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Gemma capping is a must for big models #31698

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merged 6 commits into from
Jun 28, 2024
Merged

Gemma capping is a must for big models #31698

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4535b0b
softcapping
ArthurZucker Jun 28, 2024
8d1fbce
Merge branch 'main' of github.com:huggingface/transformers into gemma…
ArthurZucker Jun 28, 2024
6ee8e53
soft cap before the mask
ArthurZucker Jun 28, 2024
60935ba
style
ArthurZucker Jun 28, 2024
33ce595
...
ArthurZucker Jun 28, 2024
957c98e
super nit
ArthurZucker Jun 28, 2024
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3 changes: 3 additions & 0 deletions src/transformers/models/gemma2/configuration_gemma2.py
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Expand Up @@ -78,6 +78,7 @@ class Gemma2Config(PretrainedConfig):
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores.
query_pre_attn_scalar (`float`, *optional*, defaults to 224): scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the
size of the sliding window.
Expand Down Expand Up @@ -116,6 +117,7 @@ def __init__(
attention_bias=False,
attention_dropout=0.0,
final_logit_softcapping=30.0,
attn_logit_softcapping=50.0,
query_pre_attn_scalar=224,
sliding_window=4096,
**kwargs,
Expand All @@ -135,6 +137,7 @@ def __init__(
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.attn_logit_softcapping = attn_logit_softcapping

super().__init__(
pad_token_id=pad_token_id,
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5 changes: 5 additions & 0 deletions src/transformers/models/gemma2/modeling_gemma2.py
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Expand Up @@ -256,6 +256,11 @@ def forward(

attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling

if self.config.attn_logit_softcapping is not None:
attn_weights = attn_weights / self.config.attn_logit_softcapping
attn_weights = torch.tanh(attn_weights)
attn_weights = attn_weights * self.config.attn_logit_softcapping

if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
Expand Down
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