Clean up v0.9.1 code (#1672)
vllm has released 0.9.2. This PR drop 0.9.1 support.
- vLLM version: v0.9.1
- vLLM main:
b942c094e3
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
@@ -24,9 +24,9 @@
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# each worker's `__init__` function.
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#
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# Then in each kind of patch, there are three folders:
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# - patch_0_9_1: contains the patches applied when vllm version is 0.9.1.
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# - patch_0_9_2: contains the patches applied when vllm version is 0.9.2.
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# - patch_main: contains the patches applied when vllm version is main branch.
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# - patch_common: contains the patches applied in both 0.9.1 and main branch.
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# - patch_common: contains the patches applied in both 0.9.2 and main branch.
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#
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# Once a new patch is added in vllm-ascend, please add the patch description into this file as well.
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# ----------------------------------------------------------------------------------
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@@ -105,32 +105,6 @@
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# Future Plan:
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# Revert it when the related pr is merged in vllm and vllm-ascend.
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#
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# ** File: worker/patch_common/patch_sampler.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.v1.sample.sampler.Sampler.apply_top_k_top_p`
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# Why:
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# We need to use the patched `apply_top_k_top_p` in `sample`.
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# The mainly reason to overwrite `apply_top_k_top_p` is
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# to improve performance.
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# How：
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# Re-implementation the `apply_top_k_top_p` function by pytorch
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm-ascend/pull/970
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# Future Plan:
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# Revert it when the ascend scatter performance improves.
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#
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# 2. `vllm.v1.sample.sampler.Sampler.apply_min_p`
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# Why:
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# We need to use the patched `apply_min_p` in `sample`.
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# The mainly reason to overwrite `apply_min_p` is
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# to improve performance.
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# How：
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# Re-implementation the `apply_min_p` function by pytorch
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# Related PR (if no, explain why):
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# - https://github.com/vllm-project/vllm-ascend/pull/970
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# Future Plan:
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# Revert it when the ascend indexput performance improves.
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#
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# ** File: worker/patch_common/patch_distributed.py **
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.parallel_state.GroupCoordinator`
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@@ -154,4 +128,4 @@
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# Related PR (if no, explain why):
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# This is the problem in vllm-ascend
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# Future Plan:
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# Remove this patch once pytorch 2.7.0 is supported for vllm ascend.
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# Remove this patch once pytorch 2.7.0 is supported for vllm ascend.
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@@ -17,8 +17,8 @@
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from vllm_ascend.utils import vllm_version_is
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# Import specific patches for different versions
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if vllm_version_is("0.9.1"):
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from vllm_ascend.patch.platform import patch_0_9_1 # noqa: F401
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if vllm_version_is("0.9.2"):
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from vllm_ascend.patch.platform import patch_0_9_2 # noqa: F401
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from vllm_ascend.patch.platform import patch_common # noqa: F401
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else:
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from vllm_ascend.patch.platform import patch_common # noqa: F401
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@@ -18,8 +18,8 @@
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from vllm_ascend.utils import vllm_version_is
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# Import specific patches for different versions
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if vllm_version_is("0.9.1"):
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from vllm_ascend.patch.worker import patch_0_9_1 # noqa: F401
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if vllm_version_is("0.9.2"):
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from vllm_ascend.patch.worker import patch_0_9_2 # noqa: F401
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from vllm_ascend.patch.worker import patch_common # noqa: F401
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else:
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from vllm_ascend.patch.worker import patch_common # noqa: F401
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@@ -1,106 +0,0 @@
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#
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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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import torch_npu
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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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k: torch.Tensor,
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p: torch.Tensor,
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) -> torch.Tensor:
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if p is not None and k is not None:
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# npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
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return torch_npu.npu_top_k_top_p(logits, p, k)
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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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@@ -14,4 +14,3 @@
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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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import vllm_ascend.patch.worker.patch_0_9_1.patch_sampler # noqa
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