[Bugfix][Multimodal] Fix DeepSeek-OCR zero-sum patch guard in _encode…

tests/models/multimodal/generation/test_deepseek_ocr.py

@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Guard tests for DeepSeek-OCR _encode_local_features.
+
+_encode_local_features must skip processing only for an empty-element
+patches tensor, not for a zero-sum one.
+
+Regression for #64075: the old guard ``torch.sum(patches).item() == 0``
+returned None for a 1536x768 half-white/half-black image whose two
+normalised crop patches cancel to sum==0, causing a shape mismatch in
+_merge_multimodal_embeddings and a fatal EngineCore crash.
+
+The correct guard is ``patches.numel() == 0``.
+"""
+
+import pytest
+import torch
+
+from vllm.model_executor.models.deepseek_ocr import DeepseekOCRForCausalLM
+from vllm.model_executor.models.deepseek_ocr2 import DeepseekOCR2ForCausalLM
+
+
+class TestDeepseekOCREncodeLocalFeaturesGuard:
+    def test_ocr2_empty_patches_returns_none(self):
+        """Zero-element tensor → skip local encoding (non-tiled image)."""
+        empty = torch.empty(0, 3, 224, 224)
+        assert DeepseekOCR2ForCausalLM._encode_local_features(None, empty) is None
+
+    def test_ocr2_zero_sum_patches_enters_model_body(self):
+        """Non-empty patches that happen to sum to zero must NOT return None.
+
+        The method will raise AttributeError when it tries to call
+        self.sam_model on a None self – that is the expected outcome here
+        because it proves the guard was passed, not that encoding succeeded.
+        The real crash was a shape-mismatch ValueError that killed EngineCore.
+        """
+        # Two patches that cancel: white (1.0) + black (-1.0) after Normalize.
+        wb = torch.cat([torch.ones(1, 3, 4, 4), -torch.ones(1, 3, 4, 4)])
+        assert wb.numel() > 0
+        assert wb.sum().item() == 0.0
+        with pytest.raises(AttributeError):
+            DeepseekOCR2ForCausalLM._encode_local_features(None, wb)
+
+    def test_ocr1_empty_patches_returns_none(self):
+        """OCR-1: zero-element tensor → skip local encoding."""
+        empty = torch.empty(0, 3, 224, 224)
+        crop_shape = torch.tensor([0, 0])
+        assert (
+            DeepseekOCRForCausalLM._encode_local_features(None, empty, crop_shape)
+            is None
+        )
+
+    def test_ocr1_zero_sum_patches_enters_model_body(self):
+        """OCR-1: non-empty zero-sum patches must pass the guard."""
+        wb = torch.cat([torch.ones(1, 3, 4, 4), -torch.ones(1, 3, 4, 4)])
+        crop_shape = torch.tensor([1, 2])
+        with pytest.raises(AttributeError):
+            DeepseekOCRForCausalLM._encode_local_features(None, wb, crop_shape)

vllm/model_executor/models/deepseek_ocr.py

@@ -499,7 +499,7 @@ def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
     def _encode_local_features(
         self, patches: torch.Tensor, crop_shape: torch.Tensor
     ) -> torch.Tensor | None:
-        if torch.sum(patches).item() == 0:
+        if patches.numel() == 0:
             return None
 
         local_features_1 = self.sam_model(patches)

vllm/model_executor/models/deepseek_ocr2.py

@@ -343,7 +343,7 @@ def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
         return features.view(-1, dim)
 
     def _encode_local_features(self, patches: torch.Tensor) -> torch.Tensor | None:
-        if torch.sum(patches).item() == 0:
+        if patches.numel() == 0:
             return None
 
         local_features = self.sam_model(patches)