From ba4ac9639972c4f3b60c3d0340ca60d2eaab7e7d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dani=C3=ABl=20de=20Kok?= <me@danieldk.eu>
Date: Wed, 9 Oct 2024 11:50:21 +0000
Subject: [PATCH 1/3] Add support for FP8 KV cache scales

Since FP8 only has limited dynamic range, we can scale keys/values
before storing them into the cache (and unscale them in attention). To
avoid rescaling the cache as the absmax values change, good scales are
usually determined per layer using calibration calibration data and stored
in the checkpoint.

This change adds support for for using key-value scales and loading them
from checkpoints in the two most common formats:

- Separate per-layer `k_scale` and `v_scale` scalars.
- Per-layer `kv_scale` scalar (older format).

Currently, scales are only used with an `float8_e4m3fn` cache.

Besides adding support for key/value scales, the `fp8_quantize` function
is also extended to support quantization with a kernel vendored from
vLLM. This is slightly faster than the PyTorch implementation, but also
scales in FP32, potentially improving accuracy.
---
 flake.lock                                    |  7 +-
 flake.nix                                     |  2 +-
 server/poetry.lock                            | 24 ++---
 server/pyproject.toml                         |  8 +-
 .../layers/attention/__init__.py              |  3 +-
 .../layers/attention/cuda.py                  | 14 ++-
 .../layers/attention/flashinfer.py            |  3 +-
 .../layers/attention/ipex.py                  |  5 +-
 .../layers/attention/kv_cache.py              | 93 ++++++++++++++++++-
 .../layers/attention/rocm.py                  |  5 +-
 server/text_generation_server/layers/fp8.py   | 17 ++++
 .../custom_modeling/flash_cohere_modeling.py  | 11 ++-
 .../custom_modeling/flash_dbrx_modeling.py    | 11 ++-
 .../flash_deepseek_v2_modeling.py             | 14 ++-
 .../custom_modeling/flash_gemma2_modeling.py  | 11 ++-
 .../custom_modeling/flash_gemma_modeling.py   | 11 ++-
 .../custom_modeling/flash_gpt2_modeling.py    | 11 ++-
 .../custom_modeling/flash_gptj_modeling.py    | 11 ++-
 .../custom_modeling/flash_llama_modeling.py   | 16 +++-
 .../custom_modeling/flash_mistral_modeling.py | 11 ++-
 .../custom_modeling/flash_mixtral_modeling.py | 11 ++-
 .../custom_modeling/flash_neox_modeling.py    | 11 ++-
 .../custom_modeling/flash_phi_modeling.py     | 11 ++-
 .../custom_modeling/flash_qwen2_modeling.py   | 12 ++-
 .../custom_modeling/flash_rw_modeling.py      | 19 +++-
 .../flash_santacoder_modeling.py              | 11 ++-
 .../flash_starcoder2_modeling.py              | 11 ++-
 .../models/flash_causal_lm.py                 |  1 +
 .../text_generation_server/utils/weights.py   |  4 +-
 29 files changed, 331 insertions(+), 48 deletions(-)

diff --git a/flake.lock b/flake.lock
index aacdd30e401..76b4ca2fe38 100644
--- a/flake.lock
+++ b/flake.lock
@@ -978,15 +978,16 @@
         "nixpkgs": "nixpkgs_6"
       },
       "locked": {
-        "lastModified": 1728381423,
-        "narHash": "sha256-gpHy1WtlA8ZTd8XmxsdCoDd4Z7DE7co37lH7P+nsADA=",
+        "lastModified": 1729531056,
+        "narHash": "sha256-dW9IOA31+j3VS19WAWAmkJW2YCzeVZGqd6HpIJfODtI=",
         "owner": "huggingface",
         "repo": "text-generation-inference-nix",
-        "rev": "93123736c97e9f7bfe825bfaf3d7de0fc9a21a1e",
+        "rev": "a84a90281a17b15762873845c947e5c78f5a8dd1",
         "type": "github"
       },
       "original": {
         "owner": "huggingface",
+        "ref": "marlin-kernels-0.3.0",
         "repo": "text-generation-inference-nix",
         "type": "github"
       }
diff --git a/flake.nix b/flake.nix
index f26a983ed93..5c05bfae7fb 100644
--- a/flake.nix
+++ b/flake.nix
@@ -5,7 +5,7 @@
       inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
     };
     nix-filter.url = "github:numtide/nix-filter";
-    tgi-nix.url = "github:huggingface/text-generation-inference-nix";
+    tgi-nix.url = "github:huggingface/text-generation-inference-nix/marlin-kernels-0.3.0";
     nixpkgs.follows = "tgi-nix/nixpkgs";
     flake-utils.url = "github:numtide/flake-utils";
     rust-overlay = {
diff --git a/server/poetry.lock b/server/poetry.lock
index 80fe72ba271..1293e883656 100644
--- a/server/poetry.lock
+++ b/server/poetry.lock
@@ -1215,12 +1215,12 @@ files = [
 
 [[package]]
 name = "marlin-kernels"
-version = "0.2.0"
+version = "0.3.0"
 description = "Marlin quantization kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:9a5afcf19b0f5917e43353cc19873fb3c4d4d0b924e2a95a37884f9ce208d0bd"},
+    {file = "marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:a2086b9e98d22071f52c5b4b4b98b1b4a988565258905173fa74c5a9eddd1a0a"},
 ]
 
 [package.dependencies]
@@ -1228,16 +1228,16 @@ torch = "*"
 
 [package.source]
 type = "url"
-url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
+url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
 
 [[package]]
 name = "marlin-kernels"
-version = "0.2.0"
+version = "0.3.0"
 description = "Marlin quantization kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:1e64fcc7ebadfaffa60091ee9201ae3daaf5c1be3be60c8c054143a3dcb72d5d"},
+    {file = "marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:f39a6946d8247629446ec170832d832c7038c363f1d8803211fe67249c2d804d"},
 ]
 
 [package.dependencies]
@@ -1245,16 +1245,16 @@ torch = "*"
 
 [package.source]
 type = "url"
-url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
+url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
 
 [[package]]
 name = "marlin-kernels"
-version = "0.2.0"
+version = "0.3.0"
 description = "Marlin quantization kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:e75f3ce9b1c13a4ed43a380d88e1d34d297259452db037ec1973ec33dc2eb78e"},
+    {file = "marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:07fd869d5289777fa866107dae676523e18b1f6ba4afce79946ddc58a6870169"},
 ]
 
 [package.dependencies]
@@ -1262,16 +1262,16 @@ torch = "*"
 
 [package.source]
 type = "url"
-url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
+url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
 
 [[package]]
 name = "marlin-kernels"
-version = "0.2.0"
+version = "0.3.0"
 description = "Marlin quantization kernels"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:2f99a27f70b391887ee6adffeeee7c3f4df7fac37393f9fb16d4cace2b3f6457"},
+    {file = "marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:0dedaa418225d490a5f1d8f85dbc75e439a8c43a8870e4ef32945bf61672d7dc"},
 ]
 
 [package.dependencies]
@@ -1279,7 +1279,7 @@ torch = "*"
 
 [package.source]
 type = "url"
-url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
+url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
 
 [[package]]
 name = "mdurl"
diff --git a/server/pyproject.toml b/server/pyproject.toml
index 6ea4718d715..d08d0b8f488 100644
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@@ -41,10 +41,10 @@ py-cpuinfo = "^9.0.0"
 numpy = "^1.26"
 
 marlin-kernels = [
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
 ]
 moe-kernels = [
   { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
diff --git a/server/text_generation_server/layers/attention/__init__.py b/server/text_generation_server/layers/attention/__init__.py
index b1d7b864a29..ebe32042c46 100644
--- a/server/text_generation_server/layers/attention/__init__.py
+++ b/server/text_generation_server/layers/attention/__init__.py
@@ -28,10 +28,11 @@
     raise ImportError(f"System {SYSTEM} doesn't support flash/paged attention")
 
 # KVCache needs `reshape_and_cache`, so ensure that it is defined already.
-from .kv_cache import KVCache
+from .kv_cache import KVCache, get_kv_scales
 
 __all__ = [
     "attention",
+    "get_kv_scales",
     "paged_attention",
     "SUPPORTS_WINDOWING",
     "KVCache",
diff --git a/server/text_generation_server/layers/attention/cuda.py b/server/text_generation_server/layers/attention/cuda.py
index 23f3404cc70..c67a79c29a0 100644
--- a/server/text_generation_server/layers/attention/cuda.py
+++ b/server/text_generation_server/layers/attention/cuda.py
@@ -1,5 +1,5 @@
 import torch
-from text_generation_server.layers.attention.kv_cache import KVCache
+from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.models.globals import (
     ATTENTION,
@@ -8,6 +8,7 @@
 from text_generation_server.layers.attention import Seqlen
 from typing import Optional
 
+
 major, minor = torch.cuda.get_device_capability()
 is_sm75 = major == 7 and minor == 5
 _PARTITION_SIZE = 512
@@ -21,6 +22,8 @@ def paged_attention(
     block_tables: torch.Tensor,
     seqlen: Seqlen,
     max_s: int,
+    *,
+    kv_scales: KVScales,
     softcap: Optional[float] = None,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
@@ -46,6 +49,8 @@ def paged_attention(
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
 
+    can_scale = kv_cache.can_scale(kv_scales)
+
     # NOTE(woosuk): We use a simple heuristic to decide whether to use
     # PagedAttention V1 or V2. If the number of partitions is 1, we use
     # V1 to avoid the overhead of reduction. Also, if the number of
@@ -59,6 +64,8 @@ def paged_attention(
             paged_kv_cache=(kv_cache.key, kv_cache.value),
             logits_soft_cap=softcap,
             sm_scale=softmax_scale,
+            k_scale=kv_scales.key_scale_cpu if can_scale else 1.0,
+            v_scale=kv_scales.value_scale_cpu if can_scale else 1.0,
         )
     elif ATTENTION == "flashdecoding":
         max_q = 1
@@ -204,6 +211,7 @@ def attention(
     key: torch.Tensor,
     value: torch.Tensor,
     kv_cache: KVCache,
+    kv_scales: KVScales,
     seqlen: Seqlen,
     block_tables: torch.Tensor,
     softmax_scale: float,
@@ -211,6 +219,8 @@ def attention(
     causal: bool = True,
     softcap: Optional[float] = None,
 ):
+    can_scale = kv_cache.can_scale(kv_scales)
+
     if ATTENTION == "flashinfer":
         from text_generation_server.layers.attention.flashinfer import (
             prefill_with_paged_kv_state,
@@ -226,6 +236,8 @@ def attention(
             logits_soft_cap=softcap,
             sm_scale=softmax_scale,
             window_left=window_size_left,
+            k_scale=kv_scales.key_scale_cpu if can_scale else 1.0,
+            v_scale=kv_scales.value_scale_cpu if can_scale else 1.0,
         )
 
     # If we are using flashdecoding or paged, we always use flash-attn for
diff --git a/server/text_generation_server/layers/attention/flashinfer.py b/server/text_generation_server/layers/attention/flashinfer.py
index d603c6f5f03..26a72d9be71 100644
--- a/server/text_generation_server/layers/attention/flashinfer.py
+++ b/server/text_generation_server/layers/attention/flashinfer.py
@@ -204,6 +204,7 @@ def use_decode_state(
     num_kv_heads: int,
     head_size: int,
     page_size: int,
+    kv_cache_dtype: torch.dtype,
     dtype: torch.dtype,
     window_left: int,
 ):
@@ -240,7 +241,7 @@ def use_decode_state(
             num_kv_heads=num_kv_heads,
             head_dim=head_size,
             page_size=page_size,
-            data_type=dtype,
+            data_type=kv_cache_dtype,
             q_data_type=dtype,
             window_left=window_left,
         )
diff --git a/server/text_generation_server/layers/attention/ipex.py b/server/text_generation_server/layers/attention/ipex.py
index e76bb1f42fd..677f3f5647d 100644
--- a/server/text_generation_server/layers/attention/ipex.py
+++ b/server/text_generation_server/layers/attention/ipex.py
@@ -1,6 +1,6 @@
 import intel_extension_for_pytorch as ipex
 import torch
-from text_generation_server.layers.attention.kv_cache import KVCache
+from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
 from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
 from text_generation_server.layers.attention import Seqlen
 from typing import Optional
@@ -14,6 +14,7 @@ def attention(
     key: torch.Tensor,
     value: torch.Tensor,
     kv_cache: KVCache,
+    kv_scales: KVScales,
     seqlen: Seqlen,
     block_tables: torch.Tensor,
     softmax_scale: float,
@@ -55,6 +56,8 @@ def paged_attention(
     block_tables: torch.Tensor,
     seqlen: Seqlen,
     max_s: int,
+    *,
+    kv_scales: KVScales,
     softcap: Optional[float] = None,
 ):
     if softcap is not None:
diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py
index d64302c651c..8a79eebb893 100644
--- a/server/text_generation_server/layers/attention/kv_cache.py
+++ b/server/text_generation_server/layers/attention/kv_cache.py
@@ -1,8 +1,38 @@
 from typing import Tuple
+from dataclasses import dataclass, field
 
+from loguru import logger
 import torch
+
+from text_generation_server.layers.fp8 import fp8_quantize
 from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE
 from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.utils.log import log_once
+from text_generation_server.utils.weights import Weights
+
+
+@dataclass
+class KVScales:
+    """
+    Key-value scales for FP8 KV cache.
+
+    This data class stores key and value scales both as a GPU tensor and
+    as a GPU float. This inconvenience is necessary because some functions
+    (e.g. scaling kernels) take scales as a GPU tensor, whereas others
+    (e.g. flashinfer) take scales as a CPU scalar.
+    """
+
+    key_scale: torch.Tensor
+    value_scale: torch.Tensor
+    key_scale_cpu: float = field(init=False)
+    value_scale_cpu: float = field(init=False)
+
+    def __post_init__(self):
+        if self.key_scale.numel() != 1 or self.value_scale.numel() != 1:
+            raise ValueError("Key and value scales must be scalar tensors.")
+
+        self.key_scale_cpu = self.key_scale.item()
+        self.value_scale_cpu = self.value_scale.item()
 
 
 class KVCache:
@@ -76,6 +106,29 @@ def __init__(
                 ),
             )
 
+    def can_scale(self, kv_scales: KVScales) -> bool:
+        """Check if the cache can be scaled by the given scales."""
+        if kv_scales.key_scale_cpu == 1.0 and kv_scales.value_scale_cpu == 1.0:
+            return False
+        elif self.dtype == torch.float8_e4m3fn and SYSTEM == "cuda":
+            log_once(
+                logger.info,
+                "Using FP8 KV cache scales",
+            )
+            return True
+        else:
+            # We have scales, but not the correct FP8 cache type, so warn once.
+            log_once(
+                logger.info,
+                "Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on CUDA is supported",
+            )
+            return False
+
+    @property
+    def dtype(self):
+        """Get the data type of the cache."""
+        return self.kv_cache[0].dtype
+
     @property
     def key(self):
         """Get the key cache."""
@@ -94,17 +147,33 @@ def store(
         key: torch.Tensor,
         value: torch.Tensor,
         slots: torch.Tensor,
+        kv_scales: KVScales,
     ):
         """Store the key and value at the given slots."""
 
         key_cache = self.kv_cache[0]
         value_cache = self.kv_cache[1]
 
+        if self.can_scale(kv_scales):
+            if kv_scales.key_scale_cpu != 1.0:
+                key = fp8_quantize(
+                    key.float(),
+                    scale=kv_scales.key_scale,
+                    qdtype=self.dtype,
+                    scalar=True,
+                )[0]
+            if kv_scales.value_scale_cpu != 1.0:
+                value = fp8_quantize(
+                    value.float(),
+                    scale=kv_scales.value_scale,
+                    qdtype=self.dtype,
+                    scalar=True,
+                )[0]
+
         if ATTENTION in {"flashdecoding", "flashinfer"}:
-            # TODO: add scale
             key = key.to(key_cache.dtype)
             value = value.to(value_cache.dtype)
-            if key_cache.dtype in {torch.float8_e5m2, torch.float8_e4m3fn}:
+            if key_cache.dtype in {torch.float8_e4m3fn, torch.float8_e5m2}:
                 # Torch index_put does not support float8_{e5m2,e4m3fn} yet, so
                 # put as raw data instead.
                 key_cache = key_cache.view(torch.uint8)
@@ -151,5 +220,23 @@ def paged_reshape_and_cache(
         )
     else:
         raise NotImplementedError(
-            f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supportedattention"
+            f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supported"
         )
+
+
+def get_kv_scales(weights: Weights, prefix: str) -> KVScales:
+    """Load KV cache scales."""
+
+    key_scale = torch.tensor(1.0, dtype=torch.float32, device=weights.device)
+    value_scale = key_scale
+    if weights.has_tensor(f"{prefix}.k_scale") and weights.has_tensor(
+        f"{prefix}.v_scale"
+    ):
+        key_scale = weights.get_tensor(f"{prefix}.k_scale", to_dtype=False).float()
+        value_scale = weights.get_tensor(f"{prefix}.v_scale", to_dtype=False).float()
+    elif weights.has_tensor(f"{prefix}.kv_scale"):
+        # Fall back to older more coarse-grained scale when available.
+        key_scale = weights.get_tensor(f"{prefix}.kv_scale").float()
+        value_scale = key_scale
+
+    return KVScales(key_scale=key_scale, value_scale=value_scale)
diff --git a/server/text_generation_server/layers/attention/rocm.py b/server/text_generation_server/layers/attention/rocm.py
index 47bf5539c56..ea11c2c2615 100644
--- a/server/text_generation_server/layers/attention/rocm.py
+++ b/server/text_generation_server/layers/attention/rocm.py
@@ -1,7 +1,7 @@
 import os
 from typing import Optional
 import torch
-from text_generation_server.layers.attention.kv_cache import KVCache
+from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import Seqlen
 from text_generation_server.utils.log import log_master
@@ -36,6 +36,8 @@ def paged_attention(
     block_tables: torch.Tensor,
     seqlen: Seqlen,
     max_s: int,
+    *,
+    kv_scales: KVScales,
     softcap: Optional[float] = None,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
@@ -210,6 +212,7 @@ def attention(
     key: torch.Tensor,
     value: torch.Tensor,
     kv_cache: KVCache,
+    kv_scales: KVScales,
     seqlen: Seqlen,
     block_tables: torch.Tensor,
     softmax_scale: float,
diff --git a/server/text_generation_server/layers/fp8.py b/server/text_generation_server/layers/fp8.py
index 18a40afa3db..a58c7f7b223 100644
--- a/server/text_generation_server/layers/fp8.py
+++ b/server/text_generation_server/layers/fp8.py
@@ -26,6 +26,12 @@ def is_fbgemm_gpu_available():
         return False
 
 
+try:
+    import marlin_kernels
+except ImportError:
+    marlin_kernels = None
+
+
 if is_fbgemm_gpu_available():
     if SYSTEM == "cuda":
         major, _ = torch.cuda.get_device_capability()
@@ -94,6 +100,17 @@ def fp8_quantize(
         )
         return qweight, scale
 
+    if marlin_kernels is not None:
+        shape = weight.shape
+        qweight, scale = marlin_kernels.scaled_fp8_quant(
+            weight.reshape(-1, shape[-1]),
+            dtype=qdtype,
+            scale=scale,
+            scale_ub=scale_upper_bound,
+        )
+
+        return qweight.reshape(shape), scale
+
     # weight, scale = quant_weights(weight, torch.int8, False)
     finfo = torch.finfo(qdtype)
 
diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
index 4eee5c20894..68719106fca 100644
--- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
@@ -30,6 +30,7 @@
     attention,
     Seqlen,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers import (
     TensorParallelRowLinear,
@@ -227,6 +228,7 @@ def __init__(
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.use_qk_norm = config.use_qk_norm
         if self.use_qk_norm:
@@ -289,7 +291,12 @@ def forward(
 
         self.rotary_emb(query, key, cos, sin)
 
-        kv_cache.store(key=key, value=value, slots=slots)
+        kv_cache.store(
+            key=key,
+            value=value,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -299,6 +306,7 @@ def forward(
                 key=key,
                 value=value,
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -313,6 +321,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(
diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
index 4ee6774171f..f70bff4f881 100644
--- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
@@ -20,6 +20,7 @@
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple, Any
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.utils.import_utils import SYSTEM
 
 if SYSTEM != "ipex":
@@ -288,6 +289,7 @@ def __init__(
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = TensorParallelRowLinear.load(
             config,
@@ -328,7 +330,12 @@ def forward(
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -338,6 +345,7 @@ def forward(
                 key=kv[:, 0],
                 value=kv[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -352,6 +360,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
index 97b3ea967d5..906a83a4151 100644
--- a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
@@ -34,6 +34,7 @@
     attention,
     paged_attention,
 )
+from text_generation_server.layers.attention.kv_cache import KVCache, get_kv_scales
 from text_generation_server.layers.layernorm import FastRMSNorm
 from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
 from text_generation_server.layers.rotary import PositionRotaryEmbedding, get_mscale
@@ -230,6 +231,8 @@ def __init__(
             ),
         )
 
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
+
         self.kv_a_layernorm = FastRMSNorm.load(
             prefix=f"{prefix}.kv_a_layernorm", weights=weights, eps=config.rms_norm_eps
         )
@@ -258,7 +261,7 @@ def forward(
         cos: torch.Tensor,
         sin: torch.Tensor,
         cu_seqlen_prefill: torch.Tensor,
-        kv_cache: Tuple[torch.Tensor, torch.Tensor],
+        kv_cache: KVCache,
         block_tables: torch.Tensor,
         slots: torch.Tensor,
         seqlen: Seqlen,
@@ -319,7 +322,12 @@ def forward(
             value, (0, self.head_pad_size - self.value_head_size), value=0
         )
 
-        kv_cache.store(key=key, value=value, slots=slots)
+        kv_cache.store(
+            key=key,
+            value=value,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -329,6 +337,7 @@ def forward(
                 key=key,
                 value=value,
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -343,6 +352,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         # Remove padding.
diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
index c962a2aff41..ebf1b80eb03 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
@@ -39,6 +39,7 @@
     TensorParallelMultiAdapterLinear,
     TensorParallelAdapterRowLinear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
@@ -206,6 +207,7 @@ def __init__(
             ],
             process_group=weights.process_group,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         o_proj = TensorParallelRowLinear.load(
             config,
@@ -251,7 +253,12 @@ def forward(
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -261,6 +268,7 @@ def forward(
                 key=kv[:, 0],
                 value=kv[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -278,6 +286,7 @@ def forward(
                 seqlen,
                 max_s,
                 softcap=self.softcap,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(
diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
index b127f2843c2..ad3be80e51d 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
@@ -37,6 +37,7 @@
     SpeculativeHead,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
@@ -185,6 +186,7 @@ def __init__(self, prefix: str, config, weights, causal: bool):
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = TensorParallelRowLinear.load(
             config,
@@ -222,7 +224,12 @@ def forward(
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -232,6 +239,7 @@ def forward(
                 key=kv[:, 0],
                 value=kv[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -247,6 +255,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
index 2d005734b89..906b34c12b2 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
@@ -36,6 +36,7 @@
     SpeculativeHead,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 
 
 def load_qkv(config, prefix: str, weights, head_size, num_heads):
@@ -193,6 +194,7 @@ def __init__(
             head_size=self.head_size,
             num_heads=self.num_heads,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = load_row(
             config,
@@ -222,7 +224,12 @@ def forward(
         key = key.view(-1, self.num_heads, self.head_size)
         value = value.view(-1, self.num_heads, self.head_size)
 
-        kv_cache.store(key=key, value=value, slots=slots)
+        kv_cache.store(
+            key=key,
+            value=value,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -232,6 +239,7 @@ def forward(
                 key=key,
                 value=value,
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -246,6 +254,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
index 2eef1dedc57..692f8ca31be 100644
--- a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
@@ -24,6 +24,7 @@
 from torch import nn
 from transformers.activations import ACT2FN
 from typing import Optional, List, Tuple
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
     paged_attention,
@@ -138,6 +139,7 @@ def __init__(
             prefix=prefix,
             weights=weights,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = load_row(
             config,
@@ -184,7 +186,12 @@ def forward(
         else:
             self.rotary_emb(query, key, cos, sin)
 
-        kv_cache.store(key=key, value=value, slots=slots)
+        kv_cache.store(
+            key=key,
+            value=value,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -194,6 +201,7 @@ def forward(
                 key=key,
                 value=value,
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -208,6 +216,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
index 20841aeb7dd..b26dd484942 100644
--- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
@@ -27,7 +27,10 @@
 from torch import nn
 from transformers.activations import ACT2FN
 
-from text_generation_server.layers.attention import KVCache
+from text_generation_server.layers.attention import (
+    KVCache,
+    get_kv_scales,
+)
 from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
@@ -179,6 +182,8 @@ def __init__(
         self.query_key_value = load_attention(config, prefix, weights, index)
         self.index = index
 
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
+
         o_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.o_proj",
@@ -224,7 +229,12 @@ def forward(
 
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -233,6 +243,7 @@ def forward(
                 query=query,
                 key=kv[:, 0],
                 value=kv[:, 1],
+                kv_scales=self.kv_scales,
                 kv_cache=kv_cache,
                 seqlen=seqlen,
                 block_tables=block_tables,
@@ -248,6 +259,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(
diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
index 7bad429c3f0..c66c732f21d 100644
--- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
@@ -26,6 +26,7 @@
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
     paged_attention,
@@ -158,6 +159,7 @@ def __init__(self, prefix: str, config, weights, layer_id):
             ],
             process_group=weights.process_group,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         o_proj = TensorParallelRowLinear.load(
             config,
@@ -208,7 +210,12 @@ def forward(
         else:
             kv_to_cache = kv
 
-        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv_to_cache[:, 0],
+            value=kv_to_cache[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -218,6 +225,7 @@ def forward(
                 key=kv_to_cache[:, 0],
                 value=kv_to_cache[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -233,6 +241,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(
diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
index 712b7bc4602..a45dd1e615e 100644
--- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
@@ -38,6 +38,7 @@
     attention,
     paged_attention,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.layernorm import FastRMSNorm
 from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
@@ -213,6 +214,7 @@ def __init__(
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = TensorParallelRowLinear.load(
             config,
@@ -256,7 +258,12 @@ def forward(
         else:
             kv_to_cache = kv
 
-        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv_to_cache[:, 0],
+            value=kv_to_cache[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -266,6 +273,7 @@ def forward(
                 key=kv_to_cache[:, 0],
                 value=kv_to_cache[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -281,6 +289,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
index 2ce69d8ea0b..2301b63cff2 100644
--- a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
@@ -38,6 +38,7 @@
     SpeculativeHead,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.layernorm import (
     FastLayerNorm,
 )
@@ -130,6 +131,7 @@ def __init__(self, config, prefix, weights):
             head_size=self.head_size,
             hidden_size=self.hidden_size,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
         self.dense = load_row(
             config, prefix=f"{prefix}.dense", weights=weights, bias=True
         )
@@ -163,7 +165,12 @@ def forward(
         qkv[:, 0] = torch.cat((query_rot, query_pass), dim=-1)
         qkv[:, 1] = torch.cat((key_rot, key_pass), dim=-1)
 
-        kv_cache.store(key=qkv[:, 1], value=qkv[:, 2], slots=slots)
+        kv_cache.store(
+            key=qkv[:, 1],
+            value=qkv[:, 2],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -173,6 +180,7 @@ def forward(
                 key=qkv[:, 1],
                 value=qkv[:, 2],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -187,6 +195,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
index 62d524c9b5b..7382a7cb9fc 100644
--- a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
@@ -18,6 +18,7 @@
     SpeculativeHead,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.layernorm import (
     FastLayerNorm,
 )
@@ -137,6 +138,7 @@ def __init__(
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         # in llama the dense layer is called "o_proj" and has bias=False
         self.dense = TensorParallelRowLinear.load(
@@ -186,7 +188,12 @@ def forward(
         )
 
         # Reshape key and value and cache
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -194,6 +201,7 @@ def forward(
                 query=query,
                 key=kv[:, 0],
                 value=kv[:, 1],
+                kv_scales=self.kv_scales,
                 kv_cache=kv_cache,
                 seqlen=seqlen,
                 block_tables=block_tables,
@@ -209,6 +217,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
index 905dd98fcd6..ab2a177db6a 100644
--- a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
@@ -16,6 +16,7 @@
     TensorParallelEmbedding,
     SpeculativeHead,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
@@ -84,6 +85,8 @@ def __init__(
 
         self.query_key_value = load_attention(config, prefix, weights)
 
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
+
         self.o_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.o_proj",
@@ -126,7 +129,12 @@ def forward(
         else:
             kv_to_cache = kv
 
-        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv_to_cache[:, 0],
+            value=kv_to_cache[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -136,6 +144,7 @@ def forward(
                 key=kv_to_cache[:, 0],
                 value=kv_to_cache[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -151,6 +160,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
index 8085ff8928b..2dcd1bf3033 100644
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@@ -12,6 +12,7 @@
     TensorParallelRowLinear,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.layernorm import FastLayerNorm
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.attention import (
@@ -158,6 +159,7 @@ def __init__(
             weights=weights,
             bias=config.bias,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
         self.dense = load_row(
             config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
         )
@@ -198,7 +200,12 @@ def forward(
         # Inplace rotary
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
-        kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv[:, 0],
+            value=kv[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -208,6 +215,7 @@ def forward(
                 key=kv[:, 0],
                 value=kv[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -222,6 +230,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
@@ -276,6 +285,7 @@ def __init__(
             weights=weights,
             bias=config.bias,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
         self.dense = load_row(
             config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
         )
@@ -311,7 +321,10 @@ def forward(
         self.rotary_emb(query, torch.select(kv, dim=2, index=0), cos, sin)
 
         kv_cache.store(
-            key=kv[:, :, 0].contiguous(), value=kv[:, :, 1].contiguous(), slots=slots
+            key=kv[:, :, 0].contiguous(),
+            value=kv[:, :, 1].contiguous(),
+            slots=slots,
+            kv_scales=self.kv_scales,
         )
 
         # Prefill
@@ -322,6 +335,7 @@ def forward(
                 key=kv[:, :, 0],
                 value=kv[:, :, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -336,6 +350,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(
diff --git a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
index 52119b64c1d..ed053eb661c 100644
--- a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
@@ -17,6 +17,7 @@
     TensorParallelEmbedding,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.gptq import GPTQWeightsLoader
 from text_generation_server.layers.layernorm import (
     FastLayerNorm,
@@ -257,6 +258,7 @@ def __init__(self, prefix, config, weights):
         self.c_proj = load_row(
             config, prefix=f"{prefix}.c_proj", weights=weights, bias=True
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
         self.kv_head_mapping = torch.zeros(
             self.num_heads, dtype=torch.int32, device=weights.device
         )
@@ -282,7 +284,12 @@ def forward(
         query = query.view(-1, self.num_heads, self.head_size)
         key_value = key_value.view(-1, 2, 1, self.head_size)
 
-        kv_cache.store(key=key_value[:, 0], value=key_value[:, 1], slots=slots)
+        kv_cache.store(
+            key=key_value[:, 0],
+            value=key_value[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -292,6 +299,7 @@ def forward(
                 key=key_value[:, 0],
                 value=key_value[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -306,6 +314,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
index fe339aee734..c793982d828 100644
--- a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
@@ -38,6 +38,7 @@
     SpeculativeHead,
     get_linear,
 )
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
 from text_generation_server.layers.layernorm import (
     FastLayerNorm,
     FastRMSNorm,
@@ -188,6 +189,7 @@ def __init__(
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = TensorParallelRowLinear.load(
             config,
@@ -231,7 +233,12 @@ def forward(
         else:
             kv_to_cache = kv
 
-        kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
+        kv_cache.store(
+            key=kv_to_cache[:, 0],
+            value=kv_to_cache[:, 1],
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
 
         # Prefill
         if cu_seqlen_prefill is not None:
@@ -241,6 +248,7 @@ def forward(
                 key=kv_to_cache[:, 0],
                 value=kv_to_cache[:, 1],
                 kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
                 seqlen=seqlen,
                 block_tables=block_tables,
                 softmax_scale=self.softmax_scale,
@@ -256,6 +264,7 @@ def forward(
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index b1270b4493f..b931671cc0c 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -2283,6 +2283,7 @@ def _forward_context(
                 num_kv_heads=self.num_kv_heads,
                 head_size=self.head_size,
                 page_size=BLOCK_SIZE,
+                kv_cache_dtype=self.kv_cache_dtype,
                 dtype=self.dtype,
                 window_left=self.sliding_window,
             )
diff --git a/server/text_generation_server/utils/weights.py b/server/text_generation_server/utils/weights.py
index 548591e5710..aae64acf3da 100644
--- a/server/text_generation_server/utils/weights.py
+++ b/server/text_generation_server/utils/weights.py
@@ -207,7 +207,9 @@ def has_tensor(self, tensor_name: str):
     def get_shape(self, tensor_name: str):
         return self._get_slice(tensor_name).get_shape()
 
-    def get_tensor(self, tensor_name: str, to_device=True, to_dtype=True):
+    def get_tensor(
+        self, tensor_name: str, to_device: bool = True, to_dtype: bool = True
+    ) -> torch.Tensor:
         filename, tensor_name = self.get_filename(tensor_name)
         f = self._get_handle(filename)
         tensor = f.get_tensor(tensor_name)

From 1f18cb6aa6b4aa1cee07962124cfeedb39c821be Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dani=C3=ABl=20de=20Kok?= <me@danieldk.eu>
Date: Mon, 21 Oct 2024 11:18:52 +0000
Subject: [PATCH 2/3] Update FP8 KV cache test to use checkpoint with scales

---
 .../test_flash_llama_fp8_kv_cache.json        |  36 ++---
 ...t_flash_llama_fp8_kv_cache_all_params.json |  70 +++++++--
 .../test_flash_llama_fp8_kv_cache_load.json   | 144 +++++++++---------
 .../models/test_flash_llama_fp8_kv_cache.py   |   8 +-
 4 files changed, 151 insertions(+), 107 deletions(-)

diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
index c55dd593a1d..b82882c00b6 100644
--- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
@@ -11,27 +11,27 @@
       },
       {
         "id": 3923,
-        "logprob": -5.6328125,
+        "logprob": -6.1875,
         "text": "What"
       },
       {
         "id": 374,
-        "logprob": -1.2265625,
+        "logprob": -0.93359375,
         "text": " is"
       },
       {
         "id": 5655,
-        "logprob": -9.1015625,
+        "logprob": -9.875,
         "text": " deep"
       },
       {
         "id": 6975,
-        "logprob": -1.8085938,
+        "logprob": -1.1796875,
         "text": " learning"
       },
       {
         "id": 30,
-        "logprob": -1.0439453,
+        "logprob": -1.75,
         "text": "?"
       }
     ],
@@ -39,66 +39,66 @@
     "tokens": [
       {
         "id": 18682,
-        "logprob": -2.1992188,
+        "logprob": -1.109375,
         "special": false,
         "text": " Deep"
       },
       {
         "id": 6975,
-        "logprob": -0.079956055,
+        "logprob": -0.005432129,
         "special": false,
         "text": " learning"
       },
       {
         "id": 374,
-        "logprob": -0.2763672,
+        "logprob": -0.028808594,
         "special": false,
         "text": " is"
       },
       {
         "id": 264,
-        "logprob": -0.37548828,
+        "logprob": -0.013671875,
         "special": false,
         "text": " a"
       },
       {
         "id": 27084,
-        "logprob": -1.4628906,
+        "logprob": -0.69921875,
         "special": false,
         "text": " subset"
       },
       {
         "id": 315,
-        "logprob": -0.02885437,
+        "logprob": -0.0005874634,
         "special": false,
         "text": " of"
       },
       {
         "id": 5780,
-        "logprob": -0.2565918,
+        "logprob": -0.026855469,
         "special": false,
         "text": " machine"
       },
       {
         "id": 6975,
-        "logprob": -0.0063438416,
+        "logprob": -0.00020885468,
         "special": false,
         "text": " learning"
       },
       {
         "id": 430,
-        "logprob": -1.3056641,
+        "logprob": -0.17773438,
         "special": false,
         "text": " that"
       },
       {
-        "id": 374,
-        "logprob": -1.6035156,
+        "id": 18065,
+        "logprob": -0.703125,
         "special": false,
-        "text": " is"
+        "text": " involves"
       }
     ],
     "top_tokens": null
   },
-  "generated_text": " Deep learning is a subset of machine learning that is"
+  "generated_text": " Deep learning is a subset of machine learning that involves"
 }
diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
index d06d6e5662d..8bce3e108d5 100644
--- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
@@ -1,8 +1,8 @@
 {
   "details": {
     "best_of_sequences": null,
-    "finish_reason": "eos_token",
-    "generated_tokens": 3,
+    "finish_reason": "length",
+    "generated_tokens": 10,
     "prefill": [
       {
         "id": 128000,
@@ -11,22 +11,22 @@
       },
       {
         "id": 374,
-        "logprob": -22.96875,
+        "logprob": -18.0,
         "text": " is"
       },
       {
         "id": 5655,
-        "logprob": -10.71875,
+        "logprob": -11.75,
         "text": " deep"
       },
       {
         "id": 6975,
-        "logprob": -2.6992188,
+        "logprob": -2.0625,
         "text": " learning"
       },
       {
         "id": 30,
-        "logprob": -4.8398438,
+        "logprob": -6.0,
         "text": "?"
       }
     ],
@@ -34,24 +34,66 @@
     "tokens": [
       {
         "id": 720,
-        "logprob": -0.4411621,
+        "logprob": 0.0,
         "special": false,
         "text": " \n"
       },
       {
-        "id": 220,
-        "logprob": -0.35864258,
+        "id": 34564,
+        "logprob": -0.11279297,
+        "special": false,
+        "text": "Deep"
+      },
+      {
+        "id": 6975,
+        "logprob": -0.16015625,
         "special": false,
-        "text": " "
+        "text": " learning"
       },
       {
-        "id": 128001,
+        "id": 320,
+        "logprob": -0.25195312,
+        "special": false,
+        "text": " ("
+      },
+      {
+        "id": 16931,
+        "logprob": -1.703125,
+        "special": false,
+        "text": "DL"
+      },
+      {
+        "id": 8,
         "logprob": 0.0,
-        "special": true,
-        "text": "<|end_of_text|>"
+        "special": false,
+        "text": ")"
+      },
+      {
+        "id": 374,
+        "logprob": -1.140625,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 264,
+        "logprob": 0.0,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 1207,
+        "logprob": -1.3125,
+        "special": false,
+        "text": " sub"
+      },
+      {
+        "id": 2630,
+        "logprob": 0.0,
+        "special": false,
+        "text": "field"
       }
     ],
     "top_tokens": null
   },
-  "generated_text": "What is deep learning? \n "
+  "generated_text": "What is deep learning? \nDeep learning (DL) is a subfield"
 }
diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
index 46670819f99..c7acee467c6 100644
--- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
+++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
@@ -12,27 +12,27 @@
         },
         {
           "id": 3923,
-          "logprob": -5.6328125,
+          "logprob": -6.1875,
           "text": "What"
         },
         {
           "id": 374,
-          "logprob": -1.2265625,
+          "logprob": -0.93359375,
           "text": " is"
         },
         {
           "id": 5655,
-          "logprob": -9.1015625,
+          "logprob": -9.875,
           "text": " deep"
         },
         {
           "id": 6975,
-          "logprob": -1.8085938,
+          "logprob": -1.1796875,
           "text": " learning"
         },
         {
           "id": 30,
-          "logprob": -1.0439453,
+          "logprob": -1.75,
           "text": "?"
         }
       ],
@@ -40,68 +40,68 @@
       "tokens": [
         {
           "id": 18682,
-          "logprob": -2.1992188,
+          "logprob": -1.109375,
           "special": false,
           "text": " Deep"
         },
         {
           "id": 6975,
-          "logprob": -0.07897949,
+          "logprob": -0.0047912598,
           "special": false,
           "text": " learning"
         },
         {
           "id": 374,
-          "logprob": -0.27734375,
+          "logprob": -0.025512695,
           "special": false,
           "text": " is"
         },
         {
           "id": 264,
-          "logprob": -0.37402344,
+          "logprob": -0.012145996,
           "special": false,
           "text": " a"
         },
         {
           "id": 27084,
-          "logprob": -1.4511719,
+          "logprob": -0.72265625,
           "special": false,
           "text": " subset"
         },
         {
           "id": 315,
-          "logprob": -0.02909851,
+          "logprob": -0.0005760193,
           "special": false,
           "text": " of"
         },
         {
           "id": 5780,
-          "logprob": -0.25854492,
+          "logprob": -0.02722168,
           "special": false,
           "text": " machine"
         },
         {
           "id": 6975,
-          "logprob": -0.0061798096,
+          "logprob": -0.00023651123,
           "special": false,
           "text": " learning"
         },
         {
           "id": 430,
-          "logprob": -1.3046875,
+          "logprob": -0.17285156,
           "special": false,
           "text": " that"
         },
         {
-          "id": 374,
-          "logprob": -1.5537109,
+          "id": 18065,
+          "logprob": -0.703125,
           "special": false,
-          "text": " is"
+          "text": " involves"
         }
       ],
       "top_tokens": null
     },
-    "generated_text": " Deep learning is a subset of machine learning that is"
+    "generated_text": " Deep learning is a subset of machine learning that involves"
   },
   {
     "details": {
@@ -116,27 +116,27 @@
         },
         {
           "id": 3923,
-          "logprob": -5.6328125,
+          "logprob": -6.21875,
           "text": "What"
         },
         {
           "id": 374,
-          "logprob": -1.2265625,
+          "logprob": -0.95703125,
           "text": " is"
         },
         {
           "id": 5655,
-          "logprob": -9.1015625,
+          "logprob": -9.9375,
           "text": " deep"
         },
         {
           "id": 6975,
-          "logprob": -1.8085938,
+          "logprob": -1.1328125,
           "text": " learning"
         },
         {
           "id": 30,
-          "logprob": -1.0439453,
+          "logprob": -1.75,
           "text": "?"
         }
       ],
@@ -144,68 +144,68 @@
       "tokens": [
         {
           "id": 18682,
-          "logprob": -2.1992188,
+          "logprob": -1.1796875,
           "special": false,
           "text": " Deep"
         },
         {
           "id": 6975,
-          "logprob": -0.07897949,
+          "logprob": -0.005432129,
           "special": false,
           "text": " learning"
         },
         {
           "id": 374,
-          "logprob": -0.27734375,
+          "logprob": -0.02758789,
           "special": false,
           "text": " is"
         },
         {
           "id": 264,
-          "logprob": -0.37402344,
+          "logprob": -0.013366699,
           "special": false,
           "text": " a"
         },
         {
           "id": 27084,
-          "logprob": -1.4511719,
+          "logprob": -0.6953125,
           "special": false,
           "text": " subset"
         },
         {
           "id": 315,
-          "logprob": -0.02909851,
+          "logprob": -0.0004863739,
           "special": false,
           "text": " of"
         },
         {
           "id": 5780,
-          "logprob": -0.25854492,
+          "logprob": -0.02709961,
           "special": false,
           "text": " machine"
         },
         {
           "id": 6975,
-          "logprob": -0.0061798096,
+          "logprob": -0.00022506714,
           "special": false,
           "text": " learning"
         },
         {
           "id": 430,
-          "logprob": -1.3046875,
+          "logprob": -0.19726562,
           "special": false,
           "text": " that"
         },
         {
-          "id": 374,
-          "logprob": -1.5537109,
+          "id": 18065,
+          "logprob": -0.77734375,
           "special": false,
-          "text": " is"
+          "text": " involves"
         }
       ],
       "top_tokens": null
     },
-    "generated_text": " Deep learning is a subset of machine learning that is"
+    "generated_text": " Deep learning is a subset of machine learning that involves"
   },
   {
     "details": {
@@ -220,27 +220,27 @@
         },
         {
           "id": 3923,
-          "logprob": -5.6328125,
+          "logprob": -6.21875,
           "text": "What"
         },
         {
           "id": 374,
-          "logprob": -1.2265625,
+          "logprob": -0.95703125,
           "text": " is"
         },
         {
           "id": 5655,
-          "logprob": -9.1015625,
+          "logprob": -9.9375,
           "text": " deep"
         },
         {
           "id": 6975,
-          "logprob": -1.8085938,
+          "logprob": -1.1328125,
           "text": " learning"
         },
         {
           "id": 30,
-          "logprob": -1.0439453,
+          "logprob": -1.75,
           "text": "?"
         }
       ],
@@ -248,68 +248,68 @@
       "tokens": [
         {
           "id": 18682,
-          "logprob": -2.1992188,
+          "logprob": -1.1796875,
           "special": false,
           "text": " Deep"
         },
         {
           "id": 6975,
-          "logprob": -0.07897949,
+          "logprob": -0.005432129,
           "special": false,
           "text": " learning"
         },
         {
           "id": 374,
-          "logprob": -0.27734375,
+          "logprob": -0.02758789,
           "special": false,
           "text": " is"
         },
         {
           "id": 264,
-          "logprob": -0.37402344,
+          "logprob": -0.013366699,
           "special": false,
           "text": " a"
         },
         {
           "id": 27084,
-          "logprob": -1.4511719,
+          "logprob": -0.6953125,
           "special": false,
           "text": " subset"
         },
         {
           "id": 315,
-          "logprob": -0.02909851,
+          "logprob": -0.0004863739,
           "special": false,
           "text": " of"
         },
         {
           "id": 5780,
-          "logprob": -0.25854492,
+          "logprob": -0.02709961,
           "special": false,
           "text": " machine"
         },
         {
           "id": 6975,
-          "logprob": -0.0061798096,
+          "logprob": -0.00022506714,
           "special": false,
           "text": " learning"
         },
         {
           "id": 430,
-          "logprob": -1.3046875,
+          "logprob": -0.19726562,
           "special": false,
           "text": " that"
         },
         {
-          "id": 374,
-          "logprob": -1.5537109,
+          "id": 18065,
+          "logprob": -0.77734375,
           "special": false,
-          "text": " is"
+          "text": " involves"
         }
       ],
       "top_tokens": null
     },
-    "generated_text": " Deep learning is a subset of machine learning that is"
+    "generated_text": " Deep learning is a subset of machine learning that involves"
   },
   {
     "details": {
@@ -324,27 +324,27 @@
         },
         {
           "id": 3923,
-          "logprob": -5.6328125,
+          "logprob": -6.21875,
           "text": "What"
         },
         {
           "id": 374,
-          "logprob": -1.2265625,
+          "logprob": -0.95703125,
           "text": " is"
         },
         {
           "id": 5655,
-          "logprob": -9.1015625,
+          "logprob": -9.9375,
           "text": " deep"
         },
         {
           "id": 6975,
-          "logprob": -1.8085938,
+          "logprob": -1.1328125,
           "text": " learning"
         },
         {
           "id": 30,
-          "logprob": -1.0439453,
+          "logprob": -1.75,
           "text": "?"
         }
       ],
@@ -352,67 +352,67 @@
       "tokens": [
         {
           "id": 18682,
-          "logprob": -2.1992188,
+          "logprob": -1.1796875,
           "special": false,
           "text": " Deep"
         },
         {
           "id": 6975,
-          "logprob": -0.07897949,
+          "logprob": -0.005432129,
           "special": false,
           "text": " learning"
         },
         {
           "id": 374,
-          "logprob": -0.27734375,
+          "logprob": -0.02758789,
           "special": false,
           "text": " is"
         },
         {
           "id": 264,
-          "logprob": -0.37402344,
+          "logprob": -0.013366699,
           "special": false,
           "text": " a"
         },
         {
           "id": 27084,
-          "logprob": -1.4511719,
+          "logprob": -0.6953125,
           "special": false,
           "text": " subset"
         },
         {
           "id": 315,
-          "logprob": -0.02909851,
+          "logprob": -0.0004863739,
           "special": false,
           "text": " of"
         },
         {
           "id": 5780,
-          "logprob": -0.25854492,
+          "logprob": -0.02709961,
           "special": false,
           "text": " machine"
         },
         {
           "id": 6975,
-          "logprob": -0.0061798096,
+          "logprob": -0.00022506714,
           "special": false,
           "text": " learning"
         },
         {
           "id": 430,
-          "logprob": -1.3046875,
+          "logprob": -0.19726562,
           "special": false,
           "text": " that"
         },
         {
-          "id": 374,
-          "logprob": -1.5537109,
+          "id": 18065,
+          "logprob": -0.77734375,
           "special": false,
-          "text": " is"
+          "text": " involves"
         }
       ],
       "top_tokens": null
     },
-    "generated_text": " Deep learning is a subset of machine learning that is"
+    "generated_text": " Deep learning is a subset of machine learning that involves"
   }
 ]
diff --git a/integration-tests/models/test_flash_llama_fp8_kv_cache.py b/integration-tests/models/test_flash_llama_fp8_kv_cache.py
index 05e9f0dd9ec..ccd7f78fe6f 100644
--- a/integration-tests/models/test_flash_llama_fp8_kv_cache.py
+++ b/integration-tests/models/test_flash_llama_fp8_kv_cache.py
@@ -4,7 +4,9 @@
 @pytest.fixture(scope="module")
 def flash_llama_fp8_kv_cache_handle(launcher):
     with launcher(
-        "meta-llama/Meta-Llama-3-8B", num_shard=2, kv_cache_dtype="fp8_e5m2"
+        "neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV",
+        num_shard=2,
+        kv_cache_dtype="fp8_e4m3fn",
     ) as handle:
         yield handle
 
@@ -25,7 +27,7 @@ async def test_flash_llama_fp8_kv_cache(flash_llama_fp8_kv_cache, response_snaps
 
     assert (
         response.generated_text
-        == " Deep learning is a subset of machine learning that is"
+        == " Deep learning is a subset of machine learning that involves"
     )
     assert response.details.generated_tokens == 10
     assert response == response_snapshot
@@ -69,7 +71,7 @@ async def test_flash_llama_fp8_kv_cache_load(
     assert len(responses) == 4
     assert (
         responses[0].generated_text
-        == " Deep learning is a subset of machine learning that is"
+        == " Deep learning is a subset of machine learning that involves"
     )
     assert all(
         [r.generated_text == responses[0].generated_text for r in responses]

From a68fae05e9f7081e9bc0eb25f7fd85dbd7ab99c5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dani=C3=ABl=20de=20Kok?= <me@danieldk.eu>
Date: Thu, 24 Oct 2024 12:35:30 +0000
Subject: [PATCH 3/3] `can_scale`: check that the attention is flashinfer

---
 .../text_generation_server/layers/attention/kv_cache.py   | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py
index 8a79eebb893..9d739da5ee1 100644
--- a/server/text_generation_server/layers/attention/kv_cache.py
+++ b/server/text_generation_server/layers/attention/kv_cache.py
@@ -110,7 +110,11 @@ def can_scale(self, kv_scales: KVScales) -> bool:
         """Check if the cache can be scaled by the given scales."""
         if kv_scales.key_scale_cpu == 1.0 and kv_scales.value_scale_cpu == 1.0:
             return False
-        elif self.dtype == torch.float8_e4m3fn and SYSTEM == "cuda":
+        elif (
+            self.dtype == torch.float8_e4m3fn
+            and ATTENTION == "flashinfer"
+            and SYSTEM == "cuda"
+        ):
             log_once(
                 logger.info,
                 "Using FP8 KV cache scales",
@@ -120,7 +124,7 @@ def can_scale(self, kv_scales: KVScales) -> bool:
             # We have scales, but not the correct FP8 cache type, so warn once.
             log_once(
                 logger.info,
-                "Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on CUDA is supported",
+                "Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on flashinfer is supported",
             )
             return False