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Bert #158

Merged
merged 45 commits into from
Jul 20, 2023
Merged

Bert #158

merged 45 commits into from
Jul 20, 2023

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ScoThunder
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@ScoThunder ScoThunder commented Jul 19, 2023

日志:root@p-kunlunxin-r480-005:/data/dufeilei/dev/code/FlagPerf/training/benchmarks/bert/pytorch/log/train_1x8.log
数据集:root@p-kunlunxin-r480-005:/data/datasets_ckpt/bert/train

@@ -83,9 83,6 @@ def evaluate(self, trainer):
total_masked = num_masked
#torch.cuda.synchronize()
dist_pytorch.barrier(config.vendor)
if config.vendor == 'kunlunxin':
import torch_xmlir.core.xpu_model as xm
xm.mark_step()
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之前跑图模式需要这个xm.mark_step,现在是eager模式,不需要了

from apex.parallel import DistributedDataParallel as APEX_DDP
from apex.parallel.distributed import flat_dist_call
except ImportError:
print("import apex error")
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kunlunxin的机器没有apex,import会报错,加入try catch

from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as NativeDDP
from torch.optim import Optimizer

import utils
import config
#from converter import convert_model
from .distributed_fused_lamb import _pipeline_block_reductions_patched, _pipeline_step_patched
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同上

from torch.optim import Optimizer
from torch_xmlir.optimizer import Lamb
from torch_xmlir.optimizer import FusedLAMB
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替换为fuse优化器

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请提供kulunxin机器上跑通的截图

# e5m2_allgather=config.dwu_e5m2_allgather)
#optimizer.set_global_scale(float(os.getenv("INIT_LOSS_SCALE", 2 ** 20)))
else:
optimizer = Lamb(optimizer_grouped_parameters,
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删除多余代码

use_ddp = dist.is_initialized()
if use_ddp and config.use_xpu:
from torch_xmlir.distributed import DistributedDataParallel as DDP
model = DDP(model)
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替换为torch原生的ddp,而不是自定的ddp

optimizer,
delay_overflow_check=self.config.
allreduce_post_accumulation) as scaled_loss:
scaled_loss.backward()
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删除冗余代码


update_step = step % config.gradient_accumulation_steps == 0
if update_step:
update_model_params(loss, optimizer, grad_scaler)
else:
xm.mark_step()
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eager模式不用xm.mark_step

param.grad = None
else:
xm.optimizer_step(optimizer, barrier=True)

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删除多余代码

@yuzhou03 yuzhou03 merged commit ddacd61 into FlagOpen:main Jul 20, 2023
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5 participants