int8_matmul_mixed_dequantize.py

import torch
from bitsandbytes.triton.triton_utils import is_triton_available

if not is_triton_available():
    def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias): return None
else:

    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_dequantize(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