f1ffb5fb19
### What this PR does / why we need it?
vllm model runner v2 use uva buffer to prepare input data, but npu
doesn't support uva yet, this pr implement a uvawrapper class to mimic
gpu's uva backend. what's more, this pr make some modifications to adapt
to the newer main branch.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM main:
13397841ab
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/sample/sampler.py.
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import numpy as np
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import torch
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from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
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from vllm.v1.worker.gpu.sample.gumbel import apply_temperature
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from vllm.v1.worker.gpu.sample.min_p import apply_min_p
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from vllm.v1.worker.gpu.sample.sampler import Sampler
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from vllm_ascend.worker.v2.sample.gumbel import gumbel_sample
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class AscendSampler(Sampler):
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def sample(
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self,
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logits: torch.Tensor,
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idx_mapping: torch.Tensor,
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idx_mapping_np: np.ndarray,
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pos: torch.Tensor,
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input_ids: torch.Tensor,
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expanded_local_pos: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Override sample method because we need to override triton operators
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called in the method.
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"""
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# Copy logits to a new FP32 tensor.
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logits = torch.empty_like(logits, dtype=torch.float32).copy_(logits)
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# Apply logit bias (e.g., allowed_token_ids, min_tokens) in place.
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self.logit_bias_state.apply_logit_bias(logits, idx_mapping, idx_mapping_np, pos)
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# Apply penalties in place.
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self.penalties_state.apply_penalties(
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logits,
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idx_mapping,
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idx_mapping_np,
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input_ids,
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expanded_local_pos,
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self.num_speculative_tokens,
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)
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# Apply temperature in place.
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apply_temperature(logits, idx_mapping, self.sampling_states.temperature.gpu)
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# Apply min_p in place if any request has a non-zero min_p.
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do_min_p = self.sampling_states.do_min_p(idx_mapping_np)
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if do_min_p:
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apply_min_p(logits, idx_mapping, self.sampling_states.min_p.gpu)
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# Apply top_k and/or top_p. This might return a new tensor.
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do_top_k = self.sampling_states.do_top_k(idx_mapping_np)
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top_k = self.sampling_states.top_k.gpu[idx_mapping] if do_top_k else None
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do_top_p = self.sampling_states.do_top_p(idx_mapping_np)
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top_p = self.sampling_states.top_p.gpu[idx_mapping] if do_top_p else None
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if do_top_k or do_top_p:
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logits = apply_top_k_top_p(logits, top_k, top_p)
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# Sample the next token.
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sampled = gumbel_sample(
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logits,
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idx_mapping,
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self.sampling_states.temperature.gpu,
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self.sampling_states.seeds.gpu,
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pos,
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apply_temperature=False,
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)
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return sampled, logits
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