Gentoo Packages

# Copyright 1999-2026 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2

EAPI=8

DISTUTILS_USE_PEP517=setuptools
DISTUTILS_EXT=1
PYTHON_COMPAT=( python3_{12..14} )
DISTUTILS_SINGLE_IMPL=1
ROCM_VERSION=7.2

RUST_MIN_VER="1.89.0"

# vllm 0.22.0 ships a Rust frontend binary (vllm-rs) built via
# setuptools-rust from the bundled rust/ workspace.  Vendor its crate
# dependencies (generated from rust/Cargo.lock) rather than relying on a
# network-sandbox bypass, per the overlay's Rust+Python convention.  The
# frontend is opt-in at runtime (VLLM_USE_RUST_FRONTEND=1, default off);
# vllm's Python API server stays the default, so the binary is a
# performance option, not load-bearing.
CRATES="
	adler2@2.0.1
	ahash@0.8.12
	aho-corasick@1.1.4
	android_system_properties@0.1.5
	anes@0.1.6
	anstream@0.6.21
	anstream@1.0.0
	anstyle-parse@0.2.7
	anstyle-parse@1.0.0
	anstyle-query@1.1.5
	anstyle-wincon@3.0.11
	anstyle@1.0.13
	anyhow@1.0.102
	arc-swap@1.9.0
	arrayref@0.3.9
	arrayvec@0.7.6
	async-io@2.6.0
	async-openai-macros@0.1.1
	async-openai@0.33.1
	async-trait@0.1.89
	asynchronous-codec@0.7.0
	asynk-strim-attr-macro@0.1.0
	asynk-strim-attr@0.1.0
	asynk-strim@0.1.5
	atomic-waker@1.1.2
	auto_enums@0.8.9
	auto_impl@1.3.0
	autocfg@1.5.0
	axum-core@0.5.6
	axum@0.8.8
	backoff@0.4.0
	base64@0.13.1
	base64@0.22.1
	base64ct@1.8.3
	bit-set@0.5.3
	bit-set@0.8.0
	bit-vec@0.6.3
	bit-vec@0.8.0
	bitflags@2.11.0
	blake3@1.8.5
	block-buffer@0.10.4
	bstr@1.12.1
	bumpalo@3.20.2
	bytemuck@1.25.0
	bytemuck_derive@1.10.2
	byteorder-lite@0.1.0
	byteorder@1.5.0
	bytes@1.12.0
	cast@0.3.0
	castaway@0.2.4
	cc@1.2.56
	cfg-if@1.0.4
	chrono@0.4.44
	ciborium-io@0.2.2
	ciborium-ll@0.2.2
	ciborium@0.2.2
	clap@4.5.60
	clap_builder@4.5.60
	clap_derive@4.5.55
	clap_lex@1.0.0
	color_quant@1.1.0
	colorchoice@1.0.4
	compact_str@0.9.0
	concurrent-queue@2.5.0
	console@0.16.2
	constant_time_eq@0.4.2
	cookie@0.18.1
	cookie_store@0.22.1
	core-foundation-sys@0.8.7
	core-foundation@0.10.1
	core-foundation@0.9.4
	cpufeatures@0.2.17
	cpufeatures@0.3.0
	crc32fast@1.5.0
	criterion-plot@0.5.0
	criterion@0.5.1
	crossbeam-deque@0.8.6
	crossbeam-epoch@0.9.18
	crossbeam-queue@0.3.12
	crossbeam-utils@0.8.21
	crunchy@0.2.4
	crypto-common@0.1.7
	daachorse@1.0.0
	darling@0.20.11
	darling@0.23.0
	darling_core@0.20.11
	darling_core@0.23.0
	darling_macro@0.20.11
	darling_macro@0.23.0
	dary_heap@0.3.8
	der@0.8.1
	deranged@0.5.8
	derive_builder@0.20.2
	derive_builder_core@0.20.2
	derive_builder_macro@0.20.2
	derive_more-impl@1.0.0
	derive_more@1.0.0
	derive_utils@0.15.1
	digest@0.10.7
	dirs-sys@0.5.0
	dirs@6.0.0
	displaydoc@0.2.5
	dissimilar@1.0.11
	document-features@0.2.12
	dtoa@1.0.11
	dyn-clone@1.0.20
	easy-ext@1.0.3
	educe@0.6.0
	either@1.15.0
	encode_unicode@1.0.0
	encoding_rs@0.8.35
	enum-as-inner@0.7.0
	enum-ordinalize-derive@4.3.2
	enum-ordinalize@4.3.2
	env_filter@1.0.1
	env_logger@0.11.10
	equivalent@1.0.2
	errno@0.3.14
	esaxx-rs@0.1.10
	eventsource-stream@0.2.3
	expect-test@1.5.1
	extended@0.1.0
	fancy-regex@0.13.0
	fancy-regex@0.17.0
	fast_image_resize@6.0.0
	fastokens@0.2.1
	fastrand@2.3.0
	fax@0.2.6
	fax_derive@0.2.0
	fdeflate@0.3.7
	find-msvc-tools@0.1.9
	fixedbitset@0.5.7
	flate2@1.1.9
	fnv@1.0.7
	foldhash@0.1.5
	foreign-types-shared@0.1.1
	foreign-types@0.3.2
	form_urlencoded@1.2.2
	fslock@0.2.1
	futures-channel@0.3.32
	futures-core@0.3.32
	futures-executor@0.3.32
	futures-io@0.3.32
	futures-lite@2.6.1
	futures-macro@0.3.32
	futures-sink@0.3.32
	futures-task@0.3.32
	futures-timer@3.0.3
	futures-util@0.3.32
	futures@0.3.32
	generic-array@0.14.7
	getopts@0.2.24
	getrandom@0.2.17
	getrandom@0.3.4
	getrandom@0.4.2
	gif@0.14.2
	h2@0.4.15
	half@2.7.1
	hashbrown@0.12.3
	hashbrown@0.14.5
	hashbrown@0.15.5
	hashbrown@0.16.1
	heck@0.5.0
	hermit-abi@0.5.2
	hex@0.4.3
	hf-hub@0.5.0
	hmac@0.12.1
	hound@3.5.1
	http-body-util@0.1.3
	http-body@1.0.1
	http@1.4.0
	httparse@1.10.1
	httpdate@1.0.3
	hyper-rustls@0.27.7
	hyper-timeout@0.5.2
	hyper-tls@0.6.0
	hyper-util@0.1.20
	hyper@1.10.1
	iana-time-zone-haiku@0.1.2
	iana-time-zone@0.1.65
	icu_collections@2.1.1
	icu_locale_core@2.1.1
	icu_normalizer@2.1.1
	icu_normalizer_data@2.1.1
	icu_properties@2.1.2
	icu_properties_data@2.1.2
	icu_provider@2.1.1
	id-arena@2.3.0
	ident_case@1.0.1
	idna@1.1.0
	idna_adapter@1.2.1
	image-webp@0.2.4
	image@0.25.10
	indexmap@1.9.3
	indexmap@2.13.0
	indicatif@0.18.4
	instant@0.1.13
	ipnet@2.12.0
	iri-string@0.7.10
	is-macro@0.3.7
	is-terminal@0.4.17
	is_terminal_polyfill@1.70.2
	itertools@0.10.5
	itertools@0.11.0
	itertools@0.14.0
	itoa@1.0.17
	jiff-static@0.2.23
	jiff@0.2.23
	jobserver@0.1.34
	js-sys@0.3.91
	lalrpop-util@0.20.2
	lazy_static@1.5.0
	leb128fmt@0.1.0
	libc@0.2.183
	libloading@0.8.9
	libm@0.2.16
	libmimalloc-sys@0.1.49
	libredox@0.1.14
	linux-raw-sys@0.12.1
	litemap@0.8.1
	litrs@1.0.0
	lock_api@0.4.14
	log@0.4.29
	macro_rules_attribute-proc_macro@0.2.2
	macro_rules_attribute@0.2.2
	malachite-base@0.4.22
	malachite-bigint@0.2.3
	malachite-nz@0.4.22
	malachite-q@0.4.22
	malachite@0.4.22
	matchers@0.2.0
	matchit@0.8.4
	matrixmultiply@0.3.10
	memchr@2.8.0
	memo-map@0.3.3
	mimalloc@0.1.52
	mime@0.3.17
	mime_guess@2.0.5
	minijinja-contrib@2.18.0
	minijinja@2.18.0
	minimal-lexical@0.2.1
	miniz_oxide@0.8.9
	mio@1.2.1
	monostate-impl@0.1.18
	monostate@0.1.18
	moxcms@0.8.1
	multimap@0.10.1
	native-tls@0.2.18
	ndarray@0.16.1
	ndarray@0.17.2
	nom@7.1.3
	nu-ansi-term@0.50.3
	num-complex@0.4.6
	num-conv@0.2.0
	num-integer@0.1.46
	num-traits@0.2.19
	num_cpus@1.17.0
	num_threads@0.1.7
	once_cell@1.21.4
	once_cell_polyfill@1.70.2
	onig@6.5.1
	onig_sys@69.9.1
	oorandom@11.1.5
	openai-protocol@1.6.0
	openssl-macros@0.1.1
	openssl-probe@0.2.1
	openssl-src@300.5.5+3.5.5
	openssl-sys@0.9.117
	openssl@0.10.81
	option-ext@0.2.0
	parking@2.2.1
	parking_lot@0.12.5
	parking_lot_core@0.9.12
	paste@1.0.15
	pcre2-sys@0.2.10
	pcre2@0.2.11
	pem-rfc7468@1.0.0
	percent-encoding@2.3.2
	petgraph@0.8.3
	phf@0.11.3
	phf_codegen@0.11.3
	phf_generator@0.11.3
	phf_shared@0.11.3
	pin-project-internal@1.1.11
	pin-project-lite@0.2.17
	pin-project@1.1.11
	pkg-config@0.3.32
	plotters-backend@0.3.7
	plotters-svg@0.3.7
	plotters@0.3.7
	png@0.18.1
	polling@3.11.0
	portable-atomic-util@0.2.6
	portable-atomic@1.13.1
	potential_utf@0.1.4
	powerfmt@0.2.0
	ppv-lite86@0.2.21
	prettyplease@0.2.37
	primal-check@0.3.4
	proc-macro-crate@3.5.0
	proc-macro-error-attr2@2.0.0
	proc-macro-error2@2.0.1
	proc-macro2@1.0.106
	prometheus-client-derive-encode@0.5.0
	prometheus-client@0.24.0
	prost-build@0.14.3
	prost-derive@0.14.3
	prost-types@0.14.3
	prost@0.14.3
	pulldown-cmark-to-cmark@22.0.0
	pulldown-cmark@0.13.3
	pxfm@0.1.29
	pyo3-build-config@0.28.3
	pyo3-ffi@0.28.3
	pyo3-macros-backend@0.28.3
	pyo3-macros@0.28.3
	pyo3@0.28.3
	pythonize@0.28.0
	quick-error@2.0.1
	quote@1.0.45
	r-efi@5.3.0
	r-efi@6.0.0
	rand@0.8.5
	rand@0.9.2
	rand_chacha@0.3.1
	rand_chacha@0.9.0
	rand_core@0.6.4
	rand_core@0.9.5
	rand_distr@0.5.1
	rawpointer@0.2.1
	rayon-cond@0.4.0
	rayon-core@1.13.0
	rayon@1.12.0
	realfft@3.5.0
	redox_syscall@0.5.18
	redox_users@0.5.2
	ref-cast-impl@1.0.25
	ref-cast@1.0.25
	regex-automata@0.4.14
	regex-lite@0.1.9
	regex-syntax@0.8.10
	regex@1.12.3
	reqwest-eventsource@0.6.0
	reqwest@0.12.28
	reqwest@0.13.4
	ring@0.17.14
	riptoken@0.3.0
	rlimit@0.11.0
	rmp-serde@1.3.1
	rmp@0.8.15
	rmpv@1.3.1
	rubato@0.16.2
	rustc-hash@1.1.0
	rustc-hash@2.1.1
	rustfft@6.4.1
	rustix@1.1.4
	rustls-pki-types@1.14.1
	rustls-webpki@0.103.9
	rustls@0.23.37
	rustpython-ast@0.4.0
	rustpython-parser-core@0.4.0
	rustpython-parser-vendored@0.4.0
	rustpython-parser@0.4.0
	rustversion@1.0.22
	ryu@1.0.23
	saa@5.5.0
	same-file@1.0.6
	scc@2.4.0
	scc@3.6.9
	schannel@0.1.29
	schemars@0.8.22
	schemars@0.9.0
	schemars@1.2.1
	schemars_derive@0.8.22
	scopeguard@1.2.0
	sdd@3.0.10
	sdd@4.7.3
	secrecy@0.10.3
	security-framework-sys@2.17.0
	security-framework@3.7.0
	semver@1.0.27
	serde-json-fmt@0.1.0
	serde@1.0.228
	serde_bytes@0.11.19
	serde_core@1.0.228
	serde_default@0.2.0
	serde_derive@1.0.228
	serde_derive_internals@0.29.1
	serde_json@1.0.149
	serde_path_to_error@0.1.20
	serde_repr@0.1.20
	serde_tuple@1.1.3
	serde_tuple_macros@1.1.3
	serde_urlencoded@0.7.1
	serde_with@3.18.0
	serde_with_macros@3.18.0
	serial_test@3.4.0
	serial_test_derive@3.4.0
	sha2@0.10.9
	sharded-slab@0.1.7
	shlex@1.3.0
	signal-hook-registry@1.4.8
	simd-adler32@0.3.8
	siphasher@1.0.2
	slab@0.4.12
	smallvec@1.15.1
	smartstring@1.0.1
	socket2@0.6.3
	socks@0.3.4
	spm_precompiled@0.1.4
	stable_deref_trait@1.2.1
	static_assertions@1.1.0
	strength_reduce@0.2.4
	strsim@0.11.1
	strum@0.27.2
	strum_macros@0.27.2
	subenum@1.1.3
	subtle@2.6.1
	symphonia-bundle-flac@0.6.0
	symphonia-bundle-mp3@0.6.0
	symphonia-codec-aac@0.6.0
	symphonia-codec-adpcm@0.6.0
	symphonia-codec-alac@0.6.0
	symphonia-codec-pcm@0.6.0
	symphonia-codec-vorbis@0.6.0
	symphonia-common@0.6.0
	symphonia-core@0.6.0
	symphonia-format-caf@0.6.0
	symphonia-format-isomp4@0.6.0
	symphonia-format-mkv@0.6.0
	symphonia-format-ogg@0.6.0
	symphonia-format-riff@0.6.0
	symphonia-metadata@0.6.0
	symphonia@0.6.0
	syn@1.0.109
	syn@2.0.117
	sync_wrapper@1.0.2
	synstructure@0.13.2
	system-configuration-sys@0.6.0
	system-configuration@0.7.0
	target-lexicon@0.13.5
	task-local@0.1.1
	tekken-rs@0.1.1
	tempfile@3.27.0
	thiserror-ext-derive@0.3.0
	thiserror-ext@0.3.0
	thiserror-impl@1.0.69
	thiserror-impl@2.0.18
	thiserror@1.0.69
	thiserror@2.0.18
	thread_local@1.1.9
	tiff@0.11.3
	tiktoken-rs@0.7.0
	tiktoken-rs@0.9.1
	time-core@0.1.8
	time-macros@0.2.27
	time@0.3.47
	tiny-keccak@2.0.2
	tinystr@0.8.2
	tinytemplate@1.2.1
	tls-listener@0.11.2
	tokenizers@0.22.2
	tokio-macros@2.7.0
	tokio-native-tls@0.3.1
	tokio-openssl@0.6.5
	tokio-rustls@0.26.4
	tokio-stream@0.1.18
	tokio-tungstenite@0.28.0
	tokio-util@0.7.18
	tokio@1.52.3
	toml_datetime@1.1.1+spec-1.1.0
	toml_edit@0.25.11+spec-1.1.0
	toml_parser@1.1.2+spec-1.1.0
	tonic-build@0.14.6
	tonic-health@0.14.6
	tonic-prost-build@0.14.6
	tonic-prost@0.14.6
	tonic@0.14.6
	tool-parser@1.2.0
	tower-http@0.6.8
	tower-layer@0.3.3
	tower-service@0.3.3
	tower@0.5.3
	tracing-attributes@0.1.31
	tracing-core@0.1.36
	tracing-futures@0.2.5
	tracing-log@0.2.0
	tracing-subscriber@0.3.22
	tracing@0.1.44
	trait-set@0.3.0
	transpose@0.2.3
	try-lock@0.2.5
	tungstenite@0.28.0
	typenum@1.19.0
	unic-char-property@0.9.0
	unic-char-range@0.9.0
	unic-common@0.9.0
	unic-emoji-char@0.9.0
	unic-ucd-ident@0.9.0
	unic-ucd-version@0.9.0
	unicase@2.9.0
	unicode-ident@1.0.24
	unicode-normalization-alignments@0.1.12
	unicode-segmentation@1.13.1
	unicode-width@0.2.2
	unicode-xid@0.2.6
	unicode_categories@0.1.1
	unicode_names2@1.3.0
	unicode_names2_generator@1.3.0
	unit-prefix@0.5.2
	untrusted@0.9.0
	ureq-proto@0.6.0
	ureq@3.3.0
	url@2.5.8
	utf-8@0.7.6
	utf16_iter@1.0.5
	utf8-zero@0.8.1
	utf8_iter@1.0.4
	utf8parse@0.2.2
	uuid@1.22.0
	validator@0.20.0
	validator_derive@0.20.0
	valuable@0.1.1
	vcpkg@0.2.15
	version_check@0.9.5
	walkdir@2.5.0
	want@0.3.1
	wasi@0.11.1+wasi-snapshot-preview1
	wasip2@1.0.2+wasi-0.2.9
	wasip3@0.4.0+wasi-0.3.0-rc-2026-01-06
	wasm-bindgen-futures@0.4.64
	wasm-bindgen-macro-support@0.2.114
	wasm-bindgen-macro@0.2.114
	wasm-bindgen-shared@0.2.114
	wasm-bindgen@0.2.114
	wasm-encoder@0.244.0
	wasm-metadata@0.244.0
	wasm-streams@0.4.2
	wasm-streams@0.5.0
	wasmparser@0.244.0
	web-sys@0.3.91
	web-time@1.1.0
	webpki-root-certs@1.0.8
	webpki-roots@1.0.8
	weezl@0.1.12
	win_uds@0.2.2
	winapi-i686-pc-windows-gnu@0.4.0
	winapi-util@0.1.11
	winapi-x86_64-pc-windows-gnu@0.4.0
	winapi@0.3.9
	windows-core@0.62.2
	windows-implement@0.60.2
	windows-interface@0.59.3
	windows-link@0.2.1
	windows-registry@0.6.1
	windows-result@0.4.1
	windows-strings@0.5.1
	windows-sys@0.52.0
	windows-sys@0.61.2
	windows-targets@0.52.6
	windows_aarch64_gnullvm@0.52.6
	windows_aarch64_msvc@0.52.6
	windows_i686_gnu@0.52.6
	windows_i686_gnullvm@0.52.6
	windows_i686_msvc@0.52.6
	windows_x86_64_gnu@0.52.6
	windows_x86_64_gnullvm@0.52.6
	windows_x86_64_msvc@0.52.6
	winnow@1.0.2
	wit-bindgen-core@0.51.0
	wit-bindgen-rust-macro@0.51.0
	wit-bindgen-rust@0.51.0
	wit-bindgen@0.51.0
	wit-component@0.244.0
	wit-parser@0.244.0
	write16@1.0.0
	writeable@0.6.2
	xgrammar-structural-tag@0.2.0+xgrammar.0.2.4.dd729e7
	yoke-derive@0.8.1
	yoke@0.8.1
	zerocopy-derive@0.8.42
	zerocopy@0.8.42
	zerofrom-derive@0.1.6
	zerofrom@0.1.6
	zeroize@1.8.2
	zeromq@0.6.0
	zerotrie@0.2.3
	zerovec-derive@0.11.2
	zerovec@0.11.5
	zmij@1.0.21
	zstd-safe@7.2.4
	zstd-sys@2.0.16+zstd.1.5.7
	zstd@0.13.3
	zune-core@0.5.1
	zune-jpeg@0.5.15
"
declare -A GIT_CRATES=(
	[llm-multimodal]='https://github.com/smg-project/llm-multimodal;15adba5e025d8636ba4a334fb379b1371f6196a1;llm-multimodal-%commit%'
	[oss-harmony]='https://github.com/oss-harmony/harmony;76e849426cc092f84509e31a17027755f67d662a;harmony-%commit%'
)

# The Rust frontend (vllm-rs) is opt-in at runtime (VLLM_USE_RUST_FRONTEND=1,
# default off) and a heavy 600+-crate build, so gate it behind USE=rust rather
# than building it for every install. CARGO_OPTIONAL stops the cargo eclass from
# auto-adding its BDEPEND/SRC_URI/phase functions; we wire those under rust?
# below and call cargo_src_unpack manually.
CARGO_OPTIONAL=1

inherit cargo cuda distutils-r1 flag-o-matic pypi rocm toolchain-funcs

# Revisions pinned by vllm's CMake files.  Keep these in lockstep with
# upstream: all CMake dependencies are pre-staged so builds stay offline.
VLLM_CUTLASS_TAG="4.4.2"
VLLM_DEEPGEMM_COMMIT="e21c821f39a2056d68067a466c64ddc942200106"
VLLM_DEEPGEMM_CUTLASS_COMMIT="f3fde58372d33e9a5650ba7b80fc48b3b49d40c8"
VLLM_DEEPGEMM_FMT_COMMIT="553ec11ec06fbe0beebfbb45f9dc3c9eabd83d28"
VLLM_FA_COMMIT="28e862d21806bc3580207aa0ad4e2759151e9827"
VLLM_FA_CUTLASS_COMMIT="62750a2b75c802660e4894434dc55e839f322277"
VLLM_FLASHMLA_COMMIT="a8f794d1251cbfd88a5011445dd5582289c727e4"
VLLM_FLASHMLA_CUTLASS_COMMIT="147f5673d0c1c3dcf66f78d677fd647e4a020219"
VLLM_FMHA_SM100_COMMIT="087c161814d4d9c735b46c21212a09e5f8eb92fa"
VLLM_FMHA_SM100_CUTLASS_COMMIT="eb61c911471867a5fd2466bfd8f29306cea6ebf8"
VLLM_ONEDNN_TAG="3.10"
VLLM_QUTLASS_COMMIT="e74319e3405ce6d71965732880f5dc1f52371f64"
VLLM_FLASHKDA_COMMIT="a3e42bbbece3bb38f7c426b880315294a336e82f"
VLLM_TML_FA4_COMMIT="b206834606ed5b5f21f8eed6b0683f528ea9cf7d"
VLLM_TRITON_KERNELS_TAG="3.5.1"

DESCRIPTION="High-throughput, memory-efficient inference and serving engine for LLMs"
HOMEPAGE="
	https://github.com/vllm-project/vllm
	https://docs.vllm.ai/
	https://pypi.org/project/vllm/
"
SRC_URI+="
	rust? ( ${CARGO_CRATE_URIS} )
	cpu? (
		https://github.com/uxlfoundation/oneDNN/archive/refs/tags/v${VLLM_ONEDNN_TAG}.tar.gz
			-> vllm-oneDNN-${VLLM_ONEDNN_TAG}.gh.tar.gz
	)
	cuda? (
		https://github.com/NVIDIA/cutlass/archive/refs/tags/v${VLLM_CUTLASS_TAG}.tar.gz
			-> vllm-cutlass-${VLLM_CUTLASS_TAG}.gh.tar.gz
		https://github.com/vllm-project/DeepGEMM/archive/${VLLM_DEEPGEMM_COMMIT}.tar.gz
			-> vllm-DeepGEMM-${VLLM_DEEPGEMM_COMMIT:0:7}.gh.tar.gz
		https://github.com/NVIDIA/cutlass/archive/${VLLM_DEEPGEMM_CUTLASS_COMMIT}.tar.gz
			-> vllm-DeepGEMM-cutlass-${VLLM_DEEPGEMM_CUTLASS_COMMIT:0:7}.gh.tar.gz
		https://github.com/fmtlib/fmt/archive/${VLLM_DEEPGEMM_FMT_COMMIT}.tar.gz
			-> vllm-DeepGEMM-fmt-${VLLM_DEEPGEMM_FMT_COMMIT:0:7}.gh.tar.gz
		https://github.com/vllm-project/flash-attention/archive/${VLLM_FA_COMMIT}.tar.gz
			-> vllm-flash-attn-${VLLM_FA_COMMIT:0:7}.gh.tar.gz
		https://github.com/NVIDIA/cutlass/archive/${VLLM_FA_CUTLASS_COMMIT}.tar.gz
			-> vllm-flash-attn-cutlass-${VLLM_FA_CUTLASS_COMMIT:0:7}.gh.tar.gz
		https://github.com/vllm-project/FlashMLA/archive/${VLLM_FLASHMLA_COMMIT}.tar.gz
			-> vllm-FlashMLA-${VLLM_FLASHMLA_COMMIT:0:7}.gh.tar.gz
		https://github.com/NVIDIA/cutlass/archive/${VLLM_FLASHMLA_CUTLASS_COMMIT}.tar.gz
			-> vllm-FlashMLA-cutlass-${VLLM_FLASHMLA_CUTLASS_COMMIT:0:7}.gh.tar.gz
		https://github.com/vllm-project/MSA/archive/${VLLM_FMHA_SM100_COMMIT}.tar.gz
			-> vllm-MSA-${VLLM_FMHA_SM100_COMMIT:0:7}.gh.tar.gz
		https://github.com/NVIDIA/cutlass/archive/${VLLM_FMHA_SM100_CUTLASS_COMMIT}.tar.gz
			-> vllm-MSA-cutlass-${VLLM_FMHA_SM100_CUTLASS_COMMIT:0:7}.gh.tar.gz
		https://github.com/IST-DASLab/qutlass/archive/${VLLM_QUTLASS_COMMIT}.tar.gz
			-> vllm-qutlass-${VLLM_QUTLASS_COMMIT:0:7}.gh.tar.gz
		https://github.com/vllm-project/tml-fa4/archive/${VLLM_TML_FA4_COMMIT}.tar.gz
			-> vllm-tml-fa4-${VLLM_TML_FA4_COMMIT:0:7}.gh.tar.gz
		https://github.com/vllm-project/FlashKDA/archive/${VLLM_FLASHKDA_COMMIT}.tar.gz
			-> vllm-FlashKDA-${VLLM_FLASHKDA_COMMIT:0:7}.gh.tar.gz
		https://github.com/triton-lang/triton/archive/refs/tags/v${VLLM_TRITON_KERNELS_TAG}.tar.gz
			-> vllm-triton-kernels-${VLLM_TRITON_KERNELS_TAG}.gh.tar.gz
	)
	rocm? (
		https://github.com/triton-lang/triton/archive/refs/tags/v${VLLM_TRITON_KERNELS_TAG}.tar.gz
			-> vllm-triton-kernels-${VLLM_TRITON_KERNELS_TAG}.gh.tar.gz
	)
"

LICENSE="Apache-2.0"
# Dependent crate licenses
LICENSE+="
	BSD-2 BSD CC0-1.0 CDLA-Permissive-2.0 ISC LGPL-3 MIT
	MPL-2.0 UoI-NCSA Unicode-3.0 Unicode-DFS-2016 Unlicense ZLIB
"
SLOT="0"
KEYWORDS="~amd64"
IUSE="cpu cuda humming rocm rust"
# VLLM_TARGET_DEVICE is single-valued; cpu, cuda, and rocm paths are
# mutually exclusive. Default (none) → empty target. USE=rust is
# orthogonal — it builds the optional vllm-rs Rust serving frontend
# (opt-in at runtime via VLLM_USE_RUST_FRONTEND=1) and combines with any
# target.
REQUIRED_USE="
	?? ( cpu cuda rocm )
	rocm? ( || ( ${ROCM_REQUIRED_USE} ) )
	humming? ( cuda )
"

# USE=cpu (default off): build with VLLM_TARGET_DEVICE=cpu so the
# Python entrypoints can actually drive inference on CPU hardware.
# Pulls torchaudio + numba (vllm's cpu.txt also lists intel-openmp on
# x86_64, but Intel ships it as a proprietary blob — we omit it; vllm
# falls back to the pthreads OpenMP shipped with sci-libs/openblas etc.)
#
# CAVEAT (historical): ::gentoo sci-ml/pytorch's caffe2::mkl public
# link interface used to drag MKL's MPI / cluster libs (scalapack,
# cdft, blacs_intelmpi) and Intel-OpenMP threading (intel_thread)
# into every consumer link, breaking the build on hosts without
# Intel Cluster Edition + Compiler. We pin >=sci-ml/caffe2-2.11.0-r90
# below — this overlay's r90 fork ships a scrub patch on
# cmake/public/mkl.cmake that filters those libs and forces
# gnu_thread. Drop the pin once an equivalent upstream fix lands.
#
# USE=cuda: build with VLLM_TARGET_DEVICE=cuda. Pulls torchaudio +
# torchvision + numba and the full Tier-0..5 CUDA stack (flashinfer
# + tilelang + nvidia-cutlass-dsl + cuda-bindings + nvidia-cudnn-
# frontend + ...). Compiles the _C / _moe_C / _vllm_fa* CUDA C++
# extensions in setup.py via nvcc and the system CUDA toolkit at
# /opt/cuda. CMAKE_CUDA_HOST_COMPILER is selected through cuda.eclass
# because nvcc rejects unsupported host compiler versions.
# All CMake dependencies are pre-staged from SRC_URI below.
#
# Same MKL-MPI link-pollution caveat as USE=cpu (above): without the
# >=sci-ml/caffe2-2.11.0-r90 pin the cumem_allocator link fails with
# "cannot find -lmkl_scalapack_ilp64" after all 339 CUDA objects build.
#
# USE=rocm: build with VLLM_TARGET_DEVICE=rocm. Pulls torchaudio +
# torchvision + numba + the runai-streamer/tensorizer/conch-triton
# trio from upstream's requirements/rocm.txt, plus the HIP libs that
# vllm's CMake `enable_language(HIP)` and the linked libtorch_hip
# resolve at link time (hipBLAS / hipBLASLt / hipFFT / hipRAND /
# hipSOLVER / hipSPARSE / hipCUB). Compiles the _C / _moe_C / _rocm_C
# extensions and csrc/rocm/*.cu via hipcc and the system ROCm
# toolchain at /opt/rocm. Inherits sci-ml/caffe2's MKL-MPI scrub
# (>=2.11.0-r90) — same link-pollution caveat as the cuda path.
# PYTORCH_ROCM_ARCH is derived from AMDGPU_TARGETS via rocm.eclass's
# get_amdgpu_flags.
#
# amd-quark (in requirements/rocm.txt as "for Quark quantization on
# ROCm") is deliberately omitted from RDEPEND: no direct `import` from
# vllm core code, only used by vllm.model_executor.layers.quantization.
# quark internals when Quark-quantized models are loaded.
# dev-python/amd-quark-bin in this overlay caps PYTHON_COMPAT at
# 3.{11,12}, which would block vllm on 3.13/3.14. Users wanting Quark
# quantization install amd-quark-bin separately.
#
# Upstream requirements/cuda.txt (0.27.1) pins nvidia-cutlass-dsl[cu13]==4.6.0,
# tilelang==0.1.12 and flashinfer-python==0.6.16.post3 exactly; we pin
# ~nvidia-cutlass-dsl-4.6.0, ~tilelang-0.1.12 and ~flashinfer-python-0.6.16_p3
# to match.
# quack-kernels floor is now >=0.4.0 (0.26.0 cuda.txt, "Required for
# tml-fa4") but we KEEP an upper cap at <0.6.2. cutlass-dsl moved to 4.6.0
# this bump; quack-kernels 0.6.1 pins ~nvidia-cutlass-dsl-4.6.0 (compatible),
# but 0.6.2+ bumped to ~nvidia-cutlass-dsl-4.6.1 (their JIT kernels ABI-lock
# to 4.6.1). vllm's ~4.6.0 pin (upstream cuda.txt ==4.6.0) is incompatible
# with 4.6.1, so an uncapped floor lets -uDN pull quack 0.6.3 -> cutlass 4.6.1
# and conflict with our own cutlass pin. Cap at <0.6.2 keeps quack on 0.6.1
# (~4.6.0). Do NOT relax to 4.6.1: not blessed by this vllm release.
# verified 2026-08-07
# The cutlass-dsl metapackage pulls nvidia-cutlass-dsl-libs-cu13
# transitively, so it already covers the [cu13] extra. The
# nvidia-cudnn-frontend floor stays >=1.19.1; that dep lives on the
# flashinfer-python ebuild — vllm has zero direct cudnn_frontend imports;
# it is for flashinfer's internal use. fastsafetensors floor >=0.3.2.
# 0.25.0 also adds torchcodec>=0.14 (GPU video decode) to cuda.txt; pulled
# in cuda? here.
# 0.25.1 (2026-07-14): patch release; both packaging AND build inputs are
# byte-identical to 0.25.0 -- rust/Cargo.lock (575 crates), GIT_CRATES
# (oss-harmony, llm-multimodal), every requirements/*.txt, the flash-attn
# pin, requires-python, setup.py, CMakeLists.txt, cmake/ and all of csrc/
# are unchanged; only 7 .py files differ (5 runtime vllm modules, the version
# stamp, and one test), all py_compile-clean. So the 0.25.0 audit below
# carries over verbatim and the compiled result is identical -- not
# separately rebuilt.
# static cuda.txt audit done 2026-07-12 against vllm-0.25.0 -- deltas vs
# 0.24.0: flashinfer-python 0.6.12->0.6.13, humming-kernels 0.1.4->0.1.10,
# +torchcodec>=0.14, flash-attn pin dd62dac->2c839c3; PyNvVideoCodec + nvtx
# were packaged with the 0.27.1 dependency audit. rocm gfx1150 + cpu + empty
# + USE=rust coverage completed 2026-07-12; CUDA sm_86 coverage on an A4500
# with CUDA 13.3 and nvcc -ccbin g++-15 completed the same day: FA2 builds, FA3 stubbed
# on sm_86, opt-125m generate() OK, USE=humming registry loads. 0.25.0 adds a
# new ninja target _vllm_fa4_cutedsl_C (cutedsl) that builds clean and needs
# no patch -- watch it alongside the FA3 stub on future bumps.
# 0.26.0 (2026-07-26): common.txt drops diskcache (outlines disk-cache removed).
# cuda.txt deltas vs 0.25.1: flashinfer-python/cubin 0.6.13->0.6.14, apache-tvm-ffi
# 0.1.9->0.1.10, nvidia-cutlass-dsl 4.5.2->4.6.0,
# quack-kernels >=0.3.3,<0.6.0 -> >=0.4.0,<0.6.2 (floor raised, cap moved to
# 0.6.2 for the cutlass-4.6.1 boundary, see above); tokenspeed-mla
# 0.1.2->0.1.8 is packaged exactly while amd-quark 0.8.99->0.12 stays omitted.
# cpu.txt is unchanged (torch
# still ==2.11.0). flash-attn pin 2c839c3->caaa4eb (fa3-skip + py314 patches apply
# clean, regenerated as caaa4eb copies). CRATES 561->594, GIT_CRATES llm-multimodal
# rev 7d74582->5390032 (still v1.7.1), oss-harmony unchanged. The rocm and cuda
# targets are not yet verified against the shipped ~2.11.0 torch pin.
#
# 0.27.1 (2026-08-12): the big one -- torch 2.11.0 -> 2.13.0 (caffe2/pytorch
# pins ->2.13.0-r90; torchvision 0.26.0->0.28.0; torchaudio stays ==2.11.0 per
# cuda.txt). cuda.txt: flashinfer-python/cubin 0.6.14->0.6.16.post3, apache-tvm-ffi
# 0.1.10->0.1.11 (rocm stays 0.1.10), tilelang 0.1.9->0.1.12 (rocm stays 0.1.10),
# quack-kernels now ==0.6.1 (our >=0.4.0<0.6.2 range still resolves it). common.txt:
# mistral-common 1.11.5->1.11.6. numba stays ==0.65.0 upstream (kept <0.66; numba
# 0.65.1 + llvmlite 0.47.0 were re-added this cycle to satisfy it). xgrammar kept
# ~0.2.2 (requirements only bound 0.2.1..<1; FFI ABI move unverified here).
# CMake: DeepGEMM repo deepseek-ai->vllm-project + a6b593d->e21c821, MSA
# 2e63ec3->087c161, qutlass 830d2c4->e74319e, FA caaa4eb->28e862d (patches
# regenerated: FA CMakeLists moved to C++20 + per-target CXX_STANDARD, fa3 guard
# folds the new set_target_properties in); NEW external project FlashKDA a3e42bb
# (cuda, no submodules, reads FLASH_KDA_SRC_DIR). All nested cutlass/fmt UNCHANGED.
# CRATES 594->595 (tonic 0.14.5->0.14.6 +tonic-health, xgrammar-structural-tag
# 0.2.2.4d145cc->0.2.4.dd729e7), GIT_CRATES llm-multimodal 5390032->15adba5 (still
# v1.7.1), oss-harmony now tag v0.0.11 (same commit 76e8494). rocm gfx1150
# build + `from vllm import LLM` import verified 2026-08-12 (against
# caffe2/pytorch 2.13.0), after two fixes the torch-2.13 stack forced: the rocm
# HIP_CLANG_PATH export below (caffe2 2.13's LoadHIP no longer finds the slotted
# llvm) and bumping rocm apache-tvm-ffi to 0.1.11 (xgrammar 0.2.2 needs its
# extra_lib_paths). CUDA sm_86 clean rebuild and non-eager OPT-125M generation
# completed 2026-08-14 with CUDA 13.3, driver 610.57.04, and GCC 15.
#
# requirements/cuda.txt pins tokenspeed-mla 0.1.8, PyNvVideoCodec 2.0.4,
# and nvtx 0.2.15. Keep the exact dependency set even though the TokenSpeed
# kernels target Blackwell SM100/SM103 and the other two imports are lazy.
#
# humming-kernels[cu13] (requirements/cuda.txt, ==0.1.10 "for quantization
# gemm") provides the optional `humming` quant backend -- pulled only
# under USE=humming. As of 0.24.0 vllm lazily imports the external
# `humming` package via vllm.utils.humming (vllm-project/vllm#44921), so
# the quant registry imports fine without it and a Humming-quantized
# model only errors at load time under USE=-humming. No import-guard
# patch is needed here -- verified 2026-07-05 that
# vllm.model_executor.layers.quantization.humming imports with
# humming-kernels absent (0.23.0 and earlier predate #44921 and still
# ship the guard patch).
#
# gfx1150 (Strix Point iGPU) rocm build verified on
# caffe2[rocm,amdgpu_targets_gfx1150,-nccl,-cusparselt] with
# AMDGPU_TARGETS=gfx1150.  Produces the HIP extensions (_C,
# _C_stable_libtorch, _moe_C, _rocm_C, cumem_allocator, spinloop) and
# installs cleanly.
# verified 2026-05-08 for 0.20.1, 2026-05-16 for 0.21.0, 2026-06-13 for
# 0.23.0, 2026-07-12 for 0.25.0 (with pytorch/caffe2 2.11.0; cpu + empty +
# USE=rust also OK).
#
# RTX A4500 Laptop (sm_86 Ampere) cuda build verified on
# caffe2-2.11.0-r90 + CUDA-13.2 + CUDAHOSTCXX=g++-15 + MAX_JOBS=4.
# Pre-FA3-skip baseline: ~2h30m wallclock, 339 CUDA template files
# (FA3 .cu compiled at nvcc's default arch — wasted on Ampere).
# Post-FA3-skip (next commit, files/vllm-flash-attn-...-fa3-only-
# when-archs.patch): ~1h35m wallclock, 144 CUDA template files.
# Peak ~14 GiB RSS in either case (16 GiB free headroom on 31 GiB
# host).  Smoke test in both shapes: `from vllm import LLM`
# succeeds, torch.cuda.is_available() True, torch reports "NVIDIA
# RTX A4500 Laptop GPU"; FA2 kernels build for sm_80+PTX (forward-
# compat with sm_86); FA3 (Hopper) does NOT build on sm_86 in the
# post-patch shape (FA3_AVAILABLE=False at runtime, vllm picks FA2).
# verified 2026-05-17 for 0.21.0 on sm_86 + CUDA 13.2 (both shapes).
#
# USE=-cpu -cuda -rocm (default): build with VLLM_TARGET_DEVICE=empty
# — Python entrypoints import cleanly, backend kernels fail at first
# model-load. Useful if you only want the API surface for development.
#
# vllm resolves its runtime platform from the host hardware (not the
# VLLM_TARGET_DEVICE built below). platforms/cuda.py / rocm.py import
# torch.distributed.PrefixStore + ProcessGroup unconditionally at module
# load (needs USE=distributed), and at engine init vllm builds a CPU
# coordination group on the gloo backend. Since our caffe2 builds CUDA
# with USE_NCCL=OFF, vllm's nccl device group also falls back to gloo, so
# USE=gloo is required too. Both flags are default-off: without
# caffe2[distributed,gloo] vllm ImportErrors at startup, or
# AssertionErrors ("Fallback Gloo backend is not available") at engine
# init. verified 2026-06-14, bug #274
#
# vllm's GPU kernels (slot mapping, attention, sampling, and the
# torch.compile/inductor path) are @triton.jit on both the cuda and
# rocm targets -- on ROCm, vllm's custom paged-attention also falls
# back to a Triton kernel on gfx targets without it (e.g. gfx1150).
# Gentoo's source-built torch does not pull Triton the way upstream's
# PyPI wheels do, so the cuda? and rocm? targets require
# virtual/triton or vllm dies at first GPU inference with
# "'function' object is not subscriptable". torch-2.11.0 pairs with
# triton 3.6.0; its AMD backend JITs gfx kernels via hipcc. cuda
# verified 2026-06-14 (bug #274); rocm gfx1150 verified 2026-06-14
# (opt-125m generated, inductor path + Triton _fwd_kernel).
# Upstream pins lark==1.2.2 and numba==0.65.0, neither of which is in the
# active repositories.  Stay within their compatible major/minor series.
# common.txt allows xgrammar 0.2.1..<1; constrain it to ~0.2.2 on CUDA/ROCm
# (newest compatible with transformers 5). xgrammar 0.2.2 calls tvm-ffi's
# load_lib_module(extra_lib_paths=...), which only exists from apache-tvm-ffi
# 0.1.11 (0.1.10 raises TypeError at `import vllm`). Upstream rocm.txt still
# pins tvm-ffi 0.1.10, but that is too old for the shipped xgrammar, so ROCm is
# bumped to ~0.1.11 here to match CUDA (rocm import-verified 2026-08-12).
RDEPEND="
	~sci-ml/pytorch-2.13.0[${PYTHON_SINGLE_USEDEP}]
	sci-ml/caffe2[distributed,gloo]
	>=sci-ml/transformers-5.5.3[${PYTHON_SINGLE_USEDEP}]
	>=sci-ml/tokenizers-0.21.1[${PYTHON_SINGLE_USEDEP}]
	>=dev-python/xgrammar-0.2.1[${PYTHON_SINGLE_USEDEP}]
	<dev-python/xgrammar-1.0.0[${PYTHON_SINGLE_USEDEP}]
	~dev-python/compressed-tensors-0.17.0[${PYTHON_SINGLE_USEDEP}]
	app-alternatives/ninja
	$(python_gen_cond_dep '
		dev-python/regex[${PYTHON_USEDEP}]
		dev-python/cachetools[${PYTHON_USEDEP}]
		dev-python/psutil[${PYTHON_USEDEP}]
		sci-ml/sentencepiece[${PYTHON_USEDEP}]
		>=sci-ml/safetensors-0.6.2[${PYTHON_USEDEP}]
		dev-python/numpy[${PYTHON_USEDEP}]
		>=dev-python/requests-2.26.0[${PYTHON_USEDEP}]
		dev-python/tqdm[${PYTHON_USEDEP}]
		dev-python/blake3[${PYTHON_USEDEP}]
		dev-python/py-cpuinfo[${PYTHON_USEDEP}]
		|| (
			~dev-python/protobuf-5.29.6[${PYTHON_USEDEP}]
			>=dev-python/protobuf-6.33.5[${PYTHON_USEDEP}]
		)
		>=dev-python/fastapi-0.133.0[${PYTHON_USEDEP}]
		<dev-python/fastapi-0.137.0[${PYTHON_USEDEP}]
		>=dev-python/starlette-1.0.1[${PYTHON_USEDEP}]
		>=dev-python/aiohttp-3.13.3[${PYTHON_USEDEP}]
		>=dev-python/openai-2.0.0[${PYTHON_USEDEP}]
		>=dev-python/pydantic-2.12.0[${PYTHON_USEDEP}]
		>=dev-python/prometheus-client-0.18.0[${PYTHON_USEDEP}]
		dev-python/pillow[${PYTHON_USEDEP}]
		>=dev-python/prometheus-fastapi-instrumentator-8.0.0[${PYTHON_USEDEP}]
		>=dev-python/tiktoken-0.6.0[${PYTHON_USEDEP}]
		~dev-python/lm-format-enforcer-0.11.3[${PYTHON_USEDEP}]
		>=dev-python/llguidance-1.7.0[${PYTHON_USEDEP}]
		<dev-python/llguidance-1.8.0[${PYTHON_USEDEP}]
		~dev-python/outlines-core-0.2.14[${PYTHON_USEDEP}]
		>=dev-python/lark-1.2.2[${PYTHON_USEDEP}]
		<dev-python/lark-2[${PYTHON_USEDEP}]
		>=dev-python/jsonschema-4.23.0[${PYTHON_USEDEP}]
		>=dev-python/typing-extensions-4.10[${PYTHON_USEDEP}]
		>=dev-python/filelock-3.16.1[${PYTHON_USEDEP}]
		dev-python/partial-json-parser[${PYTHON_USEDEP}]
		>=dev-python/pyzmq-25.0.0[${PYTHON_USEDEP}]
		dev-python/msgspec[${PYTHON_USEDEP}]
		>=dev-python/mistral-common-1.11.6[${PYTHON_USEDEP},image]
		>=media-libs/opencv-4.13.0[python,${PYTHON_USEDEP}]
		dev-python/pyyaml[${PYTHON_USEDEP}]
		>=dev-python/six-1.16.0[${PYTHON_USEDEP}]
		>=dev-python/setuptools-77.0.3[${PYTHON_USEDEP}]
		dev-python/einops[${PYTHON_USEDEP}]
		~dev-python/depyf-0.20.0[${PYTHON_USEDEP}]
		dev-python/cloudpickle[${PYTHON_USEDEP}]
		dev-python/uvloop[${PYTHON_USEDEP}]
		dev-python/watchfiles[${PYTHON_USEDEP}]
		>=dev-python/uvicorn-0.12.0[${PYTHON_USEDEP}]
		>=dev-python/jinja2-3.1.5[${PYTHON_USEDEP}]
		>=dev-python/python-multipart-0.0.18[${PYTHON_USEDEP}]
		>=dev-python/websockets-13.0[${PYTHON_USEDEP}]
		dev-python/python-json-logger[${PYTHON_USEDEP}]
		dev-python/pybase64[${PYTHON_USEDEP}]
		dev-python/cbor2[${PYTHON_USEDEP}]
		dev-python/ijson[${PYTHON_USEDEP}]
		dev-python/setproctitle[${PYTHON_USEDEP}]
		>=dev-python/openai-harmony-0.0.3[${PYTHON_USEDEP}]
		>=dev-python/anthropic-0.71.0[${PYTHON_USEDEP}]
		>=dev-python/model-hosting-container-standards-0.1.14[${PYTHON_USEDEP}]
		<dev-python/model-hosting-container-standards-1.0.0[${PYTHON_USEDEP}]
		<dev-python/mcp-2.0[${PYTHON_USEDEP}]
		>=dev-python/opentelemetry-sdk-1.27.0[${PYTHON_USEDEP}]
		>=dev-python/opentelemetry-api-1.27.0[${PYTHON_USEDEP}]
		>=dev-python/opentelemetry-exporter-otlp-1.27.0[${PYTHON_USEDEP}]
		>=dev-python/opentelemetry-semantic-conventions-ai-0.4.1[${PYTHON_USEDEP}]
	')
	cpu? (
		>=sci-ml/caffe2-2.13.0-r90[-cuda,-rocm]
		~sci-ml/torchaudio-2.11.0
		~sci-ml/torchvision-0.28.0[-cuda,-rocm,${PYTHON_SINGLE_USEDEP}]
		>=sci-ml/torchcodec-0.14[-cuda,${PYTHON_SINGLE_USEDEP}]
		$(python_gen_cond_dep '
			>=dev-python/numba-0.65.0[${PYTHON_USEDEP}]
			<dev-python/numba-0.66[${PYTHON_USEDEP}]
		')
	)
	cuda? (
		>=sci-ml/caffe2-2.13.0-r90[cuda,-rocm]
		~dev-python/pynvvideocodec-bin-2.0.4[${PYTHON_SINGLE_USEDEP}]
		~dev-python/tokenspeed-mla-bin-0.1.8[${PYTHON_SINGLE_USEDEP}]
		~sci-ml/torchaudio-2.11.0
		~sci-ml/torchvision-0.28.0[cuda,-rocm,${PYTHON_SINGLE_USEDEP}]
		~dev-python/xgrammar-0.2.2[cuda,${PYTHON_SINGLE_USEDEP}]
		~dev-python/flashinfer-python-0.6.16_p3[${PYTHON_SINGLE_USEDEP}]
		>=sci-ml/torchcodec-0.14[cuda,${PYTHON_SINGLE_USEDEP}]
		~dev-python/tilelang-0.1.12[cuda,-rocm,${PYTHON_SINGLE_USEDEP}]
		~dev-python/quack-kernels-0.6.1[${PYTHON_SINGLE_USEDEP}]
		humming? ( ~dev-python/humming-kernels-0.1.10[${PYTHON_SINGLE_USEDEP}] )
		$(python_gen_cond_dep '
			~dev-python/nvtx-0.2.15[${PYTHON_USEDEP}]
			~dev-python/apache-tvm-ffi-0.1.11[${PYTHON_USEDEP}]
			>=dev-python/numba-0.65.0[${PYTHON_USEDEP}]
			<dev-python/numba-0.66[${PYTHON_USEDEP}]
			>=dev-python/fastsafetensors-0.3.2[${PYTHON_SINGLE_USEDEP}]
			>=dev-python/nvidia-cudnn-frontend-1.19.1[${PYTHON_USEDEP}]
			~dev-python/nvidia-cutlass-dsl-4.6.0[${PYTHON_USEDEP}]
			~virtual/triton-3.6.0[${PYTHON_USEDEP}]
		')
		dev-util/nvidia-cuda-toolkit:=
	)
	rocm? (
		>=sci-ml/caffe2-2.13.0-r90[-cuda,rocm,${ROCM_USEDEP}]
		~sci-ml/torchaudio-2.11.0
		~sci-ml/torchvision-0.28.0[-cuda,rocm,${PYTHON_SINGLE_USEDEP}]
		~dev-python/xgrammar-0.2.2[${PYTHON_SINGLE_USEDEP}]
		>=dev-python/runai-model-streamer-bin-0.15.7[${PYTHON_SINGLE_USEDEP}]
		~dev-python/tensorizer-2.10.1[${PYTHON_SINGLE_USEDEP}]
		~dev-python/tilelang-0.1.10[-cuda,rocm,${PYTHON_SINGLE_USEDEP}]
		$(python_gen_cond_dep '
			~dev-python/apache-tvm-ffi-0.1.11[${PYTHON_USEDEP}]
			>=dev-python/numba-0.65.0[${PYTHON_USEDEP}]
			<dev-python/numba-0.66[${PYTHON_USEDEP}]
			~dev-python/conch-triton-kernels-1.2.1[${PYTHON_USEDEP}]
			~virtual/triton-3.6.0[${PYTHON_USEDEP}]
			>=dev-util/amdsmi-7.0.2[${PYTHON_USEDEP}]
			>=dev-python/fastsafetensors-0.3.2[${PYTHON_SINGLE_USEDEP}]
		')
		>=dev-util/hip-7.2:=
		>=sci-libs/hipBLAS-7.2:=
		>=sci-libs/hipBLASLt-7.2:=
		>=sci-libs/hipFFT-7.2:=
		>=sci-libs/hipRAND-7.2:=
		>=sci-libs/hipSOLVER-7.2:=
		>=sci-libs/hipSPARSE-7.2:=
		>=sci-libs/hipCUB-7.2:=
	)
"
# Upstream pyproject.toml caps setuptools at <81.0.0; dropped from
# BDEPEND because (a) gentoo only ships 79.0.1 + 82.0.1 (nothing in
# the 80.x/81.x line), and downgrading to 79.0.1 fights pkg-resources-
# 81.0.0 (which has !<setuptools-82 and is pulled in by html5lib /
# opcodes / python-xlib among others); and (b) vllm's setup.py uses
# only the standard setuptools surface (Extension, setup, build_ext)
# — no pkg_resources imports, no setuptools.command.* removed in 81+.
# Cap re-evaluate on bump. # verified 2026-05-16 against setup.py.
BDEPEND="
	>=dev-build/cmake-3.26.1
	app-alternatives/ninja
	~sci-ml/pytorch-2.13.0[${PYTHON_SINGLE_USEDEP}]
	$(python_gen_cond_dep '
		>=dev-python/setuptools-77.0.3[${PYTHON_USEDEP}]
		>=dev-python/setuptools-scm-8.0[${PYTHON_USEDEP}]
		>=dev-python/setuptools-rust-1.9.0[${PYTHON_USEDEP}]
		>=dev-python/packaging-24.2[${PYTHON_USEDEP}]
		dev-python/jinja2[${PYTHON_USEDEP}]
	')
	rust? (
		${RUST_DEPEND}
		dev-lang/perl
	)
	cuda? (
		dev-util/nvidia-cuda-toolkit:=
	)
	rocm? (
		>=dev-util/hip-7.2:=
		>=dev-util/hipcc-7.2:=
	)
"

# Tests need a model+inference setup; not wired up here.
RESTRICT="test"

# 0.20.x carried a patch to relax cmake/cpu_extension.cmake's libgomp
# probe so it would fall back to the system gcc-runtime libgomp when
# torch.libs/ contains no vendored copy.  Upstream 0.21.0's cmake now
# has an equivalent fallback (find_library(OPEN_MP NAMES gomp REQUIRED)
# without NO_DEFAULT_PATH) when VLLM_TORCH_GOMP_SHIM_DIR is empty, so
# the local patch is no longer needed.

# Pretend the version so setuptools-scm doesn't probe git.
export SETUPTOOLS_SCM_PRETEND_VERSION=${PV}

src_unpack() {
	if use rust; then
		# Vendor the vllm-rs crate deps and set up CARGO_HOME for the
		# offline build (cargo_src_unpack also unpacks the sdist + any
		# cuda? flash-attn tarball normally).
		cargo_src_unpack
	else
		default
	fi
}

src_prepare() {
	distutils-r1_src_prepare

	if ! use rust; then
		# vllm's setup.py unconditionally wires the vllm-rs RustExtension.
		# With USE=-rust we ship no crates and set up no cargo, so drop the
		# extension list to keep setup.py from attempting a cargo build.
		# Guard the sed: it exits 0 on a no-match, so a future upstream
		# rename of this kwarg would silently leave the rust build active
		# and break the -rust build. Fail loudly instead.
		grep -q 'rust_extensions=rust_extensions,' setup.py ||
			die "vllm-rs RustExtension wiring changed; revisit the USE=rust gate"
		sed -i 's/rust_extensions=rust_extensions,/rust_extensions=[],/' \
			setup.py || die
	fi

	if use cuda; then
		# Populate the gitlinks omitted by GitHub-generated archives.
		local deepgemm_dir="${WORKDIR}/DeepGEMM-${VLLM_DEEPGEMM_COMMIT}"
		local fa_dir="${WORKDIR}/flash-attention-${VLLM_FA_COMMIT}"
		local flashmla_dir="${WORKDIR}/FlashMLA-${VLLM_FLASHMLA_COMMIT}"
		local fmha_dir="${WORKDIR}/MSA-${VLLM_FMHA_SM100_COMMIT}"

		rmdir "${deepgemm_dir}/third-party/cutlass" || die
		mv "${WORKDIR}/cutlass-${VLLM_DEEPGEMM_CUTLASS_COMMIT}" \
			"${deepgemm_dir}/third-party/cutlass" || die
		rmdir "${deepgemm_dir}/third-party/fmt" || die
		mv "${WORKDIR}/fmt-${VLLM_DEEPGEMM_FMT_COMMIT}" \
			"${deepgemm_dir}/third-party/fmt" || die
		rmdir "${fa_dir}/csrc/cutlass" || die
		mv "${WORKDIR}/cutlass-${VLLM_FA_CUTLASS_COMMIT}" \
			"${fa_dir}/csrc/cutlass" || die
		rmdir "${flashmla_dir}/csrc/cutlass" || die
		mv "${WORKDIR}/cutlass-${VLLM_FLASHMLA_CUTLASS_COMMIT}" \
			"${flashmla_dir}/csrc/cutlass" || die
		rmdir "${fmha_dir}/python/fmha_sm100/cutlass" || die
		mv "${WORKDIR}/cutlass-${VLLM_FMHA_SM100_CUTLASS_COMMIT}" \
			"${fmha_dir}/python/fmha_sm100/cutlass" || die

		# Pre-stage vllm-flash-attn and apply our local patches before
		# vllm's CMake FetchContent reaches it.  vllm honours
		# VLLM_FLASH_ATTN_SRC_DIR (set in src_configure) and skips the
		# git fetch when the dir already exists.
		[[ -d ${fa_dir} ]] || die "expected ${fa_dir} from SRC_URI unpack"
		pushd "${fa_dir}" >/dev/null || die
		# Skip the FA3 (Hopper) target body when no Hopper arch is in
		# CUDA_ARCHS so Ampere/Ada builds don't compile unrunnable kernels.
		eapply -p0 \
			"${FILESDIR}/vllm-flash-attn-${VLLM_FA_COMMIT:0:7}-fa3-only-when-archs.patch"
		# vllm's PYTHON_COMPAT allows python3_14, but flash-attn's
		# CMakeLists hard-codes a supported-Python whitelist and
		# FATAL_ERRORs on 3.14 at configure.  The extension is abi3
		# (USE_SABI 3), so widening that whitelist is safe.  bug #274
		eapply -p0 \
			"${FILESDIR}/vllm-flash-attn-${VLLM_FA_COMMIT:0:7}-py314.patch"
		popd >/dev/null || die
	fi
}

src_configure() {
	# When the Rust frontend is requested, make its build mandatory so a
	# failure errors out instead of setuptools-rust silently skipping the
	# optional extension.
	use rust && export VLLM_REQUIRE_RUST_FRONTEND=1

	if use cuda || use rocm; then
		export TRITON_KERNELS_SRC_DIR="${WORKDIR}/triton-${VLLM_TRITON_KERNELS_TAG}/python/triton_kernels/triton_kernels"
	fi

	if use cuda; then
		export VLLM_TARGET_DEVICE=cuda
		# Point every CMake external project at its pre-staged source.
		export VLLM_CUTLASS_SRC_DIR="${WORKDIR}/cutlass-${VLLM_CUTLASS_TAG}"
		export DEEPGEMM_SRC_DIR="${WORKDIR}/DeepGEMM-${VLLM_DEEPGEMM_COMMIT}"
		export FLASH_MLA_SRC_DIR="${WORKDIR}/FlashMLA-${VLLM_FLASHMLA_COMMIT}"
		export FMHA_SM100_SRC_DIR="${WORKDIR}/MSA-${VLLM_FMHA_SM100_COMMIT}"
		export QUTLASS_SRC_DIR="${WORKDIR}/qutlass-${VLLM_QUTLASS_COMMIT}"
		export TML_FA4_SRC_DIR="${WORKDIR}/tml-fa4-${VLLM_TML_FA4_COMMIT}"
		export FLASH_KDA_SRC_DIR="${WORKDIR}/FlashKDA-${VLLM_FLASHKDA_COMMIT}"
		export VLLM_FLASH_ATTN_SRC_DIR="${WORKDIR}/flash-attention-${VLLM_FA_COMMIT}"
		# Select the newest installed compiler supported by this CUDA,
		# while still respecting an explicit user override.
		: "${CUDAHOSTCXX:=$(cuda_gccdir)/g++}"
		export CUDAHOSTCXX
		export CMAKE_ARGS+=" -DCMAKE_CUDA_HOST_COMPILER=${CUDAHOSTCXX}"

		# vllm's heavy CUDA template instantiations
		# (paged_attention_v*, layernorm_quant_kernels, w8a8/fp8/...)
		# can each peak at 3-4 GiB during cudafe++. Unrestricted ninja
		# parallelism can OOM-kill the compiler
		# (cudafe++ dies with SIGKILL, "[code=9]"). MAX_JOBS is the
		# env var vllm's setup.py reads to throttle the CMake build;
		# CMAKE_BUILD_PARALLEL_LEVEL backs it up for direct cmake
		# --build invocations. MAX_JOBS=4 is a conservative default that
		# users can override according to available memory. The OOM threshold was measured
		# against 0.20.1; 0.21.0's CUDA template set wasn't re-profiled
		# at bump time but the heavy instantiations (paged_attention,
		# layernorm_quant, w8a8/fp8) are unchanged, so MAX_JOBS=4 stays
		# a conservative default. # verified 2026-05-07 against 0.20.1.
		#
		# Caller-overridable so users on smaller/larger hosts can adjust
		# without ebuild-edit (e.g. MAX_JOBS=2 emerge … on a 16 GiB
		# host).
		export MAX_JOBS="${MAX_JOBS:-4}"
		export CMAKE_BUILD_PARALLEL_LEVEL="${CMAKE_BUILD_PARALLEL_LEVEL:-${MAX_JOBS}}"
	elif use cpu; then
		export VLLM_TARGET_DEVICE=cpu
		export FETCHCONTENT_SOURCE_DIR_ONEDNN="${WORKDIR}/oneDNN-${VLLM_ONEDNN_TAG}"
		# vllm 0.22.x cpu_extension.cmake locates OpenMP via
		# vllm_prepare_torch_gomp_shim(), which expects a libgomp vendored
		# inside PyTorch (torch.libs/libgomp-*.so — a PyPI-wheel artifact).
		# Our source-built sci-ml/caffe2 ships none, so cmake falls back to
		# find_library(NAMES gomp), which misses Gentoo's libgomp under the
		# gcc-internal dir. Point CMAKE_LIBRARY_PATH at the toolchain's
		# libgomp so the fallback resolves. # verified 2026-06-05 (0.22.1)
		local gomp_dir
		gomp_dir=$(dirname "$($(tc-getCC) -print-file-name=libgomp.so)")
		export CMAKE_ARGS+=" -DCMAKE_LIBRARY_PATH=${gomp_dir}"
	elif use rocm; then
		export VLLM_TARGET_DEVICE=rocm
		# caffe2 2.13.0's cmake/public/LoadHIP.cmake (consumed here via
		# find_package(Torch)) switched to CMake-native HIP and defaults the
		# compiler to ${ROCM_PATH}/lib/llvm/bin/clang++ (i.e. /usr/lib/llvm/bin),
		# but Gentoo slots llvm at /usr/lib/llvm/<N>/bin. LoadHIP honours
		# HIP_CLANG_PATH; point it at the real HIP clang, same as sci-ml/caffe2's
		# own build does. (torch 2.11's FindHIP path did not need this.)
		# verified 2026-08-12
		export HIP_CLANG_PATH="$(hipconfig -l)"
		filter-lto
		# rocm.eclass turns AMDGPU_TARGETS into a semicolon-joined
		# list. vllm's CMakeLists reads PYTORCH_ROCM_ARCH and feeds
		# it to enable_language(HIP). Same MAX_JOBS throttle as the
		# cuda branch — HIP template instantiation in csrc/rocm/
		# (skinny_gemms, attention) hits comparable peak RSS.
		export PYTORCH_ROCM_ARCH=$(get_amdgpu_flags)
		export MAX_JOBS="${MAX_JOBS:-4}"
		export CMAKE_BUILD_PARALLEL_LEVEL="${CMAKE_BUILD_PARALLEL_LEVEL:-${MAX_JOBS}}"
	else
		export VLLM_TARGET_DEVICE=empty
	fi
	distutils-r1_src_configure
}

pkg_postinst() {
	if use cuda; then
		elog "vllm's CUDA path pulls dev-python/flashinfer-python, which"
		elog "JIT-compiles GPU kernels with nvcc on first inference. CUDA"
		elog "13.x nvcc rejects host compilers newer than gcc 15, so if the"
		elog "active gcc is newer, vllm aborts at first run with a"
		elog "'Ninja build failed ... unsupported GNU version' error."
		elog ""
		elog "Pin nvcc's host compiler to a CUDA-supported gcc when launching vllm:"
		elog ""
		elog "  NVCC_PREPEND_FLAGS=\"-ccbin $(cuda_gccdir)/g++\" vllm serve ..."
		elog ""
		elog "or switch the system compiler via 'eselect gcc'."
	fi

	if use cuda && ! use humming; then
		elog ""
		elog "The optional 'humming' MXFP4 quantization backend is off by"
		elog "default. Enable USE=humming to pull dev-python/humming-kernels"
		elog "if you serve humming-quantized models."
	fi

	if use rocm; then
		elog "vllm initializes a torch.distributed process group at engine"
		elog "start (a TCPStore rendezvous) even for single-GPU inference."
		elog "Since torch 2.4 the TCPStore defaults to the libuv backend,"
		elog "but sci-ml/pytorch's ROCm build ships no libuv -- it rides in"
		elog "via tensorpipe, which is disabled for ROCm. Without it vllm"
		elog "aborts at engine init with:"
		elog ""
		elog "  DistStoreError: use_libuv was requested but PyTorch was"
		elog "  built without libuv support"
		elog ""
		elog "Launch vllm with USE_LIBUV=0 to use the legacy socket store:"
		elog ""
		elog "  USE_LIBUV=0 vllm serve ..."
	fi
}