init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/patch/worker/patch_kimi_k25.py

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2026 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 torch
+import torch.nn as nn
+import torch.nn.functional as F
+from vllm.model_executor.models.kimi_k25_vit import (
+    Learnable2DInterpPosEmbDivided_fixed,
+    MoonViT3dPretrainedModel,
+    get_rope_shape_decorate,
+)
+
+from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
+
+
+@get_rope_shape_decorate
+def get_rope_shape(org, interpolation_mode, shape):
+    return (
+        F.interpolate(
+            org.permute((2, 0, 1)).unsqueeze(0),
+            size=shape,
+            mode=interpolation_mode,
+        )
+        .squeeze(0)
+        .permute((1, 2, 0))
+        .flatten(end_dim=1)
+    )
+
+
+class AscendLearnable2DInterpPosEmbDivided_fixed(nn.Module):
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor | list) -> torch.Tensor:
+        pos_embs = []
+        if isinstance(grid_thws, torch.Tensor):
+            grid_list = grid_thws.tolist()
+        else:
+            grid_list = grid_thws
+
+        for t, h, w in grid_list:
+            assert t <= self.num_frames, (
+                f"[vllm-ascend/patch_kimi_k25] Invalid frame count. t={t}, num_frames={self.num_frames}"
+            )
+            if (h, w) == self.weight.shape[:-1]:
+                pos_emb_2d = self.weight.flatten(end_dim=1)
+            else:
+                pos_emb_2d = get_rope_shape(
+                    self.weight,
+                    interpolation_mode=self.interpolation_mode,
+                    shape=(h, w),
+                )
+
+            if t == 1:
+                pos_emb_3d = pos_emb_2d
+            else:
+                pos_emb_3d = pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
+
+            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
+
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+Learnable2DInterpPosEmbDivided_fixed.forward = AscendLearnable2DInterpPosEmbDivided_fixed.forward
+
+
+# Patch MoonViT3dPretrainedModel.to() to ignore the `dtype` argument.
+# When KimiK25ForConditionalGeneration.__init__ calls:
+#     self.vision_tower = self.vision_tower.to(device=..., dtype=model_config.dtype)
+# the `dtype=model_config.dtype` (e.g. bf16) would overwrite the fp8 parameters
+# created by the Ascend quantization scheme, causing a dtype mismatch later
+# in weight_loader when the checkpoint's fp8 weights are loaded.
+if get_ascend_device_type() == AscendDeviceType.A5:
+    _original_moonvit_to = MoonViT3dPretrainedModel.to
+
+    def _patched_moonvit_to(self, *args, **kwargs):
+        # Filter out dtype from positional arguments and remove from kwargs
+        # to prevent overriding quantized weight dtypes on A5.
+        new_args = tuple(a for a in args if not isinstance(a, torch.dtype))
+        kwargs.pop("dtype", None)
+        return _original_moonvit_to(self, *new_args, **kwargs)
+
+    MoonViT3dPretrainedModel.to = _patched_moonvit_to