"""
Stage B: Two MMAs + Identity Softmax with Layout Transform

Following fmha.py's synchronization pattern:
  - MMA↔softmax sync via PipelineUmmaAsync (mma_si pipeline)
  - MMA produces scores (after QK), softmax consumes
  - Softmax produces P, MMA re-acquires (before PV)
  - Identity softmax: tcgen05.ld from C-layout → F32→BF16 → tcgen05.st to A-layout

Reference: output = (Q @ K^T) @ V
"""
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 StageBIdentitySoftmax:
    def __init__(self, mma_tiler_mn):
        self.acc_dtype = Float32
        self.qk_acc_dtype = Float32
        self.q_dtype = BFloat16
        self.o_dtype = BFloat16
        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.num_c_stage = 2

        # TMEM offsets (fmha.py for 128x128)
        self.tmem_s0_offset = 0
        self.tmem_o0_offset = 256
        self.tmem_p0_offset = 32
        self.tmem_alloc_cols = 512
        self.epilog_sync_bar_id = 1

    def _setup(self, qk_mma, pv_mma):
        qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
        self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
        pv_inst_k = cute.size(pv_mma.shape_mnk, mode=[2])
        self.pv_mma_tiler = (*self.mma_tiler_mn, pv_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, 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.a_dtype, 1)
        self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.b_dtype, 1)
        self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
        self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)

        acc_shape = qk_mma.partition_shape_C(self.mma_tiler_mn)
        tCtS_fake = qk_mma.make_fragment_C(cute.append(acc_shape, 1))
        self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols(tCtS_fake, arch="sm_100")

        q_smem = cute.slice_(self.q_smem_s, (None, None, None, 0))
        k_smem = cute.slice_(self.k_smem_s, (None, None, None, 0))
        self.num_tma_bytes = (cute.size_in_bytes(self.a_dtype, q_smem) + cute.size_in_bytes(self.b_dtype, k_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.b_dtype, self.a_major, self.b_major, self.acc_dtype, self.cta_group, self.mma_tiler_mn,
            tcgen05.OperandSource.SMEM)
        pv_mma = utils.sm100.make_trivial_tiled_mma(
            self.a_dtype, self.b_dtype, cute.nvgpu.OperandMajorMode.K, self.b_major, self.acc_dtype, self.cta_group, self.mma_tiler_mn,
            tcgen05.OperandSource.TMEM)
        self._setup(qk_mma, pv_mma)

        q_smem = cute.slice_(self.q_smem_s, (None, None, None, 0))
        k_smem = cute.slice_(self.k_smem_s, (None, None, None, 0))
        tma_q, tma_tq = cute.nvgpu.make_tiled_tma_atom_A(
            utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
            a, q_smem, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
        tma_k, tma_tk = cute.nvgpu.make_tiled_tma_atom_B(
            utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
            b, k_smem, self.qk_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, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_c, tma_tc,
            self.cluster_layout_vmnk, self.q_smem_s, self.k_smem_s, self.p_tmem_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, pv_mma, tma_q, mQ, tma_k, mK, tma_c, mC, cl_vmnk,
                q_smem_s, k_smem_s, p_tmem_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:
            ab_bar: cute.struct.MemRange[cutlass.Int64, 2]       # AB pipeline (1 stage)
            mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]   # MMA↔softmax pipeline (1 stage)
            acc_bar: cute.struct.MemRange[cutlass.Int64, 2]      # ACC pipeline (1 stage)
            tmem_dealloc: cutlass.Int64
            holding: cutlass.Int32

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

        # AB pipeline (TMA load)
        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_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()

        # MMA↔softmax pipeline (following fmha.py's mma_s0 pattern)
        # Producer = MMA warp (after QK: commit scores; before PV: re-acquire P)
        # Consumer = softmax warps (wait for scores, process, release P)
        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, 128),
            cta_layout_vmnk=cl_vmnk, defer_sync=True
        ).make_participants()

        # ACC pipeline (PV output → epilogue)
        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, 128),
            cta_layout_vmnk=cl_vmnk, defer_sync=True)

        # TMEM allocator
        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)

        sQ = smem.allocate_tensor(element_type=BFloat16, layout=q_smem_s.outer, byte_alignment=128, swizzle=q_smem_s.inner)
        sK = smem.allocate_tensor(element_type=BFloat16, layout=k_smem_s.outer, byte_alignment=128, swizzle=k_smem_s.inner)
        sC = smem.allocate_tensor(element_type=BFloat16, 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))
        k_cnt = cute.size(gQ, 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)
        tAsK, tAgK = 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)]; tAgK = tAgK[(None,0,None,0)]

        tCrQ = qk_mma.make_fragment_A(sQ)
        tCrK = qk_mma.make_fragment_B(sK)
        tCrV = pv_mma.make_fragment_B(sK)

        # TMEM tensors
        qk_acc_shape = qk_thr.partition_shape_C(self.mma_tiler_mn)
        tStS = qk_thr.make_fragment_C(qk_acc_shape)
        tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)

        pv_thr = pv_mma.get_slice(0)
        pv_acc_shape = pv_mma.partition_shape_C(self.mma_tiler_mn)
        tOtO = pv_thr.make_fragment_C(pv_acc_shape)
        tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)

        # P fragment for PV MMA (A-layout from TMEM)
        tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
        tOrP_base = pv_mma.make_fragment_A(tP)
        tOrP = tOrP_base[(None, None, None, 0)]
        tOrP0 = cute.make_tensor(
            tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
            tOrP.layout)

        # Fake acc for epilogue
        tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, 1))
        tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, 1))

        pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)

        # ── TMA WARP ──
        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_q, tAgQ[(None,h.count)], tAsQ[(None,h.index)], tma_bar_ptr=h.barrier)
                cute.copy(tma_k, tAgK[(None,h.count)], tAsK[(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 WARP ──
        if warp_idx == self.mma_warp_id:
            tmem.wait_for_alloc()

            ab_c.reset(); peek = ab_c.try_wait()

            # 1. Acquire S0 buffer (before QK)
            s0_handle = mma_si_prod.acquire_and_advance()

            # 2. QK MMA: Q @ K^T → tmem_scores
            qk_mma.set(tcgen05.Field.ACCUMULATE, False)
            for kt in range(k_cnt):
                h = ab_c.wait_and_advance(peek)
                nblk = cute.size(tCrQ, mode=[2])
                for kb in cutlass.range(nblk, unroll_full=True):
                    cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tStS0)
                h.release()
                peek = cutlass.Boolean(1)
                if h.count+1<k_cnt: peek = ab_c.try_wait()

            # 3. Fence TMEM, then release S0 for softmax
            cute.arch.fence_view_async_tmem_store()
            s0_handle.commit()

            # 4. Re-acquire S0 (wait for softmax to finish and release P)
            s0_handle = mma_si_prod.acquire_and_advance()

            # 5. PV MMA: P @ V → tmem_output
            pv_mma.set(tcgen05.Field.ACCUMULATE, True)
            tCrV_s = tCrV[(None, None, None, 0)]
            nblk_pv = cute.size(tOrP0, mode=[2])
            for kb in cutlass.range(nblk_pv, unroll_full=True):
                cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)

            # 6. Release output for epilogue
            acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, 1)
            acc_pipe.producer_acquire(acc_prod_st)
            acc_pipe.producer_commit(acc_prod_st)
            acc_prod_st.advance()
            acc_pipe.producer_tail(acc_prod_st)

        # ── SOFTMAX / EPILOGUE WARPS ──
        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)

            # Identity softmax setup (following fmha.py)
            tmem_load_atom = cute.make_copy_atom(
                tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
            tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
            sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
            thr_load = tiled_tmem_load.get_slice(sfw_idx)
            tTMEM_LOADtS = thr_load.partition_S(tStS0)
            cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
            tScS = qk_thr.partition_C(cS)
            tTMEM_LOADcS = thr_load.partition_D(tScS)

            tilePlikeFP32 = self.qk_mma_tiler[1] // 32 * self.o_dtype.width
            tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, tilePlikeFP32)))
            tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
            tmem_store_atom = cute.make_copy_atom(
                tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
            tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
            thr_store = tiled_tmem_store.get_slice(sfw_idx)
            tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
            tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, tilePlikeFP32)))
            tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
            tTMEM_STOREcS = thr_store.partition_S(tScS_P)

            # Wait for scores
            si_handle = mma_si_cons.wait_and_advance()

            # Load from C-layout
            tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
            cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
            cute.arch.fence_view_async_tmem_load()

            # Identity: F32 → BF16
            tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
            tTMEM_STORErS_x4_e = cute.make_tensor(
                cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
                tTMEM_LOADrS.layout)
            s_vec = tTMEM_LOADrS.load()
            tTMEM_STORErS_x4_e.store(s_vec.to(self.q_dtype))

            # Store to A-layout
            cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
            cute.arch.fence_view_async_tmem_store()

            # Release back to MMA warp
            si_handle.release()

            # Epilogue
            tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
            acc_cons_st = 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)
            acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
                self, tidx, warp_idx, tma_c, tCtO_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)
    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')
    qf = q[:,:,0].float(); kvf = kv[:,:,0].float()
    ref = qf @ kvf.T @ kvf
    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 = StageBIdentitySoftmax((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()
    max_err = (out - ref).abs().max().item()
    print('Stage B (identity softmax, PipelineUmmaAsync sync)')
    print('  Cosine: {:.6f}, Max error: {:.6f}'.format(cos, max_err))
    print('  {}'.format('PASS' if cos>=0.99 else 'FAIL'))

if __name__ == '__main__':
    test()
