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import argparse
import datetime
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=str, help="Checkpoint path", required=True)
parser.add_argument("--max-memory-per-gpu", type=str, help="Defines maximum memory allocated to gpu", required=True)
parser.add_argument("--global-step", type=str, default=None)
parser.add_argument("--generate-max-length", type=int, default=50, help="max generation length")
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--top-k", type=int, default=0)
parser.add_argument("--top-p", type=float, default=0.)
parser.add_argument("--offload_folder", type=str, help="offload folder for accelerate", default="./offload")
return parser.parse_args()
def get_gpus_max_memory(max_memory):
max_memory = {i: max_memory for i in range(torch.cuda.device_count())}
return max_memory
def generate_from_text(model, text, tokenizer, max_length=200, greedy=False, top_k=0, top_p=0.):
input_ids = tokenizer.encode(text, return_tensors='pt').to("cuda:0")
max_length = input_ids.size(-1) + max_length
greedy_output = model.generate(
input_ids.to('cuda:0'),
max_length=max_length,
do_sample=not greedy,
top_k=None if greedy else top_k,
top_p=None if greedy else top_p
)
return tokenizer.decode(greedy_output[0], skip_special_tokens=True)
def main():
args = get_args()
print("Loading model")
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, padding_side="left")
print("Loaded tokenizer!")
start = datetime.datetime.now()
model = AutoModelForCausalLM.from_pretrained(
args.checkpoint,
device_map="auto",
max_memory=get_gpus_max_memory(args.max_memory_per_gpu),
torch_dtype=torch.bfloat16,
revision="gs{}".format(args.global_step) if args.global_step else None,
offload_folder=args.offload_folder,
)
print(f"Loaded model in {datetime.datetime.now() - start}")
texts = []
while True:
try:
dummy = input('''Enter the paragraph (Enter for to validate new input line and Ctrl-c to start generating the prompt):''')
texts.append(dummy)
except KeyboardInterrupt:
text = "\n".join(texts)
output = generate_from_text(model, text, tokenizer, max_length=args.generate_max_length, greedy=args.greedy, top_k=args.top_k, top_p=args.top_p)
print(output)
texts = []
if __name__ == "__main__":
main()
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