nvfp4-megamoe-kernel/tests/unit/test_qkonly.py

"""
Debug: QK only (no PV) with KV-tile interleaving pipeline.
Outputs P to GMEM to verify QK+softmax pipeline works.
n=128, single KV tile, identity softmax.
"""
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
from cutlass.cute.nvgpu import cpasync, tcgen05
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
from cutlass.utils import LayoutEnum
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
import cuda.bindings.driver as cuda
import cutlass.torch as ct

HEAD_DIM = 64


class QkOnlyTest:
    def __init__(self):
        self.acc_dtype = Float32; self.qk_acc_dtype = Float32
        self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
        self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
        self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
        self.epilogue_warp_id = (0,1,2,3); self.mma_warp_id = 4; self.tma_warp_id = 5
        self.threads_per_cta = 192; self.num_c_stage = 2
        self.kv_stage = 2; self.q_stage = 1

    def _setup(self, qk_mma):
        qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
        self.qk_mma_tiler = (128, 128, qk_ik * 4)
        self.mma_tiler = self.qk_mma_tiler
        self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
        self.cta_tile_shape_mnk = (self.qk_mma_tiler[0]//cute.size(qk_mma.thr_id.shape), 128, self.qk_mma_tiler[2])
        self.c_layout = LayoutEnum.ROW_MAJOR
        self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, self.o_dtype)
        self.num_ab_stage = 1; self.num_acc_stage = 1
        self.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
        self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
        self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)

        qk_thr = qk_mma.get_slice(0); qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
        tStS = qk_thr.make_fragment_C(qk_as)

        self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
        self.tmem_s0_offset = 0; self.tmem_p0_offset = 32
        self.tmem_o0_offset = 0  # Output is at S offset for QK-only

        tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
        self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS], arch="sm_100")

        cta = cute.size(qk_mma.thr_id.shape)
        q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
        k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
        self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta
        self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta

    @cute.jit
    def __call__(self, q, k, c, stream):
        self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
        self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
        self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
        self.c_layout = LayoutEnum.from_tensor(c)

        qk_mma = utils.sm100.make_trivial_tiled_mma(
            self.q_dtype, self.q_dtype, self.a_major, self.b_major,
            self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
        self._setup(qk_mma)

        q_s = cute.slice_(self.q_smem_s,(None,None,None,0))
        k_s = cute.slice_(self.k_smem_s,(None,None,None,0))

        tma_q,mQ = cute.nvgpu.make_tiled_tma_atom_A(
            utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn,qk_mma.thr_id),
            q,q_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
        tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(
            utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,qk_mma.thr_id),
            k,k_s,self.qk_mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
        epi_s = cute.select(self.c_smem_s,mode=[0,1])
        tma_c,mC = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(),c,epi_s,self.epi_tile)

        self._kernel(qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
            self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_s, self.c_smem_s, self.epi_tile
        ).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)

    @cute.kernel
    def _kernel(self, qk_mma, tma_q, mQ, tma_k, mK, tma_c, mC,
                cl_vmnk, q_smem_s, k_smem_s, c_smem_s, epi_tile):
        warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
        tidx,_,_ = cute.arch.thread_idx()

        if warp_idx == self.tma_warp_id:
            cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
            cpasync.prefetch_descriptor(tma_c)

        @cute.struct
        class SS:
            q_bar: cute.struct.MemRange[cutlass.Int64, self.q_stage*2]
            kv_bar: cute.struct.MemRange[cutlass.Int64, self.kv_stage*2]
            s_bar: cute.struct.MemRange[cutlass.Int64, 2]
            acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage*2]
            tmem_dealloc: cutlass.Int64; holding: cutlass.Int32

        smem = utils.SmemAllocator(); st = smem.allocate(SS)

        qp,qc = pipeline.PipelineTmaUmma.create(
            barrier_storage=st.q_bar.data_ptr(), num_stages=self.q_stage,
            producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
            consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
            tx_count=self.q_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()

        kvp,kvc = pipeline.PipelineTmaUmma.create(
            barrier_storage=st.kv_bar.data_ptr(), num_stages=self.kv_stage,
            producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
            consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread,1),
            tx_count=self.kv_tx_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()

        s_prod,s_cons = pipeline.PipelineUmmaAsync.create(
            barrier_storage=st.s_bar.data_ptr(), num_stages=1,
            producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
            consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32*len(self.epilogue_warp_id))
        ).make_participants()

        softmax_done_bar = pipeline.NamedBarrier(barrier_id=3, num_threads=32 + 32*len(self.epilogue_warp_id))

        acc_pipe = pipeline.PipelineUmmaAsync.create(
            barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
            producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
            consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, len(self.epilogue_warp_id)),
            cta_layout_vmnk=cl_vmnk, defer_sync=True)

        tmem_bar = pipeline.NamedBarrier(barrier_id=2,
            num_threads=32*len((self.mma_warp_id,*self.epilogue_warp_id)))
        tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
            allocator_warp_id=self.epilogue_warp_id[0],
            is_two_cta=cute.size(qk_mma.thr_id.shape)==2,
            two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)

        pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)

        sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
        sK = smem.allocate_tensor(element_type=self.q_dtype, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
        sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)

        gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler,(None,0,None)), (None,None,None))
        gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler,(0,None,None)), (None,None,None))
        gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler,(None,None,0)), (None,None,None))
        n_kv_tiles = cute.size(gK, mode=[3])

        qk_thr = qk_mma.get_slice(0)
        tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
        tCgC = qk_thr.partition_C(gC)

        a_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,0,None,0)).shape)
        tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(sQ,0,3),cute.group_modes(tCgQ,0,3))
        b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
        tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
        tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]

        tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)

        qk_as = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
        tStS = qk_thr.make_fragment_C(qk_as)
        tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)

        tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))

        pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)

        # TMA LOAD
        if warp_idx == self.tma_warp_id:
            qp.reset(); qh = qp.acquire_and_advance()
            cute.copy(tma_q, tAgQ[(None,qh.count)], tAsQ[(None,qh.index)], tma_bar_ptr=qh.barrier)
            qp.tail()

            kvp.reset(); pk = kvp.try_acquire()
            for kt in cutlass.range(n_kv_tiles, unroll=1):
                kh = kvp.acquire_and_advance(pk)
                cute.copy(tma_k, tBgK[(None,kh.count)], tBsK[(None,kh.index)], tma_bar_ptr=kh.barrier)
                pk = cutlass.Boolean(1)
            kvp.tail()

        # MMA
        if warp_idx == self.mma_warp_id:
            tmem.wait_for_alloc()
            qc.reset(); qh = qc.wait_and_advance(); qh.release()
            kvc.reset(); pk = kvc.try_wait()

            acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
            acc_pipe.producer_acquire(acc_st)

            for kt in range(n_kv_tiles):
                kh = kvc.wait_and_advance(pk); pk = cutlass.Boolean(1)

                # QK only, accumulate across KV tiles
                sh = s_prod.acquire_and_advance()
                qk_mma.set(tcgen05.Field.ACCUMULATE, kt != 0)
                for kb in cutlass.range(cute.size(tCrQ,mode=[2]), unroll_full=True):
                    cute.gemm(qk_mma, tStS0,
                        tCrQ[(None,None,kb,0)], tCrK[(None,None,kb,kh.index)], tStS0)
                    qk_mma.set(tcgen05.Field.ACCUMULATE, True)
                cute.arch.fence_view_async_tmem_store()
                sh.commit()
                kh.release()

                # Wait for softmax (identity: just signal done)
                softmax_done_bar.arrive_and_wait()

            acc_pipe.producer_commit(acc_st); acc_st.advance()
            acc_pipe.producer_tail(acc_st)

        # EPILOGUE
        if warp_idx < self.mma_warp_id:
            tmem.allocate(self.num_tmem_alloc_cols)
            tmem.wait_for_alloc()
            tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
            sfw_idx = tidx % (32 * len(self.epilogue_warp_id))

            for kt in range(n_kv_tiles):
                si_handle = s_cons.wait_and_advance()
                # Identity softmax: no-op, just signal MMA
                si_handle.release()
                softmax_done_bar.arrive()

            # Output S (QK result) to GMEM
            tCtS_base = cute.make_tensor(tmem_ptr + self.tmem_s0_offset, tCtS_fake.layout)
            acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
            c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
            c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
            acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
                self, tidx, warp_idx, tma_c, tCtS_base, sC, tCgC,
                epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
            c_pipe.producer_tail()
            tmem.relinquish_alloc_permit()
            tmem.free(tmem_ptr)


def test():
    torch.manual_seed(42)
    n = 128
    m, hd = 128, HEAD_DIM
    q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
    k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
    c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')  # (128, 128) output = S

    qf = q[:,:,0].float(); kf = k[:,:,0].float()
    ref = (qf @ kf.T)

    mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
    mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
    mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)

    kernel = QkOnlyTest()
    print('Compiling...', flush=True)
    compiled = cute.compile(kernel, mQ, mK, mC, stream)
    print('Running...', flush=True)
    compiled(mQ, mK, mC, stream)
    torch.cuda.synchronize()
    out = c[:,:,0].float()
    cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
    print(f'QK-only n={n}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
    if cos < 0.99:
        print(f'  out[0,:4]={out[0,:4].tolist()} ref[0,:4]={ref[0,:4].tolist()}')
        print(f'  out stats: min={out.min().item():.4f} max={out.max().item():.4f}')
        print(f'  ref stats: min={ref.min().item():.4f} max={ref.max().item():.4f}')

if __name__ == '__main__':
    test()