Merge branch 'main' into gptq-cuda-kernels

2025-04-29 14:02:12 +00:00 · 2023-07-19 16:58:54 +02:00 · 2023-07-19 16:58:54 +02:00 · edfbfdfb3f
commit edfbfdfb3f
parent 74e6d6e54e 5a1512c025
30 changed files with 968 additions and 563 deletions
--- a/Cargo.lock
+++ b/Cargo.lock
@ -148,18 +148,18 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
 name = "async-trait"
-version = "0.1.68"
+version = "0.1.71"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b9ccdd8f2a161be9bd5c023df56f1b2a0bd1d83872ae53b71a84a12c9bf6e842"
+checksum = "a564d521dd56509c4c47480d00b80ee55f7e385ae48db5744c67ad50c92d2ebf"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -410,9 +410,9 @@ dependencies = [
 [[package]]
 name = "clap"
-version = "4.3.10"
+version = "4.3.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "384e169cc618c613d5e3ca6404dda77a8685a63e08660dcc64abaf7da7cb0c7a"
+checksum = "1640e5cc7fb47dbb8338fd471b105e7ed6c3cb2aeb00c2e067127ffd3764a05d"
 dependencies = [
 "clap_builder",
 "clap_derive",
@ -421,9 +421,9 @@ dependencies = [
 [[package]]
 name = "clap_builder"
-version = "4.3.10"
+version = "4.3.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ef137bbe35aab78bdb468ccfba75a5f4d8321ae011d34063770780545176af2d"
+checksum = "98c59138d527eeaf9b53f35a77fcc1fad9d883116070c63d5de1c7dc7b00c72b"
 dependencies = [
 "anstream",
 "anstyle",
@ -440,7 +440,7 @@ dependencies = [
 "heck",
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -492,9 +492,9 @@ checksum = "e496a50fda8aacccc86d7529e2c1e0892dbd0f898a6b5645b5561b89c3210efa"
 [[package]]
 name = "cpufeatures"
-version = "0.2.8"
+version = "0.2.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "03e69e28e9f7f77debdedbaafa2866e1de9ba56df55a8bd7cfc724c25a09987c"
+checksum = "a17b76ff3a4162b0b27f354a0c87015ddad39d35f9c0c36607a3bdd175dde1f1"
 dependencies = [
 "libc",
 ]
@ -633,12 +633,12 @@ dependencies = [
 [[package]]
 name = "dashmap"
-version = "5.4.0"
+version = "5.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "907076dfda823b0b36d2a1bb5f90c96660a5bbcd7729e10727f07858f22c4edc"
+checksum = "6943ae99c34386c84a470c499d3414f66502a41340aa895406e0d2e4a207b91d"
 dependencies = [
 "cfg-if",
- "hashbrown 0.12.3",
+ "hashbrown 0.14.0",
 "lock_api",
 "once_cell",
 "parking_lot_core",
@ -736,6 +736,12 @@ dependencies = [
 "cfg-if",
 ]
 [[package]]
 name = "equivalent"
 version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5443807d6dff69373d433ab9ef5378ad8df50ca6298caf15de6e52e24aaf54d5"
 [[package]]
 name = "errno"
 version = "0.3.1"
@ -924,7 +930,7 @@ checksum = "89ca545a94061b6365f2c7355b4b32bd20df3ff95f02da9329b34ccc3bd6ee72"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -1023,7 +1029,7 @@ dependencies = [
 "futures-sink",
 "futures-util",
 "http",
- "indexmap",
+ "indexmap 1.9.3",
 "slab",
 "tokio",
 "tokio-util",
@ -1038,13 +1044,19 @@ checksum = "8a9ee70c43aaf417c914396645a0fa852624801b24ebb7ae78fe8272889ac888"
 [[package]]
 name = "hashbrown"
-version = "0.13.2"
+version = "0.13.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "43a3c133739dddd0d2990f9a4bdf8eb4b21ef50e4851ca85ab661199821d510e"
+checksum = "33ff8ae62cd3a9102e5637afc8452c55acf3844001bd5374e0b0bd7b6616c038"
 dependencies = [
 "ahash",
 ]
 [[package]]
 name = "hashbrown"
 version = "0.14.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2c6201b9ff9fd90a5a3bac2e56a830d0caa509576f0e503818ee82c181b3437a"
 [[package]]
 name = "heck"
 version = "0.4.1"
@ -1053,9 +1065,9 @@ checksum = "95505c38b4572b2d910cecb0281560f54b440a19336cbbcb27bf6ce6adc6f5a8"
 [[package]]
 name = "hermit-abi"
-version = "0.3.1"
+version = "0.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fed44880c466736ef9a5c5b5facefb5ed0785676d0c02d612db14e54f0d84286"
+checksum = "443144c8cdadd93ebf52ddb4056d257f5b52c04d3c804e657d19eb73fc33668b"
 [[package]]
 name = "hmac"
@ -1190,6 +1202,16 @@ checksum = "bd070e393353796e801d209ad339e89596eb4c8d430d18ede6a1cced8fafbd99"
 dependencies = [
 "autocfg",
 "hashbrown 0.12.3",
 ]
 [[package]]
 name = "indexmap"
 version = "2.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d5477fe2230a79769d8dc68e0eabf5437907c0457a5614a9e8dddb67f65eb65d"
 dependencies = [
 "equivalent",
 "hashbrown 0.14.0",
 "serde",
 ]
@ -1254,12 +1276,12 @@ checksum = "28b29a3cd74f0f4598934efe3aeba42bae0eb4680554128851ebbecb02af14e6"
 [[package]]
 name = "is-terminal"
-version = "0.4.8"
+version = "0.4.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "24fddda5af7e54bf7da53067d6e802dbcc381d0a8eef629df528e3ebf68755cb"
+checksum = "cb0889898416213fab133e1d33a0e5858a48177452750691bde3666d0fdbaf8b"
 dependencies = [
 "hermit-abi",
- "rustix 0.38.1",
+ "rustix 0.38.4",
 "windows-sys 0.48.0",
 ]
@ -1292,9 +1314,9 @@ dependencies = [
 [[package]]
 name = "itoa"
-version = "1.0.6"
+version = "1.0.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "453ad9f582a441959e5f0d088b02ce04cfe8d51a8eaf077f12ac6d3e94164ca6"
+checksum = "62b02a5381cc465bd3041d84623d0fa3b66738b52b8e2fc3bab8ad63ab032f4a"
 [[package]]
 name = "jobserver"
@ -1397,7 +1419,7 @@ version = "0.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8263075bb86c5a1b1427b5ae862e8889656f126e9f77c484496e8b47cf5c5558"
 dependencies = [
- "regex-automata",
+ "regex-automata 0.1.10",
 ]
 [[package]]
@ -1432,9 +1454,9 @@ dependencies = [
 [[package]]
 name = "metrics"
-version = "0.21.0"
+version = "0.21.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "aa8ebbd1a9e57bbab77b9facae7f5136aea44c356943bf9a198f647da64285d6"
+checksum = "fde3af1a009ed76a778cb84fdef9e7dbbdf5775ae3e4cc1f434a6a307f6f76c5"
 dependencies = [
 "ahash",
 "metrics-macros",
@ -1449,7 +1471,7 @@ checksum = "8a4964177ddfdab1e3a2b37aec7cf320e14169abb0ed73999f558136409178d5"
 dependencies = [
 "base64 0.21.2",
 "hyper",
- "indexmap",
+ "indexmap 1.9.3",
 "ipnet",
 "metrics",
 "metrics-util",
@ -1467,18 +1489,18 @@ checksum = "ddece26afd34c31585c74a4db0630c376df271c285d682d1e55012197830b6df"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
 name = "metrics-util"
-version = "0.15.0"
+version = "0.15.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "111cb375987443c3de8d503580b536f77dc8416d32db62d9456db5d93bd7ac47"
+checksum = "4de2ed6e491ed114b40b732e4d1659a9d53992ebd87490c44a6ffe23739d973e"
 dependencies = [
 "crossbeam-epoch",
 "crossbeam-utils",
- "hashbrown 0.13.2",
+ "hashbrown 0.13.1",
 "metrics",
 "num_cpus",
 "quanta",
@ -1530,9 +1552,9 @@ dependencies = [
 [[package]]
 name = "monostate"
-version = "0.1.6"
+version = "0.1.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0230b703f1ac35df1e24f6d0d2255472bcccaf657ecdfa4f1fcbcad1ad5bb98a"
+checksum = "3f3f57a8802842f648026a33c3d2e3bb41bb309a35b1609bd7ef2b060b8b6b1b"
 dependencies = [
 "monostate-impl",
 "serde",
@ -1540,13 +1562,13 @@ dependencies = [
 [[package]]
 name = "monostate-impl"
-version = "0.1.6"
+version = "0.1.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8795add3e14028f11f8e848bd3294898a8294767b3776b6f733560d33bd2530b"
+checksum = "e72f4d2e10fde62a0f2fcb4b44ccbf4f9899dcc30c9193449f8dfb9123d71377"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -1701,6 +1723,15 @@ dependencies = [
 "libc",
 ]
 [[package]]
 name = "num_threads"
 version = "0.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2819ce041d2ee131036f4fc9d6ae7ae125a3a40e97ba64d04fe799ad9dabbb44"
 dependencies = [
 "libc",
 ]
 [[package]]
 name = "number_prefix"
 version = "0.3.0"
@ -1773,7 +1804,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -1854,7 +1885,7 @@ dependencies = [
 "fnv",
 "futures-channel",
 "futures-util",
- "indexmap",
+ "indexmap 1.9.3",
 "js-sys",
 "once_cell",
 "pin-project-lite",
@ -1870,7 +1901,7 @@ dependencies = [
 "fnv",
 "futures-channel",
 "futures-util",
- "indexmap",
+ "indexmap 1.9.3",
 "once_cell",
 "pin-project-lite",
 "thiserror",
@ -1974,9 +2005,9 @@ dependencies = [
 [[package]]
 name = "paste"
-version = "1.0.12"
+version = "1.0.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9f746c4065a8fa3fe23974dd82f15431cc8d40779821001404d10d2e79ca7d79"
+checksum = "b4b27ab7be369122c218afc2079489cdcb4b517c0a3fc386ff11e1fedfcc2b35"
 [[package]]
 name = "pbkdf2"
@ -2003,34 +2034,34 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "4dd7d28ee937e54fe3080c91faa1c3a46c06de6252988a7f4592ba2310ef22a4"
 dependencies = [
 "fixedbitset",
- "indexmap",
+ "indexmap 1.9.3",
 ]
 [[package]]
 name = "pin-project"
-version = "1.1.1"
+version = "1.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6e138fdd8263907a2b0e1b4e80b7e58c721126479b6e6eedfb1b402acea7b9bd"
+checksum = "030ad2bc4db10a8944cb0d837f158bdfec4d4a4873ab701a95046770d11f8842"
 dependencies = [
 "pin-project-internal",
 ]
 [[package]]
 name = "pin-project-internal"
-version = "1.1.1"
+version = "1.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d1fef411b303e3e12d534fb6e7852de82da56edd937d895125821fb7c09436c7"
+checksum = "ec2e072ecce94ec471b13398d5402c188e76ac03cf74dd1a975161b23a3f6d9c"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
 name = "pin-project-lite"
-version = "0.2.9"
+version = "0.2.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e0a7ae3ac2f1173085d398531c705756c94a4c56843785df85a60c1a0afac116"
+checksum = "4c40d25201921e5ff0c862a505c6557ea88568a4e3ace775ab55e93f2f4f9d57"
 [[package]]
 name = "pin-utils"
@ -2046,9 +2077,9 @@ checksum = "26072860ba924cbfa98ea39c8c19b4dd6a4a25423dbdf219c1eca91aa0cf6964"
 [[package]]
 name = "portable-atomic"
-version = "1.3.3"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "767eb9f07d4a5ebcb39bbf2d452058a93c011373abf6832e24194a1c3f004794"
+checksum = "d220334a184db82b31b83f5ff093e3315280fb2b6bbc032022b2304a509aab7a"
 [[package]]
 name = "ppv-lite86"
@ -2092,9 +2123,9 @@ dependencies = [
 [[package]]
 name = "proc-macro2"
-version = "1.0.63"
+version = "1.0.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7b368fba921b0dce7e60f5e04ec15e565b3303972b42bcfde1d0713b881959eb"
+checksum = "78803b62cbf1f46fde80d7c0e803111524b9877184cfe7c3033659490ac7a7da"
 dependencies = [
 "unicode-ident",
 ]
@ -2294,13 +2325,14 @@ dependencies = [
 [[package]]
 name = "regex"
-version = "1.8.4"
+version = "1.9.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d0ab3ca65655bb1e41f2a8c8cd662eb4fb035e67c3f78da1d61dffe89d07300f"
+checksum = "b2eae68fc220f7cf2532e4494aded17545fce192d59cd996e0fe7887f4ceb575"
 dependencies = [
 "aho-corasick 1.0.2",
 "memchr",
- "regex-syntax 0.7.2",
+ "regex-automata 0.3.3",
 "regex-syntax 0.7.4",
 ]
 [[package]]
@ -2312,6 +2344,17 @@ dependencies = [
 "regex-syntax 0.6.29",
 ]
 [[package]]
 name = "regex-automata"
 version = "0.3.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "39354c10dd07468c2e73926b23bb9c2caca74c5501e38a35da70406f1d923310"
 dependencies = [
 "aho-corasick 1.0.2",
 "memchr",
 "regex-syntax 0.7.4",
 ]
 [[package]]
 name = "regex-syntax"
 version = "0.6.29"
@ -2320,9 +2363,9 @@ checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1"
 [[package]]
 name = "regex-syntax"
-version = "0.7.2"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "436b050e76ed2903236f032a59761c1eb99e1b0aead2c257922771dab1fc8c78"
+checksum = "e5ea92a5b6195c6ef2a0295ea818b312502c6fc94dde986c5553242e18fd4ce2"
 [[package]]
 name = "reqwest"
@ -2397,7 +2440,7 @@ dependencies = [
 "quote",
 "rust-embed-utils",
 "shellexpand",
- "syn 2.0.22",
+ "syn 2.0.25",
 "walkdir",
 ]
@ -2428,9 +2471,9 @@ dependencies = [
 [[package]]
 name = "rustix"
-version = "0.37.21"
+version = "0.37.23"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "62f25693a73057a1b4cb56179dd3c7ea21a7c6c5ee7d85781f5749b46f34b79c"
+checksum = "4d69718bf81c6127a49dc64e44a742e8bb9213c0ff8869a22c308f84c1d4ab06"
 dependencies = [
 "bitflags 1.3.2",
 "errno",
@ -2442,9 +2485,9 @@ dependencies = [
 [[package]]
 name = "rustix"
-version = "0.38.1"
+version = "0.38.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fbc6396159432b5c8490d4e301d8c705f61860b8b6c863bf79942ce5401968f3"
+checksum = "0a962918ea88d644592894bc6dc55acc6c0956488adcebbfb6e273506b7fd6e5"
 dependencies = [
 "bitflags 2.3.3",
 "errno",
@ -2476,15 +2519,15 @@ dependencies = [
 [[package]]
 name = "rustversion"
-version = "1.0.12"
+version = "1.0.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4f3208ce4d8448b3f3e7d168a73f5e0c43a61e32930de3bceeccedb388b6bf06"
+checksum = "dc31bd9b61a32c31f9650d18add92aa83a49ba979c143eefd27fe7177b05bd5f"
 [[package]]
 name = "ryu"
-version = "1.0.13"
+version = "1.0.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f91339c0467de62360649f8d3e185ca8de4224ff281f66000de5eb2a77a79041"
+checksum = "fe232bdf6be8c8de797b22184ee71118d63780ea42ac85b61d1baa6d3b782ae9"
 [[package]]
 name = "same-file"
@ -2497,11 +2540,11 @@ dependencies = [
 [[package]]
 name = "schannel"
-version = "0.1.21"
+version = "0.1.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "713cfb06c7059f3588fb8044c0fad1d09e3c01d225e25b9220dbfdcf16dbb1b3"
+checksum = "0c3733bf4cf7ea0880754e19cb5a462007c4a8c1914bff372ccc95b464f1df88"
 dependencies = [
- "windows-sys 0.42.0",
+ "windows-sys 0.48.0",
 ]
 [[package]]
@ -2551,29 +2594,29 @@ checksum = "bebd363326d05ec3e2f532ab7660680f3b02130d780c299bca73469d521bc0ed"
 [[package]]
 name = "serde"
-version = "1.0.164"
+version = "1.0.171"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9e8c8cf938e98f769bc164923b06dce91cea1751522f46f8466461af04c9027d"
+checksum = "30e27d1e4fd7659406c492fd6cfaf2066ba8773de45ca75e855590f856dc34a9"
 dependencies = [
 "serde_derive",
 ]
 [[package]]
 name = "serde_derive"
-version = "1.0.164"
+version = "1.0.171"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d9735b638ccc51c28bf6914d90a2e9725b377144fc612c49a611fddd1b631d68"
+checksum = "389894603bd18c46fa56231694f8d827779c0951a667087194cf9de94ed24682"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
 name = "serde_json"
-version = "1.0.99"
+version = "1.0.102"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "46266871c240a00b8f503b877622fe33430b3c7d963bdc0f2adc511e54a1eae3"
+checksum = "b5062a995d481b2308b6064e9af76011f2921c35f97b0468811ed9f6cd91dfed"
 dependencies = [
 "itoa",
 "ryu",
@ -2582,10 +2625,11 @@ dependencies = [
 [[package]]
 name = "serde_path_to_error"
-version = "0.1.11"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f7f05c1d5476066defcdfacce1f52fc3cae3af1d3089727100c02ae92e5abbe0"
+checksum = "8acc4422959dd87a76cb117c191dcbffc20467f06c9100b76721dab370f24d3a"
 dependencies = [
 "itoa",
 "serde",
 ]
@ -2697,9 +2741,9 @@ dependencies = [
 [[package]]
 name = "smallvec"
-version = "1.10.0"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a507befe795404456341dfab10cef66ead4c041f62b8b11bbb92bffe5d0953e0"
+checksum = "62bb4feee49fdd9f707ef802e22365a35de4b7b299de4763d44bfea899442ff9"
 [[package]]
 name = "socket2"
@ -2769,9 +2813,9 @@ dependencies = [
 [[package]]
 name = "syn"
-version = "2.0.22"
+version = "2.0.25"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2efbeae7acf4eabd6bcdcbd11c92f45231ddda7539edc7806bd1a04a03b24616"
+checksum = "15e3fc8c0c74267e2df136e5e5fb656a464158aa57624053375eb9c8c6e25ae2"
 dependencies = [
 "proc-macro2",
 "quote",
@ -2786,9 +2830,9 @@ checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160"
 [[package]]
 name = "sysinfo"
-version = "0.29.3"
+version = "0.29.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5bcd0346f90b6bc83526c7b180039a8acd26a5c848cc556d457f6472eb148122"
+checksum = "751e810399bba86e9326f5762b7f32ac5a085542df78da6a78d94e07d14d7c11"
 dependencies = [
 "cfg-if",
 "core-foundation-sys",
@ -2824,9 +2868,9 @@ dependencies = [
 [[package]]
 name = "tar"
-version = "0.4.38"
+version = "0.4.39"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4b55807c0344e1e6c04d7c965f5289c39a8d94ae23ed5c0b57aabac549f871c6"
+checksum = "ec96d2ffad078296368d46ff1cb309be1c23c513b4ab0e22a45de0185275ac96"
 dependencies = [
 "filetime",
 "libc",
@ -2843,13 +2887,13 @@ dependencies = [
 "cfg-if",
 "fastrand",
 "redox_syscall 0.3.5",
- "rustix 0.37.21",
+ "rustix 0.37.23",
 "windows-sys 0.48.0",
 ]
 [[package]]
 name = "text-generation-benchmark"
-version = "0.9.1"
+version = "0.9.3"
 dependencies = [
 "average",
 "clap",
@ -2869,7 +2913,7 @@ dependencies = [
 [[package]]
 name = "text-generation-client"
-version = "0.9.1"
+version = "0.9.3"
 dependencies = [
 "futures",
 "grpc-metadata",
@ -2885,7 +2929,7 @@ dependencies = [
 [[package]]
 name = "text-generation-launcher"
-version = "0.9.1"
+version = "0.9.3"
 dependencies = [
 "clap",
 "ctrlc",
@ -2901,7 +2945,7 @@ dependencies = [
 [[package]]
 name = "text-generation-router"
-version = "0.9.1"
+version = "0.9.3"
 dependencies = [
 "async-stream",
 "axum",
@ -2934,22 +2978,22 @@ dependencies = [
 [[package]]
 name = "thiserror"
-version = "1.0.40"
+version = "1.0.43"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "978c9a314bd8dc99be594bc3c175faaa9794be04a5a5e153caba6915336cebac"
+checksum = "a35fc5b8971143ca348fa6df4f024d4d55264f3468c71ad1c2f365b0a4d58c42"
 dependencies = [
 "thiserror-impl",
 ]
 [[package]]
 name = "thiserror-impl"
-version = "1.0.40"
+version = "1.0.43"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f9456a42c5b0d803c8cd86e73dd7cc9edd429499f37a3550d286d5e86720569f"
+checksum = "463fe12d7993d3b327787537ce8dd4dfa058de32fc2b195ef3cde03dc4771e8f"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -2964,11 +3008,13 @@ dependencies = [
 [[package]]
 name = "time"
-version = "0.3.22"
+version = "0.3.23"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ea9e1b3cf1243ae005d9e74085d4d542f3125458f3a81af210d901dcd7411efd"
+checksum = "59e399c068f43a5d116fedaf73b203fa4f9c519f17e2b34f63221d3792f81446"
 dependencies = [
 "itoa",
 "libc",
 "num_threads",
 "serde",
 "time-core",
 "time-macros",
@ -2982,9 +3028,9 @@ checksum = "7300fbefb4dadc1af235a9cef3737cea692a9d97e1b9cbcd4ebdae6f8868e6fb"
 [[package]]
 name = "time-macros"
-version = "0.2.9"
+version = "0.2.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "372950940a5f07bf38dbe211d7283c9e6d7327df53794992d293e534c733d09b"
+checksum = "96ba15a897f3c86766b757e5ac7221554c6750054d74d5b28844fce5fb36a6c4"
 dependencies = [
 "time-core",
 ]
@ -3078,7 +3124,7 @@ checksum = "630bdcf245f78637c13ec01ffae6187cca34625e8c63150d424b59e55af2675e"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -3209,7 +3255,7 @@ checksum = "b8fa9be0de6cf49e536ce1851f987bd21a43b771b09473c3549a6c853db37c1c"
 dependencies = [
 "futures-core",
 "futures-util",
- "indexmap",
+ "indexmap 1.9.3",
 "pin-project",
 "pin-project-lite",
 "rand",
@ -3291,7 +3337,7 @@ checksum = "5f4f31f56159e98206da9efd823404b79b6ef3143b4a7ab76e67b1751b25a4ab"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 ]
 [[package]]
@ -3413,9 +3459,9 @@ checksum = "92888ba5573ff080736b3648696b70cafad7d250551175acbaa4e0385b3e1460"
 [[package]]
 name = "unicode-ident"
-version = "1.0.9"
+version = "1.0.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b15811caf2415fb889178633e7724bad2509101cde276048e013b9def5e51fa0"
+checksum = "22049a19f4a68748a168c0fc439f9516686aa045927ff767eca0a85101fb6e73"
 [[package]]
 name = "unicode-normalization"
@ -3484,11 +3530,11 @@ checksum = "711b9620af191e0cdc7468a8d14e709c3dcdb115b36f838e601583af800a370a"
 [[package]]
 name = "utoipa"
-version = "3.3.0"
+version = "3.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "68ae74ef183fae36d650f063ae7bde1cacbe1cd7e72b617cbe1e985551878b98"
+checksum = "520434cac5c98120177d5cc15be032703f6dca7d5ef82e725c798113b375000a"
 dependencies = [
- "indexmap",
+ "indexmap 2.0.0",
 "serde",
 "serde_json",
 "utoipa-gen",
@ -3496,21 +3542,22 @@ dependencies = [
 [[package]]
 name = "utoipa-gen"
-version = "3.3.0"
+version = "3.4.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ea8ac818da7e746a63285594cce8a96f5e00ee31994e655bd827569cb8b137b"
+checksum = "6e22e88a487b6e0374533871b79b1f5ded05671bd0936bd547eb42f82fb9060d"
 dependencies = [
 "proc-macro-error",
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "regex",
 "syn 2.0.25",
 ]
 [[package]]
 name = "utoipa-swagger-ui"
-version = "3.1.3"
+version = "3.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "062bba5a3568e126ac72049a63254f4cb1da2eb713db0c1ab2a4c76be191db8c"
+checksum = "4602d7100d3cfd8a086f30494e68532402ab662fa366c9d201d677e33cee138d"
 dependencies = [
 "axum",
 "mime_guess",
@ -3536,9 +3583,9 @@ checksum = "accd4ea62f7bb7a82fe23066fb0957d48ef677f6eeb8215f372f52e48bb32426"
 [[package]]
 name = "vergen"
-version = "8.2.1"
+version = "8.2.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8b3c89c2c7e50f33e4d35527e5bf9c11d6d132226dbbd1753f0fbe9f19ef88c6"
+checksum = "bbc5ad0d9d26b2c49a5ab7da76c3e79d3ee37e7821799f8223fcb8f2f391a2e7"
 dependencies = [
 "anyhow",
 "rustc_version",
@ -3599,7 +3646,7 @@ dependencies = [
 "once_cell",
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 "wasm-bindgen-shared",
 ]
@ -3633,7 +3680,7 @@ checksum = "54681b18a46765f095758388f2d0cf16eb8d4169b639ab575a8f5693af210c7b"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn 2.0.22",
+ "syn 2.0.25",
 "wasm-bindgen-backend",
 "wasm-bindgen-shared",
 ]
@ -3706,21 +3753,6 @@ version = "0.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f"
 [[package]]
 name = "windows-sys"
 version = "0.42.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5a3e1820f08b8513f676f7ab6c1f99ff312fb97b553d30ff4dd86f9f15728aa7"
 dependencies = [
 "windows_aarch64_gnullvm 0.42.2",
 "windows_aarch64_msvc 0.42.2",
 "windows_i686_gnu 0.42.2",
 "windows_i686_msvc 0.42.2",
 "windows_x86_64_gnu 0.42.2",
 "windows_x86_64_gnullvm 0.42.2",
 "windows_x86_64_msvc 0.42.2",
 ]
 [[package]]
 name = "windows-sys"
 version = "0.45.0"
--- a/Cargo.toml
+++ b/Cargo.toml
@ -8,7 +8,7 @@ members = [
 ]
 [workspace.package]
-version = "0.9.1"
+version = "0.9.3"
 edition = "2021"
 authors = ["Olivier Dehaene"]
 homepage = "https://github.com/huggingface/text-generation-inference"
--- a/15
+++ b/15
@ -98,6 +98,16 @@ COPY server/Makefile-flash-att Makefile
 # Build specific version of flash attention
 RUN make build-flash-attention
 # Build Flash Attention v2 CUDA kernels
 FROM kernel-builder as flash-att-v2-builder
 WORKDIR /usr/src
 COPY server/Makefile-flash-att-v2 Makefile
 # Build specific version of flash attention v2
 RUN make build-flash-attention-v2
 # Build Transformers CUDA kernels
 FROM kernel-builder as custom-kernels-builder
@ -146,8 +156,11 @@ COPY --from=flash-att-builder /usr/src/flash-attention/build/lib.linux-x86_64-cp
 COPY --from=flash-att-builder /usr/src/flash-attention/csrc/layer_norm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 COPY --from=flash-att-builder /usr/src/flash-attention/csrc/rotary/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 # Copy build artifacts from flash attention v2 builder
 COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 # Copy build artifacts from custom kernels builder
-COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39/custom_kernels /usr/src/custom-kernels/src/custom_kernels
+COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 # Copy builds artifacts from vllm builder
 COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
--- a/README.md
+++ b/README.md
@ -1,5 +1,7 @@
 <div align="center">
 ![image](https://github.com/huggingface/text-generation-inference/assets/3841370/38ba1531-ea0d-4851-b31a-a6d4ddc944b0)
 # Text Generation Inference
 <a href="https://github.com/huggingface/text-generation-inference">
@ -11,9 +13,6 @@
 <a href="https://huggingface.github.io/text-generation-inference">
  <img alt="Swagger API documentation" src="https://img.shields.io/badge/API-Swagger-informational">
 </a>
 ![architecture](assets/architecture.jpg)
 </div>
 A Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co) 
@ -64,6 +63,8 @@ to power LLMs api-inference widgets.
 - [Starcoder](https://huggingface.co/bigcode/starcoder)
 - [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
 - [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b)
 - [MPT](https://huggingface.co/mosaicml/mpt-30b)
 - [Llama V2](https://huggingface.co/meta-llama)
 Other architectures are supported on a best effort basis using:
@ -133,6 +134,10 @@ print(text)
 You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route.
 The Swagger UI is also available at: [https://huggingface.github.io/text-generation-inference](https://huggingface.github.io/text-generation-inference).
 ### Using on private models or gated models
 You can use `HUGGING_FACE_HUB_TOKEN` environment variable to set the token used by `text-generation-inference` to give access to protected ressources.
 ### Distributed Tracing
 `text-generation-inference` is instrumented with distributed tracing using OpenTelemetry. You can use this feature
@ -212,7 +217,7 @@ sudo apt-get install libssl-dev gcc -y
 ### CUDA Kernels
 The custom CUDA kernels are only tested on NVIDIA A100s. If you have any installation or runtime issues, you can remove 
-the kernels by using the `BUILD_EXTENSIONS=False` environment variable.
+the kernels by using the `DISABLE_CUSTOM_KERNELS=True` environment variable.
 Be aware that the official Docker image has them enabled by default.
--- a/docs/openapi.json
+++ b/docs/openapi.json
@ -10,7 +10,7 @@
      "name": "Apache 2.0",
      "url": "https://www.apache.org/licenses/LICENSE-2.0"
    },
-    "version": "0.9.1"
+    "version": "0.9.3"
  },
  "paths": {
    "/": {
--- a/launcher/Cargo.toml
+++ b/launcher/Cargo.toml
@ -13,7 +13,7 @@ nix = "0.26.2"
 serde = { version = "1.0.152", features = ["derive"]  }
 serde_json = "1.0.93"
 tracing = "0.1.37"
-tracing-subscriber = { version = "0.3.16", features = ["json"] }
+tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
 [dev-dependencies]
 float_eq = "1.0.1"
--- a/launcher/src/main.rs
+++ b/launcher/src/main.rs
@ -4,10 +4,10 @@ use nix::unistd::Pid;
 use serde::Deserialize;
 use std::env;
 use std::ffi::OsString;
-use std::io::{BufRead, BufReader, Read};
+use std::io::{BufRead, BufReader, Lines, Read};
 use std::os::unix::process::{CommandExt, ExitStatusExt};
 use std::path::Path;
-use std::process::{Child, Command, Stdio};
+use std::process::{Child, Command, ExitStatus, Stdio};
 use std::sync::atomic::{AtomicBool, Ordering};
 use std::sync::mpsc::TryRecvError;
 use std::sync::{mpsc, Arc};
@ -15,6 +15,7 @@ use std::thread;
 use std::thread::sleep;
 use std::time::{Duration, Instant};
 use std::{fs, io};
 use tracing_subscriber::EnvFilter;
 mod env_runtime;
@ -41,6 +42,7 @@ impl std::fmt::Display for Quantization {
 #[derive(Clone, Copy, Debug, ValueEnum)]
 enum Dtype {
    Float16,
    #[clap(name = "bfloat16")]
    BFloat16,
 }
@ -182,8 +184,8 @@ struct Args {
    /// depends on other parameters like if you're using quantization, flash attention
    /// or the model implementation, text-generation-inference cannot infer this number
    /// automatically.
-    #[clap(default_value = "16000", long, env)]
+    #[clap(long, env)]
-    max_batch_total_tokens: u32,
+    max_batch_total_tokens: Option<u32>,
    /// This setting defines how many tokens can be passed before forcing the waiting
    /// queries to be put on the batch (if the size of the batch allows for it).
@ -265,17 +267,9 @@ struct Args {
    #[clap(long, env)]
    ngrok_authtoken: Option<String>,
-    /// ngrok domain name where the axum webserver will be available at
+    /// ngrok edge
    #[clap(long, env)]
-    ngrok_domain: Option<String>,
+    ngrok_edge: Option<String>,
    /// ngrok basic auth username
    #[clap(long, env)]
    ngrok_username: Option<String>,
    /// ngrok basic auth password
    #[clap(long, env)]
    ngrok_password: Option<String>,
    /// Display a lot of information about your runtime environment
    #[clap(long, short, action)]
@ -285,7 +279,7 @@ struct Args {
 #[derive(Debug)]
 enum ShardStatus {
    Ready,
-    Failed((usize, Option<String>)),
+    Failed(usize),
 }
 #[allow(clippy::too_many_arguments)]
@ -310,6 +304,9 @@ fn shard_manager(
    shutdown: Arc<AtomicBool>,
    _shutdown_sender: mpsc::Sender<()>,
 ) {
    // Enter shard-manager tracing span
    let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
    // Get UDS path
    let uds_string = format!("{uds_path}-{rank}");
    let uds = Path::new(&uds_string);
@ -319,7 +316,7 @@ fn shard_manager(
    }
    // Process args
-    let mut shard_argv = vec![
+    let mut shard_args = vec![
        "serve".to_string(),
        model_id,
        "--uds-path".to_string(),
@ -331,77 +328,71 @@ fn shard_manager(
    // Activate trust remote code
    if trust_remote_code {
-        shard_argv.push("--trust-remote-code".to_string());
+        shard_args.push("--trust-remote-code".to_string());
    }
    // Activate tensor parallelism
    if world_size > 1 {
-        shard_argv.push("--sharded".to_string());
+        shard_args.push("--sharded".to_string());
    }
    if let Some(quantize) = quantize {
-        shard_argv.push("--quantize".to_string());
+        shard_args.push("--quantize".to_string());
-        shard_argv.push(quantize.to_string())
+        shard_args.push(quantize.to_string())
    }
    if let Some(dtype) = dtype {
-        shard_argv.push("--dtype".to_string());
+        shard_args.push("--dtype".to_string());
-        shard_argv.push(dtype.to_string())
+        shard_args.push(dtype.to_string())
    }
    // Model optional revision
    if let Some(revision) = revision {
-        shard_argv.push("--revision".to_string());
+        shard_args.push("--revision".to_string());
-        shard_argv.push(revision)
+        shard_args.push(revision)
    }
    // OpenTelemetry
    if let Some(otlp_endpoint) = otlp_endpoint {
-        shard_argv.push("--otlp-endpoint".to_string());
+        shard_args.push("--otlp-endpoint".to_string());
-        shard_argv.push(otlp_endpoint);
+        shard_args.push(otlp_endpoint);
    }
    // Copy current process env
-    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+    let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
    // Use cuda allocator. It leads to less memory fragmentation
    env.push((
        "PYTORCH_CUDA_ALLOC_CONF".into(),
        "backend:cudaMallocAsync".into(),
    ));
    // Torch Distributed Env vars
-    env.push(("RANK".into(), rank.to_string().into()));
+    envs.push(("RANK".into(), rank.to_string().into()));
-    env.push(("WORLD_SIZE".into(), world_size.to_string().into()));
+    envs.push(("WORLD_SIZE".into(), world_size.to_string().into()));
-    env.push(("MASTER_ADDR".into(), master_addr.into()));
+    envs.push(("MASTER_ADDR".into(), master_addr.into()));
-    env.push(("MASTER_PORT".into(), master_port.to_string().into()));
+    envs.push(("MASTER_PORT".into(), master_port.to_string().into()));
-    env.push(("NCCL_ASYNC_ERROR_HANDLING".into(), "1".into()));
+    envs.push(("NCCL_ASYNC_ERROR_HANDLING".into(), "1".into()));
    // Safetensors load fast
-    env.push(("SAFETENSORS_FAST_GPU".into(), "1".into()));
+    envs.push(("SAFETENSORS_FAST_GPU".into(), "1".into()));
    // Enable hf transfer for insane download speeds
    let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
-    env.push((
+    envs.push((
        "HF_HUB_ENABLE_HF_TRANSFER".into(),
        enable_hf_transfer.into(),
    ));
    // Parse Inference API token
    if let Ok(api_token) = env::var("HF_API_TOKEN") {
-        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+        envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
    };
    // If huggingface_hub_cache is some, pass it to the shard
    // Useful when running inside a docker container
    if let Some(huggingface_hub_cache) = huggingface_hub_cache {
-        env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
+        envs.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
    };
    // If weights_cache_override is some, pass it to the shard
    // Useful when running inside a HuggingFace Inference Endpoint
    if let Some(weights_cache_override) = weights_cache_override {
-        env.push((
+        envs.push((
            "WEIGHTS_CACHE_OVERRIDE".into(),
            weights_cache_override.into(),
        ));
@ -409,24 +400,24 @@ fn shard_manager(
    // If disable_custom_kernels is true, pass it to the shard as an env var
    if disable_custom_kernels {
-        env.push(("DISABLE_CUSTOM_KERNELS".into(), "True".into()))
+        envs.push(("DISABLE_CUSTOM_KERNELS".into(), "True".into()))
    }
    // Watermark Gamma
    if let Some(watermark_gamma) = watermark_gamma {
-        env.push(("WATERMARK_GAMMA".into(), watermark_gamma.to_string().into()))
+        envs.push(("WATERMARK_GAMMA".into(), watermark_gamma.to_string().into()))
    }
    // Watermark Delta
    if let Some(watermark_delta) = watermark_delta {
-        env.push(("WATERMARK_DELTA".into(), watermark_delta.to_string().into()))
+        envs.push(("WATERMARK_DELTA".into(), watermark_delta.to_string().into()))
    }
    // Start process
-    tracing::info!("Starting shard {rank}");
+    tracing::info!("Starting shard");
    let mut p = match Command::new("text-generation-server")
-        .args(shard_argv)
+        .args(shard_args)
-        .envs(env)
+        .envs(envs)
        .stdout(Stdio::piped())
        .stderr(Stdio::piped())
        .process_group(0)
@ -437,30 +428,23 @@ fn shard_manager(
            if err.kind() == io::ErrorKind::NotFound {
                tracing::error!("text-generation-server not found in PATH");
                tracing::error!("Please install it with `make install-server`")
-            } else {
+            }
            {
                tracing::error!("{}", err);
            }
-            status_sender
+            status_sender.send(ShardStatus::Failed(rank)).unwrap();
                .send(ShardStatus::Failed((rank, Some(err.to_string()))))
                .unwrap();
            return;
        }
    };
    // Redirect STDOUT to the console
    let shard_stdout_reader = BufReader::new(p.stdout.take().unwrap());
-    let mut shard_stderr_reader = BufReader::new(p.stderr.take().unwrap());
+    let shard_stderr_reader = BufReader::new(p.stderr.take().unwrap());
    //stdout tracing thread
    thread::spawn(move || {
-        // Enter shard-manager tracing span
+        log_lines(shard_stdout_reader.lines());
        let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
        for line in shard_stdout_reader.lines() {
            // Parse loguru logs
            if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
                log.trace();
            }
        }
    });
    let mut ready = false;
@ -469,30 +453,25 @@ fn shard_manager(
    loop {
        // Process exited
        if let Some(exit_status) = p.try_wait().unwrap() {
-            // We read stderr in another thread as it seems that `read_to_string` can block
+            // We read stderr in another thread as it seems that lines() can block in some cases
            // indefinitely in some cases
            let (err_sender, err_receiver) = mpsc::channel();
            thread::spawn(move || {
-                let mut err = String::new();
+                for line in shard_stderr_reader.lines().flatten() {
-                shard_stderr_reader.read_to_string(&mut err).unwrap();
+                    err_sender.send(line).unwrap_or(());
-                err_sender.send(err).unwrap_or(());
+                }
            });
            let mut err = String::new();
            while let Ok(line) = err_receiver.recv_timeout(Duration::from_millis(10)) {
                err = err + "\n" + &line;
            }
-            let err = err_receiver
+            tracing::error!("Shard complete standard error output:\n{err}");
                .recv_timeout(Duration::from_millis(100))
                .map_err(|err| {
                    tracing::error!("Unable to read shard {rank} error from stderr");
                    err
                })
                .ok();
            if let Some(signal) = exit_status.signal() {
                tracing::error!("Shard process was signaled to shutdown with signal {signal}");
            }
-            status_sender
+            status_sender.send(ShardStatus::Failed(rank)).unwrap();
                .send(ShardStatus::Failed((rank, err)))
                .unwrap();
            return;
        }
@ -500,17 +479,17 @@ fn shard_manager(
        if shutdown.load(Ordering::SeqCst) {
            p.kill().unwrap();
            let _ = p.wait();
-            tracing::info!("Shard {rank} terminated");
+            tracing::info!("Shard terminated");
            return;
        }
        // Shard is ready
        if uds.exists() && !ready {
-            tracing::info!("Shard {rank} ready in {:?}", start_time.elapsed());
+            tracing::info!("Shard ready in {:?}", start_time.elapsed());
            status_sender.send(ShardStatus::Ready).unwrap();
            ready = true;
        } else if !ready && wait_time.elapsed() > Duration::from_secs(10) {
-            tracing::info!("Waiting for shard {rank} to be ready...");
+            tracing::info!("Waiting for shard to be ready...");
            wait_time = Instant::now();
        }
        sleep(Duration::from_millis(100));
@ -579,6 +558,23 @@ impl PythonLogMessage {
    }
 }
 impl TryFrom<&String> for PythonLogMessage {
    type Error = serde_json::Error;
    fn try_from(value: &String) -> Result<Self, Self::Error> {
        serde_json::from_str::<Self>(value)
    }
 }
 fn log_lines<S: Sized + BufRead>(lines: Lines<S>) {
    for line in lines.flatten() {
        match PythonLogMessage::try_from(&line) {
            Ok(log) => log.trace(),
            Err(_) => tracing::debug!("{line}"),
        }
    }
 }
 fn find_num_shards(
    sharded: Option<bool>,
    num_shard: Option<usize>,
@ -632,7 +628,10 @@ enum LauncherError {
 }
 fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), LauncherError> {
-    let mut download_argv = vec![
+    // Enter download tracing span
    let _span = tracing::span!(tracing::Level::INFO, "download").entered();
    let mut download_args = vec![
        "download-weights".to_string(),
        args.model_id.to_string(),
        "--extension".to_string(),
@ -644,35 +643,35 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
    // Model optional revision
    if let Some(revision) = &args.revision {
-        download_argv.push("--revision".to_string());
+        download_args.push("--revision".to_string());
-        download_argv.push(revision.to_string())
+        download_args.push(revision.to_string())
    }
    // Copy current process env
-    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+    let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
    // If huggingface_hub_cache is set, pass it to the download process
    // Useful when running inside a docker container
    if let Some(ref huggingface_hub_cache) = args.huggingface_hub_cache {
-        env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
+        envs.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
    };
    // Enable hf transfer for insane download speeds
    let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
-    env.push((
+    envs.push((
        "HF_HUB_ENABLE_HF_TRANSFER".into(),
        enable_hf_transfer.into(),
    ));
    // Parse Inference API token
    if let Ok(api_token) = env::var("HF_API_TOKEN") {
-        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+        envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
    };
    // If args.weights_cache_override is some, pass it to the download process
    // Useful when running inside a HuggingFace Inference Endpoint
    if let Some(weights_cache_override) = &args.weights_cache_override {
-        env.push((
+        envs.push((
            "WEIGHTS_CACHE_OVERRIDE".into(),
            weights_cache_override.into(),
        ));
@ -681,8 +680,8 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
    // Start process
    tracing::info!("Starting download process.");
    let mut download_process = match Command::new("text-generation-server")
-        .args(download_argv)
+        .args(download_args)
-        .envs(env)
+        .envs(envs)
        .stdout(Stdio::piped())
        .stderr(Stdio::piped())
        .process_group(0)
@ -693,6 +692,8 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
            if err.kind() == io::ErrorKind::NotFound {
                tracing::error!("text-generation-server not found in PATH");
                tracing::error!("Please install it with `make install-server`")
            } else {
                tracing::error!("{}", err);
            }
            return Err(LauncherError::DownloadError);
@ -701,16 +702,10 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
    // Redirect STDOUT to the console
    let download_stdout = download_process.stdout.take().unwrap();
    let stdout = BufReader::new(download_stdout);
    thread::spawn(move || {
-        // Enter download tracing span
+        log_lines(stdout.lines());
        let stdout = BufReader::new(download_stdout);
        let _span = tracing::span!(tracing::Level::INFO, "download").entered();
        for line in stdout.lines() {
            // Parse loguru logs
            if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
                log.trace();
            }
        }
    });
    loop {
@ -738,10 +733,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
            return Err(LauncherError::DownloadError);
        }
        if !running.load(Ordering::SeqCst) {
-            signal::kill(Pid::from_raw(download_process.id() as i32), Signal::SIGTERM).unwrap();
+            terminate("download", download_process, Duration::from_secs(10)).unwrap();
            tracing::info!("Waiting for download process to gracefully shutdown");
            download_process.wait().unwrap();
            tracing::info!("Download process terminated");
            return Ok(());
        }
        sleep(Duration::from_millis(100));
@ -760,16 +752,6 @@ fn spawn_shards(
    status_sender: mpsc::Sender<ShardStatus>,
    running: Arc<AtomicBool>,
 ) -> Result<(), LauncherError> {
    if args.trust_remote_code {
        tracing::warn!(
            "`trust_remote_code` is set. Trusting that model `{}` do not contain malicious code.",
            args.model_id
        );
        if args.revision.is_none() {
            tracing::warn!("Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.");
        }
    }
    // Start shard processes
    for rank in 0..num_shard {
        let model_id = args.model_id.clone();
@ -828,11 +810,8 @@ fn spawn_shards(
            Err(TryRecvError::Empty) => {
                sleep(Duration::from_millis(100));
            }
-            Ok(ShardStatus::Failed((rank, err))) => {
+            Ok(ShardStatus::Failed(rank)) => {
                tracing::error!("Shard {rank} failed to start");
                if let Some(err) = err {
                    tracing::error!("{err}");
                }
                shutdown_shards(shutdown, shutdown_receiver);
                return Err(LauncherError::ShardCannotStart);
            }
@ -854,7 +833,7 @@ fn spawn_webserver(
    // All shard started
    // Start webserver
    tracing::info!("Starting Webserver");
-    let mut argv = vec![
+    let mut router_args = vec![
        "--max-concurrent-requests".to_string(),
        args.max_concurrent_requests.to_string(),
        "--max-best-of".to_string(),
@ -867,8 +846,6 @@ fn spawn_webserver(
        args.max_total_tokens.to_string(),
        "--max-batch-prefill-tokens".to_string(),
        args.max_batch_prefill_tokens.to_string(),
        "--max-batch-total-tokens".to_string(),
        args.max_batch_total_tokens.to_string(),
        "--waiting-served-ratio".to_string(),
        args.waiting_served_ratio.to_string(),
        "--max-waiting-tokens".to_string(),
@ -885,63 +862,54 @@ fn spawn_webserver(
        args.model_id,
    ];
    // Model optional max batch total tokens
    if let Some(max_batch_total_tokens) = args.max_batch_total_tokens {
        router_args.push("--max-batch-total-tokens".to_string());
        router_args.push(max_batch_total_tokens.to_string());
    }
    // Model optional revision
    if let Some(ref revision) = args.revision {
-        argv.push("--revision".to_string());
+        router_args.push("--revision".to_string());
-        argv.push(revision.to_string())
+        router_args.push(revision.to_string())
    }
    if args.json_output {
-        argv.push("--json-output".to_string());
+        router_args.push("--json-output".to_string());
    }
    // OpenTelemetry
    if let Some(otlp_endpoint) = args.otlp_endpoint {
-        argv.push("--otlp-endpoint".to_string());
+        router_args.push("--otlp-endpoint".to_string());
-        argv.push(otlp_endpoint);
+        router_args.push(otlp_endpoint);
    }
    // CORS origins
    for origin in args.cors_allow_origin.into_iter() {
-        argv.push("--cors-allow-origin".to_string());
+        router_args.push("--cors-allow-origin".to_string());
-        argv.push(origin);
+        router_args.push(origin);
    }
    // Ngrok
    if args.ngrok {
-        let authtoken = args.ngrok_authtoken.ok_or_else(|| {
+        router_args.push("--ngrok".to_string());
-            tracing::error!("`ngrok-authtoken` must be set when using ngrok tunneling");
+        router_args.push("--ngrok-authtoken".to_string());
-            LauncherError::WebserverCannotStart
+        router_args.push(args.ngrok_authtoken.unwrap());
-        })?;
+        router_args.push("--ngrok-edge".to_string());
-
+        router_args.push(args.ngrok_edge.unwrap());
        argv.push("--ngrok".to_string());
        argv.push("--ngrok-authtoken".to_string());
        argv.push(authtoken);
        if let Some(domain) = args.ngrok_domain {
            argv.push("--ngrok-domain".to_string());
            argv.push(domain);
        }
        if let (Some(username), Some(password)) = (args.ngrok_username, args.ngrok_password) {
            argv.push("--ngrok-username".to_string());
            argv.push(username);
            argv.push("--ngrok-password".to_string());
            argv.push(password);
        }
    }
    // Copy current process env
-    let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
+    let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
    // Parse Inference API token
    if let Ok(api_token) = env::var("HF_API_TOKEN") {
-        env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
+        envs.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
    };
    let mut webserver = match Command::new("text-generation-router")
-        .args(argv)
+        .args(router_args)
-        .envs(env)
+        .envs(envs)
        .stdout(Stdio::piped())
        .stderr(Stdio::piped())
        .process_group(0)
@ -979,14 +947,49 @@ fn spawn_webserver(
    Ok(webserver)
 }
 fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::Result<ExitStatus> {
    tracing::info!("Terminating {process_name}");
    let terminate_time = Instant::now();
    signal::kill(Pid::from_raw(process.id() as i32), Signal::SIGTERM).unwrap();
    tracing::info!("Waiting for {process_name} to gracefully shutdown");
    while terminate_time.elapsed() < timeout {
        if let Some(status) = process.try_wait()? {
            tracing::info!("{process_name} terminated");
            return Ok(status);
        }
        sleep(Duration::from_millis(100));
    }
    tracing::info!("Killing {process_name}");
    process.kill()?;
    let exit_status = process.wait()?;
    tracing::info!("{process_name} killed");
    Ok(exit_status)
 }
 fn main() -> Result<(), LauncherError> {
    // Pattern match configuration
-    let args = Args::parse();
+    let args: Args = Args::parse();
    // Filter events with LOG_LEVEL
    let env_filter =
        EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));
    if args.json_output {
-        tracing_subscriber::fmt().json().init();
+        tracing_subscriber::fmt()
            .with_env_filter(env_filter)
            .json()
            .init();
    } else {
-        tracing_subscriber::fmt().compact().init();
+        tracing_subscriber::fmt()
            .with_env_filter(env_filter)
            .compact()
            .init();
    }
    if args.env {
@ -1008,29 +1011,53 @@ fn main() -> Result<(), LauncherError> {
            args.max_batch_prefill_tokens, args.max_input_length
        )));
    }
-    if args.max_batch_prefill_tokens > args.max_batch_total_tokens {
+
        return Err(LauncherError::ArgumentValidation(format!(
            "`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
            args.max_batch_prefill_tokens, args.max_batch_total_tokens
        )));
    }
    if args.max_total_tokens as u32 > args.max_batch_total_tokens {
        return Err(LauncherError::ArgumentValidation(format!(
            "`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
            args.max_total_tokens, args.max_batch_total_tokens
        )));
    }
    if args.validation_workers == 0 {
        return Err(LauncherError::ArgumentValidation(
            "`validation_workers` must be > 0".to_string(),
        ));
    }
    if args.trust_remote_code {
        tracing::warn!(
            "`trust_remote_code` is set. Trusting that model `{}` do not contain malicious code.",
            args.model_id
        );
    }
    let num_shard = find_num_shards(args.sharded, args.num_shard)?;
    if num_shard > 1 {
        tracing::info!("Sharding model on {num_shard} processes");
    }
    if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens {
        if args.max_batch_prefill_tokens > *max_batch_total_tokens {
            return Err(LauncherError::ArgumentValidation(format!(
                "`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
                args.max_batch_prefill_tokens, max_batch_total_tokens
            )));
        }
        if args.max_total_tokens as u32 > *max_batch_total_tokens {
            return Err(LauncherError::ArgumentValidation(format!(
                "`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}",
                args.max_total_tokens, max_batch_total_tokens
            )));
        }
    }
    if args.ngrok {
        if args.ngrok_authtoken.is_none() {
            return Err(LauncherError::ArgumentValidation(
                "`ngrok-authtoken` must be set when using ngrok tunneling".to_string(),
            ));
        }
        if args.ngrok_edge.is_none() {
            return Err(LauncherError::ArgumentValidation(
                "`ngrok-edge` must be set when using ngrok tunneling".to_string(),
            ));
        }
    }
    // Signal handler
    let running = Arc::new(AtomicBool::new(true));
    let r = running.clone();
@ -1042,6 +1069,11 @@ fn main() -> Result<(), LauncherError> {
    // Download and convert model weights
    download_convert_model(&args, running.clone())?;
    if !running.load(Ordering::SeqCst) {
        // Launcher was asked to stop
        return Ok(());
    }
    // Shared shutdown bool
    let shutdown = Arc::new(AtomicBool::new(false));
    // Shared shutdown channel
@ -1078,11 +1110,8 @@ fn main() -> Result<(), LauncherError> {
    let mut exit_code = Ok(());
    while running.load(Ordering::SeqCst) {
-        if let Ok(ShardStatus::Failed((rank, err))) = status_receiver.try_recv() {
+        if let Ok(ShardStatus::Failed(rank)) = status_receiver.try_recv() {
            tracing::error!("Shard {rank} crashed");
            if let Some(err) = err {
                tracing::error!("{err}");
            }
            exit_code = Err(LauncherError::ShardFailed);
            break;
        };
@ -1100,10 +1129,7 @@ fn main() -> Result<(), LauncherError> {
    }
    // Graceful termination
-    signal::kill(Pid::from_raw(webserver.id() as i32), Signal::SIGTERM).unwrap();
+    terminate("webserver", webserver, Duration::from_secs(90)).unwrap();
    tracing::info!("Waiting for webserver to gracefully shutdown");
    webserver.wait().unwrap();
    tracing::info!("Webserver terminated");
    shutdown_shards(shutdown, &shutdown_receiver);
    exit_code
--- a/proto/generate.proto
+++ b/proto/generate.proto
@ -198,9 +198,10 @@ message DecodeResponse {
 message WarmupRequest {
    /// Batch to warmup on
    Batch batch = 1;
    /// Maximum number of tokens that the client will send
    uint32 max_total_tokens = 2;
 }
 /// Empty response
-message WarmupResponse {}
+message WarmupResponse {
    /// Maximum number of tokens supported by the model
    optional uint32 max_supported_total_tokens = 1;
 }
--- a/router/client/src/client.rs
+++ b/router/client/src/client.rs
@ -103,8 +103,7 @@ impl Client {
        &mut self,
        max_input_length: u32,
        max_prefill_tokens: u32,
-        max_total_tokens: u32,
+    ) -> Result<Option<u32>> {
    ) -> Result<()> {
        let mut n_tokens = 0;
        let mut requests = Vec::new();
@ -143,13 +142,9 @@ impl Client {
            max_tokens: 0,
        };
-        let request = tonic::Request::new(WarmupRequest {
+        let request = tonic::Request::new(WarmupRequest { batch: Some(batch) }).inject_context();
-            batch: Some(batch),
+        let response = self.stub.warmup(request).await?.into_inner();
-            max_total_tokens,
+        Ok(response.max_supported_total_tokens)
        })
        .inject_context();
        self.stub.warmup(request).await?.into_inner();
        Ok(())
    }
    /// Generate one token for each request in the given batch
--- a/router/client/src/sharded_client.rs
+++ b/router/client/src/sharded_client.rs
@ -95,14 +95,11 @@ impl ShardedClient {
        &mut self,
        max_input_length: u32,
        max_prefill_tokens: u32,
-        max_total_tokens: u32,
+    ) -> Result<Option<u32>> {
    ) -> Result<()> {
        let futures: Vec<_> = self
            .clients
            .iter_mut()
-            .map(|client| {
+            .map(|client| Box::pin(client.warmup(max_input_length, max_prefill_tokens)))
                Box::pin(client.warmup(max_input_length, max_prefill_tokens, max_total_tokens))
            })
            .collect();
        // all shards return the same message
        join_all(futures).await.pop().unwrap()
--- a/router/src/infer.rs
+++ b/router/src/infer.rs
@ -53,7 +53,7 @@ impl Infer {
        generation_health: Arc<AtomicBool>,
    ) -> Self {
        // Infer shared state
-        let queue = Queue::new(requires_padding);
+        let queue = Queue::new(requires_padding, 16);
        let shared = Arc::new(Shared {
            batching_task: Notify::new(),
        });
--- a/router/src/main.rs
+++ b/router/src/main.rs
@ -37,8 +37,8 @@ struct Args {
    waiting_served_ratio: f32,
    #[clap(default_value = "4096", long, env)]
    max_batch_prefill_tokens: u32,
-    #[clap(default_value = "16000", long, env)]
+    #[clap(long, env)]
-    max_batch_total_tokens: u32,
+    max_batch_total_tokens: Option<u32>,
    #[clap(default_value = "20", long, env)]
    max_waiting_tokens: usize,
    #[clap(default_value = "0.0.0.0", long, env)]
@ -49,8 +49,8 @@ struct Args {
    master_shard_uds_path: String,
    #[clap(default_value = "bigscience/bloom", long, env)]
    tokenizer_name: String,
-    #[clap(default_value = "main", long, env)]
+    #[clap(long, env)]
-    revision: String,
+    revision: Option<String>,
    #[clap(default_value = "2", long, env)]
    validation_workers: usize,
    #[clap(long, env)]
@ -64,11 +64,7 @@ struct Args {
    #[clap(long, env)]
    ngrok_authtoken: Option<String>,
    #[clap(long, env)]
-    ngrok_domain: Option<String>,
+    ngrok_edge: Option<String>,
    #[clap(long, env)]
    ngrok_username: Option<String>,
    #[clap(long, env)]
    ngrok_password: Option<String>,
 }
 fn main() -> Result<(), RouterError> {
@ -96,9 +92,7 @@ fn main() -> Result<(), RouterError> {
        cors_allow_origin,
        ngrok,
        ngrok_authtoken,
-        ngrok_domain,
+        ngrok_edge,
        ngrok_username,
        ngrok_password,
    } = args;
    // Validate args
@ -110,18 +104,22 @@ fn main() -> Result<(), RouterError> {
    if max_input_length as u32 > max_batch_prefill_tokens {
        return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}")));
    }
-    if max_batch_prefill_tokens > max_batch_total_tokens {
+
        return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}")));
    }
    if max_total_tokens as u32 > max_batch_total_tokens {
        return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}")));
    }
    if validation_workers == 0 {
        return Err(RouterError::ArgumentValidation(
            "`validation_workers` must be > 0".to_string(),
        ));
    }
    if let Some(ref max_batch_total_tokens) = max_batch_total_tokens {
        if max_batch_prefill_tokens > *max_batch_total_tokens {
            return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}")));
        }
        if max_total_tokens as u32 > *max_batch_total_tokens {
            return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}")));
        }
    }
    // CORS allowed origins
    // map to go inside the option and then map to parse from String to HeaderValue
    // Finally, convert to AllowOrigin
@ -147,7 +145,7 @@ fn main() -> Result<(), RouterError> {
        // Download and instantiate tokenizer
        // We need to download it outside of the Tokio runtime
        let params = FromPretrainedParameters {
-            revision: revision.clone(),
+            revision: revision.clone().unwrap_or("main".to_string()),
            auth_token: authorization_token.clone(),
            ..Default::default()
        };
@ -175,7 +173,7 @@ fn main() -> Result<(), RouterError> {
                    sha: None,
                    pipeline_tag: None,
                },
-                false => get_model_info(&tokenizer_name, &revision, authorization_token)
+                false => get_model_info(&tokenizer_name, revision, authorization_token)
                    .await
                    .unwrap_or_else(|| {
                        tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
@ -210,14 +208,35 @@ fn main() -> Result<(), RouterError> {
            // Warmup model
            tracing::info!("Warming up model");
-            sharded_client
+            let max_supported_batch_total_tokens = match sharded_client
-                .warmup(
+                .warmup(max_input_length as u32, max_batch_prefill_tokens)
                    max_input_length as u32,
                    max_batch_prefill_tokens,
                    max_batch_total_tokens,
                )
                .await
-                .map_err(RouterError::Warmup)?;
+                .map_err(RouterError::Warmup)?
            {
                // Older models do not support automatic max-batch-total-tokens
                None => {
                    let max_batch_total_tokens = max_batch_total_tokens.unwrap_or(
                        16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)),
                    );
                    tracing::warn!("Model does not support automatic max batch total tokens");
                    max_batch_total_tokens
                }
                // Flash attention models return their max supported total tokens
                Some(max_supported_batch_total_tokens) => {
                    // Warn if user added his own max-batch-total-tokens as we will ignore it
                    if max_batch_total_tokens.is_some() {
                        tracing::warn!(
                            "`--max-batch-total-tokens` is deprecated for Flash \
                        Attention models."
                        );
                        tracing::warn!(
                            "Inferred max batch total tokens: {max_supported_batch_total_tokens}"
                        );
                    }
                    max_supported_batch_total_tokens
                }
            };
            tracing::info!("Setting max batch total tokens to {max_supported_batch_total_tokens}");
            tracing::info!("Connected");
            let addr = match hostname.parse() {
@ -240,7 +259,7 @@ fn main() -> Result<(), RouterError> {
                max_total_tokens,
                waiting_served_ratio,
                max_batch_prefill_tokens,
-                max_batch_total_tokens,
+                max_supported_batch_total_tokens,
                max_waiting_tokens,
                sharded_client,
                tokenizer,
@ -249,9 +268,7 @@ fn main() -> Result<(), RouterError> {
                cors_allow_origin,
                ngrok,
                ngrok_authtoken,
-                ngrok_domain,
+                ngrok_edge,
                ngrok_username,
                ngrok_password,
            )
            .await?;
            Ok(())
@ -316,9 +333,18 @@ fn init_logging(otlp_endpoint: Option<String>, json_output: bool) {
 /// get model info from the Huggingface Hub
 pub async fn get_model_info(
    model_id: &str,
-    revision: &str,
+    revision: Option<String>,
    token: Option<String>,
 ) -> Option<HubModelInfo> {
    let revision = match revision {
        None => {
            tracing::warn!("`--revision` is not set");
            tracing::warn!("We strongly advise to set it to a known supported commit.");
            "main".to_string()
        }
        Some(revision) => revision,
    };
    let client = reqwest::Client::new();
    // Poor man's urlencode
    let revision = revision.replace('/', "%2F");
@ -331,9 +357,18 @@ pub async fn get_model_info(
    let response = builder.send().await.ok()?;
    if response.status().is_success() {
-        return serde_json::from_str(&response.text().await.ok()?).ok();
+        let hub_model_info: HubModelInfo =
            serde_json::from_str(&response.text().await.ok()?).ok()?;
        if let Some(sha) = &hub_model_info.sha {
            tracing::info!(
                "Serving revision {sha} of model {}",
                hub_model_info.model_id
            );
        }
        Some(hub_model_info)
    } else {
        None
    }
    None
 }
 #[derive(Debug, Error)]
--- a/router/src/queue.rs
+++ b/router/src/queue.rs
@ -33,12 +33,12 @@ pub(crate) struct Queue {
 }
 impl Queue {
-    pub(crate) fn new(requires_padding: bool) -> Self {
+    pub(crate) fn new(requires_padding: bool, block_size: u32) -> Self {
        // Create channel
        let (queue_sender, queue_receiver) = flume::unbounded();
        // Launch background queue task
-        tokio::spawn(queue_task(requires_padding, queue_receiver));
+        tokio::spawn(queue_task(requires_padding, block_size, queue_receiver));
        Self { queue_sender }
    }
@ -81,8 +81,12 @@ impl Queue {
 }
 // Background task responsible of the queue state
-async fn queue_task(requires_padding: bool, receiver: flume::Receiver<QueueCommand>) {
+async fn queue_task(
-    let mut state = State::new(requires_padding);
+    requires_padding: bool,
    block_size: u32,
    receiver: flume::Receiver<QueueCommand>,
 ) {
    let mut state = State::new(requires_padding, block_size);
    while let Ok(cmd) = receiver.recv_async().await {
        match cmd {
@ -119,15 +123,19 @@ struct State {
    /// Whether the model is using padding
    requires_padding: bool,
    /// Paged Attention block size
    block_size: u32,
 }
 impl State {
-    fn new(requires_padding: bool) -> Self {
+    fn new(requires_padding: bool, block_size: u32) -> Self {
        Self {
            entries: VecDeque::with_capacity(128),
            next_id: 0,
            next_batch_id: 0,
            requires_padding,
            block_size,
        }
    }
@ -187,10 +195,21 @@ impl State {
                max_input_length = max_input_length.max(entry.request.input_length);
                prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
            } else {
-                prefill_tokens += entry.request.input_length;
+                // pad to block size
                prefill_tokens += ((entry.request.input_length + self.block_size - 1)
                    / self.block_size)
                    * self.block_size;
            }
-            decode_tokens += entry.request.stopping_parameters.max_new_tokens;
+            if self.requires_padding {
                decode_tokens += entry.request.stopping_parameters.max_new_tokens;
            } else {
                // pad to block size
                decode_tokens +=
                    ((entry.request.stopping_parameters.max_new_tokens + self.block_size - 1)
                        / self.block_size)
                        * self.block_size;
            }
            if prefill_tokens > prefill_token_budget
                || (prefill_tokens + decode_tokens) > token_budget
@ -321,7 +340,7 @@ mod tests {
    #[test]
    fn test_append() {
-        let mut state = State::new(false);
+        let mut state = State::new(false, 1);
        let (entry, _guard) = default_entry();
        assert_eq!(state.next_id, 0);
@ -337,7 +356,7 @@ mod tests {
    #[test]
    fn test_next_batch_empty() {
-        let mut state = State::new(false);
+        let mut state = State::new(false, 1);
        assert!(state.next_batch(None, 1, 1).is_none());
        assert!(state.next_batch(Some(1), 1, 1).is_none());
@ -345,7 +364,7 @@ mod tests {
    #[test]
    fn test_next_batch_min_size() {
-        let mut state = State::new(false);
+        let mut state = State::new(false, 1);
        let (entry1, _guard1) = default_entry();
        let (entry2, _guard2) = default_entry();
        state.append(entry1);
@ -377,7 +396,7 @@ mod tests {
    #[test]
    fn test_next_batch_token_budget() {
-        let mut state = State::new(false);
+        let mut state = State::new(false, 1);
        let (entry1, _guard1) = default_entry();
        let (entry2, _guard2) = default_entry();
        state.append(entry1);
@ -410,14 +429,14 @@ mod tests {
    #[tokio::test]
    async fn test_queue_append() {
-        let queue = Queue::new(false);
+        let queue = Queue::new(false, 1);
        let (entry, _guard) = default_entry();
        queue.append(entry);
    }
    #[tokio::test]
    async fn test_queue_next_batch_empty() {
-        let queue = Queue::new(false);
+        let queue = Queue::new(false, 1);
        assert!(queue.next_batch(None, 1, 1).await.is_none());
        assert!(queue.next_batch(Some(1), 1, 1).await.is_none());
@ -425,7 +444,7 @@ mod tests {
    #[tokio::test]
    async fn test_queue_next_batch_min_size() {
-        let queue = Queue::new(false);
+        let queue = Queue::new(false, 1);
        let (entry1, _guard1) = default_entry();
        let (entry2, _guard2) = default_entry();
        queue.append(entry1);
@ -458,7 +477,7 @@ mod tests {
    #[tokio::test]
    async fn test_queue_next_batch_token_budget() {
-        let queue = Queue::new(false);
+        let queue = Queue::new(false, 1);
        let (entry1, _guard1) = default_entry();
        let (entry2, _guard2) = default_entry();
        queue.append(entry1);
@ -483,7 +502,7 @@ mod tests {
    #[tokio::test]
    async fn test_queue_next_batch_dropped_receiver() {
-        let queue = Queue::new(false);
+        let queue = Queue::new(false, 1);
        let (entry, _) = default_entry();
        queue.append(entry);
--- a/router/src/server.rs
+++ b/router/src/server.rs
@ -524,9 +524,7 @@ pub async fn run(
    allow_origin: Option<AllowOrigin>,
    ngrok: bool,
    ngrok_authtoken: Option<String>,
-    ngrok_domain: Option<String>,
+    ngrok_edge: Option<String>,
    ngrok_username: Option<String>,
    ngrok_password: Option<String>,
 ) -> Result<(), axum::BoxError> {
    // OpenAPI documentation
    #[derive(OpenApi)]
@ -696,32 +694,25 @@ pub async fn run(
        #[cfg(feature = "ngrok")]
        {
            use ngrok::config::TunnelBuilder;
            use ngrok::tunnel::UrlTunnel;
            let _ = addr;
            let authtoken =
                ngrok_authtoken.expect("`ngrok-authtoken` must be set when using ngrok tunneling");
-            let mut tunnel = ngrok::Session::builder()
+            let edge = ngrok_edge.expect("`ngrok-edge` must be set when using ngrok tunneling");
            let tunnel = ngrok::Session::builder()
                .authtoken(authtoken)
                .connect()
                .await
                .unwrap()
-                .http_endpoint();
+                .labeled_tunnel()
-
+                .label("edge", edge);
            if let Some(domain) = ngrok_domain {
                tunnel = tunnel.domain(domain);
            }
            if let (Some(username), Some(password)) = (ngrok_username, ngrok_password) {
                tunnel = tunnel.basic_auth(username, password);
            }
            let listener = tunnel.listen().await.unwrap();
            // Run server
            tracing::info!("Ingress URL: {:?}", listener.url());
            axum::Server::builder(listener)
                .serve(app.into_make_service())
                //Wait until all requests are finished to shut down
--- a/server/Makefile
+++ b/server/Makefile
@ -1,4 +1,5 @@
 include Makefile-flash-att
 include Makefile-flash-att-v2
 include Makefile-vllm
 unit-tests:
--- a/server/Makefile-flash-att-v2
+++ b/server/Makefile-flash-att-v2
@ -0,0 +1,13 @@
 flash_att_v2_commit := 4f285b354796fb17df8636485b9a04df3ebbb7dc
 flash-attention-v2:
    # Clone flash attention
 	pip install packaging
 	git clone https://github.com/HazyResearch/flash-attention.git flash-attention-v2
 build-flash-attention-v2: flash-attention-v2
 	cd flash-attention-v2 && git fetch && git checkout $(flash_att_v2_commit)
 	cd flash-attention-v2 && python setup.py build
 install-flash-attention-v2: build-flash-attention-v2
 	cd flash-attention-v2 && python setup.py install
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@ -1,6 +1,6 @@
 [tool.poetry]
 name = "text-generation-server"
-version = "0.9.1"
+version = "0.9.3"
 description = "Text Generation Inference Python gRPC Server"
 authors = ["Olivier Dehaene <olivier@huggingface.co>"]
--- a/server/text_generation_server/cli.py
+++ b/server/text_generation_server/cli.py
@ -194,6 +194,8 @@ def quantize(
    percdamp: float = 0.01,
    act_order: bool = False,
 ):
    if revision is None:
        revision = "main"
    download_weights(
        model_id=model_id,
        revision=revision,
@ -207,6 +209,7 @@ def quantize(
        bits=4,
        groupsize=128,
        output_dir=output_dir,
        revision=revision,
        trust_remote_code=trust_remote_code,
        upload_to_model_id=upload_to_model_id,
        percdamp=percdamp,
--- a/server/text_generation_server/models/init.py
+++ b/server/text_generation_server/models/init.py
@ -42,51 +42,21 @@ __all__ = [
    "get_model",
 ]
-FLASH_ATT_ERROR_MESSAGE = (
+FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
    "{} requires CUDA and Flash Attention kernels to be installed.\n"
    "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
    "or install flash attention with `cd server && make install install-flash-attention`"
 )
 FLASH_ATTENTION = True
 try:
-    if not os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
+    from text_generation_server.models.flash_rw import FlashRWSharded
-        if not torch.cuda.is_available():
+    from text_generation_server.models.flash_neox import FlashNeoXSharded
-            FLASH_ATT_ERROR_MESSAGE = (
+    from text_generation_server.models.flash_llama import (
-                "{} requires CUDA. No compatible CUDA devices found."
+        FlashLlama,
            )
            raise ImportError("CUDA is not available")
        major, minor = torch.cuda.get_device_capability()
        is_sm75 = major == 7 and minor == 5
        is_sm8x = major == 8 and minor >= 0
        is_sm90 = major == 9 and minor == 0
        supported = is_sm75 or is_sm8x or is_sm90
        if not supported:
            FLASH_ATT_ERROR_MESSAGE = (
                "{} requires a CUDA device with capability 7.5, > 8.0 or 9.0. "
                "No compatible CUDA device found."
            )
            raise ImportError(
                f"GPU with CUDA capability {major} {minor} is not supported"
            )
        from text_generation_server.models.flash_rw import FlashRWSharded
        from text_generation_server.models.flash_neox import FlashNeoXSharded
        from text_generation_server.models.flash_llama import (
            FlashLlama,
        )
        from text_generation_server.models.flash_santacoder import (
            FlashSantacoderSharded,
        )
        FLASH_ATTENTION = True
    else:
        FLASH_ATTENTION = False
 except ImportError:
    logger.opt(exception=True).warning(
        "Could not import Flash Attention enabled models"
    )
    from text_generation_server.models.flash_santacoder import (
        FlashSantacoderSharded,
    )
 except ImportError as e:
    logger.warning(f"Could not import Flash Attention enabled models: {e}")
    FLASH_ATTENTION = False
 if FLASH_ATTENTION:
--- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
@ -23,25 +23,77 @@ import torch.distributed
 from torch import nn
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 # Flash attention imports
 import flash_attn_cuda
 import dropout_layer_norm
 # vllm imports
 import vllm_cache_ops
 import vllm_attention_ops
 from text_generation_server.utils.flash_attn import attention
 from text_generation_server.utils.layers import (
    TensorParallelRowLinear,
    TensorParallelColumnLinear,
    TensorParallelEmbedding,
    PositionRotaryEmbedding,
    TensorParallelHead,
    get_linear,
 )
 class LlamaConfig(PretrainedConfig):
    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_scaling=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.rope_scaling = rope_scaling
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
 class LlamaRMSNorm(nn.Module):
    def __init__(self, prefix, weights, eps=1e-6):
        """
@ -59,7 +111,8 @@ class LlamaRMSNorm(nn.Module):
                hidden_states += residual
            residual = hidden_states
-            variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+            hidden_states = hidden_states.to(torch.float32)
            variance = hidden_states.pow(2).mean(-1, keepdim=True)
            hidden_states = hidden_states * torch.rsqrt(
                variance + self.variance_epsilon
            )
@ -94,6 +147,27 @@ class LlamaRMSNorm(nn.Module):
            return normed_hidden_states, res
 def _load_gqa(config, prefix: str, weights):
    w = [
        weights.get_sharded(f"{prefix}.q_proj.weight", dim=0),
        weights.get_sharded(f"{prefix}.k_proj.weight", dim=0),
        weights.get_sharded(f"{prefix}.v_proj.weight", dim=0),
    ]
    weight = torch.cat(w, dim=0)
    weight = weight.to(dtype=weights.dtype).to(device=weights.device)
    bias = None
    assert config.hidden_size % config.num_attention_heads == 0
    head_size = config.hidden_size // config.num_attention_heads
    assert config.num_attention_heads % weights.process_group.size() == 0
    num_heads = config.num_attention_heads // weights.process_group.size()
    num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
    assert list(weight.shape) == [
        (num_heads + 2 * num_key_value_heads) * head_size,
        config.hidden_size,
    ], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
    return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
 class FlashLlamaAttention(torch.nn.Module):
    def __init__(
        self,
@ -118,22 +192,29 @@ class FlashLlamaAttention(torch.nn.Module):
                f"and `num_shards`: {weights.process_group.size()}"
            )
        self.num_heads = self.num_heads // weights.process_group.size()
-        self.query_key_value = TensorParallelColumnLinear.load_multi(
+        self.num_key_value_heads = (
-            config,
+            config.num_key_value_heads // weights.process_group.size()
            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
            dim=0,
            weights=weights,
            bias=False,
        )
        if config.num_attention_heads != config.num_key_value_heads:
            self.query_key_value = _load_gqa(config, prefix, weights)
        else:
            self.query_key_value = TensorParallelColumnLinear.load_multi(
                config,
                prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
                dim=0,
                weights=weights,
                bias=False,
            )
        self.o_proj = TensorParallelRowLinear.load(
            config,
            prefix=f"{prefix}.o_proj",
            weights=weights,
            bias=False,
        )
        self.num_groups = self.num_heads // self.num_key_value_heads
        self.kv_head_mapping = torch.arange(
-            0, self.num_heads, dtype=torch.int32, device=weights.device
+            0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
-        )
+        ).repeat_interleave(self.num_groups)
    def forward(
        self,
@ -148,38 +229,37 @@ class FlashLlamaAttention(torch.nn.Module):
        max_s,
    ):
        qkv = self.query_key_value(hidden_states)
-        qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
+        query, kv = qkv.split(
            [
                self.head_size * self.num_heads,
                2 * self.head_size * self.num_key_value_heads,
            ],
            dim=1,
        )
        query = query.view(-1, self.num_heads, self.head_size)
        kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
-        # Inplace rotary
+        self.rotary_emb(query, cos, sin)
-        self.rotary_emb(qkv[:, 0], cos, sin)
+        self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
        self.rotary_emb(qkv[:, 1], cos, sin)
        vllm_cache_ops.reshape_and_cache(
-            qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
+            kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
        )
        # output tensor
-        attn_output = torch.empty_like(qkv[:, 0])
+        attn_output = torch.empty_like(query)
        # Prefill
        if cu_seqlen_prefill is not None:
            # flash attention
-            flash_attn_cuda.fwd(
+            attention(
-                qkv[:, 0],
+                query,
-                qkv[:, 1],
+                torch.select(kv, dim=1, index=0),
-                qkv[:, 2],
+                torch.select(kv, dim=1, index=1),
                attn_output,
                cu_seqlen_prefill,
                cu_seqlen_prefill,
                max_s,
                max_s,
                0.0,
                self.softmax_scale,
                False,
                True,
                False,
                0,
                None,
            )
        # Decode
        else:
@ -187,7 +267,7 @@ class FlashLlamaAttention(torch.nn.Module):
            block_size = kv_cache[1].shape[3]
            vllm_attention_ops.single_query_cached_kv_attention(
                attn_output,
-                qkv[:, 0],
+                query,
                kv_cache[0],
                kv_cache[1],
                self.kv_head_mapping,
@ -323,6 +403,7 @@ class FlashLlamaModel(torch.nn.Module):
        self.head_size = self.layers[0].self_attn.head_size
        self.num_heads = self.layers[0].self_attn.num_heads
        self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
    def forward(
        self,
--- a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
@ -27,13 +27,11 @@ from transformers.modeling_utils import PreTrainedModel
 from transformers.models.gpt_neox import GPTNeoXConfig
 from typing import Optional, List, Tuple
 # Flash attention imports
 import flash_attn_cuda
 # vllm imports
 import vllm_cache_ops
 import vllm_attention_ops
 from text_generation_server.utils.flash_attn import attention
 from text_generation_server.utils.layers import (
    TensorParallelRowLinear,
    TensorParallelColumnLinear,
@ -153,22 +151,14 @@ class FlashNeoxAttention(torch.nn.Module):
        # Prefill
        if cu_seqlen_prefill is not None:
            # flash attention
-            flash_attn_cuda.fwd(
+            attention(
                qkv[:, 0],
                qkv[:, 1],
                qkv[:, 2],
                attn_output,
                cu_seqlen_prefill,
                cu_seqlen_prefill,
                max_s,
                max_s,
                0.0,
                self.softmax_scale,
                False,
                True,
                False,
                0,
                None,
            )
        # Decode
        else:
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@ -6,13 +6,11 @@ from transformers.modeling_utils import PreTrainedModel
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 # Flash attention imports
 import flash_attn_cuda
 # vllm imports
 import vllm_cache_ops
 import vllm_attention_ops
 from text_generation_server.utils.flash_attn import attention
 from text_generation_server.utils.layers import (
    TensorParallelRowLinear,
    TensorParallelColumnLinear,
@ -182,27 +180,15 @@ class FlashRWAttention(torch.nn.Module):
        # Prefill
        if cu_seqlen_prefill is not None:
            if self.num_heads_kv == 1:
                # Expand to query shape
                kv = kv.expand(-1, 2, self.num_heads, self.head_size)
            # flash attention
-            flash_attn_cuda.fwd(
+            attention(
                query,
                torch.select(kv, dim=1, index=0),
                torch.select(kv, dim=1, index=1),
                attn_output,
                cu_seqlen_prefill,
                cu_seqlen_prefill,
                max_s,
                max_s,
                0.0,
                self.softmax_scale,
                False,
                True,
                False,
                0,
                None,
            )
        # Decode
        else:
@ -314,30 +300,15 @@ class FlashRWLargeAttention(torch.nn.Module):
        # Prefill
        if cu_seqlen_prefill is not None:
            # Expand to query shape
            kv = (
                kv.unsqueeze(2)
                .expand(-1, self.num_groups, self.num_heads, 2, self.head_size)
                .reshape(-1, self.num_groups * self.num_heads, 2, self.head_size)
            )
            # flash attention
-            flash_attn_cuda.fwd(
+            attention(
                query,
                torch.select(kv, dim=2, index=0),
                torch.select(kv, dim=2, index=1),
                attn_output,
                cu_seqlen_prefill,
                cu_seqlen_prefill,
                max_s,
                max_s,
                0.0,
                self.softmax_scale,
                False,
                True,
                False,
                0,
                None,
            )
        # Decode
        else:
--- a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
@ -5,13 +5,11 @@ from torch import nn
 from transformers.activations import ACT2FN
 from typing import Optional, List, Tuple
 # Flash attention imports
 import flash_attn_cuda
 # vllm imports
 import vllm_cache_ops
 import vllm_attention_ops
 from text_generation_server.utils.flash_attn import attention
 from text_generation_server.utils.layers import (
    TensorParallelRowLinear,
    TensorParallelColumnLinear,
@ -265,26 +263,15 @@ class FlashMQAttention(torch.nn.Module):
        # Prefill
        if cu_seqlen_prefill is not None:
            # Expand from 1 to num_heads
            key_value = key_value.expand(-1, 2, self.num_heads, self.head_size)
            # flash attention
-            flash_attn_cuda.fwd(
+            attention(
                query,
                torch.select(key_value, dim=1, index=0),
                torch.select(key_value, dim=1, index=1),
                attn_output,
                cu_seqlen_prefill,
                cu_seqlen_prefill,
                max_s,
                max_s,
                0.0,
                self.softmax_scale,
                False,
                True,
                False,
                0,
                None,
            )
        # Decode
        else:
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@ -712,14 +712,14 @@ class FlashCausalLM(Model):
    def batch_type(self) -> Type[FlashCausalLMBatch]:
        return FlashCausalLMBatch
-    def warmup(self, batch: FlashCausalLMBatch, max_total_tokens: int):
+    def warmup(self, batch: FlashCausalLMBatch):
        global CACHE_MANAGER
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats(self.device)
        try:
            CACHE_MANAGER = CacheManager(
-                # Adds some wiggle room
+                batch.blocks,
                math.ceil(max_total_tokens / BLOCK_SIZE) + 10,
                self.num_layers,
                self.num_kv_heads,
                self.head_size,
@ -729,11 +729,43 @@ class FlashCausalLM(Model):
            _, batch = self.generate_token(batch)
        except Exception as e:
            raise RuntimeError(
-                f"Not enough memory to handle {max_total_tokens} total tokens with {len(batch.input_ids)} "
+                f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
-                f"prefill tokens. "
+                f"You need to decrease `--max-batch-prefill-tokens`"
                f"You need to decrease `--max-batch-total-tokens` or `--max-batch-prefill-tokens`"
            ) from e
        # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
        # Calculate the number of blocks that can be allocated with the
        # profiled peak memory.
        torch.cuda.synchronize(self.device)
        peak_memory = torch.cuda.max_memory_reserved(self.device)
        dtype_size = torch.tensor([], dtype=self.dtype).element_size()
        cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
        total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
        total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory
        # 0.98 to add some wiggle room
        num_blocks = (
            int((total_gpu_memory * 0.98 - peak_memory) // total_cache_size)
            # Add batch.blocks as we allocated it above, so it is included in the peak memory.
            + batch.blocks
        )
        del CACHE_MANAGER
        del batch
        torch.cuda.empty_cache()
        CACHE_MANAGER = CacheManager(
            num_blocks,
            self.num_layers,
            self.num_kv_heads,
            self.head_size,
            self.dtype,
            self.device,
        )
        return int(num_blocks * BLOCK_SIZE)
    def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
        return self.tokenizer.decode(
--- a/server/text_generation_server/models/flash_llama.py
+++ b/server/text_generation_server/models/flash_llama.py
@ -2,13 +2,13 @@ import torch
 import torch.distributed
 from opentelemetry import trace
 from transformers import AutoConfig
 from transformers.models.llama import LlamaTokenizer, LlamaTokenizerFast
 from typing import Optional
 from text_generation_server.models import FlashCausalLM
 from text_generation_server.models.custom_modeling.flash_llama_modeling import (
    FlashLlamaForCausalLM,
    LlamaConfig,
 )
 from text_generation_server.utils import (
    initialize_torch_distributed,
@ -52,7 +52,7 @@ class FlashLlama(FlashCausalLM):
                trust_remote_code=trust_remote_code,
            )
-        config = AutoConfig.from_pretrained(
+        config = LlamaConfig.from_pretrained(
            model_id, revision=revision, trust_remote_code=trust_remote_code
        )
@ -70,7 +70,7 @@ class FlashLlama(FlashCausalLM):
            tokenizer=tokenizer,
            config=config,
            num_layers=len(model.model.layers),
-            num_kv_heads=model.model.num_heads,
+            num_kv_heads=model.model.num_key_value_heads,
            head_size=model.model.head_size,
            dtype=dtype,
            device=device,
--- a/server/text_generation_server/models/model.py
+++ b/server/text_generation_server/models/model.py
@ -107,8 +107,9 @@ class Model(ABC):
    def generate_token(self, batch: B) -> Tuple[List[GeneratedText], Optional[B]]:
        raise NotImplementedError
-    def warmup(self, batch: B, max_total_tokens: int):
+    def warmup(self, batch: B) -> Optional[int]:
        self.generate_token(batch)
        return None
    def decode_token(
        self,
--- a/server/text_generation_server/server.py
+++ b/server/text_generation_server/server.py
@ -51,21 +51,17 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
        filtered_batch = batch.filter(request.request_ids)
        self.cache.set(filtered_batch)
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
    async def Warmup(self, request, context):
        batch = self.model.batch_type.from_pb(
            request.batch, self.model.tokenizer, self.model.dtype, self.model.device
        )
-        self.model.warmup(batch, request.max_total_tokens)
+        max_supported_total_tokens = self.model.warmup(batch)
-        if torch.cuda.is_available():
+        return generate_pb2.WarmupResponse(
-            torch.cuda.empty_cache()
+            max_supported_total_tokens=max_supported_total_tokens
-
+        )
        return generate_pb2.WarmupResponse()
    async def Prefill(self, request, context):
        batch = self.model.batch_type.from_pb(
@ -96,8 +92,6 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
        if len(batches) > 1:
            batch = self.model.batch_type.concatenate(batches)
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
        else:
            batch = batches[0]
--- a/server/text_generation_server/utils/convert.py
+++ b/server/text_generation_server/utils/convert.py
@ -94,6 +94,14 @@ def convert_files(pt_files: List[Path], sf_files: List[Path], discard_names: Lis
    # We do this instead of using tqdm because we want to parse the logs with the launcher
    for i, (pt_file, sf_file) in enumerate(zip(pt_files, sf_files)):
        # Skip blacklisted files
        if (
            "arguments" in pt_file.name
            or "args" in pt_file.name
            or "training" in pt_file.name
        ):
            continue
        start = datetime.datetime.now()
        convert_file(pt_file, sf_file, discard_names)
        elapsed = datetime.datetime.now() - start
--- a/server/text_generation_server/utils/flash_attn.py
+++ b/server/text_generation_server/utils/flash_attn.py
@ -0,0 +1,124 @@
 import os
 import torch
 from loguru import logger
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
    raise ImportError("`USE_FLASH_ATTENTION` is false.")
 if not torch.cuda.is_available():
    raise ImportError("CUDA is not available")
 major, minor = torch.cuda.get_device_capability()
 is_sm75 = major == 7 and minor == 5
 is_sm8x = major == 8 and minor >= 0
 is_sm90 = major == 9 and minor == 0
 HAS_FLASH_ATTN = False
 HAS_FLASH_ATTN_V2 = False
 try:
    try:
        import flash_attn_2_cuda
    except ImportError:
        raise ImportError(
            "Flash Attention V2 is not installed.\n"
            "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
            "or install flash attention v2 with `cd server && make install install-flash-attention-v2`"
        )
    if not (is_sm8x or is_sm90):
        raise ImportError(
            f"GPU with CUDA capability {major} {minor} is not supported for "
            "Flash Attention V2"
        )
    HAS_FLASH_ATTN_V2 = True
 except ImportError as e:
    try:
        import flash_attn_cuda
    except ImportError:
        raise ImportError(
            "Flash Attention is not installed.\n"
            "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) "
            "or install flash attention with `cd server && make install install-flash-attention`"
        ) from e
    if not (is_sm75 or is_sm8x or is_sm90):
        raise ImportError(
            f"GPU with CUDA capability {major} {minor} is not supported"
        ) from e
    logger.warning(f"Unable to use Flash Attention V2: {e}")
    HAS_FLASH_ATTN = True
 def attention(
    q,
    k,
    v,
    out,
    cu_seqlens,
    max_s,
    softmax_scale,
 ):
    if HAS_FLASH_ATTN_V2:
        return flash_attn_2_cuda.varlen_fwd(
            q,
            k,
            v,
            out,
            cu_seqlens,
            cu_seqlens,
            max_s,
            max_s,
            0.0,
            softmax_scale,
            False,
            True,
            False,
            None,
        )
    if HAS_FLASH_ATTN:
        # Flash attention v1 requires q, k and v to have the same number of heads
        if k.shape[1] != q.shape[1]:
            # MQA expand
            if k.shape[1] == 1:
                k = k.expand(-1, q.shape[1], -1)
            # Grouped attention reshape
            else:
                original_shape = k.shape
                k = (
                    k.unsqueeze(2)
                    .expand(-1, -1, q.shape[1] // k.shape[1], -1)
                    .reshape(original_shape[0], -1, original_shape[2])
                )
        if v.shape[1] != q.shape[1]:
            # MQA expand
            if v.shape[1] == 1:
                v = v.expand(-1, q.shape[1], -1)
            # Grouped attention reshape
            else:
                original_shape = v.shape
                v = (
                    v.unsqueeze(2)
                    .expand(-1, -1, q.shape[1] // v.shape[1], -1)
                    .reshape(original_shape[0], -1, original_shape[2])
                )
        return flash_attn_cuda.fwd(
            q,
            k,
            v,
            out,
            cu_seqlens,
            cu_seqlens,
            max_s,
            max_s,
            0.0,
            softmax_scale,
            False,
            True,
            False,
            0,
            None,
        )
    raise NotImplementedError("flash attention is not installed")
--- a/server/text_generation_server/utils/gptq/quantize.py
+++ b/server/text_generation_server/utils/gptq/quantize.py
@ -13,6 +13,9 @@ import transformers
 from huggingface_hub import HfApi
 import numpy as np
 import torch
 from accelerate import init_empty_weights
 from text_generation_server.utils import initialize_torch_distributed, Weights
 from text_generation_server.utils.hub import weight_files
 from text_generation_server.utils.gptq.quant_linear import QuantLinear
 from loguru import logger
 from typing import Optional
@ -38,7 +41,6 @@ class Quantizer(nn.Module):
        maxshrink=0.8,
        trits=False,
    ):
        self.maxq = torch.tensor(2**bits - 1)
        self.perchannel = perchannel
        self.sym = sym
@ -600,6 +602,8 @@ def sequential(
    nsamples,
    bits,
    groupsize,
    *,
    hooks,
    percdamp=0.01,
    sym: bool = False,
    act_order: bool = False,
@ -637,7 +641,7 @@ def sequential(
    layers[0] = Catcher(layers[0])
    for batch in dataloader:
        try:
-            model(batch[0])
+            model(batch[0].cuda())
        except ValueError:
            pass
    layers[0] = layers[0].module
@ -646,6 +650,8 @@ def sequential(
    # model.model.embed_tokens = model.model.embed_tokens.cpu()
    # model.model.norm = model.model.norm.cpu()
    torch.cuda.empty_cache()
    for hook in hooks:
        hook.remove()
    outs = torch.zeros_like(inps)
@ -662,10 +668,8 @@ def sequential(
        print("|       name       | weight_error | fp_inp_SNR | q_inp_SNR | time  |")
        print("+==================+==============+============+===========+=======+")
-        from accelerate.hooks import remove_hook_from_submodules
+        layer = layers[i]
-
+        layer.load()
        layer = layers[i].to(dev)
        remove_hook_from_submodules(layer)
        full = find_layers(layer)
        sequential = [list(full.keys())]
@ -677,6 +681,7 @@ def sequential(
                gptq[name].quantizer.configure(
                    bits, perchannel=True, sym=sym, mse=False
                )
                pass
            def add_batch(name):
                def tmp(_, inp, out):
@ -688,7 +693,6 @@ def sequential(
            for name in subset:
                handles.append(subset[name].register_forward_hook(add_batch(name)))
            for j in range(nsamples):
                outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
            for h in handles:
                h.remove()
@ -714,7 +718,7 @@ def sequential(
        for j in range(nsamples):
            outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
-        layers[i] = layer.cpu()
+        layer.unload()
        del layer
        del gptq
        torch.cuda.empty_cache()
@ -768,24 +772,136 @@ def pack(model, quantizers, bits, groupsize):
    return model
 def setdeepattr(module, full_name, tensor):
    current = module
    tokens = full_name.split(".")
    for token in tokens[:-1]:
        current = getattr(current, token)
    setattr(current, tokens[-1], tensor)
 def getdeepattr(module, full_name):
    current = module
    tokens = full_name.split(".")
    for token in tokens:
        current = getattr(current, token)
    return current
 def load_weights_pre_hook(module_name, weights, recursive=False):
    def inner(module, args):
        print(f"Pre hook {module_name}")
        local_params = {}
        for k, v in module.named_parameters():
            if not recursive and k.count(".") != 1:
                continue
            local_params[k] = v
        for k, v in module.named_buffers():
            if not recursive and k.count(".") != 1:
                continue
            local_params[k] = v
        for local_param in local_params:
            current_tensor = getdeepattr(module, local_param)
            if current_tensor.device == torch.device("meta"):
                # print(f"Loading {local_param}")
                if module_name:
                    tensor_name = f"{module_name}.{local_param}"
                else:
                    tensor_name = local_param
                tensor = weights.get_tensor(tensor_name)
                setdeepattr(module, local_param, nn.Parameter(tensor))
            else:
                setdeepattr(
                    module,
                    local_param,
                    nn.Parameter(current_tensor.to(device=torch.device("cuda:0"))),
                )
    return inner
 def load_weights_post_hook(module_name, weights, recursive=False):
    def inner(module, args, output):
        print(f"Post hook {module_name}")
        local_params = {}
        for k, v in module.named_parameters():
            if not recursive and k.count(".") != 1:
                continue
            local_params[k] = v
        for k, v in module.named_buffers():
            if not recursive and k.count(".") != 1:
                continue
            local_params[k] = v
        for local_param in local_params:
            # print(f"Unloading {local_param}")
            current_tensor = getdeepattr(module, local_param)
            setdeepattr(
                module,
                local_param,
                nn.Parameter(current_tensor.to(device=torch.device("cpu"))),
            )
        return output
    return inner
 def quantize(
    model_id: str,
    bits: int,
    groupsize: int,
    output_dir: str,
    revision: str,
    trust_remote_code: bool,
    upload_to_model_id: Optional[str],
    percdamp: float,
    act_order: bool,
 ):
    print("loading model")
-    model = AutoModelForCausalLM.from_pretrained(
+    config = AutoConfig.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        device_map="balanced_low_0",
        trust_remote_code=trust_remote_code,
    )
    with init_empty_weights():
        model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
    model = model.eval()
    print("LOADED model")
    files = weight_files(model_id, revision, extension=".safetensors")
    process_group, _, _ = initialize_torch_distributed()
    weights = Weights(
        files,
        device=torch.device("cuda:0"),
        dtype=torch.float16,
        process_group=process_group,
        aliases={"embed_tokens.weight": ["lm_head.weight"]},
    )
    hooks = []
    for name, module in model.named_modules():
        def load(module, name):
            def _load():
                load_weights_pre_hook(name, weights, recursive=True)(module, None)
            return _load
        def unload(module, name):
            def _unload():
                load_weights_post_hook(name, weights, recursive=True)(
                    module, None, None
                )
            return _unload
        module.load = load(module, name)
        module.unload = unload(module, name)
        hooks.append(
            module.register_forward_pre_hook(load_weights_pre_hook(name, weights))
        )
        hooks.append(
            module.register_forward_hook(load_weights_post_hook(name, weights))
        )
    model.seqlen = 2048
    dataset = "wikitext2"
@ -806,6 +922,7 @@ def quantize(
        groupsize,
        percdamp=percdamp,
        act_order=act_order,
        hooks=hooks,
    )
    print(time.time() - tick)
@ -858,7 +975,6 @@ def quantize(
    logger.info("Saved tokenizer")
    if upload_to_model_id:
        api = HfApi()
        api.upload_folder(