import os import sys from functools import partial from typing import List, Union import fire import numpy as np if os.path.dirname(os.path.abspath(__file__)) not in sys.path: sys.path.append(os.path.dirname(os.path.abspath(__file__))) from loaders import get_loaders, get_tokenizer from prompter import generate_prompt, prompt_types, PromptType from utils import get_githash, copy_code import torch def log(*args, **kwargs): if int(os.environ.get("LOCAL_RANK", 0)) == 0: if 'flush' not in kwargs: kwargs['flush'] = True print(*args, **kwargs) # supported by huggingface evaluate supported_metrics = ['bleu', 'rouge', 'sacrebleu', 'meteor'] def train( save_code: bool = False, run_id: int = None, base_model: str = 'h2oai/h2ogpt-oig-oasst1-512-6_9b', # base_model: str = 'h2oai/h2ogpt-oasst1-512-12b', # base_model: str = 'h2oai/h2ogpt-oasst1-512-20b', # base_model: str = 'EleutherAI/gpt-neox-20b', # base_model: str = 'EleutherAI/pythia-12b-deduped', # base_model: str = 'togethercomputer/GPT-NeoXT-Chat-Base-20B', # base_model: str = 'decapoda-research/llama-7b-hf', # base_model: str = 'decapoda-research/llama-13b-hf', # base_model: str = 'decapoda-research/llama-30b-hf', # base_model: str = 'EleutherAI/gpt-j-6B', # only needed if base_model is self-exported HF state without tokenizer tokenizer_base_model: str = None, # tokenizer_base_model: str = 'EleutherAI/gpt-neox-20b', data_path: str = "h2oai/openassistant_oasst1_h2ogpt", data_col_dict: dict = None, # data_path: str = "./dai_docs.train.json", prompt_type: Union[str, int] = "plain", # "plain", "instruct", "quality", "human_bot", "dai_faq" valid_path: str = None, # valid_path: str = "./dai_docs.valid.json", # data_mix_in_path: str = "laion/OIG", # way too big, medium quality data_mix_in_path: str = "0-hero/OIG-small-chip2", # high quality, 50 MB, good enough for now data_mix_in_factor: float = 0.0, # >1: more mix-in data, <1: more of data_path data data_mix_in_col_dict: dict = {'user': 'instruction', 'chip2': 'output'}, data_mix_in_prompt_type: str = "instruct", # just instruction->output, same as instruct output_dir: str = None, # LoRA checkpoint continuation lora_weights: str = "", # batching training hyperparams batch_size: int = 128, micro_batch_size: int = 4, gradient_checkpointing=False, # unnecessary with gradient accumulation enabled fp16=True, train_8bit=False, train_4bit=False, # general training hyperparams num_epochs: float = 1, learning_rate: float = 3e-4, # validation settings val_set_size: int = None, val_metrics: List[str] = [], eval_steps: int = None, # to control eval steps via steps eval_epochs: float = None, # to control eval steps via epochs # lora hyperparams lora_r: int = 8, lora_alpha: int = 16, lora_dropout: float = 0.05, lora_target_modules: List[str] = None, llama_type: bool = None, llama_flash_attn: bool = False, # llm hyperparams train_on_inputs: bool = True, # if False, masks out inputs in loss group_by_length: bool = False, # if True, faster, but produces an odd training loss curve resume_from_checkpoint: str = None, # either training checkpoint or final adapter cutoff_len: int = 512, # larger values use more memory drop_truncations: bool = False, # if True, drop any truncated long sequences # torch training params ddp: bool = True, # set to False if OOM with True, for multi-GPU model parallelism local_files_only: bool = False, # else will download new versions, normally unwanted resume_download: bool = True, use_auth_token: Union[str, bool] = False, # True requires CLI did huggingface-cli login before running warmup_steps: int = 100, logging_steps: int = 1, save_steps: int = None, # must be round multiple of eval_steps save_total_limit: int = 3, add_eos_token: bool = False, ): if llama_flash_attn: # Need to call this before importing transformers. from llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn replace_llama_attn_with_flash_attn() # allow set token directly use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token) prompt_type = str(prompt_type) # migration from integers assert prompt_type in prompt_types world_size = int(os.getenv("WORLD_SIZE", 1)) local_rank = int(os.getenv("LOCAL_RANK", 0)) rank = int(os.getenv("RANK", 0)) print(f"local_rank: {local_rank}") print(f"global rank: {rank}") gpus = max(world_size, torch.cuda.device_count()) run_id = run_id or 0 if not data_path: raise ValueError("No data_path provided") if not output_dir: output_dir = f"{base_model.split('/')[-1]}.{data_path.replace('/', '')}.{num_epochs}_epochs.{get_githash() or 'nogit'}.{run_id}" if os.path.exists(output_dir) and not resume_from_checkpoint: raise FileExistsError( f"output_dir {output_dir} based on run_id {run_id} already exists. Please pick a different run_id.") else: if os.path.exists(output_dir) and not resume_from_checkpoint: raise FileExistsError( f"output_dir {output_dir} already exists. Please pick a different output_dir, or specify a run_id instead.") device_map = "auto" if save_code: copy_code(run_id) if tokenizer_base_model is None: tokenizer_base_model = base_model if llama_type is None: llama_type = "llama" in base_model.lower() if llama_type and llama_flash_attn: import pkg_resources try: pkg_resources.get_distribution('flash_attn') can_do_flash_attn = True except (pkg_resources.DistributionNotFound, pkg_resources.ContextualVersionConflict): can_do_flash_attn = False if not can_do_flash_attn: raise RuntimeError("""Flash attention not installed. NOTE: for current pytorch 2.0, flash attention requires installing cuda 11.7 via https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=runfile_local and then when running, to avoid installing driver, docs, samples, just install toolkit. Then when pip installing flash attention do: CUDA_HOME=/usr/local/cuda-11.7 pip install flash-attn""") assert ( base_model ), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'" gradient_accumulation_steps = batch_size // micro_batch_size assert gradient_accumulation_steps >= world_size, "must increase batch_size for multi-GPU" device_map = "auto" locals_dict = locals() locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()]) log(f"Training model with params:\n{locals_print}") log("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), get_githash())) max_memory = None if gpus > 1: if ddp: log("Distributed: data parallel") device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} gradient_accumulation_steps = gradient_accumulation_steps // world_size else: free_in_GB = int(min(torch.cuda.mem_get_info()) / 1024 ** 3) max_memory = f"{free_in_GB - 2}GB" max_memory = {i: max_memory for i in range(gpus)} log("world_size: %d" % world_size) log("num_gpus: %d" % gpus) log("max mem: %s" % max_memory) model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=False, llama_type=llama_type) model = model_loader.from_pretrained( base_model, load_in_8bit=train_8bit, load_in_4bit=train_4bit, device_map=device_map, torch_dtype=torch.float16, max_memory=max_memory, local_files_only=local_files_only, trust_remote_code=True, resume_download=resume_download, use_auth_token=use_auth_token, ) if gpus > 1: if not ddp: log("model parallel") model.is_parallelizable = True model.model_parallel = True tokenizer = get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token) if train_8bit or train_4bit: from peft import ( prepare_model_for_kbit_training, ) model = prepare_model_for_kbit_training(model) from peft import LoraConfig, get_peft_model, set_peft_model_state_dict try: from peft import utils lora_mappings = utils.TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.copy() except AttributeError: from peft import mapping lora_mappings = mapping.TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.copy() lora_mappings['distilgpt2'] = ["c_attn"] if lora_weights: from peft import PeftModel model = PeftModel.from_pretrained( model, lora_weights, torch_dtype=torch.float16, device_map=device_map, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, ) elif lora_r > 0: if lora_target_modules is None: base_model_lower = base_model.lower() if base_model_lower in lora_mappings: lora_target_modules_cand = [lora_mappings[base_model_lower]] else: lora_target_modules_cand = [["query_key_value"], ["q_proj", "v_proj"]] else: lora_target_modules_cand = [lora_target_modules] for lora_target_modules in lora_target_modules_cand: try: config = LoraConfig( r=lora_r, lora_alpha=lora_alpha, target_modules=lora_target_modules, lora_dropout=lora_dropout, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) break except ValueError as e: if "Target modules" in str(e) and "not found" in str(e): continue else: raise from peft import PeftModel assert isinstance(model, PeftModel), "LoRA failed. Please provide --lora_target_modules explicitly." if resume_from_checkpoint: # Check the available weights and load them checkpoint_name = os.path.join( resume_from_checkpoint, "pytorch_model.bin" ) # Full checkpoint if not os.path.exists(checkpoint_name): checkpoint_name = os.path.join( resume_from_checkpoint, "adapter_model.bin" ) # only LoRA model - LoRA config above has to fit resume_from_checkpoint = False # So the trainer won't try loading its state # The two files above have a different name depending on how they were saved, but are actually the same. if os.path.exists(checkpoint_name): log(f"Restarting from {checkpoint_name}") adapters_weights = torch.load(checkpoint_name) set_peft_model_state_dict(model, adapters_weights) else: log(f"Checkpoint {checkpoint_name} not found") print(model) try: # only for PeftModel model.print_trainable_parameters() # Be more transparent about the % of trainable params. except: pass metrics = {} for name in supported_metrics: if name in val_metrics: import evaluate # Causes hang for 'python generate.py' on dual 4090 if imported early, 100% reproducible metrics[name] = evaluate.load(name) log("Using Validation Metrics: %s" % str(list(metrics.keys()))) log("Supported Metrics: %s" % supported_metrics) if val_set_size is None: if len(metrics) == 0: val_set_size = 1000 else: val_set_size = 100 log("Auto set val_set_size %s" % val_set_size) elif val_set_size < 1.0 and val_set_size != 0: raise RuntimeError("Fractional validation size not supported.") from datasets import load_dataset, concatenate_datasets if valid_path: data = load_dataset("json", data_files={"train": data_path, "valid": valid_path}) else: if "json" in data_path: data = load_dataset("json", data_files={"train": data_path}) else: data = load_dataset(data_path) data = data.rename_columns(data_col_dict or {}) valid_data = None train_data_mix_in = None valid_data_mix_in = None if data_mix_in_path and data_mix_in_factor > 0: # get mix-in training/validation data - to keep model "sane" num_rows = data["train"].num_rows log("Loading mix-in dataset: %s" % data_mix_in_path) if "json" in data_mix_in_path: data_mix_in = load_dataset("json", data_files={"train": data_mix_in_path})["train"] else: data_mix_in = load_dataset(data_mix_in_path)["train"] # can be large data_mix_in = data_mix_in.rename_columns(data_mix_in_col_dict or {}) mix_in_rows = int(num_rows * data_mix_in_factor) if mix_in_rows > data_mix_in.num_rows: # duplicate rows if mix-in is smaller than required log("Duplicating mixin to compensate for its size for training size and mixin fraction") data_mix_in = concatenate_datasets([data_mix_in] * int(np.ceil(mix_in_rows / data_mix_in.num_rows))) # only get as much as we need to balance valid_size = min(data_mix_in.num_rows // 2, val_set_size or 0) train_size = max(1, min(data_mix_in.num_rows - valid_size, mix_in_rows)) mixin_small = data_mix_in.train_test_split( test_size=train_size + valid_size, shuffle=True, seed=np.random.randint(10000), )["test"] if valid_size: mixin_train_test = mixin_small.train_test_split( test_size=valid_size, shuffle=False, ) train_data_mix_in = mixin_train_test["train"] valid_data_mix_in = mixin_train_test["test"] else: train_data_mix_in = mixin_small if "prompt_type" not in train_data_mix_in.column_names: train_data_mix_in = train_data_mix_in.add_column( "prompt_type", [data_mix_in_prompt_type] * train_data_mix_in.num_rows, ) log("Added prompt type %s to mix-in training data" % data_mix_in_prompt_type) if valid_data_mix_in and "prompt_type" not in valid_data_mix_in.column_names: valid_data_mix_in = valid_data_mix_in.add_column( "prompt_type", [data_mix_in_prompt_type] * valid_data_mix_in.num_rows, ) log("Added prompt type %s to mix-in validation data" % data_mix_in_prompt_type) log("Created mix-in data:\nTrain %s\nValid %s" % (train_data_mix_in, valid_data_mix_in)) # get our own training/validation data - for fine-tuning if val_set_size > 0 and not valid_path and not data_mix_in_path: # create valid split from train train_val = data["train"].train_test_split( test_size=val_set_size, shuffle=True, seed=42 ) train_data = train_val["train"] valid_data = train_val["test"] else: train_data = data["train"] if valid_path: # use given valid split, has priority over data_mix_in_path valid_data = data["valid"] if "prompt_type" not in train_data.column_names: train_data = train_data.add_column( "prompt_type", [prompt_type] * train_data.num_rows, ) log("Added prompt type %s to training data" % prompt_type) if valid_data and "prompt_type" not in valid_data.column_names: valid_data = valid_data.add_column( "prompt_type", [prompt_type] * valid_data.num_rows, ) log("Added prompt type %s to validation data" % prompt_type) assert train_data is not None generate_and_tokenize_prompt_fun = partial(generate_and_tokenize_prompt, prompt_type=prompt_type, train_on_inputs=train_on_inputs, add_eos_token=add_eos_token, cutoff_len=cutoff_len, tokenizer=tokenizer) # shuffle and tokenize data if train_data_mix_in: train_data = concatenate_datasets([train_data, train_data_mix_in]) log("Tokenizing %s training rows" % train_data.num_rows) train_data = train_data.shuffle().map(generate_and_tokenize_prompt_fun, num_proc=os.cpu_count() // torch.cuda.device_count()) if drop_truncations: log("avoid keeping truncated cases to avoid contaminating model with truncation cases. Original size: %s" % train_data.num_rows) prune_long_sequences_func = partial(prune_long_sequences, cutoff_len=cutoff_len) train_data = train_data.filter(prune_long_sequences_func, num_proc=os.cpu_count() // torch.cuda.device_count()) log("avoid keeping truncated cases to avoid contaminating model with truncation cases. New size: %s" % train_data.num_rows) train_set_size = len(train_data) if valid_data and valid_data_mix_in: valid_data = concatenate_datasets([valid_data, valid_data_mix_in]) elif valid_data_mix_in: valid_data = valid_data_mix_in if valid_data: log("Tokenizing %s validation rows" % valid_data.num_rows) valid_data = valid_data.shuffle().map(generate_and_tokenize_prompt_fun, num_proc=os.cpu_count() // torch.cuda.device_count()) val_set_size = len(valid_data) else: val_set_size = 0 log("Final fine-tuning data:\nTrain %s\nValid %s" % (train_data, valid_data)) sample_row_dict = train_data[:1] del sample_row_dict['input_ids'] del sample_row_dict['attention_mask'] del sample_row_dict['labels'] log("Sample input: %s" % sample_row_dict) try: import neptune from transformers.integrations import NeptuneCallback neptune_run = neptune.init_run( source_files=[], ) log("Connected to Neptune.") except ImportError: neptune_run = None log("Please pip install neptune for tracking.") except neptune.exceptions.NeptuneMissingApiTokenException: neptune_run = None os.environ["NEPTUNE_MODE"] = 'debug' log("No neptune configured, set NEPTUNE_API_TOKEN env var.") if neptune_run: neptune_callback = NeptuneCallback(run=neptune_run) callbacks = [neptune_callback] else: from transformers.integrations import TensorBoardCallback, is_tensorboard_available if is_tensorboard_available: # tensorboard --logdir=runs/ from torch.utils.tensorboard import SummaryWriter tb_writer = SummaryWriter() callbacks = [TensorBoardCallback(tb_writer=tb_writer)] else: callbacks = [] expected_steps = (train_set_size * num_epochs) // batch_size if eval_steps is None and eval_epochs is None: # 20 evaluations for a run eval_steps = max(1, int(expected_steps / 20)) log("Auto set eval_steps to %s out of %s total training steps" % (eval_steps, expected_steps)) elif eval_steps is None and eval_epochs is not None: eval_steps = max(1, int(expected_steps * eval_epochs / num_epochs)) log("Auto converted eval_epochs=%s to eval_steps %s" " out of %s total training steps" % (eval_epochs, eval_steps, expected_steps)) if save_steps is None: save_steps = eval_steps log("Auto step save_steps to %s" % save_steps) elif save_steps > eval_steps: # save steps must be round multiple of eval_steps save_steps0 = save_steps save_steps = max(1, (save_steps // eval_steps)) * eval_steps if save_steps0 != save_steps: log("Auto converted save_steps from %s to %s" % (save_steps0, save_steps)) def compute_metrics(eval_preds): # e.g. see: https://huggingface.co./docs/transformers/v4.25.1/en/tasks/translation#evaluate inputs = eval_preds.inputs label_ids = eval_preds.label_ids predictions = eval_preds.predictions # inputs = np.where(inputs != -100, inputs, tokenizer.pad_token_id) # decoded_inputs = tokenizer.batch_decode(inputs, skip_special_tokens=True) # decoded_inputs = [pred.strip() for pred in decoded_inputs] label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id) # tokenizer behavior like generate time decoded_labels = tokenizer.batch_decode(label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) decoded_labels = [pred.strip() for pred in decoded_labels] predictions = np.argmax(predictions, -1) predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id) # tokenizer behavior like generate time decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True) decoded_predictions = [pred.strip() for pred in decoded_predictions] result = {} for metric in metrics.values(): result1 = metric.compute(predictions=decoded_predictions, references=decoded_labels) # get rid of lists, for precision etc., for now numeric_results = {k: v for k, v in result1.items() if isinstance(v, (int, float))} result.update(numeric_results) return result # the callback that computes metrics of interest if val_metrics: trainer_kwargs = dict(compute_metrics=compute_metrics) else: trainer_kwargs = dict() import transformers trainer = transformers.Trainer( model=model, tokenizer=tokenizer, train_dataset=train_data, eval_dataset=valid_data, # FIXME: might need Seq2SeqTrainingArguments for some models args=transformers.TrainingArguments( per_device_train_batch_size=micro_batch_size, per_device_eval_batch_size=1, eval_accumulation_steps=10, # predict_with_generate=True, # SEQ2SEQ only include_inputs_for_metrics=True, gradient_accumulation_steps=gradient_accumulation_steps, warmup_steps=warmup_steps, num_train_epochs=num_epochs, learning_rate=learning_rate, gradient_checkpointing=gradient_checkpointing, fp16=fp16, # cosnider 8-bit adam: https://huggingface.co./docs/transformers/v4.18.0/en/performance#8bit-adam optim="adamw_torch", # consider "adafactor" to save memory logging_steps=logging_steps, logging_strategy="steps", evaluation_strategy="steps" if val_set_size > 0 else "no", save_strategy="steps", eval_steps=eval_steps if val_set_size > 0 else None, save_steps=save_steps, output_dir=output_dir, save_total_limit=save_total_limit, load_best_model_at_end=True if val_set_size > 0 else False, ddp_find_unused_parameters=False if ddp else None, group_by_length=group_by_length, # fsdp="shard_grad_op auto_wrap" if gpus > 1 and not ddp else None, # fsdp_min_num_params=20000 if gpus > 1 and not ddp else None, report_to='tensorboard' if not neptune_run else 'neptune', ), data_collator=transformers.DataCollatorForSeq2Seq( tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True ), callbacks=callbacks, **trainer_kwargs, ) model.config.use_cache = False if torch.__version__ >= "2" and sys.platform != "win32": model = torch.compile(model) # WIP (not generally replacing layers until pytorch 2.1) if not llama_flash_attn: torch.backends.cuda.enable_flash_sdp(True) if gpus > 1 and not ddp: assert trainer.is_model_parallel else: assert not trainer.is_model_parallel trainer.train(resume_from_checkpoint=resume_from_checkpoint) model.save_pretrained(output_dir) log("\n If there's a warning about missing keys above, please disregard :)") def tokenize(prompt, tokenizer, cutoff_len, add_eos_token=False): # there's probably a way to do this with the tokenizer settings # but again, gotta move fast result = tokenizer( prompt, truncation=True, max_length=cutoff_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < cutoff_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) result["labels"] = result["input_ids"].copy() return result def prune_long_sequences(data_point, cutoff_len=None): """ Prune if too long for tokenizer, so truncation doesn't lead training to learn from truncated language :param data_point: :param cutoff_len: :return: """ assert cutoff_len is not None return len(data_point['input_ids']) < cutoff_len def generate_and_tokenize_prompt(data_point, prompt_type=None, train_on_inputs=False, add_eos_token=False, cutoff_len=None, tokenizer=None): assert prompt_type is not None assert cutoff_len is not None assert tokenizer is not None prompt_dict = '' # only for custom prompt_type assert prompt_type != PromptType.custom.name, "custom not setup for finetune" full_prompt, _, _, _, _ = generate_prompt(data_point, prompt_type, prompt_dict, False, False, False) tokenized_full_prompt = tokenize(full_prompt, tokenizer, cutoff_len, add_eos_token=add_eos_token) if not train_on_inputs: user_prompt, _, _, _, _ = generate_prompt({**data_point, "output": ""}, prompt_type, prompt_dict, False, False, False) tokenized_user_prompt = tokenize(user_prompt, tokenizer, cutoff_len, add_eos_token=add_eos_token) user_prompt_len = len(tokenized_user_prompt["input_ids"]) if add_eos_token: user_prompt_len -= 1 # ignore_index=-100 ensures torch/tf don't include padding token id in CrossEntropyLoss tokenized_full_prompt["labels"] = [ -100 ] * user_prompt_len + tokenized_full_prompt["labels"][ user_prompt_len: ] # could be sped up, probably return tokenized_full_prompt def test_debug(): fire.Fire(train) def entrypoint_main(): CONFIG = "NCCL_P2P_LEVEL=LOC WORLD_SIZE=5 torchrun --nnodes=5 --master_addr=10.10.10.2 --master_port=1111 --nproc_per_node=1" CMD = "finetune.py --data_path=config.json --num_epochs=1 --base_model=decapoda-research/llama-13b-hf" log(f""" Example runs on 4 GPUs: WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='decapoda-research/llama-7b-hf' --data_path=data/config.json --run_id=0 &> 0.log WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='decapoda-research/llama-30b-hf' --data_path=data/config.json --batch_size=16 --micro_batch_size=1 --run_id=1 --save_code=True &> 1.log WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='EleutherAI/gpt-j-6B' --data_path=data/config.json --run_id=2 &> 2.log WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='EleutherAI/gpt-neox-20b' --data_path=data/config.json --run_id=8 --batch_size=16 --micro_batch_size=4 &> 8.log WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --data_path=data/config.json --prompt_type='dai_faq' --run_id=13 --batch_size=16 --micro_batch_size=4 --num_epochs=100 --val_set_size=0 data_mix_in_path='' &> 13.log WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --data_path=data/config.json --run_id=28 --batch_size=16 --micro_batch_size=4 --num_epochs=8 --val_set_size=0 --data_mix_in_factor=0.1 --data_mix_in_prompt_type='human_bot' --save_code=True --cutoff_len=512 &> 28.log All metrics: CUDA_VISIBLE_DEVICES= finetune.py --data_mix_in_factor=0 --eval_steps=100 --warmup_steps=2 --val_set_size=100 --val_metrics="['bleu', 'rouge', 'sacrebleu', 'meteor']" # Fine-tune 20B on 24GB GPUs across 3 nodes with 3+2+2 GPUs rippa> NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1,2" torchrun --node_rank 0 --nproc_per_node=3 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank0 ova> NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1" torchrun --node_rank 1 --nproc_per_node=2 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank1 timemachine> NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1" torchrun --node_rank 2 --nproc_per_node=2 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank2 """, flush=True) if os.environ.get("LOCAL_RANK") is None: # then not using torchrun, so can't do distributed, ensure CVD set assert os.environ.get( "CUDA_VISIBLE_DEVICES") is not None, "Run python script using: torchrun finetune.py OR set CUDA_VISIBLE_DEVICES to single GPU" fire.Fire(train) if __name__ == "__main__": entrypoint_main()