"""Stage B v4: Two MMAs with a_source=TMEM, minimal pipeline.
No softmax. P = raw scores. Reference: Q @ K^T @ V."""
import torch
import cutlass
import cutlass.cute as cute
import cutlass.utils as utils
import 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 StageBKernel:
    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

    @cute.jit
    def __call__(self, a, b, c, stream):
        a_dtype = a.element_type
        b_dtype = b.element_type
        c_dtype = c.element_type
        a_major = LayoutEnum.from_tensor(a).mma_major_mode()
        b_major = LayoutEnum.from_tensor(b).mma_major_mode()
        c_layout = LayoutEnum.from_tensor(c)
        self.a_dtype = a_dtype
        self.b_dtype = b_dtype
        self.c_dtype = c_dtype
        self.c_layout = c_layout

        qk_mma = utils.sm100.make_trivial_tiled_mma(
            a_dtype, b_dtype, a_major, b_major,
            self.acc_dtype, self.cta_group, self.mma_tiler_mn,
            tcgen05.OperandSource.SMEM)
        pv_mma = utils.sm100.make_trivial_tiled_mma(
            a_dtype, b_dtype, cute.nvgpu.OperandMajorMode.K, b_major,
            self.acc_dtype, self.cta_group, self.mma_tiler_mn,
            tcgen05.OperandSource.TMEM)

        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 = (self.qk_mma_tiler[0], 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.epi_tile = utils.sm100.compute_epilogue_tile_shape(self.cta_tile_shape_mnk, False, c_layout, c_dtype)

        q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, a_dtype, 1)
        k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, b_dtype, 1)
        p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, a_dtype, 1)
        c_smem_s = utils.sm100.make_smem_layout_epi(c_dtype, 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_cols_scores = utils.get_num_tmem_alloc_cols(tCtS_fake, arch="sm_100")
        acc_shape_pv = pv_mma.partition_shape_C(self.mma_tiler_mn)
        tCtO_fake = pv_mma.make_fragment_C(cute.append(acc_shape_pv, 1))
        self.num_tmem_cols_output = utils.get_num_tmem_alloc_cols(tCtO_fake, arch="sm_100")
        self.total_tmem_cols = max(self.num_tmem_cols_scores + self.num_tmem_cols_output, 256)

        q_smem = cute.slice_(q_smem_s, (None, None, None, 0))
        k_smem = cute.slice_(k_smem_s, (None, None, None, 0))
        self.num_tma_bytes = (cute.size_in_bytes(a_dtype, q_smem) + cute.size_in_bytes(b_dtype, k_smem)) * cute.size(qk_mma.thr_id.shape)

        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(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, q_smem_s, k_smem_s, p_tmem_s, 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]
            acc_bar: cute.struct.MemRange[cutlass.Int64, 2]
            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_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, 128),
            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.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)

        acc_shape = qk_mma.partition_shape_C(self.mma_tiler_mn)
        tCtS_fake = qk_mma.make_fragment_C(cute.append(acc_shape, 1))
        acc_shape_pv = pv_mma.partition_shape_C(self.mma_tiler_mn)
        tCtO_fake = pv_mma.make_fragment_C(cute.append(acc_shape_pv, 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()
            tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)

            tCtS_base = cute.make_tensor(tmem_ptr, tCtS_fake.layout)
            tCtS = tCtS_base[(None,None,None,0)]
            out_ptr = cute.recast_ptr(tmem_ptr + self.num_tmem_cols_scores, dtype=self.acc_dtype)
            tCtO_base = cute.make_tensor(out_ptr, tCtO_fake.layout)
            tCtO = tCtO_base[(None,None,None,0)]

            # P fragment for PV MMA (fmha.py pattern)
            tP = cute.make_tensor(tmem_ptr, p_tmem_s.outer)
            tOrP = pv_mma.make_fragment_A(tP)[(None,None,None,0)]

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

            # QK MMA
            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, tCtS, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tCtS)
                h.release()
                peek = cutlass.Boolean(1)
                if h.count+1<k_cnt: peek = ab_c.try_wait()

            # fence removed for debug

            # PV MMA
            pv_mma.set(tcgen05.Field.ACCUMULATE, True)
            tCrV_s = tCrV[(None,None,None,0)]
            nblk_pv = cute.size(tOrP, mode=[2])
            for kb in cutlass.range(nblk_pv, unroll_full=True):
                cute.gemm(pv_mma, tCtO, tOrP[(None,None,kb)], tCrV_s[(None,None,kb)], tCtO)

            acc_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, 1)
            acc_pipe.producer_acquire(acc_st)
            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.total_tmem_cols)
            tmem.wait_for_alloc()
            tmem_ptr = tmem.retrieve_ptr(self.acc_dtype)
            out_ptr = cute.recast_ptr(tmem_ptr + self.num_tmem_cols_scores, dtype=self.acc_dtype)
            tCtO_base = cute.make_tensor(out_ptr, tCtO_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, tCtO_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, 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 @ kv[:,:,0].float()

    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 = StageBKernel((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('Cosine: {:.6f}'.format(cos))
    print('{}'.format('PASS' if cos >= 0.99 else 'FAIL'))

if __name__ == '__main__':
    test()
