File size: 4,125 Bytes
b24b632 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | import os
import json
import torch
import wandb
from datasets import load_from_disk
from transformers import (
Trainer,
TrainingArguments,
DefaultDataCollator,
set_seed,
get_cosine_schedule_with_warmup,
AutoModelForCausalLM, AutoTokenizer,AutoConfig
)
from torch.optim import AdamW
# MODEL_ID = "phasorkinetics/pmnet"
# CONFIG_PATH = "/workspace/config_pmnet.json"
# CKPT_SAVE_DIR = "/workspace/ckpt/pmnet"
# CKPT_START_DIR = "/workspace/ckpt_start"
# compile_model = True
MODEL_ID = "phasorkinetics/pmnet"
CONFIG_PATH = "/workspace/config_pmnet_no_mem.json"
CKPT_SAVE_DIR = "/workspace/ckpt/pmnet_no_mem"
CKPT_START_DIR = "/workspace/ckpt_start_no_mem"
compile_model = True
########################################################################
DATA_DIR = "/data/copypaste_2048"
SEED = 42
BATCH_SIZE = 256
GRADIENT_ACCUMULATION_STEPS = 1
NUM_DEVICES = 2
os.environ["WANDB_PROJECT"] = "pmnet_copy_paste"
os.environ["WANDB_Run_ID"] = ""
os.environ["WANDB_RESUME"] = "allow"
########################################################################
per_device_batch_size = BATCH_SIZE // (NUM_DEVICES*GRADIENT_ACCUMULATION_STEPS)
def get_optimizer_grouped_parameters(model, weight_decay: float):
no_decay_keywords = ["bias", "norm", "embedding", "layernorm", "a_log"]
decay_params = []
no_decay_params = []
for name, param in model.named_parameters():
if not param.requires_grad: continue
if any(keyword in name.lower() for keyword in no_decay_keywords):
no_decay_params.append(param)
else:
decay_params.append(param)
return [
{"params": decay_params, "weight_decay": weight_decay},
{"params": no_decay_params, "weight_decay": 0.0},
]
def main():
set_seed(SEED)
config = AutoConfig.from_pretrained(MODEL_ID, trust_remote_code=True)
if CONFIG_PATH and os.path.exists(CONFIG_PATH):
with open(CONFIG_PATH, "r", encoding="utf-8") as f:
local_config_dict = json.load(f)
config.update(local_config_dict)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
# model size
total_params = sum(p.numel() for p in model.parameters())
print(f"Total params: {total_params/1_000_000}")
dataset_train = load_from_disk(os.path.join(DATA_DIR, "train"))
dataset_val = load_from_disk(os.path.join(DATA_DIR, "val"))
tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
training_args = TrainingArguments(
output_dir=CKPT_SAVE_DIR,
overwrite_output_dir=False,
num_train_epochs=10,
save_strategy="steps",
save_steps=100,
eval_strategy="steps",
eval_steps=100,
logging_strategy="steps",
logging_steps=10,
save_total_limit=2,
load_best_model_at_end=True,
metric_for_best_model="loss",
greater_is_better=False,
seed=SEED,
data_seed=SEED,
per_device_train_batch_size=per_device_batch_size,
per_device_eval_batch_size=per_device_batch_size,
gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
learning_rate=5e-4,
max_grad_norm=1.0,
fp16=False,
bf16=True,
dataloader_num_workers=8,
report_to="wandb",
ddp_find_unused_parameters=False,
lr_scheduler_type="cosine",
warmup_steps=100,
torch_compile=compile_model
)
data_collator = DefaultDataCollator()
optimizer_grouped_parameters = get_optimizer_grouped_parameters(model, weight_decay=0.1)
optimizer = AdamW(optimizer_grouped_parameters, lr=training_args.learning_rate, betas=(0.9, 0.95))
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset_train,
eval_dataset=dataset_val,
data_collator=data_collator,
optimizers=(optimizer, None),
)
trainer.train(resume_from_checkpoint=CKPT_START_DIR if os.path.exists(CKPT_START_DIR) else None)
trainer.save_model(os.path.join(CKPT_SAVE_DIR, "final_model"))
if __name__ == "__main__":
main() |