app.py

"""
Sample from a trained model
"""
import os
import pickle
from contextlib import nullcontext
import torch
import tiktoken
from model import GPTConfig, GPT
import gradio as gr
# -----------------------------------------------------------------------------
init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
out_dir = 'out-shakespeare-char' # ignored if init_from is not 'resume'
start = "\n" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
num_samples = 10 # number of samples to draw
max_new_tokens = 500 # number of tokens generated in each sample
temperature = 0.8 # 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 = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16'
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
# -----------------------------------------------------------------------------
def sample_from_trained_model(start="\n", init_from='resume', out_dir='out-shakespeare-char', num_samples=1, 
                              max_new_tokens=500, temperature=0.8, top_k=200, device='cpu', compile=False):
    #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)

    # model
    if init_from == 'resume':
        # 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 = 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)
    elif init_from.startswith('gpt2'):
        # init from a given GPT-2 model
        model = GPT.from_pretrained(init_from, dict(dropout=0.0))

    model.eval()
    model.to(device)
    if compile:
        model = torch.compile(model) # requires PyTorch 2.0 (optional)

    # look for the meta pickle in case it is available in the dataset folder
    load_meta = False
    if init_from == 'resume' and 'config' in checkpoint and 'dataset' in checkpoint['config']: # older checkpoints might not have these...
        meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl')
        load_meta = os.path.exists(meta_path)
    if load_meta:
        print(f"Loading meta from {meta_path}...")
        with open(meta_path, 'rb') as f:
            meta = pickle.load(f)
        # TODO want to make this more general to arbitrary encoder/decoder schemes
        stoi, itos = meta['stoi'], meta['itos']
        encode = lambda s: [stoi[c] for c in s]
        decode = lambda l: ''.join([itos[i] for i in l])
    else:
        # ok let's assume gpt-2 encodings by default
        print("No meta.pkl found, assuming GPT-2 encodings...")
        enc = tiktoken.get_encoding("gpt2")
        encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
        decode = lambda l: enc.decode(l)

    # 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 = encode(start)
    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)
                z = decode(y[0].tolist())
                return z


iface = gr.Interface(fn=sample_from_trained_model, inputs=[], outputs="textbox", 
                        title="GPT Shakespeare script Generator", description="Press button to generate shakespearean text")

iface.launch(share=True)