76 lines
2.6 KiB
Markdown
76 lines
2.6 KiB
Markdown
# GPT-J
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本文档介绍了如何使用昆仑芯XTRT-LLM在单XPU上构建和运行[GPT-J](https://huggingface.co/EleutherAI/gpt-j-6b)模型。
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## 概述
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XTRT-LLM GPT-J 示例代码位于 [`examples/gptj`](./)。 此文件夹中有以下几个主要文件：
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* [`build.py`](./build.py) 构建运行GPT-J模型所需的XTRT引擎
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* [`run.py`](./run.py) 基于输入的文字进行推理
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## 支持的矩阵
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* FP16
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## 使用说明
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### 1.从HuggingFace（HF） Transformers下载权重
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```bash
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# 1. Weights & config
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git clone https://huggingface.co/EleutherAI/gpt-j-6b ./downloads/gptj-6b
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pushd ./downloads/gptj-6b && \
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rm -f pytorch_model.bin && \
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wget https://huggingface.co/EleutherAI/gpt-j-6b/resolve/main/pytorch_model.bin && \
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popd
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# 2. Vocab and merge table
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wget https://huggingface.co/EleutherAI/gpt-j-6b/resolve/main/vocab.json
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wget https://huggingface.co/EleutherAI/gpt-j-6b/resolve/main/merges.txt
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```
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### 2. 构建XTRT引擎
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XTRT-LLM从HF checkpoint构建XTRT引擎。如果未指定checkpoint目录，XTRT-LLM将使用伪权重构建引擎。
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构建调用示例：
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```bash
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# Build a float16 engine using HF weights.
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# Enable several XTRT-LLM plugins to increase runtime performance. It also helps with build time.
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python3 build.py --dtype=float16 \
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--log_level=verbose \
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--enable_context_fmha \
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--use_gpt_attention_plugin float16 \
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--use_gemm_plugin float16 \
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--max_batch_size=32 \
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--max_input_len=1919 \
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--max_output_len=128 \
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--output_dir=./downloads/gptj-6b/trt_engines/fp16/1-XPU/ \
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--model_dir=./downloads/gptj-6b 2>&1 | tee build.log
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# Build a float16 engine using dummy weights, useful for performance tests.
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# Enable several XTRT-LLM plugins to increase runtime performance. It also helps with build time.
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python3 build.py --dtype=float16 \
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--log_level=verbose \
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--enable_context_fmha \
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--use_gpt_attention_plugin float16 \
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--use_gemm_plugin float16 \
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--max_batch_size=32 \
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--max_input_len=1919 \
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--max_output_len=128 \
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--output_dir=./downloads/gptj-6b/trt_engines/gptj_engine_dummy_weights 2>&1 | tee build.log
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```
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### 3. 运行
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要运行XTRT-LLM GPT-J模型，请执行以下操作：
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```bash
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python3 run.py --max_output_len=50 \
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--engine_dir=./downloads/gptj-6b/trt_engines/fp16/1-XPU/ \
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--hf_model_location=./downloads/gptj-6b
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``` |