mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
9edbafe0de
Instead of using hard-coded rules or rely on gpu_strategy to mark full sync data parallel ops, we need some generic rules that is applicable to both the single and distributed setting.
608 lines
22 KiB
Python
608 lines
22 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 layer_model_helper
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# Module caffe2.python.layer_model_helper
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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, model_helper, schema, scope
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from caffe2.python.modeling.parameter_info import (
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ParameterInfo,
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)
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from caffe2.python.modeling.parameter_sharing import (
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parameter_sharing_context,
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)
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from caffe2.python.modeling.net_modifier import NetModifier
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from caffe2.python.optimizer import get_param_device
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from caffe2.python.regularizer import Regularizer
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from caffe2.python.layers import layers
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from caffe2.proto import caffe2_pb2
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from future.utils import viewitems, viewvalues
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import logging
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import numpy as np
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import six
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import copy
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logger = logging.getLogger(__name__)
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class LayerModelHelper(model_helper.ModelHelper):
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"""
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Model helper for building models on top of layers abstractions.
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Each layer is the abstraction that is higher level than Operator. Layer
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is responsible for ownership of it's own parameters and can easily be
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instantiated in multiple nets possible with different sets of ops.
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As an example: one can easily instantiate predict and train nets from
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the same set of layers, where predict net will have subset of the
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operators from train net.
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"""
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def __init__(self, name, input_feature_schema, trainer_extra_schema,
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keep_blobs=False):
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''' TODO(amalevich): more documnetation on input args
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'''
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super(LayerModelHelper, self).__init__(name=name)
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self._layer_names = set()
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self._layers = []
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self._param_to_shape = {}
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# seed default
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self._seed = None
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self._sequence_seed = True
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# optimizer bookkeeping
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self.param_to_optim = {}
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self.param_to_reg = {}
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self._default_optimizer = None
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self._loss = None
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self._output_schema = None
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self._post_grad_net_modifiers = []
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self._final_net_modifiers = []
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# breakdown map; breakdown features are categorical (like dense) but not
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# necessarily used to represent data for training
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self._breakdown_map = None
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# Connect Schema to self.net. That particular instance of schmea will be
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# use for generation of the Layers accross the network and would be used
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# for connection with Readers.
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self._input_feature_schema = schema.NewRecord(
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self.net,
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input_feature_schema
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) if not keep_blobs else input_feature_schema.clone()
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self._trainer_extra_schema = schema.NewRecord(
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self.net,
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trainer_extra_schema
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) if not keep_blobs else trainer_extra_schema.clone()
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self._metrics_schema = schema.Struct()
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self._preproc_output_schema = None
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self._init_global_constants()
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self.param_init_net = self.create_init_net('param_init_net')
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self._initialize_params = True
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def clear_output_schema(self):
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self._output_schema = None
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def set_initialize_params(self, initialize_params):
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self._initialize_params = initialize_params
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def add_metric_field(self, name, value):
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assert name not in self._metrics_schema.fields, (
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"Try to add metric field twice: {}".format(name))
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self._metrics_schema = self._metrics_schema + schema.Struct(
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(name, value)
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)
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@staticmethod
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def _get_global_constant_initializer_op(
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blob_name, array=None, dtype=None, initializer=None
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):
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# to add a global constant to model, one first need to get the
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# initializer
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if array is not None:
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assert initializer is None,\
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"Only one from array and initializer should be specified"
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if dtype is None:
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array = np.array(array)
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else:
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array = np.array(array, dtype=dtype)
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# TODO: make GivenTensor generic
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op_name = None
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if array.dtype == np.int32:
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op_name = 'GivenTensorIntFill'
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elif array.dtype == np.int64:
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op_name = 'GivenTensorInt64Fill'
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elif array.dtype == np.str:
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op_name = 'GivenTensorStringFill'
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elif array.dtype == np.bool:
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op_name = 'GivenTensorBoolFill'
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else:
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op_name = 'GivenTensorFill'
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def initializer(blob_name):
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return core.CreateOperator(
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op_name, [],
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blob_name,
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shape=array.shape,
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values=array.flatten().tolist()
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)
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else:
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assert initializer is not None
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initializer_op = initializer(blob_name)
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return initializer_op
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def add_global_constant(
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self, name, array=None, dtype=None, initializer=None
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):
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assert isinstance(name, six.string_types), (
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'name should be a string as we are using it as map key')
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# This is global namescope for constants. They will be created in all
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# init_nets and there should be very few of them.
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assert name not in self.global_constants, \
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"%s already added in global_constants" % name
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blob_name = self.net.NextBlob(name)
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self.global_constants[name] = blob_name
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initializer_op = LayerModelHelper._get_global_constant_initializer_op(
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blob_name, array, dtype, initializer
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)
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assert blob_name not in self.global_constant_initializers, \
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"there is already a initializer op associated with blob %s" % \
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blob_name
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self.global_constant_initializers[blob_name] = initializer_op
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return blob_name
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def maybe_add_global_constant(self, name, *args, **kwargs):
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# To ad hoc add new global constants without duplication
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# if the name was already registered in global_constants, it will not be
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# added even if the intended value is different from its original value
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def op_equal(operator1, operator2):
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o1 = copy.deepcopy(operator1)
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o2 = copy.deepcopy(operator2)
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# debug_info is supposed to be different, and we don't need to
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# compare debug_info
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if hasattr(o1, 'debug_info'):
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o1.debug_info = ''
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if hasattr(o2, 'debug_info'):
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o2.debug_info = ''
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return o1 == o2
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if name in self.global_constants:
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blob_name = self.global_constants[name]
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initializer_op = \
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LayerModelHelper._get_global_constant_initializer_op(
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blob_name, *args, **kwargs
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)
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# check if the original initializer is the same as the one intended
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# now
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assert op_equal(initializer_op,
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self.global_constant_initializers[blob_name]), \
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"conflict initializers for global constant %s, " \
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"previous %s, now %s" % (
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blob_name, str(initializer_op),
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str(self.global_constant_initializers[blob_name]))
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return blob_name
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return self.add_global_constant(name, *args, **kwargs)
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def _init_global_constants(self):
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self.global_constants = {}
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self.global_constant_initializers = {}
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self.add_global_constant('ONE', 1.0)
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self.add_global_constant('ZERO', 0.0)
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self.add_global_constant('ZERO_RANGE', [0, 0], dtype='int32')
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def _add_global_constants(self, init_net):
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for initializer_op in viewvalues(self.global_constant_initializers):
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init_net._net.op.extend([initializer_op])
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def create_init_net(self, name):
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init_net = core.Net(name)
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self._add_global_constants(init_net)
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return init_net
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def _validate_param_shape(self, param_name, shape):
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if param_name not in self._param_to_shape:
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return
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ref_shape = self._param_to_shape[param_name]
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if shape != ref_shape:
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raise ValueError(
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"Got inconsistent shapes between shared parameters "
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"when trying to map a blob in scope {0} to {1}. ref_shape : "
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" {2}, shape : {3}".format(
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scope.CurrentNameScope(), param_name, ref_shape, shape)
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)
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def create_param(self, param_name, shape, initializer, optimizer=None,
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ps_param=None, regularizer=None):
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if isinstance(param_name, core.BlobReference):
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param_name = str(param_name)
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elif isinstance(param_name, six.string_types):
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# Parameter name will be equal to current Namescope that got
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# resolved with the respect of parameter sharing of the scopes.
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param_name = parameter_sharing_context.get_parameter_name(
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param_name)
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else:
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raise "Unsupported type for param_name"
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param_blob = core.BlobReference(param_name)
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if len(initializer) == 1:
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init_op_args = {}
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else:
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assert len(initializer) == 2
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init_op_args = copy.deepcopy(initializer[1])
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if shape is not None:
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assert 'shape' not in init_op_args
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init_op_args.update({'shape': shape})
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initializer_op = None
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if self._initialize_params:
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initializer_op = core.CreateOperator(
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initializer[0],
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[],
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param_blob,
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**init_op_args
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)
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param = layers.LayerParameter(
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parameter=param_blob,
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initializer=initializer_op,
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optimizer=optimizer,
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ps_param=ps_param,
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regularizer=regularizer
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)
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self._validate_param_shape(param_name, shape)
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self._param_to_shape[param_name] = shape
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return param
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def next_layer_name(self, prefix):
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base_name = core.ScopedName(prefix)
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name = base_name
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index = 0
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while name in self._layer_names:
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name = base_name + '_auto_' + str(index)
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index += 1
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self._layer_names.add(name)
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return name
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def add_layer(self, layer):
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self._layers.append(layer)
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for param in layer.get_parameters():
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assert isinstance(param.parameter, core.BlobReference)
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self.param_to_optim[str(param.parameter)] = \
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param.optimizer or self.default_optimizer
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self.params.append(param.parameter)
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if isinstance(param, layers.LayerParameter):
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self.param_to_reg[param.parameter] = param.regularizer
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elif isinstance(param, ParameterInfo):
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# TODO:
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# Currently, LSTM and RNNcells, which use ModelHelper instead of
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# LayerModelHelper as super class, are called in pooling_methods
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# In ModelHelper, regularization is not supported in create_param
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# We will unify the way of create_param of ModelHelper and
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# LayerModelHelper in the future.
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logger.info('regularization is unsupported for ParameterInfo object')
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else:
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raise ValueError(
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'unknown object type besides ParameterInfo and LayerParameter: {}'
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.format(param)
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)
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# The primary value of adding everything to self.net - generation of the
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# operators right away, i.e. if error happens it'll be detected
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# immediately. Other than this - create_x_net should be called.
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layer.add_operators(self.net, self.param_init_net)
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return layer.output_schema
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def get_parameter_blobs(self):
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param_blobs = []
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for layer in self._layers:
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for param in layer.get_parameters():
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param_blobs.append(param.parameter)
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return param_blobs
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def add_post_grad_net_modifiers(self, modifier):
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assert modifier not in self._post_grad_net_modifiers,\
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"{0} is already in {1}".format(modifier, self._post_grad_net_modifiers)
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assert isinstance(modifier, NetModifier),\
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"{} has to be a NetModifier instance".format(modifier)
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self._post_grad_net_modifiers.append(modifier)
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def add_final_net_modifiers(self, modifier):
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assert modifier not in self._final_net_modifiers,\
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"{0} is already in {1}".format(modifier, self._final_net_modifiers)
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assert isinstance(modifier, NetModifier),\
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"{} has to be a NetModifier instance".format(modifier)
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self._final_net_modifiers.append(modifier)
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@property
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def seed(self):
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return self._seed
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@property
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def sequence_seed(self):
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return self._sequence_seed
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def store_seed(self, seed, sequence_seed=True):
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# Store seed config that will be applied to each op in the net.
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self._seed = seed
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# If sequence_seed is True, the i-th op has rand_seed=`seed + i`
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self._sequence_seed = sequence_seed
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def apply_seed(self, net):
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if self._seed:
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net.set_rand_seed(self._seed, self._sequence_seed)
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@property
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def default_optimizer(self):
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return self._default_optimizer
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@default_optimizer.setter
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def default_optimizer(self, optimizer):
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self._default_optimizer = optimizer
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@property
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def input_feature_schema(self):
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return self._input_feature_schema
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@property
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def trainer_extra_schema(self):
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return self._trainer_extra_schema
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@property
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def metrics_schema(self):
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"""
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Returns the schema that represents model output that should be used for
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metric reporting.
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During the training/evaluation this schema will be appended to the
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schema that represents model output.
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"""
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return self._metrics_schema
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@property
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def output_schema(self):
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assert self._output_schema is not None
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return self._output_schema
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@output_schema.setter
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def output_schema(self, schema):
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assert self._output_schema is None
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self._output_schema = schema
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@property
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def preproc_output_schema(self):
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if self._preproc_output_schema is None:
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self._preproc_output_schema = self.input_feature_schema + self.trainer_extra_schema
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return self._preproc_output_schema
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@preproc_output_schema.setter
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def preproc_output_schema(self, schema):
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assert self._preproc_output_schema is None
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self._preproc_output_schema = schema
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@property
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def loss(self):
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assert self._loss is not None
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return self._loss
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@loss.setter
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def loss(self, loss):
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assert self._loss is None
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self._loss = loss
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def has_loss(self):
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return self._loss is not None
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def add_loss(self, loss, name='unnamed'):
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assert loss is not None, "Added loss should not be None"
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assert isinstance(loss, schema.Scalar) or isinstance(
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loss, schema.Struct
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), "Added loss should be a scalar or a struct"
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if self._loss is None:
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self._loss = schema.Struct((name, loss))
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else:
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prefix_base = name + '_auto_'
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index = 0
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prefix = name
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while prefix in self._loss:
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prefix = prefix_base + str(index)
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index += 1
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loss_struct = schema.Struct((prefix, loss))
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self._loss = self._loss + loss_struct
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def add_output_schema(self, name, value):
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assert value is not None, \
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'Added output schema {} should not be None'.format(name)
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assert isinstance(value, schema.Scalar) or \
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isinstance(value, schema.Struct), \
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'Added output schema {} should be a scalar or a struct.\n\
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Now it is {}.'.format(name, type(value))
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if self._output_schema is None: # be the first field
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self._output_schema = schema.Struct((name, value))
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else: # merge with other fields
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assert name not in self._output_schema.fields, \
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'Output Schema Field {} already exists'.format(name)
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self._output_schema = \
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self._output_schema + schema.Struct((name, value))
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def add_trainer_extra_schema(self, trainer_extra_schema):
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trainer_extra_record = schema.NewRecord(self.net, trainer_extra_schema)
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self._trainer_extra_schema += trainer_extra_record
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def __getattr__(self, layer):
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if layer.startswith('__'):
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raise AttributeError(layer)
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# TODO(amalevich): Add add support for ifbpy inline documentation
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if layers.layer_exists(layer):
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def wrapper(*args, **kwargs):
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new_layer = layers.create_layer(layer, self, *args, **kwargs)
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if kwargs.get("output_to_metrics", False):
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new_layer.export_output_for_metrics()
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if kwargs.get("params_to_metrics", False):
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new_layer.export_params_for_metrics()
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return self.add_layer(new_layer)
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return wrapper
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elif core.IsOperator(layer):
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def wrapper(*args, **kwargs):
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def apply_operator(net, in_record, out_record, **kwargs):
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# TODO(amalevich): Switch to net.operator as soon as it gets
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# landed
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net.__getattr__(layer)(in_record.field_blobs(),
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out_record.field_blobs(),
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**kwargs)
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if 'name' not in kwargs:
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kwargs['name'] = layer
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new_layer = layers.create_layer(
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'Functional',
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self, *args, function=apply_operator,
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**kwargs
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)
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if kwargs.get("output_to_metrics", False):
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new_layer.export_output_for_metrics()
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if kwargs.get("params_to_metrics", False):
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new_layer.export_params_for_metrics()
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return self.add_layer(new_layer)
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return wrapper
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else:
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raise ValueError(
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"Trying to create non-registered layer: {}".format(layer))
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@property
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def layers(self):
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return self._layers
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def apply_regularizers_on_loss(
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self,
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train_net,
|
|
train_init_net,
|
|
blob_to_device=None,
|
|
):
|
|
for param, regularizer in viewitems(self.param_to_reg):
|
|
if regularizer is None or regularizer.apply_after_optimizer:
|
|
continue
|
|
assert isinstance(regularizer, Regularizer)
|
|
added_loss_blob = regularizer(train_net, train_init_net, param)
|
|
self.add_loss(
|
|
schema.Scalar(blob=added_loss_blob),
|
|
str(added_loss_blob)
|
|
)
|
|
|
|
def apply_regularizers_after_optimizer(
|
|
self,
|
|
train_net,
|
|
train_init_net,
|
|
grad_map,
|
|
blob_to_device=None,
|
|
):
|
|
for param, regularizer in viewitems(self.param_to_reg):
|
|
if regularizer is None or not regularizer.apply_after_optimizer:
|
|
continue
|
|
assert isinstance(regularizer, Regularizer)
|
|
regularizer(
|
|
train_net, train_init_net, param, grad_map.get(str(param)))
|
|
|
|
def apply_post_grad_net_modifiers(
|
|
self,
|
|
trainer_net,
|
|
trainer_init_net,
|
|
grad_map,
|
|
blob_to_device=None,
|
|
):
|
|
for modifier in self._post_grad_net_modifiers:
|
|
modifier(trainer_net, trainer_init_net, grad_map,
|
|
blob_to_device=blob_to_device)
|
|
|
|
def apply_final_net_modifiers(
|
|
self,
|
|
trainer_net,
|
|
trainer_init_net,
|
|
grad_map,
|
|
blob_to_device=None,
|
|
):
|
|
for modifier in self._final_net_modifiers:
|
|
modifier(trainer_net, trainer_init_net, grad_map,
|
|
blob_to_device=blob_to_device)
|
|
|
|
def apply_optimizers(
|
|
self,
|
|
train_net,
|
|
train_init_net,
|
|
grad_map,
|
|
blob_to_device=None,
|
|
):
|
|
CPU = core.DeviceOption(caffe2_pb2.CPU)
|
|
# if given, blob_to_device is a map from blob to device_option
|
|
blob_to_device = blob_to_device or {}
|
|
for param, optimizer in viewitems(self.param_to_optim):
|
|
assert optimizer is not None, \
|
|
"default optimizer must have been set in add_layer"
|
|
# note that not all params has gradient and thus we sent None if
|
|
# gradient does not exists
|
|
device = get_param_device(
|
|
param,
|
|
grad_map.get(str(param)),
|
|
param_to_device=blob_to_device,
|
|
default_device=CPU,
|
|
)
|
|
with core.DeviceScope(device):
|
|
optimizer(
|
|
train_net, train_init_net, param, grad_map.get(str(param)))
|
|
|
|
def _GetOne(self):
|
|
return self.global_constants['ONE']
|
|
|
|
# An optimizer which allows us to do NO optimization
|
|
def NoOptim(self, *args, **kwargs):
|
|
pass
|
|
|
|
@property
|
|
def breakdown_map(self):
|
|
return self._breakdown_map
|
|
|
|
@breakdown_map.setter
|
|
def breakdown_map(self, breakdown_map):
|
|
# TODO(xlwang): provide more rich feature information in breakdown_map;
|
|
# and change the assertion accordingly
|
|
assert isinstance(breakdown_map, dict)
|
|
assert all(isinstance(k, six.string_types) for k in breakdown_map)
|
|
assert sorted(list(breakdown_map.values())) == range(len(breakdown_map))
|
|
self._breakdown_map = breakdown_map
|