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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #10) (#6173)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
|`vllm_ascend/ops/layer_shard_linear.py`|
|`vllm_ascend/ops/linear.py`|
|`vllm_ascend/ops/linear_op.py`|
|`vllm_ascend/worker/worker.py`|
| ` vllm_ascend/patch/worker/patch_bert.py` |
| ` vllm_ascend/patch/worker/patch_deepseek.py` |
| ` vllm_ascend/patch/worker/patch_distributed.py` |
| ` vllm_ascend/patch/worker/patch_module.py` |
| ` vllm_ascend/patch/worker/patch_multimodal_merge.py` |
| ` vllm_ascend/patch/worker/patch_qwen3_next.py` |
| ` vllm_ascend/patch/worker/patch_qwen3_next_mtp.py` |
| ` vllm_ascend/patch/worker/patch_rejection_sampler.py` |
| ` vllm_ascend/patch/worker/patch_rope.py` |
| ` vllm_ascend/patch/worker/patch_triton.py` |
| ` vllm_ascend/patch/worker/patch_unquantized_gemm.py` |
| ` vllm_ascend/patch/worker/patch_v2_egale.py` |
|` vllm_ascend/worker/npu_input_batch.py`|
|` vllm_ascend/worker/v2/aclgraph_utils.py`|
|` vllm_ascend/worker/v2/attn_utils.py`|
|` vllm_ascend/worker/v2/model_runner.py`|
|` vllm_ascend/worker/v2/sample/gumbel.py`|
|` vllm_ascend/worker/v2/sample/penalties.py`|
|` vllm_ascend/worker/v2/sample/sampler.py`|
|` vllm_ascend/worker/v2/spec_decode/__init__.py`|
|` vllm_ascend/worker/v2/spec_decode/eagle.py`|
|` vllm_ascend/worker/v2/states.py`|
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.0
- vLLM main:
d68209402d

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
SILONG ZENG
2026-02-06 15:35:06 +08:00
committed by GitHub
parent 65b7f716e6
commit 19b5d44ea8
33 changed files with 938 additions and 1243 deletions
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96
vllm_ascend/worker/npu_input_batch.py
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@@ -23,15 +23,13 @@ from vllm.lora.request import LoRARequest
from vllm.pooling_params import PoolingParams
from vllm.v1.outputs import LogprobsTensors
from vllm.v1.pool.metadata import PoolingStates
from vllm.v1.sample.logits_processor import (BatchUpdateBuilder,
LogitsProcessors)
from vllm.v1.sample.logits_processor import BatchUpdateBuilder, LogitsProcessors
from vllm.v1.worker.gpu_input_batch import InputBatch
from vllm_ascend.worker.block_table import MultiGroupBlockTable
class NPUInputBatch(InputBatch):
def __init__(
self,
max_num_reqs: int,
@@ -72,10 +70,9 @@ class NPUInputBatch(InputBatch):
pin_memory=False,
)
self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
self.is_token_ids_tensor = torch.zeros((max_num_reqs, max_model_len),
device="cpu",
dtype=bool,
pin_memory=False)
self.is_token_ids_tensor = torch.zeros(
(max_num_reqs, max_model_len), device="cpu", dtype=bool, pin_memory=False
)
self.is_token_ids = self.is_token_ids_tensor.numpy()
# Store prompt embeddings per request to avoid OOM from large upfront
# allocation if max_model_len is big.
@@ -85,13 +82,12 @@ class NPUInputBatch(InputBatch):
self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
self.num_computed_tokens_cpu_tensor = torch.zeros(
(max_num_reqs, ),
(max_num_reqs,),
device="cpu",
dtype=torch.int32,
pin_memory=pin_memory,
)
self.num_computed_tokens_cpu = self.num_computed_tokens_cpu_tensor.numpy(
)
self.num_computed_tokens_cpu = self.num_computed_tokens_cpu_tensor.numpy()
# Block table.
self.block_table = MultiGroupBlockTable(
@@ -107,34 +103,21 @@ class NPUInputBatch(InputBatch):
)
# Sampling-related.
self.temperature = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device=device)
self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device="cpu",
pin_memory=pin_memory)
self.temperature = torch.empty((max_num_reqs,), dtype=torch.float32, device=device)
self.temperature_cpu_tensor = torch.empty(
(max_num_reqs,), dtype=torch.float32, device="cpu", pin_memory=pin_memory
)
self.temperature_cpu = self.temperature_cpu_tensor.numpy()
self.greedy_reqs: set[str] = set()
self.random_reqs: set[str] = set()
self.top_p = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device=device)
self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device="cpu",
pin_memory=pin_memory)
self.top_p = torch.empty((max_num_reqs,), dtype=torch.float32, device=device)
self.top_p_cpu_tensor = torch.empty((max_num_reqs,), dtype=torch.float32, device="cpu", pin_memory=pin_memory)
self.top_p_cpu = self.top_p_cpu_tensor.numpy()
self.top_p_reqs: set[str] = set()
self.top_k = torch.empty((max_num_reqs, ),
dtype=torch.int32,
device=device)
self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.int32,
device="cpu",
pin_memory=pin_memory)
self.top_k = torch.empty((max_num_reqs,), dtype=torch.int32, device=device)
self.top_k_cpu_tensor = torch.empty((max_num_reqs,), dtype=torch.int32, device="cpu", pin_memory=pin_memory)
self.top_k_cpu = self.top_k_cpu_tensor.numpy()
self.top_k_reqs: set[str] = set()
@@ -142,54 +125,37 @@ class NPUInputBatch(InputBatch):
self.spec_decode_unsupported_reqs: set[str] = set()
# Frequency penalty related data structures
self.frequency_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.frequency_penalties = torch.empty((max_num_reqs,), dtype=torch.float, device=device)
self.frequency_penalties_cpu_tensor = torch.empty(
(max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.frequency_penalties_cpu = self.frequency_penalties_cpu_tensor.numpy(
(max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
)
self.frequency_penalties_cpu = self.frequency_penalties_cpu_tensor.numpy()
self.frequency_penalties_reqs: set[str] = set()
# Presence penalty related data structures
self.presence_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
self.presence_penalties = torch.empty((max_num_reqs,), dtype=torch.float, device=device)
self.presence_penalties_cpu_tensor = torch.empty(
(max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
)
self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy()
self.presence_penalties_reqs: set[str] = set()
# Repetition penalty related data structures
self.repetition_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.repetition_penalties = torch.empty((max_num_reqs,), dtype=torch.float, device=device)
self.repetition_penalties_cpu_tensor = torch.empty(
(max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.repetition_penalties_cpu = self.repetition_penalties_cpu_tensor.numpy(
(max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
)
self.repetition_penalties_cpu = self.repetition_penalties_cpu_tensor.numpy()
self.repetition_penalties_reqs: set[str] = set()
# Speculative decoding
self.num_accepted_tokens_cpu_tensor = torch.ones((max_num_reqs, ),
dtype=torch.int64,
device="cpu",
pin_memory=pin_memory)
self.num_accepted_tokens_cpu = self.num_accepted_tokens_cpu_tensor.numpy(
self.num_accepted_tokens_cpu_tensor = torch.ones(
(max_num_reqs,), dtype=torch.int64, device="cpu", pin_memory=pin_memory
)
self.num_accepted_tokens_cpu = self.num_accepted_tokens_cpu_tensor.numpy()
# lora related
self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
dtype=np.int64)
self.request_lora_mapping = np.zeros((self.max_num_reqs,), dtype=np.int64)
self.lora_id_to_request_ids: dict[int, set[str]] = {}
self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
@@ -218,8 +184,7 @@ class NPUInputBatch(InputBatch):
# req_index -> bad_words_token_ids
self.bad_words_token_ids: dict[int, list[list[int]]] = {}
self.logits_processing_needs_token_ids = np.zeros(max_num_reqs,
dtype=bool)
self.logits_processing_needs_token_ids = np.zeros(max_num_reqs, dtype=bool)
self.req_output_token_ids: list[list[int] | None] = []
@@ -229,8 +194,7 @@ class NPUInputBatch(InputBatch):
self.logitsprocs_need_output_token_ids = logitsprocs_need_output_token_ids
# Store last speculative tokens for sampler.
self.spec_token_ids: list[list[int]] = [[]
for _ in range(max_num_reqs)]
self.spec_token_ids: list[list[int]] = [[] for _ in range(max_num_reqs)]
# This is updated each time the batch constituents change.
self.sampling_metadata = self._make_sampling_metadata()