Fix CI for VLMs (#35690)
* fix some easy test * more tests * remove logit check here also * add require_torch_large_gpu in Emu3
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@@ -504,41 +504,3 @@ class LlavaNextVideoForConditionalGenerationIntegrationTest(unittest.TestCase):
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self.processor.decode(output_batched[0], skip_special_tokens=True),
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self.processor.decode(output_single[0], skip_special_tokens=True),
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)
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@slow
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@require_bitsandbytes
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def test_padding_side_when_merging_inputs(self):
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model = LlavaNextVideoForConditionalGeneration.from_pretrained(
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"llava-hf/LLaVA-NeXT-Video-7B-hf", load_in_4bit=True
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)
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inputs_batched = self.processor(
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[self.prompt_video, self.prompt_image],
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images=[self.image],
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videos=[self.video],
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return_tensors="pt",
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padding=True,
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).to(torch_device)
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# model is in eval mode by default so we should get pad on the left side
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# we can check the first hidden-states (aka inputs embeds)
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# the first element was lo-res image and we expect the first 1482 tokens to be all pads
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with torch.no_grad():
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output_eval = model(**inputs_batched, output_hidden_states=True)
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self.assertTrue((output_eval.hidden_states[0][0, :1482, ...] == 0).all().item())
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with self.assertLogs("transformers", level="WARNING") as logs:
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model.padding_side = "left"
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model.train()
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with torch.no_grad():
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model(**inputs_batched, output_hidden_states=True)
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self.assertIn("Padding side is set to 'left' but the model is in training mode. For training", logs)
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with self.assertLogs("transformers", level="WARNING") as logs:
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model.padding_side = "right"
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model.eval()
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with torch.no_grad():
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model(**inputs_batched, output_hidden_states=True)
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self.assertIn("Padding side is set to 'right' but the model is in inference mode. For correct", logs)
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