mirror of https://github.com/InternLM/InternLM
790 lines
32 KiB
Python
790 lines
32 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import math
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from functools import partial
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from itertools import product
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import torch
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import torch.distributed as dist
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from torch.optim import Optimizer
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from internlm.core.context import Config, ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.monitor import send_alert_message
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from internlm.solver.optimizer.store import (
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BucketStore,
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GradientStore,
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ParameterStore,
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TensorBucket,
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)
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from internlm.solver.optimizer.utils import (
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DynamicGradScaler,
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ParamBcastSyncHandler,
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flatten,
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get_grad_accumulate_object,
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has_inf_or_nan,
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reduce_tensor,
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release_param_grad,
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split_half_float_double,
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sync_param,
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)
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from internlm.utils.common import get_current_device
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from internlm.utils.logger import get_logger
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from internlm.utils.megatron_timers import megatron_timer as timer
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from internlm.utils.timeout import llm_timeout
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from .utils import compute_norm
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inf = math.inf
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logger = get_logger(__file__)
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class BaseOptimizer(Optimizer):
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"""
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Base Optimizer.
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"""
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def __init__(self, optim: Optimizer): # pylint: disable=W0231
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self.optim = optim
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@property
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def param_groups(self):
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return self.optim.param_groups
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@property
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def defaults(self):
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return self.optim.defaults
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def add_param_group(self, *args, **kwargs):
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return self.optim.add_param_group(*args, **kwargs)
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def step(self, *args, **kwargs):
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return self.optim.step(*args, **kwargs)
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def zero_grad(self, *args, **kwargs):
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self.optim.zero_grad(*args, **kwargs)
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def load_state_dict(self, *args, **kwargs):
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self.optim.load_state_dict(*args, **kwargs)
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def state_dict(self):
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return self.optim.state_dict()
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def backward(self, loss):
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loss.backward()
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def backward_by_grad(self, tensor, grad):
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torch.autograd.backward(tensors=tensor, grad_tensors=grad)
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def clip_grad_norm(self):
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pass
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class HybridZeroOptimizer(BaseOptimizer):
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"""
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Hybrid Zero Optimizer.
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"""
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def __init__(
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self,
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optimizer: Optimizer,
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cpu_offload=False,
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grad_scal_cfg: Config = None,
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zero_cfg: Config = None,
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param_bcast_sync_handler: ParamBcastSyncHandler = None,
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):
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# DynamicGradScaler related args
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if gpc.config.model.dtype is torch.float32:
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initial_scale = 1
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else:
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initial_scale = grad_scal_cfg.fp16.initial_scale
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min_scale = grad_scal_cfg.fp16.min_scale
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growth_interval = grad_scal_cfg.fp16.growth_interval
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growth_factor = grad_scal_cfg.growth_factor
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backoff_factor = grad_scal_cfg.backoff_factor
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hysteresis = grad_scal_cfg.hysteresis
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max_scale = grad_scal_cfg.max_scale
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# Zero related args
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reduce_bucket_size = zero_cfg.reduce_bucket_size
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clip_grad_norm = zero_cfg.clip_grad_norm
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self._overlap_sync_grad = zero_cfg.overlap_sync_grad
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self._overlap_sync_param = zero_cfg.overlap_sync_param
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super().__init__(optim=optimizer)
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self._dtype = self.optim.param_groups[0]["params"][0].dtype
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self._cpu_offload = cpu_offload
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self._zero_local_rank = gpc.get_local_rank(ParallelMode.ZERO1)
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self._zero_world_size = gpc.get_world_size(ParallelMode.ZERO1)
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self._broadcast_parallel_mode = ParallelMode.ZERO1
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# ParameterStore will manage the tensor buffers used for zero
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# it will not manage the tensors used by mixed precision training
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self._param_store = ParameterStore(ParallelMode.ZERO1)
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self._grad_store = GradientStore(ParallelMode.DATA)
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self._bucket_store = BucketStore(ParallelMode.DATA)
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# fp16 and fp32 params for mixed precision training
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self._fp16_param_groups = dict()
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self._fp32_flat_param_groups_of_current_rank = dict()
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# communication params
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# self._overlap_communication = overlap_communication
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self._reduce_bucket_size = reduce_bucket_size
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# gradient scaler
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self.grad_scaler = DynamicGradScaler(
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initial_scale=initial_scale,
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min_scale=min_scale,
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growth_factor=growth_factor,
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backoff_factor=backoff_factor,
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growth_interval=growth_interval,
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hysteresis=hysteresis,
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max_scale=max_scale,
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)
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self._found_overflow = torch.cuda.FloatTensor([0], device=get_current_device())
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# gradient clipping
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self._clip_grad_norm = clip_grad_norm
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# need to record the rank in which parameter groups are not assigned parameters.
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self.param_group_has_params = []
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self.param_group_no_params_ranks = []
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self.padding_grad = torch.zeros([32], dtype=self._dtype, device=get_current_device())
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self.padding_tensor = torch.zeros([32], dtype=self._dtype, device=get_current_device())
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self.rank_unique_id = (
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f"gpus-{gpc.get_world_size(ParallelMode.GLOBAL)}_"
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+ f"pp-{gpc.get_local_rank(ParallelMode.PIPELINE)}_"
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+ f"tp-{gpc.get_local_rank(ParallelMode.TENSOR)}_"
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+ f"zo-{self._zero_local_rank}.pt"
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)
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self.params_per_rank_id_dict = []
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self._param_bcast_sync_handler = param_bcast_sync_handler
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if self._overlap_sync_param:
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assert self._param_bcast_sync_handler is not None
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# iterate over the param group in the optimizer
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# partition these param groups for data parallel training
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# and add buffers to parameter store for future access
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for group_id, param_group in enumerate(self.optim.param_groups):
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group_params = param_group["params"]
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# add the fp16 params to fp16_param_groups for bookkeeping
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self._fp16_param_groups[group_id] = group_params
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# assign parameters to ranks the params in the list are sorted
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params_per_rank, no_params_ranks = self._partition_param_list(group_params)
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self.param_group_no_params_ranks.append(no_params_ranks)
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self.param_group_has_params.append(self._zero_local_rank not in no_params_ranks)
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# store the mapping between param to rank each param should belong to only one rank
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for rank, params in enumerate(params_per_rank):
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# check whether any rank is not assigned params.
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if len(params) != 0:
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self._param_store.add_fp16_param_list_by_rank_group(rank, group_id, params)
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for param in params:
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setattr(param, "group_id", group_id)
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self._param_store.set_param_to_rank(param, rank)
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# move to cpu to make room to create the flat tensor
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for param in group_params:
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param.data = param.data.cpu()
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# flatten the reordered tensors
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for rank in range(self._zero_world_size):
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# No flat fp16 buffer is allocated if the process has no parameters.
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if rank not in self.param_group_no_params_ranks[group_id]:
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tensor_list = self._param_store.get_fp16_params_by_rank_group(rank, group_id)
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with torch.no_grad():
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flat_tensor = flatten(tensor_list)
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flat_tensor = flat_tensor.data.cuda()
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self._param_store.add_flat_fp16_param_by_rank_group(rank, group_id, flat_tensor)
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sync_param(flat_tensor=flat_tensor, tensor_list=tensor_list)
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# create a copy of fp32 weights of the parameters for which this rank is responsible
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# No flat fp32 buffer is allocated if the process has no parameters.
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if self.param_group_has_params[group_id]:
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fp16_flat_current_rank = self._param_store.get_flat_fp16_param_by_rank_group(
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self._zero_local_rank, group_id
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)
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fp32_flat_current_rank = fp16_flat_current_rank.float()
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device = "cpu" if self._cpu_offload else get_current_device()
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fp32_flat_current_rank = fp32_flat_current_rank.to(device)
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fp32_flat_current_rank.requires_grad = True
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self._fp32_flat_param_groups_of_current_rank[group_id] = fp32_flat_current_rank
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# need to replace the params in the `params` field in the optimizer
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# so that when the optimizer calls step(), it only updates the tensors
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# managed by this data parallel rank
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param_group["params"] = [fp32_flat_current_rank]
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# set reduction state
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for param in self._fp16_param_groups[group_id]:
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self._param_store.set_param_reduction_state(param, False)
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assert len(self._fp16_param_groups) != 0
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# If a rank is not assigned any arguments, 'has_params' is False.
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self.has_params = sum(self.param_group_has_params) != 0
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# flag used to skip unnecessary gradient reduce operation when gradient accumulation is enabled.
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self.skip_grad_reduce = False
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# initialize communication stream for
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# communication-computation overlapping
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if self._overlap_sync_grad:
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self._comm_stream = torch.cuda.Stream()
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else:
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self._comm_stream = torch.cuda.current_stream()
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# reduction hook is only used if overlapping communication
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# if it is stage 1 without overlapping, no hook will be attached
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if self._overlap_sync_grad:
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self._attach_reduction_hook()
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@property
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def zero_local_rank(self):
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return self._zero_local_rank
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@property
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def zero_world_size(self):
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return self._zero_world_size
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@property
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def dtype(self):
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return self._dtype
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@property
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def loss_scale(self):
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return self.grad_scaler.scale
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@property
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def num_param_groups(self):
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return len(self._fp16_param_groups)
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def _partition_param_list(self, param_list):
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no_params_ranks = []
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params_per_rank = [[] for _ in range(self._zero_world_size)]
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numel_per_rank = [0 for _ in range(self._zero_world_size)]
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self.params_per_rank_id_dict.append([[] for _ in range(self._zero_world_size)])
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sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True)
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for i, param in enumerate(sorted_params):
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global_id = str(i)
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for j in range(len(param.size())):
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global_id = "_".join([global_id, str(param.size()[j])])
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if self._overlap_sync_param:
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rank_to_go = self._param_bcast_sync_handler.get_rank_by_param(param)
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else:
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rank_to_go = numel_per_rank.index(min(numel_per_rank))
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params_per_rank[rank_to_go].append(param)
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self.params_per_rank_id_dict[-1][rank_to_go].append(global_id)
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numel_per_rank[rank_to_go] += param.numel()
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# check whether any rank is not assigned to parameters.
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for rank, params in enumerate(params_per_rank):
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if len(params) == 0:
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no_params_ranks.append(rank)
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if gpc.is_rank_for_log():
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logger.info(f"Number of elements on ranks: {numel_per_rank}, rank:{gpc.get_global_rank()}")
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return params_per_rank, set(no_params_ranks)
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def _attach_reduction_hook(self):
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# we iterate over the fp16 params
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# on each param, we register a hook to its AccumulateGrad object
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for group_id in range(self.num_param_groups):
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param_group = self._fp16_param_groups[group_id]
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for param in param_group:
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if param.requires_grad:
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reduce_rank = None
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def _define_and_attach(param, reduce_rank=None):
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# get the AccumulateGrad object of the param itself
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# If these objects are not kept, reduction hooks may not be attached successfully.
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accum_grad_obj = get_grad_accumulate_object(param)
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self._grad_store.add_accumulate_grad_object(accum_grad_obj)
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reduction_func = partial(
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self._store_and_try_reduce_grads_by_bucket,
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param=param,
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reduce_rank=reduce_rank,
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)
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# define hook
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# NOT IMPORTANT BUT GOOD TO KNOW:
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# args here is not grad, but allow_unreacable and accumulate_grad
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def reduce_grad_hook(*args): # pylint: disable=W0613
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if self.skip_grad_reduce is False:
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reduction_func()
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accum_grad_obj.register_hook(reduce_grad_hook)
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_define_and_attach(param, reduce_rank)
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def _store_and_try_reduce_grads_by_bucket(self, param, reduce_rank=None):
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param_size = param.numel()
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# check if the bucket is full
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# if full, will reduce the grads already in the bucket
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# after reduction, the bucket will be empty
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if self._bucket_store.num_elements_in_bucket(reduce_rank) + param_size > self._reduce_bucket_size:
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self._reduce_grads_stored_in_bucket(reduce_rank, last_bucket=False)
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# the param must not be reduced to ensure correctness
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is_param_reduced = self._param_store.is_param_reduced(param)
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if is_param_reduced:
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msg = (
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f"Parameter of size ({param.size()}) has already been reduced, "
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+ "duplicate reduction will lead to arithmetic incorrectness"
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)
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raise RuntimeError(msg)
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# the param must have grad for reduction
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assert param.grad is not None, f"Parameter of size ({param.size()}) has None grad, cannot be reduced"
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self._bucket_store.add_num_elements_in_bucket(param_size, reduce_rank)
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self._bucket_store.add_grad(param.grad, reduce_rank)
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self._bucket_store.add_param(param, reduce_rank)
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def _reduce_grads_stored_in_bucket(self, reduce_rank=None, last_bucket=False):
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# reduce grads
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self._reduce_grads_by_rank(
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reduce_rank=reduce_rank,
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grads=self._bucket_store.get_grad(reduce_rank=reduce_rank),
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bucket_size=self._bucket_store.num_elements_in_bucket(reduce_rank),
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)
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params_in_bucket = self._bucket_store.get_param(reduce_rank=reduce_rank)
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for param in params_in_bucket:
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# the is_param_reduced flag should be False showing that
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# this param is not reduced before calling self._reduce_grads_by_rank
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is_param_reduced = self._param_store.is_param_reduced(param)
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if is_param_reduced:
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msg = (
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f"Parameter of size ({param.size()}) has been reduced, "
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+ "duplicate reduction will lead to arithmetic incorrectness"
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)
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raise RuntimeError(msg)
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# update the flag
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self._param_store.set_param_reduction_state(param, True)
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if self._param_store.belongs_to_current_rank(param):
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self._param_store.add_reduced_param_for_compute_norm(param, last_bucket)
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else:
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self._param_store.add_previous_reduced_param(param)
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self._bucket_store.reset_by_rank(reduce_rank)
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def _reduce_grads_by_rank(self, reduce_rank, grads, bucket_size):
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grad_buckets_by_dtype = split_half_float_double(grads)
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for tensor_list in grad_buckets_by_dtype:
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param_bucket = TensorBucket(size=bucket_size)
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for tensor in tensor_list:
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param_bucket.add_to_bucket(tensor, allow_oversize=True)
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if param_bucket.is_full_or_oversized():
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self._reduce_and_copy(bucket=param_bucket, reduce_rank=reduce_rank)
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param_bucket.empty()
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if not param_bucket.is_empty():
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self._reduce_and_copy(bucket=param_bucket, reduce_rank=reduce_rank)
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def _reduce_and_copy(self, bucket: TensorBucket, reduce_rank):
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if self._overlap_sync_grad:
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self._comm_stream.synchronize()
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self._param_store.clear_grads_of_previous_reduced_params()
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with torch.cuda.stream(self._comm_stream):
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flat = bucket.flatten()
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reduced_flat = reduce_tensor(
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tensor=flat,
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dtype=self.dtype,
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dst_rank=reduce_rank,
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parallel_mode=ParallelMode.DATA,
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)
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# update the reduced tensor
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if reduce_rank is None or reduce_rank == self._zero_local_rank:
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bucket.unflatten_and_copy(reduced_flat)
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def _has_inf_or_nan(self, tensor):
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try:
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tensor_mean = float(tensor.mean())
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except RuntimeError as instance:
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# We want to check if inst is actually an overflow exception.
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# RuntimeError could come from a different error.
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# If so, we still want the exception to propagate.
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if "value cannot be converted" not in instance.args[0]:
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raise
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return True
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else:
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if tensor_mean == float("inf") or tensor_mean == -float("inf"):
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return True
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return False
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def _sync_grad(self):
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# update param already reduced flag
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reduction_states = self._param_store.get_param_reduction_states()
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for tensor, _ in reduction_states.items():
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reduction_states[tensor] = False
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self._param_store.reset_reduced_data_for_compute_norm()
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# accumulate gradient
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avg_gradients = self._grad_store._averaged_gradients
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for group_id in range(self.num_param_groups):
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# the following operations are performed only on the rank to which parameters are assigned.
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if self._zero_local_rank not in self.param_group_no_params_ranks[group_id]:
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param_group = self._param_store.get_fp16_params_by_rank_group(self._zero_local_rank, group_id)
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if group_id not in avg_gradients:
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avg_gradients[group_id] = []
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param_idx = 0
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for param in param_group:
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if param.grad is not None:
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if len(avg_gradients[group_id]) == param_idx:
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avg_gradients[group_id].append(param.grad)
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else:
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avg_gradients[group_id][param_idx].add_(param.grad)
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param_idx += 1
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# the gradients needed are stored in the avg_gradients buffer
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# thus, can clear this
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self.zero_grad()
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def zero_grad(self, set_to_none=True):
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"""
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Set parameter gradients to zero. If set_to_none = True, gradient
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will be set to None to save memory.
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:param set_to_none: Whether set the gradient to None. Default value is True.
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:type set_to_none: bool
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"""
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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:
|
|
grads = [self.padding_grad]
|
|
params = [self.padding_tensor]
|
|
|
|
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,
|
|
)
|
|
|
|
return norm
|
|
|
|
@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]:
|
|
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
|
|
self._reduce_grads_stored_in_bucket(reduce_rank=None, last_bucket=True)
|
|
|
|
# compute norm for gradients in the before bucket
|
|
groups_norms = []
|
|
for group_id in range(self.num_param_groups):
|
|
groups_norms.append(self._compute_norm_with_stage(group_id=group_id))
|
|
|
|
# clear reduced grads
|
|
if self._overlap_sync_grad:
|
|
# grads in the last bucket is reduced
|
|
self._comm_stream.synchronize()
|
|
self._param_store.clear_grads_of_previous_reduced_params()
|
|
|
|
# compute norm for gradients in the last bucket
|
|
total_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],
|
|
)
|
|
|
|
timer("sync_grad").start()
|
|
self._sync_grad()
|
|
timer("sync_grad").stop()
|
|
|
|
return self._step(closure=closure, norms=total_norms)
|
|
|
|
def _step(self, closure=None, norms=None):
|
|
assert closure is None, "closure is not supported by step()"
|
|
|
|
# check for overflow
|
|
found_inf = 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
|
|
|
|
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.alert_address,
|
|
message="Overflow 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}"
|
|
|
|
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
|
|
)
|
|
fp32_param = self._fp32_flat_param_groups_of_current_rank[group_id]
|
|
fp16_param.data.copy_(fp32_param)
|
|
|
|
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 rank, group_id in product(range(self._zero_world_size), range(self.num_param_groups)):
|
|
# 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)[rank]
|
|
handle = dist.broadcast(
|
|
fp16_param,
|
|
src=g_rank,
|
|
group=gpc.get_group(ParallelMode.ZERO1),
|
|
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()
|
|
|
|
##################
|
|
# 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 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 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 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 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
|
|
)
|
|
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
|
|
)
|
|
# param_group["params"] is fp32 flatten optimizer states of this zero rank.
|
|
param_group["params"][0].data.copy_(fp16_flat_current_rank.float())
|