import os import math import glob from typing import Dict, List, Any import torch from datasets import load_dataset, DatasetDict from transformers import ( AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, ) MODEL_NAME = os.environ.get("MODEL_NAME", "meta-llama/Meta-Llama-3-8B") TEXT_COL = os.environ.get("TEXT_COL", "text") DATASET_ID = os.environ.get("DATASET_ID", "") LOCAL_SHARDS_GLOB = os.environ.get("LOCAL_SHARDS_GLOB", "data/raw/train_shards_clean/train_*.jsonl") TRAIN_SPLIT = os.environ.get("TRAIN_SPLIT", "train") EVAL_SPLIT = os.environ.get("EVAL_SPLIT", "validation") OUT_DIR = os.environ.get("OUT_DIR", "/workspace/outputs_cpt_llama3_8b") MAX_LEN = int(os.environ.get("MAX_LEN", "2048")) BATCH = int(os.environ.get("BATCH", "1")) EVAL_BATCH = int(os.environ.get("EVAL_BATCH", "1")) GAS = int(os.environ.get("GAS", "16")) LR = float(os.environ.get("LR", "1e-5")) STREAMING = os.environ.get("STREAMING", "1") == "1" MAX_STEPS = int(os.environ.get("MAX_STEPS", "1000")) # required when streaming EPOCHS = float(os.environ.get("EPOCHS", "1")) # used when not streaming LOGGING_STEPS = int(os.environ.get("LOGGING_STEPS", "10")) SAVE_STEPS = int(os.environ.get("SAVE_STEPS", "500")) EVAL_STEPS = int(os.environ.get("EVAL_STEPS", "500")) FP16 = os.environ.get("FP16", "0") == "1" BF16 = os.environ.get("BF16", "0") == "1" PUSH_TO_HUB = os.environ.get("PUSH_TO_HUB", "0") == "1" HF_MODEL_REPO = os.environ.get("HF_MODEL_REPO", "") def load_train_eval() -> DatasetDict: files = sorted(glob.glob(LOCAL_SHARDS_GLOB)) if files: print("LOCAL_SHARDS_GLOB:", LOCAL_SHARDS_GLOB) print("Loading local shards:", len(files), "files") data_files = {"train": files} val_file = os.environ.get("VAL_FILE", "") if val_file and os.path.exists(val_file): data_files["validation"] = val_file ds_train = load_dataset("json", data_files=data_files, split="train", streaming=STREAMING) if "validation" in data_files: ds_val = load_dataset("json", data_files=data_files, split="validation", streaming=STREAMING) else: take_n = int(os.environ.get("STREAM_EVAL_TAKE", "2000")) ds_val = ds_train.take(take_n) return DatasetDict({"train": ds_train, "validation": ds_val}) if not DATASET_ID: raise ValueError("No local shards found and DATASET_ID is empty. Set DATASET_ID or LOCAL_SHARDS_GLOB.") print("DATASET_ID:", DATASET_ID) ds = load_dataset(DATASET_ID) if TRAIN_SPLIT not in ds: raise ValueError(f"Train split '{TRAIN_SPLIT}' not found. Available: {list(ds.keys())}") if EVAL_SPLIT not in ds: raise ValueError(f"Eval split '{EVAL_SPLIT}' not found. Available: {list(ds.keys())}") return DatasetDict({"train": ds[TRAIN_SPLIT], "validation": ds[EVAL_SPLIT]}) def infer_remove_columns(ds_split): # IMPORTANT: remove ALL original columns including TEXT_COL ex = next(iter(ds_split)) cols = list(ex.keys()) if TEXT_COL not in cols: raise ValueError(f"TEXT_COL='{TEXT_COL}' not found. Columns: {cols}") return cols def normalize_text(x: Any) -> str: # some rows can be list/None/etc. if x is None: return "" if isinstance(x, str): return x if isinstance(x, list): # join tokens/parts safely return " ".join([str(t) for t in x if t is not None]) return str(x) def main(): print("MODEL_NAME:", MODEL_NAME) print("OUT_DIR:", OUT_DIR) print("STREAMING:", STREAMING) print("FP16:", FP16, "BF16:", BF16) if STREAMING: print("MAX_STEPS:", MAX_STEPS) else: print("EPOCHS:", EPOCHS) ds = load_train_eval() try: print("Train rows:", len(ds["train"])) except Exception: print("Train rows: (streaming, unknown)") try: print("Val rows:", len(ds["validation"])) except Exception: print("Val rows: (streaming, unknown)") remove_cols_train = infer_remove_columns(ds["train"]) remove_cols_val = infer_remove_columns(ds["validation"]) print("Removing columns (train):", remove_cols_train) print("Removing columns (val):", remove_cols_val) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token def tokenize(batch: Dict[str, List]): raw_texts = batch.get(TEXT_COL) if raw_texts is None: raise ValueError(f"Column '{TEXT_COL}' not found. Got: {list(batch.keys())}") texts = [normalize_text(t) for t in raw_texts] # drop empty strings in-batch (keep alignment by replacing with eos) texts = [t if t.strip() else tokenizer.eos_token for t in texts] return tokenizer( texts, truncation=True, max_length=MAX_LEN, padding=False, ) # NOTE: remove ALL original columns so only token fields remain train_tok = ds["train"].map(tokenize, batched=True, remove_columns=remove_cols_train) eval_tok = ds["validation"].map(tokenize, batched=True, remove_columns=remove_cols_val) dtype = (torch.bfloat16 if BF16 else None) # fp16: keep dtype=None, let Trainer autocast model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, dtype=dtype, device_map="auto", ) model.resize_token_embeddings(len(tokenizer)) collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) train_args = dict( output_dir=OUT_DIR, per_device_train_batch_size=BATCH, per_device_eval_batch_size=EVAL_BATCH, gradient_accumulation_steps=GAS, learning_rate=LR, logging_steps=LOGGING_STEPS, save_steps=SAVE_STEPS, eval_steps=EVAL_STEPS, do_eval=True, eval_strategy="steps", save_strategy="steps", report_to="none", fp16=FP16, bf16=BF16, gradient_checkpointing=True, save_total_limit=int(os.environ.get("SAVE_TOTAL_LIMIT", "2")), dataloader_num_workers=int(os.environ.get("NUM_WORKERS", "2")), remove_unused_columns=False, ) if STREAMING: train_args["max_steps"] = MAX_STEPS else: train_args["num_train_epochs"] = EPOCHS args = TrainingArguments(**train_args) trainer = Trainer( model=model, args=args, train_dataset=train_tok, eval_dataset=eval_tok, data_collator=collator, tokenizer=tokenizer, ) print("\nStarting CPT training...") trainer.train() metrics = trainer.evaluate() print("\nEval metrics:", metrics) if "eval_loss" in metrics: print("Perplexity:", math.exp(metrics["eval_loss"])) trainer.save_model(OUT_DIR) tokenizer.save_pretrained(OUT_DIR) print("\nSaved to:", OUT_DIR) if PUSH_TO_HUB: if not HF_MODEL_REPO: raise ValueError("PUSH_TO_HUB=1 but HF_MODEL_REPO is empty.") print("\nPushing to HF model repo:", HF_MODEL_REPO) trainer.model.push_to_hub(HF_MODEL_REPO) tokenizer.push_to_hub(HF_MODEL_REPO) print("✅ Pushed to:", HF_MODEL_REPO) if __name__ == "__main__": main()