mirror of
https://github.com/osmarks/nanogpt-experiments.git
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68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
"""
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A much shorter version of train.py for benchmarking
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"""
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import os
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import numpy as np
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import time
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import torch
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from model import GPTConfig, GPT
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device = 'cuda:3'
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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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torch.manual_seed(1337)
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batch_size = 8
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block_size = 1024
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# data loading init
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real_data = True
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if real_data:
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dataset = 'openwebtext'
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data_dir = os.path.join('data', dataset)
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train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
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def get_batch(split):
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data = train_data # note ignore split in benchmarking script
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ix = torch.randint(len(data) - block_size, (batch_size,))
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x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
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y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
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x, y = x.to(device), y.to(device)
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return x, y
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else:
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# alternatively, if fixed data is desired to not care about data loading
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x = torch.randint(50257, (batch_size, block_size), device=device)
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y = torch.randint(50257, (batch_size, block_size), device=device)
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get_batch = lambda split: (x, y)
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# model init
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gptconf = GPTConfig(
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block_size = block_size, # how far back does the model look? i.e. context size
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n_layer = 12, n_head = 12, n_embd = 768, # size of the model
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dropout = 0, # for determinism
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)
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model = GPT(gptconf)
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model.to(device)
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optimizer = model.configure_optimizers(weight_decay=1e-2, learning_rate=1e-4, betas=(0.9, 0.95))
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burn_in = 10 # number of burn in steps where we don't measure time
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num_steps = 30
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for k in range(num_steps):
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if k == burn_in:
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t0 = time.time() # start the timer
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X, Y = get_batch('train')
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with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
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logits, loss = model(X, Y)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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optimizer.step()
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lossf = loss.item()
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print(f"{k}/{num_steps} loss: {lossf:.4f}")
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torch.cuda.synchronize()
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t1 = time.time()
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print("time in ms per iteration: %.2f" % ((t1 - t0) / (num_steps - burn_in) * 1000))
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