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Enable bf16 check_grad_overflow by default (matching fp16) #8035

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Enable bf16 check_grad_overflow by default (matching fp16) #8035

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19 changes: 15 additions & 4 deletions deepspeed/runtime/precision_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
"bf16": {
"enabled": true,
"immediate_grad_update": false,
"check_grad_overflow": false
"check_grad_overflow": true
}
'''
BFLOAT16 = "bf16"
Expand Down Expand Up @@ -53,9 +53,20 @@ class DeepSpeedBF16Config(DeepSpeedConfigModel):
Apply gradient updates immediately rather than delayed.
"""

check_grad_overflow: bool = False
"""
Check for gradient overflows and underflows
check_grad_overflow: bool = True
"""
Detect gradient overflow/underflow before optimizer step and skip the step
when detected. Default True (matching fp16 default) because bf16 partition-flat
gradient accumulation in ZeRO-2 with heterogeneous per-sample loss masks (e.g.
Mixture-of-Transformers + per-sample validity dropout) can produce a bf16 element
that overflows to +inf in averaged_gradients[i]. Without this check, Adam.step
computes inf/sqrt(inf)=NaN inside a fused kernel, simultaneously corrupting
thousands of parameter tensors and ending the training run with no useful
diagnostic. Set False only if you have measured this check to be too expensive
and have separately confirmed your bf16 path cannot overflow.
See:
- github.com/deepspeedai/DeepSpeed/issues/5242
- github.com/deepspeedai/DeepSpeed/pull/6976 (introduced the option)
"""

bf16_master_weights_and_grads: bool = False
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