Support optimizers operator to be generated (#51767)

* test_get_kernel

* add invoke signature

* change reduce_max

* change frobenius_norm

* reset reduce_max according to composite and change reduce_all

* fix the bug when Scalar(*)

* fix 'scalar when support_tensor'

* change code according to review

* change 'keep_signature' to 'manual_signature' and add some erro info

* support optimizers autogen

* change sgd yaml

* change generate signature

* fix test/cpp/new_executor/CM

* reset signature generated function

* change signature funciton

* change signature funciton

paddle/fluid/operators/generator/filters.py

@@ -65,6 +65,25 @@ def to_dense_input_type(s, optional=False):
         return dense_optional_input_types_map[s]
 
 
+def assert_dense_or_sr(input_type):
+    return (
+        "ctx.IsSelectedRowsInput"
+        if input_type == "selected_rows"
+        else "ctx.IsDenseTensorInput"
+    )
+
+
+def find_optinal_inputs_name(inputs):
+    optional_inputs_name = [
+        input["fluid_name"] for input in inputs if input["optional"] is True
+    ]
+    return optional_inputs_name
+
+
+def delete_last_underline(op_name):
+    return op_name if op_name[-1] != '_' else op_name[:-1]
+
+
 # ------------------------------ output  ----------------------------------
 def to_paddle_output_type(s):
     return output_type_map[s]

paddle/fluid/operators/generator/generate_op.py

@@ -19,7 +19,10 @@ from pathlib import Path
 
 import yaml
 from filters import (
+    assert_dense_or_sr,
     cartesian_prod_mapping,
+    delete_last_underline,
+    find_optinal_inputs_name,
     to_composite_grad_opmaker_name,
     to_input_name,
     to_int_array_tensor_name,
@@ -63,6 +66,8 @@ env.filters["to_opmaker_name_cstr"] = to_opmaker_name_cstr
 env.filters["cartesian_prod_mapping"] = cartesian_prod_mapping
 env.filters["to_composite_grad_opmaker_name"] = to_composite_grad_opmaker_name
 env.filters["to_variable_names"] = to_variable_names
+env.filters["assert_dense_or_sr"] = assert_dense_or_sr
+env.filters["find_optinal_inputs_name"] = find_optinal_inputs_name
 env.tests["base_op"] = is_base_op
 env.tests["composite_op"] = is_composite_op
 env.tests["vec"] = is_vec
@@ -478,6 +483,7 @@ def parse_get_expected_kerneltype(
                     fw_op_dict[fw_name][
                         "get_expected_kernel_type"
                     ] = op_comp_map['get_expected_kernel_type'][fw_name]
+            if "backward" in op_comp_map:
                 bw_names = [
                     bw_name.split('(')[0].strip()
                     for bw_name in op_comp_map['backward'].split(',')
@@ -528,6 +534,20 @@ def split_ops_list(ops, backward_op_dict, split_num):
     return new_ops_list, new_bw_ops_list
 
 
+def to_phi_and_fluid_op_name_without_underline(op_item):
+    '''
+    If the op_name ends with '_', delete the last '_'. For an example, 'sgd_' becomes 'sgd
+    '''
+    names = op_item.split('(')
+    if len(names) == 1:
+        op_kernel_name = delete_last_underline(names[0].strip())
+        return op_kernel_name
+    else:
+        op_name = delete_last_underline(names[0].strip())
+        kernel_name = delete_last_underline(names[1].split(')')[0].strip())
+        return op_name + '(' + kernel_name + ')'
+
+
 def main(
     ops_yaml_path,
     backward_yaml_path,
@@ -539,11 +559,11 @@ def main(
     with open(ops_yaml_path, "rt") as f:
         ops = yaml.safe_load(f)
         ops = [restruct_io(op) for op in ops]
-    forward_op_dict = to_named_dict(ops)
+    forward_op_dict = to_named_dict(ops, True)
     with open(backward_yaml_path, "rt") as f:
         backward_ops = yaml.safe_load(f)
         backward_ops = [restruct_io(op) for op in backward_ops]
-    backward_op_dict = to_named_dict(backward_ops)
+    backward_op_dict = to_named_dict(backward_ops, True)
     with open(op_version_yaml_path, "rt") as f:
         op_versions = yaml.safe_load(f)
     # add op version info into op
@@ -553,6 +573,10 @@ def main(
 
     with open(op_compat_yaml_path, "rt") as f:
         op_fluid_map_list = yaml.safe_load(f)
+        for op_args in op_fluid_map_list:
+            op_args["op"] = to_phi_and_fluid_op_name_without_underline(
+                op_args["op"]
+            )
 
     for op in ops:
         op['op_name'] = op['name']

paddle/fluid/operators/generator/generate_sparse_op.py

@@ -18,7 +18,9 @@ from pathlib import Path
 
 import yaml
 from filters import (
+    assert_dense_or_sr,
     cartesian_prod_mapping,
+    find_optinal_inputs_name,
     to_composite_grad_opmaker_name,
     to_input_name,
     to_int_array_tensor_name,
@@ -59,6 +61,8 @@ env.filters["to_scalar_tensor_name"] = to_scalar_tensor_name
 env.filters["to_int_array_tensor_name"] = to_int_array_tensor_name
 env.filters["to_int_array_tensors_name"] = to_int_array_tensors_name
 env.filters["to_input_name"] = to_input_name
+env.filters["assert_dense_or_sr"] = assert_dense_or_sr
+env.filters["find_optinal_inputs_name"] = find_optinal_inputs_name
 env.filters["to_opmaker_name_cstr"] = to_opmaker_name_cstr
 env.filters["cartesian_prod_mapping"] = cartesian_prod_mapping
 env.filters["to_composite_grad_opmaker_name"] = to_composite_grad_opmaker_name

paddle/fluid/operators/generator/get_expected_kernel_func.cc

@@ -101,5 +101,27 @@ phi::KernelKey GetReduceGradExpectedKernelType(
   return phi::KernelKey(input_data_type, ctx.GetPlace());
 }
 
+phi::KernelKey GetSgdExpectedKernelType(
+    const framework::ExecutionContext& ctx,
+    const framework::OperatorWithKernel* op_ptr) {
+  auto data_type = op_ptr->IndicateVarDataType(ctx, "Param");
+
+  // NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
+  const auto* param_var = ctx.InputVar("Param");
+  const auto* grad_var = ctx.InputVar("Grad");
+
+  // supported cases
+  bool dense_param_sparse_grad = param_var->IsType<phi::DenseTensor>() &&
+                                 grad_var->IsType<phi::SelectedRows>();
+  bool dense_param_and_grad = param_var->IsType<phi::DenseTensor>() &&
+                              grad_var->IsType<phi::DenseTensor>();
+  if (!(dense_param_sparse_grad || dense_param_and_grad)) {
+    op_ptr->SetDnnFallback(true);
+  }
+  // NOTE(jiahongyu): Above codes originally enclosed by PADDLE_WITH_MKLDNN
+
+  return phi::KernelKey(data_type, ctx.GetPlace());
+}
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/generator/get_expected_kernel_func.h

@@ -28,5 +28,9 @@ phi::KernelKey GetReduceGradExpectedKernelType(
     const framework::ExecutionContext& ctx,
     const framework::OperatorWithKernel* op_ptr);
 
+phi::KernelKey GetSgdExpectedKernelType(
+    const framework::ExecutionContext& ctx,
+    const framework::OperatorWithKernel* op_ptr);
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/generator/ops_extra_info_gen.py

@@ -16,6 +16,7 @@ import argparse
 import re
 
 import yaml
+from filters import delete_last_underline
 
 
 def map_code_template(attrs_str, attrs_checker_str):
@@ -77,9 +78,9 @@ def generate_extra_info(op_compat_yaml_path, ops_extra_info_path):
     def get_op_name(api_item):
         names = api_item.split('(')
         if len(names) == 1:
-            return names[0].strip()
+            return delete_last_underline(names[0].strip())
         else:
-            return names[1].split(')')[0].strip()
+            return delete_last_underline(names[1].split(')')[0].strip())
 
     extra_map_str_list = []
     extra_checker_str_list = []

paddle/fluid/operators/generator/parse_utils.py

@@ -20,8 +20,18 @@ from tests_utils import is_attr, is_input, is_output, is_vec
 from type_mapping import opmaker_attr_types_map
 
 
-def to_named_dict(items: List[Dict]) -> Dict[str, Dict]:
+def to_named_dict(items: List[Dict], is_op=False) -> Dict[str, Dict]:
     named_dict = {}
+    if is_op:
+        for item in items:
+            if "name" not in item:
+                raise KeyError(f"name not in {item}")
+            item["name"] = (
+                item["name"] if item["name"][-1] != '_' else item["name"][:-1]
+            )
+            name = item["name"]
+            named_dict[name] = item
+    else:
         for item in items:
             if "name" not in item:
                 raise KeyError(f"name not in {item}")

paddle/fluid/operators/generator/templates/operator_utils.c.j2

@@ -119,6 +119,34 @@ static_cast<int>(phi::Place({{"phi::" if not default_value is initializer_list}}
 {%- endmacro %}
 
 
+{% macro choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, kernel_name, is_first) %} {#inline#}
+  {%- set inputs_len = inputs_type | length -%}
+  {%- if is_first -%}
+if (
+  {%- else -%}
+else if (
+  {%- endif -%}
+      {%- for input_type in inputs_type -%}
+      {%- set kernel_arg_name = kernel_args[loop.index0] | to_opmaker_name_cstr -%}
+        {%- if loop.index0 != inputs_len - 1%}
+          {%- if kernel_args[loop.index0] in optional_inputs_name %}
+  ((ctx.HasInput({{kernel_arg_name}}) && {{input_type | assert_dense_or_sr}}({{kernel_arg_name}})) || (!ctx.HasInput({{kernel_arg_name}}))) &&
+          {% else %}
+  {{input_type | assert_dense_or_sr}}({{kernel_arg_name}}) &&
+          {% endif %}
+        {% else %} {# the last param #}
+          {% if kernel_args[loop.index0] in optional_inputs_name -%}
+  ((ctx.HasInput({{kernel_arg_name}}) && {{input_type | assert_dense_or_sr}}({{kernel_arg_name}})) || (!ctx.HasInput({{kernel_arg_name}}))))
+          {%- else -%}
+  {{input_type | assert_dense_or_sr}}({{kernel_arg_name}}))
+          {%- endif %}  {
+    return KernelSignature("{{kernel_name}}", std::move(inputs), std::move(attrs), std::move(outputs));
+  }
+        {% endif %}
+      {% endfor %}
+{%- endmacro -%}
+
+
 {# --------------------------------------- name mapping ---------------------------------------------- #}
 {% macro name_map(op) %}
 /*
@@ -132,6 +160,7 @@ All possible KernelSignatures returned by {{op["name"] | to_pascal_case }}OpArgu
 
 KernelSignature {{op["op_name"] | to_pascal_case }}OpArgumentMapping(const ArgumentMappingContext& ctx) {
   {% set kernel_args = op["kernel"]["param"] %}
+  {% set optional_inputs_name = op["inputs"]| find_optinal_inputs_name %}
   {{get_input_list(op["inputs"], kernel_args)}};
   paddle::small_vector<const char*> attrs;
   {% for attr in op["attrs"]%}
@@ -140,13 +169,30 @@ KernelSignature {{op["op_name"] | to_pascal_case }}OpArgumentMapping(const Argum
     {% endfilter %}
   {% endfor %}
   {{get_output_list(op["outputs"], kernel_args)}};
-  {% if op["kernel"]["func"] | length == 1 %}
-  KernelSignature sig("{{op["kernel"]["func"][0]}}", std::move(inputs), std::move(attrs), std::move(outputs));
-  return sig;
-  {% else %}{# it has kernel for selected rows #}
-  const char* kernel_name = ctx.IsSelectedRowsInput({{kernel_args[0] | to_opmaker_name_cstr}}) ? "{{op["kernel"]["func"][1]}}" : "{{op["kernel"]["func"][0]}}";
-  KernelSignature sig (kernel_name, std::move(inputs), std::move(attrs), std::move(outputs));
-  return sig;
+  {% set kernel_num = op["kernel"]["func"] | length %}
+  {% if kernel_num == 1 %}
+  return KernelSignature("{{op["kernel"]["func"][0]}}", std::move(inputs), std::move(attrs), std::move(outputs));
+  {% elif kernel_num == 2 %}{# it has kernel for selected rows #}
+  {% set fun_name = op["kernel"]["func"][0] %}
+    {% set inputs_type = op["kernel"]["dispatch"][fun_name][0] %}
+{{choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, fun_name, true)}}
+    {%- set fun_name = op["kernel"]["func"][1] -%}
+    {%- set inputs_type = op["kernel"]["dispatch"][fun_name][0] -%}
+{{choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, fun_name, false)-}}
+  else { return KernelSignature("unregistered", {}, {}, {}); }
+  {% elif kernel_num == 3 %}{# it has kernel for selected rows #}
+    {%- set fun_name = op["kernel"]["func"][0] -%}
+    {%- set inputs_type = op["kernel"]["dispatch"][fun_name][0] -%}
+{{choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, fun_name, true)}}
+    {%- set fun_name = op["kernel"]["func"][1] -%}
+    {%- set inputs_type = op["kernel"]["dispatch"][fun_name][0] -%}
+{{choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, fun_name, false)-}}
+    {%- set fun_name = op["kernel"]["func"][2] -%}
+    {%- set inputs_type = op["kernel"]["dispatch"][fun_name][0] -%}
+{{choose_kernel_signature(inputs_type, optional_inputs_name, kernel_args, fun_name, false)-}}
+  else { return KernelSignature("unregistered", {}, {}, {}); }
+  {% else %} {# only support kernel_num <= 3 #}
+  return KernelSignature("unregistered", {}, {}, {});
   {%endif%}
 }
 {% endmacro %}
@@ -395,7 +441,7 @@ class {{op["op_name"] | to_pascal_case}}Op : public framework::OperatorWithKerne
 DECLARE_INFER_SHAPE_FUNCTOR({{op["op_name"]}}, {{op["op_name"] | to_pascal_case}}InferShapeFunctor,
                             PD_INFER_META(phi::{{op["infer_meta"]["func"]}}));
 {# inplace inferer #}
-{% if op["inplace"] is not none %}
+{% if op["inplace"] is not none and op["inplace"] | length == 1%}
   {% set inplace_map %}
   {% for source, target in op["inplace"].items() %}
 {{"{"}}{{target | to_opmaker_name}}, {{source | to_opmaker_name}}{{"}"}}{{", " if not loop.last}}

paddle/fluid/operators/generator/tests_utils.py

@@ -63,7 +63,7 @@ def supports_selected_rows_kernel(op):
 
 
 def supports_inplace(op):
-    return op['inplace'] is not None
+    return op['inplace'] is not None and len(op['inplace']) == 1
 
 
 def supports_no_need_buffer(op):

paddle/fluid/operators/optimizers/lamb_op.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 <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/phi/backends/cpu/cpu_context.h"
-#include "paddle/phi/core/infermeta_utils.h"
-#include "paddle/phi/core/kernel_registry.h"
-#include "paddle/phi/infermeta/multiary.h"
-#include "paddle/phi/kernels/lamb_kernel.h"
-
-namespace paddle {
-namespace operators {
-
-class LambOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  phi::KernelKey GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const {
-    auto input_data_type =
-        OperatorWithKernel::IndicateVarDataType(ctx, "Param");
-    return phi::KernelKey(input_data_type, ctx.GetPlace());
-  }
-  phi::KernelKey GetKernelTypeForVar(
-      const std::string &var_name,
-      const phi::DenseTensor &tensor,
-      const phi::KernelKey &expected_kernel_type) const {
-    if (var_name == "Beta1Pow" || var_name == "Beta2Pow") {
-      return phi::KernelKey(phi::Backend::ALL_BACKEND,
-                            expected_kernel_type.layout(),
-                            expected_kernel_type.dtype());
-    } else {
-      return phi::KernelKey(
-          tensor.place(), tensor.layout(), expected_kernel_type.dtype());
-    }
-  }
-};
-
-class LambOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput("Param",
-             "(phi::DenseTensor, default phi::DenseTensor<float>) "
-             "Input parameter that has to be updated.");
-    AddInput("Grad",
-             "(phi::DenseTensor, default phi::DenseTensor<float>) "
-             "Input gradient of the parameter.");
-    AddInput("LearningRate", "(Tensor) Learning rate.");
-    AddInput("Moment1", "(Tensor) Input first moment.");
-    AddInput("Moment2", "(Tensor) Input second moment.");
-    AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator.");
-    AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator.");
-    AddInput("MasterParam",
-             "(phi::DenseTensor, default phi::DenseTensor<float>) "
-             "Input master parameter that has to be updated.")
-        .AsDispensable();
-    AddInput(
-        "SkipUpdate",
-        "(Tensor) Input tensor to determine whether to update the parameter.")
-        .AsDispensable();
-
-    AddOutput("ParamOut", "(Tensor) Output parameter.");
-    AddOutput("Moment1Out", "(Tensor) Output first moment.");
-    AddOutput("Moment2Out", "(Tensor) Output second moment.");
-    AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator")
-        .AsDispensable();
-    AddOutput("Beta2PowOut", "(Tensor) Output beta2 power accumulator")
-        .AsDispensable();
-    AddOutput("MasterParamOut", "(Tensor) Output master parameter.")
-        .AsDispensable();
-    AddAttr<float>("weight_decay", "(float) Weight decay rate.");
-    AddAttr<float>("beta1",
-                   "(float, default 0.9) The exponential decay rate for the "
-                   "1st moment estimates.")
-        .SetDefault(0.9);
-    AddAttr<float>("beta2",
-                   "(float, default 0.999) The exponential decay rate for the "
-                   "2nd moment estimates.")
-        .SetDefault(0.999);
-    AddAttr<float>("epsilon",
-                   "(float, default 1.0e-6) "
-                   "Constant for numerical stability.")
-        .SetDefault(1.0e-6f);
-    AddAttr<bool>(
-        "multi_precision",
-        "(bool, default false) Whether to enable multi-precision mode.")
-        .SetDefault(false);
-
-    AddComment(R"DOC(
-LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.
-
-LAMB Optimizer is designed to scale up the batch size of training without losing
-accuracy, which supports adaptive element-wise updating and accurate layer-wise
-correction. For more information, please refer to https://arxiv.org/abs/1904.00962.
-
-The updating of parameters follows:
-
-$$
-m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t \\
-
-v_t &= \beta_2 v_{t - 1}  + (1 - \beta_2)g_t^2 \\
-
-m_t &= \frac{m_t}{\beta_1^t} \\
-
-v_t &= \frac{v_t}{\beta_2^t} \\
-
-r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon} \\
-
-w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})
-$$
-
-where $m$ is the 1st moment, and $v$ the 2nd moment, $\eta$ the
-learning rate, $\lambda$ the weight decay rate.
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-DECLARE_INFER_SHAPE_FUNCTOR(lamb,
-                            LambInferMetaFunctor,
-                            PD_INFER_META(phi::LambInferMeta));
-REGISTER_OPERATOR(
-    lamb,
-    ops::LambOp,
-    ops::LambOpMaker,
-    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    LambInferMetaFunctor);
-
-/* ==========================  register checkpoint ===========================*/
-REGISTER_OP_VERSION(lamb).AddCheckpoint(
-    R"ROC(Upgrade lamb, add two new outputs [Beta1PowOut] and [Beta2PowOut].)ROC",
-    paddle::framework::compatible::OpVersionDesc()
-        .NewInput("Beta1PowOut",
-                  "The Output beta1 power accumulator. 'Beta1PowOut' is "
-                  "dispensable.")
-        .NewInput("Beta2PowOut",
-                  "The Output beta2 power accumulator. 'Beta2PowOut' is "
-                  "dispensable."));

paddle/fluid/operators/optimizers/sgd_op.cc

-/* Copyright (c) 2016 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/operators/optimizers/sgd_op.h"
-
-#include <string>
-
-#include "paddle/fluid/framework/infershape_utils.h"
-#include "paddle/phi/core/infermeta_utils.h"
-#include "paddle/phi/infermeta/multiary.h"
-
-namespace paddle {
-namespace operators {
-
-class SGDOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  phi::KernelKey GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "Param");
-
-    // NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
-    const auto *param_var = ctx.InputVar("Param");
-    const auto *grad_var = ctx.InputVar("Grad");
-
-    // supported cases
-    bool dense_param_sparse_grad = param_var->IsType<phi::DenseTensor>() &&
-                                   grad_var->IsType<phi::SelectedRows>();
-    bool dense_param_and_grad = param_var->IsType<phi::DenseTensor>() &&
-                                grad_var->IsType<phi::DenseTensor>();
-    if (!(dense_param_sparse_grad || dense_param_and_grad)) {
-      this->SetDnnFallback(true);
-    }
-    // NOTE(jiahongyu): Above codes originally enclosed by PADDLE_WITH_MKLDNN
-
-    return phi::KernelKey(data_type, ctx.GetPlace());
-  }
-
-  phi::KernelKey GetKernelTypeForVar(
-      const std::string &var_name,
-      const phi::DenseTensor &tensor,
-      const phi::KernelKey &expected_kernel_type) const override {
-    if (var_name == "LearningRate") {
-      return phi::KernelKey(tensor.place(), tensor.layout(), tensor.dtype());
-    }
-    return phi::KernelKey(
-        tensor.place(), tensor.layout(), expected_kernel_type.dtype());
-  }
-};
-
-class SGDOpInferVarType : public framework::VarTypeInference {
- public:
-  void operator()(framework::InferVarTypeContext *ctx) const override {
-    auto in_var_type = ctx->GetInputType("Param");
-    PADDLE_ENFORCE_EQ(in_var_type == framework::proto::VarType::SELECTED_ROWS ||
-                          in_var_type == framework::proto::VarType::LOD_TENSOR,
-                      true,
-                      platform::errors::InvalidArgument(
-                          "The input Var's type should be LoDtensor or "
-                          "SelectedRows, but the received type is %s",
-                          in_var_type));
-
-    ctx->SetOutputType("ParamOut", in_var_type, framework::ALL_ELEMENTS);
-  }
-};
-
-class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput("Param", "(Tensor or SelectedRows) Input parameter");
-    AddInput("LearningRate", "(Tensor) Learning rate of SGD");
-    AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
-    AddInput("MasterParam", "FP32 master weight for AMP.").AsDispensable();
-    AddOutput("ParamOut",
-              "(Tensor or SelectedRows, same with Param) "
-              "Output parameter, should share the same memory with Param");
-    AddOutput("MasterParamOut",
-              "The updated FP32 master weight for AMP. "
-              "It shared memory with Input(MasterParam).")
-        .AsDispensable();
-
-    AddAttr<bool>(
-        "use_mkldnn",
-        "(bool, default false) Indicates if MKL-DNN kernel will be used")
-        .SetDefault(false);
-    AddAttr<bool>("multi_precision",
-                  "(bool, default false) "
-                  "Whether to use multi-precision during weight updating.")
-        .SetDefault(false);
-
-    AddComment(R"DOC(
-
-SGD operator
-
-This operator implements one step of the stochastic gradient descent algorithm.
-
-$$param\_out = param - learning\_rate * grad$$
-
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-DECLARE_INFER_SHAPE_FUNCTOR(sgd,
-                            SGDInferShapeFunctor,
-                            PD_INFER_META(phi::SgdInferMeta));
-REGISTER_OPERATOR(
-    sgd,
-    ops::SGDOp,
-    ops::SGDOpMaker,
-    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    ops::SGDOpInferVarType,
-    SGDInferShapeFunctor);

paddle/fluid/operators/optimizers/unity_build_rule.cmake

@@ -9,7 +9,6 @@ register_unity_group(
   ftrl_op.cc
   lars_momentum_op.cc
   momentum_op.cc
-  sgd_op.cc
   proximal_adagrad_op.cc
   adagrad_op.cc
   adam_op.cc
@@ -18,7 +17,6 @@ register_unity_group(
   proximal_gd_op.cc
   decayed_adagrad_op.cc
   adadelta_op.cc
-  lamb_op.cc
   dpsgd_op.cc
   rmsprop_op.cc)
 register_unity_group(

paddle/phi/api/yaml/legacy_ops.yaml

@@ -853,18 +853,6 @@
     data_type : x
   backward : kldiv_loss_grad
 
-- op : lamb_
-  args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, float weight_decay, float beta1, float beta2, float epsilon, bool multi_precision)
-  output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_outs)
-  infer_meta :
-    func : LambInferMeta
-  kernel :
-    func : lamb {dense, dense, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense},
-           lamb_sr {dense, selected_rows, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense}
-    data_type : param
-  optional : master_param, skip_update
-  inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_outs)
-
 - op : layer_norm
   args : (Tensor x, Tensor scale, Tensor bias, float epsilon, int begin_norm_axis)
   output : Tensor(out), Tensor(mean), Tensor(variance)
@@ -1455,21 +1443,6 @@
     data_type : x
   backward : segment_pool_grad
 
-- op : sgd_
-  args : (Tensor param, Tensor learning_rate, Tensor grad, Tensor master_param, bool multi_precision)
-  output : Tensor(param_out), Tensor(master_param_out)
-  infer_meta :
-    func : SgdInferMeta
-  kernel :
-    func : sgd {dense, dense, dense, dense -> dense, dense},
-           sgd_dense_param_sparse_grad {dense, dense, selected_rows, dense -> dense, dense},
-           sgd_sparse_param_sparse_grad {selected_rows, dense, selected_rows, selected_rows -> selected_rows, selected_rows}
-    data_type : param
-  data_transform :
-    support_trans_dtype : learning_rate
-  optional : master_param
-  inplace : (param -> param_out), (master_param -> master_param_out)
-
 - op : shape
   args : (Tensor input)
   output : Tensor(out)

paddle/phi/api/yaml/op_compat.yaml

@@ -984,6 +984,12 @@
   outputs :
     out : Out
 
+- op : lamb_
+  inputs :
+    {param : Param, grad : Grad, learning_rate : LearningRate, moment1 : Moment1, moment2 : Moment2, beta1_pow : Beta1Pow, beta2_pow : Beta2Pow, master_param : MasterParam, skip_update : SkipUpdate}
+  outputs :
+    {param_out : ParamOut, moment1_out : Moment1Out, moment2_out : Moment2Out, beta1_pow_out : Beta1PowOut, beta2_pow_out : Beta2PowOut, master_param_outs : MasterParamOut}
+
 - op : layer_norm
   backward : layer_norm_grad
   inputs :
@@ -1578,6 +1584,16 @@
   extra :
     attrs : [str data_format = "AnyLayout"]
 
+- op : sgd_
+  inputs :
+    {param : Param, learning_rate : LearningRate, grad : Grad, master_param : MasterParam}
+  outputs :
+    {param_out : ParamOut, master_param_out : MasterParamOut}
+  get_expected_kernel_type :
+    sgd : GetSgdExpectedKernelType #"sgd_" becomes "sgd"
+  extra :
+    attrs : [bool use_mkldnn=false]
+
 - op : shape
   extra :
     attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]

paddle/phi/api/yaml/op_version.yaml

@@ -78,6 +78,15 @@
           comment : In order to specify interpolation mode
           default : std::string("bilinear")
 
+- op : lamb
+  version :
+    - checkpoint : Upgrade lamb, add two new outputs [Beta1PowOut] and [Beta2PowOut].
+      action :
+        - add_output : Beta1PowOut
+          comment : The Output beta1 power accumulator. 'Beta1PowOut' is dispensable.
+        - add_output : Beta2PowOut
+          comment : The Output beta2 power accumulator. 'Beta2PowOut' is dispensable.
+
 - op : less_equal
   version :
     - checkpoint : Upgrade compare ops, add a new attribute [force_cpu]

paddle/phi/api/yaml/ops.yaml

@@ -835,6 +835,18 @@
   optional : prior_dist
   backward : label_smooth_grad
 
+- op : lamb_
+  args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, float weight_decay, float beta1=0.9, float beta2=0.999, float epsilon=1.0e-6f, bool multi_precision=false)
+  output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_outs)
+  infer_meta :
+    func : LambInferMeta
+  kernel :
+    func : lamb {dense, dense, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense},
+           lamb_sr {dense, selected_rows, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense}
+    data_type : param
+  optional : master_param, skip_update, beta1_pow_out, beta2_pow_out, master_param_outs
+  inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_outs)
+
 - op : leaky_relu
   args : (Tensor x, float negative_slope = 0.02f)
   output : Tensor
@@ -1312,6 +1324,21 @@
     data_type : x
   backward : send_uv_grad
 
+- op : sgd_
+  args : (Tensor param, Tensor learning_rate, Tensor grad, Tensor master_param, bool multi_precision=false)
+  output : Tensor(param_out), Tensor(master_param_out)
+  infer_meta :
+    func : SgdInferMeta
+  kernel :
+    func : sgd {dense, dense, dense, dense -> dense, dense},
+           sgd_dense_param_sparse_grad {dense, dense, selected_rows, dense -> dense, dense},
+           sgd_sparse_param_sparse_grad {selected_rows, dense, selected_rows, selected_rows -> selected_rows, selected_rows}
+    data_type : param
+  data_transform :
+    support_trans_dtype : learning_rate
+  optional : master_param, master_param_out
+  inplace : (param -> param_out), (master_param -> master_param_out)
+
 - op : shard_index
   args : (Tensor input, int index_num, int nshards, int shard_id, int ignore_value=-1)
   output : Tensor(out)

paddle/phi/ops/compat/lamb_sig.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 <string>
-
-#include "paddle/phi/core/compat/op_utils.h"
-#include "paddle/utils/small_vector.h"
-
-namespace phi {
-
-KernelSignature LambOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  paddle::small_vector<const char*> in_names = {"Param",
-                                                "Grad",
-                                                "LearningRate",
-                                                "Moment1",
-                                                "Moment2",
-                                                "Beta1Pow",
-                                                "Beta2Pow",
-                                                "MasterParam",
-                                                "SkipUpdate"};
-  paddle::small_vector<const char*> out_names = {"ParamOut",
-                                                 "Moment1Out",
-                                                 "Moment2Out",
-                                                 "Beta1PowOut",
-                                                 "Beta2PowOut",
-                                                 "MasterParamOut"};
-  paddle::small_vector<const char*> attr_names;
-
-  attr_names.emplace_back("weight_decay");
-  attr_names.emplace_back("beta1");
-  attr_names.emplace_back("beta2");
-  attr_names.emplace_back("epsilon");
-  attr_names.emplace_back("multi_precision");
-
-  if (ctx.IsSelectedRowsInput("Grad")) {
-    return KernelSignature("lamb_sr",
-                           std::move(in_names),
-                           std::move(attr_names),
-                           std::move(out_names));
-  } else if (ctx.IsDenseTensorInput("Grad")) {
-    return KernelSignature("lamb",
-                           std::move(in_names),
-                           std::move(attr_names),
-                           std::move(out_names));
-  } else {
-    return KernelSignature("unregistered", {}, {}, {});
-  }
-}
-
-}  // namespace phi
-
-PD_REGISTER_ARG_MAPPING_FN(lamb, phi::LambOpArgumentMapping);

paddle/phi/ops/compat/sgd_sig.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/core/compat/op_utils.h"
-
-namespace phi {
-
-KernelSignature SGDOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  if (ctx.IsDenseTensorInput("Grad")) {
-    return KernelSignature("sgd",
-                           {"Param", "LearningRate", "Grad", "MasterParam"},
-                           {"multi_precision"},
-                           {"ParamOut", "MasterParamOut"});
-  } else if (ctx.IsSelectedRowsInput("Grad")) {
-    if (ctx.IsDenseTensorInput("Param")) {
-      return KernelSignature("sgd_dense_param_sparse_grad",
-                             {"Param", "LearningRate", "Grad", "MasterParam"},
-                             {"multi_precision"},
-                             {"ParamOut", "MasterParamOut"});
-    } else {
-      return KernelSignature("sgd_sparse_param_sparse_grad",
-                             {"Param", "LearningRate", "Grad", "MasterParam"},
-                             {"multi_precision"},
-                             {"ParamOut", "MasterParamOut"});
-    }
-  }
-
-  return KernelSignature("unregistered", {}, {}, {});
-}
-
-}  // namespace phi
-
-PD_REGISTER_ARG_MAPPING_FN(sgd, phi::SGDOpArgumentMapping);

test/cpp/new_executor/CMakeLists.txt

@@ -30,7 +30,7 @@ if(WITH_GPU
       sum_op
       elementwise_max_op
       elementwise_div_op
-      sgd_op
+      generated_op
       squared_l2_norm_op
       memcpy_h2d_op
       memcpy_d2h_op