[Misc] Upgrade vllm hash to 12_14 (#5000)
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
1. fix https://github.com/vllm-project/vllm/pull/27938
2. fix https://github.com/vllm-project/vllm/pull/27145
pooling models now supports chunked prefill and prefix caching,
3. fix https://github.com/vllm-project/vllm/pull/30181
define the CPU fields in the field config where they really belong.
4. fix https://github.com/vllm-project/vllm/pull/28168
define the CPU fields in the field config where they really belong.
5. fix https://github.com/vllm-project/vllm/pull/30201
some moudle rename
6. fix https://github.com/vllm-project/vllm/pull/29067
fusedmoe moudle refactor
7. fix https://github.com/vllm-project/vllm/pull/29066
fusedmoe moudle refactor
8. fix https://github.com/vllm-project/vllm/pull/29624
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
@@ -121,8 +121,8 @@ from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
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AscendDeviceType, ProfileExecuteDuration,
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enable_sp, get_ascend_device_type, is_enable_nz,
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is_moe_model, lmhead_tp_enable)
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from vllm_ascend.worker.npu_input_batch import InputBatch
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is_moe_model, lmhead_tp_enable, vllm_version_is)
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from vllm_ascend.worker.npu_input_batch import NPUInputBatch
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if TYPE_CHECKING:
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import xgrammar as xgr # type: ignore[import-untyped]
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@@ -249,13 +249,24 @@ class NPUModelRunner(GPUModelRunner):
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# Set up Attention
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self.use_sparse = hasattr(self.vllm_config.model_config.hf_config,
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"index_topk")
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self.attn_backend = get_attn_backend(0,
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self.dtype,
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None,
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self.block_size,
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use_mla=self.model_config.use_mla,
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use_sparse=self.use_sparse)
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if vllm_version_is('0.12.0'):
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self.attn_backend = get_attn_backend(
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0,
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self.dtype,
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None,
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self.block_size,
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use_mla=self.model_config.use_mla,
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use_sparse=self.use_sparse)
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else:
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self.attn_backend = get_attn_backend(
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0,
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self.dtype,
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None,
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self.block_size,
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use_mla=self.model_config.use_mla,
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use_sparse=self.use_sparse,
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use_mm_prefix=self.model_config is not None
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and self.model_config.is_mm_prefix_lm)
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self.attn_mask_builder = AttentionMaskBuilder(self.device)
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self._set_up_drafter()
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@@ -353,7 +364,7 @@ class NPUModelRunner(GPUModelRunner):
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# solution, we initialize the input batch here, and re-initialize it
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# in `initialize_kv_cache` if the block_sizes here is different from
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# the block_sizes in the kv cache config.
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self.input_batch = InputBatch(
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self.input_batch = NPUInputBatch(
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max_num_reqs=self.max_num_reqs,
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max_model_len=self.model_config.max_model_len,
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max_num_batched_tokens=self.max_num_tokens,
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@@ -2000,19 +2011,36 @@ class NPUModelRunner(GPUModelRunner):
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self.speculative_config.method == "mtp":
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attn_state = AscendAttentionState.SpecDecoding
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common_metadata = CommonAttentionMetadata(
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query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
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query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
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if vllm_version_is("0.12.0"):
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common_metadata = CommonAttentionMetadata(
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query_start_loc=self.query_start_loc.gpu[:num_reqs +
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1],
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seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
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seq_lens=self.seq_lens.cpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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block_table_tensor=block_table_tensor[:num_reqs],
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slot_mapping=slot_mapping.gpu,
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num_computed_tokens_cpu=num_computed_tokens_cpu,
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max_query_len=max_query_len,
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max_seq_len=seq_lens)
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query_start_loc_cpu=self.query_start_loc.
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cpu[:num_reqs + 1],
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seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
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seq_lens=self.seq_lens.cpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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block_table_tensor=block_table_tensor[:num_reqs],
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slot_mapping=slot_mapping.gpu,
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num_computed_tokens_cpu=num_computed_tokens_cpu,
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max_query_len=max_query_len,
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max_seq_len=seq_lens)
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else:
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common_metadata = CommonAttentionMetadata(
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query_start_loc=self.query_start_loc.gpu[:num_reqs +
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1],
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query_start_loc_cpu=self.query_start_loc.
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cpu[:num_reqs + 1],
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_seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
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seq_lens=self.seq_lens.cpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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block_table_tensor=block_table_tensor[:num_reqs],
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slot_mapping=slot_mapping.gpu,
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_num_computed_tokens_cpu=num_computed_tokens_cpu,
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max_query_len=max_query_len,
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max_seq_len=seq_lens)
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for attn_group in self.attn_groups[kv_cache_group_id]:
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builder = attn_group.get_metadata_builder()
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@@ -2778,7 +2806,7 @@ class NPUModelRunner(GPUModelRunner):
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"Cannot re-initialize the input batch when CPU weight "
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"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
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"for more details.")
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self.input_batch = InputBatch(
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self.input_batch = NPUInputBatch(
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max_num_reqs=self.max_num_reqs,
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max_model_len=self.model_config.max_model_len,
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max_num_batched_tokens=self.max_num_tokens,
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