[NPU][QwenVL] Support qwen image preprocess on npu (#20189)

python/sglang/srt/hardware_backend/npu/modules/qwen_vl_processor.py

@@ -0,0 +1,153 @@
+from typing import Optional
+
+import torch
+import torchvision.transforms.v2.functional as tvF
+from transformers.image_processing_utils import BatchFeature
+from transformers.image_processing_utils_fast import (
+    group_images_by_shape,
+    reorder_images,
+)
+from transformers.image_utils import SizeDict
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+from transformers.utils import TensorType
+
+from sglang.srt.utils import apply_module_patch
+
+
+# Func refers to transformers.models.qwen2_vl.image_processing_qwen2_vl_fast.py
+# Qwen2VLImageProcessorFast._preprocess
+def npu_wrapper_preprocess(func):
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        interpolation: Optional["tvF.InterpolationMode"],
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        patch_size: int,
+        temporal_patch_size: int,
+        merge_size: int,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ):
+        # Group images by size for batched resizing
+        grouped_images, grouped_images_index = group_images_by_shape(
+            images, disable_grouping=disable_grouping
+        )
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            height, width = stacked_images.shape[-2:]
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height,
+                    width,
+                    factor=patch_size * merge_size,
+                    min_pixels=size["shortest_edge"],
+                    max_pixels=size["longest_edge"],
+                )
+                stacked_images = self.resize(
+                    image=stacked_images,
+                    size=SizeDict(height=resized_height, width=resized_width),
+                    interpolation=interpolation,
+                )
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        # Group images by size for further processing
+        # Needed in case do_resize is False, or resize returns images with different sizes
+        grouped_images, grouped_images_index = group_images_by_shape(
+            resized_images, disable_grouping=disable_grouping
+        )
+        processed_images_grouped = {}
+        processed_grids = {}
+        for shape, stacked_images in grouped_images.items():
+            resized_height, resized_width = stacked_images.shape[-2:]
+            # Fused rescale and normalize
+            patches = self.rescale_and_normalize(
+                stacked_images,
+                do_rescale,
+                rescale_factor,
+                do_normalize,
+                image_mean,
+                image_std,
+            )
+            if patches.ndim == 4:
+                # add a temporal dimension if we have images
+                patches = patches.unsqueeze(1)
+            if patches.shape[1] % temporal_patch_size != 0:
+                repeats = patches[:, -1:].repeat(1, temporal_patch_size - 1, 1, 1, 1)
+                patches = torch.cat([patches, repeats], dim=1)
+            batch_size, grid_t, channel = patches.shape[:3]
+            grid_t = grid_t // temporal_patch_size
+            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+
+            ######################################
+            # Start of modifications for sglang  #
+            ######################################
+            patches = patches.view(
+                batch_size * grid_t,
+                temporal_patch_size * channel,
+                grid_h // merge_size,
+                merge_size,
+                patch_size,
+                grid_w // merge_size,
+                merge_size,
+                patch_size,
+            )
+            patches = patches.permute(0, 1, 2, 5, 3, 6, 4, 7)
+            patches = patches.reshape(
+                batch_size,
+                grid_t,
+                temporal_patch_size,
+                channel,
+                grid_h * grid_w,
+                patch_size,
+                patch_size,
+            )
+            patches = patches.permute(0, 1, 4, 3, 2, 5, 6)
+            flatten_patches = patches.reshape(
+                batch_size,
+                grid_t * grid_h * grid_w,
+                -1,
+            )
+            ######################################
+            #  End of modifications for sglang   #
+            ######################################
+
+            processed_images_grouped[shape] = flatten_patches
+            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size
+
+        processed_images = reorder_images(
+            processed_images_grouped, grouped_images_index
+        )
+        processed_grids = reorder_images(processed_grids, grouped_images_index)
+        pixel_values = torch.cat(processed_images, dim=0)
+        image_grid_thw = torch.tensor(processed_grids)
+
+        return BatchFeature(
+            data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw},
+            tensor_type=return_tensors,
+        )
+
+    return _preprocess
+
+
+_npu_preprocess_patched = False
+
+
+def npu_apply_qwen_image_preprocess_patch():
+    global _npu_preprocess_patched
+    if _npu_preprocess_patched:
+        return
+    apply_module_patch(
+        "transformers.models.qwen2_vl.image_processing_qwen2_vl_fast.Qwen2VLImageProcessorFast",
+        "_preprocess",
+        [npu_wrapper_preprocess],
+    )
+    _npu_preprocess_patched = True

python/sglang/srt/multimodal/processors/base_processor.py

@@ -346,11 +346,13 @@ class BaseMultimodalProcessor(ABC):
                 kwargs["device"] = "xpu"
             elif not _is_npu:
                 kwargs["device"] = "cuda"
-            elif processor.__class__.__name__ not in {
+            else:
-                "Qwen2_5_VLProcessor",
-                "Qwen3VLProcessor",
-            }:
                 # Note: for qwen-vl, processor has some reshape issue because of dims restriction on Ascend.
+                from sglang.srt.hardware_backend.npu.modules.qwen_vl_processor import (
+                    npu_apply_qwen_image_preprocess_patch,
+                )
+
+                npu_apply_qwen_image_preprocess_patch()
                 kwargs["device"] = "npu"
 
         result = processor.__call__(