Optimize adam speed (#21777)

* optimize adam speed by removing _finish_update test=develop

* fix SparseAdamFunctor param list test=develop

* Remove scale_op in expect_list of adam_op test=develop

* fix test optimizer loss assert error test=develop

* fix test optimizer loss assert error test=develop

* modify PADDLE_ENFORCE usage test=develop

* fix op_type in lamb_op.cc test=develop

* fix errors ostream format bug test=develop

* add betaPowOut in ngraph op test=develop

* fix ngraph::op api for gcc8 test=develop

* clean code test=develop

* modify struct into class test=develop

* remove code of beta1Tensor in lamb_op test=develop

paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_adam_op_pass.cc

@@ -39,13 +39,6 @@ class FuseAdamOpPass : public FuseOptimizerOpPass {
       const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
     auto fused_adam_node =
         FuseAdamOps(aux_var_set, fused_vars_name, adam_ops, graph);
-    auto fused_scale1 =
-        FuseScaleOps(aux_var_set.at("Beta1Pow"), fused_vars_name.at("Beta1Pow"),
-                     adam_ops, graph);
-    auto fused_scale2 =
-        FuseScaleOps(aux_var_set.at("Beta2Pow"), fused_vars_name.at("Beta2Pow"),
-                     adam_ops, graph);
-    RemoveCycleDepsBetweenOpNodes(graph, fused_scale1, fused_scale2);
     return fused_adam_node;
   }
 
@@ -139,6 +132,8 @@ class FuseAdamOpPass : public FuseOptimizerOpPass {
     adam_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
     adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
     adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
+    adam_desc.SetOutput("Beta1PowOut", {fused_vars_name.at("Beta1Pow")});
+    adam_desc.SetOutput("Beta2PowOut", {fused_vars_name.at("Beta2Pow")});
     adam_desc.SetAttr("beta1", beta1);
     adam_desc.SetAttr("beta2", beta2);
     adam_desc.SetAttr("epsilon", epsilon);

paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc

@@ -416,6 +416,8 @@ void FuseOptimizerOpPass::FuseVarsToContinuousSpace(
       result->Get<details::ProgramDescs>(details::kProgramDescs).back();
   auto *global_block = program_desc.MutableBlock(0);
   for (auto &var_name : aux_var_names) {
+    VLOG(6) << "aux_var_names : " << var_name
+            << ". fused_vars_name: " << fused_vars_name.at(var_name);
     AppendCoalesceTensorOp(aux_var_map.at(var_name), aux_var_map.at(var_name),
                            fused_vars_name.at(var_name), dtype, global_block,
                            true);

paddle/fluid/operators/ngraph/ops/adam_op.h

@@ -68,9 +68,14 @@ void BuildAdamNode(
   auto delta = ElementwiseScalar<ngraph::op::Multiply>(updated_lr, param_grad);
   auto param_out = std::make_shared<ngraph::op::Subtract>(param, delta);
 
+  auto beta1_pow_out = ElementwiseScalar<ngraph::op::Multiply>(beta1, beta1pow);
+  auto beta2_pow_out = ElementwiseScalar<ngraph::op::Multiply>(beta2, beta2pow);
+
   platform::SetOutputNode(op, "Moment1Out", moment1out, ngb_node_map);
   platform::SetOutputNode(op, "Moment2Out", moment2out, ngb_node_map);
   platform::SetOutputNode(op, "ParamOut", param_out, ngb_node_map);
+  platform::SetOutputNode(op, "Beta1PowOut", beta1_pow_out, ngb_node_map);
+  platform::SetOutputNode(op, "Beta2PowOut", beta2_pow_out, ngb_node_map);
 }
 }  // namespace ngraphs
 }  // namespace operators

paddle/fluid/operators/optimizers/adam_op.cc

@@ -66,37 +66,63 @@ void AdamOp::InferShape(framework::InferShapeContext* ctx) const {
                         "Output(Moment2Out) of AdamOp should not be null."));
 
   auto lr_dims = ctx->GetInputDim("LearningRate");
-  PADDLE_ENFORCE_NE(framework::product(lr_dims), 0,
-                    "Maybe the Input variable LearningRate has not "
+  PADDLE_ENFORCE_NE(
+      framework::product(lr_dims), 0,
+      platform::errors::InvalidArgument(
+          "The number of LearningRate shall not be 0, but received %d. Maybe "
+          "the Input variable LearningRate has not "
           "been initialized. You may need to confirm "
           "if you put exe.run(startup_program) "
-                    "after optimizer.minimize function.");
-  PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
-                    "Learning rate should have 1 dimension");
+          "after optimizer.minimize function.",
+          framework::product(lr_dims)));
+  PADDLE_ENFORCE_EQ(
+      framework::product(lr_dims), 1,
+      platform::errors::InvalidArgument(
+          "Learning rate should have 1 dimension, but received %d",
+          framework::product(lr_dims)));
   auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
-  PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1,
-                    "Beta1 power accumulator should have 1 dimension");
+  VLOG(3) << "dims of Beta1Pow : [" << beta1_pow_dims << "]";
+  PADDLE_ENFORCE_GE(framework::product(beta1_pow_dims), 1,
+                    platform::errors::InvalidArgument(
+                        "The size of Beta1 power accumulator should be greater "
+                        "than 0, but received %d.",
+                        framework::product(beta1_pow_dims)));
   auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
-  PADDLE_ENFORCE_EQ(framework::product(beta2_pow_dims), 1,
-                    "Beta2 power accumulator should have 1 dimension");
+  VLOG(3) << "dims of Beta2Pow : [" << beta2_pow_dims << "]";
+  PADDLE_ENFORCE_GE(framework::product(beta2_pow_dims), 1,
+                    platform::errors::InvalidArgument(
+                        "The size of Beta2 power accumulator should be greater "
+                        "than 0, but received %d.",
+                        framework::product(beta2_pow_dims)));
 
   auto param_dims = ctx->GetInputDim("Param");
   if (ctx->GetInputsVarType("Grad")[0] ==
       framework::proto::VarType::LOD_TENSOR) {
     PADDLE_ENFORCE_EQ(
         param_dims, ctx->GetInputDim("Grad"),
-        "Param and Grad input of AdamOp should have same dimension");
+        platform::errors::InvalidArgument(
+            "Param and Grad input of AdamOp should have same dimension. But "
+            "received Param dims: [%s], Grad dims: [%s].",
+            param_dims, ctx->GetInputDim("Grad")));
   }
   PADDLE_ENFORCE_EQ(
       param_dims, ctx->GetInputDim("Moment1"),
-      "Param and Moment1 input of AdamOp should have same dimension");
+      platform::errors::InvalidArgument(
+          "Param and Moment1 input of AdamOp should have same dimension. But "
+          "received Param dims: [%s], Moment1 dims: [%s].",
+          param_dims, ctx->GetInputDim("Moment1")));
   PADDLE_ENFORCE_EQ(
       param_dims, ctx->GetInputDim("Moment2"),
-      "Param and Moment2 input of AdamOp should have same dimension");
+      platform::errors::InvalidArgument(
+          "Param and Moment2 input of AdamOp should have same dimension. But "
+          "received Param dims: [%s], Moment2 dims: [%s].",
+          param_dims, ctx->GetInputDim("Moment2")));
 
   ctx->SetOutputDim("ParamOut", param_dims);
   ctx->SetOutputDim("Moment1Out", param_dims);
   ctx->SetOutputDim("Moment2Out", param_dims);
+  ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims);
+  ctx->SetOutputDim("Beta2PowOut", beta2_pow_dims);
 }
 
 framework::OpKernelType AdamOp::GetExpectedKernelType(
@@ -130,6 +156,8 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("ParamOut", "(Tensor) Output parameter");
     AddOutput("Moment1Out", "(Tensor) Output first moment");
     AddOutput("Moment2Out", "(Tensor) Output second moment");
+    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator");
+    AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator");
 
     AddAttr<float>("beta1",
                    "(float, default 0.9) "

paddle/fluid/operators/optimizers/adam_op.h

@@ -52,10 +52,48 @@ struct GPUAdam;
 struct CPUAdam;
 
 template <typename T, typename Flavour>
-struct AdamFunctor;
+class AdamFunctor;
 
 template <typename T>
-struct AdamFunctor<T, GPUAdam> {
+class BetaPowFunctor {
+ private:
+  T beta1_;
+  T beta2_;
+  const T* beta1_pow_;
+  const T* beta2_pow_;
+  T* beta1_pow_out_;
+  T* beta2_pow_out_;
+
+ public:
+  BetaPowFunctor(T beta1, T beta2, const T* beta1_pow, const T* beta2_pow,
+                 T* beta1_pow_out, T* beta2_pow_out)
+      : beta1_(beta1),
+        beta2_(beta2),
+        beta1_pow_(beta1_pow),
+        beta2_pow_(beta2_pow),
+        beta1_pow_out_(beta1_pow_out),
+        beta2_pow_out_(beta2_pow_out) {}
+
+  inline HOSTDEVICE void update_step(size_t i) const {
+    T beta1_pow_i = beta1_pow_[i];
+    T beta2_pow_i = beta2_pow_[i];
+
+    beta1_pow_out_[i] = beta1_pow_i * beta1_;
+    beta2_pow_out_[i] = beta2_pow_i * beta2_;
+  }
+
+  inline HOSTDEVICE void operator()(size_t i) const { update_step(i); }
+
+  inline HOSTDEVICE void apply_update(size_t limit) const {
+    for (size_t i = 0; i < limit; ++i) {
+      update_step(i);
+    }
+  }
+};
+
+template <typename T>
+class AdamFunctor<T, GPUAdam> {
+ private:
   T beta1_;
   T beta2_;
   T epsilon_;
@@ -71,6 +109,7 @@ struct AdamFunctor<T, GPUAdam> {
   const T* param_;
   T* param_out_;
 
+ public:
   AdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
               const T* beta2_pow, const T* mom1, T* mom1_out, const T* mom2,
               T* mom2_out, const T* lr, const T* grad, const T* param,
@@ -114,7 +153,8 @@ struct AdamFunctor<T, GPUAdam> {
 };
 
 template <typename T>
-struct AdamFunctor<T, CPUAdam> {
+class AdamFunctor<T, CPUAdam> {
+ private:
   T beta1_;
   T beta2_;
   T epsilon_;
@@ -130,6 +170,7 @@ struct AdamFunctor<T, CPUAdam> {
   const T* param_;
   T* param_out_;
 
+ public:
   AdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
               const T* beta2_pow, const T* mom1, T* mom1_out, const T* mom2,
               T* mom2_out, const T* lr, const T* grad, const T* param,
@@ -179,10 +220,11 @@ struct AdamFunctor<T, CPUAdam> {
 };
 
 template <typename T, typename Flavour>
-struct SparseAdamFunctor;
+class SparseAdamFunctor;
 
 template <typename T>
-struct SparseAdamFunctor<T, GPUAdam> {
+class SparseAdamFunctor<T, GPUAdam> {
+ private:
   T beta1_;
   T beta2_;
   T epsilon_;
@@ -203,6 +245,7 @@ struct SparseAdamFunctor<T, GPUAdam> {
   int64_t row_count_;
   bool lazy_mode_;
 
+ public:
   SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                     const T* beta2_pow, const T* mom1, T* mom1_out,
                     const T* mom2, T* mom2_out, const T* lr, const T* grad,
@@ -261,7 +304,8 @@ struct SparseAdamFunctor<T, GPUAdam> {
 };
 
 template <typename T>
-struct SparseAdamFunctor<T, CPUAdam> {
+class SparseAdamFunctor<T, CPUAdam> {
+ private:
   T beta1_;
   T beta2_;
   T epsilon_;
@@ -281,6 +325,7 @@ struct SparseAdamFunctor<T, CPUAdam> {
   int64_t row_numel_;
   int64_t row_count_;
 
+ public:
   SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                     const T* beta2_pow, const T* mom1, T* mom1_out,
                     const T* mom2, T* mom2_out, const T* lr, const T* grad,
@@ -397,6 +442,10 @@ class AdamOpKernel : public framework::OpKernel<T> {
         Ref(ctx.Output<LoDTensor>("Moment1Out"), "Must set Moment1Out");
     auto& mom2_out =
         Ref(ctx.Output<LoDTensor>("Moment2Out"), "Must set Moment1Out");
+    auto& beta1_pow_out =
+        Ref(ctx.Output<LoDTensor>("Beta1PowOut"), "Must set Beta1PowOut");
+    auto& beta2_pow_out =
+        Ref(ctx.Output<LoDTensor>("Beta2PowOut"), "Must set Beta2PowOut");
 
     T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
     if (ctx.HasInput("Beta1Tensor")) {
@@ -408,6 +457,14 @@ class AdamOpKernel : public framework::OpKernel<T> {
       auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
       beta2 = static_cast<T>(GetAttrFromTensor(beta2_tensor));
     }
+    VLOG(3) << "beta1_pow.numel() : " << beta1_pow.numel()
+            << "beta2_pow.numel() : " << beta2_pow.numel();
+    VLOG(3) << "param.numel(): " << param.numel();
+    BetaPowFunctor<T> beta_functor(
+        beta1, beta2, beta1_pow.template data<T>(),
+        beta2_pow.template data<T>(),
+        beta1_pow_out.template mutable_data<T>(ctx.GetPlace()),
+        beta2_pow_out.template mutable_data<T>(ctx.GetPlace()));
 
     if (grad_var->IsType<framework::LoDTensor>()) {
       auto& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");
@@ -423,6 +480,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
             param.template data<T>(),
             param_out.template mutable_data<T>(ctx.GetPlace()));
         functor(param.numel());
+        beta_functor.apply_update(beta2_pow.numel());
       } else if (platform::is_gpu_place(ctx.GetPlace())) {
         AdamFunctor<T, GPUAdam> functor(
             beta1, beta2, epsilon, beta1_pow.template data<T>(),
@@ -433,11 +491,16 @@ class AdamOpKernel : public framework::OpKernel<T> {
             lr.template data<T>(), grad.template data<T>(),
             param.template data<T>(),
             param_out.template mutable_data<T>(ctx.GetPlace()));
-
+        // update param and moment
         platform::ForRange<DeviceContext> for_range(
             static_cast<const DeviceContext&>(ctx.device_context()),
             param.numel());
         for_range(functor);
+        // update beta1 and beta2
+        platform::ForRange<DeviceContext> for_range_beta(
+            static_cast<const DeviceContext&>(ctx.device_context()),
+            beta2_pow.numel());
+        for_range_beta(beta_functor);
       }
     } else if (grad_var->IsType<framework::SelectedRows>()) {
       auto& grad =
@@ -485,6 +548,8 @@ class AdamOpKernel : public framework::OpKernel<T> {
             lr.template data<T>(), grad_data, param.template data<T>(),
             param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
             grad_merge.rows().size(), lazy_mode);
+        // update beta1 and beta2
+        beta_functor.apply_update(beta2_pow.numel());
         if (lazy_mode) {
           VLOG(3) << "run cpu lazy mode";
           size_t row_count = grad_merge.rows().size();
@@ -574,6 +639,11 @@ class AdamOpKernel : public framework::OpKernel<T> {
             static_cast<const DeviceContext&>(ctx.device_context()),
             param.numel());
         for_range(functor);
+        // update beta1 and beta2
+        platform::ForRange<DeviceContext> for_range_beta(
+            static_cast<const DeviceContext&>(ctx.device_context()),
+            beta2_pow.numel());
+        for_range_beta(beta_functor);
       }
     } else {
       PADDLE_THROW("Variable type not supported by adam_op");

paddle/fluid/operators/optimizers/lamb_op.cc

@@ -13,11 +13,111 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/optimizers/lamb_op.h"
-#include "paddle/fluid/operators/optimizers/adam_op.h"
 
 namespace paddle {
 namespace operators {
 
+class LambOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Param"), true,
+                      platform::errors::NotFound(
+                          "Input(Param) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Grad"), true,
+                      platform::errors::NotFound(
+                          "Input(Grad) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Moment1"), true,
+                      platform::errors::NotFound(
+                          "Input(Moment1) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Moment2"), true,
+                      platform::errors::NotFound(
+                          "Input(Moment2) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("LearningRate"), true,
+                      platform::errors::NotFound(
+                          "Input(LearningRate) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Beta1Pow"), true,
+                      platform::errors::NotFound(
+                          "Input(Beta1Pow) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Beta2Pow"), true,
+                      platform::errors::NotFound(
+                          "Input(Beta2Pow) of LambOp should not be null."));
+
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("ParamOut"), true,
+                      platform::errors::NotFound(
+                          "Output(ParamOut) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment1Out"), true,
+                      platform::errors::NotFound(
+                          "Output(Moment1Out) of LambOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Moment2Out"), true,
+                      platform::errors::NotFound(
+                          "Output(Moment2Out) of LambOp should not be null."));
+
+    auto lr_dims = ctx->GetInputDim("LearningRate");
+    PADDLE_ENFORCE_NE(
+        framework::product(lr_dims), 0,
+        platform::errors::InvalidArgument(
+            "The number of LearningRate shall not be 0, but received %d. Maybe "
+            "the Input variable LearningRate has not "
+            "been initialized. You may need to confirm "
+            "if you put exe.run(startup_program) "
+            "after optimizer.minimize function.",
+            framework::product(lr_dims)));
+    PADDLE_ENFORCE_EQ(
+        framework::product(lr_dims), 1,
+        platform::errors::InvalidArgument(
+            "Learning rate should have 1 dimension, but received %d.",
+            framework::product(lr_dims)));
+    auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow");
+    PADDLE_ENFORCE_GE(framework::product(beta1_pow_dims), 1,
+                      platform::errors::InvalidArgument(
+                          "The size of Beta1 power accumulator should be "
+                          "greater than 0, but received %d.",
+                          framework::product(beta1_pow_dims)));
+    auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow");
+    PADDLE_ENFORCE_GE(framework::product(beta2_pow_dims), 1,
+                      platform::errors::InvalidArgument(
+                          "The size of Beta2 power accumulator should be "
+                          "greater than 0, but received %d.",
+                          framework::product(beta2_pow_dims)));
+
+    auto param_dims = ctx->GetInputDim("Param");
+    if (ctx->GetInputsVarType("Grad")[0] ==
+        framework::proto::VarType::LOD_TENSOR) {
+      PADDLE_ENFORCE_EQ(
+          param_dims, ctx->GetInputDim("Grad"),
+          platform::errors::InvalidArgument(
+              "Param and Grad input of LambOp should have same dimension. But "
+              "received Param dims: [%s], Grad dims: [%s].",
+              param_dims, ctx->GetInputDim("Grad")));
+    }
+    PADDLE_ENFORCE_EQ(
+        param_dims, ctx->GetInputDim("Moment1"),
+        platform::errors::InvalidArgument(
+            "Param and Moment1 input of LambOp should have same dimension. But "
+            "received Param dims: [%s], Moment1 dims: [%s].",
+            param_dims, ctx->GetInputDim("Moment1")));
+    PADDLE_ENFORCE_EQ(
+        param_dims, ctx->GetInputDim("Moment2"),
+        platform::errors::InvalidArgument(
+            "Param and Moment2 input of LambOp should have same dimension. But "
+            "received Param dims: [%s], Moment2 dims: [%s].",
+            param_dims, ctx->GetInputDim("Moment2")));
+
+    ctx->SetOutputDim("ParamOut", param_dims);
+    ctx->SetOutputDim("Moment1Out", param_dims);
+    ctx->SetOutputDim("Moment2Out", param_dims);
+  }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const {
+    auto input_data_type =
+        OperatorWithKernel::IndicateVarDataType(ctx, "Param");
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
+};
+
 class LambOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -79,7 +179,7 @@ learning rate, $\lambda$ the weight decay rate.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(lamb, ops::AdamOp, ops::LambOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(lamb, ops::LambOp, ops::LambOpMaker);
 REGISTER_OP_CPU_KERNEL(
     lamb, ops::LambOpKernel<paddle::platform::CPUDeviceContext, float>,
     ops::LambOpKernel<paddle::platform::CPUDeviceContext, double>);

python/paddle/fluid/optimizer.py

@@ -1683,7 +1683,9 @@ class AdamOptimizer(Optimizer):
         outputs = {
             "ParamOut": param_and_grad[0],
             "Moment1Out": moment1,
-            "Moment2Out": moment2
+            "Moment2Out": moment2,
+            "Beta1PowOut": beta1_pow_acc,
+            "Beta2PowOut": beta2_pow_acc,
         }
         attrs = {
             "epsilon": self._epsilon,
@@ -1709,46 +1711,6 @@ class AdamOptimizer(Optimizer):
 
         return adam_op
 
-    def _finish_update(self, block, param_and_grads):
-        """Update Beta1 and Beta2 Power accumulators
-        """
-        assert isinstance(block, framework.Block)
-        main_block = block.program.global_block()
-        for param, grad in param_and_grads:
-            if grad is None or param.trainable is False:
-                continue
-            with param.block.program._optimized_guard(
-                [param, grad]), name_scope("optimizer"):
-                beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
-                                                      param)
-                beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
-                                                      param)
-                inputs = {"X": beta1_pow_acc}
-                attrs = {}
-                if isinstance(self._beta1, Variable):
-                    inputs['ScaleTensor'] = self._beta1
-                else:
-                    attrs['scale'] = self._beta1
-                main_block.append_op(
-                    type="scale",
-                    inputs=inputs,
-                    outputs={"Out": beta1_pow_acc},
-                    attrs=attrs,
-                    stop_gradient=True)
-
-                inputs = {"X": beta2_pow_acc}
-                attrs = {}
-                if isinstance(self._beta2, Variable):
-                    inputs['ScaleTensor'] = self._beta2
-                else:
-                    attrs['scale'] = self._beta2
-                main_block.append_op(
-                    type="scale",
-                    inputs=inputs,
-                    outputs={"Out": beta2_pow_acc},
-                    attrs=attrs,
-                    stop_gradient=True)
-
 
 class AdamaxOptimizer(Optimizer):
     """

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

@@ -58,7 +58,9 @@ class TestAdamOp1(OpTest):
         self.outputs = {
             'Moment1Out': moment1_out,
             'Moment2Out': moment2_out,
-            'ParamOut': param_out
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
         }
 
     def test_check_output(self):
@@ -101,7 +103,9 @@ class TestAdamOp2(OpTest):
         self.outputs = {
             'Moment1Out': moment1_out,
             'Moment2Out': moment2_out,
-            'ParamOut': param_out
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
         }
 
     def test_check_output(self):
@@ -122,11 +126,11 @@ class TestAdamOpMultipleSteps(OpTest):
         moment2 = np.random.random((102, 105)).astype("float32")
 
         learning_rate = 0.001
-        beta1 = 0.9
-        beta2 = 0.999
+        self.beta1 = 0.9
+        self.beta2 = 0.999
         epsilon = 1e-8
-        beta1_pow = beta1**10
-        beta2_pow = beta2**10
+        self.beta1_pow = self.beta1**10
+        self.beta2_pow = self.beta2**10
 
         self.inputs = {
             'Param': param,
@@ -134,21 +138,29 @@ class TestAdamOpMultipleSteps(OpTest):
             'Moment1': moment1,
             'Moment2': moment2,
             'LearningRate': np.array([learning_rate]).astype("float32"),
-            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
-            'Beta2Pow': np.array([beta2_pow]).astype("float32")
+            'Beta1Pow': np.array([self.beta1_pow]).astype("float32"),
+            'Beta2Pow': np.array([self.beta2_pow]).astype("float32")
         }
 
-        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
+        self.attrs = {
+            'epsilon': epsilon,
+            'beta1': self.beta1,
+            'beta2': self.beta2
+        }
 
     def test_check_output(self):
         for _ in range(self.num_steps):
             param_out, moment1_out, \
                 moment2_out = adam_step(self.inputs, self.attrs)
 
+            beta1_pow_out = self.inputs['Beta1Pow'] * self.beta1
+            beta2_pow_out = self.inputs['Beta2Pow'] * self.beta2
             self.outputs = {
                 'Moment1Out': moment1_out,
                 'Moment2Out': moment2_out,
-                'ParamOut': param_out
+                'ParamOut': param_out,
+                'Beta1PowOut': beta1_pow_out,
+                'Beta2PowOut': beta2_pow_out
             }
 
             # Verify output for this step
@@ -160,8 +172,8 @@ class TestAdamOpMultipleSteps(OpTest):
             self.inputs['Moment2'] = moment2_out
 
             # Update powers of Beta1 and Beta2 for next time step
-            self.inputs['Beta1Pow'] *= self.attrs['beta1']
-            self.inputs['Beta2Pow'] *= self.attrs['beta1']
+            self.inputs['Beta1Pow'] = beta1_pow_out
+            self.inputs['Beta2Pow'] = beta2_pow_out
 
             # Randomize gradient for next step
             self.inputs['Grad'] = np.random.uniform(
@@ -254,6 +266,8 @@ class TestSparseAdamOp(unittest.TestCase):
         beta1 = 0.78
         beta2 = 0.836
         epsilon = 1e-4
+        beta1_pow = np.array([beta1**10]).astype("float32")
+        beta2_pow = np.array([beta2**10]).astype("float32")
 
         height = 10
         rows = [0, 4, 7]
@@ -264,8 +278,8 @@ class TestSparseAdamOp(unittest.TestCase):
             "Param": np.full((height, row_numel), 5.0).astype("float32"),
             "Moment1": np.full((height, row_numel), 5.0).astype("float32"),
             "Moment2": np.full((height, row_numel), 5.0).astype("float32"),
-            'Beta1Pow': np.array([beta1**10]).astype("float32"),
-            'Beta2Pow': np.array([beta2**10]).astype("float32"),
+            'Beta1Pow': beta1_pow,
+            'Beta2Pow': beta2_pow,
             "LearningRate": np.full((1), 2.0).astype("float32")
         }
         self.init_output = np.full((height, row_numel), 0.0).astype("float32")
@@ -294,7 +308,9 @@ class TestSparseAdamOp(unittest.TestCase):
         self.outputs = {
             "ParamOut": param_out,
             "Moment1Out": mom1,
-            "Moment2Out": mom2
+            "Moment2Out": mom2,
+            'Beta1PowOut': beta1_pow * beta1,
+            'Beta2PowOut': beta2_pow * beta2
         }
 
     def check_with_place(self, place, lazy_mode):
@@ -376,7 +392,9 @@ class TestAdamOpBetaVariable(OpTest):
         self.outputs = {
             'Moment1Out': moment1_out,
             'Moment2Out': moment2_out,
-            'ParamOut': param_out
+            'ParamOut': param_out,
+            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
+            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
         }
 
     def test_check_output(self):

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

@@ -320,9 +320,8 @@ class TestAdamOptimizer(unittest.TestCase):
         self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
         with framework.program_guard(program, init_program):
             opts = adam_optimizer.apply_gradients(params_grads)
-        self.assertEqual(len(opts), 4)
-        self.assertEqual([op.type for op in opts],
-                         ["scale", "adam", "scale", "scale"])
+        self.assertEqual(len(opts), 2)
+        self.assertEqual([op.type for op in opts], ["scale", "adam"])
 
         # Check accumulators
         accumulators = adam_optimizer.get_accumulators()

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

@@ -68,7 +68,7 @@ class TestTrainable(unittest.TestCase):
             test_trainable,
             feed_dict,
             op_count={'adam': 1,
-                      'scale': 2,
+                      'scale': 0,
                       'mul_grad': 0})
         self.check_trainable(
             test_trainable,