import os
import enum
import re as regex
from collections import defaultdict
from functools import partial
import torch
import torch.nn as nn
import torch.distributed as dist
from deepspeed.utils import logger
from .. import utils as ds_utils
from ..activation_checkpointing import checkpointing
from .topology import PipeDataParallelTopology, PipelineParallelGrid
class PipelineError(Exception):
"""Errors related to the use of deepspeed.PipelineModule """
[docs]class LayerSpec:
"""Building block for specifying pipeline-parallel modules.
LayerSpec stores the type information and parameters for each stage in a
PipelineModule. For example:
.. code-block:: python
nn.Sequence(
torch.nn.Linear(self.in_dim, self.hidden_dim, bias=False),
torch.nn.Linear(self.hidden_hidden, self.out_dim)
)
becomes
.. code-block:: python
layer_specs = [
LayerSpec(torch.nn.Linear, self.in_dim, self.hidden_dim, bias=False),
LayerSpec(torch.nn.Linear, self.hidden_hidden, self.out_dim)]
]
"""
def __init__(self, typename, *module_args, **module_kwargs):
self.typename = typename
self.module_args = module_args
self.module_kwargs = module_kwargs
if not issubclass(typename, nn.Module):
raise RuntimeError('LayerSpec only supports torch.nn.Module types.')
if dist.is_initialized():
self.global_rank = dist.get_rank()
else:
self.global_rank = -1
def __repr__(self):
return ds_utils.call_to_str(self.typename.__name__,
self.module_args,
self.module_kwargs)
[docs] def build(self, log=False):
"""Build the stored specification."""
if log:
logger.info(f'RANK={self.global_rank} building {repr(self)}')
return self.typename(*self.module_args, **self.module_kwargs)
[docs]class TiedLayerSpec(LayerSpec):
def __init__(self,
key,
typename,
*module_args,
forward_fn=None,
tied_weight_attr='weight',
**module_kwargs):
super().__init__(typename, *module_args, **module_kwargs)
self.key = key
self.forward_fn = forward_fn
self.tied_weight_attr = tied_weight_attr
[docs]class PipelineModule(nn.Module):
def __init__(self,
layers,
num_stages=None,
topology=None,
loss_fn=None,
seed_layers=False,
seed_fn=None,
base_seed=1234,
partition_method='parameters',
activation_checkpoint_interval=0,
activation_checkpoint_func=checkpointing.checkpoint):
"""Modules to be parallelized with pipeline parallelism.
The key constraint that enables pipeline parallelism is the
representation of the forward pass as a sequence of layers
and the enforcement of a simple interface between them. The
forward pass is implicitly defined by the module ``layers``. The key
assumption is that the output of each layer can be directly fed as
input to the next, like a ``torch.nn.Sequence``. The forward pass is
implicitly:
.. code-block:: python
def forward(self, inputs):
x = inputs
for layer in self.layers:
x = layer(x)
return x
Args:
layers (Iterable): A sequence of layers defining pipeline structure. Can be a ``torch.nn.Sequential`` module.
num_stages (int, optional): The degree of pipeline parallelism. If not specified, ``topology`` must be provided.
topology (``deepseed.pipe.ProcessTopology``, optional): Defines the axes of parallelism axes for training. Must be provided if ``num_stages`` is ``None``.
loss_fn (callable, optional): Loss is computed ``loss = loss_fn(outputs, label)``
base_seed (int, optional): [description]. Defaults to 1234.
partition_method (str, optional): [description]. Defaults to 'parameters'.
activation_checkpoint_interval (int, optional): The granularity activation checkpointing in terms of number of layers. 0 disables activation checkpointing.
activation_checkpoint_func (callable, optional): The function to use for activation checkpointing. Defaults to ``deepspeed.checkpointing.checkpoint``.
"""
super().__init__()
if num_stages is None and topology is None:
raise RuntimeError('must provide num_stages or topology')
self.micro_offset = 0
self.loss_fn = loss_fn
self.seed_layers = seed_layers
self.seed_fn = seed_fn
self.base_seed = base_seed
if dist.get_rank() == 0:
try:
seed_str = self.seed_fn.__name__
except AttributeError:
seed_str = None
print(
f'SEED_LAYERS={self.seed_layers} BASE_SEED={self.base_seed} SEED_FN={seed_str}'
)
# Setup world info
self.world_group = dist.new_group(ranks=range(dist.get_world_size()))
self.global_rank = dist.get_rank(group=self.world_group)
self.world_size = dist.get_world_size(group=self.world_group)
if topology:
self._topo = topology
self.num_stages = self._topo.get_dim('pipe')
else:
self.num_stages = num_stages
if topology is None:
if self.world_size % self.num_stages != 0:
raise RuntimeError(
f'num_stages ({self.num_stages}) must divide distributed world size ({self.world_size})'
)
dp = self.world_size // num_stages
topology = PipeDataParallelTopology(num_pp=num_stages, num_dp=dp)
self._topo = topology
# Contruct communicators for pipeline topology
self._grid = PipelineParallelGrid(process_group=self.world_group,
topology=self._topo)
self.stage_id = self._topo.get_coord(self.global_rank).pipe
# Initialize partition information
self._layer_specs = list(layers)
self._num_layers = len(self._layer_specs)
self._local_start = 0
self._local_stop = None
self._partition_layers(method=partition_method)
self.forward_funcs = []
self.tied_modules = nn.ModuleDict()
self.tied_weight_attrs = {}
# Offset the random seed by the stage ID.
#newseed = torch.cuda.initial_seed() + self._grid.get_stage_id()
#ds_utils.set_random_seed(newseed)
#with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
self._build()
self.to('cuda')
self.tied_comms = self._index_tied_modules()
self._synchronize_tied_weights()
self.activation_checkpoint_interval = activation_checkpoint_interval
self.activation_checkpoint_func = activation_checkpoint_func
def _build(self):
specs = self._layer_specs
for local_idx, layer in enumerate(specs[self._local_start:self._local_stop]):
layer_idx = local_idx + self._local_start
if self.seed_layers:
if self.seed_fn:
self.seed_fn(self.base_seed + layer_idx)
else:
ds_utils.set_random_seed(self.base_seed + layer_idx)
# Recursively build PipelineModule objects
if isinstance(layer, PipelineModule):
raise NotImplementedError('RECURSIVE BUILD NOT YET IMPLEMENTED')
# LayerSpec objects contain an nn.Module that should be allocated now.
elif isinstance(layer, nn.Module):
name = str(layer_idx)
self.forward_funcs.append(layer)
self.add_module(name, layer)
# TiedLayerSpec objects contain an nn.Module that should be allocated now.
elif isinstance(layer, TiedLayerSpec):
# Build and register the module if we haven't seen it before.
if layer.key not in self.tied_modules:
self.tied_modules[layer.key] = layer.build()
self.tied_weight_attrs[layer.key] = layer.tied_weight_attr
if layer.forward_fn is None:
# Just use forward()
self.forward_funcs.append(self.tied_modules[layer.key])
else:
# User specified fn with args (module, input)
self.forward_funcs.append(
partial(layer.forward_fn,
self.tied_modules[layer.key]))
# LayerSpec objects contain an nn.Module that should be allocated now.
elif isinstance(layer, LayerSpec):
module = layer.build()
name = str(layer_idx)
self.forward_funcs.append(module)
self.add_module(name, module)
# Last option: layer may be a functional (e.g., lambda). We do nothing in
# that case and just use it in forward()
else:
self.forward_funcs.append(layer)
# All pipeline parameters should be considered as model parallel in the context
# of our FP16 optimizer
for p in self.parameters():
p.model_parallel = True
def _count_layer_params(self):
"""Count the trainable parameters in individual layers.
This routine will only build one layer at a time.
Returns:
A list of the number of parameters in each layer.
"""
param_counts = [0] * len(self._layer_specs)
for idx, layer in enumerate(self._layer_specs):
if isinstance(layer, LayerSpec):
l = layer.build()
params = filter(lambda p: p.requires_grad, l.parameters())
param_counts[idx] = sum(p.numel() for p in params)
elif isinstance(layer, nn.Module):
params = filter(lambda p: p.requires_grad, layer.parameters())
param_counts[idx] = sum(p.numel() for p in params)
return param_counts
def _find_layer_type(self, layername):
idxs = []
typeregex = regex.compile(layername, regex.IGNORECASE)
for idx, layer in enumerate(self._layer_specs):
name = None
if isinstance(layer, LayerSpec):
name = layer.typename.__name__
elif isinstance(layer, nn.Module):
name = layer.__class__.__name__
else:
try:
name = layer.__name__
except AttributeError:
continue
if typeregex.search(name):
idxs.append(idx)
if len(idxs) == 0:
raise RuntimeError(
f"Partitioning '{layername}' found no valid layers to partition.")
return idxs
def forward(self, forward_input):
# We need to offset the seed by the microbatch ID. Save it in a local var to
# ensure it is preserved in the closure. Otherwise checkpointed forward funcs
# will see a different offset.
self.micro_offset += 1
def exec_range_func(start, end):
''' Helper function to be used with checkpoint()
Adapted from torch.utils.checkpoint:checkpoint_sequential()
'''
local_micro_offset = self.micro_offset + 1
def exec_func(*inputs):
# Single tensor inputs need to be unwrapped
if len(inputs) == 1:
inputs = inputs[0]
for idx, layer in enumerate(self.forward_funcs[start:end]):
self.curr_layer = idx + self._local_start
if self.seed_layers:
new_seed = (self.base_seed *
local_micro_offset) + self.curr_layer
if self.seed_fn:
self.seed_fn(new_seed)
else:
ds_utils.set_random_seed(new_seed)
inputs = layer(inputs)
return inputs
return exec_func
if self.activation_checkpoint_interval == 0:
func = exec_range_func(0, len(self.forward_funcs))
x = func(forward_input)
else:
num_layers = len(self.forward_funcs)
x = forward_input
for start_idx in range(0, num_layers, self.activation_checkpoint_interval):
end_idx = min(start_idx + self.activation_checkpoint_interval,
num_layers)
funcs = self.forward_funcs[start_idx:end_idx]
# Since we either pass tensors or tuples of tensors without unpacking, we
# need to be careful not to double-wrap tensors with tuple.
if not isinstance(x, tuple):
x = (x, )
if self._is_checkpointable(funcs):
x = self.activation_checkpoint_func(
exec_range_func(start_idx,
end_idx),
*x)
else:
x = exec_range_func(start_idx, end_idx)(*x)
return x
def _partition_layers(self, method='uniform'):
num_stages = self._topo.get_dim('pipe')
stage_id = self._topo.get_coord(self.global_rank).pipe
if self.global_rank == 0:
logger.info(f'Partitioning pipeline stages with method {method}')
method = method.lower()
# Each stage gets a simple uniform number of layers.
if method == 'uniform':
num_layers = len(self._layer_specs)
self.parts = ds_utils.partition_uniform(num_items=num_layers,
num_parts=num_stages)
elif method == 'parameters':
param_counts = self._count_layer_params()
self.parts = ds_utils.partition_balanced(weights=param_counts,
num_parts=num_stages)
elif method.startswith('type:'):
layertype = method.split(':')[1]
binary_weights = [0] * len(self._layer_specs)
for idx in self._find_layer_type(layertype):
binary_weights[idx] = 1
else:
self.parts = ds_utils.partition_balanced(weights=binary_weights,
num_parts=num_stages)
elif method == 'profile':
raise NotImplementedError(f'Partitioning method {method} not implemented.')
else:
raise NotImplementedError(f'Partitioning method {method} not implemented.')
# Print some information on the partitioning.
if self.global_rank == 0:
for stage in range(num_stages):
start = self.parts[stage]
stop = self.parts[stage + 1]
print(f'stage={stage} layers={stop - start}')
for idx, layer in enumerate(self._layer_specs[start:stop]):
name = str(layer)
if isinstance(layer, LayerSpec):
name = layer.typename.__name__
if isinstance(layer, nn.Module):
name = layer.__class__.__name__
else:
try:
name = layer.__name__
except AttributeError:
pass
print(f' {idx+start:2d}: {name}')
if self.loss_fn:
try:
print(f' loss: {self.loss_fn.__name__}')
except AttributeError:
print(f' loss: {self.loss_fn.__class__.__name__}')
self._set_bounds(start=self.parts[stage_id], stop=self.parts[stage_id + 1])
[docs] def allreduce_tied_weight_gradients(self):
'''All reduce the gradients of the tied weights between tied stages'''
for key, comm in self.tied_comms.items():
weight = getattr(self.tied_modules[key], comm['weight_attr'])
dist.all_reduce(weight.grad, group=comm['group'])
def _synchronize_tied_weights(self):
for key, comm in self.tied_comms.items():
dist.broadcast(
getattr(comm['module'],
comm['weight_attr']),
src=min(comm['ranks']),
group=comm['group'],
)
def _index_tied_modules(self):
''' Build communication structures for tied modules. '''
tied_comms = {}
if self._topo.get_dim('pipe') == 1:
return tied_comms
specs = self._layer_specs
tie_keys = set(s.key for s in specs if isinstance(s, TiedLayerSpec))
for key in tie_keys:
# Find the layers that the tied module appears in
tied_layers = []
for idx, layer in enumerate(specs):
if isinstance(layer, TiedLayerSpec) and layer.key == key:
tied_layers.append(idx)
# Find all stages with this tied module
# TODO: Would be nice to remove the nested data/model parallelism loops and
# TODO: instead generalize in some way, since we really just care about the
# TODO: stage that owns the tied layer. Then loop over each (dp, mp, ...)
# TODO: fiber to generate process groups.
tied_stages = set(self.stage_owner(idx) for idx in tied_layers)
for dp in range(self._grid.data_parallel_size):
for mp in range(self._grid.model_parallel_size):
tied_ranks = []
for s in sorted(tied_stages):
if self._grid.model_parallel_size > 1:
tied_ranks.append(
self._grid.stage_to_global(stage_id=s,
data=dp,
model=mp))
else:
tied_ranks.append(
self._grid.stage_to_global(stage_id=s,
data=dp))
group = dist.new_group(ranks=tied_ranks)
# Record this tied module if we own a local copy of it.
if self.global_rank in tied_ranks:
assert key in self.tied_modules
if key in self.tied_modules:
tied_comms[key] = {
'ranks': tied_ranks,
'group': group,
'weight_attr': self.tied_weight_attrs[key],
'module': self.tied_modules[key],
}
# Only count the tied module once in the eyes of the FP16 optimizer
if self.global_rank != tied_ranks[0]:
for p in self.tied_modules[key].parameters():
p.model_parallel = False
'''
if len(tied_comms) > 0:
print(f'RANK={self.global_rank} tied_comms={tied_comms}')
'''
return tied_comms
def partitions(self):
return self.parts
def stage_owner(self, layer_idx):
assert 0 <= layer_idx < self._num_layers
for stage in range(self._topo.get_dim('pipe')):
if self.parts[stage] <= layer_idx < self.parts[stage + 1]:
return stage
raise RuntimeError(f'Layer {layer_idx} not owned? parts={self.parts}')
def _set_bounds(self, start=None, stop=None):
"""Manually define the range of layers that will be built on this process.
These boundaries are treated as list slices and so start is inclusive and stop is
exclusive. The default of None for both results in all layers being built
locally.
"""
self._local_start = start
self._local_stop = stop
def set_checkpoint_interval(self, interval):
assert interval >= 0
self.checkpoint_interval = interval
[docs] def topology(self):
""" ProcessTopology object to query process mappings. """
return self._topo
def mpu(self):
return self._grid
def num_pipeline_stages(self):
return self._topo.get_dim('pipe')
[docs] def ckpt_prefix(self, checkpoints_path, tag):
"""Build a prefix for all checkpoint files written by this module. """
# All checkpoint files start with this
rank_name = 'module'
# Data parallelism is omitted from the naming convention because we are agnostic
# to this in the checkpoint.
omit_dims = frozenset(['data'])
axes = [a for a in self._grid._topo.get_axis_names() if a not in omit_dims]
for dim in axes:
rank = getattr(self._grid._topo.get_coord(rank=self.global_rank), dim)
rank_name += f'-{dim}_{rank:02d}'
ckpt_name = os.path.join(checkpoints_path, str(tag), rank_name)
return ckpt_name
[docs] def ckpt_layer_path(self, ckpt_dir, local_layer_idx):
"""Customize a prefix for a specific pipeline module layer. """
idx = local_layer_idx + self._local_start
layer_ckpt_path = os.path.join(ckpt_dir, f'layer_{idx:02d}')
rank_repr = self._grid._topo.get_rank_repr(rank=self.global_rank)
if rank_repr is not '':
layer_ckpt_path += f'-{rank_repr}'
layer_ckpt_path += '-model_states.pt'
return layer_ckpt_path
def save_state_dict(self, save_dir):
if self._grid.data_parallel_id != 0:
return
os.makedirs(save_dir, exist_ok=True)
layer_offset = self._local_start
for idx, layer in enumerate(self.forward_funcs):
model_ckpt_path = self.ckpt_layer_path(save_dir, idx)
if not hasattr(layer, 'state_dict'):
continue
torch.save(layer.state_dict(), model_ckpt_path)
def load_state_dir(self, load_dir, strict=True):
rank = dist.get_rank()
layer_offset = self._local_start
for idx, layer in enumerate(self.forward_funcs):
# Functions, etc. will not have state_dicts
if not hasattr(layer, 'load_state_dict'):
continue
model_ckpt_path = self.ckpt_layer_path(load_dir, idx)
layer.load_state_dict(torch.load(model_ckpt_path,
map_location=lambda storage,
loc: storage),
strict=strict)
if self._grid.data_parallel_id == 0:
logger.info(
f'RANK={self.global_rank} Loaded layer={idx+layer_offset} file={model_ckpt_path}'
)
self._synchronize_tied_weights()
def _is_checkpointable(self, funcs):
if self.__class__.__name__ == 'GPT2ModelPipe':
return all('ParallelTransformerLayerPipe' in f.__class__.__name__
for f in funcs)
params = [f.parameters() for f in funcs if isinstance(f, torch.nn.Module)]
return any(len(list(p)) > 0 for p in params)