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Fix qwen2_5_vl
tests (#38845)
* fix * breakpoint() * breakpoint() * update * update * update * update * update * update --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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@ -14,7 +14,6 @@
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"""Testing suite for the PyTorch Qwen2.5-VL model."""
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import copy
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import gc
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import tempfile
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import unittest
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@ -29,7 +28,7 @@ from transformers import (
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is_vision_available,
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)
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from transformers.testing_utils import (
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backend_empty_cache,
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cleanup,
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is_flaky,
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require_cv2,
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require_flash_attn,
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@ -408,9 +407,10 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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url = "https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg"
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self.image = Image.open(requests.get(url, stream=True).raw)
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cleanup(torch_device, gc_collect=True)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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cleanup(torch_device, gc_collect=True)
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@slow
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def test_small_model_integration_test(self):
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@ -422,7 +422,7 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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inputs = self.processor(text=[text], images=[self.image], return_tensors="pt")
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expected_input_ids = [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655] # fmt: skip
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assert torch.allclose(expected_input_ids, inputs.input_ids[0].tolist()[:17], atol=3e-3)
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torch.testing.assert_close(expected_input_ids, inputs.input_ids[0].tolist()[:17])
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expected_pixel_slice = torch.tensor(
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[
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@ -436,13 +436,13 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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dtype=torch.float32,
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device="cpu",
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)
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assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=3e-3)
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torch.testing.assert_close(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=5e-4, rtol=1e-5)
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# verify generation
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inputs = inputs.to(torch_device)
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output = model.generate(**inputs, max_new_tokens=30)
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EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets"
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EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in"
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self.assertEqual(
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self.processor.decode(output[0], skip_special_tokens=True),
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@ -463,9 +463,10 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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output = model.generate(**inputs, max_new_tokens=30)
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EXPECTED_DECODED_TEXT = [
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets'
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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] # fmt: skip
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self.assertEqual(
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self.processor.batch_decode(output, skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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@ -482,10 +483,11 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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output = model.generate(**inputs, max_new_tokens=30, num_return_sequences=3)
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EXPECTED_DECODED_TEXT = [
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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] # fmt: skip
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self.assertEqual(
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self.processor.batch_decode(output, skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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@ -510,9 +512,10 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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output = model.generate(**inputs, max_new_tokens=30)
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EXPECTED_DECODED_TEXT = [
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets',
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'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am Qwen, a large language model created by Alibaba Cloud. I am designed to assist with various tasks and answer questions to the best of my'
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'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in',
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'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\n addCriterion',
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] # fmt: skip
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self.assertEqual(
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self.processor.batch_decode(output, skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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@ -537,9 +540,10 @@ class Qwen2_5_VLIntegrationTest(unittest.TestCase):
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output = model.generate(**inputs, max_new_tokens=30)
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EXPECTED_DECODED_TEXT = [
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"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets",
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"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets",
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"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in",
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"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\n addCriterion\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and gentle nature, which is",
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]
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self.assertEqual(
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self.processor.batch_decode(output, skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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