Update app.py

app.py

@@ -1,3 +1,6 @@
+"""
+Sample from a trained model
+"""
 import os
 import pickle
 from contextlib import nullcontext
@@ -5,74 +8,88 @@ 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, seed=1337, device='cpu', compile=False):
-    # Set the dtype
-    dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16'
-    
-    # Setup seed and device
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    device_type = 'cuda' if 'cuda' in device else 'cpu'
-    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)
+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)
 
-    # Load model
-    if init_from == 'resume':
-        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'):
-        model = GPT.from_pretrained(init_from, dict(dropout=0.0))
+# 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)
+model.eval()
+model.to(device)
+if compile:
+    model = torch.compile(model) # requires PyTorch 2.0 (optional)
 
-    # Load meta data if available
-    load_meta = False
-    if init_from == 'resume' and 'config' in checkpoint and 'dataset' in checkpoint['config']:
-        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)
-        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:
-        print("No meta.pkl found, assuming GPT-2 encodings...")
-        enc = tiktoken.get_encoding("gpt2")
-        encode = lambda s: enc.encode(s, allowed_special={""})
-        decode = lambda l: enc.decode(l)
+# 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, ...])
+# 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)
-                return decode(y[0].tolist())
+# 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())
 
-iface = gr.Interface(fn=sample_from_trained_model, inputs="text", outputs="textbox", 
+def show_text(prompt=z):
+    return prompt
+iface = gr.Interface(fn=show_text, inputs=[], outputs="textbox", 
                      title="GPT Text Generator", description="Enter a prompt to generate text.")
 
 iface.launch(share=True)