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import re |
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import sys |
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import os |
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import shutil |
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from pathlib import Path |
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from argparse import ArgumentParser |
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import torch |
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from tqdm.auto import tqdm |
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def permute_qkv( |
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qkv_w: torch.Tensor, |
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dim: int, |
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n_heads: int, |
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n_heads_kv: int, |
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n_hidden_per_head=None, |
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revert: bool = False, |
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) -> torch.Tensor: |
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def permute(x): |
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if revert: |
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return x.view(head_dim // 2, 2, dim).transpose(0, 1).reshape(head_dim, dim) |
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return x.view(2, head_dim // 2, dim).transpose(0, 1).reshape(head_dim, dim) |
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if n_hidden_per_head is None: |
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head_dim = dim // n_heads |
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else: |
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head_dim = n_hidden_per_head |
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n_qs_per_kv = n_heads // n_heads_kv |
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n_groups = qkv_w.size(0) // head_dim // (n_qs_per_kv + 2) |
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groups = torch.chunk(qkv_w, n_groups, dim=0) |
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new = [] |
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for group in groups: |
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*qs, k, v = torch.split(group, head_dim, dim=0) |
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assert len(qs) == n_qs_per_kv, f"{len(qs)}, {n_qs_per_kv}" |
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new += list(map(permute, qs)) + [permute(k), v] |
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return torch.cat(new, dim=0) |
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def update_checkpoint(input_dir: Path, output_dir: Path, overwrite_ok: bool = False): |
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sys.path.append( |
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os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) |
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) |
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if output_dir.exists(): |
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if not overwrite_ok: |
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raise FileExistsError(f"Output directory {output_dir} already exists") |
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print(f"Removing {output_dir}") |
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shutil.rmtree(output_dir) |
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output_dir.mkdir(exist_ok=True) |
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with open(input_dir / "latest_checkpointed_iteration.txt") as f: |
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it = f.read() |
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print("Updating weights of iteration", it) |
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with open(output_dir / "latest_checkpointed_iteration.txt", "w+") as f: |
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f.write(it) |
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if it != "release": |
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it = f"iter_{int(it):07d}" |
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(output_dir / it).mkdir() |
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for fname in tqdm(list((input_dir / it).iterdir())): |
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checkpoint = torch.load(fname / "model_optim_rng.pt", map_location="cpu") |
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args = checkpoint["args"] |
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args = (args.hidden_size, args.num_attention_heads, args.num_attention_heads_kv) |
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if "transformer" in checkpoint["model"]["language_model"]: |
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key = "transformer" |
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attn_key = "attention" |
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else: |
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key = "encoder" |
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attn_key = "self_attention" |
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states = checkpoint["model"]["language_model"][key] |
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for name, weight in states.items(): |
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if re.match( |
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rf"^layers\.[0-9]+\.{attn_key}\.query_key_value\.weight$", name |
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): |
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states[name] = permute_qkv(weight, *args) |
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(output_dir / it / fname.stem).mkdir() |
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torch.save(checkpoint, output_dir / it / fname.stem / "model_optim_rng.pt") |
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if __name__ == "__main__": |
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parser = ArgumentParser() |
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parser.add_argument("--input-dir", type=Path) |
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parser.add_argument("--output-dir", type=Path) |
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parser.add_argument("--overwrite-ok", action="store_true") |
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args = parser.parse_args() |
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update_checkpoint(args.input_dir, args.output_dir, args.overwrite_ok) |
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