chore: import upstream snapshot with attribution

python/paddle/jit/dy2static/transformers/transform.py

@@ -0,0 +1,144 @@
+#   Copyright (c) 2020 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.
+
+# gast is a generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST).
+# It provides a compatibility layer between the AST of various Python versions,
+# as produced by ast.parse from the standard ast module.
+# See details in https://github.com/serge-sans-paille/gast/
+
+import os
+
+from paddle.framework import use_pir_api
+
+from .. import logging_utils
+from ..utils import ast_to_source_code
+from .assert_transformer import AssertTransformer
+from .base import BaseTransformer
+from .break_continue_transformer import (
+    BreakContinueTransformer,
+    BreakTransformOptimizer,
+)
+from .call_transformer import CallTransformer
+from .cast_transformer import CastTransformer
+from .create_variable_transformer import CreateVariableTransformer
+from .decorator_transformer import DecoratorTransformer
+from .early_return_transformer import EarlyReturnTransformer
+from .ifelse_transformer import IfElseTransformer
+from .logical_transformer import LogicalTransformer
+from .loop_transformer import LoopTransformer
+from .name_load_transformer import (
+    AttributeJstTransformer,
+    NameloadJstTransformer,
+)
+from .return_transformer import ReturnTransformer
+from .super_transformer import SuperTransformer
+from .tensor_shape_transformer import TensorShapeTransformer
+from .tensorhook_transformer import RegisterHookTransformer
+from .typehint_transformer import TypeHintTransformer
+
+__all__ = []
+
+
+def apply_optimization(transformers):
+    """
+    Judge whether to apply optimized transformation, such as BreakTransformOptimizer.
+    And not all optimized transformations are applied by default. It's controlled by
+    'export FLAGS_optim_transformation=1'
+    """
+    flag = str(os.environ.get('FLAGS_optim_transformation')) in [
+        '1',
+        'True',
+        'true',
+    ]
+    if flag:
+        transformers.insert(3, BreakTransformOptimizer)
+
+
+class DygraphToStaticAst(BaseTransformer):
+    """
+    Main class to transform Dygraph to Static Graph
+    """
+
+    def __init__(self):
+        self.translator_logger = logging_utils.TranslatorLogger()
+
+    def get_static_ast(self, root):
+        self.root = root
+        self.decorate_func_name = None
+
+        # inplace transfer
+        self.transfer_from_node_type(self.root)
+        return self.root
+
+    def _apply(self, transformer, node, log_level):
+        transformer(node).transform()
+        self.translator_logger.log_transformed_code(
+            log_level, self.root, transformer.__name__
+        )
+
+    def transfer_from_node_type(self, node):
+        self.translator_logger.log(
+            1, f"Source code: \n{ast_to_source_code(self.root)}"
+        )
+        # Generic transformation
+        self.visit(node)
+
+        transformers = [
+            TypeHintTransformer,  # remove all typehint
+            SuperTransformer,  # super() -> super(__class__, <first argument>)
+            RegisterHookTransformer,
+            EarlyReturnTransformer,
+            AttributeJstTransformer,  # Tensor.size -> Tensor.size(), it's unnecessary in PIR mode
+            TensorShapeTransformer,  # Tensor.shape -> paddle.shape(Tensor)
+            BreakContinueTransformer,  # break/continue in loops
+            ReturnTransformer,  # return in functions
+            LogicalTransformer,  # logical and/or/not
+            CreateVariableTransformer,  # create undefined var for if / while / for
+            LoopTransformer,  # for/while -> while_op
+            IfElseTransformer,  # if/else -> if_op
+            AssertTransformer,  # assert statement
+            CallTransformer,  # transform call recursively
+            CastTransformer,  # type casting statement
+            DecoratorTransformer,  # transform decorators to function call
+            NameloadJstTransformer,
+        ]
+
+        if use_pir_api():
+            # It's unnecessary in PIR mode
+            transformers.remove(AttributeJstTransformer)
+
+        apply_optimization(transformers)
+
+        for index, transformer in enumerate(transformers):
+            self._apply(transformer, node, log_level=index + 1)
+
+        self.translator_logger.log_transformed_code(
+            logging_utils.LOG_AllTransformer, self.root, "All Transformers"
+        )
+
+    def visit_FunctionDef(self, node):
+        if self.decorate_func_name is None:
+            self.decorate_func_name = node.name
+
+        self.generic_visit(node)
+        return node
+
+    def get_module_name(self):
+        """
+        Return the main function name which will be used as module name
+        in ast_to_func.
+        """
+        # Should consider BaseAPITransformer which add new module name in Yamei's PR.
+        assert self.decorate_func_name, "decorate_func_name shall not be None."
+        return self.decorate_func_name