Add type hints for forward functions in Gemma2 (#35034)

* feat: add gemma2 type hints

* fix: mask is optional

src/transformers/models/gemma2/modeling_gemma2.py

@@ -170,7 +170,14 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
-def eager_attention_forward(config, query, key, value, mask, **_kwargs):
+def eager_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    **_kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
     key_states = repeat_kv(key, config.num_key_value_groups)
     value_states = repeat_kv(value, config.num_key_value_groups)
 
@@ -192,7 +199,15 @@ def eager_attention_forward(config, query, key, value, mask, **_kwargs):
     return attn_output, attn_weights
 
 
-def flash_attention_forward(config, query, key, value, mask, target_dtype=torch.float16, **_kwargs):
+def flash_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    target_dtype: torch.dtype = torch.float16,
+    **_kwargs,
+) -> Tuple[torch.Tensor, None]:
     if mask is not None:
         seq_len = mask.shape[1]
         query = query[:, :, :seq_len]
@@ -229,7 +244,15 @@ def flash_attention_forward(config, query, key, value, mask, target_dtype=torch.
     return attn_output, None
 
 
-def flex_attention_forward(config, query, key, value, mask, output_attentions=False, **_kwargs):
+def flex_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    output_attentions: bool = False,
+    **_kwargs,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
     def tanh_softcap(score, b, h, q_idx, kv_idx):
         soft_cap = config.attn_logit_softcapping
         score = soft_cap * torch.tanh(score / soft_cap)
@@ -255,7 +278,14 @@ def tanh_softcap(score, b, h, q_idx, kv_idx):
     return attn_output, attn_weights
 
 
-def sdpa_attention_forward(config, query, key, value, mask, **_kwargs):
+def sdpa_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    **_kwargs,
+) -> Tuple[torch.Tensor, None]:
     key = repeat_kv(key, config.num_key_value_groups)
     value = repeat_kv(value, config.num_key_value_groups)
 

src/transformers/models/gemma2/modular_gemma2.py

@@ -213,7 +213,14 @@ class Gemma2RotaryEmbedding(GemmaRotaryEmbedding):
     pass
 
 
-def eager_attention_forward(config, query, key, value, mask, **_kwargs):
+def eager_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    **_kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
     key_states = repeat_kv(key, config.num_key_value_groups)
     value_states = repeat_kv(value, config.num_key_value_groups)
 
@@ -235,7 +242,15 @@ def eager_attention_forward(config, query, key, value, mask, **_kwargs):
     return attn_output, attn_weights
 
 
-def flash_attention_forward(config, query, key, value, mask, target_dtype=torch.float16, **_kwargs):
+def flash_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    target_dtype: torch.dtype = torch.float16,
+    **_kwargs,
+) -> Tuple[torch.Tensor, None]:
     if mask is not None:
         seq_len = mask.shape[1]
         query = query[:, :, :seq_len]
@@ -272,7 +287,15 @@ def flash_attention_forward(config, query, key, value, mask, target_dtype=torch.
     return attn_output, None
 
 
-def flex_attention_forward(config, query, key, value, mask, output_attentions=False, **_kwargs):
+def flex_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    output_attentions: bool = False,
+    **_kwargs,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
     def tanh_softcap(score, b, h, q_idx, kv_idx):
         soft_cap = config.attn_logit_softcapping
         score = soft_cap * torch.tanh(score / soft_cap)
@@ -298,7 +321,14 @@ def tanh_softcap(score, b, h, q_idx, kv_idx):
     return attn_output, attn_weights
 
 
-def sdpa_attention_forward(config, query, key, value, mask, **_kwargs):
+def sdpa_attention_forward(
+    config: Gemma2Config,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    mask: Optional[torch.Tensor],
+    **_kwargs,
+) -> Tuple[torch.Tensor, None]:
     key = repeat_kv(key, config.num_key_value_groups)
     value = repeat_kv(value, config.num_key_value_groups)