mirror of
https://github.com/osmarks/meme-search-engine.git
synced 2024-11-10 22:09:54 +00:00
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
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import torch.nn
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import torch.nn.functional as F
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import torch
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import sqlite3
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import random
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import numpy
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import json
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import time
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from tqdm import tqdm
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from torch.func import functional_call, vmap, grad
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from model import Config, BradleyTerry
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import shared
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steps = 855
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batch_size = 128
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num_pairs = batch_size * 1024
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device = "cuda"
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config = Config(
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d_emb=1152,
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n_hidden=1,
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n_ensemble=1,
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device=device,
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dtype=torch.bfloat16
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)
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model = BradleyTerry(config)
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modelc, _ = shared.checkpoint_for(855)
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model.load_state_dict(torch.load(modelc))
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params = sum(p.numel() for p in model.parameters())
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print(f"{params/1e6:.1f}M parameters")
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print(model)
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files = shared.fetch_all_files()
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importance = {}
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params = {k: v.detach() for k, v in model.named_parameters()}
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buffers = {k: v.detach() for k, v in model.named_buffers()}
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# https://pytorch.org/tutorials/intermediate/per_sample_grads.html
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def compute_loss(params, buffers, sample, target):
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batch = sample.unsqueeze(0)
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targets = target.unsqueeze(0)
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predictions = functional_call(model, (params, buffers), (batch,))
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loss = F.binary_cross_entropy(predictions, targets)
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return loss
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ft_compute_grad = grad(compute_loss)
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ft_compute_sample_grad = vmap(ft_compute_grad, in_dims=(None, None, 1, 1))
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pairs = []
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for _ in range(num_pairs):
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pairs.append(tuple(random.sample(files, 2)))
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for bstart in tqdm(range(0, len(pairs), batch_size)):
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batch = pairs[bstart:bstart + batch_size]
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filenames = [ (f1, f2) for ((f1, e1), (f2, e2)) in batch ]
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embs = torch.stack([ torch.stack((torch.Tensor(e1).to(config.dtype), torch.Tensor(e2).to(config.dtype))) for ((f1, e1), (f2, e2)) in batch ])
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inputs = embs.unsqueeze(0).expand((config.n_ensemble, batch_size, 2, config.d_emb)).to(device)
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#win_probs = model(inputs)
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# TODO gradients
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# don't take variance: do backwards pass and compute gradient norm
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grads = ft_compute_sample_grad(params, buffers, inputs, torch.full((1, batch_size), 0.95).to(device))
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total_grad_norms = torch.zeros(batch_size).to(device)
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for k, v in grads.items():
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param_dims = tuple(range(1, len(v.shape)))
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total_grad_norms += torch.linalg.vector_norm(v, dim=param_dims)
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tgn = total_grad_norms.cpu().numpy()
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for filename, tg in zip(filenames, tgn):
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importance[filename] = float(tg)
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top = sorted(importance.items(), key=lambda x: -x[1])
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with open("top.json", "w") as f:
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json.dump(top[:256], f)
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