From 9a3cf248db4b3618c024c1bedac1ece5d9d57b2d Mon Sep 17 00:00:00 2001 From: biondizzle Date: Fri, 22 May 2026 23:29:15 +0000 Subject: [PATCH] auto: pre-test commit --- tests/diag_tma_shapes2.py | 122 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 tests/diag_tma_shapes2.py diff --git a/tests/diag_tma_shapes2.py b/tests/diag_tma_shapes2.py new file mode 100644 index 00000000..faa33d0f --- /dev/null +++ b/tests/diag_tma_shapes2.py @@ -0,0 +1,122 @@ +"""Diagnostic: print tma_partition output shapes for K, V tensors only.""" +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 +import math + +HEAD_DIM = 64 + +class DiagShapes: + def __init__(self, s_k=256): + self.s_k = s_k + self.n_kv_tiles = s_k // 128 + 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.kv_stage = 2; self.q_stage = 1; self.num_c_stage = 2 + self.scale_softmax = 1.0 / math.sqrt(HEAD_DIM) + self.scale_softmax_log2 = self.scale_softmax * math.log2(math.e) + + 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]) + self.pv_mma_tiler = (128, HEAD_DIM, pv_ik * (128 // pv_ik)) + 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.q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage) + self.k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage) + self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage) + self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2) + self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1) + + @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() + v_fmha = cute.make_tensor( + v.iterator, + cute.make_layout((HEAD_DIM, self.s_k, 1), stride=(1, HEAD_DIM, HEAD_DIM * self.s_k)), + ) + self.v_major = LayoutEnum.from_tensor(v_fmha).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.q_smem_s,(None,None,None,0)) + k_s = cute.slice_(self.k_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.qk_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.qk_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_fmha,v_s,self.pv_mma_tiler,pv_mma,self.cluster_layout_vmnk.shape) + + 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)) + gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None)) + + print(f"gQ shape: {cute.shape(gQ)}") + print(f"gK shape: {cute.shape(gK)}") + print(f"gV shape: {cute.shape(gV)}") + + 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) + tCgV = pv_thr.partition_B(gV) + + print(f"tCgQ shape: {cute.shape(tCgQ)}") + print(f"tCgK shape: {cute.shape(tCgK)}") + print(f"tCgV shape: {cute.shape(tCgV)}") + + a_lay = cute.make_layout(cute.slice_(self.cluster_layout_vmnk,(0,0,None,0)).shape) + b_lay = cute.make_layout(cute.slice_(self.cluster_layout_vmnk,(0,None,0,0)).shape) + + # K and V only (Q tma_partition fails in this context) + tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(k_s,0,3),cute.group_modes(tCgK,0,3)) + tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(v_s,0,3),cute.group_modes(tCgV,0,3)) + + print(f"tBsK shape: {cute.shape(tBsK)} stride: {tBsK.layout.stride}") + print(f"tBgK shape: {cute.shape(tBgK)} stride: {tBgK.layout.stride}") + print(f"tVsV shape: {cute.shape(tVsV)} stride: {tVsV.layout.stride}") + print(f"tVgV shape: {cute.shape(tVgV)} stride: {tVgV.layout.stride}") + + # Try the (None,0,None,0) pre-slice + tBgK_sliced = tBgK[(None,0,None,0)] + tVgV_sliced = tVgV[(None,0,None,0)] + print(f"tBgK after (None,0,None,0) shape: {cute.shape(tBgK_sliced)} stride: {tBgK_sliced.layout.stride}") + print(f"tVgV after (None,0,None,0) shape: {cute.shape(tVgV_sliced)} stride: {tVgV_sliced.layout.stride}") + + +def test(): + torch.manual_seed(42) + n = 256 + 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 = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda') + c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda') + v_kernel = v.unsqueeze(-1) + + 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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel)) + 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 = DiagShapes(s_k=n) + try: + kernel(mQ, mK, mV, mC, stream) + except Exception as e: + print(f"Error (expected — just needed the prints): {e}") + +if __name__ == '__main__': + test()