import os
import sys
from functools import partial
from typing import List, Union
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__)))
if os.path.dirname('src') not in sys.path:
sys.path.append('src')
from src.loaders import get_loaders, get_tokenizer
from src.prompter import generate_prompt, prompt_types, PromptType
from src.utils import get_githash, copy_code, H2O_Fire
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-4096-llama2-7b',
# base_model: str = 'h2oai/h2ogpt-4096-llama2-13b',
# base_model: str = 'h2oai/h2ogpt-4096-llama2-70b',
# 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
bf16=False, # needed (and automatically enabled) for llama2-7b
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 src.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
replace_llama_attn_with_flash_attn()
if "llama2-7b" in base_model:
fp16 = False
bf16 = True
# allow set token directly
use_auth_token = os.environ.get("HUGGING_FACE_HUB_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:
from importlib.metadata import distribution, PackageNotFoundError
try:
distribution('flash_attn')
can_do_flash_attn = True
except (PackageNotFoundError, AssertionError):
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, conditional_type = (
get_loaders(model_name=base_model, reward_type=False, llama_type=llama_type))
model = model_loader(
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,
token=use_auth_token,
)
print(model)
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,
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,
bf16=bf16,
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=gpus > 1 and not ddp,
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)
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():
H2O_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"
H2O_Fire(train)
if __name__ == "__main__":
entrypoint_main()