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(llama_type=llama_type, model_name=base_model, reward_type=False)

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

old_state_dict = model.state_dict

from peft import get_peft_model_state_dict

model.state_dict = (

lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())

).__get__(model, type(model))

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)

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)

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()