import argparse import json import os import shutil import torch """ Sample usage: ``` python src/transformers/models/llama/convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path ``` Thereafter, models can be loaded via: ``` tokenizer = transformers.LLaMATokenizer.from_pretrained("/output/path/tokenizer/") model = transformers.LLaMAForCausalLM.from_pretrained("/output/path/llama-7b/") ``` """ INTERMEDIATE_SIZE_MAP = { "7B": 11008, "13B": 13824, "30B": 17920, "65B": 22016, } NUM_SHARDS = { "7B": 1, "13B": 2, "30B": 4, "65B": 8, } def read_json(path): with open(path, "r") as f: return json.loads(f.read()) def write_json(text, path): with open(path, "w") as f: f.write(json.dumps(text)) def write_model(model_path, input_base_path, model_size): assert model_size in INTERMEDIATE_SIZE_MAP os.makedirs(model_path, exist_ok=True) params = read_json(os.path.join(input_base_path, "params.json")) num_shards = NUM_SHARDS[model_size] n_layers = params["n_layers"] n_heads = params["n_heads"] n_heads_per_shard = n_heads // num_shards dim = params["dim"] dims_per_head = dim // n_heads # Load weights if model_size == "7B": # Not shared # (The sharded implementation would also work, but this is simpler.) loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu") else: # Sharded loaded = [ torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu") for i in range(num_shards) ] param_count = 0 index_dict = {"weight_map": {}} for layer_i in range(n_layers): filename = "pytorch_model-{:05d}-of-{:05d}.bin".format( layer_i, n_layers + 1, ) if model_size == "7B": # Unsharded state_dict = { f"model.decoder.layers.{layer_i}.self_attn.q_proj.weight": loaded[ f"layers.{layer_i}.attention.wq.weight" ], f"model.decoder.layers.{layer_i}.self_attn.k_proj.weight": loaded[ f"layers.{layer_i}.attention.wk.weight" ], f"model.decoder.layers.{layer_i}.self_attn.v_proj.weight": loaded[ f"layers.{layer_i}.attention.wv.weight" ], f"model.decoder.layers.{layer_i}.self_attn.o_proj.weight": loaded[ f"layers.{layer_i}.attention.wo.weight" ], f"model.decoder.layers.{layer_i}.feed_forward.w1.weight": loaded[ f"layers.{layer_i}.feed_forward.w1.weight" ], f"model.decoder.layers.{layer_i}.feed_forward.w2.weight": loaded[ f"layers.{layer_i}.feed_forward.w2.weight" ], f"model.decoder.layers.{layer_i}.feed_forward.w3.weight": loaded[ f"layers.{layer_i}.feed_forward.w3.weight" ], f"model.decoder.layers.{layer_i}.attention_norm.weight": loaded[ f"layers.{layer_i}.attention_norm.weight" ], f"model.decoder.layers.{layer_i}.ffn_norm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"], } else: # Sharded state_dict = { f"model.decoder.layers.{layer_i}.attention_norm.weight": loaded[0][ f"layers.{layer_i}.attention_norm.weight" ], f"model.decoder.layers.{layer_i}.ffn_norm.weight": loaded[0][f"layers.{layer_i}.ffn_norm.weight"], } state_dict[f"model.decoder.layers.{layer_i}.self_attn.q_proj.weight"] = torch.cat( [ loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim) for i in range(num_shards) ], dim=0, ).reshape(dim, dim) state_dict[f"model.decoder.layers.{layer_i}.self_attn.k_proj.weight"] = torch.cat( [ loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim) for i in range(num_shards) ], dim=0, ).reshape(dim, dim) state_dict[f"model.decoder.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat( [ loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim) for i in range(num_shards) ], dim=0, ).reshape(dim, dim) state_dict[f"model.decoder.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat( [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1 ) state_dict[f"model.decoder.layers.{layer_i}.feed_forward.w1.weight"] = torch.cat( [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0 ) state_dict[f"model.decoder.layers.{layer_i}.feed_forward.w2.weight"] = torch.cat( [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1 ) state_dict[f"model.decoder.layers.{layer_i}.feed_forward.w3.weight"] = torch.cat( [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0 ) for k, v in state_dict.items(): index_dict["weight_map"][k] = filename param_count += v.numel() torch.save(state_dict, os.path.join(model_path, filename)) filename = "pytorch_model-{:05d}-of-{:05d}.bin".format( n_layers, n_layers + 1, ) if model_size == "7B": # Unsharded state_dict = { "model.decoder.embed_tokens.weight": loaded["tok_embeddings.weight"], "model.decoder.norm.weight": loaded["norm.weight"], "lm_head.weight": loaded["output.weight"], } else: state_dict = { "model.decoder.norm.weight": loaded[0]["norm.weight"], "model.decoder.embed_tokens.weight": torch.cat( [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1 ), "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0), } for k, v in state_dict.items(): index_dict["weight_map"][k] = filename param_count += v.numel() torch.save(state_dict, os.path.join(model_path, filename)) # Write configs index_dict["metadata"] = {"total_size": param_count * 2} write_json(index_dict, os.path.join(model_path, "pytorch_model.bin.index.json")) config_out = { "architectures": ["LLaMAForCausalLM"], "bos_token_id": 0, "eos_token_id": 1, "hidden_act": "silu", "hidden_size": params["dim"], "intermediate_size": INTERMEDIATE_SIZE_MAP[model_size], "initializer_range": 0.02, "max_sequence_length": 2048, "model_type": "llama", "num_attention_heads": params["n_heads"], "num_hidden_layers": params["n_layers"], "pad_token_id": -1, "rms_norm_eps": params["norm_eps"], "torch_dtype": "float16", "transformers_version": "4.27.0.dev0", "use_cache": True, "vocab_size": 32000, } write_json( config_out, os.path.join(model_path, "config.json"), ) generation_config = { "_from_model_config": True, "bos_token_id": 0, "eos_token_id": 1, "pad_token_id": -1, "transformers_version": "4.27.0.dev0", } write_json( generation_config, os.path.join(model_path, "generation_config.json"), ) def write_tokenizer(tokenizer_path, input_tokenizer_path): os.makedirs(tokenizer_path, exist_ok=True) write_json({}, os.path.join(tokenizer_path, "special_tokens_map.json")) write_json( { "bos_token": "", "eos_token": "", "model_max_length": int(1e30), "tokenizer_class": "LLaMATokenizer", "unk_token": "", }, os.path.join(tokenizer_path, "tokenizer_config.json"), ) shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model")) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--input_dir", help="Location of LLaMA weights, which contains tokenizer.model and model folders", ) parser.add_argument( "--model_size", choices=["7B", "13B", "30B", "65B"], ) parser.add_argument( "--output_dir", help="Location to write HF model and tokenizer", ) args = parser.parse_args() write_model( model_path=os.path.join(args.output_dir, "llama-{}".format(args.model_size).lower()), input_base_path=os.path.join(args.input_dir, args.model_size), model_size=args.model_size, ) write_tokenizer( tokenizer_path=os.path.join(args.output_dir, "tokenizer"), input_tokenizer_path=os.path.join(args.input_dir, "tokenizer.model"), ) if __name__ == "__main__": main()