mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
2bfacff426
Summary: Adds support for generating and training pfp16 models. Added SGD optimizer for multi-precision trainers and a new callback to data_parallel_model in order to help multi-precision models keep their different copies of parameters in sync during training. Closes https://github.com/caffe2/caffe2/pull/697 Differential Revision: D5159712 Pulled By: salexspb fbshipit-source-id: 60a889494d2e2f4df1d720331e19f638c5eb95cc
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
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 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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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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def cloned_init_net(self):
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if not self._cloned_init_net:
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init_net, outputs = self.blob.Net().ClonePartial(
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'param_%d_%s_init' % (self.param_id, self.name),
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inputs=[],
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outputs=[self.blob])
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self._cloned_init_net = (init_net, outputs[0])
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return self._cloned_init_net
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def __str__(self):
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return self.name
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