Support static graph code-gen for squeeze and unsqueeze op (#49430)

* support static graph code-gen for squeeze op

* generate static graph code of unsqueeze

* refine op name

* add extra output in op_compat

* remove debug log

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

@@ -739,6 +739,14 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
             self.backward_returns_list,
         ) = ParseYamlBackward(backward_args_str, backward_returns_str)
 
+        # Remove the output which is intermediate
+        if 'intermediate' in grad_api_contents:
+            backward_returns_list_new = []
+            for return_item in self.backward_returns_list:
+                if return_item[0] not in grad_api_contents['intermediate']:
+                    backward_returns_list_new.append(return_item)
+            self.backward_returns_list = backward_returns_list_new
+
     def CollectForwardInfoFromBackwardContents(self):
 
         backward_forward_str = self.backward_forward_str
@@ -1979,7 +1987,6 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
                         fill_zero_str += f"{indent}egr::EagerUtils::FillZeroForEmptyGradInput(&grads[{fwd_position}], input_metas[{fwd_position}]);\n"
 
         inplace_grad_input_str = ""
-        inplaced_tensor_wrapper = False
         inplace_check_str = ""
         optional_inplace_var_name = []
         # Grad Ins from TensorWrappers

paddle/fluid/operators/generator/generate_op.py

@@ -131,9 +131,10 @@ def process_int_array(op_item, int_array_configs):
                 )
                 if attr_item['is_support_tensor']:
                     attr_item['typename'] = (
-                        data_type_map[int_array_config['data_type']]
+                        'int[]'
                         if 'data_type' in int_array_config
-                        else 'std::vector<int64_t>'
+                        and int_array_config['data_type'] == 'int'
+                        else 'int64_t[]'
                     )
                 else:
                     attr_item['data_type'] = (
@@ -153,21 +154,95 @@ def process_int_array(op_item, int_array_configs):
 
 
 # replace name of op and params for OpMaker
-def replace_compat_name(op_op_map, forward_op_dict, backward_op_dict):
-    def get_op_and_op_name(op_item):
+def replace_compat_name(op_fluid_map_list, forward_op_dict, backward_op_dict):
+    def get_phi_and_fluid_op_name(op_item):
         names = op_item.split('(')
         if len(names) == 1:
             return names[0].strip(), names[0].strip()
         else:
             return names[0].strip(), names[1].split(')')[0].strip()
 
-    def update_op_attr_name(attrs, attrs_alias_map):
-        for attr_item in attrs:
-            if attr_item['name'] in attrs_alias_map:
-                attr_item['name'] = attrs_alias_map[attr_item['name']]
+    def update_op_param_name(op_args, args_alias_map):
+        for item in op_args:
+            if item['name'] in args_alias_map:
+                item['name'] = args_alias_map[item['name']]
+
+    def update_grad_args_name(op_args, args_alias_map):
+        for item in op_args:
+            if (
+                item['name'].endswith('_grad')
+                and item['name'][:-5] in args_alias_map
+            ):
+                args_alias_map[item['name']] = (
+                    args_alias_map[item['name'][:-5]] + '_grad'
+                )
+                item['name'] = args_alias_map[item['name'][:-5]] + '_grad'
+
+    def get_param_list_alias(param_list, args_map):
+        return [
+            args_map[param] if param in args_map else param
+            for param in param_list
+        ]
 
-    for op_args in op_op_map:
-        new_op_name, op_name = get_op_and_op_name(op_args['op'])
+    def update_common_params_name(
+        op_item, args_name_map, scalar_configs, int_array_configs
+    ):
+        if 'inplace' in op_item and op_item['inplace']:
+            inplace_map = {}
+            for key, val in op_item['inplace'].items():
+                if key in args_map:
+                    key = args_map[key]
+                if val in args_map:
+                    val = args_map[val]
+                inplace_map[key] = val
+            op_item['inplace'] = inplace_map
+        if 'no_need_buffer' in op_item and op_item['no_need_buffer']:
+            op_item['no_need_buffer'] = get_param_list_alias(
+                op_item['no_need_buffer'], args_map
+            )
+
+        process_scalar(op_item, scalar_configs)
+        process_int_array(op_item, int_array_configs)
+
+        if 'invoke' in op_item:
+            op_item['invoke']['args'] = [
+                args_map[param.strip()]
+                if param.strip() in args_map
+                else param.strip()
+                for param in op_item['invoke']['args'].split(',')
+            ]
+            return
+        op_item['infer_meta']['param'] = get_param_list_alias(
+            op_item['infer_meta']['param'], args_name_map
+        )
+        op_item['kernel']['param'] = get_param_list_alias(
+            op_item['kernel']['param'], args_name_map
+        )
+        if op_item['kernel']['data_type']:
+            op_item['kernel']['data_type']['candidates'] = get_param_list_alias(
+                op_item['kernel']['data_type']['candidates'], args_name_map
+            )
+        if op_item['kernel']['backend']:
+            op_item['kernel']['backend']['candidates'] = get_param_list_alias(
+                op_item['kernel']['backend']['candidates'], args_name_map
+            )
+        if op_item['kernel']['layout']:
+            op_item['kernel']['layout']['candidates'] = get_param_list_alias(
+                op_item['kernel']['layout']['candidates'], args_name_map
+            )
+
+    def update_grad_op_compat_name(grad_op_item, args_name_map):
+        update_op_param_name(grad_op_item['inputs'], args_name_map)
+        update_op_param_name(grad_op_item['outputs'], args_name_map)
+        update_op_param_name(grad_op_item['attrs'], args_name_map)
+        update_op_param_name(grad_op_item['forward']['inputs'], args_name_map)
+        update_op_param_name(grad_op_item['forward']['outputs'], args_name_map)
+        update_op_param_name(grad_op_item['forward']['attrs'], args_name_map)
+        update_grad_args_name(grad_op_item['inputs'], args_map)
+        update_grad_args_name(grad_op_item['outputs'], args_map)
+
+    for op_args in op_fluid_map_list:
+        new_op_name, op_name = get_phi_and_fluid_op_name(op_args['op'])
         if new_op_name not in forward_op_dict:
             continue
         forward_op_item = forward_op_dict[new_op_name]
@@ -179,189 +254,102 @@ def replace_compat_name(op_op_map, forward_op_dict, backward_op_dict):
 
         scalar_configs = None
         int_array_configs = None
-
         if 'scalar' in op_args:
             scalar_configs = op_args['scalar']
         if 'int_array' in op_args:
             int_array_configs = op_args['int_array']
+        if 'extra' in op_args and 'outputs' in op_args['extra']:
+            for out_item in forward_op_item['outputs']:
+                if out_item['name'] in op_args['extra']['outputs']:
+                    out_item['is_extra'] = True
 
-        process_scalar(forward_op_item, scalar_configs)
-        process_int_array(forward_op_item, int_array_configs)
+        key_set = ['inputs', 'attrs', 'outputs']
+        args_map = {}
+        for key in key_set:
+            if key in op_args:
+                args_map.update(op_args[key])
+                for args_item in forward_op_item[key]:
+                    if args_item['name'] in op_args[key]:
+                        if (
+                            scalar_configs
+                            and args_item['name'] in scalar_configs
+                        ):
+                            scalar_configs[
+                                op_args[key][args_item['name']]
+                            ] = scalar_configs[args_item['name']]
+                        if (
+                            int_array_configs
+                            and args_item['name'] in int_array_configs
+                        ):
+                            int_array_configs[
+                                op_args[key][args_item['name']]
+                            ] = int_array_configs[args_item['name']]
+                        args_item['name'] = op_args[key][args_item['name']]
+                if has_backward:
+                    for args_item in backward_op_item['forward'][key]:
+                        if args_item['name'] in op_args[key]:
+                            args_item['name'] = op_args[key][args_item['name']]
+        forward_op_item["attr_dict"] = to_named_dict(forward_op_item["attrs"])
+        update_common_params_name(
+            forward_op_item, args_map, scalar_configs, int_array_configs
+        )
+
+        if has_backward:
+            update_grad_op_compat_name(backward_op_item, args_map)
+            update_common_params_name(
+                backward_op_item, args_map, scalar_configs, int_array_configs
+            )
+            backward_op_item["attr_dict"] = to_named_dict(
+                backward_op_item["attrs"]
+            )
+
+            if 'backward' not in op_args:
+                continue
 
-        if 'backward' in op_args and has_backward:
             backward_op_list = op_args['backward'].split(',')
-            _, bw_op_name = get_op_and_op_name(backward_op_list[0])
+            _, bw_op_name = get_phi_and_fluid_op_name(backward_op_list[0])
             forward_op_item['backward'] = bw_op_name
             backward_op_item['op_name'] = bw_op_name
 
-            process_scalar(backward_op_item, scalar_configs)
-            process_int_array(backward_op_item, int_array_configs)
-
             # for double grad
             if len(backward_op_list) > 1:
                 (
-                    new_double_grad_op_name,
+                    phi_double_grad_op_name,
                     double_grad_op_name,
-                ) = get_op_and_op_name(backward_op_list[1])
-                double_grad_item = backward_op_dict[new_double_grad_op_name]
+                ) = get_phi_and_fluid_op_name(backward_op_list[1])
+                double_grad_item = backward_op_dict[phi_double_grad_op_name]
                 backward_op_item['backward'] = double_grad_op_name
                 double_grad_item['op_name'] = double_grad_op_name
-                if 'attrs' in op_args:
-                    update_op_attr_name(
-                        double_grad_item['attrs'], op_args['attrs']
-                    )
-                    update_op_attr_name(
-                        double_grad_item['forward']['attrs'], op_args['attrs']
-                    )
-
-                process_scalar(double_grad_item, scalar_configs)
-                process_int_array(double_grad_item, int_array_configs)
+                update_grad_op_compat_name(double_grad_item, args_map)
+                update_common_params_name(
+                    double_grad_item,
+                    args_map,
+                    scalar_configs,
+                    int_array_configs,
+                )
+                double_grad_item["attr_dict"] = to_named_dict(
+                    double_grad_item["attrs"]
+                )
 
                 # for triple grad
                 if len(backward_op_list) > 2:
                     (
-                        new_triple_grad_op_name,
+                        phi_triple_grad_op_name,
                         triple_grad_op_name,
-                    ) = get_op_and_op_name(backward_op_list[2])
-                    triple_grad_item = backward_op_dict[new_triple_grad_op_name]
+                    ) = get_phi_and_fluid_op_name(backward_op_list[2])
+                    triple_grad_item = backward_op_dict[phi_triple_grad_op_name]
                     double_grad_item['backward'] = triple_grad_op_name
                     triple_grad_item['op_name'] = triple_grad_op_name
-                    if 'attrs' in op_args:
-                        update_op_attr_name(
-                            triple_grad_item['attrs'], op_args['attrs']
-                        )
-                        update_op_attr_name(
-                            triple_grad_item['forward']['attrs'],
-                            op_args['attrs'],
-                        )
-
-                    process_scalar(triple_grad_item, scalar_configs)
-                    process_int_array(triple_grad_item, int_array_configs)
-
-        key_set = ['inputs', 'attrs', 'outputs']
-        args_map = {}
-        for key in key_set:
-            if key in op_args:
-                args_map.update(op_args[key])
-                for args_item in forward_op_item[key]:
-                    if args_item['name'] in op_args[key]:
-                        args_item['name'] = op_args[key][args_item['name']]
-                if has_backward:
-                    for args_item in backward_op_item['forward'][key]:
-                        if args_item['name'] in op_args[key]:
-                            args_item['name'] = op_args[key][args_item['name']]
-        forward_op_item['infer_meta']['param'] = [
-            args_map[param] if param in args_map else param
-            for param in forward_op_item['infer_meta']['param']
-        ]
-        forward_op_item['kernel']['param'] = [
-            args_map[param] if param in args_map else param
-            for param in forward_op_item['kernel']['param']
-        ]
-        if forward_op_item['kernel']['data_type']:
-            forward_op_item['kernel']['data_type']['candidates'] = [
-                args_map[param] if param in args_map else param
-                for param in forward_op_item['kernel']['data_type'][
-                    'candidates'
-                ]
-            ]
-        if forward_op_item['kernel']['backend']:
-            forward_op_item['kernel']['backend']['candidates'] = [
-                args_map[param] if param in args_map else param
-                for param in forward_op_item['kernel']['backend']['candidates']
-            ]
-        if forward_op_item['kernel']['layout']:
-            forward_op_item['kernel']['layout']['candidates'] = [
-                args_map[param] if param in args_map else param
-                for param in forward_op_item['kernel']['layout']['candidates']
-            ]
-        if forward_op_item['inplace']:
-            inplace_map = {}
-            for key, val in forward_op_item['inplace'].items():
-                if key in args_map:
-                    key = args_map[key]
-                if val in args_map:
-                    val = args_map[val]
-                inplace_map[key] = val
-            forward_op_item['inplace'] = inplace_map
-
-        if has_backward:
-            for args_item in backward_op_item['inputs']:
-                if args_item['name'] in args_map:
-                    args_item['name'] = args_map[args_item['name']]
-                elif (
-                    args_item['name'].endswith('_grad')
-                    and args_item['name'][:-5] in args_map
-                ):
-                    args_map[args_item['name']] = (
-                        args_map[args_item['name'][:-5]] + '_grad'
+                    update_grad_op_compat_name(triple_grad_item, args_map)
+                    update_common_params_name(
+                        triple_grad_item,
+                        args_map,
+                        scalar_configs,
+                        int_array_configs,
                     )
-                    args_item['name'] = args_map[args_item['name']]
-            for args_item in backward_op_item['attrs']:
-                if args_item['name'] in args_map:
-                    args_item['name'] = args_map[args_item['name']]
-            for args_item in backward_op_item['outputs']:
-                if (
-                    args_item['name'].endswith('_grad')
-                    and args_item['name'][:-5] in args_map
-                ):
-                    args_map[args_item['name']] = (
-                        args_map[args_item['name'][:-5]] + '_grad'
+                    triple_grad_item["attr_dict"] = to_named_dict(
+                        triple_grad_item["attrs"]
                     )
-                    args_item['name'] = args_map[args_item['name']]
-
-            if 'invoke' in backward_op_item:
-                backward_op_item['invoke']['args'] = [
-                    args_map[param.strip()]
-                    if param.strip() in args_map
-                    else param.strip()
-                    for param in backward_op_item['invoke']['args'].split(',')
-                ]
-                continue
-
-            backward_op_item['infer_meta']['param'] = [
-                args_map[param] if param in args_map else param
-                for param in backward_op_item['infer_meta']['param']
-            ]
-            backward_op_item['kernel']['param'] = [
-                args_map[param] if param in args_map else param
-                for param in backward_op_item['kernel']['param']
-            ]
-            if backward_op_item['kernel']['data_type']:
-                backward_op_item['kernel']['data_type']['candidates'] = [
-                    args_map[param] if param in args_map else param
-                    for param in backward_op_item['kernel']['data_type'][
-                        'candidates'
-                    ]
-                ]
-            if backward_op_item['kernel']['backend']:
-                backward_op_item['kernel']['backend']['candidates'] = [
-                    args_map[param] if param in args_map else param
-                    for param in backward_op_item['kernel']['backend'][
-                        'candidates'
-                    ]
-                ]
-            if backward_op_item['kernel']['layout']:
-                backward_op_item['kernel']['layout']['candidates'] = [
-                    args_map[param] if param in args_map else param
-                    for param in backward_op_item['kernel']['layout'][
-                        'candidates'
-                    ]
-                ]
-            if backward_op_item['no_need_buffer']:
-                backward_op_item['no_need_buffer'] = [
-                    args_map[param] if param in args_map else param
-                    for param in backward_op_item['no_need_buffer']
-                ]
-            if backward_op_item['inplace']:
-                inplace_map = {}
-                for key, val in backward_op_item['inplace'].items():
-                    if key in args_map:
-                        key = args_map[key]
-                    if val in args_map:
-                        val = args_map[val]
-                    inplace_map[key] = val
-                backward_op_item['inplace'] = inplace_map
 
 
 def process_invoke_op(forward_op_dict, backward_op_dict):
@@ -372,6 +360,7 @@ def process_invoke_op(forward_op_dict, backward_op_dict):
             args_index = 0
             if invoke_op in forward_op_dict:
                 reuse_op = forward_op_dict[invoke_op]
+                bw_op['invoke']['func'] = reuse_op['op_name']
                 bw_op['invoke']['inputs'] = []
                 bw_op['invoke']['attrs'] = []
                 bw_op['invoke']['outputs'] = []
@@ -430,14 +419,14 @@ def main(
         forward_op_dict[op_version['op']]['version'] = op_version['version']
 
     with open(op_compat_yaml_path, "rt") as f:
-        op_op_map = yaml.safe_load(f)
+        op_fluid_map_list = yaml.safe_load(f)
 
     for op in ops:
         op['op_name'] = op['name']
     for bw_op in backward_ops:
         bw_op['op_name'] = bw_op['name']
 
-    replace_compat_name(op_op_map, forward_op_dict, backward_op_dict)
+    replace_compat_name(op_fluid_map_list, forward_op_dict, backward_op_dict)
 
     # prepare for invoke case
     process_invoke_op(forward_op_dict, backward_op_dict)

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

@@ -54,6 +54,10 @@ AddOutput({{name | to_opmaker_name}}, "({{typename}}), output {{i}} of {{op_name
 
     .AsIntermediate()
   {%- endif %}
+  {%- if "is_extra" in output and output["is_extra"] %}
+
+    .AsExtra()
+  {%- endif %}
 {%- endmacro %}
 
 {# add attribute, and process default value if needed #}
@@ -115,7 +119,7 @@ KernelSignature {{op["op_name"] | to_pascal_case }}OpArgumentMapping(const Argum
   paddle::small_vector<const char*> attrs;
   {% for attr in op["attrs"]%}
   {% filter indent(2)%}
-  {{get_an_attr(attr)}}
+  {{get_an_attr(attr, kernel_args)}}
   {% endfilter %}
   {% endfor %}
   {{get_output_list(op["outputs"], kernel_args)}};
@@ -170,7 +174,7 @@ KernelSignature {{op["op_name"] | to_pascal_case }}OpArgumentMapping(const Argum
   paddle::small_vector<const char*> attrs;
   {% for attr in op["attrs"]%}
   {% filter indent(2)%}
-  {{get_an_attr(attr)}}
+  {{get_an_attr(attr, kernel_args)}}
   {% endfilter %}
   {% endfor %}
   {{get_output_list(op["outputs"], kernel_args)}};
@@ -209,8 +213,9 @@ paddle::small_vector<const char*> inputs {
 }
 {%- endmacro %}
 
-{% macro get_an_attr(attr) %}{# inline #}
+{% macro get_an_attr(attr, kernel_args) %}{# inline #}
 {% set typename = attr["typename"] %}
+{%- if attr["name"] in kernel_args %}
 {% set name = attr["name"] %}
 {% if typename is scalar %}{# scalar correspond to a dispensable input and an attr in opmaker #}
 attrs.emplace_back(ctx.HasInput("{{attr | to_scalar_tensor_name}}") ? "{{attr | to_scalar_tensor_name}}" : "{{name}}");
@@ -236,6 +241,7 @@ attrs.emplace_back(
 {%- else %}
 attrs.emplace_back("{{name}}");
 {%- endif %}
+{%- endif %}
 {%- endmacro %}
 
 {% macro get_output_list(outputs, kernel_args) %}{# inline #}
@@ -502,10 +508,9 @@ OutputGrad({{name_in_forward_orig | to_opmaker_name}})
     {% set name_in_forward = name[:-5] %}
     {% set name_in_forward_orig = input_orig_names[input_names.index(name_in_forward)]%}
 InputGrad({{name_in_forward_orig | to_opmaker_name}})
-  {%- elif (name | to_input_name) in input_names %}
-    {% set name_in_forward = name | to_input_name %}
-    {% set name_in_forward_orig = input_orig_names[input_names.index(name_in_forward)]%}
-InputGrad({{name | to_input_name | to_opmaker_name}})
+  {%- elif (name) in input_names %}
+    {% set name_in_forward_orig = input_orig_names[input_names.index(name)]%}
+Input({{name  | to_opmaker_name}})
   {%- endif %}
 {%- endmacro %}
 

paddle/fluid/operators/squeeze_op.cc

@@ -195,17 +195,6 @@ class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-class Squeeze2Op : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    auto input_data_type =
-        framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
-    return framework::OpKernelType(input_data_type, ctx.GetPlace());
-  }
-};
-
 template <typename T>
 class SqueezeGradOpMaker : public framework::SingleGradOpMaker<T> {
  public:
@@ -220,32 +209,6 @@ class SqueezeGradOpMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-class Squeeze2GradOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext *context) const override {
-    OP_INOUT_CHECK(
-        context->HasInput("XShape"), "Input", "XShape", "Squeeze2Grad");
-    OP_INOUT_CHECK(context->HasInput(framework::GradVarName("Out")),
-                   "Input",
-                   framework::GradVarName("Out"),
-                   "Squeeze2Grad");
-    auto xshape_dims = context->GetInputDim("XShape");
-    auto x_dims = phi::slice_ddim(xshape_dims, 1, xshape_dims.size());
-    context->SetOutputDim(framework::GradVarName("X"), x_dims);
-    context->ShareLoD("XShape", framework::GradVarName("X"));
-  }
-
- protected:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    auto input_data_type = framework::OperatorWithKernel::IndicateVarDataType(
-        ctx, framework::GradVarName("Out"));
-    return framework::OpKernelType(input_data_type, ctx.GetPlace());
-  }
-};
-
 template <typename T>
 class SqueezeDoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
  public:
@@ -259,82 +222,6 @@ class SqueezeDoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-// FIXME(zcd): squeeze2 adds an intermediate output(XShape) based on squeeze,
-// the XShape is used to carry the shape and lod of X which will be used in
-// squeeze_grad, in this way, the framework can reuse the memory of X
-// immediately the squeeze2_op is finished.
-// Considering compatibility issues, we could not fix squeeze2_op
-class Squeeze2OpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput("X", "(Tensor). The input tensor of squeeze operator.");
-    AddOutput("Out", "(Tensor). The output tensor of squeeze operator.");
-    AddOutput("XShape",
-              "XShape is just used to store the shape and lod of X, which will "
-              "be used in SqueezeGradOp.")
-        .AsIntermediate()
-        .AsExtra();
-    AddAttr<std::vector<int>>("axes",
-                              "(std::vector<int>). List of integers,"
-                              " indicating the dimensions to squeeze.")
-        .SetDefault({})
-        .SupportTensor();
-    AddComment(R"DOC(
-        Squeeze2 Operator.
-
-        Remove single-dimensional entries from the shape of a tensor.
-        Takes a parameter axes with a list of axes to squeeze.
-        If axes is not provided, all the single dimensions will be removed from the shape.
-        If an axis is selected with shape entry not equal to one, an error is raised.
-
-        Examples:
-        Case 1:
-          Given
-            X.shape = (1, 3, 1, 5)
-          and
-            axes = [0]
-          we get:
-            Out.shape = (3, 1, 5)
-
-        Case 2:
-          Given
-            X.shape = (1, 3, 1, 5)
-          and
-            axes = []
-          we get:
-            Out.shape = (3, 5)
-    )DOC");
-  }
-};
-
-template <typename T>
-class Squeeze2GradOpMaker : public framework::SingleGradOpMaker<T> {
- public:
-  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
-
-  void Apply(GradOpPtr<T> grad_op) const override {
-    grad_op->SetType("squeeze2_grad");
-    grad_op->SetInput("XShape", this->Output("XShape"));
-    grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
-    grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
-    grad_op->SetAttrMap(this->Attrs());
-  }
-};
-
-template <typename T>
-class Squeeze2DoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
- public:
-  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
-
-  void Apply(GradOpPtr<T> grad_op) const override {
-    grad_op->SetType("squeeze2");
-    grad_op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
-    grad_op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
-    grad_op->SetOutput("XShape", this->Input("XShape"));
-    grad_op->SetAttrMap(this->Attrs());
-  }
-};
-
 DECLARE_INPLACE_OP_INFERER(SqueezeInplaceInferer, {"X", "Out"});
 DECLARE_INPLACE_OP_INFERER(SqueezeGradInplaceInferer,
                            {framework::GradVarName("Out"),
@@ -345,10 +232,6 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(SqueezeGradNoNeedBufferVarsInferer, "X");
 
 namespace ops = paddle::operators;
 
-DECLARE_INFER_SHAPE_FUNCTOR(squeeze2,
-                            SqueezeInferShapeFunctor,
-                            PD_INFER_META(phi::SqueezeWithXShapeInferMeta));
-
 REGISTER_OPERATOR(squeeze,
                   ops::SqueezeOp,
                   ops::SqueezeOpMaker,
@@ -360,19 +243,6 @@ REGISTER_OPERATOR(squeeze_grad,
                   ops::SqueezeDoubleGradOpMaker<paddle::imperative::OpBase>,
                   ops::SqueezeGradNoNeedBufferVarsInferer);
 
-REGISTER_OPERATOR(squeeze2,
-                  ops::Squeeze2Op,
-                  ops::Squeeze2OpMaker,
-                  ops::Squeeze2GradOpMaker<paddle::framework::OpDesc>,
-                  ops::Squeeze2GradOpMaker<paddle::imperative::OpBase>,
-                  ops::SqueezeInplaceInferer,
-                  SqueezeInferShapeFunctor);
-REGISTER_OPERATOR(squeeze2_grad,
-                  ops::Squeeze2GradOp,
-                  ops::Squeeze2DoubleGradOpMaker<paddle::framework::OpDesc>,
-                  ops::Squeeze2DoubleGradOpMaker<paddle::imperative::OpBase>,
-                  ops::SqueezeGradInplaceInferer);
-
 REGISTER_OP_CPU_KERNEL(
     squeeze,
     ops::SqueezeKernel<phi::CPUContext, float>,

paddle/fluid/operators/unsqueeze_op.cc

@@ -260,83 +260,6 @@ class UnsqueezeDoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
-// FIXME(zcd): unsqueeze2 adds an intermediate output(XShape) based on
-// unsqueeze, the XShape is used to carry the shape and lod of X which
-// will be used in unsqueeze_grad, in this way, the framework can reuse
-// the memory of X immediately the unsqueeze2_op is finished.
-// Considering compatibility issues, we could not fix unsqueeze2_op
-class Unsqueeze2Op : public UnsqueezeOp {
- public:
-  using UnsqueezeOp::UnsqueezeOp;
-};
-
-class Unsqueeze2OpMaker : public UnsqueezeOpMaker {
- public:
-  void Make() override {
-    UnsqueezeOpMaker::Make();
-    AddOutput("XShape",
-              "XShape is just used to store the shape and lod of X, which will "
-              "be used in UnsqueezeGradOp.")
-        .AsIntermediate()
-        .AsExtra();
-  }
-};
-
-template <typename T>
-class Unsqueeze2GradOpMaker : public framework::SingleGradOpMaker<T> {
- public:
-  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
-
-  void Apply(GradOpPtr<T> grad_op) const override {
-    grad_op->SetType("unsqueeze2_grad");
-    grad_op->SetInput("XShape", this->Output("XShape"));
-    grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
-    grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
-    grad_op->SetAttrMap(this->Attrs());
-  }
-};
-
-class Unsqueeze2GradOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-  void InferShape(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE_EQ(
-        context->HasInput("XShape"),
-        true,
-        platform::errors::InvalidArgument("Input(XShape) shouldn't be null."));
-    PADDLE_ENFORCE_EQ(context->HasInput(framework::GradVarName("Out")),
-                      true,
-                      platform::errors::InvalidArgument(
-                          "Input(Out@GRAD) shouldn't be null."));
-    auto xshape_dims = context->GetInputDim("XShape");
-    auto x_dims = phi::slice_ddim(xshape_dims, 1, xshape_dims.size());
-    context->SetOutputDim(framework::GradVarName("X"), x_dims);
-    context->ShareLoD("XShape", framework::GradVarName("X"));
-  }
-
- protected:
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
-                                       ctx, framework::GradVarName("Out")),
-                                   ctx.device_context());
-  }
-};
-
-template <typename T>
-class Unsqueeze2DoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
- public:
-  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
-
-  void Apply(GradOpPtr<T> grad_op) const override {
-    grad_op->SetType("unsqueeze2");
-    grad_op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
-    grad_op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
-    grad_op->SetOutput("XShape", this->Input("XShape"));
-    grad_op->SetAttrMap(this->Attrs());
-  }
-};
-
 DECLARE_INPLACE_OP_INFERER(UnsqueezeInplaceInferer, {"X", "Out"});
 DECLARE_INPLACE_OP_INFERER(UnsqueezeGradInplaceInferer,
                            {framework::GradVarName("Out"),
@@ -345,10 +268,6 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(UnsqueezeGradOpNoNeedBufferVarInferer, "X");
 }  // namespace operators
 }  // namespace paddle
 
-DECLARE_INFER_SHAPE_FUNCTOR(unsqueeze2,
-                            Unsqueeze2InferShapeFunctor,
-                            PD_INFER_META(phi::UnsqueezeWithXShapeInferMeta));
-
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(unsqueeze,
                   ops::UnsqueezeOp,
@@ -362,20 +281,6 @@ REGISTER_OPERATOR(unsqueeze_grad,
                   ops::UnsqueezeDoubleGradOpMaker<paddle::imperative::OpBase>,
                   ops::UnsqueezeGradOpNoNeedBufferVarInferer);
 
-REGISTER_OPERATOR(unsqueeze2,
-                  ops::Unsqueeze2Op,
-                  ops::Unsqueeze2OpMaker,
-                  ops::Unsqueeze2GradOpMaker<paddle::framework::OpDesc>,
-                  ops::Unsqueeze2GradOpMaker<paddle::imperative::OpBase>,
-                  Unsqueeze2InferShapeFunctor,
-                  ops::UnsqueezeInplaceInferer);
-
-REGISTER_OPERATOR(unsqueeze2_grad,
-                  ops::Unsqueeze2GradOp,
-                  ops::Unsqueeze2DoubleGradOpMaker<paddle::framework::OpDesc>,
-                  ops::Unsqueeze2DoubleGradOpMaker<paddle::imperative::OpBase>,
-                  ops::UnsqueezeGradInplaceInferer);
-
 REGISTER_OP_CPU_KERNEL(
     unsqueeze,
     ops::UnsqueezeKernel<phi::CPUContext, float>,

paddle/phi/api/yaml/backward.yaml

@@ -1186,6 +1186,26 @@
   backward : square_double_grad
   inplace : (out_grad -> x_grad)
 
+- backward_op : squeeze_double_grad
+  forward : squeeze_grad(Tensor xshape, Tensor grad_out, IntArray axis) -> Tensor(grad_x)
+  args : (Tensor grad_x_grad, IntArray axis)
+  output : Tensor(grad_out_grad), Tensor(xshape)
+  invoke: squeeze(grad_x_grad, axis)
+  intermediate : xshape
+
+- backward_op : squeeze_grad
+  forward : squeeze(Tensor x, IntArray axis) -> Tensor(out), Tensor(xshape)
+  args : (Tensor xshape, Tensor out_grad, IntArray axis)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : KernelWithXShapeInferMeta
+    param: [xshape]
+  kernel :
+    func : squeeze_grad
+    data_type : out_grad
+  inplace : (out_grad -> x_grad)
+  backward: squeeze_double_grad
+
 - backward_op : svd_grad
   forward : svd (Tensor x, bool full_matrices = false) -> Tensor(u), Tensor(s), Tensor(vh)
   args : (Tensor x, Tensor u, Tensor vh, Tensor s, Tensor u_grad, Tensor vh_grad, Tensor s_grad, bool full_matrices)
@@ -1321,6 +1341,27 @@
     data_type : out_grad
   no_need_buffer : x
 
+- backward_op : unsqueeze_double_grad
+  forward : unsqueeze_grad(Tensor xshape, Tensor grad_out, IntArray axes) -> Tensor(grad_x)
+  args : (Tensor grad_x_grad, IntArray axes)
+  output : Tensor(grad_out_grad), Tensor(xshape)
+  invoke : unsqueeze(grad_x_grad, axes)
+  intermediate : xshape
+
+- backward_op : unsqueeze_grad
+  forward : unsqueeze(Tensor x, IntArray axes) -> Tensor(out), Tensor(xshape)
+  args : (Tensor xshape, Tensor out_grad, IntArray axes)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : KernelWithXShapeInferMeta
+    param: [xshape]
+  kernel :
+    func : unsqueeze_grad
+    param : [xshape, out_grad]
+    data_type : out_grad
+  inplace : (out_grad -> x_grad)
+  backward : unsqueeze_double_grad
+
 - backward_op : unstack_grad
   forward : unstack (Tensor x, int axis=0, int num=0) -> Tensor[](out)
   args : (Tensor[] out_grad, int axis)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -1363,24 +1363,6 @@
   kernel :
     func : squared_l2_norm_grad
 
-- backward_op : squeeze_double_grad
-  forward : squeeze_grad(Tensor xshape, Tensor grad_out, IntArray axis) -> Tensor(grad_x)
-  args : (Tensor grad_x_grad, IntArray axis)
-  output : Tensor(grad_out_grad)
-  invoke: squeeze(grad_x_grad, axis)
-
-- backward_op : squeeze_grad
-  forward : squeeze(Tensor x, IntArray axis) -> Tensor(out), Tensor(xshape)
-  args : (Tensor xshape, Tensor out_grad, IntArray axis)
-  output : Tensor(x_grad)
-  infer_meta :
-    func : KernelWithXShapeInferMeta
-    param: [xshape]
-  kernel :
-    func : squeeze_grad
-  inplace : (out_grad -> x_grad)
-  backward: squeeze_double_grad
-
 - backward_op : stack_grad
   forward : stack (Tensor[] x, int axis) -> Tensor(out)
   args : (Tensor[] x, Tensor out_grad, int axis)
@@ -1574,25 +1556,6 @@
     func : uniform_inplace_grad
   inplace : (out_grad -> x_grad)
 
-- backward_op : unsqueeze_double_grad
-  forward : unsqueeze_grad(Tensor xshape, Tensor grad_out, IntArray axes) -> Tensor(grad_x)
-  args : (Tensor grad_x_grad, IntArray axes)
-  output : Tensor(grad_out_grad)
-  invoke : unsqueeze(grad_x_grad, axes)
-
-- backward_op : unsqueeze_grad
-  forward : unsqueeze(Tensor x, IntArray axes) -> Tensor(out), Tensor(xshape)
-  args : (Tensor xshape, Tensor out_grad, IntArray axes)
-  output : Tensor(x_grad)
-  infer_meta :
-    func : KernelWithXShapeInferMeta
-    param: [xshape]
-  kernel :
-    func : unsqueeze_grad
-    param: [xshape, out_grad]
-  inplace : (out_grad -> x_grad)
-  backward : unsqueeze_double_grad
-
 - backward_op : warpctc_grad
   forward : warpctc (Tensor logits, Tensor label, Tensor logits_length, Tensor labels_length, int blank, bool norm_by_times) -> Tensor(loss), Tensor(warpctcgrad)
   args : (Tensor logits, Tensor logits_length, Tensor warpctcgrad, Tensor loss_grad, int blank, bool norm_by_times)

paddle/phi/api/yaml/legacy_ops.yaml

@@ -1777,18 +1777,6 @@
     func : squared_l2_norm
   backward : squared_l2_norm_grad
 
-- op : squeeze
-  args : (Tensor x, IntArray axis)
-  output : Tensor(out), Tensor(xshape)
-  infer_meta :
-    func : SqueezeWithXShapeInferMeta
-  kernel :
-    func : squeeze_with_xshape
-  inplace : (x -> out)
-  view: (x -> out)
-  intermediate : xshape
-  backward : squeeze_grad
-
 - op : stack
   args : (Tensor[] x, int axis)
   output : Tensor
@@ -2022,18 +2010,6 @@
     data_type: x
   backward: unpool3d_grad
 
-- op : unsqueeze
-  args : (Tensor x, IntArray axis)
-  output : Tensor(out), Tensor(xshape)
-  infer_meta :
-    func : UnsqueezeWithXShapeInferMeta
-  kernel :
-    func : unsqueeze_with_xshape
-  inplace : (x -> out)
-  view: (x -> out)
-  intermediate : xshape
-  backward : unsqueeze_grad
-
 - op : update_loss_scaling_
   args : (Tensor[] x, Tensor found_infinite, Tensor prev_loss_scaling, Tensor in_good_steps, Tensor in_bad_steps, int incr_every_n_steps, int decr_every_n_nan_or_inf, float incr_ratio, float decr_ratio, Scalar stop_update)
   output : Tensor[](out){x.size()}, Tensor(loss_scaling), Tensor(out_good_steps), Tensor(out_bad_steps)

paddle/phi/api/yaml/op_compat.yaml

@@ -1270,9 +1270,20 @@
     attrs : [bool use_mkldnn = false, bool use_cudnn = false]
 
 - op : squeeze (squeeze2)
-  backward : squeeze_grad (squeeze2_grad)
+  backward : squeeze_grad (squeeze2_grad), squeeze_double_grad(squeeze2_double_grad)
+  inputs :
+    x : X
+  attrs :
+   axis : axes
+  outputs :
+    {out : Out, xshape : XShape}
+  int_array:
+    axis :
+      data_type : int
+      support_tensor : true
   extra :
     attrs : [bool use_mkldnn = false, str mkldnn_data_type = "float32"]
+    outputs : [xshape]
 
 - op : stack
   backward : stack_grad
@@ -1389,6 +1400,22 @@
   outputs :
     out : Y
 
+- op : unsqueeze (unsqueeze2)
+  backward : unsqueeze_grad (unsqueeze2_grad), unsqueeze_double_grad(unsqueeze2_double_grad)
+  inputs :
+    x : X
+  attrs :
+   axis : axes
+  outputs :
+    {out : Out, xshape : XShape}
+  int_array:
+    axis :
+      data_type : int
+      tensor_name : AxesTensor
+      tensors_name : AxesTensorList
+  extra :
+    outputs : [xshape]
+
 - op : unstack
   backward : unstack_grad
   inputs :

paddle/phi/api/yaml/ops.yaml

@@ -1054,6 +1054,19 @@
            square_sr {selected_rows -> selected_rows}
   backward : square_grad
 
+- op : squeeze
+  args : (Tensor x, IntArray axis={})
+  output : Tensor(out), Tensor(xshape)
+  infer_meta :
+    func : SqueezeWithXShapeInferMeta
+  kernel :
+    func : squeeze_with_xshape
+    data_type : x
+  inplace : (x -> out)
+  view: (x -> out)
+  intermediate : xshape
+  backward : squeeze_grad
+
 - op : svd
   args : (Tensor x, bool full_matrices = false)
   output : Tensor(u), Tensor(s), Tensor(vh)
@@ -1149,6 +1162,19 @@
     func : unfold
   backward : unfold_grad
 
+- op : unsqueeze
+  args : (Tensor x, IntArray axis = {})
+  output : Tensor(out), Tensor(xshape)
+  infer_meta :
+    func : UnsqueezeWithXShapeInferMeta
+  kernel :
+    func : unsqueeze_with_xshape
+    data_type : x
+  inplace : (x -> out)
+  view: (x -> out)
+  intermediate : xshape
+  backward : unsqueeze_grad
+
 - op : unstack
   args : (Tensor x, int axis=0, int num=0)
   output : Tensor[](out){num}

paddle/phi/ops/compat/squeeze_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 SqueezeOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  return KernelSignature(
-      "squeeze_with_xshape", {"X"}, {"axes"}, {"Out", "XShape"});
-}
-
-KernelSignature SqueezeGradOpArgumentMapping(
-    const ArgumentMappingContext& ctx) {
-  return KernelSignature(
-      "squeeze_grad", {"XShape", "Out@GRAD"}, {"axes"}, {"X@GRAD"});
-}
-
-}  // namespace phi
-PD_REGISTER_BASE_KERNEL_NAME(squeeze2, squeeze);
-PD_REGISTER_BASE_KERNEL_NAME(squeeze2_grad, squeeze_grad);
-PD_REGISTER_ARG_MAPPING_FN(squeeze2, phi::SqueezeOpArgumentMapping);
-PD_REGISTER_ARG_MAPPING_FN(squeeze2_grad, phi::SqueezeGradOpArgumentMapping);

paddle/phi/ops/compat/unsqueeze_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 UnsqueezeOpArgumentMapping(const ArgumentMappingContext& ctx) {
-  if (ctx.InputSize("AxesTensorList") > 0) {
-    VLOG(2) << "unsqueeze2 in AxesTensorList";
-    return KernelSignature(
-        "unsqueeze_with_xshape", {"X"}, {"AxesTensorList"}, {"Out", "XShape"});
-  } else if (ctx.InputSize("AxesTensor") > 0) {
-    VLOG(2) << "unsqueeze2 in AxesTensor";
-    return KernelSignature(
-        "unsqueeze_with_xshape", {"X"}, {"AxesTensor"}, {"Out", "XShape"});
-  } else {
-    VLOG(2) << "unsqueeze2 in axes";
-    return KernelSignature(
-        "unsqueeze_with_xshape", {"X"}, {"axes"}, {"Out", "XShape"});
-  }
-}
-
-KernelSignature UnsqueezeGradOpArgumentMapping(
-    const ArgumentMappingContext& ctx) {
-  return KernelSignature(
-      "unsqueeze_grad", {"XShape", "Out@GRAD"}, {}, {"X@GRAD"});
-}
-}  // namespace phi
-PD_REGISTER_BASE_KERNEL_NAME(unsqueeze2, unsqueeze);
-PD_REGISTER_BASE_KERNEL_NAME(unsqueeze2_grad, unsqueeze_grad);
-
-PD_REGISTER_ARG_MAPPING_FN(unsqueeze2, phi::UnsqueezeOpArgumentMapping);
-PD_REGISTER_ARG_MAPPING_FN(unsqueeze2_grad,
-                           phi::UnsqueezeGradOpArgumentMapping);