unify the output num of optimizer ops

mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc

@@ -292,7 +292,6 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
     ir_fusion_pm->AddPass(std::make_shared<EraseVisitAttr>());
   }
   ir_fusion_pm->AddPass(std::make_shared<InsertMemcpyAsyncForHcclOp>());
-  ir_fusion_pm->AddPass(std::make_shared<AddInputToOutput>());
   ir_fusion_pm->AddPass(std::make_shared<InsertTranspose>());
   ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
   ir_fusion_pm->AddPass(std::make_shared<EraseVisitAttr>());

mindspore/ccsrc/backend/optimizer/ascend/mindir/optimizer_unify_output.cc

@@ -0,0 +1,126 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 "backend/optimizer/ascend/mindir/optimizer_unify_output.h"
+
+#include <vector>
+#include <memory>
+
+#include "abstract/abstract_value.h"
+#include "backend/session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+constexpr size_t kFtrlOutputNum = 3;
+constexpr size_t kMomentumOutputNum = 2;
+constexpr size_t kRMSPropOutputNum = 3;
+constexpr size_t kCenteredRMSPropOutputNum = 4;
+
+CNodePtr ProcessOutput(const FuncGraphPtr &graph, const AnfNodePtr &node, const size_t output_size) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(node);
+
+  auto cnode_ptr = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode_ptr);
+
+  auto abstract = cnode_ptr->abstract();
+  MS_EXCEPTION_IF_NULL(abstract);
+
+  if (AnfAlgo::HasNodeAttr("optim_output_passed", cnode_ptr) && abstract->isa<abstract::AbstractTuple>()) {
+    return nullptr;
+  }
+  AnfAlgo::SetNodeAttr("optim_output_passed", MakeValue(true), cnode_ptr);
+
+  std::vector<AbstractBasePtr> abstract_list;
+  for (size_t i = 0; i < output_size; i++) {
+    abstract_list.push_back(abstract->Clone());
+  }
+  auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
+  cnode_ptr->set_abstract(abstract_tuple);
+
+  auto index = NewValueNode(static_cast<int64_t>(0));
+  auto get_item = graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), cnode_ptr, index});
+  MS_EXCEPTION_IF_NULL(get_item);
+
+  get_item->set_abstract(abstract->Clone());
+  return get_item;
+}
+}  // namespace
+
+const BaseRef FtrlUnifyOutput::DefinePattern() const {
+  VarPtr var = std::make_shared<Var>();
+  VarPtr accum = std::make_shared<Var>();
+  VarPtr linear = std::make_shared<Var>();
+  VarPtr grad = std::make_shared<Var>();
+  VarPtr lr = std::make_shared<Var>();
+  VarPtr l1 = std::make_shared<Var>();
+  VarPtr l2 = std::make_shared<Var>();
+  VarPtr lr_power = std::make_shared<Var>();
+  VectorRef pattern({prim::kPrimApplyFtrl, var, accum, linear, grad, lr, l1, l2, lr_power});
+  return pattern;
+}
+
+const AnfNodePtr FtrlUnifyOutput::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const {
+  return ProcessOutput(graph, node, kFtrlOutputNum);
+}
+
+const BaseRef MomentumUnifyOutput::DefinePattern() const {
+  VarPtr var = std::make_shared<Var>();
+  VarPtr accum = std::make_shared<Var>();
+  VarPtr lr = std::make_shared<Var>();
+  VarPtr grad = std::make_shared<Var>();
+  VarPtr momentum = std::make_shared<Var>();
+  VectorRef pattern({prim::kPrimApplyMomentum, var, accum, lr, grad, momentum});
+  return pattern;
+}
+
+const AnfNodePtr MomentumUnifyOutput::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
+                                              const EquivPtr &) const {
+  return ProcessOutput(graph, node, kMomentumOutputNum);
+}
+
+const BaseRef RMSPropUnifyOutput::DefinePattern() const {
+  VarPtr inputs = std::make_shared<SeqVar>();
+  VectorRef pattern({prim::kPrimApplyRMSProp, inputs});
+  return pattern;
+}
+
+const AnfNodePtr RMSPropUnifyOutput::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
+                                             const EquivPtr &) const {
+  return ProcessOutput(graph, node, kRMSPropOutputNum);
+}
+
+const BaseRef CenteredRMSPropUnifyOutput::DefinePattern() const {
+  VarPtr var = std::make_shared<Var>();
+  VarPtr mg = std::make_shared<Var>();
+  VarPtr ms = std::make_shared<Var>();
+  VarPtr mom = std::make_shared<Var>();
+  VarPtr grad = std::make_shared<Var>();
+  VarPtr lr = std::make_shared<Var>();
+  VarPtr rho = std::make_shared<Var>();
+  VarPtr momentum = std::make_shared<Var>();
+  VarPtr epsilon = std::make_shared<Var>();
+  VectorRef pattern({prim::kPrimApplyCenteredRMSProp, var, mg, ms, mom, grad, lr, rho, momentum, epsilon});
+  return pattern;
+}
+
+const AnfNodePtr CenteredRMSPropUnifyOutput::Process(const FuncGraphPtr &graph, const AnfNodePtr &node,
+                                                     const EquivPtr &) const {
+  return ProcessOutput(graph, node, kCenteredRMSPropOutputNum);
+}
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/backend/optimizer/ascend/mindir/optimizer_unify_output.h

@@ -0,0 +1,58 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_MINDIR_OPTIMIZER_UNIFY_OUTPUT_H_
+#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_MINDIR_OPTIMIZER_UNIFY_OUTPUT_H_
+
+#include <memory>
+#include "backend/optimizer/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class FtrlUnifyOutput : public PatternProcessPass {
+ public:
+  explicit FtrlUnifyOutput(bool multigraph = true) : PatternProcessPass("ftrl_unify_output", multigraph) {}
+  ~FtrlUnifyOutput() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+
+class MomentumUnifyOutput : public PatternProcessPass {
+ public:
+  explicit MomentumUnifyOutput(bool multigraph = true) : PatternProcessPass("momentum_unify_output", multigraph) {}
+  ~MomentumUnifyOutput() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+
+class CenteredRMSPropUnifyOutput : public PatternProcessPass {
+ public:
+  explicit CenteredRMSPropUnifyOutput(bool multigraph = true)
+      : PatternProcessPass("centered_rmsprop_unify_output", multigraph) {}
+  ~CenteredRMSPropUnifyOutput() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+
+class RMSPropUnifyOutput : public PatternProcessPass {
+ public:
+  explicit RMSPropUnifyOutput(bool multigraph = true) : PatternProcessPass("rmsprop_unify_output", multigraph) {}
+  ~RMSPropUnifyOutput() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_MINDIR_OPTIMIZER_UNIFY_OUTPUT_H_

mindspore/ccsrc/backend/session/ascend_session.cc

@@ -38,6 +38,7 @@
 #include "backend/optimizer/ascend/mindir/maxpool_to_maxpool_with_argmax.h"
 #include "backend/optimizer/ascend/mindir/maxpool_with_argmax_unify_mindir.h"
 #include "backend/optimizer/ascend/mindir/conv2d_unify_mindir.h"
+#include "backend/optimizer/ascend/mindir/optimizer_unify_output.h"
 #include "backend/optimizer/ascend/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.h"
 #include "backend/optimizer/ascend/mindir/slice_grad_unify_mindir.h"
 #include "runtime/device/kernel_adjust.h"
@@ -217,6 +218,10 @@ void AscendSession::UnifyMindIR(const KernelGraphPtr &graph) {
   unify_mindir_pm->AddPass(std::make_shared<opt::Conv2DBackpropInputUnifyMindIR>());
   unify_mindir_pm->AddPass(std::make_shared<opt::Conv2DBackpropFilterUnifyMindIR>());
   unify_mindir_pm->AddPass(std::make_shared<opt::SliceGradUnifyMindIR>());
+  unify_mindir_pm->AddPass(std::make_shared<opt::FtrlUnifyOutput>());
+  unify_mindir_pm->AddPass(std::make_shared<opt::MomentumUnifyOutput>());
+  unify_mindir_pm->AddPass(std::make_shared<opt::RMSPropUnifyOutput>());
+  unify_mindir_pm->AddPass(std::make_shared<opt::CenteredRMSPropUnifyOutput>());
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
   if (ms_context->get_param<int>(MS_CTX_EXECUTION_MODE) == kGraphMode) {

mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.

mindspore/ccsrc/transform/graph_ir/op_declare/nn_training_ops_declare.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -143,13 +143,13 @@ ATTR_MAP(SparseApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<b
 OUTPUT_MAP(SparseApplyFtrlD) = {{0, OUTPUT_DESC(var)}};
 REG_ADPT_DESC(SparseApplyFtrlD, kNameSparseApplyFtrlD, ADPT_DESC(SparseApplyFtrlD))
 
-// ApplyFtrlD
+// ApplyFtrl
-INPUT_MAP(ApplyFtrlD) = {{1, INPUT_DESC(var)},  {2, INPUT_DESC(accum)},   {3, INPUT_DESC(linear)},
+INPUT_MAP(ApplyFtrl) = {{1, INPUT_DESC(var)},  {2, INPUT_DESC(accum)},   {3, INPUT_DESC(linear)},
-                         {4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)},      {6, INPUT_DESC(l1)},
+                        {4, INPUT_DESC(grad)}, {5, INPUT_DESC(lr)},      {6, INPUT_DESC(l1)},
-                         {7, INPUT_DESC(l2)},   {8, INPUT_DESC(lr_power)}};
+                        {7, INPUT_DESC(l2)},   {8, INPUT_DESC(lr_power)}};
-ATTR_MAP(ApplyFtrlD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+ATTR_MAP(ApplyFtrl) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
-OUTPUT_MAP(ApplyFtrlD) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(accum)}, {2, OUTPUT_DESC(linear)}};
+OUTPUT_MAP(ApplyFtrl) = {{0, OUTPUT_DESC(var)}};
-REG_ADPT_DESC(ApplyFtrlD, kNameApplyFtrl, ADPT_DESC(ApplyFtrlD))
+REG_ADPT_DESC(ApplyFtrl, kNameApplyFtrl, ADPT_DESC(ApplyFtrl))
 
 // ApplyRMSPropD
 INPUT_MAP(ApplyRMSPropD) = {
@@ -161,12 +161,11 @@ ATTR_MAP(ApplyRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool
 OUTPUT_MAP(ApplyRMSPropD) = {{0, OUTPUT_DESC(var)}};
 REG_ADPT_DESC(ApplyRMSPropD, kNameApplyRMSProp, ADPT_DESC(ApplyRMSPropD))
 
-// ApplyCenteredRMSPropD
+// ApplyCenteredRMSProp
-INPUT_MAP(ApplyCenteredRMSPropD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
+INPUT_MAP(ApplyCenteredRMSProp) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
-                                    {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
+                                   {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
-                                    {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
+                                   {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
-ATTR_MAP(ApplyCenteredRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+ATTR_MAP(ApplyCenteredRMSProp) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
-OUTPUT_MAP(ApplyCenteredRMSPropD) = {
+OUTPUT_MAP(ApplyCenteredRMSProp) = {{0, OUTPUT_DESC(var)}};
-  {0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(mg)}, {2, OUTPUT_DESC(ms)}, {3, OUTPUT_DESC(mom)}};
+REG_ADPT_DESC(ApplyCenteredRMSProp, kNameApplyCenteredRMSProp, ADPT_DESC(ApplyCenteredRMSProp))
-REG_ADPT_DESC(ApplyCenteredRMSPropD, kNameApplyCenteredRMSProp, ADPT_DESC(ApplyCenteredRMSPropD))
 }  // namespace mindspore::transform

mindspore/ccsrc/transform/graph_ir/op_declare/nn_training_ops_declare.h

@@ -62,8 +62,8 @@ DECLARE_OP_USE_OUTPUT(ApplyProximalAdagradD)
 DECLARE_OP_ADAPTER(LarsV2Update)
 DECLARE_OP_USE_OUTPUT(LarsV2Update)
 
-DECLARE_OP_ADAPTER(ApplyFtrlD)
+DECLARE_OP_ADAPTER(ApplyFtrl)
-DECLARE_OP_USE_OUTPUT(ApplyFtrlD)
+DECLARE_OP_USE_OUTPUT(ApplyFtrl)
 
 DECLARE_OP_ADAPTER(SparseApplyFtrlD)
 DECLARE_OP_USE_OUTPUT(SparseApplyFtrlD)
@@ -72,7 +72,7 @@ DECLARE_OP_ADAPTER(ApplyRMSPropD)
 DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD)
 DECLARE_OP_USE_OUTPUT(ApplyRMSPropD)
 
-DECLARE_OP_ADAPTER(ApplyCenteredRMSPropD)
+DECLARE_OP_ADAPTER(ApplyCenteredRMSProp)
-DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSPropD)
+DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp)
 }  // namespace mindspore::transform
 #endif  // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_DECLARE_NN_TRAINING_OPS_DECLARE_H_

mindspore/core/base/core_ops.h

@@ -239,6 +239,7 @@ inline const PrimitivePtr kPrimSparseSoftmaxCrossEntropyWithLogits =
   std::make_shared<Primitive>("SparseSoftmaxCrossEntropyWithLogits");
 inline const PrimitivePtr kPrimMomentum = std::make_shared<Primitive>("Momentum");
 inline const PrimitivePtr kPrimApplyMomentum = std::make_shared<Primitive>("ApplyMomentum");
+inline const PrimitivePtr kPrimApplyFtrl = std::make_shared<Primitive>("ApplyFtrl");
 inline const PrimitivePtr kPrimLayerNorm = std::make_shared<Primitive>("LayerNorm");
 inline const PrimitivePtr kPrimLrn = std::make_shared<Primitive>("Lrn");
 inline const PrimitivePtr kPrimLayerNormGrad = std::make_shared<Primitive>("LayerNormGrad");
@@ -452,7 +453,7 @@ inline const PrimitivePtr kPrimGetRefKey = std::make_shared<Primitive>("get_ref_
 inline const PrimitivePtr kPrimMakeRef = std::make_shared<Primitive>("make_ref");
 inline const PrimitivePtr kPrimGetRefValue = std::make_shared<Primitive>("get_ref_value");
 
-// Other primitve not used by backend but used in core;
+// Other primitive not used by backend but used in core;
 inline const PrimitivePtr kPrimStateSetItem = std::make_shared<Primitive>("state_setitem");
 inline const PrimitivePtr kPrimJ = std::make_shared<Primitive>("J");
 

mindspore/ops/_grad/grad_array_ops.py

@@ -308,7 +308,6 @@ def _concat_grad_uniform(input_shapes, input_nums):
 def get_bprop_concat(self):
     """Generate bprop for Concat"""
     axis = self.axis
-    is_ascend = context.get_context('device_target') == "Ascend"
 
     def bprop(x, out, dout):
         dx = ()
@@ -318,7 +317,7 @@ def get_bprop_concat(self):
         for i in range(input_nums):
             input_shapes = input_shapes + (shape_op(x[i]),)
         is_uniform = _concat_grad_uniform(input_shapes, input_nums)
-        if is_uniform and is_ascend:
+        if is_uniform:
             dx = P.Split(axis, input_nums)(dout)
         else:
             for i in range(input_nums):

mindspore/ops/operations/nn_ops.py

@@ -2413,12 +2413,8 @@ class ApplyMomentum(PrimitiveWithInfer):
         validator.check_value_type('gradient_scale', gradient_scale, [float], self.name)
         self.init_prim_io_names(inputs=['variable', 'accumulation', 'learning_rate', 'gradient', 'momentum'],
                                 outputs=['output'])
-        self.is_tbe = context.get_context("device_target") == "Ascend"
-        self.is_ge = context.get_context("enable_ge")
 
     def infer_shape(self, v_shape, a_shape, l_shape, g_shape, m_shape):
-        if not self.is_ge and self.is_tbe:
-            return v_shape, v_shape
         return v_shape
 
     def infer_dtype(self, v_dtype, a_dtype, l_dtype, g_dtype, m_dtype):
@@ -2429,9 +2425,7 @@ class ApplyMomentum(PrimitiveWithInfer):
         validator.check_scalar_or_tensor_types_same({"l_dtype": l_dtype}, valid_dtypes, self.name)
         validator.check_scalar_or_tensor_types_same({"g_dtype": g_dtype}, valid_dtypes, self.name)
         validator.check_scalar_or_tensor_types_same({"m_dtype": m_dtype}, valid_dtypes, self.name)
-        if not self.is_ge and self.is_tbe:
+        return v_dtype
-            return g_dtype, g_dtype
-        return g_dtype
 
 
 class SmoothL1Loss(PrimitiveWithInfer):
@@ -2763,9 +2757,8 @@ class ApplyRMSProp(PrimitiveWithInfer):
         >>> momentum = 1e-10
         >>> epsilon = 0.001
         >>> output = apply_rms(input_x, mean_square, moment, learning_rate, grad, decay, momentum, epsilon)
-        >>> print(output)
+        >>> output
-        (Tensor(shape=[], dtype=Float32, value= 0.100112), Tensor(shape=[], dtype=Float32, value= 4),
+        Tensor(shape=[], dtype=Float32, value= 0.100112)
-        Tensor(shape=[], dtype=Float32, value= 0.899888))
     """
 
     @prim_attr_register
@@ -2773,16 +2766,12 @@ class ApplyRMSProp(PrimitiveWithInfer):
         self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
         self.init_prim_io_names(inputs=['var', 'mean_square', 'moment', 'learning_rate', 'grad',
                                         'rho', 'momentum', 'epsilon'], outputs=['output'])
-        self.is_ge = context.get_context("enable_ge")
-        self.is_d = context.get_context("device_target") == "Ascend"
 
     def infer_shape(self, var_shape, mean_square_shape, moment_shape, learning_rate_shape, grad_shape, decay_shape,
                     momentum_shape, epsilon_shape):
         validator.check("var_shape", var_shape, "mean_square_shape", mean_square_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "moment_shape", moment_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name)
-        if not self.is_ge and self.is_d:
-            return var_shape, var_shape, var_shape
         return var_shape
 
     def infer_dtype(self, var_dtype, mean_square_dtype, moment_dtype, learning_rate_dtype, grad_dtype, decay_dtype,
@@ -2795,8 +2784,6 @@ class ApplyRMSProp(PrimitiveWithInfer):
         validator.check_types_same_and_valid(args_decay, valid_dtypes, self.name)
         args_lr = {"learning_rate": learning_rate_dtype, "decay": decay_dtype}
         validator.check_scalar_or_tensor_types_same(args_lr, valid_dtypes, self.name, allow_mix=True)
-        if not self.is_ge and self.is_d:
-            return var_dtype, var_dtype, var_dtype
         return var_dtype
 
     def infer_value(self, var, mean_square, moment, learning_rate, grad, decay, momentum, epsilon):
@@ -2867,22 +2854,15 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         >>> epsilon = 0.05
         >>> output = centered_rms_prop(input_x, mean_grad, mean_square, moment, grad,
         ...                            learning_rate, decay, momentum, epsilon)
-        >>> print(output)
+        >>> output
-        (Tensor(shape=[2, 2], dtype=Float32, value=
+        Tensor(shape=[2, 2], dtype=Float32, value=
         [[-2.00000000e+00, -5.02492237e+00],
-         [-8.04984474e+00, -1.10747662e+01]]), Tensor(shape=[2, 2], dtype=Float32, value=
+         [-8.04984474e+00, -1.10747662e+01]])
-        [[ 0.00000000e+00,  1.00000000e+00],
-         [ 2.00000000e+00,  3.00000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
-        [[ 0.00000000e+00,  1.00000000e+00],
-         [ 4.00000000e+00,  9.00000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
-        [[ 0.00000000e+00,  4.02492237e+00],
-         [ 8.04984474e+00,  1.20747662e+01]]))
     """
 
     @prim_attr_register
     def __init__(self, use_locking=False):
         self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
-        self.is_ascend = context.get_context("device_target") == "Ascend"
 
     def infer_shape(self, var_shape, mean_gradient_shape, mean_square_shape, moment_shape, grad_shape,
                     learning_rate_shape, decay_shape, momentum_shape, epsilon_shape):
@@ -2890,8 +2870,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         validator.check("var_shape", var_shape, "mean_square_shape", mean_square_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "moment_shape", moment_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name)
-        if self.is_ascend:
-            return var_shape, mean_gradient_shape, mean_square_shape, moment_shape
         return var_shape
 
     def infer_dtype(self, var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype, grad_dtype,
@@ -2905,8 +2883,6 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         validator.check_types_same_and_valid(args_rho, valid_dtypes, self.name)
         args_lr = {"learning_rate": learning_rate_dtype, "rho": rho_dtype}
         validator.check_scalar_or_tensor_types_same(args_lr, valid_dtypes, self.name, allow_mix=True)
-        if self.is_ascend:
-            return var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype
         return var_dtype
 
 
@@ -6176,15 +6152,8 @@ class ApplyFtrl(PrimitiveWithInfer):
           Default: -0.5. It must be a float number or a scalar tensor with float16 or float32 data type.
 
     Outputs:
-        There are three outputs for Ascend environment.
+        - **var** (Tensor) - represents the updated `var`. As the input parameters has been updated in-place, this
-
+          value is always zero when the platforms is GPU.
-        - **var** (Tensor) - represents the updated `var`.
-        - **accum** (Tensor) - represents the updated `accum`.
-        - **linear** (Tensor) - represents the updated `linear`.
-
-        There is only one output for GPU environment.
-
-        - **var** (Tensor) - This value is always zero and the input parameters has been updated in-place.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -6217,26 +6186,10 @@ class ApplyFtrl(PrimitiveWithInfer):
         >>> net = ApplyFtrlNet()
         >>> input_x = Tensor(np.random.randint(-4, 4, (2, 2)), mindspore.float32)
         >>> output = net(input_x)
-        >>> is_tbe = context.get_context("device_target") == "Ascend"
+        >>> output
-        >>> if is_tbe:
+        Tensor(shape=[2, 2], dtype=Float32, value=
-        ...     print(output)
-        (Tensor(shape=[2, 2], dtype=Float32, value=
         [[ 4.61418092e-01,  5.30964255e-01],
-         [ 2.68715084e-01,  3.82065028e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
+         [ 2.68715084e-01,  3.82065028e-01]])
-        [[ 1.64236546e+01,  9.64589405e+00],
-         [ 1.43758726e+00,  9.89177322e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
-        [[-1.86994812e+03, -1.64906018e+03],
-         [-3.22187836e+02, -1.20163989e+03]]))
-         ... else:
-         ...    print(net.var.asnumpy())
-         [[0.4614181  0.5309642 ]
-          [0.2687151  0.38206503]]
-         ...    print(net.accum.asnumpy())
-         [[16.423655  9.645894 ]
-          [ 1.4375873 9.891773 ]]
-         ...    print(net.linear.asnumpy())
-         [[-1869.9479 -1649.0599]
-          [ -322.1879 -1201.6399]]
     """
 
     @prim_attr_register
@@ -6244,14 +6197,11 @@ class ApplyFtrl(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['var', 'accum', 'linear', 'grad', 'lr', 'l1', 'l2', 'lr_power'],
                                 outputs=['output'])
         self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
-        self.is_tbe = context.get_context("device_target") == "Ascend"
 
     def infer_shape(self, var_shape, accum_shape, linear_shape, grad_shape, lr_shape, l1_shape, l2_shape,
                     lr_power_shape):
         validator.check('var shape', var_shape, 'accum shape', accum_shape, Rel.EQ, self.name)
         validator.check('var shape', var_shape, 'linear shape', linear_shape, Rel.EQ, self.name)
-        if self.is_tbe:
-            return var_shape, var_shape, var_shape
         return var_shape
 
     def infer_dtype(self, var_type, accum_type, linear_type, grad_type, lr_type, l1_type, l2_type, lr_power_type):
@@ -6263,8 +6213,6 @@ class ApplyFtrl(PrimitiveWithInfer):
         validator.check_scalar_or_tensor_types_same({"l1": l1_type}, valid_dtypes, self.name)
         validator.check_scalar_or_tensor_types_same({"l2": l2_type}, valid_dtypes, self.name)
         validator.check_scalar_or_tensor_types_same({"lr_power": lr_power_type}, valid_dtypes, self.name)
-        if self.is_tbe:
-            return var_type, var_type, var_type
         return var_type
 
 

tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_hccl_op_test.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -48,7 +48,8 @@ class MockInsertMemcpyForHcclKernelQuery : public KernelQuery {
     if (!node->isa<CNode>()) {
       return false;
     }
-    return AnfAlgo::GetCNodeName(node->cast<CNodePtr>()) == "ApplyMomentum";
+    auto node_name = AnfAlgo::GetCNodeName(node->cast<CNodePtr>());
+    return node_name == "ApplyMomentum" || node_name == "AssignAdd";
   }
 };
 
@@ -103,9 +104,9 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond3) {
   get_py_fun_.SetDoResolve(true);
   FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_insert_memcpy_async_for_hccl_op_cond3", "before");
   ASSERT_TRUE(g != nullptr);
-  std::vector<int64_t> shp_x{1, 64, 112, 112};
+  std::vector<int64_t> shp_x{3, 2};
   auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
-  AbstractBasePtrList args_spec_list{x_abstract, x_abstract, x_abstract, x_abstract, x_abstract};
+  AbstractBasePtrList args_spec_list{x_abstract, x_abstract};
   auto kg = GetKernelGraph(g, args_spec_list);
   EXPECT_NE(kg, nullptr);
 

tests/ut/cpp/pre_activate/ascend/ir_fusion/add_input_to_output_test.cc

@@ -1,74 +0,0 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * 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 "common/backend_common_test.h"
-#include "common/py_func_graph_fetcher.h"
-#include "debug/anf_ir_dump.h"
-
-#define private public
-#define protected public
-#include "backend/optimizer/ascend/ir_fusion/add_input_to_output.h"
-#undef private
-#undef protected
-
-namespace mindspore {
-namespace opt {
-class TestHWAddInputToOutput : public BackendCommon {
- public:
-  TestHWAddInputToOutput() : getPyFun_("gtest_input.pre_activate.add_input_to_output_test", true) {}
-  ~TestHWAddInputToOutput() override = default;
-
- public:
-  UT::PyFuncGraphFetcher getPyFun_;
-};
-
-class MockOpFinder : public OpFinder {
- public:
-  MockOpFinder() = default;
-  ~MockOpFinder() override = default;
-  int GetOpRegisteredOutputNum(const std::string &op_name, const CNodePtr &cnode) override { return 2; }
-};
-
-TEST_F(TestHWAddInputToOutput, test_add_input_to_output) {
-  FuncGraphPtr g = getPyFun_.CallAndParseRet("test_add_input_to_output", "before");
-  EXPECT_NE(g, nullptr);
-  std::vector<int64_t> shp{2, 32, 224, 224};
-  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
-  AbstractBasePtrList args_spec_list;
-  for (size_t i = 0; i < 5; ++i) {
-    args_spec_list.push_back(x_abstract);
-  }
-  auto kg = GetKernelGraph(g, args_spec_list);
-  EXPECT_NE(kg, nullptr);
-  auto ret = kg->get_return();
-  EXPECT_NE(ret, nullptr);
-  auto make_tuple = ret->input(1);
-  EXPECT_NE(make_tuple, nullptr);
-  auto momentum = make_tuple->cast<CNodePtr>()->input(1);
-  EXPECT_NE(momentum, nullptr);
-  EXPECT_NE(momentum->abstract(), nullptr);
-  EXPECT_FALSE(momentum->abstract()->isa<abstract::AbstractTuple>());
-
-  auto optimizer = std::make_shared<opt::GraphOptimizer>();
-  auto pm = std::make_shared<opt::PassManager>();
-  auto pass = std::make_shared<opt::AddInputToOutput>();
-  pass->op_finder_ = std::make_shared<MockOpFinder>();
-  pm->AddPass(pass);
-  optimizer->AddPassManager(pm);
-  (void)optimizer->Optimize(kg);
-  EXPECT_TRUE(momentum->abstract()->isa<abstract::AbstractTuple>());
-}
-}  // namespace opt
-}  // namespace mindspore

tests/ut/cpp/python_input/gtest_input/pre_activate/add_input_to_output_test.py

@@ -1,39 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-from mindspore.ops import operations as P
-
-ApplyMomentum = P.ApplyMomentum()
-
-
-class FnDict:
-    def __init__(self):
-        self.fnDict = {}
-
-    def __call__(self, fn):
-        self.fnDict[fn.__name__] = fn
-
-    def __getitem__(self, name):
-        return self.fnDict[name]
-
-
-def test_add_input_to_output(tag):
-    fns = FnDict()
-
-    @fns
-    def before(input0, input1, input2, input3, input4):
-        return ApplyMomentum(input0, input1, input2, input3, input4)
-
-    return fns[tag]

tests/ut/cpp/python_input/gtest_input/pre_activate/insert_memcpy_async_for_hccl_op.py

@@ -22,7 +22,7 @@ broadcast = P.Broadcast(1)
 memcpy_async = Primitive('memcpy_async')
 make_tuple = Primitive('make_tuple')
 tuple_getitem = Primitive(Constants.kTupleGetItem)
-apply_momentun = P.ApplyMomentum()
+assign_add = P.AssignAdd()
 control_depend = P.ControlDepend()
 relu = P.ReLU()
 
@@ -84,14 +84,14 @@ def test_insert_memcpy_async_for_hccl_op_cond3(tag):
     fns = FnDict()
 
     @fns
-    def before(a, b, c, d, e):
+    def before(a, b):
-        res = apply_momentun(a, b, c, d, e)
+        res = assign_add(a, b)
         res = all_reduce(res)
         return res
 
     @fns
-    def after(a, b, c, d, e):
+    def after(a, b):
-        res = apply_momentun(a, b, c, d, e)
+        res = assign_add(a, b)
         res = memcpy_async(res)
         res = all_reduce(res)
         return make_tuple(res)

tests/ut/cpp/python_input/gtest_input/pre_activate/momentum_lossscale_fusion_test.py

@@ -48,6 +48,6 @@ def test_momentum_lossscale_fusion(tag):
 
     @fns
     def after(input0, input1, input2, input3, input4):
-        return make_tuple(FusedMulApplyMomentum(input0, input1, input2, input3, input4, constant))
+        return make_tuple(tuple_getitem(FusedMulApplyMomentum(input0, input1, input2, input3, input4, constant), 0))
 
     return fns[tag]