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import argparse | |
import gc | |
import re | |
import math | |
import os | |
import time | |
import pickle | |
import torch | |
from tqdm import tqdm | |
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer | |
def get_args(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--prompts_file", type=str, help="pickle file containing prompts to query") | |
parser.add_argument("--output_file", type=str, help="file to save output translations") | |
parser.add_argument("--local_rank", required=False, type=int, help="used by dist launchers") | |
parser.add_argument("--max-gpu-memory", type=str, default='80GB') | |
parser.add_argument("--max-new-tokens", type=int, default=64) | |
parser.add_argument("--name", type=str, help="Name path", required=True) | |
parser.add_argument("--batch_size", default=1, type=int, help="batch size") | |
parser.add_argument("--no-repeat-ngram-size", type=int, default=4) | |
parser.add_argument("--num-beams", type=int, default=0) | |
parser.add_argument("--early-stopping", action="store_true", help="Early stopping for beam search") | |
return parser.parse_args() | |
t_start = time.time() | |
args = get_args() | |
num_tokens = args.max_new_tokens | |
local_rank = int(os.getenv("LOCAL_RANK", "0")) | |
rank = local_rank | |
def print_rank0(*msg): | |
if rank != 0: | |
return | |
print(*msg) | |
model_name = args.name | |
print_rank0(f"Loading model {model_name}") | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
dtype = torch.int8 | |
max_memory = {i: args.max_gpu_memory for i in range(world_size)} | |
print(f'Max memory : {max_memory}') | |
kwargs = dict( | |
device_map="balanced", | |
max_memory=max_memory, | |
load_in_8bit=True | |
) | |
model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs) | |
### Generate | |
print_rank0(f"*** Starting to generate... ***") | |
input_sentences = pickle.load(open(args.prompts_file, "rb")) | |
generate_kwargs = dict(max_new_tokens=num_tokens, num_beams=args.num_beams, early_stopping=args.early_stopping, no_repeat_ngram_size=args.no_repeat_ngram_size) | |
print_rank0(f"Generate args {generate_kwargs}") | |
def generate(inputs): | |
"""returns a list of zipped inputs, outputs and number of new tokens""" | |
input_tokens = tokenizer.batch_encode_plus(inputs, return_tensors="pt", padding=True) | |
for t in input_tokens: | |
if torch.is_tensor(input_tokens[t]): | |
input_tokens[t] = input_tokens[t].to("cuda:0") | |
outputs = model.generate(**input_tokens, **generate_kwargs) | |
input_tokens_lengths = [x.shape[0] for x in input_tokens.input_ids] | |
output_tokens_lengths = [x.shape[0] for x in outputs] | |
total_new_tokens = [o - i for i, o in zip(input_tokens_lengths, output_tokens_lengths)] | |
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) | |
return zip(inputs, outputs, total_new_tokens) | |
def postprocess(result): | |
query, response, _ = result | |
if response: | |
# Remove input query | |
resp = response.replace(query, "").strip() | |
# Remove start and end quotes | |
resp = re.sub("^\"","", resp) | |
resp = re.sub("\"$","", resp) | |
# Get first line as output (preceding hallucination) | |
response = resp.strip().split("\n")[0].strip() | |
return response | |
print_rank0("*** Running generate") | |
t_generate_start = time.time() | |
results = [] | |
for idx in tqdm(range(0, len(input_sentences), args.batch_size)): | |
inputs = input_sentences[idx*args.batch_size: (idx+1)*args.batch_size] | |
print (inputs) | |
if not inputs[0]: | |
results.append((inputs, "")) | |
else: | |
generated = generate(inputs) | |
output = postprocess(generated) | |
print (output) | |
results.append(output) | |
t_generate_span = time.time() - t_generate_start | |
open(args.output_file,"w+").write("\n".join(results) + "\n") |
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