"""Stage B debug v5: Minimal two-MMA with debug printf to find deadlock location."""
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
import cuda.bindings.driver as cuda

class StageBDebug5:
    def __init__(self, mma_tiler_mn):
        self.acc_dtype = Float32; self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
        self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE; self.use_2cta_instrs = False
        self.epilogue_warp_id = (0, 1, 2, 3); self.mma_warp_id = 4; self.tma_warp_id = 5
        self.threads_per_cta = 192; self.epilog_sync_bar_id = 1; self.num_c_stage = 2

    def _setup(self, qk_mma):
        qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
        self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
        self.mma_tiler = self.qk_mma_tiler
        self.cta_tile_shape_mnk = tuple(self.qk_mma_tiler)
        self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
        self.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, self.c_layout, BFloat16)
        self.num_ab_stage = 1; self.num_acc_stage = 1
        self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, BFloat16, 1)
        self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, BFloat16, 1)
        self.c_smem_s = utils.sm100.make_smem_layout_epi(BFloat16, self.c_layout, self.epi_tile, 2)
        # Use QK fragment for tmem allocation
        acc_shape = qk_mma.partition_shape_C(self.mma_tiler_mn)
        tCtAcc_fake = qk_mma.make_fragment_C(cute.append(acc_shape, 1))
        self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols(tCtAcc_fake, arch="sm_100")
        a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
        b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
        self.num_tma_load_bytes = (cute.size_in_bytes(BFloat16, a_smem) + cute.size_in_bytes(BFloat16, b_smem)) * cute.size(qk_mma.thr_id.shape)

    @cute.jit
    def __call__(self, a, b, c, stream):
        self.a_dtype = a.element_type; self.b_dtype = b.element_type; self.c_dtype = c.element_type
        self.a_major = LayoutEnum.from_tensor(a).mma_major_mode()
        self.b_major = LayoutEnum.from_tensor(b).mma_major_mode()
        self.c_layout = LayoutEnum.from_tensor(c)
        qk_mma = utils.sm100.make_trivial_tiled_mma(
            self.a_dtype, self.a_major, self.b_major, self.acc_dtype, self.cta_group, self.mma_tiler_mn)
        self._setup(qk_mma)
        a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
        b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
        tma_a, tma_ta = cute.nvgpu.make_tiled_tma_atom_A(
            utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
            a, a_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
        tma_b, tma_tb = cute.nvgpu.make_tiled_tma_atom_B(
            utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
            b, b_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
        epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
        tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
        self._kernel(qk_mma, tma_a, tma_ta, tma_b, tma_tb, tma_c, tma_tc,
            self.cluster_layout_vmnk, self.a_smem_s, self.b_smem_s, self.c_smem_s, self.epi_tile
        ).launch(grid=(1,1,1), block=[192,1,1], stream=stream)

    @cute.kernel
    def _kernel(self, qk_mma, tma_a, mA, tma_b, mB, tma_c, mC, cl_vmnk,
                a_smem_s, b_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_a); cpasync.prefetch_descriptor(tma_b); cpasync.prefetch_descriptor(tma_c)

        @cute.struct
        class SS:
            ab_bar: cute.struct.MemRange[cutlass.Int64, 2]
            acc_bar: cute.struct.MemRange[cutlass.Int64, 2]
            tmem_dealloc: cutlass.Int64
            holding: cutlass.Int32

        smem = utils.SmemAllocator(); st = smem.allocate(SS)
        ab_p, ab_c = pipeline.PipelineTmaUmma.create(
            barrier_storage=st.ab_bar.data_ptr(), num_stages=1,
            producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
            consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
            tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()
        acc_pipe = pipeline.PipelineUmmaAsync.create(
            barrier_storage=st.acc_bar.data_ptr(), num_stages=1,
            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=160)
        tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
                                   allocator_warp_id=0, is_two_cta=False,
                                   two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
        pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)

        sA = smem.allocate_tensor(element_type=BFloat16, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
        sB = smem.allocate_tensor(element_type=BFloat16, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_smem_s.inner)
        sC = smem.allocate_tensor(element_type=BFloat16, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
        gA = cute.local_tile(mA, cute.slice_(self.mma_tiler, (None,0,None)), (None,None,None))
        gB = cute.local_tile(mB, cute.slice_(self.mma_tiler, (0,None,None)), (None,None,None))
        gC = cute.local_tile(mC, cute.slice_(self.mma_tiler, (None,None,0)), (None,None,None))
        k_cnt = cute.size(gA, mode=[3])

        qk_thr = qk_mma.get_slice(0)
        tCgA = qk_thr.partition_A(gA); tCgB = qk_thr.partition_B(gB); tCgC = qk_thr.partition_C(gC)
        a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,0,None,0)).shape)
        tAsA, tAgA = cpasync.tma_partition(tma_a, 0, a_lay, cute.group_modes(sA,0,3), cute.group_modes(tCgA,0,3))
        b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,None,0,0)).shape)
        tBsB, tBgB = cpasync.tma_partition(tma_b, 0, b_lay, cute.group_modes(sB,0,3), cute.group_modes(tCgB,0,3))
        tAgA = tAgA[(None,0,None,0)]; tBgB = tBgB[(None,0,None,0)]

        tCrA = qk_mma.make_fragment_A(sA); tCrB = qk_mma.make_fragment_B(sB)
        # NO pv_mma.make_fragment_B

        acc_shape = qk_mma.partition_shape_C(self.mma_tiler_mn)
        tCtAcc_fake = qk_mma.make_fragment_C(cute.append(acc_shape, 1))

        pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)

        # TMA
        if warp_idx == self.tma_warp_id:
            ab_p.reset(); peek = ab_p.try_acquire()
            for kt in cutlass.range(k_cnt, unroll=1):
                h = ab_p.acquire_and_advance(peek)
                cute.copy(tma_a, tAgA[(None,h.count)], tAsA[(None,h.index)], tma_bar_ptr=h.barrier)
                cute.copy(tma_b, tBgB[(None,h.count)], tBsB[(None,h.index)], tma_bar_ptr=h.barrier)
                peek = cutlass.Boolean(1)
                if h.count+1<k_cnt: peek = ab_p.try_acquire()
            ab_p.tail()

        # MMA — identical to Stage A
        if warp_idx == self.mma_warp_id:
            tmem.wait_for_alloc()
            tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            tCtAcc_base = cute.make_tensor(tmem_ptr, tCtAcc_fake.layout)
            tCtAcc = tCtAcc_base[(None,None,None,0)]

            ab_c.reset(); peek = ab_c.try_wait()
            acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, 1)
            acc_pipe.producer_acquire(acc_st)
            qk_mma.set(tcgen05.Field.ACCUMULATE, False)
            for kt in range(k_cnt):
                h = ab_c.wait_and_advance(peek)
                nblk = cute.size(tCrA, mode=[2])
                for kb in cutlass.range(nblk, unroll_full=True):
                    cute.gemm(qk_mma, tCtAcc, tCrA[(None,None,kb,h.index)], tCrB[(None,None,kb,h.index)], tCtAcc)
                    qk_mma.set(tcgen05.Field.ACCUMULATE, True)
                h.release(); peek = cutlass.Boolean(1)
                if h.count+1<k_cnt: peek = ab_c.try_wait()

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

        # Epilogue — identical to Stage A
        if warp_idx < self.mma_warp_id:
            tmem.allocate(self.num_tmem_alloc_cols)
            tmem.wait_for_alloc()
            tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            tCtAcc_base = cute.make_tensor(tmem_ptr, tCtAcc_fake.layout)
            cons = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, 1)
            c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 128)
            c_pipe = pipeline.PipelineTmaStore.create(num_stages=2, producer_group=c_grp)
            cons = utils.gemm.sm100.epilogue_tma_store(
                self, tidx, warp_idx, tma_c, tCtAcc_base, sC, tCgC,
                epi_tile, 0, const_expr(lambda x: x), (0,0,0), cons, acc_pipe, c_pipe)
            c_pipe.producer_tail()
            tmem.relinquish_alloc_permit()
            tmem.free(tmem_ptr)


def test():
    torch.manual_seed(42)
    m,n,k = 128,128,512
    a = torch.randn(m,k,1,dtype=torch.bfloat16,device='cuda')
    b = torch.randn(n,k,1,dtype=torch.bfloat16,device='cuda')
    c = torch.zeros(m,n,1,dtype=torch.bfloat16,device='cuda')
    import cutlass.torch as ct
    mA = ct.from_dlpack(a).mark_layout_dynamic(leading_dim=ct.get_leading_dim(a))
    mB = ct.from_dlpack(b).mark_layout_dynamic(leading_dim=ct.get_leading_dim(b))
    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 = StageBDebug5((128,128))
    print('Compiling...', flush=True)
    compiled = cute.compile(kernel, mA, mB, mC, stream)
    print('Running...', flush=True)
    compiled(mA, mB, mC, stream)
    torch.cuda.synchronize()
    print('No deadlock!')

if __name__ == '__main__':
    test()
