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import os
from contextlib import nullcontext
import torch
import tiktoken
from model import GPTConfig, GPT
from transformers import GPT2Tokenizer, GPT2Model
start = "\n" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
num_samples = 1 # number of samples to draw
max_new_tokens = 100 # number of tokens generated in each sample
temperature = 0.6 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
seed = 1337
device = 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
dtype = 'float16'
output_dir = "."
model = GPT.from_pretrained(output_dir)
tokenizer = GPT2Tokenizer.from_pretrained(output_dir)
def infer():
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
ptdtype = {
'float32': torch.float32,
'bfloat16': torch.bfloat16,
'float16': torch.float16
}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(
device_type=device_type, dtype=ptdtype)
# ckpt_path = os.path.join(os.getcwd(), out_dir, 'ckpt.pt')
# checkpoint = torch.load(ckpt_path, map_location=device)
# gptconf = GPTConfig(**checkpoint['model_args'])
# model = GPT(gptconf)
# state_dict = checkpoint['model']
# unwanted_prefix = '_orig_mod.'
# for k, v in list(state_dict.items()):
# if k.startswith(unwanted_prefix):
# state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
# model.load_state_dict(state_dict)
model.eval()
model.to(device)
# enc = tiktoken.get_encoding("gpt2")
# encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
# decode = lambda l: enc.decode(l)
encode = lambda s: tokenizer.encode(s)
decode = lambda l: tokenizer.decode(l)
start_ids = encode(start)
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
with torch.no_grad():
with ctx:
for k in range(num_samples):
y = model.generate(x,
max_new_tokens,
temperature=temperature,
top_k=top_k)
return decode(y[0].tolist()) |