"""

Sample from the trained model with PyTorch

"""

import os

import pickle

from contextlib import nullcontext

import torch

import tiktoken

from model import ModelArgs, Transformer

from tokenizer import Tokenizer

# -----------------------------------------------------------------------------

out_dir = 'out' # ignored if init_from is not 'resume'

start = "" # 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 = 1.0 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions

top_k = 300 # retain only the top_k most likely tokens, clamp others to have 0 probability

seed = 1337

device = 'cuda' if torch.cuda.is_available() else 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.

#dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16'

dtype = "float32"

compile = False # use PyTorch 2.0 to compile the model to be faster

exec(open('configurator.py').read()) # overrides from command line or config file

# -----------------------------------------------------------------------------

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)

# init from a model saved in a specific directory

ckpt_path = os.path.join(out_dir, 'ckpt.pt')

checkpoint = torch.load(ckpt_path, map_location=device)

gptconf = ModelArgs(**checkpoint['model_args'])

model = Transformer(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, strict=False)

model.eval()

model.to(device)

if compile:

print("Compiling the model...")

model = torch.compile(model) # requires PyTorch 2.0 (optional)

# load the tokenizer

enc = Tokenizer()

# encode the beginning of the prompt

if start.startswith('FILE:'):

with open(start[5:], 'r', encoding='utf-8') as f:

start = f.read()

start_ids = enc.encode(start, bos=True, eos=False)

x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])

# run generation

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)

print(enc.decode(y[0].tolist()))

print('---------------')