mirror of
https://github.com/zebrajr/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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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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##############################################################################
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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import numpy as np
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class ParameterTags(object):
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BIAS = 'BIAS'
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WEIGHT = 'WEIGHT'
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COMPUTED_PARAM = 'COMPUTED_PARAM'
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class ParameterType(object):
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DENSE = 'dense'
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SPARSE = 'sparse'
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class ParameterInfo(object):
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def __init__(
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self, param_id, param, key=None, shape=None, length=None,
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grad=None, blob_copy=None):
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assert isinstance(param, core.BlobReference)
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self.param_id = param_id
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self.name = str(param)
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self.blob = param
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self.key = key
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self.shape = shape
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self.size = None if shape is None else np.prod(shape)
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self.length = max(1, length if length is not None else 1)
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self.grad = grad
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self._cloned_init_net = None
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# Optionally store equivalent copies of the blob
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# in different precisions (i.e. half and float copies)
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# stored as a dict of TensorProto.DataType -> BlobReference
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self.blob_copy = blob_copy
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# each param_info can have its own optimizer. It can be set within
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# OptimizerContext (caffe2/python/optimizer.py)
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self._optimizer = None
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def grad_type(self):
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# self.grad could be None for model parallelism with parameter server
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if self.grad is None:
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return
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return (
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ParameterType.SPARSE if isinstance(self.grad, core.GradientSlice)
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else ParameterType.DENSE)
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@property
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def parameter(self):
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return self.blob
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@property
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def optimizer(self):
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return self._optimizer
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@optimizer.setter
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def optimizer(self, value):
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assert self._optimizer is None, "optimizer has already been set"
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self._optimizer = value
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def __str__(self):
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return self.name
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