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train.py
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from dataclasses import dataclass, field
from typing import cast
import torch
from datasets import load_dataset
from transformers import HfArgumentParser, Trainer, TrainingArguments
from magicoder.llm_wrapper import (
DecodingConfig,
EncodingConfig,
TokenizationContext,
get_model_context,
pad_sequences,
)
from magicoder.prompt_template import MAGICODER_PROMPT
from magicoder.utils import N_CORES
@dataclass(frozen=True)
class ModelArguments:
model_key: str
model_name_or_path: str | None = None
# Ignored index in CrossEntropyLoss
IGNORED_INDEX = -100
def map_dataset(
examples: dict[str, list[str]],
args: "Args",
context: TokenizationContext,
) -> dict:
instructions = examples["instruction"]
responses = examples["response"]
prompts = [
MAGICODER_PROMPT.format(instruction=instruction, response="")
for instruction in instructions
]
completions = responses
assert len(prompts) == len(completions)
prompt_config = EncodingConfig(add_bos=True, add_eos=False)
completion_config = EncodingConfig(add_bos=False, add_eos=True)
prompt_id_batches = context.encode(prompt_config, prompts)
completion_id_batches = context.encode(completion_config, completions)
# prompt_id_batches = context.tokenization_context.encode(prompt_config, prompts)
# completion_id_batches = context.tokenization_context.encode(
# completion_config, completions
# )
assert len(prompt_id_batches) == len(completion_id_batches)
untruncated_input_ids = [
(instruction_ids + response_ids)
for instruction_ids, response_ids in zip(
prompt_id_batches, completion_id_batches
)
]
exceeding_length = [
len(input_id) > args.max_training_seq_length
for input_id in untruncated_input_ids
]
input_ids = [
input_id[: args.max_training_seq_length] for input_id in untruncated_input_ids
]
# NOTE: no need to set EOF to IGNORED_INDEX as it is *implicitly* ignored inside
# the model.forward that shifts the logits left by 1
labels = [
(list(map(lambda _: IGNORED_INDEX, instruction_ids)) + response_ids)[
: args.max_training_seq_length
]
for instruction_ids, response_ids in zip(
prompt_id_batches, completion_id_batches
)
]
# `len` of each returned value must be the same, which is required by `tokenizer.map`
# After `map`, they are treated as individual pieces of data, not as a batch.
assert len(input_ids) == len(labels)
for input_id_batch, label_batch in zip(input_ids, labels):
assert len(input_id_batch) == len(label_batch)
print(context.decode(DecodingConfig.default(), input_ids[0:])[0])
return {
"input_ids": input_ids,
"labels": labels,
"exceeding_length": exceeding_length,
}
def get_data_collator(args: "Args", pad_token_id: int):
"""Pad input_ids to the right, create labels by setting the padding tokens to -100, and
create attention_mask to ignore the padding tokens"""
def collate(examples: list[dict[str, list[int]]]) -> dict[str, torch.Tensor]:
input_ids_unpadded = [example["input_ids"] for example in examples]
labels_unpadded = [example["labels"] for example in examples]
padding_length = (
args.max_training_seq_length if args.pad_to_max_length else None
)
input_ids = pad_sequences(
input_ids_unpadded, pad_token_id, "right", padding_length=padding_length
)
labels = pad_sequences(
labels_unpadded, IGNORED_INDEX, "right", padding_length=padding_length
)
assert input_ids.shape == labels.shape
assert len(input_ids) == len(examples)
# Enforced in `map_raw_dataset`
assert input_ids.shape[-1] <= args.max_training_seq_length
if args.pad_to_max_length:
assert input_ids.shape[-1] == args.max_training_seq_length
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": input_ids.ne(pad_token_id),
}
return collate
@dataclass(frozen=True)
class Args:
datafile_paths: list[str] = field(default_factory=list)
max_training_seq_length: int = field(default=1216)
pad_to_max_length: bool = field(default=False)
eval_dataset_size: float = field(
default=0.05, metadata={"help": "0--1 means ratio, >1 means number of examples"}
)
use_flash_attention: bool = field(default=False)
def train():
parser = HfArgumentParser((ModelArguments, TrainingArguments, Args))
model_args, training_args, args = cast(
tuple[ModelArguments, TrainingArguments, Args],
parser.parse_args_into_dataclasses(),
)
dataset = load_dataset("json", data_files=args.datafile_paths, split="train")
model_key = model_args.model_key
if (model_name_or_path := model_args.model_name_or_path) is None:
model_name_or_path = model_key
tokenization_context = TokenizationContext.from_model_key(
model_key, model_name_or_path
)
# if dataset_config.dpo_jsonl_path is None or dataset_config.dpo_sft:
train_dataset = dataset.map(
function=map_dataset,
fn_kwargs=dict(args=args, context=tokenization_context),
batched=True,
num_proc=N_CORES,
remove_columns=dataset.column_names,
load_from_cache_file=False, # not args.overwrite_cache
desc="Running tokenizer on train dataset",
)
msg = f"#Examples truncated: {sum(train_dataset['exceeding_length'])} / {len(train_dataset)}"
print(msg)
# else:
# train_dataset = dataset
# Shuffling
if training_args.eval_steps is None and training_args.evaluation_strategy == "no":
train_dataset = train_dataset.shuffle(seed=training_args.seed)
eval_dataset = None
else:
print("Splitting dataset")
split_dataset = train_dataset.train_test_split(
test_size=args.eval_dataset_size,
shuffle=True,
seed=training_args.seed,
)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
state = get_model_context(
model_key,
model_name_or_path,
tokenization_context,
inference_mode=False,
use_flash_attention=args.use_flash_attention,
)
print("Parallel mode:", training_args.parallel_mode)
data_collator = get_data_collator(args, state.tokenization_context.pad_token_id)
# neftune_noise_alpha
trainer = Trainer(
model=state.model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
# eval_dataset=small_eval_dataset,
# compute_metrics=compute_metrics,
)
# NOTE: the checkpoint will override the initialized model
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(training_args.output_dir)
state.tokenization_context.tokenizer.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
train()