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#!/usr/bin/env python3
from .promptops import PF_SMUGRI_MT
from .aux import log, CmdlineArgs
from .data import load_training_data
import json
import os, socket, torch
from datetime import datetime
from accelerate import Accelerator
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling,
logging,
TrainerCallback
)
"""
1/3 This simply reads in command-line arguments
"""
def _cmdline_args():
description = """Train or tune decoder models"""
result = CmdlineArgs(description,
pos_arg_list=["mdl_id", "save_location", "train_file"],
pos_arg_types=[str, str, str],
kw_arg_dict={ "continue_training": False, "save_steps": 100, "lr": 1.5e-5,
"batch_size": 1024, "nr_sents_per_gpu": 4, "log_steps": 1, "epochs": 4,
"max_length": 2000, "prompt_format": PF_SMUGRI_MT,
"deepspeed": "none"})
# if the directory args.save_location already exists, raise an exception:
if not result.continue_training and os.path.exists(result.save_location):
raise Exception(f"Save location '{result.save_location}' already exists, don't want to overwrite.")
if result.nr_sents_per_gpu == 0:
result.nr_sents_per_gpu = result.batch_size
if result.deepspeed == "none":
result.deepspeed = None
return result
"""
2/3 This here is used in training in order to report timing and predictions
"""
class StepTimerCallback(TrainerCallback):
def __init__(self):
self._step_start = None
self.lengths = []
self.abs_start = datetime.now()
self.actual_first_step = None
self.zero = self.abs_start - self.abs_start
def on_step_begin(self, args, state, control, **kwargs):
# called right before each training step
self._step_start = datetime.now()
def on_step_end(self, args, state, control, **kwargs):
if self.actual_first_step is None:
self.actual_first_step = state.global_step - 1
# called right after each training step
now = datetime.now()
elapsed = now - self._step_start
tot_elapsed = now - self.abs_start
self.lengths.append(elapsed)
avg = sum(self.lengths, start=self.zero) / len(self.lengths)
remaining = state.max_steps - self.actual_first_step - state.global_step
prediction = (tot_elapsed/(state.global_step - self.actual_first_step)) * remaining
# you can use logging.get_logger(...) instead of print
print(f"[step {state.global_step}/{state.max_steps}] took {elapsed}, avg {avg}; approx {prediction} remaining")
"""
3/3 Finally, the filling of TrainingArguments and the launching of Trainer:
"""
def get_training_args(cmdline_args, acc):
world_size = acc.num_processes
assert cmdline_args.batch_size % (cmdline_args.nr_sents_per_gpu * world_size) == 0, \
"Batch size must be divisible by the number of GPUs and nr of sents per GPU"
accum_steps = cmdline_args.batch_size // (cmdline_args.nr_sents_per_gpu * world_size)
log(f"Nr of processes (GPUs): {world_size}, per-device batch: {cmdline_args.nr_sents_per_gpu}, accum. steps: {accum_steps}")
if cmdline_args.deepspeed is not None:
with open(cmdline_args.deepspeed, "r") as f:
dpspd = json.load(f)
#correct the dictionary with current values, so that we wouldn't need to update the JSON every time
dpspd['train_batch_size'] = cmdline_args.batch_size
dpspd['train_micro_batch_size_per_gpu'] = cmdline_args.nr_sents_per_gpu
dpspd['gradient_accumulation_steps'] = accum_steps
log(f"Using deepspeed with config {dpspd}")
else:
dpspd = None
tr_args = TrainingArguments(
output_dir=cmdline_args.save_location,
per_device_train_batch_size=cmdline_args.nr_sents_per_gpu,
gradient_accumulation_steps=accum_steps,
num_train_epochs=cmdline_args.epochs,
save_steps=cmdline_args.save_steps,
save_total_limit=10,
logging_steps=cmdline_args.log_steps,
deepspeed=dpspd,
learning_rate=cmdline_args.lr,
save_strategy="epoch",
disable_tqdm=True,
report_to="none",
# Optional but often helpful on LUMI/ROCm if you enable it in your args:
bf16=True,
ddp_find_unused_parameters=False,
#dataloader_num_workers=1,
#group_by_length=True,
log_level="debug",
#gradient_checkpointing=True,
#dataloader_persistent_workers=True
)
return tr_args
def load_model(mdl_id, device, accelerator=None, attention="flash_attention_2"):
log(f"Load model", accelerator=accelerator)
model = AutoModelForCausalLM.from_pretrained(mdl_id,
low_cpu_mem_usage=False,
torch_dtype=torch.bfloat16,
attn_implementation=attention)
model.config.use_cache = False
model = model.to(device)
log(f"Model loaded on device: {model.device}.", accelerator=accelerator)
return model
def load_tokenizer(mdl_id, accelerator=None):
log(f"Load tokenizer", accelerator=accelerator)
tokenizer = AutoTokenizer.from_pretrained(mdl_id)
# LLaMA 3.x: no pad token by default
if tokenizer.pad_token is None:
tokenizer.pad_token = "<|reserved_special_token_100|>"
return tokenizer
def simple_train():
cmd_args = _cmdline_args()
acc = Accelerator()
device = acc.device # it seems that the accelerator loses/changes this info later
training_args = get_training_args(cmd_args, acc)
tokenizer = load_tokenizer(cmd_args.mdl_id, acc)
model = load_model(cmd_args.mdl_id, device, acc)
if getattr(model.config, "pad_token_id", None) is None:
model.config.pad_token_id = tokenizer.pad_token_id
log(f"Load data", accelerator=acc)
tokenized_train_data = load_training_data(cmd_args.train_file, tokenizer, cmd_args)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False,
pad_to_multiple_of=8, # GPT says this helps performance
)
log(f"Preparing to train", accelerator=acc)
clbks = [StepTimerCallback] if acc.is_main_process else []
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train_data,
tokenizer=tokenizer,
data_collator=data_collator,
callbacks=clbks,
)
logging.set_verbosity_debug()
log(f"Starting training", accelerator=acc)
trainer.train(resume_from_checkpoint=cmd_args.continue_training)
log(f"Done, saving model", accelerator=acc)
trainer.save_model()
def env_stuff():
os.environ.setdefault("LOCAL_RANK", os.environ.get("SLURM_LOCALID", "---"))
os.environ.setdefault("RANK", os.environ.get("SLURM_PROCID", "0"))
os.environ.setdefault("WORLD_SIZE", os.environ.get("SLURM_NTASKS", "1"))
os.environ.setdefault("MASTER_ADDR", os.environ.get("SLURM_LAUNCH_NODE_IPADDR", "127.0.0.1"))
os.environ.setdefault("MASTER_PORT", "29500") # pick an open port
# Optional: make sure each process selects its own GPU
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
try:
log(
f"host={socket.gethostname()} "
f"RANK={os.environ['RANK']}/{os.environ['WORLD_SIZE']} "
f"LOCAL_RANK={os.environ['LOCAL_RANK']} "
f"HIP_VISIBLE_DEVICES={os.environ.get('HIP_VISIBLE_DEVICES')} "
f"ROCR_VISIBLE_DEVICES={os.environ.get('ROCR_VISIBLE_DEVICES')} "
f"cuda_count={torch.cuda.device_count()} curr_dev={torch.cuda.current_device()}"
)
except AssertionError:
log(
f"host={socket.gethostname()} "
f"RANK={os.environ['RANK']}/{os.environ['WORLD_SIZE']} "
f"LOCAL_RANK={os.environ['LOCAL_RANK']} "
f"HIP_VISIBLE_DEVICES={os.environ.get('HIP_VISIBLE_DEVICES')} "
f"ROCR_VISIBLE_DEVICES={os.environ.get('ROCR_VISIBLE_DEVICES')} "
f"no cuda"
)
"""
This replaces the trainer, in order to
print out the final batch when training,
and commit harakiri. So only for temporary
debugging-related usage
"""
class LoggingKillingTrainer(Trainer):
def compute_loss(self, model, inputs, **kwargs):
log(f"Here is the batch for training: {inputs}")
raise NotImplementedError
#return super().compute_loss(model, inputs, **kwargs)
if __name__ == "__main__":
env_stuff()
simple_train()
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