swiglu.py

import torch
import ctypes
from ctypes import POINTER, Structure, c_int32, c_void_p
from libinfiniop import (
    infiniopHandle_t,
    infiniopTensorDescriptor_t,
    open_lib,
    to_tensor,
    get_test_devices,
    check_error,
    rearrange_if_needed,
    test_operator,
    get_args,
    debug,
    get_tolerance,
    profile_operation,
)
from enum import Enum, auto

# ==============================================================================
#  Configuration (Internal Use Only)
# ==============================================================================
# These are not meant to be imported from other modules
_TEST_CASES_ = [
    # shape, a_stride, b_stride, c_stride
    ((13, 4), None, None, None),
    ((13, 4), (10, 1), (10, 1), (10, 1)),
    # ((13, 4, 4), None, None, None),
    # ((13, 4, 4), (20, 4, 1), (20, 4, 1), (20, 4, 1)),
    ((16, 5632), None, None, None),
    ((16, 5632), (13312, 1), (13312, 1), (13312, 1)),
    # ((4, 4, 5632), None, None, None),
    # ((4, 4, 5632), (45056, 5632, 1), (45056, 5632, 1), (45056, 5632, 1)),
]

# Inplace options applied for each test case in _TEST_CASES_
_INPLACE = [
    "Inplace.OUT_OF_PLACE",
    "Inplace.INPLACE_A",
    "Inplace.INPLACE_B",
]

# Form the test cases by appending each element of _INPLACE to each tuple in _TEST_CASES_
_TEST_CASES = [
    test_case + (inplace_item,)
    for test_case in _TEST_CASES_
    for inplace_item in _INPLACE
]

# Data types used for testing
_TENSOR_DTYPES = [torch.float16]

# Tolerance map for different data types
_TOLERANCE_MAP = {
    torch.float16: {"atol": 1e-4, "rtol": 1e-2},
}

DEBUG = False
PROFILE = False
NUM_PRERUN = 10
NUM_ITERATIONS = 1000


class Inplace(Enum):
    OUT_OF_PLACE = auto()
    INPLACE_A = auto()
    INPLACE_B = auto()


class SwiGLUDescriptor(Structure):
    _fields_ = [("device", c_int32)]


infiniopSwiGLUDescriptor_t = POINTER(SwiGLUDescriptor)


def swiglu(a, b):
    return a * b / (1 + torch.exp(-b.float()).to(b.dtype))


def test(
    lib,
    handle,
    torch_device,
    shape,
    a_stride=None,
    b_stride=None,
    c_stride=None,
    inplace=Inplace.OUT_OF_PLACE,
    dtype=torch.float16,
    sync=None,
):
    print(
        f"Testing SwiGLU on {torch_device} with shape:{shape} a_stride:{a_stride} b_stride:{b_stride} c_stride:{c_stride} "
        f"dtype:{dtype} inplace:{inplace}"
    )

    a = torch.rand(shape, dtype=dtype).to(torch_device)
    b = torch.rand(shape, dtype=dtype).to(torch_device)
    c = torch.rand(shape, dtype=dtype).to(torch_device)

    ans = swiglu(a, b)

    a, b, c = [
        rearrange_if_needed(tensor, stride)
        for tensor, stride in zip([a, b, c], [a_stride, b_stride, c_stride])
    ]
    c = (
        c
        if inplace == Inplace.OUT_OF_PLACE
        else (a if inplace == Inplace.INPLACE_A else b)
    )
    a_tensor, b_tensor = [to_tensor(tensor, lib) for tensor in [a, b]]
    c_tensor = (
        to_tensor(c, lib)
        if inplace == Inplace.OUT_OF_PLACE
        else (a_tensor if inplace == Inplace.INPLACE_A else b_tensor)
    )
    if sync is not None:
        sync()

    descriptor = infiniopSwiGLUDescriptor_t()
    check_error(
        lib.infiniopCreateSwiGLUDescriptor(
            handle,
            ctypes.byref(descriptor),
            c_tensor.descriptor,
            a_tensor.descriptor,
            b_tensor.descriptor,
        )
    )

    # Invalidate the shape and strides in the descriptor to prevent them from being directly used by the kernel
    for tensor in [a_tensor, b_tensor, c_tensor]:
        tensor.descriptor.contents.invalidate()

    def lib_swiglu():
        check_error(
            lib.infiniopSwiGLU(
                descriptor, c_tensor.data, a_tensor.data, b_tensor.data, None
            )
        )

    lib_swiglu()

    atol, rtol = get_tolerance(_TOLERANCE_MAP, dtype)
    if DEBUG:
        debug(c, ans, atol=atol, rtol=rtol)
    assert torch.allclose(c, ans, atol=atol, rtol=rtol)

    # Profiling workflow
    if PROFILE:
        # fmt: off
        profile_operation("PyTorch", lambda: swiglu(a, b), torch_device, NUM_PRERUN, NUM_ITERATIONS)
        profile_operation("    lib", lambda: lib_swiglu(), torch_device, NUM_PRERUN, NUM_ITERATIONS)
        # fmt: on
    check_error(lib.infiniopDestroySwiGLUDescriptor(descriptor))


if __name__ == "__main__":
    args = get_args()
    lib = open_lib()

    lib.infiniopCreateSwiGLUDescriptor.restype = c_int32
    lib.infiniopCreateSwiGLUDescriptor.argtypes = [
        infiniopHandle_t,
        POINTER(infiniopSwiGLUDescriptor_t),
        infiniopTensorDescriptor_t,
        infiniopTensorDescriptor_t,
        infiniopTensorDescriptor_t,
    ]

    lib.infiniopSwiGLU.restype = c_int32
    lib.infiniopSwiGLU.argtypes = [
        infiniopSwiGLUDescriptor_t,
        c_void_p,
        c_void_p,
        c_void_p,
        c_void_p,
    ]

    lib.infiniopDestroySwiGLUDescriptor.restype = c_int32
    lib.infiniopDestroySwiGLUDescriptor.argtypes = [
        infiniopSwiGLUDescriptor_t,
    ]

    # Configure testing options
    DEBUG = args.debug
    PROFILE = args.profile
    NUM_PRERUN = args.num_prerun
    NUM_ITERATIONS = args.num_iterations

    for device in get_test_devices(args):
        test_operator(lib, device, test, _TEST_CASES, _TENSOR_DTYPES)

    print("\033[92mTest passed!\033[0m")