84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
|
#
|
||
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||
|
|
# SPDX-License-Identifier: Apache-2.0
|
||
|
|
# This file is a part of the vllm-ascend project.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
#
|
||
|
|
|
||
|
|
from typing import Optional
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch_npu
|
||
|
|
from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler, random_sample
|
||
|
|
|
||
|
|
from vllm_ascend import envs
|
||
|
|
|
||
|
|
|
||
|
|
def apply_top_k_top_p(
|
||
|
|
logits: torch.Tensor,
|
||
|
|
k: torch.Tensor,
|
||
|
|
p: torch.Tensor,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
if p is not None and k is not None:
|
||
|
|
# npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
|
||
|
|
return torch_npu.npu_top_k_top_p(logits, p, k)
|
||
|
|
|
||
|
|
probs = logits.softmax(dim=-1)
|
||
|
|
probs_sort, _ = probs.sort(dim=-1, descending=False)
|
||
|
|
|
||
|
|
if k is not None:
|
||
|
|
top_k_count = probs_sort.size(1) - k.to(torch.long) # shape: (batch, )
|
||
|
|
top_k_count = top_k_count.unsqueeze(dim=1)
|
||
|
|
top_k_cutoff = probs_sort.gather(-1, top_k_count)
|
||
|
|
|
||
|
|
# Make sure the no top-k rows are no-op.
|
||
|
|
no_top_k_mask = (k == logits.shape[1]).unsqueeze(dim=1)
|
||
|
|
top_k_cutoff.masked_fill_(no_top_k_mask, -float("inf"))
|
||
|
|
|
||
|
|
elements_to_discard = probs < top_k_cutoff
|
||
|
|
logits.masked_fill_(elements_to_discard, -float("inf"))
|
||
|
|
|
||
|
|
if p is not None:
|
||
|
|
cumprob = torch.cumsum(probs_sort, dim=-1)
|
||
|
|
top_p_mask = cumprob <= 1 - p.unsqueeze(dim=1)
|
||
|
|
top_p_mask[:, -1] = False # at least one
|
||
|
|
|
||
|
|
top_p_count = top_p_mask.sum(dim=-1).unsqueeze(1)
|
||
|
|
top_p_cutoff = probs_sort.gather(-1, top_p_count)
|
||
|
|
elements_to_discard = probs < top_p_cutoff
|
||
|
|
logits.masked_fill_(elements_to_discard, -float("inf"))
|
||
|
|
|
||
|
|
return logits
|
||
|
|
|
||
|
|
|
||
|
|
def topk_topp_forward_native(
|
||
|
|
self,
|
||
|
|
logits: torch.Tensor,
|
||
|
|
generators: dict[int, torch.Generator],
|
||
|
|
k: Optional[torch.Tensor],
|
||
|
|
p: Optional[torch.Tensor],
|
||
|
|
) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
PyTorch-native implementation of top-k and top-p sampling.
|
||
|
|
|
||
|
|
The logits tensor may be updated in-place.
|
||
|
|
"""
|
||
|
|
logits = apply_top_k_top_p(logits, k, p)
|
||
|
|
probs = logits.softmax(dim=-1, dtype=torch.float32)
|
||
|
|
return random_sample(probs, generators)
|
||
|
|
|
||
|
|
|
||
|
|
if envs.VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION:
|
||
|
|
TopKTopPSampler.forward_native = topk_topp_forward_native
|