"""Absolute minimal: ld FP32 from S0, st FP32 to S1, epi reads S1.
No recast, no BF16, no packing. Pure FP32 copy between TMEM regions."""
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

class BF16PackTest:
    def __init__(self, mma_tiler_mn):
        self.qk_acc_dtype = Float32; self.q_dtype = BFloat16; self.o_dtype = BFloat16
        self.c_dtype = BFloat16; 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.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.use_2cta_instrs = False
        self.epilog_sync_bar_id = 1

    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 = (
            self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
            self.qk_mma_tiler[1], self.qk_mma_tiler[2])
        self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
        self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
        self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
        c_layout = LayoutEnum.ROW_MAJOR; self.c_layout = c_layout
        self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
            self.cta_tile_shape_mnk, False, c_layout, self.o_dtype)
        self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, c_layout, self.epi_tile, 2)
        self.num_ab_stage = 1; self.num_acc_stage = 1
        qk_thr = qk_mma.get_slice(0)
        qk_acc_shape = qk_thr.partition_shape_C(self.mma_tiler[:2])
        tStS = qk_thr.make_fragment_C(qk_acc_shape)
        self.s_cols = find_tmem_tensor_col_offset(tStS)
        self.tmem_alloc_cols = self.s_cols * 2
        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(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem)
        ) * cute.size(qk_mma.thr_id.shape)

    @cute.jit
    def __call__(self, a: cute.Tensor, b: cute.Tensor, c: cute.Tensor, stream: cuda.CUstream):
        qk_mma = utils.sm100.make_trivial_tiled_mma(
            self.q_dtype, self.q_dtype,
            LayoutEnum.from_tensor(a).mma_major_mode(),
            LayoutEnum.from_tensor(b).mma_major_mode(),
            self.qk_acc_dtype, self.cta_group, self.mma_tiler_mn,
            tcgen05.OperandSource.SMEM)
        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=[self.threads_per_cta,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, self.num_ab_stage * 2]
            mma_si_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)
        ab_p, ab_c = pipeline.PipelineTmaUmma.create(
            barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_stage,
            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()
        mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
            barrier_storage=st.mma_si_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)),
            cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()
        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=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=self.q_dtype, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
        sB = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_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)
        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)
        qk_acc_shape = qk_thr.partition_shape_C(self.mma_tiler[:2])
        tStS = qk_thr.make_fragment_C(qk_acc_shape)
        tStS0 = cute.make_tensor(tStS.iterator, tStS.layout)
        tStS1 = cute.make_tensor(tStS.iterator + self.s_cols, tStS.layout)

        # LD and ST on same layout
        tmem_ld = cute.make_copy_atom(tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
        tmem_st = cute.make_copy_atom(tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
        tiled_ld = tcgen05.make_tmem_copy(tmem_ld, tStS0)
        tiled_st = tcgen05.make_tmem_copy(tmem_st, tStS1)
        sfw = tidx % (32 * len(self.epilogue_warp_id))
        thr_ld = tiled_ld.get_slice(sfw)
        thr_st = tiled_st.get_slice(sfw)
        tLdS = thr_ld.partition_S(tStS0)
        tStS = thr_st.partition_D(tStS1)
        cS_id = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
        tScS = qk_thr.partition_C(cS_id)
        tLdcS = thr_ld.partition_D(tScS)
        tStcS = thr_st.partition_S(tScS)

        tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, 1))
        pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)

        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()

        if warp_idx == self.mma_warp_id:
            tmem.wait_for_alloc()
            ab_c.reset(); peek = ab_c.try_wait()
            s0_handle = mma_si_prod.acquire_and_advance()
            acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
            acc_pipe.producer_acquire(acc_prod_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, tStS0, tCrA[(None,None,kb,h.index)], tCrB[(None,None,kb,h.index)], tStS0)
                    qk_mma.set(tcgen05.Field.ACCUMULATE, True)
                h.release(); peek = cutlass.Boolean(1)
                if h.count+1<k_cnt: peek = ab_c.try_wait()
            cute.arch.fence_view_async_tmem_store()
            s0_handle.commit()
            acc_pipe.producer_commit(acc_prod_st)
            acc_prod_st.advance()
            acc_pipe.producer_tail(acc_prod_st)

        if warp_idx < self.mma_warp_id:
            tmem.allocate(self.tmem_alloc_cols)
            tmem.wait_for_alloc()
            tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)

            si_handle = mma_si_cons.wait_and_advance()

            # FP32 ld → FP32 st, NO recast
            rLd = cute.make_rmem_tensor(tLdcS.shape, self.qk_acc_dtype)
            cute.copy(tiled_ld, tLdS, rLd)
            cute.arch.fence_view_async_tmem_load()

            # Direct copy: ld register → st register (same shape since same layout)
            rSt = cute.make_rmem_tensor(tStcS.shape, self.qk_acc_dtype)
            # Since ld and st have the same C-fragment layout and same identity tensor,
            # the register shapes should match. Copy element by element.
            for i in cutlass.range(cute.size(rLd), vectorize=True):
                rSt[i] = rLd[i].to(self.q_dtype).to(self.qk_acc_dtype)

            cute.copy(tiled_st, rSt, tStS)
            cute.arch.fence_view_async_tmem_store()
            si_handle.release()

            # epi reads S1
            tCtS1 = cute.make_tensor(tmem_ptr + self.s_cols, 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, tCtS1, 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)
    m, n, k = 128, 128, 128
    q = torch.randn(m, k, 1, dtype=torch.bfloat16, device='cuda')
    kv = torch.randn(n, k, 1, dtype=torch.bfloat16, device='cuda')
    c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
    ref = q[:,:,0].float() @ kv[:,:,0].float().T
    import cutlass.torch as ct
    mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
    mK = ct.from_dlpack(kv).mark_layout_dynamic(leading_dim=ct.get_leading_dim(kv))
    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 = BF16PackTest(mma_tiler_mn=(128, 128))
    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('BF16 elemwise ld→st to S1: cos={:.6f} {}'.format(cos, 'PASS' if cos >= 0.99 else 'FAIL'))

if __name__ == '__main__':
    test()