consts.py

import dataclasses
from enum import Enum, IntEnum
from typing import Type


class Timeout(IntEnum):
    TEST = 180
    BENCHMARK = 180
    RANKED = 180
    COMPILE = 120
    SCRIPT = 120


class SchedulerType(Enum):
    GITHUB = "github"
    MODAL = "modal"
    SLURM = "slurm"


class GitHubGPU(Enum):
    NVIDIA = "NVIDIA"
    MI300 = "MI300"
    MI250 = "MI250"


class ModalGPU(Enum):
    T4 = "T4"
    L4 = "L4"
    A100 = "A100"
    H100 = "H100"
    B200 = "B200"
    H200 = "H200"


@dataclasses.dataclass
class GPU:
    name: str
    value: str
    runner: str


def _make_gpu_lookup(runner_map: dict[str, Type[Enum]]):
    lookup = {}
    for runner, gpus in runner_map.items():
        for name, member in gpus.__members__.items():
            if name.lower() in lookup:
                raise ValueError(f"Duplicate gpu name '{name}' found across Enums.")
            lookup[name.lower()] = GPU(name=name, value=member.value, runner=runner)
    return lookup


_GPU_LOOKUP = _make_gpu_lookup({"Modal": ModalGPU, "GitHub": GitHubGPU})


def get_gpu_by_name(name: str) -> GPU:
    name = name.lower()
    return _GPU_LOOKUP.get(name, None)


class ExitCode(IntEnum):
    """
    Exit codes for our runners. These are just the codes actively return,
    others are possible (e.g., exiting due to segfault, permissions, signal, ...)
    """

    # program ran successfully
    SUCCESS = 0
    # a cuda API call failed
    CUDA_FAIL = 110
    # could not setup file descriptor for custom pipe
    PIPE_FAILED = 111
    # didn't crash, but tests failed
    VALIDATE_FAIL = 112
    # problem parsing test/benchmark
    TEST_SPEC = 113
    # process was shut down because it timed out
    TIMEOUT_EXPIRED = 114


class SubmissionMode(Enum):
    """
    Different types of submission that can be made:
    Test: Run tests and give detailed results about passed/failed tests. These have short timeouts.
    Benchmark: Run larger benchmarks. Each benchmark is tested once, and then run multiple times.
    Profile: Gather profiling information. One selected benchmark is run under the profiler. No
        testing is performed in this mode (sometimes, you need to profile deliberately broken code)
    Leaderboard: Official submission to the leaderboard. This first runs public tests, then a
        repeated invocation of a single benchmark. Feedback for the secret benchmark is only very
        limited (no stdout/stderr).
    Private: Special run that does test followed by leaderboard (on a secret seed), but gives only
        very limited feedback.
    Script: Submit an arbitrary script.
    """

    TEST = "test"
    BENCHMARK = "benchmark"
    PROFILE = "profile"
    LEADERBOARD = "leaderboard"
    PRIVATE = "private"
    SCRIPT = "script"


class Language(Enum):
    Python = "py"
    CUDA = "cu"


class RankCriterion(Enum):
    LAST = "last"  # only last benchmark counts
    MEAN = "mean"  # arithmetic mean of all benchmarks
    GEOM = "geom"  # geometric mean of all benchmarks


GPU_TO_SM = {
    "T4": "75",
    "L4": "80",
    "A100": "80",
    "H100": "90a",
    "H200": "90a",
    "B200": "100",
    "NVIDIA": None,
    "MI300": None,
    "MI250": None,
}


# Modal-specific constants
MODAL_PATH = "/tmp/dcs/"
MODAL_EVAL_CODE_PATH = "/tmp/dcs/eval.py"
MODAL_REFERENCE_CODE_PATH = "/tmp/dcs/reference.py"
MODAL_SUBMISSION_CODE_PATH = "/tmp/dcs/submission.py"


# Compilation flags for Modal
CUDA_FLAGS = [
    "--std=c++20",
    "-DNDEBUG",
    "-Xcompiler=-Wno-psabi",
    "-Xcompiler=-fno-strict-aliasing",
    "--expt-extended-lambda",
    "--expt-relaxed-constexpr",
    "-forward-unknown-to-host-compiler",
    "-O3",
    "-Xnvlink=--verbose",
    "-Xptxas=--verbose",
    "-Xptxas=--warn-on-spills",
]
MODAL_CUDA_INCLUDE_DIRS = ["/ThunderKittens/include"]

NVIDIA_REQUIREMENTS = """
numpy
torch
setuptools
ninja
triton
"""

AMD_REQUIREMENTS = """
--index-url https://download.pytorch.org/whl/rocm6.2.4
torch
"""