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Fix Aria tests (#37444)

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* update aria tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* add cuda tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check outputs for cpu and cuda and xpu

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check outputs for cpu and cuda and xpu

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check outputs for cpu and cuda and xpu

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check output for each device

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix style

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix style

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix xpu output

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* add comments and use assert list equal

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* rm pad token assign

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
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jiqing-feng 2025-04-24 16:51:29 +08:00 committed by GitHub
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commit b7f7aa78a0
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1 changed files with 44 additions and 16 deletions
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tests/models/aria/test_modeling_aria.py
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@ -420,8 +420,11 @@ class AriaForConditionalGenerationIntegrationTest(unittest.TestCase):
@require_vision
@require_bitsandbytes
def test_batched_generation(self):
model = AriaForConditionalGeneration.from_pretrained("rhymes-ai/Aria", load_in_4bit=True)
# Skip multihead_attn for 4bit because MHA will read the original weight without dequantize.
# See https://github.com/huggingface/transformers/pull/37444#discussion_r2045852538.
model = AriaForConditionalGeneration.from_pretrained(
"rhymes-ai/Aria", load_in_4bit=True, llm_int8_skip_modules=["multihead_attn"]
)
processor = AutoProcessor.from_pretrained("rhymes-ai/Aria")
prompt1 = "<image>\n<image>\nUSER: What's the difference of two images?\nASSISTANT:"
@ -432,24 +435,49 @@ class AriaForConditionalGenerationIntegrationTest(unittest.TestCase):
image1 = Image.open(requests.get(url1, stream=True).raw)
image2 = Image.open(requests.get(url2, stream=True).raw)
inputs = processor(
images=[image1, image2, image1, image2],
text=[prompt1, prompt2, prompt3],
return_tensors="pt",
padding=True,
).to(torch_device)
model = model.eval()
EXPECTED_OUTPUT = [
"\n \nUSER: What's the difference of two images?\nASSISTANT: The difference between the two images is that one shows a dog standing on a grassy field, while",
"\nUSER: Describe the image.\nASSISTANT: The image features a brown and white dog sitting on a sidewalk. The dog is holding a small",
"\nUSER: Describe the image.\nASSISTANT: The image features a lone llama standing on a grassy hill. The llama is the",
# Create inputs
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": prompt1},
{"type": "image"},
{"type": "text", "text": prompt2},
],
},
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": prompt3},
],
},
]
prompts = [processor.apply_chat_template([message], add_generation_prompt=True) for message in messages]
images = [[image1, image2], [image2]]
inputs = processor(text=prompts, images=images, padding=True, return_tensors="pt").to(
device=model.device, dtype=model.dtype
)
EXPECTED_OUTPUT = {
"cpu": [
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n <image>\n USER: What's the difference of two images?\n ASSISTANT:<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The first image features a cute, light-colored puppy sitting on a paved surface with",
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The image shows a young alpaca standing on a grassy hill. The alpaca has",
], # cpu output
"cuda": [
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n <image>\n USER: What's the difference of two images?\n ASSISTANT:<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The first image features a cute, light-colored puppy sitting on a paved surface with",
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The image shows a young alpaca standing on a patch of ground with some dry grass. The",
], # cuda output
"xpu": [
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n <image>\n USER: What's the difference of two images?\n ASSISTANT:<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The first image features a cute, light-colored puppy sitting on a paved surface with",
"<|im_start|>user\n<fim_prefix><fim_suffix> <image>\n USER: Describe the image.\n ASSISTANT:<|im_end|>\n <|im_start|>assistant\n The image shows a young alpaca standing on a grassy hill. The alpaca has",
], # xpu output
}
generate_ids = model.generate(**inputs, max_new_tokens=20)
outputs = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
self.assertEqual(outputs, EXPECTED_OUTPUT)
self.assertListEqual(outputs, EXPECTED_OUTPUT[model.device.type])
def test_tokenizer_integration(self):
model_id = "rhymes-ai/Aria"