"""
FMHA Pipeline v1: Modified v30 with pv_mma_tiler=(128,64) for head_dim=64.
Step 1: Test if the V SMEM layout works for head_dim=64 PV.
"""
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 PvHeadDimKernel:
    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

    def _setup(self, qk_mma, pv_mma):
        qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
        self.qk_mma_tiler = (128, 128, qk_ik * 4)
        pv_ik = cute.size(pv_mma.shape_mnk, mode=[2])
        # PV tiler: (M, N, K) = (QK_M, head_dim, QK_N)
        # K of PV = sequence per tile = QK's N = 128
        pv_nphases = 128 // pv_ik  # 128/16 = 8 k-phases
        self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * pv_nphases)  # (128, 64, 128)
        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), HEAD_DIM, 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.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)
        self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
        v_s = cute.slice_(self.v_smem_s, (None,None,None,0))
        v_sz = cute.size_in_bytes(self.q_dtype, v_s)
        print(f"[DIAG] pv_mma_tiler={self.pv_mma_tiler} V SMEM per stage: {v_sz} bytes ({v_sz//2} BF16)")
        pv_thr = pv_mma.get_slice(0)
        tCrV = pv_mma.make_fragment_B(v_s)
        print(f"[DIAG] tCrV shape={tCrV.shape} size={cute.size(tCrV)} k_phases={cute.size(tCrV,mode=[2])}")
        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)
        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)
        pv_thr = pv_mma.get_slice(0); pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
        tOtO = pv_thr.make_fragment_C(pv_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 = find_tmem_tensor_col_offset(tOtO)
        tCS = qk_mma.make_fragment_C(cute.append(qk_as, self.num_acc_stage))
        tCO = pv_mma.make_fragment_C(cute.append(pv_as, self.num_acc_stage))
        self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCS, tCO], arch="sm_100")
        a = cute.slice_(self.a_smem_s,(None,None,None,0)); b = cute.slice_(self.b_smem_s,(None,None,None,0))
        vs = cute.slice_(self.v_smem_s,(None,None,None,0))
        self.num_tma_load_bytes = (cute.size_in_bytes(self.q_dtype,a)+cute.size_in_bytes(self.q_dtype,b)+cute.size_in_bytes(self.q_dtype,vs))*cute.size(qk_mma.thr_id.shape)

    @cute.jit
    def __call__(self, q, k, v, 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.v_major = LayoutEnum.from_tensor(v).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)
        pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, self.v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
        self._setup(qk_mma, pv_mma)
        q_s = cute.slice_(self.a_smem_s,(None,None,None,0)); k_s = cute.slice_(self.b_smem_s,(None,None,None,0))
        v_s = cute.slice_(self.v_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.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.mma_tiler,qk_mma,self.cluster_layout_vmnk.shape)
        tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v,v_s,self.pv_mma_tiler,pv_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,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.a_smem_s,self.b_smem_s,self.v_smem_s,self.p_tmem_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, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_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_v); 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))).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)*(2 if cute.size(qk_mma.thr_id.shape)==2 else 1)),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=a_smem_s.outer,byte_alignment=128,swizzle=a_smem_s.inner)
        sK = smem.allocate_tensor(element_type=self.q_dtype,layout=b_smem_s.outer,byte_alignment=128,swizzle=b_smem_s.inner)
        sV = smem.allocate_tensor(element_type=self.q_dtype,layout=v_smem_s.outer,byte_alignment=128,swizzle=v_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.pv_mma_tiler,(None,None,0)),(None,None,None))
        k_cnt = cute.size(gQ,mode=[3])
        qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
        tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = pv_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)]
        gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
        tCgV = pv_thr.partition_B(gV)
        tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3))
        tVgV = tVgV[(None,0,None,0)]
        tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
        tCrV = pv_mma.make_fragment_B(sV)
        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)
        pv_as = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
        tOtO = pv_thr.make_fragment_C(pv_as)
        tOtO0 = cute.make_tensor(tOtO.iterator+self.tmem_o0_offset,tOtO.layout)
        tP = cute.make_tensor(tStS.iterator,p_tmem_s.outer)
        tOrP_base = pv_thr.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)
        tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_as,self.num_acc_stage))
        tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_as,self.num_acc_stage))
        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_q,tAgQ[(None,h.count)],tAsQ[(None,h.index)],tma_bar_ptr=h.barrier)
                cute.copy(tma_k,tBgK[(None,h.count)],tBsK[(None,h.index)],tma_bar_ptr=h.barrier)
                cute.copy(tma_v,tVgV[(None,h.count)],tVsV[(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(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)
                    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()
            s0_handle = mma_si_prod.acquire_and_advance()
            pv_mma.set(tcgen05.Field.ACCUMULATE, False)
            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)
                pv_mma.set(tcgen05.Field.ACCUMULATE, True)
            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.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))
            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)
            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)
            tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.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, self.tilePlikeFP32)))
            tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
            tTMEM_STOREcS = thr_store.partition_S(tScS_P)
            si_handle = mma_si_cons.wait_and_advance()
            tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
            cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
            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)
            frg_cnt = 4; frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
            tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
            tTMEM_STORErS_x4_e_frg = cute.logical_divide(tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
            for j in range(frg_cnt):
                s_vec = tTMEM_LOADrS_frg[None, j].load()
                tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
            cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
            cute.arch.fence_view_async_tmem_store()
            si_handle.release()
            tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_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, 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)
    # Full softmax attention with real V
    for n in [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')
        # V in MN-major for PV's B-operand: (K=n, N=hd)
        v = torch.ones(n, hd, 1, dtype=torch.bfloat16, device='cuda')
        c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')

        qf = q[:,:,0].float(); kf = k[:,:,0].float(); vf = v[:,:,0].float()
        ref = (qf @ kf.T).bfloat16().float() @ vf  # identity softmax: S.bf16() @ V
        ref = ref.bfloat16().float()

        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))
        mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
        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 = PvHeadDimKernel()
        print(f'n={n}: Compiling...', flush=True)
        compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
        print(f'n={n}: Running...', flush=True)
        compiled(mQ, mK, mV, 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'PV head_dim={HEAD_DIM} n={n} softmax: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
        if cos < 0.99:
            print(f'  out[0,:4] = {out[0,:4].tolist()}')
            print(f'  ref[0,:4] = {ref[0,:4].tolist()}')

if __name__ == '__main__':
    test()
