[CI] Fix mypy for vllm/v1/worker (#29037)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

tools/pre_commit/mypy.py

@@ -38,6 +38,7 @@ FILES = [
     "vllm/usage",
     "vllm/v1/core",
     "vllm/v1/engine",
+    "vllm/v1/worker",
 ]
 
 # After fixing errors resulting from changing follow_imports
@@ -62,7 +63,6 @@ SEPARATE_GROUPS = [
     "vllm/v1/sample",
     "vllm/v1/spec_decode",
     "vllm/v1/structured_output",
-    "vllm/v1/worker",
 ]
 
 # TODO(woosuk): Include the code from Megatron and HuggingFace.

vllm/model_executor/utils.py

@@ -10,7 +10,7 @@ import torch
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 
-def set_random_seed(seed: int) -> None:
+def set_random_seed(seed: int | None) -> None:
     from vllm.platforms import current_platform
 
     current_platform.seed_everything(seed)

vllm/multimodal/utils.py

@@ -3,7 +3,7 @@
 
 import asyncio
 import atexit
-from collections.abc import Iterable, Set
+from collections.abc import Generator, Set
 from concurrent.futures import ThreadPoolExecutor
 from itertools import groupby
 from pathlib import Path
@@ -403,7 +403,7 @@ def group_mm_kwargs_by_modality(
     pin_memory: bool = False,
     merge_by_field_config: bool | None = None,
     multimodal_cpu_fields: Set[str] = frozenset(),
-) -> Iterable[tuple[str, int, BatchedTensorInputs]]:
+) -> Generator[tuple[str, int, BatchedTensorInputs], None, None]:
     """Group consecutive `MultiModalKwargsItem`s from `mm_kwargs` with the same
     modality together into the same `MultiModalKwargs` instance.
 

vllm/v1/worker/cpu_worker.py

@@ -3,6 +3,7 @@
 import os
 import platform
 from collections.abc import Callable
+from typing import Any
 
 import torch
 
@@ -37,6 +38,9 @@ class CPUWorker(Worker):
 
         self.parallel_config.disable_custom_all_reduce = True
 
+        # Torch profiler. Enabled and configured through env vars:
+        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        self.profiler: Any | None = None
         if envs.VLLM_TORCH_PROFILER_DIR:
             torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
@@ -80,13 +84,13 @@ class CPUWorker(Worker):
             self.local_omp_cpuid = "nobind"
         else:
             local_dp_rank = self.parallel_config.data_parallel_rank_local
-            omp_cpuids = omp_cpuids.split("|")
+            omp_cpuids_list = omp_cpuids.split("|")
             if local_dp_rank is not None:
                 world_size = self.parallel_config.world_size
-                omp_cpuids = omp_cpuids[
+                omp_cpuids_list = omp_cpuids_list[
                     local_dp_rank * world_size : (local_dp_rank + 1) * world_size
                 ]
-            self.local_omp_cpuid = omp_cpuids[self.rank]
+            self.local_omp_cpuid = omp_cpuids_list[self.rank]
 
         if self.local_omp_cpuid != "nobind":
             ret = torch.ops._C_utils.init_cpu_threads_env(self.local_omp_cpuid)
@@ -120,7 +124,7 @@ class CPUWorker(Worker):
         pass
 
     def determine_available_memory(self) -> int:
-        return self.cache_config.cpu_kvcache_space_bytes  # type: ignore
+        return self.cache_config.cpu_kvcache_space_bytes or 0
 
     def compile_or_warm_up_model(self) -> None:
         # Reset the seed to ensure that the random state is not affected by

vllm/v1/worker/gpu_model_runner.py

@@ -5,7 +5,7 @@ import gc
 import itertools
 import time
 from collections import defaultdict
-from collections.abc import Iterator
+from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
 from copy import copy, deepcopy
 from functools import reduce
@@ -53,6 +53,7 @@ from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
 from vllm.model_executor.models.interfaces import (
+    SupportsMRoPE,
     SupportsMultiModal,
     is_mixture_of_experts,
     supports_eagle3,
@@ -126,6 +127,7 @@ from vllm.v1.outputs import (
 )
 from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
+from vllm.v1.sample.logits_processor.interface import LogitsProcessor
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.rejection_sampler import RejectionSampler
 from vllm.v1.sample.sampler import Sampler
@@ -404,7 +406,10 @@ class GPUModelRunner(
         # solution, we initialize the input batch here, and re-initialize it
         # in `initialize_kv_cache` if the block_sizes here is different from
         # the block_sizes in the kv cache config.
-        custom_logitsprocs = model_config.logits_processors
+        logits_processors = model_config.logits_processors
+        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
+            tuple(logits_processors) if logits_processors is not None else ()
+        )
         self.input_batch = InputBatch(
             max_num_reqs=self.max_num_reqs,
             # We need to use the encoder length for encoder-decoer
@@ -959,9 +964,13 @@ class GPUModelRunner(
     def _init_mrope_positions(self, req_state: CachedRequestState):
         model = self.get_model()
         assert supports_mrope(model), "M-RoPE support is not implemented."
+        assert req_state.prompt_token_ids is not None, (
+            "M-RoPE requires prompt_token_ids to be available."
+        )
+        mrope_model = cast(SupportsMRoPE, model)
 
         req_state.mrope_positions, req_state.mrope_position_delta = (
-            model.get_mrope_input_positions(
+            mrope_model.get_mrope_input_positions(
                 req_state.prompt_token_ids,
                 req_state.mm_features,
             )
@@ -1762,6 +1771,7 @@ class GPUModelRunner(
                 dst_start = mrope_pos_ptr
                 dst_end = mrope_pos_ptr + completion_part_len
 
+                assert req.mrope_position_delta is not None
                 MRotaryEmbedding.get_next_input_positions_tensor(
                     out=self.mrope_positions.np,
                     out_offset=dst_start,
@@ -1907,6 +1917,8 @@ class GPUModelRunner(
 
             for mm_input_id in encoder_input_ids:
                 mm_feature = req_state.mm_features[mm_input_id]
+                if mm_feature.data is None:
+                    continue
                 mm_hash = mm_feature.identifier
                 mm_kwargs.append(mm_feature.data)
                 mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
@@ -1930,7 +1942,7 @@ class GPUModelRunner(
         # multimodal inputs. The proper solution should be reordering the
         # encoder outputs.
         model = cast(SupportsMultiModal, self.model)
-        encoder_outputs = []
+        encoder_outputs: list[torch.Tensor] = []
         for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
             mm_kwargs,
             device=self.device,
@@ -1938,7 +1950,7 @@ class GPUModelRunner(
             merge_by_field_config=model.merge_by_field_config,
             multimodal_cpu_fields=model.multimodal_cpu_fields,
         ):
-            curr_group_outputs = []
+            curr_group_outputs: list[torch.Tensor] = []
 
             # EVS-related change.
             # (ekhvedchenia): Temporary hack to limit peak memory usage when
@@ -1980,7 +1992,7 @@ class GPUModelRunner(
                 # 2. A list or tuple (length: num_items) of tensors,
                 # each of shape (feature_size, hidden_size) in case the feature
                 # size is dynamic depending on the input multimodal items.
-                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
+                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)  # type: ignore[assignment]
 
             sanity_check_mm_encoder_outputs(
                 curr_group_outputs,
@@ -2180,7 +2192,7 @@ class GPUModelRunner(
     def sync_and_slice_intermediate_tensors(
         self,
         num_tokens: int,
-        intermediate_tensors: IntermediateTensors,
+        intermediate_tensors: IntermediateTensors | None,
         sync_self: bool,
     ) -> IntermediateTensors:
         assert self.intermediate_tensors is not None
@@ -2397,6 +2409,7 @@ class GPUModelRunner(
         if is_first_rank:
             intermediate_tensors = None
         else:
+            assert intermediate_tensors is not None
             intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                 num_input_tokens, intermediate_tensors, True
             )
@@ -2765,14 +2778,14 @@ class GPUModelRunner(
             uniform_decode = (
                 max_num_scheduled_tokens == self.uniform_decode_query_len
             ) and (num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
-            batch_descriptor = BatchDescriptor(
+            batch_desc = BatchDescriptor(
                 num_tokens=num_input_tokens,
                 uniform_decode=uniform_decode,
                 has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
             )
             cudagraph_runtime_mode, batch_descriptor = (
                 self.cudagraph_dispatcher.dispatch(
-                    batch_descriptor,
+                    batch_desc,
                     use_cascade_attn=cascade_attn_prefix_lens is not None,
                 )
             )
@@ -2856,15 +2869,15 @@ class GPUModelRunner(
                 else:
                     logits = self.model.compute_logits(sample_hidden_states)
 
-                model_output_broadcast_data = {}
+                model_output_broadcast_data: dict[str, Any] = {}
                 if logits is not None:
                     model_output_broadcast_data["logits"] = logits.contiguous()
 
-                model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
+                broadcasted = get_pp_group().broadcast_tensor_dict(
                     model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                 )
-                assert model_output_broadcast_data is not None
-                logits = model_output_broadcast_data["logits"]
+                assert broadcasted is not None
+                logits = broadcasted["logits"]
 
         self.execute_model_state = ExecuteModelState(
             scheduler_output,
@@ -2889,7 +2902,7 @@ class GPUModelRunner(
         if self.execute_model_state is None:
             # Nothing to do (PP non-final rank case), output isn't used.
             if not kv_connector_output:
-                return None  # noqa
+                return None  # type: ignore[return-value]
 
             # In case of PP with kv transfer, we need to pass through the
             # kv_connector_output
@@ -2941,33 +2954,37 @@ class GPUModelRunner(
                     spec_decode_common_attn_metadata,
                 )
 
+        spec_config = self.speculative_config
         use_padded_batch_for_eagle = (
-            self.speculative_config
-            and self.speculative_config.use_eagle()
-            and not self.speculative_config.disable_padded_drafter_batch
+            spec_config is not None
+            and spec_config.use_eagle()
+            and not spec_config.disable_padded_drafter_batch
         )
         effective_drafter_max_model_len = self.max_model_len
         if effective_drafter_max_model_len is None:
             effective_drafter_max_model_len = self.model_config.max_model_len
         if (
-            self.speculative_config
-            and self.speculative_config.draft_model_config is not None
-            and self.speculative_config.draft_model_config.max_model_len is not None
+            spec_config is not None
+            and spec_config.draft_model_config is not None
+            and spec_config.draft_model_config.max_model_len is not None
         ):
             effective_drafter_max_model_len = (
-                self.speculative_config.draft_model_config.max_model_len
+                spec_config.draft_model_config.max_model_len
             )
         input_fits_in_drafter = spec_decode_common_attn_metadata and (
             spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
             <= effective_drafter_max_model_len
         )
         if use_padded_batch_for_eagle:
+            assert self.speculative_config is not None
+            assert isinstance(self.drafter, EagleProposer)
             sampled_token_ids = sampler_output.sampled_token_ids
             if input_fits_in_drafter:
                 # EAGLE speculative decoding can use the GPU sampled tokens
                 # as inputs, and does not need to wait for bookkeeping to finish.
                 propose_draft_token_ids(sampled_token_ids)
             elif self.valid_sampled_token_count_event is not None:
+                assert spec_decode_common_attn_metadata is not None
                 next_token_ids, valid_sampled_tokens_count = (
                     self.drafter.prepare_next_token_ids_padded(
                         spec_decode_common_attn_metadata,
@@ -3105,7 +3122,9 @@ class GPUModelRunner(
         common_attn_metadata: CommonAttentionMetadata,
     ) -> torch.Tensor | list[list[int]]:
         num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
-        if self.speculative_config.method == "ngram":
+        spec_config = self.speculative_config
+        assert spec_config is not None
+        if spec_config.method == "ngram":
             assert isinstance(sampled_token_ids, list)
             assert isinstance(self.drafter, NgramProposer)
             draft_token_ids = self.drafter.propose(
@@ -3115,11 +3134,11 @@ class GPUModelRunner(
                 self.input_batch.token_ids_cpu,
                 self.input_batch.spec_decode_unsupported_reqs,
             )
-        elif self.speculative_config.method == "suffix":
+        elif spec_config.method == "suffix":
             assert isinstance(sampled_token_ids, list)
             assert isinstance(self.drafter, SuffixDecodingProposer)
             draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
-        elif self.speculative_config.method == "medusa":
+        elif spec_config.method == "medusa":
             assert isinstance(sampled_token_ids, list)
             assert isinstance(self.drafter, MedusaProposer)
 
@@ -3144,10 +3163,10 @@ class GPUModelRunner(
                 target_hidden_states=hidden_states,
                 sampling_metadata=sampling_metadata,
             )
-        elif self.speculative_config.use_eagle():
+        elif spec_config.use_eagle():
             assert isinstance(self.drafter, EagleProposer)
 
-            if self.speculative_config.disable_padded_drafter_batch:
+            if spec_config.disable_padded_drafter_batch:
                 # When padded-batch is disabled, the sampled_token_ids should be
                 # the cpu-side list[list[int]] of valid sampled tokens for each
                 # request, with invalid requests having empty lists.
@@ -3197,7 +3216,7 @@ class GPUModelRunner(
                 else:
                     target_hidden_states = hidden_states[:num_scheduled_tokens]
             else:
-                if self.speculative_config.disable_padded_drafter_batch:
+                if spec_config.disable_padded_drafter_batch:
                     token_indices_to_sample = None
                     common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                         common_attn_metadata,
@@ -3292,9 +3311,12 @@ class GPUModelRunner(
                     and is_mixture_of_experts(self.drafter.model)
                     and self.parallel_config.enable_eplb
                 ):
+                    spec_config = self.vllm_config.speculative_config
+                    assert spec_config is not None
+                    assert spec_config.draft_model_config is not None
                     logger.info_once(
                         "EPLB is enabled for drafter model %s.",
-                        self.vllm_config.speculative_config.draft_model_config.model,
+                        spec_config.draft_model_config.model,
                     )
 
                     global_expert_load = (
@@ -3311,7 +3333,7 @@ class GPUModelRunner(
                         self.eplb_state = EplbState(self.parallel_config, self.device)
                     self.eplb_state.add_model(
                         self.drafter.model,
-                        self.vllm_config.speculative_config.draft_model_config,
+                        spec_config.draft_model_config,
                         global_expert_load,
                         old_global_expert_indices,
                         rank_mapping,
@@ -3346,9 +3368,11 @@ class GPUModelRunner(
             scope="local",
         )
         prepare_communication_buffer_for_model(self.model)
+        mm_config = self.model_config.multimodal_config
         self.is_multimodal_pruning_enabled = (
             supports_multimodal_pruning(self.get_model())
-            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
+            and mm_config is not None
+            and mm_config.is_multimodal_pruning_enabled()
         )
 
         if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
@@ -3383,15 +3407,14 @@ class GPUModelRunner(
         # CudagraphWraper and CudagraphDispatcher of vllm.
 
         # wrap the model with full cudagraph wrapper if needed.
-        if (
-            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
-            and not self.parallel_config.enable_dbo
-        ):
+        cudagraph_mode = self.compilation_config.cudagraph_mode
+        assert cudagraph_mode is not None
+        if cudagraph_mode.has_full_cudagraphs() and not self.parallel_config.enable_dbo:
             self.model = CUDAGraphWrapper(
                 self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
             )
         elif self.parallel_config.enable_dbo:
-            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            if cudagraph_mode.has_full_cudagraphs():
                 self.model = UBatchWrapper(
                     self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                 )
@@ -4071,7 +4094,8 @@ class GPUModelRunner(
     def profile_run(self) -> None:
         # Profile with multimodal encoder & encoder cache.
         if self.supports_mm_inputs:
-            if self.model_config.multimodal_config.skip_mm_profiling:
+            mm_config = self.model_config.multimodal_config
+            if mm_config is not None and mm_config.skip_mm_profiling:
                 logger.info(
                     "Skipping memory profiling for multimodal encoder and "
                     "encoder cache."
@@ -4333,8 +4357,9 @@ class GPUModelRunner(
         def get_attn_backends_for_group(
             kv_cache_group_spec: KVCacheGroupSpec,
         ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
+            layer_type = cast(type[Any], AttentionLayerBase)
             layers = get_layers_from_vllm_config(
-                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
+                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
             )
             attn_backends = {}
             attn_backend_layers = defaultdict(list)
@@ -4349,7 +4374,7 @@ class GPUModelRunner(
                 if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                     attn_backend = create_fast_prefill_custom_backend(
                         "FastPrefill",
-                        attn_backend,
+                        attn_backend,  # type: ignore[arg-type]
                     )
 
                 full_cls_name = attn_backend.full_cls_name()
@@ -4448,6 +4473,7 @@ class GPUModelRunner(
                     min_cg_backend_name = attn_backend.__name__
         # Flexible resolve the cudagraph mode
         cudagraph_mode = self.compilation_config.cudagraph_mode
+        assert cudagraph_mode is not None
         # check cudagraph for mixed batch is supported
         if (
             cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
@@ -4562,12 +4588,17 @@ class GPUModelRunner(
             self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                 self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
             )
-            self.cudagraph_batch_sizes = self.compilation_config.cudagraph_capture_sizes
+            capture_sizes = self.compilation_config.cudagraph_capture_sizes
+            self.cudagraph_batch_sizes = (
+                capture_sizes if capture_sizes is not None else []
+            )
 
         # Trigger cudagraph dispatching keys initialization after
         # resolved cudagraph mode.
+        cudagraph_mode = self.compilation_config.cudagraph_mode
+        assert cudagraph_mode is not None
         self.cudagraph_dispatcher.initialize_cudagraph_keys(
-            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
+            cudagraph_mode, self.uniform_decode_query_len
         )
 
     def calculate_reorder_batch_threshold(self) -> None:
@@ -4579,7 +4610,7 @@ class GPUModelRunner(
         """
         min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)
 
-        reorder_batch_thresholds = [
+        reorder_batch_thresholds: list[int | None] = [
             group.get_metadata_builder().reorder_batch_threshold
             for group in self._attn_group_iterator()
         ]
@@ -4588,7 +4619,7 @@ class GPUModelRunner(
         if len(reorder_batch_thresholds) == 0:
             self.reorder_batch_threshold = None
             return
-        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
+        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
 
     @staticmethod
     def select_common_block_size(
@@ -5048,12 +5079,16 @@ class GPUModelRunner(
             kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
 
         if self.dcp_world_size > 1:
-            layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
+            layer_type = cast(type[Any], AttentionLayerBase)
+            layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
             for layer in layers.values():
-                assert layer.impl.need_to_return_lse_for_decode, (
+                layer_impl = getattr(layer, "impl", None)
+                if layer_impl is None:
+                    continue
+                assert layer_impl.need_to_return_lse_for_decode, (
                     "DCP requires attention impls to return"
                     " the softmax lse for decode, but the impl "
-                    f"{layer.impl.__class__.__name__} "
+                    f"{layer_impl.__class__.__name__} "
                     "does not return the softmax lse for decode."
                 )
 
@@ -5094,7 +5129,8 @@ class GPUModelRunner(
         if has_ec_transfer() and get_ec_transfer().is_producer:
             return {}
         kv_cache_spec: dict[str, KVCacheSpec] = {}
-        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
+        layer_type = cast(type[Any], AttentionLayerBase)
+        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
         for layer_name, attn_module in attn_layers.items():
             if isinstance(attn_module, Attention) and (
                 kv_tgt_layer := attn_module.kv_sharing_target_layer_name

vllm/v1/worker/gpu_ubatch_wrapper.py

@@ -121,18 +121,24 @@ class UBatchWrapper:
 
     @staticmethod
     def _create_sm_control_context(vllm_config: VllmConfig):
-        comm_sms = envs.VLLM_DBO_COMM_SMS
+        comm_sms: int = envs.VLLM_DBO_COMM_SMS
 
         set_comm_sms = lambda sms: None
         if vllm_config.parallel_config.enable_expert_parallel:
             # Currently only DeepEP highthroughput supports SM control so this
             # only affects that case.
-            all2all_manager = get_ep_group().device_communicator.all2all_manager
+            ep_group = get_ep_group()
+            device_communicator = ep_group.device_communicator
+            all2all_manager = None
+            if device_communicator is not None:
+                all2all_manager = device_communicator.all2all_manager
 
-            if all2all_manager.max_sms_used() is not None:
-                comm_sms = min(comm_sms, all2all_manager.max_sms_used())
+            if all2all_manager is not None:
+                max_sms_used = all2all_manager.max_sms_used()
+                if max_sms_used is not None:
+                    comm_sms = min(comm_sms, max_sms_used)
 
-            if comm_sms > 0:
+            if comm_sms > 0 and all2all_manager is not None:
                 set_comm_sms = lambda sms: all2all_manager.set_num_sms(sms)
 
         # TODO(lucas): support other kernels besides DeepGEMM

vllm/v1/worker/gpu_worker.py

@@ -6,7 +6,7 @@ import gc
 import os
 from contextlib import AbstractContextManager, nullcontext
 from types import NoneType
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 import torch
 import torch.distributed
@@ -87,8 +87,10 @@ class Worker(WorkerBase):
         # Buffers saved before sleep
         self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
 
-        # Torch profiler. Enabled and configured through env vars:
+        # Torch/CUDA profiler. Enabled and configured through env vars:
         # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        # VLLM_TORCH_CUDA_PROFILE=1
+        self.profiler: Any | None = None
         if envs.VLLM_TORCH_PROFILER_DIR:
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
             self.profiler = TorchProfilerWrapper(
@@ -146,17 +148,17 @@ class Worker(WorkerBase):
                 assert allocator.get_current_usage() == 0, (
                     "Sleep mode can only be used for one instance per process."
                 )
-            context = allocator.use_memory_pool(tag=tag)
+            return allocator.use_memory_pool(tag=tag)
         else:
-            context = nullcontext()
-        return context
+            return nullcontext()
 
     def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
         self.cache_config.num_gpu_blocks = num_gpu_blocks
         self.cache_config.num_cpu_blocks = num_cpu_blocks
 
     def init_device(self):
-        if self.device_config.device.type == "cuda":
+        device = self.device_config.device
+        if isinstance(device, torch.device) and device.type == "cuda":
             # This env var set by Ray causes exceptions with graph building.
             os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
             if (
@@ -375,23 +377,21 @@ class Worker(WorkerBase):
             from vllm.device_allocator.cumem import CuMemAllocator
 
             allocator = CuMemAllocator.get_instance()
-            context = allocator.use_memory_pool(tag="kv_cache")
+            with allocator.use_memory_pool(tag="kv_cache"):
+                self.model_runner.initialize_kv_cache(kv_cache_config)
         else:
-            context = nullcontext()
-        with context:
             self.model_runner.initialize_kv_cache(kv_cache_config)
 
     def compile_or_warm_up_model(self) -> None:
         # warm up sizes that are not in cudagraph capture sizes,
         # but users still want to compile for better performance,
         # e.g. for the max-num-batched token size in chunked prefill.
-        warmup_sizes = self.vllm_config.compilation_config.compile_sizes.copy()
+        compile_sizes = self.vllm_config.compilation_config.compile_sizes
+        warmup_sizes = compile_sizes.copy() if compile_sizes is not None else []
         if not self.model_config.enforce_eager:
-            warmup_sizes = [
-                x
-                for x in warmup_sizes
-                if x not in self.vllm_config.compilation_config.cudagraph_capture_sizes
-            ]
+            capture_sizes = self.vllm_config.compilation_config.cudagraph_capture_sizes
+            if capture_sizes is not None:
+                warmup_sizes = [x for x in warmup_sizes if x not in capture_sizes]
         # We skip EPLB here since we don't want to record dummy metrics
         for size in sorted(warmup_sizes, reverse=True):
             logger.info("Compile and warming up model for size %d", size)
@@ -532,12 +532,12 @@ class Worker(WorkerBase):
             )
         }
         if forward_pass and not get_pp_group().is_first_rank:
-            intermediate_tensors = IntermediateTensors(
-                get_pp_group().recv_tensor_dict(
-                    all_gather_group=get_tp_group(),
-                    all_gather_tensors=all_gather_tensors,
-                )
+            tensor_dict = get_pp_group().recv_tensor_dict(
+                all_gather_group=get_tp_group(),
+                all_gather_tensors=all_gather_tensors,
             )
+            assert tensor_dict is not None
+            intermediate_tensors = IntermediateTensors(tensor_dict)
 
         with self.annotate_profile(scheduler_output):
             output = self.model_runner.execute_model(
@@ -605,7 +605,7 @@ class Worker(WorkerBase):
         assert self.model_runner.eplb_state is not None
         self.model_runner.eplb_state.rearrange(
             execute_shuffle=True,
-            global_expert_load=None,
+            global_expert_loads=None,
             rank_mapping=rank_mapping,
         )
         torch.cuda.synchronize()
@@ -661,7 +661,7 @@ class Worker(WorkerBase):
 
     def _reconfigure_moe(
         self, old_ep_size: int, new_ep_size: int
-    ) -> torch.Tensor | None:
+    ) -> list[torch.Tensor] | None:
         """
         Reconfigure MoE modules with provided reconfig_request
 
@@ -728,26 +728,29 @@ class Worker(WorkerBase):
             num_local_physical_experts = num_local_experts
             assert self.model_runner.eplb_state is not None
             new_physical_experts = (
-                self.model_runner.eplb_state.physical_to_logical_map.shape[1]
+                self.model_runner.eplb_state.physical_to_logical_map.shape[1]  # type: ignore[attr-defined]
             )
             parallel_config.eplb_config.num_redundant_experts = (
                 new_physical_experts
-                - self.model_runner.eplb_state.logical_replica_count.shape[1]
+                - self.model_runner.eplb_state.logical_replica_count.shape[1]  # type: ignore[attr-defined]
             )
             global_expert_loads = None
         else:
-            num_local_physical_experts = torch.tensor(
+            num_local_physical_experts_tensor = torch.tensor(
                 [num_local_experts], dtype=torch.int32, device="cpu"
             )
             torch.distributed.broadcast(
-                num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0
+                num_local_physical_experts_tensor,
+                group=get_ep_group().cpu_group,
+                group_src=0,
             )
-            num_local_physical_experts = num_local_physical_experts.item()
+            num_local_physical_experts = int(num_local_physical_experts_tensor.item())
             new_physical_experts = num_local_physical_experts * new_ep_size
             assert self.model_runner.eplb_state is not None
-            global_expert_loads = self.model_runner.eplb_state.rearrange(
+            global_expert_loads_any = self.model_runner.eplb_state.rearrange(
                 execute_shuffle=False
             )
+            global_expert_loads = cast(list[torch.Tensor], global_expert_loads_any)
             parallel_config.eplb_config.num_redundant_experts = (
                 new_physical_experts - global_expert_loads[0].shape[1]
             )
@@ -849,8 +852,9 @@ def init_worker_distributed_environment(
     init_batch_invariance()
     set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
 
+    init_method = distributed_init_method or "env://"
     init_distributed_environment(
-        parallel_config.world_size, rank, distributed_init_method, local_rank, backend
+        parallel_config.world_size, rank, init_method, local_rank, backend
     )
 
     ensure_model_parallel_initialized(

vllm/v1/worker/kv_connector_model_runner_mixin.py

@@ -59,7 +59,7 @@ class KVConnectorModelRunnerMixin:
     @staticmethod
     def ensure_kv_transfer_shutdown() -> None:
         # has_kv_transfer_group can be None during interpreter shutdown.
-        if has_kv_transfer_group and has_kv_transfer_group():
+        if has_kv_transfer_group and has_kv_transfer_group():  # type: ignore[truthy-function]
             ensure_kv_transfer_shutdown()
 
     @staticmethod

vllm/v1/worker/tpu_model_runner.py

@@ -572,7 +572,10 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             format. Layers that do not need KV cache are not included.
         """
 
-        layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
+        layers = get_layers_from_vllm_config(
+            self.vllm_config,
+            AttentionLayerBase,  # type: ignore[type-abstract]
+        )
         block_size = self.vllm_config.cache_config.block_size
         cache_dtype_str = self.vllm_config.cache_config.cache_dtype
 
@@ -725,7 +728,11 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             req_id = self.input_batch.req_ids[i]
             assert req_id is not None
             num_tokens = scheduler_output.num_scheduled_tokens[req_id]
-            if not use_max_model_len and num_tokens > self.most_model_len:
+            if (
+                not use_max_model_len
+                and self.most_model_len is not None
+                and num_tokens > self.most_model_len
+            ):
                 use_max_model_len = True
             num_scheduled_tokens_per_req.append(num_tokens)
         if use_max_model_len:
@@ -737,6 +744,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             else:
                 end_index = num_reqs
         else:
+            assert self.num_reqs_most_model_len is not None
             if len(num_scheduled_tokens_per_req) > self.num_reqs_most_model_len:
                 num_scheduled_tokens_per_req = num_scheduled_tokens_per_req[
                     : self.num_reqs_most_model_len
@@ -829,6 +837,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             ].to(self.device)
             seq_lens = self.seq_lens_cpu[: self.num_reqs_max_model_len].to(self.device)
         else:
+            assert self.num_reqs_most_model_len is not None
             block_tables = self.block_table_cpu[
                 : self.num_reqs_most_model_len, : self.num_blocks_per_most_len_req
             ]
@@ -931,6 +940,8 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
             for mm_input_id in encoder_input_ids:
                 mm_feature = req_state.mm_features[mm_input_id]
+                if mm_feature.data is None:
+                    continue
                 mm_hash = mm_feature.identifier
                 mm_kwargs.append(mm_feature.data)
                 mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
@@ -1114,7 +1125,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
     ) -> ModelRunnerOutput:
         if self.scheduler_output is None:
             # Nothing to do (PP non-final rank case), output isn't used.
-            return None  # noqa
+            return None  # type: ignore[return-value]
         scheduler_output = self.scheduler_output
         mm_embed_inputs = self.mm_embed_inputs
         self.scheduler_output = None
@@ -1696,7 +1707,8 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
     ) -> None:
         # Profile with multimodal encoder & encoder cache.
         if self.supports_mm_inputs:
-            if self.model_config.multimodal_config.skip_mm_profiling:
+            mm_config = self.model_config.multimodal_config
+            if mm_config is not None and mm_config.skip_mm_profiling:
                 logger.info(
                     "Skipping memory profiling for multimodal encoder and "
                     "encoder cache."
@@ -2166,5 +2178,9 @@ def replace_set_lora(model):
         if isinstance(module, BaseLayerWithLoRA):
             module._original_set_lora = module.set_lora
             module._original_reset_lora = module.reset_lora
-            module.set_lora = _tpu_set_lora.__get__(module, module.__class__)
-            module.reset_lora = _tpu_reset_lora.__get__(module, module.__class__)
+            module.set_lora = _tpu_set_lora.__get__(  # type: ignore[method-assign]
+                module, module.__class__
+            )
+            module.reset_lora = _tpu_reset_lora.__get__(  # type: ignore[method-assign]
+                module, module.__class__
+            )

vllm/v1/worker/tpu_worker.py

@@ -141,8 +141,7 @@ class TPUWorker:
 
         # Set random seed.
         set_random_seed(self.model_config.seed)
-        if self.model_config.seed is not None:
-            xm.set_rng_state(self.model_config.seed, self.device)
+        xm.set_rng_state(self.model_config.seed, self.device)
 
         # Increase the cache size limit, which is the maximum number of
         # dynamo graphs that can be compiled.
@@ -332,7 +331,7 @@ class TPUWorker:
             world_size=parallel_config.world_size,
             rank=rank,
             local_rank=local_rank,
-            distributed_init_method=distributed_init_method,
+            distributed_init_method=distributed_init_method or "env://",
             backend=current_platform.dist_backend,
         )
         ensure_model_parallel_initialized(

vllm/v1/worker/utils.py

@@ -280,7 +280,7 @@ def bind_kv_cache(
     kv_caches: dict[str, torch.Tensor],
     forward_context: dict[str, "Attention"],
     runner_kv_caches: list[torch.Tensor],
-    num_attn_module: int | None = 1,
+    num_attn_module: int = 1,
 ) -> None:
     """
     Bind the allocated KV cache to both ModelRunner and forward context so
@@ -362,5 +362,7 @@ def is_residual_scattered_for_sp(
         or vllm_config.compilation_config.use_inductor_graph_partition
     ):
         return True
-
-    return num_input_tokens in vllm_config.compilation_config.compile_sizes
+    compile_sizes = vllm_config.compilation_config.compile_sizes
+    if compile_sizes is None:
+        return False
+    return num_input_tokens in compile_sizes

vllm/v1/worker/worker_base.py

@@ -315,10 +315,12 @@ class WorkerWrapperBase:
 
     def initialize_from_config(self, kv_cache_configs: list[Any]) -> None:
         kv_cache_config = kv_cache_configs[self.global_rank]
+        assert self.vllm_config is not None
         with set_current_vllm_config(self.vllm_config):
             self.worker.initialize_from_config(kv_cache_config)  # type: ignore
 
     def init_device(self):
+        assert self.vllm_config is not None
         with set_current_vllm_config(self.vllm_config):
             # To make vLLM config available during device initialization
             self.worker.init_device()  # type: ignore

vllm/v1/worker/xpu_worker.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
+from typing import Any
 
 import torch
 import torch.distributed
@@ -37,6 +38,7 @@ class XPUWorker(Worker):
 
         # Torch profiler. Enabled and configured through env vars:
         # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        self.profiler: Any | None = None
         if envs.VLLM_TORCH_PROFILER_DIR:
             torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
@@ -148,7 +150,12 @@ class XPUWorker(Worker):
         return int(available_kv_cache_memory)
 
     def init_device(self):
-        if self.device_config.device.type == "xpu" and current_platform.is_xpu():
+        device = self.device_config.device
+        if (
+            isinstance(device, torch.device)
+            and device.type == "xpu"
+            and current_platform.is_xpu()
+        ):
             self.device = torch.device(f"xpu:{self.local_rank}")
             current_platform.set_device(self.device)
             current_platform.check_if_supports_dtype(self.model_config.dtype)