b69b04d3a9
### What this PR does / why we need it?
This PR aim to implement model runner v2 basic framework in vllm-ascend,
the e2e function is not guaranteed by this pr.
### Does this PR introduce _any_ user-facing change?
use envs.VLLM_USE_V2_MODEL_RUNNER to decide if choose model_runenr_v2.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
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Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
from contextlib import contextmanager
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import torch
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from vllm.v1.utils import CpuGpuBuffer
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from vllm.v1.worker.gpu.states import RequestState, UvaBuffer
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class AscendRequestState(RequestState):
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"""Request state for Ascend NPUs."""
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def __init__(
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self,
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max_num_reqs: int,
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max_model_len: int,
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max_num_batched_tokens: int,
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num_speculative_steps: int,
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vocab_size: int,
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device: torch.device,
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pin_memory: bool,
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):
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with uva_wrapper():
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super().__init__(
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max_num_reqs,
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max_model_len,
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max_num_batched_tokens,
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num_speculative_steps,
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vocab_size,
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device,
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pin_memory,
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)
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# because we will override these attribute, delete these attribute to
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# make sure it's collected by python gc immediately.
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del self.prefill_token_ids
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# vllm gpu_model_runner_v2 deprecate the seqs_lens_cpu attribute,
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# because they think most attention backends do not need it.
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# However, Ascend attention backend muse uses seqs_lens_cpu,
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# so we keep num_computed_tokens_cpu here, seq_lens_cpu need to be
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# calculated by num_computed_tokens_cpu + decode_token_per_req outside.
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self.num_computed_tokens_cpu: torch.Tensor = torch.zeros(
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self.max_num_reqs,
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dtype=torch.int32,
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device="cpu",
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)
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# NOTE(Ronald1995): Ascend NPUs do not support UVA yet,
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# so we use CpuGpuBuffer to allocate prefill_token_ids buffer.
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self.prefill_token_ids: CpuGpuBuffer = self._make_buffer( # type: ignore
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(self.max_num_reqs, self.max_model_len),
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dtype=torch.int32)
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def add_request(
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self,
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req_id,
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prompt_len,
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prefill_token_ids,
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num_computed_tokens,
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sampling_params,
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lora_request,
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):
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super().add_request(
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req_id,
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prompt_len,
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prefill_token_ids,
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num_computed_tokens,
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sampling_params,
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lora_request,
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)
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req_idx = self.req_id_to_index[req_id]
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self.num_computed_tokens_cpu[req_idx] = num_computed_tokens
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@contextmanager
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def uva_wrapper():
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"""Context manager to disable UVA for Ascend NPUs."""
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class UvaBufferWrapper:
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def __init__(self, *args, **kwargs):
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pass
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# TODO(Ronald1995): rectify this when NPU support uva.
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global UvaBuffer
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ori_class = UvaBuffer
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try:
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UvaBuffer = UvaBufferWrapper
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yield
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finally:
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UvaBuffer = ori_class
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