e7e1a7dc05
### What this PR does / why we need it?
#5051 only implement a basic framework for model runner v2, but there
are still some bugs for e2e functionality, this PR aim to enable basic
functionality.
model runner v2 plans:
https://github.com/vllm-project/vllm-ascend/issues/5208
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
59 lines
2.2 KiB
Python
59 lines
2.2 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 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.metadata import SamplingMetadata
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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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from vllm_ascend.worker.v2.sample.penalties import \
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apply_penalties_and_temperature
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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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sampling_metadata: SamplingMetadata,
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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 penalties and temperature in place.
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apply_penalties_and_temperature(logits, sampling_metadata)
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# Apply min_p in place.
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if sampling_metadata.min_p is not None:
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apply_min_p(logits, sampling_metadata.min_p)
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# Apply top_k and/or top_p. This might return a new tensor.
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logits = apply_top_k_top_p(logits, sampling_metadata.top_k,
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sampling_metadata.top_p)
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sampled = gumbel_sample(
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logits,
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sampling_metadata.temperature,
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sampling_metadata.seeds,
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sampling_metadata.pos,
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apply_temperature=False,
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)
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return sampled, logits
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