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Fix for Neuron #30259

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Merged
michaelbenayoun merged 16 commits into from
May 2, 2024
Merged

Fix for Neuron #30259

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d32aee0
Fix _update_causal_mask for Neuron
michaelbenayoun Apr 15, 2024
9429cd9
Remove the use of Ellipsis
michaelbenayoun Apr 16, 2024
5f6526c
Update warning message
michaelbenayoun Apr 16, 2024
32be4b7
Fixup
michaelbenayoun Apr 17, 2024
9e72932
Fix
michaelbenayoun Apr 17, 2024
3198401
Fix _update_causal_mask for Neuron, it works
michaelbenayoun Apr 18, 2024
446cb62
Fix FX when defining custom leaf module
michaelbenayoun Apr 25, 2024
65ebf45
Partial fix
michaelbenayoun Apr 25, 2024
1483dbb
Fix
michaelbenayoun Apr 26, 2024
206b007
Merge branch 'main' into fix_for_neuron
michaelbenayoun Apr 29, 2024
c991c89
[WIP] use metaclasses instead
michaelbenayoun Apr 30, 2024
c8362c0
Works
michaelbenayoun Apr 30, 2024
2eae3e0
Cleanup
michaelbenayoun Apr 30, 2024
8ed56cb
Fix
michaelbenayoun Apr 30, 2024
0d547f4
Removing comment
michaelbenayoun Apr 30, 2024
2a24b86
nit
michaelbenayoun Apr 30, 2024
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7 changes: 5 additions & 2 deletions src/transformers/models/cohere/modeling_cohere.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1021,8 +1021,11 @@ def _update_causal_mask(
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
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@ArthurZucker ArthurZucker Apr 30, 2024

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you risk overflows here no?
If you have torch_min + torch_min -> produced inf

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@amyeroberts amyeroberts Apr 30, 2024

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+1 - we should try to avoid this. I think it's technically OK because of the masked_fill below

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@michaelbenayoun michaelbenayoun Apr 30, 2024

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Even if you overflow it's ok because the next line you create a boolean tensor: padding_mask = padding_mask == 0. So in the end you get the same result as long as the elements in the tensor that need to be zero end up having this value.

This code, which overflows in the context of the function:

padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask *= 2
padding_mask = padding_mask == 0

produces the same output as this code, which does not overflow:

padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0

padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
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7 changes: 5 additions & 2 deletions src/transformers/models/gemma/modeling_gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1007,8 +1007,11 @@ def _update_causal_mask(
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
Expand Down
7 changes: 5 additions & 2 deletions src/transformers/models/llama/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1099,8 +1099,11 @@ def _update_causal_mask(
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
Expand Down
7 changes: 5 additions & 2 deletions src/transformers/models/olmo/modeling_olmo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1082,8 +1082,11 @@ def _update_causal_mask(
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/training_args.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,12 +84,12 @@
if os.environ.get("TORCHELASTIC_RUN_ID"):
if is_optimum_neuron_available():
logger.info(
"Make sure that you are performing the training with the TrainiumTrainer from optimum[neuron], this "
"Make sure that you are performing the training with the NeuronTrainer from optimum[neuron], this "
"will fail otherwise."
)
else:
logger.warning(
"Please use the TrainiumTrainer from optimum[neuron] instead of the Transformers library to perform "
"Please use the NeuronTrainer from optimum[neuron] instead of the Transformers library to perform "
"training on AWS Trainium instances. More information here: "
"https://github.com/huggingface/optimum-neuron"
)
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