EngineX/xc-llm-ascend
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[CI] upgrade to vllm 0.9.0 (#959)

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Upgrade to vllm 0.9.0.
0.8.5 will not be supported any more.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-05-28 21:18:41 +08:00
committed by GitHub
parent e2a0c19cea
commit f6e5decc10
16 changed files with 79 additions and 146 deletions
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37
vllm_ascend/worker/model_runner_v1.py
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@@ -61,7 +61,6 @@ from vllm_ascend.attention.attention import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.sample.rejection_sampler import AscendRejectionSampler
from vllm_ascend.utils import vllm_version_is
if TYPE_CHECKING:
import xgrammar as xgr # type: ignore[import-untyped]
@@ -210,16 +209,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# Request states.
self.requests: Dict[str, CachedRequestState] = {}
# Persistent batch.
# Remove this after we drop 0.8.5 support
if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
self.input_batch = InputBatch(
max_num_reqs=self.max_num_reqs,
max_model_len=self.model_config.max_model_len,
max_num_blocks_per_req=self.max_num_blocks_per_req,
device=self.device,
pin_memory=True,
vocab_size=self.model_config.get_vocab_size(),
)
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
@@ -573,10 +562,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
block_table_indices = (req_indices * self.max_num_blocks_per_req +
positions_np // self.block_size)
if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
else:
block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
block_offsets = positions_np % self.block_size
np.add(block_numbers * self.block_size,
@@ -1182,16 +1169,16 @@ class NPUModelRunner(LoRAModelRunnerMixin):
"""
import torch_npu
kv_caches: Dict[str, torch.Tensor] = {}
if not (vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1")):
self.input_batch = InputBatch(
max_num_reqs=self.max_num_reqs,
max_model_len=self.model_config.max_model_len,
max_num_batched_tokens=self.max_num_tokens,
device=self.device,
pin_memory=True,
vocab_size=self.model_config.get_vocab_size(),
block_size=self.cache_config.block_size,
)
self.input_batch = InputBatch(
max_num_reqs=self.max_num_reqs,
max_model_len=self.model_config.max_model_len,
max_num_batched_tokens=self.max_num_tokens,
device=self.device,
pin_memory=True,
vocab_size=self.model_config.get_vocab_size(),
block_size=self.cache_config.block_size,
)
for kv_cache_group in kv_cache_config.kv_cache_groups:
kv_cache_spec = kv_cache_group.kv_cache_spec