wehub-resource-sync
811 lines
24 KiB
Python
811 lines
24 KiB
Python
# Copyright (c) 2023 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,tes
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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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from __future__ import annotations
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import collections
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import logging
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import warnings
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from collections.abc import Sequence
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from functools import lru_cache
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from typing import Any
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from paddle import pir
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from paddle.base import core
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from paddle.base.libpaddle.pir import (
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get_used_external_value,
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)
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from paddle.base.wrapped_decorator import signature_safe_contextmanager
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# TODO(CZ): to be removed when we support dynamic shape by default.
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ALLOW_DYNAMIC_SHAPE_VJP_OPS = [
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"pd_op.abs",
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"pd_op.add",
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"pd_op.amax",
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"pd_op.amin",
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"pd_op.angle",
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"pd_op.argsort",
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"pd_op.assign",
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"pd_op.batch_norm_",
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"pd_op.cast",
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"pd_op.ceil",
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"pd_op.concat",
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"pd_op.cos",
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"pd_op.cumprod",
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"pd_op.cumsum",
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"pd_op.divide",
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"pd_op.dot",
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"pd_op.dropout",
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"pd_op.elementwise_pow",
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"pd_op.erf",
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"pd_op.exp",
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"pd_op.expand",
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"pd_op.floor",
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"pd_op.fmax",
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"pd_op.fmin",
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"pd_op.gather",
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"pd_op.gather_nd",
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"pd_op.gelu",
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"pd_op.group_norm",
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"pd_op.hardsigmoid",
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"pd_op.hardswish",
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"pd_op.kron",
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"pd_op.kthvalue",
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"pd_op.layer_norm",
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"pd_op.leaky_relu",
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"pd_op.log",
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"pd_op.logcumsumexp",
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"pd_op.logsumexp",
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"pd_op.linear_v2",
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"pd_op.matmul",
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"pd_op.max",
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"pd_op.maximum",
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"pd_op.mean",
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"pd_op.minimum",
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"pd_op.multiply",
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"pd_op.pad",
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"pd_op.pow",
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"pd_op.prod",
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"pd_op.reduce_as",
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"pd_op.relu",
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"pd_op.relu6",
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"pd_op.reshape",
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"pd_op.roll",
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"pd_op.rsqrt",
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"pd_op.scale",
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"pd_op.scatter",
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"pd_op.scatter_nd_add",
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"pd_op.sigmoid",
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"pd_op.silu",
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"pd_op.sin",
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"pd_op.softmax",
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"pd_op.softsign",
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"pd_op.split",
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"pd_op.sqrt",
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"pd_op.square",
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"pd_op.squeeze",
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"pd_op.stack",
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"pd_op.subtract",
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"pd_op.sum",
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"pd_op.swiglu",
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"pd_op.swish",
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"pd_op.take_along_axis",
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"pd_op.tanh",
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"pd_op.tile",
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"pd_op.topk",
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"pd_op.transpose",
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"pd_op.trunc",
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"pd_op.unsqueeze",
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"pd_op.where",
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"pd_op.p_norm",
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"pd_op.index_put",
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"pd_op.index_add",
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"pd_op.elu",
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"pd_op.masked_fill",
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"pd_op.masked_select",
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"pd_op.var",
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]
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class ValueWrapper:
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def __init__(self, value) -> None:
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if isinstance(value, ValueWrapper):
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assert isinstance(value._value, (type(None), pir.Value))
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else:
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if not isinstance(value, (type(None), pir.Value)):
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raise TypeError(
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"Value Wrapper is only support None and pir.Value"
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)
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self._value = value._value if isinstance(value, ValueWrapper) else value
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def __hash__(self) -> int:
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if isinstance(self._value, pir.Value):
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return self._value.hash()
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else:
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return hash(self._value)
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def __eq__(self, other) -> bool:
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if not isinstance(other, ValueWrapper):
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warnings.warn(
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f'In ValueWrapper.__eq__ expected type of `other` is ValueWrapper but received {other.__class__}.'
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)
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return False
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if self._value is None or other._value is None:
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return self._value is None and other._value is None
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return self._value.is_same(other._value)
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class ValueDict:
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def __init__(
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self,
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iter=None,
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*,
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default_factory=None,
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):
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self._items: dict[ValueWrapper] = {}
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self._default_factory = default_factory
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if iter is not None:
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for key, val in iter.items():
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self[key] = val
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def copy(self):
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ret = ValueDict()
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ret._items = self._items.copy()
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ret._default_factory = self._default_factory
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return ret
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def update(self, other_dict):
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for key, val in other_dict.items():
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self[key] = val
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def keys(self):
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for key in self._items.keys():
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yield key._value
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def values(self):
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return self._items.values()
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def items(self):
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for key, val in self._items.items():
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yield key._value, val
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def get(self, key, default=None):
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if not self.__contains__(key):
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return default
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return self._items[ValueWrapper(key)]
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def pop(self, key):
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if not self.__contains__(key):
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raise KeyError(f'{key} is not in ValueDict')
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return self._items.pop(ValueWrapper(key))
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def setdefault(self, key, default=None):
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if not self.__contains__(key):
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self[key] = default
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return self[key]
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def __setitem__(self, key, val: Any):
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self._items[ValueWrapper(key)] = val
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def __getitem__(self, key):
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if not self.__contains__(key):
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if self._default_factory is not None:
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self[key] = self._default_factory()
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else:
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raise KeyError(f'{key} is not in ValueDict')
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return self._items[ValueWrapper(key)]
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def __bool__(self):
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return bool(self._items)
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def __len__(self):
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return len(self._items)
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def __iter__(self):
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return self.keys()
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def __contains__(self, key):
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return ValueWrapper(key) in self._items
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def __repr__(self) -> str:
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items_str = ", ".join(f"{key}: {val}" for key, val in self.items())
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return f'ValueDict({items_str})'
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class ValueSet:
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def __init__(
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self, iter: Sequence[ValueWrapper] | set[ValueWrapper] | None = None
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):
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self._set: set[ValueWrapper] = set()
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if iter is not None:
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for val in iter:
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self.add(val)
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def copy(self):
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ret = ValueSet()
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ret._set = self._set.copy()
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return ret
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def add(self, val):
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if not self.__contains__(val):
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self._set.add(ValueWrapper(val))
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def update(self, other: set):
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for val in other:
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self.add(val)
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def pop(self):
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return self._set.pop()._value
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def remove(self, val):
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self._set.remove(ValueWrapper(val))
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def discard(self, val):
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self._set.discard(ValueWrapper(val))
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def __and__(self, other: ValueSet):
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return ValueSet(self._set & other._set)
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def __sub__(self, other: ValueSet):
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return ValueSet(self._set - other._set)
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def __or__(self, other: ValueSet):
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return ValueSet(self._set | other._set)
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def __bool__(self):
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return bool(self._set)
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def __len__(self):
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return len(self._set)
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def __iter__(self):
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for val in self._set:
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yield val._value
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def __contains__(self, val):
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return ValueWrapper(val) in self._set
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def __repr__(self) -> str:
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items_str = ", ".join(repr(item) for item in self)
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return f'ValueSet({items_str})'
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class State:
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"""
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record relationship of forward op/value and backward op/value
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one state must be binding with a block, if block has parent block,
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state will include parent block info.
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"""
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def __init__(self, block):
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self.block = block
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# value -> list(list(value))
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self.value_to_valuegrad = ValueDict(default_factory=list)
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self.value_to_sumvaluegrad = ValueDict(default_factory=list)
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# operation -> list(operation)
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self.op_to_opgrad = collections.defaultdict(list)
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# value -> list(value)
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self.valuegrad_to_value = ValueDict(default_factory=list)
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self.sumvaluegrad_to_value = ValueDict(default_factory=list)
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# operation -> list(operation)
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self.opgrad_to_op = collections.defaultdict(list)
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# only for controlflow
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# inside_value is sub block value, which will yield to parent block,
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# parent block value is outside_value
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self.inside_value_to_outside_value_map = ValueDict()
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def turn_map(self) -> None:
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self.valuegrad_to_value = ValueDict(default_factory=list)
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self.sumvaluegrad_to_value = ValueDict(default_factory=list)
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self.opgrad_to_op = collections.defaultdict(list)
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for k, v in self.value_to_valuegrad.items():
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if v != []:
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for value in v[0]:
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self.valuegrad_to_value[value] = [k]
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for k, v in self.value_to_sumvaluegrad.items():
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if v != []:
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for value in v[0]:
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self.sumvaluegrad_to_value[value] = [k]
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for k, v in self.op_to_opgrad.items():
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if v != []:
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self.opgrad_to_op[v[0]] = [k]
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def copy(self, new_block):
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state = State(new_block)
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state.value_to_valuegrad = self.value_to_valuegrad.copy()
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state.value_to_sumvaluegrad = self.value_to_sumvaluegrad.copy()
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# operation -> list(operation)
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state.op_to_opgrad = self.op_to_opgrad.copy()
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# value -> list(value)
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state.valuegrad_to_value = self.valuegrad_to_value.copy()
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state.sumvaluegrad_to_value = self.sumvaluegrad_to_value.copy()
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# operation -> list(operation)
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state.opgrad_to_op = self.opgrad_to_op.copy()
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# only for controlflow
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state.inside_value_to_outside_value_map = (
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self.inside_value_to_outside_value_map.copy()
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)
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return state
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def _check_vjp_dynamic_shape(op, inputs):
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for items in inputs:
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for item in items:
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if (
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item.is_dense_tensor_type()
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and item.initialized()
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and -1 in item.shape
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):
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return True
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# Prim currently does not support dynamic shape, when dynamic shape exits in shape of op inputs, prim will be skipped its vjp op.
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@signature_safe_contextmanager
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def dynamic_shape_prim_vjp_guard(op, inputs):
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origin_prim = core._is_bwd_prim_enabled()
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if op.name() == "cf.tuple_push":
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skip_prim = True
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else:
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skip_prim = (
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origin_prim
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and core._enable_prim_skip_dynamic_shape()
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and _check_vjp_dynamic_shape(op, inputs)
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and op.name() not in ALLOW_DYNAMIC_SHAPE_VJP_OPS
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)
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try:
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if origin_prim and skip_prim:
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core._set_prim_backward_enabled(False)
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yield
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finally:
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if origin_prim:
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core._set_prim_backward_enabled(True)
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def check_type(input, input_name, expected_type, op_name, extra_message=''):
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if not isinstance(input, expected_type):
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raise TypeError(
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f"The type of '{input_name}' in {op_name} must be {expected_type}, but received {type(input)}. {extra_message}"
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)
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def _as_list(x):
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if x is None:
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return []
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return list(x) if isinstance(x, Sequence) else [x]
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def some_in_set(value_list, value_set):
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return any(v in value_set for v in value_list)
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def is_control_flow(op):
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return op.name() == "pd_op.if" or op.name() == "pd_op.while"
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def is_builtin_op(op):
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dialect_name, opname = op.name().split(".")
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return dialect_name == "builtin"
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def update_no_grad_set_by_stopgradient(block, no_grad_set):
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for op in block.ops:
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if is_control_flow(op):
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for sub_block in op.blocks():
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update_no_grad_set_by_stopgradient(sub_block, no_grad_set)
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for value in op.results():
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if value.stop_gradient and value not in no_grad_set:
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no_grad_set.add(value)
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def get_real_op_inputs(op):
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if op.name() == "pd_op.if":
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return get_used_external_value(op)
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elif op.name() == "pd_op.while":
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return op.operands_source() + get_used_external_value(
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op.as_while_op().body()
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)
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elif op.name() == "pd_op.pylayer":
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return get_used_external_value(op)
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else:
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return op.operands_source()
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def get_real_op_outputs(op):
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outputs = op.results()
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if op.name() == "pd_op.array_write_":
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for x in op.operands():
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outputs.append(x.source())
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if op.name() == "pd_op.while":
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for internal_op in op.as_while_op().body().ops:
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if internal_op.name() == "pd_op.array_write_":
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for x in internal_op.operands():
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outputs.append(x.source())
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return outputs
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def inverse_sort_op(old_ops):
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'''
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if topo graph is op1 -> op2 -> op3
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return [op3, op2, op1]
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'''
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# init pending_count[op] which describes number of
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# pending edges for its grad_op
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pending_count = collections.defaultdict(int)
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ops = []
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[ops.append(x) for x in old_ops if x not in ops]
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ops_set = set(ops)
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sorted_list = []
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for op in ops:
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for x in get_real_op_inputs(op):
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if not pir.is_fake_value(x) and x.get_defining_op() in ops_set:
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pending_count[x.get_defining_op()] += 1
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queue = collections.deque()
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for op in ops:
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if pending_count[op] == 0:
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queue.append(op)
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while queue:
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op = queue.popleft()
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sorted_list.append(op)
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for x in get_real_op_inputs(op):
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x_op = x.get_defining_op()
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pending_count[x_op] -= 1
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if pending_count[x_op] == 0:
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queue.append(x_op)
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if len(sorted_list) != len(ops):
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raise ValueError(
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"inverse_sort_op wrong, sorted_list size is not equal to origin_list size"
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)
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change_list = []
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# true %0 = op1, 1% = increment(0%), 3% = op2(0%), tuple_push(%0, 1%, 3%),
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# no one use 1% so increment be the first op, actually op2 use 1% ,
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# sorted_list = [increment, op2, op1] should be [op2, increment, op1],
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# tuple_push(0%) must be forward last op, backward first op, so skip it.
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for op in reversed(sorted_list):
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if op.name() == 'pd_op.increment_':
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idx_1 = sorted_list.index(op)
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idx_2 = sorted_list.index(op)
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for op_in in reversed(sorted_list[: sorted_list.index(op)]):
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if (
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some_in_set(
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op.operands_source(),
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ValueSet(get_real_op_inputs(op_in)),
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)
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and op_in.name() != "cf.tuple_push"
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):
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idx_2 = sorted_list.index(op_in)
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if idx_1 != idx_2:
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change_list.append((idx_1, idx_2))
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for idx_1, idx_2 in change_list:
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sorted_list[idx_1], sorted_list[idx_2] = (
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sorted_list[idx_2],
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sorted_list[idx_1],
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)
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return sorted_list
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def is_inplace_net(op_list):
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'''
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when program has inplace op , it's difficult to find the actual pending_count.
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'''
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for op in op_list:
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if op.name() in ["pd_op.array_write_", "pd_op.assign_out_"]:
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return True
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if is_control_flow(op):
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for block in op.blocks():
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if is_inplace_net(block.ops):
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return True
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return False
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def remove_op(block, op, state):
|
|
'''
|
|
remove op from block
|
|
'''
|
|
if state.opgrad_to_op[op] != []:
|
|
fwd_op = state.opgrad_to_op[op][0]
|
|
state.op_to_opgrad[fwd_op].remove(op)
|
|
|
|
for valuegrad in op.results():
|
|
if state.valuegrad_to_value[valuegrad] != []:
|
|
value = state.valuegrad_to_value[valuegrad][0]
|
|
state.value_to_valuegrad[value] = []
|
|
|
|
if value in state.sumvaluegrad_to_value:
|
|
raise ValueError(
|
|
f'input_grad in [%s] is value which need to sum {op.name()}'
|
|
)
|
|
# NOTE(SigureMo): Ensure access to the op's results before removing it.
|
|
# Otherwise, the op will be deconstructed and access the num_results
|
|
# will be undefined behavior, it always cause hanging on the macOS.
|
|
block.remove_op(op)
|
|
|
|
|
|
def while_prune_check(while_tuple_ops):
|
|
if len(while_tuple_ops) != 0:
|
|
for opresult in while_tuple_ops[0].results():
|
|
if not opresult.use_empty():
|
|
return False
|
|
return True
|
|
return False
|
|
|
|
|
|
def remove_useless_full_like_ops(block, ops, state):
|
|
'''
|
|
remove ops which are not in use recursively,
|
|
|
|
'''
|
|
remove_ops = []
|
|
inverse_ops = inverse_sort_op(list(ops))
|
|
# from output to input
|
|
for op in inverse_ops:
|
|
if op.name() == "pd_op.full_like":
|
|
if op.result(0).use_empty():
|
|
full_op = op.operand_source(1).get_defining_op()
|
|
remove_ops.append(op)
|
|
remove_ops.append(full_op)
|
|
elif is_control_flow(op):
|
|
for sub_block in op.blocks():
|
|
remove_useless_full_like_ops(sub_block, sub_block.ops, state)
|
|
|
|
for op in remove_ops:
|
|
remove_op(block, op, state)
|
|
|
|
|
|
def all_stop_gradient_true(block):
|
|
for op in block.ops:
|
|
for value in op.results():
|
|
if value.stop_gradient is False:
|
|
return False
|
|
return True
|
|
|
|
|
|
def all_input_stop_gradient_true(list_of_list):
|
|
for list_ in list_of_list:
|
|
for stop_gradient in list_:
|
|
if stop_gradient is False:
|
|
return False
|
|
return True
|
|
|
|
|
|
def all_output_grad_none(list_of_list):
|
|
for list_ in list_of_list:
|
|
for value in list_:
|
|
if value is not None:
|
|
return False
|
|
return True
|
|
|
|
|
|
def op_has_vjp(op):
|
|
# NOTE(MarioLulab): In PIR mode, even though the `PyLayer` op does
|
|
# not have a vjp interface, we still need to generate the backward
|
|
# block based on its registered backward function. To achieve this,
|
|
# we add more handling logic for `PyLayer` Op in the `call_vjp` function
|
|
return core.has_vjp(op) or op.name() == "pd_op.pylayer"
|
|
|
|
|
|
def parent_total_ops(block):
|
|
'''
|
|
when block is sub_block, forward op should include its parent block ops
|
|
(sub block nest should Add on demand to avoid block copy)
|
|
'''
|
|
total_ops = []
|
|
if block.parent_block is not None:
|
|
if block.parent_block.parent_block:
|
|
total_ops += block.parent_block.parent_block.ops
|
|
total_ops += block.parent_block.ops
|
|
total_ops += block.ops
|
|
|
|
return total_ops
|
|
|
|
|
|
# only for control_flow to find corresponding value or value_list
|
|
def return_map_value(value, map):
|
|
output = value
|
|
while output in map:
|
|
output = map[output]
|
|
return output
|
|
|
|
|
|
def return_map_value_list(value, map):
|
|
output = []
|
|
for i in range(len(value)):
|
|
if value[i] in map:
|
|
output.append(return_map_value(value[i], map))
|
|
else:
|
|
output.append(value[i])
|
|
return output
|
|
|
|
|
|
def argument_to_value(while_op):
|
|
'''
|
|
return while op's relationship of (block_argument to input value) and (input value to block_argument).
|
|
'''
|
|
if while_op.name() != "pd_op.while":
|
|
return ValueDict(), ValueDict()
|
|
|
|
assert len(while_op.as_while_op().block_arguments()) + 1 == len(
|
|
while_op.operands_source()
|
|
), (
|
|
"while op's block_arguments size + 1 should same to while op's operands_source size"
|
|
)
|
|
arg_to_value_map = ValueDict()
|
|
value_to_arg_map = ValueDict()
|
|
for arg, value in zip(
|
|
while_op.as_while_op().block_arguments(),
|
|
while_op.operands_source()[1:],
|
|
):
|
|
arg_to_value_map[arg] = value
|
|
value_to_arg_map[value] = arg
|
|
return arg_to_value_map, value_to_arg_map
|
|
|
|
|
|
def get_grad_semantic_info(op):
|
|
'''
|
|
return whether op's inputs has grad, usually handled from yaml.
|
|
some op has uncertain inputs need special handling.
|
|
'''
|
|
if op.name() in [
|
|
"builtin.combine",
|
|
"pd_op.if",
|
|
"pd_op.while",
|
|
"pd_op.pylayer",
|
|
"cf.tuple_push",
|
|
"dist_op.moe_global_mesh_tensor",
|
|
"dist_op.moe_sub_mesh_tensors",
|
|
"dist_op.dist_reshape",
|
|
]:
|
|
grad_semantic_info = [True for _ in range(len(get_real_op_inputs(op)))]
|
|
if op.name() == "pd_op.if":
|
|
grad_semantic_info[0] = False
|
|
else:
|
|
grad_semantic_info = op.get_input_grad_semantics()
|
|
return grad_semantic_info
|
|
|
|
|
|
def get_split_op(value):
|
|
for op in value.all_used_ops():
|
|
if op.name() == "builtin.split":
|
|
return op
|
|
return None
|
|
|
|
|
|
@lru_cache
|
|
def warning_once(message: str):
|
|
logging.warning(message)
|
|
|
|
|
|
def update_if_output_stopgradient(if_op, true_yield_op, false_yield_op):
|
|
"""
|
|
Update if_op's stop_gradient based on true_yield_op and false_yield_op.
|
|
|
|
Args:
|
|
true_yield_op: true block of if_op's last op.
|
|
false_yield_op: false block of if_op's last op.
|
|
if_op: update it's op_results()'s stop_gradient.
|
|
"""
|
|
if (
|
|
true_yield_op.name() != 'cf.yield'
|
|
or false_yield_op.name() != 'cf.yield'
|
|
):
|
|
raise ValueError("param is not yield op")
|
|
|
|
# Check if operands_source sizes match
|
|
if len(true_yield_op.operands_source()) != len(
|
|
false_yield_op.operands_source()
|
|
):
|
|
raise ValueError("Mismatched yield operands_source sizes")
|
|
|
|
# Check if op_results size matches operands_source
|
|
if len(if_op.results()) != len(true_yield_op.operands_source()):
|
|
raise ValueError(
|
|
"Mismatched if op_results size with yield operands_source"
|
|
)
|
|
|
|
# Update if_op's stop_gradient
|
|
for i in range(len(true_yield_op.operands_source())):
|
|
stop_grad1 = true_yield_op.operand_source(i).stop_gradient
|
|
stop_grad2 = false_yield_op.operand_source(i).stop_gradient
|
|
|
|
# Set to False if either stop_gradient is False
|
|
if not stop_grad1 or not stop_grad2:
|
|
if_op.result(i).stop_gradient = False
|
|
|
|
|
|
def update_while_output_stopgradient(while_op, yield_op):
|
|
"""
|
|
Update while_op's stop_gradient based on yield_op.
|
|
|
|
Args:
|
|
yield_op: The yield operation associated with the while loop.
|
|
while_op: The while operation whose op_results()'s stop_gradient needs to be updated.
|
|
"""
|
|
# Check if yield_op is indeed a yield operation
|
|
if yield_op.name() != 'cf.yield':
|
|
raise ValueError("yield_op is not a yield operation")
|
|
|
|
# Check if operands_source size of yield_op matches op_results size of while_op
|
|
if len(while_op.results()) + 1 != len(yield_op.operands_source()):
|
|
raise ValueError(
|
|
f"Mismatched while op_results size %d with yield operands_source %d. {len(while_op.results()) + 1, len(yield_op.operands_source())}"
|
|
)
|
|
|
|
# Update while_op's stop_gradient
|
|
for i in range(1, len(yield_op.operands_source())):
|
|
stop_grad = yield_op.operand_source(i).stop_gradient
|
|
|
|
# Set to False if stop_gradient is False
|
|
if not stop_grad:
|
|
while_op.result(i - 1).stop_gradient = False
|
|
|
|
|
|
def find_index_of_yield(value, yield_op):
|
|
for i, v in enumerate(yield_op.operands_source()):
|
|
if v.is_same(value):
|
|
return i
|
|
return -1
|
|
|
|
|
|
def update_tuple_pop_origin_inputs(tuple_pop_outputs):
|
|
if tuple_pop_outputs == []:
|
|
return tuple_pop_outputs
|
|
op = tuple_pop_outputs[0][0].get_defining_op()
|
|
assert op.name() == "cf.tuple_pop"
|
|
stack_op = op.operand_source(0).get_defining_op()
|
|
tuple_push_inputs = stack_op.result(1).first_use().owner().operands_source()
|
|
tuple_push_inputs_with_if = []
|
|
for input in tuple_push_inputs:
|
|
if input.first_use().owner().name() == "cf.yield":
|
|
yield_op = input.first_use().owner()
|
|
index = find_index_of_yield(input, yield_op)
|
|
assert index != -1
|
|
tuple_push_inputs_with_if.append(
|
|
yield_op.get_parent_block().parent_op.result(index)
|
|
)
|
|
else:
|
|
tuple_push_inputs_with_if.append(input)
|
|
|
|
# pass inlets
|
|
return tuple_push_inputs_with_if[1:]
|
|
|
|
|
|
def value_in_block(value, block):
|
|
value_block = value.get_defining_op().get_parent_block()
|
|
while block.parent_op.name() != "builtin.module":
|
|
if block == value_block:
|
|
return True
|
|
block = block.parent_block
|
|
# now block is module op's block
|
|
if block == value_block:
|
|
return True
|
|
|
|
return False
|