[Typing] Mypy typing part 2 (#4043)

Co-authored-by: SangBin Cho <sangcho@sangcho-LT93GQWG9C.local>

.github/workflows/mypy.yaml

@@ -41,10 +41,10 @@ jobs:
         mypy vllm/*.py --follow-imports=skip --config-file pyproject.toml
         mypy vllm/transformers_utils/*.py --follow-imports=skip --config-file pyproject.toml
 
+        mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/spec_decode/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
         # TODO(sang): Follow up
-        # mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
-        # mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
-        # mypy vllm/spec_decoding/*.py --follow-imports=skip --config-file pyproject.toml
-        # mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
         # mypy vllm/lora/*.py --follow-imports=skip --config-file pyproject.toml
 

format.sh

@@ -104,10 +104,10 @@ mypy vllm/*.py --follow-imports=skip --config-file pyproject.toml
 mypy vllm/transformers_utils/*.py --follow-imports=skip --config-file pyproject.toml
 
 # TODO(sang): Follow up
-# mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
-# mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
-# mypy vllm/spec_decoding/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/spec_decode/*.py --follow-imports=skip --config-file pyproject.toml
-# mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
 # mypy vllm/lora/*.py --follow-imports=skip --config-file pyproject.toml
 
 

vllm/engine/async_llm_engine.py

@@ -2,8 +2,8 @@ import asyncio
 import os
 import time
 from functools import partial
-from typing import (AsyncIterator, Callable, Dict, Iterable, List, Optional,
+from typing import (Any, AsyncIterator, Callable, Dict, Iterable, List,
-                    Set, Tuple, Type, Union)
+                    Optional, Set, Tuple, Type, Union)
 
 from transformers import PreTrainedTokenizer
 
@@ -52,7 +52,7 @@ class AsyncStream:
 
     def __init__(self, request_id: str) -> None:
         self.request_id = request_id
-        self._queue = asyncio.Queue()
+        self._queue: asyncio.Queue = asyncio.Queue()
         self._finished = False
 
     def put(self, item: Union[RequestOutput, Exception]) -> None:
@@ -312,15 +312,17 @@ class AsyncLLMEngine:
         self.max_log_len = max_log_len
         self.engine = self._init_engine(*args, **kwargs)
 
-        self.background_loop = None
+        self.background_loop: Optional[asyncio.Future] = None
         # We need to keep a reference to unshielded
         # task as well to prevent it from being garbage
         # collected
-        self._background_loop_unshielded = None
+        self._background_loop_unshielded: Optional[asyncio.Task[Any]] = None
         self.start_engine_loop = start_engine_loop
-        self._request_tracker: Optional[RequestTracker] = None
         self._errored_with: Optional[BaseException] = None
 
+        # Lazy initialized fields
+        self._request_tracker: RequestTracker
+
     @classmethod
     def from_engine_args(
         cls,
@@ -361,11 +363,13 @@ class AsyncLLMEngine:
     @property
     def is_running(self) -> bool:
         return (self.background_loop is not None
+                and self._background_loop_unshielded is not None
                 and not self._background_loop_unshielded.done())
 
     @property
     def is_stopped(self) -> bool:
-        return self.errored or (self.background_loop is not None
+        return self.errored or (self.background_loop is not None and
+                                self._background_loop_unshielded is not None
                                 and self._background_loop_unshielded.done())
 
     @property
@@ -381,7 +385,7 @@ class AsyncLLMEngine:
 
     async def get_tokenizer(self) -> "PreTrainedTokenizer":
         if self.engine_use_ray:
-            return await self.engine.get_tokenizer.remote()
+            return await self.engine.get_tokenizer.remote()  # type: ignore
         else:
             return self.engine.get_tokenizer()
 
@@ -434,7 +438,8 @@ class AsyncLLMEngine:
             # TODO: Maybe add add_request_batch to reduce Ray overhead
             try:
                 if self.engine_use_ray:
-                    await self.engine.add_request.remote(**new_request)
+                    await self.engine.add_request.remote(  # type: ignore
+                        **new_request)
                 else:
                     await self.engine.add_request_async(**new_request)
             except ValueError as e:
@@ -449,7 +454,7 @@ class AsyncLLMEngine:
             await self._engine_abort(finished_requests)
 
         if self.engine_use_ray:
-            request_outputs = await self.engine.step.remote()
+            request_outputs = await self.engine.step.remote()  # type: ignore
         else:
             request_outputs = await self.engine.step_async()
 
@@ -462,7 +467,7 @@ class AsyncLLMEngine:
 
     async def _engine_abort(self, request_ids: Iterable[str]):
         if self.engine_use_ray:
-            await self.engine.abort_request.remote(request_ids)
+            await self.engine.abort_request.remote(request_ids)  # type: ignore
         else:
             self.engine.abort_request(request_ids)
 
@@ -525,11 +530,12 @@ class AsyncLLMEngine:
             arrival_time = time.time()
 
         if self.engine_use_ray:
-            prompt_token_ids = await self.engine.encode_request_async.remote(
+            prompt_token_ids = await (
-                request_id=request_id,
+                self.engine.encode_request_async.remote(  # type: ignore
-                prompt=prompt,
+                    request_id=request_id,
-                prompt_token_ids=prompt_token_ids,
+                    prompt=prompt,
-                lora_request=lora_request)
+                    prompt_token_ids=prompt_token_ids,
+                    lora_request=lora_request))
         else:
             prompt_token_ids = await self.engine.encode_request_async(
                 request_id=request_id,
@@ -676,13 +682,13 @@ class AsyncLLMEngine:
     async def get_model_config(self) -> ModelConfig:
         """Get the model configuration of the vLLM engine."""
         if self.engine_use_ray:
-            return await self.engine.get_model_config.remote()
+            return await self.engine.get_model_config.remote()  # type: ignore
         else:
             return self.engine.get_model_config()
 
     async def do_log_stats(self) -> None:
         if self.engine_use_ray:
-            await self.engine.do_log_stats.remote()
+            await self.engine.do_log_stats.remote()  # type: ignore
         else:
             self.engine.do_log_stats()
 
@@ -695,7 +701,7 @@ class AsyncLLMEngine:
 
         if self.engine_use_ray:
             try:
-                await self.engine.check_health.remote()
+                await self.engine.check_health.remote()  # type: ignore
             except ray.exceptions.RayActorError as e:
                 raise RuntimeError("Engine is dead.") from e
         else:

vllm/lora/worker_manager.py

@@ -107,12 +107,12 @@ class WorkerLoRAManager(AbstractWorkerLoRAManager):
         self._lora_manager: LoRAModelManager = lora_manager
         return lora_manager.model
 
-    def set_active_loras(self, lora_requests: List[LoRARequest],
+    def set_active_loras(self, lora_requests: Set[LoRARequest],
                          lora_mapping: LoRAMapping) -> None:
         self._apply_loras(lora_requests)
         self._lora_manager.set_lora_mapping(lora_mapping)
 
-    def _apply_loras(self, lora_requests: List[LoRARequest]) -> None:
+    def _apply_loras(self, lora_requests: Set[LoRARequest]) -> None:
         loras_that_exist = self.list_loras()
         loras_map = {
             lora_request.lora_int_id: lora_request

vllm/model_executor/guided_decoding/outlines_decoding.py

@@ -55,7 +55,7 @@ global_thread_pool = None  # used for generating logits processor fsm
 
 async def get_outlines_guided_decoding_logits_processor(
         request: Union[CompletionRequest, ChatCompletionRequest],
-        tokenizer) -> Union[JSONLogitsProcessor, RegexLogitsProcessor]:
+        tokenizer) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, None]:
     """
     Given an OpenAI-compatible request, check for guided decoding parameters
     and get the necessary logits processor for the given guide.
@@ -84,7 +84,7 @@ async def get_outlines_guided_decoding_logits_processor(
 
 def _get_guide_and_mode(
     request: Union[CompletionRequest, ChatCompletionRequest]
-) -> Tuple[str, GuidedDecodingMode]:
+) -> Union[Tuple[str, GuidedDecodingMode], Tuple[None, None]]:
 
     if request.guided_json:
         json = request.guided_json

vllm/model_executor/guided_decoding/outlines_logits_processors.py

@@ -21,7 +21,7 @@ from functools import lru_cache
 from typing import Callable, DefaultDict, Dict, List, Optional, Union
 
 import torch
-from outlines.fsm.fsm import CFGFSM, RegexFSM
+from outlines.fsm.fsm import CFGFSM, FSM, RegexFSM
 from outlines.fsm.json_schema import build_regex_from_schema
 from pydantic import BaseModel
 from transformers import PreTrainedTokenizerBase
@@ -29,6 +29,10 @@ from transformers import PreTrainedTokenizerBase
 
 class BaseLogitsProcessor:
 
+    def __init__(self):
+        # Child class should use initialize in their init.
+        self.fsm: FSM
+
     def init_state(self):
         """Initialize the FSM states."""
         self.fsm_state: DefaultDict[int, int] = defaultdict(int)

vllm/model_executor/model_loader/neuron.py

@@ -1,7 +1,7 @@
 """Utilities for selecting and loading neuron models."""
 import importlib
 import os
-from typing import Optional, Type
+from typing import Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -27,7 +27,7 @@ TORCH_DTYPE_TO_NEURON_AMP = {
 }
 
 # Models supported by Neuron.
-_NEURON_SUPPORTED_MODELS = {
+_NEURON_SUPPORTED_MODELS: Dict[str, Tuple[str, str, str]] = {
     "LlamaForCausalLM": ("transformers_neuronx.llama.model",
                          "LlamaForSampling", "LlamaForCausalLM"),
     "MistralForCausalLM": ("transformers_neuronx.mistral.model",
@@ -43,11 +43,13 @@ class NeuronCasualLM(nn.Module):
     ) -> None:
         super().__init__()
         self.config = config
-        self.model = None
         self.logits_processor = LogitsProcessor(config.vocab_size,
                                                 logits_as_input=True)
         self.sampler = Sampler()
 
+        # Lazy initialized
+        self.model: nn.Module
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -74,17 +76,17 @@ class NeuronCasualLM(nn.Module):
 
     def load_weights(self, model_name_or_path: str, **kwargs):
         arch = _get_model_architecture(self.config)
-        neuronx_module_path, neuronx_model_cls, hf_model_cls = (
+        neuronx_module_path, neuronx_model_cls_name, hf_model_cls_name = (
             _NEURON_SUPPORTED_MODELS[arch])
         neuronx_module = importlib.import_module(neuronx_module_path)
-        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
 
         split_model_dir = f"{model_name_or_path}-split"
         if os.path.isdir(os.path.join(model_name_or_path,
                                       "pytorch_model.bin")):
             split_model_dir = model_name_or_path
         elif not os.path.exists(f"{model_name_or_path}-split"):
-            hf_model_cls = getattr(transformers, hf_model_cls)
+            hf_model_cls = getattr(transformers, hf_model_cls_name)
             from transformers_neuronx.module import save_pretrained_split
 
             hf_model = hf_model_cls.from_pretrained(model_name_or_path,
@@ -96,7 +98,7 @@ class NeuronCasualLM(nn.Module):
         self.model.to_neuron()
 
 
-def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
+def _get_model_architecture(config: PretrainedConfig) -> str:
     architectures = getattr(config, "architectures", [])
     for arch in architectures:
         if arch in _NEURON_SUPPORTED_MODELS:

vllm/model_executor/model_loader/tensorizer.py

@@ -167,6 +167,7 @@ class TensorizerArgs:
                 decryption_params = DecryptionParams.from_key(key)
                 self.deserializer_params['encryption'] = decryption_params
 
+    @staticmethod
     def add_cli_args(
             parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
         """Tensorizer CLI arguments"""

vllm/model_executor/sampling_metadata.py

@@ -113,6 +113,8 @@ class SamplingTensors:
                              get_num_triton_sampler_splits(vocab_size))
 
         sample_indices_start_idx = 0
+        assert sampling_metadata.seq_groups is not None
+        assert sampling_metadata.seq_data is not None
         for i, seq_group in enumerate(sampling_metadata.seq_groups):
             seq_ids, sampling_params = seq_group
             temperature = sampling_params.temperature
@@ -147,6 +149,7 @@ class SamplingTensors:
                     and sampling_params.prompt_logprobs is not None):
                 # For tokens in the prompt that we only need to get
                 # their logprobs
+                assert sampling_metadata.prompt_lens is not None
                 prompt_len = sampling_metadata.prompt_lens[i]
                 temperatures += [temperature] * (prompt_len - 1)
                 top_ps += [top_p] * (prompt_len - 1)
@@ -172,6 +175,7 @@ class SamplingTensors:
             is_prompt = i < sampling_metadata.num_prompts
             if is_prompt:
                 prompt_best_of.append(sampling_params.best_of)
+                assert sampling_metadata.prompt_lens is not None
                 prompt_len = sampling_metadata.prompt_lens[i]
 
                 if sampling_params.prompt_logprobs is not None:

vllm/spec_decode/batch_expansion.py

@@ -106,7 +106,7 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
     def _expand_batch(
         self,
         seq_group_metadata_list: List[SequenceGroupMetadata],
-        proposal_token_ids_list: List[TokenId],
+        proposal_token_ids_list: List[List[TokenId]],
         proposal_lens_list: List[int],
     ) -> Tuple[List[int], List[int], List[SequenceGroupMetadata], int]:
         """Given the input sequences and potentially multiple corresponding
@@ -218,7 +218,7 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
     def _create_target_seq_group_metadata(
         self,
         input_seq_group_metadata: SequenceGroupMetadata,
-        proposal_token_ids: List[TokenId],  # shape: [batch_size, k]
+        proposal_token_ids: List[List[TokenId]],  # shape: [batch_size, k]
         batch_index: int,
         target_seq_ids_iter: Iterator[TargetSeqId],
     ) -> List[SequenceGroupMetadata]:
@@ -360,7 +360,7 @@ class BatchExpansionTop1Scorer(SpeculativeScorer):
                 [0, 1, 2]
                 [0, 1, 2, 3]
         """
-        empty_token_ids = []
+        empty_token_ids: List[TokenId] = []
 
         token_ids_to_score = [empty_token_ids]
         token_ids_to_score.extend([

vllm/spec_decode/interfaces.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional
 
 import torch
 
@@ -73,5 +73,5 @@ class SpeculativeScorer(ABC):
         blocks_to_copy: Optional[Dict[int, List[int]]],
         k: int,
         proposals: SpeculativeProposals,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> SpeculativeScores:
         raise NotImplementedError

vllm/spec_decode/metrics.py

@@ -112,6 +112,7 @@ class AsyncMetricsCollector:
 
         Returns a CUDA event recording when the copy is complete.
         """
+        assert self._copy_stream is not None
         self._copy_stream.wait_stream(torch.cuda.current_stream())
 
         with torch.cuda.stream(self._copy_stream):

vllm/spec_decode/multi_step_worker.py

@@ -26,7 +26,8 @@ class MultiStepWorker(Worker):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-        self._proposer: Optional[DraftModelTop1Proposer] = None
+        # Lazy initialization list.
+        self._proposer: DraftModelTop1Proposer
 
     def init_device(self):
         super().init_device()
@@ -338,10 +339,10 @@ class DraftModelTop1Proposer(SpeculativeProposer):
                                          self._vocab_size,
                                          dtype=torch.float32,
                                          device=self._device)
-            proposal_lens = torch.zeros(len(proposal_lens),
+            proposal_lens_tensor = torch.zeros(len(proposal_lens),
-                                        dtype=torch.long,
+                                               dtype=torch.long,
-                                        device=self._device)
+                                               device=self._device)
-            return proposal_tokens, proposal_probs, proposal_lens
+            return proposal_tokens, proposal_probs, proposal_lens_tensor
 
         sampler_output = maybe_sampler_output
 
@@ -376,9 +377,9 @@ class DraftModelTop1Proposer(SpeculativeProposer):
         proposal_tokens, proposal_probs = (entire_proposal_tokens,
                                            entire_proposal_probs)
 
-        proposal_lens = torch.zeros(batch_size,
+        proposal_lens_tensor = torch.zeros(batch_size,
-                                    dtype=torch.long,
+                                           dtype=torch.long,
-                                    device=self._device)
+                                           device=self._device)
-        proposal_lens[nonzero_proposal_len_indices] = max_proposal_len
+        proposal_lens_tensor[nonzero_proposal_len_indices] = max_proposal_len
 
-        return proposal_tokens, proposal_probs, proposal_lens
+        return proposal_tokens, proposal_probs, proposal_lens_tensor

vllm/spec_decode/spec_decode_worker.py

@@ -89,7 +89,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         self.probs_dtype = self.rejection_sampler.probs_dtype
         self.token_id_dtype = self.rejection_sampler.token_id_dtype
 
-        self.scorer: SpeculativeScorer = None
+        # Lazy initiazliation.
+        self.scorer: SpeculativeScorer
 
     def init_device(self) -> None:
         """Initialize both scorer and proposer models.
@@ -233,6 +234,9 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
         logger.info("get spec proposals")
         # Generate proposals using draft worker.
+        assert blocks_to_swap_in is not None
+        assert blocks_to_swap_out is not None
+        assert blocks_to_copy is not None
         proposals = self.proposer_worker.get_spec_proposals(
             seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out,
             blocks_to_copy, k)

vllm/worker/cpu_model_runner.py

@@ -1,6 +1,7 @@
 from typing import Dict, List, Optional, Tuple
 
 import torch
+from torch import nn
 
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.config import (DeviceConfig, LoadConfig, LoRAConfig, ModelConfig,
@@ -48,14 +49,15 @@ class CPUModelRunner:
                               if device_config is not None else DeviceConfig())
         self.device = self.device_config.device
 
-        self.model = None
-        self.block_size = None  # Set after initial profiling.
-
         self.kv_cache_dtype = kv_cache_dtype
 
         self.attn_backend = get_attn_backend(
             self.model_config.dtype if model_config is not None else None)
 
+        # Lazy initialization.
+        self.model: nn.Module  # Set after init_Model
+        self.block_size: int  # Set after initial profiling.
+
     def load_model(self) -> None:
         self.model = get_model(model_config=self.model_config,
                                load_config=self.load_config,
@@ -245,7 +247,11 @@ class CPUModelRunner:
         selected_token_indices: List[int] = []
         generators: List[torch.Generator] = []
         selected_token_start_idx = 0
-        categorized_sample_indices = {t: [] for t in SamplingType}
+        categorized_sample_indices: Dict[SamplingType,
+                                         List[Tuple[int, int]]] = {
+                                             t: []
+                                             for t in SamplingType
+                                         }
         categorized_sample_indices_start_idx = 0
         categorized_sampled_token_indices_start_idx = 0
 
@@ -262,10 +268,9 @@ class CPUModelRunner:
                     categorized_sample_indices_start_idx += subquery_len - 1
 
                 categorized_sample_indices[
-                    sampling_params.sampling_type].append([
+                    sampling_params.sampling_type].append(
-                        categorized_sample_indices_start_idx,
+                        (categorized_sample_indices_start_idx,
-                        categorized_sampled_token_indices_start_idx
+                         categorized_sampled_token_indices_start_idx))
-                    ])
                 categorized_sample_indices_start_idx += 1
                 categorized_sampled_token_indices_start_idx += 1
 
@@ -328,7 +333,7 @@ class CPUModelRunner:
 
     def prepare_input_tensors(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata,
                SamplingMetadata]:
         if self.is_driver_worker:
@@ -381,7 +386,7 @@ class CPUModelRunner:
     @torch.inference_mode()
     def execute_model(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
         kv_caches: List[torch.Tensor],
     ) -> Optional[SamplerOutput]:
         (input_tokens, input_positions, attn_metadata, sampling_metadata

vllm/worker/cpu_worker.py

@@ -1,5 +1,5 @@
 """A CPU worker class."""
-from typing import Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 import torch.distributed
@@ -152,8 +152,8 @@ class CPUWorker(LoraNotSupportedWorkerBase):
                                            is_driver_worker=is_driver_worker)
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.
-        self.cache_engine = None
+        self.cache_engine: CPUCacheEngine
-        self.cpu_cache = None
+        self.cpu_cache: List[torch.Tensor]
 
     def init_device(self) -> None:
         self.init_distributed_environment()
@@ -257,13 +257,13 @@ class CPUWorker(LoraNotSupportedWorkerBase):
     ) -> List[SamplerOutput]:
         if self.is_driver_worker:
             assert seq_group_metadata_list is not None
-            num_seq_groups = len(seq_group_metadata_list)
+            num_seq_groups: int = len(seq_group_metadata_list)
             assert blocks_to_swap_in is not None
             assert blocks_to_swap_out is not None
             assert blocks_to_copy is not None
             assert len(blocks_to_swap_in) == 0
             assert len(blocks_to_swap_out) == 0
-            data = {
+            data: Dict[str, Any] = {
                 "num_seq_groups": num_seq_groups,
                 "blocks_to_copy": blocks_to_copy,
             }
@@ -273,6 +273,7 @@ class CPUWorker(LoraNotSupportedWorkerBase):
             num_seq_groups = data["num_seq_groups"]
             blocks_to_copy = data["blocks_to_copy"]
 
+        assert blocks_to_copy is not None
         self.cache_copy(blocks_to_copy)
 
         # If there is no input, we don't need to execute the model.

vllm/worker/model_runner.py

@@ -128,23 +128,17 @@ class ModelRunner:
                               if device_config is not None else DeviceConfig())
         self.device = self.device_config.device
 
-        self.model = None
+        # Set after load_model.
-        self.block_size = None  # Set after initial profiling.
+        self.lora_manager: LRUCacheWorkerLoRAManager = None
-        self.lora_manager = None
 
         self.graph_runners: Dict[int, CUDAGraphRunner] = {}
-        self.graph_memory_pool = None  # Set during graph capture.
+        self.graph_memory_pool: Optional[Tuple[
+            int, int]] = None  # Set during graph capture.
 
         self.max_context_len_to_capture = (
             self.model_config.max_context_len_to_capture
             if self.model_config is not None else 0)
-        # When using CUDA graph, the input block tables must be padded to
+
-        # max_context_len_to_capture. However, creating the block table in
-        # Python can be expensive. To optimize this, we cache the block table
-        # in numpy and only copy the actual input content at every iteration.
-        # The shape of the cached block table will be
-        # (max batch size to capture, max context len to capture / block size).
-        self.graph_block_tables = None  # Set after initial profiling.
         self.pin_memory = is_pin_memory_available()
         self.kv_cache_dtype = kv_cache_dtype
         self.vision_language_config = vision_language_config
@@ -152,6 +146,17 @@ class ModelRunner:
         self.attn_backend = get_attn_backend(
             self.model_config.dtype if model_config is not None else None)
 
+        # Lazy initialization
+        self.model: torch.nn.Module  # Set after load_model
+        self.block_size: int  # Set after initial profiling.
+        # When using CUDA graph, the input block tables must be padded to
+        # max_context_len_to_capture. However, creating the block table in
+        # Python can be expensive. To optimize this, we cache the block table
+        # in numpy and only copy the actual input content at every iteration.
+        # The shape of the cached block table will be
+        # (max batch size to capture, max context len to capture / block size).
+        self.graph_block_tables: torch.Tensor  # Set after initial profiling.
+
     def load_model(self) -> None:
         with CudaMemoryProfiler() as m:
             self.model = get_model(
@@ -489,16 +494,16 @@ class ModelRunner:
                 lora_index_mapping.append(0)
             batch_size = graph_batch_size
 
-        context_lens = torch.tensor(context_lens,
+        context_lens_tensor = torch.tensor(context_lens,
-                                    dtype=torch.int,
+                                           dtype=torch.int,
-                                    device=self.device)
+                                           device=self.device)
 
         if use_captured_graph:
             # When using cuda-graph all these tensors should be
             # padded.
-            assert context_lens.shape[0] == len(input_tokens)
+            assert context_lens_tensor.shape[0] == len(input_tokens)
-            assert context_lens.shape[0] == len(input_positions)
+            assert context_lens_tensor.shape[0] == len(input_positions)
-            assert context_lens.shape[0] == len(slot_mapping)
+            assert context_lens_tensor.shape[0] == len(slot_mapping)
 
             # The shape of graph_block_tables is
             # [max batch size, max context len // block size].
@@ -527,7 +532,7 @@ class ModelRunner:
             max_prompt_len=None,
             subquery_start_loc=None,
             seq_start_loc=None,
-            context_lens=context_lens,
+            context_lens=context_lens_tensor,
             block_tables=block_tables,
             use_cuda_graph=use_captured_graph,
         )
@@ -551,7 +556,11 @@ class ModelRunner:
         selected_token_indices: List[int] = []
         generators: List[torch.Generator] = []
         selected_token_start_idx = 0
-        categorized_sample_indices = {t: [] for t in SamplingType}
+        categorized_sample_indices: Dict[SamplingType,
+                                         List[Tuple[int, int]]] = {
+                                             t: []
+                                             for t in SamplingType
+                                         }
         categorized_sample_indices_start_idx = 0
         categorized_sampled_token_indices_start_idx = 0
 
@@ -569,10 +578,9 @@ class ModelRunner:
                     categorized_sample_indices_start_idx += subquery_len - 1
 
                 categorized_sample_indices[
-                    sampling_params.sampling_type].append([
+                    sampling_params.sampling_type].append(
-                        categorized_sample_indices_start_idx,
+                        (categorized_sample_indices_start_idx,
-                        categorized_sampled_token_indices_start_idx
+                         categorized_sampled_token_indices_start_idx))
-                    ])
                 categorized_sample_indices_start_idx += 1
                 categorized_sampled_token_indices_start_idx += 1
 
@@ -596,15 +604,16 @@ class ModelRunner:
 
                 categorized_sample_indices[
                     sampling_params.sampling_type].extend(
-                        zip(
+                        list(
-                            range(
+                            zip(
-                                categorized_sample_indices_start_idx,
+                                range(
-                                categorized_sample_indices_start_idx +
+                                    categorized_sample_indices_start_idx,
-                                num_seqs),
+                                    categorized_sample_indices_start_idx +
-                            range(
+                                    num_seqs),
-                                categorized_sampled_token_indices_start_idx,
+                                range(
-                                categorized_sampled_token_indices_start_idx +
+                                    categorized_sampled_token_indices_start_idx,
-                                num_seqs)))
+                                    categorized_sampled_token_indices_start_idx
+                                    + num_seqs))))
                 categorized_sample_indices_start_idx += num_seqs
                 categorized_sampled_token_indices_start_idx += num_seqs
 
@@ -641,9 +650,9 @@ class ModelRunner:
 
     def prepare_input_tensors(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
-               Set[int], LoRAMapping, torch.Tensor]:
+               Set[LoRARequest], LoRAMapping, torch.Tensor]:
         if self.is_driver_worker:
             prefill_reqs = []
             decode_reqs = []
@@ -741,6 +750,7 @@ class ModelRunner:
             if prefill_attn_metadata is not None:
                 metadata_dict.update(prefill_attn_metadata.asdict_zerocopy())
             else:
+                assert decode_attn_metadata is not None
                 metadata_dict.update(decode_attn_metadata.asdict_zerocopy())
             broadcast_tensor_dict(metadata_dict, src=0)
 
@@ -809,7 +819,7 @@ class ModelRunner:
     @torch.inference_mode()
     def execute_model(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
         kv_caches: List[torch.Tensor],
     ) -> Optional[SamplerOutput]:
         (input_tokens, input_positions, attn_metadata, sampling_metadata,
@@ -923,7 +933,7 @@ class ModelRunner:
             raise RuntimeError("LoRA is not enabled.")
         return self.lora_manager.remove_all_loras()
 
-    def set_active_loras(self, lora_requests: List[LoRARequest],
+    def set_active_loras(self, lora_requests: Set[LoRARequest],
                          lora_mapping: LoRAMapping) -> None:
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")
@@ -1065,10 +1075,16 @@ class CUDAGraphRunner:
 
     def __init__(self, model: nn.Module):
         self.model = model
-        self.graph = None
         self.input_buffers: Dict[str, torch.Tensor] = {}
         self.output_buffers: Dict[str, torch.Tensor] = {}
 
+        self._graph: Optional[torch.cuda.CUDAGraph] = None
+
+    @property
+    def graph(self):
+        assert self._graph is not None
+        return self._graph
+
     def capture(
         self,
         input_ids: torch.Tensor,
@@ -1078,7 +1094,7 @@ class CUDAGraphRunner:
         memory_pool,
         **kwargs,
     ) -> None:
-        assert self.graph is None
+        assert self._graph is None
         # Run the model once without capturing the graph.
         # This is to make sure that the captured graph does not include the
         # kernel launches for initial benchmarking (e.g., Triton autotune).
@@ -1095,8 +1111,8 @@ class CUDAGraphRunner:
         # Capture the graph.
         # NOTE(woosuk): Python 3.8 does not support multi-line with statements.
         # https://stackoverflow.com/questions/31039022/python-multi-line-with-statement
-        self.graph = torch.cuda.CUDAGraph()
+        self._graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(self.graph, pool=memory_pool):  # noqa: SIM117
+        with torch.cuda.graph(self._graph, pool=memory_pool):  # noqa: SIM117
             with _maybe_pynccl():
                 hidden_states = self.model(
                     input_ids,

vllm/worker/neuron_model_runner.py

@@ -1,6 +1,7 @@
 from typing import Dict, List, Optional, Tuple
 
 import torch
+from torch import nn
 
 from vllm.config import (DeviceConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig)
@@ -34,9 +35,11 @@ class NeuronModelRunner:
         self.device_config = (device_config
                               if device_config is not None else DeviceConfig())
         self.device = self.device_config.device
-        self.model = None
         self.pin_memory = is_pin_memory_available()
 
+        # Lazy initialization.
+        self.model: nn.Module  # initialize after load_model.
+
     def load_model(self) -> None:
         self.model = get_neuron_model(self.model_config,
                                       parallel_config=self.parallel_config,
@@ -147,7 +150,11 @@ class NeuronModelRunner:
         selected_token_indices: List[int] = []
         generators: List[torch.Generator] = []
         selected_token_start_idx = 0
-        categorized_sample_indices = {t: [] for t in SamplingType}
+        categorized_sample_indices: Dict[SamplingType,
+                                         List[Tuple[int, int]]] = {
+                                             t: []
+                                             for t in SamplingType
+                                         }
         categorized_sample_indices_start_idx = 0
         categorized_sampled_token_indices_start_idx = 0
 
@@ -165,10 +172,9 @@ class NeuronModelRunner:
                     categorized_sample_indices_start_idx += prompt_len - 1
 
                 categorized_sample_indices[
-                    sampling_params.sampling_type].append([
+                    sampling_params.sampling_type].append(
-                        categorized_sample_indices_start_idx,
+                        (categorized_sample_indices_start_idx,
-                        categorized_sampled_token_indices_start_idx
+                         categorized_sampled_token_indices_start_idx))
-                    ])
                 categorized_sample_indices_start_idx += 1
                 categorized_sampled_token_indices_start_idx += 1
 
@@ -237,7 +243,7 @@ class NeuronModelRunner:
 
     def prepare_input_tensors(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, SamplingMetadata]:
         # NOTE: We assume that all sequences in the group are all prompts or
         # all decodes.
@@ -259,7 +265,7 @@ class NeuronModelRunner:
     @torch.inference_mode()
     def execute_model(
         self,
-        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Optional[SamplerOutput]:
         (input_tokens, input_positions, input_block_ids, sampling_metadata
          ) = self.prepare_input_tensors(seq_group_metadata_list)

vllm/worker/worker.py

@@ -1,7 +1,7 @@
 """A GPU worker class."""
 import gc
 import os
-from typing import Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple
 
 import torch
 import torch.distributed
@@ -82,8 +82,8 @@ class Worker(WorkerBase):
         )
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.
-        self.cache_engine = None
+        self.cache_engine: CacheEngine
-        self.gpu_cache = None
+        self.gpu_cache: List[torch.Tensor]
 
     def init_device(self) -> None:
         if self.device_config.device.type == "cuda":
@@ -223,7 +223,7 @@ class Worker(WorkerBase):
             assert blocks_to_swap_in is not None
             assert blocks_to_swap_out is not None
             assert blocks_to_copy is not None
-            data = {
+            data: Dict[str, Any] = {
                 "num_seq_groups": num_seq_groups,
                 "blocks_to_swap_in": blocks_to_swap_in,
                 "blocks_to_swap_out": blocks_to_swap_out,
@@ -237,6 +237,9 @@ class Worker(WorkerBase):
             blocks_to_swap_out = data["blocks_to_swap_out"]
             blocks_to_copy = data["blocks_to_copy"]
 
+        assert blocks_to_swap_in is not None
+        assert blocks_to_swap_out is not None
+        assert blocks_to_copy is not None
         self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy)
 
         # If there is no input, we don't need to execute the model.

vllm/worker/worker_base.py

@@ -1,7 +1,7 @@
 import importlib
 import os
 from abc import ABC, abstractmethod
-from typing import Dict, List, Tuple
+from typing import Dict, List, Set, Tuple
 
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -56,7 +56,7 @@ class WorkerBase(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def get_cache_block_size_bytes() -> int:
+    def get_cache_block_size_bytes(self) -> int:
         """Return the size of a single cache block, in bytes. Used in
         speculative decoding.
         """
@@ -71,7 +71,7 @@ class WorkerBase(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def list_loras(self) -> List[int]:
+    def list_loras(self) -> Set[int]:
         raise NotImplementedError
 
 
@@ -86,7 +86,7 @@ class LoraNotSupportedWorkerBase(WorkerBase):
     def remove_lora(self, lora_id: int) -> bool:
         raise ValueError(f"{type(self)} does not support LoRA")
 
-    def list_loras(self) -> List[int]:
+    def list_loras(self) -> Set[int]:
         raise ValueError(f"{type(self)} does not support LoRA")