mirror of
https://github.com/zebrajr/pytorch.git
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2f68632a32
Summary: Add SparseNN workflow for feed. I haven't fully thought about the change needed for ads, as I added a property called 'preproc_output_schema' for LayerModelHelper. Reviewed By: xianjiec Differential Revision: D4585796 fbshipit-source-id: 060d08f4beb928e7e7863f2e563f612c358951fb
120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import schema, scope
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from caffe2.python.layers.tags import TagContext
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from collections import namedtuple
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import numpy as np
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# Some types to simplify descriptions of things traveling between ops
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IdList = schema.List(np.int64)
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IdScoreList = schema.Map(np.int64, np.float32)
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class InstantiationContext(object):
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"""
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List of contexts where layer could be instantitated
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"""
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TRAINING = 'training'
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PREDICTION = 'prediction'
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_LAYER_REGISTRY = {}
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def register_layer(name, layer):
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assert name not in _LAYER_REGISTRY, "{0} already exists".format(name)
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_LAYER_REGISTRY[name] = layer
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def layer_exists(name):
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return name in _LAYER_REGISTRY
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def create_layer(layer_name, *args, **kwargs):
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return _LAYER_REGISTRY[layer_name](*args, **kwargs)
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# TODO(amalevich): Modify this to some better struct, something closer to
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# ParameterInfo.
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LayerParameter = namedtuple(
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'LayerParameter', ['parameter', 'optimizer', 'initializer'])
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def _is_request_only_scalar(scalar):
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if len(scalar.field_metadata()) == 0:
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return False
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for metadata in scalar.field_metadata():
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if not (metadata and metadata.feature_specs and getattr(
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metadata.feature_specs, 'feature_is_request_only', False)):
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return False
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return True
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class ModelLayer(object):
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def __init__(self, model, prefix, input_record, tags=set(), **kwargs):
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self.name = model.next_layer_name(prefix)
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self.model = model
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self.kwargs = kwargs
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self.input_record = input_record
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self.request_only = True
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if len(input_record.all_scalars()) == 0:
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self.request_only = False
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for scalar in input_record.all_scalars():
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if not _is_request_only_scalar(scalar):
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self.request_only = False
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break
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self.output_schema = None
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self.tags = set(tags)
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self.tags.update(TagContext.current().tags)
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self.params = []
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def get_type(self):
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return self.__class__.__name__
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def get_output_schema(self):
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assert self.output_schema is not None, "Schema is not initialized"
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return self.output_schema
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def get_parameters(self):
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return self.params
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def get_fp16_compatible_parameters(self):
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"""Return a subset of parameters which can be converted to fp16"""
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return []
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def get_memory_usage(self):
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return 0
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def add_operators(self, net, init_net=None,
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context=InstantiationContext.TRAINING):
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# Namescope below should warranty that all intermediate blobs will be
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# assiciated with the layer that produces them
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with scope.NameScope(self.name):
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if context != InstantiationContext.PREDICTION:
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assert init_net,\
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"Only prediction context can be used without init_net"
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if init_net:
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for param in self.params:
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# TODO(amalevich): Either return back to lambdas, that add
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# all params (looks a bit safer and breaking less
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# abstractions) or extend Net interface to this type of
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# operations better
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init_net._net.op.extend([param.initializer])
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if context == InstantiationContext.TRAINING:
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self.add_train_ops(net)
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else:
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self.add_ops(net)
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def add_ops(self, net):
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raise NotImplementedError
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def add_train_ops(self, net):
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# Default train layer implementation is completely matching predict
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# layer implementation.
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self.add_ops(net)
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