wehub-resource-sync
145 lines
5.2 KiB
Python
145 lines
5.2 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# gast is a generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST).
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# It provides a compatibility layer between the AST of various Python versions,
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# as produced by ast.parse from the standard ast module.
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# See details in https://github.com/serge-sans-paille/gast/
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import os
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from paddle.framework import use_pir_api
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from .. import logging_utils
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from ..utils import ast_to_source_code
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from .assert_transformer import AssertTransformer
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from .base import BaseTransformer
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from .break_continue_transformer import (
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BreakContinueTransformer,
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BreakTransformOptimizer,
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)
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from .call_transformer import CallTransformer
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from .cast_transformer import CastTransformer
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from .create_variable_transformer import CreateVariableTransformer
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from .decorator_transformer import DecoratorTransformer
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from .early_return_transformer import EarlyReturnTransformer
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from .ifelse_transformer import IfElseTransformer
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from .logical_transformer import LogicalTransformer
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from .loop_transformer import LoopTransformer
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from .name_load_transformer import (
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AttributeJstTransformer,
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NameloadJstTransformer,
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)
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from .return_transformer import ReturnTransformer
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from .super_transformer import SuperTransformer
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from .tensor_shape_transformer import TensorShapeTransformer
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from .tensorhook_transformer import RegisterHookTransformer
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from .typehint_transformer import TypeHintTransformer
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__all__ = []
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def apply_optimization(transformers):
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"""
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Judge whether to apply optimized transformation, such as BreakTransformOptimizer.
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And not all optimized transformations are applied by default. It's controlled by
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'export FLAGS_optim_transformation=1'
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"""
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flag = str(os.environ.get('FLAGS_optim_transformation')) in [
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'1',
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'True',
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'true',
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]
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if flag:
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transformers.insert(3, BreakTransformOptimizer)
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class DygraphToStaticAst(BaseTransformer):
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"""
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Main class to transform Dygraph to Static Graph
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"""
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def __init__(self):
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self.translator_logger = logging_utils.TranslatorLogger()
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def get_static_ast(self, root):
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self.root = root
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self.decorate_func_name = None
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# inplace transfer
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self.transfer_from_node_type(self.root)
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return self.root
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def _apply(self, transformer, node, log_level):
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transformer(node).transform()
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self.translator_logger.log_transformed_code(
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log_level, self.root, transformer.__name__
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)
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def transfer_from_node_type(self, node):
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self.translator_logger.log(
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1, f"Source code: \n{ast_to_source_code(self.root)}"
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)
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# Generic transformation
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self.visit(node)
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transformers = [
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TypeHintTransformer, # remove all typehint
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SuperTransformer, # super() -> super(__class__, <first argument>)
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RegisterHookTransformer,
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EarlyReturnTransformer,
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AttributeJstTransformer, # Tensor.size -> Tensor.size(), it's unnecessary in PIR mode
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TensorShapeTransformer, # Tensor.shape -> paddle.shape(Tensor)
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BreakContinueTransformer, # break/continue in loops
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ReturnTransformer, # return in functions
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LogicalTransformer, # logical and/or/not
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CreateVariableTransformer, # create undefined var for if / while / for
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LoopTransformer, # for/while -> while_op
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IfElseTransformer, # if/else -> if_op
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AssertTransformer, # assert statement
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CallTransformer, # transform call recursively
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CastTransformer, # type casting statement
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DecoratorTransformer, # transform decorators to function call
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NameloadJstTransformer,
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]
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if use_pir_api():
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# It's unnecessary in PIR mode
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transformers.remove(AttributeJstTransformer)
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apply_optimization(transformers)
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for index, transformer in enumerate(transformers):
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self._apply(transformer, node, log_level=index + 1)
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self.translator_logger.log_transformed_code(
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logging_utils.LOG_AllTransformer, self.root, "All Transformers"
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)
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def visit_FunctionDef(self, node):
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if self.decorate_func_name is None:
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self.decorate_func_name = node.name
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self.generic_visit(node)
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return node
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def get_module_name(self):
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"""
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Return the main function name which will be used as module name
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in ast_to_func.
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"""
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# Should consider BaseAPITransformer which add new module name in Yamei's PR.
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assert self.decorate_func_name, "decorate_func_name shall not be None."
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return self.decorate_func_name
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