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#
# 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.v1.sample.sampler import Sampler, _SAMPLING_EPS
from vllm.v1.sample.metadata import SamplingMetadata
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)
def apply_top_n_sigma(
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
):
if sampling_metadata.no_top_n_sigma:
return logits
top_n_sigma = sampling_metadata.top_n_sigma[:, None]
top_n_sigma_mask = (top_n_sigma != -1)
filter_value = -3.4028e+38
max_vals, _ = logits.max(dim=-1, keepdim=True)
std_vals = logits.std(dim=-1, keepdim=True)
threshold = max_vals - top_n_sigma * std_vals
threshold[~top_n_sigma_mask] = filter_value
mask = (logits < threshold)
logits = torch.where(mask, filter_value, logits)
return logits
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
"""Sample logits based on sampling metadata.
The various logits processing functions called in this method
may update the logits tensor in-place.
"""
assert not (sampling_metadata.all_greedy
and sampling_metadata.all_random)
if sampling_metadata.all_random:
greedy_sampled = None
else:
greedy_sampled = self.greedy_sample(logits)
if sampling_metadata.all_greedy:
return greedy_sampled
assert sampling_metadata.temperature is not None
# Apply temperature.
logits = self.apply_temperature(logits, sampling_metadata.temperature)
# Apply logits processors that only apply to random sampling
# (argmax invariant)
for processor in sampling_metadata.logitsprocs.argmax_invariant:
logits = processor.apply(logits)
# Apply top_n_sigma
logits = apply_top_n_sigma(logits, sampling_metadata)
# Apply top_k and/or top_p.
random_sampled = self.topk_topp_sampler(
logits,
sampling_metadata.generators,
sampling_metadata.top_k,
sampling_metadata.top_p,
)
if greedy_sampled is None:
return random_sampled
sampled = torch.where(
sampling_metadata.temperature < _SAMPLING_EPS,
greedy_sampled,
random_sampled,
out=greedy_sampled, # Reuse tensor
)
return sampled
if envs.VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION:
TopKTopPSampler.forward_native = topk_topp_forward_native
if envs.VLLM_ASCEND_ENABLE_TOP_N_SIGMA:
Sampler.sample = sample |