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fix

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon 2025-09-14 15:56:42 -07:00
parent 633f9f006d
commit 9a6fcca030
1 changed files with 25 additions and 30 deletions
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55
vllm/v1/worker/gpu_worker_states.py
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@ -8,11 +8,9 @@ import numpy as np
import torch
import triton
import triton.language as tl
from typing_extensions import deprecated
from vllm.lora.request import LoRARequest
from vllm.multimodal.inputs import (MultiModalKwargsItem,
MultiModalKwargsItems, PlaceholderRange)
from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.v1.sample.logits_processor import LogitsProcessors
@ -37,15 +35,6 @@ class RequestData:
lora_request: Optional[LoRARequest] = None
# Temporary back-compatibility for plugins that define model runner
@property
@deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
"removed in v0.13. Please use `mm_kwargs` instead.")
def mm_inputs(self) -> list[MultiModalKwargsItems]:
return [
MultiModalKwargsItems.from_seq([item]) for item in self.mm_kwargs
]
class RequestState:
@ -81,26 +70,33 @@ class RequestState:
self.req_data: dict[int, RequestData] = {}
# TODO(woosuk): Because the token_ids tensor can be very big, we only
# initialize it on CPU memory.
self.token_ids = self._make_param(
num_cols=self.max_model_len,
dtype=torch.int32,
cpu_only=True,
self.token_ids = np.zeros(
(self.max_num_cached_reqs, self.max_model_len),
dtype=np.int32,
)
self.num_prompt_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
self.num_prompt_tokens = np.zeros(self.max_num_cached_reqs,
dtype=np.int32)
self.num_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
self.num_computed_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
self.num_computed_tokens = np.zeros(self.max_num_cached_reqs,
dtype=np.int32)
# Last sampled token ids.
self.last_sampled_token = torch.zeros(
self.max_num_cached_reqs,
dtype=torch.int32,
device=self.device,
)
self.temperature = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
self.greedy_req_indices: set[int] = set()
self.top_p = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
self.top_p_req_indices: set[int] = set()
self.top_k = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
self.top_k_req_indices: set[int] = set()
self.frequency_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
self.presence_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
self.repetition_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
self.penalty_req_indices: set[int] = set()
self.frequency_penalties = np.zeros(self.max_num_cached_reqs,
dtype=np.float32)
self.presence_penalties = np.zeros(self.max_num_cached_reqs,
dtype=np.float32)
self.repetition_penalties = np.zeros(self.max_num_cached_reqs,
dtype=np.float32)
self.generators: dict[int, torch.Generator] = {}
@ -179,9 +175,9 @@ class RequestState:
frequency_penalties = self.frequency_penalties[idx_mapping]
presence_penalties = self.presence_penalties[idx_mapping]
repetition_penalties = self.repetition_penalties[idx_mapping]
no_penalties = (np.all(frequency_penalties == 0.0) and
np.all(presence_penalties == 0.0) and
np.all(repetition_penalties == 1.0))
no_penalties = (np.all(frequency_penalties == 0.0)
and np.all(presence_penalties == 0.0)
and np.all(repetition_penalties == 1.0))
if no_penalties:
frequency_penalties = None
presence_penalties = None
@ -194,8 +190,7 @@ class RequestState:
if self.generators:
generators = {
req_idx: self.generators[req_idx]
for req_idx in idx_mapping
if req_idx in self.generators
for req_idx in idx_mapping if req_idx in self.generators
}
else:
generators = {}