Why Low-Precision Transformer Training Fails: An Analysis on Flash
  Attention
The pursuit of computational efficiency has driven the adoption of
low-precision formats for training transformer models. However, this progress
is often hindered by notorious training instabilities. This paper provides the
first mechanistic explanation for a long-standing and unresolved failure case
where training with flash attention in low-precision settings leads to
catastrophic loss explosions. Our in-depth analysis reveals that the failure is
not a random artifact but caused by two intertwined phenomena: the emergence of
similar low-rank representations within the attention mechanism and the
compounding effect of biased rounding errors inherent in low-precision
arithmetic. We demonstrate how these factors create a vicious cycle of error
accumulation that corrupts weight updates, ultimately derailing the training
dynamics. To validate our findings, we introduce a minimal modification to the
flash attention that mitigates the bias in rounding errors. This simple change
stabilizes the training process, confirming our analysis and offering a
practical solution to this persistent problem.
68e7159d7ae125f9582e6916
Haiquan Qiu
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2025-10-09T06:50:30.338Z
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Haiquan Qiu
user
huggingaaaaa
68e7159d7ae125f9582e6917
Quanming Yao
Why Low-Precision Transformer Training Fails: An Analysis on Flash
  Attention
The pursuit of computational efficiency has driven the adoption of
low-precision formats for training transformer models. However, this progress
is often hindered by notorious training instabilities. This paper provides the
first mechanistic explanation for a long-standing and unresolved failure case
where training with flash attention in low-precision settings leads to
catastrophic loss explosions. Our in-depth analysis reveals that the failure is
not a random artifact but caused by two intertwined phenomena: the emergence of
similar low-rank representations within the attention mechanism and the
compounding effect of biased rounding errors inherent in low-precision
arithmetic. We demonstrate how these factors create a vicious cycle of error
accumulation that corrupts weight updates, ultimately derailing the training
dynamics. To validate our findings, we introduce a minimal modification to the
flash attention that mitigates the bias in rounding errors. This simple change
stabilizes the training process, confirming our analysis and offering a
practical solution to this persistent problem.
Low-precision training of transformer models with flash attention suffers from catastrophic loss explosions due to low-rank representations and biased rounding errors, which are addressed by a minimal modification to the flash attention mechanism.
low-precision formats transformer models flash attention training instabilities low-rank representations biased rounding errors error accumulation weight updates training dynamics