Convert formatting to use ruff instead of yapf + isort (#26247)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/internlm2_ve.py

@@ -12,14 +12,16 @@ from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.models.internlm2 import (InternLM2Attention,
-                                                  InternLM2ForCausalLM,
-                                                  InternLM2MLP, InternLM2Model)
+from vllm.model_executor.models.internlm2 import (
+    InternLM2Attention,
+    InternLM2ForCausalLM,
+    InternLM2MLP,
+    InternLM2Model,
+)
 from vllm.sequence import IntermediateTensors
 
 
 class InternLM2VEDecoderLayer(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,
@@ -31,8 +33,7 @@ class InternLM2VEDecoderLayer(nn.Module):
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.attention = InternLM2Attention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -58,8 +59,7 @@ class InternLM2VEDecoderLayer(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.feed_forward_ve",
         )
-        self.attention_norm = RMSNorm(config.hidden_size,
-                                      eps=config.rms_norm_eps)
+        self.attention_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def forward(
@@ -74,8 +74,7 @@ class InternLM2VEDecoderLayer(nn.Module):
             residual = hidden_states
             hidden_states = self.attention_norm(hidden_states)
         else:
-            hidden_states, residual = self.attention_norm(
-                hidden_states, residual)
+            hidden_states, residual = self.attention_norm(hidden_states, residual)
         hidden_states = self.attention(
             positions=positions,
             hidden_states=hidden_states,
@@ -84,27 +83,25 @@ class InternLM2VEDecoderLayer(nn.Module):
         # Fully Connected
         hidden_states, residual = self.ffn_norm(hidden_states, residual)
         if visual_token_mask is not None and visual_token_mask.any():
-            visual_token_mask = visual_token_mask.repeat(
-                1, self.hidden_size).bool()
+            visual_token_mask = visual_token_mask.repeat(1, self.hidden_size).bool()
             text_token_mask = ~visual_token_mask
             hidden_states[visual_token_mask] = self.feed_forward_ve(
-                hidden_states[visual_token_mask].reshape(
-                    -1, self.hidden_size)).flatten()
+                hidden_states[visual_token_mask].reshape(-1, self.hidden_size)
+            ).flatten()
             if text_token_mask.any():
                 hidden_states[text_token_mask] = self.feed_forward(
-                    hidden_states[text_token_mask].reshape(
-                        -1, self.hidden_size)).flatten()
+                    hidden_states[text_token_mask].reshape(-1, self.hidden_size)
+                ).flatten()
         else:
             hidden_states = self.feed_forward(hidden_states)
         return hidden_states, residual
 
 
 class InternLM2VEModel(InternLM2Model):
-
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__(vllm_config=vllm_config,
-                         prefix=prefix,
-                         layer_type=InternLM2VEDecoderLayer)
+        super().__init__(
+            vllm_config=vllm_config, prefix=prefix, layer_type=InternLM2VEDecoderLayer
+        )
 
     def forward(
         self,
@@ -132,17 +129,15 @@ class InternLM2VEModel(InternLM2Model):
                 visual_token_mask=visual_token_mask,
             )
         if not get_pp_group().is_last_rank:
-            return IntermediateTensors({
-                "hidden_states": hidden_states,
-                "residual": residual
-            })
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
 
 class InternLM2VEForCausalLM(InternLM2ForCausalLM):
-
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__(vllm_config=vllm_config,
-                         prefix=prefix,
-                         model_type=InternLM2VEModel)
+        super().__init__(
+            vllm_config=vllm_config, prefix=prefix, model_type=InternLM2VEModel
+        )