Update app.py

app.py

@@ -22,74 +22,75 @@ dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported
 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):
+    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)
 
-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
-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)
 
-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)
 
-# 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)
+                z = decode(y[0].tolist())
+                return z
 
-# 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())
 
-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 = 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)