EngineX/xc-llm-ascend
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[CI] Upgrade vllm to 0.9.1 (#1165)

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1. upgrade vllm to 0.9.1. 0.9.0 is not supported for main branch now.
keep doc to 0.9.0 until we release the first 0.9.1 release.
2. disable V0 test for PR
3. move actionlint check to lint job

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-06-11 16:33:11 +08:00
committed by GitHub
parent e46dc142bf
commit 4f5964420e
19 changed files with 72 additions and 320 deletions
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51
vllm_ascend/worker/model_runner_v1.py
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@@ -74,7 +74,7 @@ from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.mla_v1 import CommonAttentionMetadata
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.sample.rejection_sampler import AscendRejectionSampler
from vllm_ascend.utils import ProfileExecuteDuration, vllm_version_is
from vllm_ascend.utils import ProfileExecuteDuration
from vllm_ascend.worker.mtp_proposer_v1 import MtpProposer
if TYPE_CHECKING:
@@ -1614,44 +1614,27 @@ class NPUModelRunner(LoRAModelRunnerMixin):
import torch_npu
kv_caches: Dict[str, torch.Tensor] = {}
# Remove this after we drop 0.9.0 support
if vllm_version_is("0.9.0"):
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,
)
else:
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_sizes=[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_sizes=[self.cache_config.block_size],
)
if not vllm_version_is("0.9.0"):
kv_cache_sizes = {}
for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
assert len(kv_cache_tensor.shared_by) == 1, (
"KV cache tensor shared by multiple layers is not supported in "
"NPU.")
kv_cache_sizes[
kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
kv_cache_sizes = {}
for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
assert len(kv_cache_tensor.shared_by) == 1, (
"KV cache tensor shared by multiple layers is not supported in "
"NPU.")
kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
for kv_cache_group in kv_cache_config.kv_cache_groups:
kv_cache_spec = kv_cache_group.kv_cache_spec
for layer_name in kv_cache_group.layer_names:
if vllm_version_is("0.9.0"):
tensor_size = kv_cache_config.tensors[layer_name].size
else:
tensor_size = kv_cache_sizes[layer_name]
tensor_size = kv_cache_sizes[layer_name]
assert tensor_size % kv_cache_spec.page_size_bytes == 0
num_blocks = tensor_size // kv_cache_spec.page_size_bytes