[VLM] Split Pixtral multi-image features before the CUDA IPC wrap

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

@@ -1726,29 +1726,60 @@ def process_and_combine_mm_data(
         from sglang.srt.managers.mm_utils import get_new_expanded_mm_items
 
         all_collected_items = get_new_expanded_mm_items(all_collected_items)
+        all_collected_items = self._finalize_mm_items(
+            all_collected_items,
+            images=base_output.images,
+        )
+
+        return all_collected_items, input_ids, ret
+
+    def _finalize_mm_items(
+        self,
+        mm_items: List[MultimodalDataItem],
+        *,
+        images: Optional[List[Any]],
+    ) -> List[MultimodalDataItem]:
+        mm_items = self._postprocess_mm_items_before_transport(
+            mm_items,
+            images=images,
+        )
 
-        for item in all_collected_items:
+        for item in mm_items:
             if item.format in (
                 MultimodalInputFormat.PROCESSOR_OUTPUT,
                 MultimodalInputFormat.PRECOMPUTED_EMBEDDING,
             ):
                 item.set_pad_value()
 
-        self._precompute_hashes_before_cpu_transfer(all_collected_items)
-
-        # Wrap GPU features in the bounded IPC pool; pool misses fall back to a
-        # plain CPU tensor. The scheduler copies out and releases each slice.
-        if self.use_cuda_ipc:
-            # post-process, prepare for cuda-ipc transfer
-            for item in all_collected_items:
-                if isinstance(item.feature, torch.Tensor):
-                    item.feature = self._wrap_tensor_for_cuda_ipc(item.feature)
-                if isinstance(item.precomputed_embeddings, torch.Tensor):
-                    item.precomputed_embeddings = self._wrap_tensor_for_cuda_ipc(
-                        item.precomputed_embeddings
-                    )
+        self._precompute_hashes_before_cpu_transfer(mm_items)
+        return self._prepare_mm_items_for_transport(mm_items)
 
-        return all_collected_items, input_ids, ret
+    def _postprocess_mm_items_before_transport(
+        self,
+        mm_items: List[MultimodalDataItem],
+        *,
+        images: Optional[List[Any]],
+    ) -> List[MultimodalDataItem]:
+        """Apply model-specific item reshaping while features are still tensors."""
+        return mm_items
+
+    def _prepare_mm_items_for_transport(
+        self, mm_items: List[MultimodalDataItem]
+    ) -> List[MultimodalDataItem]:
+        """Wrap final GPU features for dispatch to the scheduler."""
+        if not self.use_cuda_ipc:
+            return mm_items
+
+        # Pool misses fall back to plain CPU tensors. The scheduler copies out
+        # and releases each successful pool slice.
+        for item in mm_items:
+            if isinstance(item.feature, torch.Tensor):
+                item.feature = self._wrap_tensor_for_cuda_ipc(item.feature)
+            if isinstance(item.precomputed_embeddings, torch.Tensor):
+                item.precomputed_embeddings = self._wrap_tensor_for_cuda_ipc(
+                    item.precomputed_embeddings
+                )
+        return mm_items
 
     async def process_and_combine_mm_data_async(
         self,

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

@@ -1,12 +1,17 @@
+import copy
 import math
-from typing import List, Union
+from typing import Any, List, Optional, Union
 
 from transformers import PreTrainedTokenizerBase
 from transformers.models.pixtral.image_processing_pixtral import (
     _num_image_tokens as _get_pixtral_hf_num_image_tokens,
 )
 
-from sglang.srt.managers.schedule_batch import Modality, MultimodalProcessorOutput
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalProcessorOutput,
+)
 from sglang.srt.models.pixtral import (
     PixtralForConditionalGeneration,
     PixtralVisionModel,
@@ -41,6 +46,7 @@ def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
             "spatial_merge_size",
             getattr(hf_config, "spatial_merge_size", 1),
         )
+        self._effective_patch_size = self.patch_size * self._spatial_merge_size
 
         self._processor.patch_size = self.patch_size
         if self._spatial_merge_size > 1:
@@ -77,58 +83,86 @@ async def process_mm_data_async(
             image_data=image_data,
             return_text=True,
         )
-        if mm_data.images:
-            effective_patch = self.patch_size * self._spatial_merge_size
-            image_nrows = []
-            for img in mm_data.images:
-                w, h = img.size
-                ratio = max(w / self.image_size, h / self.image_size)
-                if ratio > 1:
-                    w = int(math.floor(w / ratio))
-                    h = int(math.floor(h / ratio))
-                nrows, _ = _get_pixtral_hf_num_image_tokens(
-                    (h, w), (effective_patch, effective_patch)
-                )
-                image_nrows.append(nrows)
-
-            mm_items, input_ids, _ = self.process_and_combine_mm_data(
-                mm_data, self.mm_tokens
-            )
-
-            # For multi-image: split single IMAGE mm_item into per-image items
-            if len(mm_data.images) > 1:
-                from sglang.srt.managers.schedule_batch import MultimodalDataItem
-
-                old_item = next(
-                    item for item in mm_items if item.modality == Modality.IMAGE
-                )
-                all_offsets = old_item.offsets
-                old_feature = old_item.feature
-                old_image_sizes = getattr(old_item, "image_sizes", None)
-
-                mm_items = [
-                    item for item in mm_items if item.modality != Modality.IMAGE
-                ]
-                offset_idx = 0
-                for i, img in enumerate(mm_data.images):
-                    nr = image_nrows[i]
-                    item_offsets = all_offsets[offset_idx : offset_idx + nr]
-                    offset_idx += nr
-                    new_item = MultimodalDataItem(modality=Modality.IMAGE)
-                    new_item.feature = old_feature[i : i + 1]
-                    new_item.offsets = item_offsets
-                    if old_image_sizes is not None:
-                        new_item.model_specific_data["image_sizes"] = old_image_sizes[
-                            i : i + 1
-                        ]
-                    mm_items.append(new_item)
-        else:
-            mm_items, input_ids, _ = self.process_and_combine_mm_data(
-                mm_data, self.mm_tokens
-            )
+        mm_items, input_ids, _ = self.process_and_combine_mm_data(
+            mm_data, self.mm_tokens
+        )
 
         return MultimodalProcessorOutput(
             mm_items=mm_items,
             input_ids=input_ids.tolist(),
             im_token_id=self.IM_TOKEN_ID,
         )
+
+    def _postprocess_mm_items_before_transport(
+        self,
+        mm_items: List[MultimodalDataItem],
+        *,
+        images: Optional[List[Any]],
+    ) -> List[MultimodalDataItem]:
+        if not images or len(images) <= 1:
+            return mm_items
+
+        image_items = [item for item in mm_items if item.modality == Modality.IMAGE]
+        if len(image_items) == len(images):
+            return mm_items
+        if len(image_items) != 1:
+            raise ValueError(
+                "Pixtral multi-image processing expected one bundled IMAGE item or "
+                f"{len(images)} split items, but found {len(image_items)}"
+            )
+
+        old_item = image_items[0]
+        all_offsets = old_item.offsets
+        old_feature = old_item.feature
+        old_image_sizes = old_item.model_specific_data.get("image_sizes")
+        image_nrows = self._get_image_nrows(images)
+        if old_feature is None or len(old_feature) != len(image_nrows):
+            raise ValueError(
+                "Pixtral multi-image feature count does not match the number of "
+                f"images: features={0 if old_feature is None else len(old_feature)}, "
+                f"images={len(image_nrows)}"
+            )
+        if all_offsets is None or sum(image_nrows) != len(all_offsets):
+            raise ValueError(
+                "Pixtral image patch rows do not match the computed offsets: "
+                f"rows={sum(image_nrows)}, "
+                f"offsets={0 if all_offsets is None else len(all_offsets)}"
+            )
+
+        split_items = [item for item in mm_items if item.modality != Modality.IMAGE]
+        offset_idx = 0
+        for image_idx, num_rows in enumerate(image_nrows):
+            item_offsets = all_offsets[offset_idx : offset_idx + num_rows]
+            offset_idx += num_rows
+            new_item = copy.copy(old_item)
+            new_item.feature = old_feature[image_idx : image_idx + 1]
+            new_item.offsets = item_offsets
+            new_item.model_specific_data = copy.copy(old_item.model_specific_data)
+            if old_image_sizes is not None:
+                new_item.model_specific_data["image_sizes"] = old_image_sizes[
+                    image_idx : image_idx + 1
+                ]
+            new_item.hash = None
+            new_item.pad_value = None
+            split_items.append(new_item)
+        if offset_idx != len(all_offsets):
+            raise ValueError(
+                "Pixtral multi-image split did not consume every offset: "
+                f"consumed={offset_idx}, offsets={len(all_offsets)}"
+            )
+        return split_items
+
+    def _get_image_nrows(self, images: List[Any]) -> List[int]:
+        image_nrows = []
+        for image in images:
+            width, height = image.size
+            ratio = max(width / self.image_size, height / self.image_size)
+            if ratio > 1:
+                width = int(math.floor(width / ratio))
+                height = int(math.floor(height / ratio))
+            num_rows, _ = _get_pixtral_hf_num_image_tokens(
+                (height, width),
+                (self._effective_patch_size, self._effective_patch_size),
+            )
+            image_nrows.append(num_rows)
+        return image_nrows

test/registered/unit/multimodal/test_pixtral_processor.py

@@ -0,0 +1,117 @@
+"""Regression tests for Pixtral multimodal item processing."""
+
+import unittest
+from unittest.mock import MagicMock
+
+import torch
+from PIL import Image
+
+from sglang.srt.managers.mm_utils import get_new_expanded_mm_items
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputFormat,
+)
+from sglang.srt.multimodal.processors.pixtral import PixtralProcessor
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=2, suite="base-a-test-cpu")
+
+
+class TestPixtralProcessor(CustomTestCase):
+    def _make_processor(self):
+        processor = object.__new__(PixtralProcessor)
+        processor.use_cuda_ipc = True
+        processor.image_size = 1024
+        processor._effective_patch_size = 28
+        processor._precompute_hashes_before_cpu_transfer = MagicMock()
+        return processor
+
+    def test_multi_image_features_are_split_before_transport(self):
+        """CUDA IPC dispatch must receive per-image tensors, not a bundled proxy."""
+        processor = self._make_processor()
+        proxies = [object(), object()]
+        processor._wrap_tensor_for_cuda_ipc = MagicMock(side_effect=proxies)
+
+        feature = torch.arange(8).reshape(2, 4)
+        image_sizes = torch.tensor([[10, 20], [30, 40]])
+        bundled_item = MultimodalDataItem(
+            modality=Modality.IMAGE,
+            feature=feature,
+            offsets=[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)],
+            format=MultimodalInputFormat.PROCESSOR_OUTPUT,
+            model_specific_data={"image_sizes": image_sizes, "extra_key": "keep"},
+        )
+        images = [Image.new("RGB", (28, 56)), Image.new("RGB", (28, 84))]
+
+        items = processor._finalize_mm_items(
+            [bundled_item],
+            images=images,
+        )
+
+        self.assertEqual(len(items), 2)
+        self.assertEqual(
+            [item.offsets for item in items],
+            [[(0, 0), (1, 1)], [(2, 2), (3, 3), (4, 4)]],
+        )
+        self.assertTrue(
+            all(item.format == MultimodalInputFormat.PROCESSOR_OUTPUT for item in items)
+        )
+        self.assertTrue(
+            all(item.model_specific_data["extra_key"] == "keep" for item in items)
+        )
+        self.assertTrue(all(item.pad_value is not None for item in items))
+        self.assertTrue(
+            torch.equal(items[0].model_specific_data["image_sizes"], image_sizes[:1])
+        )
+        self.assertTrue(
+            torch.equal(items[1].model_specific_data["image_sizes"], image_sizes[1:])
+        )
+        wrapped_features = [
+            call.args[0] for call in processor._wrap_tensor_for_cuda_ipc.call_args_list
+        ]
+        self.assertTrue(torch.equal(wrapped_features[0], feature[:1]))
+        self.assertTrue(torch.equal(wrapped_features[1], feature[1:]))
+        self.assertEqual([item.feature for item in items], proxies)
+
+    def test_already_split_one_row_images_are_preserved(self):
+        """Generic per-image splits must not be collapsed and re-sliced by Pixtral."""
+        processor = self._make_processor()
+        processor._wrap_tensor_for_cuda_ipc = MagicMock(
+            side_effect=[object(), object()]
+        )
+        bundled_item = MultimodalDataItem(
+            modality=Modality.IMAGE,
+            feature=torch.arange(8).reshape(2, 4),
+            offsets=[(0, 0), (2, 2)],
+        )
+        items = get_new_expanded_mm_items([bundled_item])
+        images = [Image.new("RGB", (28, 28)), Image.new("RGB", (56, 28))]
+
+        items = processor._finalize_mm_items(items, images=images)
+
+        self.assertEqual(len(items), 2)
+        self.assertEqual([item.offsets for item in items], [[(0, 0)], [(2, 2)]])
+        wrapped_features = [
+            call.args[0] for call in processor._wrap_tensor_for_cuda_ipc.call_args_list
+        ]
+        self.assertTrue(torch.equal(wrapped_features[0], bundled_item.feature[:1]))
+        self.assertTrue(torch.equal(wrapped_features[1], bundled_item.feature[1:]))
+
+    def test_mismatched_patch_rows_fail_loudly(self):
+        """Derived row counts cannot silently leave image placeholders unassigned."""
+        processor = self._make_processor()
+        item = MultimodalDataItem(
+            modality=Modality.IMAGE,
+            feature=torch.arange(8).reshape(2, 4),
+            offsets=[(0, 0), (1, 1)],
+        )
+        images = [Image.new("RGB", (28, 56)), Image.new("RGB", (28, 84))]
+
+        with self.assertRaisesRegex(ValueError, "patch rows"):
+            processor._finalize_mm_items([item], images=images)
+
+
+if __name__ == "__main__":
+    unittest.main()