Migrate linter from `pylint` to `ruff` (#1665)

vllm/model_executor/layers/attention.py

@@ -18,7 +18,6 @@ _PARTITION_SIZE = 512
 
 
 class PagedAttention(nn.Module):
-    # pylint: disable=line-too-long
     """GPT-style multi-head PagedAttention.
 
     This class takes query, key, and value tensors as input. The input tensors

vllm/model_executor/layers/quantization/awq.py

@@ -50,7 +50,7 @@ class AWQConfig(QuantizationConfig):
     def get_config_filenames() -> List[str]:
         return [
             "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
-            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq  # pylint: disable=line-too-long
+            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
         ]
 
     @classmethod

vllm/model_executor/model_loader.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
-from vllm.model_executor.models import *  # pylint: disable=wildcard-import
+from vllm.model_executor.models import *
 from vllm.model_executor.weight_utils import (get_quant_config,
                                               initialize_dummy_weights)
 

vllm/model_executor/models/aquila.py

@@ -261,10 +261,7 @@ class AquilaModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states = layer(
                 positions,

vllm/model_executor/models/baichuan.py

@@ -281,10 +281,7 @@ class BaiChuanModel(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,

vllm/model_executor/models/bloom.py

@@ -256,10 +256,7 @@ class BloomModel(nn.Module):
         hidden_states = self.word_embeddings(input_ids)
         hidden_states = self.word_embeddings_layernorm(hidden_states)
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(
                 position_ids,

vllm/model_executor/models/chatglm.py

@@ -269,10 +269,7 @@ class GLMTransformer(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         for i in range(self.num_layers):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states = layer(
                 hidden_states=hidden_states,

vllm/model_executor/models/falcon.py

@@ -353,10 +353,7 @@ class FalconModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.word_embeddings(input_ids)
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(
                 positions,

vllm/model_executor/models/gpt2.py

@@ -206,10 +206,7 @@ class GPT2Model(nn.Module):
         hidden_states = inputs_embeds + position_embeds
 
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
                                   cache_event)

vllm/model_executor/models/gpt_bigcode.py

@@ -225,10 +225,7 @@ class GPTBigCodeModel(nn.Module):
         hidden_states = inputs_embeds + position_embeds
 
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
                                   cache_event)

vllm/model_executor/models/gpt_j.py

@@ -147,10 +147,7 @@ class GPTJBlock(nn.Module):
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        if config.n_inner is None:
-            inner_dim = 4 * config.n_embd
-        else:
-            inner_dim = config.n_inner
+        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
         self.attn = GPTJAttention(config, linear_method)
         self.mlp = GPTJMLP(inner_dim, config, linear_method)
@@ -205,10 +202,7 @@ class GPTJModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.wte(input_ids)
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(
                 position_ids,

vllm/model_executor/models/gpt_neox.py

@@ -216,10 +216,7 @@ class GPTNeoXModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.embed_in(input_ids)
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states = layer(
                 position_ids,

vllm/model_executor/models/internlm.py

@@ -213,10 +213,7 @@ class InternLMModel(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,

vllm/model_executor/models/llama.py

@@ -253,10 +253,7 @@ class LlamaModel(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,

vllm/model_executor/models/mistral.py

@@ -248,10 +248,7 @@ class MistralModel(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,

vllm/model_executor/models/mpt.py

@@ -203,10 +203,10 @@ class MPTModel(nn.Module):
         self.norm_f = nn.LayerNorm(config.d_model)
         if config.no_bias:
             for module in self.modules():
-                if hasattr(module, "bias"):
-                    if isinstance(module.bias, nn.Parameter):
-                        # Remove the bias term in Linear and LayerNorm.
-                        module.register_parameter("bias", None)
+                if hasattr(module, "bias") and isinstance(
+                        module.bias, nn.Parameter):
+                    # Remove the bias term in Linear and LayerNorm.
+                    module.register_parameter("bias", None)
 
     def forward(
         self,
@@ -218,10 +218,7 @@ class MPTModel(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.wte(input_ids)
         for i in range(len(self.blocks)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             block = self.blocks[i]
             hidden_states = block(
                 position_ids,

vllm/model_executor/models/opt.py

@@ -257,10 +257,7 @@ class OPTDecoder(nn.Module):
         hidden_states = inputs_embeds + pos_embeds
 
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
                                   cache_event)

vllm/model_executor/models/phi_1_5.py

@@ -258,10 +258,7 @@ class PhiModel(nn.Module):
     ) -> SamplerOutput:
         hidden_states = self.embd(input_ids)
         for i in range(self.config.num_hidden_layers):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states = layer(
                 positions,

vllm/model_executor/models/qwen.py

@@ -213,10 +213,7 @@ class QWenModel(nn.Module):
         hidden_states = self.wte(input_ids)
         residual = None
         for i in range(len(self.h)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.h[i]
             hidden_states, residual = layer(
                 positions,

vllm/model_executor/models/yi.py

@@ -249,10 +249,7 @@ class YiModel(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
+            cache_event = None if cache_events is None else cache_events[i]
             layer = self.layers[i]
             hidden_states, residual = layer(
                 positions,