quantizer/kernels/python/sgl/int8_kernel.py

import functools
import json
import logging
import os
from typing import Any, Dict, List, Optional, Tuple
from functools import lru_cache, partial

import torch
import triton
import triton.language as tl

@lru_cache(maxsize=1)
def is_musa() -> bool:
    try:
        import torchada  # noqa: F401
    except ImportError:
        return False
    return hasattr(torch.version, "musa") and torch.version.musa is not None

def get_device_name(device_id: int = 0) -> str:
    if (hasattr(torch, "cuda") and torch.cuda.is_available()) or is_musa():
        return torch.cuda.get_device_name(device_id)

    if hasattr(torch, "xpu") and torch.xpu.is_available():
        return torch.xpu.get_device_name(device_id)

    if hasattr(torch, "hpu") and torch.hpu.is_available():
        return torch.hpu.get_device_name(device_id)

    if hasattr(torch, "npu") and torch.npu.is_available():
        return torch.npu.get_device_name(device_id)

@lru_cache(maxsize=1)
def is_cuda():
    return torch.cuda.is_available() and torch.version.cuda is not None

_is_cuda = is_cuda()
if _is_cuda:
    # Temporary
    try:
        from sgl_kernel import sgl_per_token_group_quant_8bit

        enable_sgl_per_token_group_quant_8bit = True
    except ImportError:
        from sgl_kernel import sgl_per_token_group_quant_int8

        enable_sgl_per_token_group_quant_8bit = False

logger = logging.getLogger(__name__)


@triton.jit
def _per_token_quant_int8(
    x_ptr,
    xq_ptr,
    scale_ptr,
    x_sum_ptr,
    stride_x,
    stride_xq,
    N,
    CAL_SUM: tl.constexpr,
    BLOCK: tl.constexpr,
):
    # Adapted from https://github.com/InternLM/lmdeploy/blob/086481ed84b59bee3b8e4274e5fc69620040c048/lmdeploy/pytorch/kernels/cuda/w8a8_triton_kernels.py#L282
    row_id = tl.program_id(0)

    cols = tl.arange(0, BLOCK)
    mask = cols < N

    x = tl.load(x_ptr + row_id * stride_x + cols, mask=mask, other=0.0).to(tl.float32)
    absmax = tl.maximum(tl.max(tl.abs(x)), 1e-10)
    scale_x = absmax / 127
    x_q = x * (127 / absmax)
    x_q = tl.extra.cuda.libdevice.round(x_q).to(tl.int8)
    if CAL_SUM:
        x_sum = tl.sum(x, axis=0)
        tl.store(x_sum_ptr + row_id, x_sum.to(x_sum_ptr.dtype.element_ty))

    tl.store(xq_ptr + row_id * stride_xq + cols, x_q, mask=mask)
    tl.store(scale_ptr + row_id, scale_x.to(scale_ptr.dtype.element_ty))


def per_token_quant_int8(x, scale_dtype=torch.float32, cal_sum=False):
    M = x.numel() // x.shape[-1]
    N = x.shape[-1]
    x_q = torch.empty_like(x, device=x.device, dtype=torch.int8)
    scales = torch.empty(x.shape[:-1] + (1,), device=x.device, dtype=scale_dtype)
    if cal_sum:
        x_sum = torch.empty(x.shape[:-1], device=x.device, dtype=x.dtype)
    else:
        x_sum = None
    BLOCK = triton.next_power_of_2(N)
    # heuristics for number of warps
    num_warps = min(max(BLOCK // 256, 1), 8)

    assert x.is_contiguous()
    _per_token_quant_int8[(M,)](
        x,
        x_q,
        scales,
        x_sum,
        stride_x=x.stride(-2),
        stride_xq=x_q.stride(-2),
        N=N,
        CAL_SUM=cal_sum,
        BLOCK=BLOCK,
        num_warps=num_warps,
        num_stages=1,
    )
    if cal_sum:
        return x_q, scales, x_sum
    else:
        return x_q, scales


@triton.jit
def _per_token_group_quant_int8(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    # Stride of input
    y_stride,
    # Columns of input
    N,
    # Avoid to divide zero
    eps,
    # Information for int8
    int8_min,
    int8_max,
    # Meta-parameters
    BLOCK: tl.constexpr,
):
    """A Triton-accelerated function to perform per-token-group quantization on a
    tensor.

    This function converts the tensor values into int8 values.
    """
    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    y_ptr += g_id * y_stride
    y_q_ptr += g_id * y_stride
    y_s_ptr += g_id

    cols = tl.arange(0, BLOCK)  # N <= BLOCK
    mask = cols < N

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    y_s = _absmax / int8_max
    y_q = tl.clamp(y / y_s, int8_min, int8_max).to(y_q_ptr.dtype.element_ty)

    tl.store(y_q_ptr + cols, y_q, mask=mask)
    tl.store(y_s_ptr, y_s)


def per_token_group_quant_int8(
    x: torch.Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: torch.dtype = torch.int8,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Function to perform per-token-group quantization on an input tensor `x`.

    It converts the tensor values into signed int8 values and returns the
    quantized tensor along with the scaling factor used for quantization.

    Args:
        x: The input tensor with ndim >= 2.
        group_size: The group size used for quantization.
        eps: The minimum to avoid dividing zero.
        dtype: The dype of output tensor. Note that only `torch.int8` is supported for now.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
    """
    assert (
        x.shape[-1] % group_size == 0
    ), "the last dimension of `x` cannot be divisible by `group_size`"
    assert x.is_contiguous(), "`x` is not contiguous"

    iinfo = torch.iinfo(dtype)
    int8_max = iinfo.max
    int8_min = iinfo.min

    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
    M = x.numel() // group_size
    N = group_size
    x_s = torch.empty(
        x.shape[:-1] + (x.shape[-1] // group_size,),
        device=x.device,
        dtype=torch.float32,
    )

    BLOCK = triton.next_power_of_2(N)
    # heuristics for number of warps
    num_warps = min(max(BLOCK // 256, 1), 8)
    num_stages = 1
    _per_token_group_quant_int8[(M,)](
        x,
        x_q,
        x_s,
        group_size,
        N,
        eps,
        int8_min=int8_min,
        int8_max=int8_max,
        BLOCK=BLOCK,
        num_warps=num_warps,
        num_stages=num_stages,
    )

    return x_q, x_s


def sglang_per_token_group_quant_int8(
    x: torch.Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: torch.dtype = torch.int8,
    enable_v2: Optional[bool] = None,
):
    assert (
        x.shape[-1] % group_size == 0
    ), "the last dimension of `x` cannot be divisible by `group_size`"
    assert x.is_contiguous(), "`x` is not contiguous"

    iinfo = torch.iinfo(dtype)
    int8_max = iinfo.max
    int8_min = iinfo.min

    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
    x_s = torch.empty(
        x.shape[:-1] + (x.shape[-1] // group_size,),
        device=x.device,
        dtype=torch.float32,
    )

    # Temporary
    if enable_sgl_per_token_group_quant_8bit:
        sgl_per_token_group_quant_8bit(
            x, x_q, x_s, group_size, eps, int8_min, int8_max, enable_v2=enable_v2
        )
    else:
        assert not enable_v2
        sgl_per_token_group_quant_int8(x, x_q, x_s, group_size, eps, int8_min, int8_max)

    return x_q, x_s


@triton.jit
def _w8a8_block_int8_matmul(
    # Pointers to inputs and output
    A,
    B,
    C,
    As,
    Bs,
    # Shape for matmul
    M,
    N,
    K,
    # Block size for block-wise quantization
    group_n,
    group_k,
    # Stride for inputs and output
    stride_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    stride_As_m,
    stride_As_k,
    stride_Bs_k,
    stride_Bs_n,
    # Meta-parameters
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
    GROUP_SIZE_M: tl.constexpr,
):
    """Triton-accelerated function used to perform linear operations (dot
    product) on input tensors `A` and `B` with block-wise quantization, and store the result in output
    tensor `C`.
    """

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + (pid % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = A + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
    b_ptrs = B + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)

    As_ptrs = As + offs_am * stride_As_m
    offs_bsn = offs_bn // group_n
    Bs_ptrs = Bs + offs_bsn * stride_Bs_n

    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)

        k_start = k * BLOCK_SIZE_K
        offs_ks = k_start // group_k
        a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
        b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)

        accumulator += tl.dot(a, b).to(tl.float32) * a_s[:, None] * b_s[None, :]
        a_ptrs += BLOCK_SIZE_K * stride_ak
        b_ptrs += BLOCK_SIZE_K * stride_bk

    if C.dtype.element_ty == tl.bfloat16:
        c = accumulator.to(tl.bfloat16)
    elif C.dtype.element_ty == tl.float16:
        c = accumulator.to(tl.float16)
    else:
        c = accumulator.to(tl.float32)

    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    c_ptrs = C + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, c, mask=c_mask)


@functools.lru_cache
def get_w8a8_block_int8_configs(
    N: int, K: int, block_n: int, block_k: int
) -> Optional[Dict[int, Any]]:
    """
    Return optimized configurations for the w8a8 block fp8 kernel.

    The return value will be a dictionary that maps an irregular grid of
    batch sizes to configurations of the w8a8 block fp8 kernel. To evaluate the
    kernel on a given batch size bs, the closest batch size in the grid should
    be picked and the associated configuration chosen to invoke the kernel.
    """

    # First look up if an optimized configuration is available in the configs
    # directory
    device_name = get_device_name().replace(" ", "_")
    json_file_name = f"N={N},K={K},device_name={device_name},dtype=int8_w8a8,block_shape=[{block_n}, {block_k}].json"

    config_file_path = os.path.join(
        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name
    )
    if os.path.exists(config_file_path):
        with open(config_file_path) as f:
            logger.info(
                "Using configuration from %s for W8A8 Block INT8 kernel.",
                config_file_path,
            )
            # If a configuration has been found, return it
            return {int(key): val for key, val in json.load(f).items()}

    # If no optimized configuration is available, we will use the default
    # configuration
    logger.warning(
        (
            "Using default W8A8 Block INT8 kernel config. Performance might be sub-optimal! "
            "Config file not found at %s"
        ),
        config_file_path,
    )
    return None


def w8a8_block_int8_matmul(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    block_size: List[int],
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    """This function performs matrix multiplication with block-wise quantization.

    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
    The output is returned in the specified `output_dtype`.

    Args:
        A: The input tensor, e.g., activation.
        B: The input tensor, e.g., weight.
        As: The per-token-group quantization scale for `A`.
        Bs: The per-block quantization scale for `B`.
        block_size: The block size for per-block quantization. It should be 2-dim, e.g., [128, 128].
        output_dytpe: The dtype of the returned tensor.

    Returns:
        torch.Tensor: The result of matmul.
    """
    assert len(block_size) == 2
    block_n, block_k = block_size[0], block_size[1]

    assert A.shape[-1] == B.shape[-1]
    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
    M = A.numel() // A.shape[-1]

    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
    N, K = B.shape
    assert triton.cdiv(N, block_n) == Bs.shape[0]
    assert triton.cdiv(K, block_k) == Bs.shape[1]

    C_shape = A.shape[:-1] + (N,)
    C = A.new_empty(C_shape, dtype=output_dtype)

    configs = get_w8a8_block_int8_configs(N, K, block_size[0], block_size[1])
    if configs:
        # If an optimal configuration map has been found, look up the
        # optimal config
        config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
    else:
        # Default config
        # Block-wise quant: BLOCK_SIZE_K must be divisible by block_size[1]
        config = {
            "BLOCK_SIZE_M": 64,
            "BLOCK_SIZE_N": block_size[0],
            "BLOCK_SIZE_K": block_size[1],
            "GROUP_SIZE_M": 32,
            "num_warps": 4,
            "num_stages": 3,
        }

    def grid(META):
        return (
            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
        )

    _w8a8_block_int8_matmul[grid](
        A,
        B,
        C,
        As,
        Bs,
        M,
        N,
        K,
        block_n,
        block_k,
        A.stride(-2),
        A.stride(-1),
        B.stride(1),
        B.stride(0),
        C.stride(-2),
        C.stride(-1),
        As.stride(-2),
        As.stride(-1),
        Bs.stride(1),
        Bs.stride(0),
        **config,
    )

    return C