::VarType::FP64;
+        if (!is_persistable && is_float) {
+          int suffix = 0;
+          for (auto* pre_node_input : pre_node->inputs) {
+            if (!pre_node_input->IsOp()) continue;
+            const auto& type = pre_node_input->Op()->Type();
+            if (!cast_list.count(type) && type != "cast") {
+              std::string old_name = pre_node->Name();
+              std::string new_name =
+                  old_name + "_cast.tmp_" + std::to_string(suffix);
+              suffix++;
+
+              framework::OpDesc new_op_desc(op_node->Op()->Block());
+              // 4 for fp16, 5 for fp32
+              update_cast_desc(new_op_desc, old_name, new_name, 4, 5);
+              auto* new_op = graph->CreateOpNode(&new_op_desc);
+
+              VarDesc out_var(new_name);
+              out_var.SetPersistable(false);
+              auto* node_var = graph->CreateVarNode(&out_var);
+
+              op_node->Op()->RenameInput(old_name, new_name);
+              IR_NODE_LINK_TO(pre_node, new_op);
+              IR_NODE_LINK_TO(new_op, node_var);
+              IR_NODE_LINK_TO(node_var, op_node);
+            }
+          }
+        }
+      }
+    }
+  };
+
+  auto cast_output = [&](Graph* graph, Node* op_node,
+                         const StringSet& cast_list) {
+    auto outlinks = op_node->outputs;
+    for (auto* next_node : outlinks) {
+      if (next_node->IsVar()) {
+        const auto is_persistable = next_node->Var()->Persistable();
+        const auto is_float =
+            next_node->Var()->GetDataType() == proto::VarType::FP16 ||
+            next_node->Var()->GetDataType() == proto::VarType::FP32 ||
+            next_node->Var()->GetDataType() == proto::VarType::FP64;
+        if (!is_persistable && is_float) {
+          int suffix = 0;
+          for (auto* next_node_output : next_node->outputs) {
+            if (!next_node_output->IsOp()) continue;
+
+            const auto& type = next_node_output->Op()->Type();
+            if (!cast_list.count(type) && type != "cast") {
+              std::string old_name = next_node->Name();
+              std::string new_name =
+                  old_name + "_cast.tmp_" + std::to_string(suffix);
+              suffix++;
+
+              framework::OpDesc new_op_desc(op_node->Op()->Block());
+              // 4 for fp16, 5 for fp32
+              update_cast_desc(new_op_desc, old_name, new_name, 5, 4);
+              auto* new_op = graph->CreateOpNode(&new_op_desc);
+
+              VarDesc out_var(new_name);
+              out_var.SetPersistable(false);
+              auto* node_var = graph->CreateVarNode(&out_var);
+
+              next_node_output->Op()->RenameInput(old_name, new_name);
+              IR_NODE_LINK_TO(next_node, new_op);
+              IR_NODE_LINK_TO(new_op, node_var);
+              IR_NODE_LINK_TO(node_var, next_node_output);
+            }
+          }
+        }
+      }
+    }
+  };
+
+  for (auto* op_node :
+       ir::TopologyVarientSort(*graph, static_cast<ir::SortKind>(0))) {
+    if (!op_node->IsOp() || op_node->Op()->Type() == "feed" ||
+        op_node->Op()->Type() == "fetch")
+      continue;
+
+    const auto& type = op_node->Op()->Type();
+    if (blacklist.count(type)) {
+      cast_input(graph, op_node, blacklist);
+      cast_output(graph, op_node, blacklist);
+    }
+  }
+}
+
+void MixedPrecisionConfigurePass::ApplyImpl(Graph* graph) const {
+  const auto blacklist =
+      Get<std::unordered_set<std::string>>("gpu_fp16_disabled_op_types");
+  InsertCastOps(graph, blacklist);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(mixed_precision_configure_pass,
+              paddle::framework::ir::MixedPrecisionConfigurePass);

paddle/fluid/framework/ir/mixed_precision_configure_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 "paddle/fluid/framework/ir/fuse_pass_base.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+using StringSet = std::unordered_set<std::string>;
+
+class MixedPrecisionConfigurePass : public FusePassBase {
+ public:
+  MixedPrecisionConfigurePass() = default;
+  virtual ~MixedPrecisionConfigurePass() {}
+
+ protected:
+  void ApplyImpl(Graph* graph) const override;
+
+ private:
+  void InsertCastOps(Graph* graph, const StringSet& blacklist) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/inference/analysis/argument.h

@@ -188,6 +188,9 @@ struct Argument {
   DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
   DECL_ARGUMENT_FIELD(use_fc_padding, UseFcPadding, bool);
   DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
+  DECL_ARGUMENT_FIELD(use_gpu_fp16, UseGPUFp16, bool);
+  DECL_ARGUMENT_FIELD(gpu_fp16_disabled_op_types, GpuFp16DisabledOpTypes,
+                      std::unordered_set<std::string>);
 
   // Usually use for trt dynamic shape.
   // TRT will select the best kernel according to opt shape

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -189,6 +189,10 @@ void IRPassManager::CreatePasses(Argument *argument,
                 new int(argument->dlnne_min_subgraph_size()));
       pass->Set("program",
                 new framework::ProgramDesc *(&argument->main_program()));
+    } else if (pass_name == "mixed_precision_configure_pass") {
+      pass->Set("gpu_fp16_disabled_op_types",
+                new std::unordered_set<std::string>(
+                    argument->gpu_fp16_disabled_op_types()));
     }
     if (pass_name == "lite_subgraph_pass") {
       bool lite_enable_int8 =

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

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.h"
 #include "paddle/fluid/framework/data_layout.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -65,6 +66,26 @@ void IrParamsSyncAmongDevicesPass::CopyParamsToNpu(Argument *argument) {
 
 #else
 
+void IrParamsSyncAmongDevicesPass::GetVarNameToOpTypeMap(
+    const framework::ir::Graph &graph,
+    std::unordered_map<std::string, std::string> *var_name_op_type_map) {
+  std::vector<framework::ir::Node *> node_list =
+      framework::ir::TopologyVarientSort(
+          graph, static_cast<framework::ir::SortKind>(0));
+  for (auto *op_node : node_list) {
+    if (!op_node->IsOp() || op_node->Op()->Type() == "feed" ||
+        op_node->Op()->Type() == "fetch")
+      continue;
+
+    for (auto *pre_node : op_node->inputs) {
+      if (pre_node->IsVar() && pre_node->Var()->Persistable()) {
+        var_name_op_type_map->insert(std::pair<std::string, std::string>(
+            pre_node->Var()->Name(), op_node->Op()->Type()));
+      }
+    }
+  }
+}
+
 void IrParamsSyncAmongDevicesPass::CopyParamsToGpu(Argument *argument) {
   // The parameters are on the cpu, therefore, synchronization is not necessary.
   if (!argument->use_gpu()) return;
@@ -102,6 +123,16 @@ void IrParamsSyncAmongDevicesPass::CopyParamsToGpu(Argument *argument) {
   if (with_dynamic_shape) {
     reserve_cpu_weights = true;
   }
+
+  bool mixed_precision_mode =
+      argument->Has("use_gpu_fp16") && argument->use_gpu_fp16();
+  std::unordered_map<std::string, std::string> var_name_op_type_map{};
+  std::unordered_set<std::string> blacklist{};
+  if (mixed_precision_mode) {
+    GetVarNameToOpTypeMap(graph, &var_name_op_type_map);
+    blacklist = argument->gpu_fp16_disabled_op_types();
+  }
+
   for (auto &var_name : all_vars) {
     if (std::count(repetitive_params.begin(), repetitive_params.end(),
                    var_name)) {
@@ -117,18 +148,29 @@ void IrParamsSyncAmongDevicesPass::CopyParamsToGpu(Argument *argument) {
         var->IsType<framework::Tensor>()) {
       auto *t = var->GetMutable<framework::LoDTensor>();
 
-      platform::CPUPlace cpu_place;
-      framework::LoDTensor temp_tensor;
-      temp_tensor.Resize(t->dims());
-      temp_tensor.mutable_data<float>(cpu_place);
-
-      // Copy the parameter data to a tmp tensor.
-      paddle::framework::TensorCopySync(*t, cpu_place, &temp_tensor);
-      // Reallocation the space on GPU
-      t->clear();
-
-      // Copy parameter data to newly allocated GPU space.
-      paddle::framework::TensorCopySync(temp_tensor, place, t);
+      bool is_float = t->dtype() == paddle::experimental::DataType::FLOAT32 ||
+                      t->dtype() == paddle::experimental::DataType::FLOAT64;
+      if (mixed_precision_mode &&
+          !blacklist.count(var_name_op_type_map[var_name]) && is_float) {
+        framework::Tensor half_tensor;
+        half_tensor.set_type(paddle::experimental::DataType::FLOAT16);
+        half_tensor.Resize(t->dims());
+        auto *half_data =
+            half_tensor.mutable_data<float16>(platform::CPUPlace());
+        for (int i = 0; i < t->numel(); i++) {
+          auto *data = t->mutable_data<float>(platform::CPUPlace());
+          half_data[i] = static_cast<float16>(data[i]);
+        }
+        t->clear();
+        paddle::framework::TensorCopySync(half_tensor, place, t);
+      } else {
+        platform::CPUPlace cpu_place;
+        framework::LoDTensor temp_tensor;
+        temp_tensor.Resize(t->dims());
+        paddle::framework::TensorCopySync(*t, cpu_place, &temp_tensor);
+        t->clear();
+        paddle::framework::TensorCopySync(temp_tensor, place, t);
+      }
     }
   }
 }

paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.h

@@ -38,7 +38,12 @@ class IrParamsSyncAmongDevicesPass : public AnalysisPass {
 #ifdef PADDLE_WITH_ASCEND_CL
   void CopyParamsToNpu(Argument *argument);
 #else
-  void CopyParamsToGpu(Argument *argument);
+
+  void GetVarNameToOpTypeMap(
+      const framework::ir::Graph& graph,
+      std::unordered_map<std::string, std::string>* var_name_op_type_map);
+
+  void CopyParamsToGpu(Argument* argument);
 #endif
 };
 

paddle/fluid/inference/api/analysis_config.cc

@@ -83,6 +83,7 @@ void AnalysisConfig::SetModel(const std::string &prog_file_path,
 
   Update();
 }
+
 void AnalysisConfig::EnableUseGpu(uint64_t memory_pool_init_size_mb,
                                   int device_id) {
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
@@ -97,12 +98,26 @@ void AnalysisConfig::EnableUseGpu(uint64_t memory_pool_init_size_mb,
 
   Update();
 }
+
 void AnalysisConfig::DisableGpu() {
   use_gpu_ = false;
 
   Update();
 }
 
+void AnalysisConfig::Exp_EnableUseGpuFp16(
+    std::unordered_set<std::string> op_list) {
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  use_gpu_fp16_ = true;
+  gpu_fp16_disabled_op_types_.insert(op_list.begin(), op_list.end());
+#else
+  LOG(ERROR) << "Please compile with gpu to Exp_EnableUseGpuFp16()";
+  use_gpu_fp16_ = false;
+#endif
+
+  Update();
+}
+
 void AnalysisConfig::DisableFCPadding() {
   use_fc_padding_ = false;
 
@@ -213,6 +228,8 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
   CP_MEMBER(use_cudnn_);
   CP_MEMBER(gpu_device_id_);
   CP_MEMBER(memory_pool_init_size_mb_);
+  CP_MEMBER(use_gpu_fp16_);
+  CP_MEMBER(gpu_fp16_disabled_op_types_);
 
   CP_MEMBER(enable_memory_optim_);
   // TensorRT related.
@@ -573,6 +590,20 @@ void AnalysisConfig::Update() {
 #endif
   }
 
+  if (use_gpu_fp16_) {
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+    if (!enable_ir_optim_) {
+      LOG(ERROR) << "Exp_EnableUseGpuFp16() only works when IR optimization is "
+                    "enabled.";
+    } else if (!use_gpu()) {
+      LOG(ERROR)
+          << "Exp_EnableUseGpuFp16() only works when use_gpu is enabled.";
+    } else {
+      pass_builder()->Exp_EnableUseGpuFp16();
+    }
+#endif
+  }
+
   if (use_mkldnn_) {
 #ifdef PADDLE_WITH_MKLDNN
     if (!enable_ir_optim_) {
@@ -669,6 +700,8 @@ std::string AnalysisConfig::SerializeInfoCache() {
   ss << params_file_;
 
   ss << use_gpu_;
+  ss << use_gpu_fp16_;
+  for (auto &item : gpu_fp16_disabled_op_types_) ss << item;
   ss << use_fc_padding_;
   ss << gpu_device_id_;
   ss << xpu_device_id_;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -872,6 +872,11 @@ void AnalysisPredictor::PrepareArgument() {
     argument_.SetDlnneMinSubgraphSize(config_.dlnne_min_subgraph_size_);
   }
 
+  if (config_.gpu_fp16_enabled()) {
+    argument_.SetUseGPUFp16(true);
+    argument_.SetGpuFp16DisabledOpTypes(config_.gpu_fp16_disabled_op_types_);
+  }
+
   if (config_.lite_engine_enabled()) {
     argument_.SetCpuMathLibraryNumThreads(
         config_.cpu_math_library_num_threads());

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -375,6 +375,19 @@ TEST(AnalysisPredictor, enable_onnxruntime) {
   ASSERT_TRUE(!config.use_onnxruntime());
 }
 
+TEST(AnalysisPredictor, exp_enable_use_gpu_fp16) {
+  AnalysisConfig config;
+  config.SwitchIrOptim();
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  config.EnableUseGpu(100, 0);
+  config.Exp_EnableUseGpuFp16();
+  ASSERT_TRUE(config.gpu_fp16_enabled());
+#else
+  config.DisableGpu();
+#endif
+  LOG(INFO) << config.Summary();
+}
+
 }  // namespace paddle
 
 namespace paddle_infer {
@@ -434,6 +447,19 @@ TEST(Predictor, EnableONNXRuntime) {
   auto predictor = CreatePredictor(config);
 }
 
+TEST(Predictor, Exp_EnableUseGpuFp16) {
+  Config config;
+  config.SetModel(FLAGS_dirname);
+  config.SwitchIrOptim();
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+  config.EnableUseGpu(100, 0);
+  config.Exp_EnableUseGpuFp16();
+#else
+  config.DisableGpu();
+#endif
+  auto predictor = CreatePredictor(config);
+}
+
 TEST(Tensor, CpuShareExternalData) {
   Config config;
   config.SetModel(FLAGS_dirname);

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -253,6 +253,19 @@ struct PD_INFER_DECL AnalysisConfig {
   ///
   ///
   void DisableGpu();
+  ///
+  /// \brief Enable GPU fp16 precision computation, in experimental state.
+  ///
+  /// \param op_list The operator type list.
+  ///
+  void Exp_EnableUseGpuFp16(std::unordered_set<std::string> op_list = {});
+  ///
+  /// \brief A boolean state telling whether the GPU fp16 precision is turned
+  /// on.
+  ///
+  /// \return bool Whether the GPU fp16 precision is turned on.
+  ///
+  bool gpu_fp16_enabled() const { return use_gpu_fp16_; }
 
   ///
   /// \brief Turn on XPU.
@@ -859,6 +872,9 @@ struct PD_INFER_DECL AnalysisConfig {
   int gpu_device_id_{0};
   uint64_t memory_pool_init_size_mb_{100};  // initial size is 100MB.
   bool thread_local_stream_{false};
+  bool use_gpu_fp16_{false};
+  std::unordered_set<std::string> gpu_fp16_disabled_op_types_{
+      "conv2d_fusion", "conv2d", "roll", "strided_slice"};
 
   bool use_cudnn_{false};
 

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -172,6 +172,40 @@ void GpuPassStrategy::EnableCUDNN() {
   use_cudnn_ = true;
 }
 
+void GpuPassStrategy::Exp_EnableUseGpuFp16() {
+  passes_.assign({
+    "is_test_pass",                               //
+        "simplify_with_basic_ops_pass",           //
+        "conv_bn_fuse_pass",                      //
+        "conv_eltwiseadd_bn_fuse_pass",           //
+        "embedding_eltwise_layernorm_fuse_pass",  //
+        "multihead_matmul_fuse_pass_v2",          //
+        "gpu_cpu_squeeze2_matmul_fuse_pass",      //
+        "gpu_cpu_reshape2_matmul_fuse_pass",      //
+        "gpu_cpu_flatten2_matmul_fuse_pass",      //
+        "gpu_cpu_map_matmul_v2_to_mul_pass",      //
+        "gpu_cpu_map_matmul_v2_to_matmul_pass",   //
+        "gpu_cpu_map_matmul_to_mul_pass",         //
+        // "fc_fuse_pass",                        //
+        "fc_elementwise_layernorm_fuse_pass",  //
+#if CUDNN_VERSION >= 7100  // To run conv_fusion, the version of cudnn must be
+                           // guaranteed at least v7
+// cudnn8.0 has memory leak problem in conv + eltwise + act, so we
+// disable the pass.
+#if !(CUDNN_VERSION >= 8000 && CUDNN_VERSION < 8100)
+        "conv_elementwise_add_act_fuse_pass",   //
+        "conv_elementwise_add2_act_fuse_pass",  //
+#endif
+        "conv_elementwise_add_fuse_pass",      //
+#endif                                         //
+        "transpose_flatten_concat_fuse_pass",  //
+        "mixed_precision_configure_pass",      //
+        "runtime_context_cache_pass"           //
+  });
+
+  use_gpu_fp16_ = true;
+}
+
 void GpuPassStrategy::EnableMKLDNN() {
   LOG(ERROR) << "GPU not support MKLDNN yet";
 }

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -125,6 +125,9 @@ class PD_INFER_DECL PassStrategy : public PaddlePassBuilder {
   /// \brief Enable the use of cuDNN kernel.
   virtual void EnableCUDNN() {}
 
+  /// \brief Enable use gpu fp16 kernel.
+  virtual void Exp_EnableUseGpuFp16() {}
+
   /// \brief Enable the use of MKLDNN.
   /// The MKLDNN control exists in both CPU and GPU mode, because there can
   /// still be some CPU kernels running in GPU mode.
@@ -140,6 +143,10 @@ class PD_INFER_DECL PassStrategy : public PaddlePassBuilder {
   /// \return A bool variable implying whether we are in gpu mode.
   bool use_gpu() const { return use_gpu_; }
 
+  /// \brief Check if we are using gpu fp16 kernel.
+  /// \return A bool variable implying whether we are in gpu fp16 mode.
+  bool use_gpu_fp16() const { return use_gpu_fp16_; }
+
   /// \brief Check if we are using xpu.
   /// \return A bool variable implying whether we are in xpu mode.
   bool use_xpu() const { return use_xpu_; }
@@ -162,6 +169,7 @@ class PD_INFER_DECL PassStrategy : public PaddlePassBuilder {
   bool use_npu_{false};
   bool use_ipu_{false};
   bool use_mkldnn_{false};
+  bool use_gpu_fp16_{false};
   /// \endcond
 };
 
@@ -223,6 +231,9 @@ class PD_INFER_DECL GpuPassStrategy : public PassStrategy {
   /// \brief Enable the use of cuDNN kernel.
   void EnableCUDNN() override;
 
+  /// \brief Enable the use of gpu fp16 kernel.
+  void Exp_EnableUseGpuFp16() override;
+
   /// \brief Not supported in GPU mode yet.
   void EnableMKLDNN() override;
 
@@ -238,6 +249,7 @@ class PD_INFER_DECL GpuPassStrategy : public PassStrategy {
  protected:
   /// \cond Protected
   bool use_cudnn_{false};
+  bool use_gpu_fp16_{false};
   /// \endcond
 };
 

paddle/fluid/operators/fake_quantize_op.cu

@@ -28,13 +28,14 @@ __global__ void FindAbsMaxKernel(const T* in, const int n, T* out) {
   extern __shared__ char* shared_max_data_tmp[];
   auto shared_max_data = reinterpret_cast<T*>(shared_max_data_tmp);
   if (gridDim.x > 1) {
-    shared_max_data[tid] = T(0);
+    T local_max_data = T(0);
     for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
       T tmp = abs(in[i]);
-      if (tmp > shared_max_data[tid]) {
-        shared_max_data[tid] = tmp;
+      if (tmp > local_max_data) {
+        local_max_data = tmp;
       }
     }
+    shared_max_data[tid] = local_max_data;
   } else {
     if (bid < n) {
       shared_max_data[tid] = abs(in[bid]);
@@ -83,13 +84,14 @@ __global__ void FindChannelAbsMaxKernelQuantAxis0(const T* in, const int n,
   int channel_size = n / c;
   const T* in_c = in + blockIdx.x * channel_size;
   extern __shared__ T shared_max_data[];
-  shared_max_data[tid] = T(0);
+  T local_max_data = T(0);
   for (int i = tid; i < channel_size; i += blockDim.x) {
     T tmp = fabs(in_c[i]);
-    if (tmp > shared_max_data[tid]) {
-      shared_max_data[tid] = tmp;
+    if (tmp > local_max_data) {
+      local_max_data = tmp;
     }
   }
+  shared_max_data[tid] = local_max_data;
   __syncthreads();
   for (int i = blockDim.x / 2; i > 0; i >>= 1) {
     if (tid < i && (shared_max_data[tid] < shared_max_data[tid + i])) {
@@ -113,13 +115,14 @@ __global__ void FindChannelAbsMaxKernelQuantAxis1(const T* in, const int n,
   int tid = threadIdx.x;
   int bid = blockIdx.x;
   const T* in_current = in + tid * cout_wh_size + bid * wh_size;
-  shared_max_data[tid] = T(0);
+  T local_max_data = T(0);
   for (int i = 0; i < wh_size; i++) {
     T tmp = fabs(in_current[i]);
-    if (tmp > shared_max_data[tid]) {
-      shared_max_data[tid] = tmp;
+    if (tmp > local_max_data) {
+      local_max_data = tmp;
     }
   }
+  shared_max_data[tid] = local_max_data;
   __syncthreads();
 
   int len = blockDim.x;
@@ -404,6 +407,19 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
   }
 };
 
+template <typename T>
+__global__ void FindMovingAverageAbsMaxKernel(const T* in_state,
+                                              const T* in_accum,
+                                              const T* cur_scale, const T rate,
+                                              T* out_state, T* out_accum,
+                                              T* out_scale) {
+  T state = rate * (*in_state) + T(1.0f);
+  T accum = rate * (*in_accum) + (*cur_scale);
+  *out_state = state;
+  *out_accum = accum;
+  *out_scale = accum / state;
+}
+
 template struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, float>;
 
 template <typename T>
@@ -415,29 +431,14 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
                   framework::Tensor* out_accum, framework::Tensor* out_scale) {
     const auto gpu_place = ctx.GetPlace();
 
-    T accum;
-    T state;
-    T scale;
-    memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
-                 sizeof(T), ctx.stream());
-    memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
-                 sizeof(T), ctx.stream());
-    memory::Copy(platform::CPUPlace(), &scale, gpu_place, cur_scale, sizeof(T),
-                 ctx.stream());
-    ctx.Wait();
-
     T rate_t = static_cast<T>(rate);
-    state = rate_t * state + static_cast<T>(1.0);
-    accum = rate_t * accum + scale;
-    scale = accum / state;
-
-    memory::Copy(gpu_place, out_accum->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &accum, sizeof(T), ctx.stream());
-    memory::Copy(gpu_place, out_state->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &state, sizeof(T), ctx.stream());
-    memory::Copy(gpu_place, out_scale->mutable_data<T>(gpu_place),
-                 platform::CPUPlace(), &scale, sizeof(T), ctx.stream());
-    ctx.Wait();
+    T* out_state_data = out_state->mutable_data<T>(gpu_place);
+    T* out_accum_data = out_accum->mutable_data<T>(gpu_place);
+    T* out_scale_data = out_scale->mutable_data<T>(gpu_place);
+
+    FindMovingAverageAbsMaxKernel<T><<<1, 1, 0, ctx.stream()>>>(
+        in_state.data<T>(), in_accum.data<T>(), cur_scale, rate_t,
+        out_state_data, out_accum_data, out_scale_data);
   }
 };
 

paddle/fluid/operators/grid_sampler_op.cc

@@ -15,9 +15,13 @@ limitations under the License. */
 #include <memory>
 #include <string>
 
+#include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/backward.h"
+#include "paddle/phi/infermeta/binary.h"
 
 namespace paddle {
 namespace operators {
@@ -27,43 +31,6 @@ using Tensor = framework::Tensor;
 class GridSampleOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GridSampler");
-    OP_INOUT_CHECK(ctx->HasInput("Grid"), "Input", "Grid", "GridSampler");
-    OP_INOUT_CHECK(ctx->HasOutput("Output"), "Output", "Output", "GridSampler");
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto grid_dims = ctx->GetInputDim("Grid");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 4,
-                      platform::errors::InvalidArgument(
-                          "Input(X) of GridSampleOp should be 4-D Tensor, but "
-                          "received X dimension size(%d)",
-                          x_dims.size()));
-    PADDLE_ENFORCE_EQ(grid_dims.size(), 4,
-                      platform::errors::InvalidArgument(
-                          "Input(Grid) of GridSampleOp should be 4-D Tensor, "
-                          "but received X dimension size(%d)",
-                          grid_dims.size()));
-    if (ctx->IsRuntime() || grid_dims[3] > 0) {
-      PADDLE_ENFORCE_EQ(
-          grid_dims[3], 2,
-          platform::errors::InvalidArgument(
-              "Input(Grid) dimension[3] should be 2, but received %d",
-              grid_dims[3]));
-    }
-    if (ctx->IsRuntime()) {
-      PADDLE_ENFORCE_EQ(
-          grid_dims[0], x_dims[0],
-          platform::errors::InvalidArgument(
-              "Input(X) and Input(Grid) dimension[0] should be equal, but "
-              "received X dimension[0](%d) != Grid dimension[0](%d)",
-              x_dims[0], grid_dims[0]));
-    }
-
-    ctx->SetOutputDim("Output",
-                      {x_dims[0], x_dims[1], grid_dims[1], grid_dims[2]});
-    ctx->ShareLoD("X", "Output");
-  }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
@@ -173,18 +140,6 @@ class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
 class GridSampleOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
-                   framework::GradVarName("X"), "grid_sampler");
-    auto input_dims = ctx->GetInputDim("X");
-    auto grid_dims = ctx->GetInputDim("Grid");
-    if (ctx->HasOutput(framework::GradVarName("X"))) {
-      ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
-    }
-    if (ctx->HasOutput(framework::GradVarName("Grid"))) {
-      ctx->SetOutputDim(framework::GradVarName("Grid"), grid_dims);
-    }
-  }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
@@ -224,10 +179,16 @@ class GridSampleGradMaker : public framework::SingleGradOpMaker<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(grid_sampler, GridSamplerInferShapeFunctor,
+                            PD_INFER_META(phi::GridSampleBaseInferMeta));
 REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker,
                   ops::GridSampleGradMaker<paddle::framework::OpDesc>,
-                  ops::GridSampleGradMaker<paddle::imperative::OpBase>);
-REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad);
+                  ops::GridSampleGradMaker<paddle::imperative::OpBase>,
+                  GridSamplerInferShapeFunctor);
+DECLARE_INFER_SHAPE_FUNCTOR(grid_sampler_grad, GridSamplerGradInferShapeFunctor,
+                            PD_INFER_META(phi::GeneralBinaryGradInferMeta));
+REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad,
+                  GridSamplerGradInferShapeFunctor);
 
 REGISTER_OP_VERSION(grid_sampler)
     .AddCheckpoint(

paddle/fluid/pybind/eager_functions.cc

@@ -122,13 +122,33 @@ static PyObject* eager_api_run_backward(PyObject* self, PyObject* args,
   EAGER_TRY
   auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
   auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
-  egr::RunBackward(tensors, grad_tensors,
-                   CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2));
+  egr::Backward(tensors, grad_tensors,
+                CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 2), 2));
   Py_INCREF(Py_None);
   return Py_None;
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* eager_api_run_partial_grad(PyObject* self, PyObject* args,
+                                            PyObject* kwargs) {
+  EAGER_TRY
+  auto tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 0), 0);
+  auto inputs = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 1), 1);
+  auto grad_tensors = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 2), 2);
+  auto retain_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 3), 3);
+  auto create_graph = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 4), 4);
+  auto only_inputs = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 5), 5);
+  auto allow_unused = CastPyArg2AttrBoolean(PyTuple_GET_ITEM(args, 6), 6);
+  auto no_grad_vars = CastPyArg2VectorOfTensor(PyTuple_GET_ITEM(args, 7), 7);
+
+  std::vector<paddle::experimental::Tensor> result =
+      egr::Grad(tensors, inputs, grad_tensors, retain_graph, create_graph,
+                only_inputs, allow_unused, no_grad_vars);
+  VLOG(1) << " in eager_api_run_partial_grad, after runing egr::Grad";
+  return ToPyObject(result, true /* return_py_none_if_not_initialize */);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* eager_api_tensor_copy(PyObject* self, PyObject* args,
                                        PyObject* kwargs) {
   EAGER_TRY
@@ -452,6 +472,9 @@ PyMethodDef variable_functions[] = {
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"run_backward", (PyCFunction)(void (*)(void))eager_api_run_backward,
      METH_VARARGS | METH_KEYWORDS, NULL},
+    {"run_partial_grad",
+     (PyCFunction)(void (*)(void))eager_api_run_partial_grad,
+     METH_VARARGS | METH_KEYWORDS, NULL},
     {"_run_custom_op", (PyCFunction)(void (*)(void))eager_api_run_costum_op,
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"tensor_copy", (PyCFunction)(void (*)(void))eager_api_tensor_copy,

paddle/fluid/pybind/eager_method.cc

@@ -226,6 +226,19 @@ static PyObject* tensor_method__copy_to(TensorObject* self, PyObject* args,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* tensor_method_cpu(TensorObject* self, PyObject* args,
+                                   PyObject* kwargs) {
+  EAGER_TRY
+  auto cp_tensor =
+      self->tensor.copy_to(phi::TransToPhiBackend(phi::CPUPlace()), true);
+  egr::EagerUtils::autograd_meta(&cp_tensor)->SetStopGradient(true);
+  egr::EagerUtils::autograd_meta(&cp_tensor)
+      ->SetPersistable(
+          egr::EagerUtils::autograd_meta(&(self->tensor))->Persistable());
+  return ToPyObject(cp_tensor);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* tensor_method_reconstruct_from_(TensorObject* self,
                                                  PyObject* args,
                                                  PyObject* kwargs) {
@@ -264,7 +277,7 @@ static PyObject* tensor_method_copy_(TensorObject* self, PyObject* args,
             egr::EagerUtils::autograd_meta(&(src_tensor))->Persistable());
   }
 
-  self->tensor.copy_(src_tensor, blocking);
+  self->tensor.copy_(src_tensor, self->tensor.inner_place(), blocking);
 
   VLOG(6) << "Finish Copy Tensor " << src_tensor.name() << " to "
           << self->tensor.name();

paddle/fluid/pybind/eager_properties.cc

@@ -96,7 +96,7 @@ int tensor_properties_set_grad(TensorObject* self, PyObject* value,
                      "Detected NULL grad"
                      "Please check if you have manually cleared"
                      "the grad inside autograd_meta"));
-  grad->copy_(src, true);
+  grad->copy_(src, self->tensor.inner_place(), true);
   return 0;
   EAGER_CATCH_AND_THROW_RETURN_ZERO
 }

paddle/fluid/pybind/eager_utils.cc

@@ -492,20 +492,26 @@ PyObject* ToPyObject(const std::vector<double>& value) {
   return result;
 }
 
-PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value) {
+PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
+                     bool return_py_none_if_not_initialize) {
   PyObject* result = PyList_New((Py_ssize_t)value.size());
 
   for (size_t i = 0; i < value.size(); i++) {
-    PyObject* obj = p_tensor_type->tp_alloc(p_tensor_type, 0);
-    if (obj) {
-      auto v = reinterpret_cast<TensorObject*>(obj);
-      new (&(v->tensor)) paddle::experimental::Tensor();
-      v->tensor = value[i];
+    if (!value[i].initialized() && return_py_none_if_not_initialize) {
+      Py_INCREF(Py_None);
+      PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), Py_None);
     } else {
-      PADDLE_THROW(platform::errors::Fatal(
-          "tp_alloc return null, can not new a PyObject."));
+      PyObject* obj = p_tensor_type->tp_alloc(p_tensor_type, 0);
+      if (obj) {
+        auto v = reinterpret_cast<TensorObject*>(obj);
+        new (&(v->tensor)) paddle::experimental::Tensor();
+        v->tensor = value[i];
+      } else {
+        PADDLE_THROW(platform::errors::Fatal(
+            "tp_alloc return null, can not new a PyObject."));
+      }
+      PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), obj);
     }
-    PyList_SET_ITEM(result, static_cast<Py_ssize_t>(i), obj);
   }
 
   return result;

paddle/fluid/pybind/eager_utils.h

@@ -68,7 +68,8 @@ PyObject* ToPyObject(const std::vector<int>& value);
 PyObject* ToPyObject(const std::vector<int64_t>& value);
 PyObject* ToPyObject(const std::vector<float>& value);
 PyObject* ToPyObject(const std::vector<double>& value);
-PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value);
+PyObject* ToPyObject(const std::vector<paddle::experimental::Tensor>& value,
+                     bool return_py_none_if_not_initialize = false);
 PyObject* ToPyObject(const platform::Place& value);
 PyObject* ToPyObject(const framework::LoDTensor* value);
 PyObject* ToPyObject(const paddle::framework::proto::VarType::Type& dtype);

paddle/fluid/pybind/inference_api.cc

@@ -551,6 +551,9 @@ void BindAnalysisConfig(py::module *m) {
       .def("params_file", &AnalysisConfig::params_file)
       .def("enable_use_gpu", &AnalysisConfig::EnableUseGpu,
            py::arg("memory_pool_init_size_mb"), py::arg("device_id") = 0)
+      .def("exp_enable_use_gpu_fp16", &AnalysisConfig::Exp_EnableUseGpuFp16,
+           py::arg("gpu_fp16_disabled_op_types") =
+               std::unordered_set<std::string>({}))
       .def("enable_xpu", &AnalysisConfig::EnableXpu,
            py::arg("l3_workspace_size") = 16 * 1024 * 1024,
            py::arg("locked") = false, py::arg("autotune") = true,

paddle/infrt/CMakeLists.txt

@@ -3,12 +3,22 @@ if (NOT WITH_INFRT)
 endif()
 
 option(INFRT_WITH_PHI  "Compile INFRT with PHI"    ON)
+option(INFRT_WITH_GPU  "Compile INFRT with GPU"    OFF)
+option(INFRT_WITH_TRT  "Compile INFRT with TensorRT"    OFF)
 
 #TODO(xiaowei) remove fluid
 include_directories(${PADDLE_SOURCE_DIR}/paddle/fluid/platform)
 
 if (INFRT_WITH_PHI)
-    add_definitions("-DINFRT_WITH_PHI")
+  add_definitions("-DINFRT_WITH_PHI")
+
+  # TODO(wilber): Now Infrt gpu/trt depends on phi's components, Modify compile dependency options later.
+  if (INFRT_WITH_GPU)
+    add_definitions("-DINFRT_WITH_GPU")
+    if (INFRT_WITH_TRT)
+      add_definitions("-DINFRT_WITH_TRT")
+    endif()
+  endif()
 endif()
 
 # compile flags
@@ -92,7 +102,6 @@ set(infrt_mlir_incs
         test_kernels_inc
         tensor_shape_inc
         dense_tensor_inc
-        pd_ops_inc
         pd_extra_ops_inc
         trt_ops_inc
         )
@@ -106,6 +115,9 @@ if (INFRT_WITH_PHI)
 endif()
 
 cc_library(infrt SHARED SRCS ${infrt_src} DEPS glog boost ${mlir_libs} ${phi_libs} paddle_framework_proto infrt_naive)
+if (INFRT_WITH_TRT)
+  target_link_libraries(infrt infrt_trt)
+endif()
 cc_library(infrt_static SRCS ${infrt_src} DEPS glog boost ${mlir_libs} ${phi_libs} paddle_framework_proto)
 add_dependencies(infrt ${infrt_mlir_incs} mlir-headers)
 

paddle/infrt/backends/host/phi_allocator.h

@@ -13,6 +13,10 @@ limitations under the License. */
 
 #include "paddle/phi/core/allocator.h"
 
+#ifdef INFRT_WITH_GPU
+#include <cuda_runtime.h>
+#endif
+
 namespace infrt {
 namespace backends {
 
@@ -29,5 +33,22 @@ class CpuPhiAllocator : public phi::Allocator {
   }
 };
 
+#ifdef INFRT_WITH_GPU
+// TODO(wilber): Just for demo test. we need a more efficient gpu allocator.
+class GpuPhiAllocator : public phi::Allocator {
+ public:
+  static void deleter(phi::Allocation* ptr) { cudaFree(ptr->ptr()); }
+
+  AllocationPtr Allocate(size_t bytes_size) {
+    void* ptr;
+    cudaMalloc(&ptr, bytes_size);
+    return AllocationPtr(
+        new phi::Allocation(
+            ptr, bytes_size, phi::Place(phi::AllocationType::GPU)),
+        deleter);
+  }
+};
+#endif
+
 }  // namespace backends
 }  // namespace infrt

paddle/infrt/backends/host/phi_context.h

@@ -13,6 +13,7 @@ limitations under the License. */
 
 #include "paddle/infrt/backends/host/phi_allocator.h"
 #include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
 
 namespace infrt {
 namespace backends {
@@ -31,5 +32,16 @@ class CpuPhiContext : public phi::CPUContext {
   std::unique_ptr<phi::Allocator> alloc_{std::make_unique<CpuPhiAllocator>()};
 };
 
+class GpuPhiContext : public phi::GPUContext {
+ public:
+  using Base = phi::GPUContext;
+  using phi::GPUContext::SetStream;
+  using phi::GPUContext::SetEigenDevice;
+  using phi::GPUContext::SetBlasHandle;
+  using phi::GPUContext::SetDnnHandle;
+  using phi::GPUContext::SetSolverHandle;
+  using phi::GPUContext::SetSparseHandle;
+};
+
 }  // namespace backends
 }  // namespace infrt

paddle/infrt/backends/tensorrt/test_trt_engine.cc

@@ -37,9 +37,9 @@ namespace infrt {
 namespace backends {
 namespace tensorrt {
 
-const char* model_input = "model_input";
-const char* model_output = "model_output1";
-const char* model_output2 = "model_output2";
+const char* model_input = "input_0";
+const char* model_output = "output_0";
+const char* model_output2 = "output_1";
 
 TrtUniquePtr<nvinfer1::INetworkDefinition> ConstructNetwork(
     nvinfer1::IBuilder* builder, nvinfer1::Dims dims, bool is_static_shape) {
@@ -122,27 +122,26 @@ TEST(trt, run_static) {
 
   std::unordered_map<std::string, phi::DenseTensor*> inputs;
   inputs.emplace(std::make_pair(model_input, &input));
-  phi::DenseTensor output, output2;
-  std::unordered_map<std::string, phi::DenseTensor*> outputs;
-  outputs.emplace(std::make_pair(model_output, &output));
-  outputs.emplace(std::make_pair(model_output2, &output2));
-
-  static_trt_engine.SetUpInference(inference_options, inputs, &outputs);
+  static_trt_engine.PrepareOutputHandle("output_0");
+  static_trt_engine.PrepareOutputHandle("output_1");
+  static_trt_engine.SetUpInference(inference_options, inputs);
   static_trt_engine.GetEngineInfo();
   static_trt_engine.Run(context);
 
+  phi::DenseTensor* output0 = static_trt_engine.GetOutput("output_0");
+  phi::DenseTensor* output1 = static_trt_engine.GetOutput("output_1");
   std::vector<float> output_data1(inference_options.batch * 1 * 28 * 28, 0);
   std::vector<float> output_data2(inference_options.batch * 2 * 28 * 28, 0);
   paddle::memory::Copy(phi::CPUPlace(),
                        output_data1.data(),
                        place,
-                       output.data<float>(),
+                       output0->data<float>(),
                        sizeof(float) * output_data1.size(),
                        context.stream());
   paddle::memory::Copy(phi::CPUPlace(),
                        output_data2.data(),
                        place,
-                       output2.data<float>(),
+                       output1->data<float>(),
                        sizeof(float) * output_data2.size(),
                        context.stream());
   cudaStreamSynchronize(context.stream());
@@ -208,27 +207,27 @@ TEST(trt, run_dynamic) {
                        context.stream());
 
   std::unordered_map<std::string, phi::DenseTensor*> inputs;
-  std::unordered_map<std::string, phi::DenseTensor*> outputs;
   inputs.emplace(std::make_pair(model_input, &input));
-  outputs.emplace(std::make_pair(model_output, &output));
-  outputs.emplace(std::make_pair(model_output2, &output2));
-
-  engine.SetUpInference(inference_options, inputs, &outputs);
+  engine.PrepareOutputHandle("output_0");
+  engine.PrepareOutputHandle("output_1");
+  engine.SetUpInference(inference_options, inputs);
   engine.GetEngineInfo();
   engine.Run(context);
+  phi::DenseTensor* output0 = engine.GetOutput("output_0");
+  phi::DenseTensor* output1 = engine.GetOutput("output_1");
 
   std::vector<float> output_data1(inference_options.batch * 1 * 16 * 16, 0);
   std::vector<float> output_data2(inference_options.batch * 2 * 16 * 16, 0);
   paddle::memory::Copy(phi::CPUPlace(),
                        output_data1.data(),
                        place,
-                       output.data<float>(),
+                       output0->data<float>(),
                        sizeof(float) * output_data1.size(),
                        context.stream());
   paddle::memory::Copy(phi::CPUPlace(),
                        output_data2.data(),
                        place,
-                       output2.data<float>(),
+                       output1->data<float>(),
                        sizeof(float) * output_data2.size(),
                        context.stream());
   cudaStreamSynchronize(context.stream());

paddle/infrt/backends/tensorrt/trt_engine.cc

@@ -21,6 +21,7 @@
 #include "paddle/phi/backends/dynload/tensorrt.h"
 #include "paddle/phi/backends/gpu/gpu_info.h"
 #include "paddle/phi/core/ddim.h"
+#include "paddle/phi/core/dense_tensor.h"
 
 namespace infrt {
 namespace backends {
@@ -235,10 +236,20 @@ bool TrtEngine::SetupNetworkAndConfig(const BuildOptions& build,
   return true;
 }
 
+void TrtEngine::PrepareOutputHandle(const std::string& out_name) {
+  phi::DenseTensor t;
+  outputs_.emplace(out_name, t);
+}
+
+phi::DenseTensor* TrtEngine::GetOutput(const std::string& name) {
+  return &outputs_[name];
+}
+
+size_t TrtEngine::GetOutputNum() const { return outputs_.size(); }
+
 bool TrtEngine::SetUpInference(
     const InferenceOptions& inference,
-    const std::unordered_map<std::string, phi::DenseTensor*>& inputs,
-    std::unordered_map<std::string, phi::DenseTensor*>* outputs) {
+    const std::unordered_map<std::string, phi::DenseTensor*>& inputs) {
   // TODO(wilber): now only create one exec_context
   FreshDeviceId();
   CHECK(engine_ != nullptr);
@@ -252,10 +263,10 @@ bool TrtEngine::SetUpInference(
     bindings_.front()->AddBinding(
         bind_index, it.first, true, it.second, nvinfer1::DataType::kFLOAT);
   }
-  for (auto& it : *outputs) {
+  for (auto& it : outputs_) {
     const int bind_index = engine_->getBindingIndex(it.first.c_str());
     bindings_.front()->AddBinding(
-        bind_index, it.first, false, it.second, nvinfer1::DataType::kFLOAT);
+        bind_index, it.first, false, &it.second, nvinfer1::DataType::kFLOAT);
   }
 
   return true;
@@ -290,11 +301,13 @@ void TrtEngine::StaticRun(const phi::GPUContext& ctx) {
     const int bind_index = engine_->getBindingIndex(bind.name.c_str());
     std::vector<int32_t> ddim;
     auto dims = engine_->getBindingDimensions(bind_index);
+    CHECK_NE(runtime_batch, -1) << "runtime_batch should not be -1.";
     ddim.push_back(runtime_batch);
     for (int i = 0; i < dims.nbDims; ++i) {
       ddim.push_back(dims.d[i]);
     }
     bind.buffer->Resize(phi::make_ddim(ddim));
+    // TODO(wilber): now only support float output.
     ctx.Alloc<float>(bind.buffer, sizeof(float) * bind.buffer->numel());
     buffers[bind_index] = static_cast<void*>(bind.buffer->data<float>());
   }

paddle/infrt/backends/tensorrt/trt_engine.h

@@ -81,11 +81,17 @@ class TrtEngine {
   // TODO(wilber): How to support multiple execution contexts?
   bool SetUpInference(
       const InferenceOptions& inference,
-      const std::unordered_map<std::string, phi::DenseTensor*>& inputs,
-      std::unordered_map<std::string, phi::DenseTensor*>* outputs);
+      const std::unordered_map<std::string, phi::DenseTensor*>& inputs);
 
   void GetEngineInfo();
 
+  void PrepareOutputHandle(const std::string& out_name);
+
+  // TODO(wilber): The output tensor names are: output_0, output_1, ...
+  phi::DenseTensor* GetOutput(const std::string&);
+
+  size_t GetOutputNum() const;
+
  private:
   void FreshDeviceId();
 
@@ -112,6 +118,7 @@ class TrtEngine {
   std::vector<std::unique_ptr<Bindings>> bindings_;
   int device_id_{0};
   bool is_dynamic_shape_{false};
+  std::unordered_map<std::string, phi::DenseTensor> outputs_;
 };
 
 }  // namespace tensorrt

paddle/infrt/dialect/CMakeLists.txt

@@ -7,16 +7,10 @@ gather_srcs(infrt_src SRCS
     dense_tensor.cc
     mlir_loader.cc
     diagnostic_utils.cc
-    pd_ops.cc
     )
 
 mlir_tablegen_on(tensor_shape DIALECT ts)
 mlir_tablegen_on(dense_tensor DIALECT dt)
-mlir_tablegen_on(pd_op_base DIALECT pd)
-mlir_tablegen_on(pd_ops)
-mlir_tablegen_on(pd_extra_ops)
-
-mlir_add_rewriter(rewrite)
 
 # TODO(Superjomn) add a cmake function cc_executable to ecapsulate the following code
 add_executable(infrtopt opt.cc)
@@ -24,10 +18,10 @@ target_link_libraries(infrtopt infrt)
 
 add_executable(print-ir print_ir.cc)
 target_link_libraries(print-ir infrt ${mlir_libs})
-add_dependencies(print-ir pd_ops_inc)
 cc_test_tiny(test_infrt_mlir_loader SRCS mlir_loader_test.cc DEPS infrt ${MLIR_IR_LIBS})
 
 add_subdirectory(infrt)
+add_subdirectory(pd)
 add_subdirectory(tensorrt)
 
 if (INFRT_WITH_PHI)

paddle/infrt/dialect/dense_tensor.td

@@ -130,7 +130,7 @@ def TensorMapGetTensorOp : DT_Op<"tensor_map_get_tensor", [NoSideEffect]> {
 }
 
 def TensorMapGetSizeOp : DT_Op<"tensor_map_get_size", [NoSideEffect]> {
-  let summary = "ddt.tensor_map_get_size operation";
+  let summary = "dt.tensor_map_get_size operation";
 
   let description = [{
     An operation that get the size of a TensorMap.
@@ -141,6 +141,32 @@ def TensorMapGetSizeOp : DT_Op<"tensor_map_get_size", [NoSideEffect]> {
   let assemblyFormat = "`(` $map `)` attr-dict `->` type($size)";
 }
 
+def Infrt_TensorListGetTensorOp : DT_Op<"tensor_list_get_tensor", [NoSideEffect]> {
+  let summary = "dt.tensor_list_get_tensor operation";
+
+  let description = [{
+    An operation that can get a tensor from a TensorList.
+  }];
+
+  let arguments = (ins
+          DenseTensorList:$l,
+          I32Attr:$id
+          );
+  let results = (outs DenseTensor:$output);
+  let verifier = ?;
+}
+
+def TensorListGetSizeOp : DT_Op<"tensor_list_get_size", [NoSideEffect]> {
+  let summary = "dt.tensor_list_get_size operation";
+
+  let description = [{
+    An operation that get the size of a TensorList.
+  }];
+
+  let arguments = (ins DenseTensorList:$map);
+  let results = (outs I32:$size);
+}
+
 def GetTensorShapeOp : DT_Op<"get_tensor_shape", [NoSideEffect]> {
   let summary = "dt.get_tensor_shape operation";
 

paddle/infrt/dialect/infrt/ir/infrt_base.td

@@ -89,6 +89,13 @@ def DenseTensorMap :  Infrt_Type<"DenseTensorMap"> {
   let parameters = (ins);
 }
 
+// TODO(wilber): Add !infrt.vec type.
+def DenseTensorList :  Infrt_Type<"DenseTensorList"> {
+  let summary = "infrt dense tensor map";
+  let description = [{dense_tensor map}];
+  let parameters = (ins);
+}
+
 // Type Constrait for concrete DenseTensor type.
 class DenseTensor<string target, string precision, string layout> :
     Type<CPred<"$_self == ::infrt::DenseTensorType::get($_self.getContext(), ::infrt::TargetType::"#target#",::infrt::PrecisionType::"#precision#",::infrt::LayoutType::"#layout#")">, 

paddle/infrt/dialect/infrt/ir/infrt_dialect.cc

@@ -138,6 +138,10 @@ mlir::Type InfrtDialect::parseType(::mlir::DialectAsmParser &parser) const {
         parser.getContext(), *targetType, *precisionType, *layoutType);
   }
 
+  if (keyword == "tensor_list") {
+    return infrt::DenseTensorListType::get(parser.getContext());
+  }
+
   if (keyword == "dense_tensor_map") {
     return DenseTensorMapType::get(parser.getContext());
   }
@@ -175,6 +179,9 @@ void InfrtDialect::printType(::mlir::Type type,
     return;
   }
 
+  if (type.isa<infrt::DenseTensorListType>()) {
+    os << "tensor_list";
+  }
   // print DenseTensorType, for example: !infrt.dense_tensor<CPU, FP32, NCHW>
   if (type.isa<DenseTensorMapType>()) {
     os << "dense_tensor_map";

paddle/infrt/dialect/infrt/pass/infrt_op_fuse.td

@@ -3,7 +3,7 @@
 
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "paddle/infrt/dialect/infrt/ir/infrt_ops.td"
-include "paddle/infrt/dialect/pd_ops.td"
+include "paddle/infrt/dialect/pd/ir/pd_ops.td"
 
 def FuseTensorCastPattern : Pat<
        (Infrt_TensorCastOp (Infrt_TensorCastOp $arg)),

paddle/infrt/dialect/infrt/pass/infrt_op_fuse_pass.cc

@@ -16,7 +16,7 @@
 
 #include <mlir/Transforms/GreedyPatternRewriteDriver.h>
 #include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 namespace {
 #include "paddle/infrt/dialect/infrt/pass/infrt_op_fuse.cpp.inc"  // NOLINT
 

paddle/infrt/dialect/init_dialects.cc

@@ -20,12 +20,13 @@
 #include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
 #include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
 
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 #include "paddle/infrt/dialect/phi/ir/infrt_phi_tensor.h"
 #include "paddle/infrt/dialect/phi/ir/phi_base.h"
 #include "paddle/infrt/dialect/phi/ir/phi_kernels.h"
 
 #include "paddle/infrt/dialect/tensor_shape.h"
+#include "paddle/infrt/dialect/tensorrt/trt_ops.h"
 
 namespace infrt {
 void registerCinnDialects(mlir::DialectRegistry &registry) {  // NOLINT
@@ -37,7 +38,8 @@ void registerCinnDialects(mlir::DialectRegistry &registry) {  // NOLINT
                   phi::PHIDenseTensorDialect,
                   phi::PHICPUKernelDialect,
                   phi::PHIGPUKernelDialect,
-                  phi::PHIDialect
+                  phi::PHIDialect,
+                  infrt::trt::TensorRTDialect
 #endif
                   >();
 }

paddle/infrt/dialect/pd/CMakeLists.txt

+add_subdirectory(common)
+add_subdirectory(ir)
+add_subdirectory(pass)

paddle/infrt/dialect/pd/common/CMakeLists.txt

+core_gather_headers()
+
+gather_srcs(infrt_src SRCS
+    )

paddle/infrt/dialect/pd/ir/CMakeLists.txt

+core_gather_headers()
+
+gather_srcs(infrt_src SRCS
+    pd_ops.cc
+    )
+add_mlir_dialect(pd_ops pd)
+mlir_tablegen_on(pd_extra_ops)

paddle/infrt/dialect/pd_extra_ops.td → paddle/infrt/dialect/pd/ir/pd_extra_ops.td

@@ -4,7 +4,7 @@
 include "mlir/Interfaces/InferTypeOpInterface.td"
 include "mlir/Interfaces/LoopLikeInterface.td"
 include "mlir/IR/OpBase.td"
-include "paddle/infrt/dialect/pd_op_base.td"
+include "paddle/infrt/dialect/pd/ir/pd_op_base.td"
 
 def PD_FusedFC : PD_Op<"FC", [NoSideEffect]> {
     let summary = "Computes the Fully Connected result of two tensors";

paddle/infrt/dialect/pd_op_base.td → paddle/infrt/dialect/pd/ir/pd_op_base.td

@@ -8,7 +8,7 @@ include "mlir/IR/OpBase.td"
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "paddle/infrt/dialect/infrt/ir/infrt_base.td"
 
-def PD_Dialect : Dialect {
+def Paddle_Dialect : Dialect {
   let name = "pd";
 
   let description = [{
@@ -16,12 +16,12 @@ def PD_Dialect : Dialect {
 
     This dialect contains the PaddlePaddle operators.
   }];
-
+  let hasConstantMaterializer = 1;
   let cppNamespace = "mlir::pd";
 }
 
 class PD_Op<string mnemonic, list<OpTrait> traits = []> :
-      Op<PD_Dialect, mnemonic, traits>;
+      Op<Paddle_Dialect, mnemonic, traits>;
 
 
 class PD_PaddleAttr <string name, string description> :

paddle/infrt/dialect/pd_ops.cc → paddle/infrt/dialect/pd/ir/pd_ops.cc

@@ -12,29 +12,27 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 
 #include <mlir/IR/Matchers.h>
 #include <mlir/IR/PatternMatch.h>
 
+#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
+#include "paddle/infrt/dialect/pd/ir/pd_opsDialect.cpp.inc"
 #define GET_OP_CLASSES
-#include "paddle/infrt/dialect/pd_ops.cpp.inc"  // NOLINT
+#include "paddle/infrt/dialect/pd/ir/pd_ops.cpp.inc"  // NOLINT
 #define GET_OP_CLASSES
-#include "paddle/infrt/dialect/pd_extra_ops.cpp.inc"  // NOLINT
+#include "paddle/infrt/dialect/pd/ir/pd_extra_ops.cpp.inc"  // NOLINT
 
 namespace mlir {
 namespace pd {
-
-#include "paddle/infrt/dialect/rewrite.cpp.inc"  // NOLINT
-
-PaddleDialect::PaddleDialect(MLIRContext *context)
-    : Dialect("pd", context, TypeID::get<PaddleDialect>()) {
+void PaddleDialect::initialize() {
   addOperations<
 #define GET_OP_LIST
-#include "paddle/infrt/dialect/pd_ops.cpp.inc"  // NOLINT
+#include "paddle/infrt/dialect/pd/ir/pd_ops.cpp.inc"  // NOLINT
       ,
 #define GET_OP_LIST
-#include "paddle/infrt/dialect/pd_extra_ops.cpp.inc"  // NOLINT
+#include "paddle/infrt/dialect/pd/ir/pd_extra_ops.cpp.inc"  // NOLINT
       >();
 }
 
@@ -73,106 +71,5 @@ mlir::OpFoldResult ConstantOp::fold(
     ::llvm::ArrayRef<mlir::Attribute> operands) {
   return value();
 }
-/*
-LogicalResult ElementwiseAdd::inferReturnTypes(
-    MLIRContext *context,
-    Optional<Location> location,
-    ValueRange operands,
-    DictionaryAttr attributes,
-    RegionRange regions,
-    SmallVectorImpl<Type> &inferredReturnTypes) {
-  inferredReturnTypes.push_back(operands[0].getType());
-  return success();
-}
-*/
-
-void Elementwise_addOp::getCanonicalizationPatterns(
-    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
-  results.insert<FuseMulAdd>(context);
-}
-
-/*
-mlir::OpFoldResult ElementwiseAdd::fold(
-    llvm::ArrayRef<mlir::Attribute> operands) {
-  if (getElementTypeOrSelf(getType()).isa<FloatType>()) {
-    if (!operands[0] || !operands[1]) return {};
-    DenseElementsAttr lhs = operands[0].dyn_cast<DenseElementsAttr>();
-    DenseElementsAttr rhs = operands[1].dyn_cast<DenseElementsAttr>();
-    if (!lhs || !rhs) return {};
-    ShapedType type = getType().template cast<ShapedType>();
-    if (!type.hasStaticShape()) return {};
-    Type etype = type.getElementType();
-    if (!etype.isa<FloatType>()) return {};
-    SmallVector<APFloat, 6> values;
-    values.reserve(lhs.getNumElements());
-    for (const auto zip :
-         llvm::zip(lhs.getValues<APFloat>(), rhs.getValues<APFloat>())) {
-      values.push_back(
-          std::plus<APFloat>()(std::get<0>(zip), std::get<1>(zip)));
-    }
-    return DenseElementsAttr::get(type, values);
-  }
-  return {};
-}
-
-LogicalResult ElementwiseDiv::inferReturnTypes(
-    MLIRContext *context,
-    Optional<Location> location,
-    ValueRange operands,
-    DictionaryAttr attributes,
-    RegionRange regions,
-    SmallVectorImpl<Type> &inferredReturnTypes) {
-  inferredReturnTypes.push_back(operands[0].getType());
-  return success();
-}
-
-LogicalResult ElementwiseMul::inferReturnTypes(
-    MLIRContext *context,
-    Optional<Location> location,
-    ValueRange operands,
-    DictionaryAttr attributes,
-    RegionRange regions,
-    SmallVectorImpl<Type> &inferredReturnTypes) {
-  inferredReturnTypes.push_back(operands[0].getType());
-  return success();
-}
-
-LogicalResult ElementwiseSub::inferReturnTypes(
-    MLIRContext *context,
-    Optional<Location> location,
-    ValueRange operands,
-    DictionaryAttr attributes,
-    RegionRange regions,
-    SmallVectorImpl<Type> &inferredReturnTypes) {
-  inferredReturnTypes.push_back(operands[0].getType());
-  return success();
-}
-
-LogicalResult MulOp::inferReturnTypes(
-    MLIRContext *context,
-    Optional<Location> location,
-    ValueRange operands,
-    DictionaryAttr attributes,
-    RegionRange regions,
-    SmallVectorImpl<Type> &inferredReturnTypes) {
-  inferredReturnTypes.push_back(operands[0].getType());
-  return success();
-}
-
-void ReluOp::getCanonicalizationPatterns(
-    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
-  results.insert<FuseFCRelu>(context);
-}
-
-void FusedRepeatedFCRelu::getCanonicalizationPatterns(
-    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
-  results.insert<FuseRepeatedFCRelu2>(context);
-}
-
-void BatchNormOp::getCanonicalizationPatterns(
-    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
-  results.insert<FuseBatchNormWithConvPattern>(context);
-}*/
-
 }  // namespace pd
 }  // namespace mlir

paddle/infrt/dialect/pd_ops.h → paddle/infrt/dialect/pd/ir/pd_ops.h

-// Copyright (c) 2021 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.
@@ -14,49 +14,20 @@
 
 #pragma once
 
-#include <mlir/Dialect/Traits.h>
-#include <mlir/IR/Attributes.h>
-#include <mlir/IR/Builders.h>
-#include <mlir/IR/BuiltinOps.h>
+//===----------------------------------------------------------------------===//
+// Dialect
+//===----------------------------------------------------------------------===//
+#include <llvm/ADT/StringMap.h>
 #include <mlir/IR/BuiltinTypes.h>
 #include <mlir/IR/Dialect.h>
-#include <mlir/IR/Matchers.h>
+#include <mlir/IR/OpDefinition.h>
 #include <mlir/IR/OpImplementation.h>
-#include <mlir/IR/TypeUtilities.h>
-#include <mlir/Interfaces/CallInterfaces.h>
-#include <mlir/Interfaces/DerivedAttributeOpInterface.h>
 #include <mlir/Interfaces/InferTypeOpInterface.h>
-#include <mlir/Interfaces/LoopLikeInterface.h>
 #include <mlir/Interfaces/SideEffectInterfaces.h>
-#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
-
-namespace mlir {
-namespace pd {
-
-class PaddleDialect : public Dialect {
- public:
-  explicit PaddleDialect(MLIRContext* context);
-
-  static StringRef getDialectNamespace() { return "pd"; }
-
-  /// A hook used to materialize constant values with the given type.
-  Operation* materializeConstant(OpBuilder& builder,
-                                 Attribute value,
-                                 Type type,
-                                 Location loc) override;
-
-  Type parseType(DialectAsmParser& parser) const override {
-    return Dialect::parseType(parser);
-  }
-  void printType(Type type, DialectAsmPrinter& printer) const override {
-    Dialect::printType(type, printer);
-  }
-};
-
-}  // namespace pd
-}  // namespace mlir
 
+#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
+#include "paddle/infrt/dialect/pd/ir/pd_opsDialect.h.inc"
 #define GET_OP_CLASSES
-#include "paddle/infrt/dialect/pd_ops.hpp.inc"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h.inc"
 #define GET_OP_CLASSES
-#include "paddle/infrt/dialect/pd_extra_ops.hpp.inc"
+#include "paddle/infrt/dialect/pd/ir/pd_extra_ops.hpp.inc"

paddle/infrt/dialect/pd/pass/CMakeLists.txt

+
+core_gather_headers()
+
+gather_srcs(infrt_src SRCS
+    pd_op_fuse_pass.cc
+    )
+
+mlir_add_rewriter(pd_op_fuse)

paddle/infrt/dialect/rewrite.td → paddle/infrt/dialect/pd/pass/pd_op_fuse.td

@@ -3,8 +3,8 @@
 
 include "paddle/infrt/dialect/infrt/ir/infrt_base.td"
 include "mlir/Interfaces/SideEffectInterfaces.td"
-include "paddle/infrt/dialect/pd_ops.td"
-include "paddle/infrt/dialect/pd_extra_ops.td"
+include "paddle/infrt/dialect/pd/ir/pd_ops.td"
+include "paddle/infrt/dialect/pd/ir/pd_extra_ops.td"
 
 //===----------------------------------------------------------------------===//
 // This is to fuse the composition: 'Matmul o ElementwiseAdd' into 'PD_FusedFC'.

paddle/infrt/dialect/pd/pass/pd_op_fuse_pass.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/infrt/dialect/pd/pass/pd_op_fuse_pass.h"  // NOLINT
+
+#include <mlir/Transforms/GreedyPatternRewriteDriver.h>
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
+
+namespace {
+#include "paddle/infrt/dialect/pd/pass/pd_op_fuse.cpp.inc"  // NOLINT
+
+/*
+ * PdOpFusePass.
+ */
+struct PdOpFusePass
+    : public mlir::PassWrapper<PdOpFusePass, mlir::FunctionPass> {
+ public:
+  ::llvm::StringRef getName() const override { return "PdOpFusePass"; }
+
+  llvm::StringRef getArgument() const override { return "pd-op-fuse"; }
+
+  void runOnFunction() override;
+};
+
+// Implementation of the PdOpFusePass.
+void PdOpFusePass::runOnFunction() {
+  ::mlir::RewritePatternSet patterns(&getContext());
+  populateWithGenerated(patterns);
+  (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
+}
+
+}  // namespace
+
+mlir::PassRegistration<PdOpFusePass> infrt_op_fuse_pass;

paddle/infrt/dialect/pd/pass/pd_op_fuse_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 <mlir/Pass/Pass.h>
+
+namespace infrt {
+/*
+ * PdOpFusePass.
+ */
+std::unique_ptr<mlir::Pass> CreatePdOpFusePass();
+
+}  // namespace infrt

paddle/infrt/dialect/phi/ir/infrt_phi_tensor.td

@@ -21,8 +21,8 @@ def PHI_DenseTensorDialect : Dialect {
 class PDT_Op<string mnemonic, list<OpTrait> traits = []> : Op<PHI_DenseTensorDialect,
   mnemonic, !listconcat(traits, [PhiOpTrait, IsolatedFromAbove])> {}
 
-class CreateDenseTensorOp 
-      : PDT_Op<"create_dense_tensor", [NoSideEffect]> {
+class CreateDenseTensorOp<string target>
+      : PDT_Op<"create_dense_tensor." # target, [NoSideEffect]> {
   let arguments = (ins Context:$context, I64ArrayAttr:$dims, 
     LayoutAttr:$layout, I64ArrayAttr:$lod, PrecisionAttr:$precision);
   let results = (outs DenseTensor:$output);
@@ -51,9 +51,11 @@ class CreateContextOp<string target>
   let results = (outs Context:$output);
 }
 
-def PDT_CreateDenseTensorOp : CreateDenseTensorOp;
+def PDT_CreateCPUDenseTensorOp : CreateDenseTensorOp<"cpu">;
+def PDT_CreateGPUDenseTensorOp : CreateDenseTensorOp<"gpu">;
 def PDT_FillDenseTensorOp_f32 : FillDenseTensorOp<F32ArrayAttr, "f32">;
 def PDT_CreateCPUContextOp : CreateContextOp<"cpu">;
+def PDT_CreateGPUContextOp : CreateContextOp<"gpu">;
 def PDT_PrintDenseTensor : PrintDenseTensorOp;
 
 def FakeKernelOp : PDT_Op<"fake_phi_kernel"> {

paddle/infrt/dialect/phi/pass/phi_op_convert_pass.cc

@@ -32,6 +32,7 @@
 #include "paddle/infrt/dialect/phi/pass/kernel_op_desc.h"
 #include "paddle/infrt/dialect/phi/pass/proto_arg_map_context.h"
 #include "paddle/phi/core/compat/op_utils.h"
+#include "paddle/phi/core/kernel_factory.h"
 #include "paddle/phi/ops/compat/signatures.h"
 
 namespace {
@@ -94,42 +95,49 @@ void PhiOpConvertPass::convertStage() {
       // Todo: print log
       continue;
     }
-
-    ::phi::KernelSignature kernel_sign =
-        ::phi::OpUtilsMap::Instance().GetArgumentMappingFn(op_name)(
-            infrt::ProtoArgumentMappingContext(op));
-    // resort input&output according to kernel_sign
-    ::llvm::SmallVector<mlir::Value, 4> inputs, ori_output;
-    ::llvm::SmallVector<mlir::Type, 4> output_types;
-    for (const std::string &str : std::get<0>(kernel_sign.args)) {
-      if (pd_dialect_inputs_info_map_.at(op_name).count(str) == 0) {
-        LOG(ERROR) << "No input info for Op " << op_name << " and argument "
-                   << str;
-        return;
+    auto loc = getFunction().getLoc();
+    builder.setInsertionPoint(op);
+    if (phi::KernelFactory::Instance().HasCompatiblePhiKernel(op_name)) {
+      std::string kernel_name = phi::TransToPhiKernelName(op_name);
+      auto kernel_op = builder.create<infrt::KernelOp>(loc,
+                                                       op->getResultTypes(),
+                                                       op->getOperands(),
+                                                       kernel_name,
+                                                       op->getAttrDictionary());
+      op->replaceAllUsesWith(kernel_op.getResults());
+    } else {
+      ::phi::KernelSignature kernel_sign =
+          ::phi::OpUtilsMap::Instance().GetArgumentMappingFn(op_name)(
+              infrt::ProtoArgumentMappingContext(op));
+      // resort input&output according to kernel_sign
+      ::llvm::SmallVector<mlir::Value, 4> inputs, ori_output;
+      ::llvm::SmallVector<mlir::Type, 4> output_types;
+      for (const std::string &str : std::get<0>(kernel_sign.args)) {
+        if (pd_dialect_inputs_info_map_.at(op_name).count(str) == 0) {
+          LOG(ERROR) << "No input info for Op " << op_name << " and argument "
+                     << str;
+          return;
+        }
+        uint8_t index = pd_dialect_inputs_info_map_.at(op_name).at(str);
+        inputs.push_back(op->getOperands()[index]);
       }
-      uint8_t index = pd_dialect_inputs_info_map_.at(op_name).at(str);
-      inputs.push_back(op->getOperands()[index]);
-    }
 
-    for (const std::string &str : std::get<2>(kernel_sign.args)) {
-      if (pd_dialect_outputs_info_map_.at(op_name).count(str) == 0) {
-        LOG(ERROR) << "No output info for Op " << op_name << " and argument "
-                   << str;
-        return;
+      for (const std::string &str : std::get<2>(kernel_sign.args)) {
+        if (pd_dialect_outputs_info_map_.at(op_name).count(str) == 0) {
+          LOG(ERROR) << "No output info for Op " << op_name << " and argument "
+                     << str;
+          return;
+        }
+        uint8_t index = pd_dialect_outputs_info_map_.at(op_name).at(str);
+        output_types.push_back(op->getResultTypes()[index]);
+        ori_output.push_back(op->getResult(index));
+      }
+      auto kernel_op = builder.create<infrt::KernelOp>(
+          loc, output_types, inputs, kernel_sign.name, op->getAttrDictionary());
+      for (size_t index = 0; index < ori_output.size(); ++index) {
+        ori_output[index].replaceAllUsesWith(kernel_op.getResult(index));
       }
-      uint8_t index = pd_dialect_outputs_info_map_.at(op_name).at(str);
-      output_types.push_back(op->getResultTypes()[index]);
-      ori_output.push_back(op->getResult(index));
-    }
-
-    auto loc = getFunction().getLoc();
-    builder.setInsertionPoint(op);
-    auto kernel_op = builder.create<infrt::KernelOp>(
-        loc, output_types, inputs, kernel_sign.name, op->getAttrDictionary());
-    for (size_t index = 0; index < ori_output.size(); ++index) {
-      ori_output[index].replaceAllUsesWith(kernel_op.getResult(index));
     }
-
     CHECK(op->use_empty());
     op->erase();
   }

paddle/infrt/dialect/phi/pass/proto_arg_map_context.h

@@ -16,7 +16,7 @@ limitations under the License. */
 
 #include <mlir/IR/Operation.h>
 #include <unordered_map>
-#include "paddle/infrt/dialect/pd_ops_info.h"
+#include "paddle/infrt/dialect/pd/common/pd_ops_info.h"
 #include "paddle/phi/core/compat/arg_map_context.h"
 
 namespace infrt {

paddle/infrt/dialect/tensorrt/pd_lower_to_trt.td

@@ -3,7 +3,7 @@
 
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "paddle/infrt/dialect/infrt/ir/infrt_base.td"
-include "paddle/infrt/dialect/pd_ops.td"
+include "paddle/infrt/dialect/pd/ir/pd_ops.td"
 include "paddle/infrt/dialect/tensorrt/trt_ops.td"
 
 def PD2TRT_Matmul_Lower : Pat<

paddle/infrt/dialect/tensorrt/trt_graph_fuse_pass.cc

@@ -17,11 +17,12 @@
 #include <llvm/ADT/SetVector.h>
 #include <mlir/Analysis/SliceAnalysis.h>
 #include <mlir/IR/Builders.h>
-#include <paddle/infrt/dialect/pd_ops.h>
 #include <list>
 #include <unordered_set>
 #include <vector>
 
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
+
 namespace infrt {
 namespace trt {
 namespace {

paddle/infrt/dialect/tensorrt/trt_graph_split_pass.cc

@@ -15,7 +15,7 @@
 #include "paddle/infrt/dialect/tensorrt/trt_graph_split_pass.h"
 
 #include <mlir/IR/Builders.h>
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 
 namespace infrt {
 namespace trt {

paddle/infrt/dialect/tensorrt/trt_op_converter_pass.cc

@@ -14,7 +14,7 @@
 #include "paddle/infrt/dialect/tensorrt/trt_op_converter_pass.h"
 #include <mlir/IR/Builders.h>
 #include <mlir/Transforms/DialectConversion.h>
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 #include "paddle/infrt/dialect/tensorrt/trt_dialect_types.h"
 
 namespace infrt {

paddle/infrt/dialect/tensorrt/trt_op_teller_pass.cc

@@ -17,7 +17,7 @@
 #include <mlir/IR/Builders.h>
 #include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
 #include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 
 namespace infrt {
 namespace trt {

paddle/infrt/dialect/tensorrt/trt_ops.cc

@@ -21,6 +21,10 @@
 #include "paddle/infrt/common/global.h"
 #include "paddle/infrt/dialect/tensorrt/trt_dialect_types.h"
 
+#include "paddle/infrt/dialect/dense_tensor.h"
+#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
+#include "paddle/infrt/dialect/phi/ir/phi_base.h"
+
 namespace infrt {
 namespace trt {
 

paddle/infrt/dialect/tensorrt/trt_ops.h

@@ -30,7 +30,7 @@
 #include <mlir/Interfaces/SideEffectInterfaces.h>
 #include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
 #include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 
 namespace infrt {
 namespace trt {

paddle/infrt/dialect/tensorrt/trt_ops.td

@@ -7,6 +7,8 @@ include "mlir/Interfaces/CallInterfaces.td"
 include "mlir/IR/OpBase.td"
 include "paddle/infrt/dialect/tensorrt/trt_op_base.td"
 
+include "paddle/infrt/dialect/infrt/ir/infrt_base.td"
+include "paddle/infrt/dialect/phi/ir/infrt_phi_base.td"
 
 def TRT_CreateEngineOp : TRT_Op<"create_engine", [SingleBlockImplicitTerminator<"::infrt::ReturnOp">]> {
   let summary = "trt CreateEngine Op";
@@ -14,8 +16,8 @@ def TRT_CreateEngineOp : TRT_Op<"create_engine", [SingleBlockImplicitTerminator<
     Describe a tensorrt subgraph.
   }];
   let regions = (region SizedRegion<1>:$body);
-  let arguments = (ins Variadic<TRT_Tensor>:$inputs, DefaultValuedAttr<BoolAttr, "true">:$run_once);
-  let results = (outs TRT_EngineType:$output);
+  let arguments = (ins Variadic<DenseTensor>:$inputs, DefaultValuedAttr<BoolAttr, "true">:$run_once);
+  let results = (outs TRT_EngineType:$engine);
 }
 
 def TRT_ExecuteOp : TRT_Op<"execute", [NoSideEffect]> {
@@ -23,8 +25,25 @@ def TRT_ExecuteOp : TRT_Op<"execute", [NoSideEffect]> {
   let description = [{
     Describe a tensorrt runtime.
   }];
-  let arguments = (ins TRT_EngineType:$engine, Variadic<TRT_Tensor>:$inputs);
-  let results = (outs Variadic<TRT_Tensor>:$output);
+  let arguments = (ins TRT_EngineType:$engine, Variadic<DenseTensor>:$inputs);
+  let results = (outs Variadic<DenseTensor>:$output);
+}
+
+def TRT_EngineComputeOp : TRT_Op<"compute", [NoSideEffect]> {
+  let summary = "trt compute engine";
+  let description = [{
+    execute engine
+  }];
+  let arguments = (ins TRT_EngineType:$engine, Context:$context);
+  let results = (outs DenseTensorList:$outputs);
+}
+
+def TRT_InspectEngineOp : TRT_Op<"inspect_engine", [NoSideEffect]> {
+  let summary = "trt inspect engine";
+  let description = [{
+    Show engine
+  }];
+  let arguments = (ins TRT_EngineType:$engine);
 }
 
 def TRT_ActivationOp : TRT_Op<"Activation", [NoSideEffect]> {
@@ -34,11 +53,11 @@ def TRT_ActivationOp : TRT_Op<"Activation", [NoSideEffect]> {
     TensorRT IActivationLayer.
     
   }];
-  let arguments = (ins  TRT_Tensor:$input, SI32Attr:$activation_type,
+  let arguments = (ins  DenseTensor:$input, SI32Attr:$activation_type,
                         DefaultValuedAttr<F32Attr, "0.0">:$alpha,
                         DefaultValuedAttr<F32Attr, "0.0">:$beta);
 
-  let results = (outs TRT_Tensor:$output);
+  let results = (outs DenseTensor:$output);
 }
 
 def TRT_ElementWiseOp : TRT_Op<"ElementWise", [NoSideEffect]> {
@@ -48,9 +67,9 @@ def TRT_ElementWiseOp : TRT_Op<"ElementWise", [NoSideEffect]> {
     TensorRT IElementWiseLayer.
     
   }];
-  let arguments = (ins  TRT_Tensor:$input1, TRT_Tensor:$input2, SI32Attr:$elementwise_operation);
+  let arguments = (ins  DenseTensor:$input1, DenseTensor:$input2, SI32Attr:$elementwise_operation);
 
-  let results = (outs TRT_Tensor:$output);
+  let results = (outs DenseTensor:$output);
 }
 
 def TRT_MatrixMultiplyOp : TRT_Op<"MatrixMultiply", [NoSideEffect]> {
@@ -60,10 +79,10 @@ def TRT_MatrixMultiplyOp : TRT_Op<"MatrixMultiply", [NoSideEffect]> {
     TensorRT IMatrixMultiplyLayer.
     
   }];
-  let arguments = (ins  TRT_Tensor:$input1, BoolAttr:$transpose1,
-                        TRT_Tensor:$input2, BoolAttr:$transpose2);
+  let arguments = (ins  DenseTensor:$input1, BoolAttr:$transpose1,
+                        DenseTensor:$input2, BoolAttr:$transpose2);
 
-  let results = (outs TRT_Tensor:$output);
+  let results = (outs DenseTensor:$output);
 }
 
 #endif  // TRT_OPS

paddle/infrt/host_context/mlir_exec.cc

@@ -33,7 +33,10 @@
 #include "paddle/infrt/dialect/phi/pass/phi_op_convert_pass.h"
 #include "paddle/infrt/kernel/phi/infershaped/infershaped_kernel_launchers.h"
 #include "paddle/infrt/kernel/phi/registry.h"
-#endif
+#if defined(INFRT_WITH_GPU) && defined(INFRT_WITH_TRT)
+#include "paddle/infrt/kernel/tensorrt/registry.h"
+#endif  // INFRT_WITH_GPU && INFRT_WITH_TRT
+#endif  // INFRT_WITH_PHI
 
 static llvm::cl::list<std::string> cl_shared_libs(  // NOLINT
     "shared_libs",
@@ -62,6 +65,9 @@ int main(int argc, char** argv) {
 #ifdef INFRT_WITH_PHI
   kernel::RegisterPhiKernels(&registry);
   kernel::RegisterInferShapeLaunchers(&registry);
+#if defined(INFRT_WITH_GPU) && defined(INFRT_WITH_TRT)
+  kernel::RegisterTrtKernels(&registry);
+#endif  // INFRT_WITH_GPU && INFRT_WITH_TRT
 #endif
 
   // load extra shared library

paddle/infrt/host_context/mlir_to_runtime_translate.cc

@@ -16,12 +16,14 @@
 
 #include <llvm/Support/SourceMgr.h>
 #include <mlir/Dialect/StandardOps/IR/Ops.h>
+#include <mlir/IR/BuiltinAttributes.h>
 #include <mlir/IR/BuiltinOps.h>
 #include <mlir/IR/BuiltinTypes.h>
 #include <mlir/IR/Diagnostics.h>
 #include <mlir/IR/OperationSupport.h>
 #include <mlir/Parser.h>
 
+#include <glog/logging.h>
 #include <iostream>
 #include <memory>
 #include <string>
@@ -42,6 +44,13 @@
 #include "paddle/infrt/host_context/value.h"
 #include "paddle/infrt/tensor/tensor_shape.h"
 
+#ifdef INFRT_WITH_PHI
+#ifdef INFRT_WITH_TRT
+#include "paddle/infrt/kernel/tensorrt/trt_kernels.h"
+#endif
+#include "paddle/phi/core/dense_tensor.h"
+#endif
+
 namespace infrt {
 namespace host_context {
 
@@ -277,33 +286,58 @@ bool MlirToRuntimeTranslator::EmitGeneralOp(
       impl_->runtime->NewOpExecutable(op->getName().getStringRef().str());
 
   VLOG(3) << "processing general op : " << op->getName().getStringRef().str();
+  // TODO(wilber): Find a more appropriate way to handle special cases.
+  if (op->getName().getStringRef() == "trt.create_engine") {
+#ifdef INFRT_WITH_TRT
+    auto* symbols = impl_->runtime->symbol_table();
+    ::infrt::kernel::tensorrt::MlirOperationWithInfrtSymbol mlir_operation;
+    mlir_operation.operation = op;
+    mlir_operation.symbol_table = symbols;
+    impl_->cur_op->AppendArgument(new Value(mlir_operation));
+    // TODO(wilber): how to pass DenseTensor to create_engine op? temporialiy
+    // add a naive implement.
+    for (int i = 0, e = op->getNumOperands(); i < e; ++i) {
+      auto operand = op->getOperand(i);
+      if (operand.isa<mlir::BlockArgument>()) {
+        mlir::BlockArgument arg = operand.dyn_cast<mlir::BlockArgument>();
+        Value* arg_value = GetValue(arg);
+        if (arg_value->is_type<phi::DenseTensor>()) {
+          impl_->runtime->FeedInArgs(
+              std::make_pair(std::to_string(i), ValueRef(arg_value)));
+        }
+      }
+    }
+#else
+    CHECK(false) << "should not reach here";
+#endif
+  } else {
+    // process operands
+    for (int i = 0, e = op->getNumOperands(); i < e; i++) {
+      // function argument as value
+      auto operand = op->getOperand(i);
+      /// if (operand.getKind() == mlir::Value::Kind::BlockArgument) {
+      if (operand.isa<mlir::BlockArgument>()) {
+        mlir::BlockArgument arg = operand.dyn_cast<mlir::BlockArgument>();
+        Value* arg_value = GetValue(arg);
+        impl_->cur_op->AppendArgument(arg_value);
+        VLOG(3) << "* op mlir operand: " << DumpToString(arg) << " "
+                << GetValue(arg);
+        continue;
+      }
 
-  // process operands
-  for (int i = 0, e = op->getNumOperands(); i < e; i++) {
-    // function argument as value
-    auto operand = op->getOperand(i);
-    /// if (operand.getKind() == mlir::Value::Kind::BlockArgument) {
-    if (operand.isa<mlir::BlockArgument>()) {
-      mlir::BlockArgument arg = operand.dyn_cast<mlir::BlockArgument>();
-      Value* arg_value = GetValue(arg);
+      // normal value
+      Value* arg_value = GetValue(operand);
+      if (!arg_value) {
+        auto upstream_op = operand.getDefiningOp();
+        arg_value = GetOpResult(upstream_op);
+      }
+      CHECK(arg_value) << "No-exist argument value found: "
+                       << DumpToString(operand);
       impl_->cur_op->AppendArgument(arg_value);
-      VLOG(3) << "* op mlir operand: " << DumpToString(arg) << " "
-              << GetValue(arg);
-      continue;
-    }
 
-    // normal value
-    Value* arg_value = GetValue(operand);
-    if (!arg_value) {
-      auto upstream_op = operand.getDefiningOp();
-      arg_value = GetOpResult(upstream_op);
+      VLOG(3) << "* op mlir operand: " << DumpToString(operand) << " "
+              << GetValue(operand) << " vs " << arg_value;
     }
-    CHECK(arg_value) << "No-exist argument value found: "
-                     << DumpToString(operand);
-    impl_->cur_op->AppendArgument(arg_value);
-
-    VLOG(3) << "* op mlir operand: " << DumpToString(operand) << " "
-            << GetValue(operand) << " vs " << arg_value;
   }
 
   // process attributes
@@ -383,33 +417,6 @@ bool MlirToRuntimeTranslator::EmitGeneralOp(
     impl_->cur_op->AppendAttribute(tmp[i]);
   }
 
-  // process results
-  llvm::SmallVector<Value*, 4> res_values;
-  for (int i = 0, e = op->getNumResults(); i < e; i++) {
-    auto res = op->getResult(i);
-    if (res.getType().isa<::infrt::DenseTensorType>()) {
-      auto r = impl_->value_map.try_emplace(
-          res, ValueRef(new Value{::phi::DenseTensor()}));
-      CHECK(r.second) << "Duplicate add mlir value [" << DumpToString(res)
-                      << "]";
-      res_values.push_back(r.first->second.get());
-    } else {
-      res_values.push_back(AddValue(res));
-    }
-
-    VLOG(3) << "* op mlir res: " << DumpToString(res) << " " << GetValue(res);
-  }
-  impl_->cur_op->SetResults(res_values);
-
-#ifdef INFRT_DEBUG
-  {
-    VLOG(3) << "check result";
-    for (int i = 0; i < impl_->cur_op->frame().GetNumResults(); i++) {
-      VLOG(3) << "+ res value: " << impl_->cur_op->frame().GetResults()[i];
-    }
-  }
-#endif
-
   // process regions, we treat regions as attribute.
   auto num_regions = op->getNumRegions();
   if (num_regions > 0) {
@@ -438,6 +445,33 @@ bool MlirToRuntimeTranslator::EmitGeneralOp(
     impl_->cur_op->AppendAttribute(new Value(function));
   }
 
+  // process results
+  llvm::SmallVector<Value*, 4> res_values;
+  for (int i = 0, e = op->getNumResults(); i < e; i++) {
+    auto res = op->getResult(i);
+    if (res.getType().isa<::infrt::DenseTensorType>()) {
+      auto r = impl_->value_map.try_emplace(
+          res, ValueRef(new Value{::phi::DenseTensor()}));
+      CHECK(r.second) << "Duplicate add mlir value [" << DumpToString(res)
+                      << "]";
+      res_values.push_back(r.first->second.get());
+    } else {
+      res_values.push_back(AddValue(res));
+    }
+
+    VLOG(3) << "* op mlir res: " << DumpToString(res) << " " << GetValue(res);
+  }
+  impl_->cur_op->SetResults(res_values);
+
+#ifdef INFRT_DEBUG
+  {
+    VLOG(3) << "check result";
+    for (int i = 0; i < impl_->cur_op->frame().GetNumResults(); i++) {
+      VLOG(3) << "+ res value: " << impl_->cur_op->frame().GetResults()[i];
+    }
+  }
+#endif
+
   return true;
 }
 

paddle/infrt/host_context/paddle_mlir.cc

@@ -15,7 +15,7 @@
 #include "paddle/infrt/host_context/paddle_mlir.h"
 #include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
 #include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
-#include "paddle/infrt/dialect/pd_ops_info.h"
+#include "paddle/infrt/dialect/pd/common/pd_ops_info.h"
 
 MLIRModelGenImpl::MLIRModelGenImpl()
     : context_(infrt::Global::getMLIRContext()), builder_(context_) {

paddle/infrt/host_context/paddle_mlir.h

@@ -14,22 +14,22 @@
 #ifndef PADDLE_INFRT_HOST_CONTEXT_PADDLE_MLIR_H_
 #define PADDLE_INFRT_HOST_CONTEXT_PADDLE_MLIR_H_
 
+#include <llvm/Support/CommandLine.h>
+#include <mlir/Dialect/StandardOps/IR/Ops.h>
+#include <mlir/IR/AsmState.h>
+#include <mlir/IR/Builders.h>
+#include <mlir/IR/BuiltinOps.h>
+#include <mlir/IR/MLIRContext.h>
 #include <fstream>
 #include <iostream>
 #include <string>
 
-#include "llvm/Support/CommandLine.h"
-#include "mlir/Dialect/StandardOps/IR/Ops.h"
-#include "mlir/IR/AsmState.h"
-#include "mlir/IR/Builders.h"
-#include "mlir/IR/MLIRContext.h"
 #include "paddle/infrt/common/global.h"
 #include "paddle/infrt/common/string.h"
 #include "paddle/infrt/dialect/dense_tensor.h"
 #include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
-
 #include "paddle/infrt/dialect/init_dialects.h"
-#include "paddle/infrt/dialect/pd_ops.h"
+#include "paddle/infrt/dialect/pd/ir/pd_ops.h"
 #include "paddle/infrt/dialect/tensor_shape.h"
 #include "paddle/infrt/paddle/model_parser.h"
 

paddle/infrt/host_context/value.h

@@ -24,6 +24,7 @@
 #include "paddle/infrt/common/shared.h"
 #include "paddle/infrt/dialect/infrt/common/types.h"
 #include "paddle/infrt/host_context/function.h"
+#include "paddle/infrt/host_context/symbol_table.h"
 #include "paddle/infrt/support/variant.h"
 #include "paddle/infrt/tensor/dense_host_tensor.h"
 #include "paddle/infrt/tensor/dense_tensor_view.h"
@@ -41,7 +42,15 @@
 #include "paddle/phi/common/scalar_array.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/meta_tensor.h"
-#endif
+
+#ifdef INFRT_WITH_GPU
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#endif  // INFRT_WITH_GPU
+#ifdef INFRT_WITH_TRT
+#include "paddle/infrt/backends/tensorrt/trt_engine.h"
+#include "paddle/infrt/kernel/tensorrt/trt_kernels.h"
+#endif  // INFRT_WITH_TRT
+#endif  // INFRT_WITH_PHI
 
 namespace infrt {
 namespace host_context {
@@ -72,8 +81,13 @@ using ValueVariantType =
             ::phi::MetaTensor,
             ::phi::DenseTensor,
             backends::CpuPhiContext,
+#ifdef INFRT_WITH_GPU
+            backends::GpuPhiContext,
+            ::phi::GPUContext,
+#endif
             ::phi::CPUContext,
             std::vector<const phi::DenseTensor*>,
+            std::vector<phi::DenseTensor*>,
             paddle::experimental::ScalarBase<phi::DenseTensor>,
             paddle::experimental::ScalarArrayBase<phi::DenseTensor>,
             std::vector<phi::MetaTensor*>,
@@ -81,6 +95,10 @@ using ValueVariantType =
             paddle::experimental::Backend,
             paddle::experimental::DataLayout,
             paddle::experimental::DataType,
+#ifdef INFRT_WITH_TRT
+            ::infrt::backends::tensorrt::TrtEngine,
+            ::infrt::kernel::tensorrt::MlirOperationWithInfrtSymbol,
+#endif  // INFRT_WITH_TRT
 #endif
             std::vector<int16_t>,
             std::vector<int32_t>,
@@ -120,8 +138,18 @@ class Value : public common::Object {
 #ifdef INFRT_WITH_PHI
   explicit Value(::phi::CPUContext&& x) : data(std::move(x)) {}
   explicit Value(backends::CpuPhiContext&& x) : data(std::move(x)) {}
+#ifdef INFRT_WITH_GPU
+  explicit Value(::phi::GPUContext&& x) : data(std::move(x)) {}
+  explicit Value(backends::GpuPhiContext&& x) : data(std::move(x)) {}
+#endif
   explicit Value(::phi::DenseTensor&& x) : data(std::move(x)) {}
   explicit Value(::phi::MetaTensor&& x) : data(std::move(x)) {}
+#ifdef INFRT_WITH_TRT
+  explicit Value(::infrt::backends::tensorrt::TrtEngine&& x)
+      : data(std::move(x)) {}
+  explicit Value(::infrt::kernel::tensorrt::MlirOperationWithInfrtSymbol x)
+      : data(x) {}
+#endif  // INFRT_WITH_TRT
 #endif
 
   template <typename T>

paddle/infrt/kernel/CMakeLists.txt

 add_subdirectory(phi)
+add_subdirectory(tensorrt)
 
 core_gather_headers()
 

paddle/infrt/kernel/phi/context_kernels.cc

@@ -25,6 +25,16 @@ namespace phi {
   return ctx;
 }
 
+#ifdef INFRT_WITH_GPU
+::phi::GPUContext CreateGPUContext() {
+  ::phi::GPUContext context;
+  context.PartialInitWithoutAllocator();
+  context.SetAllocator(new ::infrt::backends::GpuPhiAllocator{});
+  context.PartialInitWithAllocator();
+  return context;
+}
+#endif
+
 }  // namespace phi
 }  // namespace kernel
 }  // namespace infrt

paddle/infrt/kernel/phi/context_kernels.h

@@ -25,6 +25,10 @@ namespace phi {
 
 ::phi::CPUContext CreateCPUContext();
 
+#ifdef INFRT_WITH_GPU
+::phi::GPUContext CreateGPUContext();
+#endif
+
 }  // namespace phi
 }  // namespace kernel
 }  // namespace infrt

paddle/infrt/kernel/phi/dense_tensor_kernels.cc

@@ -15,6 +15,12 @@
 #include "paddle/infrt/kernel/phi/dense_tensor_kernels.h"
 #include "paddle/infrt/dialect/phi/data_type.h"
 #include "paddle/infrt/kernel/phi/context_kernels.h"
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/common/place.h"
+
+#ifdef INFRT_WITH_GPU
+#include <cuda_runtime.h>
+#endif
 
 namespace infrt {
 namespace kernel {
@@ -34,26 +40,83 @@ namespace phi {
                              {}));
 }
 
+::phi::DenseTensor CreateGPUDenseTensor(
+    const ::phi::GPUContext& context,
+    host_context::Attribute<std::vector<int64_t>> dims,
+    host_context::Attribute<std::vector<int64_t>> lod,
+    host_context::Attribute<::infrt::LayoutType> layout,
+    host_context::Attribute<::infrt::PrecisionType> precision) {
+  return ::phi::DenseTensor(
+      const_cast<::phi::Allocator*>(&context.GetAllocator()),
+      ::phi::DenseTensorMeta(ConvertPrecisionToPhi(precision.get()),
+                             ::phi::make_ddim(dims.get()),
+                             ConvertLayoutToPhi(layout.get()),
+                             {}));
+}
+
 void FillDenseTensorF32(::phi::DenseTensor* dense_tensor,
                         host_context::Attribute<std::vector<float>> value) {
-  auto place = ::phi::CPUPlace();
+  auto place = dense_tensor->place();
   float* a_data = dense_tensor->mutable_data<float>(place);
-  for (int64_t i = 0; i < dense_tensor->numel(); ++i) {
-    a_data[i] = (value.get())[i];
+  if (place.GetType() == ::phi::AllocationType::CPU) {
+    for (int64_t i = 0; i < dense_tensor->numel(); ++i) {
+      a_data[i] = (value.get())[i];
+    }
+  } else if (place.GetType() == ::phi::AllocationType::GPU) {
+#ifdef INFRT_WITH_GPU
+    // TODO(wilber): how to set the stream parameter to copy with stream.
+    cudaMemcpy(a_data,
+               value.get().data(),
+               sizeof(float) * value.get().size(),
+               cudaMemcpyHostToDevice);
+#endif
+  } else {
+    llvm_unreachable("temporarily not support other target.");
   }
 }
 
 void PrintDenseTensor(::phi::DenseTensor* dense_tensor) {
-#define PRINT_META_DATA(PHI_DATATYPE, DTYPE)              \
-  case ::phi::DataType::PHI_DATATYPE: {                   \
-    DTYPE* data = dense_tensor->data<DTYPE>();            \
-    if (dense_tensor->numel() == 0) break;                \
-    std::cout << data[0];                                 \
-    for (int64_t i = 1; i < dense_tensor->numel(); i++) { \
-      std::cout << "," << data[i];                        \
-    }                                                     \
-    break;                                                \
+#ifndef INFRT_WITH_GPU
+#define PRINT_META_DATA(PHI_DATATYPE, DTYPE)                \
+  case ::phi::DataType::PHI_DATATYPE: {                     \
+    auto place = dense_tensor->place();                     \
+    if (place.GetType() == ::phi::AllocationType::CPU) {    \
+      DTYPE* data = dense_tensor->data<DTYPE>();            \
+      if (dense_tensor->numel() == 0) break;                \
+      std::cout << data[0];                                 \
+      for (int64_t i = 1; i < dense_tensor->numel(); i++) { \
+        std::cout << "," << data[i];                        \
+      }                                                     \
+    }                                                       \
+    break;                                                  \
+  }
+#else
+#define PRINT_META_DATA(PHI_DATATYPE, DTYPE)                     \
+  case ::phi::DataType::PHI_DATATYPE: {                          \
+    auto place = dense_tensor->place();                          \
+    DTYPE* data = dense_tensor->data<DTYPE>();                   \
+    if (dense_tensor->numel() == 0) break;                       \
+    if (place.GetType() == ::phi::AllocationType::CPU) {         \
+      std::cout << data[0];                                      \
+      for (int64_t i = 1; i < dense_tensor->numel(); i++) {      \
+        std::cout << "," << data[i];                             \
+      }                                                          \
+    } else if (place.GetType() == ::phi::AllocationType::GPU) {  \
+      std::vector<DTYPE> host_data(dense_tensor->numel(), 0);    \
+      cudaMemcpy(host_data.data(),                               \
+                 data,                                           \
+                 sizeof(DTYPE) * dense_tensor->numel(),          \
+                 cudaMemcpyDeviceToHost);                        \
+      std::cout << host_data[0];                                 \
+      for (int64_t i = 1; i < dense_tensor->numel(); i++) {      \
+        std::cout << "," << host_data[i];                        \
+      }                                                          \
+    } else {                                                     \
+      llvm_unreachable("temporarily not support other target."); \
+    }                                                            \
+    break;                                                       \
   }
+#endif
 
   ::phi::DDim dims = dense_tensor->dims();
   std::cout << "dense_tensor: shape=shape" << dims.to_str() << ","

paddle/infrt/kernel/phi/dense_tensor_kernels.h

@@ -30,6 +30,13 @@ namespace phi {
     host_context::Attribute<::infrt::LayoutType> layout,
     host_context::Attribute<::infrt::PrecisionType> precision);
 
+::phi::DenseTensor CreateGPUDenseTensor(
+    const ::phi::GPUContext& context,
+    host_context::Attribute<std::vector<int64_t>> dims,
+    host_context::Attribute<std::vector<int64_t>> lod,
+    host_context::Attribute<::infrt::LayoutType> layout,
+    host_context::Attribute<::infrt::PrecisionType> precision);
+
 void FillDenseTensorF32(::phi::DenseTensor* dense_tensor,
                         host_context::Attribute<std::vector<float>> values);
 void PrintDenseTensor(::phi::DenseTensor* dense_tensor);

paddle/infrt/kernel/phi/registry.cc

@@ -35,7 +35,7 @@ void RegisterPhiKernels(host_context::KernelRegistry* registry) {
   registry->AddKernel("phi_dt.create_context.cpu",
                       INFRT_KERNEL(infrt::kernel::phi::CreateCPUContext));
   registry->AddKernelWithAttrs(
-      "phi_dt.create_dense_tensor",
+      "phi_dt.create_dense_tensor.cpu",
       INFRT_KERNEL(infrt::kernel::phi::CreateDenseTensor),
       {"dims", "lod", "layout", "precision"});
   registry->AddKernelWithAttrs(
@@ -44,6 +44,15 @@ void RegisterPhiKernels(host_context::KernelRegistry* registry) {
       {"value"});
   registry->AddKernel("phi_dt.print_tensor",
                       INFRT_KERNEL(infrt::kernel::phi::PrintDenseTensor));
+
+#ifdef INFRT_WITH_GPU
+  registry->AddKernel("phi_dt.create_context.gpu",
+                      INFRT_KERNEL(infrt::kernel::phi::CreateGPUContext));
+  registry->AddKernelWithAttrs(
+      "phi_dt.create_dense_tensor.gpu",
+      INFRT_KERNEL(infrt::kernel::phi::CreateGPUDenseTensor),
+      {"dims", "lod", "layout", "precision"});
+#endif
 }
 
 }  // namespace kernel

paddle/infrt/kernel/tensor_kernels.cc

@@ -25,6 +25,10 @@
 #include "paddle/infrt/tensor/tensor_map.h"
 #include "paddle/infrt/tensor/tensor_shape.h"
 
+#ifdef INFRT_WITH_PHI
+#include "paddle/phi/core/dense_tensor.h"
+#endif
+
 namespace infrt {
 namespace kernel {
 using namespace host_context;  // NOLINT
@@ -62,6 +66,20 @@ DenseHostTensor TensorMapGetTensor(TensorMap map, Attribute<std::string> name) {
 
 int32_t TensorMapGetSize(TensorMap map) { return map.size(); }
 
+// TODO(wilber): Maybe we should place TensorList type in dt dialect.
+#ifdef INFRT_WITH_PHI
+phi::DenseTensor TensorListGetTensor(std::vector<phi::DenseTensor *> list,
+                                     Attribute<int32_t> idx) {
+  CHECK_LT(idx.get(), static_cast<int>(list.size()))
+      << "idx should less than list size";
+  return *list[idx.get()];
+}
+
+int32_t TensorListGetSize(const std::vector<phi::DenseTensor *> &list) {
+  return list.size();
+}
+#endif
+
 DenseHostTensor ShallowCopyTensor(DenseHostTensor v) { return v; }
 
 template <typename T>
@@ -126,6 +144,14 @@ void RegisterTensorKernels(host_context::KernelRegistry *registry) {
                       INFRT_KERNEL(TensorMapGetTensor));
   registry->AddKernel("dt.tensor_map_get_size", INFRT_KERNEL(TensorMapGetSize));
 
+// TensorList related methods.
+#ifdef INFRT_WITH_PHI
+  registry->AddKernel("dt.tensor_list_get_tensor",
+                      INFRT_KERNEL(TensorListGetTensor));
+  registry->AddKernel("dt.tensor_list_get_size",
+                      INFRT_KERNEL(TensorListGetSize));
+#endif
+
   registry->AddKernel("dt.shallow_copy_tensor",
                       INFRT_KERNEL(ShallowCopyTensor));
 

paddle/infrt/kernel/tensorrt/CMakeLists.txt

+if (NOT (INFRT_WITH_PHI AND INFRT_WITH_GPU AND INFRT_WITH_TRT))
+  return()
+endif()
+
+core_gather_headers()
+
+gather_srcs(infrt_src SRCS
+    registry.cc
+    trt_kernels.cc
+)

paddle/infrt/kernel/tensorrt/registry.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/infrt/kernel/tensorrt/registry.h"
+
+#include "paddle/infrt/host_context/kernel_registry.h"
+#include "paddle/infrt/host_context/kernel_utils.h"
+#include "paddle/infrt/kernel/tensorrt/trt_kernels.h"
+
+namespace infrt {
+namespace kernel {
+
+void RegisterTrtKernels(host_context::KernelRegistry* registry) {
+  registry->AddKernel("trt.create_engine",
+                      INFRT_KERNEL(tensorrt::CreateTrtEngine));
+  registry->AddKernel("trt.inspect_engine",
+                      INFRT_KERNEL(tensorrt::PrintTrtLayer));
+  registry->AddKernel("trt.compute", INFRT_KERNEL(tensorrt::TrtEngineCompute));
+}
+
+}  // namespace kernel
+}  // namespace infrt

paddle/infrt/kernel/tensorrt/registry.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>
+
+namespace infrt {
+namespace host_context {
+
+struct KernelRegistry;
+
+}  // namespace host_context
+}  // namespace infrt
+
+namespace infrt {
+namespace kernel {
+
+/**
+ * Register all the trt kernels to registry.
+ */
+void RegisterTrtKernels(host_context::KernelRegistry* registry);
+
+}  // namespace kernel
+}  // namespace infrt

paddle/infrt/kernel/tensorrt/trt_kernels.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/infrt/kernel/tensorrt/trt_kernels.h"
+#include <string>
+#include "NvInfer.h"
+#include "NvInferRuntime.h"
+#include "NvInferRuntimeCommon.h"
+#include "glog/logging.h"
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/Support/Casting.h"
+#include "llvm/Support/raw_ostream.h"
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/Operation.h"
+#include "mlir/IR/Value.h"
+#include "paddle/infrt/backends/tensorrt/trt_engine.h"
+#include "paddle/infrt/backends/tensorrt/trt_options.h"
+#include "paddle/infrt/dialect/tensorrt/trt_ops.h"
+#include "paddle/infrt/host_context/symbol_table.h"
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace infrt {
+namespace kernel {
+namespace tensorrt {
+
+::infrt::backends::tensorrt::TrtEngine CreateTrtEngine(
+    MlirOperationWithInfrtSymbol
+        create_engine_op /*, input_tensors, output_tensors, weights*/) {
+  // TODO(wilber): The device_id needs to get from mlir.
+  int device_id = 0;
+  backends::tensorrt::TrtEngine engine(device_id);
+
+  auto* builder = engine.GetTrtBuilder();
+  // TODO(wilber): How to process weights?
+  backends::tensorrt::TrtUniquePtr<nvinfer1::INetworkDefinition> network;
+  // TODO(wilber): static_shape or dynamic_shape network? The code is just
+  // static_shape test.
+  network.reset(builder->createNetworkV2(0));
+
+  // TODO(wilber): The build option shoule be fiiled from mlir info.
+  backends::tensorrt::BuildOptions options;
+  options.max_batch = 4;
+
+  // Parse mlir Region which only has one block.
+  mlir::Operation& operation = *create_engine_op.operation;
+  auto* symbol_table = create_engine_op.symbol_table;
+  CHECK_NOTNULL(symbol_table);
+
+  unsigned int num_regions = operation.getNumRegions();
+  CHECK_EQ(num_regions, 1U) << "only support one region case.";
+  auto& region = operation.getRegion(0);
+  auto& block = region.getBlocks().front();
+
+  llvm::DenseMap<mlir::Value, nvinfer1::ITensor*> map_info;
+  std::unordered_map<std::string, phi::DenseTensor*> trt_bind_inputs;
+
+  for (auto index_operand : llvm::enumerate(operation.getOperands())) {
+    mlir::Value operand = index_operand.value();
+    size_t idx = index_operand.index();
+
+    const std::string input_name = "input_" + std::to_string(idx);
+    auto* v = symbol_table->GetValue(std::to_string(idx));
+    CHECK_NOTNULL(v);
+    auto* t = &v->get<phi::DenseTensor>();
+    trt_bind_inputs[input_name] = t;
+    // TODO(wilber): get input info from mlir.
+    // TODO(wilber): input dims, now only support static_shape, and just remove
+    // the first dimension.
+    // TODO(wilber): now only suppot float input.
+    nvinfer1::Dims dims;
+    dims.nbDims = t->dims().size() - 1;
+    for (int i = 0; i < dims.nbDims; ++i) {
+      dims.d[i] = t->dims()[i + 1];
+    }
+    auto* in =
+        network->addInput(input_name.c_str(), nvinfer1::DataType::kFLOAT, dims);
+    map_info[operand] = in;
+  }
+
+  // TODO(wilber): Find a way to add layer.
+  for (auto& inner_op : block.without_terminator()) {
+    if (inner_op.getName().getStringRef() == "trt.Activation") {
+      trt::ActivationOp act_op = llvm::dyn_cast<trt::ActivationOp>(inner_op);
+      auto in_arg = act_op.getOperand();
+      if (!map_info.count(in_arg)) {
+        CHECK(false) << "map_info not has in_arg.";
+      }
+      nvinfer1::ActivationType act_type =
+          static_cast<nvinfer1::ActivationType>(act_op.activation_type());
+      auto* act_layer = network->addActivation(*map_info[in_arg], act_type);
+      act_layer->setAlpha(act_op.alpha().convertToFloat());
+      act_layer->setBeta(act_op.beta().convertToFloat());
+      for (size_t i = 0; i < act_op->getNumResults(); ++i) {
+        nvinfer1::ITensor* act_out_tensor = act_layer->getOutput(i);
+        mlir::Value act_out = act_op->getResult(i);
+        map_info[act_out] = act_out_tensor;
+      }
+    }
+
+    // if (inner_op.getName().getStringRef() == "trt.Constant") {
+    //   trt::ConstantOp op = llvm::dyn_cast<trt::ConstantOp>(inner_op);
+    //   mlir::Value op_out = op.getResult();
+    //   std::vector<float> weight_data{1};
+    //   auto* layer = network->addConstant(nvinfer1::Dims2(1, 1),
+    //   nvinfer1::Weights{nvinfer1::DataType::kFLOAT, weight_data.data(), 1});
+    //   auto* op_out_tenor = layer->getOutput(0);
+    //   map_info[op_out] = op_out_tenor;
+    // }
+  }
+  for (auto& inner_op : block.without_terminator()) {
+    for (mlir::Value v : inner_op.getResults()) {
+      for (mlir::Operation* user : v.getUsers()) {
+        if (user->getName().getStringRef() == "infrt.return") {
+          if (!map_info.count(v)) {
+            CHECK(false) << "map_info not has value";
+          }
+          network->markOutput(*map_info[v]);
+        }
+      }
+    }
+  }
+  // std::unordered_map<std::string, phi::DenseTensor*> trt_bind_outputs;
+  mlir::Operation* ret = block.getTerminator();
+  for (unsigned int i = 0; i < ret->getNumOperands(); ++i) {
+    mlir::Value arg = ret->getOperand(i);
+    CHECK(map_info.count(arg));
+    map_info[arg]->setName(("output_" + std::to_string(i)).c_str());
+  }
+  for (int i = 0; i < network->getNbOutputs(); ++i) {
+    engine.PrepareOutputHandle(network->getOutput(i)->getName());
+  }
+
+  VLOG(3) << "trt engine build start.";
+  engine.Build(std::move(network), options);
+  VLOG(3) << "trt engine build done.";
+
+  // TODO(wilber): get inference options from mlir.
+  backends::tensorrt::InferenceOptions inference_options;
+  inference_options.batch = 1;
+  // TODO(wilber): bind trt input/output tensors.
+  engine.SetUpInference(inference_options, trt_bind_inputs);
+  return engine;
+}
+
+void PrintTrtLayer(backends::tensorrt::TrtEngine* engine) {
+  engine->GetEngineInfo();
+}
+
+std::vector<phi::DenseTensor*> TrtEngineCompute(
+    backends::tensorrt::TrtEngine* engine, const phi::GPUContext& context) {
+  engine->Run(context);
+  std::vector<phi::DenseTensor*> res;
+  for (size_t i = 0; i < engine->GetOutputNum(); ++i) {
+    res.push_back(engine->GetOutput("output_" + std::to_string(i)));
+  }
+  return res;
+}
+
+}  // namespace tensorrt
+}  // namespace kernel
+}  // namespace infrt

paddle/infrt/kernel/tensorrt/trt_kernels.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 <tuple>
+#include <utility>
+
+#include "mlir/IR/Operation.h"
+
+#include "paddle/infrt/backends/tensorrt/trt_engine.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+
+namespace infrt {
+namespace host_context {
+class SymbolTable;
+}  // namespace host_context
+
+namespace kernel {
+namespace tensorrt {
+
+struct MlirOperationWithInfrtSymbol {
+  mlir::Operation* operation;
+  ::infrt::host_context::SymbolTable* symbol_table;
+};
+
+::infrt::backends::tensorrt::TrtEngine CreateTrtEngine(
+    MlirOperationWithInfrtSymbol engine_op);
+
+void PrintTrtLayer(backends::tensorrt::TrtEngine* engine);
+
+std::vector<phi::DenseTensor*> TrtEngineCompute(
+    backends::tensorrt::TrtEngine* engine, const phi::GPUContext& context);
+
+}  // namespace tensorrt
+}  // namespace kernel
+}  // namespace infrt

paddle/infrt/tests/dialect/disabled_trt.mlir

+// RUN: infrtexec -i %s | FileCheck %s
+
+// CHECK-LABEL: @run_trt
+func @run_trt(%0 : !infrt.dense_tensor<GPU, FP32, NCHW>, %ctx : !phi.context<GPU>) {
+  %a = "trt.create_engine"(%0) ({
+    %1 = "trt.Activation"(%0) {activation_type = 1 : si32, alpha = 1.0 : f32, beta = 6.0 : f32} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+    "infrt.return"(%1) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
+  }) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !trt.engine
+  "trt.inspect_engine"(%a) {} : (!trt.engine) -> ()
+
+  %res = "trt.compute"(%a, %ctx) {} : (!trt.engine, !phi.context<GPU>) -> (!infrt.tensor_list)
+  %size = "dt.tensor_list_get_size"(%res) {} : (!infrt.tensor_list) -> (i32)
+  "infrt.print.i32"(%size) {} : (i32) -> ()
+
+  %ts0 = "dt.tensor_list_get_tensor"(%res) {id = 0 : i32} : (!infrt.tensor_list) -> (!infrt.dense_tensor<GPU, FP32, NCHW>)
+  "phi_dt.print_tensor" (%ts0) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
+
+  infrt.return
+}
+
+// CHECK-LABEL: @main
+func @main() {
+  %ctx = "phi_dt.create_context.gpu" (): () -> !phi.context<GPU>
+  %t = "phi_dt.create_dense_tensor.gpu" (%ctx) {
+    precision=#infrt.precision<FP32>,
+    layout=#infrt.layout<NCHW>,
+    dims=[1:i64, 3:i64, 1:i64, 1:i64], lod=[1:i64]}: (!phi.context<GPU>) -> (!infrt.dense_tensor<GPU, FP32, NCHW>)
+
+  "phi_dt.fill_dense_tensor.f32"(%t) {value=[3.8:f32, 2.4:f32, 1.3:f32]} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
+  "phi_dt.print_tensor" (%t) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
+
+  //%res = 
+  infrt.call @run_trt(%t, %ctx) : (!infrt.dense_tensor<GPU, FP32, NCHW>, !phi.context<GPU>) -> ()
+  //-> (!infrt.dense_tensor<GPU, FP32, NCHW>)
+
+  infrt.return
+}

paddle/infrt/tests/dialect/rewrite.mlir → paddle/infrt/tests/dialect/pd/rewrite.mlir

-// RUN: infrtopt --canonicalize %s | FileCheck %s
+// RUN: infrtopt --pd-op-fuse %s | FileCheck %s
 // CHECK-LABEL: @main
 func @main() -> tensor<?xf32> {
   %a = "pd.feed"() {name="input0"} : () -> tensor<?xf32>

paddle/infrt/tests/dialect/phi/dense_tensor.mlir

@@ -3,7 +3,7 @@
 // CHECK-LABEL: @sign_any_float32_execute
 func @sign_any_float32_execute() {
   %ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
-  %t = "phi_dt.create_dense_tensor" (%ctx) {
+  %t = "phi_dt.create_dense_tensor.cpu" (%ctx) {
     precision=#infrt.precision<FP32>, 
     layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
   "phi_dt.fill_dense_tensor.f32"(%t) {value=[3.8:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()

paddle/infrt/tests/dialect/phi/phi_test.mlir

@@ -6,7 +6,7 @@ module  {
   }
   func @main() {
     %ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
-    %t = "phi_dt.create_dense_tensor" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    %t = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
     "phi_dt.fill_dense_tensor.f32"(%t) {value=[3.8:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
     %2 = infrt.call@predict(%t) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
     phi_dt.print_tensor(%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)

paddle/infrt/tests/dialect/trt_ops.mlir

 // RUN: trt-exec %s
 // CHECK-LABEL: @main
-func @main(%bias:tensor<?xf32>, %c:tensor<?xf32>, %b1:tensor<?xf32>, %b2:tensor<?xf32>, %bias1:tensor<?xf32>, %bias2:tensor<?xf32>) -> tensor<?xf32> {
-  %d = "pd.elementwise_add"(%c, %bias) {axis=-1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e = "pd.relu6"(%d) {} : (tensor<?xf32>) -> tensor<?xf32>
+func @main(%bias:!infrt.dense_tensor<GPU, FP32, NCHW>, %c:!infrt.dense_tensor<GPU, FP32, NCHW>, %b1:!infrt.dense_tensor<GPU, FP32, NCHW>, %b2:!infrt.dense_tensor<GPU, FP32, NCHW>, %bias1:!infrt.dense_tensor<GPU, FP32, NCHW>, %bias2:!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW> {
+  %d = "pd.elementwise_add"(%c, %bias) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+  %e = "pd.relu6"(%d) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
 
-  %c1 = "pd.matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %d1 = "pd.elementwise_add"(%c1, %bias1) {axis=-1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e1 = "pd.relu"(%d1) {} : (tensor<?xf32>) -> tensor<?xf32>
+  %c1 = "pd.matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+  %d1 = "pd.elementwise_add"(%c1, %bias1) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+  %e1 = "pd.relu"(%d1) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
 
-  %c2 = "pd.matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %d2 = "pd.elementwise_add"(%c2, %bias2) {axis=-1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e2 = "pd.relu"(%d2) {} : (tensor<?xf32>) -> tensor<?xf32>
+  %c2 = "pd.matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+  %d2 = "pd.elementwise_add"(%c2, %bias2) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
+  %e2 = "pd.relu"(%d2) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
   
-  infrt.return %e2 : tensor<?xf32>
+  infrt.return %e2 : !infrt.dense_tensor<GPU, FP32, NCHW>
 }

paddle/phi/api/include/tensor.h

@@ -324,7 +324,7 @@ class PADDLE_API Tensor final {
    *
    * @return std::shared_ptr<phi::TensorBase>
    */
-  std::shared_ptr<phi::TensorBase> impl() const;
+  const std::shared_ptr<phi::TensorBase>& impl() const;
 
   /**
    * @brief Set the implemention of current Tensor.
@@ -333,6 +333,13 @@ class PADDLE_API Tensor final {
    */
   void set_impl(const std::shared_ptr<phi::TensorBase>& impl);
 
+  /**
+   * @brief Set the implemention of current Tensor.
+   *
+   * @param impl
+   */
+  void set_impl(std::shared_ptr<phi::TensorBase>&& impl);
+
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   /**
    * @brief Get the stream where the tensor is currently located
@@ -397,7 +404,9 @@ class PADDLE_API Tensor final {
    * @param blocking, Should we copy this in sync way.
    * @return void
    */
-  void copy_(const Tensor& src, const bool blocking);
+  void copy_(const Tensor& src,
+             const phi::Place& target_place,
+             const bool blocking);
   /**
    * @brief Cast datatype from one to another
    *

paddle/phi/api/lib/CMakeLists.txt

@@ -148,4 +148,4 @@ cc_library(phi_bw_function_api SRCS ${bw_api_source_file} DEPS phi_tensor_raw ph
 cc_library(sparse_api SRCS ${sparse_api_source_file} DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils sparse_api_custom_impl)
 cc_library(sparse_bw_api SRCS ${sparse_bw_api_source_file} DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils sparse_api sparse_api_custom_impl)
 
-cc_library(phi_tensor SRCS tensor_method.cc DEPS phi_tensor_raw phi_function_api)
+cc_library(phi_tensor SRCS tensor_method.cc DEPS phi_tensor_raw phi_function_api api_gen_utils kernel_dispatch infermeta)

paddle/phi/api/lib/api_gen_utils.cc

@@ -95,12 +95,8 @@ paddle::optional<phi::MetaTensor> MakeMetaTensor(
 /* ------------------ for output ----------------------- */
 
 phi::DenseTensor* SetKernelOutput(Backend backend, Tensor* out) {
-  if (!out->initialized()) {
-    auto dense_tensor = std::make_shared<phi::DenseTensor>(
-        phi::make_intrusive<SharedStorage>(phi::TransToPhiPlace(backend)),
-        phi::DenseTensorMeta());
-    out->set_impl(dense_tensor);
-    return dense_tensor.get();
+  if (out->impl() == nullptr) {
+    out->set_impl(std::make_shared<phi::DenseTensor>());
   }
   return static_cast<phi::DenseTensor*>(out->impl().get());
 }
@@ -111,9 +107,7 @@ std::vector<phi::DenseTensor*> SetKernelOutput(size_t out_size,
   out->reserve(out_size);
   std::vector<phi::DenseTensor*> results(out_size);
   for (size_t i = 0; i < out_size; ++i) {
-    auto tensor_ptr = std::make_shared<phi::DenseTensor>(
-        phi::make_intrusive<SharedStorage>(phi::TransToPhiPlace(backend)),
-        phi::DenseTensorMeta());
+    auto tensor_ptr = std::make_shared<phi::DenseTensor>();
     results[i] = tensor_ptr.get();
     out->emplace_back();
     out->back().set_impl(tensor_ptr);

paddle/phi/api/lib/data_transform.cc

@@ -167,10 +167,7 @@ phi::DenseTensor TransformData(const phi::DenseTensor& tensor,
 
   if (NeedTransformPlace(
           out.place(), target_args_def.backend, transform_flag)) {
-    phi::DenseTensor result(
-        phi::make_intrusive<paddle::experimental::SharedStorage>(
-            phi::TransToPhiPlace(target_args_def.backend)),
-        {out.dtype(), out.dims(), out.layout()});
+    phi::DenseTensor result;
     framework::TransDataDevice(
         out, phi::TransToPhiPlace(target_args_def.backend), &result);
     out = result;
@@ -190,14 +187,14 @@ std::shared_ptr<phi::DenseTensor> PrepareData(
            tensor_in->dtype(), target_args_def.dtype, transform_flag) &&
        !NeedTransformLayout(
            tensor_in->layout(), target_args_def.layout, transform_flag))) {
-    return std::dynamic_pointer_cast<phi::DenseTensor>(tensor_in);
+    return std::static_pointer_cast<phi::DenseTensor>(tensor_in);
   }
 
   phi::DenseTensor out =
       TransformData(*(static_cast<phi::DenseTensor*>(tensor_in.get())),
                     target_args_def,
                     transform_flag);
-  return std::make_shared<phi::DenseTensor>(out);
+  return std::make_shared<phi::DenseTensor>(std::move(out));
 }
 
 std::shared_ptr<phi::DenseTensor> PrepareData(

paddle/phi/api/lib/tensor.cc

@@ -46,6 +46,7 @@ limitations under the License. */
  * In the future, the necessary components will be moved to the this library,
  * or the corresponding components will be re-implemented.
  */
+
 #include "paddle/fluid/memory/memory.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/platform/stream/cuda_stream.h"
@@ -142,7 +143,12 @@ PlaceType Tensor::place() const {
 }
 
 paddle::platform::Place Tensor::inner_place() const {
-  return ConvertExtPlaceToInnerPlace(place());
+  PADDLE_ENFORCE_NOT_NULL(
+      impl_,
+      phi::errors::PermissionDenied(
+          "Null pointer error, the impl_ of Tensor should not be "
+          "Null when calling Tensor::inner_place()."));
+  return impl_->place();
 }
 
 bool Tensor::is_cpu() const {
@@ -286,12 +292,16 @@ Tensor Tensor::slice(int64_t begin_idx, int64_t end_idx) const {
   }
 }
 
-std::shared_ptr<phi::TensorBase> Tensor::impl() const { return impl_; }
+const std::shared_ptr<phi::TensorBase> &Tensor::impl() const { return impl_; }
 
 void Tensor::set_impl(const std::shared_ptr<phi::TensorBase> &impl) {
   impl_ = impl;
 }
 
+void Tensor::set_impl(std::shared_ptr<phi::TensorBase> &&impl) {
+  impl_ = std::move(impl);
+}
+
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
 gpuStream_t Tensor::stream() const {
   return platform::stream::get_current_stream(-1)->raw_stream();

paddle/phi/api/lib/tensor_method.cc

@@ -19,9 +19,12 @@ limitations under the License. */
 #include "paddle/phi/core/compat/convert_utils.h"
 #include "paddle/phi/core/tensor_base.h"
 
+#include "paddle/phi/api/lib/api_gen_utils.h"
+#include "paddle/phi/api/lib/kernel_dispatch.h"
+#include "paddle/phi/infermeta/unary.h"
+
 namespace paddle {
 namespace experimental {
-
 // declare cast api
 Tensor cast(const Tensor &x, DataType out_dtype);
 Tensor copy_to(const Tensor &x, Backend backend, bool blocking);
@@ -67,12 +70,18 @@ template PADDLE_API Tensor Tensor::copy_to<phi::dtype::complex<double>>(
 template PADDLE_API Tensor
 Tensor::copy_to<phi::dtype::float16>(const PlaceType &target_place) const;
 
-void Tensor::copy_(const Tensor &src, bool blocking) {
+void Tensor::copy_(const Tensor &src,
+                   const phi::Place &target_place,
+                   bool blocking) {
   if (!src.is_initialized()) {
+    VLOG(8) << "Src is empty, skip copy";
     return;
   }
+  // Prepare copy kernel key and outputs
+  auto kernel_key_set = ParseKernelKeyByInputArgs(src);
+  KernelType kernel_type = ParseKernelTypeByInputArgs(src);
   VLOG(3) << "Deep copy Tensor from " << src.name() << " to " << name();
-  if (defined()) {
+  if (is_initialized()) {
     PADDLE_ENFORCE_EQ(dtype(),
                       src.dtype(),
                       platform::errors::PreconditionNotMet(
@@ -87,10 +96,91 @@ void Tensor::copy_(const Tensor &src, bool blocking) {
                           "Copy cannot be performed!",
                           name(),
                           src.name()));
+    PADDLE_ENFORCE_EQ(target_place,
+                      inner_place(),
+                      platform::errors::PreconditionNotMet(
+                          "Place is different of dst tensor and args %s, which "
+                          "current tensor holds %s "
+                          "Copy cannot be performed!",
+                          target_place.DebugString(),
+                          inner_place().DebugString()));
+    kernel_key_set.backend_set =
+        kernel_key_set.backend_set |
+        BackendSet(phi::TransToPhiBackend(inner_place()));
+  } else {
+    // Deep Copy AutoGrad info from src to self.
+    *autograd_meta_ = *(src.autograd_meta_);
+  }
+
+  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+  auto *dev_ctx = GetDeviceContextByBackend(kernel_key.backend());
+  Backend kernel_backend = Backend::UNDEFINED;
+  DataLayout kernel_layout = DataLayout::UNDEFINED;
+  DataType kernel_data_type = DataType::UNDEFINED;
+
+  if (kernel_backend == Backend::UNDEFINED ||
+      kernel_layout == DataLayout::UNDEFINED ||
+      kernel_data_type == DataType::UNDEFINED) {
+    if (kernel_backend == Backend::UNDEFINED) {
+      kernel_backend = kernel_key.backend();
+    }
+    if (kernel_layout == DataLayout::UNDEFINED) {
+      kernel_layout = kernel_key.layout();
+    }
+    if (kernel_data_type == DataType::UNDEFINED) {
+      kernel_data_type = kernel_key.dtype();
+    }
+  }
+
+  if (kernel_type == KernelType::DENSE_TENSOR_KENREL) {
+    auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+        "copy", {kernel_backend, kernel_layout, kernel_data_type});
+    VLOG(6) << "copy API kernel key: " << kernel_key;
+    VLOG(6) << "copy API kernel: " << kernel;
+    using kernel_signature = void (*)(const platform::DeviceContext &,
+                                      const phi::DenseTensor &,
+                                      phi::Place,
+                                      bool,
+                                      phi::DenseTensor *);
+    SetKernelOutput(kernel_backend, this);
+    phi::MetaTensor meta_out(impl_.get());
+    phi::UnchangedInferMeta(
+        MakeMetaTensor(
+            *(std::static_pointer_cast<phi::DenseTensor>(src.impl_))),
+        &meta_out);
+    auto *kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+    (*kernel_fn)(*dev_ctx,
+                 (*(std::static_pointer_cast<phi::DenseTensor>(src.impl_))),
+                 target_place,
+                 blocking,
+                 static_cast<phi::DenseTensor *>(impl_.get()));
+  } else if (kernel_type == KernelType::SELECTED_ROWS_KENREL) {
+    auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+        "copy_sr", {kernel_backend, kernel_layout, kernel_data_type});
+    VLOG(6) << "copy API kernel key: " << kernel_key;
+    VLOG(6) << "copy API kernel: " << kernel;
+    using kernel_signature = void (*)(const platform::DeviceContext &,
+                                      const phi::SelectedRows &,
+                                      phi::Place,
+                                      bool,
+                                      phi::SelectedRows *);
+    SetSelectedRowsKernelOutput(kernel_backend, this);
+    phi::MetaTensor meta_out(impl_.get());
+    phi::UnchangedInferMeta(
+        MakeMetaTensor(
+            *(std::static_pointer_cast<phi::SelectedRows>(src.impl_))),
+        &meta_out);
+    auto *kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+    (*kernel_fn)(*dev_ctx,
+                 (*(std::static_pointer_cast<phi::SelectedRows>(src.impl_))),
+                 target_place,
+                 blocking,
+                 static_cast<phi::SelectedRows *>(impl_.get()));
+  } else {
+    PADDLE_THROW(paddle::platform::errors::InvalidArgument(
+        "We currently only support dense tensor copy for now and if u need to "
+        "copy selected rows please raise a issue."));
   }
-  auto copy_tensor =
-      src.copy_to(phi::TransToPhiBackend(src.inner_place()), blocking);
-  set_impl(copy_tensor.impl());
 }
 
 }  // namespace experimental

paddle/phi/backends/gpu/gpu_context.cc

@@ -741,6 +741,10 @@ struct GPUContext::Impl {
 
 GPUContext::GPUContext() : DeviceContext(), impl_(std::make_unique<Impl>()) {}
 
+GPUContext::GPUContext(GPUContext&&) = default;
+
+GPUContext& GPUContext::operator=(GPUContext&&) = default;
+
 GPUContext::GPUContext(const GPUPlace& place)
     : DeviceContext(), impl_(std::make_unique<Impl>(place)) {}
 

paddle/phi/backends/gpu/gpu_context.h

@@ -77,6 +77,8 @@ class DnnWorkspaceHandle {
 class GPUContext : public DeviceContext {
  public:
   GPUContext();
+  GPUContext(GPUContext&&);
+  GPUContext& operator=(GPUContext&&);
 
   explicit GPUContext(const GPUPlace& place);
 

paddle/phi/common/CMakeLists.txt

 cc_library(phi_place SRCS place.cc)
-cc_library(scalar SRCS scalar.cc)
+cc_library(scalar SRCS scalar.cc DEPS phi_enforce)

paddle/phi/core/kernel_factory.h

@@ -197,8 +197,16 @@ class Kernel {
 
   const KernelArgsDef& args_def() const { return args_def_; }
 
+  const TensorArgDef& InputAt(size_t idx) const {
+    return args_def_.input_defs().at(idx);
+  }
+
   TensorArgDef& InputAt(size_t idx) { return args_def_.input_defs().at(idx); }
 
+  const TensorArgDef& OutputAt(size_t idx) const {
+    return args_def_.output_defs().at(idx);
+  }
+
   TensorArgDef& OutputAt(size_t idx) { return args_def_.output_defs().at(idx); }
 
   bool IsValid() { return fn_ != nullptr; }

paddle/phi/infermeta/binary.cc

@@ -571,6 +571,48 @@ void GatherTreeMeta(const MetaTensor& ids,
   out->set_dims(ids_dims);
 }
 
+void GridSampleBaseInferMeta(const MetaTensor& x,
+                             const MetaTensor& grid,
+                             MetaTensor* out,
+                             MetaConfig config) {
+  auto x_dims = x.dims();
+  auto grid_dims = grid.dims();
+  PADDLE_ENFORCE_EQ(x_dims.size(),
+                    4,
+                    phi::errors::InvalidArgument(
+                        "Input(X) of GridSampleOp should be 4-D Tensor, but "
+                        "received X dimension size(%d)",
+                        x_dims.size()));
+  PADDLE_ENFORCE_EQ(grid_dims.size(),
+                    4,
+                    phi::errors::InvalidArgument(
+                        "Input(Grid) of GridSampleOp should be 4-D Tensor, "
+                        "but received X dimension size(%d)",
+                        grid_dims.size()));
+  if (config.is_runtime || grid_dims[3] > 0) {
+    PADDLE_ENFORCE_EQ(
+        grid_dims[3],
+        2,
+        phi::errors::InvalidArgument(
+            "Input(Grid) dimension[3] should be 2, but received %d",
+            grid_dims[3]));
+  }
+  if (config.is_runtime) {
+    PADDLE_ENFORCE_EQ(
+        grid_dims[0],
+        x_dims[0],
+        phi::errors::InvalidArgument(
+            "Input(X) and Input(Grid) dimension[0] should be equal, but "
+            "received X dimension[0](%d) != Grid dimension[0](%d)",
+            x_dims[0],
+            grid_dims[0]));
+  }
+
+  out->set_dims({x_dims[0], x_dims[1], grid_dims[1], grid_dims[2]});
+  out->set_dtype(x.dtype());
+  out->share_lod(x);
+}
+
 void HuberLossInferMeta(const MetaTensor& input,
                         const MetaTensor& label,
                         float delta,

paddle/phi/infermeta/binary.h

@@ -103,6 +103,11 @@ void GatherTreeMeta(const MetaTensor& ids,
                     const MetaTensor& parents,
                     MetaTensor* out);
 
+void GridSampleBaseInferMeta(const MetaTensor& x,
+                             const MetaTensor& grid,
+                             MetaTensor* out,
+                             MetaConfig config = MetaConfig());
+
 void HuberLossInferMeta(const MetaTensor& input_meta,
                         const MetaTensor& label_meta,
                         float delta,

paddle/phi/kernels/selected_rows/copy_kernel.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/phi/kernels/selected_rows/copy_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/bfloat16.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/copy_kernel.h"
+namespace phi {
+namespace sr {
+
+template <typename Context>
+void Copy(const Context& dev_ctx,
+          const SelectedRows& src,
+          Place dst_place,
+          bool blocking,
+          SelectedRows* dst) {
+  if (src.value().Holder() != dst->value().Holder() ||
+      src.value().data() != dst->value().data()) {
+    dst->set_rows(src.rows());
+    dst->set_height(src.height());
+  }
+  phi::Copy<Context>(
+      dev_ctx, src.value(), dst_place, blocking, dst->mutable_value());
+}
+
+}  // namespace sr
+}  // namespace phi
+
+PD_REGISTER_GENERAL_KERNEL(
+    copy_sr, CPU, ALL_LAYOUT, phi::sr::Copy<phi::CPUContext>, ALL_DTYPE) {}
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_GENERAL_KERNEL(
+    copy_sr, GPU, ALL_LAYOUT, phi::sr::Copy<phi::GPUContext>, ALL_DTYPE) {}
+#endif

paddle/phi/kernels/selected_rows/copy_kernel.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 "paddle/phi/core/selected_rows.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
+
+namespace phi {
+namespace sr {
+
+template <typename Context>
+void Copy(const Context& dev_ctx,
+          const SelectedRows& src,
+          Place dst_place,
+          bool blocking,
+          SelectedRows* dst);
+
+}  // namespace sr
+}  // namespace phi

python/paddle/fluid/dygraph/base.py

@@ -565,16 +565,25 @@ def grad(outputs,
         if isinstance(in_out_list, (list, tuple)):
             assert len(in_out_list) > 0, "{} cannot be empty".format(name)
             for each_var in in_out_list:
-                assert isinstance(
-                    each_var,
-                    core.VarBase), "Elements of {} must be Variable".format(
-                        name)
+                if core._in_eager_mode():
+                    assert isinstance(
+                        each_var, core.eager.
+                        Tensor), "Elements of {} must be Tensor".format(name)
+                else:
+                    assert isinstance(
+                        each_var,
+                        core.VarBase), "Elements of {} must be Variable".format(
+                            name)
             return in_out_list
         else:
-            assert isinstance(
-                in_out_list,
-                core.VarBase), "{} must be Variable or list of Variable".format(
-                    name)
+            if core._in_eager_mode():
+                assert isinstance(
+                    in_out_list, core.eager.
+                    Tensor), "{} must be Tensor or list of Tensor".format(name)
+            else:
+                assert isinstance(
+                    in_out_list, core.VarBase
+                ), "{} must be Variable or list of Variable".format(name)
             return [in_out_list]
 
     outputs = check_in_out(outputs, 'outputs')
@@ -586,9 +595,14 @@ def grad(outputs,
 
         for each_var in grad_outputs:
             if each_var is not None:
-                assert isinstance(
-                    each_var, core.VarBase
-                ), "grad_outputs must be None, a Variable or a list containing None or Variables"
+                if core._in_eager_mode():
+                    assert isinstance(
+                        each_var, core.eager.Tensor
+                    ), "grad_outputs must be None, a Variable or a list containing None or Variables"
+                else:
+                    assert isinstance(
+                        each_var, core.VarBase
+                    ), "grad_outputs must be None, a Variable or a list containing None or Variables"
     else:
         grad_outputs = []
 
@@ -600,14 +614,27 @@ def grad(outputs,
         no_grad_vars = []
     elif isinstance(no_grad_vars, core.VarBase):
         no_grad_vars = [no_grad_vars]
+    elif isinstance(no_grad_vars, core.eager.Tensor):
+        no_grad_vars = [no_grad_vars]
     elif isinstance(no_grad_vars, (list, tuple, set)):
         no_grad_vars = list(no_grad_vars)
         for var in no_grad_vars:
-            assert isinstance(
-                var, core.VarBase), "no_grad_vars can only contains Variable"
+            if core._in_eager_mode():
+                assert isinstance(
+                    var,
+                    core.eager.Tensor), "no_grad_vars can only contains Tensor"
+            else:
+                assert isinstance(
+                    var,
+                    core.VarBase), "no_grad_vars can only contains Variable"
     else:
-        raise AssertionError(
-            "no_grad_vars must be None, Variable or list/tuple/set of Variables")
+        if core._in_eager_mode():
+            raise AssertionError(
+                "no_grad_vars must be None, Tensor or list/tuple/set of Tensors")
+        else:
+            raise AssertionError(
+                "no_grad_vars must be None, Variable or list/tuple/set of Variables"
+            )
 
     assert isinstance(create_graph, bool), "create_graph must be True or False"
 
@@ -622,6 +649,11 @@ def grad(outputs,
     assert isinstance(only_inputs, bool), "only_inputs must be True or False"
     assert only_inputs, "only_inputs=False is not supported yet"
 
+    if core._in_eager_mode():
+        return core.eager.run_partial_grad(
+            outputs, inputs, grad_outputs, retain_graph, create_graph,
+            only_inputs, allow_unused, no_grad_vars)
+
     place = core.Place()
     place.set_place(framework._current_expected_place())
     return core.dygraph_partial_grad(inputs, outputs, grad_outputs,

python/paddle/fluid/dygraph/io.py

@@ -30,6 +30,7 @@ from paddle.fluid.layers import nn
 from paddle.fluid.layers.utils import _hash_with_id
 from paddle.fluid.dygraph.base import switch_to_static_graph
 from paddle.fluid.framework import in_dygraph_mode
+from paddle import _C_ops
 
 __all__ = ['TranslatedLayer']
 
@@ -761,6 +762,21 @@ def _construct_params_and_buffers(model_path,
     return var_dict
 
 
+def _valid_vars(vars):
+    if vars:
+        return vars
+    if framework._in_eager_mode():
+        return [
+            core.eager.Tensor(core.VarDesc.VarType.FP32, [], "Fake_var",
+                              core.VarDesc.VarType.RAW, False)
+        ]
+    else:
+        return [
+            core.VarBase(core.VarDesc.VarType.FP32, [], "Fake_var",
+                         core.VarDesc.VarType.RAW, False)
+        ]
+
+
 def _run_dygraph(instance, input, program_holder):
 
     # 1. prepare inputs, outputs, attrs
@@ -826,17 +842,12 @@ def _run_dygraph(instance, input, program_holder):
 
     # hold forward variables
     if framework._in_eager_mode():
-        tmp_scope_vec = core.eager.Tensor(
-            dtype=core.VarDesc.VarType.FP32,
-            dims=[],
-            name="program_out_scope",
-            type=core.VarDesc.VarType.STEP_SCOPES,
-            persistable=True)
+        tmp_scope_vec = [program_holder.scope]
     else:
         tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
                                      "program_out_scope",
                                      core.VarDesc.VarType.STEP_SCOPES, True)
-    tmp_scope_vec.value().set_scope(program_holder.scope)
+        tmp_scope_vec.value().set_scope(program_holder.scope)
 
     double_grad_vars = []
     for var_desc in program_holder.double_grad_descs:
@@ -852,41 +863,18 @@ def _run_dygraph(instance, input, program_holder):
                                var_desc.shape(),
                                var_desc.name(), var_desc.type(), False)
         double_grad_vars.append(var)
-    if len(double_grad_vars) == 0:
-        if framework._in_eager_mode():
-            double_grad_vars = [
-                core.eager.Tensor(
-                    value=[1],
-                    name='Fake_var',
-                    place=framework._current_expected_place())
-            ]
-        else:
-            double_grad_vars = [
-                core.VarBase(
-                    value=[1],
-                    name='Fake_var',
-                    place=framework._current_expected_place())
-            ]
 
     # 2. run program by op
     trace_program = program_holder.infer_program if instance._is_test else program_holder.train_program
     end_op_index = program_holder.infer_program.block(0).op_size()
-    framework._dygraph_tracer().trace_op(
-        type='run_program',
-        inputs={'X': input_vars,
-                'Params': persistable_vars},
-        outputs={
-            'Out': output_vars,
-            'OutScope': tmp_scope_vec,
-            'DOut': double_grad_vars
-        },
-        attrs={
-            'global_block': trace_program.block(0),
-            'start_op_index': 0,
-            'end_op_index': end_op_index,
-            'is_test': instance._is_test,
-            'program_id': _hash_with_id(trace_program, instance)
-        })
+    attrs = ('global_block', trace_program.block(0), 'start_op_index', 0,
+             'end_op_index', end_op_index, 'is_test', instance._is_test,
+             'program_id', _hash_with_id(trace_program, instance))
+    _C_ops.run_program(
+        _valid_vars(input_vars),
+        _valid_vars(persistable_vars),
+        _valid_vars(output_vars), tmp_scope_vec,
+        _valid_vars(double_grad_vars), *attrs)
     # NOTE: [ why need set param's gradient type here ]
     # if user set sparse gradient mode, the param's gradient
     # will be SelectedRows, not LoDTensor. But tracer will just
@@ -914,8 +902,10 @@ def _run_dygraph(instance, input, program_holder):
 
 def drop_scope_if_no_grad(instance, scope_vec):
     tracer = framework._dygraph_tracer()
+    scope = scope_vec.value().get_scope() if isinstance(scope_vec, (
+        core.VarBase)) else scope_vec[0]
     if (not instance._is_test) and (not tracer._has_grad):
-        scope_vec.value().get_scope().drop_kids()
+        scope.drop_kids()
 
 
 def _run_static_graph(input, program_holder, trace_program):

python/paddle/fluid/dygraph/jit.py

@@ -821,7 +821,7 @@ def save(layer, path, input_spec=None, **configs):
         for var in flatten(input_spec):
             if isinstance(var, paddle.static.InputSpec):
                 inner_input_spec.append(var)
-            elif isinstance(var, (core.VarBase, Variable)):
+            elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)):
                 inner_input_spec.append(
                     paddle.static.InputSpec.from_tensor(var))
             else:

python/paddle/fluid/dygraph/layers.py

@@ -760,7 +760,8 @@ class Layer(object):
             raise KeyError("The name of buffer can not be empty.")
         elif hasattr(self, name) and name not in self._buffers:
             raise KeyError("attribute '{}' already exists.".format(name))
-        elif tensor is not None and not type(tensor) == core.VarBase:
+        elif tensor is not None and not (type(tensor) == core.VarBase or
+                                         type(tensor) == core.eager.Tensor):
             raise TypeError(
                 "The registered buffer should be a core.VarBase, but received {}.".
                 format(type(tensor).__name__))

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -1118,9 +1118,9 @@ set_tests_properties(test_cumprod_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_split_program PROPERTIES TIMEOUT 120)
 if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL)
     set_tests_properties(test_parallel_dygraph_dataparallel PROPERTIES TIMEOUT 120)
-    set_tests_properties(test_parallel_dygraph_unused_variables PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_parallel_dygraph_unused_variables PROPERTIES TIMEOUT 150)
     set_tests_properties(test_parallel_dygraph_control_flow PROPERTIES TIMEOUT 200)
-    set_tests_properties(test_parallel_dygraph_no_sync PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_parallel_dygraph_no_sync PROPERTIES TIMEOUT 150)
     set_tests_properties(test_parallel_dygraph_no_sync_gradient_check PROPERTIES TIMEOUT 30)
     set_tests_properties(test_parallel_dygraph_pipeline_parallel PROPERTIES TIMEOUT 200)
     set_tests_properties(test_parallel_dygraph_tensor_parallel PROPERTIES TIMEOUT 200)

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py

@@ -27,6 +27,7 @@ from paddle.fluid.dygraph.nn import Conv2D, Linear, Pool2D
 from paddle.fluid.optimizer import AdamOptimizer
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
+from paddle.fluid.framework import _test_eager_guard
 
 from predictor_utils import PredictorTools
 
@@ -155,6 +156,13 @@ class TestMNISTWithToStatic(TestMNIST):
             np.allclose(dygraph_loss, static_loss),
             msg='dygraph is {}\n static_res is \n{}'.format(dygraph_loss,
                                                             static_loss))
+        with _test_eager_guard():
+            dygraph_loss = self.train_dygraph()
+            static_loss = self.train_static()
+            self.assertTrue(
+                np.allclose(dygraph_loss, static_loss),
+                msg='dygraph is {}\n static_res is \n{}'.format(dygraph_loss,
+                                                                static_loss))
 
     def test_mnist_declarative_cpu_vs_mkldnn(self):
         dygraph_loss_cpu = self.train_dygraph()

python/paddle/fluid/tests/unittests/parallel_dygraph_dataparallel_in_eager_mode.py

@@ -19,6 +19,7 @@ import unittest
 import os
 import numpy as np
 import random
+import socket
 
 import paddle
 import paddle.nn as nn
@@ -31,13 +32,26 @@ from paddle.optimizer import SGD
 from paddle.fluid.initializer import NumpyArrayInitializer
 
 
+def net_is_used(port, ip='127.0.0.1'):
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        s.connect((ip, port))
+        s.shutdown(2)
+        return True
+    except Exception as e:
+        return False
+
+
 def init_process_group(strategy=None):
     nranks = ParallelEnv().nranks
     rank = ParallelEnv().local_rank
     is_master = True if rank == 0 else False
-    store = paddle.fluid.core.TCPStore("127.0.0.1", 6172, is_master, nranks)
-    group = core.ProcessGroupNCCL(store, rank, nranks)
-    return group
+    for port in range(20000, 21000):
+        if not net_is_used(port):
+            store = paddle.fluid.core.TCPStore("127.0.0.1", port, is_master,
+                                               nranks)
+            group = core.ProcessGroupNCCL(store, rank, nranks)
+            return group
 
 
 class LinearModel(nn.Layer):

python/paddle/fluid/tests/unittests/test_egr_python_api.py

@@ -52,7 +52,7 @@ class EagerScaleTestCase(unittest.TestCase):
             out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True)
             self.assertIsNone(data_eager.grad)
             out_eager.backward(grad_eager, False)
-            self.assertTrue(data_eager.grad._is_initialized())
+            self.assertIsNotNone(data_eager.grad)
             self.assertTrue(np.array_equal(data_eager.grad.numpy(), input_data))
 
     def test_retain_grad_and_run_backward_raises(self):

python/paddle/fluid/tests/unittests/test_imperative_double_grad.py

-# Copyright (c) 2020 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.
@@ -19,6 +19,9 @@ from paddle.vision.models import resnet50, resnet101
 import unittest
 from unittest import TestCase
 import numpy as np
+import paddle.compat as cpt
+from paddle.fluid.framework import _test_eager_guard
+import paddle.fluid.core as core
 
 
 def _dygraph_guard_(func):
@@ -40,6 +43,80 @@ def random_var(size, low=-1, high=1, dtype='float32'):
     return fluid.dygraph.to_variable(x_np)
 
 
+class TestEagerGrad(TestCase):
+    def func_simple_example_eager_grad(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = np.random.random((3, 1))
+        x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False)
+        y = paddle.to_tensor(np_y, dtype="float64", stop_gradient=False)
+        out = paddle.matmul(x, y)
+        dx = fluid.dygraph.grad(out, x)
+
+        dout = np.ones_like(np_y)
+        expected_dx = np.matmul(dout, np.transpose(np_y))
+
+        # stop_gradient = !create_graph, create_graph default false
+        self.assertEqual(dx[0].stop_gradient, True)
+        self.assertTrue(np.allclose(dx[0].numpy(), expected_dx[0]))
+
+    def test_simple_example_eager_grad(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad()
+        self.func_simple_example_eager_grad()
+
+    def func_simple_example_eager_grad_allow_unused(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = np.random.random((3, 1))
+        np_z = np.random.random((3, 1))
+        x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False)
+        y = paddle.to_tensor(np_y, dtype="float64", stop_gradient=False)
+        z = paddle.to_tensor(np_z, dtype="float64", stop_gradient=False)
+        out_z = paddle.nn.functional.sigmoid(z)
+        out = paddle.matmul(x, y)
+
+        dx = fluid.dygraph.grad(out, [x, z], allow_unused=True)
+        dout = np.ones_like(np_y)
+        expected_dx = np.matmul(dout, np.transpose(np_y))
+        self.assertTrue(np.allclose(dx[0].numpy(), expected_dx[0]))
+        # stop_gradient = !create_graph, create_graph default false
+        self.assertEqual(dx[0].stop_gradient, True)
+        # x is unused input in the graph
+        self.assertEqual(dx[1], None)
+
+    def test_simple_example_eager_grad_allow_unused(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_allow_unused()
+        self.func_simple_example_eager_grad_allow_unused()
+
+    def func_simple_example_eager_grad_not_allow_unused(self):
+        np.random.seed(2021)
+        paddle.set_device('cpu')
+        np_x = np.random.random((3, 3))
+        np_y = np.random.random((3, 1))
+        np_z = np.random.random((3, 1))
+        x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False)
+        y = paddle.to_tensor(np_y, dtype="float64", stop_gradient=False)
+        z = paddle.to_tensor(np_z, dtype="float64", stop_gradient=False)
+        out_z = paddle.nn.functional.sigmoid(z)
+        out = paddle.matmul(x, y)
+
+        try:
+            # allow_unused is false in default
+            dx = fluid.dygraph.grad(out, [x, z])
+        except ValueError as e:
+            error_msg = cpt.get_exception_message(e)
+            assert error_msg.find("allow_unused") > 0
+
+    def test_simple_example_eager_grad_not_allow_unused(self):
+        with _test_eager_guard():
+            self.func_simple_example_eager_grad_not_allow_unused()
+        self.func_simple_example_eager_grad_not_allow_unused()
+
+
 class TestDygraphDoubleGrad(TestCase):
     def setUp(self):
         self.sort_sum_gradient = False
@@ -64,7 +141,7 @@ class TestDygraphDoubleGrad(TestCase):
             allow_unused=allow_unused)
 
     @dygraph_guard
-    def test_exception(self):
+    def func_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -93,8 +170,13 @@ class TestDygraphDoubleGrad(TestCase):
         with self.assertRaises(AssertionError):
             self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1)
 
+    def test_exception(self):
+        with _test_eager_guard():
+            self.func_exception()
+        self.func_exception()
+
     @dygraph_guard
-    def test_simple_example(self):
+    def func_simple_example(self):
         x = random_var(self.shape)
         x.stop_gradient = False
         y = x + 1
@@ -123,8 +205,44 @@ class TestDygraphDoubleGrad(TestCase):
             self.assertNotEqual(grad_with_none_and_not_none.stop_gradient,
                                 create_graph)
 
+    def test_simple_example(self):
+        with _test_eager_guard():
+            self.func_simple_example()
+        self.func_simple_example()
+
     @dygraph_guard
-    def test_none_one_initial_gradient(self):
+    def func_example_no_grad_vars(self):
+        x = random_var(self.shape)
+        x_np = x.numpy()
+        numel = x_np.size
+        x.stop_gradient = False
+
+        y1 = fluid.layers.relu(x)
+        y2 = fluid.layers.relu(x)
+        z = y1 + y2
+        w = z * z
+
+        w_mean = fluid.layers.reduce_mean(w)
+        del y1, z, w
+
+        dx_actual, = self.grad(
+            [w_mean], [x], create_graph=True, no_grad_vars=[y2])
+
+        self.assertFalse(y2.stop_gradient)
+        self.assertFalse(dx_actual.stop_gradient)
+
+        dx_expected = (1.0 / float(numel) * (np.maximum(x_np, 0) + y2.numpy()) *
+                       (x_np > 0) * 2).astype('float32')
+
+        self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
+
+    def test_example_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_no_grad_vars()
+        self.func_example_no_grad_vars()
+
+    @dygraph_guard
+    def func_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -190,8 +308,13 @@ class TestDygraphDoubleGrad(TestCase):
                             np.array_equal(grad_z.numpy(),
                                            original_random_grad_z))
 
+    def test_none_one_initial_gradient(self):
+        with _test_eager_guard():
+            self.func_none_one_initial_gradient()
+        self.func_none_one_initial_gradient()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_create_graph(self):
+    def func_example_with_gradient_accumulation_and_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -214,25 +337,33 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward(retain_graph=True)
-
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 2 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
-
-        for i in range(5):
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
             loss.backward(retain_graph=True)
+
             x_grad_actual = x.gradient()
-            x_grad_expected = (i + 2) * (2.0 / float(numel) * (
+            x_grad_expected = (2.0 / float(numel) * (
                 x_np + dx_expected *
                 (x_np > 0) * 2 / float(numel))).astype('float32')
             self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
 
+            for i in range(5):
+                loss.backward(retain_graph=True)
+                x_grad_actual = x.gradient()
+                x_grad_expected = (i + 2) * (2.0 / float(numel) * (
+                    x_np + dx_expected *
+                    (x_np > 0) * 2 / float(numel))).astype('float32')
+                self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_create_graph()
+        self.func_example_with_gradient_accumulation_and_create_graph()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+    def func_example_with_gradient_accumulation_and_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -256,17 +387,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 4 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 4 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_no_grad_vars()
+        self.func_example_with_gradient_accumulation_and_no_grad_vars()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+    def func_example_with_gradient_accumulation_and_not_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -289,12 +428,20 @@ class TestDygraphDoubleGrad(TestCase):
 
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_not_create_graph()
+        self.func_example_with_gradient_accumulation_and_not_create_graph()
 
 
 class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
@@ -304,7 +451,7 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
 
 
 class TestDygraphDoubleGradVisitedUniq(TestCase):
-    def test_compare(self):
+    def func_compare(self):
         value = np.random.uniform(-0.5, 0.5, 100).reshape(10, 2,
                                                           5).astype("float32")
 
@@ -349,6 +496,11 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
         self.assertTrue(np.array_equal(grad_1, grad_2))
 
+    def test_compare(self):
+        with _test_eager_guard():
+            self.func_compare()
+        self.func_compare()
+
 
 class TestRaiseNoDoubleGradOp(TestCase):
     def raise_no_grad_op(self):

python/paddle/fluid/tests/unittests/test_paddle_imperative_double_grad.py

-# Copyright (c) 2020 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.
@@ -18,6 +18,8 @@ import unittest
 from unittest import TestCase
 import numpy as np
 import paddle
+from paddle.fluid.framework import _test_eager_guard
+import paddle.fluid.core as core
 
 
 def _dygraph_guard_(func):
@@ -62,7 +64,7 @@ class TestDygraphDoubleGrad(TestCase):
             allow_unused=allow_unused)
 
     @dygraph_guard
-    def test_exception(self):
+    def func_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -91,8 +93,13 @@ class TestDygraphDoubleGrad(TestCase):
         with self.assertRaises(AssertionError):
             self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1)
 
+    def test_exception(self):
+        with _test_eager_guard():
+            self.func_exception()
+        self.func_exception()
+
     @dygraph_guard
-    def test_simple_example(self):
+    def func_simple_example(self):
         x = random_var(self.shape)
         x.stop_gradient = False
         y = x + 1
@@ -121,8 +128,13 @@ class TestDygraphDoubleGrad(TestCase):
             self.assertNotEqual(grad_with_none_and_not_none.stop_gradient,
                                 create_graph)
 
+    def test_simple_example(self):
+        with _test_eager_guard():
+            self.func_simple_example()
+        self.func_simple_example()
+
     @dygraph_guard
-    def test_none_one_initial_gradient(self):
+    def func_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -188,8 +200,13 @@ class TestDygraphDoubleGrad(TestCase):
                             np.array_equal(grad_z.numpy(),
                                            original_random_grad_z))
 
+    def test_none_one_initial_gradient(self):
+        with _test_eager_guard():
+            self.func_none_one_initial_gradient()
+        self.func_none_one_initial_gradient()
+
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_create_graph(self):
+    def func_example_with_gradient_accumulation_and_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -212,17 +229,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 2 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 2 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_create_graph()
+        self.func_example_with_gradient_accumulation_and_create_graph()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+    def func_example_with_gradient_accumulation_and_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -246,17 +271,25 @@ class TestDygraphDoubleGrad(TestCase):
                        (x_np > 0) * 2).astype('float32')
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
+
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 / float(numel) * (
+                x_np + dx_expected *
+                (x_np > 0) * 4 / float(numel))).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 / float(numel) *
-                           (x_np + dx_expected *
-                            (x_np > 0) * 4 / float(numel))).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_no_grad_vars()
+        self.func_example_with_gradient_accumulation_and_no_grad_vars()
 
     @dygraph_guard
-    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+    def func_example_with_gradient_accumulation_and_not_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -279,12 +312,20 @@ class TestDygraphDoubleGrad(TestCase):
 
         self.assertTrue(np.allclose(dx_actual.numpy(), dx_expected))
 
-        loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
-        loss.backward()
+        if core._in_eager_mode():
+            pass
+        else:
+            loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
+            loss.backward()
 
-        x_grad_actual = x.gradient()
-        x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
-        self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+            x_grad_actual = x.gradient()
+            x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
+            self.assertTrue(np.allclose(x_grad_actual, x_grad_expected))
+
+    def test_example_with_gradient_accumulation_and_not_create_graph(self):
+        with _test_eager_guard():
+            self.func_example_with_gradient_accumulation_and_not_create_graph()
+        self.func_example_with_gradient_accumulation_and_not_create_graph()
 
 
 class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):

python/paddle/static/input.py

@@ -193,7 +193,7 @@ class InputSpec(object):
                 print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
 
         """
-        if isinstance(tensor, (Variable, core.VarBase)):
+        if isinstance(tensor, (Variable, core.VarBase, core.eager.Tensor)):
             return cls(tensor.shape, tensor.dtype, name or tensor.name)
         else:
             raise ValueError(

python/paddle/utils/code_gen/api_base.py

@@ -698,7 +698,7 @@ PADDLE_API {self.gene_return_type_code()} {self.get_api_func_name() + '_'}({self
             self.outputs['types'], 'SetKernelOutput', code_indent, inplace_flag)
         api_func_name = self.get_api_func_name() + ('_' if inplace_flag else '')
         return f"""
-{code_indent}  auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+{code_indent}  const auto& kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
 {code_indent}      "{self.kernel['func'][0]}", {{kernel_backend, kernel_layout, kernel_data_type}});
 {code_indent}  VLOG(6) << "{self.api} API kernel key: [" << kernel_backend << ", " << kernel_layout << ", "<< kernel_data_type << "]";
 {code_indent}  VLOG(6) << "{self.api} API kernel: " << kernel;

tools/infrt/custom_pdop.td

@@ -23,16 +23,6 @@ def PD_FetchOp : PD_Op<"fetch", [Terminator]> {
   let arguments = (ins PD_Tensor :$inputs, StrAttr:$name);
 }
 
-def PD_ReturnOp : PD_Op<"return", [Terminator]> {
-  let summary = "return Op";
-
-  let description = [{
-    Fetch tensor from the graph.
-  }];
-
-  let arguments = (ins Variadic<PD_Tensor>:$inputs);
-}
-
 def PD_GraphOp : PD_Op<"graph", [SingleBlockImplicitTerminator<"::infrt::ReturnOp">]> {
   let summary = "paddle graph Op";
   let description = [{

tools/infrt/generate_pd_op_dialect_from_paddle_op_maker.py

@@ -16,8 +16,6 @@ import paddle.fluid.framework as framework
 from paddle.fluid import core
 from paddle import compat as cpt
 
-ops_having_canonicalization = {"elementwise_add", }
-
 
 # collect original ops: op which has both inference and grid defination
 def get_original_ops():
@@ -186,7 +184,7 @@ def generate_all_ops_inputs_outputs_map(op_descs):
     cpp_style_ops_outputs_map_str = start_ + ops_outputs_str + "\n};"
 
     # 3. Write to header file
-    dst_head_file = "../../paddle/infrt/dialect/pd_ops_info.h"
+    dst_head_file = "../../paddle/infrt/dialect/pd/common/pd_ops_info.h"
     with open(dst_head_file, 'w') as ops_inputs_outputs_head_file:
         ops_inputs_outputs_head_file.write(cpp_style_ops_inputs_map_str)
         ops_inputs_outputs_head_file.write("\n\n")
@@ -195,7 +193,7 @@ def generate_all_ops_inputs_outputs_map(op_descs):
 
 # funtion to generate paddle op dialect file
 def convert_op_proto_into_mlir(op_descs):
-    dst_dialect_file = "../../paddle/infrt/dialect/pd_ops.td"
+    dst_dialect_file = "../../paddle/infrt/dialect/pd/ir/pd_ops.td"
     custom_dialect_file = "custom_pdop.td"
 
     # 1. Head files
@@ -214,7 +212,7 @@ def convert_op_proto_into_mlir(op_descs):
         "include \"mlir/Interfaces/InferTypeOpInterface.td\"",
         "include \"mlir/Interfaces/LoopLikeInterface.td\"",
         "include \"mlir/IR/OpBase.td\"",
-        "include \"paddle/infrt/dialect/pd_op_base.td\"",
+        "include \"paddle/infrt/dialect/pd/ir/pd_op_base.td\"",
         "",
     ]
 
@@ -245,7 +243,6 @@ def convert_op_proto_into_mlir(op_descs):
             op_type=op_type,
             left_brace="{")
         SUMMARY = '  let summary = "{} op";\n'.format(op_type)
-        CANONICALIZATION = "let hasCanonicalizer = 1;" if op_type in ops_having_canonicalization else ""
 
         # 2.2 Description
         contents = ""
@@ -348,7 +345,6 @@ def convert_op_proto_into_mlir(op_descs):
             ops_mlir_file.write(DESCRIPTION)
             ops_mlir_file.write(ARGUMENTS)
             ops_mlir_file.write(RESULTS)
-            ops_mlir_file.write(CANONICALIZATION)
             ops_mlir_file.write("}\n")
 
     print("Skipped ops num: " + str(len(skipped_op_list)))