b80a484864
### What this PR does / why we need it? Fix typo of VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed Signed-off-by: linfeng-yuan <1102311262@qq.com>
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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# This file is a part of the vllm-ascend project.
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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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#
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from typing import Optional
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import torch
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from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler, random_sample
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from vllm.v1.sample.sampler import Sampler
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from vllm_ascend import envs
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def apply_min_p(
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self,
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logits: torch.Tensor,
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min_p: torch.Tensor,
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) -> torch.Tensor:
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"""
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Filters logits using adaptive probability thresholding.
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"""
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# Convert logits to probability distribution
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probability_values = torch.nn.functional.softmax(logits, dim=-1)
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# Calculate maximum probabilities per sequence
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max_probabilities = torch.amax(probability_values, dim=-1, keepdim=True)
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# Reshape min_p for broadcasting
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adjusted_min_p = min_p.unsqueeze(1) * max_probabilities
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# Identify valid tokens using threshold comparison
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# Apply mask using boolean indexing
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logits = logits.masked_fill(probability_values < adjusted_min_p,
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-float('inf'))
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return logits
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def _apply_top_k_top_p(
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logits: torch.Tensor,
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p: torch.Tensor,
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k: torch.Tensor,
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) -> torch.Tensor:
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probs = logits.softmax(dim=-1)
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probs_sort, _ = probs.sort(dim=-1, descending=False)
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if k is not None:
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top_k_count = probs_sort.size(1) - k.to(torch.long) # shape: (batch, )
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top_k_count = top_k_count.unsqueeze(dim=1)
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top_k_cutoff = probs_sort.gather(-1, top_k_count)
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# Make sure the no top-k rows are no-op.
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no_top_k_mask = (k == logits.shape[1]).unsqueeze(dim=1)
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top_k_cutoff.masked_fill_(no_top_k_mask, -float("inf"))
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elements_to_discard = probs < top_k_cutoff
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logits.masked_fill_(elements_to_discard, -float("inf"))
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if p is not None:
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cumprob = torch.cumsum(probs_sort, dim=-1)
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top_p_mask = cumprob <= 1 - p.unsqueeze(dim=1)
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top_p_mask[:, -1] = False # at least one
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top_p_count = top_p_mask.sum(dim=-1).unsqueeze(1)
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top_p_cutoff = probs_sort.gather(-1, top_p_count)
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elements_to_discard = probs < top_p_cutoff
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logits.masked_fill_(elements_to_discard, -float("inf"))
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return logits
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def topk_topp_forward_native(
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self,
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logits: torch.Tensor,
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generators: dict[int, torch.Generator],
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k: Optional[torch.Tensor],
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p: Optional[torch.Tensor],
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) -> torch.Tensor:
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"""
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PyTorch-native implementation of top-k and top-p sampling.
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The logits tensor may be updated in-place.
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"""
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logits = _apply_top_k_top_p(logits, k, p)
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probs = logits.softmax(dim=-1, dtype=torch.float32)
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return random_sample(probs, generators)
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Sampler.apply_min_p = apply_min_p
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if envs.VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE:
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TopKTopPSampler.forward_native = topk_topp_forward_native
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