Skip to content

fix template #1321

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jul 8, 2024
Merged

fix template #1321

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
49 changes: 25 additions & 24 deletions swift/llm/utils/template.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -929,7 +929,7 @@ def _load_image(img_path: Union[str, 'PIL.Image.Image']) -> 'PIL.Image.Image':
return image


def _load_video(video_path: str) -> np.ndarray:
def _load_video_llava(video_path: str) -> np.ndarray:
import av
container = av.open(video_path)
total_frames = container.streams.video[0].frames
Expand Down Expand Up @@ -1036,7 +1036,7 @@ def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any
if idx_list:
idx = idx_list[0]
images_path = example.get('images') or []
image = _load_image(images_path[0])
image = _read_batch(images_path)[0]
placeholder = '<|begin_of_image|><|endoftext|><|end_of_image|>'
placeholder_id = self.tokenizer.encode(placeholder, add_special_tokens=False)
input_ids = (input_ids[:idx] + placeholder_id + input_ids[idx + 1:])
Expand Down Expand Up @@ -1627,7 +1627,7 @@ def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any
else:
videos_path.append(media_file)
if len(videos_path) > 0:
videos = _read_batch(videos_path, _load_video)
videos = _read_batch(videos_path, _load_video_llava)
video_processor = self.tokenizer.processor.video_processor
video_inputs = video_processor(videos, return_tensors='pt').to(self.model.dtype)
inputs['pixel_values_videos'] = video_inputs['pixel_values_videos']
Expand Down Expand Up @@ -1766,9 +1766,9 @@ def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any
if len(inputs) == 0:
return inputs, {}
image_path = example.get('images') or []
if image_path:
raw_image = _load_image(image_path[0])
pixel_values = self.tokenizer.processor.image_processor(raw_image, return_tensors='pt')['pixel_values']
raw_image = _read_batch(image_path)
if raw_image:
pixel_values = self.tokenizer.processor.image_processor(raw_image[0], return_tensors='pt')['pixel_values']
inputs['pixel_values'] = pixel_values.to(self.model.dtype)
return inputs, {}

Expand Down Expand Up @@ -1806,9 +1806,9 @@ def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any
inputs['token_type_ids'] = [0] * n + [1] * n2
else:
inputs['token_type_ids'] = [0] * len(inputs['input_ids'])
if image_path:
raw_image = _load_image(image_path[0])
model_inputs = processor(text=example['query'], images=raw_image, return_tensors='pt')
raw_image = _read_batch(image_path)
if raw_image:
model_inputs = processor(text=example['query'], images=raw_image[0], return_tensors='pt')
inputs['pixel_values'] = model_inputs['pixel_values']
return inputs, {}

Expand Down Expand Up @@ -1949,9 +1949,9 @@ def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any
example['query'], example['history'], images_path = replace_img_tag(example['query'], history,
'<image_placeholder>')
inputs, _ = super().encode(example)
images_path.extend(example.get('images') or [])
if len(inputs) == 0:
return inputs, {}
images_path.extend(example.get('images') or [])
images = _read_batch(images_path)
processor = self.tokenizer.processor
input_ids, labels = inputs['input_ids'], inputs['labels']
Expand Down Expand Up @@ -2024,34 +2024,35 @@ def replace_tag(self, media_type: Literal['image', 'video', 'audio'], index, exa

def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
inputs, _ = super().encode(example)
images_path = example.get('images') or []
image = _load_image(images_path[0]) if len(images_path) >= 1 else []
if len(inputs) == 0:
return inputs, {}
images_path = example.get('images') or []
image = _read_batch(images_path)
inputs.pop('loss_scale', None)
model = self.model
inputs2 = model.build_conversation_input_ids(
self.tokenizer, query=example['query'], history=example.get('history'), images=[image])
image_token_len = inputs2['token_type_ids'].sum()
self.tokenizer, query=example['query'], history=example.get('history'), images=image)
image_token_len = inputs2['token_type_ids'].sum().item()
input_ids = inputs['input_ids']
labels = inputs['labels']
inputs['token_type_ids'] = [0] + [1] * image_token_len + [0] * len(input_ids[1:])
inputs['input_ids'] = input_ids[:1] + [self.tokenizer.pad_token_id] * image_token_len + input_ids[1:]
if labels is not None:
inputs['labels'] = labels[:1] + [-100] * image_token_len + labels[1:]
dtype = model.dtype
inputs['images'] = [[img.to(dtype=dtype)] for img in inputs2['images']]
if 'cross_images' in inputs2:
# is cogagent
inputs['cross_images'] = [[cross_img.to(dtype=dtype)] for cross_img in inputs2['cross_images']]
if len(image) > 0:
dtype = model.dtype
inputs['images'] = [[img.to(dtype=dtype)] for img in inputs2['images']]
if 'cross_images' in inputs2:
# is cogagent
inputs['cross_images'] = [[cross_img.to(dtype=dtype)] for cross_img in inputs2['cross_images']]
return inputs, {}

def data_collator(self, batch: List[Dict[str, Any]], padding_to: Optional[int] = None) -> Dict[str, Any]:
res = super().data_collator(batch, padding_to)
is_cogagent = 'cross_images' in batch[0]
keys = ['images', 'cross_images'] if is_cogagent else ['images']
keys = ['images', 'cross_images']
for key in keys:
res[key] = [b[key][0] for b in batch]
if key in batch[0]:
res[key] = [b[key][0] for b in batch]
token_type_ids = [torch.tensor(b['token_type_ids']) for b in batch]
token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=0)
res['token_type_ids'] = token_type_ids
Expand Down Expand Up @@ -2107,10 +2108,10 @@ def check_example(self, example):

def encode(self, example: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
inputs, _ = super().encode(example)
images_path = example['images']
image = _load_image(images_path[0])
if len(inputs) == 0:
return inputs, {}
images_path = example['images']
image = _load_image(images_path[0])
input_ids = inputs['input_ids']
labels = inputs['labels']
idx_list = _findall(input_ids, -1)
Expand Down
Loading