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import os |
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import re |
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from pathlib import Path |
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from typing import Optional |
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from collections import OrderedDict |
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import torch |
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from tqdm.auto import tqdm |
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from transformers import LlamaForCausalLM |
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scale2emb = { |
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'7B': 4096, |
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'13B': 5120, |
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'30B': 6656, |
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'34B': 8192, |
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'65B': 8192, |
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'70B': 8192, |
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} |
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key_to_dim = { |
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"w1": 0, |
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"w2": -1, |
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"w3": 0, |
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"wo": -1, |
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"wq": 0, |
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"wk": 0, |
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"wv": 0, |
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"output": 0, |
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"tok_embeddings": -1, |
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"ffn_norm": None, |
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"attention_norm": None, |
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"norm": None, |
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"rope": None, |
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} |
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def init_merged_ckpt(pth_00, num_pth=8, emb_dim=8192): |
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merged_ckpt = OrderedDict() |
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for parameter_name, parameter in pth_00.items(): |
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short_name = parameter_name.split(".")[-2] |
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if key_to_dim[short_name] is None: |
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merged_ckpt[parameter_name] = parameter |
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del parameter |
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elif key_to_dim[short_name] == 0: |
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size = parameter.shape[0] |
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merged_param_shape = [ parameter.shape[0] * num_pth, parameter.shape[1] ] |
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merged_ckpt[parameter_name] = torch.zeros(merged_param_shape) |
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merged_ckpt[parameter_name][0 : size, :] = parameter |
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del parameter |
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elif key_to_dim[short_name] == -1: |
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size = parameter.shape[-1] |
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merged_param_shape = [ parameter.shape[0], parameter.shape[1] * num_pth] |
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merged_ckpt[parameter_name] = torch.zeros(merged_param_shape) |
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merged_ckpt[parameter_name][:, 0 : size] = parameter |
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del parameter |
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return merged_ckpt |
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def merge_meta_llama(size: int, root_dir: Path): |
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paths = sorted(path for path in root_dir.iterdir() |
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if re.match(r"^consolidated\.[0-9]+\.pth$", path.name)) |
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if len(paths) == 1: |
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return torch.load(paths[0], map_location=torch.device("cpu")) |
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num_pth = len(paths) |
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for i, ckpt_path in enumerate(tqdm(paths, desc="Merging llama")): |
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llama_config = torch.load(ckpt_path, map_location=torch.device('cpu')) |
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if i == 0: |
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merged_ckpt = init_merged_ckpt(llama_config, num_pth=num_pth, |
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emb_dim=scale2emb[f"{size}B"]) |
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else: |
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for parameter_name, parameter in llama_config.items(): |
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short_name = parameter_name.split(".")[-2] |
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if key_to_dim[short_name] == 0: |
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size = parameter.shape[0] |
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merged_param_shape = [ parameter.shape[0] * num_pth, parameter.shape[1] ] |
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merged_ckpt[parameter_name][size * i : size * (i + 1), :] = parameter |
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del parameter |
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if key_to_dim[short_name] == -1: |
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size = parameter.shape[-1] |
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merged_param_shape = [ parameter.shape[0], parameter.shape[1] * num_pth] |
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merged_ckpt[parameter_name][:, size * i : size * (i + 1)] = parameter |
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del parameter |
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del llama_config |
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return merged_ckpt |
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def merge_hf_llama(size: int, version: int, cache_dir: Optional[Path] = None, |
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model_path: Optional[str] = None): |
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if model_path is None and version == 1: |
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model_path = f"decapoda-research/llama-{size}b-hf" |
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elif model_path is None and version == 2: |
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model_path = f"meta-llama/Llama-2-{size}b-hf" |
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weights = LlamaForCausalLM.from_pretrained(model_path, cache_dir=cache_dir).state_dict() |
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weights["tok_embeddings.weight"] = weights.pop("model.embed_tokens.weight") |
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weights["norm.weight"] = weights.pop("model.norm.weight") |
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weights["output.weight"] = weights.pop("lm_head.weight") |
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for key in list(weights.keys()): |
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if rmatch := re.match(r"^model\.(layers\.[0-9]+\.)(.+)(\.weight)$", key): |
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new_key = { |
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"self_attn.q_proj": "attention.wq", |
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"self_attn.k_proj": "attention.wk", |
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"self_attn.v_proj": "attention.wv", |
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"self_attn.o_proj": "attention.wo", |
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"mlp.gate_proj": "feed_forward.w1", |
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"mlp.down_proj": "feed_forward.w2", |
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"mlp.up_proj": "feed_forward.w3", |
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"input_layernorm": "attention_norm", |
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"post_attention_layernorm": "ffn_norm" |
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}[rmatch.group(2)] |
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weights[rmatch.group(1) + new_key + rmatch.group(3)] = weights.pop(key) |
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return weights |
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def merge_llama(size: int, version: int, root_dir: Optional[Path] = None, |
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model_path: Optional[str] = None): |
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if root_dir is not None and (root_dir/"consolidated.00.pth").exists(): |
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return merge_meta_llama(size, root_dir), "meta" |
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print(f"Weights at {root_dir} do not look like a meta checkpoint, assuming " |
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"huggingface cache_dir instead") |
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return merge_hf_llama(size, version, root_dir, model_path), "hf" |
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