File size: 5,067 Bytes
af6e330 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | """Script to generate text from a trained model, without HuggingFace wrappers.
This script is useful for simple generation, and to debug any issues with HuggingFace integration.
The output of this script should match that of generate.py when `--temperature 0` is passed.
"""
# Thanks to Gabriel for this code.
import argparse
import os
import glob
import yaml
from dataclasses import dataclass
from typing import List
from yaml import Loader
import torch
from transformers import GPTNeoXTokenizerFast
from open_lm.model import Transformer, create_model
@dataclass
class GenerationArgs:
max_gen_len: int = 200
temperature: float = 0.8
top_p: float = 0.95
class Generator:
def __init__(self, model: Transformer):
self.model = model
self.tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
self.pad_token_id = 50282
self.seq_len = 2048
@torch.inference_mode()
def generate(
self,
prompts: List[str],
gen_args: GenerationArgs = GenerationArgs(),
) -> List[str]:
bsz = len(prompts)
prompt_tokens = [self.tokenizer.encode(x) for x in prompts]
min_prompt_size = min([len(t) for t in prompt_tokens])
max_prompt_size = max([len(t) for t in prompt_tokens])
total_len = min(self.seq_len, gen_args.max_gen_len + max_prompt_size)
tokens = torch.full((bsz, total_len), self.pad_token_id).cuda().long()
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t).long()
input_text_mask = tokens != self.pad_token_id
start_pos = min_prompt_size
prev_pos = 0
for cur_pos in range(start_pos, total_len):
last_logits = self.model(tokens[:, prev_pos:cur_pos].clone())[0][:, -1, :]
if gen_args.temperature > 0:
probs = torch.softmax(last_logits / gen_args.temperature, dim=-1)
next_token = sample_top_p(probs, gen_args.top_p)
else:
next_token = torch.argmax(last_logits, dim=-1)
next_token = next_token.reshape(-1)
# only replace token if prompt has already been generated
next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token)
tokens[:, cur_pos] = next_token
# TODO: enable caching again for inference
# prev_pos = cur_pos
decoded = []
for i, t in enumerate(tokens.tolist()):
t = t[: len(prompt_tokens[i]) + gen_args.max_gen_len]
decoded_i = self.tokenizer.decode(t)
decoded = []
for t in decoded_i:
decoded.append(t)
return decoded
def sample_top_p(probs, p):
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token
class ModelArgs:
def __init__(self, path: str):
with open(path, "r") as f:
params = yaml.load(f, Loader=Loader)
for k, v in params.items():
setattr(self, k, v)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", default="")
# TODO: Make this take as input --model-config, similar to generate.py
parser.add_argument("--params", default="")
parser.add_argument("--wandb-dir", default="")
parser.add_argument("--input-text", required=True)
parser.add_argument("--max-gen-len", default=200, type=int)
parser.add_argument("--temperature", default=0.8, type=float)
parser.add_argument("--top-p", default=0.95, type=float)
args = parser.parse_args()
if args.wandb_dir != "":
if args.params == "":
args.params = os.path.join(args.wandb_dir, "params.txt")
if args.checkpoint == "":
chkpt_dir = os.path.join(args.wandb_dir, "checkpoints", "epoch_*.pt")
list_of_files = glob.glob(chkpt_dir)
latest_file = max(list_of_files, key=os.path.getctime)
args.checkpoint = latest_file
else:
assert args.params != "", "Must provide params file or a wandb directory."
assert args.checkpoint != "", "Must provide checkpoint file or a wandb directory."
checkpoint = torch.load(args.checkpoint)
open_lm = create_model(ModelArgs(args.params)).half()
state_dict = checkpoint["state_dict"]
state_dict = {x.replace("module.", ""): y for x, y in state_dict.items()}
open_lm.load_state_dict(state_dict)
open_lm.eval().cuda()
generator = Generator(open_lm)
input_text = [
args.input_text,
]
output = generator.generate(
input_text,
GenerationArgs(args.max_gen_len, args.temperature, args.top_p),
)
print("".join(output))
if __name__ == "__main__":
main()
|