mirror of
https://github.com/zebrajr/pytorch.git
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2caca70a37
Summary: (Work in progress). This diff will allow shifting of activations to other GPUs, in case the model does not fit into memory. To see the API, check the code in data_parallel_model_test, which tests shifting two activations from 0 and 1 to gpu 4, and from gpu 2 and 3 to gpu 5. I will need to further test on ResNets, and probablly add copy operations to handle device change points. Reviewed By: asaadaldien Differential Revision: D5591674 fbshipit-source-id: eb12d23651a56d64fa4db91090c6474218705270
1830 lines
65 KiB
Python
1830 lines
65 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 data_parallel_model
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# Module caffe2.python.data_parallel_model
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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 collections import OrderedDict
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from future.utils import viewitems, viewkeys, viewvalues
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import logging
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import copy
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from caffe2.python import model_helper, dyndep, scope, workspace, core, memonger
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from caffe2.proto import caffe2_pb2
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import numpy as np
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dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/nccl:nccl_ops")
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dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/gloo:gloo_ops")
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dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/gloo:gloo_ops_gpu")
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log = logging.getLogger("data_parallel_model")
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log.setLevel(logging.INFO)
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_DEFAULT_TIMEOUT_SEC = 30
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def Parallelize_GPU(*args, **kwargs):
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kwargs['cpu_device'] = False
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Parallelize(*args, **kwargs)
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def Parallelize_CPU(*args, **kwargs):
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kwargs['cpu_device'] = True
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Parallelize(*args, **kwargs)
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def Parallelize(
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model_helper_obj,
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input_builder_fun,
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forward_pass_builder_fun,
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param_update_builder_fun=None,
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optimizer_builder_fun=None,
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post_sync_builder_fun=None,
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devices=None,
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rendezvous=None,
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net_type='dag',
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broadcast_computed_params=True,
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optimize_gradient_memory=False,
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dynamic_memory_management=False,
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blobs_to_keep=None,
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use_nccl=False,
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max_concurrent_distributed_ops=16,
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cpu_device=False,
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num_threads_per_device=4,
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shared_model=False,
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):
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'''
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Function to create a model that can run on many GPUs or CPUs.
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model_helper_obj: an object of ModelHelper
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input_builder_fun:
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Function that adds the input operators
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Note: Remember to instantiate reader outside of this
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function so all devices share same reader object.
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Signature: input_builder_fun(model)
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forward_pass_builder_fun:
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Function to add the operators to the model.
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Must return list of loss-blob references that
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are used to build the gradient. Loss scale parameter
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is passed, as you should scale the loss of your model
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by 1.0 / the total number of devices.
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Signature: forward_pass_builder_fun(model, loss_scale)
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param_update_builder_fun:
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Function that adds operators that are run after
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gradient update, such as updating the weights and
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weight decaying. This is called for each GPU separately.
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Signature: param_update_builder_fun(model)
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optimizer_builder_fun:
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Alternative to param_update_builder_fun, allows one
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to add an optimizer for the whole model. Called only
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once, without name or devicescope.
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post_sync_builder_fun:
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Function applied after initial parameter sync has been
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completed, such as keeping multi-precision parameters
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in sync.
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Signature: post_sync_builder_fun(model)
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devices: List of GPU ids, such as [0, 1, 2, 3],
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rendezvous: used for rendezvous in distributed computation, if None
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then only one node is used. To create rendezvous,
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use <TBD>.
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net_type: Network type
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optimize_gradient_memory: whether to apply 'memonger' to share blobs
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shared_model (only for CPU) use same parameters on each device
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in gradient computation to reduce memory footprint.
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dynamic_memory_management: Whether to apply dynamic memory optimization
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by freeing unused blobs. The underlying (de)allocation
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uses cached allocator. For GPU training PLEASE MAKE SURE
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caffe2_cuda_memory_pool is set.
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blobs_to_keep : A list of blob names to keep and don't free during
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dynamic memory optimization (for example loss blob).
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cpu_device Use CPU instead of GPU.
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'''
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assert scope.CurrentDeviceScope() is None \
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or scope.CurrentDeviceScope().device_type == caffe2_pb2.CPU, \
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"Parallelize must be called without device-scope, \
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device scope was: {}".format(scope.CurrentDeviceScope())
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if devices is None:
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devices = list(range(0, workspace.NumCudaDevices())),
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if not cpu_device:
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for gpu in devices:
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if gpu >= workspace.NumCudaDevices():
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log.warning("** Only {} GPUs available, GPUs {} requested".format(
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workspace.NumCudaDevices(), devices))
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break
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model_helper_obj._device_type = caffe2_pb2.CUDA
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model_helper_obj._device_prefix = "gpu"
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model_helper_obj._shared_model = False
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device_name = "GPU"
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assert shared_model is False, "Shared model only supported on CPU"
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else:
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model_helper_obj._device_type = caffe2_pb2.CPU
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model_helper_obj._device_prefix = "cpu"
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device_name = "CPU"
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model_helper_obj._shared_model = shared_model
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if shared_model and rendezvous is not None:
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assert "Shared model only supported on single-node currently"
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log.info("Parallelizing model for devices: {}".format(devices))
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extra_workers = 8 if rendezvous is not None else 0 # best-guess
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num_workers = len(devices) * num_threads_per_device + extra_workers
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max_concurrent_distributed_ops =\
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min(max_concurrent_distributed_ops, num_workers - 1)
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model_helper_obj.net.Proto().num_workers = num_workers
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model_helper_obj.net.Proto().type = net_type
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# Store some information in the model -- a bit ugly
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model_helper_obj._devices = devices
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model_helper_obj._rendezvous = rendezvous
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model_helper_obj._broadcast_context = None
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model_helper_obj._grad_names = []
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assert isinstance(model_helper_obj, model_helper.ModelHelper)
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# Keep track of params that were in the model before: they are not
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# data parallel, so we need to handle them separately
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non_datapar_params = copy.copy(model_helper_obj.params)
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# Add input and model
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log.info("Create input and model training operators")
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losses_by_gpu = {}
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num_shards = 1 if rendezvous is None else rendezvous['num_shards']
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loss_scale = 1.0 / (len(devices) * num_shards)
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has_parameter_updates = param_update_builder_fun is not None or \
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optimizer_builder_fun is not None
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assert not (
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param_update_builder_fun is not None and
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optimizer_builder_fun is not None
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), 'Can only specify one of param_update_builder_fun, optimizer_builder_fun'
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# Check that a model that is used for validation/testing has
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# init_params False, otherwise running the param init net will overwrite
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# synchronized values by the training net
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if not has_parameter_updates and model_helper_obj.init_params:
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log.warning('')
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log.warning("############# WARNING #############")
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log.warning("Model {}/{} is used for testing/validation but".format(
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model_helper_obj.name, model_helper_obj))
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log.warning("has init_params=True!")
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log.warning("This can conflict with model training.")
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log.warning("Please ensure model = ModelHelper(init_params=False)")
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log.warning('####################################')
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log.warning('')
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# TODO: make into assert
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for device in devices:
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device_opt = core.DeviceOption(model_helper_obj._device_type, device)
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with core.DeviceScope(device_opt):
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with core.NameScope("{}_{}".format(model_helper_obj._device_prefix,
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device)):
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log.info("Model for {} : {}".format(device_name, device))
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input_builder_fun(model_helper_obj)
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losses = forward_pass_builder_fun(model_helper_obj, loss_scale)
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# Losses are not needed for test net
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if has_parameter_updates:
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assert isinstance(losses, list), \
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'Model builder function must return list of loss blobs'
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for loss in losses:
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assert isinstance(loss, core.BlobReference), \
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'Model builder func must return list of loss blobs'
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losses_by_gpu[device] = losses
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_ValidateParams(model_helper_obj.params)
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# Create parameter map
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model_helper_obj._device_grouped_blobs =\
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_GroupByDevice(model_helper_obj, devices,
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model_helper_obj.params, non_datapar_params)
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# computed params
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computed_params_grouped =\
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_GroupByDevice(model_helper_obj, devices,
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model_helper_obj.GetComputedParams(''), [])
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model_helper_obj._device_grouped_blobs.update(computed_params_grouped)
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model_helper_obj._param_names =\
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list(viewkeys(model_helper_obj._device_grouped_blobs))
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model_helper_obj._computed_param_names =\
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list(viewkeys(computed_params_grouped))
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if not has_parameter_updates:
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log.info("Parameter update function not defined --> only forward")
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_InferBlobDevice(model_helper_obj)
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return
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log.info("Adding gradient operators")
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_AddGradientOperators(devices, model_helper_obj, losses_by_gpu)
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_ValidateParams(model_helper_obj.params)
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# Group gradients by device and register to blob lookup
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param_to_grad = model_helper_obj.param_to_grad
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grads_ordered = [param_to_grad[p] for p in
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model_helper_obj.params if p in param_to_grad]
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non_datapar_grads = [param_to_grad[p] for p in non_datapar_params]
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gradients_grouped = _GroupByDevice(
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model_helper_obj,
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devices,
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grads_ordered,
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non_datapar_grads
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)
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model_helper_obj._device_grouped_blobs.update(gradients_grouped)
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model_helper_obj._grad_names = list(viewkeys(gradients_grouped))
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model_helper_obj._losses_by_gpu = losses_by_gpu
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_InferBlobDevice(model_helper_obj)
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log.info("Add gradient all-reduces for SyncSGD")
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if broadcast_computed_params:
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_BroadcastComputedParams(devices, model_helper_obj, rendezvous, use_nccl)
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if len(model_helper_obj._grad_names) > 0:
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# Gradients in reverse order
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reverse_ordered_grads = _GetReverseOrderedGrads(model_helper_obj)
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assert(len(reverse_ordered_grads) > 0)
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_AllReduceBlobs(
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reverse_ordered_grads,
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devices,
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model_helper_obj,
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model_helper_obj.net,
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rendezvous,
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use_nccl,
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max_concurrent_distributed_ops,
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)
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else:
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log.info("NOTE: Param builder function did not create any parameters.")
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log.info("Post-iteration operators for updating params")
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num_shards = 1 if rendezvous is None else rendezvous['num_shards']
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all_params = set(model_helper_obj.GetParams(''))
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if shared_model:
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_PruneParametersForSharing(model_helper_obj)
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if param_update_builder_fun is not None:
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for device in devices:
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device_opt = core.DeviceOption(model_helper_obj._device_type, device)
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with core.DeviceScope(device_opt):
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with core.NameScope(
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"{}_{}".format(model_helper_obj._device_prefix, device)
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):
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param_update_builder_fun(model_helper_obj)
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else:
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log.info("Calling optimizer builder function")
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optimizer = optimizer_builder_fun(model_helper_obj)
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model_helper_obj._optimizer = optimizer
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(sync_blobs, sync_names) = _ComputeBlobsToSync(model_helper_obj)
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sync_blobs_grouped = _GroupByDevice(
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model_helper_obj,
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devices,
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sync_blobs,
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[],
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)
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model_helper_obj._device_grouped_blobs.update(sync_blobs_grouped)
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_InferBlobDevice(model_helper_obj)
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_AnalyzeOperators(model_helper_obj)
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# Configure dagnet to run with only one worker on the first iteration,
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# to prevent concurrency problems with allocs and nccl.
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arg = model_helper_obj.Proto().arg.add()
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arg.name = "first_iter_only_one_worker"
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arg.i = 1
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# Add initial parameter syncs
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log.info("Add initial parameter sync")
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_SyncAllParams(
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devices,
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model_helper_obj,
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model_helper_obj.param_init_net,
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model_helper_obj.param_init_net,
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rendezvous,
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sync_names,
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max_concurrent_distributed_ops=1
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)
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# Handle any operations that need to be done after parameter sync
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# i.e. making sure multi-precision copies of parameters are up-to-date
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if post_sync_builder_fun is not None:
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for device in devices:
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device_opt = core.DeviceOption(model_helper_obj._device_type, device)
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with core.DeviceScope(device_opt):
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with core.NameScope(
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"{}_{}".format(model_helper_obj._device_prefix, device)
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):
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post_sync_builder_fun(model_helper_obj)
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assert not (optimize_gradient_memory and dynamic_memory_management), \
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"""It is not advised to use gradient optimization ('memonger')
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with dynamic memory management."""
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if optimize_gradient_memory:
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_OptimizeGradientMemorySimple(model_helper_obj, losses_by_gpu, devices)
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if dynamic_memory_management:
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_AddDynamicMemoryOptimization(model_helper_obj, blobs_to_keep, devices)
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model_helper_obj._data_parallel_model_init_nets = [
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model_helper_obj.param_init_net,
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]
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model_helper_obj._data_parallel_model_nets = [model_helper_obj.net]
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if shared_model:
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_RemapParameterBlobsForSharedModel(model_helper_obj, all_params)
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def Parallelize_GPU_BMUF(*args, **kwargs):
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kwargs['cpu_device'] = False
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Parallelize_BMUF(*args, **kwargs)
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def Parallelize_CPU_BMUF(*args, **kwargs):
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kwargs['cpu_device'] = True
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Parallelize_BMUF(*args, **kwargs)
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def Parallelize_BMUF(
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model_helper_obj,
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input_builder_fun,
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forward_pass_builder_fun,
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param_update_builder_fun,
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block_learning_rate=1.0,
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block_momentum=None,
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devices=None,
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rendezvous=None,
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net_type='dag',
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master_device=None,
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use_nccl=False,
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nesterov=False,
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optimize_gradient_memory=False,
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reset_momentum_sgd=False,
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warmup_iterations=None,
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max_concurrent_distributed_ops=4,
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add_blobs_to_sync=None,
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num_threads_per_device=4,
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cpu_device=False
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):
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'''
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Function to create model that run on many GPUs and creates a net for
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parameter_updates that can be run independently for number of iterations
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then followed by another net that runs once to compute the final parameter
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updates according to block wise model update filtering rule described
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in : Scalable Training of Deep Learning Machines by Incremental Block
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Training with Intra-block Parallel Optimization and Blockwise Model-Update
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Filtering (ICASSP 2016).
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'''
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assert scope.CurrentDeviceScope() is None \
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or scope.CurrentDeviceScope().device_type == caffe2_pb2.CPU, \
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"Parallelize must be called without device-scope, \
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device scope was: {}".format(scope.CurrentDeviceScope())
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assert isinstance(model_helper_obj, model_helper.ModelHelper)
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if devices is None:
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devices = list(range(0, workspace.NumCudaDevices()))
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if master_device is None:
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master_device = devices[0]
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if not cpu_device:
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for gpu in devices:
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if gpu >= workspace.NumCudaDevices():
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log.warning("** Only {} GPUs available, GPUs {} requested".format(
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workspace.NumCudaDevices(), devices))
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break
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model_helper_obj._device_type = caffe2_pb2.CUDA
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model_helper_obj._device_prefix = "gpu"
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else:
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model_helper_obj._device_type = caffe2_pb2.CPU
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model_helper_obj._device_prefix = "cpu"
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model_helper_obj._devices = devices
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model_helper_obj._rendezvous = rendezvous
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model_helper_obj._broadcast_context = None
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model_helper_obj._shared_model = False
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master_dev_opt = core.DeviceOption(model_helper_obj._device_type, master_device)
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# question: rendezvous structure
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num_shards = rendezvous['num_shards'] if rendezvous else 1
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# num_devices is #devices across all machines
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num_devices = len(devices) * num_shards
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# num_workers is #threads to execute the DAG per shard
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num_workers = num_threads_per_device * len(devices)
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if rendezvous:
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num_workers += 8
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loss_scale = 1.0 / num_devices
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if block_momentum is None:
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block_momentum = 1.0 - 1.0 / num_devices
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max_concurrent_distributed_ops = min(
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max_concurrent_distributed_ops,
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num_workers - 1
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)
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model_helper_obj.net.Proto().num_workers = num_workers
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model_helper_obj.net.Proto().type = net_type
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# A net for initializing global model parameters. Its called once in the
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# same step as net parameters initialization.
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model_helper_obj._global_model_init_net = core.Net('global_model_init')
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model_helper_obj._global_model_init_net.Proto().type = net_type
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model_helper_obj._global_model_init_net.Proto().num_workers = \
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num_workers
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# A net for computing final parameter updates. Its will run once after
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# running net (local models updates) for `num_local_iterations` times.
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model_helper_obj._global_model_param_updates_net = core.Net('global_model')
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model_helper_obj._global_model_param_updates_net.Proto().type = net_type
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model_helper_obj._global_model_param_updates_net.Proto().num_workers = \
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num_workers
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def _v(param):
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return "{}_v".format(param)
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def _g(param):
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return "{}_g".format(param)
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|
|
def _v_prev(param):
|
|
return "{}_prev".format(param)
|
|
|
|
# Keep track of params that were in the model before: they are not
|
|
# data parallel, so we need to handle them separately
|
|
non_datapar_params = copy.copy(model_helper_obj.params)
|
|
model_helper_obj._losses_by_gpu = {}
|
|
|
|
def _InitializeModels(gpu_id):
|
|
input_builder_fun(model_helper_obj)
|
|
loss = forward_pass_builder_fun(model_helper_obj, loss_scale)
|
|
model_helper_obj._losses_by_gpu[gpu_id] = loss
|
|
_ForEachDevice(
|
|
devices,
|
|
_InitializeModels,
|
|
device_type=model_helper_obj._device_type,
|
|
device_prefix=model_helper_obj._device_prefix,
|
|
scoped=True
|
|
)
|
|
_ValidateParams(model_helper_obj.params)
|
|
|
|
model_helper_obj._device_grouped_blobs =\
|
|
_GroupByDevice(model_helper_obj, devices,
|
|
model_helper_obj.params, non_datapar_params)
|
|
|
|
model_helper_obj._param_names =\
|
|
list(viewkeys(model_helper_obj._device_grouped_blobs))
|
|
|
|
_AddGradientOperators(
|
|
devices, model_helper_obj, model_helper_obj._losses_by_gpu
|
|
)
|
|
_ValidateParams(model_helper_obj.params)
|
|
|
|
_InferBlobDevice(model_helper_obj)
|
|
|
|
def _InitializeParamUpdate(gpu_id):
|
|
param_update_builder_fun(model_helper_obj)
|
|
_ForEachDevice(
|
|
devices,
|
|
_InitializeParamUpdate,
|
|
device_type=model_helper_obj._device_type,
|
|
device_prefix=model_helper_obj._device_prefix,
|
|
scoped=True
|
|
)
|
|
|
|
model_parameter_names = list(
|
|
viewkeys(model_helper_obj._device_grouped_blobs)
|
|
)
|
|
if warmup_iterations is not None:
|
|
model_helper_obj._warmup_iterations = warmup_iterations
|
|
# A net for broadcasting gpu-0 (master shard) parameters after
|
|
# running net for `warmup_iterartions`.
|
|
model_helper_obj._warmup_broadcast = core.Net('warmup-broadcast')
|
|
model_helper_obj._warmup_broadcast.Proto().type = net_type
|
|
model_helper_obj._warmup_broadcast.Proto().num_workers = \
|
|
num_workers
|
|
|
|
_SyncAllParams(
|
|
devices,
|
|
model_helper_obj,
|
|
model_helper_obj.param_init_net,
|
|
model_helper_obj._warmup_broadcast,
|
|
rendezvous,
|
|
model_parameter_names,
|
|
max_concurrent_distributed_ops
|
|
)
|
|
for param_name in viewkeys(model_helper_obj._device_grouped_blobs):
|
|
param = model_helper_obj._device_grouped_blobs[param_name][master_device]
|
|
with core.DeviceScope(master_dev_opt):
|
|
model_helper_obj._warmup_broadcast.Copy(param, _g(param))
|
|
|
|
# (Step-0) Initialize momentum parameters on master device.
|
|
for param_name in viewkeys(model_helper_obj._device_grouped_blobs):
|
|
param = model_helper_obj._device_grouped_blobs[param_name][master_device]
|
|
with core.DeviceScope(master_dev_opt):
|
|
model_helper_obj._global_model_init_net.ConstantFill(
|
|
param, _v(param), value=0.0
|
|
)
|
|
model_helper_obj._global_model_init_net.Copy(param, _g(param))
|
|
if nesterov:
|
|
model_helper_obj._global_model_init_net.ConstantFill(
|
|
param, _v_prev(param), value=0.0
|
|
)
|
|
|
|
# (Step-1) Update models for num_local_iterations.
|
|
|
|
# (Step-2) Compute post-local-updates average of the params.
|
|
# Sum model params across GPUs and store resutls in param_avg blob.
|
|
_AllReduceBlobs(
|
|
model_parameter_names,
|
|
devices,
|
|
model_helper_obj,
|
|
model_helper_obj._global_model_param_updates_net,
|
|
rendezvous,
|
|
use_nccl,
|
|
max_concurrent_distributed_ops
|
|
)
|
|
|
|
# (Step-3) Update momentum params :
|
|
# param_v = block_momentum * param_v
|
|
# + block_learning_Rate * (param_avg - param)
|
|
# if nesterov momentum:
|
|
# param = param + param_v
|
|
# - block_momentum * (param_v - param_v_prev)
|
|
# param_v_prev = param_v
|
|
# else:
|
|
# param = param + param_v
|
|
for param_name in model_parameter_names:
|
|
param = model_helper_obj._device_grouped_blobs[param_name][master_device]
|
|
with core.DeviceScope(master_dev_opt):
|
|
# TODO(ataei) : Stop building the graph here to get model average ?
|
|
model_helper_obj._global_model_param_updates_net.Scale(
|
|
param, param, scale=1.0 / num_devices
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Sub(
|
|
[param, _g(param)], param
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Scale(
|
|
param, param, scale=block_learning_rate
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Scale(
|
|
_v(param), _v(param), scale=block_momentum
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Add(
|
|
[_v(param), param], _v(param)
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Add(
|
|
[_g(param), _v(param)], _g(param)
|
|
)
|
|
if nesterov:
|
|
model_helper_obj._global_model_param_updates_net.Sub(
|
|
[_v(param), _v_prev(param)], _v_prev(param)
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Scale(
|
|
_v_prev(param), _v_prev(param), scale=block_momentum
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Sub(
|
|
[_g(param), _v_prev(param)], _g(param)
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Copy(
|
|
_v(param), _v_prev(param)
|
|
)
|
|
model_helper_obj._global_model_param_updates_net.Copy(
|
|
_g(param), param
|
|
)
|
|
|
|
|
|
_SyncAllParams(
|
|
devices,
|
|
model_helper_obj,
|
|
model_helper_obj.param_init_net,
|
|
model_helper_obj._global_model_param_updates_net,
|
|
rendezvous,
|
|
model_parameter_names,
|
|
max_concurrent_distributed_ops
|
|
)
|
|
|
|
# Add additional syncs
|
|
if add_blobs_to_sync is not None:
|
|
AddBlobSync(
|
|
model_helper_obj,
|
|
add_blobs_to_sync,
|
|
net=model_helper_obj._global_model_param_updates_net)
|
|
|
|
# Reset momentum-SGD parameters
|
|
if reset_momentum_sgd:
|
|
momentum_ops = [op for op in model_helper_obj.net.Proto().op
|
|
if op.type == 'MomentumSGDUpdate']
|
|
for op in momentum_ops:
|
|
momentum_blob = op.input[1]
|
|
with core.DeviceScope(op.device_option):
|
|
model_helper_obj._global_model_param_updates_net.ConstantFill(
|
|
[momentum_blob], momentum_blob, value=0.0
|
|
)
|
|
|
|
if optimize_gradient_memory:
|
|
_OptimizeGradientMemorySimple(
|
|
model_helper_obj, model_helper_obj._losses_by_gpu, devices
|
|
)
|
|
|
|
model_helper_obj._data_parallel_model_init_nets = [
|
|
model_helper_obj.param_init_net,
|
|
model_helper_obj._global_model_init_net
|
|
]
|
|
|
|
model_helper_obj._data_parallel_model_nets = [
|
|
model_helper_obj.net,
|
|
(model_helper_obj._global_model_param_updates_net, 1)
|
|
]
|
|
|
|
|
|
def RunInitNet(model):
|
|
for init_net in model._data_parallel_model_init_nets:
|
|
workspace.RunNetOnce(init_net)
|
|
for net_iters in model._data_parallel_model_nets:
|
|
if isinstance(net_iters, tuple):
|
|
workspace.CreateNet(net_iters[0])
|
|
else:
|
|
workspace.CreateNet(net_iters)
|
|
|
|
|
|
def RunWarmup(model):
|
|
workspace.RunNet(model.net, model._warmup_iterations)
|
|
workspace.RunNetOnce(model._warmup_broadcast)
|
|
|
|
|
|
def RunNet(model, num_iterations):
|
|
for net_iter in model._data_parallel_model_nets:
|
|
if isinstance(net_iter, tuple):
|
|
workspace.RunNet(net_iter[0].Proto().name, net_iter[1])
|
|
else:
|
|
workspace.RunNet(net_iter, num_iterations)
|
|
|
|
|
|
barrier_instance = 0
|
|
|
|
|
|
def Synchronize(model, timeout_sec=_DEFAULT_TIMEOUT_SEC):
|
|
log.info("Creating synchronization barrier net")
|
|
assert model._rendezvous is not None, "Missing rendezvous"
|
|
assert model._rendezvous['engine'] == 'GLOO', "Engine does not support barrier"
|
|
assert model._rendezvous['num_shards'] > 1, \
|
|
"synchronization barrier requires multiple shards"
|
|
global barrier_instance
|
|
instance = barrier_instance
|
|
barrier_instance += 1
|
|
barrier_net = core.Net("sync_barrier_net_" + str(instance))
|
|
comm_world = _CreateOrCloneCommonWorld(
|
|
barrier_net,
|
|
"sync_barrier_cw_" + str(instance),
|
|
rendezvous=model._rendezvous,
|
|
status_blob="sync_barrier_cw_status_" + str(instance),
|
|
timeout_sec=timeout_sec,
|
|
)
|
|
barrier_net.Barrier(
|
|
inputs=[comm_world],
|
|
outputs=[],
|
|
engine=model._rendezvous['engine'],
|
|
status_blob="sync_barrier_status_" + str(instance),
|
|
)
|
|
workspace.RunNetOnce(barrier_net)
|
|
|
|
|
|
def ConvertNetForDevice(net, device=None):
|
|
'''
|
|
Converts all blobs in the net to have namescope gpu_X, and correct
|
|
device scope. You can use this to enable AppendNet with a
|
|
forward_pass_builder_fun:
|
|
|
|
def builder_fun(model):
|
|
...
|
|
model.net.AppendNet(
|
|
data_parallel_model.ConvertNetForDevice(othermodel.net))
|
|
model.param_init_net.AppendNet(
|
|
data_parallel_model.ConvertNetForDevice(othermodel.param_init_net))
|
|
'''
|
|
mnet = copy.deepcopy(net)
|
|
|
|
if device is None:
|
|
device = scope.CurrentDeviceScope()
|
|
|
|
device_prefix = "gpu" if device.device_type == caffe2_pb2.CUDA else "cpu"
|
|
|
|
namescope = "{}_{}/".format(device_prefix, device.cuda_gpu_id)
|
|
for op in mnet.Proto().op:
|
|
if "RecurrentNetwork" in op.type:
|
|
raise("RecurrentNetwork conversion not yet supported")
|
|
for i, inputb in enumerate(op.input):
|
|
op.input[i] = namescope + inputb
|
|
for i, outputb in enumerate(op.output):
|
|
op.output[i] = namescope + outputb
|
|
for i, blob in enumerate(op.control_input):
|
|
op.control_input[i] = namescope + blob
|
|
op.device_option.CopyFrom(device)
|
|
for i, einp in enumerate(mnet.Proto().external_input):
|
|
mnet.Proto().external_input[i] = namescope + einp
|
|
for i, eoutp in enumerate(mnet.Proto().external_output):
|
|
mnet.Proto().external_output[i] = namescope + eoutp
|
|
return mnet
|
|
|
|
|
|
def _ForEachDevice(devices, f, device_type, device_prefix, scoped=False,
|
|
*args, **kwargs):
|
|
for device in devices:
|
|
device_opt = core.DeviceOption(device_type, device)
|
|
with core.DeviceScope(device_opt):
|
|
if scoped:
|
|
with core.NameScope("{}_{}".format(device_prefix, device)):
|
|
f(device, *args, **kwargs)
|
|
else:
|
|
f(device, *args, **kwargs)
|
|
|
|
|
|
def _AddGradientOperators(devices, model, losses_by_gpu):
|
|
def create_grad(lossp):
|
|
return model.ConstantFill(lossp, str(lossp) + "_grad", value=1.0)
|
|
|
|
loss_grad = {}
|
|
# Explicitly need to create gradients on each GPU
|
|
for gpu_id in devices:
|
|
device = core.DeviceOption(model._device_type, gpu_id)
|
|
with core.DeviceScope(device):
|
|
for l in losses_by_gpu[gpu_id]:
|
|
lg = create_grad(l)
|
|
loss_grad[str(l)] = str(lg)
|
|
|
|
model.AddGradientOperators(loss_grad)
|
|
|
|
|
|
def ExtractPredictorNet(model, inputs, outputs, device):
|
|
'''
|
|
Returns (net, params) that can be exported to be used as a prediction
|
|
net.
|
|
'''
|
|
master_device = model._devices[0]
|
|
prefix = "{}_{}/".format(model._device_prefix, master_device)
|
|
prefix_inputs = [prefix + str(b) for b in inputs]
|
|
prefix_outputs = [prefix + str(b) for b in outputs]
|
|
(predictor_net, export_blobs) = model_helper.ExtractPredictorNet(
|
|
net_proto=model.net.Proto(),
|
|
input_blobs=prefix_inputs,
|
|
output_blobs=prefix_outputs,
|
|
device=device,
|
|
renames={
|
|
a: b
|
|
for (a, b) in zip(prefix_inputs + prefix_outputs, inputs + outputs)
|
|
},
|
|
)
|
|
|
|
return (predictor_net, export_blobs)
|
|
|
|
|
|
def GetCheckpointParams(model):
|
|
'''
|
|
Returns a set of blobs that are needed for a complete check point.
|
|
They are blobs for the first gpu and iteration blobs.
|
|
'''
|
|
(all_blobs, _) = _ComputeBlobsToSync(model)
|
|
first_gpu_blobs = {
|
|
b
|
|
for b in all_blobs
|
|
if str(b)
|
|
.startswith("{}_{}/".format(model._device_prefix, model._devices[0]))
|
|
}
|
|
|
|
# Add iteration blobs that do not have namescope separately, since
|
|
# it is important to checkpoint iteration counter
|
|
iteration_blobs = set()
|
|
for op in model.net.Proto().op:
|
|
if op.type == 'Iter' or op.type == 'AtomicIter':
|
|
if not op.output[0].startswith("{}_".format(model._device_prefix)):
|
|
iteration_blobs.add(op.output[0])
|
|
|
|
return first_gpu_blobs.union(iteration_blobs)
|
|
|
|
|
|
def FinalizeAfterCheckpoint(model, blobs=None):
|
|
'''
|
|
This function should be called after loading parameters from a
|
|
checkpoint / initial parameters file.
|
|
'''
|
|
|
|
if not hasattr(model, "_checkpoint_net"):
|
|
if blobs is None:
|
|
(_, uniq_blob_names) = _ComputeBlobsToSync(model)
|
|
else:
|
|
uniq_blob_names = [stripBlobName(p) for p in blobs]
|
|
|
|
# Synchronize to the blob lookup map, as the provided
|
|
# blobs might have non-parameters, such as momemtum blobs.
|
|
log.info("Creating checkpoint synchronization net")
|
|
devices = model.GetDevices()
|
|
for name in uniq_blob_names:
|
|
if name not in model._device_grouped_blobs:
|
|
grouped = {
|
|
d:
|
|
core.BlobReference("{}_{}{}{}".format(
|
|
model._device_prefix,
|
|
d,
|
|
scope._NAMESCOPE_SEPARATOR,
|
|
name)
|
|
) for d in devices}
|
|
model._device_grouped_blobs[name] = grouped
|
|
|
|
model._checkpoint_net = core.Net("checkpoint_sync_net")
|
|
model._checkpoint_net.RunAllOnGPU()
|
|
|
|
checkpoint_init_net = None
|
|
if (model._rendezvous is not None and model._rendezvous['num_shards'] > 1):
|
|
checkpoint_init_net = core.Net("checkpoint_init_net")
|
|
checkpoint_init_net.RunAllOnGPU()
|
|
|
|
_SyncAllParams(
|
|
devices,
|
|
model,
|
|
checkpoint_init_net,
|
|
model._checkpoint_net,
|
|
model._rendezvous,
|
|
uniq_blob_names,
|
|
max_concurrent_distributed_ops=1
|
|
)
|
|
if (checkpoint_init_net):
|
|
workspace.RunNetOnce(checkpoint_init_net)
|
|
|
|
workspace.CreateNet(model._checkpoint_net)
|
|
|
|
# Run the sync
|
|
log.info("Run checkpoint net")
|
|
workspace.RunNet(model._checkpoint_net.Proto().name)
|
|
|
|
|
|
def GetLearningRateBlobNames(model):
|
|
'''
|
|
Returns a list of learning rates blob names used in the optimizer.
|
|
'''
|
|
if model._optimizer is not None:
|
|
if model._device_type == caffe2_pb2.CPU:
|
|
return [model._optimizer.get_cpu_blob_name('lr')]
|
|
elif model._device_type == caffe2_pb2.CUDA:
|
|
return [model._optimizer.get_gpu_blob_name('lr', gpu, '')
|
|
for gpu in model._devices]
|
|
else:
|
|
raise Exception(
|
|
"Unsupported device type : {}".format(model._device_type)
|
|
)
|
|
else:
|
|
lr_blob_names = []
|
|
for op in model.net.Proto().op:
|
|
if op.type == "LearningRate":
|
|
lr_blob_names.append(op.output(0))
|
|
return lr_blob_names
|
|
|
|
|
|
def _Broadcast(devices, model, net, param, use_nccl=False):
|
|
# Copy params from gpu_0 to other
|
|
master_dev = devices[0]
|
|
|
|
if use_nccl:
|
|
if _IsGPUBlob(model, param):
|
|
master_device_opt = core.DeviceOption(model._device_type, master_dev)
|
|
with core.DeviceScope(master_device_opt):
|
|
# Note that the root is the root _rank_ and not the root
|
|
# _device_. Thus we always use root=0, regardless of the
|
|
# devices used.
|
|
net.NCCLBroadcast(
|
|
list(viewvalues(model._device_grouped_blobs[param])),
|
|
list(viewvalues(model._device_grouped_blobs[param])),
|
|
root=0,
|
|
)
|
|
return
|
|
|
|
for dev_idx in devices[1:]:
|
|
if _IsGPUBlob(model, param):
|
|
device_opt = core.DeviceOption(caffe2_pb2.CUDA, dev_idx)
|
|
else:
|
|
device_opt = core.DeviceOption(caffe2_pb2.CPU, 0)
|
|
with core.DeviceScope(device_opt):
|
|
net.Copy(
|
|
model._device_grouped_blobs[param][master_dev],
|
|
model._device_grouped_blobs[param][dev_idx]
|
|
)
|
|
|
|
|
|
def _AllReduce(devices, model, net, param, use_nccl=False, control_input=None):
|
|
blobs_group = list(viewvalues(model._device_grouped_blobs[param]))
|
|
if model._device_type == caffe2_pb2.CUDA and use_nccl:
|
|
# TODO: for _shared_model, do only NCCLReduce
|
|
model.NCCLAllreduce(
|
|
blobs_group, blobs_group, control_input=control_input
|
|
)
|
|
return
|
|
|
|
if model._device_type == caffe2_pb2.CUDA:
|
|
p2p_access_pattern = workspace.GetCudaPeerAccessPattern()
|
|
else:
|
|
p2p_access_pattern = None
|
|
|
|
def sumN(*dev_indices):
|
|
"""Create a Sum op for 2 or more blobs on different devices.
|
|
Saves the result on the first device.
|
|
|
|
Arguments:
|
|
dev_indices -- a list of device indices, which can be translated into
|
|
CUDA identifiers with model._devices
|
|
"""
|
|
devices = [model._devices[idx] for idx in dev_indices]
|
|
blobs = [blobs_group[idx] for idx in dev_indices]
|
|
for i, peer in enumerate(devices):
|
|
if i == 0:
|
|
continue # Skip the first device
|
|
if p2p_access_pattern is not None and not p2p_access_pattern[
|
|
devices[0], peer
|
|
]:
|
|
# Copy from peer to d0
|
|
blobs[i] = model.Copy(
|
|
blobs[i],
|
|
'gpu_{}/{}_gpu{}_copy'.format(devices[0], param, peer)
|
|
)
|
|
device_opt = core.DeviceOption(model._device_type, devices[0])
|
|
with core.DeviceScope(device_opt):
|
|
net.Sum(blobs, [blobs[0]], name='dpm')
|
|
|
|
if len(devices) == 16:
|
|
# Special tree reduction for 16 gpus, TODO generalize like in muji.py
|
|
for j in range(8):
|
|
sumN(j * 2, j * 2 + 1)
|
|
for j in range(4):
|
|
sumN(j * 4, j * 4 + 2)
|
|
for j in range(2):
|
|
sumN(j * 8, j * 8 + 4)
|
|
sumN(0, 8)
|
|
elif len(devices) == 8:
|
|
for j in range(4):
|
|
sumN(j * 2, j * 2 + 1)
|
|
for j in range(2):
|
|
sumN(j * 4, j * 4 + 2)
|
|
sumN(0, 4)
|
|
elif len(devices) == 4:
|
|
sumN(0, 1)
|
|
sumN(2, 3)
|
|
sumN(0, 2)
|
|
else:
|
|
sumN(*range(len(devices)))
|
|
# TODO: for _shared_model, no need to broadcast
|
|
_Broadcast(devices, model, net, param)
|
|
|
|
|
|
def _SyncAllParams(
|
|
devices,
|
|
model,
|
|
init_net,
|
|
net,
|
|
rendezvous,
|
|
unique_param_names,
|
|
max_concurrent_distributed_ops=4
|
|
):
|
|
if rendezvous is None or rendezvous['num_shards'] <= 1:
|
|
_SyncAllParamsSingleHost(devices, model, net, unique_param_names)
|
|
else:
|
|
_SyncAllParamsDistributed(
|
|
devices,
|
|
model,
|
|
init_net,
|
|
net,
|
|
rendezvous,
|
|
unique_param_names,
|
|
max_concurrent_distributed_ops
|
|
)
|
|
|
|
|
|
def AddBlobSync(model, blobs, net=None):
|
|
'''
|
|
Sync a blob across devices and hosts
|
|
'''
|
|
if len(blobs) == 0:
|
|
return
|
|
net = model.net if net is None else net
|
|
for b in blobs:
|
|
assert not b.startswith(model._device_prefix), \
|
|
"Provide unprefixed blob name: {}".format(b)
|
|
model._device_grouped_blobs[b] = {
|
|
d: core.BlobReference("{}_{}/{}".format(model._device_prefix, d, b))
|
|
for d in model._devices
|
|
}
|
|
|
|
_SyncAllParams(
|
|
model._devices,
|
|
model,
|
|
model.param_init_net,
|
|
net,
|
|
model._rendezvous,
|
|
set(blobs))
|
|
|
|
|
|
def AddDistributedBlobSync(model, blobs):
|
|
'''
|
|
Sync blobs across machines (but not across devices)
|
|
'''
|
|
if model._rendezvous is None:
|
|
return
|
|
synth_name = "_".join([str(b) for b in blobs])
|
|
comm_world = _CreateOrCloneCommonWorld(
|
|
model.param_init_net,
|
|
"blob_sync_cw_" + synth_name,
|
|
rendezvous=model._rendezvous,
|
|
status_blob="create_blob_sync_cw_{}_cw_status".format(
|
|
synth_name,
|
|
),
|
|
)
|
|
|
|
model.net.Allreduce(
|
|
inputs=[comm_world] + blobs,
|
|
outputs=blobs,
|
|
engine=model._rendezvous['engine'],
|
|
status_blob="blob_sync_allred_{}_status".format(synth_name),
|
|
)
|
|
|
|
|
|
def _SyncAllParamsDistributed(
|
|
devices,
|
|
model,
|
|
init_net,
|
|
net,
|
|
rendezvous,
|
|
unique_param_names,
|
|
max_concurrent_distributed_ops
|
|
):
|
|
assert rendezvous['num_shards'] > 1
|
|
|
|
gpu_device_opt = core.DeviceOption(model._device_type, devices[0])
|
|
cpu_device_opt = core.DeviceOption(caffe2_pb2.CPU)
|
|
|
|
if model._broadcast_context is None:
|
|
model._broadcast_context = CollectivesConcurrencyControl(
|
|
"broadcast",
|
|
max_concurrent_distributed_ops,
|
|
init_net,
|
|
rendezvous
|
|
)
|
|
context = model._broadcast_context
|
|
|
|
for param_name in sorted(unique_param_names):
|
|
master_param = model._device_grouped_blobs[param_name][devices[0]]
|
|
params_group = list(viewvalues(model._device_grouped_blobs[param_name]))
|
|
|
|
def broadcast(params):
|
|
comm_world, control_input = context.get_control_and_context(params)
|
|
net.Broadcast(
|
|
inputs=[comm_world] + params,
|
|
outputs=params,
|
|
name=param_name,
|
|
engine=rendezvous['engine'],
|
|
status_blob="broadcast_{}_status".format(str(param_name)),
|
|
control_input=control_input
|
|
)
|
|
|
|
device_opt = gpu_device_opt if _IsGPUBlob(
|
|
model, param_name
|
|
) else cpu_device_opt
|
|
|
|
if rendezvous['engine'] == 'GLOO':
|
|
with core.DeviceScope(device_opt):
|
|
broadcast(params_group)
|
|
else:
|
|
# Copy between GPU and CPU
|
|
with core.DeviceScope(device_opt):
|
|
param_cpu = net.CopyGPUToCPU(
|
|
master_param,
|
|
str(master_param) + "cpu"
|
|
)
|
|
with core.DeviceScope(cpu_device_opt):
|
|
broadcast([param_cpu])
|
|
with core.DeviceScope(device_opt):
|
|
net.CopyCPUToGPU(param_cpu, master_param)
|
|
|
|
# Broadcast locally
|
|
_Broadcast(devices, model, net, param_name)
|
|
|
|
|
|
def _SyncAllParamsSingleHost(devices, model, net, unique_param_names):
|
|
for param in unique_param_names:
|
|
_Broadcast(devices, model, net, param)
|
|
|
|
|
|
def _AllReduceBlobs(blob_names, devices, model, net, rendezvous, use_nccl,
|
|
max_concurrent_distributed_ops):
|
|
if rendezvous is None or rendezvous['num_shards'] <= 1:
|
|
_AllReduceBlobsSingleHost(
|
|
blob_names,
|
|
devices,
|
|
model,
|
|
net,
|
|
use_nccl
|
|
)
|
|
else:
|
|
_AllReduceBlobsDistributed(
|
|
blob_names,
|
|
devices,
|
|
model,
|
|
net,
|
|
rendezvous,
|
|
max_concurrent_distributed_ops,
|
|
)
|
|
|
|
|
|
def _PruneParametersForSharing(model):
|
|
assert model._shared_model
|
|
master_prefix = "{}_{}/".format(model._device_prefix, model._devices[0])
|
|
|
|
# Remove non-master parameters so that they will not receive parameter
|
|
# update operators.
|
|
model.params = model.GetParams(master_prefix)
|
|
paramset = set(model.params)
|
|
|
|
model.param_to_grad = {
|
|
p: model.param_to_grad[p]
|
|
for p in model.param_to_grad if p in paramset
|
|
}
|
|
model.weights = [w for w in model.weights if w in paramset]
|
|
model.biases = [w for w in model.biases if w in paramset]
|
|
|
|
|
|
def _RemapParameterBlobsForSharedModel(model, all_params):
|
|
assert model._shared_model
|
|
master_prefix = "{}_{}/".format(
|
|
model._device_prefix, model._devices[0])
|
|
log.info("Remapping param blobs to master -> {}".format(master_prefix))
|
|
master_params = set(model.GetParams())
|
|
|
|
# Remove all but master params
|
|
def modify_ops(net):
|
|
ops = []
|
|
for op in net.Proto().op:
|
|
delete_op = False
|
|
# Delete ops that output non-master version of parameter
|
|
for outp in op.output:
|
|
if outp in all_params and outp not in master_params:
|
|
delete_op = True
|
|
log.debug("Delete b/c {}: {}".format(outp, str(op)))
|
|
break
|
|
if delete_op:
|
|
continue
|
|
# Remap inputs to point to the master param
|
|
for j, inp in enumerate(op.input):
|
|
if inp in all_params and inp not in master_params:
|
|
op.input[j] = master_prefix + stripBlobName(inp)
|
|
ops.append(op)
|
|
del net.Proto().op[:]
|
|
net.Proto().op.extend(ops)
|
|
|
|
modify_ops(model.param_init_net)
|
|
modify_ops(model.net)
|
|
|
|
|
|
class CollectivesConcurrencyControl(object):
|
|
"""
|
|
Creates common worlds (up to max_concurrent_context) and manage the
|
|
sequential execution of collectives that shares the same context with
|
|
cyclic control inputs.
|
|
"""
|
|
def __init__(
|
|
self,
|
|
name,
|
|
max_concurrent_context,
|
|
param_init_net,
|
|
rendezvous
|
|
):
|
|
self.name = name
|
|
self.param_init_net = param_init_net
|
|
self.max_concurrent_context = max_concurrent_context
|
|
self.counter = 0
|
|
self.common_worlds = []
|
|
self.control_inputs = []
|
|
self.rendezvous = rendezvous
|
|
|
|
def get_control_and_context(self, control_output_blob):
|
|
common_world, control_input = [None, None]
|
|
current_slot = self.counter % self.max_concurrent_context
|
|
if len(self.common_worlds) < self.max_concurrent_context:
|
|
common_world = _CreateOrCloneCommonWorld(
|
|
self.param_init_net,
|
|
"{}_{}_cw".format(self.name, current_slot),
|
|
rendezvous=self.rendezvous,
|
|
status_blob="create_{}_cw_{}_status".format(
|
|
self.name,
|
|
current_slot
|
|
),
|
|
)
|
|
self.common_worlds.append(common_world)
|
|
self.control_inputs.append(control_output_blob)
|
|
else:
|
|
common_world = self.common_worlds[current_slot]
|
|
control_input = self.control_inputs[current_slot]
|
|
self.control_inputs[current_slot] = control_output_blob
|
|
self.counter += 1
|
|
return common_world, control_input
|
|
|
|
|
|
def _AllReduceBlobsDistributed(
|
|
blob_names,
|
|
devices,
|
|
model,
|
|
net,
|
|
rendezvous,
|
|
max_concurrent_distributed_ops,
|
|
):
|
|
num_workers = model.net.Proto().num_workers
|
|
assert num_workers > 1, "Please specify more than 1 worker"
|
|
all_reduce_engine = rendezvous['engine']
|
|
|
|
master_device_opt = core.DeviceOption(model._device_type, devices[0])
|
|
|
|
reducing_device_opt = master_device_opt
|
|
|
|
context = CollectivesConcurrencyControl(
|
|
"allreduce",
|
|
max_concurrent_distributed_ops,
|
|
model.param_init_net,
|
|
rendezvous
|
|
)
|
|
|
|
nccl_control_blob = None
|
|
|
|
for blob_name in blob_names:
|
|
master_blob = model._device_grouped_blobs[blob_name][devices[0]]
|
|
blobs_group = list(viewvalues(model._device_grouped_blobs[blob_name]))
|
|
|
|
assert master_blob in blobs_group
|
|
|
|
# Remark: NCCLReduce does not support in-place modifications
|
|
# so we need a temporary blob
|
|
reduced_blob = str(master_blob) + "_red"
|
|
|
|
def allreduce(blobs, **kwargs):
|
|
with core.DeviceScope(reducing_device_opt):
|
|
comm_world, control_input = \
|
|
context.get_control_and_context(blobs[0])
|
|
net.Allreduce(
|
|
inputs=[comm_world] + blobs,
|
|
outputs=blobs,
|
|
name=blob_name,
|
|
engine=all_reduce_engine,
|
|
control_input=control_input,
|
|
status_blob="allreduce_{}_status".format(blob_name),
|
|
**kwargs
|
|
)
|
|
|
|
if rendezvous['engine'] == 'GLOO':
|
|
# With Gloo cross GPU and cross machine allreduce
|
|
# can be executed in a single operation.
|
|
# Try to use GPUDirect if transport == ibverbs.
|
|
allreduce(
|
|
blobs_group,
|
|
gpu_direct=(rendezvous.get("transport", None) == "ibverbs"),
|
|
)
|
|
else:
|
|
# Step 1: sum blobs from local GPUs to master GPU
|
|
with core.DeviceScope(master_device_opt):
|
|
model.ConstantFill(master_blob, reduced_blob, value=0.0)
|
|
|
|
# Temp fix since NCCLReduce does not work
|
|
net.NCCLAllreduce(
|
|
blobs_group,
|
|
blobs_group,
|
|
control_input=nccl_control_blob,
|
|
)
|
|
nccl_control_blob = blobs_group[0]
|
|
net.Copy(master_blob, reduced_blob)
|
|
|
|
# Step 2: allreduce between all hosts, between master GPUs
|
|
allreduce([reduced_blob])
|
|
|
|
with core.DeviceScope(master_device_opt):
|
|
net.Copy(reduced_blob, master_blob)
|
|
|
|
# Step 3: broadcast locally
|
|
_Broadcast(devices, model, net, blob_name)
|
|
|
|
|
|
def _AllReduceBlobsSingleHost(blob_names, devices, model, net, use_nccl):
|
|
"""Performs NCCL AllReduce to distribute blobs to all the GPUs."""
|
|
|
|
if len(devices) == 1:
|
|
return
|
|
|
|
# Now we need to Allreduce blobs on all the GPUs.
|
|
# Pick GPU #0 as a master GPU.
|
|
master_device_opt = core.DeviceOption(model._device_type, devices[0])
|
|
last_out = None
|
|
concatenated_idx = set()
|
|
|
|
for blob_name in blob_names:
|
|
# Group by blob_name for reduce.
|
|
blobs_group = list(viewvalues(model._device_grouped_blobs[blob_name]))
|
|
assert len(blobs_group) == len(devices), \
|
|
"Each GPU from {}, should have a copy of {}.".format(
|
|
devices, blob_name)
|
|
|
|
if _IsGPUBlob(model, blob_name):
|
|
with core.DeviceScope(master_device_opt):
|
|
if not isinstance(blobs_group[0], core.GradientSlice):
|
|
_AllReduce(
|
|
devices, model, net, blob_name, use_nccl, last_out
|
|
)
|
|
# last_out is used to serialize the execution of nccls
|
|
last_out = blobs_group[0]
|
|
|
|
else:
|
|
# Sparse gradients: all-gather for indices and values
|
|
master_ns = "{}_{}".format(model._device_prefix, devices[0])
|
|
'''
|
|
Skip if we have already copied concatenated indices
|
|
to the indices of GradientSlice. This happens when two
|
|
or more grad blobs are gathered with the same indices
|
|
blob
|
|
'''
|
|
skip_idx_concat = False
|
|
for g in blobs_group:
|
|
if g.indices in concatenated_idx:
|
|
skip_idx_concat = True
|
|
|
|
if not skip_idx_concat:
|
|
grad_idx_concat, _ = net.Concat(
|
|
[g.indices for g in blobs_group],
|
|
["{}/{}_index_concat".format(master_ns, blob_name),
|
|
"{}/{}_index_splitinfo".format(master_ns, blob_name)],
|
|
axis=0,
|
|
name="note:data_parallel_model")
|
|
|
|
for gpu, g in viewitems(model._device_grouped_blobs[blob_name]):
|
|
device_opt = core.DeviceOption(model._device_type, gpu)
|
|
with core.DeviceScope(device_opt):
|
|
model.Copy(grad_idx_concat, g.indices)
|
|
concatenated_idx.add(g.indices)
|
|
|
|
grad_val_concat, _ = net.Concat(
|
|
[g.values for g in blobs_group],
|
|
["{}/{}_val_concat".format(master_ns, blob_name),
|
|
"{}/{}_val_splitinfo".format(master_ns, blob_name)],
|
|
axis=0, name="note:data_parallel_model")
|
|
|
|
for gpu, g in viewitems(model._device_grouped_blobs[blob_name]):
|
|
device_opt = core.DeviceOption(model._device_type, gpu)
|
|
with core.DeviceScope(device_opt):
|
|
model.Copy(grad_val_concat, g.values)
|
|
|
|
else:
|
|
assert not isinstance(blobs_group[0], core.GradientSlice), \
|
|
"Synchronizing gradient slices not supported"
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
# Poor man's allreduce
|
|
net.Sum(blobs_group, [blobs_group[0]])
|
|
if not model._shared_model:
|
|
_Broadcast(devices, model, net, blob_name)
|
|
|
|
|
|
def _BroadcastComputedParams(devices, model, rendezvous, use_nccl=False):
|
|
if rendezvous is None:
|
|
_BroadcastComputedParamsSingleHost(devices, model, use_nccl)
|
|
else:
|
|
_BroadcastComputedParamsDistributed(devices, model, rendezvous, use_nccl)
|
|
|
|
|
|
def _BroadcastComputedParamsDistributed(
|
|
devices,
|
|
model,
|
|
rendezvous,
|
|
use_nccl=False
|
|
):
|
|
_BroadcastComputedParamsSingleHost(devices, model, use_nccl)
|
|
log.warn("Distributed broadcast of computed params is not implemented yet")
|
|
|
|
|
|
def _BroadcastComputedParamsSingleHost(devices, model, use_nccl=False):
|
|
'''
|
|
Average computed params over all devices
|
|
'''
|
|
if len(devices) == 1:
|
|
return
|
|
|
|
for param_name in model._computed_param_names:
|
|
# Copy from master to others -- averaging would be perhaps better,
|
|
# but currently NCCLAllReduce is too prone to deadlock
|
|
_Broadcast(devices, model, model.net, param_name, use_nccl)
|
|
|
|
|
|
def _GetReverseOrderedGrads(model):
|
|
'''
|
|
Returns the gradients in reverse order (namespace stripped),
|
|
for the optimal synchronization order.
|
|
'''
|
|
return list(reversed(model._grad_names))
|
|
|
|
|
|
# A helper function to extract a parameter's name
|
|
def stripBlobName(param):
|
|
# Format is "a/b/c/d" -> "b/c/d"
|
|
if isinstance(param, core.GradientSlice):
|
|
return stripBlobName(param.indices) + ":" + stripBlobName(param.values)
|
|
else:
|
|
name = str(param)
|
|
return name[name.index(scope._NAMESCOPE_SEPARATOR) + 1:]
|
|
|
|
|
|
def _AnalyzeOperators(model):
|
|
'''
|
|
Look at all the operators and check that they do not cross device scopes
|
|
'''
|
|
for op in model.Proto().op:
|
|
if "NCCL" in op.type or "Copy" in op.type or "Concat" in op.type:
|
|
continue
|
|
if "Sum" == op.type and op.name == "dpm":
|
|
continue
|
|
if "Allreduce" in op.type and "GLOO" in op.engine:
|
|
continue
|
|
|
|
op_dev = op.device_option
|
|
op_gpu = op_dev.cuda_gpu_id
|
|
|
|
# This avoids failing on operators that are only for CPU
|
|
if op_dev.device_type != caffe2_pb2.CUDA:
|
|
continue
|
|
|
|
namescope = "{}_{}/".format(model._device_prefix, op_gpu)
|
|
for inp in list(op.input) + list(op.output):
|
|
if inp.startswith("{}_".format(model._device_prefix)
|
|
) and not inp.startswith(namescope):
|
|
raise Exception(
|
|
"Blob {} of op {}, should have namescope {}. Op: {}".format(
|
|
inp,
|
|
op.type,
|
|
"{}_{}/".format(model._device_prefix, op_gpu),
|
|
str(op),
|
|
)
|
|
)
|
|
|
|
|
|
def _InferBlobDevice(model):
|
|
'''
|
|
Assign blob to device option based on the operator outputing it
|
|
'''
|
|
mapping = {}
|
|
|
|
def map_ops(proto):
|
|
for op in proto.op:
|
|
device_option = op.device_option
|
|
if op.type == "Iter":
|
|
# Hack for Iters which have blob in CPU context
|
|
device_option = caffe2_pb2.DeviceOption()
|
|
device_option.device_type = caffe2_pb2.CPU
|
|
for b in list(op.input) + list(op.output):
|
|
if b not in mapping:
|
|
mapping[b] = device_option
|
|
if op.type.startswith('RecurrentNetwork'):
|
|
step_args = [a for a in op.arg if a.name.endswith("step_net")]
|
|
for step_arg in step_args:
|
|
map_ops(step_arg.n)
|
|
map_ops(model.param_init_net.Proto())
|
|
map_ops(model.net.Proto())
|
|
model._blob_to_device = mapping
|
|
|
|
def _IsGPUBlob(model, blob_name):
|
|
if blob_name in model._blob_to_device:
|
|
return model._blob_to_device[blob_name].device_type == caffe2_pb2.CUDA
|
|
else:
|
|
blob_name = "{}_{}/{}".format(
|
|
model._device_prefix, model._devices[0], blob_name
|
|
)
|
|
if blob_name not in model._blob_to_device:
|
|
return model._device_type == caffe2_pb2.CUDA
|
|
return model._blob_to_device[blob_name].device_type == caffe2_pb2.CUDA
|
|
|
|
|
|
def _GroupByDevice(model, devices, params, non_data_params):
|
|
'''
|
|
Groups blobs by device, returning a map of [blobname] = {0: BlobRef, 1: ..}.
|
|
Returns ordered dictionary, ensuring the original order.
|
|
'''
|
|
grouped = OrderedDict()
|
|
# Only consider params that were created to be "data parallel"
|
|
params = params[len(non_data_params):]
|
|
assert len(params) % len(devices) == 0,\
|
|
"There should be equal number of params per device"
|
|
|
|
num_params_per_device = int(len(params) / len(devices))
|
|
|
|
for i, p in enumerate(params):
|
|
assert isinstance(p, core.BlobReference) or \
|
|
isinstance(p, core.GradientSlice), \
|
|
"Param {} is not BlobReference or GradientSlice".format(p)
|
|
|
|
name = stripBlobName(p)
|
|
gpuid = devices[i // num_params_per_device]
|
|
|
|
if isinstance(p, core.BlobReference):
|
|
assert "{}_{}/".format(model._device_prefix, gpuid) in p.GetNameScope(),\
|
|
"Param {} expected to have namescope '{}_{}'".format(str(p), model._device_prefix, gpuid)
|
|
else:
|
|
assert "{}_{}/".format(model._device_prefix, gpuid) in p.indices.GetNameScope(),\
|
|
"Indices {} expected to have namescope '{}_{}'".format(str(p), model._device_prefix, gpuid)
|
|
assert "{}_{}/".format(model._device_prefix, gpuid) in p.values.GetNameScope(),\
|
|
"Values {} expected to have namescope '{}_{}'".format(str(p), model._device_prefix, gpuid)
|
|
|
|
if name not in grouped:
|
|
grouped[name] = {}
|
|
grouped[name][gpuid] = p
|
|
|
|
# Confirm consistency
|
|
for j, (p, ps) in enumerate(viewitems(grouped)):
|
|
assert \
|
|
len(ps) == len(devices), \
|
|
"Param {} does not have value for each device (only {}: {})".format(
|
|
p, len(ps), ps,
|
|
)
|
|
# Ensure ordering
|
|
if (ps[devices[0]] != params[j]):
|
|
log.error("Params: {}".format(params))
|
|
log.error("Grouped: {}".format(list(viewkeys(grouped))))
|
|
assert ps[devices[0]] == params[j], \
|
|
"Incorrect ordering: {}".format(ps)
|
|
|
|
return grouped
|
|
|
|
|
|
def _ValidateParams(params):
|
|
set_params = set(params)
|
|
if len(params) > len(set_params):
|
|
dupes = []
|
|
sp = sorted(params)
|
|
for j, p in enumerate(sp):
|
|
if j > 0 and sp[j - 1] == p:
|
|
dupes.append(p)
|
|
|
|
assert len(params) == len(set_params), \
|
|
"Duplicate entries in params: {}".format(dupes)
|
|
|
|
|
|
def _ComputeBlobsToSync(model):
|
|
'''
|
|
We sync all blobs that are generated by param init net and
|
|
are 'data parallel', i.e assigned to a device
|
|
'''
|
|
sync_names = set()
|
|
|
|
# We don't sync params if the model is shared
|
|
if model._shared_model:
|
|
blobs_to_sync = [str(p) for p in model.GetComputedParams('')]
|
|
sync_names = [stripBlobName(p) for p in blobs_to_sync]
|
|
else:
|
|
blobs_to_sync = []
|
|
|
|
for op in model.param_init_net.Proto().op:
|
|
dp_outputs = [
|
|
o for o in op.output
|
|
if o.startswith("{}_".format(model._device_prefix))
|
|
]
|
|
sync_names.update([stripBlobName(o) for o in dp_outputs])
|
|
blobs_to_sync.extend(dp_outputs)
|
|
|
|
# Sanity check
|
|
diff = set(model._param_names) - sync_names
|
|
assert diff == set(), \
|
|
"Some params not instantiated in param init net: {}".format(diff)
|
|
|
|
# Remove duplicates and sort
|
|
prefixlen = len(model._device_prefix) + 1
|
|
|
|
def extract_sort_key(b):
|
|
# Sort first based on device id, and then by whole string
|
|
deviceid = int(b[prefixlen:b.index(scope._NAMESCOPE_SEPARATOR)])
|
|
return (deviceid, b)
|
|
|
|
blobs_to_sync = sorted(
|
|
list(set(blobs_to_sync)),
|
|
key=extract_sort_key)
|
|
|
|
blobs_to_sync = [core.BlobReference(b) for b in blobs_to_sync]
|
|
return (blobs_to_sync, sync_names)
|
|
|
|
|
|
def _OptimizeGradientMemorySimple(model, losses_by_gpu, devices):
|
|
log.warning("------- DEPRECATED API, please use " +
|
|
"data_parallel_model.OptimizeGradientMemory() ----- ")
|
|
for device in devices:
|
|
namescope = "{}_{}/".format(model._device_prefix, device)
|
|
model.net._net = memonger.share_grad_blobs(
|
|
model.net,
|
|
losses_by_gpu[device],
|
|
set(viewvalues(model.param_to_grad)),
|
|
namescope,
|
|
share_activations=False,
|
|
)
|
|
|
|
|
|
def _AddDynamicMemoryOptimization(model, blobs_to_keep, devices):
|
|
blobs_to_keep_all_devices = set()
|
|
if blobs_to_keep is not None:
|
|
for device in devices:
|
|
for blob_name in blobs_to_keep:
|
|
blobs_to_keep_all_devices.add(
|
|
"{}_{}/{}".format(model._device_prefix, device, blob_name)
|
|
)
|
|
|
|
if model._rendezvous is not None:
|
|
# GLOO operators expect the tensor addresses to remain same over
|
|
# iterations so we need to remove param grads from the dynamic memory
|
|
# management.
|
|
blobs_to_keep_all_devices.update(
|
|
[str(b) for b in viewvalues(model.param_to_grad)]
|
|
)
|
|
|
|
model.net._net = memonger.release_blobs_when_used(
|
|
model.net.Proto(),
|
|
blobs_to_keep_all_devices
|
|
)
|
|
|
|
|
|
def OptimizeGradientMemory(model,
|
|
input_shapes,
|
|
excluded_blobs,
|
|
recycle_activations):
|
|
"""
|
|
Optimize memory usage of the backward pass by recycling blobs for gradient
|
|
inputs that have been 'used'.
|
|
input_shapes: dict of blob name to shape for the inputs of the model.
|
|
Pass empty dictionary if not known.
|
|
excluded_blobs: list of blobs that cannot be recycled. These are blobs
|
|
that you will access externally.
|
|
recycle_activations: whether to also recycle forward pass activations
|
|
"""
|
|
if input_shapes is not None:
|
|
input_shapes_all_devices = {}
|
|
for b, shp in viewitems(input_shapes):
|
|
for d in model._devices:
|
|
input_shapes_all_devices["{}_{}/{}".
|
|
format(model._device_prefix, d, b)] = shp
|
|
|
|
(shapes, types) = workspace.InferShapesAndTypes(
|
|
[model.param_init_net, model.net],
|
|
input_shapes_all_devices,
|
|
)
|
|
else:
|
|
shapes = None
|
|
|
|
for device in model._devices:
|
|
namescope = "{}_{}/".format(model._device_prefix, device)
|
|
excluded_blobs_by_device = set(namescope + b for b in excluded_blobs)
|
|
model.net._net = memonger.share_grad_blobs(
|
|
model.net,
|
|
model._losses_by_gpu[device],
|
|
set(viewvalues(model.param_to_grad)),
|
|
namescope,
|
|
dont_share_blobs=excluded_blobs_by_device,
|
|
share_activations=recycle_activations,
|
|
blob_shapes=shapes,
|
|
)
|
|
|
|
|
|
def _CreateOrCloneCommonWorld(
|
|
net,
|
|
common_world_blob,
|
|
rendezvous,
|
|
name=None,
|
|
status_blob=None,
|
|
timeout_sec=None):
|
|
|
|
if timeout_sec is None:
|
|
timeout_sec = _DEFAULT_TIMEOUT_SEC
|
|
|
|
timeout_ms = timeout_sec * 1000
|
|
|
|
# Check if there is an existing CreateCommonWorld
|
|
# with the same timeout we're looking for. If so,
|
|
# we can clone it instead of creating a new one.
|
|
existing = None
|
|
for op in net.Proto().op:
|
|
if op.type != "CreateCommonWorld":
|
|
continue
|
|
|
|
# Find common world timeout
|
|
op_timeout_ms = -1
|
|
for arg in op.arg:
|
|
if arg.name == 'timeout_ms':
|
|
op_timeout_ms = arg.i
|
|
break
|
|
if op_timeout_ms != timeout_ms:
|
|
continue
|
|
|
|
# This common world was created with the same timeout we're
|
|
# looking for, so we can clone it
|
|
existing = op.output[0]
|
|
break
|
|
|
|
if name is None:
|
|
name = "{}_op".format(common_world_blob)
|
|
|
|
if existing is not None:
|
|
comm_world = net.CloneCommonWorld(
|
|
[existing],
|
|
common_world_blob,
|
|
name=name,
|
|
engine=rendezvous['engine'],
|
|
status_blob=status_blob,
|
|
)
|
|
else:
|
|
kwargs=dict()
|
|
if 'transport' in rendezvous:
|
|
kwargs['transport'] = rendezvous['transport']
|
|
if 'interface' in rendezvous:
|
|
kwargs['interface'] = rendezvous['interface']
|
|
if 'mpi_rendezvous' in rendezvous:
|
|
kwargs['mpi_rendezvous'] = rendezvous['mpi_rendezvous']
|
|
comm_world = net.CreateCommonWorld(
|
|
rendezvous['kv_handler'] or [],
|
|
common_world_blob,
|
|
name=name,
|
|
size=rendezvous['num_shards'],
|
|
rank=rendezvous['shard_id'],
|
|
engine=rendezvous['engine'],
|
|
status_blob=status_blob,
|
|
timeout_ms=timeout_ms,
|
|
**kwargs
|
|
)
|
|
|
|
return comm_world
|
|
|
|
|
|
def _RunComparison(model, blob_name, device=None):
|
|
if device is None:
|
|
device = model._blob_to_device[blob_name]
|
|
with core.DeviceScope(device):
|
|
rendezvous = model._rendezvous
|
|
if rendezvous is None or rendezvous['num_shards'] == 1:
|
|
return True
|
|
|
|
test_data_arr = np.zeros(rendezvous['num_shards']).astype(np.float32)
|
|
test_data_arr[rendezvous['shard_id']] = 1
|
|
workspace.FeedBlob("compare_arr", test_data_arr)
|
|
|
|
comparison_net = core.Net("allcompare_net")
|
|
|
|
kwargs=dict()
|
|
if 'mpi_rendezvous' in rendezvous:
|
|
kwargs['mpi_rendezvous'] = rendezvous['mpi_rendezvous']
|
|
comm_world = comparison_net.CreateCommonWorld(
|
|
rendezvous['kv_handler'] or [],
|
|
"initial_sync",
|
|
name=model.net.Proto().name + ".cw_master_select",
|
|
size=rendezvous['num_shards'],
|
|
rank=rendezvous['shard_id'],
|
|
engine=rendezvous['engine'],
|
|
status_blob="cw_master_select",
|
|
**kwargs
|
|
)
|
|
|
|
blob_name_checksum = blob_name + "_checksum"
|
|
comparison_net.SumSqrElements(
|
|
[blob_name], [blob_name_checksum], average=False
|
|
)
|
|
|
|
blob_name_gather = blob_name + "_gather"
|
|
comparison_net.Mul(
|
|
inputs=["compare_arr", blob_name_checksum],
|
|
outputs=blob_name_gather,
|
|
broadcast=1
|
|
)
|
|
|
|
comparison_net.Allreduce(
|
|
inputs=[comm_world, blob_name_gather],
|
|
outputs=[blob_name_gather],
|
|
engine=rendezvous['engine'],
|
|
status_blob="all_reduce_master_select_status",
|
|
)
|
|
|
|
workspace.RunNetOnce(comparison_net)
|
|
gather_arr = workspace.FetchBlob(blob_name_gather)
|
|
|
|
baseline = gather_arr[0]
|
|
for i in range(rendezvous['num_shards']):
|
|
assert gather_arr[i] == baseline, \
|
|
"allcompare failed on shard {}.".format(rendezvous['shard_id'])
|
|
|
|
return True
|