utils_block.py

# SPDX-License-Identifier: Apache-2.0

import torch


def native_w8a8_block_matmul(A: torch.Tensor, B: torch.Tensor,
                             As: torch.Tensor, Bs: torch.Tensor, block_size,
                             output_dtype):
    """This function performs matrix multiplication with block-wise
    quantization using native torch.
    It is agnostic to the input data type and can be used for both int8 and
    fp8 data types.

    It takes two input tensors `A` and `B` (int8) with scales `As` and 
    `Bs` (float32).
    The output is returned in the specified `output_dtype`.
    """
    A = A.to(torch.float32)
    B = B.to(torch.float32)
    assert A.shape[-1] == B.shape[-1]
    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
    assert len(block_size) == 2
    block_n, block_k = block_size[0], block_size[1]
    assert (A.shape[-1] + block_k - 1) // block_k == As.shape[-1]
    assert A.shape[:-1] == As.shape[:-1]

    M = A.numel() // A.shape[-1]
    N, K = B.shape
    origin_C_shape = A.shape[:-1] + (N, )
    A = A.reshape(M, A.shape[-1])
    As = As.reshape(M, As.shape[-1])
    n_tiles = (N + block_n - 1) // block_n
    k_tiles = (K + block_k - 1) // block_k
    assert n_tiles == Bs.shape[0]
    assert k_tiles == Bs.shape[1]

    C_shape = (M, N)
    C = torch.zeros(C_shape, dtype=torch.float32, device=A.device)

    A_tiles = [
        A[:, i * block_k:min((i + 1) * block_k, K)] for i in range(k_tiles)
    ]
    B_tiles = [[
        B[
            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)]
    C_tiles = [
        C[:, j * block_n:min((j + 1) * block_n, N)] for j in range(n_tiles)
    ]
    As_tiles = [As[:, i:i + 1] for i in range(k_tiles)]

    for i in range(k_tiles):
        for j in range(n_tiles):
            a = A_tiles[i]
            b = B_tiles[j][i]
            c = C_tiles[j]
            s = As_tiles[i] * Bs[j][i]
            c[:, :] += torch.matmul(a, b.t()) * s

    C = C.reshape(origin_C_shape).to(output_dtype)
    return C