mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
Merge branch 'huggingface:main' into fix/dockerfile-triton
This commit is contained in:
Yaser Jaradeh
GitHub
commit
ad4dcb68df
214
Cargo.lock
generated
214
Cargo.lock
generated
@ -456,18 +456,18 @@ dependencies = [
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[[package]]
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[[package]]
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name = "bit-set"
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name = "bit-set"
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||||||
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version = "0.8.0"
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||||||
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source = "registry+https://github.com/rust-lang/crates.io-index"
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||||||
checksum = "0700ddab506f33b20a03b13996eccd309a48e5ff77d0d95926aa0210fb4e95f1"
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checksum = "08807e080ed7f9d5433fa9b275196cfc35414f66a0c79d864dc51a0d825231a3"
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dependencies = [
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dependencies = [
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"bit-vec",
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"bit-vec",
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]
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]
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[[package]]
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[[package]]
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name = "bit-vec"
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name = "bit-vec"
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version = "0.6.3"
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version = "0.8.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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||||||
checksum = "349f9b6a179ed607305526ca489b34ad0a41aed5f7980fa90eb03160b69598fb"
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checksum = "5e764a1d40d510daf35e07be9eb06e75770908c27d411ee6c92109c9840eaaf7"
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[[package]]
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[[package]]
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name = "bit_field"
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name = "bit_field"
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@ -502,6 +502,12 @@ dependencies = [
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"generic-array",
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"generic-array",
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]
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]
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[[package]]
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name = "borrow-or-share"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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[[package]]
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[[package]]
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name = "built"
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name = "built"
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version = "0.7.5"
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version = "0.7.5"
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@ -1139,6 +1145,15 @@ version = "1.13.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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[[package]]
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name = "email_address"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "e079f19b08ca6239f47f8ba8509c11cf3ea30095831f7fed61441475edd8c449"
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dependencies = [
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"serde",
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]
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[[package]]
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[[package]]
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name = "encode_unicode"
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name = "encode_unicode"
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version = "0.3.6"
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version = "0.3.6"
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@ -1196,12 +1211,13 @@ dependencies = [
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[[package]]
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[[package]]
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name = "fancy-regex"
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name = "fancy-regex"
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version = "0.11.0"
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version = "0.14.0"
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||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "b95f7c0680e4142284cf8b22c14a476e87d61b004a3a0861872b32ef7ead40a2"
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checksum = "6e24cb5a94bcae1e5408b0effca5cd7172ea3c5755049c5f3af4cd283a165298"
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dependencies = [
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dependencies = [
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"bit-set",
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"bit-set",
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"regex",
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"regex-automata 0.4.9",
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"regex-syntax 0.8.5",
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]
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]
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[[package]]
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[[package]]
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@ -1247,6 +1263,17 @@ version = "1.0.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
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checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
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[[package]]
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name = "fluent-uri"
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version = "0.3.2"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "1918b65d96df47d3591bed19c5cca17e3fa5d0707318e4b5ef2eae01764df7e5"
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dependencies = [
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"borrow-or-share",
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"ref-cast",
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"serde",
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]
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[[package]]
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[[package]]
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name = "fnv"
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name = "fnv"
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version = "1.0.7"
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version = "1.0.7"
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@ -1285,9 +1312,9 @@ dependencies = [
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[[package]]
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[[package]]
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name = "fraction"
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name = "fraction"
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version = "0.13.1"
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version = "0.15.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "3027ae1df8d41b4bed2241c8fdad4acc1e7af60c8e17743534b545e77182d678"
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checksum = "0f158e3ff0a1b334408dc9fb811cd99b446986f4d8b741bb08f9df1604085ae7"
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dependencies = [
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dependencies = [
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"lazy_static",
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"lazy_static",
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"num",
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"num",
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@ -1414,10 +1441,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
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checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
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dependencies = [
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dependencies = [
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"cfg-if",
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"cfg-if",
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"js-sys",
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"libc",
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"libc",
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"wasi",
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"wasi",
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"wasm-bindgen",
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]
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]
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[[package]]
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[[package]]
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@ -1573,7 +1598,7 @@ dependencies = [
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"native-tls",
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"native-tls",
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"num_cpus",
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"num_cpus",
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"rand",
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"rand",
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"reqwest",
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"reqwest 0.11.27",
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"serde",
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"serde",
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"serde_json",
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"serde_json",
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"thiserror",
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"thiserror",
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@ -2051,15 +2076,6 @@ version = "1.70.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
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checksum = "7943c866cc5cd64cbc25b2e01621d07fa8eb2a1a23160ee81ce38704e97b8ecf"
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[[package]]
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name = "iso8601"
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version = "0.6.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "924e5d73ea28f59011fec52a0d12185d496a9b075d360657aed2a5707f701153"
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dependencies = [
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"nom",
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]
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[[package]]
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[[package]]
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name = "itertools"
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name = "itertools"
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version = "0.10.5"
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version = "0.10.5"
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@ -2128,32 +2144,27 @@ dependencies = [
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[[package]]
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name = "jsonschema"
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name = "jsonschema"
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version = "0.17.1"
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version = "0.28.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "2a071f4f7efc9a9118dfb627a0a94ef247986e1ab8606a4c806ae2b3aa3b6978"
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checksum = "74d8eb539cdb4222da29bb658cc9881aa2477b33fb1a74c5c31450395fc1a4b2"
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dependencies = [
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dependencies = [
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"ahash",
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"ahash",
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"anyhow",
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"base64 0.22.1",
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"base64 0.21.7",
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"bytecount",
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"bytecount",
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"clap 4.5.21",
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"email_address",
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"fancy-regex",
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"fancy-regex",
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"fraction",
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"fraction",
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"getrandom",
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"idna",
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"iso8601",
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"itoa",
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"itoa",
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"memchr",
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"num-cmp",
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"num-cmp",
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"once_cell",
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"once_cell",
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"parking_lot",
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"percent-encoding",
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"regex",
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"referencing",
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"reqwest",
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"regex-syntax 0.8.5",
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"reqwest 0.12.9",
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"serde",
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"serde",
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"serde_json",
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"serde_json",
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"time",
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"uuid-simd",
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"url",
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"uuid",
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]
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]
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"serde_json",
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"serde_json",
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]
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]
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name = "outref"
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version = "0.5.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "4030760ffd992bef45b0ae3f10ce1aba99e33464c90d14dd7c039884963ddc7a"
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name = "overload"
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name = "overload"
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version = "0.1.1"
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version = "0.1.1"
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"thiserror",
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"thiserror",
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]
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name = "ref-cast"
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version = "1.0.23"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "ccf0a6f84d5f1d581da8b41b47ec8600871962f2a528115b542b362d4b744931"
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dependencies = [
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"ref-cast-impl",
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]
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name = "ref-cast-impl"
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version = "1.0.23"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "bcc303e793d3734489387d205e9b186fac9c6cfacedd98cbb2e8a5943595f3e6"
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dependencies = [
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"proc-macro2",
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"quote",
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"syn 2.0.89",
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]
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name = "referencing"
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version = "0.28.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "093a875008827c0ae15c746189966e162faa05bf347719d06302c548ac63630f"
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dependencies = [
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"ahash",
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"serde_json",
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name = "regex"
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version = "1.11.1"
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name = "reqwest"
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version = "0.12.9"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a77c62af46e79de0a562e1a9849205ffcb7fc1238876e9bd743357570e04046f"
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dependencies = [
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"base64 0.22.1",
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"windows-registry",
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]
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name = "rgb"
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name = "rgb"
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version = "0.8.50"
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version = "0.8.50"
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version = "1.0.2"
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version = "1.0.2"
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source = "registry+https://github.com/rust-lang/crates.io-index"
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
checksum = "0bf256ce5efdfa370213c1dabab5935a12e49f2c58d15e9eac2870d3b4f27263"
|
checksum = "0bf256ce5efdfa370213c1dabab5935a12e49f2c58d15e9eac2870d3b4f27263"
|
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|
dependencies = [
|
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|
"futures-core",
|
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|
]
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name = "synstructure"
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name = "synstructure"
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|
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"once_cell",
|
"once_cell",
|
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"pyo3",
|
"pyo3",
|
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"regex",
|
"regex",
|
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"reqwest",
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"reqwest 0.11.27",
|
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"serde",
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"serde",
|
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"serde_json",
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"serde_json",
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"thiserror",
|
"thiserror",
|
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@ -4445,7 +4534,7 @@ dependencies = [
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"pyo3",
|
"pyo3",
|
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"rand",
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"rand",
|
||||||
"regex",
|
"regex",
|
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"reqwest",
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"reqwest 0.11.27",
|
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"serde",
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"serde",
|
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"serde_json",
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"serde_json",
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"sysinfo",
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"sysinfo",
|
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@ -4493,7 +4582,7 @@ dependencies = [
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"prost-build",
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"prost-build",
|
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"rand",
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|
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"regex",
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"reqwest",
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"reqwest 0.11.27",
|
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"serde",
|
"serde",
|
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"serde_json",
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"serde_json",
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"slotmap",
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|
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|
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"prost-build",
|
"prost-build",
|
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"rand",
|
"rand",
|
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"regex",
|
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|
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"reqwest",
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"reqwest 0.11.27",
|
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"serde",
|
"serde",
|
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"serde_json",
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"serde_json",
|
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"slotmap",
|
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|
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@ -5298,6 +5387,17 @@ dependencies = [
|
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"syn 2.0.89",
|
"syn 2.0.89",
|
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]
|
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|
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|
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|
name = "uuid-simd"
|
||||||
|
version = "0.8.0"
|
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|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "23b082222b4f6619906941c17eb2297fff4c2fb96cb60164170522942a200bd8"
|
||||||
|
dependencies = [
|
||||||
|
"outref",
|
||||||
|
"uuid",
|
||||||
|
"vsimd",
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "v_frame"
|
name = "v_frame"
|
||||||
version = "0.3.8"
|
version = "0.3.8"
|
||||||
@ -5349,6 +5449,12 @@ version = "0.9.5"
|
|||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
|
checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "vsimd"
|
||||||
|
version = "0.8.0"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "5c3082ca00d5a5ef149bb8b555a72ae84c9c59f7250f013ac822ac2e49b19c64"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "walkdir"
|
name = "walkdir"
|
||||||
version = "2.5.0"
|
version = "2.5.0"
|
||||||
@ -5558,6 +5664,36 @@ dependencies = [
|
|||||||
"windows-targets 0.52.6",
|
"windows-targets 0.52.6",
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "windows-registry"
|
||||||
|
version = "0.2.0"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "e400001bb720a623c1c69032f8e3e4cf09984deec740f007dd2b03ec864804b0"
|
||||||
|
dependencies = [
|
||||||
|
"windows-result",
|
||||||
|
"windows-strings",
|
||||||
|
"windows-targets 0.52.6",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "windows-result"
|
||||||
|
version = "0.2.0"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "1d1043d8214f791817bab27572aaa8af63732e11bf84aa21a45a78d6c317ae0e"
|
||||||
|
dependencies = [
|
||||||
|
"windows-targets 0.52.6",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "windows-strings"
|
||||||
|
version = "0.1.0"
|
||||||
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
|
checksum = "4cd9b125c486025df0eabcb585e62173c6c9eddcec5d117d3b6e8c30e2ee4d10"
|
||||||
|
dependencies = [
|
||||||
|
"windows-result",
|
||||||
|
"windows-targets 0.52.6",
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "windows-sys"
|
name = "windows-sys"
|
||||||
version = "0.45.0"
|
version = "0.45.0"
|
||||||
|
|||||||
@ -234,6 +234,7 @@ FROM kernel-builder AS vllm-builder
|
|||||||
WORKDIR /usr/src
|
WORKDIR /usr/src
|
||||||
|
|
||||||
COPY server/Makefile-vllm Makefile
|
COPY server/Makefile-vllm Makefile
|
||||||
|
RUN pip install setuptools_scm
|
||||||
|
|
||||||
# Build specific version of vllm
|
# Build specific version of vllm
|
||||||
RUN make build-vllm-rocm
|
RUN make build-vllm-rocm
|
||||||
@ -267,6 +268,15 @@ COPY server/exllamav2_kernels/ .
|
|||||||
|
|
||||||
RUN python setup.py build
|
RUN python setup.py build
|
||||||
|
|
||||||
|
FROM kernel-builder AS moe-kernels
|
||||||
|
WORKDIR /usr/src
|
||||||
|
ENV MOE_KERNELS_BRANCH=a67b35841774b2056a73806c36661134b5054edd
|
||||||
|
ENV VLLM_TARGET_DEVICE=rocm
|
||||||
|
RUN git clone https://github.com/danieldk/moe-kernels.git && \
|
||||||
|
cd moe-kernels && \
|
||||||
|
git checkout ${MOE_KERNELS_BRANCH} && \
|
||||||
|
python setup.py install
|
||||||
|
|
||||||
FROM install_deps AS base-copy
|
FROM install_deps AS base-copy
|
||||||
|
|
||||||
# Text Generation Inference base env
|
# Text Generation Inference base env
|
||||||
@ -289,6 +299,9 @@ COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-311
|
|||||||
# Copy build artifacts from exllamav2 kernels builder
|
# Copy build artifacts from exllamav2 kernels builder
|
||||||
COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
|
COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
|
||||||
|
|
||||||
|
# Copy build artifacts from moe kernels
|
||||||
|
COPY --from=moe-kernels /usr/src/moe-kernels/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
|
||||||
|
|
||||||
# Install server
|
# Install server
|
||||||
COPY proto proto
|
COPY proto proto
|
||||||
COPY server server
|
COPY server server
|
||||||
|
|||||||
@ -97,11 +97,10 @@ ENV HF_HOME=/data \
|
|||||||
|
|
||||||
|
|
||||||
WORKDIR /usr/src
|
WORKDIR /usr/src
|
||||||
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torch-2.5.0a0%2Bgite84e33f-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
||||||
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torchaudio-2.5.0a0%2B56bc006-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torchaudio-2.5.0a0%2B56bc006-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
||||||
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/torchvision-0.20.0a0%2B8e8a208-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/torchvision-0.20.0a0%2B8e8a208-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
||||||
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.5.10%2Bgit9d489a8-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
RUN pip install https://intel-optimized-pytorch.s3.cn-north-1.amazonaws.com.cn/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
||||||
RUN pip install https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/oneccl_bind_pt-2.5.0%2Bxpu-cp311-cp311-linux_x86_64.whl --no-cache-dir
|
|
||||||
|
|
||||||
RUN pip install triton-xpu==3.0.0b2 --no-cache-dir
|
RUN pip install triton-xpu==3.0.0b2 --no-cache-dir
|
||||||
|
|
||||||
@ -119,6 +118,9 @@ ENV CCL_ZE_IPC_EXCHANGE=sockets
|
|||||||
#ENV TORCH_LLM_ALLREDUCE=1
|
#ENV TORCH_LLM_ALLREDUCE=1
|
||||||
#ENV CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0
|
#ENV CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0
|
||||||
|
|
||||||
|
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout 033af6f63745ac748cccdadee5c6140c7971edf6
|
||||||
|
RUN cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc,ats-m150' BUILD_SEPARATE_OPS=OFF BUILD_WITH_CPU=OFF USE_XETLA=ON python setup.py install && rm -rf /usr/src/intel-extension-for-pytorch
|
||||||
|
|
||||||
# Install benchmarker
|
# Install benchmarker
|
||||||
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
|
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
|
||||||
# Install router
|
# Install router
|
||||||
|
|||||||
12
README.md
12
README.md
@ -1,7 +1,7 @@
|
|||||||
<div align="center">
|
<div align="center">
|
||||||
|
|
||||||
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
|
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
|
||||||
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
|
<img width=560 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
|
||||||
</a>
|
</a>
|
||||||
|
|
||||||
# Text Generation Inference
|
# Text Generation Inference
|
||||||
@ -141,8 +141,8 @@ You have the option to utilize the `HF_TOKEN` environment variable for configuri
|
|||||||
For example, if you want to serve the gated Llama V2 model variants:
|
For example, if you want to serve the gated Llama V2 model variants:
|
||||||
|
|
||||||
1. Go to https://huggingface.co/settings/tokens
|
1. Go to https://huggingface.co/settings/tokens
|
||||||
2. Copy your cli READ token
|
2. Copy your CLI READ token
|
||||||
3. Export `HF_TOKEN=<your cli READ token>`
|
3. Export `HF_TOKEN=<your CLI READ token>`
|
||||||
|
|
||||||
or with Docker:
|
or with Docker:
|
||||||
|
|
||||||
@ -157,7 +157,7 @@ docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/da
|
|||||||
### A note on Shared Memory (shm)
|
### A note on Shared Memory (shm)
|
||||||
|
|
||||||
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
|
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
|
||||||
`PyTorch` to do distributed training/inference. `text-generation-inference` make
|
`PyTorch` to do distributed training/inference. `text-generation-inference` makes
|
||||||
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
|
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
|
||||||
|
|
||||||
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
|
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
|
||||||
@ -196,7 +196,7 @@ Detailed blogpost by Adyen on TGI inner workings: [LLM inference at scale with T
|
|||||||
|
|
||||||
You can also opt to install `text-generation-inference` locally.
|
You can also opt to install `text-generation-inference` locally.
|
||||||
|
|
||||||
First clone the repository and change directoy into it:
|
First clone the repository and change directory into it:
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
git clone https://github.com/huggingface/text-generation-inference
|
git clone https://github.com/huggingface/text-generation-inference
|
||||||
@ -213,7 +213,7 @@ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
|||||||
conda create -n text-generation-inference python=3.11
|
conda create -n text-generation-inference python=3.11
|
||||||
conda activate text-generation-inference
|
conda activate text-generation-inference
|
||||||
|
|
||||||
#using pyton venv
|
#using python venv
|
||||||
python3 -m venv .venv
|
python3 -m venv .venv
|
||||||
source .venv/bin/activate
|
source .venv/bin/activate
|
||||||
```
|
```
|
||||||
|
|||||||
@ -23,7 +23,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
|
|||||||
grpc-metadata = { path = "../grpc-metadata" }
|
grpc-metadata = { path = "../grpc-metadata" }
|
||||||
futures = "0.3.28"
|
futures = "0.3.28"
|
||||||
hf-hub = { workspace = true }
|
hf-hub = { workspace = true }
|
||||||
jsonschema = { version = "0.17.1", features = ["draft202012"] }
|
jsonschema = { version = "0.28.0" }
|
||||||
metrics = { workspace = true }
|
metrics = { workspace = true }
|
||||||
metrics-exporter-prometheus = { workspace = true }
|
metrics-exporter-prometheus = { workspace = true }
|
||||||
nohash-hasher = "0.2.0"
|
nohash-hasher = "0.2.0"
|
||||||
|
|||||||
@ -23,7 +23,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
|
|||||||
grpc-metadata = { path = "../grpc-metadata" }
|
grpc-metadata = { path = "../grpc-metadata" }
|
||||||
futures = "0.3.28"
|
futures = "0.3.28"
|
||||||
hf-hub = { workspace = true }
|
hf-hub = { workspace = true }
|
||||||
jsonschema = { version = "0.17.1", features = ["draft202012"] }
|
jsonschema = { version = "0.28.0" }
|
||||||
metrics = { workspace = true }
|
metrics = { workspace = true }
|
||||||
metrics-exporter-prometheus = { workspace = true }
|
metrics-exporter-prometheus = { workspace = true }
|
||||||
nohash-hasher = "0.2.0"
|
nohash-hasher = "0.2.0"
|
||||||
|
|||||||
@ -187,8 +187,6 @@ In addition to the grammar parameter, we've also introduced a set of tools and f
|
|||||||
|
|
||||||
Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the LLM's capabilities. Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
|
Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the LLM's capabilities. Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
|
||||||
|
|
||||||
Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API.
|
|
||||||
|
|
||||||
```json
|
```json
|
||||||
curl localhost:3000/v1/chat/completions \
|
curl localhost:3000/v1/chat/completions \
|
||||||
-X POST \
|
-X POST \
|
||||||
|
|||||||
@ -5,6 +5,7 @@ Text Generation Inference enables serving optimized models. The following sectio
|
|||||||
|
|
||||||
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
|
- [Deepseek V2](https://huggingface.co/deepseek-ai/DeepSeek-V2)
|
||||||
- [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
|
- [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
|
||||||
|
- [Idefics 3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (Multimodal)
|
||||||
- [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)
|
- [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)
|
||||||
- [Llama](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f)
|
- [Llama](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f)
|
||||||
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
|
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
|
||||||
|
|||||||
@ -978,11 +978,11 @@
|
|||||||
"nixpkgs": "nixpkgs_6"
|
"nixpkgs": "nixpkgs_6"
|
||||||
},
|
},
|
||||||
"locked": {
|
"locked": {
|
||||||
"lastModified": 1732218602,
|
"lastModified": 1736436388,
|
||||||
"narHash": "sha256-BElslL34KjOJCFMPkNtilOz6S/7iY7Vd72FNbRRWKDY=",
|
"narHash": "sha256-CIyxVPpM9RrSwthNT/4DQ10YPk/uwzP7AeE83kBNsrE=",
|
||||||
"owner": "huggingface",
|
"owner": "huggingface",
|
||||||
"repo": "text-generation-inference-nix",
|
"repo": "text-generation-inference-nix",
|
||||||
"rev": "f79638ac4e420e661321261744e745a3a747e182",
|
"rev": "5103c3fb1f9ad1fd33b6e09ff05e957884b112d5",
|
||||||
"type": "github"
|
"type": "github"
|
||||||
},
|
},
|
||||||
"original": {
|
"original": {
|
||||||
|
|||||||
@ -354,6 +354,7 @@ def launcher(event_loop):
|
|||||||
kv_cache_dtype: Optional[str] = None,
|
kv_cache_dtype: Optional[str] = None,
|
||||||
revision: Optional[str] = None,
|
revision: Optional[str] = None,
|
||||||
max_input_length: Optional[int] = None,
|
max_input_length: Optional[int] = None,
|
||||||
|
max_input_tokens: Optional[int] = None,
|
||||||
max_batch_prefill_tokens: Optional[int] = None,
|
max_batch_prefill_tokens: Optional[int] = None,
|
||||||
max_total_tokens: Optional[int] = None,
|
max_total_tokens: Optional[int] = None,
|
||||||
lora_adapters: Optional[List[str]] = None,
|
lora_adapters: Optional[List[str]] = None,
|
||||||
@ -402,6 +403,9 @@ def launcher(event_loop):
|
|||||||
if max_input_length:
|
if max_input_length:
|
||||||
args.append("--max-input-length")
|
args.append("--max-input-length")
|
||||||
args.append(str(max_input_length))
|
args.append(str(max_input_length))
|
||||||
|
if max_input_tokens:
|
||||||
|
args.append("--max-input-tokens")
|
||||||
|
args.append(str(max_input_tokens))
|
||||||
if max_batch_prefill_tokens:
|
if max_batch_prefill_tokens:
|
||||||
args.append("--max-batch-prefill-tokens")
|
args.append("--max-batch-prefill-tokens")
|
||||||
args.append(str(max_batch_prefill_tokens))
|
args.append(str(max_batch_prefill_tokens))
|
||||||
|
|||||||
@ -32,7 +32,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 1101,
|
"id": 1101,
|
||||||
"logprob": -1.0947266,
|
"logprob": -1.0136719,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " also"
|
"text": " also"
|
||||||
},
|
},
|
||||||
@ -56,13 +56,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 4009,
|
"id": 4009,
|
||||||
"logprob": -0.15563965,
|
"logprob": -0.21923828,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " network"
|
"text": " network"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 477,
|
"id": 477,
|
||||||
"logprob": -1.4003906,
|
"logprob": -1.4824219,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " or"
|
"text": " or"
|
||||||
}
|
}
|
||||||
|
|||||||
@ -8,7 +8,7 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 1939,
|
"id": 1939,
|
||||||
"logprob": -2.2675781,
|
"logprob": -2.2460938,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "?\n\n"
|
"text": "?\n\n"
|
||||||
},
|
},
|
||||||
@ -20,13 +20,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 20909,
|
"id": 20909,
|
||||||
"logprob": -0.37695312,
|
"logprob": -0.48608398,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " Learning"
|
"text": " Learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 4102,
|
"id": 4102,
|
||||||
"logprob": -1.9316406,
|
"logprob": -2.265625,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " "
|
"text": " "
|
||||||
},
|
},
|
||||||
@ -38,25 +38,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 458,
|
"id": 458,
|
||||||
"logprob": -0.80859375,
|
"logprob": -0.6328125,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " an"
|
"text": " an"
|
||||||
},
|
},
|
||||||
{
|
|
||||||
"id": 3082,
|
|
||||||
"logprob": -1.4541016,
|
|
||||||
"special": false,
|
|
||||||
"text": " area"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 315,
|
|
||||||
"logprob": 0.0,
|
|
||||||
"special": false,
|
|
||||||
"text": " of"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"id": 20443,
|
"id": 20443,
|
||||||
"logprob": -0.5136719,
|
"logprob": -0.1796875,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " artificial"
|
"text": " artificial"
|
||||||
},
|
},
|
||||||
@ -65,9 +53,21 @@
|
|||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " intelligence"
|
"text": " intelligence"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 320,
|
||||||
|
"logprob": -0.37695312,
|
||||||
|
"special": false,
|
||||||
|
"text": " ("
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 15469,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": "AI"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"top_tokens": null
|
"top_tokens": null
|
||||||
},
|
},
|
||||||
"generated_text": "What is deep learning?\n\nDeep Learning is an area of artificial intelligence"
|
"generated_text": "What is deep learning?\n\nDeep Learning is an artificial intelligence (AI"
|
||||||
}
|
}
|
||||||
|
|||||||
@ -9,61 +9,61 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 18183,
|
"id": 18183,
|
||||||
"logprob": -1.6669922,
|
"logprob": -1.4912109,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " Deep"
|
"text": " Deep"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.08959961,
|
"logprob": -0.075683594,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 374,
|
"id": 374,
|
||||||
"logprob": -0.14685059,
|
"logprob": -0.12408447,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " is"
|
"text": " is"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 264,
|
"id": 264,
|
||||||
"logprob": -0.125,
|
"logprob": -0.12768555,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " a"
|
"text": " a"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 25993,
|
"id": 25993,
|
||||||
"logprob": -0.81640625,
|
"logprob": -0.82128906,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " subset"
|
"text": " subset"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 315,
|
"id": 315,
|
||||||
"logprob": -0.0013418198,
|
"logprob": -0.0012636185,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " of"
|
"text": " of"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5662,
|
"id": 5662,
|
||||||
"logprob": -0.16259766,
|
"logprob": -0.12878418,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " machine"
|
"text": " machine"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.0016393661,
|
"logprob": -0.0015888214,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 429,
|
"id": 429,
|
||||||
"logprob": -0.4477539,
|
"logprob": -0.49194336,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " that"
|
"text": " that"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5711,
|
"id": 5711,
|
||||||
"logprob": -1.2802734,
|
"logprob": -1.2626953,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " uses"
|
"text": " uses"
|
||||||
}
|
}
|
||||||
@ -82,61 +82,61 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 18183,
|
"id": 18183,
|
||||||
"logprob": -1.6669922,
|
"logprob": -1.4912109,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " Deep"
|
"text": " Deep"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.08959961,
|
"logprob": -0.075683594,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 374,
|
"id": 374,
|
||||||
"logprob": -0.14685059,
|
"logprob": -0.12408447,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " is"
|
"text": " is"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 264,
|
"id": 264,
|
||||||
"logprob": -0.125,
|
"logprob": -0.12768555,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " a"
|
"text": " a"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 25993,
|
"id": 25993,
|
||||||
"logprob": -0.81640625,
|
"logprob": -0.82128906,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " subset"
|
"text": " subset"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 315,
|
"id": 315,
|
||||||
"logprob": -0.0013418198,
|
"logprob": -0.0012636185,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " of"
|
"text": " of"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5662,
|
"id": 5662,
|
||||||
"logprob": -0.16259766,
|
"logprob": -0.12878418,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " machine"
|
"text": " machine"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.0016393661,
|
"logprob": -0.0015888214,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 429,
|
"id": 429,
|
||||||
"logprob": -0.4477539,
|
"logprob": -0.49194336,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " that"
|
"text": " that"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5711,
|
"id": 5711,
|
||||||
"logprob": -1.2802734,
|
"logprob": -1.2626953,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " uses"
|
"text": " uses"
|
||||||
}
|
}
|
||||||
@ -155,61 +155,61 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 18183,
|
"id": 18183,
|
||||||
"logprob": -1.6669922,
|
"logprob": -1.4912109,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " Deep"
|
"text": " Deep"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.08959961,
|
"logprob": -0.075683594,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 374,
|
"id": 374,
|
||||||
"logprob": -0.14685059,
|
"logprob": -0.12408447,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " is"
|
"text": " is"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 264,
|
"id": 264,
|
||||||
"logprob": -0.125,
|
"logprob": -0.12768555,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " a"
|
"text": " a"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 25993,
|
"id": 25993,
|
||||||
"logprob": -0.81640625,
|
"logprob": -0.82128906,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " subset"
|
"text": " subset"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 315,
|
"id": 315,
|
||||||
"logprob": -0.0013418198,
|
"logprob": -0.0012636185,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " of"
|
"text": " of"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5662,
|
"id": 5662,
|
||||||
"logprob": -0.16259766,
|
"logprob": -0.12878418,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " machine"
|
"text": " machine"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.0016393661,
|
"logprob": -0.0015888214,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 429,
|
"id": 429,
|
||||||
"logprob": -0.4477539,
|
"logprob": -0.49194336,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " that"
|
"text": " that"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5711,
|
"id": 5711,
|
||||||
"logprob": -1.2802734,
|
"logprob": -1.2626953,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " uses"
|
"text": " uses"
|
||||||
}
|
}
|
||||||
@ -228,61 +228,61 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 18183,
|
"id": 18183,
|
||||||
"logprob": -1.6669922,
|
"logprob": -1.4912109,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " Deep"
|
"text": " Deep"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.08959961,
|
"logprob": -0.075683594,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 374,
|
"id": 374,
|
||||||
"logprob": -0.14685059,
|
"logprob": -0.12408447,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " is"
|
"text": " is"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 264,
|
"id": 264,
|
||||||
"logprob": -0.125,
|
"logprob": -0.12768555,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " a"
|
"text": " a"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 25993,
|
"id": 25993,
|
||||||
"logprob": -0.81640625,
|
"logprob": -0.82128906,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " subset"
|
"text": " subset"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 315,
|
"id": 315,
|
||||||
"logprob": -0.0013418198,
|
"logprob": -0.0012636185,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " of"
|
"text": " of"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5662,
|
"id": 5662,
|
||||||
"logprob": -0.16259766,
|
"logprob": -0.12878418,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " machine"
|
"text": " machine"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 6832,
|
"id": 6832,
|
||||||
"logprob": -0.0016393661,
|
"logprob": -0.0015888214,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " learning"
|
"text": " learning"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 429,
|
"id": 429,
|
||||||
"logprob": -0.4477539,
|
"logprob": -0.49194336,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " that"
|
"text": " that"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 5711,
|
"id": 5711,
|
||||||
"logprob": -1.2802734,
|
"logprob": -1.2626953,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " uses"
|
"text": " uses"
|
||||||
}
|
}
|
||||||
|
|||||||
@ -44,7 +44,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 38397,
|
"id": 38397,
|
||||||
"logprob": -0.12695312,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " subset"
|
"text": " subset"
|
||||||
},
|
},
|
||||||
|
|||||||
@ -14,60 +14,60 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 573,
|
"id": 573,
|
||||||
"logprob": -0.18493652,
|
"logprob": -0.19030762,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " the"
|
"text": " the"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 16819,
|
"id": 16819,
|
||||||
"logprob": -1.4804688,
|
"logprob": -1.4863281,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " detection"
|
"text": " detection"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 576,
|
"id": 576,
|
||||||
"logprob": -0.7011719,
|
"logprob": -0.7089844,
|
||||||
|
"special": false,
|
||||||
|
"text": " of"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 573,
|
||||||
|
"logprob": -2.0410156,
|
||||||
|
"special": false,
|
||||||
|
"text": " the"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 8566,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": " presence"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 689,
|
||||||
|
"logprob": -0.16491699,
|
||||||
|
"special": false,
|
||||||
|
"text": " or"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 14862,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": " absence"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 576,
|
||||||
|
"logprob": -0.9970703,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " of"
|
"text": " of"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 671,
|
"id": 671,
|
||||||
"logprob": -2.1738281,
|
"logprob": -0.5292969,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " an"
|
"text": " an"
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 24646,
|
|
||||||
"logprob": -3.0449219,
|
|
||||||
"special": false,
|
|
||||||
"text": " RNA"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 12369,
|
|
||||||
"logprob": -0.19299316,
|
|
||||||
"special": false,
|
|
||||||
"text": " virus"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 575,
|
|
||||||
"logprob": -0.10632324,
|
|
||||||
"special": false,
|
|
||||||
"text": " in"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 6022,
|
|
||||||
"logprob": -0.98095703,
|
|
||||||
"special": false,
|
|
||||||
"text": " patients"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 1064,
|
|
||||||
"logprob": -1.3095703,
|
|
||||||
"special": false,
|
|
||||||
"text": " who"
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"top_tokens": null
|
"top_tokens": null
|
||||||
},
|
},
|
||||||
"generated_text": "Test request for the detection of an RNA virus in patients who"
|
"generated_text": "Test request for the detection of the presence or absence of an"
|
||||||
}
|
}
|
||||||
|
|||||||
@ -8,7 +8,7 @@
|
|||||||
"tokens": [
|
"tokens": [
|
||||||
{
|
{
|
||||||
"id": 2284,
|
"id": 2284,
|
||||||
"logprob": -0.296875,
|
"logprob": -0.31323242,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "():"
|
"text": "():"
|
||||||
},
|
},
|
||||||
@ -38,13 +38,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 10914,
|
"id": 10914,
|
||||||
"logprob": -0.7734375,
|
"logprob": -0.7871094,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " World"
|
"text": " World"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 16013,
|
"id": 16013,
|
||||||
"logprob": -0.61816406,
|
"logprob": -0.64746094,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "!\")"
|
"text": "!\")"
|
||||||
},
|
},
|
||||||
@ -62,7 +62,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 610,
|
"id": 610,
|
||||||
"logprob": -0.4152832,
|
"logprob": -0.41064453,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "def"
|
"text": "def"
|
||||||
},
|
},
|
||||||
@ -92,7 +92,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 444,
|
"id": 444,
|
||||||
"logprob": -0.21618652,
|
"logprob": -0.21655273,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "name"
|
"text": "name"
|
||||||
},
|
},
|
||||||
@ -139,28 +139,16 @@
|
|||||||
"text": "Hello"
|
"text": "Hello"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 925,
|
"id": 332,
|
||||||
"logprob": -3.3476562,
|
"logprob": -0.034698486,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " %"
|
"text": " \""
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 120,
|
"id": 494,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "s"
|
"text": " +"
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 11571,
|
|
||||||
"logprob": -0.08892822,
|
|
||||||
"special": false,
|
|
||||||
"text": "!\""
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 925,
|
|
||||||
"logprob": 0.0,
|
|
||||||
"special": false,
|
|
||||||
"text": " %"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 655,
|
"id": 655,
|
||||||
@ -169,10 +157,22 @@
|
|||||||
"text": " name"
|
"text": " name"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 46,
|
"id": 494,
|
||||||
|
"logprob": -0.20141602,
|
||||||
|
"special": false,
|
||||||
|
"text": " +"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 332,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": ")"
|
"text": " \""
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 16013,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": "!\")"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 222,
|
"id": 222,
|
||||||
@ -230,7 +230,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 400,
|
"id": 400,
|
||||||
"logprob": -0.074279785,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "age"
|
"text": "age"
|
||||||
},
|
},
|
||||||
@ -289,22 +289,34 @@
|
|||||||
"text": "Hello"
|
"text": "Hello"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 925,
|
"id": 332,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " %"
|
"text": " \""
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 120,
|
"id": 494,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "s"
|
"text": " +"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 49,
|
"id": 655,
|
||||||
"logprob": -0.07891846,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": ","
|
"text": " name"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 494,
|
||||||
|
"logprob": 0.0,
|
||||||
|
"special": false,
|
||||||
|
"text": " +"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 3021,
|
||||||
|
"logprob": -0.5761719,
|
||||||
|
"special": false,
|
||||||
|
"text": " \","
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 863,
|
"id": 863,
|
||||||
@ -319,55 +331,43 @@
|
|||||||
"text": " are"
|
"text": " are"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 925,
|
"id": 332,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " %"
|
"text": " \""
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 105,
|
"id": 494,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "d"
|
"text": " +"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 11339,
|
"id": 615,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " years"
|
"text": " str"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 3627,
|
"id": 45,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " old"
|
"text": "("
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 11571,
|
"id": 400,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": "!\""
|
"text": "age"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"id": 925,
|
"id": 46,
|
||||||
"logprob": 0.0,
|
"logprob": 0.0,
|
||||||
"special": false,
|
"special": false,
|
||||||
"text": " %"
|
"text": ")"
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 327,
|
|
||||||
"logprob": 0.0,
|
|
||||||
"special": false,
|
|
||||||
"text": " ("
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": 444,
|
|
||||||
"logprob": 0.0,
|
|
||||||
"special": false,
|
|
||||||
"text": "name"
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"top_tokens": null
|
"top_tokens": null
|
||||||
},
|
},
|
||||||
"generated_text": "():\n print(\"Hello World!\")\n\ndef print_hello_name(name):\n print(\"Hello %s!\" % name)\n\ndef print_hello_name_age(name, age):\n print(\"Hello %s, you are %d years old!\" % (name"
|
"generated_text": "():\n print(\"Hello World!\")\n\ndef print_hello_name(name):\n print(\"Hello \" + name + \"!\")\n\ndef print_hello_name_age(name, age):\n print(\"Hello \" + name + \", you are \" + str(age)"
|
||||||
}
|
}
|
||||||
|
|||||||
@ -0,0 +1,67 @@
|
|||||||
|
{
|
||||||
|
"details": {
|
||||||
|
"best_of_sequences": null,
|
||||||
|
"finish_reason": "eos_token",
|
||||||
|
"generated_tokens": 9,
|
||||||
|
"prefill": [],
|
||||||
|
"seed": null,
|
||||||
|
"tokens": [
|
||||||
|
{
|
||||||
|
"id": 2684,
|
||||||
|
"logprob": -0.24902344,
|
||||||
|
"special": false,
|
||||||
|
"text": " There"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 374,
|
||||||
|
"logprob": -0.0703125,
|
||||||
|
"special": false,
|
||||||
|
"text": " is"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 264,
|
||||||
|
"logprob": -0.23535156,
|
||||||
|
"special": false,
|
||||||
|
"text": " a"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 35372,
|
||||||
|
"logprob": -0.125,
|
||||||
|
"special": false,
|
||||||
|
"text": " statue"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 304,
|
||||||
|
"logprob": -0.30273438,
|
||||||
|
"special": false,
|
||||||
|
"text": " in"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 279,
|
||||||
|
"logprob": -0.20507812,
|
||||||
|
"special": false,
|
||||||
|
"text": " the"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 2217,
|
||||||
|
"logprob": -0.076171875,
|
||||||
|
"special": false,
|
||||||
|
"text": " image"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 13,
|
||||||
|
"logprob": -0.053710938,
|
||||||
|
"special": false,
|
||||||
|
"text": "."
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 128258,
|
||||||
|
"logprob": -0.011352539,
|
||||||
|
"special": true,
|
||||||
|
"text": "<end_of_utterance>"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"top_tokens": null
|
||||||
|
},
|
||||||
|
"generated_text": " There is a statue in the image."
|
||||||
|
}
|
||||||
@ -0,0 +1,61 @@
|
|||||||
|
{
|
||||||
|
"details": {
|
||||||
|
"best_of_sequences": null,
|
||||||
|
"finish_reason": "eos_token",
|
||||||
|
"generated_tokens": 8,
|
||||||
|
"prefill": [],
|
||||||
|
"seed": null,
|
||||||
|
"tokens": [
|
||||||
|
{
|
||||||
|
"id": 330,
|
||||||
|
"logprob": -0.118652344,
|
||||||
|
"special": false,
|
||||||
|
"text": " A"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 11426,
|
||||||
|
"logprob": -0.28320312,
|
||||||
|
"special": false,
|
||||||
|
"text": " bee"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 335,
|
||||||
|
"logprob": -0.95703125,
|
||||||
|
"special": false,
|
||||||
|
"text": " on"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 253,
|
||||||
|
"logprob": -0.06982422,
|
||||||
|
"special": false,
|
||||||
|
"text": " a"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 11986,
|
||||||
|
"logprob": -0.49414062,
|
||||||
|
"special": false,
|
||||||
|
"text": " pink"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 8525,
|
||||||
|
"logprob": -0.07763672,
|
||||||
|
"special": false,
|
||||||
|
"text": " flower"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 30,
|
||||||
|
"logprob": -1.0703125,
|
||||||
|
"special": false,
|
||||||
|
"text": "."
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 49154,
|
||||||
|
"logprob": -0.092285156,
|
||||||
|
"special": true,
|
||||||
|
"text": "<end_of_utterance>"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"top_tokens": null
|
||||||
|
},
|
||||||
|
"generated_text": " A bee on a pink flower."
|
||||||
|
}
|
||||||
@ -64,7 +64,7 @@ async def test_compressed_tensors_w8a8_int_dynamic_weight_all_params(
|
|||||||
assert response.details.generated_tokens == 10
|
assert response.details.generated_tokens == 10
|
||||||
assert (
|
assert (
|
||||||
response.generated_text
|
response.generated_text
|
||||||
== "What is deep learning?\n\nDeep Learning is an area of artificial intelligence"
|
== "What is deep learning?\n\nDeep Learning is an artificial intelligence (AI"
|
||||||
)
|
)
|
||||||
assert response == response_snapshot
|
assert response == response_snapshot
|
||||||
|
|
||||||
|
|||||||
31
integration-tests/models/test_idefics3.py
Normal file
31
integration-tests/models/test_idefics3.py
Normal file
@ -0,0 +1,31 @@
|
|||||||
|
import pytest
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
def flash_idefics3_next_handle(launcher):
|
||||||
|
with launcher("HuggingFaceM4/Idefics3-8B-Llama3") as handle:
|
||||||
|
yield handle
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
async def flash_idefics3_next(flash_idefics3_next_handle):
|
||||||
|
await flash_idefics3_next_handle.health(300)
|
||||||
|
return flash_idefics3_next_handle.client
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
@pytest.mark.private
|
||||||
|
async def test_flash_idefics3_next_simple_url(flash_idefics3_next, response_snapshot):
|
||||||
|
ny_skyline = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
|
||||||
|
query = "What is in this image?"
|
||||||
|
response = await flash_idefics3_next.generate(
|
||||||
|
f"<|begin_of_text|><|begin_of_text|>User:{query}<end_of_utterance>\nAssistant:",
|
||||||
|
max_new_tokens=10,
|
||||||
|
seed=1337,
|
||||||
|
)
|
||||||
|
print(response)
|
||||||
|
assert (
|
||||||
|
response.generated_text == " There is a statue in the image."
|
||||||
|
), f"{repr(response.generated_text)}"
|
||||||
|
assert response.details.generated_tokens == 9
|
||||||
|
assert response == response_snapshot
|
||||||
31
integration-tests/models/test_smolvlm.py
Normal file
31
integration-tests/models/test_smolvlm.py
Normal file
@ -0,0 +1,31 @@
|
|||||||
|
import pytest
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
def flash_smolvlm_next_handle(launcher):
|
||||||
|
with launcher("HuggingFaceTB/SmolVLM-Instruct") as handle:
|
||||||
|
yield handle
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="module")
|
||||||
|
async def flash_smolvlm_next(flash_smolvlm_next_handle):
|
||||||
|
await flash_smolvlm_next_handle.health(300)
|
||||||
|
return flash_smolvlm_next_handle.client
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.asyncio
|
||||||
|
@pytest.mark.private
|
||||||
|
async def test_flash_smolvlm_next_simple_url(flash_smolvlm_next, response_snapshot):
|
||||||
|
ny_skyline = "https://huggingface.co/spaces/merve/chameleon-7b/resolve/main/bee.jpg"
|
||||||
|
query = "What is in this image?"
|
||||||
|
response = await flash_smolvlm_next.generate(
|
||||||
|
f"<|begin_of_text|><|begin_of_text|>User:{query}<end_of_utterance>\nAssistant:",
|
||||||
|
max_new_tokens=10,
|
||||||
|
seed=1337,
|
||||||
|
)
|
||||||
|
print(response)
|
||||||
|
assert (
|
||||||
|
response.generated_text == " A bee on a pink flower."
|
||||||
|
), f"{repr(response.generated_text)}"
|
||||||
|
assert response.details.generated_tokens == 8
|
||||||
|
assert response == response_snapshot
|
||||||
@ -1652,7 +1652,11 @@ impl From<&str> for Gpu {
|
|||||||
"nvidia-l40s" => Gpu::L40S,
|
"nvidia-l40s" => Gpu::L40S,
|
||||||
"nvidia-a10g" => Gpu::A10G,
|
"nvidia-a10g" => Gpu::A10G,
|
||||||
"nvidia-h100-80gb-hbm3" => Gpu::H100,
|
"nvidia-h100-80gb-hbm3" => Gpu::H100,
|
||||||
|
"nvidia-h100-nvl" => Gpu::H100,
|
||||||
|
"nvidia-h100" => Gpu::H100,
|
||||||
"nvidia-a100-sxm4-80gb" => Gpu::A100,
|
"nvidia-a100-sxm4-80gb" => Gpu::A100,
|
||||||
|
"nvidia-a100-sxm4-40gb" => Gpu::A100,
|
||||||
|
"nvidia-a100-80gb-pcie" => Gpu::A100,
|
||||||
"nvidia-a100" => Gpu::A100,
|
"nvidia-a100" => Gpu::A100,
|
||||||
card => Gpu::Unknown(card.to_string()),
|
card => Gpu::Unknown(card.to_string()),
|
||||||
}
|
}
|
||||||
|
|||||||
@ -17,7 +17,7 @@ clap = { version = "4.4.5", features = ["derive", "env"] }
|
|||||||
futures = "0.3.28"
|
futures = "0.3.28"
|
||||||
hf-hub = { workspace = true }
|
hf-hub = { workspace = true }
|
||||||
itertools = "0.10"
|
itertools = "0.10"
|
||||||
jsonschema = { version = "0.17.1", features = ["draft202012"] }
|
jsonschema = { version = "0.28.0" }
|
||||||
metrics = { workspace = true }
|
metrics = { workspace = true }
|
||||||
metrics-exporter-prometheus = { workspace = true }
|
metrics-exporter-prometheus = { workspace = true }
|
||||||
nohash-hasher = "0.2.0"
|
nohash-hasher = "0.2.0"
|
||||||
@ -25,7 +25,7 @@ opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
|
|||||||
opentelemetry-otlp = "0.13.0"
|
opentelemetry-otlp = "0.13.0"
|
||||||
outlines-core = { git = "https://github.com/dottxt-ai/outlines-core.git", rev = "ba10c619fc9bf3c487e43f49bdecb95a24bb465c" }
|
outlines-core = { git = "https://github.com/dottxt-ai/outlines-core.git", rev = "ba10c619fc9bf3c487e43f49bdecb95a24bb465c" }
|
||||||
rand = "0.8.5"
|
rand = "0.8.5"
|
||||||
reqwest = { version = "0.11.20", features = [] }
|
reqwest = { version = "0.11.20", features = ["blocking"] }
|
||||||
serde = "1.0.188"
|
serde = "1.0.188"
|
||||||
serde_json = "1.0.107"
|
serde_json = "1.0.107"
|
||||||
thiserror = "1.0.48"
|
thiserror = "1.0.48"
|
||||||
|
|||||||
@ -110,6 +110,24 @@ pub struct ClipVisionModel {
|
|||||||
patch_size: usize,
|
patch_size: usize,
|
||||||
}
|
}
|
||||||
|
|
||||||
|
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||||
|
#[serde(rename_all = "snake_case")]
|
||||||
|
pub struct Idefics3 {}
|
||||||
|
|
||||||
|
impl Idefics3 {
|
||||||
|
pub fn get_max_longest_edge(&self) -> usize {
|
||||||
|
364
|
||||||
|
}
|
||||||
|
|
||||||
|
pub fn get_number_of_features(&self) -> usize {
|
||||||
|
169
|
||||||
|
}
|
||||||
|
|
||||||
|
pub fn get_max_longest_edge_for_image_resize(&self) -> usize {
|
||||||
|
1456
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||||
#[serde(rename_all = "snake_case")]
|
#[serde(rename_all = "snake_case")]
|
||||||
pub struct Idefics2 {}
|
pub struct Idefics2 {}
|
||||||
@ -178,6 +196,7 @@ pub enum Config {
|
|||||||
Idefics,
|
Idefics,
|
||||||
Mllama,
|
Mllama,
|
||||||
Idefics2(Idefics2),
|
Idefics2(Idefics2),
|
||||||
|
Idefics3(Idefics3),
|
||||||
Ssm,
|
Ssm,
|
||||||
GptBigcode,
|
GptBigcode,
|
||||||
Granite,
|
Granite,
|
||||||
|
|||||||
@ -205,6 +205,7 @@ pub async fn kserve_model_infer(
|
|||||||
let generate_request = GenerateRequest {
|
let generate_request = GenerateRequest {
|
||||||
inputs: str_input.to_string(),
|
inputs: str_input.to_string(),
|
||||||
parameters: payload.parameters.clone(),
|
parameters: payload.parameters.clone(),
|
||||||
|
add_special_tokens: true,
|
||||||
};
|
};
|
||||||
let infer = infer.clone();
|
let infer = infer.clone();
|
||||||
let compute_type = compute_type.clone();
|
let compute_type = compute_type.clone();
|
||||||
@ -212,7 +213,7 @@ pub async fn kserve_model_infer(
|
|||||||
async move {
|
async move {
|
||||||
generate_internal(infer, compute_type, Json(generate_request), span)
|
generate_internal(infer, compute_type, Json(generate_request), span)
|
||||||
.await
|
.await
|
||||||
.map(|(_, Json(generation))| {
|
.map(|(_, _, Json(generation))| {
|
||||||
let generation_as_bytes = generation.generated_text.as_bytes().to_vec();
|
let generation_as_bytes = generation.generated_text.as_bytes().to_vec();
|
||||||
OutputChunk {
|
OutputChunk {
|
||||||
name: output.name.clone(),
|
name: output.name.clone(),
|
||||||
|
|||||||
@ -170,6 +170,7 @@ impl TokenizerConfigToken {
|
|||||||
#[serde(tag = "processor_class")]
|
#[serde(tag = "processor_class")]
|
||||||
pub enum HubPreprocessorConfig {
|
pub enum HubPreprocessorConfig {
|
||||||
Idefics2Processor(Idefics2Preprocessor),
|
Idefics2Processor(Idefics2Preprocessor),
|
||||||
|
Idefics3Processor(Idefics2Preprocessor),
|
||||||
}
|
}
|
||||||
|
|
||||||
impl HubPreprocessorConfig {
|
impl HubPreprocessorConfig {
|
||||||
|
|||||||
@ -7,7 +7,6 @@ use crate::{
|
|||||||
use crate::{PyTokenizer, Tokenizer};
|
use crate::{PyTokenizer, Tokenizer};
|
||||||
use base64::{engine::general_purpose::STANDARD, Engine};
|
use base64::{engine::general_purpose::STANDARD, Engine};
|
||||||
use image::{ImageFormat, ImageReader};
|
use image::{ImageFormat, ImageReader};
|
||||||
use jsonschema::{Draft, JSONSchema};
|
|
||||||
use outlines_core::json_schema::to_regex as json_schema_to_regex;
|
use outlines_core::json_schema::to_regex as json_schema_to_regex;
|
||||||
use rand::{thread_rng, Rng};
|
use rand::{thread_rng, Rng};
|
||||||
use serde_json::Value;
|
use serde_json::Value;
|
||||||
@ -355,9 +354,7 @@ impl Validation {
|
|||||||
}?;
|
}?;
|
||||||
|
|
||||||
// Check if the json is a valid JSONSchema
|
// Check if the json is a valid JSONSchema
|
||||||
JSONSchema::options()
|
jsonschema::draft202012::meta::validate(&json)
|
||||||
.with_draft(Draft::Draft202012)
|
|
||||||
.compile(&json)
|
|
||||||
.map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?;
|
.map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?;
|
||||||
|
|
||||||
// The schema can be valid but lack properties.
|
// The schema can be valid but lack properties.
|
||||||
@ -614,6 +611,73 @@ fn image_tokens(
|
|||||||
|
|
||||||
image_string
|
image_string
|
||||||
}
|
}
|
||||||
|
Idefics3(config) => {
|
||||||
|
const FAKE: &str = "<fake_token_around_image>";
|
||||||
|
const IMAGE: &str = "<image>";
|
||||||
|
const GLOBAL_IMG: &str = "<global-img>";
|
||||||
|
|
||||||
|
let max_longest_edge_for_image_resize = config.get_max_longest_edge_for_image_resize();
|
||||||
|
|
||||||
|
// resize image if it is larger than max_longest_edge_for_image_resize keeping aspect ratio
|
||||||
|
let (height, width) = if height > max_longest_edge_for_image_resize
|
||||||
|
|| width > max_longest_edge_for_image_resize
|
||||||
|
{
|
||||||
|
let aspect_ratio = height as f32 / width as f32;
|
||||||
|
if height > width {
|
||||||
|
(
|
||||||
|
max_longest_edge_for_image_resize,
|
||||||
|
(max_longest_edge_for_image_resize as f32 / aspect_ratio) as usize,
|
||||||
|
)
|
||||||
|
} else {
|
||||||
|
(
|
||||||
|
(max_longest_edge_for_image_resize as f32 * aspect_ratio) as usize,
|
||||||
|
max_longest_edge_for_image_resize,
|
||||||
|
)
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
(height, width)
|
||||||
|
};
|
||||||
|
|
||||||
|
let image_seq_len = config.get_number_of_features();
|
||||||
|
let max_edge = config.get_max_longest_edge();
|
||||||
|
|
||||||
|
let (image_rows, image_cols) = if height > max_edge || width > max_edge {
|
||||||
|
(
|
||||||
|
(height as f32 / max_edge as f32).ceil() as usize,
|
||||||
|
(width as f32 / max_edge as f32).ceil() as usize,
|
||||||
|
)
|
||||||
|
} else {
|
||||||
|
(0, 0)
|
||||||
|
};
|
||||||
|
|
||||||
|
let mut image_string = String::new();
|
||||||
|
|
||||||
|
if image_rows == 0 && image_cols == 0 {
|
||||||
|
// Single image case
|
||||||
|
image_string.push_str(FAKE);
|
||||||
|
image_string.push_str(GLOBAL_IMG);
|
||||||
|
image_string.push_str(&IMAGE.repeat(image_seq_len));
|
||||||
|
image_string.push_str(FAKE);
|
||||||
|
} else {
|
||||||
|
// Split image case
|
||||||
|
for n_h in 0..image_rows {
|
||||||
|
for n_w in 0..image_cols {
|
||||||
|
image_string.push_str(FAKE);
|
||||||
|
image_string.push_str(&format!("<row_{}_col_{}>", n_h + 1, n_w + 1));
|
||||||
|
image_string.push_str(&IMAGE.repeat(image_seq_len));
|
||||||
|
}
|
||||||
|
image_string.push('\n');
|
||||||
|
}
|
||||||
|
|
||||||
|
image_string.push('\n');
|
||||||
|
image_string.push_str(FAKE);
|
||||||
|
image_string.push_str(GLOBAL_IMG);
|
||||||
|
image_string.push_str(&IMAGE.repeat(image_seq_len));
|
||||||
|
image_string.push_str(FAKE);
|
||||||
|
}
|
||||||
|
|
||||||
|
image_string
|
||||||
|
}
|
||||||
Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
|
Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
|
||||||
LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
|
LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
|
||||||
Qwen2Vl(config) => format!(
|
Qwen2Vl(config) => format!(
|
||||||
@ -647,7 +711,8 @@ fn prepare_input<T: TokenizerTrait>(
|
|||||||
static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
|
static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
|
||||||
let (tokenizer_query, input_chunks) = match config {
|
let (tokenizer_query, input_chunks) = match config {
|
||||||
Some(
|
Some(
|
||||||
config @ (Idefics | Mllama | Idefics2(_) | Paligemma(_) | LlavaNext(_) | Qwen2Vl(_)),
|
config @ (Idefics | Mllama | Idefics2(_) | Idefics3(_) | Paligemma(_) | LlavaNext(_)
|
||||||
|
| Qwen2Vl(_)),
|
||||||
) => {
|
) => {
|
||||||
let mut input_chunks = Vec::new();
|
let mut input_chunks = Vec::new();
|
||||||
let mut tokenizer_query = String::with_capacity(inputs.len());
|
let mut tokenizer_query = String::with_capacity(inputs.len());
|
||||||
|
|||||||
@ -1,5 +1,5 @@
|
|||||||
flash_att_v2_commit_cuda := v2.6.1
|
flash_att_v2_commit_cuda := v2.6.1
|
||||||
flash_att_v2_commit_rocm := 2092111b9f975b3347c652ff7fabd431130256c4
|
flash_att_v2_commit_rocm := 47bd46e0204a95762ae48712fd1a3978827c77fd
|
||||||
|
|
||||||
build-flash-attention-v2-cuda:
|
build-flash-attention-v2-cuda:
|
||||||
pip install -U packaging wheel
|
pip install -U packaging wheel
|
||||||
|
|||||||
@ -1,2 +1,5 @@
|
|||||||
install-flashinfer:
|
install-flashinfer:
|
||||||
pip install flashinfer==0.1.6 -i https://flashinfer.ai/whl/cu124/torch2.4
|
# We need fsspec as an additional dependency, but
|
||||||
|
# `pip install flashinfer` cannot resolve it.
|
||||||
|
pip install fsspec
|
||||||
|
pip install flashinfer==0.2.0.post1 -i https://flashinfer.ai/whl/cu124/torch2.4
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247
|
commit_rocm := de990cd12537f78f74e40b5c8ee1a62d63d734dd
|
||||||
|
|
||||||
build-vllm-rocm:
|
build-vllm-rocm:
|
||||||
if [ ! -d 'vllm' ]; then \
|
if [ ! -d 'vllm' ]; then \
|
||||||
|
|||||||
28
server/poetry.lock
generated
28
server/poetry.lock
generated
@ -1,4 +1,4 @@
|
|||||||
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
|
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "accelerate"
|
name = "accelerate"
|
||||||
@ -1289,12 +1289,12 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "marlin-kernels"
|
name = "marlin-kernels"
|
||||||
version = "0.3.6"
|
version = "0.3.7"
|
||||||
description = "Marlin quantization kernels"
|
description = "Marlin quantization kernels"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:afedaa9a15e8991442bc8c81f62833fbf5c1556ae9d7a5a9e13b747ce97beef9"},
|
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:bb416d14623dc0ad0eeb2835446c37a41f994555f1baec8701de6d4c1fc17ec8"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -1302,16 +1302,16 @@ torch = "*"
|
|||||||
|
|
||||||
[package.source]
|
[package.source]
|
||||||
type = "url"
|
type = "url"
|
||||||
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
|
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "marlin-kernels"
|
name = "marlin-kernels"
|
||||||
version = "0.3.6"
|
version = "0.3.7"
|
||||||
description = "Marlin quantization kernels"
|
description = "Marlin quantization kernels"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:c0c05621d5e87144415d8a6e439072bd844d5f3cb55e4c4c69eabdc4c94610f4"},
|
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:a89bb61d718002d4432158641bce95c6fd68f9ee1a7d5402dd283903397f3185"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -1319,16 +1319,16 @@ torch = "*"
|
|||||||
|
|
||||||
[package.source]
|
[package.source]
|
||||||
type = "url"
|
type = "url"
|
||||||
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
|
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "marlin-kernels"
|
name = "marlin-kernels"
|
||||||
version = "0.3.6"
|
version = "0.3.7"
|
||||||
description = "Marlin quantization kernels"
|
description = "Marlin quantization kernels"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:3be4662c8d25a3cdb1793dafe0e2e76dd600913a69a468e2c68d1fed4e149255"},
|
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:ed938d196fc5e9cce9fc44cd2b889d5adc5ca7475c8a23858f1474d29e38bdbf"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -1336,16 +1336,16 @@ torch = "*"
|
|||||||
|
|
||||||
[package.source]
|
[package.source]
|
||||||
type = "url"
|
type = "url"
|
||||||
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
|
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "marlin-kernels"
|
name = "marlin-kernels"
|
||||||
version = "0.3.6"
|
version = "0.3.7"
|
||||||
description = "Marlin quantization kernels"
|
description = "Marlin quantization kernels"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:89eac9d46bc084a256b538afda6053683eb7e505db0e0d4f6dbeca32368caac6"},
|
{file = "marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:113c54f68565ad476ca12366b4de92131fa3e9ddb16cbe8ad63272972a15ac28"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -1353,7 +1353,7 @@ torch = "*"
|
|||||||
|
|
||||||
[package.source]
|
[package.source]
|
||||||
type = "url"
|
type = "url"
|
||||||
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
|
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "mdurl"
|
name = "mdurl"
|
||||||
@ -4097,4 +4097,4 @@ torch = ["torch"]
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = ">=3.9,<3.13"
|
python-versions = ">=3.9,<3.13"
|
||||||
content-hash = "c7fdcff2b752cd3beb3995c1ecd15f0f4d9b4e117048b06ab991c6d0e0c86ff3"
|
content-hash = "25f96d5dea777bfa7a959f863e35d2e05e1a6172d0dd45193dbe25ac2f32cc25"
|
||||||
|
|||||||
@ -48,10 +48,10 @@ attention-kernels = [
|
|||||||
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
|
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
|
||||||
]
|
]
|
||||||
marlin-kernels = [
|
marlin-kernels = [
|
||||||
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
|
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
|
||||||
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
|
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
|
||||||
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
|
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
|
||||||
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.6/marlin_kernels-0.3.6+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
|
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
|
||||||
]
|
]
|
||||||
moe-kernels = [
|
moe-kernels = [
|
||||||
{ url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
|
{ url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
|
||||||
|
|||||||
@ -60,8 +60,7 @@ def paged_attention(
|
|||||||
from text_generation_server.layers.attention.flashinfer import decode_state
|
from text_generation_server.layers.attention.flashinfer import decode_state
|
||||||
|
|
||||||
return decode_state.get().forward(
|
return decode_state.get().forward(
|
||||||
# TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged.
|
query,
|
||||||
query.contiguous(),
|
|
||||||
paged_kv_cache=(kv_cache.key, kv_cache.value),
|
paged_kv_cache=(kv_cache.key, kv_cache.value),
|
||||||
logits_soft_cap=softcap,
|
logits_soft_cap=softcap,
|
||||||
sm_scale=softmax_scale,
|
sm_scale=softmax_scale,
|
||||||
@ -231,8 +230,7 @@ def attention(
|
|||||||
softcap = 0.0
|
softcap = 0.0
|
||||||
|
|
||||||
return prefill_with_paged_kv_state.get().forward(
|
return prefill_with_paged_kv_state.get().forward(
|
||||||
# TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged.
|
query,
|
||||||
query.contiguous(),
|
|
||||||
causal=causal,
|
causal=causal,
|
||||||
paged_kv_cache=(kv_cache.key, kv_cache.value),
|
paged_kv_cache=(kv_cache.key, kv_cache.value),
|
||||||
logits_soft_cap=softcap,
|
logits_soft_cap=softcap,
|
||||||
|
|||||||
@ -50,7 +50,8 @@ def use_prefill_with_paged_kv_state(
|
|||||||
num_kv_heads: int,
|
num_kv_heads: int,
|
||||||
head_size: int,
|
head_size: int,
|
||||||
page_size: int,
|
page_size: int,
|
||||||
dtype: torch.dtype,
|
kv_dtype: torch.dtype,
|
||||||
|
q_dtype: torch.dtype,
|
||||||
window_left: int,
|
window_left: int,
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
@ -91,9 +92,10 @@ def use_prefill_with_paged_kv_state(
|
|||||||
num_qo_heads=num_heads,
|
num_qo_heads=num_heads,
|
||||||
num_kv_heads=num_kv_heads,
|
num_kv_heads=num_kv_heads,
|
||||||
head_dim=head_size,
|
head_dim=head_size,
|
||||||
q_data_type=dtype,
|
kv_data_type=kv_dtype,
|
||||||
|
q_data_type=q_dtype,
|
||||||
page_size=page_size,
|
page_size=page_size,
|
||||||
window_left=window_left,
|
window_left=-1 if window_left is None else window_left,
|
||||||
)
|
)
|
||||||
yield
|
yield
|
||||||
finally:
|
finally:
|
||||||
@ -113,41 +115,6 @@ def create_prefill_state(
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@contextmanager
|
|
||||||
def use_prefill_state(
|
|
||||||
*,
|
|
||||||
state: flashinfer.BatchPrefillWithRaggedKVCacheWrapper,
|
|
||||||
cu_seqlens: torch.Tensor,
|
|
||||||
num_heads: int,
|
|
||||||
num_kv_heads: int,
|
|
||||||
head_size: int,
|
|
||||||
dtype: torch.dtype,
|
|
||||||
window_left: int,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Context manager to set the active flashinfer prefill state to the given
|
|
||||||
`state` and parameters. This state will be used by all calls to the
|
|
||||||
`attention` function while the context manager is active.
|
|
||||||
"""
|
|
||||||
|
|
||||||
token = prefill_state.set(state)
|
|
||||||
try:
|
|
||||||
state.begin_forward(
|
|
||||||
qo_indptr=cu_seqlens,
|
|
||||||
kv_indptr=cu_seqlens,
|
|
||||||
num_qo_heads=num_heads,
|
|
||||||
num_kv_heads=num_kv_heads,
|
|
||||||
head_dim=head_size,
|
|
||||||
q_data_type=dtype,
|
|
||||||
window_left=window_left,
|
|
||||||
)
|
|
||||||
yield
|
|
||||||
finally:
|
|
||||||
state.end_forward()
|
|
||||||
if token is not None:
|
|
||||||
prefill_state.reset(token)
|
|
||||||
|
|
||||||
|
|
||||||
def create_decode_state(
|
def create_decode_state(
|
||||||
*,
|
*,
|
||||||
device: torch.device,
|
device: torch.device,
|
||||||
@ -205,7 +172,7 @@ def use_decode_state(
|
|||||||
head_size: int,
|
head_size: int,
|
||||||
page_size: int,
|
page_size: int,
|
||||||
kv_cache_dtype: torch.dtype,
|
kv_cache_dtype: torch.dtype,
|
||||||
dtype: torch.dtype,
|
q_dtype: torch.dtype,
|
||||||
window_left: int,
|
window_left: int,
|
||||||
):
|
):
|
||||||
"""
|
"""
|
||||||
@ -242,8 +209,8 @@ def use_decode_state(
|
|||||||
head_dim=head_size,
|
head_dim=head_size,
|
||||||
page_size=page_size,
|
page_size=page_size,
|
||||||
data_type=kv_cache_dtype,
|
data_type=kv_cache_dtype,
|
||||||
q_data_type=dtype,
|
q_data_type=q_dtype,
|
||||||
window_left=window_left,
|
window_left=-1 if window_left is None else window_left,
|
||||||
)
|
)
|
||||||
yield
|
yield
|
||||||
finally:
|
finally:
|
||||||
|
|||||||
@ -215,7 +215,9 @@ def paged_reshape_and_cache(
|
|||||||
raise ImportError(
|
raise ImportError(
|
||||||
f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
|
f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
|
||||||
)
|
)
|
||||||
ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
|
ops.reshape_and_cache(
|
||||||
|
key, value, key_cache, value_cache, slots, "auto", 1.0, 1.0
|
||||||
|
)
|
||||||
elif SYSTEM == "ipex":
|
elif SYSTEM == "ipex":
|
||||||
import intel_extension_for_pytorch as ipex
|
import intel_extension_for_pytorch as ipex
|
||||||
|
|
||||||
|
|||||||
@ -5,27 +5,47 @@ from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
|
|||||||
from text_generation_server.utils.import_utils import SYSTEM
|
from text_generation_server.utils.import_utils import SYSTEM
|
||||||
from text_generation_server.layers.attention import Seqlen
|
from text_generation_server.layers.attention import Seqlen
|
||||||
from text_generation_server.utils.log import log_master
|
from text_generation_server.utils.log import log_master
|
||||||
|
from text_generation_server.models.globals import (
|
||||||
|
ATTENTION,
|
||||||
|
BLOCK_SIZE,
|
||||||
|
)
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
|
import vllm._custom_ops as ops
|
||||||
|
|
||||||
major, minor = torch.cuda.get_device_capability()
|
major, minor = torch.cuda.get_device_capability()
|
||||||
is_sm75 = major == 7 and minor == 5
|
is_sm75 = major == 7 and minor == 5
|
||||||
|
|
||||||
_PARTITION_SIZE_V1V2 = 512
|
_PARTITION_SIZE_V1V2 = 1024
|
||||||
_PARTITION_SIZE_CUSTOM = 256
|
_PARTITION_SIZE_CUSTOM = 256
|
||||||
|
|
||||||
|
_GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
|
||||||
|
_ON_MI250_MI300 = any(
|
||||||
|
arch in _GPU_ARCH for arch in ["gfx90a", "gfx940", "gfx941", "gfx942"]
|
||||||
|
)
|
||||||
|
|
||||||
use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"}
|
use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"}
|
||||||
ENGINE = "triton" if use_triton else "ck"
|
ENGINE = "triton" if use_triton else "ck"
|
||||||
|
|
||||||
use_rocm_custom_paged_attn = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0"
|
use_rocm_custom_paged_attn = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0"
|
||||||
try:
|
|
||||||
if use_rocm_custom_paged_attn:
|
|
||||||
from vllm._custom_C import paged_attention_custom
|
def _use_rocm_custom_paged_attention(
|
||||||
except ImportError as e:
|
qtype: torch.dtype,
|
||||||
log_master(
|
head_size: int,
|
||||||
logger.info,
|
block_size: int,
|
||||||
f"Custom Paged Attention not available. Complete error: {e}",
|
gqa_ratio: int,
|
||||||
|
max_seq_len: int,
|
||||||
|
) -> bool:
|
||||||
|
# rocm custom page attention not support on navi (gfx1*)
|
||||||
|
return (
|
||||||
|
use_rocm_custom_paged_attn
|
||||||
|
and _ON_MI250_MI300
|
||||||
|
and (qtype == torch.half or qtype == torch.bfloat16)
|
||||||
|
and (head_size == 64 or head_size == 128)
|
||||||
|
and (block_size == 16 or block_size == 32)
|
||||||
|
and (gqa_ratio >= 1 and gqa_ratio <= 16)
|
||||||
|
and max_seq_len <= 131072
|
||||||
)
|
)
|
||||||
use_rocm_custom_paged_attn = False
|
|
||||||
|
|
||||||
|
|
||||||
def paged_attention(
|
def paged_attention(
|
||||||
@ -57,22 +77,50 @@ def paged_attention(
|
|||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
#
|
#
|
||||||
|
|
||||||
|
if ATTENTION == "flashdecoding":
|
||||||
|
max_q = 1
|
||||||
|
max_k = max_s
|
||||||
|
import flash_attn_2_cuda
|
||||||
|
|
||||||
|
if softcap is None:
|
||||||
|
softcap = 0.0
|
||||||
|
out = flash_attn_2_cuda.varlen_fwd(
|
||||||
|
query,
|
||||||
|
kv_cache.key,
|
||||||
|
kv_cache.value,
|
||||||
|
None,
|
||||||
|
seqlen.cu_seqlen_q,
|
||||||
|
seqlen.cu_seqlen_k,
|
||||||
|
None, # pad_k
|
||||||
|
None,
|
||||||
|
block_tables,
|
||||||
|
None,
|
||||||
|
max_q,
|
||||||
|
max_k,
|
||||||
|
0.0, # dropout
|
||||||
|
softmax_scale,
|
||||||
|
False, # zero_tensors
|
||||||
|
True, # causal
|
||||||
|
-1, # Window_left
|
||||||
|
-1, # Window right
|
||||||
|
softcap,
|
||||||
|
False, # return softmax
|
||||||
|
None, # generator
|
||||||
|
)
|
||||||
|
return out[0]
|
||||||
|
|
||||||
if softcap is not None:
|
if softcap is not None:
|
||||||
raise RuntimeError("Paged attention doesn't support softcapping")
|
raise RuntimeError("Paged attention doesn't support softcapping")
|
||||||
|
|
||||||
# value_cache => [num_blocks, num_heads, head_size, block_size]
|
# value_cache => [num_blocks, num_heads, head_size, block_size]
|
||||||
block_size = kv_cache.value.shape[3]
|
# block_size = kv_cache.value.shape[3]
|
||||||
|
block_size = BLOCK_SIZE
|
||||||
num_seqs, num_heads, head_size = query.shape
|
num_seqs, num_heads, head_size = query.shape
|
||||||
|
|
||||||
num_kv_heads = kv_cache.key.shape[1]
|
num_kv_heads = kv_cache.key.shape[1]
|
||||||
gqa_ratio = num_heads // num_kv_heads
|
gqa_ratio = num_heads // num_kv_heads
|
||||||
use_custom = (
|
use_custom = _use_rocm_custom_paged_attention(
|
||||||
use_rocm_custom_paged_attn
|
query.dtype, head_size, block_size, gqa_ratio, max_s
|
||||||
and (query.dtype == torch.half or query.dtype == torch.bfloat16)
|
|
||||||
and (head_size == 128 or head_size == 64)
|
|
||||||
and (block_size == 16 or block_size == 32)
|
|
||||||
and (gqa_ratio >= 1 and gqa_ratio <= 16)
|
|
||||||
and max_s <= 32768
|
|
||||||
)
|
)
|
||||||
|
|
||||||
if not use_custom:
|
if not use_custom:
|
||||||
@ -90,8 +138,6 @@ def paged_attention(
|
|||||||
# V1 to avoid the overhead of reduction. Also, if the number of
|
# V1 to avoid the overhead of reduction. Also, if the number of
|
||||||
# sequences or heads is large, we use V1 since there is enough work
|
# sequences or heads is large, we use V1 since there is enough work
|
||||||
# to parallelize.
|
# to parallelize.
|
||||||
import vllm._custom_ops as ops
|
|
||||||
|
|
||||||
use_v1 = (
|
use_v1 = (
|
||||||
max_s <= 8192
|
max_s <= 8192
|
||||||
and (max_num_partitions == 1 or num_seqs * num_heads > 512)
|
and (max_num_partitions == 1 or num_seqs * num_heads > 512)
|
||||||
@ -103,7 +149,7 @@ def paged_attention(
|
|||||||
query,
|
query,
|
||||||
kv_cache.key,
|
kv_cache.key,
|
||||||
kv_cache.value,
|
kv_cache.value,
|
||||||
kv_head_mapping,
|
num_kv_heads,
|
||||||
softmax_scale,
|
softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
@ -112,6 +158,7 @@ def paged_attention(
|
|||||||
None,
|
None,
|
||||||
"auto",
|
"auto",
|
||||||
1.0,
|
1.0,
|
||||||
|
1.0,
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
# Run PagedAttention V2.
|
# Run PagedAttention V2.
|
||||||
@ -137,7 +184,7 @@ def paged_attention(
|
|||||||
query,
|
query,
|
||||||
kv_cache.key,
|
kv_cache.key,
|
||||||
kv_cache.value,
|
kv_cache.value,
|
||||||
kv_head_mapping,
|
num_kv_heads,
|
||||||
softmax_scale,
|
softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
@ -146,9 +193,10 @@ def paged_attention(
|
|||||||
None,
|
None,
|
||||||
"auto",
|
"auto",
|
||||||
1.0,
|
1.0,
|
||||||
|
1.0,
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
paged_attention_custom(
|
ops.paged_attention_rocm(
|
||||||
out,
|
out,
|
||||||
exp_sums,
|
exp_sums,
|
||||||
max_logits,
|
max_logits,
|
||||||
@ -164,6 +212,10 @@ def paged_attention(
|
|||||||
max_s,
|
max_s,
|
||||||
None,
|
None,
|
||||||
"auto",
|
"auto",
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
None,
|
||||||
|
_PARTITION_SIZE,
|
||||||
)
|
)
|
||||||
|
|
||||||
return out
|
return out
|
||||||
@ -232,14 +284,15 @@ def attention(
|
|||||||
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
|
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
|
||||||
return flash_attn_2_cuda.varlen_fwd(
|
return flash_attn_2_cuda.varlen_fwd(
|
||||||
query,
|
query,
|
||||||
key,
|
# flashdecoding: pass the KV caches, paged: pass the KV.
|
||||||
value,
|
kv_cache.key if ATTENTION == "flashdecoding" else key,
|
||||||
|
kv_cache.value if ATTENTION == "flashdecoding" else value,
|
||||||
out,
|
out,
|
||||||
seqlen.cu_seqlen_q,
|
seqlen.cu_seqlen_q,
|
||||||
seqlen.cu_seqlen_q,
|
seqlen.cu_seqlen_k,
|
||||||
None,
|
|
||||||
None,
|
None,
|
||||||
None,
|
None,
|
||||||
|
block_tables if ATTENTION == "flashdecoding" else None,
|
||||||
None,
|
None,
|
||||||
seqlen.max_q,
|
seqlen.max_q,
|
||||||
seqlen.max_k,
|
seqlen.max_k,
|
||||||
|
|||||||
@ -72,7 +72,7 @@ if SYSTEM == "cuda":
|
|||||||
return normed_hidden_states, residual
|
return normed_hidden_states, residual
|
||||||
|
|
||||||
elif SYSTEM == "rocm":
|
elif SYSTEM == "rocm":
|
||||||
from vllm._C import ops
|
import vllm._custom_ops as ops
|
||||||
|
|
||||||
class FastLayerNorm(nn.LayerNorm):
|
class FastLayerNorm(nn.LayerNorm):
|
||||||
def forward(self, hidden_states, residual=None):
|
def forward(self, hidden_states, residual=None):
|
||||||
@ -121,6 +121,27 @@ class FastRMSNorm(nn.Module):
|
|||||||
residual is not None,
|
residual is not None,
|
||||||
)
|
)
|
||||||
return out, residual if residual is not None else hidden_states
|
return out, residual if residual is not None else hidden_states
|
||||||
|
elif SYSTEM == "rocm":
|
||||||
|
# We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not.
|
||||||
|
if residual is not None:
|
||||||
|
ops.fused_add_rms_norm(
|
||||||
|
hidden_states,
|
||||||
|
residual,
|
||||||
|
self.weight.data,
|
||||||
|
self.variance_epsilon,
|
||||||
|
)
|
||||||
|
return hidden_states, residual
|
||||||
|
|
||||||
|
residual = hidden_states
|
||||||
|
|
||||||
|
out = torch.empty_like(hidden_states)
|
||||||
|
ops.rms_norm(
|
||||||
|
out,
|
||||||
|
hidden_states,
|
||||||
|
self.weight.data,
|
||||||
|
self.variance_epsilon,
|
||||||
|
)
|
||||||
|
return out, residual
|
||||||
elif hidden_states.shape[-1] > 8192:
|
elif hidden_states.shape[-1] > 8192:
|
||||||
if residual is not None:
|
if residual is not None:
|
||||||
hidden_states += residual
|
hidden_states += residual
|
||||||
@ -164,20 +185,6 @@ class FastRMSNorm(nn.Module):
|
|||||||
res = hidden_states
|
res = hidden_states
|
||||||
|
|
||||||
return normed_hidden_states, res
|
return normed_hidden_states, res
|
||||||
elif SYSTEM == "rocm":
|
|
||||||
# We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not.
|
|
||||||
if residual is not None:
|
|
||||||
hidden_states += residual
|
|
||||||
residual = hidden_states
|
|
||||||
|
|
||||||
out = torch.empty_like(hidden_states)
|
|
||||||
ops.rms_norm(
|
|
||||||
out,
|
|
||||||
hidden_states,
|
|
||||||
self.weight.data,
|
|
||||||
self.variance_epsilon,
|
|
||||||
)
|
|
||||||
return out, residual
|
|
||||||
else:
|
else:
|
||||||
raise ValueError(
|
raise ValueError(
|
||||||
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
|
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
|
||||||
|
|||||||
@ -11,10 +11,10 @@ if SYSTEM == "rocm":
|
|||||||
|
|
||||||
if ROCM_USE_SKINNY_GEMM:
|
if ROCM_USE_SKINNY_GEMM:
|
||||||
try:
|
try:
|
||||||
from vllm import _custom_C
|
import vllm._custom_ops as ops
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise ImportError(
|
raise ImportError(
|
||||||
f"Could not load `vllm._custom_C` for ROCm skinny gemm. Full error: {e}"
|
f"Could not load `vllm._custom_ops` for ROCm skinny gemm. Full error: {e}"
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@ -95,12 +95,12 @@ class FastLinearROCm(torch.nn.Module):
|
|||||||
out = torch.empty(
|
out = torch.empty(
|
||||||
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
|
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
|
||||||
)
|
)
|
||||||
_custom_C.wvSpltK(weight, inp, out, n, self.cu_count)
|
ops.wvSpltK(weight, inp, out, n, self.cu_count)
|
||||||
elif m % 4 == 0 and n == 1 and k <= 8192:
|
elif m % 4 == 0 and n == 1 and k <= 8192:
|
||||||
out = torch.empty(
|
out = torch.empty(
|
||||||
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
|
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
|
||||||
)
|
)
|
||||||
_custom_C.LLMM1(weight, inp, out, 4)
|
ops.LLMM1(weight, inp, out, 4)
|
||||||
else:
|
else:
|
||||||
out = F.linear(inp, weight)
|
out = F.linear(inp, weight)
|
||||||
|
|
||||||
|
|||||||
@ -24,10 +24,7 @@ from text_generation_server.utils.weights import (
|
|||||||
UnquantizedWeight,
|
UnquantizedWeight,
|
||||||
)
|
)
|
||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "ipex":
|
||||||
from .fused_moe_rocm import grouped_topk
|
|
||||||
from vllm.model_executor.layers.fused_moe import fused_topk
|
|
||||||
elif SYSTEM == "ipex":
|
|
||||||
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
||||||
else:
|
else:
|
||||||
from moe_kernels.fused_moe import fused_topk, grouped_topk
|
from moe_kernels.fused_moe import fused_topk, grouped_topk
|
||||||
|
|||||||
@ -1,52 +0,0 @@
|
|||||||
# coding=utf-8
|
|
||||||
# Copyright 2023, 2024 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
|
|
||||||
from typing import Tuple
|
|
||||||
|
|
||||||
import torch
|
|
||||||
import torch.distributed
|
|
||||||
|
|
||||||
|
|
||||||
# TODO: Remove the functions once moe_kernel are built for ROCM
|
|
||||||
def grouped_topk(
|
|
||||||
hidden_states: torch.Tensor,
|
|
||||||
gating_output: torch.Tensor,
|
|
||||||
topk: int,
|
|
||||||
renormalize: bool,
|
|
||||||
num_expert_group: int = 0,
|
|
||||||
topk_group: int = 0,
|
|
||||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
||||||
scores = torch.softmax(gating_output, dim=-1)
|
|
||||||
num_token = scores.shape[0]
|
|
||||||
group_scores = (
|
|
||||||
scores.view(num_token, num_expert_group, -1).max(dim=-1).values
|
|
||||||
) # [n, n_group]
|
|
||||||
group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
|
|
||||||
1
|
|
||||||
] # [n, top_k_group]
|
|
||||||
group_mask = torch.zeros_like(group_scores) # [n, n_group]
|
|
||||||
group_mask.scatter_(1, group_idx, 1) # [n, n_group]
|
|
||||||
score_mask = (
|
|
||||||
group_mask.unsqueeze(-1)
|
|
||||||
.expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
|
|
||||||
.reshape(num_token, -1)
|
|
||||||
) # [n, e]
|
|
||||||
tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
|
|
||||||
topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
|
|
||||||
|
|
||||||
if renormalize:
|
|
||||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
|
||||||
|
|
||||||
return topk_weights, topk_ids
|
|
||||||
@ -6,9 +6,7 @@ import torch.nn as nn
|
|||||||
from text_generation_server.utils.import_utils import SYSTEM
|
from text_generation_server.utils.import_utils import SYSTEM
|
||||||
from text_generation_server.utils.weights import UnquantizedWeight, Weights
|
from text_generation_server.utils.weights import UnquantizedWeight, Weights
|
||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "ipex":
|
||||||
from vllm.model_executor.layers.fused_moe import fused_moe
|
|
||||||
elif SYSTEM == "ipex":
|
|
||||||
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
||||||
else:
|
else:
|
||||||
from moe_kernels.fused_moe import fused_moe
|
from moe_kernels.fused_moe import fused_moe
|
||||||
|
|||||||
@ -7,7 +7,7 @@ from text_generation_server.utils.import_utils import SYSTEM
|
|||||||
if SYSTEM == "cuda":
|
if SYSTEM == "cuda":
|
||||||
import rotary_emb
|
import rotary_emb
|
||||||
elif SYSTEM == "rocm":
|
elif SYSTEM == "rocm":
|
||||||
from vllm._C import ops
|
import vllm._custom_ops as ops
|
||||||
elif SYSTEM == "ipex":
|
elif SYSTEM == "ipex":
|
||||||
import intel_extension_for_pytorch as ipex
|
import intel_extension_for_pytorch as ipex
|
||||||
|
|
||||||
|
|||||||
@ -152,6 +152,9 @@ try:
|
|||||||
from text_generation_server.models.custom_modeling.idefics2 import (
|
from text_generation_server.models.custom_modeling.idefics2 import (
|
||||||
Idefics2ForConditionalGeneration,
|
Idefics2ForConditionalGeneration,
|
||||||
)
|
)
|
||||||
|
from text_generation_server.models.custom_modeling.idefics3 import (
|
||||||
|
Idefics3ForConditionalGeneration,
|
||||||
|
)
|
||||||
from text_generation_server.models.custom_modeling.qwen2_vl import (
|
from text_generation_server.models.custom_modeling.qwen2_vl import (
|
||||||
Qwen2VLForConditionalGeneration,
|
Qwen2VLForConditionalGeneration,
|
||||||
)
|
)
|
||||||
@ -188,6 +191,12 @@ class ModelType(enum.Enum):
|
|||||||
"url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
|
"url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
|
||||||
"multimodal": True,
|
"multimodal": True,
|
||||||
}
|
}
|
||||||
|
IDEFICS3 = {
|
||||||
|
"type": "idefics3",
|
||||||
|
"name": "Idefics 3",
|
||||||
|
"url": "https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3",
|
||||||
|
"multimodal": True,
|
||||||
|
}
|
||||||
LLAVA_NEXT = {
|
LLAVA_NEXT = {
|
||||||
"type": "llava_next",
|
"type": "llava_next",
|
||||||
"name": "Llava Next (1.6)",
|
"name": "Llava Next (1.6)",
|
||||||
@ -1253,6 +1262,24 @@ def get_model(
|
|||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
|
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
|
||||||
|
if model_type == IDEFICS3:
|
||||||
|
if FLASH_ATTENTION:
|
||||||
|
return VlmCausalLM(
|
||||||
|
model_id=model_id,
|
||||||
|
model_class=Idefics3ForConditionalGeneration,
|
||||||
|
revision=revision,
|
||||||
|
quantize=quantize,
|
||||||
|
speculator=speculator,
|
||||||
|
dtype=dtype,
|
||||||
|
default_dtype=torch.bfloat16,
|
||||||
|
trust_remote_code=trust_remote_code,
|
||||||
|
lora_adapter_ids=lora_adapter_ids,
|
||||||
|
# XXX: Extremely important to cap resolution in order to limit
|
||||||
|
# VRAM usage.
|
||||||
|
processor_kwargs={"size": {"longest_edge": 1456}},
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
|
||||||
if model_type == PALIGEMMA:
|
if model_type == PALIGEMMA:
|
||||||
if FLASH_ATTENTION:
|
if FLASH_ATTENTION:
|
||||||
return VlmCausalLM(
|
return VlmCausalLM(
|
||||||
|
|||||||
@ -75,7 +75,7 @@ class CohereRotary(PositionRotaryEmbedding):
|
|||||||
|
|
||||||
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
||||||
elif SYSTEM == "rocm":
|
elif SYSTEM == "rocm":
|
||||||
from vllm._C import ops
|
import vllm._custom_ops as ops
|
||||||
|
|
||||||
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
||||||
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
||||||
|
|||||||
@ -23,9 +23,7 @@ from typing import Optional, List, Tuple, Any
|
|||||||
from text_generation_server.layers.attention.kv_cache import get_kv_scales
|
from text_generation_server.layers.attention.kv_cache import get_kv_scales
|
||||||
from text_generation_server.utils.import_utils import SYSTEM
|
from text_generation_server.utils.import_utils import SYSTEM
|
||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "ipex":
|
||||||
from vllm.model_executor.layers.fused_moe import fused_moe
|
|
||||||
elif SYSTEM == "ipex":
|
|
||||||
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
|
||||||
else:
|
else:
|
||||||
from moe_kernels.fused_moe import fused_moe
|
from moe_kernels.fused_moe import fused_moe
|
||||||
|
|||||||
@ -43,9 +43,9 @@ from text_generation_server.utils.weights import Weights
|
|||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "rocm":
|
||||||
try:
|
try:
|
||||||
from vllm import _custom_C
|
import vllm._custom_ops as ops
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
|
raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}")
|
||||||
|
|
||||||
|
|
||||||
class DeepseekV2Config(PretrainedConfig):
|
class DeepseekV2Config(PretrainedConfig):
|
||||||
@ -408,7 +408,7 @@ class DeepseekV2MLP(nn.Module):
|
|||||||
dtype=hidden_states.dtype,
|
dtype=hidden_states.dtype,
|
||||||
device="cuda",
|
device="cuda",
|
||||||
)
|
)
|
||||||
_custom_C.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8)
|
ops.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8)
|
||||||
return self.down_proj(out, reduce=reduce)
|
return self.down_proj(out, reduce=reduce)
|
||||||
else:
|
else:
|
||||||
gate_up_states = self.gate_up_proj(hidden_states)
|
gate_up_states = self.gate_up_proj(hidden_states)
|
||||||
|
|||||||
@ -91,7 +91,7 @@ class GPTJRotary(PositionRotaryEmbedding):
|
|||||||
|
|
||||||
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
||||||
elif SYSTEM == "rocm":
|
elif SYSTEM == "rocm":
|
||||||
from vllm._C import ops
|
import vllm._custom_ops as ops
|
||||||
|
|
||||||
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
||||||
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
||||||
|
|||||||
@ -64,9 +64,9 @@ if SYSTEM != "ipex":
|
|||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "rocm":
|
||||||
try:
|
try:
|
||||||
from vllm import _custom_C
|
import vllm._custom_ops as ops
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
|
raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}")
|
||||||
|
|
||||||
|
|
||||||
def load_attention(config, prefix: str, weights, layer_id):
|
def load_attention(config, prefix: str, weights, layer_id):
|
||||||
@ -392,7 +392,7 @@ class LlamaMLP(nn.Module):
|
|||||||
dtype=hidden_states.dtype,
|
dtype=hidden_states.dtype,
|
||||||
device="cuda",
|
device="cuda",
|
||||||
)
|
)
|
||||||
_custom_C.LLMM_Silu(
|
ops.LLMM_Silu(
|
||||||
self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8
|
self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8
|
||||||
)
|
)
|
||||||
return self.down_proj(out, adapter_data)
|
return self.down_proj(out, adapter_data)
|
||||||
@ -515,9 +515,7 @@ class FlashLlamaModel(torch.nn.Module):
|
|||||||
self.layers.append(
|
self.layers.append(
|
||||||
FlashLlamaLayer(
|
FlashLlamaLayer(
|
||||||
index=0,
|
index=0,
|
||||||
prefix=(
|
prefix=f"{prefix}.layers.0",
|
||||||
"model.layers.0" if not prefix else f"{prefix}.model.layers.0"
|
|
||||||
),
|
|
||||||
config=config,
|
config=config,
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
@ -533,11 +531,7 @@ class FlashLlamaModel(torch.nn.Module):
|
|||||||
self.layers.append(
|
self.layers.append(
|
||||||
FlashLlamaCrossLayer(
|
FlashLlamaCrossLayer(
|
||||||
index=layer_id,
|
index=layer_id,
|
||||||
prefix=(
|
prefix=(f"{prefix}.layers.{layer_id}"),
|
||||||
f"model.layers.{layer_id}"
|
|
||||||
if not prefix
|
|
||||||
else f"{prefix}.model.layers.{layer_id}"
|
|
||||||
),
|
|
||||||
config=config,
|
config=config,
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
@ -546,11 +540,7 @@ class FlashLlamaModel(torch.nn.Module):
|
|||||||
self.layers.append(
|
self.layers.append(
|
||||||
FlashLlamaLayer(
|
FlashLlamaLayer(
|
||||||
index=layer_id,
|
index=layer_id,
|
||||||
prefix=(
|
prefix=(f"{prefix}.layers.{layer_id}"),
|
||||||
f"model.layers.{layer_id}"
|
|
||||||
if not prefix
|
|
||||||
else f"{prefix}.model.layers.{layer_id}"
|
|
||||||
),
|
|
||||||
config=config,
|
config=config,
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
@ -561,18 +551,14 @@ class FlashLlamaModel(torch.nn.Module):
|
|||||||
self.layers.append(
|
self.layers.append(
|
||||||
FlashLlamaLayer(
|
FlashLlamaLayer(
|
||||||
index=last_layer_id,
|
index=last_layer_id,
|
||||||
prefix=(
|
prefix=(f"{prefix}.layers.{last_layer_id}"),
|
||||||
f"model.layers.{last_layer_id}"
|
|
||||||
if not prefix
|
|
||||||
else f"{prefix}.model.layers.{last_layer_id}"
|
|
||||||
),
|
|
||||||
config=config,
|
config=config,
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
|
||||||
self.norm = FastRMSNorm.load(
|
self.norm = FastRMSNorm.load(
|
||||||
prefix="model.norm" if not prefix else f"{prefix}.model.norm",
|
prefix=f"{prefix}.norm",
|
||||||
weights=weights,
|
weights=weights,
|
||||||
eps=config.rms_norm_eps,
|
eps=config.rms_norm_eps,
|
||||||
)
|
)
|
||||||
@ -629,19 +615,24 @@ class FlashLlamaModel(torch.nn.Module):
|
|||||||
|
|
||||||
|
|
||||||
class FlashLlamaForCausalLM(torch.nn.Module):
|
class FlashLlamaForCausalLM(torch.nn.Module):
|
||||||
def __init__(self, prefix: str, config, weights):
|
def __init__(self, prefix: str, config, weights, name=None):
|
||||||
|
if name is None:
|
||||||
|
name = "model"
|
||||||
super().__init__()
|
super().__init__()
|
||||||
|
|
||||||
with no_fp8(weights):
|
with no_fp8(weights):
|
||||||
self.embed_tokens = TensorParallelEmbedding(
|
self.embed_tokens = TensorParallelEmbedding(
|
||||||
prefix=(
|
prefix=(
|
||||||
"model.embed_tokens"
|
f"{name}.embed_tokens"
|
||||||
if not prefix
|
if not prefix
|
||||||
else f"{prefix}.model.embed_tokens"
|
else f"{prefix}.{name}.embed_tokens"
|
||||||
),
|
),
|
||||||
weights=weights,
|
weights=weights,
|
||||||
)
|
)
|
||||||
self.model = FlashLlamaModel(prefix, config, weights)
|
self.model = FlashLlamaModel(
|
||||||
|
prefix=name if not prefix else f"{prefix}.{name}",
|
||||||
|
config=config,
|
||||||
|
weights=weights,
|
||||||
|
)
|
||||||
if config.tie_word_embeddings:
|
if config.tie_word_embeddings:
|
||||||
suffix = "model.embed_tokens"
|
suffix = "model.embed_tokens"
|
||||||
else:
|
else:
|
||||||
@ -652,11 +643,13 @@ class FlashLlamaForCausalLM(torch.nn.Module):
|
|||||||
if embedding_multiplier is not None:
|
if embedding_multiplier is not None:
|
||||||
self.embed_tokens.weight.data *= embedding_multiplier
|
self.embed_tokens.weight.data *= embedding_multiplier
|
||||||
|
|
||||||
|
prefix = "lm_head" if not prefix or name != "model" else f"{prefix}.{suffix}"
|
||||||
|
|
||||||
with no_fp8(weights):
|
with no_fp8(weights):
|
||||||
self.lm_head = SpeculativeHead.load(
|
self.lm_head = SpeculativeHead.load(
|
||||||
config,
|
config,
|
||||||
prefix=suffix if not prefix else f"{prefix}.{suffix}",
|
prefix,
|
||||||
weights=weights,
|
weights,
|
||||||
)
|
)
|
||||||
|
|
||||||
# Used in Granite
|
# Used in Granite
|
||||||
|
|||||||
@ -49,9 +49,9 @@ from text_generation_server.layers.layernorm import (
|
|||||||
|
|
||||||
if SYSTEM == "rocm":
|
if SYSTEM == "rocm":
|
||||||
try:
|
try:
|
||||||
from vllm import _custom_C
|
import vllm._custom_ops as ops
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
|
raise ImportError(f"Could not load `vllm._custom_ops`. Full error: {e}")
|
||||||
|
|
||||||
|
|
||||||
class MistralConfig(PretrainedConfig):
|
class MistralConfig(PretrainedConfig):
|
||||||
@ -318,7 +318,7 @@ class MistralMLP(nn.Module):
|
|||||||
dtype=hidden_states.dtype,
|
dtype=hidden_states.dtype,
|
||||||
device="cuda",
|
device="cuda",
|
||||||
)
|
)
|
||||||
_custom_C.LLMM_Silu(
|
ops.LLMM_Silu(
|
||||||
self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8
|
self.gate_up_proj.base_layer.linear.weight, hidden_states, out, 8
|
||||||
)
|
)
|
||||||
return self.down_proj(out, adapter_data)
|
return self.down_proj(out, adapter_data)
|
||||||
|
|||||||
584
server/text_generation_server/models/custom_modeling/idefics3.py
Normal file
584
server/text_generation_server/models/custom_modeling/idefics3.py
Normal file
@ -0,0 +1,584 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
""" PyTorch Idefics3 model."""
|
||||||
|
|
||||||
|
from typing import List, Optional, Tuple
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.utils.checkpoint
|
||||||
|
from torch import nn
|
||||||
|
|
||||||
|
from transformers.activations import ACT2FN
|
||||||
|
from text_generation_server.models.custom_modeling.vlm import (
|
||||||
|
load_text_model,
|
||||||
|
)
|
||||||
|
from text_generation_server.layers.attention import Seqlen
|
||||||
|
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
|
||||||
|
|
||||||
|
from text_generation_server.layers import (
|
||||||
|
TensorParallelColumnLinear,
|
||||||
|
TensorParallelEmbedding,
|
||||||
|
TensorParallelRowLinear,
|
||||||
|
)
|
||||||
|
from text_generation_server.utils.weights import DefaultWeightsLoader, UnquantizedWeight
|
||||||
|
|
||||||
|
|
||||||
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||||
|
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||||
|
"""
|
||||||
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||||
|
if n_rep == 1:
|
||||||
|
return hidden_states
|
||||||
|
hidden_states = hidden_states[:, :, None, :, :].expand(
|
||||||
|
batch, num_key_value_heads, n_rep, slen, head_dim
|
||||||
|
)
|
||||||
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3VisionEmbeddings(nn.Module):
|
||||||
|
"""
|
||||||
|
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable
|
||||||
|
resolution.
|
||||||
|
|
||||||
|
The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
|
||||||
|
which allows treating images in their native aspect ratio and without the need to resize them to the same
|
||||||
|
fixed size. In particular, we start from the original pre-trained SigLIP model
|
||||||
|
(which uses images of fixed-size square images) and adapt it by training on images of variable resolutions.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.embed_dim = config.hidden_size
|
||||||
|
self.image_size = config.image_size
|
||||||
|
self.patch_size = config.patch_size
|
||||||
|
|
||||||
|
self.patch_embedding = nn.Conv2d(
|
||||||
|
in_channels=config.num_channels,
|
||||||
|
out_channels=self.embed_dim,
|
||||||
|
kernel_size=self.patch_size,
|
||||||
|
stride=self.patch_size,
|
||||||
|
padding="valid",
|
||||||
|
)
|
||||||
|
self.patch_embedding.weight = nn.Parameter(
|
||||||
|
weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
|
||||||
|
)
|
||||||
|
self.patch_embedding.bias = nn.Parameter(
|
||||||
|
weights.get_tensor(f"{prefix}.patch_embedding.bias"), requires_grad=False
|
||||||
|
)
|
||||||
|
|
||||||
|
self.num_patches_per_side = self.image_size // self.patch_size
|
||||||
|
self.num_patches = self.num_patches_per_side**2
|
||||||
|
self.num_positions = self.num_patches
|
||||||
|
self.position_embedding = TensorParallelEmbedding(
|
||||||
|
prefix=f"{prefix}.position_embedding", weights=weights
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor
|
||||||
|
) -> torch.Tensor:
|
||||||
|
batch_size, _, max_im_h, max_im_w = pixel_values.shape
|
||||||
|
|
||||||
|
patch_embeds = self.patch_embedding(pixel_values)
|
||||||
|
embeddings = patch_embeds.flatten(2).transpose(1, 2)
|
||||||
|
|
||||||
|
max_nb_patches_h, max_nb_patches_w = (
|
||||||
|
max_im_h // self.patch_size,
|
||||||
|
max_im_w // self.patch_size,
|
||||||
|
)
|
||||||
|
boundaries = torch.arange(
|
||||||
|
1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side
|
||||||
|
)
|
||||||
|
position_ids = torch.full(
|
||||||
|
size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0
|
||||||
|
)
|
||||||
|
|
||||||
|
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
|
||||||
|
nb_patches_h = p_attn_mask[:, 0].sum()
|
||||||
|
nb_patches_w = p_attn_mask[0].sum()
|
||||||
|
|
||||||
|
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
|
||||||
|
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
|
||||||
|
|
||||||
|
bucket_coords_h = torch.bucketize(
|
||||||
|
fractional_coords_h, boundaries, right=True
|
||||||
|
)
|
||||||
|
bucket_coords_w = torch.bucketize(
|
||||||
|
fractional_coords_w, boundaries, right=True
|
||||||
|
)
|
||||||
|
|
||||||
|
pos_ids = (
|
||||||
|
bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w
|
||||||
|
).flatten()
|
||||||
|
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
|
||||||
|
|
||||||
|
position_ids = position_ids.to(self.position_embedding.weight.device)
|
||||||
|
embeddings = embeddings + self.position_embedding(position_ids)
|
||||||
|
return embeddings
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3VisionAttention(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.config = config
|
||||||
|
self.embed_dim = config.hidden_size
|
||||||
|
self.num_heads = config.num_attention_heads
|
||||||
|
self.head_size = self.embed_dim // self.num_heads
|
||||||
|
if self.head_size * self.num_heads != self.embed_dim:
|
||||||
|
raise ValueError(
|
||||||
|
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
||||||
|
f" {self.num_heads})."
|
||||||
|
)
|
||||||
|
self.scale = self.head_size**-0.5
|
||||||
|
self.dropout = config.attention_dropout
|
||||||
|
|
||||||
|
self.num_heads = self.num_heads // weights.process_group.size()
|
||||||
|
self.embed_dim = self.embed_dim // weights.process_group.size()
|
||||||
|
|
||||||
|
self.qkv = TensorParallelColumnLinear.load_multi(
|
||||||
|
config,
|
||||||
|
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
||||||
|
dim=0,
|
||||||
|
weights=weights,
|
||||||
|
bias=True,
|
||||||
|
)
|
||||||
|
self.out_proj = TensorParallelRowLinear.load(
|
||||||
|
config=config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
|
||||||
|
)
|
||||||
|
self.is_causal = False
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
hidden_states: torch.Tensor,
|
||||||
|
attention_mask: Optional[torch.Tensor] = None,
|
||||||
|
) -> torch.Tensor:
|
||||||
|
batch_size, q_len, _ = hidden_states.size()
|
||||||
|
|
||||||
|
qkv = self.qkv(hidden_states)
|
||||||
|
query_states, key_states, value_states = qkv.split(
|
||||||
|
[
|
||||||
|
self.head_size * self.num_heads,
|
||||||
|
self.head_size * self.num_heads,
|
||||||
|
self.head_size * self.num_heads,
|
||||||
|
],
|
||||||
|
dim=2,
|
||||||
|
)
|
||||||
|
|
||||||
|
query_states = query_states.view(
|
||||||
|
batch_size, q_len, self.num_heads, self.head_size
|
||||||
|
).transpose(1, 2)
|
||||||
|
key_states = key_states.view(
|
||||||
|
batch_size, q_len, self.num_heads, self.head_size
|
||||||
|
).transpose(1, 2)
|
||||||
|
value_states = value_states.view(
|
||||||
|
batch_size, q_len, self.num_heads, self.head_size
|
||||||
|
).transpose(1, 2)
|
||||||
|
|
||||||
|
k_v_seq_len = key_states.shape[-2]
|
||||||
|
attn_weights = (
|
||||||
|
torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
|
||||||
|
)
|
||||||
|
|
||||||
|
if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
|
||||||
|
raise ValueError(
|
||||||
|
f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
|
||||||
|
f" {attn_weights.size()}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if attention_mask is not None:
|
||||||
|
if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
|
||||||
|
raise ValueError(
|
||||||
|
f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
|
||||||
|
)
|
||||||
|
attn_weights = attn_weights + attention_mask
|
||||||
|
|
||||||
|
# upcast attention to fp32
|
||||||
|
attn_weights = nn.functional.softmax(
|
||||||
|
attn_weights, dim=-1, dtype=torch.float32
|
||||||
|
).to(query_states.dtype)
|
||||||
|
attn_weights = nn.functional.dropout(
|
||||||
|
attn_weights, p=self.dropout, training=self.training
|
||||||
|
)
|
||||||
|
attn_output = torch.matmul(attn_weights, value_states)
|
||||||
|
|
||||||
|
if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_size):
|
||||||
|
raise ValueError(
|
||||||
|
f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_size)}, but is"
|
||||||
|
f" {attn_output.size()}"
|
||||||
|
)
|
||||||
|
|
||||||
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||||
|
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
|
||||||
|
|
||||||
|
attn_output = self.out_proj(attn_output)
|
||||||
|
|
||||||
|
return attn_output
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3VisionMLP(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.config = config
|
||||||
|
self.activation_fn = ACT2FN[config.hidden_act]
|
||||||
|
self.fc1 = TensorParallelColumnLinear.load(
|
||||||
|
prefix=f"{prefix}.fc1", config=config, weights=weights, bias=True
|
||||||
|
)
|
||||||
|
self.fc2 = TensorParallelRowLinear.load(
|
||||||
|
prefix=f"{prefix}.fc2", config=config, weights=weights, bias=True
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||||
|
hidden_states = self.fc1(hidden_states)
|
||||||
|
hidden_states = self.activation_fn(hidden_states)
|
||||||
|
hidden_states = self.fc2(hidden_states)
|
||||||
|
return hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3EncoderLayer(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.embed_dim = config.hidden_size
|
||||||
|
self.self_attn = Idefics3VisionAttention(
|
||||||
|
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||||
|
)
|
||||||
|
self.layer_norm1 = nn.LayerNorm.load(
|
||||||
|
prefix=f"{prefix}.layer_norm1", eps=config.layer_norm_eps, weights=weights
|
||||||
|
)
|
||||||
|
self.layer_norm2 = nn.LayerNorm.load(
|
||||||
|
prefix=f"{prefix}.layer_norm2", eps=config.layer_norm_eps, weights=weights
|
||||||
|
)
|
||||||
|
self.mlp = Idefics3VisionMLP(
|
||||||
|
prefix=f"{prefix}.mlp", config=config, weights=weights
|
||||||
|
)
|
||||||
|
|
||||||
|
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoderLayer.forward
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
hidden_states: torch.Tensor,
|
||||||
|
attention_mask: torch.Tensor,
|
||||||
|
) -> torch.Tensor:
|
||||||
|
residual = hidden_states
|
||||||
|
|
||||||
|
hidden_states = self.layer_norm1(hidden_states)
|
||||||
|
hidden_states = self.self_attn(
|
||||||
|
hidden_states=hidden_states,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
)
|
||||||
|
hidden_states = residual + hidden_states
|
||||||
|
|
||||||
|
residual = hidden_states
|
||||||
|
hidden_states = self.layer_norm2(hidden_states)
|
||||||
|
hidden_states = self.mlp(hidden_states)
|
||||||
|
hidden_states = residual + hidden_states
|
||||||
|
|
||||||
|
return hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3Encoder(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.config = config
|
||||||
|
self.layers = nn.ModuleList(
|
||||||
|
[
|
||||||
|
Idefics3EncoderLayer(
|
||||||
|
prefix=f"{prefix}.layers.{i}", config=config, weights=weights
|
||||||
|
)
|
||||||
|
for i in range(config.num_hidden_layers)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Ignore copy
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
inputs_embeds,
|
||||||
|
attention_mask: Optional[torch.Tensor] = None,
|
||||||
|
):
|
||||||
|
hidden_states = inputs_embeds
|
||||||
|
for encoder_layer in self.layers:
|
||||||
|
hidden_states = encoder_layer(
|
||||||
|
hidden_states,
|
||||||
|
attention_mask,
|
||||||
|
)
|
||||||
|
return hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3VisionTransformer(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.config = config
|
||||||
|
self.embeddings = Idefics3VisionEmbeddings(
|
||||||
|
prefix=f"{prefix}.embeddings", config=config, weights=weights
|
||||||
|
)
|
||||||
|
self.encoder = Idefics3Encoder(
|
||||||
|
prefix=f"{prefix}.encoder", config=config, weights=weights
|
||||||
|
)
|
||||||
|
self.post_layernorm = nn.LayerNorm.load(
|
||||||
|
prefix=f"{prefix}.post_layernorm",
|
||||||
|
weights=weights,
|
||||||
|
eps=config.layer_norm_eps,
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
pixel_values,
|
||||||
|
patch_attention_mask: Optional[torch.BoolTensor] = None,
|
||||||
|
):
|
||||||
|
batch_size = pixel_values.size(0)
|
||||||
|
if patch_attention_mask is None:
|
||||||
|
patch_size = self.config.patch_size
|
||||||
|
patch_attention_mask = torch.ones(
|
||||||
|
(
|
||||||
|
batch_size,
|
||||||
|
pixel_values.size(2) // patch_size,
|
||||||
|
pixel_values.size(3) // patch_size,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
patch_attention_mask = patch_attention_mask.to(
|
||||||
|
dtype=torch.bool, device=pixel_values.device
|
||||||
|
)
|
||||||
|
|
||||||
|
hidden_states = self.embeddings(
|
||||||
|
pixel_values=pixel_values, patch_attention_mask=patch_attention_mask
|
||||||
|
)
|
||||||
|
|
||||||
|
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
|
||||||
|
# The call to `_upad_input` in `_flash_attention_forward` is expensive
|
||||||
|
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
|
||||||
|
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
|
||||||
|
if not torch.any(~patch_attention_mask):
|
||||||
|
patch_attention_mask = None
|
||||||
|
else:
|
||||||
|
patch_attention_mask = _prepare_4d_attention_mask(
|
||||||
|
patch_attention_mask, hidden_states.dtype
|
||||||
|
)
|
||||||
|
|
||||||
|
encoder_outputs = self.encoder(
|
||||||
|
inputs_embeds=hidden_states,
|
||||||
|
attention_mask=patch_attention_mask,
|
||||||
|
)
|
||||||
|
|
||||||
|
last_hidden_state = encoder_outputs
|
||||||
|
last_hidden_state = self.post_layernorm(last_hidden_state)
|
||||||
|
|
||||||
|
return last_hidden_state
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3SimpleMLP(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
input_size = config.vision_config.hidden_size * (config.scale_factor**2)
|
||||||
|
output_size = config.text_config.hidden_size
|
||||||
|
proj = nn.Parameter(
|
||||||
|
weights.get_tensor(f"{prefix}.modality_projection.proj.weight"),
|
||||||
|
requires_grad=False,
|
||||||
|
).to(weights.dtype)
|
||||||
|
self.proj = nn.Linear(input_size, output_size, bias=False)
|
||||||
|
self.proj.weight = proj
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
return self.proj(x)
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3Connector(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
self.modality_projection = Idefics3SimpleMLP(prefix, config, weights)
|
||||||
|
self.scale_factor = config.scale_factor
|
||||||
|
|
||||||
|
def pixel_shuffle(self, x, scale_factor=2):
|
||||||
|
bsz, seq, embed_dim = x.size()
|
||||||
|
height = width = int(seq**0.5)
|
||||||
|
x = x.view(bsz, height, width, embed_dim)
|
||||||
|
x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
|
||||||
|
x = x.permute(0, 2, 1, 3)
|
||||||
|
x = x.reshape(
|
||||||
|
bsz,
|
||||||
|
int(width / scale_factor),
|
||||||
|
int(height / scale_factor),
|
||||||
|
embed_dim * (scale_factor**2),
|
||||||
|
)
|
||||||
|
x = x.permute(0, 2, 1, 3)
|
||||||
|
x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
|
||||||
|
return x
|
||||||
|
|
||||||
|
def forward(self, image_hidden_states):
|
||||||
|
image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
|
||||||
|
image_hidden_states = self.modality_projection(image_hidden_states)
|
||||||
|
return image_hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
class Idefics3ForConditionalGeneration(nn.Module):
|
||||||
|
def __init__(self, prefix, config, weights):
|
||||||
|
super().__init__()
|
||||||
|
config.vision_config.quantize = None
|
||||||
|
config.vision_config.speculator = config.speculator
|
||||||
|
config.text_config.quantize = config.quantize
|
||||||
|
config.text_config.speculator = config.speculator
|
||||||
|
# set tie_word_embeddings to True to load `.embed_tokens.weight` instead of `.lm_head.weight`
|
||||||
|
# since Idefics3 uses the `embed_tokens` for the final prediction
|
||||||
|
# config.text_config.tie_word_embeddings = True
|
||||||
|
|
||||||
|
vision_config = config.vision_config
|
||||||
|
self.text_model = load_text_model(
|
||||||
|
prefix="model" if not prefix else f"{prefix}.model",
|
||||||
|
config=config.text_config,
|
||||||
|
weights=weights,
|
||||||
|
name="text_model",
|
||||||
|
)
|
||||||
|
self.dtype = weights.dtype
|
||||||
|
|
||||||
|
# The vision and connector models are not quantized.
|
||||||
|
with weights.use_loader(DefaultWeightsLoader(UnquantizedWeight)):
|
||||||
|
self.vision_model = Idefics3VisionTransformer(
|
||||||
|
prefix=(
|
||||||
|
f"{prefix}.model.vision_model" if prefix else "model.vision_model"
|
||||||
|
),
|
||||||
|
config=vision_config,
|
||||||
|
weights=weights,
|
||||||
|
)
|
||||||
|
|
||||||
|
config.quantize = None
|
||||||
|
self.connector = Idefics3Connector(
|
||||||
|
prefix=f"{prefix}.model.connector" if prefix else "model.connector",
|
||||||
|
config=config,
|
||||||
|
weights=weights,
|
||||||
|
)
|
||||||
|
|
||||||
|
self.config = config
|
||||||
|
self.image_token_id = config.image_token_id
|
||||||
|
self.pad_token_id = (
|
||||||
|
config.pad_token_id if config.pad_token_id is not None else -1
|
||||||
|
)
|
||||||
|
|
||||||
|
def _merge_input_ids_with_image_features(
|
||||||
|
self,
|
||||||
|
input_ids: torch.Tensor,
|
||||||
|
inputs_embeds: torch.Tensor,
|
||||||
|
image_features: torch.Tensor,
|
||||||
|
):
|
||||||
|
"""In place merges in vision_embeddings with inputs_embeds."""
|
||||||
|
# mask = input_ids == self.config.image_token_index
|
||||||
|
mask = input_ids == self.config.image_token_id
|
||||||
|
# Let's pray we have enabled enough slots !
|
||||||
|
inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
|
||||||
|
return inputs_embeds
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
input_ids: torch.Tensor,
|
||||||
|
position_ids: torch.Tensor,
|
||||||
|
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||||
|
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||||
|
block_tables: torch.Tensor,
|
||||||
|
slots: torch.Tensor,
|
||||||
|
seqlen: Seqlen,
|
||||||
|
max_s: int,
|
||||||
|
prefill_cache_indices: Optional[torch.Tensor],
|
||||||
|
lm_head_indices: Optional[torch.Tensor] = None,
|
||||||
|
pixel_values: torch.FloatTensor = None,
|
||||||
|
pixel_attention_mask: Optional[torch.BoolTensor] = None,
|
||||||
|
# Unused here
|
||||||
|
image_sizes: Optional[torch.Tensor] = None,
|
||||||
|
adapter_data: Optional[torch.Tensor] = None,
|
||||||
|
image_grid_thw: Optional[torch.LongTensor] = None,
|
||||||
|
video_grid_thw: Optional[torch.LongTensor] = None,
|
||||||
|
cross_attention_states: Optional[torch.Tensor] = None,
|
||||||
|
image_indices=None,
|
||||||
|
):
|
||||||
|
inputs_embeds = self.text_model.embed_tokens(input_ids)
|
||||||
|
if pixel_values is not None:
|
||||||
|
batch_size, num_images, num_channels, height, width = pixel_values.shape
|
||||||
|
all_states = []
|
||||||
|
all_pixel_values = pixel_values
|
||||||
|
all_pixel_mask = pixel_attention_mask
|
||||||
|
for i in range(batch_size):
|
||||||
|
pixel_values = all_pixel_values.to(
|
||||||
|
dtype=self.dtype
|
||||||
|
) # fp16 compatibility
|
||||||
|
pixel_values = pixel_values[i : i + 1]
|
||||||
|
pixel_values = pixel_values.view(num_images, *pixel_values.shape[2:])
|
||||||
|
|
||||||
|
# Remove padding images - padding images are full 0.
|
||||||
|
nb_values_per_image = pixel_values.shape[1:].numel()
|
||||||
|
real_images_inds = (pixel_values == 0.0).sum(
|
||||||
|
dim=(-1, -2, -3)
|
||||||
|
) != nb_values_per_image
|
||||||
|
pixel_values = pixel_values[real_images_inds].contiguous()
|
||||||
|
# Handle the vision attention mask
|
||||||
|
if pixel_attention_mask is None:
|
||||||
|
pixel_attention_mask = torch.ones(
|
||||||
|
size=(
|
||||||
|
pixel_values.size(0),
|
||||||
|
pixel_values.size(2),
|
||||||
|
pixel_values.size(3),
|
||||||
|
),
|
||||||
|
dtype=torch.bool,
|
||||||
|
device=pixel_values.device,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
# Remove padding images from the mask/pP p
|
||||||
|
pixel_attention_mask = all_pixel_mask[i : i + 1]
|
||||||
|
pixel_attention_mask = pixel_attention_mask.view(
|
||||||
|
1 * num_images, *pixel_attention_mask.shape[2:]
|
||||||
|
)
|
||||||
|
pixel_attention_mask = pixel_attention_mask[
|
||||||
|
real_images_inds
|
||||||
|
].contiguous()
|
||||||
|
|
||||||
|
patch_size = self.config.vision_config.patch_size
|
||||||
|
patches_subgrid = pixel_attention_mask.unfold(
|
||||||
|
dimension=1, size=patch_size, step=patch_size
|
||||||
|
)
|
||||||
|
patches_subgrid = patches_subgrid.unfold(
|
||||||
|
dimension=2, size=patch_size, step=patch_size
|
||||||
|
)
|
||||||
|
patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
|
||||||
|
|
||||||
|
# Get sequence from the vision encoder
|
||||||
|
image_hidden_states = self.vision_model(
|
||||||
|
pixel_values=pixel_values,
|
||||||
|
patch_attention_mask=patch_attention_mask,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Modality projection & resampling
|
||||||
|
image_hidden_states = self.connector(
|
||||||
|
image_hidden_states,
|
||||||
|
)
|
||||||
|
|
||||||
|
all_states.append(image_hidden_states)
|
||||||
|
image_hidden_states = torch.stack(all_states, dim=0)
|
||||||
|
|
||||||
|
inputs_embeds = self._merge_input_ids_with_image_features(
|
||||||
|
input_ids, inputs_embeds, image_hidden_states
|
||||||
|
)
|
||||||
|
|
||||||
|
hidden_states = self.text_model.model(
|
||||||
|
inputs_embeds=inputs_embeds,
|
||||||
|
position_ids=position_ids,
|
||||||
|
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||||
|
kv_cache=kv_cache,
|
||||||
|
block_tables=block_tables,
|
||||||
|
slots=slots,
|
||||||
|
seqlen=seqlen,
|
||||||
|
max_s=max_s,
|
||||||
|
true_max_s=max_s,
|
||||||
|
prefill_cache_indices=None,
|
||||||
|
adapter_data=adapter_data,
|
||||||
|
)
|
||||||
|
if lm_head_indices is not None:
|
||||||
|
hidden_states = hidden_states[lm_head_indices]
|
||||||
|
logits, speculative_logits = self.text_model.lm_head(hidden_states)
|
||||||
|
return logits, speculative_logits
|
||||||
@ -52,7 +52,7 @@ from loguru import logger
|
|||||||
if SYSTEM == "cuda":
|
if SYSTEM == "cuda":
|
||||||
import dropout_layer_norm
|
import dropout_layer_norm
|
||||||
elif SYSTEM == "rocm":
|
elif SYSTEM == "rocm":
|
||||||
from vllm._C import ops
|
import vllm._custom_ops as ops
|
||||||
else:
|
else:
|
||||||
dropout_layer_norm = None
|
dropout_layer_norm = None
|
||||||
|
|
||||||
|
|||||||
@ -450,7 +450,7 @@ class Qwen2VLForConditionalGeneration(nn.Module):
|
|||||||
width //= self.spatial_merge_size
|
width //= self.spatial_merge_size
|
||||||
|
|
||||||
# calculate the length of the text and image tokens
|
# calculate the length of the text and image tokens
|
||||||
text_length = next_image_pos - current_pos
|
text_length = next_image_pos
|
||||||
start_idx = (
|
start_idx = (
|
||||||
llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
|
llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
|
||||||
)
|
)
|
||||||
@ -480,7 +480,7 @@ class Qwen2VLForConditionalGeneration(nn.Module):
|
|||||||
)
|
)
|
||||||
llm_pos_ids_list.append(image_pos_ids)
|
llm_pos_ids_list.append(image_pos_ids)
|
||||||
|
|
||||||
current_pos = next_image_pos + time_steps * height * width
|
current_pos += next_image_pos + time_steps * height * width
|
||||||
image_index += 1
|
image_index += 1
|
||||||
|
|
||||||
if current_pos < batch_input_ids.size(1):
|
if current_pos < batch_input_ids.size(1):
|
||||||
|
|||||||
@ -4,7 +4,7 @@ def load_text_model(prefix, config, weights, name=None):
|
|||||||
FlashLlamaForCausalLM,
|
FlashLlamaForCausalLM,
|
||||||
)
|
)
|
||||||
|
|
||||||
return FlashLlamaForCausalLM(prefix, config, weights)
|
return FlashLlamaForCausalLM(prefix, config, weights, name=name)
|
||||||
elif config.model_type == "mistral":
|
elif config.model_type == "mistral":
|
||||||
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
|
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
|
||||||
FlashMistralForCausalLM,
|
FlashMistralForCausalLM,
|
||||||
|
|||||||
@ -1288,7 +1288,7 @@ class FlashCausalLM(Model):
|
|||||||
weights_loader=weights_loader,
|
weights_loader=weights_loader,
|
||||||
)
|
)
|
||||||
|
|
||||||
prefix = ""
|
prefix = None
|
||||||
model = model_class(prefix, config, weights)
|
model = model_class(prefix, config, weights)
|
||||||
torch.distributed.barrier(group=self.process_group)
|
torch.distributed.barrier(group=self.process_group)
|
||||||
|
|
||||||
@ -1663,7 +1663,7 @@ class FlashCausalLM(Model):
|
|||||||
|
|
||||||
for seqlen in tuning_sequences:
|
for seqlen in tuning_sequences:
|
||||||
log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
|
log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
|
||||||
self.tunableop_warmup(seqlen)
|
self.tunableop_warmup(seqlen, max_total_tokens)
|
||||||
torch.cuda.tunable.write_file(tunableop_filepath)
|
torch.cuda.tunable.write_file(tunableop_filepath)
|
||||||
if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
|
if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
|
||||||
torch.cuda.tunable.tuning_enable(False)
|
torch.cuda.tunable.tuning_enable(False)
|
||||||
@ -1710,7 +1710,7 @@ class FlashCausalLM(Model):
|
|||||||
assert max_total_tokens is not None
|
assert max_total_tokens is not None
|
||||||
return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
|
return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
|
||||||
|
|
||||||
def tunableop_warmup(self, seqlen: int):
|
def tunableop_warmup(self, seqlen: int, max_bt: int):
|
||||||
input_ids = torch.zeros(seqlen, dtype=torch.int64, device=self.device)
|
input_ids = torch.zeros(seqlen, dtype=torch.int64, device=self.device)
|
||||||
position_ids = torch.zeros(seqlen, dtype=torch.int32, device=self.device)
|
position_ids = torch.zeros(seqlen, dtype=torch.int32, device=self.device)
|
||||||
slots = torch.arange(seqlen, dtype=torch.int64, device=self.device)
|
slots = torch.arange(seqlen, dtype=torch.int64, device=self.device)
|
||||||
@ -1724,11 +1724,15 @@ class FlashCausalLM(Model):
|
|||||||
[0, seqlen], device=self.device, dtype=torch.int32
|
[0, seqlen], device=self.device, dtype=torch.int32
|
||||||
)
|
)
|
||||||
max_s = seqlen
|
max_s = seqlen
|
||||||
|
|
||||||
|
block_tables = torch.arange(
|
||||||
|
max_bt, dtype=torch.int32, device=self.device
|
||||||
|
).repeat(seqlen)
|
||||||
|
block_tables = block_tables.reshape((seqlen, max_bt))
|
||||||
|
|
||||||
seqlen = Seqlen(
|
seqlen = Seqlen(
|
||||||
input_lengths=input_lengths,
|
input_lengths=input_lengths,
|
||||||
cache_lengths=cache_lengths_tensor,
|
cache_lengths=cache_lengths_tensor,
|
||||||
cu_seqlen_q=cu_seqlen_prefill,
|
|
||||||
max_q=1,
|
|
||||||
max_k=seqlen,
|
max_k=seqlen,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -1738,7 +1742,7 @@ class FlashCausalLM(Model):
|
|||||||
position_ids=position_ids,
|
position_ids=position_ids,
|
||||||
cu_seqlen_prefill=cu_seqlen_prefill,
|
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||||
kv_cache=self.kv_cache,
|
kv_cache=self.kv_cache,
|
||||||
block_tables=None,
|
block_tables=block_tables,
|
||||||
seqlen=seqlen,
|
seqlen=seqlen,
|
||||||
slots=slots,
|
slots=slots,
|
||||||
max_s=max_s,
|
max_s=max_s,
|
||||||
@ -2480,7 +2484,8 @@ class FlashCausalLM(Model):
|
|||||||
num_kv_heads=self.num_kv_heads,
|
num_kv_heads=self.num_kv_heads,
|
||||||
head_size=self.head_size,
|
head_size=self.head_size,
|
||||||
page_size=BLOCK_SIZE,
|
page_size=BLOCK_SIZE,
|
||||||
dtype=self.dtype,
|
kv_dtype=self.kv_cache_dtype,
|
||||||
|
q_dtype=self.dtype,
|
||||||
window_left=self.sliding_window,
|
window_left=self.sliding_window,
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
@ -2494,6 +2499,6 @@ class FlashCausalLM(Model):
|
|||||||
head_size=self.head_size,
|
head_size=self.head_size,
|
||||||
page_size=BLOCK_SIZE,
|
page_size=BLOCK_SIZE,
|
||||||
kv_cache_dtype=self.kv_cache_dtype,
|
kv_cache_dtype=self.kv_cache_dtype,
|
||||||
dtype=self.dtype,
|
q_dtype=self.dtype,
|
||||||
window_left=self.sliding_window,
|
window_left=self.sliding_window,
|
||||||
)
|
)
|
||||||
|
|||||||
@ -13,6 +13,7 @@ from text_generation_server.models.flash_causal_lm import (
|
|||||||
FlashCausalLM,
|
FlashCausalLM,
|
||||||
)
|
)
|
||||||
from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
|
from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
|
||||||
|
from loguru import logger
|
||||||
from text_generation_server.utils.log import log_master
|
from text_generation_server.utils.log import log_master
|
||||||
from transformers import AutoProcessor
|
from transformers import AutoProcessor
|
||||||
from text_generation_server.layers.attention import Seqlen
|
from text_generation_server.layers.attention import Seqlen
|
||||||
@ -23,6 +24,40 @@ tracer = trace.get_tracer(__name__)
|
|||||||
IDEFICS2_FAKE_TOKEN = "<fake_token_around_image>"
|
IDEFICS2_FAKE_TOKEN = "<fake_token_around_image>"
|
||||||
IDEFICS2_IMAGE_TOKEN = "<image>"
|
IDEFICS2_IMAGE_TOKEN = "<image>"
|
||||||
|
|
||||||
|
IDEFICS3_IMAGE_TOKEN = "<image>"
|
||||||
|
IDEFICS3_FAKE_IMAGE_TOKEN = "<fake_token_around_image>"
|
||||||
|
IDEFICS3_GLOBAL_IMG_TOKEN = "<global-img>"
|
||||||
|
|
||||||
|
|
||||||
|
# copied from: https://github.com/huggingface/transformers/blob/02ed609285c2448b3b54c31e362f2c389fa952ab/src/transformers/models/idefics3/processing_idefics3.py#L44-L60
|
||||||
|
def _prompt_split_image(
|
||||||
|
*,
|
||||||
|
image_seq_len: int,
|
||||||
|
image_rows: int,
|
||||||
|
image_cols: int,
|
||||||
|
fake_token_around_image: str,
|
||||||
|
image_token: str,
|
||||||
|
global_img_token: str,
|
||||||
|
):
|
||||||
|
"""Prompt with expanded image tokens for when the image is split into patches."""
|
||||||
|
text_split_images = ""
|
||||||
|
for n_h in range(image_rows):
|
||||||
|
for n_w in range(image_cols):
|
||||||
|
text_split_images += (
|
||||||
|
f"{fake_token_around_image}"
|
||||||
|
+ f"<row_{n_h + 1}_col_{n_w + 1}>"
|
||||||
|
+ f"{image_token}" * image_seq_len
|
||||||
|
)
|
||||||
|
text_split_images += "\n"
|
||||||
|
|
||||||
|
text_split_images += (
|
||||||
|
f"\n{fake_token_around_image}"
|
||||||
|
+ f"{global_img_token}"
|
||||||
|
+ f"{image_token}" * image_seq_len
|
||||||
|
+ f"{fake_token_around_image}"
|
||||||
|
)
|
||||||
|
return text_split_images
|
||||||
|
|
||||||
|
|
||||||
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
|
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
|
||||||
"""
|
"""
|
||||||
@ -54,10 +89,26 @@ def image_text_replacement(processor, image_input, config, image_id: int) -> str
|
|||||||
if processor.image_processor.do_image_splitting:
|
if processor.image_processor.do_image_splitting:
|
||||||
image_str *= 5
|
image_str *= 5
|
||||||
return image_str
|
return image_str
|
||||||
|
if config.model_type == "idefics3":
|
||||||
|
# TODO: implement this in a more general way
|
||||||
|
n_rows = image_input["rows"][0][image_id]
|
||||||
|
n_cols = image_input["cols"][0][image_id]
|
||||||
|
image_seq_len = int(
|
||||||
|
((config.vision_config.image_size // config.vision_config.patch_size) ** 2)
|
||||||
|
/ (config.scale_factor**2)
|
||||||
|
)
|
||||||
|
image_str = _prompt_split_image(
|
||||||
|
image_seq_len=image_seq_len,
|
||||||
|
image_rows=n_rows,
|
||||||
|
image_cols=n_cols,
|
||||||
|
fake_token_around_image=IDEFICS3_FAKE_IMAGE_TOKEN,
|
||||||
|
image_token=IDEFICS3_IMAGE_TOKEN,
|
||||||
|
global_img_token=IDEFICS3_GLOBAL_IMG_TOKEN,
|
||||||
|
)
|
||||||
|
return image_str
|
||||||
elif config.model_type == "llava_next":
|
elif config.model_type == "llava_next":
|
||||||
height, width = image_input["image_sizes"][image_id]
|
height, width = image_input["image_sizes"][image_id]
|
||||||
num_features = get_number_of_features(height, width, config)
|
num_features = get_number_of_features(height, width, config)
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
log_master(
|
log_master(
|
||||||
logger.info,
|
logger.info,
|
||||||
@ -68,7 +119,8 @@ def image_text_replacement(processor, image_input, config, image_id: int) -> str
|
|||||||
elif config.model_type == "paligemma":
|
elif config.model_type == "paligemma":
|
||||||
return "<image>" * config.text_config.num_image_tokens
|
return "<image>" * config.text_config.num_image_tokens
|
||||||
elif config.model_type == "qwen2_vl":
|
elif config.model_type == "qwen2_vl":
|
||||||
num_pads = image_input.pixel_values.shape[0] // 4
|
grid_t, grid_h, grid_w = image_input["image_grid_thw"][image_id]
|
||||||
|
num_pads = grid_t * grid_h * grid_w // 4
|
||||||
padding = "<|image_pad|>" * num_pads
|
padding = "<|image_pad|>" * num_pads
|
||||||
return f"<|vision_start|>{padding}<|vision_end|>"
|
return f"<|vision_start|>{padding}<|vision_end|>"
|
||||||
else:
|
else:
|
||||||
@ -193,12 +245,21 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
|
|||||||
raise RuntimeError(f"Invalid chunk type {chunk_type}")
|
raise RuntimeError(f"Invalid chunk type {chunk_type}")
|
||||||
|
|
||||||
if images:
|
if images:
|
||||||
image_inputs = processor.image_processor(images, return_tensors="pt")
|
kwargs = {}
|
||||||
|
if (
|
||||||
|
hasattr(processor, "image_processor_class")
|
||||||
|
and processor.image_processor_class == "Idefics3ImageProcessor"
|
||||||
|
):
|
||||||
|
kwargs["return_row_col_info"] = True
|
||||||
|
|
||||||
|
image_inputs = processor.image_processor(
|
||||||
|
images, return_tensors="pt", **kwargs
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
image_inputs = None
|
image_inputs = None
|
||||||
|
|
||||||
batch_inputs = []
|
batch_tokenized_inputs = []
|
||||||
max_truncation = 0
|
max_length = 0
|
||||||
image_id = 0
|
image_id = 0
|
||||||
for r in requests:
|
for r in requests:
|
||||||
full_text = ""
|
full_text = ""
|
||||||
@ -213,16 +274,14 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
|
|||||||
image_id += 1
|
image_id += 1
|
||||||
|
|
||||||
full_text = image_text_replacement_fixup(config, full_text)
|
full_text = image_text_replacement_fixup(config, full_text)
|
||||||
|
input_ids = tokenizer(
|
||||||
batch_inputs.append(full_text)
|
full_text,
|
||||||
max_truncation = max(max_truncation, r.truncate)
|
|
||||||
|
|
||||||
batch_tokenized_inputs = tokenizer(
|
|
||||||
batch_inputs,
|
|
||||||
truncation=True,
|
truncation=True,
|
||||||
max_length=max_truncation,
|
max_length=r.truncate,
|
||||||
add_special_tokens=not config.model_type == "paligemma",
|
add_special_tokens=r.add_special_tokens,
|
||||||
)["input_ids"]
|
)["input_ids"]
|
||||||
|
max_length = max(max_length, len(input_ids))
|
||||||
|
batch_tokenized_inputs.append(input_ids)
|
||||||
|
|
||||||
return batch_tokenized_inputs, image_inputs
|
return batch_tokenized_inputs, image_inputs
|
||||||
|
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
import os
|
import os
|
||||||
import torch
|
import torch
|
||||||
|
from torch.distributed import ProcessGroup
|
||||||
from datetime import timedelta
|
from datetime import timedelta
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
from text_generation_server.utils.import_utils import SYSTEM
|
from text_generation_server.utils.import_utils import SYSTEM
|
||||||
@ -18,10 +18,11 @@ class FakeBarrier:
|
|||||||
pass
|
pass
|
||||||
|
|
||||||
|
|
||||||
class FakeGroup:
|
class FakeGroup(ProcessGroup):
|
||||||
def __init__(self, rank, size):
|
def __init__(self, rank, size):
|
||||||
self._rank = rank
|
self._rank = rank
|
||||||
self._size = size
|
self._size = size
|
||||||
|
super().__init__(rank, size)
|
||||||
|
|
||||||
def allreduce(self, *args, **kwargs):
|
def allreduce(self, *args, **kwargs):
|
||||||
return FakeBarrier()
|
return FakeBarrier()
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user