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Merge branch 'main' into main

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Former-commit-id: 154f504fc2cebaae2b58c0121d6d8d8016db1bb2
This commit is contained in:
hoshi-hiyouga
2024-11-02 21:20:27 +08:00
committed by GitHub
parent a7feab45d5 fa9c32bb8d
commit c2ea5fb618
125 changed files with 3063 additions and 1811 deletions
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38
src/llamafactory/data/aligner.py
View File

@@ -16,7 +16,7 @@ import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
@@ -29,45 +29,51 @@ if TYPE_CHECKING:
from .parser import DatasetAttr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _convert_images(
images: Sequence["ImageInput"],
images: Union["ImageInput", Sequence["ImageInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["ImageInput"]]:
r"""
Optionally concatenates image path to dataset dir when loading from local disk.
"""
if len(images) == 0:
if not isinstance(images, list):
images = [images]
elif len(images) == 0:
return None
else:
images = images[:]
images = images[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(images)):
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, images[i])):
images[i] = os.path.join(data_args.dataset_dir, images[i])
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
images[i] = os.path.join(data_args.image_dir, images[i])
return images
def _convert_videos(
videos: Sequence["VideoInput"],
videos: Union["VideoInput", Sequence["VideoInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["VideoInput"]]:
r"""
Optionally concatenates video path to dataset dir when loading from local disk.
"""
if len(videos) == 0:
if not isinstance(videos, list):
videos = [videos]
elif len(videos) == 0:
return None
else:
videos = videos[:]
videos = videos[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(videos)):
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, videos[i])):
videos[i] = os.path.join(data_args.dataset_dir, videos[i])
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
videos[i] = os.path.join(data_args.image_dir, videos[i])
return videos
@@ -161,7 +167,7 @@ def convert_sharegpt(
broken_data = False
for turn_idx, message in enumerate(messages):
if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
logger.warning("Invalid role tag in {}.".format(messages))
logger.warning_rank0(f"Invalid role tag in {messages}.")
broken_data = True
aligned_messages.append(
@@ -171,7 +177,7 @@ def convert_sharegpt(
if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
dataset_attr.ranking and len(aligned_messages) % 2 == 0
):
logger.warning("Invalid message count in {}.".format(messages))
logger.warning_rank0(f"Invalid message count in {messages}.")
broken_data = True
if dataset_attr.kto_tag and isinstance(example[dataset_attr.kto_tag], bool): # kto example
@@ -192,7 +198,7 @@ def convert_sharegpt(
chosen[dataset_attr.role_tag] not in accept_tags[-1]
or rejected[dataset_attr.role_tag] not in accept_tags[-1]
):
logger.warning("Invalid role tag in {}.".format([chosen, rejected]))
logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
broken_data = True
prompt = aligned_messages
@@ -205,7 +211,7 @@ def convert_sharegpt(
response = aligned_messages[-1:]
if broken_data:
logger.warning("Skipping this abnormal example.")
logger.warning_rank0("Skipping this abnormal example.")
prompt, response = [], []
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)

9
src/llamafactory/data/collator.py
View File

@@ -99,6 +99,9 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
features: Dict[str, "torch.Tensor"] = super().__call__(features)
features.update(mm_inputs)
if isinstance(features.get("pixel_values"), list): # for pixtral inputs
features = features.data # use default_collate() instead of BatchEncoding.to()
return features
@@ -137,9 +140,9 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
for key in ("chosen", "rejected"):
for feature in features:
target_feature = {
"input_ids": feature["{}_input_ids".format(key)],
"attention_mask": feature["{}_attention_mask".format(key)],
"labels": feature["{}_labels".format(key)],
"input_ids": feature[f"{key}_input_ids"],
"attention_mask": feature[f"{key}_attention_mask"],
"labels": feature[f"{key}_labels"],
"images": feature["images"],
"videos": feature["videos"],
}

10
src/llamafactory/data/data_utils.py
View File

@@ -17,7 +17,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict
from datasets import DatasetDict, concatenate_datasets, interleave_datasets
from ..extras.logging import get_logger
from ..extras import logging
if TYPE_CHECKING:
@@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
@@ -56,12 +56,12 @@ def merge_dataset(
return all_datasets[0]
elif data_args.mix_strategy == "concat":
if data_args.streaming:
logger.warning("The samples between different datasets will not be mixed in streaming mode.")
logger.warning_once("The samples between different datasets will not be mixed in streaming mode.")
return concatenate_datasets(all_datasets)
elif data_args.mix_strategy.startswith("interleave"):
if not data_args.streaming:
logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
logger.warning_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
return interleave_datasets(
datasets=all_datasets,
@@ -70,7 +70,7 @@ def merge_dataset(
stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
)
else:
raise ValueError("Unknown mixing strategy: {}.".format(data_args.mix_strategy))
raise ValueError(f"Unknown mixing strategy: {data_args.mix_strategy}.")
def split_dataset(

10
src/llamafactory/data/formatter.py
View File

@@ -83,14 +83,14 @@ class StringFormatter(Formatter):
if isinstance(slot, str):
for name, value in kwargs.items():
if not isinstance(value, str):
raise RuntimeError("Expected a string, got {}".format(value))
raise RuntimeError(f"Expected a string, got {value}")
slot = slot.replace("{{" + name + "}}", value, 1)
elements.append(slot)
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@@ -113,7 +113,7 @@ class FunctionFormatter(Formatter):
functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
except json.JSONDecodeError:
functions = []
raise RuntimeError(f"Invalid JSON format in function message: {str([content])}") # flat string
elements = []
for name, arguments in functions:
@@ -124,7 +124,7 @@ class FunctionFormatter(Formatter):
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@@ -141,7 +141,7 @@ class ToolFormatter(Formatter):
tools = json.loads(content)
return [self.tool_utils.tool_formatter(tools) if len(tools) != 0 else ""]
except json.JSONDecodeError:
return [""]
raise RuntimeError(f"Invalid JSON format in tool description: {str([content])}") # flat string
@override
def extract(self, content: str) -> Union[str, List["FunctionCall"]]:

59
src/llamafactory/data/loader.py
View File

@@ -20,8 +20,8 @@ import numpy as np
from datasets import DatasetDict, load_dataset, load_from_disk
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import FILEEXT2TYPE
from ..extras.logging import get_logger
from ..extras.misc import has_tokenized_data
from .aligner import align_dataset
from .data_utils import merge_dataset, split_dataset
@@ -39,7 +39,7 @@ if TYPE_CHECKING:
from .template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _load_single_dataset(
@@ -51,9 +51,9 @@ def _load_single_dataset(
r"""
Loads a single dataset and aligns it to the standard format.
"""
logger.info("Loading dataset {}...".format(dataset_attr))
logger.info_rank0(f"Loading dataset {dataset_attr}...")
data_path, data_name, data_dir, data_files = None, None, None, None
if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
if dataset_attr.load_from in ["hf_hub", "ms_hub", "om_hub"]:
data_path = dataset_attr.dataset_name
data_name = dataset_attr.subset
data_dir = dataset_attr.folder
@@ -69,25 +69,24 @@ def _load_single_dataset(
if os.path.isdir(local_path): # is directory
for file_name in os.listdir(local_path):
data_files.append(os.path.join(local_path, file_name))
if data_path is None:
data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
raise ValueError("File types should be identical.")
elif os.path.isfile(local_path): # is file
data_files.append(local_path)
data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
else:
raise ValueError("File {} not found.".format(local_path))
raise ValueError(f"File {local_path} not found.")
data_path = FILEEXT2TYPE.get(os.path.splitext(data_files[0])[-1][1:], None)
if data_path is None:
raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
if any(data_path != FILEEXT2TYPE.get(os.path.splitext(data_file)[-1][1:], None) for data_file in data_files):
raise ValueError("File types should be identical.")
else:
raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
if dataset_attr.load_from == "ms_hub":
require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
from modelscope import MsDataset
from modelscope.utils.config_ds import MS_DATASETS_CACHE
from modelscope import MsDataset # type: ignore
from modelscope.utils.config_ds import MS_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
dataset = MsDataset.load(
@@ -98,10 +97,27 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.ms_hub_token,
use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
use_streaming=data_args.streaming,
)
if isinstance(dataset, MsDataset):
dataset = dataset.to_hf_dataset()
elif dataset_attr.load_from == "om_hub":
require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
from openmind import OmDataset # type: ignore
from openmind.utils.hub import OM_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
dataset = OmDataset.load_dataset(
path=data_path,
name=data_name,
data_dir=data_dir,
data_files=data_files,
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.om_hub_token,
streaming=data_args.streaming,
)
else:
dataset = load_dataset(
path=data_path,
@@ -111,13 +127,10 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=model_args.cache_dir,
token=model_args.hf_hub_token,
streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
streaming=data_args.streaming,
trust_remote_code=True,
)
if data_args.streaming and (dataset_attr.load_from == "file"): # faster than specifying streaming=True
dataset = dataset.to_iterable_dataset() # TODO: add num shards parameter
if dataset_attr.num_samples is not None and not data_args.streaming:
target_num = dataset_attr.num_samples
indexes = np.random.permutation(len(dataset))[:target_num] # all samples should be included
@@ -128,7 +141,7 @@ def _load_single_dataset(
assert len(indexes) == dataset_attr.num_samples, "Sample num mismatched."
dataset = dataset.select(indexes)
logger.info("Sampled {} examples from dataset {}.".format(dataset_attr.num_samples, dataset_attr))
logger.info_rank0(f"Sampled {dataset_attr.num_samples} examples from dataset {dataset_attr}.")
if data_args.max_samples is not None: # truncate dataset
max_samples = min(data_args.max_samples, len(dataset))
@@ -224,9 +237,9 @@ def get_dataset(
# Load tokenized dataset
if data_args.tokenized_path is not None:
if has_tokenized_data(data_args.tokenized_path):
logger.warning("Loading dataset from disk will ignore other data arguments.")
logger.warning_rank0("Loading dataset from disk will ignore other data arguments.")
dataset_dict: "DatasetDict" = load_from_disk(data_args.tokenized_path)
logger.info("Loaded tokenized dataset from {}.".format(data_args.tokenized_path))
logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
dataset_module: Dict[str, "Dataset"] = {}
if "train" in dataset_dict:
@@ -277,8 +290,8 @@ def get_dataset(
if data_args.tokenized_path is not None:
if training_args.should_save:
dataset_dict.save_to_disk(data_args.tokenized_path)
logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
logger.info_rank0(f"Tokenized dataset saved at {data_args.tokenized_path}.")
logger.info_rank0(f"Please restart the training with `tokenized_path: {data_args.tokenized_path}`.")
sys.exit(0)

285
src/llamafactory/data/mm_plugin.py
View File

@@ -4,6 +4,7 @@ from io import BytesIO
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, TypedDict, Union
import numpy as np
from transformers.image_utils import get_image_size, to_numpy_array
from typing_extensions import override
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@@ -110,7 +111,7 @@ class BasePlugin:
image = Image.open(image["path"])
if not isinstance(image, ImageObject):
raise ValueError("Expect input is a list of Images, but got {}.".format(type(image)))
raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
results.append(self._preprocess_image(image, **kwargs))
@@ -157,6 +158,7 @@ class BasePlugin:
It holds num_patches == torch.prod(image_grid_thw)
"""
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
input_dict = {"images": None} # default key
if len(images) != 0:
images = self._regularize_images(
@@ -174,10 +176,16 @@ class BasePlugin:
)
input_dict["videos"] = videos
if input_dict.get("images", None) is not None or input_dict.get("videos", None) is not None:
return image_processor(**input_dict, return_tensors="pt")
else:
return {}
mm_inputs = {}
if image_processor != video_processor:
if input_dict.get("images") is not None:
mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
if input_dict.get("videos") is not None:
mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
elif input_dict.get("images") is not None or input_dict.get("videos") is not None: # same processor (qwen2-vl)
mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
return mm_inputs
def process_messages(
self,
@@ -218,6 +226,14 @@ class BasePlugin:
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
r"""
Builds batched multimodal inputs for VLMs.
Arguments:
images: a list of image inputs, shape (num_images,)
videos: a list of video inputs, shape (num_videos,)
imglens: number of images in each sample, shape (batch_size,)
vidlens: number of videos in each sample, shape (batch_size,)
seqlens: number of tokens in each sample, shape (batch_size,)
processor: a processor for pre-processing images and videos
"""
self._validate_input(images, videos)
return {}
@@ -245,7 +261,123 @@ class LlavaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", self.image_token * image_seqlen)
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class LlavaNextPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "image_sizes" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
if "pixel_values" in mm_inputs:
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
res = self._get_mm_inputs(images, videos, processor)
return res
class LlavaNextVideoPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "pixel_values" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if "pixel_values_videos" in mm_inputs:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size)
video_seqlen = image_seqlen // 4 * num_frames # divide by 4 needed for avg pooling layer
for message in messages:
content = message["content"]
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@@ -284,7 +416,7 @@ class PaliGemmaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", "")
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@@ -324,6 +456,68 @@ class PaliGemmaPlugin(BasePlugin):
return mm_inputs
class PixtralPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
patch_size = getattr(processor, "patch_size")
image_token = getattr(processor, "image_token")
image_break_token = getattr(processor, "image_break_token")
image_end_token = getattr(processor, "image_end_token")
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
image_input_sizes = mm_inputs.get("image_sizes", None)
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if image_input_sizes is None:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
image_size = image_input_sizes[0][num_image_tokens]
height, width = image_size
num_height_tokens = height // patch_size
num_width_tokens = width // patch_size
replace_tokens = [[image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
replace_tokens = [item for sublist in replace_tokens for item in sublist] # flatten list
replace_tokens[-1] = image_end_token
replace_str = "".join(replace_tokens)
content = content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
num_image_tokens += 1
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if mm_inputs.get("pixel_values"):
mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
mm_inputs.pop("image_sizes", None)
return mm_inputs
class Qwen2vlPlugin(BasePlugin):
@override
def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
@@ -369,7 +563,7 @@ class Qwen2vlPlugin(BasePlugin):
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if num_image_tokens >= len(image_grid_thw):
raise ValueError("`len(images)` is less than the number of {} tokens.".format(IMAGE_PLACEHOLDER))
raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
content = content.replace(
IMAGE_PLACEHOLDER,
@@ -382,7 +576,7 @@ class Qwen2vlPlugin(BasePlugin):
while VIDEO_PLACEHOLDER in content:
if num_video_tokens >= len(video_grid_thw):
raise ValueError("`len(videos)` is less than the number of {} tokens.".format(VIDEO_PLACEHOLDER))
raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
content = content.replace(
VIDEO_PLACEHOLDER,
@@ -396,10 +590,73 @@ class Qwen2vlPlugin(BasePlugin):
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError("The number of videos does not match the number of {} tokens".format(VIDEO_PLACEHOLDER))
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class VideoLlavaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
num_frames = 0
exist_images = "pixel_values_images" in mm_inputs
exist_videos = "pixel_values_videos" in mm_inputs
if exist_videos or exist_images:
if exist_images:
height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
num_frames = 1
if exist_videos:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size) + 1
video_seqlen = image_seqlen * num_frames
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
for message in messages:
content = message["content"]
while self.image_token in content:
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}", 1)
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
content = content.replace("{{image}}", self.image_token * image_seqlen)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {self.image_token} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {self.video_token} tokens")
return messages
@@ -420,8 +677,12 @@ class Qwen2vlPlugin(BasePlugin):
PLUGINS = {
"base": BasePlugin,
"llava": LlavaPlugin,
"llava_next": LlavaNextPlugin,
"llava_next_video": LlavaNextVideoPlugin,
"paligemma": PaliGemmaPlugin,
"pixtral": PixtralPlugin,
"qwen2_vl": Qwen2vlPlugin,
"video_llava": VideoLlavaPlugin,
}
@@ -432,6 +693,6 @@ def get_mm_plugin(
) -> "BasePlugin":
plugin_class = PLUGINS.get(name, None)
if plugin_class is None:
raise ValueError("Multimodal plugin `{}` not found.".format(name))
raise ValueError(f"Multimodal plugin `{name}` not found.")
return plugin_class(image_token, video_token)

24
src/llamafactory/data/parser.py
View File

@@ -20,7 +20,7 @@ from typing import Any, Dict, List, Literal, Optional, Sequence
from transformers.utils import cached_file
from ..extras.constants import DATA_CONFIG
from ..extras.misc import use_modelscope
from ..extras.misc import use_modelscope, use_openmind
@dataclass
@@ -30,7 +30,7 @@ class DatasetAttr:
"""
# basic configs
load_from: Literal["hf_hub", "ms_hub", "script", "file"]
load_from: Literal["hf_hub", "ms_hub", "om_hub", "script", "file"]
dataset_name: str
formatting: Literal["alpaca", "sharegpt"] = "alpaca"
ranking: bool = False
@@ -87,31 +87,39 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
config_path = os.path.join(dataset_dir, DATA_CONFIG)
try:
with open(config_path, "r") as f:
with open(config_path) as f:
dataset_info = json.load(f)
except Exception as err:
if len(dataset_names) != 0:
raise ValueError("Cannot open {} due to {}.".format(config_path, str(err)))
raise ValueError(f"Cannot open {config_path} due to {str(err)}.")
dataset_info = None
dataset_list: List["DatasetAttr"] = []
for name in dataset_names:
if dataset_info is None: # dataset_dir is ONLINE
load_from = "ms_hub" if use_modelscope() else "hf_hub"
if use_modelscope():
load_from = "ms_hub"
elif use_openmind():
load_from = "om_hub"
else:
load_from = "hf_hub"
dataset_attr = DatasetAttr(load_from, dataset_name=name)
dataset_list.append(dataset_attr)
continue
if name not in dataset_info:
raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
raise ValueError(f"Undefined dataset {name} in {DATA_CONFIG}.")
has_hf_url = "hf_hub_url" in dataset_info[name]
has_ms_url = "ms_hub_url" in dataset_info[name]
has_om_url = "om_hub_url" in dataset_info[name]
if has_hf_url or has_ms_url:
if (use_modelscope() and has_ms_url) or (not has_hf_url):
if has_hf_url or has_ms_url or has_om_url:
if has_ms_url and (use_modelscope() or not has_hf_url):
dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
elif has_om_url and (use_openmind() or not has_hf_url):
dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
else:
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
elif "script_url" in dataset_info[name]:

10
src/llamafactory/data/processors/feedback.py
View File

@@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_feedback_example(
@@ -94,7 +94,9 @@ def preprocess_feedback_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
@@ -123,6 +125,6 @@ def preprocess_feedback_dataset(
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
if desirable_num == 0 or undesirable_num == 0:
logger.warning("Your dataset only has one preference type.")
logger.warning_rank0("Your dataset only has one preference type.")
return model_inputs

12
src/llamafactory/data/processors/pairwise.py
View File

@@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_pairwise_example(
@@ -77,7 +77,9 @@ def preprocess_pairwise_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
@@ -110,8 +112,8 @@ def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "Pr
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
print(f"chosen_labels:\n{tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
print(f"rejected_labels:\n{tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")

16
src/llamafactory/data/processors/supervised.py
View File

@@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import greedy_knapsack, infer_seqlen
@@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_supervised_example(
@@ -99,7 +99,9 @@ def preprocess_supervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@@ -141,7 +143,9 @@ def preprocess_packed_supervised_dataset(
length2indexes = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@@ -160,7 +164,7 @@ def preprocess_packed_supervised_dataset(
)
length = len(input_ids)
if length > data_args.cutoff_len:
logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
logger.warning_rank0(f"Dropped lengthy example with length {length} > {data_args.cutoff_len}.")
else:
lengths.append(length)
length2indexes[length].append(valid_num)
@@ -212,4 +216,4 @@ def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))
print(f"labels:\n{tokenizer.decode(valid_labels, skip_special_tokens=False)}")

8
src/llamafactory/data/processors/unsupervised.py
View File

@@ -15,7 +15,7 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.logging import get_logger
from ...extras import logging
from ..data_utils import Role
from .processor_utils import infer_seqlen
@@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_unsupervised_example(
@@ -71,7 +71,9 @@ def preprocess_unsupervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_unsupervised_example(

183
src/llamafactory/data/template.py
View File

@@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
from transformers.utils.versions import require_version
from typing_extensions import override
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
from .mm_plugin import get_mm_plugin
@@ -32,7 +32,7 @@ if TYPE_CHECKING:
from .mm_plugin import BasePlugin
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@dataclass
@@ -49,6 +49,7 @@ class Template:
stop_words: List[str]
efficient_eos: bool
replace_eos: bool
replace_jinja_template: bool
mm_plugin: "BasePlugin"
def encode_oneturn(
@@ -146,7 +147,7 @@ class Template:
elif "eos_token" in elem and tokenizer.eos_token_id is not None:
token_ids += [tokenizer.eos_token_id]
else:
raise ValueError("Input must be string, set[str] or dict[str, str], got {}".format(type(elem)))
raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")
return token_ids
@@ -214,6 +215,7 @@ def _register_template(
stop_words: Sequence[str] = [],
efficient_eos: bool = False,
replace_eos: bool = False,
replace_jinja_template: bool = True,
mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
) -> None:
r"""
@@ -263,6 +265,7 @@ def _register_template(
stop_words=stop_words,
efficient_eos=efficient_eos,
replace_eos=replace_eos,
replace_jinja_template=replace_jinja_template,
mm_plugin=mm_plugin,
)
@@ -272,12 +275,12 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})
if is_added:
logger.info("Add eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Add eos token: {tokenizer.eos_token}")
else:
logger.info("Replace eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}")
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
def _jinja_escape(content: str) -> str:
@@ -353,24 +356,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
r"""
Gets chat template and fixes the tokenizer.
"""
if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
require_version(
"transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
)
require_version("accelerate>=0.34.0", "To fix: pip install accelerate>=0.34.0")
if data_args.template is None:
template = TEMPLATES["empty"] # placeholder
else:
template = TEMPLATES.get(data_args.template, None)
if template is None:
raise ValueError("Template {} does not exist.".format(data_args.template))
raise ValueError(f"Template {data_args.template} does not exist.")
if template.mm_plugin.__class__.__name__ != "BasePlugin":
require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
if data_args.train_on_prompt and template.efficient_eos:
raise ValueError("Current template does not support `train_on_prompt`.")
if data_args.tool_format is not None:
logger.info("Using tool format: {}.".format(data_args.tool_format))
logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
eos_slots = [] if template.efficient_eos else [{"eos_token"}]
template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
@@ -388,20 +388,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
logger.info("Add pad token: {}".format(tokenizer.pad_token))
logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")
if stop_words:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
)
logger.info("Add {} to stop words.".format(",".join(stop_words)))
logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError:
logger.info("Cannot add this chat template to tokenizer.")
if tokenizer.chat_template is None or template.replace_jinja_template:
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError as e:
logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
return template
@@ -640,6 +641,14 @@ _register_template(
)
_register_template(
name="exaone",
format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
)
_register_template(
name="falcon",
format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
@@ -664,6 +673,7 @@ _register_template(
format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
efficient_eos=True,
replace_jinja_template=False,
)
@@ -681,6 +691,14 @@ _register_template(
)
_register_template(
name="index",
format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
format_system=StringFormatter(slots=["<unk>{{content}}"]),
efficient_eos=True,
)
_register_template(
name="intern",
format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
@@ -740,6 +758,7 @@ _register_template(
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
)
@@ -754,6 +773,107 @@ _register_template(
)
_register_template(
name="llava_next",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_llama3",
format_user=StringFormatter(
slots=[
(
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
format_observation=StringFormatter(
slots=[
(
"<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_video",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
@@ -831,6 +951,14 @@ _register_template(
replace_eos=True,
)
_register_template(
name="pixtral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
)
_register_template(
name="qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
@@ -840,6 +968,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
)
@@ -852,6 +981,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
)
@@ -907,6 +1037,17 @@ _register_template(
)
_register_template(
name="video_llava",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
)
_register_template(
name="xuanyuan",
format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),

2
src/llamafactory/data/tool_utils.py
View File

@@ -177,6 +177,6 @@ TOOLS = {
def get_tool_utils(name: str) -> "ToolUtils":
tool_utils = TOOLS.get(name, None)
if tool_utils is None:
raise ValueError("Tool utils `{}` not found.".format(name))
raise ValueError(f"Tool utils `{name}` not found.")
return tool_utils