pytorch/caffe2/python/modeling/parameter_info.py
Aapo Kyrola 0f8c8f37a8 Revert D5159712: [caffe2][PR] Fp16 training initializers
Summary: This reverts commit 60a889494d2e2f4df1d720331e19f638c5eb95cc

Differential Revision: D5159712

fbshipit-source-id: 16040c911b260648857f656f92b165f92c2daae0
2017-06-01 00:17:14 -07:00

49 lines
1.5 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
import numpy as np
class ParameterType(object):
DENSE = 'dense'
SPARSE = 'sparse'
class ParameterInfo(object):
def __init__(
self, param_id, param, key=None, shape=None, length=None,
grad=None):
assert isinstance(param, core.BlobReference)
self.param_id = param_id
self.name = str(param)
self.blob = param
self.key = key
self.shape = shape
self.size = None if shape is None else np.prod(shape)
self.length = max(1, length if length is not None else 1)
self.grad = grad
self._cloned_init_net = None
def grad_type(self):
# self.grad could be None for model parallelism with parameter server
if self.grad is None:
return
return (
ParameterType.SPARSE if isinstance(self.grad, core.GradientSlice)
else ParameterType.DENSE)
def cloned_init_net(self):
if not self._cloned_init_net:
init_net, outputs = self.blob.Net().ClonePartial(
'param_%d_%s_init' % (self.param_id, self.name),
inputs=[],
outputs=[self.blob])
self._cloned_init_net = (init_net, outputs[0])
return self._cloned_init_net
def __str__(self):
return self.name