Create sft.py

sft.py

@@ -0,0 +1,186 @@
+# flake8: noqa
+"""
+pip install -U transformers accelerate trl wandb wheel packaging peft bitsandbytes liger-kernel flash_attn
+
+python sft.py \
+ --run_name="llama3.1-8b-continued2" \
+ --model_name_or_path="meta-llama/Meta-Llama-3.1-8B" \
+ --dataset_name="mlfoundations/dclm-baseline-1.0-parquet,mlabonne/FineTome-100k" \
+ --report_to="wandb" \
+ --optim="adamw_torch_fused" \
+ --lr_scheduler_type="cosine" \
+ --max_steps=10000000 \
+ --max_seq_length=64000 \
+ --learning_rate=0.0001 \
+ --attn_implementation="flash_attention_2" \
+ --save_strategy="steps" \
+ --save_steps 50 \
+ --save_total_limit=10 \
+ --per_device_train_batch_size=1 \
+ --gradient_accumulation_steps=8 \
+ --logging_steps=1 \
+ --num_train_epochs=1 \
+ --load_in_4bit \
+ --push_to_hub \
+ --hub_model_id="ericflo/Llama-3.1-8B-ContinuedTraining2-LoRA" \
+ --hub_strategy="all_checkpoints" \
+ --gradient_checkpointing \
+ --use_peft \
+ --lora_r=128 \
+ --lora_alpha=256 \
+ --lora_dropout=0.05 \
+ --use_liger=true \
+ --packing=true \
+ --torch_dtype="bfloat16" \
+ --output_dir="continuedtraining2_output"
+"""
+
+import logging
+import os
+import random
+from contextlib import nullcontext
+
+from trl.commands.cli_utils import init_zero_verbose, SFTScriptArguments, TrlParser
+from trl.env_utils import strtobool
+
+TRL_USE_RICH = strtobool(os.getenv("TRL_USE_RICH", "0"))
+
+if TRL_USE_RICH:
+ init_zero_verbose()
+ FORMAT = "%(message)s"
+
+ from rich.console import Console
+ from rich.logging import RichHandler
+
+import torch
+from datasets import load_dataset, interleave_datasets
+
+from tqdm.rich import tqdm
+from transformers import AutoTokenizer
+
+from trl import (
+ ModelConfig,
+ RichProgressCallback,
+ SFTConfig,
+ SFTTrainer,
+ get_peft_config,
+ get_quantization_config,
+ get_kbit_device_map,
+)
+
+tqdm.pandas()
+
+if TRL_USE_RICH:
+ logging.basicConfig(format=FORMAT, datefmt="[%X]", handlers=[RichHandler()], level=logging.INFO)
+
+print("Loading tokenizers...")
+METAML_TOK = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
+CHATML_TOK = AutoTokenizer.from_pretrained("NousResearch/Hermes-3-Llama-3.1-8B")
+print("Tokenizers loaded.")
+
+def formatting_prompts_func(example):
+ try:
+ language = example.get('language')
+ url = example.get('url')
+ text = example.get('text')
+ title = example.get('title')
+ conversations = example.get('conversations')
+ source = example.get('source')
+ repo_name = example.get('max_stars_repo_name')
+ repo_path = example.get('max_stars_repo_path')
+ star_count = example.get('max_stars_count')
+ content = example.get('content')
+ # mlfoundations/dclm-baseline-1.0-parquet
+ if language and url and text:
+ return f'{language} {url} {text}'
+ elif title and url and text: # wikimedia/wikipedia
+ return f'{title} {url} {text}'
+ elif conversations: # mlabonne/FineTome-100k
+ rows = [{
+ "role": {"system": "system", "gpt": "assistant", "human": "user"}[row["from"]],
+ "content": row["value"],
+ } for row in conversations]
+ tok = random.choice([METAML_TOK, CHATML_TOK])
+ return f'{source} {tok.apply_chat_template(rows, tokenize=False)}'
+ elif "max_stars_repo_name" in example: # bigcode/starcoderdata
+ return f'{example["max_stars_repo_name"]} {example["max_stars_repo_path"]} {example["max_stars_count"]} {example["content"]}'
+ print(f"Unknown example: {example}")
+ raise ValueError(f"Unknown example: {example}")
+ except Exception as e:
+ print(e)
+ raise e
+
+if __name__ == "__main__":
+ parser = TrlParser((SFTScriptArguments, SFTConfig, ModelConfig))
+ args, training_args, model_config = parser.parse_args_and_config()
+
+ # Force use our print callback
+ if TRL_USE_RICH:
+ training_args.disable_tqdm = True
+ console = Console()
+
+ ################
+ # Model init kwargs & Tokenizer
+ ################
+ model_config.lora_target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
+ quantization_config = get_quantization_config(model_config)
+ model_kwargs = dict(
+ revision=model_config.model_revision,
+ trust_remote_code=model_config.trust_remote_code,
+ attn_implementation=model_config.attn_implementation,
+ torch_dtype=model_config.torch_dtype,
+ use_cache=False if training_args.gradient_checkpointing else True,
+ device_map=get_kbit_device_map() if quantization_config is not None else None,
+ quantization_config=quantization_config,
+ )
+ training_args.model_init_kwargs = model_kwargs
+ tokenizer = AutoTokenizer.from_pretrained(
+ model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, use_fast=True
+ )
+ tokenizer.pad_token = tokenizer.eos_token
+
+ ################
+ # Dataset
+ ################
+ dataset_names = args.dataset_name.split(',')
+ train_datasets = [load_dataset(name, split="train", streaming=True) for name in dataset_names]
+ train_datasets.append(load_dataset("bigcode/starcoderdata", data_dir="python", split="train", streaming=True))
+ train_datasets.append(load_dataset("wikimedia/wikipedia", "20231101.en", split="train", streaming=True))
+ train_datasets.append(load_dataset("wikimedia/wikipedia", "20231101.es", split="train", streaming=True))
+ train_datasets.append(load_dataset("wikimedia/wikipedia", "20231101.fr", split="train", streaming=True))
+ interleaved_dataset = interleave_datasets(train_datasets)
+ eval_dataset = interleaved_dataset.take(100)
+ train_dataset = interleaved_dataset.skip(100)
+
+ print(train_dataset)
+ print(eval_dataset)
+
+ ################
+ # Optional rich context managers
+ ###############
+ init_context = nullcontext() if not TRL_USE_RICH else console.status("[bold green]Initializing the SFTTrainer...")
+ save_context = (
+ nullcontext()
+ if not TRL_USE_RICH
+ else console.status(f"[bold green]Training completed! Saving the model to {training_args.output_dir}")
+ )
+
+ ################
+ # Training
+ ################
+ with init_context:
+ trainer = SFTTrainer(
+ model=model_config.model_name_or_path,
+ args=training_args,
+ train_dataset=train_dataset,
+ eval_dataset=eval_dataset,
+ tokenizer=tokenizer,
+ peft_config=get_peft_config(model_config),
+ callbacks=[RichProgressCallback] if TRL_USE_RICH else None,
+ formatting_func=formatting_prompts_func,
+ )
+
+ trainer.train()
+
+ with save_context:
+ trainer.save_model(training_args.output_dir)