VLMs: fix number of image tokens (#34332)
* fix * fix tests * add tests * style * style * fix qwen after rebase * fix video llava
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@@ -235,6 +235,35 @@ class LlavaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterM
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out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
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self.assertTrue(torch.allclose(out_embeds, out_ids))
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def test_mismatching_num_image_tokens(self):
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"""
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Tests that VLMs through an error with explicit message saying what is wrong
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when number of images don't match number of image tokens in the text.
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Also we need to test multi-image cases when one prompr has multiple image tokens.
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"""
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config).to(torch_device)
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_ = model(**input_dict) # successfull forward with no modifications
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# remove one image but leave the image token in text
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input_dict["pixel_values"] = input_dict["pixel_values"][-1:, ...]
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with self.assertRaises(ValueError):
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_ = model(**input_dict)
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# simulate multi-image case by concatenating inputs where each has exactly one image/image-token
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input_ids = input_dict["input_ids"][:1]
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pixel_values = input_dict["pixel_values"][:1]
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input_ids = torch.cat([input_ids, input_ids], dim=0)
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# one image and two image tokens raise an error
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with self.assertRaises(ValueError):
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_ = model(input_ids=input_ids, pixel_values=pixel_values)
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# two images and two image tokens don't raise an error
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pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
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_ = model(input_ids=input_ids, pixel_values=pixel_values)
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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