Source code for horovod.torch.functions

# Copyright 2020 Uber Technologies, Inc. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

import collections
import io

from import Iterable

import cloudpickle
import torch

from horovod.torch.mpi_ops import allgather, broadcast_, broadcast_async_
from horovod.torch.mpi_ops import synchronize
from horovod.torch.mpi_ops import rank, size
from horovod.torch.mpi_ops import ProcessSet, global_process_set

[docs]def broadcast_parameters(params, root_rank): """ Broadcasts the parameters from root rank to all other processes. Typical usage is to broadcast the ``model.state_dict()``, ``model.named_parameters()``, or ``model.parameters()``. Arguments: params: One of the following: - list of parameters to broadcast - dict of parameters to broadcast root_rank: The rank of the process from which parameters will be broadcasted to all other processes. """ if isinstance(params, dict): params = sorted(params.items()) elif isinstance(params, list): # support both named_parameters() and regular parameters() params = [p if isinstance(p, tuple) else (None, p) for p in params] else: raise ValueError('invalid params of type: %s' % type(params)) # Run asynchronous broadcasts. handles = [] for name, p in params: handle = broadcast_async_(p, root_rank, name) handles.append(handle) # Wait for completion. for handle in handles: synchronize(handle)
[docs]def broadcast_optimizer_state(optimizer, root_rank): """ Broadcasts an optimizer state from root rank to all other processes. Arguments: optimizer: An optimizer. root_rank: The rank of the process from which the optimizer will be broadcasted to all other processes. """ from horovod.torch.optimizer import DistributedOptimizer if isinstance(optimizer, torch.optim.LBFGS): # TODO(travis): L-BFGS cannot be easily supported without serializing # the entire state_dict, as its structure is deeply nested and contains # None type parameter values raise ValueError('cannot broadcast torch.optim.LBFGS state') state_dict = optimizer.state_dict() # Newly created optimizers will not have their state initialized, so # do that initialization here if len(state_dict['state']) == 0: for group in optimizer.param_groups: for p in group['params']: if p.requires_grad and id(p) not in state_dict['state']: p.grad = if isinstance(optimizer, torch.optim.SparseAdam): p.grad = p.grad.to_sparse() # This function accepts a torch.optim.Optimizer or a DistributedOptimizer # wrapped around a torch optimizer. Calling step() with a DistributedOptimizer # forces allreduce on all model parameters, which will result in deadlock # unless every rank calls step(). Therefore, to finish state initialization # only call optimizer.step() with a torch.optim.Optimizer. if optimizer.__module__ == DistributedOptimizer.__module__: super(optimizer.__class__, optimizer).step() else: optimizer.step() state_dict = optimizer.state_dict() # If the state_dict is still empty after initialization, then # the optimizer is stateless, and there is nothing to broadcast. # Furthermore, attempting to access the state dict would result in # an error. if len(state_dict['state']) == 0: return params = [] scalars = {} callbacks = {} occurrences = collections.defaultdict(int) # Returns the full type structure of the possibly nested objects for recursive casting back def _get_types(x): if isinstance(x, Iterable): return type(x), [_get_types(xi) for xi in x] else: return type(x) # Casts an object encoded in a tensor back into its original type and subtypes def _recursive_cast(x, dtype): if isinstance(dtype, tuple): t, dtypes = dtype x = t(x) return t([_recursive_cast(x[i], dtypes[i]) for i in range(len(x))]) else: return dtype(x) # Some optimizer parameters may be represented as scalars instead of # tensors. In such cases, we place the scalars into a single dict, # then pickle and broadcast with broadcast_object (under the assumption # that there are not many scalars, and so the overhead of pickling will # be relatively low). Because broadcast_obect is performed out-of-place, # we then use a callback to assign the new value to the correct element # of the optimizer state. def _create_state_callback(pid, name): def _assign_state(v): state_dict['state'][pid][name] = v return _assign_state def _create_option_callback(index, option_key): def _assign_option(v): optimizer.param_groups[index][option_key] = v return _assign_option # Param groups are an ordered list, normally there is only one per model, # but users can add additional param groups for example to train # previously frozen layers for index, group in enumerate(state_dict['param_groups']): # Broadcast options like learning rate for option_key, option_value in group.items(): if option_key == 'params': continue # Options like the learning rate are scalar, and need to be broadcast separately key = '%s.%d' % (option_key, index) scalars[key] = option_value callbacks[key] = _create_option_callback(index, option_key) # The params list here is ordered by the layers in the model for pid in group['params']: if pid not in state_dict['state']: # The param has not set requires_grad, so skip broadcast continue param_state = state_dict['state'][pid] for name, p in param_state.items(): # Some parameter names may appear more than once, in which # case we ensure they have a unique identifier defined by # their order occurrences[name] += 1 key = '%s.%d' % (str(name), occurrences[name]) if torch.is_tensor(p): # Tensor -> use broadcast_parameters params.append((key, p)) else: # Scalar -> use broadcast_object scalars[key] = p callbacks[key] = _create_state_callback(pid, name) # Synchronized broadcast of all tensor parameters broadcast_parameters(params, root_rank) # Broadcast and cleanup for non-tensor parameters scalars = broadcast_object(scalars, root_rank) for key, p in scalars.items(): callbacks[key](p)
[docs]def broadcast_object(obj, root_rank=0, name=None, process_set=global_process_set): """ Serializes and broadcasts an object from root rank to all other processes. Typical usage is to broadcast the `optimizer.state_dict()`, for example: .. code-block:: python state_dict = broadcast_object(optimizer.state_dict(), 0) if hvd.rank() > 0: optimizer.load_state_dict(state_dict) Arguments: obj: An object capable of being serialized without losing any context. root_rank: The rank of the process from which parameters will be broadcasted to all other processes. name: Optional name to use during broadcast, will default to the class type. process_set: Process set object to limit this operation to a subset of Horovod processes. Default is the global process set. Returns: The object that was broadcast from the `root_rank`. """ if name is None: name = type(obj).__name__ if rank() == root_rank: b = io.BytesIO() cloudpickle.dump(obj, b) t = torch.ByteTensor(bytearray(b.getvalue())) sz = torch.IntTensor([t.shape[0]]) broadcast_(sz, root_rank, name + '.sz', process_set) else: sz = torch.IntTensor([0]) broadcast_(sz, root_rank, name + '.sz', process_set) t = torch.ByteTensor(sz.tolist()[0]) broadcast_(t, root_rank, name + '.t', process_set) if rank() != root_rank: buf = io.BytesIO(t.numpy().tobytes()) obj = cloudpickle.load(buf) return obj
[docs]def allgather_object(obj, name=None): """ Serializes and allgathers an object from all other processes. Arguments: obj: An object capable of being serialized without losing any context. name: Optional name to use during allgather, will default to the class type. Returns: The list of objects that were allgathered across all ranks. """ if name is None: name = type(obj).__name__ def load(byte_array): buf = io.BytesIO(byte_array.tobytes()) return cloudpickle.load(buf) b = io.BytesIO() cloudpickle.dump(obj, b) t = torch.ByteTensor(bytearray(b.getvalue())) sz = torch.IntTensor([t.shape[0]]) sizes = allgather(sz, name=name + '.sz').numpy() gathered = allgather(t, name=name + '.t').numpy() def select(i): start = sum(sizes[:i]) end = start + sizes[i] return gathered[start:end] return [load(select(i)) for i in range(size())]