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sft.py

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+import sys
+import logging
+
+import datasets
+from datasets import load_dataset
+import torch
+import transformers
+from trl import SFTTrainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
+from typing import Dict, List
+
+logger = logging.getLogger(__name__)
+
+"""
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch  --gradient_clipping=1.0  --multi_gpu   --num_processes=8   --num_machines=1   --mixed_precision=bf16   --zero_stage=3   sft.py
+"""
+###################
+# Hyper-parameters
+###################
+
+training_config = {
+    "bf16": True,
+    "do_eval": False,
+    "learning_rate": 1e-04,
+    "log_level": "info",
+    "logging_steps": 20,
+    "logging_strategy": "steps",
+    "lr_scheduler_type": "cosine",
+    "num_train_epochs": 1,
+    "max_steps": -1,
+    "output_dir": "./ckpts",
+    "overwrite_output_dir": True,
+    "per_device_eval_batch_size": 8,
+    "per_device_train_batch_size": 8,
+    "remove_unused_columns": True,
+    "save_steps": 1000,
+    "save_total_limit": 1,
+    "seed": 0,
+    "gradient_checkpointing": True,
+    "gradient_checkpointing_kwargs":{"use_reentrant": False},
+    "gradient_accumulation_steps": 1,
+    "warmup_ratio": 0.03,
+    }
+train_conf = TrainingArguments(**training_config)
+
+
+###############
+# Setup logging
+###############
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+log_level = train_conf.get_process_log_level()
+logger.setLevel(log_level)
+datasets.utils.logging.set_verbosity(log_level)
+transformers.utils.logging.set_verbosity(log_level)
+transformers.utils.logging.enable_default_handler()
+transformers.utils.logging.enable_explicit_format()
+
+# Log on each process a small summary
+logger.warning(
+    f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
+    + f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
+)
+logger.info(f"Training/evaluation parameters {train_conf}")
+
+
+################
+# Model Loading
+################
+
+checkpoint_path = "./"
+model_kwargs = dict(
+    use_cache=False,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",
+    torch_dtype=torch.bfloat16,
+    device_map=None
+)
+model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
+tokenizer.model_max_length = 2048
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
+tokenizer.padding_side = 'right'
+
+
+##################
+# Data Processing
+##################
+def apply_chat_template(
+    example,
+    tokenizer,
+):
+    messages = example["messages"]
+    example["text"] = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=False)
+    return example
+
+raw_dataset = load_dataset("allenai/tulu-v2-sft-mixture")
+train_dataset = raw_dataset["train"]
+column_names = list(train_dataset.features)
+
+processed_dataset = train_dataset.map(
+    apply_chat_template,
+    fn_kwargs={"tokenizer": tokenizer},
+    num_proc=64,
+    remove_columns=column_names,
+    desc="Applying chat template to train_sft",
+)
+
+
+###########
+# Freeze Transformer
+###########
+for param in model.parameters():
+    param.requires_grad = False
+
+for name, param in model.named_parameters():
+    if 'router' in name.lower():
+        param.requires_grad = True
+
+###########
+# Training
+###########
+trainer = SFTTrainer(
+    model=model,
+    args=train_conf,
+    peft_config=None,
+    train_dataset=processed_dataset,
+    eval_dataset=None,
+    max_seq_length=2048,
+    dataset_text_field="text",
+    tokenizer=tokenizer,
+    packing=False
+)
+
+train_result = trainer.train()
+metrics = train_result.metrics
+trainer.log_metrics("train", metrics)
+trainer.save_metrics("train", metrics)
+trainer.save_state()
+
+# ############
+# # Save model
+# ############
+trainer.save_model(train_conf.output_dir)