causal_softmax.py

import torch
import ctypes
from ctypes import POINTER, Structure, c_int32, c_size_t, c_uint64, c_void_p, c_float
from libinfiniop import (
    infiniopHandle_t,
    infiniopTensorDescriptor_t,
    open_lib,
    to_tensor,
    get_test_devices,
    check_error,
    rearrange_if_needed,
    create_workspace,
    test_operator,
    get_args,
    debug,
    get_tolerance,
    profile_operation,
)

# ==============================================================================
#  Configuration (Internal Use Only)
# ==============================================================================
# These are not meant to be imported from other modules

_TEST_CASES = [
        # x_shape, x_stride
        ((32, 512), None),
        ((32, 512), (1024, 1)),
        ((32, 5, 5), None),
        ((32, 20, 512), None),
        ((32, 20, 512), (20480, 512, 1)),  # Ascend 暂不支持非连续
        ((32, 20, 4, 512), None),
        ((32, 20, 4, 512), (81920, 2048, 512, 1)),
    ]
# Data types used for testing
_TENSOR_DTYPES = [torch.float16, torch.float32]

# Tolerance map for different data types
_TOLERANCE_MAP = {
    torch.float16: {'atol': 0, 'rtol': 1e-2},
    torch.float32: {'atol': 0, 'rtol': 1e-3},
}

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

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


infiniopCausalSoftmaxDescriptor_t = POINTER(CausalSoftmaxDescriptor)


def causal_softmax(x):
    type = x.dtype
    mask = torch.tril(torch.ones_like(x), diagonal=-1).flip(dims=[-2, -1])
    y = x.clone()
    masked = torch.where(mask == 1, -torch.inf, y.to(torch.float32))
    return torch.nn.functional.softmax(masked, dim=-1).to(type)


def test(
    lib, 
    handle, 
    torch_device, 
    x_shape, 
    x_stride=None, 
    dtype=torch.float16
):
    print(
        f"Testing CausalSoftmax on {torch_device} with x_shape:{x_shape} x_stride:{x_stride} dtype:{dtype}"
    )
    x = torch.rand(x_shape, dtype=dtype).to(torch_device)

    ans = causal_softmax(x)

    
    x = rearrange_if_needed(x, x_stride)
    
    x_tensor = to_tensor(x, lib)

    descriptor = infiniopCausalSoftmaxDescriptor_t()
    check_error(
        lib.infiniopCreateCausalSoftmaxDescriptor(
            handle, 
            ctypes.byref(descriptor),
            x_tensor.descriptor
        )
    )

    # Invalidate the shape and strides in the descriptor to prevent them from being directly used by the kernel
    x_tensor.descriptor.contents.invalidate()


    workspace_size = c_uint64(0)
    check_error(
        lib.infiniopGetCausalSoftmaxWorkspaceSize(
            descriptor, ctypes.byref(workspace_size)
        )
    )
    workspace = create_workspace(workspace_size.value, x.device)
    def lib_causal_softmax():
        check_error(
            lib.infiniopCausalSoftmax(
                descriptor,
                workspace.data_ptr() if workspace is not None else None,
                workspace_size.value,
                x_tensor.data,
                None,
            )
        )
    lib_causal_softmax()
    
    atol, rtol = get_tolerance(_TOLERANCE_MAP, dtype)
    if DEBUG:
        debug(x, ans, atol=atol, rtol=rtol)
    assert torch.allclose(x, ans, atol=atol, rtol=rtol)

    # Profiling workflow
    if PROFILE:
        # fmt: off
        profile_operation("PyTorch", lambda: causal_softmax(x), torch_device, NUM_PRERUN, NUM_ITERATIONS)
        profile_operation("    lib", lambda: lib_causal_softmax(), torch_device, NUM_PRERUN, NUM_ITERATIONS)
        # fmt: on

    check_error(lib.infiniopDestroyCausalSoftmaxDescriptor(descriptor))





if __name__ == "__main__":
    
    args = get_args()
    lib = open_lib()
    lib.infiniopCreateCausalSoftmaxDescriptor.restype = c_int32
    lib.infiniopCreateCausalSoftmaxDescriptor.argtypes = [
        infiniopHandle_t,
        POINTER(infiniopCausalSoftmaxDescriptor_t),
        infiniopTensorDescriptor_t,
    ]
    lib.infiniopGetCausalSoftmaxWorkspaceSize.restype = c_int32
    lib.infiniopGetCausalSoftmaxWorkspaceSize.argtypes = [
        infiniopCausalSoftmaxDescriptor_t,
        POINTER(c_uint64),
    ]
    lib.infiniopCausalSoftmax.restype = c_int32
    lib.infiniopCausalSoftmax.argtypes = [
        infiniopCausalSoftmaxDescriptor_t,
        c_void_p,
        c_uint64,
        c_void_p,
        c_void_p,
    ]
    lib.infiniopDestroyCausalSoftmaxDescriptor.restype = c_int32
    lib.infiniopDestroyCausalSoftmaxDescriptor.argtypes = [
        infiniopCausalSoftmaxDescriptor_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")