pyproject.toml

[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
include = ["skyrl*"]

[project]
name = "skyrl"
dynamic = ["version"]
description = "Unified API for training and inference"
readme = "README.md"
requires-python = ">=3.12,<3.15"
dependencies = [
    "datasets>=4.0.0",
    "pillow>=11.3.0",
    "rich>=14.1.0",
    "safetensors>=0.6.2",
    # Floor tracks the transformers pin below: transformers 5.16.1 requires
    # tokenizers>=0.23.1.
    "tokenizers>=0.23.1",
    "transformers>=5.6.1,<=5.16.1",
    "typer>=0.17.4",
    "peft==0.18.1",
    "hf_transfer",
    "cloudpathlib>=0.23.0",
    "zstandard>=0.23.0",
    "xxhash>=3.0.0",
]

[project.optional-dependencies]
gpu = [
    "jax[cuda13]>=0.7.2; sys_platform == 'linux'",
]

tpu = [
    # libtpu is published for Linux x86_64, not Linux aarch64.
    "jax[tpu]>=0.7.2; sys_platform == 'linux' and platform_machine == 'x86_64'",
]

tinker = [
    "tinker>=0.25.0,<0.26.0",
    "fastapi[standard]",
    "sqlmodel",
    "sqlalchemy[asyncio]",
    "aiosqlite",
    "asyncpg",
    "psycopg2-binary",
]

ray = [
    "ray[default]==2.57.0",
]

aws = [
    "cloudpathlib[s3]",
    "s5cmd>=0.3.3; sys_platform == 'linux'",
]

gcp = [
    "cloudpathlib[gs]",
]

azure = [
    "cloudpathlib[azure]",
]

# The extras "jax", "fsdp", and "megatron" are the dependencies the
# engine needs for --backend="jax", --backend="fsdp", and --backend="megatron",
# respectively.

jax = [
    "jax>=0.8,<1.0",
    "flax>=0.12.10",
    "optax>=0.2.5",
]

skyrl-train = [
    "loguru",
    "tqdm",
    "ninja",
    "tensorboard",
    "func_timeout",
    "hydra-core==1.3.4",
    "accelerate",
    "torchdata",
    "omegaconf",
    "ray==2.57.0",
    "peft==0.18.1",
    "debugpy==1.8.0",
    "hf_transfer",
    "wandb",
    "datasets>=4.0.0",
    "tensordict",
    "jaxtyping",
    "skyrl-gym",
    "flash-attn==2.8.3; sys_platform == 'linux'",
    "polars",
    "s3fs",
    "s5cmd>=0.3.3; sys_platform == 'linux'",
    "gcsfs",
    "fastapi",
    "orjson>=3.11.9",
    "pybase64>=1.4.2",
    "uvicorn",
    "vllm-router; sys_platform == 'linux'",
    "pybind11",
    "setuptools",
    # The `nixl` shim provides that namespace and dispatches on `torch.version.cuda`,
    # so with a cu13 torch it loads `nixl_cu13`. `nixl-cu13` ships the `nixl_cu13`
    # module, but vLLM imports `nixl._api`. Its metadata hard-depends on `nixl-cu12`
    # too; that variant is overridden out below (it would drag the CUDA-12 runtime
    # into the image for a module the shim would never load).
    "nixl; sys_platform == 'linux'",
]

fsdp = [
    "skyrl[skyrl-train]",
    "vllm==0.30.0; sys_platform == 'linux'",
    "vllm-router; sys_platform == 'linux'",
    # The `nixl` shim provides that namespace and dispatches on `torch.version.cuda`,
    # so with a cu13 torch it loads `nixl_cu13`. `nixl-cu13` ships the `nixl_cu13`
    # module, but vLLM imports `nixl._api`. Its metadata hard-depends on `nixl-cu12`
    # too; that variant is overridden out below (it would drag the CUDA-12 runtime
    # into the image for a module the shim would never load).
    "nixl; sys_platform == 'linux'",
    "flash-linear-attention; sys_platform == 'linux'",
    "causal-conv1d==1.6.2.post1; sys_platform == 'linux'",
    "flash-attn==2.8.3; sys_platform == 'linux'",
    "torch==2.13.0; sys_platform == 'linux'",
    "flashinfer-python==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "flashinfer-jit-cache==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "flashinfer-cubin==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "torchvision; sys_platform == 'linux'",
]

megatron = [
    "skyrl[skyrl-train]",
    "transformer-engine[pytorch]==2.16.0; sys_platform == 'linux'",
    # Declared directly (not just via transformer-engine[pytorch]) so the
    # [tool.uv.sources] index below is honored — uv ignores sources for
    # transitive-only packages, which would otherwise fall back to the PyPI sdist.
    "transformer-engine-torch==2.16.0; sys_platform == 'linux'",
    # TE core CUDA lib (libtransformer_engine.so). Declared directly because the
    # meta package only pulls a core via its `core*` extras, which we don't request
    # — without it the meta package's sanity check fails with "Found empty
    # `transformer-engine` meta package installed". cu13 is the only core Astral
    # publishes on the cu130 index, and it is what `transformer-engine-torch`'s
    # metadata requires there, so the two agree without an override.
    "transformer-engine-cu13==2.16.0+cu.13.0; sys_platform == 'linux'",
    "flash-attn==2.8.3; sys_platform == 'linux'",
    "flash-linear-attention; sys_platform == 'linux'",
    "causal-conv1d==1.6.2.post1; sys_platform == 'linux'",
    "mamba-ssm==2.3.2.post1; sys_platform == 'linux'",
    "vllm==0.30.0; sys_platform == 'linux'",
    "vllm-router; sys_platform == 'linux'",
    # The `nixl` shim provides that namespace and dispatches on `torch.version.cuda`,
    # so with a cu13 torch it loads `nixl_cu13`. `nixl-cu13` ships the `nixl_cu13`
    # module, but vLLM imports `nixl._api`. Its metadata hard-depends on `nixl-cu12`
    # too; that variant is overridden out below (it would drag the CUDA-12 runtime
    # into the image for a module the shim would never load).
    "nixl; sys_platform == 'linux'",
    "torch==2.13.0; sys_platform == 'linux'",
    "flashinfer-python==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "torchvision; sys_platform == 'linux'",
    "megatron-bridge; sys_platform == 'linux'",
    "megatron-core; sys_platform == 'linux'",
    "nvidia-resiliency-ext; sys_platform == 'linux'",
    "flashinfer-jit-cache==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "flashinfer-cubin==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "nvidia-modelopt; sys_platform == 'linux'",
    "fast-hadamard-transform; sys_platform == 'linux'",
]

# Opt-in FlashAttention 4. NOT part of `megatron`, so the default install runs FA2.
#
# The combined `flash-attn` wheel (see [tool.uv.sources]) always carries FA4's
# `flash_attn/cute/` tree -- that is what stops it colliding with flash-attn 2's
# stale copy. But both consumers gate on the *`flash-attn-4` distribution
# metadata*, not on the tree:
#   * Transformer Engine: `get_pkg_version("flash-attn-4")` raising
#     PackageNotFoundError leaves `flash_attn_func_v4 = None`, so TE never
#     imports or dispatches to FA4.
#   * megatron-core: `HAVE_FA4` is False for the same reason.
# So installing this companion is exactly the switch that turns FA4 on, and
# leaving it out keeps FA2 as the default with no shim and no runtime patching.
#
# Markers: `< '3.15'` matches the companion's own `Requires-Python`, and the
# `platform_machine` bound keeps it off Linux arches with no combined wheel
# (there `flash-attn` resolves to plain PyPI 2.8.3, which cannot satisfy the
# companion's hard dep on ==2.8.3+cu.13.0.torch.2.13.fa4b28.skyrl1). Dropping
# either bound makes resolution fail outright.
#
# FA4 4.0.0b28 is a beta, and TE disables it under context parallelism (TE <= 2.18;
# main lifts this for cp_comm_type p2p/all_gather/a2a). Note TE 2.16's arch gate
# only excludes < sm80, so FA4 is selected on sm86/sm87/sm89 (A10, L4, L40S, 4090)
# where its kernels cannot launch -- set SKYRL_DISABLE_FA4=1 there, or just do not
# install this extra.
fa4 = [
    "skyrl[megatron]",
    "flash-attn-4[cu13]==4.0.0b28; sys_platform == 'linux' and (platform_machine == 'x86_64' or platform_machine == 'aarch64') and python_version < '3.15'",
]

miniswe = [
    "skyrl[skyrl-train]",
    # NOTE (sumanthrh): Needs to be a commit after https://github.com/SWE-agent/mini-swe-agent/commit/4f5d445e99d13b5482478c23508bf2fbf7c0670c
    "mini-swe-agent>=1.12.0",
    "litellm",
]
harbor = [
    "harbor[daytona,modal]",
]
# Mooncake distributed KV for vLLM: the store connector (MooncakeStoreConnector,
# KV-cache offloading) and P2P PD transfer (MooncakeConnector). Provides the python
# bindings and the `mooncake_master` binary. Off-the-shelf PyPI wheel; Linux-only.
# Mooncake currently has Linux wheels through CPython 3.13 only.
mooncake = [
    "mooncake-transfer-engine-cuda13; sys_platform == 'linux' and python_version < '3.14'",
]

dev = [
    "mkdocs",
    "mkdocs-material",
    "mkdocstrings[python]>=0.24.0",
    "pymdown-extensions>=10.7",
    "pytest",
    "pytest-forked",
    "pytest-asyncio",
    "pre-commit",
    "litellm",
    "torch",
    "ty",
    "cloudpathlib[s3]",
    "alembic",
    "griffe2md",
]

[tool.setuptools]
include-package-data = true

# Runtime patch payloads (skyrl/backends/skyrl_train/patches/**/*.patch) are read
# from disk at runtime, so they must ship inside the package. `include-package-data`
# alone does not cover them
[tool.setuptools.package-data]
"*" = ["*.patch"]

[tool.setuptools.dynamic]
version = {attr = "skyrl.__version__"}

[project.scripts]
# The following is for supporting the skyrl-train dependency

[tool.uv]
# Resolve only the Linux architectures covered by the CUDA wheels, plus the
# supported macOS development target. CUDA-only extras cannot resolve on other
# Linux architectures.
required-environments = [
    "sys_platform == 'linux' and (platform_machine == 'x86_64' or platform_machine == 'aarch64')",
    "sys_platform == 'darwin' and platform_machine == 'arm64'",
]

# each backend should have separate dependencies that can potentially clash
# megatron also clashes with the jax dependency from gpu and tpu extras
conflicts = [
    [
        { extra = "jax" },
        { extra = "megatron" },
        { extra = "fsdp" },
    ],
    [
        { extra = "megatron" },
        { extra = "gpu" },
        { extra = "tpu" },
        { extra = "miniswe" },
    ]
]
# disable build isolation for megatron related dependencies
no-build-isolation-package = [
    "nv-grouped-gemm",
]
# override unnecessary dependencies and pin versions to override Megatron-Bridge
# unpinned dependencies.
override-dependencies = [
    "transformer-engine[pytorch]==2.16.0; sys_platform == 'linux'",
    "transformers>=5.6.1,<=5.16.1; sys_platform == 'linux'",
    "megatron-core>=0.16.0; sys_platform == 'linux'",
    "ml_dtypes>=0.5.0; sys_platform == 'linux'",
    # `nixl` hard-depends on both nixl-cu12 and nixl-cu13; drop the cu12 variant
    # so it doesn't pull a second, unused CUDA runtime in alongside the cu13 one.
    "nixl-cu12; sys_platform == 'never'",
    # Megatron-Bridge pins flashinfer-python==0.6.8.post1, which conflicts with
    # our pin, so all three flashinfer packages are held here. vLLM 0.30.0 needs
    # >=0.6.16: it imports `flashinfer.autotuner.set_autotune_process_group`,
    # which 0.6.14 does not have, and the engine fails to start without it.
    # flashinfer hard-errors when an installed cubin's version differs from its
    # own, so the three must move together.
    # They must also be >=0.6.13: older flashinfer rejects the `layout_code` vLLM's
    # allreduce+RMS fusion pass hands the MNNVL backend ("MNNVL AllReduce does
    # not support quantization fusion").
    #
    # flashinfer-cubin is not published to PyPI past 0.6.13, so it (like
    # flashinfer-jit-cache) comes from flashinfer's own index -- see the
    # `flashinfer` index and [tool.uv.sources] below.
    "flashinfer-python==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "flashinfer-jit-cache==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    "flashinfer-cubin==0.6.18.post1; sys_platform == 'linux' and platform_machine == 'x86_64'",
    # mamba-ssm 2.3.2.post1 pins tilelang==0.1.8 and apache-tvm-ffi<=0.1.9, while
    # vLLM 0.30.0 pins tilelang==0.1.12 and apache-tvm-ffi==0.1.11. Both are
    # overridden here; these two MUST be bumped together.
    #
    # FA4 requires apache-tvm-ffi>=0.1.12, newer than either package's metadata
    # allows. tilelang 0.1.9's own bound is loose enough (`>=0.1.2,~=0.1.0`) that
    # uv happily resolves it against 0.1.12 -- and then `import tilelang` aborts
    # with `AttributeError: attribute '__dict__' of 'type' objects is not writable`
    # from a duplicate ffi field registration, taking `mamba_ssm` and `fla` (the
    # default FLA_TILELANG path) down with it. Measured:
    #     tilelang 0.1.9  + ffi 0.1.10  -> import OK   (pre-FA4 state)
    #     tilelang 0.1.9  + ffi 0.1.12  -> AttributeError
    #     tilelang 0.1.13 + ffi 0.1.12  -> import OK
    # 0.1.13 is the first tilelang whose bound (`>=0.1.11,<0.1.13`) admits 0.1.12.
    "tilelang==0.1.13; sys_platform == 'linux'",
    "apache-tvm-ffi==0.1.12; sys_platform == 'linux'",
    "nvidia-cutlass-dsl[cu13]==4.7.1; sys_platform == 'linux'",
    "quack-kernels[cu13]==0.6.5; sys_platform == 'linux'",
    # vLLM allows xgrammar>=0.2.1,<1.0.0, but 0.2.4/0.2.5 never published a
    # cp312 linux x86_64 wheel (only cp310/cp311 + aarch64/macOS), so resolving
    # to them breaks install on x86_64 py3.12. 0.2.3 has the wheel.
    "xgrammar==0.2.3",
    "torch==2.13.0; sys_platform == 'linux'",
    # torchvision is NOT stable-ABI (unlike vLLM), and each release hard-pins one
    # torch: 0.30.0 wants torch 2.13.0, 0.26.0 wants 2.11.0. vLLM 0.30.0 pulls
    # torchvision==0.28.0, so without this the torch override above would resolve
    # a torchvision that cannot import against the torch we actually install.
    "torchvision==0.28.0; sys_platform == 'linux'",
]

[tool.uv.extra-build-dependencies]
flash-attn = [{requirement = "torch", match-runtime = true}]
fast-hadamard-transform = ["torch==2.13.0", "ninja"]

[tool.uv.extra-build-variables]
flash-attn = { FLASH_ATTENTION_SKIP_CUDA_BUILD = "TRUE"}
fast-hadamard-transform = { FAST_HADAMARD_TRANSFORM_FORCE_BUILD = "TRUE"}
# SkyRL only uses NVRx's async checkpointing. Its CUPTI straggler-detection extension
# only adds CUPTI's include dir and fails with `cuda.h: No such file or directory` unless
# the toolkit's headers are on the default include path, so skip building it.
nvidia-resiliency-ext = { STRAGGLER_DET_SKIP_CUPTI_EXT_BUILD = "1" }


[[tool.uv.index]]
name = "pytorch-cu130"
url = "https://download.pytorch.org/whl/cu130"
explicit = true

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[[tool.uv.index]]
name = "jax-tpu"
url = "https://storage.googleapis.com/jax-releases/libtpu_releases.html"

[[tool.uv.index]]
name = "flashinfer-cu130"
url = "https://flashinfer.ai/whl/cu130"
explicit = true

# flashinfer's CUDA-agnostic index. flashinfer-cubin is a pure-python wheel
# (py3-none-any) that stopped being published to PyPI after 0.6.13, so it is
# sourced here instead; the cu130 index above only carries flashinfer-jit-cache.
[[tool.uv.index]]
name = "flashinfer"
url = "https://flashinfer.ai/whl/"
explicit = true

# Astral's prebuilt GPU wheels (https://wheels.astral.sh). cu130 to match torch's
# CUDA variant. Wheels are published per (CUDA, torch) pair and carry that pair in
# a local version segment (e.g. `+cu.13.0.torch.2.11`), so the exact build must be
# pinned in the requirement — see the pins in the `fsdp` / `megatron` extras.
[[tool.uv.index]]
name = "astral-cu130"
url = "https://wheels.astral.sh/simple/cu130/"
explicit = true

[tool.uv.sources]
skyrl-gym = { path = "./skyrl-gym", editable = true }
# Match torch's CUDA variant (cu130).
flashinfer-jit-cache = { index = "flashinfer-cu130", marker = "sys_platform == 'linux'" }
flashinfer-cubin = { index = "flashinfer", marker = "sys_platform == 'linux'" }

# TEMPORARY: torch-2.13 builds of these four are not published anywhere yet --
# Astral's cu130 index stops at `torch.2.12` (mamba-ssm at `torch.2.11`), so the
# usual `+cu.13.0.torch.<ver>` pins cannot resolve. These are wheels we built
# ourselves against torch 2.13.0 + CUDA 13.0.88 and host on a GitHub release.
#
#   https://github.com/NovaSky-AI/skyrl-wheels/releases/tag/cu13torch2.13
#
# They cover cp312/cp313/cp314 on both x86_64 and aarch64 -- the whole of this
# project's `requires-python = ">=3.12"` range, matching the coverage Astral gave us.
# Anything outside that matrix falls through to the PyPI sdist and builds from source.
#
# NOTE: `TORCH_CUDA_ARCH_LIST` does NOT control what these wheels contain -- four of
# the five packages ignore it and hardcode their own gencode lists (mamba-ssm's
# setup.py explicitly clears the variable; flash-attn reads `FLASH_ATTN_CUDA_ARCHS`,
# default `80;90;100;120`). Actual embedded cubins, per `cuobjdump --list-elf`:
#   flash-attn                                sm_80 90 100 120
#   mamba-ssm / causal-conv1d / fast-hadamard sm_75 80 87 90 100 103 110 120 121
#   transformer-engine-torch                  none (kernels live in -cu13)
# So H100/GH200 (sm_90) and B200/GB200 (sm_100) are covered on both arches.
#
# TODO: DELETE THIS and go back to `{ index = "astral-cu130" }` with
# `+cu.13.0.torch.2.13` pins as soon as Astral publishes those builds. Check with:
#   curl -sL https://wheels.astral.sh/simple/cu130/mamba-ssm/ | grep -o 'torch\.2\.[0-9]*'
#
# flash-attn must stay at exactly 2.8.3, NOT 2.8.3.post1: TE gates flash-attn on
# `max_version = 2.8.3` and 2.8.3.post1 > 2.8.3. TE >= 2.16 strips the local segment
# before comparing (`PkgVersion(...).public`).
# WARNING: every `flash-attn` entry below MUST stay a combined FA2+FA4 wheel.
# The stock 2.8.3 wheel ships a stale `flash_attn/cute/` built against the
# nvidia-cutlass-dsl 4.0/4.1 API, and megatron-core imports that tree
# unconditionally -- before its own `flash-attn-4` metadata check -- under a
# bare `except ImportError`. Against cutlass-dsl 4.6.x the stale tree raises
# `AttributeError: ThrMma`, which escapes that handler and takes down every
# `megatron.core.transformer.attention` import. SkyRL used to defuse this at
# runtime in `skyrl/_compat.py`; that shim is gone, so pointing any of these
# back at a plain wheel (e.g. the `astral-cu130` TODO above) reintroduces the
# crash. Rebuild combined wheels instead.
flash-attn = [
    # Both architectures get the combined FA2+FA4 wheels: the plain
    # cu13torch2.13 FA2 build ships a stale `flash_attn/cute/` tree (21 files,
    # built against the nvidia-cutlass-dsl 4.0/4.1 API) that collides with real
    # FA4 (52 files). This wheel is the same torch-2.13 FA2 extension with that
    # tree replaced by FA4 4.0.0b28's, so one package owns `flash_attn.cute`
    # outright and FA2 and FA4 are importable side by side. See `flash-attn-4`
    # below for the companion.
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp312-cp312-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp313-cp313-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp314-cp314-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.14'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp312-cp312-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp313-cp313-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn-2.8.3%2Bcu.13.0.torch.2.13.fa4b28.skyrl1-cp314-cp314-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.14'" },
]
# Metadata-only companion: it carries no runtime files, declares the `cu13`
# extra, and hard-depends on the exact combined wheel above (which is what
# actually ships `flash_attn/cute/`). Both must come from the same release --
# the companion cannot find the combined wheel on PyPI. Transformer Engine
# gates FA4 on this distribution's metadata, so without it TE never uses FA4.
flash-attn-4 = [
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13-fa4b28-skyrl1/flash_attn_4-4.0.0b28%2Bskyrl1-py3-none-any.whl", marker = "sys_platform == 'linux' and (platform_machine == 'x86_64' or platform_machine == 'aarch64') and python_version < '3.15'" },
]
causal-conv1d = [
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp312-cp312-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp313-cp313-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp314-cp314-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.14'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp312-cp312-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp313-cp313-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/causal_conv1d-1.6.2.post1-cp314-cp314-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.14'" },
]
mamba-ssm = [
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp312-cp312-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp313-cp313-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp314-cp314-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.14'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp312-cp312-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp313-cp313-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/mamba_ssm-2.3.2.post1-cp314-cp314-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.14'" },
]
# The TE meta and core packages carry no torch coupling in their versions
# (2.16.0 / 2.16.0+cu.13.0), so they stay on Astral; only the torch bindings are
# rebuilt.
transformer-engine = { index = "astral-cu130", marker = "sys_platform == 'linux'" }
transformer-engine-cu13 = { index = "astral-cu130", marker = "sys_platform == 'linux'" }
transformer-engine-torch = [
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp312-cp312-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp313-cp313-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp314-cp314-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.14'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp312-cp312-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp313-cp313-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/transformer_engine_torch-2.16.0-cp314-cp314-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.14'" },
]
# CUDA torch on Linux, CPU torch on macOS (must match skyrl-train).
# Linux uses the cu130 index (torch 2.13 wheels are published there).
torch = [
    { index = "pytorch-cu130", marker = "sys_platform == 'linux'" },
    { index = "pytorch-cpu", marker = "sys_platform == 'darwin'" },
]
torchvision = [
    { index = "pytorch-cu130", marker = "sys_platform == 'linux'" },
    { index = "pytorch-cpu", marker = "sys_platform == 'darwin'" },
]
harbor = { git = "https://github.com/laude-institute/harbor", rev = "3de07a0e01f3368921766437fc7afece3ddec23d" }
# NVRx 0.6 wheels require glibc 2.39; its supported source build also works on Ubuntu 22.04.
nvidia-resiliency-ext = { git = "https://github.com/NVIDIA/nvidia-resiliency-ext", rev = "6c5f2a13c7688d92a7ac7ee6e464721eb8b7345d", marker = "sys_platform == 'linux'" }
# Megatron-Bridge upstream snapshot, with the matching Core revision below.
megatron-bridge = {git = "https://github.com/NVIDIA-NeMo/Megatron-Bridge", rev = "8e7077c6826d17eb4d4d54e6eb15c5a581eda4c0", marker = "sys_platform == 'linux'"}
# megatron-core must match the `3rdparty/Megatron-LM` submodule pin of the
# megatron-bridge rev above -- that is the only megatron-core commit Bridge's
# own CI validates against. Bump the two together, reading the submodule rev with
# `git ls-tree <bridge-rev> 3rdparty/Megatron-LM`.
#
# Upstream PRs not yet in these pins (GLM-5.3-Flash's KDA / mHC / k-pool DSA, and a few bug
# fixes) are carried in skyrl/backends/skyrl_train/patches/megatron/. When bumping either pin,
# follow skyrl/backends/skyrl_train/patches/megatron/README.md to retire whatever has landed.
megatron-core = {git = "https://github.com/NVIDIA/Megatron-LM", rev = "d476c21be396dc666d2e04fa8c199c3851a1551f", marker = "sys_platform == 'linux'"}
# NOTE (sumanthrh): This custom wheel of vllm-router includes a fix for /chat/completions endpoint: https://github.com/vllm-project/router/pull/162  on top of 0.1.14 as well as a new `sticky_least_loaded` policy: https://github.com/SumanthRH/router/tree/session-aware-lb
vllm-router = [
    { url = "https://github.com/SumanthRH/router/releases/download/0.1.14.post1/vllm_router-0.1.14.post1-cp38-abi3-manylinux_2_35_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
    { url = "https://github.com/SumanthRH/router/releases/download/0.1.14.post1/vllm_router-0.1.14.post1-cp38-abi3-manylinux_2_28_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" }
]
# Built against torch 2.13 + CUDA 13.0.88 alongside the other CUDA extensions above
# TODO (sumanthrh): Move towards official torch 13 + CUDA 13 wheels when available
fast-hadamard-transform = [
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp312-cp312-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp313-cp313-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp314-cp314-linux_x86_64.whl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64' and python_version == '3.14'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp312-cp312-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.12'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp313-cp313-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.13'" },
    { url = "https://github.com/NovaSky-AI/skyrl-wheels/releases/download/cu13torch2.13/fast_hadamard_transform-1.1.0-cp314-cp314-linux_aarch64.whl", marker = "sys_platform == 'linux' and platform_machine == 'aarch64' and python_version == '3.14'" }
]


[tool.pytest.ini_options]
# Registering markers silences PytestUnknownMarkWarning and makes `-m` selection
# in ci/gpu_ci_run_*.sh explicit rather than relying on free-form names.
markers = [
    "vllm: requires the vllm package (present under the fsdp/megatron extras)",
    "megatron: requires the megatron extra",
    "megatron_models: long-running Megatron model-parity tests",
    "integrations: third-party integration tests with extra install steps",
    "mooncake: requires the mooncake extra (mooncake-transfer-engine)",
]

[tool.black]
line-length = 120
include = '\.pyi?$'
extend-exclude = '''
/(
  # directories
  \.eggs
  | \.git
  | \.hg
  | \.mypy_cache
  | \.tox
  | \.venv
  | build
  | dist
)/
'''

[tool.flake8]
max-line-length = 120
max-doc-length = 120
extend-ignore = [
    # Default ignored errors by flake8
    "E121", "E123", "E126", "E226", "E24", "E704",
    # F401 module imported but unused
    "F401",
    # E203 whitespace before ':' (conflict with black)
    "E203",
    # E231 missing whitespace after ',' (conflict with black)
    "E231",
    # E501 line too long (conflict with black)
    "E501",
    # E741 do not use variables named 'l', 'O', or 'I'
    "E741",
    # W503 line break before binary operator (conflict with black)
    "W503",
    # W504 line break after binary operator (conflict with black)
    "W504",
    # W505 doc line too long (conflict with black)
    "W505",
    # W605 invalid escape sequence 'x' (conflict with latex within docs)
    "W605",
]

[tool.ruff]
exclude = [
    "skyrl-agent/",
    "examples/",
]

[tool.ruff.lint]
extend-select = ["I"]
ignore = [
    "F722" # Syntax error in annotation - ignored because this doesn't play well with jaxtyping
]

[tool.ruff.lint.isort]
known-first-party = ["skyrl", "skyrl_gym", "skycap"]