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51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
"""
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Sample from a trained model
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"""
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import os
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import torch
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import tiktoken
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from model import GPTConfig, GPT
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# -----------------------------------------------------------------------------
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# todo make these overridable like in train.py
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out_dir = 'out'
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device = 'cuda:2'
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compile = False
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start = "\n" # or "<|endoftext|>" or whatever you like
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num_samples = 10 # number of samples to draw
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max_new_tokens = 500 # number of tokens generated in each sample
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temperature = 0.8 # higher temperature (up to 1) is more random, lower (down to 0) means more greedy
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top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
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seed = 1337
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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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# model
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ckpt_path = os.path.join(out_dir, 'ckpt.pt')
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checkpoint = torch.load(ckpt_path, map_location=device)
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gptconf = GPTConfig(**checkpoint['model_args'])
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model = GPT(gptconf)
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model.load_state_dict(checkpoint['model'])
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model.eval()
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model.to(device)
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if compile:
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model = torch.compile(model) # requires PyTorch 2.0 (optional)
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# encode the beginning of the prompt
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enc = tiktoken.get_encoding("gpt2")
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start_ids = enc.encode(start, allowed_special={"<|endoftext|>"})
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x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
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for k in range(num_samples):
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with torch.no_grad():
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with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
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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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