mirror of https://github.com/InternLM/InternLM
980 lines
43 KiB
Python
980 lines
43 KiB
Python
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
import math
|
|
from functools import partial
|
|
from typing import List, Optional
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.optim import Optimizer
|
|
|
|
from internlm.core.context import IS_SEQUENCE_PARALLEL, Config, ParallelMode
|
|
from internlm.core.context import global_context as gpc
|
|
from internlm.monitor import send_alert_message
|
|
from internlm.solver.optimizer.store import (
|
|
BucketStore,
|
|
GradientStore,
|
|
ParameterStore,
|
|
TensorBucket,
|
|
)
|
|
from internlm.solver.optimizer.utils import (
|
|
DynamicGradScaler,
|
|
ParamBcastSyncHandler,
|
|
flatten,
|
|
get_grad_accumulate_object,
|
|
has_inf_or_nan,
|
|
reduce_tensor,
|
|
release_param_grad,
|
|
split_half_float_double,
|
|
sync_param,
|
|
)
|
|
from internlm.utils.common import get_current_device
|
|
from internlm.utils.logger import get_logger
|
|
from internlm.utils.megatron_timers import megatron_timer as timer
|
|
from internlm.utils.timeout import llm_timeout
|
|
|
|
from .base_optimizer import BaseOptimizer
|
|
from .utils import compute_layer_norm, compute_norm, compute_param_norm
|
|
|
|
inf = math.inf
|
|
logger = get_logger(__file__)
|
|
|
|
|
|
class HybridZeroOptimizer(BaseOptimizer):
|
|
"""
|
|
Hybrid Zero Optimizer.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
optimizer: Optimizer,
|
|
cpu_offload=False,
|
|
grad_scal_cfg: Config = None,
|
|
zero_cfg: Config = None,
|
|
param_bcast_sync_handler: ParamBcastSyncHandler = None,
|
|
):
|
|
# DynamicGradScaler related args
|
|
if gpc.config.model.dtype is torch.float32:
|
|
initial_scale = 1
|
|
else:
|
|
initial_scale = grad_scal_cfg.fp16.initial_scale
|
|
min_scale = grad_scal_cfg.fp16.min_scale
|
|
growth_interval = grad_scal_cfg.fp16.growth_interval
|
|
growth_factor = grad_scal_cfg.growth_factor
|
|
backoff_factor = grad_scal_cfg.backoff_factor
|
|
hysteresis = grad_scal_cfg.hysteresis
|
|
max_scale = grad_scal_cfg.max_scale
|
|
|
|
# Zero related args
|
|
reduce_bucket_size = zero_cfg.reduce_bucket_size
|
|
clip_grad_norm = zero_cfg.clip_grad_norm
|
|
self._overlap_sync_grad = zero_cfg.overlap_sync_grad
|
|
self._overlap_sync_param = zero_cfg.overlap_sync_param
|
|
|
|
super().__init__(optim=optimizer)
|
|
|
|
self._cpu_offload = cpu_offload
|
|
self._zero_local_rank = []
|
|
self._zero_world_size = []
|
|
self._broadcast_parallel_mode = []
|
|
|
|
# ParameterStore will manage the tensor buffers used for zero
|
|
# it will not manage the tensors used by mixed precision training
|
|
self._param_store = ParameterStore(ParallelMode.ZERO1)
|
|
self._grad_store = GradientStore(ParallelMode.DATA)
|
|
self._bucket_store: List[BucketStore] = []
|
|
self._accum_grad_buckets: List[BucketStore] = []
|
|
self._bucket_in_progress = []
|
|
|
|
# fp16 and fp32 params for mixed precision training
|
|
self._fp16_param_groups = dict()
|
|
self._fp32_flat_param_groups_of_current_rank = dict()
|
|
|
|
# communication params
|
|
# self._overlap_communication = overlap_communication
|
|
self._reduce_bucket_size = reduce_bucket_size
|
|
|
|
self._comm_bcast_stream = torch.cuda.Stream()
|
|
|
|
# gradient scaler
|
|
self.grad_scaler = DynamicGradScaler(
|
|
initial_scale=initial_scale,
|
|
min_scale=min_scale,
|
|
growth_factor=growth_factor,
|
|
backoff_factor=backoff_factor,
|
|
growth_interval=growth_interval,
|
|
hysteresis=hysteresis,
|
|
max_scale=max_scale,
|
|
)
|
|
self._found_overflow = torch.cuda.FloatTensor([0], device=get_current_device())
|
|
|
|
# gradient clipping
|
|
self._clip_grad_norm = clip_grad_norm
|
|
|
|
# need to record the rank in which parameter groups are not assigned parameters.
|
|
self.param_group_has_params = []
|
|
self.param_group_no_params_ranks = []
|
|
self.padding_grad = torch.zeros([32], dtype=gpc.config.model.dtype, device=get_current_device())
|
|
self.padding_tensor = torch.zeros([32], dtype=gpc.config.model.dtype, device=get_current_device())
|
|
|
|
self.rank_unique_id = (
|
|
f"gpus-{gpc.get_world_size(ParallelMode.GLOBAL)}_"
|
|
+ f"pp-{gpc.get_local_rank(ParallelMode.PIPELINE)}_"
|
|
+ f"tp-{gpc.get_local_rank(ParallelMode.TENSOR)}_"
|
|
+ f"zo-{gpc.get_local_rank(ParallelMode.ZERO1)}.pt"
|
|
)
|
|
self.params_per_rank_id_dict = []
|
|
self._param_bcast_sync_handler = param_bcast_sync_handler
|
|
if self._overlap_sync_param:
|
|
assert self._param_bcast_sync_handler is not None
|
|
|
|
if gpc.config.parallel["tensor"]["sp"] == "intern" and gpc.config.parallel["tensor"]["intern_overlap"] is True:
|
|
self._fstp_handler = gpc.fstp_handler
|
|
else:
|
|
self._fstp_handler = None
|
|
|
|
# iterate over the param group in the optimizer
|
|
# partition these param groups for data parallel training
|
|
# and add buffers to parameter store for future access
|
|
for group_id, param_group in enumerate(self.optim.param_groups):
|
|
group_params = param_group["params"]
|
|
|
|
# set the dtype for each param group
|
|
param_group["dtype"] = group_params[0].dtype if len(group_params) != 0 else None
|
|
|
|
# add the fp16 params to fp16_param_groups for bookkeeping
|
|
self._fp16_param_groups[group_id] = group_params
|
|
|
|
# to find real zero mode. if zero is not used, set all param group as ParallelMode.ZERO1
|
|
# if zero is used, expert dp group will use ParallelMode.EXPERT_DATA as the real zero mode
|
|
zero_mode = (
|
|
ParallelMode.ZERO1
|
|
if param_group["dp_mode"] == gpc.get_world_size(ParallelMode.ZERO1) == 1 or ParallelMode.DATA
|
|
else ParallelMode.EXPERT_DATA
|
|
)
|
|
self._zero_local_rank.append(gpc.get_local_rank(zero_mode))
|
|
self._zero_world_size.append(gpc.get_world_size(zero_mode))
|
|
# TODO _broadcast_parallel_mode is not only used in broadcast, maybe can change its name
|
|
self._broadcast_parallel_mode.append(zero_mode)
|
|
self._bucket_store.append(BucketStore(group_id, param_group["dp_mode"]))
|
|
self._accum_grad_buckets.append(BucketStore(group_id, param_group["dp_mode"]))
|
|
|
|
# assign parameters to ranks the params in the list are sorted
|
|
params_per_rank, no_params_ranks = self._partition_param_list(group_id, param_group)
|
|
self.param_group_no_params_ranks.append(no_params_ranks)
|
|
self.param_group_has_params.append(self._zero_local_rank[group_id] not in no_params_ranks)
|
|
|
|
# store the mapping between param to rank each param should belong to only one rank.
|
|
# we can skip the moe param and do not keep them in _param_store to save memory
|
|
# (means we need to deal with moe param in a different way), but it will increase
|
|
# complexity and reduce code readablity.
|
|
for rank, params in enumerate(params_per_rank):
|
|
# check whether any rank is not assigned params.
|
|
if len(params) != 0:
|
|
self._param_store.add_fp16_param_list_by_rank_group(rank, group_id, params)
|
|
for param in params:
|
|
setattr(param, "group_id", group_id)
|
|
self._param_store.set_param_to_rank(param, rank)
|
|
|
|
# move to cpu to make room to create the flat tensor
|
|
for param in group_params:
|
|
param.data = param.data.cpu()
|
|
|
|
# flatten the reordered tensors
|
|
for rank in range(self._zero_world_size[group_id]):
|
|
# No flat fp16 buffer is allocated if the process has no parameters.
|
|
if rank not in self.param_group_no_params_ranks[group_id]:
|
|
tensor_list = self._param_store.get_fp16_params_by_rank_group(rank, group_id)
|
|
with torch.no_grad():
|
|
flat_tensor = flatten(tensor_list)
|
|
flat_tensor = flat_tensor.data.cuda()
|
|
self._param_store.add_flat_fp16_param_by_rank_group(rank, group_id, flat_tensor)
|
|
sync_param(flat_tensor=flat_tensor, tensor_list=tensor_list)
|
|
|
|
# create a copy of fp32 weights of the parameters for which this rank is responsible
|
|
# No flat fp32 buffer is allocated if the process has no parameters.
|
|
if self.param_group_has_params[group_id]:
|
|
fp16_flat_current_rank = self._param_store.get_flat_fp16_param_by_rank_group(
|
|
self._zero_local_rank[group_id], group_id
|
|
)
|
|
fp32_flat_current_rank = fp16_flat_current_rank.float()
|
|
device = "cpu" if self._cpu_offload else get_current_device()
|
|
fp32_flat_current_rank = fp32_flat_current_rank.to(device)
|
|
fp32_flat_current_rank.requires_grad = True
|
|
self._fp32_flat_param_groups_of_current_rank[group_id] = fp32_flat_current_rank
|
|
|
|
# need to replace the params in the `params` field in the optimizer
|
|
# so that when the optimizer calls step(), it only updates the tensors
|
|
# managed by this data parallel rank
|
|
param_group["params"] = [fp32_flat_current_rank]
|
|
|
|
# set reduction state
|
|
for param in self._fp16_param_groups[group_id]:
|
|
self._param_store.set_param_reduction_state(param, False)
|
|
|
|
assert len(self._fp16_param_groups) != 0
|
|
|
|
# If a rank is not assigned any arguments, 'has_params' is False.
|
|
self.has_params = sum(self.param_group_has_params) != 0
|
|
# flag used to skip unnecessary gradient reduce operation when gradient accumulation is enabled.
|
|
self.skip_grad_reduce = False
|
|
|
|
# reduction hook is only used if overlapping communication
|
|
# if it is stage 1 without overlapping, no hook will be attached
|
|
self._attach_reduction_hook()
|
|
|
|
@property
|
|
def zero_local_rank(self):
|
|
return self._zero_local_rank
|
|
|
|
@property
|
|
def zero_world_size(self):
|
|
return self._zero_world_size
|
|
|
|
@property
|
|
def loss_scale(self):
|
|
return self.grad_scaler.scale
|
|
|
|
@property
|
|
def num_param_groups(self):
|
|
return len(self._fp16_param_groups)
|
|
|
|
def _partition_param_list(self, group_id, param_group):
|
|
no_params_ranks = []
|
|
params_per_rank = [[] for _ in range(self._zero_world_size[group_id])]
|
|
numel_per_rank = [0 for _ in range(self._zero_world_size[group_id])]
|
|
self.params_per_rank_id_dict.append([[] for _ in range(self._zero_world_size[group_id])])
|
|
param_list = param_group["params"]
|
|
|
|
sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True)
|
|
for i, param in enumerate(sorted_params):
|
|
global_id = str(i)
|
|
for j in range(len(param.size())):
|
|
global_id = "_".join([global_id, str(param.size()[j])])
|
|
if self._overlap_sync_param:
|
|
rank_to_go = self._param_bcast_sync_handler.get_rank_by_param(param)
|
|
else:
|
|
rank_to_go = numel_per_rank.index(min(numel_per_rank))
|
|
params_per_rank[rank_to_go].append(param)
|
|
self.params_per_rank_id_dict[-1][rank_to_go].append(global_id)
|
|
numel_per_rank[rank_to_go] += param.numel()
|
|
|
|
# check whether any rank is not assigned to parameters.
|
|
for rank, params in enumerate(params_per_rank):
|
|
if len(params) == 0:
|
|
no_params_ranks.append(rank)
|
|
|
|
if gpc.is_rank_for_log():
|
|
logger.info( # pylint: disable=W1203
|
|
f"Number of elements on ranks: {numel_per_rank}, rank:{gpc.get_global_rank()}"
|
|
)
|
|
|
|
return params_per_rank, set(no_params_ranks)
|
|
|
|
def _is_moe_group(self, param_group):
|
|
return "moe" in param_group.keys() and param_group["moe"]
|
|
|
|
def _is_norm_group(self, param_group):
|
|
return "norm" in param_group.keys() and param_group["norm"]
|
|
|
|
def _is_gate_group(self, param_group):
|
|
return "gate" in param_group.keys() and param_group["gate"]
|
|
|
|
# TODO check expert dp is correct when enable moe and overlap both
|
|
def _attach_reduction_hook(self):
|
|
# we iterate over the fp16 params
|
|
# on each param, we register a hook to its AccumulateGrad object
|
|
for group_id in range(self.num_param_groups):
|
|
param_group = self._fp16_param_groups[group_id]
|
|
for param in param_group:
|
|
# we should not reduce the param in moe
|
|
if not param.requires_grad:
|
|
continue
|
|
|
|
reduce_rank = None
|
|
|
|
def _define_and_attach(param, reduce_rank=None):
|
|
reduction_func = partial(
|
|
self._store_and_try_reduce_grads_by_bucket,
|
|
param=param,
|
|
reduce_rank=reduce_rank,
|
|
)
|
|
|
|
reduce_scatter_checker = partial(
|
|
self._wait_reduce_scatter_and_accumulate_grads,
|
|
param=param,
|
|
reduce_rank=reduce_rank,
|
|
)
|
|
|
|
def reduction_sp_func():
|
|
handle = reduce_tensor(
|
|
param.grad,
|
|
dtype=None,
|
|
dst_rank=reduce_rank,
|
|
parallel_mode=ParallelMode.TENSOR,
|
|
)
|
|
handle.wait()
|
|
|
|
# define hook
|
|
# NOT IMPORTANT BUT GOOD TO KNOW:
|
|
# args here is not grad, but allow_unreacable and accumulate_grad
|
|
def reduce_grad_hook(*args): # pylint: disable=W0613
|
|
if self.skip_grad_reduce is False:
|
|
reduction_func()
|
|
|
|
# define hook for real gradient accumulation.
|
|
def accum_grad_hook(*args): # pylint: disable=W0613
|
|
reduce_scatter_checker()
|
|
|
|
# define hook for sequence_parallel
|
|
def reduce_grad_hook_sp(*args): # pylint: disable=W0613
|
|
if self.skip_grad_reduce is False:
|
|
reduction_sp_func()
|
|
|
|
# get the AccumulateGrad object of the param itself
|
|
# If these objects are not kept, reduction hooks may not be attached successfully.
|
|
accum_grad_obj = get_grad_accumulate_object(param)
|
|
self._grad_store.add_accumulate_grad_object(accum_grad_obj)
|
|
|
|
# if sequence_parallel is True,
|
|
# the grad of norm should be all-reduce across the tp process group
|
|
if (
|
|
gpc.config.parallel.sequence_parallel is True
|
|
and hasattr(param, IS_SEQUENCE_PARALLEL)
|
|
and getattr(param, IS_SEQUENCE_PARALLEL) is True
|
|
):
|
|
accum_grad_obj.register_hook(reduce_grad_hook_sp)
|
|
|
|
# we should not only register for parameters which have _fstp_reduce_scatter_str attr.
|
|
# we must keep up with reduce_grad_hook.
|
|
if self._fstp_handler is not None:
|
|
accum_grad_obj.register_hook(accum_grad_hook)
|
|
|
|
if self._overlap_sync_grad:
|
|
accum_grad_obj.register_hook(reduce_grad_hook)
|
|
|
|
_define_and_attach(param, reduce_rank)
|
|
|
|
def accumulate_left_grads_after_backward(self):
|
|
if self._fstp_handler is None:
|
|
return
|
|
|
|
for group_id in range(self.num_param_groups):
|
|
self._accum_grads_store_in_bucket(self._accum_grad_buckets[group_id])
|
|
|
|
def belongs_to_current_rank(self, param) -> bool:
|
|
"""
|
|
Check whether a parameter is supposed to be updated by the process of the current rank
|
|
|
|
:param tensor: A :class:`torch.Tensor` object
|
|
:type tensor: torch.Tensor
|
|
|
|
:return: True if the parameter should be updated by the current rank. Otherwise false.
|
|
:rtype: bool
|
|
"""
|
|
tensor_rank = self._param_store.get_param_rank(param)
|
|
group_id = getattr(param, "group_id")
|
|
return tensor_rank == gpc.get_local_rank(self._broadcast_parallel_mode[group_id])
|
|
|
|
def _accum_grads_store_in_bucket(self, bucket: BucketStore, reduce_rank: Optional[int] = None) -> None:
|
|
for _param in bucket.get_param(reduce_rank):
|
|
if not hasattr(_param, "_fstp_reduce_scatter_str"):
|
|
continue
|
|
|
|
# wait and accumulate gardient.
|
|
_key = getattr(_param, "_fstp_reduce_scatter_str")
|
|
_comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[_key]
|
|
_comm_handle.wait()
|
|
_param.grad.add_(_grad)
|
|
|
|
# release cuda memory.
|
|
self._fstp_handler.release_reduce_scatter_memory(key=tuple(_grad.size()), index=_grad.index)
|
|
self._fstp_handler.reduce_scatter_handlers[_key] = None
|
|
|
|
bucket.reset_by_rank(reduce_rank)
|
|
|
|
def _wait_reduce_scatter_and_accumulate_grads(self, param, reduce_rank: Optional[int] = None):
|
|
param_size = param.numel()
|
|
|
|
group_id = getattr(param, "group_id")
|
|
current_bucket = self._accum_grad_buckets[group_id]
|
|
|
|
# check if the bucket is full
|
|
# if full, will reduce the grads already in the bucket
|
|
# after reduction, the bucket will be empty
|
|
if current_bucket.num_elements_in_bucket(reduce_rank) >= self._reduce_bucket_size:
|
|
self._accum_grads_store_in_bucket(current_bucket, reduce_rank)
|
|
|
|
# otherwise, add the parameter into bucket.
|
|
current_bucket.add_num_elements_in_bucket(param_size, reduce_rank)
|
|
current_bucket.add_param(param, reduce_rank)
|
|
|
|
def _store_and_try_reduce_grads_by_bucket(self, param, reduce_rank=None):
|
|
param_size = param.numel()
|
|
|
|
# check if the bucket is full
|
|
# if full, will reduce the grads already in the bucket
|
|
# after reduction, the bucket will be empty
|
|
group_id = getattr(param, "group_id")
|
|
current_bucket = self._bucket_store[group_id]
|
|
|
|
if current_bucket.num_elements_in_bucket(reduce_rank) + param_size > self._reduce_bucket_size:
|
|
self._reduce_grads_stored_in_bucket(current_bucket, reduce_rank, last_bucket=False)
|
|
|
|
# the param must not be reduced to ensure correctness
|
|
is_param_reduced = self._param_store.is_param_reduced(param)
|
|
if is_param_reduced:
|
|
msg = (
|
|
f"Parameter of size ({param.size()}) has already been reduced, "
|
|
+ "duplicate reduction will lead to arithmetic incorrectness"
|
|
)
|
|
raise RuntimeError(msg)
|
|
|
|
# the param must have grad for reduction
|
|
assert param.grad is not None, f"Parameter of size ({param.size()}) has None grad, cannot be reduced"
|
|
|
|
current_bucket.add_num_elements_in_bucket(param_size, reduce_rank)
|
|
current_bucket.add_grad(param.grad, reduce_rank)
|
|
current_bucket.add_param(param, reduce_rank)
|
|
|
|
def _reduce_grads_stored_in_bucket(self, current_bucket, reduce_rank=None, last_bucket=False):
|
|
# reduce grads
|
|
self._reduce_grads_by_rank(
|
|
reduce_rank=reduce_rank,
|
|
grads=current_bucket.get_grad(reduce_rank=reduce_rank),
|
|
bucket_size=current_bucket.num_elements_in_bucket(reduce_rank),
|
|
group_id=current_bucket.get_param_group_id(),
|
|
dp_parallel_mode=current_bucket.get_dp_parallel_mode(),
|
|
)
|
|
|
|
params_in_bucket = current_bucket.get_param(reduce_rank=reduce_rank)
|
|
|
|
for param in params_in_bucket:
|
|
# the is_param_reduced flag should be False showing that
|
|
# this param is not reduced before calling self._reduce_grads_by_rank
|
|
is_param_reduced = self._param_store.is_param_reduced(param)
|
|
|
|
if is_param_reduced:
|
|
msg = (
|
|
f"Parameter of size ({param.size()}) has been reduced, "
|
|
+ "duplicate reduction will lead to arithmetic incorrectness"
|
|
)
|
|
raise RuntimeError(msg)
|
|
|
|
# update the flag
|
|
self._param_store.set_param_reduction_state(param, True)
|
|
|
|
if self.belongs_to_current_rank(param):
|
|
self._param_store.add_reduced_param_for_compute_norm(param, last_bucket)
|
|
else:
|
|
self._param_store.add_previous_reduced_param(param)
|
|
|
|
current_bucket.reset_by_rank(reduce_rank)
|
|
|
|
def _reduce_grads_by_rank(self, reduce_rank, grads, bucket_size, group_id, dp_parallel_mode):
|
|
grad_buckets_by_dtype = split_half_float_double(grads)
|
|
next_bucket_list = []
|
|
# add parameters into bucket for reduction
|
|
for tensor_list in grad_buckets_by_dtype:
|
|
param_bucket = TensorBucket(size=bucket_size)
|
|
for tensor in tensor_list:
|
|
param_bucket.add_to_bucket(tensor, allow_oversize=True)
|
|
if not param_bucket.is_empty():
|
|
self._reduce_and_copy(
|
|
bucket=param_bucket, reduce_rank=reduce_rank, group_id=group_id, dp_parallel_mode=dp_parallel_mode
|
|
)
|
|
next_bucket_list.append(param_bucket)
|
|
|
|
# wait for the completion of previouce bucket list reduction, and do unflatten_and_copy()
|
|
# here we can also overlap the communication with some memcpy operation caused by bucket.flatten()
|
|
for bucket in self._bucket_in_progress:
|
|
bucket.commu_handle.wait()
|
|
bucket.unflatten_and_copy()
|
|
bucket.empty()
|
|
self._bucket_in_progress = []
|
|
self._param_store.clear_grads_of_previous_reduced_params()
|
|
|
|
# after the completion of bucket list reduction, add new buckets into _bucket_in_progress
|
|
self._bucket_in_progress = next_bucket_list.copy()
|
|
|
|
def _reduce_and_copy(self, bucket: TensorBucket, reduce_rank, group_id, dp_parallel_mode):
|
|
# flatten the tensors and do allreduce
|
|
bucket.flatten()
|
|
bucket.commu_handle = reduce_tensor(
|
|
tensor=bucket.get_flat_tensor(),
|
|
dtype=None,
|
|
dst_rank=reduce_rank,
|
|
parallel_mode=dp_parallel_mode,
|
|
)
|
|
|
|
# update the reduced tensor
|
|
if reduce_rank is None or reduce_rank == self._zero_local_rank[group_id]:
|
|
bucket.set_unflatten_and_copy_flag(flag=True)
|
|
|
|
def _has_inf_or_nan(self, tensor):
|
|
try:
|
|
tensor_mean = float(tensor.mean())
|
|
except RuntimeError as instance:
|
|
# We want to check if inst is actually an overflow exception.
|
|
# RuntimeError could come from a different error.
|
|
# If so, we still want the exception to propagate.
|
|
if "value cannot be converted" not in instance.args[0]:
|
|
raise
|
|
return True
|
|
else:
|
|
if tensor_mean == float("inf") or tensor_mean == -float("inf"):
|
|
return True
|
|
return False
|
|
|
|
def _sync_grad(self):
|
|
# update param already reduced flag
|
|
reduction_states = self._param_store.get_param_reduction_states()
|
|
for tensor, _ in reduction_states.items():
|
|
reduction_states[tensor] = False
|
|
self._param_store.reset_reduced_data_for_compute_norm()
|
|
|
|
# accumulate gradient
|
|
avg_gradients = self._grad_store._averaged_gradients
|
|
for group_id in range(self.num_param_groups):
|
|
# the following operations are performed only on the rank to which parameters are assigned.
|
|
if self._zero_local_rank[group_id] not in self.param_group_no_params_ranks[group_id]:
|
|
param_group = self._param_store.get_fp16_params_by_rank_group(self._zero_local_rank[group_id], group_id)
|
|
|
|
if group_id not in avg_gradients:
|
|
avg_gradients[group_id] = []
|
|
|
|
param_idx = 0
|
|
for param in param_group:
|
|
if param.grad is not None:
|
|
if len(avg_gradients[group_id]) == param_idx:
|
|
avg_gradients[group_id].append(param.grad)
|
|
else:
|
|
avg_gradients[group_id][param_idx].add_(param.grad)
|
|
param_idx += 1
|
|
|
|
# the gradients needed are stored in the avg_gradients buffer
|
|
# thus, can clear this
|
|
self.zero_grad()
|
|
|
|
def zero_grad(self, set_to_none=True):
|
|
"""
|
|
Set parameter gradients to zero. If set_to_none = True, gradient
|
|
will be set to None to save memory.
|
|
|
|
:param set_to_none: Whether set the gradient to None. Default value is True.
|
|
:type set_to_none: bool
|
|
"""
|
|
for _, param_group in self._fp16_param_groups.items():
|
|
for param in param_group:
|
|
if set_to_none:
|
|
param.grad = None
|
|
elif param.grad is not None:
|
|
param.grad.detach()
|
|
param.grad.zero_()
|
|
else:
|
|
pass
|
|
|
|
def backward(self, loss, retain_graph=False):
|
|
loss = self.loss_scale * loss
|
|
loss.backward(retain_graph=retain_graph)
|
|
|
|
# Gradients may not be fully synchronized here.
|
|
|
|
def _compute_norm_with_stage(
|
|
self,
|
|
group_id: int = 0,
|
|
last_bucket: bool = False,
|
|
last_stage: bool = False,
|
|
previous_norm=None,
|
|
):
|
|
# compute norm for gradients that have been reduced
|
|
params, grads = self._param_store.get_reduced_param_for_compute_norm(group_id=group_id, last_bucket=last_bucket)
|
|
if len(params) == 0:
|
|
dtype = self.param_groups[group_id]["dtype"]
|
|
grads = [self.padding_grad.to(dtype)]
|
|
params = [self.padding_tensor.to(dtype)]
|
|
|
|
norm = 0
|
|
if self._clip_grad_norm > 0:
|
|
# this norm is before scaling, it will be very large
|
|
norm = compute_norm(
|
|
gradients=grads,
|
|
parameters=params,
|
|
last_stage=last_stage,
|
|
previous_norm=previous_norm,
|
|
zero_mode=self._broadcast_parallel_mode[group_id],
|
|
)
|
|
|
|
return norm
|
|
|
|
def _compute_param_norm_stage(
|
|
self, group_id: int = 0, last_bucket: bool = False, last_stage: bool = False, previous_param_norms=None
|
|
):
|
|
# compute norm for gradients that have been reduced
|
|
params, grads = self._param_store.get_reduced_param_for_compute_norm(group_id=group_id, last_bucket=last_bucket)
|
|
|
|
total_param_norms = {}
|
|
if len(params) == 0:
|
|
dtype = self.param_groups[group_id]["dtype"]
|
|
grads = [self.padding_grad.to(dtype)]
|
|
params = [self.padding_tensor.to(dtype)]
|
|
|
|
if self._clip_grad_norm > 0:
|
|
total_param_norms = compute_param_norm(
|
|
grads,
|
|
params,
|
|
last_stage=last_stage,
|
|
previous_param_norms=previous_param_norms,
|
|
zero_mode=self._broadcast_parallel_mode[group_id],
|
|
is_moe_group=self._is_moe_group(self.optim.param_groups[group_id]),
|
|
)
|
|
return total_param_norms
|
|
|
|
@llm_timeout(func_name="optim_step")
|
|
def step(self, closure=None):
|
|
"""Performs a single optimization step.
|
|
|
|
Args:
|
|
closure (Callable, optional): A closure that reevaluates the model
|
|
and returns the loss.
|
|
Returns:
|
|
Union[bool, float]: Whether the gradient is success updated, and the gradient.
|
|
"""
|
|
assert closure is None, "closure is not supported by step()"
|
|
|
|
# if not overlapping communication (no reduction hook is attached)
|
|
# we need to manually reduce these gradients
|
|
if not self._overlap_sync_grad:
|
|
for group_id in range(len(self._fp16_param_groups)):
|
|
for param in self._fp16_param_groups[group_id]:
|
|
# we should not reduce the param in moe
|
|
if param.grad is not None:
|
|
self._store_and_try_reduce_grads_by_bucket(param)
|
|
|
|
# we need to reduce the gradients left in the communication bucket
|
|
for group_id in range(self.num_param_groups):
|
|
self._reduce_grads_stored_in_bucket(self._bucket_store[group_id], reduce_rank=None, last_bucket=True)
|
|
|
|
# compute norm for gradients in the before bucket
|
|
groups_norms = []
|
|
groups_param_norms = []
|
|
for group_id in range(self.num_param_groups):
|
|
groups_norms.append(self._compute_norm_with_stage(group_id=group_id))
|
|
if gpc.config.get("grad_norm_profiling", False):
|
|
groups_param_norms.append(self._compute_param_norm_stage(group_id=group_id))
|
|
|
|
# clear reduced grads
|
|
# grads in the last bucket is reduced
|
|
for bucket in self._bucket_in_progress:
|
|
bucket.commu_handle.wait()
|
|
bucket.unflatten_and_copy()
|
|
bucket.empty()
|
|
self._bucket_in_progress = []
|
|
self._param_store.clear_grads_of_previous_reduced_params()
|
|
# compute norm for gradients in the last bucket
|
|
total_norms = {}
|
|
total_param_norms = {}
|
|
total_layer_norms = {}
|
|
for group_id in range(self.num_param_groups):
|
|
group_name = self.param_groups[group_id]["name"] if "name" in self.param_groups[group_id] else "default"
|
|
group_name = f"{group_id}_{group_name}"
|
|
total_norms[group_name] = self._compute_norm_with_stage(
|
|
group_id=group_id,
|
|
last_bucket=True,
|
|
last_stage=True,
|
|
previous_norm=groups_norms[group_id],
|
|
)
|
|
if gpc.config.get("grad_norm_profiling", False):
|
|
param_norms = self._compute_param_norm_stage(
|
|
group_id=group_id,
|
|
last_bucket=True,
|
|
last_stage=True,
|
|
previous_param_norms=groups_param_norms[group_id],
|
|
)
|
|
total_layer_norms[group_name], total_param_norms[group_name] = compute_layer_norm(
|
|
param_norms=param_norms, loss_scale=self.loss_scale.item()
|
|
)
|
|
|
|
# Need to allreduce(avg) the norms across different ranks because moe params will not be synced
|
|
# during allreduce
|
|
if self._is_moe_group(self.optim.param_groups[group_id]):
|
|
# model and zero have been reduced!!!
|
|
pg = gpc.get_group(ParallelMode.EXPERT)
|
|
scaled_norm = total_norms[group_name] * 1.0 / float(gpc.get_world_size(ParallelMode.EXPERT))
|
|
scaled_norm_tensor = torch.tensor(scaled_norm, device=get_current_device(), dtype=torch.float)
|
|
dist.all_reduce(scaled_norm_tensor, group=pg)
|
|
total_norms[group_name] = scaled_norm_tensor.item()
|
|
timer("sync_grad").start()
|
|
self._sync_grad()
|
|
timer("sync_grad").stop()
|
|
|
|
state, global_norms = self._step(closure=closure, norms=total_norms)
|
|
if gpc.config.get("grad_norm_profiling", False):
|
|
global_norms["layer_norms"] = total_layer_norms
|
|
global_norms["param_norms"] = total_param_norms
|
|
|
|
return state, global_norms
|
|
|
|
def _step(self, closure=None, norms=None):
|
|
assert closure is None, "closure is not supported by step()"
|
|
|
|
# check for overflow
|
|
found_inf = False
|
|
found_nan = False
|
|
# if there is INF values in grades, compute_norm func would also returns -1
|
|
# thus, we try to avoid call _check_overflow here
|
|
# found_inf = self._check_overflow()
|
|
# Because you may encounter inf when computing norm
|
|
|
|
if -1 in norms.values():
|
|
found_inf = True
|
|
|
|
if -2 in norms.values():
|
|
found_nan = True
|
|
|
|
loss_scale = float(self.loss_scale.item()) # backup
|
|
if gpc.config.model.dtype is not torch.float32:
|
|
self.grad_scaler.update(found_inf)
|
|
|
|
# update loss scale if overflow occurs
|
|
if found_inf:
|
|
if gpc.is_rank_for_log():
|
|
logger.warning("Overflow occurs, please check it.")
|
|
send_alert_message(
|
|
address=gpc.config.monitor.alert.feishu_alert_address,
|
|
message="Overflow occurs, please check it.",
|
|
)
|
|
self._grad_store._averaged_gradients = dict()
|
|
self.zero_grad()
|
|
return False, norms
|
|
|
|
if found_nan:
|
|
if gpc.is_rank_for_log():
|
|
logger.warning("Nan grad norm occurs, please check it.")
|
|
send_alert_message(
|
|
address=gpc.config.monitor.alert.feishu_alert_address,
|
|
message="Nan grad norm occurs, please check it.",
|
|
)
|
|
self._grad_store._averaged_gradients = dict()
|
|
self.zero_grad()
|
|
return False, norms
|
|
# copy the grad of fp16 param to fp32 param
|
|
single_grad_partition_groups = []
|
|
for group_id in range(self.num_param_groups):
|
|
# compute norm
|
|
# The following operations are performed only on the rank to which parameters are assigned.
|
|
if not self.param_group_has_params[group_id]:
|
|
continue
|
|
|
|
# create flat gradient for the flat fp32 params
|
|
gradients = self._grad_store.get_averaged_gradients_by_group(group_id)
|
|
with torch.no_grad():
|
|
flat_fp16_avg_grads = flatten(gradients)
|
|
self._grad_store.reset_average_gradients_by_group(group_id)
|
|
gradients = None # release cuda memory
|
|
|
|
dtype = self._fp32_flat_param_groups_of_current_rank[group_id].dtype
|
|
flat_fp32_avg_grads = flat_fp16_avg_grads.to(dtype)
|
|
flat_fp16_avg_grads = None # release cuda memory
|
|
|
|
param_shape = self._fp32_flat_param_groups_of_current_rank[group_id].shape
|
|
assert (
|
|
param_shape == flat_fp32_avg_grads.shape
|
|
), f"fp32 param and grad have different shape {param_shape} vs {flat_fp32_avg_grads.shape}"
|
|
|
|
# Parameters shared within a TP group, such as norm and moe gate, have precision inconsistency in gradients.
|
|
# Therefore, it is recommended to synchronize gradients within the TP group to eliminate accumulated errors.
|
|
is_tp_sync_groups = (
|
|
self._is_norm_group(self.optim.param_groups[group_id]),
|
|
self._is_gate_group(self.optim.param_groups[group_id]),
|
|
)
|
|
if any(is_tp_sync_groups):
|
|
dist.all_reduce(
|
|
flat_fp32_avg_grads,
|
|
op=dist.ReduceOp.AVG,
|
|
group=gpc.get_group(ParallelMode.TENSOR),
|
|
)
|
|
|
|
single_grad_partition_groups.append(flat_fp32_avg_grads)
|
|
device = self._fp32_flat_param_groups_of_current_rank[group_id].device
|
|
self._fp32_flat_param_groups_of_current_rank[group_id].grad = flat_fp32_avg_grads.to(device)
|
|
# unscale and clip grads
|
|
# get the global norm
|
|
global_norm_groups = {}
|
|
if self._clip_grad_norm > 0:
|
|
for group_name, norm in norms.items():
|
|
global_norm_groups[group_name] = norm**0.5
|
|
|
|
# the following operations are performed only on the rank to which parameters are assigned.
|
|
if gpc.config.model.dtype is not torch.float32:
|
|
if len(single_grad_partition_groups) != 0 and self._clip_grad_norm > 0:
|
|
self._unscale_and_clip_grads(
|
|
single_grad_partition_groups,
|
|
list(global_norm_groups.values()),
|
|
loss_scale,
|
|
)
|
|
|
|
# update the parameters
|
|
timer("step").start()
|
|
|
|
# For those ranks that are not assigned parameters, we just wait for other ranks
|
|
# to send them updated their own parameters.
|
|
if self.has_params:
|
|
self.optim.step()
|
|
# release the fp32 grad
|
|
release_param_grad(self._fp32_flat_param_groups_of_current_rank.values())
|
|
# update fp16 partition updated by the current rank
|
|
for group_id in range(len(self._fp16_param_groups)):
|
|
if self.param_group_has_params[group_id]:
|
|
fp16_param = self._param_store.get_flat_fp16_param_by_rank_group(
|
|
rank=self._zero_local_rank[group_id], group_id=group_id
|
|
)
|
|
fp32_param = self._fp32_flat_param_groups_of_current_rank[group_id]
|
|
fp16_param.data.copy_(fp32_param)
|
|
torch.cuda.synchronize()
|
|
with torch.cuda.stream(self._comm_bcast_stream):
|
|
self.broadcast_params()
|
|
|
|
timer("step").stop()
|
|
|
|
# update gradients may not be needed here, because the sync_params function is used in initialization,
|
|
# so synchronization is maintained
|
|
for group_name, global_norm in global_norm_groups.items():
|
|
global_norm_groups[group_name] = global_norm / loss_scale
|
|
return True, global_norm_groups
|
|
|
|
def broadcast_params(self):
|
|
handles = []
|
|
|
|
for group_id in range(self.num_param_groups):
|
|
for rank in range(self._zero_world_size[group_id]):
|
|
# The following operations are performed only on the rank to which parameters are assigned.
|
|
if rank in self.param_group_no_params_ranks[group_id]:
|
|
continue
|
|
fp16_param = self._param_store.get_flat_fp16_param_by_rank_group(rank=rank, group_id=group_id)
|
|
# grank = gpc.get_ranks_in_group(group_type)[rank] # need to convert to the global rank
|
|
# assert grank == rank, f"{grank} == {rank}"
|
|
g_rank = gpc.get_ranks_in_group(self._broadcast_parallel_mode[group_id])[rank]
|
|
handle = dist.broadcast(
|
|
fp16_param,
|
|
src=g_rank,
|
|
group=gpc.get_group(self._broadcast_parallel_mode[group_id]),
|
|
async_op=True,
|
|
)
|
|
|
|
if self._overlap_sync_param:
|
|
self._param_bcast_sync_handler.add_bcast_handle(rank, handle)
|
|
else:
|
|
handles.append(handle)
|
|
|
|
for handle in handles:
|
|
handle.wait()
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
##################
|
|
# FP16 Utilities #
|
|
##################
|
|
|
|
def _check_overflow(self):
|
|
# clear previous overflow record
|
|
self._found_overflow.fill_(0.0)
|
|
|
|
# check for overflow
|
|
for group_id in range(len(self._fp16_param_groups)):
|
|
# The following operations are performed only on the rank to which parameters are assigned.
|
|
if self._zero_local_rank[group_id] not in self.param_group_no_params_ranks[group_id]:
|
|
for avg_grad in self._grad_store.get_averaged_gradients_by_group(group_id):
|
|
if avg_grad is not None and has_inf_or_nan(avg_grad):
|
|
self._found_overflow.fill_(1.0)
|
|
break
|
|
dist.all_reduce(
|
|
self._found_overflow,
|
|
op=dist.ReduceOp.MAX,
|
|
group=gpc.get_group(ParallelMode.GLOBAL),
|
|
)
|
|
|
|
return self._found_overflow.item() > 0
|
|
|
|
def _unscale_and_clip_grads(self, grad_groups_flat, total_norm_groups, loss_scale):
|
|
# compute combined scale factor for this group
|
|
combined_scale_groups = []
|
|
|
|
if self._clip_grad_norm > 0.0:
|
|
# norm is in fact norm*scale
|
|
for group_id, total_norm in enumerate(total_norm_groups):
|
|
combined_scale_groups.append(loss_scale)
|
|
clip = ((total_norm / loss_scale) + 1e-6) / self._clip_grad_norm
|
|
if clip > 1.0:
|
|
combined_scale_groups[group_id] = clip * loss_scale
|
|
|
|
for group_id, grad in enumerate(grad_groups_flat):
|
|
grad.data.mul_(1.0 / combined_scale_groups[group_id])
|
|
|
|
def clip_grad_norm(self, model, max_norm):
|
|
# will conduct in the step()
|
|
pass
|
|
|
|
def state_dict(self):
|
|
states = {}
|
|
grad_scaler = self.grad_scaler.state_dict()
|
|
states["grad_scaler"] = grad_scaler
|
|
optim_states = self.optim.state_dict()
|
|
states["base_optim_states"] = optim_states
|
|
|
|
flat_fp32_weights = {}
|
|
for group_id, param in self._fp32_flat_param_groups_of_current_rank.items():
|
|
if self._zero_local_rank[group_id] not in self.param_group_no_params_ranks[group_id]:
|
|
assert param.grad is None
|
|
flat_fp32_weights[group_id] = param
|
|
states["flat_fp32_weights"] = flat_fp32_weights
|
|
states["zero_devide_optim_plan"] = self.params_per_rank_id_dict
|
|
|
|
return states
|
|
|
|
def load_state_dict(self, states):
|
|
# TODO: Need to take into account the change in the number of DP.
|
|
assert "grad_scaler" in states, "Not found grad_scaler state!"
|
|
grad_scaler = states["grad_scaler"]
|
|
self.grad_scaler.load_state_dict(grad_scaler)
|
|
optim_states = states["base_optim_states"]
|
|
self.optim.load_state_dict(optim_states)
|
|
|
|
# load fp32 model weight.
|
|
flat_fp32_weights = states["flat_fp32_weights"]
|
|
assert set(flat_fp32_weights.keys()) == set(self._fp32_flat_param_groups_of_current_rank)
|
|
for group_id, param in flat_fp32_weights.items():
|
|
if self._zero_local_rank[group_id] not in self.param_group_no_params_ranks[group_id]:
|
|
self_param = self._fp32_flat_param_groups_of_current_rank[group_id]
|
|
assert (
|
|
self_param.shape == param.shape
|
|
), f"The loaded parameter shape is inconsistent, {self_param.shape} != {param.shape}"
|
|
self_param.data.copy_(param.data)
|
|
|
|
# Load the fp16 model weights.
|
|
for group_id in range(len(self._fp16_param_groups)):
|
|
if self._zero_local_rank[group_id] not in self.param_group_no_params_ranks[group_id]:
|
|
fp16_param = self._param_store.get_flat_fp16_param_by_rank_group(
|
|
rank=self._zero_local_rank[group_id], group_id=group_id
|
|
)
|
|
fp32_param = self._fp32_flat_param_groups_of_current_rank[group_id]
|
|
fp16_param.data.copy_(fp32_param)
|
|
|
|
if "zero_devide_optim_plan" in states:
|
|
self.params_per_rank_id_dict = states["zero_devide_optim_plan"]
|
|
|
|
|
|
def reload_zero_fp32_buff(optimizer):
|
|
# If we use AMP optimizer, we need to update its fp32 buffer as newly loaded weights value.
|
|
# Or we must ensure that loading model weights must be done before zero is initialized.
|
|
if isinstance(optimizer, HybridZeroOptimizer):
|
|
for group_id, param_group in enumerate(optimizer.optim.param_groups):
|
|
if optimizer.param_group_has_params[group_id]:
|
|
# flatten fp16 params have already been updated by 'load_model_checkpoint'
|
|
fp16_flat_current_rank = optimizer._param_store.get_flat_fp16_param_by_rank_group(
|
|
optimizer._zero_local_rank[group_id], group_id
|
|
)
|
|
# param_group["params"] is fp32 flatten optimizer states of this zero rank.
|
|
param_group["params"][0].data.copy_(fp16_flat_current_rank.float())
|