venv/lib/python3.11/site-packages/deepspeed/ops/sparse_attention/matmul.py

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

# DeepSpeed note, code taken & adapted from commit 9aa94789f13ada713af36cfd8cca2fc9a7f6b79a
# https://github.com/ptillet/torch-blocksparse/blob/master/torch_blocksparse/matmul.py
import importlib
import torch

import triton
import triton.language as tl
import triton._C.libtriton as libtriton
from deepspeed.accelerator import get_accelerator


@triton.jit
def _kernel(A, B, C, stride_za, stride_ha, stride_ma, stride_ka, stride_zb, stride_hb, stride_kb, stride_nb, stride_zc,
            stride_hc, stride_mc, stride_nc, DS0, DS1, SDD_K, SDD_off_width, lut, locks, nlocks, **meta):
    TM = meta['TM']
    TN = meta['TN']
    TK = meta['TK']
    TZ = meta['TZ']
    BLOCK = meta['BLOCK']
    #------------#
    #- Prologue -#
    #------------#
    pid0 = tl.program_id(0)
    pid1 = tl.program_id(1)
    pidz = tl.program_id(2)
    if meta['SDD']:
        pid1 = pid1 + SDD_off_width
        blockidm = tl.arange(0, TM) // BLOCK
        blockidn = tl.arange(0, TN) // BLOCK
        offlutm = blockidm * (TN // BLOCK) * 4
        offlutn = blockidn * 4
        header = lut + pid1 * (TM // BLOCK) * (TN // BLOCK) * 4
        z = tl.load(header + 0)
        i = tl.load(header + 1 + offlutm)
        j = tl.load(header + 2 + offlutn)
        AS1 = SDD_K // TZ
        lockid = tl.where(TZ > 1, 1, 0)
        offka = pid0 * AS1
        offkb = pid0 * AS1
        offmc = 0
        offnc = 0
        offpa = 0
        offpb = 0
        maxid = TZ
        offhc = 0
        offha = z
        offhb = z
        ram = i * BLOCK + (tl.arange(0, TM) % BLOCK)
        rbn = j * BLOCK + (tl.arange(0, TN) % BLOCK)
    else:
        header = lut + pid0 * 6
        offset = tl.load(header + 0)
        AS1 = tl.load(header + 1)
        column = tl.load(header + 2)
        depth = tl.load(header + 3)
        lockid = tl.load(header + 4)
        maxid = tl.load(header + 5)
        pinc = lut + offset
        offhc = depth
        if meta['DSD']:
            # output offset
            offnc = pid1 * TN
            offmc = column * TM
            offpc = 0
            # dense input offset
            offnb = pid1 * TN
            offkb = tl.load(pinc)
            offkb = tl.multiple_of(offkb, 8)  # compiler hint
            offpb = 0
            # sparse input offset
            offma = 0
            offka = 0
            offpa = tl.load(pinc + 1)
            offpa = tl.multiple_of(offpa, 8)  # compiler hint
            offpa = offpa * BLOCK * BLOCK
            offha = 0
            offhb = depth
        else:
            # output offset
            offmc = pid1 * TM
            offnc = column * TN
            offpc = 0
            # dense input offset
            offma = pid1 * TM
            offka = tl.load(pinc)
            offka = tl.multiple_of(offka, 8)  # compiler hint
            offpa = 0
            # sparse input offset
            offnb = 0
            offkb = 0
            offpb = tl.load(pinc + 1)
            offpb = tl.multiple_of(offpb, 8)  # compiler hint
            offpb = offpb * BLOCK * BLOCK
            offha = depth
            offhb = 0
        ram = offma + tl.arange(0, TM)
        rbn = offnb + tl.arange(0, TN)

    # initialize a, b pointers
    rka = offka + tl.arange(0, TK)
    rkb = offkb + tl.arange(0, TK)
    pa = A + pidz * stride_za + offha * stride_ha + offpa + ram[:, None] * stride_ma + rka[None, :] * stride_ka
    pb = B + pidz * stride_zb + offhb * stride_hb + offpb + rbn[None, :] * stride_nb + rkb[:, None] * stride_kb
    if meta['DDS']:
        checkam = ram[:, None] < DS0
    else:
        checkam = AS1 > 0
    if meta['DSD']:
        checkbn = rbn[None, :] < DS0
    else:
        checkbn = AS1 > 0
    a = tl.load(pa, mask=checkam, other=0.)
    b = tl.load(pb, mask=checkbn, other=0.)

    ## ---------------- ##
    ##    Inner Loop    ##
    ## ---------------- ##
    acc = tl.zeros((TM, TN), dtype=tl.float32)
    for k in range(AS1, 0, -TK):
        acc += tl.dot(a, b)
        if meta['SDD']:
            inc_a = TK * stride_ka
            inc_b = TK * stride_kb
        else:
            pinc += 2
        if meta['DSD']:
            inc_b = tl.load(pinc)
            inc_a = tl.load(pinc + 1)
            inc_b = tl.multiple_of(inc_b, 8)
            inc_a = tl.multiple_of(inc_a, 8)
            inc_b = inc_b * stride_kb
        if meta['DDS']:
            inc_a = tl.load(pinc)
            inc_b = tl.load(pinc + 1)
            inc_a = tl.multiple_of(inc_a, 8)
            inc_b = tl.multiple_of(inc_b, 8)
            inc_a = inc_a * stride_ka
        pa += inc_a
        pb += inc_b
        # pre-fetch
        checkak = k > TK
        checkbk = k > TK
        checka = checkam & checkak
        checkb = checkbn & checkbk
        a = tl.load(pa, mask=checka)
        b = tl.load(pb, mask=checkb)
    c = acc.to(C.dtype.element_ty)

    if meta['SDD']:
        checkc = True
        rr_blockidm = tl.arange(0, TM) // BLOCK
        rr_blockidn = tl.arange(0, TN) // BLOCK
        rr_offlutm = rr_blockidm * (TN // BLOCK) * 4
        rr_offlutn = rr_blockidn * 4
        off_bkid = 3 + rr_offlutm[:, None] + rr_offlutn[None, :]
        bkid = tl.load(header + off_bkid)
        offpc = bkid * BLOCK * BLOCK
        rcm = tl.arange(0, TM) % BLOCK
        rcn = tl.arange(0, TN) % BLOCK
    else:
        rcm = offmc + tl.arange(0, TM)
        rcn = offnc + tl.arange(0, TN)
    if meta['DSD']:
        checkc = rcn[None, :] < DS0
    if meta['DDS']:
        checkc = rcm[:, None] < DS0

    pc = C + offpc + offhc * stride_hc + pidz * stride_zc + rcm[:, None] * stride_mc + rcn[None, :] * stride_nc
    # write-back directly
    if lockid == 0:
        tl.store(pc, c, mask=checkc)
    # accumulate partial results using spin-locks
    else:
        plock = locks + tl.program_id(2) * nlocks * tl.num_programs(1) + tl.program_id(1) * nlocks + lockid - 1
        pcount = plock + tl.num_programs(2) * tl.num_programs(1) * nlocks
        while tl.atomic_cas(plock, 0, 1) == 1:
            pass
        count = tl.load(pcount)
        if count == 0:
            tl.store(pc, c, mask=checkc)
        else:
            d = tl.load(pc, mask=checkc)
            tl.store(pc, d + c, mask=checkc)
        tl.atomic_xchg(pcount, (count + 1) % maxid)
        tl.atomic_xchg(plock, 0)


##############
#  MAIN API  #
##############
class _sparse_matmul(torch.autograd.Function):

    sdd_cache = dict()
    dsd_cache = dict()
    dds_cache = dict()
    locks = dict()

    # Given an array sizes representing reduction size for each
    # column of a block-mode matrix multiplication,
    # performs load-balancing to achieve more smaller reductions
    # between `seg_size` elements
    @staticmethod
    def load_balance(sizes, block):
        #global triton
        #if triton is None:
        #    triton = importlib.import_module('triton')
        # segment size
        # heuristics taken from OpenAI blocksparse code
        # https://github.com/openai/blocksparse/blob/master/blocksparse/matmul.py#L95
        max_size = sizes.max()
        min_size = sizes[sizes != 0].min()
        #if max_size > min_size * 2.0:
        #  seg_max = max(triton.cdiv(max_size, 4), min_size*2)
        #else:
        #  seg_max = max_size
        seg_max = max_size
        seg_min = max(triton.cdiv(seg_max, 4), 4)
        # split reduction into segments
        div = sizes // seg_max
        rem = sizes % seg_max
        packs = div + (sizes < seg_min).long() + (rem >= seg_min).long()
        width = packs.sum()
        segments = torch.empty(width, dtype=sizes.dtype)
        column = torch.empty_like(segments)
        lockid = torch.zeros_like(segments)
        maxid = torch.zeros_like(segments)
        nlocks = 0
        current = 0
        col_idx = 0
        for i in range(len(sizes)):
            d, r = div[i], rem[i]
            isempty = sizes[i] < seg_min
            last = current + d + (r >= seg_min) + isempty
            # column id
            column[current:last] = col_idx
            # lock id
            if d > 1 or (d == 1 and r >= seg_min):
                nlocks += 1
                lockid[current:last] = nlocks
                maxid[current:last] = last - current
            # segment size
            segments[current:current + d] = seg_max
            if r < seg_min and not isempty:
                segments[current + d - 1] += r
            if r >= seg_min or isempty:
                segments[current + d] = r
            current = last
            col_idx += 1
        offsets = torch.zeros_like(segments)
        offsets[1:] = torch.cumsum(segments[:-1], dim=0)
        return segments, column, lockid, maxid, offsets

    @staticmethod
    def get_locks(size, dev):
        if dev not in _sparse_matmul.locks or \
            size > _sparse_matmul.locks[dev].size(0):
            _sparse_matmul.locks[dev] = torch.zeros(size, dtype=torch.int32, device=dev)
        return _sparse_matmul.locks[dev]

    ##########################
    # SPARSE = DENSE x DENSE #
    ##########################

    @staticmethod
    def make_sdd_lut(layout, block, dtype, device):
        #_sparse_matmul._load_utils()
        #start_width = 64 // block
        #segmented = _sparse_matmul.sdd_segment(layout.type(torch.int32), start_width)
        start_width = (128 if block > 16 else 32) // block
        layout = layout.type(torch.int32)
        segmented = libtriton.superblock(layout.data_ptr(), layout.shape[0], layout.shape[1], layout.shape[2],
                                         start_width)
        luts, widths, packs = [], [], []
        for size, nnz in segmented:
            """ width = nnz.shape[0] // (size * size)
            h = nnz[:, 0]
            i = nnz[:, 1]
            j = nnz[:, 2]
            b = nnz[:, 3]
            lut = torch.stack((h, i, j, b), dim=1).view(-1).contiguous()
            luts.append(lut.type(torch.int32).to(device))
            widths.append(width)
            packs.append(size) """
            nnz = nnz.reshape(-1, 4)
            width = nnz.shape[0] // (size * size)
            luts.append(torch.from_numpy(nnz).type(torch.int32).to(device))
            widths.append(width)
            packs.append(size)
        # create locks
        return luts, None, widths, packs

    @staticmethod
    def _sdd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, luts, num_locks, widths, packs, bench, time):
        if trans_c:
            a, b = b, a
            trans_a, trans_b = not trans_b, not trans_a
        AS0 = a.size(0)
        # Shape check
        a_dim = -2 if trans_a else -1
        b_dim = -1 if trans_b else -2
        a_inner, b_inner = a.shape[a_dim], b.shape[b_dim]
        if a_inner != b_inner:
            raise ValueError(f"Size of tensor A along the {a_dim} dim ({a_inner}) must match size "
                             f"of tensor B along the {b_dim} dim ({b_inner})")
        if a_inner % 16 != 0:
            raise ValueError('Reduction size for SDD must be a multiple of 16')

        batch_size = a.size(0)
        a_outer = a.size(3 if trans_a else 2)
        dtype = a.dtype
        is_16_multiple = a_inner % 16 == 0
        is_32_multiple = a_inner % 32 == 0
        is_64_multiple = a_inner % 64 == 0
        if not is_16_multiple:
            raise ValueError('Reduction size for SDD must be a multiple of 16')
        device = a.device
        # create kernel
        total_width = sum([width * pack * pack for width, pack in zip(widths, packs)])
        c = torch.empty((batch_size, total_width, block, block), dtype=dtype, device=a.device)
        for lut, width, pack in zip(luts, widths, packs):
            F32TK = [8, 16]
            F16TK = [16]
            F16TK += [32] if is_32_multiple else []
            F16TK += [64] if is_64_multiple else []
            TK = {torch.float32: F32TK, torch.float16: F16TK}[dtype]
            num_lock = 1
            meta = {
                'TM': block * pack,
                'TN': block * pack,
                'BLOCK': block,
                'TK': TK[0],
                'TZ': 1,
                'SDD': True,
                'DSD': False,
                'DDS': False
            }
            # create output
            locks = _sparse_matmul.get_locks(2 * width * AS0 * num_lock, a.device)
            # maximum grid size is 65535
            # so operation might be decomposed into multiple
            # kernel calls
            max_width = 49152
            total = 0 if bench else None
            for off_width in range(0, width, max_width):
                grid = lambda meta: [meta['TZ'], min(max_width, width - off_width), batch_size]
                _kernel[grid](a,
                              b,
                              c,
                              a.stride(0),
                              a.stride(1),
                              a.stride(3 if trans_a else 2),
                              a.stride(2 if trans_a else 3),
                              b.stride(0),
                              b.stride(1),
                              b.stride(3 if trans_b else 2),
                              b.stride(2 if trans_b else 3),
                              c.stride(0),
                              c.stride(0),
                              c.stride(2),
                              c.stride(3),
                              a_outer,
                              a_outer,
                              a_inner,
                              off_width,
                              lut,
                              locks,
                              num_lock,
                              num_warps=4,
                              **meta)
        # save for backward pass
        return c

    ##########################
    # DENSE = DENSE x SPARSE #
    ##########################

    # Given a binary layout of 0s and 1s,
    # Construct look-up table for efficient execution on GPUs
    @staticmethod
    def make_dxx_lut(layout, block, step, trans, device, transform=lambda idx: idx):
        # load-balancing
        _empty = torch.tensor([], dtype=torch.int64, device=layout.device)
        segments = _empty.clone()
        column = _empty.clone()
        depth = _empty.clone()
        lockid = _empty.clone()
        maxid = _empty.clone()
        offsets = _empty.clone()
        current_offset = 0
        current_maxid = 0
        for z in range(layout.size(0)):
            if trans:
                sizes = torch.sum(layout[z, :, :], 1)
            else:
                sizes = torch.sum(layout[z, :, :], 0)
            z_segments, z_column, z_lockid, z_maxid, z_offsets = _sparse_matmul.load_balance(sizes, block)
            z_depth = z * torch.ones_like(z_segments)
            z_lockid[z_lockid > 0] += current_maxid
            current_maxid = z_lockid.max()
            # concatenate depth
            segments = torch.cat((segments, z_segments))
            column = torch.cat((column, z_column))
            depth = torch.cat((depth, z_depth))
            maxid = torch.cat((maxid, z_maxid))
            offsets = torch.cat((offsets, current_offset + z_offsets))
            lockid = torch.cat((lockid, z_lockid))
            current_offset += layout[z, :, :].sum()
        segments *= step
        # pointer increments
        if trans:
            nnz = layout.nonzero()
        else:
            nnz = layout.transpose(1, 2).nonzero()
        num_blocks = nnz.size(0)
        offsets = torch.min(offsets, (num_blocks - 1) * torch.ones_like(offsets))
        idx = transform(nnz[:, 2] * block)
        xincs = idx.clone()
        xincs[1:] -= idx[:-1]
        # divide block into multiple steps
        div = block // step
        xincs = xincs.view(-1, 1).repeat(1, div)
        xincs[:, 1:] = step
        xincs[:, 0] -= (div - 1) * step
        # first increment for each reduction is actually the offset
        xincs[offsets[segments > 0], 0] = idx[offsets[segments > 0]]
        xincs = xincs.view(-1)
        # block-mode input increments
        if trans:
            widx = torch.arange(num_blocks)
        else:
            widx = _empty.clone()
            current_offset = 0
            for z in range(layout.size(0)):
                layoutw = layout[z, :, :].clone()
                msum = layoutw.sum()
                layoutw[layoutw > 0] = 1 + torch.arange(msum)
                widx = torch.cat((widx, current_offset + layoutw.T[layoutw.T > 0] - 1))
                current_offset += msum
        widx = widx
        wincs = widx * block * block
        wincs[1:] -= widx[:-1] * block * block
        wincs = wincs.view(-1, 1).repeat(1, div)
        if trans:
            wincs[:, 1:] = step
            wincs[:, 0] -= (div - 1) * step
        else:
            wincs[:, 1:] = step * block
            wincs[:, 0] -= (div - 1) * step * block
        wincs[offsets[segments > 0], 0] = widx[offsets[segments > 0]]
        wincs = wincs.view(-1)
        # adjust offset and segment size
        offsets *= 2 * div
        segments *= div
        # create header
        width = column.size(0)
        offsets += 6 * width
        header = torch.stack((offsets, segments, column, depth, lockid, maxid), dim=1).view(-1).contiguous()
        incs = torch.stack((xincs, wincs), dim=1).view(-1).contiguous()
        incs = torch.cat((incs, torch.zeros(2, device=incs.device, dtype=incs.dtype)))
        # create lut
        lut = torch.cat((header, incs))
        lut = lut.type(torch.int32).to(device)
        # create locks
        num_locks = max(1, lockid.max())
        return lut, num_locks, width, None

    @staticmethod
    def _dds_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, num_locks, width, packs, bench, time):
        global triton
        if triton is None:
            triton = importlib.import_module('triton')

        # shapes / dtypes
        AS0 = a.size(0)
        AS1 = a.size(1)
        AS2 = a.size(3 if trans_a else 2)
        AS3 = a.size(2 if trans_a else 3)
        BS0 = spdims[0]
        BS1 = block * spdims[2 if trans_b else 1]
        BS2 = block * spdims[1 if trans_b else 2]
        dtype = a.dtype
        # kernel
        meta = {'TN': block, 'TM': 128, 'TK': 16, 'BLOCK': block, 'TZ': 1, 'SDD': False, 'DSD': False, 'DDS': True}
        # output
        CS0 = AS0
        CS1 = AS1
        CS2 = BS2 if trans_c else AS2
        CS3 = AS2 if trans_c else BS2
        locks = _sparse_matmul.get_locks(2 * AS0 * AS2 // 32 * num_locks, a.device)
        c = torch.empty((CS0, CS1, CS2, CS3), dtype=dtype, device=a.device)
        grid = lambda meta: [width, triton.cdiv(AS2, meta['TM']), AS0]
        _kernel[grid](a,
                      b,
                      c,
                      a.stride(0),
                      a.stride(1),
                      a.stride(3 if trans_a else 2),
                      a.stride(2 if trans_a else 3),
                      b.stride(0),
                      b.stride(1),
                      b.stride(3 if trans_b else 2),
                      b.stride(2 if trans_b else 3),
                      c.stride(0),
                      c.stride(1),
                      c.stride(3 if trans_c else 2),
                      c.stride(2 if trans_c else 3),
                      AS2,
                      BS2,
                      0,
                      0,
                      lut,
                      locks,
                      num_locks,
                      num_warps=4,
                      **meta)
        return c

    @staticmethod
    def _dsd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, num_locks, width, packs, bench, time):
        global triton
        if triton is None:
            triton = importlib.import_module('triton')

        # shapes / dtypes
        AS0 = spdims[0]
        AS1 = block * spdims[2 if trans_a else 1]
        AS2 = block * spdims[1 if trans_a else 2]
        BS0 = b.size(0)
        BS1 = b.size(1)
        BS2 = b.size(3 if trans_b else 2)
        BS3 = b.size(2 if trans_b else 3)
        dtype = a.dtype
        # kernel

        meta = {'TM': block, 'TN': 128, 'TK': 16, 'BLOCK': block, 'TZ': 1, 'SDD': False, 'DSD': True, 'DDS': False}
        # output
        CS0 = BS0
        CS1 = BS1
        CS2 = BS3 if trans_c else AS1
        CS3 = AS1 if trans_c else BS3
        locks = _sparse_matmul.get_locks(2 * BS0 * BS3 // 32 * num_locks, a.device)
        c = torch.empty((CS0, CS1, CS2, CS3), dtype=dtype, device=a.device)
        grid = lambda meta: [width, triton.cdiv(BS3, meta['TN']), BS0]
        _kernel[grid](a,
                      b,
                      c,
                      a.stride(0),
                      a.stride(1),
                      a.stride(3 if trans_a else 2),
                      a.stride(2 if trans_a else 3),
                      b.stride(0),
                      b.stride(1),
                      b.stride(3 if trans_b else 2),
                      b.stride(2 if trans_b else 3),
                      c.stride(0),
                      c.stride(1),
                      c.stride(2),
                      c.stride(3),
                      BS3,
                      AS1,
                      0,
                      0,
                      lut,
                      locks,
                      num_locks,
                      num_warps=4,
                      **meta)
        return c

    fn = {'sdd': _sdd_matmul.__get__(object), 'dsd': _dsd_matmul.__get__(object), 'dds': _dds_matmul.__get__(object)}

    @staticmethod
    def forward(ctx, a, b, trans_a, trans_b, trans_c, mode, spdims, block, c_lut, c_num_locks, c_width, c_packs,
                c_bench, c_time, da_lut, da_num_locks, da_width, da_packs, da_bench, da_time, db_lut, db_num_locks,
                db_width, db_packs, db_bench, db_time):
        c = _sparse_matmul.fn[mode](a, b, trans_a, trans_b, trans_c, spdims, block, c_lut, c_num_locks, c_width,
                                    c_packs, c_bench, c_time)
        # save for backward
        ctx.save_for_backward(a, b)
        ctx.da_num_locks = da_num_locks
        ctx.da_lut = da_lut
        ctx.da_width = da_width
        ctx.da_packs = da_packs
        ctx.da_bench = da_bench
        ctx.da_time = da_time
        ctx.db_lut = db_lut
        ctx.db_num_locks = db_num_locks
        ctx.db_width = db_width
        ctx.db_bench = db_bench
        ctx.db_packs = db_packs
        ctx.db_time = db_time
        ctx.mode = mode
        ctx.spdims = spdims
        ctx.block = block
        ctx.trans_a = trans_a
        ctx.trans_b = trans_b
        return c

    @staticmethod
    def backward(ctx, dc):
        # saved for backward
        a, b = ctx.saved_tensors
        mode = ctx.mode
        # gradients w.r.t. a
        if ctx.needs_input_grad[0]:
            mode_da = mode[1] + mode[0] + mode[2]
            da = _sparse_matmul.fn[mode_da](dc, b, False, not ctx.trans_b, ctx.trans_a, ctx.spdims, ctx.block,
                                            ctx.da_lut, ctx.da_num_locks, ctx.da_width, ctx.da_packs, ctx.da_bench,
                                            ctx.da_time)
        # gradients w.r.t. b
        if ctx.needs_input_grad[1]:
            mode_db = mode[2] + mode[1] + mode[0]
            db = _sparse_matmul.fn[mode_db](a, dc, not ctx.trans_a, False, ctx.trans_b, ctx.spdims, ctx.block,
                                            ctx.db_lut, ctx.db_num_locks, ctx.db_width, ctx.db_packs, ctx.db_bench,
                                            ctx.db_time)
        return da, db, None, None, None,\
               None, None, None, None,\
               None, None, None, None, None, None,\
               None, None, None, None, None, None,\
               None, None, None, None, None, None


class MatMul:
    """Block-Sparse MatMul class; this class handles three types of matrix-multiplication:
       - sparse = dense X dense
       - dense = sparse X dense
       - dense = dense X sparse

    For more details about sparsity config, please see `Generative Modeling with Sparse Transformers`: https://arxiv.org/abs/1904.10509
    """

    def make_lut(self, dtype, device):
        """Generates the sparsity layout/s used in block-sparse matmul
        """
        key = (dtype, device)
        if key in self.lut_cache:
            return self.lut_cache[key]
        # C look-up table
        layout, block = self.layout, self.block
        step = 16
        if self.mode == 'sdd':
            c_lut, c_num_locks, c_width, c_packs = _sparse_matmul.make_sdd_lut(layout, block, dtype, device)
        elif self.mode == 'dsd':
            c_lut, c_num_locks, c_width, c_packs = _sparse_matmul.make_dxx_lut(layout, block, step, not self.trans_a,
                                                                               device)
        elif self.mode == 'dds':
            c_lut, c_num_locks, c_width, c_packs = _sparse_matmul.make_dxx_lut(layout, block, step, self.trans_b,
                                                                               device)
        # DA look-up table
        if self.mode == 'sdd':
            da_lut, da_num_locks, da_width, da_packs = _sparse_matmul.make_dxx_lut(layout, block, step, True, device)
        elif self.mode == 'dsd':
            da_lut, da_num_locks, da_width, da_packs = _sparse_matmul.make_sdd_lut(layout, block, dtype, device)
        elif self.mode == 'dds':
            da_lut, da_num_locks, da_width, da_packs = _sparse_matmul.make_dxx_lut(layout, block, step,
                                                                                   not self.trans_b, device)
        # DB look-up table
        if self.mode == 'sdd':
            db_lut, db_num_locks, db_width, db_packs = _sparse_matmul.make_dxx_lut(layout, block, step, False, device)
        elif self.mode == 'dsd':
            db_lut, db_num_locks, db_width, db_packs = _sparse_matmul.make_dxx_lut(layout, block, step, self.trans_a,
                                                                                   device)
        elif self.mode == 'dds':
            db_lut, db_num_locks, db_width, db_packs = _sparse_matmul.make_sdd_lut(layout, block, dtype, device)
        self.lut_cache[key] = (c_lut, c_num_locks, c_width, c_packs,\
                               da_lut, da_num_locks, da_width, da_packs,\
                               db_lut, db_num_locks, db_width, db_packs)
        return self.lut_cache[key]

    def __init__(self, layout, block, mode, trans_a=False, trans_b=False, bench=False):
        """Initialize the Block-Sparse MatMul class.

        Arguments:
             layout: required: sparsity layout tensor
             block: required: an integer determining the block size.
             mode: required: a string determining type of matmul; ('sdd') sparse = dense X dense, ('dsd') dense = sparse X dense, ('dds') dense = dense X sparse
             trans_a: optional: a boolean determining if multiplication needs to be applied on transpose of input a; default is false
             trans_b: optional: a boolean determining if multiplication needs to be applied on transpose of input b; default is false
             bench: optional: set if you want to do benchmarking
        """

        if mode not in ['sdd', 'dsd', 'dds']:
            raise NotImplementedError('Supported modes are: sdd, dsd, dds')
        # look-up table cache
        self.lut_cache = dict()
        # attributes
        self.trans_a = trans_a
        self.trans_b = trans_b
        self.mode = mode
        self.block = block
        self.layout = layout
        layout_dim = layout.ndim
        assert layout_dim in (2, 3), "Layout should be a 2 or 3 dimensional tensor of 0s and 1s"
        if not mode == 'sdd':
            # Dims to be reduced on the 'inside' of the matmul, either -1 or -2
            trans_dense, trans_sparse, sparse_inner = (trans_b, trans_a, -1) if mode == 'dsd' else (trans_a, trans_b,
                                                                                                    -2)
            self.dense_inner_dim = -((sparse_inner % 2) + 1) if not trans_dense else sparse_inner
            sparse_inner = sparse_inner if not trans_sparse else -((sparse_inner % 2) + 1)

            # Inner dim of the dense input should be equal to the inner dim of the sparse input
            self.dense_inner_size = layout.shape[sparse_inner] * block
            # Expected shape for sparse inputs
            self.sparse_shape = (layout.sum().item(), block, block)

        # Support using the same layout across attention heads etc.
        if layout_dim == 2:
            layout = layout.unsqueeze(0)

        layout = layout.long()  # Above code assumes the layout tensor is an integral type

        self.spdims = layout.shape
        # timings
        self.bench = bench
        self.time_c = None
        self.time_da = None
        self.time_db = None

    # pad shapes of a tensor to make it
    # compatible with kernel calls
    @staticmethod
    def _pad_shape(x, is_sparse):
        max_dim = 3 if is_sparse else 4
        for i in range(max_dim - x.dim()):
            x = x.unsqueeze(0)
        return x

    def __call__(self, a, b):
        """Applies Block-Sparse MatMul.

        For more details about sparsity config, please see `Generative Modeling with Sparse Transformers`: https://arxiv.org/abs/1904.10509

        Arguments:
             a: required: a dense/block-sparse tensor; first input of mat-mul
             b: required: a dense/block-sparse tensor; second input of mat-mul

        Return:
             c: a dense/block-sparse tensor result of a X b
        """


        c_lut, c_num_locks, c_width, c_packs,\
        da_lut, da_num_locks, da_width, da_packs,\
        db_lut, db_num_locks, db_width, db_packs = self.make_lut(a.dtype, a.device)
        # timings
        time_c = [None]
        time_da = [None]
        time_db = [None]

        original_dims = max(a.ndim, b.ndim)
        a, b = self._validate_inputs(a, b)

        # pad shapes with ones
        a = MatMul._pad_shape(a, self.mode == 'dsd')
        b = MatMul._pad_shape(b, self.mode == 'dds')
        # execute

        c = _sparse_matmul.apply(a, b, self.trans_a, self.trans_b, False, self.mode, self.spdims, self.block, c_lut,
                                 c_num_locks, c_width, c_packs, self.bench, time_c, da_lut, da_num_locks, da_width,
                                 da_packs, self.bench, time_da, db_lut, db_num_locks, db_width, db_packs, self.bench,
                                 time_db)

        # This removes any leading singleton dimensions we may have added to the tensor that weren't in the input
        dims_to_trim = c.ndim - original_dims
        for _ in range(dims_to_trim):
            c = c.squeeze(0)

        self.time_c = time_c[0]
        self.time_da = time_da[0]
        self.time_db = time_db[0]
        return c

    def _validate_inputs(self, a, b):
        if a.device != b.device:
            raise ValueError(f"Inputs must be on the same device; got {a.device} for tensor A "
                             f"and {b.device} for tensor B")
        if not get_accelerator().on_accelerator(a):
            raise ValueError("Only GPU devices are supported for now")

        # When autocast is enabled, torch.matmul autocasts to float16, so we do the same here
        if torch.is_autocast_enabled():
            a, b = a.half(), b.half()
        elif a.dtype != b.dtype:
            raise ValueError(f"Inputs must be the same dtype; got {a.dtype} for A and {b.dtype} for B")

        mode, trans_a, trans_b = self.mode, self.trans_a, self.trans_b
        if mode != 'sdd':
            # One input is sparse
            dense, dense_name, sparse, sparse_name = (a, 'A', b, 'B') if mode == 'dds' else (b, 'B', a, 'A')
            dense_inner = dense.shape[self.dense_inner_dim]
            if dense_inner != self.dense_inner_size:
                raise ValueError(f"Expected tensor {dense_name} to have size {self.dense_inner_size} at dim "
                                 f"{self.dense_inner_dim % dense.ndim}, got {dense_inner}.")

            if sparse.shape[-len(self.sparse_shape):] != self.sparse_shape:
                raise ValueError(f"Expected tensor with trailing dimensions of shape {self.sparse_shape} for argument "
                                 f"{sparse_name}, got {sparse.shape}")

        def add_extra_dims(x):
            # Add extra leading singleton dimensions if needed
            dims_needed = 4 - x.ndim
            if dims_needed > 0:
                singletons = [1] * dims_needed
                x = x.view(*singletons, *x.shape)
            elif dims_needed < 0:
                raise ValueError("Tensors with more than 4 dimensions are not currently supported")

            return x

        # Pad shapes with leading singleton dimensions
        a = add_extra_dims(a)
        b = add_extra_dims(b)

        return a, b