vllm/vllm/model_executor/layers/quantization/utils/fp8_utils.py

# Adapted from https://github.com/sgl-project/sglang/pull/2575
from typing import List, Optional, Tuple

import torch
import triton
import triton.language as tl

from vllm.platforms import current_platform


def apply_w8a8_block_fp8_linear(
    input: torch.Tensor,
    weight: torch.Tensor,
    block_size: List[int],
    weight_scale: torch.Tensor,
    input_scale: Optional[torch.Tensor] = None,
    bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    assert input_scale is None
    # View input as 2D matrix for fp8 methods
    input_2d = input.view(-1, input.shape[-1])
    output_shape = [*input.shape[:-1], weight.shape[0]]

    q_input, x_scale = per_token_group_quant_fp8(input_2d, block_size[1])
    output = w8a8_block_fp8_matmul(q_input,
                                   weight,
                                   x_scale,
                                   weight_scale,
                                   block_size,
                                   output_dtype=input.dtype)

    if bias is not None:
        output = output + bias
    return output.to(dtype=input.dtype).view(*output_shape)


def input_to_float8(
        x: torch.Tensor,
        dtype: Optional[torch.dtype] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
    """This function quantizes input values to float8 values "
    "with tensor-wise quantization."""
    if dtype is None:
        dtype = (torch.float8_e4m3fnuz
                 if current_platform.is_rocm() else torch.float8_e4m3fn)
    finfo = torch.finfo(dtype)
    min_val, max_val = x.aminmax()
    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
    scale = finfo.max / amax
    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()


def block_quant_to_tensor_quant(
    x_q_block: torch.Tensor,
    x_s: torch.Tensor,
    block_size: List[int],
) -> Tuple[torch.Tensor, torch.Tensor]:
    """This function converts block-wise quantization to tensor-wise
    quantization. The inputs are block-wise quantization tensor `x_q_block`,
    block-wise quantization scale and the block size.
    The outputs are tensor-wise quantization tensor and tensor-wise
    quantization scale. Note only float8 is supported for now.
    """
    block_n, block_k = block_size[0], block_size[1]
    n, k = x_q_block.shape
    n_tiles = (n + block_n - 1) // block_n
    k_tiles = (k + block_k - 1) // block_k
    assert n_tiles == x_s.shape[0]
    assert k_tiles == x_s.shape[1]

    x_dq_block = x_q_block.to(torch.float32)

    x_dq_block_tiles = [[
        x_dq_block[j * block_n:min((j + 1) * block_n, n),
                   i * block_k:min((i + 1) * block_k, k), ]
        for i in range(k_tiles)
    ] for j in range(n_tiles)]

    for i in range(k_tiles):
        for j in range(n_tiles):
            x_dq_block_tiles[j][i][:, :] = x_dq_block_tiles[j][i] * x_s[j][i]

    x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
    return x_q_tensor, scale


@triton.jit
def _per_token_group_quant_fp8(
    # 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 float8
    fp8_min,
    fp8_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 float8 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 / fp8_max
    y_q = tl.clamp(y / y_s, fp8_min, fp8_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_fp8(
    x: torch.Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: Optional[torch.dtype] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Function to perform per-token-group quantization on an input tensor `x`.
    It converts the tensor values into signed float8 values and returns the
    quantized tensor along with the scaling factor used for quantization.
    Args:
        x: The input tenosr 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.float8_e4m3fn`
        is supported for now.
    Returns:
        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the
        scaling factor for quantization.
    """
    if dtype is None:
        dtype = (torch.float8_e4m3fnuz
                 if current_platform.is_rocm() else torch.float8_e4m3fn)
    assert (x.shape[-1] % group_size == 0), (
        f"the last dimension of `x` {x.shape[-1]} must be divisible "
        f"by `group_size` {group_size}")
    assert x.is_contiguous(), "`x` must be contiguous"

    finfo = torch.finfo(dtype)
    fp8_min = finfo.min
    fp8_max = finfo.max

    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_fp8[(M, )](
        x,
        x_q,
        x_s,
        group_size,
        N,
        eps,
        fp8_min=fp8_min,
        fp8_max=fp8_max,
        BLOCK=BLOCK,
        num_warps=num_warps,
        num_stages=num_stages,
    )

    return x_q, x_s


@triton.jit
def _w8a8_block_fp8_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) * 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)


def w8a8_block_fp8_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)

    # TODO:
    # BLOCK_SIZE_M, BLOCK_SIZE_K, BLOCK_SIZE_N can be optimized.
    # BLOCK_SIZE_K must be divisible by block_k
    # BLOCK_SIZE_N and BLOCK_SIZE_M has no requirements
    BLOCK_SIZE_M = 128
    if M < BLOCK_SIZE_M:
        BLOCK_SIZE_M = triton.next_power_of_2(M)
        BLOCK_SIZE_M = max(BLOCK_SIZE_M, 16)
    BLOCK_SIZE_K = block_k
    assert block_k % BLOCK_SIZE_K == 0
    BLOCK_SIZE_N = block_n

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

    _w8a8_block_fp8_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),
        BLOCK_SIZE_M=BLOCK_SIZE_M,
        BLOCK_SIZE_N=BLOCK_SIZE_N,
        BLOCK_SIZE_K=BLOCK_SIZE_K,
        GROUP_SIZE_M=8,
    )

    return C