mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-29 20:14:01 +00:00
FP16 optimizer automatically detect DeepSpeed compatibility (#18084)
### FP16 optimizer automatically detect DeepSpeed compatibility Optimum/Transformers are using accelerate lib to prepare models, so our FP16 optimizer wrapper does not work for long time. Because the namespace is `accelerate.utils.deepspeed.DeepSpeedOptimizerWrapper`, which underlying is still calling into DeepSpeed stage1and2 optimizer. This PR includes following changes: 1. Add `accelerate.utils.deepspeed.DeepSpeedOptimizerWrapper` in the modifier registry, plus a check on its contained `optimizer` property MUST be DeepSpeed stage 1 and 2 optimizer. (let's cover Stage 3 optimizer later) 2. For DeepSpeed version > 0.9.1, we will store the source code in a version list. As long as the related function in DeepSpeed remains unchanged during its new release, we won't need manually upgrade the version check any more. If some day, the source code did not match, a warning will be raised to users, to add a new version of source code in the list. With the above change, we will have our FP16 Optimizer working again in Optimum. 
This commit is contained in:
pengwa
GitHub
parent
ae8561979f
commit
2c6b31c5aa
5 changed files with 223 additions and 31 deletions
|
|
@ -45,6 +45,7 @@ exclude_patterns = [
|
|||
'cmake/external/**',
|
||||
# ignore generated flatbuffers code
|
||||
'onnxruntime/core/flatbuffers/ort_flatbuffers_py/**',
|
||||
'orttraining/orttraining/python/training/optim/_ds_code_store.py',
|
||||
]
|
||||
command = [
|
||||
'python',
|
||||
|
|
@ -76,6 +77,7 @@ exclude_patterns = [
|
|||
'cmake/**',
|
||||
'orttraining/*',
|
||||
'onnxruntime/core/flatbuffers/**',
|
||||
'orttraining/orttraining/python/training/optim/_ds_code_store.py',
|
||||
]
|
||||
command = [
|
||||
'python',
|
||||
|
|
|
|||
|
|
@ -0,0 +1,81 @@
|
|||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
#
|
||||
# Copyright 2020 The Microsoft DeepSpeed Team
|
||||
#
|
||||
# !!!IMPORTANT: This file is a copy of the original one in DeepSpeed repo at given version,
|
||||
# It is used to compare with the source code of current installed DeepSpeed during runtime.
|
||||
# Please don't modify it or do any code formatting for it.
|
||||
# 'orttraining/orttraining/python/training/optim/_ds_code_store.py' is removed from lintrunner config by intention.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
# Wrap code in this to make sure the indentation is correct compared with raw DeepSpeed.
|
||||
|
||||
class Stage1And2_DeepSpeedZeroOptimizer_0_9_2:
|
||||
|
||||
def has_overflow_serial(self, params, is_grad_list=False):
|
||||
for p in params:
|
||||
if p.grad is not None and self._has_inf_or_nan(p.grad.data):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def get_grad_norm_direct(self, gradients, params, norm_type=2):
|
||||
"""Clips gradient norm of an iterable of parameters.
|
||||
|
||||
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
|
||||
added functionality to handle model parallel parameters. Note that
|
||||
the gradients are modified in place.
|
||||
|
||||
Arguments:
|
||||
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
|
||||
single Tensor that will have gradients normalized
|
||||
max_norm (float or int): max norm of the gradients
|
||||
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
|
||||
infinity norm.
|
||||
|
||||
Returns:
|
||||
Total norm of the parameters (viewed as a single vector).
|
||||
"""
|
||||
norm_type = float(norm_type)
|
||||
if norm_type == inf:
|
||||
total_norm = max(g.data.abs().max() for g in gradients)
|
||||
total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)])
|
||||
dist.all_reduce(total_norm_cuda, op=dist.ReduceOp.MAX, group=self.dp_process_group)
|
||||
|
||||
# Take max across all GPUs.
|
||||
self._model_parallel_all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX)
|
||||
total_norm = total_norm_cuda[0].item()
|
||||
else:
|
||||
total_norm = 0.0
|
||||
# if dist.get_rank() == 0:
|
||||
# logger.info(f"Total Norm beginning {total_norm}")
|
||||
for g, p in zip(gradients, params):
|
||||
# Pipeline parallelism may replicate parameters. Avoid multi-counting.
|
||||
if hasattr(p, PIPE_REPLICATED) and p.ds_pipe_replicated:
|
||||
continue
|
||||
if is_model_parallel_parameter(p) or (self.model_parallel_rank == 0):
|
||||
param_norm = g.data.double().norm(2)
|
||||
total_norm += param_norm.item()**2
|
||||
# Sum across all model parallel GPUs.
|
||||
total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)])
|
||||
dist.all_reduce(total_norm_cuda, op=dist.ReduceOp.SUM, group=self.dp_process_group)
|
||||
|
||||
self._model_parallel_all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.SUM)
|
||||
|
||||
total_norm = total_norm_cuda[0].item()**(1. / norm_type)
|
||||
|
||||
if total_norm == float('inf') or total_norm == -float('inf') or total_norm != total_norm:
|
||||
total_norm = -1
|
||||
|
||||
return total_norm
|
||||
|
||||
|
||||
def has_overflow_partitioned_grads_serial(self):
|
||||
for i in range(len(self.bit16_groups)):
|
||||
for j, grad in enumerate(self.averaged_gradients[i]):
|
||||
if grad is not None and self._has_inf_or_nan(grad.data, j):
|
||||
return True
|
||||
return False
|
||||
|
|
@ -10,6 +10,9 @@
|
|||
# - has_overflow_partitioned_grads_serial : https://github.com/microsoft/DeepSpeed/blob/d8e9ef6f99e27bb95e10bd146d145b3372b4cfda/deepspeed/runtime/zero/stage2.py#L1799
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import inspect
|
||||
import types
|
||||
import warnings
|
||||
|
||||
|
|
@ -17,12 +20,69 @@ import torch
|
|||
from numpy import inf
|
||||
from packaging.version import Version
|
||||
|
||||
from ._ds_code_store import Stage1And2_DeepSpeedZeroOptimizer_0_9_2
|
||||
from ._modifier import FP16OptimizerModifier, check_overflow, check_overflow_for_grads
|
||||
from ._multi_tensor_apply import MultiTensorApply
|
||||
|
||||
multi_tensor_applier = MultiTensorApply(2048 * 32)
|
||||
|
||||
|
||||
def _get_normalized_str(function) -> str:
|
||||
return inspect.getsource(function)
|
||||
|
||||
|
||||
def _dynamic_checks(cur_ds_version: Version, optimizer) -> bool:
|
||||
_functions_to_override = ["has_overflow_serial", "get_grad_norm_direct", "has_overflow_partitioned_grads_serial"]
|
||||
|
||||
_version_to_source_code_map = {"0.9.2": Stage1And2_DeepSpeedZeroOptimizer_0_9_2}
|
||||
|
||||
# Try to find the biggest version that is smaller than or equal to cur_ds_version.
|
||||
# then compare the source code (in case the found version is the latest version supported);
|
||||
# If current code does not match the found version, return False, and raise a warning to
|
||||
# add the new version to the list.
|
||||
versions = [Version(v) for v in _version_to_source_code_map]
|
||||
sorted_versions = sorted(versions, reverse=True)
|
||||
version_to_compare = None
|
||||
for sv in sorted_versions:
|
||||
if cur_ds_version >= sv:
|
||||
version_to_compare = sv
|
||||
break
|
||||
|
||||
if version_to_compare is None:
|
||||
warnings.warn(
|
||||
"Unable to find a DeepSpeed version that is smaller than or equal to the current version "
|
||||
f"{cur_ds_version}. Skip modifying optimizer.",
|
||||
UserWarning,
|
||||
)
|
||||
return False
|
||||
|
||||
v_optimizer_cls = _version_to_source_code_map[str(version_to_compare)]
|
||||
all_match = True
|
||||
for func_name in _functions_to_override:
|
||||
if not getattr(optimizer, func_name):
|
||||
warnings.warn(
|
||||
f"DeepSpeed function {func_name} is not found in optimizer. Skip modifying optimizer.", UserWarning
|
||||
)
|
||||
all_match = False
|
||||
cur_code_str = _get_normalized_str(getattr(optimizer, func_name))
|
||||
v_code_str = _get_normalized_str(getattr(v_optimizer_cls, func_name))
|
||||
if cur_code_str != v_code_str:
|
||||
warnings.warn(
|
||||
f"DeepSpeed function {func_name} has changed after version {version_to_compare}. "
|
||||
f"Please append new version {cur_ds_version} in _version_to_source_code_map and _ds_code_store.py.\n"
|
||||
f"---[{func_name}] Old Source Code Start----\n"
|
||||
f"{v_code_str}\n"
|
||||
f"---{func_name} Old Source Code End----\n"
|
||||
f"---[{func_name}] New Source Code Start----\n"
|
||||
f"{cur_code_str}\n"
|
||||
f"---{func_name} New Source Code End----",
|
||||
UserWarning,
|
||||
)
|
||||
all_match = False
|
||||
|
||||
return all_match
|
||||
|
||||
|
||||
class DeepSpeedZeROModifier(FP16OptimizerModifier):
|
||||
def __init__(self, optimizer, **kwargs) -> None:
|
||||
super().__init__(optimizer)
|
||||
|
|
@ -30,19 +90,32 @@ class DeepSpeedZeROModifier(FP16OptimizerModifier):
|
|||
def can_be_modified(self):
|
||||
import deepspeed
|
||||
|
||||
# Note 1:
|
||||
# This modifier relies on the implementation of has_overflow_serial, get_grad_norm_direct,
|
||||
# and has_overflow_partitioned_grads_serial
|
||||
# in https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/runtime/zero/stage_1_and_2.py.
|
||||
# Everytime if we want to update this version supporting list to a newer version,
|
||||
# we need to check if the implementation of these functions are changed.
|
||||
# An easy way to check is to check the history of this file, if there is no change during the update,
|
||||
# The minimum version supported is 0.4.0, all versions in between [0.4.0, 0.9.1]
|
||||
# are manually checked to make sure the implementation of these functions are "logically" not changed.
|
||||
# The way we did the check is to check the history of this file, if there is no change during the update,
|
||||
# it's safe to update the version supporting list. Otherwise, or the file is moved or renamed,
|
||||
# we need to check the implementation of these functions in detail.
|
||||
#
|
||||
# Note 2:
|
||||
# Since version 0.9.2, we added dynamic source code check, by comparing installed version of code with
|
||||
# the source code in our code store. If the source code is changed, we will raise a warning to ask user
|
||||
# to add the new version to the code store. Otherwise, we will override the functions.
|
||||
|
||||
ds_version = Version(deepspeed.__version__)
|
||||
if ds_version > Version("0.9.1") or ds_version < Version("0.4.0"):
|
||||
if ds_version < Version("0.4.0"):
|
||||
warnings.warn(
|
||||
"Skip modifying optimizer because of unsupported DeepSpeed version {}, "
|
||||
"supported version: 0.4.0 - 0.9.1.".format(deepspeed.__version__),
|
||||
f"Skip modifying optimizer because of unsupported DeepSpeed version {ds_version}, "
|
||||
"minimum supported version: 0.4.0, current version",
|
||||
UserWarning,
|
||||
)
|
||||
return False
|
||||
if ds_version > Version("0.9.1") and not _dynamic_checks(ds_version, self._optimizer):
|
||||
warnings.warn(
|
||||
f"Skip modifying optimizer because of unsupported DeepSpeed version {ds_version}.",
|
||||
UserWarning,
|
||||
)
|
||||
return False
|
||||
|
|
|
|||
|
|
@ -3,13 +3,59 @@
|
|||
# Licensed under the MIT License.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import warnings
|
||||
from typing import ClassVar
|
||||
|
||||
from ._apex_amp_modifier import ApexAMPModifier
|
||||
from ._ds_modifier import DeepSpeedZeROModifier
|
||||
from ._megatron_modifier import LegacyMegatronLMModifier
|
||||
from ._modifier import FP16OptimizerModifier
|
||||
|
||||
OptimizerModifierTypeRegistry = {
|
||||
"megatron.fp16.fp16.FP16_Optimizer": LegacyMegatronLMModifier,
|
||||
"deepspeed.runtime.zero.stage2.FP16_DeepSpeedZeroOptimizer": DeepSpeedZeROModifier,
|
||||
"deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer": DeepSpeedZeROModifier,
|
||||
"apex.amp.optimizer.unique_name_as_id": ApexAMPModifier,
|
||||
}
|
||||
|
||||
class _AccelerateDeepSpeedZeROModifier(DeepSpeedZeROModifier):
|
||||
"""
|
||||
Modifier for wrapper of DeepSpeed Optimizer in accelerator.
|
||||
https://github.com/huggingface/accelerate/blob/7843286f2e1c50735d259fbc0084a7f1c85e00e3/src/accelerate/utils/deepspeed.py#L182C19-L182C19
|
||||
"""
|
||||
|
||||
def __init__(self, accelerator_optimizer, **kwargs) -> None:
|
||||
super().__init__(accelerator_optimizer.optimizer)
|
||||
|
||||
|
||||
def get_full_qualified_type_name(o):
|
||||
klass = o.__class__
|
||||
module = klass.__module__
|
||||
if module == "builtins":
|
||||
return klass.__qualname__
|
||||
return module + "." + klass.__qualname__
|
||||
|
||||
|
||||
class OptimizerModifierTypeRegistry:
|
||||
_MAP: ClassVar[dict[str, FP16OptimizerModifier]] = {
|
||||
"megatron.fp16.fp16.FP16_Optimizer": LegacyMegatronLMModifier,
|
||||
"deepspeed.runtime.zero.stage2.FP16_DeepSpeedZeroOptimizer": DeepSpeedZeROModifier,
|
||||
"deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer": DeepSpeedZeROModifier,
|
||||
"apex.amp.optimizer.unique_name_as_id": ApexAMPModifier,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def create_modifier(optimizer_full_qualified_name: str, optimizer, **kwargs) -> FP16OptimizerModifier | None:
|
||||
"""Create modifier for optimizer."""
|
||||
if optimizer_full_qualified_name in OptimizerModifierTypeRegistry._MAP:
|
||||
return OptimizerModifierTypeRegistry._MAP[optimizer_full_qualified_name](optimizer, **kwargs)
|
||||
|
||||
if optimizer_full_qualified_name == "accelerate.utils.deepspeed.DeepSpeedOptimizerWrapper":
|
||||
if (
|
||||
hasattr(optimizer, "optimizer")
|
||||
and get_full_qualified_type_name(optimizer.optimizer) in OptimizerModifierTypeRegistry._MAP
|
||||
):
|
||||
return _AccelerateDeepSpeedZeROModifier(optimizer, **kwargs)
|
||||
|
||||
warnings.warn(
|
||||
"Skip modifying optimizer because of optimizer name not found in the registry: "
|
||||
f"{optimizer_full_qualified_name}",
|
||||
UserWarning,
|
||||
)
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -3,9 +3,8 @@
|
|||
# Licensed under the MIT License.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
import warnings
|
||||
|
||||
from ._modifier_registry import OptimizerModifierTypeRegistry
|
||||
from ._modifier_registry import OptimizerModifierTypeRegistry, get_full_qualified_type_name
|
||||
|
||||
|
||||
def FP16_Optimizer(optimizer, **kwargs): # noqa: N802
|
||||
|
|
@ -80,22 +79,13 @@ def FP16_Optimizer(optimizer, **kwargs): # noqa: N802
|
|||
|
||||
"""
|
||||
|
||||
def get_full_qualified_type_name(o):
|
||||
if hasattr(optimizer, "_amp_stash"):
|
||||
return "apex.amp.optimizer.unique_name_as_id"
|
||||
|
||||
klass = o.__class__
|
||||
module = klass.__module__
|
||||
if module == "builtins":
|
||||
return klass.__qualname__
|
||||
return module + "." + klass.__qualname__
|
||||
|
||||
optimizer_full_qualified_name = get_full_qualified_type_name(optimizer)
|
||||
if optimizer_full_qualified_name not in OptimizerModifierTypeRegistry:
|
||||
warnings.warn("Skip modifying optimizer because of optimizer name not found in registry.", UserWarning)
|
||||
return optimizer
|
||||
|
||||
modifier = OptimizerModifierTypeRegistry[optimizer_full_qualified_name](optimizer, **kwargs)
|
||||
modifier.apply()
|
||||
optimizer_full_qualified_name = (
|
||||
"apex.amp.optimizer.unique_name_as_id"
|
||||
if hasattr(optimizer, "_amp_stash")
|
||||
else get_full_qualified_type_name(optimizer)
|
||||
)
|
||||
modifier = OptimizerModifierTypeRegistry.create_modifier(optimizer_full_qualified_name, optimizer, **kwargs)
|
||||
if modifier is not None:
|
||||
modifier.apply()
|
||||
|
||||
return optimizer
|
||||
|
|
|
|||
Loading…
Reference in a new issue