96 lines
4.7 KiB
Markdown
96 lines
4.7 KiB
Markdown
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# 使用 Intel® Extension for Transformers 推理 GLM-4-9B-Chat 模型
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本示例介绍如何使用 Intel® Extension for Transformers 推理 GLM-4-9B-Chat 模型。
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## 设备和依赖检查
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### 相关推理测试数据
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**本文档的数据均在以下硬件环境测试,实际运行环境需求和运行占用的显存略有不同，请以实际运行环境为准。**
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测试硬件信息:
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+ OS: Ubuntu 22.04 (本教程一定需要在Linux环境下执行)
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+ Memory: 512GB
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+ Python: 3.10.12
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+ CPU: Intel(R) Xeon(R) Platinum 8358 CPU / 12th Gen Intel i5-12400
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## 安装依赖
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在开始推理之前，请你先安装`basic_demo`中的依赖，同时您需要安装本目录下的依赖项：
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```shell
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pip install -r requirements.txt
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```
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## 运行模型推理
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```shell
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python itrex_cli_demo.py
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```
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如果您是第一次推理，会有一次模型转换权重的过程，转换后的模型权重存放在`runtime_outputs`文件夹下，这大概会消耗`60G`的硬盘空间。
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转换完成后，文件夹下有两个文件：
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+ ne_chatglm2_f32.bin 52G(如果您不使用FP32进行推理，可以删掉这个文件)
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+ ne_chatglm2_q_nf4_bestla_cfp32_sym_sfp32_g32.bin 8.1G
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如果您不是第一次推理，则会跳过这个步骤，直接开始对话，推理效果如下：
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```shell
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Welcome to the CLI chat. Type your messages below.
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User: 你好
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AVX:1 AVX2:1 AVX512F:1 AVX512BW:1 AVX_VNNI:0 AVX512_VNNI:1 AMX_INT8:0 AMX_BF16:0 AVX512_BF16:0 AVX512_FP16:0
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beam_size: 1, do_sample: 1, top_k: 40, top_p: 0.900, continuous_batching: 0, max_request_num: 1, early_stopping: 0, scratch_size_ratio: 1.000
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model_file_loader: loading model from runtime_outs/ne_chatglm2_q_nf4_bestla_cfp32_sym_sfp32_g32.bin
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Loading the bin file with NE format...
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load_ne_hparams 0.hparams.n_vocab = 151552
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load_ne_hparams 1.hparams.n_embd = 4096
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load_ne_hparams 2.hparams.n_mult = 0
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load_ne_hparams 3.hparams.n_head = 32
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load_ne_hparams 4.hparams.n_head_kv = 0
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load_ne_hparams 5.hparams.n_layer = 40
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load_ne_hparams 6.hparams.n_rot = 0
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load_ne_hparams 7.hparams.ftype = 0
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load_ne_hparams 8.hparams.max_seq_len = 131072
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load_ne_hparams 9.hparams.alibi_bias_max = 0.000
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load_ne_hparams 10.hparams.clip_qkv = 0.000
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load_ne_hparams 11.hparams.par_res = 0
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load_ne_hparams 12.hparams.word_embed_proj_dim = 0
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load_ne_hparams 13.hparams.do_layer_norm_before = 0
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load_ne_hparams 14.hparams.multi_query_group_num = 2
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load_ne_hparams 15.hparams.ffn_hidden_size = 13696
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load_ne_hparams 16.hparams.inner_hidden_size = 0
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load_ne_hparams 17.hparams.n_experts = 0
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load_ne_hparams 18.hparams.n_experts_used = 0
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load_ne_hparams 19.hparams.n_embd_head_k = 0
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load_ne_hparams 20.hparams.norm_eps = 0.000000
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load_ne_hparams 21.hparams.freq_base = 5000000.000
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load_ne_hparams 22.hparams.freq_scale = 1.000
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load_ne_hparams 23.hparams.rope_scaling_factor = 0.000
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load_ne_hparams 24.hparams.original_max_position_embeddings = 0
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load_ne_hparams 25.hparams.use_yarn = 0
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load_ne_vocab 26.vocab.bos_token_id = 1
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load_ne_vocab 27.vocab.eos_token_id = 151329
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load_ne_vocab 28.vocab.pad_token_id = 151329
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load_ne_vocab 29.vocab.sep_token_id = -1
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init: hparams.n_vocab = 151552
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init: hparams.n_embd = 4096
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init: hparams.n_mult = 0
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init: hparams.n_head = 32
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init: hparams.n_layer = 40
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init: hparams.n_rot = 0
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init: hparams.ffn_hidden_size = 13696
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init: n_parts = 1
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load: ctx size = 16528.38 MB
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load: layers[0].ffn_fusion = 1
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load: scratch0 = 4096.00 MB
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load: scratch1 = 2048.00 MB
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load: scratch2 = 4096.00 MB
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load: mem required = 26768.38 MB (+ memory per state)
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.............................................................................................
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model_init_from_file: support_bestla_kv = 1
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kv_cache_init: run_mha_reordered = 1
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model_init_from_file: kv self size = 690.00 MB
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Assistant:
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你好👋！我是人工智能助手，很高兴为你服务。有什么可以帮助你的吗？
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```
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