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support rank0 logger

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Former-commit-id: c38aa29336f286266553da4909a7267d7ef21f37
This commit is contained in:
hiyouga 2024-11-02 18:31:04 +08:00
parent 4b2c47fcae
commit e83cb17f97
42 changed files with 316 additions and 252 deletions
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1
.env.local
View File

@ -5,6 +5,7 @@ API_PORT=
API_KEY=
API_MODEL_NAME=
FASTAPI_ROOT_PATH=
MAX_CONCURRENT=
# general
DISABLE_VERSION_CHECK=
FORCE_CHECK_IMPORTS=

10
src/llamafactory/api/app.py
View File

@ -68,7 +68,7 @@ async def lifespan(app: "FastAPI", chat_model: "ChatModel"): # collects GPU mem
def create_app(chat_model: "ChatModel") -> "FastAPI":
root_path = os.environ.get("FASTAPI_ROOT_PATH", "")
root_path = os.getenv("FASTAPI_ROOT_PATH", "")
app = FastAPI(lifespan=partial(lifespan, chat_model=chat_model), root_path=root_path)
app.add_middleware(
CORSMiddleware,
@ -77,7 +77,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
allow_methods=["*"],
allow_headers=["*"],
)
api_key = os.environ.get("API_KEY", None)
api_key = os.getenv("API_KEY")
security = HTTPBearer(auto_error=False)
async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
@ -91,7 +91,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
dependencies=[Depends(verify_api_key)],
)
async def list_models():
model_card = ModelCard(id=os.environ.get("API_MODEL_NAME", "gpt-3.5-turbo"))
model_card = ModelCard(id=os.getenv("API_MODEL_NAME", "gpt-3.5-turbo"))
return ModelList(data=[model_card])
@app.post(
@ -128,7 +128,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
def run_api() -> None:
chat_model = ChatModel()
app = create_app(chat_model)
api_host = os.environ.get("API_HOST", "0.0.0.0")
api_port = int(os.environ.get("API_PORT", "8000"))
api_host = os.getenv("API_HOST", "0.0.0.0")
api_port = int(os.getenv("API_PORT", "8000"))
print(f"Visit http://localhost:{api_port}/docs for API document.")
uvicorn.run(app, host=api_host, port=api_port)

6
src/llamafactory/api/chat.py
View File

@ -21,7 +21,7 @@ import uuid
from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
from ..data import Role as DataRole
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
from .common import dictify, jsonify
from .protocol import (
@ -57,7 +57,7 @@ if TYPE_CHECKING:
from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
ROLE_MAPPING = {
Role.USER: DataRole.USER.value,
Role.ASSISTANT: DataRole.ASSISTANT.value,
@ -70,7 +70,7 @@ ROLE_MAPPING = {
def _process_request(
request: "ChatCompletionRequest",
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional[List["ImageInput"]]]:
logger.info(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
logger.info_rank0(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
if len(request.messages) == 0:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")

10
src/llamafactory/chat/hf_engine.py
View File

@ -23,8 +23,8 @@ from transformers import GenerationConfig, TextIteratorStreamer
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_logits_processor
from ..model import load_model, load_tokenizer
from .base_engine import BaseEngine, Response
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class HuggingfaceEngine(BaseEngine):
@ -63,11 +63,11 @@ class HuggingfaceEngine(BaseEngine):
try:
asyncio.get_event_loop()
except RuntimeError:
logger.warning("There is no current event loop, creating a new one.")
logger.warning_once("There is no current event loop, creating a new one.")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
@staticmethod
def _process_args(
@ -119,7 +119,7 @@ class HuggingfaceEngine(BaseEngine):
stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
if stop is not None:
logger.warning("Stop parameter is not supported by the huggingface engine yet.")
logger.warning_rank0("Stop parameter is not supported by the huggingface engine yet.")
generating_args = generating_args.copy()
generating_args.update(

6
src/llamafactory/chat/vllm_engine.py
View File

@ -18,8 +18,8 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_device_count
from ..extras.packages import is_pillow_available, is_vllm_available
from ..model import load_config, load_tokenizer
@ -43,7 +43,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class VllmEngine(BaseEngine):
@ -87,7 +87,7 @@ class VllmEngine(BaseEngine):
if getattr(config, "is_yi_vl_derived_model", None):
import vllm.model_executor.models.llava
logger.info("Detected Yi-VL model, applying projector patch.")
logger.info_rank0("Detected Yi-VL model, applying projector patch.")
vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))

6
src/llamafactory/cli.py
View File

@ -22,8 +22,8 @@ from . import launcher
from .api.app import run_api
from .chat.chat_model import run_chat
from .eval.evaluator import run_eval
from .extras import logging
from .extras.env import VERSION, print_env
from .extras.logging import get_logger
from .extras.misc import get_device_count
from .train.tuner import export_model, run_exp
from .webui.interface import run_web_demo, run_web_ui
@ -56,7 +56,7 @@ WELCOME = (
+ "-" * 58
)
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@unique
@ -90,7 +90,7 @@ def main():
if force_torchrun or get_device_count() > 1:
master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
master_port = os.getenv("MASTER_PORT", str(random.randint(20001, 29999)))
logger.info(f"Initializing distributed tasks at: {master_addr}:{master_port}")
logger.info_rank0(f"Initializing distributed tasks at: {master_addr}:{master_port}")
process = subprocess.run(
(
"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "

12
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
@ -29,7 +29,7 @@ if TYPE_CHECKING:
from .parser import DatasetAttr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _convert_images(
@ -167,7 +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(f"Invalid role tag in {messages}.")
logger.warning_rank0(f"Invalid role tag in {messages}.")
broken_data = True
aligned_messages.append(
@ -177,7 +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(f"Invalid message count in {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
@ -198,7 +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(f"Invalid role tag in {[chosen, rejected]}.")
logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
broken_data = True
prompt = aligned_messages
@ -211,7 +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)

8
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,

16
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(
@ -51,7 +51,7 @@ def _load_single_dataset(
r"""
Loads a single dataset and aligns it to the standard format.
"""
logger.info(f"Loading dataset {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", "om_hub"]:
data_path = dataset_attr.dataset_name
@ -141,7 +141,7 @@ def _load_single_dataset(
assert len(indexes) == dataset_attr.num_samples, "Sample num mismatched."
dataset = dataset.select(indexes)
logger.info(f"Sampled {dataset_attr.num_samples} examples from dataset {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))
@ -237,9 +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(f"Loaded tokenized dataset from {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:
@ -290,8 +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(f"Tokenized dataset saved at {data_args.tokenized_path}.")
logger.info(f"Please restart the training with `tokenized_path: {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)

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

8
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(

14
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(f"Dropped lengthy example with length {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)

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(

20
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
@ -275,12 +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(f"Add eos token: {tokenizer.eos_token}")
logger.info_rank0(f"Add eos token: {tokenizer.eos_token}")
else:
logger.info(f"Replace eos token: {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:
@ -370,7 +370,7 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
raise ValueError("Current template does not support `train_on_prompt`.")
if data_args.tool_format is not None:
logger.info(f"Using tool 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)
@ -388,21 +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(f"Add pad token: {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.")
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(f"Cannot add this chat template to tokenizer: {e}.")
logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
return template

72
src/llamafactory/extras/logging.py
View File

@ -20,6 +20,7 @@ import os
import sys
import threading
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from typing import Optional
from .constants import RUNNING_LOG
@ -37,12 +38,11 @@ class LoggerHandler(logging.Handler):
def __init__(self, output_dir: str) -> None:
super().__init__()
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S"
self._formatter = logging.Formatter(
fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
self.setLevel(logging.INFO)
self.setFormatter(formatter)
os.makedirs(output_dir, exist_ok=True)
self.running_log = os.path.join(output_dir, RUNNING_LOG)
if os.path.exists(self.running_log):
@ -58,7 +58,7 @@ class LoggerHandler(logging.Handler):
if record.name == "httpx":
return
log_entry = self.format(record)
log_entry = self._formatter.format(record)
self.thread_pool.submit(self._write_log, log_entry)
def close(self) -> None:
@ -66,6 +66,21 @@ class LoggerHandler(logging.Handler):
return super().close()
class _Logger(logging.Logger):
r"""
A logger that supports info_rank0 and warning_once.
"""
def info_rank0(self, *args, **kwargs) -> None:
self.info(*args, **kwargs)
def warning_rank0(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def warning_once(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def _get_default_logging_level() -> "logging._Level":
r"""
Returns the default logging level.
@ -84,7 +99,7 @@ def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> "logging.Logger":
def _get_library_root_logger() -> "_Logger":
return logging.getLogger(_get_library_name())
@ -95,12 +110,12 @@ def _configure_library_root_logger() -> None:
global _default_handler
with _thread_lock:
if _default_handler:
if _default_handler: # already configured
return
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
_default_handler = logging.StreamHandler(sys.stdout)
_default_handler.setFormatter(formatter)
@ -110,7 +125,7 @@ def _configure_library_root_logger() -> None:
library_root_logger.propagate = False
def get_logger(name: Optional[str] = None) -> "logging.Logger":
def get_logger(name: Optional[str] = None) -> "_Logger":
r"""
Returns a logger with the specified name. It it not supposed to be accessed externally.
"""
@ -119,3 +134,40 @@ def get_logger(name: Optional[str] = None) -> "logging.Logger":
_configure_library_root_logger()
return logging.getLogger(name)
def add_handler(handler: "logging.Handler") -> None:
r"""
Adds a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().addHandler(handler)
def remove_handler(handler: logging.Handler) -> None:
r"""
Removes a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().removeHandler(handler)
def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.info(*args, **kwargs)
def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
@lru_cache(None)
def warning_once(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
logging.Logger.info_rank0 = info_rank0
logging.Logger.warning_rank0 = warning_rank0
logging.Logger.warning_once = warning_once

8
src/llamafactory/extras/misc.py
View File

@ -32,7 +32,7 @@ from transformers.utils import (
)
from transformers.utils.versions import require_version
from .logging import get_logger
from . import logging
_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@ -48,7 +48,7 @@ if TYPE_CHECKING:
from ..hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class AverageMeter:
@ -76,8 +76,8 @@ def check_dependencies() -> None:
r"""
Checks the version of the required packages.
"""
if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
else:
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
require_version("datasets>=2.16.0,<=3.0.2", "To fix: pip install datasets>=2.16.0,<=3.0.2")

6
src/llamafactory/extras/ploting.py
View File

@ -19,7 +19,7 @@ from typing import Any, Dict, List
from transformers.trainer import TRAINER_STATE_NAME
from .logging import get_logger
from . import logging
from .packages import is_matplotlib_available
@ -28,7 +28,7 @@ if is_matplotlib_available():
import matplotlib.pyplot as plt
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def smooth(scalars: List[float]) -> List[float]:
@ -86,7 +86,7 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning(f"No metric {key} to plot.")
logger.warning_rank0(f"No metric {key} to plot.")
continue
plt.figure()

39
src/llamafactory/hparams/parser.py
View File

@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
from typing import Any, Dict, Optional, Tuple
@ -29,8 +28,8 @@ from transformers.training_args import ParallelMode
from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import CHECKPOINT_NAMES
from ..extras.logging import get_logger
from ..extras.misc import check_dependencies, get_current_device
from .data_args import DataArguments
from .evaluation_args import EvaluationArguments
@ -39,7 +38,7 @@ from .generating_args import GeneratingArguments
from .model_args import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
check_dependencies()
@ -73,8 +72,8 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
return (*parsed_args,)
def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
transformers.utils.logging.set_verbosity(log_level)
def _set_transformers_logging() -> None:
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
@ -104,7 +103,7 @@ def _verify_model_args(
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
if data_args.template == "yi" and model_args.use_fast_tokenizer:
logger.warning("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
model_args.use_fast_tokenizer = False
@ -261,7 +260,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
if data_args.neat_packing and not data_args.packing:
logger.warning("`neat_packing` requires `packing` is True. Change `packing` to True.")
logger.warning_rank0("`neat_packing` requires `packing` is True. Change `packing` to True.")
data_args.packing = True
_verify_model_args(model_args, data_args, finetuning_args)
@ -274,22 +273,26 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
and model_args.resize_vocab
and finetuning_args.additional_target is None
):
logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")
logger.warning_rank0(
"Remember to add embedding layers to `additional_target` to make the added tokens trainable."
)
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
logger.warning_rank0("We recommend enable `upcast_layernorm` in quantized training.")
if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
logger.warning("We recommend enable mixed precision training.")
logger.warning_rank0("We recommend enable mixed precision training.")
if training_args.do_train and finetuning_args.use_galore and not finetuning_args.pure_bf16:
logger.warning("Using GaLore with mixed precision training may significantly increases GPU memory usage.")
logger.warning_rank0(
"Using GaLore with mixed precision training may significantly increases GPU memory usage."
)
if (not training_args.do_train) and model_args.quantization_bit is not None:
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
logger.warning_rank0("Evaluating model in 4/8-bit mode may cause lower scores.")
if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
logger.warning("Specify `ref_model` for computing rewards at evaluation.")
logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
# Post-process training arguments
if (
@ -297,13 +300,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
and training_args.ddp_find_unused_parameters is None
and finetuning_args.finetuning_type == "lora"
):
logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
logger.warning_rank0("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
training_args.ddp_find_unused_parameters = False
if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
can_resume_from_checkpoint = False
if training_args.resume_from_checkpoint is not None:
logger.warning("Cannot resume from checkpoint in current stage.")
logger.warning_rank0("Cannot resume from checkpoint in current stage.")
training_args.resume_from_checkpoint = None
else:
can_resume_from_checkpoint = True
@ -323,15 +326,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if last_checkpoint is not None:
training_args.resume_from_checkpoint = last_checkpoint
logger.info(f"Resuming training from {training_args.resume_from_checkpoint}.")
logger.info("Change `output_dir` or use `overwrite_output_dir` to avoid.")
logger.info_rank0(f"Resuming training from {training_args.resume_from_checkpoint}.")
logger.info_rank0("Change `output_dir` or use `overwrite_output_dir` to avoid.")
if (
finetuning_args.stage in ["rm", "ppo"]
and finetuning_args.finetuning_type == "lora"
and training_args.resume_from_checkpoint is not None
):
logger.warning(
logger.warning_rank0(
"Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
)

28
src/llamafactory/model/adapter.py
View File

@ -20,7 +20,7 @@ from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ..extras.logging import get_logger
from ..extras import logging
from .model_utils.misc import find_all_linear_modules, find_expanded_modules
from .model_utils.quantization import QuantizationMethod
from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
@ -33,7 +33,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _setup_full_tuning(
@ -45,7 +45,7 @@ def _setup_full_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Full")
logger.info_rank0("Fine-tuning method: Full")
forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
for name, param in model.named_parameters():
if not any(forbidden_module in name for forbidden_module in forbidden_modules):
@ -64,7 +64,7 @@ def _setup_freeze_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Freeze")
logger.info_rank0("Fine-tuning method: Freeze")
if hasattr(model.config, "text_config"): # composite models
config = getattr(model.config, "text_config")
else:
@ -133,7 +133,7 @@ def _setup_freeze_tuning(
else:
param.requires_grad_(False)
logger.info("Set trainable layers: {}".format(",".join(trainable_layers)))
logger.info_rank0("Set trainable layers: {}".format(",".join(trainable_layers)))
def _setup_lora_tuning(
@ -145,7 +145,7 @@ def _setup_lora_tuning(
cast_trainable_params_to_fp32: bool,
) -> "PeftModel":
if is_trainable:
logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
adapter_to_resume = None
@ -182,7 +182,7 @@ def _setup_lora_tuning(
model = model.merge_and_unload()
if len(adapter_to_merge) > 0:
logger.info(f"Merged {len(adapter_to_merge)} adapter(s).")
logger.info_rank0(f"Merged {len(adapter_to_merge)} adapter(s).")
if adapter_to_resume is not None: # resume lora training
if model_args.use_unsloth:
@ -190,7 +190,7 @@ def _setup_lora_tuning(
else:
model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
logger.info_rank0("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
if is_trainable and adapter_to_resume is None: # create new lora weights while training
if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@ -219,7 +219,7 @@ def _setup_lora_tuning(
module_names.add(name.split(".")[-1])
finetuning_args.additional_target = module_names
logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
peft_kwargs = {
"r": finetuning_args.lora_rank,
@ -236,10 +236,10 @@ def _setup_lora_tuning(
else:
if finetuning_args.pissa_init:
if finetuning_args.pissa_iter == -1:
logger.info("Using PiSSA initialization.")
logger.info_rank0("Using PiSSA initialization.")
peft_kwargs["init_lora_weights"] = "pissa"
else:
logger.info(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"
lora_config = LoraConfig(
@ -284,11 +284,11 @@ def init_adapter(
if not is_trainable:
pass
elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
logger.info("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
logger.info("ZeRO3 / FSDP detected, remaining trainable params in float32.")
logger.info_rank0("ZeRO3 / FSDP detected, remaining trainable params in float32.")
else:
logger.info("Upcasting trainable params to float32.")
logger.info_rank0("Upcasting trainable params to float32.")
cast_trainable_params_to_fp32 = True
if finetuning_args.finetuning_type == "full":

12
src/llamafactory/model/loader.py
View File

@ -18,7 +18,7 @@ import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
from .adapter import init_adapter
from .model_utils.liger_kernel import apply_liger_kernel
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class TokenizerModule(TypedDict):
@ -90,10 +90,10 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
dict(additional_special_tokens=model_args.new_special_tokens),
replace_additional_special_tokens=False,
)
logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
logger.info_rank0("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
if num_added_tokens > 0 and not model_args.resize_vocab:
model_args.resize_vocab = True
logger.warning("New tokens have been added, changed `resize_vocab` to True.")
logger.warning_rank0("New tokens have been added, changed `resize_vocab` to True.")
patch_tokenizer(tokenizer)
try:
@ -180,7 +180,7 @@ def load_model(
vhead_params = load_valuehead_params(vhead_path, model_args)
if vhead_params is not None:
model.load_state_dict(vhead_params, strict=False)
logger.info(f"Loaded valuehead from checkpoint: {vhead_path}")
logger.info_rank0(f"Loaded valuehead from checkpoint: {vhead_path}")
if not is_trainable:
model.requires_grad_(False)
@ -200,7 +200,7 @@ def load_model(
else:
param_stats = f"all params: {all_param:,}"
logger.info(param_stats)
logger.info_rank0(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():

22
src/llamafactory/model/model_utils/attention.py
View File

@ -17,7 +17,7 @@ from typing import TYPE_CHECKING
from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
from transformers.utils.versions import require_version
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_attn_implementation(
@ -38,13 +38,15 @@ def configure_attn_implementation(
require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
if model_args.flash_attn != "fa2":
logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = "fa2"
else:
logger.warning("FlashAttention-2 is not installed, use eager attention.")
logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.")
model_args.flash_attn = "disabled"
elif model_args.flash_attn == "sdpa":
logger.warning("Gemma-2 should use soft-capping attention, while the SDPA attention does not support it.")
logger.warning_rank0(
"Gemma-2 should use soft-capping attention, while the SDPA attention does not support it."
)
if model_args.flash_attn == "auto":
return
@ -54,13 +56,13 @@ def configure_attn_implementation(
elif model_args.flash_attn == "sdpa":
if not is_torch_sdpa_available():
logger.warning("torch>=2.1.1 is required for SDPA attention.")
logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.")
return
requested_attn_implementation = "sdpa"
elif model_args.flash_attn == "fa2":
if not is_flash_attn_2_available():
logger.warning("FlashAttention-2 is not installed.")
logger.warning_rank0("FlashAttention-2 is not installed.")
return
requested_attn_implementation = "flash_attention_2"
@ -80,8 +82,8 @@ def print_attn_implementation(config: "PretrainedConfig") -> None:
attn_implementation = getattr(config, "_attn_implementation", None)
if attn_implementation == "flash_attention_2":
logger.info("Using FlashAttention-2 for faster training and inference.")
logger.info_rank0("Using FlashAttention-2 for faster training and inference.")
elif attn_implementation == "sdpa":
logger.info("Using torch SDPA for faster training and inference.")
logger.info_rank0("Using torch SDPA for faster training and inference.")
else:
logger.info("Using vanilla attention implementation.")
logger.info_rank0("Using vanilla attention implementation.")

14
src/llamafactory/model/model_utils/checkpointing.py
View File

@ -25,8 +25,8 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
import torch
from ...extras import logging
from ...extras.constants import LAYERNORM_NAMES
from ...extras.logging import get_logger
if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def get_unsloth_gradient_checkpointing_func() -> Callable:
@ -122,7 +122,7 @@ def _gradient_checkpointing_enable(
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
logger.warning_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
@ -141,14 +141,14 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
(3) add the upcasting of the lm_head in fp32
"""
if model_args.upcast_layernorm:
logger.info("Upcasting layernorm weights in float32.")
logger.info_rank0("Upcasting layernorm weights in float32.")
for name, param in model.named_parameters():
if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
param.data = param.data.to(torch.float32)
if not model_args.disable_gradient_checkpointing:
if not getattr(model, "supports_gradient_checkpointing", False):
logger.warning("Current model does not support gradient checkpointing.")
logger.warning_rank0("Current model does not support gradient checkpointing.")
else:
# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
# According to: https://github.com/huggingface/transformers/issues/28339
@ -158,10 +158,10 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
setattr(model.config, "use_cache", False) # turn off when gradient checkpointing is enabled
logger.info("Gradient checkpointing enabled.")
logger.info_rank0("Gradient checkpointing enabled.")
if model_args.upcast_lmhead_output:
output_layer = model.get_output_embeddings()
if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
logger.info("Upcasting lm_head outputs in float32.")
logger.info_rank0("Upcasting lm_head outputs in float32.")
output_layer.register_forward_hook(_fp32_forward_post_hook)

6
src/llamafactory/model/model_utils/embedding.py
View File

@ -19,14 +19,14 @@ from typing import TYPE_CHECKING
import torch
from transformers.integrations import is_deepspeed_zero3_enabled
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _noisy_mean_initialization(embed_weight: "torch.Tensor", num_new_tokens: int) -> None:
@ -69,4 +69,4 @@ def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedToken
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info(f"Resized token embeddings from {current_embedding_size} to {new_embedding_size}.")
logger.info_rank0(f"Resized token embeddings from {current_embedding_size} to {new_embedding_size}.")

10
src/llamafactory/model/model_utils/liger_kernel.py
View File

@ -15,7 +15,7 @@
import inspect
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -24,7 +24,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def apply_liger_kernel(
@ -54,14 +54,14 @@ def apply_liger_kernel(
elif model_type == "qwen2_vl":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
else:
logger.warning("Current model does not support liger kernel.")
logger.warning_rank0("Current model does not support liger kernel.")
return
if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
logger.info("Current training stage does not support chunked cross entropy.")
logger.info_rank0("Current training stage does not support chunked cross entropy.")
kwargs = {"fused_linear_cross_entropy": False}
else:
kwargs = {}
apply_liger_kernel(**kwargs)
logger.info("Liger kernel has been applied to the model.")
logger.info_rank0("Liger kernel has been applied to the model.")

12
src/llamafactory/model/model_utils/longlora.py
View File

@ -22,6 +22,7 @@ from typing import TYPE_CHECKING, Optional, Tuple
import torch
import torch.nn as nn
import transformers
from transformers.models.llama.modeling_llama import (
Cache,
LlamaAttention,
@ -30,11 +31,10 @@ from transformers.models.llama.modeling_llama import (
apply_rotary_pos_emb,
repeat_kv,
)
from transformers.utils import logging
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_greater_than_4_43
@ -44,7 +44,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
transformers_logger = logging.get_logger(__name__)
transformers_logger = transformers.utils.logging.get_logger(__name__)
# Modified from:
@ -363,11 +363,11 @@ def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments",
if not is_trainable or not model_args.shift_attn:
return
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
_apply_llama_patch()
logger.info("Using shift short attention with group_size_ratio=1/4.")
logger.info_rank0("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
logger.warning_rank0("Current model does not support shift short attention.")

8
src/llamafactory/model/model_utils/misc.py
View File

@ -14,14 +14,14 @@
from typing import TYPE_CHECKING, List
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
@ -53,7 +53,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
if "Linear" in module.__class__.__name__ and "Embedding" not in module.__class__.__name__:
module_names.add(name.split(".")[-1])
logger.info("Found linear modules: {}".format(",".join(module_names)))
logger.info_rank0("Found linear modules: {}".format(",".join(module_names)))
return list(module_names)
@ -80,7 +80,7 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
):
module_names.append(name)
logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
logger.info_rank0("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
return module_names

6
src/llamafactory/model/model_utils/packing.py
View File

@ -43,8 +43,8 @@ import torch
import torch.nn.functional as F
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_greater_than_4_43
@ -54,7 +54,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
@ -152,6 +152,6 @@ def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments",
model_type = getattr(config, "model_type", None)
if model_type in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
_patch_for_block_diag_attn(model_type)
logger.info("Using block diagonal attention for sequence packing without cross-attention.")
logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")
else:
raise ValueError("Current model does not support block diagonal attention.")

16
src/llamafactory/model/model_utils/quantization.py
View File

@ -28,8 +28,8 @@ from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
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 get_current_device
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@unique
@ -109,7 +109,7 @@ def configure_quantization(
"""
if getattr(config, "quantization_config", None): # ptq
if model_args.quantization_bit is not None:
logger.warning("`quantization_bit` will not affect on the PTQ-quantized models.")
logger.warning_rank0("`quantization_bit` will not affect on the PTQ-quantized models.")
if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
@ -130,7 +130,7 @@ def configure_quantization(
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info(f"Loading {quant_bits}-bit {quant_method.upper()}-quantized model.")
logger.info_rank0(f"Loading {quant_bits}-bit {quant_method.upper()}-quantized model.")
elif model_args.export_quantization_bit is not None: # auto-gptq
if model_args.export_quantization_bit not in [8, 4, 3, 2]:
@ -149,7 +149,7 @@ def configure_quantization(
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info(f"Quantizing model to {model_args.export_quantization_bit} bit with AutoGPTQ.")
logger.info_rank0(f"Quantizing model to {model_args.export_quantization_bit} bit with AutoGPTQ.")
elif model_args.quantization_bit is not None: # on-the-fly
if model_args.quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
@ -179,7 +179,7 @@ def configure_quantization(
else:
init_kwargs["device_map"] = {"": get_current_device()} # change auto device map for inference
logger.info(f"Quantizing model to {model_args.quantization_bit} bit with bitsandbytes.")
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with bitsandbytes.")
elif model_args.quantization_method == QuantizationMethod.HQQ.value:
if model_args.quantization_bit not in [8, 6, 5, 4, 3, 2, 1]:
raise ValueError("HQQ only accepts 1/2/3/4/5/6/8-bit quantization.")
@ -191,7 +191,7 @@ def configure_quantization(
init_kwargs["quantization_config"] = HqqConfig(
nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
) # use ATEN kernel (axis=0) for performance
logger.info(f"Quantizing model to {model_args.quantization_bit} bit with HQQ.")
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with HQQ.")
elif model_args.quantization_method == QuantizationMethod.EETQ.value:
if model_args.quantization_bit != 8:
raise ValueError("EETQ only accepts 8-bit quantization.")
@ -201,4 +201,4 @@ def configure_quantization(
require_version("eetq", "To fix: pip install eetq")
init_kwargs["quantization_config"] = EetqConfig()
logger.info(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")

16
src/llamafactory/model/model_utils/rope.py
View File

@ -19,7 +19,7 @@
import math
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
@ -36,26 +36,28 @@ def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_
return
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
logger.warning_rank0("Current model does not support RoPE scaling.")
return
if model_args.model_max_length is not None:
if is_trainable and model_args.rope_scaling == "dynamic":
logger.warning(
logger.warning_rank0(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
logger.info(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
logger.info_rank0(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
setattr(config, "max_position_embeddings", model_args.model_max_length)
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
logger.warning_rank0("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info(f"Using {model_args.rope_scaling} scaling strategy and setting scaling factor to {scaling_factor}")
logger.info_rank0(
f"Using {model_args.rope_scaling} scaling strategy and setting scaling factor to {scaling_factor}"
)

6
src/llamafactory/model/model_utils/unsloth.py
View File

@ -14,7 +14,7 @@
from typing import TYPE_CHECKING, Any, Dict, Optional
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.misc import get_current_device
@ -24,7 +24,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _get_unsloth_kwargs(
@ -56,7 +56,7 @@ def load_unsloth_pretrained_model(
try:
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
logger.warning_rank0("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
model = None
model_args.use_unsloth = False

8
src/llamafactory/model/model_utils/valuehead.py
View File

@ -17,8 +17,8 @@ from typing import TYPE_CHECKING, Dict
import torch
from transformers.utils import cached_file
from ...extras import logging
from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ...extras.logging import get_logger
if TYPE_CHECKING:
@ -27,7 +27,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
@ -54,8 +54,8 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
except Exception as err:
err_text = str(err)
logger.info(f"Provided path ({path_or_repo_id}) does not contain value head weights: {err_text}.")
logger.info("Ignore the above message if you are not resuming the training of a value head model.")
logger.info_rank0(f"Provided path ({path_or_repo_id}) does not contain value head weights: {err_text}.")
logger.info_rank0("Ignore the above message if you are not resuming the training of a value head model.")
return None

12
src/llamafactory/model/model_utils/visual.py
View File

@ -18,11 +18,11 @@
from typing import TYPE_CHECKING, List, Sequence, Set, Tuple, Union
import torch
import transformers
import transformers.models
from transformers.activations import ACT2FN
from transformers.utils import logging
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -31,8 +31,8 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
transformers_logger = logging.get_logger(__name__)
logger = logging.get_logger(__name__)
transformers_logger = transformers.utils.logging.get_logger(__name__)
class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
@ -99,7 +99,7 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
else:
return
logger.info(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
logger.info_rank0(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
@ -119,7 +119,7 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
if getattr(config, "is_yi_vl_derived_model", None):
logger.info("Detected Yi-VL model, applying projector patch.")
logger.info_rank0("Detected Yi-VL model, applying projector patch.")
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL

8
src/llamafactory/model/patcher.py
View File

@ -22,7 +22,7 @@ from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.misc import infer_optim_dtype
from .model_utils.attention import configure_attn_implementation, print_attn_implementation
from .model_utils.checkpointing import prepare_model_for_training
@ -49,7 +49,7 @@ if TYPE_CHECKING:
from ..hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
@ -100,7 +100,7 @@ def patch_config(
if model_args.use_cache and not is_trainable:
setattr(config, "use_cache", True)
logger.info("Using KV cache for faster generation.")
logger.info_rank0("Using KV cache for faster generation.")
if getattr(config, "model_type", None) == "qwen":
setattr(config, "use_flash_attn", model_args.flash_attn == "fa2")
@ -165,7 +165,7 @@ def patch_model(
try:
model.add_model_tags(["llama-factory"])
except Exception:
logger.warning("Cannot properly tag the model.")
logger.warning_rank0("Cannot properly tag the model.")
def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:

19
src/llamafactory/train/callbacks.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
import json
import logging
import os
import signal
import sys
@ -34,8 +33,8 @@ from transformers.utils import (
)
from typing_extensions import override
from ..extras import logging
from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import LoggerHandler, get_logger
from ..extras.misc import get_peak_memory
@ -48,7 +47,7 @@ if TYPE_CHECKING:
from trl import AutoModelForCausalLMWithValueHead
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def fix_valuehead_checkpoint(
@ -92,7 +91,7 @@ def fix_valuehead_checkpoint(
else:
torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))
logger.info(f"Value head model saved at: {output_dir}")
logger.info_rank0(f"Value head model saved at: {output_dir}")
class FixValueHeadModelCallback(TrainerCallback):
@ -145,7 +144,7 @@ class PissaConvertCallback(TrainerCallback):
if args.should_save:
model = kwargs.pop("model")
pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
logger.info(f"Initial PiSSA adapter will be saved at: {pissa_init_dir}.")
logger.info_rank0(f"Initial PiSSA adapter will be saved at: {pissa_init_dir}.")
if isinstance(model, PeftModel):
init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
setattr(model.peft_config["default"], "init_lora_weights", True)
@ -159,7 +158,7 @@ class PissaConvertCallback(TrainerCallback):
pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
pissa_backup_dir = os.path.join(args.output_dir, "pissa_backup")
pissa_convert_dir = os.path.join(args.output_dir, "pissa_converted")
logger.info(f"Converted PiSSA adapter will be saved at: {pissa_convert_dir}.")
logger.info_rank0(f"Converted PiSSA adapter will be saved at: {pissa_convert_dir}.")
# 1. save a pissa backup with init_lora_weights: True
# 2. save a converted lora with init_lora_weights: pissa
# 3. load the pissa backup with init_lora_weights: True
@ -200,8 +199,8 @@ class LogCallback(TrainerCallback):
self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
if self.webui_mode:
signal.signal(signal.SIGABRT, self._set_abort)
self.logger_handler = LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
logging.root.addHandler(self.logger_handler)
self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
logging.add_handler(self.logger_handler)
transformers.logging.add_handler(self.logger_handler)
def _set_abort(self, signum, frame) -> None:
@ -243,7 +242,7 @@ class LogCallback(TrainerCallback):
and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
and args.overwrite_output_dir
):
logger.warning("Previous trainer log in this folder will be deleted.")
logger.warning_once("Previous trainer log in this folder will be deleted.")
os.remove(os.path.join(args.output_dir, TRAINER_LOG))
@override
@ -310,7 +309,7 @@ class LogCallback(TrainerCallback):
logs = {k: v for k, v in logs.items() if v is not None}
if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
logger.info(
logger.info_rank0(
"{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}, 'throughput': {}}}".format(
logs["loss"], logs["learning_rate"], logs["epoch"], logs.get("throughput", "N/A")
)

37
src/llamafactory/train/ppo/trainer.py
View File

@ -37,7 +37,7 @@ from trl.core import PPODecorators, logprobs_from_logits
from trl.models.utils import unwrap_model_for_generation
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor
from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -58,7 +58,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class CustomPPOTrainer(PPOTrainer, Trainer):
@ -112,7 +112,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
]
ppo_config.accelerator_kwargs["deepspeed_plugin"] = training_args.deepspeed_plugin
if ppo_config.log_with is not None:
logger.warning("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
logger.warning_rank0("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
ppo_config.log_with = None
# Create optimizer and scheduler
@ -160,7 +160,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
callbacks, self.accelerator.unwrap_model(self.model), self.tokenizer, self.optimizer, self.lr_scheduler
)
if self.args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
logger.info_rank0("max_steps is given, it will override any value given in num_train_epochs")
self.amp_context = torch.autocast(self.current_device.type)
warnings.simplefilter("ignore") # remove gc warnings on ref model
@ -216,20 +216,19 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
self.state.is_local_process_zero = self.is_local_process_zero()
self.state.is_world_process_zero = self.is_world_process_zero()
if self.is_world_process_zero():
logger.info("***** Running training *****")
logger.info(f" Num examples = {num_examples:,}")
logger.info(f" Num Epochs = {num_train_epochs:,}")
logger.info(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
logger.info(
" Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
total_train_batch_size
)
logger.info_rank0("***** Running training *****")
logger.info_rank0(f" Num examples = {num_examples:,}")
logger.info_rank0(f" Num Epochs = {num_train_epochs:,}")
logger.info_rank0(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
logger.info_rank0(
" Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
total_train_batch_size
)
logger.info(f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps:,}")
logger.info(f" Num optimization epochs per batch = {self.finetuning_args.ppo_epochs:,}")
logger.info(f" Total training steps = {max_steps:,}")
logger.info(f" Number of trainable parameters = {count_parameters(self.model)[0]:,}")
)
logger.info_rank0(f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps:,}")
logger.info_rank0(f" Num optimization epochs per batch = {self.finetuning_args.ppo_epochs:,}")
logger.info_rank0(f" Total training steps = {max_steps:,}")
logger.info_rank0(f" Number of trainable parameters = {count_parameters(self.model)[0]:,}")
dataiter = iter(self.dataloader)
loss_meter = AverageMeter()
@ -269,7 +268,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True)
self.log_stats(stats, batch, rewards)
except Exception:
logger.warning("Failed to save stats due to unknown errors.")
logger.warning_rank0("Failed to save stats due to unknown errors.")
self.state.global_step += 1
self.callback_handler.on_step_end(self.args, self.state, self.control)
@ -498,7 +497,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
if self.args.should_save:
self._save(output_dir, state_dict=state_dict)
except ValueError:
logger.warning(
logger.warning_rank0(
" stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead,"
" use zero_to_fp32.py to recover weights"
)

4
src/llamafactory/train/pt/trainer.py
View File

@ -18,7 +18,6 @@ from typing import TYPE_CHECKING, Optional
from transformers import Trainer
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -31,9 +30,6 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
class CustomTrainer(Trainer):
r"""
Inherits Trainer for custom optimizer.

6
src/llamafactory/train/rm/trainer.py
View File

@ -24,7 +24,7 @@ import torch
from transformers import Trainer
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -37,7 +37,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class PairwiseTrainer(Trainer):
@ -118,7 +118,7 @@ class PairwiseTrainer(Trainer):
return
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info(f"Saving prediction results to {output_prediction_file}")
logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
chosen_scores, rejected_scores = predict_results.predictions
with open(output_prediction_file, "w", encoding="utf-8") as writer:

6
src/llamafactory/train/sft/trainer.py
View File

@ -25,8 +25,8 @@ import torch
from transformers import Seq2SeqTrainer
from typing_extensions import override
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -40,7 +40,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class CustomSeq2SeqTrainer(Seq2SeqTrainer):
@ -142,7 +142,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
return
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info(f"Saving prediction results to {output_prediction_file}")
logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
labels = np.where(
predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id

26
src/llamafactory/train/trainer_utils.py
View File

@ -28,8 +28,8 @@ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.trainer_pt_utils import get_parameter_names
from typing_extensions import override
from ..extras import logging
from ..extras.constants import IGNORE_INDEX
from ..extras.logging import get_logger
from ..extras.packages import is_galore_available
from ..hparams import FinetuningArguments, ModelArguments
from ..model import find_all_linear_modules, load_model, load_tokenizer, load_valuehead_params
@ -46,7 +46,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class DummyOptimizer(torch.optim.Optimizer):
@ -116,7 +116,7 @@ def create_ref_model(
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
logger.info(f"Created reference model from {finetuning_args.ref_model}")
logger.info_rank0(f"Created reference model from {finetuning_args.ref_model}")
else:
if finetuning_args.finetuning_type == "lora":
ref_model = None
@ -127,7 +127,7 @@ def create_ref_model(
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
logger.info("Created reference model from the model itself.")
logger.info_rank0("Created reference model from the model itself.")
return ref_model
@ -140,7 +140,7 @@ def create_reward_model(
"""
if finetuning_args.reward_model_type == "api":
assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
logger.info(f"Use reward server {finetuning_args.reward_model}")
logger.info_rank0(f"Use reward server {finetuning_args.reward_model}")
return finetuning_args.reward_model
elif finetuning_args.reward_model_type == "lora":
model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
@ -157,7 +157,7 @@ def create_reward_model(
model.register_buffer(
"default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
)
logger.info(f"Loaded adapter weights of reward model from {finetuning_args.reward_model}")
logger.info_rank0(f"Loaded adapter weights of reward model from {finetuning_args.reward_model}")
return None
else:
reward_model_args = ModelArguments.copyfrom(
@ -171,8 +171,8 @@ def create_reward_model(
reward_model = load_model(
tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
)
logger.info(f"Loaded full weights of reward model from {finetuning_args.reward_model}")
logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
logger.info_rank0(f"Loaded full weights of reward model from {finetuning_args.reward_model}")
logger.warning_rank0("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
return reward_model
@ -265,7 +265,7 @@ def _create_galore_optimizer(
]
optimizer = optim_class(param_groups, **optim_kwargs)
logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
logger.info_rank0("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
return optimizer
@ -305,7 +305,7 @@ def _create_loraplus_optimizer(
dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
]
optimizer = optim_class(param_groups, **optim_kwargs)
logger.info(f"Using LoRA+ optimizer with loraplus lr ratio {finetuning_args.loraplus_lr_ratio:.2f}.")
logger.info_rank0(f"Using LoRA+ optimizer with loraplus lr ratio {finetuning_args.loraplus_lr_ratio:.2f}.")
return optimizer
@ -343,7 +343,7 @@ def _create_badam_optimizer(
verbose=finetuning_args.badam_verbose,
ds_zero3_enabled=is_deepspeed_zero3_enabled(),
)
logger.info(
logger.info_rank0(
f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
f"switch block every {finetuning_args.badam_switch_interval} steps, "
f"default start block is {finetuning_args.badam_start_block}"
@ -362,7 +362,7 @@ def _create_badam_optimizer(
include_embedding=False,
**optim_kwargs,
)
logger.info(
logger.info_rank0(
f"Using BAdam optimizer with ratio-based update, update ratio is {finetuning_args.badam_update_ratio}, "
f"mask mode is {finetuning_args.badam_mask_mode}"
)
@ -391,7 +391,7 @@ def _create_adam_mini_optimizer(
n_heads=num_q_head,
n_kv_heads=num_kv_head,
)
logger.info("Using Adam-mini optimizer.")
logger.info_rank0("Using Adam-mini optimizer.")
return optimizer

12
src/llamafactory/train/tuner.py
View File

@ -20,8 +20,8 @@ import torch
from transformers import PreTrainedModel
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import get_logger
from ..hparams import get_infer_args, get_train_args
from ..model import load_model, load_tokenizer
from .callbacks import LogCallback
@ -37,7 +37,7 @@ if TYPE_CHECKING:
from transformers import TrainerCallback
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
@ -91,7 +91,7 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
setattr(model.config, "torch_dtype", output_dtype)
model = model.to(output_dtype)
logger.info(f"Convert model dtype to: {output_dtype}.")
logger.info_rank0(f"Convert model dtype to: {output_dtype}.")
model.save_pretrained(
save_directory=model_args.export_dir,
@ -117,13 +117,13 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
os.path.join(vhead_path, V_HEAD_SAFE_WEIGHTS_NAME),
os.path.join(model_args.export_dir, V_HEAD_SAFE_WEIGHTS_NAME),
)
logger.info(f"Copied valuehead to {model_args.export_dir}.")
logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
elif os.path.exists(os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME)):
shutil.copy(
os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME),
os.path.join(model_args.export_dir, V_HEAD_WEIGHTS_NAME),
)
logger.info(f"Copied valuehead to {model_args.export_dir}.")
logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
try:
tokenizer.padding_side = "left" # restore padding side
@ -138,4 +138,4 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
processor.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
except Exception as e:
logger.warning(f"Cannot save tokenizer, please copy the files manually: {e}.")
logger.warning_rank0(f"Cannot save tokenizer, please copy the files manually: {e}.")

10
src/llamafactory/webui/common.py
View File

@ -19,6 +19,7 @@ from typing import Any, Dict, Optional, Tuple
from yaml import safe_dump, safe_load
from ..extras import logging
from ..extras.constants import (
CHECKPOINT_NAMES,
DATA_CONFIG,
@ -30,7 +31,6 @@ from ..extras.constants import (
VISION_MODELS,
DownloadSource,
)
from ..extras.logging import get_logger
from ..extras.misc import use_modelscope, use_openmind
from ..extras.packages import is_gradio_available
@ -39,7 +39,7 @@ if is_gradio_available():
import gradio as gr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
DEFAULT_CACHE_DIR = "cache"
@ -56,7 +56,7 @@ def get_save_dir(*paths: str) -> os.PathLike:
Gets the path to saved model checkpoints.
"""
if os.path.sep in paths[-1]:
logger.warning("Found complex path, some features may be not available.")
logger.warning_rank0("Found complex path, some features may be not available.")
return paths[-1]
paths = (path.replace(" ", "").strip() for path in paths)
@ -172,14 +172,14 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
Loads dataset_info.json.
"""
if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
logger.info(f"dataset_dir is {dataset_dir}, using online dataset.")
logger.info_rank0(f"dataset_dir is {dataset_dir}, using online dataset.")
return {}
try:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
return json.load(f)
except Exception as err:
logger.warning(f"Cannot open {os.path.join(dataset_dir, DATA_CONFIG)} due to {str(err)}.")
logger.warning_rank0(f"Cannot open {os.path.join(dataset_dir, DATA_CONFIG)} due to {str(err)}.")
return {}

4
src/llamafactory/webui/runner.py
View File

@ -22,7 +22,7 @@ from transformers.trainer import TRAINING_ARGS_NAME
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_gpu_or_npu_available, torch_gc
from ..extras.packages import is_gradio_available
from ..extras.packages import is_gradio_available, is_transformers_version_equal_to_4_46
from .common import DEFAULT_CACHE_DIR, DEFAULT_CONFIG_DIR, QUANTIZATION_BITS, get_save_dir, load_config
from .locales import ALERTS, LOCALES
from .utils import abort_process, gen_cmd, get_eval_results, get_trainer_info, load_args, save_args, save_cmd
@ -152,7 +152,7 @@ class Runner:
pure_bf16=(get("train.compute_type") == "pure_bf16"),
plot_loss=True,
ddp_timeout=180000000,
include_num_input_tokens_seen=True,
include_num_input_tokens_seen=False if is_transformers_version_equal_to_4_46() else True, # FIXME
**json.loads(get("train.extra_args")),
)