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upgrade to 0.18.0 (#7502)

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### What this PR does / why we need it?
1. upgrade to 0.18.0
2. ensure kernel_block_sizes is int for Eagle drafter
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.17.0
- vLLM main:
8b6325758c

---------

Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
This commit is contained in:
meihanc
2026-03-21 16:05:38 +08:00
committed by GitHub
parent 80a4265717
commit bff4fbfca5
16 changed files with 139 additions and 258 deletions
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73
vllm_ascend/worker/model_runner_v1.py
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@@ -126,7 +126,6 @@ from vllm_ascend.utils import (
is_moe_model,
lmhead_tp_enable,
set_weight_prefetch_method,
vllm_version_is,
)
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
from vllm_ascend.worker.pcp_utils import PCPManager
@@ -292,27 +291,15 @@ class NPUModelRunner(GPUModelRunner):
if self.use_sparse_c8_indexer:
self.c8_k_cache_dtype = torch.int8
self.c8_k_scale_cache_dtype = torch.float16
from vllm_ascend.utils import vllm_version_is
if vllm_version_is("0.17.0"):
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
self.block_size,
use_mla=self.model_config.use_mla,
use_sparse=self.use_sparse,
use_mm_prefix=self.model_config is not None and self.model_config.is_mm_prefix_lm,
)
else:
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
use_mla=self.model_config.use_mla,
use_sparse=self.use_sparse,
use_mm_prefix=self.model_config is not None and self.model_config.is_mm_prefix_lm,
)
self.attn_backend = get_attn_backend(
0,
self.dtype,
None,
use_mla=self.model_config.use_mla,
use_sparse=self.use_sparse,
use_mm_prefix=self.model_config is not None and self.model_config.is_mm_prefix_lm,
)
try:
self.dcp_size = get_dcp_group().world_size
@@ -1389,9 +1376,7 @@ class NPUModelRunner(GPUModelRunner):
self.maybe_get_kv_connector_output(
scheduler_output,
**(
{"clear_metadata": clear_kv_metadata}
if vllm_version_is("0.17.0")
else {"defer_finalize": not clear_kv_metadata}
{"defer_finalize": not clear_kv_metadata}
),
) as kv_connector_output,
):
@@ -2567,17 +2552,14 @@ class NPUModelRunner(GPUModelRunner):
with get_tp_context(self.drafter):
self.drafter.load_model(self.model)
if self.use_aux_hidden_state_outputs:
if vllm_version_is("0.17.0"):
self.model.set_aux_hidden_state_layers(self.model.get_eagle3_aux_hidden_state_layers())
else:
from vllm.model_executor.models.interfaces import supports_eagle3
if not supports_eagle3(self.model):
raise RuntimeError(
"Model does not support EAGLE3 interface but "
"aux_hidden_state_outputs was requested"
)
aux_layers = self.model.get_eagle3_default_aux_hidden_state_layers()
self.model.set_aux_hidden_state_layers(aux_layers)
from vllm.model_executor.models.interfaces import supports_eagle3
if not supports_eagle3(self.model):
raise RuntimeError(
"Model does not support EAGLE3 interface but "
"aux_hidden_state_outputs was requested"
)
aux_layers = self.model.get_eagle3_default_aux_hidden_state_layers()
self.model.set_aux_hidden_state_layers(aux_layers)
if self.lora_config:
self.model = self.load_lora_model(self.model, self.vllm_config, self.device)
@@ -2617,7 +2599,9 @@ class NPUModelRunner(GPUModelRunner):
self.speculative_config.use_eagle() or self.speculative_config.uses_draft_model()
):
assert isinstance(self.drafter, AscendEagleProposer | AscendDraftModelProposer)
self.drafter.initialize_attn_backend(kv_cache_config, self.kernel_block_sizes)
block_size = (self.kernel_block_sizes[0] if isinstance(
self.kernel_block_sizes, list) else self.kernel_block_sizes)
self.drafter.initialize_attn_backend(kv_cache_config, block_size)
if has_kv_transfer_group():
get_kv_transfer_group().register_kv_caches(kv_caches)
@@ -3091,18 +3075,11 @@ class NPUModelRunner(GPUModelRunner):
max_num_blocks.append(max_num_blocks_per_req)
if block_sizes != [self.cache_config.block_size] or self.kernel_block_sizes != [[self.cache_config.block_size]]:
if vllm_version_is("0.17.0"):
assert self.cache_config.cpu_offload_gb == 0, (
"Cannot re-initialize the input batch when CPU weight "
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
"for more details."
)
else:
assert self.offload_config.uva.cpu_offload_gb == 0, (
"Cannot re-initialize the input batch when CPU weight "
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
"for more details."
)
assert self.offload_config.uva.cpu_offload_gb == 0, (
"Cannot re-initialize the input batch when CPU weight "
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
"for more details."
)
self.input_batch = NPUInputBatch(
max_num_reqs=self.max_num_reqs,
max_model_len=max_model_len,