Add support for FP8 KV cache scales (#2628)

* 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.

* Update FP8 KV cache test to use checkpoint with scales

* `can_scale`: check that the attention is flashinfer

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"
       }

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 = {

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"
 }

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": " "
+        "text": "Deep"
       },
       {
-        "id": 128001,
+        "id": 6975,
+        "logprob": -0.16015625,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "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"
 }

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"
   }
 ]

integration-tests/models/test_flash_llama_fp8_kv_cache.py

@@ -4,7 +4,9 @@ import pytest
 @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]

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"

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 },

server/text_generation_server/layers/attention/__init__.py

@@ -28,10 +28,11 @@ else:
     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",

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.models.globals import (
 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
@@ -60,6 +65,8 @@ 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
@@ -205,6 +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,
@@ -212,6 +220,8 @@ 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,
@@ -228,6 +238,8 @@ 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

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,
         )

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:

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 +106,33 @@ class KVCache:
                 ),
             )
 
+    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 ATTENTION == "flashinfer"
+            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 flashinfer 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 +151,33 @@ class KVCache:
         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 +224,23 @@ 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)

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,

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)
 

server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py

@@ -30,6 +30,7 @@ from text_generation_server.layers.attention import (
     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 @@ class FlashCohereAttention(torch.nn.Module):
         )
 
         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 @@ class FlashCohereAttention(torch.nn.Module):
 
         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 @@ class FlashCohereAttention(torch.nn.Module):
                 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 @@ class FlashCohereAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -20,6 +20,7 @@ from torch import nn
 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 @@ class DbrxAttention(torch.nn.Module):
         )
 
         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 @@ class DbrxAttention(torch.nn.Module):
 
         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 @@ class DbrxAttention(torch.nn.Module):
                 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 @@ class DbrxAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py

@@ -34,6 +34,7 @@ from text_generation_server.layers.attention import (
     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 @@ class DeepseekV2Attention(torch.nn.Module):
             ),
         )
 
+        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 @@ class DeepseekV2Attention(torch.nn.Module):
         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 @@ class DeepseekV2Attention(torch.nn.Module):
             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 @@ class DeepseekV2Attention(torch.nn.Module):
                 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 @@ class DeepseekV2Attention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         # Remove padding.

server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py

@@ -39,6 +39,7 @@ from text_generation_server.layers import (
     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 @@ class FlashGemma2Attention(torch.nn.Module):
             ],
             process_group=weights.process_group,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         o_proj = TensorParallelRowLinear.load(
             config,
@@ -251,7 +253,12 @@ class FlashGemma2Attention(torch.nn.Module):
 
         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 @@ class FlashGemma2Attention(torch.nn.Module):
                 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 @@ class FlashGemma2Attention(torch.nn.Module):
                 seqlen,
                 max_s,
                 softcap=self.softcap,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -37,6 +37,7 @@ from text_generation_server.layers import (
     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 @@ class FlashGemmaAttention(torch.nn.Module):
         )
 
         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 @@ class FlashGemmaAttention(torch.nn.Module):
 
         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 @@ class FlashGemmaAttention(torch.nn.Module):
                 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 @@ class FlashGemmaAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py

@@ -36,6 +36,7 @@ from text_generation_server.layers import (
     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 @@ class FlashGPT2Attention(torch.nn.Module):
             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 @@ class FlashGPT2Attention(torch.nn.Module):
         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 @@ class FlashGPT2Attention(torch.nn.Module):
                 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 @@ class FlashGPT2Attention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py

@@ -24,6 +24,7 @@ import torch.distributed
 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 @@ class FlashGPTJAttention(torch.nn.Module):
             prefix=prefix,
             weights=weights,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         self.o_proj = load_row(
             config,
@@ -184,7 +186,12 @@ class FlashGPTJAttention(torch.nn.Module):
         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 @@ class FlashGPTJAttention(torch.nn.Module):
                 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 @@ class FlashGPTJAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -27,7 +27,10 @@ import torch.distributed
 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 @@ class FlashLlamaAttention(torch.nn.Module):
         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 @@ class FlashLlamaAttention(torch.nn.Module):
 
         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 @@ class FlashLlamaAttention(torch.nn.Module):
                 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 @@ class FlashLlamaAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -26,6 +26,7 @@ from transformers.activations import ACT2FN
 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 @@ class MistralAttention(torch.nn.Module):
             ],
             process_group=weights.process_group,
         )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
 
         o_proj = TensorParallelRowLinear.load(
             config,
@@ -208,7 +210,12 @@ class MistralAttention(torch.nn.Module):
         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 @@ class MistralAttention(torch.nn.Module):
                 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 @@ class MistralAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -38,6 +38,7 @@ from text_generation_server.layers.attention import (
     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 @@ class MixtralAttention(torch.nn.Module):
         )
 
         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 @@ class MixtralAttention(torch.nn.Module):
         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 @@ class MixtralAttention(torch.nn.Module):
                 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 @@ class MixtralAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -38,6 +38,7 @@ from text_generation_server.layers import (
     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 @@ class FlashNeoxAttention(torch.nn.Module):
             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 @@ class FlashNeoxAttention(torch.nn.Module):
         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 @@ class FlashNeoxAttention(torch.nn.Module):
                 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 @@ class FlashNeoxAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_phi_modeling.py

@@ -18,6 +18,7 @@ from text_generation_server.layers import (
     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 @@ class FlashPhiAttention(torch.nn.Module):
         )
 
         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 @@ class FlashPhiAttention(torch.nn.Module):
         )
 
         # 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 @@ class FlashPhiAttention(torch.nn.Module):
                 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 @@ class FlashPhiAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -16,6 +16,7 @@ from text_generation_server.layers import (
     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 @@ class Qwen2Attention(torch.nn.Module):
 
         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 @@ class Qwen2Attention(torch.nn.Module):
         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 @@ class Qwen2Attention(torch.nn.Module):
                 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 @@ class Qwen2Attention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -12,6 +12,7 @@ from text_generation_server.layers import (
     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 @@ class FlashRWAttention(torch.nn.Module):
             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 @@ class FlashRWAttention(torch.nn.Module):
         # 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 @@ class FlashRWAttention(torch.nn.Module):
                 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 @@ class FlashRWAttention(torch.nn.Module):
                 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 @@ class FlashRWLargeAttention(torch.nn.Module):
             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 @@ class FlashRWLargeAttention(torch.nn.Module):
         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 @@ class FlashRWLargeAttention(torch.nn.Module):
                 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 @@ class FlashRWLargeAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.dense(

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -17,6 +17,7 @@ from text_generation_server.layers import (
     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 @@ class FlashMQAttention(torch.nn.Module):
         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 @@ class FlashMQAttention(torch.nn.Module):
         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 @@ class FlashMQAttention(torch.nn.Module):
                 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 @@ class FlashMQAttention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py

@@ -38,6 +38,7 @@ from text_generation_server.layers import (
     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 @@ class Starcoder2Attention(torch.nn.Module):
         )
 
         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 @@ class Starcoder2Attention(torch.nn.Module):
         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 @@ class Starcoder2Attention(torch.nn.Module):
                 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 @@ class Starcoder2Attention(torch.nn.Module):
                 block_tables,
                 seqlen,
                 max_s,
+                kv_scales=self.kv_scales,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))

server/text_generation_server/models/flash_causal_lm.py

@@ -2283,6 +2283,7 @@ class FlashCausalLM(Model):
                 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,
             )

server/text_generation_server/utils/weights.py

@@ -207,7 +207,9 @@ class Weights:
     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)