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量化
FastDeploy支持FP8、INT8、INT4、2-bit等多种量化推理精度，支持模型权重、激活和KVCache 3种张量的不同精度推理，可以满足低成本、低时延、长上下文等不同场景的推理需求。
1. 精度支持列表
| 量化方法 | 权重精度 | 激活精度 | KVCache精度 | 在线/离线 | 支持硬件 |
|---|---|---|---|---|---|
| WINT8 | INT8 | BF16 | BF16 | 在线 | GPU, XPU |
| WINT4 | INT4 | BF16 | BF16 | 在线 | GPU, XPU |
| block_wise_fp8 | block-wise static FP8 | token-wise dynamic FP8 | BF16 | 在线 | GPU |
| WINT2 | 2Bits | BF16 | BF16 | 离线 | GPU |
| MixQuant | INT4/INT8 | INT8/BF16 | INT8/BF16 | 离线 | GPU, XPU |
说明
- 量化方法：对应量化配置文件中的"quantization"字段；
- 在线/离线量化：主要用于区分权重的量化时间
- 在线量化：推理引擎在加载 BF16 权重后，再对权重做量化；
- 离线量化：在推理之前，将权重离线地量化并存储为低比特数值类型，推理时，加载量化后的低比特数值。
- 动态量化/静态量化：主要用于区别激活的量化方式
- 静态量化（static）：在推理之前，确定并存储量化系数，推理时加载提前计算好的量化系数。因为量化系数在推理时是固定不变的，所以叫静态量化（static quantization）；
- 动态量化（dynamic）：在推理时，即时地统计当前batch的量化系数。因为量化系数在推理时是动态地变化的，所以叫动态量化（dynamic quantization）。
2. 模型支持列表
| 模型名称 | 支持量化精度 |
|---|---|
| ERNIE-4.5-300B-A47B | WINT8, WINT4, Block_wise= FP8, MixQuant |
3. 量化精度术语
FastDeploy 按以下格式命名各种量化精度：
{tensor缩写}{数值类型}{tensor缩写}{数值类型}{tensor缩写}{数值类型}
部分示例如下：
- W8A8C8：W=weights，A=activations，C=CacheKV；8默认为INT8
- W8A8C16：16默认为BF16，其它同上
- W4A16C16 / WInt4 / weight-only int4：4默认为INT4
- WNF4A8C8：NF4指4bit norm-float数值类型
- Wfp8Afp8：权重和激活均为FP8精度
- W4Afp8：权重为INT4, 激活为FP8