Andrej Karpathy
75 lines
3.1 KiB
Python
75 lines
3.1 KiB
Python
"""
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Sample from the trained model with PyTorch
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"""
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import os
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import pickle
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from contextlib import nullcontext
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import torch
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from model import ModelArgs, Transformer
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from tokenizer import Tokenizer
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from tinystories import get_tokenizer_model_path
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# -----------------------------------------------------------------------------
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checkpoint = 'out/ckpt.pt'
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start = "" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
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num_samples = 1 # number of samples to draw
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max_new_tokens = 100 # number of tokens generated in each sample
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temperature = 1.0 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
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top_k = 300 # retain only the top_k most likely tokens, clamp others to have 0 probability
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tokenizer = "" # override the tokenizer model path
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seed = 1337
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device = 'cuda' if torch.cuda.is_available() else 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
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#dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16'
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dtype = "float32"
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compile = False # use PyTorch 2.0 to compile the model to be faster
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exec(open('configurator.py').read()) # overrides from command line or config file
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# -----------------------------------------------------------------------------
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
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torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
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device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
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ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
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ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
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# init from a model saved in a specific directory
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checkpoint_dict = torch.load(checkpoint, map_location=device)
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gptconf = ModelArgs(**checkpoint_dict['model_args'])
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model = Transformer(gptconf)
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state_dict = checkpoint_dict['model']
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unwanted_prefix = '_orig_mod.'
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for k,v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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model.load_state_dict(state_dict, strict=False)
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model.eval()
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model.to(device)
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if compile:
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print("Compiling the model...")
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model = torch.compile(model) # requires PyTorch 2.0 (optional)
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# load the tokenizer, either provided, or attempt to find it
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if tokenizer:
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tokenizer_model = tokenizer
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else:
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tokenizer_model = get_tokenizer_model_path(vocab_size=gptconf.vocab_size)
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enc = Tokenizer(tokenizer_model=tokenizer_model)
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# encode the beginning of the prompt
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if start.startswith('FILE:'):
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with open(start[5:], 'r', encoding='utf-8') as f:
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start = f.read()
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start_ids = enc.encode(start, bos=True, eos=False)
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x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
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# run generation
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with torch.no_grad():
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with ctx:
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for k in range(num_samples):
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y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
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print(enc.decode(y[0].tolist()))
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print('---------------')
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