mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
84067bc17d
Summary: Current version of the code is not supporting type and shape inference that is going to make all places that rely on it fail misserably. I'm still leaving option of doing init in the old way in case if some places are already failing this inference logic. Reviewed By: ffjiang Differential Revision: D6241270 fbshipit-source-id: e9080ffe93d610b5ada58ebe66579acfa57c6b3c
1068 lines
35 KiB
Python
1068 lines
35 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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## @package optimizer
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# Module caffe2.python.optimizer
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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 collections import namedtuple, defaultdict
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from past.builtins import basestring
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import numpy as np
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from caffe2.python import core, scope, workspace
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from caffe2.python.modeling import parameter_info
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from caffe2.proto import caffe2_pb2
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_OPTIMIZER_ITERATION_NAME = "optimizer_iteration"
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_LEARNING_RATE_INJECTION = "lr_injection"
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AuxOptimizerParams = namedtuple("AuxOptimizerParams", ["local", "shared"])
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_optimizer_instance_count = defaultdict(int)
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class Optimizer(object):
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def __init__(self):
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self._aux_params = AuxOptimizerParams(local=[], shared=[])
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self._instance_num = _optimizer_instance_count[self.__class__.__name__]
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_optimizer_instance_count[self.__class__.__name__] += 1
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self._lr_multiplier = None
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'''
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Adds optimization operators to the net for given parameter and its gradient
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Parameter is specified by either 'param' being a ParameterInfo object.
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In this case param.grad has to be set
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Or by 'param' being a BlobReference and 'grad' being a BlobReference for its
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gradient.
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'''
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def __call__(self, net, param_init_net, param, grad=None):
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if grad is None:
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assert isinstance(param, parameter_info.ParameterInfo)
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assert param.grad is not None
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else:
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if isinstance(param, basestring):
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param = core.BlobReference(param)
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param = parameter_info.ParameterInfo(
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param_id=None, param=param, grad=grad)
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self._run(net, param_init_net, param)
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def _run(self, net, param_init_net, param_info):
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raise Exception("Not Implemented")
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def get_cpu_blob_name(self, base_str, node_name=''):
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classname = self.__class__.__name__
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return '%s_%d_%s%s_cpu' % (classname, self._instance_num, base_str, node_name)
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def get_gpu_blob_name(self, base_str, gpu_id, node_name):
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classname = self.__class__.__name__
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return '%s_%d_%s%s_gpu%d' % (
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classname, self._instance_num, base_str, node_name, gpu_id,
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)
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def make_unique_blob_name(self, base_str):
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"""
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Returns a blob name that will be unique to the current device
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and optimizer instance.
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"""
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current_scope = scope.CurrentDeviceScope()
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if current_scope is None:
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return self.get_cpu_blob_name(base_str)
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if current_scope.device_type == caffe2_pb2.CUDA:
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return self.get_gpu_blob_name(
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base_str, current_scope.cuda_gpu_id, current_scope.node_name
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)
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else:
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return self.get_cpu_blob_name(base_str, current_scope.node_name)
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def build_lr(self, net, param_init_net, base_learning_rate,
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learning_rate_blob=None, policy="fixed",
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iter_val=0, **kwargs):
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if learning_rate_blob is None:
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learning_rate_blob = self.make_unique_blob_name('lr')
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# Each node needs its own iteration counter
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current_scope = scope.CurrentDeviceScope()
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node_name = current_scope.node_name if current_scope else ''
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optimization_iter_blob = _OPTIMIZER_ITERATION_NAME + node_name
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if not param_init_net.BlobIsDefined(optimization_iter_blob):
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# Add training operators.
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with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
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iteration = param_init_net.ConstantFill(
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[], optimization_iter_blob, shape=[1],
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value=iter_val,
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dtype=core.DataType.INT64)
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iter_mutex = param_init_net.CreateMutex(
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[], ["iteration_mutex" + node_name]
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)
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net.AtomicIter([iter_mutex, iteration], [iteration])
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else:
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iteration = param_init_net.GetBlobRef(optimization_iter_blob)
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if not net.BlobIsDefined(learning_rate_blob):
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# There is one interesting thing here: since we are minimizing, we are
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# doing "descent" so the learning rate is set to be negative.
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lr = net.LearningRate(
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[iteration],
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learning_rate_blob,
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base_lr=-base_learning_rate,
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policy=policy,
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**kwargs
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)
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else:
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lr = net.GetBlobRef(learning_rate_blob)
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if self._lr_multiplier is not None:
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lr_multiplier = net.CopyFromCPUInput(
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self._lr_multiplier, self.make_unique_blob_name('lr_multiplier')
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)
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scaled_lr = net.Mul(
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[lr, lr_multiplier],
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self.make_unique_blob_name('scaled_lr'),
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broadcast=1,
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)
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lr = scaled_lr
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return lr, iteration
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def add_lr_multiplier(self, lr_multiplier):
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self._lr_multiplier = lr_multiplier
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@staticmethod
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def dedup(net, sparse_dedup_aggregator, grad):
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assert (isinstance(grad, core.GradientSlice))
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if sparse_dedup_aggregator:
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return net.DeduplicateGradientSlices(
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grad, aggregator=sparse_dedup_aggregator)
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else:
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return grad
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def get_auxiliary_parameters(self):
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"""Returns a list of auxiliary parameters.
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Returns:
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aux_params: A namedtuple, AuxParams.
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aux_params.local stores a list of blobs. Each blob is a local
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auxiliary parameter. A local auxiliary parameter is a parameter in
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parallel to a learning rate parameter. Take adagrad as an example,
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the local auxiliary parameter is the squared sum parameter, because
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every learning rate has a squared sum associated with it.
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aux_params.shared also stores a list of blobs. Each blob is a shared
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auxiliary parameter. A shared auxiliary parameter is a parameter
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that is shared across all the learning rate parameters. Take adam as
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an example, the iteration parameter is a shared parameter, because
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all the learning rates share the same iteration parameter.
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"""
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return self._aux_params
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# TODO(xlwang): In transfer learning, parameter initialized from pretrained
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# model might require a different learning rate than otherwise initialized.
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# To this end, here we implement a python solution where
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# `base_learning_rate` is scaled by `scale`, by calling
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# `scale_learning_rate`; Alternatively, we can achieve same effect by
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# rewriting the LearningRate operator in C++
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# Note that it is the responsibility of specific optimizer to decide what
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# logic should be used for `scale_learning_rate`
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def scale_learning_rate(self, *args, **kwargs):
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raise NotImplementedError(
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"Optimizer Need to Implement `scale_learning_rate` method.")
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class SgdOptimizer(Optimizer):
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def __init__(self, base_learning_rate=0.01, policy='fixed',
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momentum=0.0, nesterov=1, sparse_dedup_aggregator=None,
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**kwargs):
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super(SgdOptimizer, self).__init__()
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self.base_learning_rate = base_learning_rate
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self.policy = policy
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self.momentum = momentum
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self.nesterov = nesterov
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self.sparse_dedup_aggregator = sparse_dedup_aggregator
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self.init_kwargs = kwargs
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def _run(self, net, param_init_net, param_info):
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param = param_info.blob
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grad = param_info.grad
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if self.base_learning_rate == 0:
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return
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assert self.base_learning_rate > 0
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# We need negative sign for LR when used directly with WeightedSum
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# below.
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lr_sign = -1 if self.momentum else 1
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lr, _ = self.build_lr(
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net, param_init_net,
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base_learning_rate=self.base_learning_rate * lr_sign,
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policy=self.policy,
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**(self.init_kwargs)
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)
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dev = scope.CurrentDeviceScope()
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if dev is None:
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dev = core.DeviceOption(caffe2_pb2.CPU)
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# Each GPU/CPU must have its own ONE blob, thus modify the name
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# to include device information.
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ONE = param_init_net.ConstantFill(
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[],
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"ONE_{}_{}{}".format(dev.device_type, dev.cuda_gpu_id, dev.node_name),
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shape=[1],
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value=1.0
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)
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self._aux_params.shared.append(ONE)
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if self.momentum > 0:
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momentum_data = param_init_net.ConstantFill(
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param, str(param) + "_momentum", value=0.)
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self._aux_params.local.append(momentum_data)
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if isinstance(grad, core.GradientSlice):
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grad = self.dedup(net, self.sparse_dedup_aggregator, grad)
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if self.momentum > 0.:
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net.SparseMomentumSGDUpdate(
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[grad.values, momentum_data, lr, param, grad.indices],
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[grad.values, momentum_data, param],
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momentum=self.momentum,
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nesterov=self.nesterov)
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else:
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net.ScatterWeightedSum(
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[param, ONE, grad.indices, grad.values, lr],
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param
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)
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else:
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if self.momentum > 0.:
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net.MomentumSGDUpdate(
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[grad, momentum_data, lr, param],
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[grad, momentum_data, param],
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momentum=self.momentum,
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nesterov=self.nesterov)
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else:
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coeff = lr
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net.WeightedSum(
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[param, ONE, grad, coeff],
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param
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)
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def scale_learning_rate(self, scale):
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self.base_learning_rate *= scale
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return
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class MultiPrecisionSgdOptimizer(SgdOptimizer):
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def __init__(self, base_learning_rate=0.1, momentum=0.0,
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policy="fixed", nesterov=1, sparse_dedup_aggregator=None,
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**kwargs):
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super(SgdOptimizer, self).__init__()
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self.base_learning_rate = base_learning_rate
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self.momentum = momentum
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self.policy = policy
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self.nesterov = nesterov
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self.sparse_dedup_aggregator = sparse_dedup_aggregator
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self.init_kwargs = kwargs
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def _run(self, net, param_init_net, param_info):
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param = param_info.blob
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param_fp32 = param_info.blob_copy[core.DataType.FLOAT] \
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if param_info.blob_copy is not None else None
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# If we have a straight fp32 parameter, run the base class
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if param_fp32 is None:
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return SgdOptimizer._run(self, net, param_init_net, param_info)
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grad = param_info.grad
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if self.base_learning_rate == 0:
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return
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assert self.base_learning_rate > 0
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lr, _ = self.build_lr(
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net, param_init_net,
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base_learning_rate=-self.base_learning_rate,
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policy=self.policy,
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**(self.init_kwargs)
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)
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momentum_data = param_init_net.ConstantFill(
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param_fp32, str(param) + "_momentum", value=0.)
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self._aux_params.local.append(momentum_data)
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assert not isinstance(grad, core.GradientSlice), \
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"Doesn't support sparse gradients"
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# Copy gradient to fp32
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grad_fp32 = net.HalfToFloat(grad, grad + "_fp32")
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# update (fused) in fp32
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net.MomentumSGDUpdate(
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[grad_fp32, momentum_data, lr, param_fp32],
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[grad_fp32, momentum_data, param_fp32],
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momentum=self.momentum,
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nesterov=self.nesterov)
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# Copy updated param back to fp16
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net.FloatToHalf(param_fp32, param)
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class FP16SgdOptimizer(SgdOptimizer):
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def __init__(self, base_learning_rate=0.1, momentum=0.0,
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policy="fixed", nesterov=1, weight_decay=0.0001,
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sparse_dedup_aggregator=None,
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**kwargs):
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super(SgdOptimizer, self).__init__()
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self.base_learning_rate = base_learning_rate
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self.momentum = momentum
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self.policy = policy
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self.nesterov = nesterov
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self.sparse_dedup_aggregator = sparse_dedup_aggregator
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self.init_kwargs = kwargs
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self.weight_decay = weight_decay
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def _run(self, net, param_init_net, param_info, fp32_update=False):
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fp32_update_flag = 0
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param_name = str(param_info.blob)
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# should only be triggered in FP16 training by SpatialBN, which
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# requires FP32 params in CuDNN.
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if param_name.find("spatbn") != -1:
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fp32_update = True
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if fp32_update:
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# doing a 32bit update
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# Have to assume param_info.blob is FP32 as there is no way
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# (that i currently know of) to query a blob's type in python
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fp32_update_flag = 1
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param = param_info.blob
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param_fp32 = param_info.blob
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else:
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if param_info.blob_copy is None:
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# doing a 32bit update
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# Have to assume param_info.blob is FP32 as there is no way
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# (that i currently know of) to query a blob's type in python
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fp32_update_flag = 1
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param = param_info.blob
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param_fp32 = param_info.blob
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else:
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if core.DataType.FLOAT in param_info.blob_copy:
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param = param_info.blob
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param_fp32 = param_info.blob_copy[core.DataType.FLOAT]
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elif core.DataType.FLOAT16 in param_info.blob_copy:
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param = param_info.blob_copy[core.DataType.FLOAT16]
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param_fp32 = param_info.blob
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else:
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assert (False), (
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"Unrecognized parameter format to be updated "
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"by FP16 Optimizer. Parameter: {}".format(param_info.name)
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)
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grad = param_info.grad
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if self.base_learning_rate == 0:
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return
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assert self.base_learning_rate > 0
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lr, _ = self.build_lr(
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net, param_init_net,
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base_learning_rate=-self.base_learning_rate,
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policy=self.policy,
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**(self.init_kwargs)
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)
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momentum_data_fp32 = param_init_net.ConstantFill(
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param_fp32, str(param) + "_momentum_fp32", value=0.)
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momentum_data = param_init_net.FloatToHalf(
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momentum_data_fp32, str(param) + "_momentum")
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self._aux_params.local.append(momentum_data)
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assert not isinstance(grad, core.GradientSlice), \
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"Doesn't support sparse gradients"
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if fp32_update_flag == 0:
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net.FP16MomentumSGDUpdate(
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[grad, momentum_data, lr, param],
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[grad, momentum_data, param],
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momentum=self.momentum,
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nesterov=self.nesterov,
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weight_decay=self.weight_decay)
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else:
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# flag set to 1, therefore doing FP32 update
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net.FP32MomentumSGDUpdate(
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[grad, momentum_data_fp32, lr, param],
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[grad, momentum_data_fp32, param],
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momentum=self.momentum,
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nesterov=self.nesterov,
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weight_decay=self.weight_decay)
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class WeightDecayBuilder(Optimizer):
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def __init__(self, weight_decay):
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self.weight_decay = weight_decay
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def _run(self, net, param_init_net, param_info):
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dev = scope.CurrentDeviceScope()
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if dev is None:
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dev = core.DeviceOption(caffe2_pb2.CPU)
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ONE = param_init_net.ConstantFill(
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[],
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"ONE_{}_{}".format(dev.device_type, dev.cuda_gpu_id),
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shape=[1],
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value=1.0
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)
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WD = param_init_net.ConstantFill(
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[], "wd_{}_{}".format(dev.device_type, dev.cuda_gpu_id),
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shape=[1], value=self.weight_decay
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)
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if isinstance(param_info.grad, core.GradientSlice):
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assert "Weight decay does not yet support sparse gradients"
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else:
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net.WeightedSum(
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[param_info.grad, ONE, param_info.blob, WD],
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param_info.grad,
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)
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class AdagradOptimizer(Optimizer):
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def __init__(self, alpha=0.01, epsilon=1e-4, decay=1, policy="fixed",
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sparse_dedup_aggregator=None, rowWise=False,
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engine='', **kwargs):
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super(AdagradOptimizer, self).__init__()
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self.alpha = alpha
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self.epsilon = epsilon
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self.decay = decay
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self.policy = policy
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self.sparse_dedup_aggregator = sparse_dedup_aggregator
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self.engine = engine
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self.init_kwargs = kwargs
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self.rowWise = rowWise
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def _run(self, net, param_init_net, param_info):
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param = param_info.blob
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grad = param_info.grad
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if self.alpha <= 0:
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return
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lr, _ = self.build_lr(
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net, param_init_net,
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base_learning_rate=self.alpha,
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policy=self.policy,
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**(self.init_kwargs)
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)
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if self.rowWise:
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shapes, types = workspace.InferShapesAndTypes([param_init_net])
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if str(param) not in shapes:
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# Type/shape inference is not available for this param, fallback
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# on Shape/Slice logic
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shape = param_init_net.Shape(param, str(param) + "_shape")
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num_rows = param_init_net.Slice(
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[shape],
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str(shape) + "_numrows",
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|
starts=[0], ends=[1]
|
|
)
|
|
param_squared_sum = param_init_net.ConstantFill(
|
|
num_rows,
|
|
str(param) + "_avg_squared_sum",
|
|
input_as_shape=1,
|
|
value=0.0
|
|
)
|
|
else:
|
|
param_squared_sum = param_init_net.ConstantFill(
|
|
[],
|
|
str(param) + "_avg_squared_sum",
|
|
shape=[shapes[str(param)][0]],
|
|
value=0.0
|
|
)
|
|
|
|
else:
|
|
param_squared_sum = param_init_net.ConstantFill(
|
|
[param],
|
|
str(param) + "_squared_sum",
|
|
value=0.0
|
|
)
|
|
|
|
self._aux_params.local.append(param_squared_sum)
|
|
|
|
if self.rowWise:
|
|
assert isinstance(grad, core.GradientSlice),\
|
|
'If SparseAdagrad with rowWise=True, gradient must be '\
|
|
'a gradientslice. PLease ensure that rowWise is not enabled '\
|
|
'for the dense Adagrad optimizer, as it is not supported.'
|
|
if isinstance(grad, core.GradientSlice):
|
|
assert self.decay == 1.,\
|
|
'Decay is not implemented for SparseAdagrad and must be set to 1'
|
|
grad = self.dedup(net, self.sparse_dedup_aggregator, grad)
|
|
if self.rowWise:
|
|
op = 'RowWiseSparseAdagrad'
|
|
else:
|
|
op = 'SparseAdagrad'
|
|
net.__getattr__(op)(
|
|
[param, param_squared_sum, grad.indices, grad.values, lr],
|
|
[param, param_squared_sum],
|
|
epsilon=self.epsilon,
|
|
engine=self.engine
|
|
)
|
|
else:
|
|
net.Adagrad(
|
|
[param, param_squared_sum, grad, lr],
|
|
[param, param_squared_sum],
|
|
epsilon=self.epsilon,
|
|
decay=float(self.decay),
|
|
engine=self.engine
|
|
)
|
|
|
|
def scale_learning_rate(self, scale):
|
|
self.alpha *= scale
|
|
return
|
|
|
|
|
|
class FtrlOptimizer(Optimizer):
|
|
def __init__(self, alpha=0.01, beta=1e-4, lambda1=0, lambda2=0,
|
|
sparse_dedup_aggregator=None, engine=''):
|
|
super(FtrlOptimizer, self).__init__()
|
|
self.alpha = alpha
|
|
self.beta = beta
|
|
self.lambda1 = lambda1
|
|
self.lambda2 = lambda2
|
|
self.sparse_dedup_aggregator = sparse_dedup_aggregator
|
|
self.engine = engine
|
|
|
|
def _run(self, net, param_init_net, param_info):
|
|
param = param_info.blob
|
|
grad = param_info.grad
|
|
|
|
if self.alpha <= 0:
|
|
return
|
|
|
|
nz = param_init_net.ConstantFill(
|
|
[param],
|
|
str(param) + "_ftrl_nz",
|
|
extra_shape=[2],
|
|
value=0.0
|
|
)
|
|
self._aux_params.local.append(nz)
|
|
if isinstance(grad, core.GradientSlice):
|
|
grad = self.dedup(net, self.sparse_dedup_aggregator, grad)
|
|
net.SparseFtrl(
|
|
[param, nz, grad.indices, grad.values],
|
|
[param, nz],
|
|
engine=self.engine,
|
|
alpha=self.alpha,
|
|
beta=self.beta,
|
|
lambda1=self.lambda1,
|
|
lambda2=self.lambda2
|
|
)
|
|
else:
|
|
net.Ftrl(
|
|
[param, nz, grad],
|
|
[param, nz],
|
|
engine=self.engine,
|
|
alpha=self.alpha,
|
|
beta=self.beta,
|
|
lambda1=self.lambda1,
|
|
lambda2=self.lambda2
|
|
)
|
|
|
|
def scale_learning_rate(self, scale):
|
|
self.alpha *= scale
|
|
return
|
|
|
|
|
|
class AdamOptimizer(Optimizer):
|
|
def __init__(self, alpha=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
|
|
policy='fixed', sparse_dedup_aggregator=None,
|
|
engine='', **kwargs):
|
|
super(AdamOptimizer, self).__init__()
|
|
self.alpha = alpha
|
|
self.beta1 = beta1
|
|
self.beta2 = beta2
|
|
self.epsilon = epsilon
|
|
self.policy = policy
|
|
self.sparse_dedup_aggregator = sparse_dedup_aggregator
|
|
self.engine = engine
|
|
self.init_kwargs = kwargs
|
|
|
|
def _run(self, net, param_init_net, param_info):
|
|
param = param_info.blob
|
|
grad = param_info.grad
|
|
|
|
if self.alpha <= 0:
|
|
return
|
|
|
|
lr, iteration = self.build_lr(
|
|
net, param_init_net,
|
|
base_learning_rate=self.alpha,
|
|
policy=self.policy,
|
|
**(self.init_kwargs)
|
|
)
|
|
|
|
m1 = param_init_net.ConstantFill(
|
|
[param],
|
|
param + "_first_moment",
|
|
value=0.0
|
|
)
|
|
m2 = param_init_net.ConstantFill(
|
|
[param],
|
|
param + "_second_moment",
|
|
value=0.0
|
|
)
|
|
self._aux_params.shared.append(iteration)
|
|
self._aux_params.local.append(m1)
|
|
self._aux_params.local.append(m2)
|
|
if isinstance(grad, core.GradientSlice):
|
|
grad = self.dedup(net, self.sparse_dedup_aggregator, grad)
|
|
net.SparseAdam(
|
|
[param, m1, m2, grad.indices, grad.values, lr, iteration],
|
|
[param, m1, m2],
|
|
beta1=self.beta1,
|
|
beta2=self.beta2,
|
|
epsilon=self.epsilon
|
|
)
|
|
|
|
else:
|
|
net.Adam(
|
|
[param, m1, m2, grad, lr, iteration],
|
|
[param, m1, m2],
|
|
beta1=self.beta1,
|
|
beta2=self.beta2,
|
|
epsilon=self.epsilon)
|
|
|
|
def scale_learning_rate(self, scale):
|
|
self.alpha *= scale
|
|
return
|
|
|
|
|
|
class YellowFinOptimizer(Optimizer):
|
|
"""YellowFin: An automatic tuner for momentum SGD
|
|
|
|
See https://arxiv.org/abs/1706.03471 for more details. This implementation
|
|
has separate learning rate and momentum per each parameter."""
|
|
|
|
def __init__(self,
|
|
alpha=0.1,
|
|
mu=0.0,
|
|
beta=0.999,
|
|
curv_win_width=20,
|
|
zero_debias=True,
|
|
epsilon=0.1**6,
|
|
policy='fixed',
|
|
sparse_dedup_aggregator=None,
|
|
**kwargs):
|
|
super(YellowFinOptimizer, self).__init__()
|
|
self.alpha = alpha
|
|
self.mu = mu
|
|
self.beta = beta
|
|
self.curv_win_width = curv_win_width
|
|
self.zero_debias = zero_debias
|
|
self.epsilon = epsilon
|
|
self.policy = policy
|
|
self.sparse_dedup_aggregator = sparse_dedup_aggregator
|
|
self.init_kwargs = kwargs
|
|
|
|
def _run(self, net, param_init_net, param_info):
|
|
|
|
# Note: This is number of persistent scalars in YellowFin optimizer.
|
|
# It should always be the number of scalars being used. The same
|
|
# number should be used in class for the operation.
|
|
SCALARS_MEMORY_SIZE = 5
|
|
|
|
param = param_info.blob
|
|
grad = param_info.grad
|
|
moment = param_init_net.ConstantFill(
|
|
[param],
|
|
param + "_moment",
|
|
value=0.0
|
|
)
|
|
curv_win = param_init_net.ConstantFill(
|
|
[],
|
|
param + "_curv_win",
|
|
shape=[self.curv_win_width],
|
|
value=0.0
|
|
)
|
|
g_avg = param_init_net.ConstantFill(
|
|
[param],
|
|
param + "_g_avg",
|
|
value=0.0
|
|
)
|
|
g2_avg = param_init_net.ConstantFill(
|
|
[param],
|
|
param + "_g2_avg",
|
|
value=0.0
|
|
)
|
|
lr_avg = param_init_net.ConstantFill(
|
|
[],
|
|
param + "_lr_avg",
|
|
shape=[1],
|
|
value=self.alpha
|
|
)
|
|
mu_avg = param_init_net.ConstantFill(
|
|
[],
|
|
param + "_mu_avg",
|
|
shape=[1],
|
|
value=self.mu
|
|
)
|
|
scalars_memory = param_init_net.ConstantFill(
|
|
[],
|
|
param + "_scalars_memory",
|
|
shape=[SCALARS_MEMORY_SIZE],
|
|
value=0.0
|
|
)
|
|
|
|
assert self.alpha > 0
|
|
assert not isinstance(grad, core.GradientSlice), \
|
|
"Doesn't support sparse gradients"
|
|
|
|
if not param_init_net.BlobIsDefined(_OPTIMIZER_ITERATION_NAME):
|
|
# Add training operators.
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
iteration = param_init_net.ConstantFill(
|
|
[],
|
|
_OPTIMIZER_ITERATION_NAME,
|
|
shape=[1],
|
|
value=0,
|
|
dtype=core.DataType.INT64)
|
|
iter_mutex = param_init_net.CreateMutex([],
|
|
["iteration_mutex"])
|
|
net.AtomicIter([iter_mutex, iteration], [iteration])
|
|
else:
|
|
iteration = param_init_net.GetBlobRef(_OPTIMIZER_ITERATION_NAME)
|
|
|
|
self._aux_params.shared.append(iteration)
|
|
self._aux_params.local.append(moment)
|
|
self._aux_params.local.append(lr_avg)
|
|
self._aux_params.local.append(mu_avg)
|
|
self._aux_params.local.append(curv_win)
|
|
self._aux_params.local.append(g_avg)
|
|
self._aux_params.local.append(g2_avg)
|
|
self._aux_params.local.append(scalars_memory)
|
|
|
|
yf_in_out_args = [
|
|
param,
|
|
moment,
|
|
lr_avg,
|
|
mu_avg,
|
|
curv_win,
|
|
g_avg,
|
|
g2_avg,
|
|
scalars_memory
|
|
]
|
|
|
|
net.YellowFin(
|
|
yf_in_out_args + [grad, iteration],
|
|
yf_in_out_args,
|
|
beta=self.beta,
|
|
epsilon=self.epsilon,
|
|
curv_win_width=self.curv_win_width,
|
|
zero_debias=self.zero_debias)
|
|
|
|
def scale_learning_rate(self, scale):
|
|
self.alpha *= scale
|
|
return
|
|
|
|
|
|
def _get_param_to_device(model):
|
|
# Infer blob devices by going through the net and param_init_net
|
|
# ops and observing the device used to create or use the blob.
|
|
param_to_device = core.InferBlobDevices(model.net)
|
|
param_to_device.update(core.InferBlobDevices(model.param_init_net))
|
|
return param_to_device
|
|
|
|
|
|
def get_param_device(param_name, grad, param_to_device=None, default_device=None):
|
|
device = default_device
|
|
param_to_device = param_to_device or {}
|
|
# We first check if parameter's device has been inferred. If not,
|
|
# we check the gradient. This can happen if parameter is not output
|
|
# by any blob but created by a FetchBlob.
|
|
if param_name in param_to_device:
|
|
device = param_to_device[param_name]
|
|
else:
|
|
if isinstance(grad, core.GradientSlice):
|
|
grad = grad
|
|
if str(grad.values) in param_to_device:
|
|
device = param_to_device[str(grad.values)]
|
|
elif str(grad.indices) in param_to_device:
|
|
device = param_to_device[str(grad.indices)]
|
|
else:
|
|
grad_name = str(grad)
|
|
if grad_name in param_to_device:
|
|
device = param_to_device[grad_name]
|
|
|
|
assert device is not None,\
|
|
"Cannot infer device for {}: no op creates it".format(param_name)
|
|
return device
|
|
|
|
|
|
def get_lr_injection():
|
|
"""
|
|
Gets current value for lr_injection, a multiplier for all base
|
|
learning rates.
|
|
Must set allow_lr_injection=True when building optimizer, as it
|
|
relies on synchronization over CPU.
|
|
"""
|
|
return workspace.FetchBlob(_LEARNING_RATE_INJECTION)
|
|
|
|
|
|
def set_lr_injection(lr_injection_value):
|
|
"""
|
|
Sets lr_injection, a multiplier for all base learning rates.
|
|
Must set allow_lr_injection=True when building optimizer, as it
|
|
relies on synchronization over CPU.
|
|
"""
|
|
workspace.FeedBlob(
|
|
_LEARNING_RATE_INJECTION,
|
|
np.array(
|
|
[float(lr_injection_value)],
|
|
dtype=np.float32,
|
|
),
|
|
)
|
|
|
|
|
|
def _calc_norm_ratio(
|
|
model, params, name_scope, param_to_device, max_gradient_norm
|
|
):
|
|
with core.NameScope(name_scope):
|
|
grad_squared_sums = []
|
|
for i, param in enumerate(params):
|
|
device = get_param_device(
|
|
str(param.blob), param.grad, param_to_device
|
|
)
|
|
|
|
with core.DeviceScope(device):
|
|
grad = (
|
|
param.grad
|
|
if not isinstance(
|
|
param.grad,
|
|
core.GradientSlice,
|
|
) else param.grad.values
|
|
)
|
|
|
|
grad_squared_sum_name = 'grad_{}_squared_sum'.format(i)
|
|
grad_squared_sum = model.net.SumSqrElements(
|
|
grad,
|
|
grad_squared_sum_name,
|
|
)
|
|
grad_squared_sum_cpu = model.net.EnsureCPUOutput(
|
|
grad_squared_sum
|
|
)
|
|
grad_squared_sums.append(grad_squared_sum_cpu)
|
|
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
grad_squared_full_sum = model.net.Sum(
|
|
grad_squared_sums,
|
|
'grad_squared_full_sum',
|
|
)
|
|
global_norm = model.net.Pow(
|
|
grad_squared_full_sum,
|
|
'global_norm',
|
|
exponent=0.5,
|
|
)
|
|
clip_norm = model.param_init_net.ConstantFill(
|
|
[],
|
|
'clip_norm',
|
|
shape=[],
|
|
value=float(max_gradient_norm),
|
|
)
|
|
max_norm = model.net.Max(
|
|
[global_norm, clip_norm],
|
|
'max_norm',
|
|
)
|
|
norm_ratio = model.net.Div(
|
|
[clip_norm, max_norm],
|
|
'norm_ratio',
|
|
)
|
|
return norm_ratio
|
|
|
|
|
|
def _build(
|
|
model,
|
|
optimizer,
|
|
weights_only=False,
|
|
use_param_info_optim=True,
|
|
max_gradient_norm=None,
|
|
allow_lr_injection=False,
|
|
):
|
|
param_to_device = _get_param_to_device(model)
|
|
|
|
# Validate there are no duplicate params
|
|
model.Validate()
|
|
|
|
params = []
|
|
for param_info in model.GetOptimizationParamInfo():
|
|
if weights_only and param_info.blob not in model.weights:
|
|
continue
|
|
params.append(param_info)
|
|
|
|
lr_multiplier = None
|
|
if max_gradient_norm is not None:
|
|
lr_multiplier = _calc_norm_ratio(
|
|
model,
|
|
params,
|
|
'norm_clipped_grad_update',
|
|
param_to_device,
|
|
max_gradient_norm,
|
|
)
|
|
|
|
if allow_lr_injection:
|
|
if not model.net.BlobIsDefined(_LEARNING_RATE_INJECTION):
|
|
lr_injection = model.param_init_net.ConstantFill(
|
|
[],
|
|
_LEARNING_RATE_INJECTION,
|
|
shape=[1],
|
|
value=1.0,
|
|
)
|
|
else:
|
|
lr_injection = _LEARNING_RATE_INJECTION
|
|
|
|
if lr_multiplier is None:
|
|
lr_multiplier = lr_injection
|
|
else:
|
|
lr_multiplier = model.net.Mul(
|
|
[lr_multiplier, lr_injection],
|
|
'lr_multiplier',
|
|
broadcast=1,
|
|
)
|
|
optimizer.add_lr_multiplier(lr_multiplier)
|
|
|
|
for param_info in params:
|
|
param_name = str(param_info.blob)
|
|
|
|
device = get_param_device(param_name, param_info.grad, param_to_device)
|
|
|
|
with core.DeviceScope(device):
|
|
if param_info.optimizer and use_param_info_optim:
|
|
param_info.optimizer(model.net, model.param_init_net, param_info)
|
|
else:
|
|
optimizer(model.net, model.param_init_net, param_info)
|
|
return optimizer
|
|
|
|
|
|
def add_weight_decay(model, weight_decay):
|
|
"""Adds a decay to weights in the model.
|
|
|
|
This is a form of L2 regularization.
|
|
|
|
Args:
|
|
weight_decay: strength of the regularization
|
|
"""
|
|
_build(
|
|
model,
|
|
WeightDecayBuilder(weight_decay=weight_decay),
|
|
weights_only=True,
|
|
use_param_info_optim=False,
|
|
)
|
|
|
|
|
|
def build_sgd(
|
|
model,
|
|
base_learning_rate,
|
|
max_gradient_norm=None,
|
|
allow_lr_injection=False,
|
|
**kwargs
|
|
):
|
|
sgd_optimizer = SgdOptimizer(base_learning_rate, **kwargs)
|
|
return _build(
|
|
model,
|
|
sgd_optimizer,
|
|
max_gradient_norm=max_gradient_norm,
|
|
allow_lr_injection=allow_lr_injection,
|
|
)
|
|
|
|
|
|
def build_multi_precision_sgd(
|
|
model,
|
|
base_learning_rate,
|
|
max_gradient_norm=None,
|
|
allow_lr_injection=False,
|
|
**kwargs
|
|
):
|
|
multi_prec_sgd_optimizer = MultiPrecisionSgdOptimizer(
|
|
base_learning_rate, **kwargs
|
|
)
|
|
return _build(
|
|
model,
|
|
multi_prec_sgd_optimizer,
|
|
max_gradient_norm=max_gradient_norm,
|
|
allow_lr_injection=allow_lr_injection,
|
|
)
|
|
|
|
|
|
def build_fp16_sgd(model, base_learning_rate, **kwargs):
|
|
fp16_sgd_optimizer = FP16SgdOptimizer(
|
|
base_learning_rate, **kwargs
|
|
)
|
|
return _build(model, fp16_sgd_optimizer)
|
|
|
|
|
|
def build_ftrl(model, engine="SIMD", **kwargs):
|
|
if engine == "SIMD":
|
|
assert core.IsOperator('Ftrl_ENGINE_SIMD')
|
|
assert core.IsOperator('SparseFtrl_ENGINE_SIMD')
|
|
ftrl_optimizer = FtrlOptimizer(engine=engine, **kwargs)
|
|
return _build(model, ftrl_optimizer)
|
|
|
|
|
|
def build_adagrad(
|
|
model,
|
|
base_learning_rate,
|
|
parameters=None,
|
|
max_gradient_norm=None,
|
|
allow_lr_injection=False,
|
|
**kwargs
|
|
):
|
|
adagrad_optimizer = AdagradOptimizer(alpha=base_learning_rate, **kwargs)
|
|
return _build(
|
|
model,
|
|
adagrad_optimizer,
|
|
max_gradient_norm=max_gradient_norm,
|
|
allow_lr_injection=allow_lr_injection,
|
|
)
|
|
|
|
|
|
def build_adam(
|
|
model,
|
|
base_learning_rate,
|
|
max_gradient_norm=None,
|
|
allow_lr_injection=False,
|
|
**kwargs
|
|
):
|
|
adam_optimizer = AdamOptimizer(alpha=base_learning_rate, **kwargs)
|
|
return _build(
|
|
model,
|
|
adam_optimizer,
|
|
max_gradient_norm=max_gradient_norm,
|
|
allow_lr_injection=allow_lr_injection,
|
|
)
|
|
|
|
|
|
def build_yellowfin(model, base_learning_rate=0.1, **kwargs):
|
|
yellowfin_optimizer = YellowFinOptimizer(
|
|
alpha=base_learning_rate,
|
|
**kwargs)
|
|
return _build(model, yellowfin_optimizer)
|