Fix paligemma detection inference (#31587)

* fix extended attention mask

* add slow test for detection instance

* [run-slow]paligemma

src/transformers/models/paligemma/modeling_paligemma.py

@@ -448,13 +448,11 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
 
                     # Get the target length
                     target_seqlen = cache_position[-1] + 1
-
                     extended_attention_mask = torch.ones(
-                        (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
+                        (attention_mask.shape[0], target_seqlen - attention_mask.shape[1] + 1),
                         dtype=attention_mask.dtype,
                         device=attention_mask.device,
                     )
-
                     # Filter out only the tokens that can be un-attended, this can happen
                     # if one uses PaliGemma+ Fused modules where the cache on the
                     # first iteration is already big enough, or if one passes custom cache
@@ -467,6 +465,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
 
                     attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
                     position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+
         attention_mask = attention_mask.to(inputs_embeds.dtype)
         outputs = self.language_model(
             attention_mask=attention_mask,

tests/models/paligemma/test_modeling_paligemma.py

@@ -430,6 +430,32 @@ class PaliGemmaForConditionalGenerationIntegrationTest(unittest.TestCase):
         EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"]  # fmt: skip
         self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
 
+    @slow
+    @require_torch
+    @require_read_token
+    def test_integration_detection_bug(self):
+        # this is a reproducer of https://github.com/huggingface/transformers/issues/31425 where not enough context
+        # impacted negatively segmentation generations.
+        model_id = "google/paligemma-3b-pt-224"
+        model = PaliGemmaForConditionalGeneration.from_pretrained(
+            model_id, revision="bfloat16", torch_dtype=torch.bfloat16
+        ).to(torch_device)
+        prompt = ("detect shoe",)
+
+        image = Image.open(
+            requests.get(
+                "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/shoe.png",
+                stream=True,
+            ).raw
+        )
+
+        inputs = self.processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20)
+
+        EXPECTED_DECODED_TEXT = "detect shoe\n<loc0051><loc0309><loc0708><loc0646> shoe"  # fmt: skip
+        self.assertEqual(self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT)
+
     @slow
     @require_read_token
     def test_paligemma_index_error_bug(self):