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[Quickfix] update CachedRequestState as NewRequestData changed (#2367)

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### What this PR does / why we need it?
1. update `CachedRequestState` as `NewRequestData` changed in
https://github.com/vllm-project/vllm/pull/22570
2. drop maintenance of vllm v0.10.0 in the branch main

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.


- vLLM version: v0.10.0
- vLLM main:
92ff41abea

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2025-08-15 07:35:27 +08:00
committed by GitHub
parent 2ad7e1251e
commit 61866b8ac6
18 changed files with 77 additions and 285 deletions
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11
vllm_ascend/patch/worker/__init__.py
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@@ -15,12 +15,5 @@
# limitations under the License.
#
from vllm_ascend.utils import vllm_version_is
# Import specific patches for different versions
if vllm_version_is("0.10.0"):
from vllm_ascend.patch.worker import patch_0_10_0 # noqa: F401
from vllm_ascend.patch.worker import patch_common # noqa: F401
else:
from vllm_ascend.patch.worker import patch_common # noqa: F401
from vllm_ascend.patch.worker import patch_main # noqa: F401
from vllm_ascend.patch.worker import patch_common # noqa: F401
from vllm_ascend.patch.worker import patch_main # noqa: F401

18
vllm_ascend/patch/worker/patch_0_10_0/__init__.py
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@@ -1,18 +0,0 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# 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.
#
import vllm_ascend.patch.worker.patch_0_10_0.patch_sampler_gather_logprobs # noqa

87
vllm_ascend/patch/worker/patch_0_10_0/patch_sampler_gather_logprobs.py
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@@ -1,87 +0,0 @@
#
# 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.
#
import torch
from vllm.platforms import current_platform
from vllm.v1.outputs import LogprobsTensors
from vllm.v1.sample.sampler import Sampler
@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
def batched_count_greater_than(x: torch.Tensor,
values: torch.Tensor) -> torch.Tensor:
"""
Counts elements in each row of x that are greater than the corresponding
value in values. Use torch.compile to generate an optimized kernel for
this function. otherwise, it will create additional copies of the input
tensors and cause memory issues.
Args:
x (torch.Tensor): A 2D tensor of shape (batch_size, n_elements).
values (torch.Tensor): A 2D tensor of shape (batch_size, 1).
Returns:
torch.Tensor: A 1D tensor of shape (batch_size,) with the counts.
"""
return (x >= values).sum(-1)
def gather_logprobs(
self,
logprobs: torch.Tensor,
num_logprobs: int,
token_ids: torch.Tensor,
) -> LogprobsTensors:
"""
Gather logprobs for topk and sampled/prompt token.
Args:
logprobs: (num tokens) x (vocab) tensor
num_logprobs: minimum number of logprobs to
retain per token
token_ids: prompt tokens (if prompt logprobs)
or sampled tokens (if sampled
logprobs); 1D token ID tensor
with (num tokens) elements
Must be int64.
Returns:
Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
Sampled token rank tensor, (num tokens)
"""
assert token_ids.dtype == torch.int64
# Find the topK values.
topk_logprobs, topk_indices = torch.topk(logprobs, num_logprobs, dim=-1)
# Get with the logprob of the prompt or sampled token.
token_ids = token_ids.unsqueeze(-1)
token_logprobs = logprobs.gather(-1, token_ids)
# Compute the ranks of the actual token.
token_ranks = batched_count_greater_than(logprobs, token_logprobs)
# Concatenate together with the topk.
indices = torch.cat((token_ids, topk_indices), dim=1)
logprobs = torch.cat((token_logprobs, topk_logprobs), dim=1)
# Use int32 to reduce the tensor size.
indices = indices.to(torch.int32)
return LogprobsTensors(indices, logprobs, token_ranks)
Sampler.gather_logprobs = gather_logprobs