ed051737e9
### What this PR does / why we need it?
This PR supports the Kimi-K2.5 models on the NPU of bf16 and w4a8
weights.
The corresponding PR in the vllm community has been merged:
https://github.com/vllm-project/vllm/pull/34501
### Does this PR introduce _any_ user-facing change?
- No.
### How was this patch tested?
We test the Kimi-K2.5 weights. The weights path:
https://modelscope.cn/models/Eco-Tech/Kimi-K2.5-W4A8
Successfully ran on 910B NPU using vllm-ascend by the w4a8 weights.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: LoganJane <LoganJane73@hotmail.com>
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from vllm.logger import init_logger
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from vllm.model_executor.models.kimi_k25_vit import Learnable2DInterpPosEmbDivided_fixed, get_rope_shape_decorate
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logger = init_logger(__name__)
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@get_rope_shape_decorate
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def get_rope_shape(org, interpolation_mode, shape):
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return (
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F.interpolate(
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org.permute((2, 0, 1)).unsqueeze(0),
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size=shape,
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mode=interpolation_mode,
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)
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.squeeze(0)
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.permute((1, 2, 0))
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.flatten(end_dim=1)
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)
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class AscendLearnable2DInterpPosEmbDivided_fixed(nn.Module):
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def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
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pos_embs = []
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for t, h, w in grid_thws.tolist():
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x_device = x.device
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x_dtype = x.dtype
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assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
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if (h, w) == self.weight.shape[:-1]:
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pos_emb_2d = self.weight.flatten(end_dim=1)
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else:
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weight_fp32 = self.weight.to(dtype=torch.float32)
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weight_cpu = weight_fp32.to("cpu")
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pos_emb_2d = get_rope_shape(
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weight_cpu,
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interpolation_mode=self.interpolation_mode,
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shape=(h, w),
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)
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pos_emb_2d = pos_emb_2d.to(x_device, dtype=x_dtype)
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if t == 1:
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pos_emb_3d = pos_emb_2d
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else:
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pos_emb_3d = pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
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pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
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out = x + torch.cat(pos_embs)
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return out
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Learnable2DInterpPosEmbDivided_fixed.forward = AscendLearnable2DInterpPosEmbDivided_fixed.forward
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