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| import os | |
| import pickle | |
| from contextlib import nullcontext | |
| import torch | |
| import tiktoken | |
| from model import GPTConfig, GPT | |
| import gradio as gr | |
| def nanogpt(start:str , max_new_tokens = 500, num_samples =2): | |
| # ----------------------------------------------------------------------------- | |
| init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl') | |
| 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 | |
| # ----------------------------------------------------------------------------- | |
| 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 = '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) | |
| 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) | |
| start_ids = encode(start) | |
| x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) | |
| # run generation | |
| with torch.no_grad(): | |
| with ctx: | |
| y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k) | |
| #print(decode(y[0].tolist())) | |
| output = decode(y[0].tolist()) | |
| return output | |
| INTERFACE = gr.Interface(fn=nanogpt, inputs=[gr.Textbox(label= "Prompt", value= 'My mind is tossing on the ocean.'),gr.Slider(300,500, "number",value= 250, label= "Maximum number of tokens to be generated")] , outputs=gr.Text(label= "Generated Text"), title="NanoGPT", | |
| description="NanoGPT is a transformer-based language model with only 10.65 million parameters, trained on a small dataset of Shakespeare work (size: 1MB only). It is trained with character level tokenization with a simple objective: predict the next char, given all of the previous chars within a text.", | |
| examples = [['We know what we are, but know not what we may be',300], | |
| ['Sweet are the uses of adversity which, like the toad, ugly and venomous, wears yet a precious jewel in his head',300],] | |
| ).launch(debug=True) |