revert operator.cc (#50895)

paddle/fluid/framework/operator.cc

@@ -1618,57 +1618,6 @@ void OperatorWithKernel::CheckWhetherPreparePhiData(
   }
 }
 
-// When do we need to reset runtime context?
-// 1. When enable cache runtime context, if the program runs for the first time,
-//   runtime_ctx_.get() == nullptr, we need to create a new runtime context.
-// 2. When enable cache runtime context, if the program is not running for the
-// first time,
-//   but the input shape or tensor layout of the operator has changed, we cannot
-//   use the runtime context stored in the cache at this time, and need to
-//   create a new one.
-bool OperatorWithKernel::NeedResetRuntimeContext(const Scope& scope) const {
-  if (runtime_ctx_.get() == nullptr) return true;
-  const auto& name_map = Inputs();
-  for (auto& var_name_item : name_map) {
-    auto& name_vec = var_name_item.second;
-    std::vector<Variable*>& cache_input_vars =
-        runtime_ctx_->inputs[var_name_item.first];
-    PADDLE_ENFORCE_EQ(
-        name_vec.size(),
-        cache_input_vars.size(),
-        platform::errors::InvalidArgument(
-            "The size of input variable names (%d) must be equal to "
-            "the size of cache input variable ptrs (%d).",
-            name_vec.size(),
-            cache_input_vars.size()));
-    for (size_t i = 0; i < name_vec.size(); i++) {
-      auto var_name = name_vec[i];
-      auto* cache_input_var = cache_input_vars[i];
-      if (!VarIsTensor(*cache_input_var)) continue;
-      auto* cache_input_tensor =
-          GetMutableLoDTensorOrSelectedRowsValueFromVar(cache_input_var);
-      auto cache_input_tensor_dims = cache_input_tensor->dims();
-      auto* current_input_var = scope.FindVar(var_name);
-      PADDLE_ENFORCE_NOT_NULL(
-          current_input_var,
-          platform::errors::NotFound(
-              "The variable %s is not found when "
-              "enable_cache_runtime_context_cache in origin scope.",
-              var_name));
-      auto* current_input_tensor =
-          GetMutableLoDTensorOrSelectedRowsValueFromVar(current_input_var);
-      auto current_input_tensor_dims = current_input_tensor->dims();
-      if (cache_input_tensor_dims != current_input_tensor_dims ||
-          NeedTransformLayout(current_input_tensor->layout(),
-                              cache_input_tensor->layout())) {
-        need_prepare_data_ = true;
-        return true;
-      }
-    }
-  }
-  return false;
-}
-
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
   // To reduce the elapsed time of HasAttr, we use bool variable to record the
@@ -1678,6 +1627,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   if (!all_kernels_must_compute_runtime_shape_ &&
       HasAttr(kAllKernelsMustComputeRuntimeShape))
     all_kernels_must_compute_runtime_shape_ = true;
+  const Scope* cur_scope = &scope;
   CheckWhetherPreparePhiData(Inputs(), Outputs(), scope);
   if (!enable_cache_runtime_context_) {
     RuntimeContext ctx(Inputs(), Outputs(), scope);
@@ -1689,9 +1639,12 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     }
     (*phi_kernel_)(impl_->getKernelContext());
   } else {
-    if (NeedResetRuntimeContext(scope)) {
+    if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) {
       std::lock_guard<std::mutex> lock(cache_update_mutex_);
+      if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) {
         runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope));
+        pre_scope_ = cur_scope;
+      }
     }
     RunImpl(scope, place, runtime_ctx_.get());
   }
@@ -2086,9 +2039,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   // To solve issue #15032, have a discussion with @Luotao for cpu inference,
   // do not cache transfer scope, hence in this case delete transfer scope
   // after run to avoid memory leak
-  if (cache_transfer_scope_ && !run_by_executor_ &&
-      !enable_cache_transfer_scope_) {
-    scope.DeleteScope(cache_transfer_scope_);
+  if (transfer_scope && !run_by_executor_ && !enable_cache_transfer_scope_) {
+    scope.DeleteScope(transfer_scope);
   }
 }
 
@@ -2623,25 +2575,33 @@ Scope* OperatorWithKernel::PrepareData(
               kernel_type_for_var.backend() == phi::Backend::GPUDNN ||
               new_expected_kernel_key->backend() == phi::Backend::GPU ||
               new_expected_kernel_key->backend() == phi::Backend::GPUDNN) {
-            cache_transfer_scope_ = TryCreateTransferScope(
+            new_scope = TryCreateTransferScope(
                 kernel_type_for_var, *new_expected_kernel_key, &scope);
             enable_cache_transfer_scope_ = true;
-            new_scope = cache_transfer_scope_;
           }
         } else if (kernel_type_for_var.backend() == phi::Backend::GPU ||
                    kernel_type_for_var.backend() == phi::Backend::GPUDNN ||
                    expected_kernel_key.backend() == phi::Backend::GPU ||
                    expected_kernel_key.backend() == phi::Backend::GPUDNN) {
-          cache_transfer_scope_ = TryCreateTransferScope(
+          new_scope = TryCreateTransferScope(
               kernel_type_for_var, expected_kernel_key, &scope);
           enable_cache_transfer_scope_ = true;
-          new_scope = cache_transfer_scope_;
         }
       }
 
       if (!new_scope) {
         new_scope = &scope.NewScope();
       }
+      // For inference, if a gpu model has an op which could only run on CPU,
+      // each result of different input will be the same with the first one.
+      // The reason is that if a gpu tensor is the input of a cpu kernel,
+      // we will create a new cpu tensor in new scope.
+      // However, if enable_cache_runtime_context_, we get the cpu tensor each
+      // time, not the gpu tensor. Thus, we set pre_scope_ = nullptr
+      // to trigger `new RuntimeContext()` in RunImpl().
+      if (enable_cache_runtime_context_) {
+        pre_scope_ = nullptr;
+      }
 
       // Create new var with the same name in transfer scopes
       auto* trans_var = new_scope->Var(var_name);
@@ -2727,13 +2687,18 @@ Scope* OperatorWithKernel::PrepareData(
     }
   }
 
+  // If pre_scope = &scope, it means that scope is cached and the op is not in
+  // while block. If new_scope = nullptr, it means that for each input of this
+  // Op, there is no need to do PrepareData. So PrepareData could be skipped at
+  // the rest iterations to save the elapsed time.
   // We do not support skipping PrepareData in while block, because the Op's
   // input may be changed by subsequent Ops, which may cause an error.
+
   // For inference, ops that behind conditional branch aren't supported well,
   // so disable prepare optimization conservatively.
   bool force_prepare_data = HasAttr("inference_force_prepare_data") &&
                             Attr<bool>("inference_force_prepare_data");
-  if (enable_cache_runtime_context_ && !force_prepare_data) {
+  if (pre_scope_ == &scope && new_scope == nullptr && !force_prepare_data) {
     need_prepare_data_ = false;
   }
 

paddle/fluid/framework/operator.h

@@ -781,19 +781,18 @@ class OperatorWithKernel : public OperatorBase {
   // used for IndicateOrPromoteVarDataTypes
   phi::DenseTensor* GetTensorFormInputSafely(const ExecutionContext& ctx,
                                              const std::string& name) const;
-  bool NeedResetRuntimeContext(const Scope& scope) const;
 
  protected:
   mutable std::unique_ptr<OpKernelType> kernel_type_;
   mutable std::unique_ptr<OpKernelFunc> kernel_func_;
   mutable std::unique_ptr<RuntimeContext> runtime_ctx_;
+  mutable const Scope* pre_scope_ = nullptr;
   mutable bool need_prepare_data_ = true;
   mutable bool need_prepare_phi_data_ = false;
   mutable bool enable_cache_runtime_context_ = false;
   mutable bool all_kernels_must_compute_runtime_shape_ = false;
   mutable std::mutex cache_update_mutex_;
   mutable bool enable_cache_transfer_scope_ = false;
-  mutable Scope* cache_transfer_scope_ = nullptr;
   // NOTE(jiahongyu): Whether fallback to plain kernel after calling
   // GetExpectedKernelType, use this bool flag to solve mkldnn and cudnn hard
   // code