bitsandbytes-rocm/bitsandbytes/nn/triton_utils/v0/int8_matmul_mixed_dequanitze.py

import torch

import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time


# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and global quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.

def init_to_zero(name):
    return lambda nargs: nargs[name].zero_()

def get_configs_io_bound():
    configs = []
    for num_stages in [2, 3, 4, 5, 6]:
        for block_m in [16, 32]:
            for block_k in [32, 64]:
                for block_n in [32, 64, 128, 256]:
                    num_warps = 2 if block_n <= 64 else 4
                    configs.append(
                        triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
                                      num_stages=num_stages, num_warps=num_warps))
                    # split_k
                    for split_k in [2, 4, 8, 16]:
                        configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
                                                     num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
    return configs


@triton.autotune(
    configs=[
        # basic configs for compute-bound matmuls
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
        # good for int8
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
    ] + get_configs_io_bound(),
    key=['M', 'N', 'K'],
    prune_configs_by={
        'early_config_prune': early_config_prune,
        'perf_model': estimate_matmul_time,
        'top_k': 10
    },
)
@triton.heuristics({
    'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
            stride_am, stride_ak,
            stride_bk, stride_bn,
            stride_cm, stride_cn,
            BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
            GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
            ACC_TYPE: tl.constexpr
            ):
    # matrix multiplication
    pid = tl.program_id(0)
    pid_z = tl.program_id(1)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    # do matrix multiplication
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
    # pointers
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)

    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    w_factor = tl.load(state_w_ptr)
    x_factor = tl.load(state_x_ptr + ram)[:, None]

    # acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
    for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            k_remaining = K - k * (BLOCK_K * SPLIT_K)
            a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
            b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
        acc += tl.dot(a, b)
        A += BLOCK_K * SPLIT_K * stride_ak
        B += BLOCK_K * SPLIT_K * stride_bk
    
    acc = (w_factor * (x_factor * (acc * divfactor)))
    acc = acc.to(C.dtype.element_ty)

    # conditionally add bias
    if has_bias:
        bias = tl.load(bias + rn).to(C.dtype.element_ty)
        acc = acc + bias[None, :]

    C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
    mask = (rm < M)[:, None] & (rn < N)[None, :]
    # handles write-back with reduction-splitting
    if SPLIT_K == 1:
        tl.store(C, acc, mask=mask)
    else:
        tl.atomic_add(C, acc, mask=mask)


def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
    device = a.device
    divfactor = 1. / (127. * 127.)
    has_bias = 0 if bias is None else 1
    # handle non-contiguous inputs if necessary
    if a.stride(0) > 1 and a.stride(1) > 1:
        a = a.contiguous()
    if b.stride(0) > 1 and b.stride(1) > 1:
        b = b.contiguous()
    # checks constraints
    assert a.shape[1] == b.shape[0], "incompatible dimensions"
    M, K = a.shape
    _, N = b.shape
    # allocates output
    c = torch.empty((M, N), device=device, dtype=torch.float16)
    # accumulator types
    ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
    # launch int8_matmul_mixed_dequantize kernel
    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
    _int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
                    a.stride(0), a.stride(1),
                    b.stride(0), b.stride(1),
                    c.stride(0), c.stride(1),
                    GROUP_M=8, ACC_TYPE=ACC_TYPE)
    return c