Remove unused kwargs from model definitions (#13555)

vllm/model_executor/models/olmo2.py

@@ -24,12 +24,12 @@
 """Inference-only OLMo2 model compatible with HuggingFace weights."""
 
 from functools import partial
-from typing import Iterable, List, Optional, Tuple, Union
+from typing import Iterable, Optional, Tuple, Union
 
 import torch
 from torch import nn
 
-from vllm.attention import Attention, AttentionMetadata
+from vllm.attention import Attention
 from vllm.config import VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.distributed.communication_op import tensor_model_parallel_all_gather
@@ -153,14 +153,12 @@ class Olmo2Attention(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         q, k = self._apply_qk_norm(q, k)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -239,13 +237,10 @@ class Olmo2DecoderLayer(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         # Attention block.
         residual = hidden_states
-        hidden_states = self.self_attn(positions, hidden_states, kv_cache,
-                                       attn_metadata)
+        hidden_states = self.self_attn(positions, hidden_states)
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = hidden_states + residual
 
@@ -287,8 +282,6 @@ class Olmo2Model(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors],
     ) -> Union[torch.Tensor, IntermediateTensors]:
         """
@@ -307,14 +300,9 @@ class Olmo2Model(nn.Module):
             assert isinstance(hidden_states, torch.Tensor)
 
         # Apply blocks one-by-one.
-        for i in range(self.start_layer, self.end_layer):
+        for layer in self.layers[self.start_layer:self.end_layer]:
             # shape: (batch_size, seq_len, d_model)
-            hidden_states = self.layers[i](
-                positions,
-                hidden_states,
-                kv_caches[i - self.start_layer],
-                attn_metadata,
-            )
+            hidden_states = layer(positions, hidden_states)
 
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({"hidden_states": hidden_states})
@@ -357,15 +345,11 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(
             input_ids=input_ids,
             positions=positions,
-            kv_caches=kv_caches,
-            attn_metadata=attn_metadata,
             intermediate_tensors=intermediate_tensors,
         )
         return hidden_states