wehub-resource-sync
142 lines
4.7 KiB
Python
142 lines
4.7 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,
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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 contextlib import contextmanager
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import paddle
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from paddle.autograd.backward_utils import ValueDict
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from paddle.framework import core
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from ..dy2static.program_translator import _program_hash, synchronized
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@contextmanager
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def append_op_in_top_block():
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current_insertion_point = paddle.pir.get_current_insertion_point()
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top_block = paddle.static.default_main_program().global_block()
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paddle.pir.set_insertion_point_to_block_end(top_block)
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try:
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yield
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finally:
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paddle.pir.set_insertion_point(current_insertion_point)
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class ParametersRecorder:
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def __init__(self):
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self.params_dict = {}
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self.tensor2value = {}
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@synchronized
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def get(self, program, tensor):
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from paddle.pir.core import create_parameter, vartype_to_datatype
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"""use the default_program as key, append tensor the parameter list."""
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key = _program_hash(program)
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if key not in self.params_dict:
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self.params_dict[key] = set()
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self.tensor2value[key] = {}
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params = self.params_dict[key]
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mappings = self.tensor2value[key]
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if id(tensor) not in mappings:
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non_used_initializer = paddle.nn.initializer.Constant(0.0)
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dtype = tensor.dtype
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if isinstance(dtype, core.VarDesc.VarType):
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dtype = vartype_to_datatype[dtype]
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with append_op_in_top_block():
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value = create_parameter(
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dtype=dtype,
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shape=tensor.shape,
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type=tensor.type,
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name=tensor.name,
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initializer=non_used_initializer,
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trainable=(not tensor.stop_gradient),
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placements=tensor.placements,
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process_mesh=tensor.process_mesh,
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)
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if isinstance(tensor, paddle.Tensor):
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params.add(tensor)
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mappings[id(tensor)] = value
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return mappings[id(tensor)]
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def pop(self, program):
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hash_id = _program_hash(program)
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params = self.params_dict.get(hash_id)
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if params is None:
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return [], []
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params = list(params)
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params.sort(key=lambda x: x.name)
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params_values = [self.tensor2value[hash_id][id(x)] for x in params]
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del self.params_dict[hash_id]
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del self.tensor2value[hash_id]
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return params, params_values
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class InplaceMap:
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def __init__(self):
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self.params_dict = {}
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@synchronized
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def add(self, program, origin_value, new_value):
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key = _program_hash(program)
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if key not in self.params_dict:
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self.params_dict[key] = ValueDict()
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inplace_dict = self.params_dict[key]
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inplace_dict[origin_value] = new_value
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def get(self, program, value):
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inplace_dict = self.params_dict.get(_program_hash(program))
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if inplace_dict is None:
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return None
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if value not in inplace_dict:
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return None
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root_var = inplace_dict[value]
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saved = []
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while root_var in inplace_dict:
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saved.append(root_var)
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root_var = inplace_dict[root_var]
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for var in saved:
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inplace_dict[var] = root_var
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return root_var
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def pop(self, program):
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key = _program_hash(program)
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if key not in self.params_dict:
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return
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del self.params_dict[key]
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def restore_checkpoint(self, checkpoint):
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# InplaceMap is a nested effect.
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# when enter a block, we should save a checkpoint
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# when exit a block, we should restore a checkpoint
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# for example:
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# if cond > 0:
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# x [:] = 0
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# return x
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# x[:] only effect current cond block, we should restore in false block.
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self.params_dict = checkpoint
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def save_checkpoint(self):
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checkpoint = {}
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for program_id, params in self.params_dict.items():
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new_params = dict(params.items())
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checkpoint[program_id] = new_params
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return checkpoint
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_global_parameter_recorder = ParametersRecorder()
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_global_inplace_map = InplaceMap()
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