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Fix Llava chat template examples (#30130)

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This commit is contained in:
lewtun
2024-04-11 10:38:24 +02:00
committed by GitHub
parent b752ad3019
commit fbdb978eb5
3 changed files with 13 additions and 12 deletions
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2
docs/source/en/model_doc/llava.md
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@@ -49,7 +49,7 @@ The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/
For multiple turns conversation:
```bash
"USER: <image>\n<prompt1>ASSISTANT: <answer1>USER: <prompt2>ASSISTANT: <answer2>USER: <prompt3>ASSISTANT:"
"USER: <image>\n<prompt1> ASSISTANT: <answer1></s>USER: <prompt2> ASSISTANT: <answer2></s>USER: <prompt3> ASSISTANT:"
```
### Using Flash Attention 2

7
src/transformers/models/llava/modeling_llava.py
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@@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Llava model."""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
@@ -388,16 +389,16 @@ class LlavaForConditionalGeneration(LlavaPreTrainedModel):
>>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
>>> prompt = "<image>\nUSER: What's the content of the image?\nASSISTANT:"
>>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_length=30)
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"\nUSER: What's the content of the image?\nASSISTANT: The image features a stop sign on a street corner"
"USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

10
tests/models/llava/test_modeling_llava.py
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@@ -398,13 +398,13 @@ class LlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", load_in_4bit=True)
processor = AutoProcessor.from_pretrained(model_id)
prompt = "USER: <image>\nWhat are the things I should be cautious about when I visit this place?\nASSISTANT:"
prompt = "USER: <image>\nWhat are the things I should be cautious about when I visit this place? ASSISTANT:"
image_file = "https://llava-vl.github.io/static/images/view.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors="pt").to(torch_device, torch.float16)
output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
EXPECTED_DECODED_TEXT = "USER: \nWhat are the things I should be cautious about when I visit this place?\nASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, there are a few things to be cautious about. First, be aware of the weather conditions, as sudden changes in weather can make the pier unsafe to walk on. Second, be mindful of the water depth and any potential hazards, such as submerged rocks or debris, that could cause accidents or injuries. Additionally, be cautious of the presence of wildlife, such as birds or fish, and avoid disturbing their natural habitats. Lastly, be aware of any local regulations or guidelines for the use of the pier, as some areas may be restricted or prohibited for certain activities." # fmt: skip
EXPECTED_DECODED_TEXT = "USER: \nWhat are the things I should be cautious about when I visit this place? ASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, there are a few things to be cautious about. First, be aware of the weather conditions, as sudden changes in weather can make the pier unsafe to walk on. Second, be mindful of the water depth and any potential hazards, such as submerged rocks or debris, that could cause accidents or injuries. Additionally, be cautious of the tides and currents, as they can change rapidly and pose a risk to swimmers or those who venture too close to the edge of the pier. Finally, be respectful of the environment and other visitors, and follow any posted rules or guidelines for the area." # fmt: skip
self.assertEqual(
processor.decode(output[0], skip_special_tokens=True),
@@ -421,8 +421,8 @@ class LlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
processor = AutoProcessor.from_pretrained(model_id)
prompts = [
"USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT:",
"USER: <image>\nWhat is this?\nASSISTANT:",
"USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me? ASSISTANT:",
"USER: <image>\nWhat is this? ASSISTANT:",
]
image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
@@ -431,7 +431,7 @@ class LlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ['USER: \nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT: When visiting this place, which appears to be a dock or pier extending over a body of water', 'USER: \nWhat is this?\nASSISTANT: The image features two cats lying down on a pink couch. One cat is located on'] # fmt: skip
EXPECTED_DECODED_TEXT = ['USER: \nWhat are the things I should be cautious about when I visit this place? What should I bring with me? ASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, you', 'USER: \nWhat is this? ASSISTANT: The image features two cats lying down on a pink couch. One cat is located on'] # fmt: skip
self.assertEqual(processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)