diff --git a/tests/unit/test_tma_shapes.py b/tests/unit/test_tma_shapes.py index b66870bb..89d49ae8 100644 --- a/tests/unit/test_tma_shapes.py +++ b/tests/unit/test_tma_shapes.py @@ -1,32 +1,77 @@ """ -Diagnostic: print tBgK and tVgV shapes after tma_partition. -Just need to see how many modes and which is the KV tile dim. +Diagnostic: print tBgK and tVgV shapes BEFORE pre-slicing. +This runs at JIT trace time, so Python print gives us static shape info. """ 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, 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 TmaShapeDiag: 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.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.kv_stage = 2; self.q_stage = 1 - self.threads_per_cta = 192 - self.qk_mma_tiler = (128, 128, 4) - self.pv_mma_tiler = (128, HEAD_DIM, 4) + 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.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) + 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.tmem_s0_offset = 0; self.tmem_p0_offset = 32 + p_cols_fp32 = self.pv_mma_tiler[2] * self.q_dtype.width // self.qk_acc_dtype.width + p_end = self.tmem_p0_offset + p_cols_fp32 + s_cols = self.qk_mma_tiler[1] + o_after = max(s_cols, p_end) + self.tmem_o0_offset = ((o_after + 31) // 32) * 32 + o_cols = find_tmem_tensor_col_offset(tOtO) + total = self.tmem_o0_offset + o_cols + self.num_tmem_alloc_cols = 1 + while self.num_tmem_alloc_cols < total: + self.num_tmem_alloc_cols *= 2 + cta = cute.size(qk_mma.thr_id.shape) + 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)) + self.q_tx_bytes = cute.size_in_bytes(self.q_dtype, q_s) * cta + self.kv_tx_bytes = (cute.size_in_bytes(self.q_dtype, k_s) + + cute.size_in_bytes(self.q_dtype, v_s)) * cta @cute.jit - def __call__(self, q, k, v, stream): - a_major = LayoutEnum.from_tensor(q).mma_major_mode() - b_major = LayoutEnum.from_tensor(k).mma_major_mode() + 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( @@ -34,21 +79,28 @@ class TmaShapeDiag: stride=(1, HEAD_DIM, HEAD_DIM * self.s_k), ), ) - v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode() - qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, a_major, 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, v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM) + 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) + 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) + # Stop here — just check shapes + self._diag_kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,self.cluster_layout_vmnk).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream) - q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage) - k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage) - v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage) - - q_s = cute.slice_(q_smem_s,(None,None,None,0)) - k_s = cute.slice_(k_smem_s,(None,None,None,0)) - v_s = cute.slice_(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,(1,1,1)) - 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,(1,1,1)) - 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,(1,1,1)) + @cute.kernel + def _diag_kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, cl_vmnk): + 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)) + sQ = cute.make_tensor(BFloat16, q_s.outer) + sK = cute.make_tensor(BFloat16, k_s.outer) + sV = cute.make_tensor(BFloat16, v_s.outer) 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)) @@ -58,29 +110,39 @@ class TmaShapeDiag: tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK) tCgV = pv_thr.partition_B(gV) - a_lay = cute.make_layout(1) - tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(q_s,0,3),cute.group_modes(tCgQ,0,3)) - b_lay = cute.make_layout(1) - 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)) + 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)) + tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(sV,0,3),cute.group_modes(tCgV,0,3)) - print(f"tAgQ shape: {cute.shape(tAgQ)} rank: {tAgQ.rank}") - print(f"tBgK shape: {cute.shape(tBgK)} rank: {tBgK.rank}") - print(f"tVgV shape: {cute.shape(tVgV)} rank: {tVgV.rank}") - print(f"tAsQ shape: {cute.shape(tAsQ)} rank: {tAsQ.rank}") - print(f"tBsK shape: {cute.shape(tBsK)} rank: {tBsK.rank}") - print(f"tVsV shape: {cute.shape(tVsV)} rank: {tVsV.rank}") - # Print size of each mode - for i in range(tBgK.rank): - try: - print(f" tBgK mode {i} size: {cute.size(tBgK, mode=[i])}") - except: - print(f" tBgK mode {i}: error getting size") - for i in range(tVgV.rank): - try: - print(f" tVgV mode {i} size: {cute.size(tVgV, mode=[i])}") - except: - print(f" tVgV mode {i}: error getting size") + # Print shapes BEFORE any pre-slice + print(f"=== TMA partition shapes (n_kv_tiles={self.n_kv_tiles}) ===") + print(f"tAgQ: shape={cute.shape(tAgQ)} rank={tAgQ.rank}") + print(f"tBgK: shape={cute.shape(tBgK)} rank={tBgK.rank}") + print(f"tVgV: shape={cute.shape(tVgV)} rank={tVgV.rank}") + print(f"tAsQ: shape={cute.shape(tAsQ)} rank={tAsQ.rank}") + print(f"tBsK: shape={cute.shape(tBsK)} rank={tBsK.rank}") + print(f"tVsV: shape={cute.shape(tVsV)} rank={tVsV.rank}") + + # Print per-mode sizes + for name, t in [("tAgQ", tAgQ), ("tBgK", tBgK), ("tVgV", tVgV)]: + for i in range(t.rank): + sz = cute.size(t, mode=[i]) + print(f" {name} mode {i} size={sz}") + + # Also print after the original pre-slice + tAgQ2 = tAgQ[(None,0,None,0)] + tBgK2 = tBgK[(None,None,0,0)] + tVgV2 = tVgV[(None,0,None,0)] + print(f"\nAfter pre-slice:") + print(f"tAgQ[(None,0,None,0)]: shape={cute.shape(tAgQ2)} rank={tAgQ2.rank}") + print(f"tBgK[(None,None,0,0)]: shape={cute.shape(tBgK2)} rank={tBgK2.rank}") + print(f"tVgV[(None,0,None,0)]: shape={cute.shape(tVgV2)} rank={tVgV2.rank}") + for name, t in [("tAgQ2", tAgQ2), ("tBgK2", tBgK2), ("tVgV2", tVgV2)]: + for i in range(t.rank): + sz = cute.size(t, mode=[i]) + print(f" {name} mode {i} size={sz}") def test(): @@ -90,16 +152,21 @@ def test(): k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda') v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda') v_kernel = v.unsqueeze(-1) + c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda') 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) diag = TmaShapeDiag(s_k=n) - compiled = cute.compile(diag, mQ, mK, mV, stream) - compiled(mQ, mK, mV, stream) + print('Compiling...', flush=True) + compiled = cute.compile(diag, mQ, mK, mV, mC, stream) + print('Running...', flush=True) + compiled(mQ, mK, mV, mC, stream) torch.cuda.synchronize() + print('Done.') if __name__ == '__main__':