Remove unused kwargs from model definitions (#13555)

vllm/model_executor/models/phi.py

@@ -36,13 +36,13 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """Inference-only Phi-1.5 model compatible with HuggingFace weights."""
-from typing import Iterable, List, Optional, Set, Tuple, Union
+from typing import Iterable, Optional, Set, Tuple, Union
 
 import torch
 from torch import nn
 from transformers import PhiConfig
 
-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_world_size
@@ -126,13 +126,11 @@ class PhiAttention(nn.Module):
         self,
         position_ids: 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.chunk(chunks=3, dim=-1)
         q, k = self.rotary_emb(position_ids, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.dense(attn_output)
         return output
 
@@ -186,16 +184,12 @@ class PhiLayer(nn.Module):
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         residual = hidden_states
         hidden_states = self.input_layernorm(hidden_states)
         attn_outputs = self.self_attn(
             position_ids=position_ids,
             hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         feed_forward_hidden_states = self.mlp(hidden_states)
         hidden_states = attn_outputs + feed_forward_hidden_states + residual
@@ -234,8 +228,6 @@ class PhiModel(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]:
@@ -247,14 +239,8 @@ class PhiModel(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})
@@ -304,13 +290,10 @@ class PhiForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         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