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Gemma2: fix config initialization (cache_implementation) #33684

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Merged
gante merged 1 commit into from
Sep 24, 2024
Merged

Gemma2: fix config initialization (cache_implementation) #33684

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4 changes: 3 additions & 1 deletion src/transformers/models/gemma2/configuration_gemma2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,7 @@ class Gemma2Config(PretrainedConfig):
size of the sliding window.
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.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.

```python
>>> from transformers import Gemma2Model, Gemma2Config
Expand All @@ -98,7 +99,6 @@ class Gemma2Config(PretrainedConfig):

model_type = "gemma2"
keys_to_ignore_at_inference = ["past_key_values"]
cache_implementation = "hybrid"
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@gante gante Sep 24, 2024 •
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TIL: attributes defined at a class level don't are not present in __dict__.

Not present in __dict__ -> config.to_dict() doesn't contain cache_implementation -> generation_config is not initialized with cache_implementation -> tests fail because they expect HybridCache


def __init__(
self,
Expand All @@ -125,6 +125,7 @@ def __init__(
sliding_window=4096,
final_logit_softcapping=30.0,
attn_logit_softcapping=50.0,
cache_implementation="hybrid",
**kwargs,
):
super().__init__(
Expand Down Expand Up @@ -153,3 +154,4 @@ def __init__(
self.sliding_window = sliding_window
self.final_logit_softcapping = final_logit_softcapping
self.attn_logit_softcapping = attn_logit_softcapping
self.cache_implementation = cache_implementation
4 changes: 3 additions & 1 deletion src/transformers/models/gemma2/modular_gemma2.py
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Expand Up @@ -117,6 +117,7 @@ class Gemma2Config(PretrainedConfig):
size of the sliding window.
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.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.

```python
>>> from transformers import Gemma2Model, Gemma2Config
Expand All @@ -130,7 +131,6 @@ class Gemma2Config(PretrainedConfig):

model_type = "gemma2"
keys_to_ignore_at_inference = ["past_key_values"]
cache_implementation = "hybrid"

def __init__(
self,
Expand All @@ -157,6 +157,7 @@ def __init__(
sliding_window=4096,
final_logit_softcapping=30.0,
attn_logit_softcapping=50.0,
cache_implementation="hybrid",
**kwargs,
):
super().__init__(
Expand Down Expand Up @@ -185,6 +186,7 @@ def __init__(
self.sliding_window = sliding_window
self.final_logit_softcapping = final_logit_softcapping
self.attn_logit_softcapping = attn_logit_softcapping
self.cache_implementation = cache_implementation


class Gemma2RMSNorm(GemmaRMSNorm):
Expand Down
4 changes: 3 additions & 1 deletion utils/check_config_attributes.py
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Expand Up @@ -44,7 +44,9 @@
"Qwen2Config": ["use_sliding_window"],
"Qwen2MoeConfig": ["use_sliding_window"],
"Qwen2VLConfig": ["use_sliding_window"],
"Gemma2Config": ["tie_word_embeddings"],
# `cache_implementation` should be in the default generation config, but we don't yet support per-model
# generation configs (TODO joao)
"Gemma2Config": ["tie_word_embeddings", "cache_implementation"],
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used to compute the property `self.layers_block_type`
Expand Down