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add code for reading from multi files in one directory

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Former-commit-id: b7ebb83a96619e5111b0faa9da9d0feb8d9cdff0
This commit is contained in:
BUAADreamer 2023-06-10 15:53:47 +08:00
parent 03c92c79ff
commit ef6c5ae18a
2 changed files with 76 additions and 54 deletions
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70
src/utils/common.py
View File

@ -56,7 +56,6 @@ require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
require_version("trl>=0.4.1", "To fix: pip install trl>=0.4.1")
logger = get_logger(__name__)
@ -92,10 +91,12 @@ def _init_adapter(
if model_args.checkpoint_dir is not None:
if finetuning_args.finetuning_type != "lora":
assert is_mergeable and len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
assert is_mergeable and len(
model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
else:
assert is_mergeable or len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
assert is_mergeable or len(
model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
if finetuning_args.finetuning_type == "lora":
logger.info("Fine-tuning method: LoRA")
@ -105,7 +106,8 @@ def _init_adapter(
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
"The given checkpoint is not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
if (is_trainable and model_args.resume_lora_training) or (
not is_mergeable): # continually train on the lora weights
checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
else:
checkpoints_to_merge = model_args.checkpoint_dir
@ -117,10 +119,10 @@ def _init_adapter(
if len(checkpoints_to_merge) > 0:
logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
if lastest_checkpoint is not None: # resume lora training or quantized inference
if lastest_checkpoint is not None: # resume lora training or quantized inference
model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)
if is_trainable and lastest_checkpoint is None: # create new lora weights while training
if is_trainable and lastest_checkpoint is None: # create new lora weights while training
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
@ -168,7 +170,7 @@ def load_pretrained(
padding_side="left",
**config_kwargs
)
tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
is_mergeable = True
@ -184,9 +186,11 @@ def load_pretrained(
)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
require_version("transformers>=4.30.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git")
require_version("transformers>=4.30.0.dev0",
"To fix: pip install git+https://github.com/huggingface/transformers.git")
require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
require_version("accelerate>=0.20.0.dev0", "To fix: pip install git+https://github.com/huggingface/accelerate.git")
require_version("accelerate>=0.20.0.dev0",
"To fix: pip install git+https://github.com/huggingface/accelerate.git")
config_kwargs["load_in_4bit"] = True
config_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
@ -214,10 +218,10 @@ def load_pretrained(
model = prepare_model_for_training(model) if is_trainable else model
model = _init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)
if stage == "rm" or stage == "ppo": # add value head
if stage == "rm" or stage == "ppo": # add value head
model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
if stage == "ppo": # load reward model
if stage == "ppo": # load reward model
assert is_trainable, "PPO stage cannot be performed at evaluation."
assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
logger.info("Load reward model from {}".format(model_args.reward_model))
@ -230,8 +234,8 @@ def load_pretrained(
model._is_int8_training_enabled = True
if not is_trainable:
model.requires_grad_(False) # fix all model params
model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
model.requires_grad_(False) # fix all model params
model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
print_trainable_params(model)
@ -241,11 +245,11 @@ def load_pretrained(
def prepare_args(
stage: Literal["pt", "sft", "rm", "ppo"]
) -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
model_args, data_args, training_args, finetuning_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()
@ -286,7 +290,7 @@ def prepare_args(
logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
training_args.ddp_find_unused_parameters = False
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
if model_args.quantization_bit is not None:
if training_args.fp16:
@ -310,10 +314,9 @@ def prepare_args(
def prepare_infer_args() -> Tuple[ModelArguments, DataTrainingArguments, FinetuningArguments]:
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FinetuningArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
model_args, data_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, finetuning_args = parser.parse_args_into_dataclasses()
@ -331,7 +334,6 @@ def prepare_data(
model_args: ModelArguments,
data_args: DataTrainingArguments
) -> Dataset:
def checksum(file_path, hash):
with open(file_path, "rb") as datafile:
binary_data = datafile.read()
@ -340,7 +342,7 @@ def prepare_data(
logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
max_samples = data_args.max_samples
all_datasets: List[Dataset] = [] # support multiple datasets
all_datasets: List[Dataset] = [] # support multiple datasets
for dataset_attr in data_args.dataset_list:
@ -361,7 +363,7 @@ def prepare_data(
checksum(data_file, dataset_attr.file_sha1)
else:
logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
print(extension)
raw_datasets = load_dataset(
extension if extension in ["csv", "json"] else "text",
data_files=data_file,
@ -383,11 +385,11 @@ def prepare_data(
("query_column", "query"),
("response_column", "response"),
("history_column", "history")
]: # every dataset will have 4 columns same as each other
]: # every dataset will have 4 columns same as each other
if getattr(dataset_attr, column_name) != target_name:
if getattr(dataset_attr, column_name):
dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
else: # None or empty string
else: # None or empty string
dataset = dataset.add_column(target_name, dummy_data)
all_datasets.append(dataset)
@ -406,7 +408,6 @@ def preprocess_data(
training_args: Seq2SeqTrainingArguments,
stage: Literal["pt", "sft", "rm", "ppo"]
) -> Dataset:
column_names = list(dataset.column_names)
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
prompt_template = Template(data_args.prompt_template)
@ -429,7 +430,8 @@ def preprocess_data(
# we drop the small remainder, and if the total_length < block_size, we exclude this batch
total_length = (total_length // data_args.max_source_length) * data_args.max_source_length
# split by chunks of max_source_length
result = [concatenated_ids[i: i+data_args.max_source_length] for i in range(0, total_length, data_args.max_source_length)]
result = [concatenated_ids[i: i + data_args.max_source_length] for i in
range(0, total_length, data_args.max_source_length)]
return {
"input_ids": result,
"labels": result.copy()
@ -442,9 +444,9 @@ def preprocess_data(
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 1: # bos token
if len(source_ids) > data_args.max_source_length - 1: # bos token
source_ids = source_ids[:data_args.max_source_length - 1]
if len(target_ids) > data_args.max_target_length - 1: # eos token
if len(target_ids) > data_args.max_target_length - 1: # eos token
target_ids = target_ids[:data_args.max_target_length - 1]
input_ids = source_ids + [tokenizer.bos_token_id] + target_ids + [tokenizer.eos_token_id]
@ -461,9 +463,9 @@ def preprocess_data(
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 1: # bos token
if len(source_ids) > data_args.max_source_length - 1: # bos token
source_ids = source_ids[:data_args.max_source_length - 1]
if len(target_ids) > data_args.max_target_length - 1: # bos token
if len(target_ids) > data_args.max_target_length - 1: # bos token
target_ids = target_ids[:data_args.max_target_length - 1]
input_ids = source_ids + [tokenizer.bos_token_id]
@ -481,11 +483,11 @@ def preprocess_data(
accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 1: # bos token
if len(source_ids) > data_args.max_source_length - 1: # bos token
source_ids = source_ids[:data_args.max_source_length - 1]
if len(accept_ids) > data_args.max_target_length - 1: # eos token
if len(accept_ids) > data_args.max_target_length - 1: # eos token
accept_ids = accept_ids[:data_args.max_target_length - 1]
if len(reject_ids) > data_args.max_target_length - 1: # eos token
if len(reject_ids) > data_args.max_target_length - 1: # eos token
reject_ids = reject_ids[:data_args.max_target_length - 1]
accept_ids = source_ids + [tokenizer.bos_token_id] + accept_ids + [tokenizer.eos_token_id]

60
src/utils/config.py
View File

@ -7,7 +7,6 @@ from dataclasses import asdict, dataclass, field
@dataclass
class DatasetAttr:
load_from: str
dataset_name: Optional[str] = None
file_name: Optional[str] = None
@ -68,7 +67,8 @@ class ModelArguments:
)
checkpoint_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
metadata={
"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
)
reward_model: Optional[str] = field(
default=None,
@ -76,7 +76,8 @@ class ModelArguments:
)
resume_lora_training: Optional[bool] = field(
default=True,
metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
metadata={
"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
)
plot_loss: Optional[bool] = field(
default=False,
@ -84,7 +85,7 @@ class ModelArguments:
)
def __post_init__(self):
if self.checkpoint_dir is not None: # support merging multiple lora weights
if self.checkpoint_dir is not None: # support merging multiple lora weights
self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
@ -146,7 +147,7 @@ class DataTrainingArguments:
metadata={"help": "Which template to use for constructing prompts in training and inference."}
)
def __post_init__(self): # support mixing multiple datasets
def __post_init__(self): # support mixing multiple datasets
dataset_names = [ds.strip() for ds in self.dataset.split(",")]
with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
dataset_info = json.load(f)
@ -155,25 +156,42 @@ class DataTrainingArguments:
for name in dataset_names:
if name not in dataset_info:
raise ValueError("Undefined dataset {} in dataset_info.json.".format(name))
dataset_attrs = []
dataset_attr = None
if "hf_hub_url" in dataset_info[name]:
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
elif "script_url" in dataset_info[name]:
dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
else:
elif os.path.isfile(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
dataset_attr = DatasetAttr(
"file",
file_name=dataset_info[name]["file_name"],
file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
)
if "columns" in dataset_info[name]:
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
self.dataset_list.append(dataset_attr)
else:
# Support Directory
for file_name in os.listdir(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
path = os.path.join(dataset_info[name]["file_name"], file_name)
dataset_attrs.append(DatasetAttr(
"file",
file_name=path,
file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
))
if dataset_attr is not None:
if "columns" in dataset_info[name]:
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
self.dataset_list.append(dataset_attr)
else:
for i, dataset_attr in enumerate(dataset_attrs):
if "columns" in dataset_info[name]:
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
self.dataset_list.append(dataset_attr)
@dataclass
@ -216,14 +234,16 @@ class FinetuningArguments:
def __post_init__(self):
if isinstance(self.lora_target, str):
self.lora_target = [target.strip() for target in self.lora_target.split(",")] # support custom target modules of LoRA
self.lora_target = [target.strip() for target in
self.lora_target.split(",")] # support custom target modules of LoRA
if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = [27-k for k in range(self.num_layer_trainable)]
else: # fine-tuning the first n layers if num_layer_trainable < 0
if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = [27 - k for k in range(self.num_layer_trainable)]
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in
trainable_layer_ids]
assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."