Fix per file ruff ignores related to simplification

pyproject.toml

@@ -101,40 +101,6 @@ include = ["vllm*"]
 "vllm/v1/engine/utils.py" = ["E501"]
 "vllm/v1/utils.py" = ["E501"]
 "vllm/v1/worker/gpu_model_runner.py" = ["E501"]
-## Simplification rules
-"tests/distributed/test_expert_placement.py" = ["SIM108"]
-"tests/kernels/attention/test_cutlass_mla_decode.py" = ["SIM108"]
-"tests/kernels/attention/test_flashmla.py" = ["SIM108"]
-"tests/kernels/attention/test_lightning_attn.py" = ["SIM108"]
-"tests/kernels/moe/test_pplx_moe.py" = ["SIM108"]
-"tests/kernels/quantization/test_cutlass_scaled_mm.py" = ["SIM108"]
-"tests/kernels/test_onednn.py" = ["SIM108"]
-"tests/kernels/utils.py" = ["SIM108"]
-"tests/multimodal/test_processing.py" = ["SIM108"]
-"vllm/attention/ops/triton_reshape_and_cache_flash.py" = ["SIM108"]
-"vllm/distributed/parallel_state.py" = ["SIM108"]
-"vllm/entrypoints/chat_utils.py" = ["SIM108"]
-"vllm/entrypoints/llm.py" = ["SIM108"]
-"vllm/executor/ray_distributed_executor.py" = ["SIM108", "SIM112"]
-"vllm/model_executor/layers/batch_invariant.py" = ["SIM108"]
-"vllm/model_executor/layers/fla/ops/chunk_o.py" = ["SIM108"]
-"vllm/model_executor/layers/fused_moe/fused_moe.py" = ["SIM108"]
-"vllm/model_executor/layers/fused_moe/layer.py" = ["SIM108"]
-"vllm/model_executor/layers/fused_moe/modular_kernel.py" = ["SIM108"]
-"vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py" = ["SIM108"]
-"vllm/model_executor/layers/layernorm.py" = ["SIM108"]
-"vllm/model_executor/layers/lightning_attn.py" = ["SIM108"]
-"vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py" = ["SIM103"]
-"vllm/model_executor/layers/quantization/compressed_tensors/utils.py" = ["SIM110"]
-"vllm/model_executor/layers/quantization/quark/utils.py" = ["SIM110"]
-"vllm/utils/__init__.py" = ["SIM108"]
-"vllm/v1/sample/ops/bad_words.py" = ["SIM108"]
-"vllm/v1/sample/rejection_sampler.py" = ["SIM108"]
-"vllm/v1/worker/tpu_model_runner.py" = ["SIM108"]
-"vllm/_custom_ops.py" = ["SIM108"]
-"tools/profiler/print_layerwise_table.py" = ["SIM118"]
-## Loop variable binding issues
-"tests/kernels/mamba/test_mamba_ssm_ssd.py" = ["B023"]
 # End of temporary ignores
 
 [tool.ruff.lint]

tests/distributed/test_expert_placement.py

@@ -12,10 +12,7 @@ def verify_round_robin_pattern(expert_map, ep_rank, ep_size, global_num_experts)
     base_experts = global_num_experts // ep_size
     remainder = global_num_experts % ep_size
 
-    if ep_rank < remainder:
-        local_num_experts = base_experts + 1
-    else:
-        local_num_experts = base_experts
+    local_num_experts = base_experts + 1 if ep_rank < remainder else base_experts
 
     # Expected expert IDs for this rank in round_robin pattern
     # For non-divisible cases, ranks with extra experts start earlier

tests/kernels/attention/test_cutlass_mla_decode.py

@@ -66,10 +66,7 @@ def test_cutlass_mla_decode(
     b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal, varlen, torch_dtype
 ):
     device = torch.device("cuda:0")
-    if torch_dtype == torch.float8_e4m3fn:
-        init_dtype = torch.bfloat16
-    else:
-        init_dtype = torch_dtype
+    init_dtype = torch.bfloat16 if torch_dtype == torch.float8_e4m3fn else torch_dtype
     torch.set_default_dtype(init_dtype)
     torch.set_default_device(device)
     torch.cuda.set_device(device)

tests/kernels/attention/test_flashmla.py

@@ -52,10 +52,7 @@ def test_flash_mla(
     b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal, varlen, torch_dtype
 ):
     device = torch.device("cuda:0")
-    if torch_dtype == torch.float8_e4m3fn:
-        init_dtype = torch.bfloat16
-    else:
-        init_dtype = torch_dtype
+    init_dtype = torch.bfloat16 if torch_dtype == torch.float8_e4m3fn else torch_dtype
     torch.set_default_dtype(init_dtype)
     torch.set_default_device(device)
     torch.cuda.set_device(device)

tests/kernels/attention/test_lightning_attn.py

@@ -33,10 +33,7 @@ def reference_lightning_attention(q, k, v, ed, block_size, kv_history):
 
     # More efficient implementation
     # Convert decay factors to matrix form
-    if ed.dim() == 1:
-        decay = torch.exp(-ed).view(1, -1, 1, 1)
-    else:
-        decay = torch.exp(-ed)
+    decay = torch.exp(-ed).view(1, -1, 1, 1) if ed.dim() == 1 else torch.exp(-ed)
 
     for b in range(B):
         for step in range(S):

tests/kernels/moe/test_pplx_moe.py

@@ -705,10 +705,7 @@ def _pplx_moe(
         with set_current_vllm_config(vllm_config), override_config(moe_config):
             topk_weight, topk_ids, _ = fused_topk(a, score, topk, False)
 
-            if shared_experts is not None:
-                shared_output = shared_experts(a)
-            else:
-                shared_output = None
+            shared_output = shared_experts(a) if shared_experts is not None else None
 
             torch_output = torch_experts(
                 a,

tests/kernels/quantization/test_cutlass_scaled_mm.py

@@ -88,10 +88,7 @@ def cutlass_fp8_gemm_helper(
     # make scales K-major for blockwise quant, doesn't affect 1D scales
     scale_b = scale_b.t().contiguous().t()
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
@@ -122,10 +119,7 @@ def cutlass_int8_gemm_helper(
     scale_a = torch.randn(a_scales_shape, device=device, dtype=torch.float32)
     scale_b = torch.randn(b_scales_shape, device=device, dtype=torch.float32)
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

tests/kernels/test_onednn.py

@@ -84,10 +84,7 @@ def onednn_int8_gemm_test_helper(
         azp = None
         azp_adj = None
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     handler = ops.create_onednn_scaled_mm(
         b,

tests/kernels/utils.py

@@ -963,13 +963,9 @@ def make_test_metadata(
             None if encoder_seq_lens is None else (sum(encoder_seq_lens))
         )
 
-    if cross_test_params is None:
-        cross_kv_mmap = None
-    else:
-        # Encoder/decoder or encoder-only models only:
-        # * Extract *cross-attention* slot_mapping and block table
-        #   (kv_mmap)
-        cross_kv_mmap = cross_test_params.kv_mmap
+    # For encoder/decoder or encoder-only models only, extract *cross-attention*
+    # slot_mapping and block table (kv_mmap)
+    cross_kv_mmap = None if cross_test_params is None else cross_test_params.kv_mmap
 
     attn_backend_obj = make_backend(attn_backend.name)
 

tests/multimodal/test_processing.py

@@ -941,10 +941,7 @@ def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
 
     profiler = MultiModalProfiler(processor)
 
-    if is_valid:
-        exc_ctx = nullcontext()
-    else:
-        exc_ctx = pytest.raises(ValueError, match="At most")
+    exc_ctx = nullcontext() if is_valid else pytest.raises(ValueError, match="At most")
 
     with exc_ctx:
         profiler.get_decoder_dummy_data(
@@ -985,10 +982,7 @@ def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
     else:
         mm_data = {"image": [image] * num_images}
 
-    if is_valid:
-        exc_ctx = nullcontext()
-    else:
-        exc_ctx = pytest.raises(ValueError, match="At most")
+    exc_ctx = nullcontext() if is_valid else pytest.raises(ValueError, match="At most")
 
     with exc_ctx:
         processor.apply(

tools/profiler/print_layerwise_table.py

@@ -58,7 +58,7 @@ def get_entries(node, curr_depth=0):
 
     assert args.phase in profile_data, (
         f"Cannot find phase {args.phase} in profile data. Choose one among"
-        f"{[x for x in profile_data.keys() if 'prefill' in x or 'decode' in x]}"
+        f"{[x for x in profile_data if 'prefill' in x or 'decode' in x]}"
     )  # noqa
 
     if args.table == "summary":

vllm/_custom_ops.py

@@ -2370,10 +2370,7 @@ def __del__(self):
             torch.ops._C.release_dnnl_matmul_handler(self.handler)
 
 
-if hasattr(torch.ops._C, "create_onednn_mm_handler"):
-    _supports_onednn = True
-else:
-    _supports_onednn = False
+_supports_onednn = bool(hasattr(torch.ops._C, "create_onednn_mm_handler"))
 
 
 def is_onednn_acl_supported():

vllm/attention/ops/triton_reshape_and_cache_flash.py

@@ -52,12 +52,9 @@ def reshape_and_cache_kernel_flash(
         key_ptr + src_key_idx + tile_pos, mask=tile_pos < (num_heads * head_size)
     )
     if FP8_KV_CACHE:
-        if key_load.dtype.is_fp8():
-            key_tile = key_load
-        else:
-            # tl.store will do the correct implicit cast to fp8,
-            #  based on the key_cache_ptr.dtype.element_ty
-            key_tile = key_load / tl.load(k_scale)
+        # tl.store will do the correct implicit cast to fp8,
+        # based on the key_cache_ptr.dtype.element_ty
+        key_tile = key_load if key_load.dtype.is_fp8() else key_load / tl.load(k_scale)
     else:
         key_tile = key_load
 

vllm/distributed/parallel_state.py

@@ -1097,10 +1097,7 @@ def init_distributed_environment(
     if local_rank == -1:
         # local rank not set, this usually happens in single-node
         # setting, where we can use rank as local rank
-        if distributed_init_method == "env://":
-            local_rank = envs.LOCAL_RANK
-        else:
-            local_rank = rank
+        local_rank = envs.LOCAL_RANK if distributed_init_method == "env://" else rank
     global _WORLD, _NODE_COUNT
     if _WORLD is None:
         ranks = list(range(torch.distributed.get_world_size()))

vllm/entrypoints/chat_utils.py

@@ -1310,10 +1310,7 @@ def _parse_chat_message_content_part(
 
     modality = None
     if part_type == "image_pil":
-        if content is not None:
-            image_content = cast(Image.Image, content)
-        else:
-            image_content = None
+        image_content = cast(Image.Image, content) if content is not None else None
         mm_parser.parse_image_pil(image_content, uuid)
         modality = "image"
     elif part_type in ("image_url", "input_image"):

vllm/entrypoints/llm.py

@@ -1018,10 +1018,7 @@ def encode(
             pooling_task = "encode"
 
         if pooling_task is None:
-            if "embed" in self.supported_tasks:
-                pooling_task = "embed"
-            else:
-                pooling_task = "encode"
+            pooling_task = "embed" if "embed" in self.supported_tasks else "encode"
 
             logger.warning_once(
                 "`LLM.encode` is currently using `pooling_task = %s`.\n"

vllm/executor/ray_distributed_executor.py

@@ -458,10 +458,7 @@ def execute_model(
         else:
             serialized_data = self.input_encoder.encode(execute_model_req)
         outputs = ray.get(self.forward_dag.execute(serialized_data))
-        if self.use_v1:
-            output = outputs[0]
-        else:
-            output = self.output_decoder.decode(outputs[0])
+        output = outputs[0] if self.use_v1 else self.output_decoder.decode(outputs[0])
         return output
 
     def _run_workers(
@@ -482,10 +479,7 @@ def _run_workers(
           rather than blocking on the results.
         - args/kwargs: All workers share the same args/kwargs
         """
-        if isinstance(method, str):
-            sent_method = method
-        else:
-            sent_method = cloudpickle.dumps(method)
+        sent_method = method if isinstance(method, str) else cloudpickle.dumps(method)
         del method
         if self.use_ray_spmd_worker:
             assert not async_run_tensor_parallel_workers_only, (
@@ -573,8 +567,9 @@ def _compiled_ray_dag(self, enable_asyncio: bool):
         from ray.dag import InputNode, MultiOutputNode
 
         logger.info(
-            "RAY_CGRAPH_get_timeout is set to %s", os.environ["RAY_CGRAPH_get_timeout"]
-        )  # noqa: SIM112
+            "RAY_CGRAPH_get_timeout is set to %s",
+            os.environ["RAY_CGRAPH_get_timeout"],  # noqa: SIM112
+        )
         logger.info(
             "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE = %s",
             envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE,

vllm/model_executor/layers/batch_invariant.py

@@ -439,10 +439,7 @@ def mean_dim(
     output = torch.empty(output_shape, dtype=dtype, device=input.device)
 
     # Reshape output for kernel
-    if keepdim:
-        output_2d = output.reshape(M, 1, K).squeeze(1)
-    else:
-        output_2d = output.reshape(M, K)
+    output_2d = output.reshape(M, 1, K).squeeze(1) if keepdim else output.reshape(M, K)
 
     # Launch kernel
     grid = (M * K,)

vllm/model_executor/layers/fla/ops/chunk_o.py

@@ -151,10 +151,7 @@ def chunk_fwd_o(
 ) -> torch.Tensor:
     B, T, Hg, K, V = *q.shape, v.shape[-1]
     H = v.shape[-2]
-    if FLA_GDN_FIX_BT:
-        BT = 64
-    else:
-        BT = min(chunk_size, max(16, triton.next_power_of_2(T)))
+    BT = 64 if FLA_GDN_FIX_BT else min(chunk_size, max(16, triton.next_power_of_2(T)))
     chunk_indices = (
         prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
     )

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1746,10 +1746,7 @@ def fused_experts_impl(
     else:
         raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
 
-    if inplace:
-        out_hidden_states = hidden_states
-    else:
-        out_hidden_states = torch.empty_like(hidden_states)
+    out_hidden_states = hidden_states if inplace else torch.empty_like(hidden_states)
 
     if use_mxfp4_w4a4:
         # Weight has to be dequantized for mxfp4 emulation.

vllm/model_executor/layers/fused_moe/layer.py

@@ -886,10 +886,7 @@ def determine_expert_map(
     # Distribute experts as evenly as possible to each rank.
     base_experts = global_num_experts // ep_size
     remainder = global_num_experts % ep_size
-    if ep_rank < remainder:
-        local_num_experts = base_experts + 1
-    else:
-        local_num_experts = base_experts
+    local_num_experts = base_experts + 1 if ep_rank < remainder else base_experts
 
     # Create a tensor of size num_experts filled with -1
     expert_map = torch.full((global_num_experts,), -1, dtype=torch.int32)

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -948,10 +948,7 @@ def forward(
         """
 
         a1 = hidden_states
-        if inplace and self.shared_experts is None:
-            output = a1
-        else:
-            output = torch.zeros_like(a1)
+        output = a1 if inplace and self.shared_experts is None else torch.zeros_like(a1)
 
         local_num_experts = w1.size(0)
         if global_num_experts == -1:

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -355,10 +355,7 @@ def rocm_aiter_fused_experts(
     topk_weights = topk_weights.to(torch.float32)
     topk_ids = topk_ids.to(torch.int32)
 
-    if expert_map is not None:
-        expert_mask = (expert_map > -1).to(torch.int32)
-    else:
-        expert_mask = None
+    expert_mask = (expert_map > -1).to(torch.int32) if expert_map is not None else None
 
     # w8a8 per-channel quantization
     if (

vllm/model_executor/layers/layernorm.py

@@ -318,10 +318,7 @@ def forward_static(
         """PyTorch-native implementation equivalent to forward()."""
         orig_dtype = x.dtype
         if residual is not None:
-            if orig_dtype == torch.float16:
-                x = x + residual.float()
-            else:
-                x = x + residual
+            x = x + residual.float() if orig_dtype == torch.float16 else x + residual
             residual = x
 
         x = x.float()

vllm/model_executor/layers/lightning_attn.py

@@ -207,10 +207,7 @@ def _fwd_kv_parallel(
     kv = tl.zeros([D_FBLOCK, E_FBLOCK], dtype=tl.float32)
 
     # Handle the last block which might be smaller than BLOCK
-    if off_block == NUM_BLOCK - 1:
-        split_n = n - (NUM_BLOCK - 1) * BLOCK
-    else:
-        split_n = BLOCK
+    split_n = n - (NUM_BLOCK - 1) * BLOCK if off_block == NUM_BLOCK - 1 else BLOCK
     left_shift = tl.cdiv(split_n, CBLOCK) * CBLOCK - split_n
     num_blocks = min(tl.cdiv(split_n, CBLOCK), NUM_CBLOCK)
     k_decay_ptr += (NUM_CBLOCK - num_blocks) * CBLOCK