沅江市建设局网站,教育培训类网站建设模板,linux安装WordPress80端口,服装网站开发方案swotLLama2是meta最新开源的语言大模型#xff0c;训练数据集2万亿token#xff0c;上下文长度由llama的2048扩展到4096#xff0c;可以理解和生成更长的文本#xff0c;包括7B、13B和70B三个模型#xff0c;在各种基准集的测试上表现突出#xff0c;该模型可用于研究和商业用…
LLama2是meta最新开源的语言大模型训练数据集2万亿token上下文长度由llama的2048扩展到4096可以理解和生成更长的文本包括7B、13B和70B三个模型在各种基准集的测试上表现突出该模型可用于研究和商业用途。
LLama2模型权重和tokenizer下载需要申请访问。
申请链接https://ai.meta.com/resources/models-and-libraries/llama-downloads/
由于下载的原始LLama2模型权重文件不能直接调用huggingface的transformers库进行使用如果要使用huggingface transformer训练LLaMA2需要使用额外的转换脚本。
转换脚本https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
现在huggingface上已发布了llama的hf版本可以直接使用。
现在介绍LLama2模型的原始权重获取和转换脚本。
LLama2模型原始权重获取
在MetaAI申请通过后将会在邮件中提及到PRESIGNED_URL运行download.sh按照提示输入即可。
set -eread -p Enter the URL from email: PRESIGNED_URL
echo
read -p Enter the list of models to download without spaces (7B,13B,70B,7B-chat,13B-chat,70B-chat), or press Enter for all: MODEL_SIZE
TARGET_FOLDER../target/file # where all files should end up
mkdir -p ${TARGET_FOLDER}if [[ $MODEL_SIZE ]]; thenMODEL_SIZE7B,13B,70B,7B-chat,13B-chat,70B-chat
fiecho Downloading LICENSE and Acceptable Usage Policy
wget --continue ${PRESIGNED_URL/*/LICENSE} -O ${TARGET_FOLDER}/LICENSE
wget --continue ${PRESIGNED_URL/*/USE_POLICY.md} -O ${TARGET_FOLDER}/USE_POLICY.mdecho Downloading tokenizer
wget --continue ${PRESIGNED_URL/*/tokenizer.model} -O ${TARGET_FOLDER}/tokenizer.model
wget --continue ${PRESIGNED_URL/*/tokenizer_checklist.chk} -O ${TARGET_FOLDER}/tokenizer_checklist.chk
CPU_ARCH$(uname -m)if [ $CPU_ARCH arm64 ]; then(cd ${TARGET_FOLDER} md5 tokenizer_checklist.chk)else(cd ${TARGET_FOLDER} md5sum -c tokenizer_checklist.chk)fifor m in ${MODEL_SIZE//,/ }
doif [[ $m 7B ]]; thenSHARD0MODEL_PATHllama-2-7belif [[ $m 7B-chat ]]; thenSHARD0MODEL_PATHllama-2-7b-chatelif [[ $m 13B ]]; thenSHARD1MODEL_PATHllama-2-13belif [[ $m 13B-chat ]]; thenSHARD1MODEL_PATHllama-2-13b-chatelif [[ $m 70B ]]; thenSHARD7MODEL_PATHllama-2-70belif [[ $m 70B-chat ]]; thenSHARD7MODEL_PATHllama-2-70b-chatfiecho Downloading ${MODEL_PATH}mkdir -p ${TARGET_FOLDER}/${MODEL_PATH}for s in $(seq -f 0%g 0 ${SHARD})dowget ${PRESIGNED_URL/*/${MODEL_PATH}/consolidated.${s}.pth} -O ${TARGET_FOLDER}/${MODEL_PATH}/consolidated.${s}.pthdonewget --continue ${PRESIGNED_URL/*/${MODEL_PATH}/params.json} -O ${TARGET_FOLDER}/${MODEL_PATH}/params.jsonwget --continue ${PRESIGNED_URL/*/${MODEL_PATH}/checklist.chk} -O ${TARGET_FOLDER}/${MODEL_PATH}/checklist.chkecho Checking checksumsif [ $CPU_ARCH arm64 ]; then(cd ${TARGET_FOLDER}/${MODEL_PATH} md5 checklist.chk)else(cd ${TARGET_FOLDER}/${MODEL_PATH} md5sum -c checklist.chk)fi
done运行download.sh:
sh download.sh代码注释
# 导入包
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer# 判断LlamaTokenizerFast是否可用LlamaTokenizerFast可以加速tokenization
try:from transformers import LlamaTokenizerFast
except ImportError as e:warnings.warn(e)warnings.warn(The converted tokenizer will be the slow tokenizer. To use the fast, update your tokenizers library and re-run the tokenizer conversion)LlamaTokenizerFast None# 不同版本的LLama模型的分片数目
NUM_SHARDS {7B: 1,7Bf: 1,13B: 2,13Bf: 2,34B: 4,30B: 4,65B: 8,70B: 8,70Bf: 8,
}# 计算中间层大小优化计算效率
def compute_intermediate_size(n, ffn_dim_multiplier1, multiple_of256):return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) multiple_of - 1) // multiple_of)# 读取json文件
def read_json(path):with open(path, r) as f:return json.load(f)# 写入json文件
def write_json(text, path):with open(path, w) as f:json.dump(text, f)def write_model(model_path, input_base_path, model_size, tokenizer_pathNone, safe_serializationTrue):# 检查参数文件路径if not os.path.isfile(os.path.join(input_base_path, params.json)):input_base_path os.path.join(input_base_path, model_size)# 创建模型临时保存目录os.makedirs(model_path, exist_okTrue)tmp_model_path os.path.join(model_path, tmp)os.makedirs(tmp_model_path, exist_okTrue)# 读取参数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_shardsdim params[dim]dims_per_head dim // n_headsbase params.get(rope_theta, 10000.0)inv_freq 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))if base 10000.0:max_position_embeddings 16384else:max_position_embeddings 2048# 初始化tokenizertokenizer_class LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFastif tokenizer_path is not None:tokenizer tokenizer_class(tokenizer_path)tokenizer.save_pretrained(model_path)vocab_size tokenizer.vocab_size if tokenizer_path is not None else 32000# 处理键值对头信息if n_kv_heads in params:num_key_value_heads params[n_kv_heads] # for GQA / MQAnum_local_key_value_heads n_heads_per_shard // num_key_value_headskey_value_dim dim // num_key_value_headselse: # compatibility with other checkpointsnum_key_value_heads n_headsnum_local_key_value_heads n_heads_per_shardkey_value_dim dim# 张量变换def permute(w, n_headsn_heads, dim1dim, dim2dim):return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)print(fFetching all parameters from the checkpoint at {input_base_path}.)# 加载权重if num_shards 1:loaded torch.load(os.path.join(input_base_path, consolidated.00.pth), map_locationcpu)else:loaded [torch.load(os.path.join(input_base_path, fconsolidated.{i:02d}.pth), map_locationcpu)for i in range(num_shards)]param_count 0index_dict {weight_map: {}}# 处理每一层的原始权重,并转化为bin文件for layer_i in range(n_layers):filename fpytorch_model-{layer_i 1}-of-{n_layers 1}.binif num_shards 1:# Unshardedstate_dict {fmodel.layers.{layer_i}.self_attn.q_proj.weight: permute(loaded[flayers.{layer_i}.attention.wq.weight]),fmodel.layers.{layer_i}.self_attn.k_proj.weight: permute(loaded[flayers.{layer_i}.attention.wk.weight]),fmodel.layers.{layer_i}.self_attn.v_proj.weight: loaded[flayers.{layer_i}.attention.wv.weight],fmodel.layers.{layer_i}.self_attn.o_proj.weight: loaded[flayers.{layer_i}.attention.wo.weight],fmodel.layers.{layer_i}.mlp.gate_proj.weight: loaded[flayers.{layer_i}.feed_forward.w1.weight],fmodel.layers.{layer_i}.mlp.down_proj.weight: loaded[flayers.{layer_i}.feed_forward.w2.weight],fmodel.layers.{layer_i}.mlp.up_proj.weight: loaded[flayers.{layer_i}.feed_forward.w3.weight],fmodel.layers.{layer_i}.input_layernorm.weight: loaded[flayers.{layer_i}.attention_norm.weight],fmodel.layers.{layer_i}.post_attention_layernorm.weight: loaded[flayers.{layer_i}.ffn_norm.weight],}else:# Sharded# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.state_dict {fmodel.layers.{layer_i}.input_layernorm.weight: loaded[0][flayers.{layer_i}.attention_norm.weight].clone(),fmodel.layers.{layer_i}.post_attention_layernorm.weight: loaded[0][flayers.{layer_i}.ffn_norm.weight].clone(),}state_dict[fmodel.layers.{layer_i}.self_attn.q_proj.weight] permute(torch.cat([loaded[i][flayers.{layer_i}.attention.wq.weight].view(n_heads_per_shard, dims_per_head, dim)for i in range(num_shards)],dim0,).reshape(dim, dim))state_dict[fmodel.layers.{layer_i}.self_attn.k_proj.weight] permute(torch.cat([loaded[i][flayers.{layer_i}.attention.wk.weight].view(num_local_key_value_heads, dims_per_head, dim)for i in range(num_shards)],dim0,).reshape(key_value_dim, dim),num_key_value_heads,key_value_dim,dim,)state_dict[fmodel.layers.{layer_i}.self_attn.v_proj.weight] torch.cat([loaded[i][flayers.{layer_i}.attention.wv.weight].view(num_local_key_value_heads, dims_per_head, dim)for i in range(num_shards)],dim0,).reshape(key_value_dim, dim)state_dict[fmodel.layers.{layer_i}.self_attn.o_proj.weight] torch.cat([loaded[i][flayers.{layer_i}.attention.wo.weight] for i in range(num_shards)], dim1)state_dict[fmodel.layers.{layer_i}.mlp.gate_proj.weight] torch.cat([loaded[i][flayers.{layer_i}.feed_forward.w1.weight] for i in range(num_shards)], dim0)state_dict[fmodel.layers.{layer_i}.mlp.down_proj.weight] torch.cat([loaded[i][flayers.{layer_i}.feed_forward.w2.weight] for i in range(num_shards)], dim1)state_dict[fmodel.layers.{layer_i}.mlp.up_proj.weight] torch.cat([loaded[i][flayers.{layer_i}.feed_forward.w3.weight] for i in range(num_shards)], dim0)state_dict[fmodel.layers.{layer_i}.self_attn.rotary_emb.inv_freq] inv_freqfor k, v in state_dict.items():index_dict[weight_map][k] filenameparam_count v.numel()torch.save(state_dict, os.path.join(tmp_model_path, filename))# 处理最后一层权重并保存filename fpytorch_model-{n_layers 1}-of-{n_layers 1}.binif num_shards 1:state_dict {model.embed_tokens.weight: loaded[tok_embeddings.weight],model.norm.weight: loaded[norm.weight],lm_head.weight: loaded[output.weight],}else:state_dict {model.norm.weight: loaded[0][norm.weight],model.embed_tokens.weight: torch.cat([loaded[i][tok_embeddings.weight] for i in range(num_shards)], dim1),lm_head.weight: torch.cat([loaded[i][output.weight] for i in range(num_shards)], dim0),}for k, v in state_dict.items():index_dict[weight_map][k] filenameparam_count v.numel()torch.save(state_dict, os.path.join(tmp_model_path, filename))# 写入配置文件index_dict[metadata] {total_size: param_count * 2}write_json(index_dict, os.path.join(tmp_model_path, pytorch_model.bin.index.json))ffn_dim_multiplier params[ffn_dim_multiplier] if ffn_dim_multiplier in params else 1multiple_of params[multiple_of] if multiple_of in params else 256config LlamaConfig(hidden_sizedim,intermediate_sizecompute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),num_attention_headsparams[n_heads],num_hidden_layersparams[n_layers],rms_norm_epsparams[norm_eps],num_key_value_headsnum_key_value_heads,vocab_sizevocab_size,rope_thetabase,max_position_embeddingsmax_position_embeddings,)config.save_pretrained(tmp_model_path)# 释放内存空间以便正确加载模型del state_dictdel loadedgc.collect()print(Loading the checkpoint in a Llama model.)# 从临时文件中加载模型model LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtypetorch.bfloat16, low_cpu_mem_usageTrue)# 避免将此作为配置的一部分保存del model.config._name_or_pathmodel.config.torch_dtype torch.float16print(Saving in the Transformers format.)# 保存LLama模型到指定的路径model.save_pretrained(model_path, safe_serializationsafe_serialization)# 删除临时文件中的所有内容shutil.rmtree(tmp_model_path)# 保存tokenizer
def write_tokenizer(tokenizer_path, input_tokenizer_path):# Initialize the tokenizer based on the spm modeltokenizer_class LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFastprint(fSaving a {tokenizer_class.__name__} to {tokenizer_path}.)tokenizer tokenizer_class(input_tokenizer_path)tokenizer.save_pretrained(tokenizer_path)def main():# 参数处理parser argparse.ArgumentParser()parser.add_argument(--input_dir,helpLocation of LLaMA weights, which contains tokenizer.model and model folders,)parser.add_argument(--model_size,choices[7B, 7Bf, 13B, 13Bf, 30B, 34B, 65B, 70B, 70Bf, tokenizer_only],helpf models correspond to the finetuned versions, and are specific to the Llama2 official release. For more details on Llama2, checkout the original repo: https://huggingface.co/meta-llama,)parser.add_argument(--output_dir,helpLocation to write HF model and tokenizer,)parser.add_argument(--safe_serialization, typebool, helpWhether or not to save using safetensors.)args parser.parse_args()spm_path os.path.join(args.input_dir, tokenizer.model)# 判断转换的对象if args.model_size ! tokenizer_only:write_model(model_pathargs.output_dir,input_base_pathargs.input_dir,model_sizeargs.model_size,safe_serializationargs.safe_serialization,tokenizer_pathspm_path,)else:write_tokenizer(args.output_dir, spm_path)if __name__ __main__:main()脚本运行
python convert_llama_weights_to_hf.py --input_dir raw-llama2-7b --output_dir llama2_7b_hfraw-llama2-7b文件夹内容 llama2_7b_hf转换文件内容
