'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
import sys
import types
from typing import Optional, Union
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from packaging import version as pkg_version
from . import ops
from . import module_inject
from .runtime.engine import DeepSpeedEngine, DeepSpeedOptimizerCallable, DeepSpeedSchedulerCallable
from .runtime.engine import ADAM_OPTIMIZER, LAMB_OPTIMIZER
from .runtime.pipe.engine import PipelineEngine
from .inference.engine import InferenceEngine
from .runtime.lr_schedules import add_tuning_arguments
from .runtime.config import DeepSpeedConfig, DeepSpeedConfigError
from .runtime.activation_checkpointing import checkpointing
from .ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
from .module_inject import replace_transformer_layer, revert_transformer_layer
from .utils import log_dist
from .utils.distributed import init_distributed
from .runtime import zero
from .pipe import PipelineModule
from .git_version_info import version, git_hash, git_branch
def _parse_version(version_str):
'''Parse a version string and extract the major, minor, and patch versions.'''
ver = pkg_version.parse(version_str)
return ver.major, ver.minor, ver.micro
# Export version information
__version__ = version
__version_major__, __version_minor__, __version_patch__ = _parse_version(__version__)
__git_hash__ = git_hash
__git_branch__ = git_branch
[docs]def initialize(args=None,
model: torch.nn.Module = None,
optimizer: Optional[Union[Optimizer,
DeepSpeedOptimizerCallable]] = None,
model_parameters: Optional[torch.nn.Module] = None,
training_data: Optional[torch.utils.data.Dataset] = None,
lr_scheduler: Optional[Union[_LRScheduler,
DeepSpeedSchedulerCallable]] = None,
mpu=None,
dist_init_required: Optional[bool] = None,
collate_fn=None,
config=None,
config_params=None):
"""Initialize the DeepSpeed Engine.
Arguments:
args: an object containing local_rank and deepspeed_config fields.
This is optional if `config` is passed.
model: Required: nn.module class before apply any wrappers
optimizer: Optional: a user defined Optimizer or Callable that returns an Optimizer object.
This overrides any optimizer definition in the DeepSpeed json config.
model_parameters: Optional: An iterable of torch.Tensors or dicts.
Specifies what Tensors should be optimized.
training_data: Optional: Dataset of type torch.utils.data.Dataset
lr_scheduler: Optional: Learning Rate Scheduler Object or a Callable that takes an Optimizer and returns a Scheduler object.
The scheduler object should define a get_lr(), step(), state_dict(), and load_state_dict() methods
mpu: Optional: A model parallelism unit object that implements
get_{model,data}_parallel_{rank,group,world_size}()
dist_init_required: Optional: None will auto-initialize torch.distributed if needed,
otherwise the user can force it to be initialized or not via boolean.
collate_fn: Optional: Merges a list of samples to form a
mini-batch of Tensor(s). Used when using batched loading from a
map-style dataset.
config: Optional: Instead of requiring args.deepspeed_config you can pass your deepspeed config
as an argument instead, as a path or a dictionary.
config_params: Optional: Same as `config`, kept for backwards compatibility.
Returns:
A tuple of ``engine``, ``optimizer``, ``training_dataloader``, ``lr_scheduler``
* ``engine``: DeepSpeed runtime engine which wraps the client model for distributed training.
* ``optimizer``: Wrapped optimizer if a user defined ``optimizer`` is supplied, or if
optimizer is specified in json config else ``None``.
* ``training_dataloader``: DeepSpeed dataloader if ``training_data`` was supplied,
otherwise ``None``.
* ``lr_scheduler``: Wrapped lr scheduler if user ``lr_scheduler`` is passed, or
if ``lr_scheduler`` specified in JSON configuration. Otherwise ``None``.
"""
log_dist("DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
__version__,
__git_hash__,
__git_branch__),
ranks=[0])
assert model is not None, "deepspeed.initialize requires a model"
if not isinstance(model, PipelineModule):
engine = DeepSpeedEngine(args=args,
model=model,
optimizer=optimizer,
model_parameters=model_parameters,
training_data=training_data,
lr_scheduler=lr_scheduler,
mpu=mpu,
dist_init_required=dist_init_required,
collate_fn=collate_fn,
config=config,
config_params=config_params)
else:
assert mpu is None, "mpu must be None with pipeline parallelism"
engine = PipelineEngine(args=args,
model=model,
optimizer=optimizer,
model_parameters=model_parameters,
training_data=training_data,
lr_scheduler=lr_scheduler,
mpu=model.mpu(),
dist_init_required=dist_init_required,
collate_fn=collate_fn,
config=config,
config_params=config_params)
return_items = [
engine,
engine.optimizer,
engine.training_dataloader,
engine.lr_scheduler
]
return tuple(return_items)
def _add_core_arguments(parser):
r"""Helper (internal) function to update an argument parser with an argument group of the core DeepSpeed arguments.
The core set of DeepSpeed arguments include the following:
1) --deepspeed: boolean flag to enable DeepSpeed
2) --deepspeed_config <json file path>: path of a json configuration file to configure DeepSpeed runtime.
This is a helper function to the public add_config_arguments()
Arguments:
parser: argument parser
Return:
parser: Updated Parser
"""
group = parser.add_argument_group('DeepSpeed', 'DeepSpeed configurations')
group.add_argument(
'--deepspeed',
default=False,
action='store_true',
help=
'Enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)')
group.add_argument('--deepspeed_config',
default=None,
type=str,
help='DeepSpeed json configuration file.')
group.add_argument(
'--deepscale',
default=False,
action='store_true',
help=
'Deprecated enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)'
)
group.add_argument('--deepscale_config',
default=None,
type=str,
help='Deprecated DeepSpeed json configuration file.')
group.add_argument(
'--deepspeed_mpi',
default=False,
action='store_true',
help=
"Run via MPI, this will attempt to discover the necessary variables to initialize torch "
"distributed from the MPI environment")
return parser
[docs]def add_config_arguments(parser):
r"""Update the argument parser to enabling parsing of DeepSpeed command line arguments.
The set of DeepSpeed arguments include the following:
1) --deepspeed: boolean flag to enable DeepSpeed
2) --deepspeed_config <json file path>: path of a json configuration file to configure DeepSpeed runtime.
Arguments:
parser: argument parser
Return:
parser: Updated Parser
"""
parser = _add_core_arguments(parser)
return parser
[docs]def init_inference(model,
triangular_masking=True,
mp_size=1,
mpu=None,
ep_group=None,
expert_mp_group=None,
checkpoint=None,
dtype=None,
injection_policy=None,
replace_method='auto',
quantization_setting=None,
replace_with_kernel_inject=False,
return_tuple=True,
ep_size=1,
moe=False,
moe_experts=1,
moe_type='standard'):
"""Initialize the DeepSpeed InferenceEngine.
Arguments:
model: Required: nn.module class before apply any wrappers
triangular_masking: Required: this shows the type of masking for attention scores in transformer layer
note that the masking is application specific.
mp_size: Optional: Desired model parallel size, default is 1 meaning no
model parallelism.
mpu: Optional: A model parallelism unit object that implements
get_{model,data}_parallel_{rank,group,world_size}()
checkpoint: Optional: Path to deepspeed compatible checkpoint or path to
JSON with load policy.
dtype: Optional: Desired model data type, will convert model to this type.
Supported target types: torch.half, torch.int8, torch.float
injection_policy: Optional: Dictionary mapping a client nn.Module to its corresponding
injection policy. e.g., {BertLayer : deepspeed.inference.HFBertLayerPolicy}
replace_method: Optional: If 'auto' DeepSpeed will automatically try and replace
model modules with its optimized versions. If an injection_policy is set this will
override the automatic replacement behavior.
quantization_setting: Optional: Quantization settings used for quantizing your model using the MoQ.
The setting can be one element or a tuple. If one value is passed in, we consider it as the number
of groups used in quantization. A tuple is passed in if we want to mention that there is extra-grouping
for the MLP part of a Transformer layer (e.g. (True, 8) shows we quantize the model using 8 groups for
all the network except the MLP part that we use 8 extra grouping).
replace_with_kernel_inject: If set we inject kernel as we initialize the inference-engine
Returns:
A deepspeed.InferenceEngine wrapped model.
"""
log_dist("DeepSpeed info: version={}, git-hash={}, git-branch={}".format(
__version__,
__git_hash__,
__git_branch__),
ranks=[0])
if isinstance(model, PipelineModule):
raise NotImplementedError("pipeline module support is not implemented yet")
else:
engine = InferenceEngine(model,
triangular_masking,
mp_size,
ep_size,
mpu,
ep_group,
expert_mp_group,
checkpoint,
dtype,
injection_policy,
return_tuple,
replace_method,
quantization_setting,
replace_with_kernel_inject,
moe,
moe_experts,
moe_type)
return engine