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Fxed other device issues and more expectations
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@ -2228,7 +2228,12 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel, GenerationMixin):
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# otherwise we expand manually by concatenating
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if getattr(self.config, "image_token_id", None) is not None:
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special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1).expand_as(inputs_embeds)
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inputs_embeds = inputs_embeds.to(language_model_inputs.device)
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special_image_mask = special_image_mask.to(language_model_inputs.device)
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inputs_embeds[special_image_mask] = language_model_inputs.flatten()
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attention_mask = attention_mask.to(language_attention_mask.device)
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else:
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logger.warning_once(
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"Expanding inputs for image tokens in BLIP-2 should be done in processing. "
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@ -1786,7 +1786,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
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generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
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# Test output
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self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
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expected_ids = [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118]
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self.assertEqual(predictions[0].tolist(), [50265] * 32 + expected_ids) # 50265 is the img token id
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self.assertEqual("a woman sitting on the beach with a dog", generated_text)
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# image and context
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@ -1797,10 +1798,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
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generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
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# Test output
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self.assertEqual(
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predictions[0].tolist(),
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[2, 45641, 35, 61, 343, 16, 42, 116, 31652, 35, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
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)
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expected_ids = [2, 45641, 35, 61, 343, 16, 42, 116, 31652, 35, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118]
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self.assertEqual(predictions[0].tolist(), [50265] * 32 + expected_ids) # 50265 is the img token id
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self.assertEqual(generated_text, "Question: which city is this? Answer: it's not a city, it's a beach")
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@require_torch_multi_accelerator
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@ -1826,8 +1825,18 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
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generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
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# Test output
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self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
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self.assertEqual("woman playing with dog on the beach", generated_text)
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expected_ids_and_text = Expectations({
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("cuda", None): (
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[0, 2335, 1556, 28, 1782, 30, 8, 2608, 1],
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"woman playing with dog on the beach"
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),
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("rocm", (9, 5)): (
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[0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1],
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"a woman is playing with her dog on the beach"
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)
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}).get_expectation()
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self.assertEqual(predictions[0].tolist(), expected_ids_and_text[0])
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self.assertEqual(generated_text, expected_ids_and_text[1])
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# image and context
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prompt = "Question: which city is this? Answer:"
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@ -1837,11 +1846,18 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
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generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
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# Test output
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self.assertEqual(
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predictions[0].tolist(),
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[0, 3, 7, 152, 67, 839, 1],
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)
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self.assertEqual(generated_text, "san diego")
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expected_ids_and_text = Expectations({
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("cuda", None): (
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[0, 3, 7, 152, 67, 839, 1],
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"san diego"
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),
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("rocm", (9, 5)): (
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[0, 3, 7, 152, 2515, 11389, 3523, 1],
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"san francisco" # TODO: check if this is ok
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)
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}).get_expectation()
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self.assertEqual(predictions[0].tolist(), expected_ids_and_text[0])
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self.assertEqual(generated_text, expected_ids_and_text[1])
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def test_expansion_in_processing(self):
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processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
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