Fp8kv support

docs/CN/source/index.rst

@@ -49,6 +49,7 @@ Lightllm 整合了众多的开源方案的优点，包括但不限于 FasterTran
    :caption: 部署教程
 
    DeepSeek R1 部署 <tutorial/deepseek_deployment>
+   FP8 KV 量化与校准 <tutorial/fp8_kv_quantization>
    多级缓存部署 <tutorial/multi_level_cache_deployment>
    多模态部署 <tutorial/multimodal>
    奖励模型部署 <tutorial/reward_model>

docs/CN/source/tutorial/api_server_args.rst

@@ -337,7 +337,10 @@ PD 分离模式参数
 
 .. option:: --llm_kv_type
 
-    推理后端使用什么类型的数据存储kv cache, 可选值为 "None", "int8kv", "int4kv", "fp8kv"
+    推理后端使用什么类型的数据存储kv cache, 可选值为 "None", "int8kv", "int4kv", "fp8kv_sph", "fp8kv_spt"
+
+    - ``fp8kv_sph``: FP8 静态按 head 量化，对应 fa3 后端
+    - ``fp8kv_spt``: FP8 静态按 tensor 量化，对应 flashinfer 后端
 
 .. option:: --disable_cudagraph
 

docs/CN/source/tutorial/fp8_kv_quantization.rst

@@ -0,0 +1,102 @@
+.. _tutorial/fp8_kv_quantization_cn:
+
+FP8 KV 量化与校准指南
+======================
+
+本章节介绍 LightLLM 中 FP8 KV 推理的使用方式，包括：
+
+- 使用校准文件进行推理（``fp8kv_sph`` 或 ``fp8kv_spt``）
+- FP8 静态按 head 和按 tensor 的量化模式
+- 常见报错与排查建议
+
+功能概览
+--------
+
+LightLLM 的 FP8 KV 推理需要准备好的校准文件（``kv_cache_calib.json``），
+并通过 ``--kv_quant_calibration_config_path`` 加载。
+你可以直接使用 ``test/advanced_config/`` 目录下已有的校准文件，
+也可以使用 `LightCompress <https://github.com/ModelTC/LightCompress>`_ 工具导出，或使用自有兼容文件。
+
+量化模式与后端对应
+------------------
+
+LightLLM 支持两种 FP8 KV 量化模式：
+
+- ``fp8kv_sph``: FP8 静态按 head 量化（Static Per-Head），每个 head 独立 scale，对应 ``fa3`` 后端
+- ``fp8kv_spt``: FP8 静态按 tensor 量化（Static Per-Tensor），K/V 各一个标量 scale，对应 ``flashinfer`` 后端
+
+校准文件与量化模式强相关：
+
+- ``fp8kv_sph`` 对应 ``per_head`` 校准文件
+- ``fp8kv_spt`` 对应 ``per_tensor`` 校准文件
+
+不建议混用不同模式的校准文件。
+
+使用校准文件启动 FP8 推理
+-------------------------
+
+推理模式示例：
+
+.. code-block:: console
+
+    $ python -m lightllm.server.api_server \
+        --model_dir /path/to/model \
+        --llm_kv_type fp8kv_sph \
+        --kv_quant_calibration_config_path /path/to/kv_cache_calib.json
+
+.. code-block:: console
+
+    $ python -m lightllm.server.api_server \
+        --model_dir /path/to/model \
+        --llm_kv_type fp8kv_spt \
+        --kv_quant_calibration_config_path /path/to/kv_cache_calib.json
+
+说明：
+
+- ``fp8kv_sph`` 和 ``fp8kv_spt`` 模式必须提供 ``--kv_quant_calibration_config_path``。
+- attention backend 会根据量化模式自动选择，无需手动指定。
+
+.. note::
+
+   使用 ``fp8kv_spt`` 模式（FP8 静态按 tensor 量化，使用 flashinfer 后端）时，
+   必须安装 ``flashinfer-python==0.6.5``。默认安装的版本是 0.6.3，
+   可能导致运行错误。请使用以下命令安装正确版本：
+
+   .. code-block:: console
+
+       $ pip install flashinfer-python==0.6.5
+
+校准文件格式
+------------
+
+``kv_cache_calib.json`` 主要字段包括：
+
+- ``quant_type``: ``per_head`` 或 ``per_tensor``
+- ``num_layers``: 层数
+- ``num_head``: 总 head 数
+- ``scales_shape``: scale 张量形状
+- ``scales``: 实际 scale 数值
+- ``qmin`` / ``qmax``: FP8 范围参数
+
+加载校准文件时，会校验模型架构、层数、head 数及量化类型是否匹配。
+
+多卡说明
+--------
+
+在多卡（TP）场景下，系统会根据当前 rank 自动切分本地需要的 head 对应 scale。
+你仍然只需要提供一份全量 ``kv_cache_calib.json``。
+
+常见问题
+--------
+
+1. 启动时报错需要 ``--kv_quant_calibration_config_path``
+
+   说明你使用了 ``--llm_kv_type fp8kv_sph`` 或 ``fp8kv_spt`` 但未传入校准文件路径。
+
+2. 报错 ``quant_type not match``
+
+   通常是量化模式与校准文件类型不一致。例如拿 ``per_tensor`` 文件去跑 ``fp8kv_sph``。
+
+3. 切换量化模式后效果异常
+
+   建议使用与目标量化模式匹配的校准文件，不要跨模式复用不兼容文件。

docs/EN/source/index.rst

@@ -48,6 +48,7 @@ Documentation List
    :caption: Deployment Tutorials
 
    DeepSeek R1 Deployment <tutorial/deepseek_deployment>
+   FP8 KV Quantization and Calibration <tutorial/fp8_kv_quantization>
    Multi-Level Cache Deployment <tutorial/multi_level_cache_deployment>
    Multimodal Deployment <tutorial/multimodal>
    Reward Model Deployment <tutorial/reward_model>

docs/EN/source/tutorial/api_server_args.rst

@@ -333,6 +333,16 @@ Performance Optimization Parameters
     * ``flashinfer``: Use FlashInfer backend
     * ``triton``: Use Triton backend
 
+.. option:: --llm_kv_type
+
+    Set the KV cache data type for inference. Available options:
+
+    * ``None``: Use the dtype from model's config.json
+    * ``int8kv``: INT8 KV quantization
+    * ``int4kv``: INT4 KV quantization
+    * ``fp8kv_sph``: FP8 static per-head quantization, uses fa3 backend
+    * ``fp8kv_spt``: FP8 static per-tensor quantization, uses flashinfer backend
+
 .. option:: --disable_cudagraph
 
     Disable cudagraph in the decoding phase

docs/EN/source/tutorial/fp8_kv_quantization.rst

@@ -0,0 +1,102 @@
+.. _tutorial/fp8_kv_quantization_en:
+
+FP8 KV Quantization and Calibration Guide
+=========================================
+
+This chapter describes FP8 KV inference in LightLLM, including:
+
+- Running inference with calibration data (``fp8kv_sph`` or ``fp8kv_spt``)
+- FP8 static per-head and per-tensor quantization modes
+- Common errors and troubleshooting
+
+Overview
+--------
+
+LightLLM FP8 KV inference requires a prepared calibration file (``kv_cache_calib.json``),
+which is loaded by ``--kv_quant_calibration_config_path``.
+You can use calibration files provided in ``test/advanced_config/``,
+export one with `LightCompress <https://github.com/ModelTC/LightCompress>`_, or use your own compatible file.
+
+Quantization Modes and Backend Mapping
+------------------------------------------
+
+LightLLM supports two FP8 KV quantization modes:
+
+- ``fp8kv_sph``: FP8 Static Per-Head quantization, independent scale per head, uses ``fa3`` backend
+- ``fp8kv_spt``: FP8 Static Per-Tensor quantization, one scalar for K and one scalar for V, uses ``flashinfer`` backend
+
+Calibration files are mode-dependent:
+
+- ``fp8kv_sph`` corresponds to ``per_head`` calibration files
+- ``fp8kv_spt`` corresponds to ``per_tensor`` calibration files
+
+Avoid mixing calibration files across different modes.
+
+Start FP8 Inference with Calibration
+------------------------------------
+
+Inference mode example:
+
+.. code-block:: console
+
+    $ python -m lightllm.server.api_server \
+        --model_dir /path/to/model \
+        --llm_kv_type fp8kv_sph \
+        --kv_quant_calibration_config_path /path/to/kv_cache_calib.json
+
+.. code-block:: console
+
+    $ python -m lightllm.server.api_server \
+        --model_dir /path/to/model \
+        --llm_kv_type fp8kv_spt \
+        --kv_quant_calibration_config_path /path/to/kv_cache_calib.json
+
+Notes:
+
+- ``fp8kv_sph`` and ``fp8kv_spt`` require ``--kv_quant_calibration_config_path``.
+- The attention backend will be automatically selected based on the quantization mode, no need to manually specify.
+
+.. note::
+
+   When using ``fp8kv_spt`` mode (FP8 static per-tensor quantization with flashinfer backend), 
+   you must install ``flashinfer-python==0.6.5``. The default installed version is 0.6.3, 
+   which may cause runtime issues. Install the correct version with:
+
+   .. code-block:: console
+
+       $ pip install flashinfer-python==0.6.5
+
+Calibration File Schema
+-----------------------
+
+Key fields in ``kv_cache_calib.json``:
+
+- ``quant_type``: ``per_head`` or ``per_tensor``
+- ``num_layers``: number of layers
+- ``num_head``: total number of heads
+- ``scales_shape``: shape of the scale tensor
+- ``scales``: actual scale values
+- ``qmin`` / ``qmax``: FP8 numeric range parameters
+
+At load time, LightLLM validates architecture, layer count, head count, and quantization type.
+
+Multi-GPU Note
+--------------
+
+In multi-GPU (TP) setups, LightLLM slices the global scales to local rank heads automatically.
+You only need to provide one full ``kv_cache_calib.json`` file.
+
+Common Issues
+-------------
+
+1. Error says ``--kv_quant_calibration_config_path`` is required
+
+   You are using ``--llm_kv_type fp8kv_sph`` or ``fp8kv_spt`` without a calibration file path.
+
+2. ``quant_type not match`` error
+
+   Usually caused by quantization mode/file mismatch (for example, using a ``per_tensor`` file with ``fp8kv_sph``).
+
+3. Abnormal quality after mode switch
+
+   Use a calibration file that matches the target quantization mode instead of reusing an incompatible file.

lightllm/common/basemodel/attention/create_utils.py

@@ -35,6 +35,12 @@
         # "fa3": Fp8Fa3AttBackend,
         # "flashinfer": Fp8FlashInferAttBackend,
     },
+    "fp8kv_sph": {
+        "fa3": Fp8Fa3AttBackend,
+    },
+    "fp8kv_spt": {
+        "flashinfer": Fp8FlashInferAttBackend,
+    },
 }
 
 mla_data_type_to_backend = {

lightllm/common/basemodel/attention/fa3/fp8.py

@@ -45,24 +45,9 @@ def init_state(self):
             torch.arange(batch_size, device=device), self.infer_state.b_q_seq_len
         )
         # 为了减少推理计算量，在推理外部初始化k_descale和v_descale
-        self.k_descale = (
-            offline_scales[:, :head_num].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
-            if offline_scales is not None
-            else torch.ones(
-                (mem_manager.layer_num, batch_size, head_num),
-                dtype=torch.float32,
-                device=device,
-            )
-        )
-        self.v_descale = (
-            offline_scales[:, head_num:].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
-            if offline_scales is not None
-            else torch.ones(
-                (mem_manager.layer_num, batch_size, head_num),
-                dtype=torch.float32,
-                device=device,
-            )
-        )
+        self.k_descale = offline_scales[:, :head_num].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
+        self.v_descale = offline_scales[:, head_num:].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
+
 
     def prefill_att(
         self,
@@ -89,19 +74,21 @@ def _fp8_prefill_att(
     ) -> torch.Tensor:
         self.backend: Fp8Fa3AttBackend = self.backend  # for typing
 
+        q_head_num = q.shape[1]
+        q_head_dim = q.shape[2]
+        k_head_num = k.shape[1]
         q, q_scale = q_per_head_fp8_quant(
-            q,
+            q.reshape(q.shape[0], k_head_num, -1),
             self.infer_state.b_seq_len,
             self.cu_seqlens_q,
-            self.mid_token_batch_ids,
+            token_batch_ids=self.mid_token_batch_ids,
         )
-        k_head_num = k.shape[1]
         k_head_dim = k.shape[2]
         cache_k = k.view(-1, 1, k_head_num, k_head_dim).view(torch.float8_e4m3fn)
         cache_v = v.view(-1, 1, k_head_num, k_head_dim).view(torch.float8_e4m3fn)
         layer_index = self.backend._find_layer_index(k=cache_k, v=cache_v, att_state=self)
         o = flash_attn_with_kvcache(
-            q=q,
+            q=q.reshape(-1, q_head_num, q_head_dim),
             k_cache=cache_k,
             v_cache=cache_v,
             page_table=self.page_table,
@@ -141,24 +128,9 @@ def init_state(self):
         head_num = mem_manager.head_num
 
         # 为了减少推理计算量，在推理外部初始化k_descale和v_descale
-        self.k_descale = (
-            offline_scales[:, :head_num].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
-            if offline_scales is not None
-            else torch.ones(
-                (mem_manager.layer_num, batch_size, head_num),
-                dtype=torch.float32,
-                device=device,
-            )
-        )
-        self.v_descale = (
-            offline_scales[:, head_num:].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
-            if offline_scales is not None
-            else torch.ones(
-                (mem_manager.layer_num, batch_size, head_num),
-                dtype=torch.float32,
-                device=device,
-            )
-        )
+        self.k_descale = offline_scales[:, :head_num].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
+        self.v_descale = offline_scales[:, head_num:].view(-1, 1, head_num).expand(offline_scales.shape[0], batch_size, head_num)
+
         return
 
     def copy_for_decode_cuda_graph(self, new_state: "Fp8Fa3DecodeAttState"):
@@ -200,9 +172,11 @@ def _fp8_decode_att(
         layer_index = self.backend._find_layer_index(k=cache_k, v=cache_v, att_state=self)
 
         q_head_num = q.shape[1]
-        q, q_scale = scaled_fp8_quant(q.view(q.shape[0] * k_head_num, -1), use_per_token_if_dynamic=True)
+        if scaled_fp8_quant is None:
+            raise ImportError("scaled_fp8_quant is unavailable. Please install vllm to enable FP8 decode attention.")
+        q, q_scale = scaled_fp8_quant(q.reshape(q.shape[0] * k_head_num, -1), use_per_token_if_dynamic=True)
         o = flash_attn_with_kvcache(
-            q=q.view(-1, q_head_num, k_head_dim),
+            q=q.reshape(-1, q_head_num, k_head_dim),
             k_cache=cache_k,
             v_cache=cache_v,
             page_table=self.page_table,