Remove unused kwargs from model definitions (#13555)

vllm/model_executor/models/arctic.py

@@ -5,7 +5,7 @@ from typing import Iterable, List, Optional, Set, Tuple, Union
 import torch
 from torch import nn
 
-from vllm.attention import Attention, AttentionMetadata
+from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
@@ -283,13 +283,11 @@ class ArcticAttention(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.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
 
@@ -336,16 +334,12 @@ class ArcticDecoderLayer(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         residual_input = hidden_states
         hidden_states = self.input_layernorm(hidden_states)
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         hidden_states = residual_input + hidden_states
 
@@ -400,8 +394,6 @@ class ArcticModel(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors],
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
@@ -413,11 +405,8 @@ class ArcticModel(nn.Module):
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
-            hidden_states = layer(positions, hidden_states,
-                                  kv_caches[i - self.start_layer],
-                                  attn_metadata)
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({"hidden_states": hidden_states})
         hidden_states = self.norm(hidden_states)
@@ -458,13 +447,10 @@ class ArcticForCausalLM(nn.Module, SupportsPP, SupportsQuant):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = self.model(input_ids, positions, kv_caches,
-                                   attn_metadata, intermediate_tensors,
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
                                    inputs_embeds)
         return hidden_states