Delete app.py

app.py

@@ -1,89 +0,0 @@
-"""
-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-python' # ignored if init_from is not 'resume'
-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 = 'cuda' # 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
-# -----------------------------------------------------------------------------
-
-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
-
-def generator(start, tokens):
-    tokens = int(tokens)
-    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, tokens, temperature=temperature, top_k=top_k)
-    return decode(y[0].tolist())
-
-demo = gr.Interface(fn=generator, inputs=["text", "number"], outputs="text")
-    
-demo.launch(share=True)