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EasyLM/models/llama/convert_easylm_to_hf.py
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338
EasyLM/models/llama/convert_easylm_to_hf.py
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# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
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# Copyright 2023 Xinyang Geng
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This script converts LLaMA model checkpoint trained by EsayLM to the
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# HuggingFace transformers LLaMA PyTorch format, which can then be loaded
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# by HuggingFace transformers.
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import gc
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import json
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import math
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import os
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import shutil
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import numpy as np
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import mlxu
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import jax
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import jax.numpy as jnp
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import flax
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from flax.traverse_util import flatten_dict
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import torch
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from transformers import LlamaConfig, LlamaForCausalLM
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from EasyLM.checkpoint import StreamingCheckpointer
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from EasyLM.jax_utils import float_tensor_to_dtype
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FLAGS, FLAGS_DEF = mlxu.define_flags_with_default(
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load_checkpoint='',
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tokenizer_path='',
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model_size='13b',
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output_dir='',
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)
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LLAMA_STANDARD_CONFIGS = {
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'small': {
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'vocab_size': 64256,
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'dim': 768,
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'intermediate_size': 3072,
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'n_layers': 12,
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'n_heads': 12,
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'norm_eps': 1e-6,
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},
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'medium': {
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'vocab_size': 64256,
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'dim': 1024,
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'intermediate_size': 4096,
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'n_layers': 24,
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'n_heads': 16,
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'norm_eps': 1e-6,
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},
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'large': {
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'vocab_size': 64256,
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'dim': 1536,
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'intermediate_size': 6144,
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'n_layers': 24,
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'n_heads': 16,
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'norm_eps': 1e-6,
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},
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'xlarge': {
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'vocab_size': 64256,
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'dim': 2048,
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'intermediate_size': 8192,
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'n_layers': 24,
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'n_heads': 32,
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'norm_eps': 1e-6,
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},
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'1b': {
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'vocab_size': 64256,
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'dim': 2048,
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'intermediate_size': 5504,
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'n_layers': 22,
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'n_heads': 16,
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'norm_eps': 1e-6,
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},
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'3b': {
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'vocab_size': 64256,
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'dim': 3200,
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'intermediate_size': 8640,
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'n_layers': 26,
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'n_heads': 32,
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'norm_eps': 1e-6,
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},
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'7b': {
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'vocab_size': 64256,
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'dim': 4096,
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'intermediate_size': 11008,
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'n_layers': 32,
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'n_heads': 32,
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'norm_eps': 1e-6,
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},
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'13b': {
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'vocab_size': 64256,
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'dim': 5120,
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'intermediate_size': 13824,
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'n_layers': 40,
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'n_heads': 40,
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'norm_eps': 1e-6,
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},
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'30b': {
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'vocab_size': 64256,
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'dim': 6656,
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'intermediate_size': 17920,
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'n_layers': 60,
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'n_heads': 52,
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'norm_eps': 1e-6,
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},
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'65b': {
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'vocab_size': 64256,
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'dim': 8192,
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'intermediate_size': 22016,
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'n_layers': 80,
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'n_heads': 64,
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'norm_eps': 1e-5,
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},
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}
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def match_keywords(string, positives, negatives):
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for positive in positives:
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if positive not in string:
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return False
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for negative in negatives:
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if negative in string:
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return False
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return True
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def load_and_convert_checkpoint(path):
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_, flax_params = StreamingCheckpointer.load_trainstate_checkpoint(path)
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flax_params = flatten_dict(flax_params['params'], sep='.')
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torch_params = {}
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for key, tensor in flax_params.items():
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if match_keywords(key, ["kernel"], ["norm", 'ln_f']):
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tensor = tensor.T
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torch_params[key] = torch.tensor(
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float_tensor_to_dtype(tensor, 'fp32'), dtype=torch.float16
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)
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return torch_params
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def read_json(path):
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with open(path, "r") as f:
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return json.load(f)
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def write_json(text, path):
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with open(path, "w") as f:
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json.dump(text, f)
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def write_model(loaded, model_path, model_size):
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os.makedirs(model_path, exist_ok=True)
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tmp_model_path = os.path.join(model_path, "tmp")
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os.makedirs(tmp_model_path, exist_ok=True)
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params = LLAMA_STANDARD_CONFIGS[model_size]
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n_layers = params["n_layers"]
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n_heads = params["n_heads"]
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dim = params["dim"]
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dims_per_head = dim // n_heads
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base = 10000.0
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inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
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# permute for sliced rotary
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def permute(w):
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return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)
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param_count = 0
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index_dict = {"weight_map": {}}
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for layer_i in range(n_layers):
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filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
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state_dict = {
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f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
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loaded[f"transformer.h.{layer_i}.attention.wq.kernel"]
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),
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f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
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loaded[f"transformer.h.{layer_i}.attention.wk.kernel"]
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),
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f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"transformer.h.{layer_i}.attention.wv.kernel"],
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f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"transformer.h.{layer_i}.attention.wo.kernel"],
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f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"transformer.h.{layer_i}.feed_forward.w1.kernel"],
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f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"transformer.h.{layer_i}.feed_forward.w2.kernel"],
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f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"transformer.h.{layer_i}.feed_forward.w3.kernel"],
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f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"transformer.h.{layer_i}.attention_norm.kernel"],
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f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"transformer.h.{layer_i}.ffn_norm.kernel"],
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}
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state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
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for k, v in state_dict.items():
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index_dict["weight_map"][k] = filename
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param_count += v.numel()
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torch.save(state_dict, os.path.join(tmp_model_path, filename))
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filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
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# Unsharded
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state_dict = {
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"model.embed_tokens.weight": loaded["transformer.wte.embedding"],
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"model.norm.weight": loaded["transformer.ln_f.kernel"],
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"lm_head.weight": loaded["lm_head.kernel"],
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}
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for k, v in state_dict.items():
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index_dict["weight_map"][k] = filename
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param_count += v.numel()
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torch.save(state_dict, os.path.join(tmp_model_path, filename))
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# Write configs
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index_dict["metadata"] = {"total_size": param_count * 2}
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write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
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config = LlamaConfig(
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vocab_size=params["vocab_size"],
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hidden_size=dim,
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intermediate_size=params["intermediate_size"],
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num_attention_heads=params["n_heads"],
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num_hidden_layers=params["n_layers"],
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rms_norm_eps=params["norm_eps"],
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)
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config.save_pretrained(tmp_model_path)
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# Make space so we can load the model properly now.
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del state_dict
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del loaded
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gc.collect()
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print("Loading the checkpoint in a Llama model.")
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model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float16)
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# Avoid saving this as part of the config.
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print("Model parameter count", model.num_parameters())
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del model.config._name_or_path
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print("Saving in the Transformers format.")
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model.save_pretrained(model_path, safe_serialization=True)
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shutil.rmtree(tmp_model_path)
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def write_tokenizer(tokenizer_path, input_tokenizer_path):
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print(f"Fetching the tokenizer from {input_tokenizer_path}.")
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os.makedirs(tokenizer_path, exist_ok=True)
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write_json(
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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},
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os.path.join(tokenizer_path, "special_tokens_map.json")
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)
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write_json(
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{
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"add_bos_token": True,
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"add_eos_token": False,
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"model_max_length": 2048,
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"pad_token": None,
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"clean_up_tokenization_spaces": False,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": False,
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"normalized": True,
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"rstrip": False,
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"single_word": False
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},
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},
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os.path.join(tokenizer_path, "tokenizer_config.json"),
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)
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shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model"))
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def main(argv):
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assert FLAGS.load_checkpoint != "" and FLAGS.output_dir != ""# and FLAGS.tokenizer_path != ""
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assert FLAGS.model_size in LLAMA_STANDARD_CONFIGS
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# write_tokenizer(
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# tokenizer_path=FLAGS.output_dir,
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# input_tokenizer_path=FLAGS.tokenizer_path,
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# )
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write_model(
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load_and_convert_checkpoint(FLAGS.load_checkpoint),
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model_path=FLAGS.output_dir,
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model_size=FLAGS.model_size,
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)
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if __name__ == "__main__":
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mlxu.run(main)
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