From 24bc318480970f5303f4f79eb0ab4e6702d752c4 Mon Sep 17 00:00:00 2001 From: biondizzle Date: Sat, 23 May 2026 23:58:57 +0000 Subject: [PATCH] shit left dangling --- tests/unit/test_smem_p_diag.py | 516 +++++++++++++++++++++++++++++++++ 1 file changed, 516 insertions(+) create mode 100644 tests/unit/test_smem_p_diag.py diff --git a/tests/unit/test_smem_p_diag.py b/tests/unit/test_smem_p_diag.py new file mode 100644 index 00000000..7fd12126 --- /dev/null +++ b/tests/unit/test_smem_p_diag.py @@ -0,0 +1,516 @@ +""" +Diagnostic test for SMEM-P: verify the TV layout mapping from TMEM-load to sP. + +This test does NOT compute attention. It: +1. Creates the TMEM-load copy and extracts its TV layout +2. Creates sP with PV A-operand layout +3. Builds atom_layout_tv mapping (tid,vid) → sP address +4. Validates the mapping is correct (all 16384 elements covered, no overlaps) +5. Tests the make_cotiled_copy approach +""" +import torch, math +import cutlass, cutlass.cute as cute +import cutlass.utils as utils +import cutlass.utils.blackwell_helpers as bh +from cutlass.cute.nvgpu import tcgen05, cpasync +from cutlass import Float32, BFloat16, Int32 +import cutlass.torch as ct +import cuda.bindings.driver as cuda + + +def diag_smem_p_tv(): + """Print TV layout info from TMEM-load copy and sP to build the R→S copy.""" + head_dim = 256 # First SMEM-P head dim (not 64 which uses TMEM-P) + s_k = 128 + m = 128 + pv_n_tile = min(head_dim, 256) + scale_softmax = 1.0 / math.sqrt(head_dim) + scale_softmax_log2 = scale_softmax * math.log2(math.e) + + # Create tensors (shapes don't matter much, we just need the layouts) + q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda') + k = torch.randn(s_k, head_dim, 1, dtype=torch.bfloat16, device='cuda') + v = torch.randn(s_k, head_dim, dtype=torch.bfloat16, device='cuda') + c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda') + + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + + # Create CuTe tensors + 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)) + + # V layout: (pv_n_tile, s_k, 1) for FMHA + v_fmha = v[:, 0:pv_n_tile].contiguous() + v_kernel = v_fmha.unsqueeze(-1) + mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel)) + mC = ct.from_dlpack(c[:, 0:pv_n_tile, :]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c[:, 0:pv_n_tile, :])) + + # MMA setup + a_major = cute.LayoutEnum.from_tensor(mQ).mma_major_mode() + b_major = cute.LayoutEnum.from_tensor(mK).mma_major_mode() + v_major = cute.LayoutEnum.from_tensor(mV).mma_major_mode() + + qk_mma = utils.sm100.make_trivial_tiled_mma( + BFloat16, BFloat16, a_major, b_major, Float32, + tcgen05.CtaGroup.ONE, (128, 128), tcgen05.OperandSource.SMEM + ) + pv_a_major = a_major # SMEM-P: A from SMEM + pv_source = tcgen05.OperandSource.SMEM + pv_mma = utils.sm100.make_trivial_tiled_mma( + BFloat16, BFloat16, pv_a_major, v_major, Float32, + tcgen05.CtaGroup.ONE, (128, pv_n_tile), pv_source + ) + + # Compute layouts (mirrors FmhaKernel._setup) + qk_ik = cute.size(qk_mma.shape_mnk, mode=[2]) + qk_mma_tiler = (128, 128, qk_ik * 4) + pv_ik = cute.size(pv_mma.shape_mnk, mode=[2]) + pv_mma_tiler = (128, pv_n_tile, pv_ik * (128 // pv_ik)) + + # P SMEM layout (PV A-operand, 1 stage) + p_smem_s = utils.sm100.make_smem_layout_a(pv_mma, pv_mma_tiler, BFloat16, 1) + + # P TMEM layout (for reference) + p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, pv_mma_tiler, BFloat16, 1) + + # QK C-fragment + qk_thr = qk_mma.get_slice(0) + qk_as = qk_thr.partition_shape_C(qk_mma_tiler[:2]) + tStS = qk_thr.make_fragment_C(qk_as) + tStS0 = cute.make_tensor(tStS.iterator, tStS.layout) # offset 0 for diagnostics + + # PV C-fragment + pv_thr = pv_mma.get_slice(0) + pv_as = pv_thr.partition_shape_C(pv_mma_tiler[:2]) + tOtO = pv_thr.make_fragment_C(pv_as) + + # ====== TMEM-load copy (softmax reads S from TMEM) ====== + tmem_load_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), Float32 + ) + tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0) + + print("=== TMEM-Load Copy Info ===") + print(f" layout_tv_tiled: {tiled_tmem_load.layout_tv_tiled}") + print(f" layout_dst_tv_tiled: {tiled_tmem_load.layout_dst_tv_tiled}") + print(f" layout_src_tv_tiled: {tiled_tmem_load.layout_src_tv_tiled}") + print(f" TV shape: {tiled_tmem_load.layout_tv_tiled.shape}") + print(f" TV stride: {tiled_tmem_load.layout_tv_tiled.stride}") + + # Softmax thread partition + sfw_idx = 0 # thread 0 in softmax warps + thr_load = tiled_tmem_load.get_slice(sfw_idx) + tTMEM_LOADtS = thr_load.partition_S(tStS0) + + # Coordinate identity tensor + cS = cute.make_identity_tensor((128, 128)) + tScS = qk_thr.partition_C(cS) + tTMEM_LOADcS = thr_load.partition_D(tScS) + + print(f"\n=== Per-Thread Partition (thread 0) ===") + print(f" tTMEM_LOADtS shape: {cute.shape(tTMEM_LOADtS)}") + print(f" tTMEM_LOADtS layout: {tTMEM_LOADtS.layout}") + print(f" tTMEM_LOADcS shape: {cute.shape(tTMEM_LOADcS)}") + print(f" tTMEM_LOADcS layout: {tTMEM_LOADcS.layout}") + + # ====== sP layout (PV A-operand SMEM) ====== + # Remove stage dim + sP_stage = p_smem_s.outer + if len(cute.shape(sP_stage)) > 3: + # Try removing the stage dimension + sP_stage = sP_stage # might need slicing + + print(f"\n=== sP Layout (PV A-operand SMEM) ===") + print(f" p_smem_s.outer shape: {cute.shape(p_smem_s.outer)}") + print(f" p_smem_s.outer layout: {p_smem_s.outer}") + print(f" p_smem_s.inner (swizzle): {p_smem_s.inner}") + + # Flatten sP to 1D for address computation + sP_flat = cute.coalesce(p_smem_s.outer) + print(f" sP_flat shape: {cute.shape(sP_flat)}") + print(f" sP_flat layout: {sP_flat}") + + # ====== Build atom_layout_tv ====== + # The TV layout of tiled_tmem_load maps (tid, vid) → tStS0 coordinate + # We need to compose with (m, k) → sP address mapping + # + # Approach: + # 1. Get the TV layout: (tid, vid) → flat S/P address in tStS0 + # 2. Convert tStS0 flat address to (m, k) coordinate + # 3. Convert (m, k) to sP coordinate + # 4. Convert sP coordinate to sP flat address + # + # The TV layout from tiled_tmem_load is in terms of tStS0's data layout. + # tStS0 has layout ((128,128),1,1):((65536,1),0,0) + # So the flat address IS (m * 65536 + k), which is just m*65536 + k + # But tStS0's actual coordinate mapping is (m, k) where m is row, k is column + + # Let's look at what layout_dst_tv_tiled gives us + dst_tv = tiled_tmem_load.layout_dst_tv_tiled + print(f"\n=== Destination TV Layout ===") + print(f" shape: {dst_tv.shape}") + print(f" stride: {dst_tv.stride}") + print(f" size: {cute.size(dst_tv)}") + + # The destination TV layout maps (tid, vid) to the destination coordinate space. + # For the TMEM-load, destination is tStS0. + # tStS0 layout: ((128,128),1,1):((65536,1),0,0) + # So (tid, vid) → (addr_m * 65536 + addr_k * 1, 0, 0) + # The first component encodes (m, k) in the 128x128 S matrix + + # We need to understand how (tid, vid) maps to (m, k) + # For a 128-thread partition (4 softmax warps × 32 threads/warp): + # Total values: 128 * 128 = 16384 + + num_softmax_threads = 128 # 4 warps + # Each thread has tTMEM_LOADcS size = 32 * 4 = 128 values (from shape ((32,1),4,1,1)) + + # The TV layout should have shape (num_threads, values_per_thread) + # From the TMEM-load TV layout: + tv_shape = dst_tv.shape + tv_stride = dst_tv.stride + print(f"\n TV shape breakdown: tid_dim={tv_shape[0] if tv_shape else '?'}, vid_dim={tv_shape[1] if len(tv_shape) > 1 else '?'}") + + # Now let's try to build the sP address mapping + # sP outer layout (from diagnostics): ((128,16),1,(4,2),1):(((64,1),0,(16,8192)),0) + # With swizzle S<3,4,3> + + # For make_cotiled_copy, we need: + # atom_layout_tv: (tid, vid) → sP flat address + # data_layout: sP flat layout (coalesced) + + # Step 1: Extract (m, k) from dst_tv + # dst_tv maps (tid, vid) → tStS0 address + # tStS0 address = m * 65536 + k (from layout ((128,128),1,1):((65536,1),0,0)) + # So m = addr // 65536, k = addr % 65536 + # But since tStS0 has stride (65536, 1) for the first group ((128, 128)): + # addr = m * 65536 + k + # This gives us the (m, k) mapping implicitly. + + # Step 2: Map (m, k) to sP address + # sP coordinate: ((m, k%16), 0, ((k//16)%4, k//64), 0) + # sP layout applies swizzle and stride + # sP flat address = sP.layout(((m, k%16), 0, ((k//16)%4, k//64), 0)) + + # For make_cotiled_copy, we need: + # atom_layout_tv: (tid, vid) → sP flat address + # We can compute this by composing the TV layout with a mapping from + # tStS0 address to sP address. + + # Let me think about this differently. + # We know dst_tv maps (tid, vid) → tStS0 address (a flat integer) + # tStS0 address encodes (m, k) as m*65536 + k + # We need to convert this to an sP address + + # Actually, let's use the identity tensor approach. + # cS = make_identity_tensor((128, 128)) maps (m, k) → (m, k) + # tScS = qk_thr.partition_C(cS) maps thread's C-fragment coordinates to (m, k) + # tTMEM_LOADcS = thr_load.partition_D(tScS) maps TMEM-load partition coordinates to (m, k) + + # The key: tTMEM_LOADcS already gives us (m, k) per (thread_coord, vid_coord) + # We just need to evaluate ALL (thread, vid) pairs to get the full TV → (m, k) mapping + # Then compose with (m, k) → sP address + + # Let's print some coordinate values to verify + print(f"\n=== Coordinate Verification ===") + # For thread 0: tTMEM_LOADcS shape is ((32,1),4,1,1) + # Values should be (m, k) pairs covering some subset of [0,128) x [0,128) + # Let's iterate and print a few + + # We need to do this at trace time inside @cute.kernel + # For now, print the layout properties + print(f" tScS shape: {cute.shape(tScS)}") + print(f" tScS layout: {tScS.layout}") + print(f" tTMEM_LOADcS shape: {cute.shape(tTMEM_LOADcS)}") + print(f" tTMEM_LOADcS layout: {tTMEM_LOADcS.layout}") + + # Try to understand the TV mapping from the layout properties + # The tTMEM_LOADcS layout: ((32,1),4,1,1):((1@1,0),32@1,0,0) + # This means: for index ((j0,0), j1, 0, 0): + # j0 ranges 0..31, j1 ranges 0..3 + # coordinate = (j0 * 1, j1 * 32) = (j0, j1 * 32) + # But wait, the @1 in stride means there's a tmem-specific indexing + # Actually, the stride is ((1@1, 0), 32@1, 0, 0) + # @1 might mean "stride 1 in the second dimension of a 2D coordinate" + + # Let me look at it from the TMEM load's perspective: + # Ld32x32bOp with Repetition(32) loads a 32x32 tile of FP32 from TMEM + # Each warp has 32 threads. 4 warps = 128 threads. + # Each thread loads 32 values per invocation, repeated 32 times? + # No - Repetition(32) means 32 repetitions of the base operation + # Ld32x32bOp base: 32 threads each load 32 values from a 32x32 TMEM tile + # Repetition(32): each thread loads 32 * 32 = 1024 values total? + # That's too many. With 128 threads, that's 128 * 1024 = 131072 >> 16384 + + # Hmm, let me reconsider. The partition_S/D shapes tell the story: + # tTMEM_LOADtS shape: (((32,32),1),4,1,1) - source (TMEM data) + # tTMEM_LOADcS shape: ((32,1),4,1,1) - destination (coordinate) + # + # The (32,1) in cS vs (32,32) in tS: + # The coordinate is 2D (m,k) so (32,1) means 32 coordinates + # The data is FP32 so (32,32) means 32*32 = 1024 values? No that's too many + # + # Actually, looking at the shapes: + # tTMEM_LOADtS: (((32,32),1),4,1,1) - this is the data register layout + # Inner (32,32) = 1024 values per fragment, 4 fragments = 4096 per thread + # 128 threads * 4096 = 524288 ≠ 16384 + # + # This doesn't add up. The shape must represent the register layout differently. + # Let me just count: 32*1*4*1*1 = 128 elements per thread (coordinate) + # And 32*32*1*4*1*1 = 4096 elements per thread (data) + # 128 * 128 = 16384 coordinates ✓ (each coord is 2D = 32768 scalars) + # But 128 * 4096 = 524288 data elements ≠ 16384 + # + # Something's off. The tTMEM_LOADtS data shape includes the FP32 register layout, + # not element count. The actual element count per thread matches the coordinate count + # because each (m,k) coordinate corresponds to one P value. + + print(f"\n=== Element Count Verification ===") + cs_size = cute.size(tTMEM_LOADcS) # should be 128 (coordinates per thread) + ts_size = cute.size(tTMEM_LOADtS) # data elements per thread + print(f" tTMEM_LOADcS size: {cs_size} (coordinates per thread)") + print(f" tTMEM_LOADtS size: {ts_size} (data elements per thread)") + print(f" 128 threads * {cs_size} coords = {128 * cs_size} (should be 16384)") + + # ====== Try make_cotiled_copy approach ====== + # We need atom_layout_tv: (tid, vid) → sP flat address + # The TMEM-load TV layout gives us (tid, vid) → tStS0 address + # tStS0 address encodes (m, k) + # We need to compose with a (m, k) → sP address mapping + + # Step 1: Get the TV layout that maps to the S/P matrix coordinates + # dst_tv = tiled_tmem_load.layout_dst_tv_tiled + # This maps (tid, vid) → coordinate in tStS0 + # But tStS0 has a specific layout ((128,128),1,1):((65536,1),0,0) + # So the TV output is in terms of tStS0's flat address space + + # Step 2: Build a layout that maps tStS0 flat address → sP flat address + # tStS0 address = m * 65536 + k (from stride) + # We need to map this to the sP address + # sP address = sP.layout(m, k%16, (k//16)%4, k//64) (after swizzle) + + # This requires iterating over all (m, k) and computing sP addresses + # which we can do at Python time since both layouts are static + + # Let's compute the mapping table + sP_outer = p_smem_s.outer + sP_swizzle = p_smem_s.inner + + # Coalesce sP to get a flat 1D layout for address computation + sP_coalesced = cute.coalesce(sP_outer) + print(f"\n=== sP Coalesced Layout ===") + print(f" shape: {cute.shape(sP_coalesced)}") + print(f" layout: {sP_coalesced}") + + # Compute (m, k) → sP address mapping for all 16384 elements + # For verification: build the address table + sP_shape = cute.shape(sP_outer) + print(f"\n=== Full sP shape: {sP_shape} ===") + + # Let's try a different approach: use composition directly + # We know that: + # 1. The TMEM-load TV layout maps (tid, vid) → tStS0 coordinate + # 2. tStS0's layout has shape (128, 128) in the first group + # 3. We need (128, 128) → sP address + + # Build a layout that maps the 128x128 P matrix to sP addresses + # For each (m, k) in [0,128) x [0,128): + # sP index = ((m, k%16), 0, ((k//16)%4, k//64), 0) + # sP address = sP_outer(sP index) (then swizzle applied by CuTe) + + # We can build this as a CuTe layout: + # shape = (128, 128), mapping to sP addresses + # For k in [0, 128): + # k0 = k % 16 + # k1 = (k // 16) % 4 + # k2 = k // 64 + # sP_addr = sP_outer((m, k0), 0, (k1, k2), 0) + + # But CuTe layouts are affine (linear). The (k%16, (k//16)%4, k//64) decomposition + # is NOT affine unless 128 = 16 * 4 * 2 exactly (which it is!) + # k = k0 + 16*k1 + 64*k2 where k0∈[0,16), k1∈[0,4), k2∈[0,2) + # So k0 = k % 16, k1 = (k//16) % 4, k2 = k//64 + # And the sP shape ((128, 16), 1, (4, 2), 1) has: + # mode 0: (128, 16) → stride (64, 1) + # mode 2: (4, 2) → stride (16, 8192) + # So sP_addr(m, k0, k1, k2) = m*64 + k0*1 + k1*16 + k2*8192 + + # Wait, but sP has swizzle S<3,4,3> which XORs some bits + # The swizzle is applied by CuTe when we use the tensor directly + # For make_cotiled_copy, we need the unswizzled (logical) layout + # because CuTe handles the swizzle at access time + + # Actually, let me re-read make_cotiled_copy's requirement: + # atom_layout_tv: (tid, vid) -> data addr + # data_layout: data coord -> data addr + # + # The "data addr" here is the LOGICAL address (before swizzle) + # because CuTe applies swizzle at tensor access time + # So we need the sP outer layout WITHOUT swizzle for data_layout + # But p_smem_s.outer IS the logical layout (swizzle is in p_smem_s.inner) + + # So for make_cotiled_copy: + # data_layout = sP_outer (logical, no swizzle) + # atom_layout_tv: (tid, vid) -> sP logical address + + # The sP logical layout with shape ((128,16),1,(4,2),1) and strides + # can be coalesced to a 1D layout mapping (m, k) → address + + # Let me compute the sP logical layout as a (128, 128) → address mapping + # sP_outer((m, k0), 0, (k1, k2), 0) = m*64 + k0*1 + k1*16 + k2*8192 + + # If we view this as a function of (m, k) where k = k0 + 16*k1 + 64*k2: + # sP_addr(m, k) = m*64 + (k%16)*1 + ((k//16)%4)*16 + (k//64)*8192 + # = m*64 + (k%16) + 16*((k//16)%4) + 8192*(k//64) + + # This is NOT a simple affine function of k alone (due to the modular decomposition) + # But it IS affine if we treat k as decomposed into (k0, k1, k2) + # k = k0 + 16*k1 + 64*k2 + # sP_addr = 64*m + 1*k0 + 16*k1 + 8192*k2 + + # So the sP logical layout, viewed as a (128, 128) → address map, + # has stride (64, ?) where the k-stride varies based on k's position + # This is the standard column-major with subtiling + + # For make_cotiled_copy, we need to express this as a CuTe layout + # The sP outer layout in its native form IS the correct data_layout + + print("\n=== Plan for make_cotiled_copy ===") + print("1. data_layout = coalesce(sP_outer) — flat 1D sP address space") + print("2. atom_layout_tv = compose(tiled_tmem_load.layout_dst_tv_tiled, tStS_to_sP_mapping)") + print("3. tStS_to_sP_mapping: tStS address → sP address") + print(" tStS layout: (128,128) → address, stride=(65536,1)") + print(" sP layout: (128,16,4,2) → address (after coalescing), strides vary") + print("") + print("The problem: tStS has stride 65536 for m, while sP has stride 64 for m") + print("These are fundamentally different address spaces.") + print("We need to: (tid,vid) → (m,k) via identity, then (m,k) → sP_addr via sP layout") + + # Actually, the cleaner approach: + # 1. Build identity tensor for (128, 128) P matrix + # 2. The TMEM-load partition + coordinate partition gives us (tid, vid) → (m, k) + # 3. The sP layout gives us (m, k) → sP address + # 4. Compose these to get (tid, vid) → sP address + + # The identity tensor cS = make_identity_tensor((128, 128)) + # maps (m, k) → (m, k) with layout (128, 128):((1, 128)) — row-major + # Wait, identity tensor layout is just (128, 128) with stride (1, 128)? Let me check. + + # Actually, make_identity_tensor creates a tensor where each element holds its coordinate + # The layout is just an enumerate layout: (128, 128):(1, 128) in row-major + # So cS(m, k) = m + 128*k (flat index in row-major order) + + # The tScS = qk_thr.partition_C(cS) partitions the identity by QK C-fragment threads + # The tTMEM_LOADcS = thr_load.partition_D(tScS) further partitions by TMEM-load threads + + # So tTMEM_LOADcS maps (thread_local_index) → (m, k) coordinate + # The shape is ((32,1),4,1,1) with layout ((1@1,0),32@1,0,0) + # For index ((j0,0), j1, 0, 0): + # value = (j0, j1*32) — thread 0's coordinates + + # But we need the FULL (tid, vid) → (m, k) mapping for ALL 128 threads + # The tTMEM_LOADcS is for a SINGLE thread (thread 0) + + # The TV layout (layout_dst_tv_tiled) gives us the FULL mapping: + # (tid, vid) → destination coordinate (in tStS0's address space) + # But the destination is tStS0 which has layout ((128,128),1,1):((65536,1),0,0) + # So the TV output is in tStS0's flat address: m*65536 + k + + # To convert to sP address, we need to: + # 1. Extract (m, k) from tStS0 flat address: m = addr // 65536, k = addr % 65536 + # But this isn't simple because CuTe layouts aren't easily decomposable + # 2. Map (m, k) to sP address: use sP_outer((m, k%16), 0, ((k//16)%4, k//64), 0) + + # Let's try using composition directly. + # We have: + # dst_tv: (tid, vid) → tStS0_addr + # tStS0 layout: ((128,128),1,1):((65536,1),0,0) + # We need: (tid, vid) → sP_addr + + # Approach: build a "reindex" layout that maps tStS0_addr → sP_addr + # For each possible tStS0_addr, compute the corresponding sP_addr + + # But tStS0_addr = m*65536 + k, and we need to decompose back to (m, k) + # With m ∈ [0, 128), k ∈ [0, 128), and stride 65536 for m and 1 for k + # We can build the inverse: given addr, m = addr // 65536, k = addr % 65536 + # Then sP_addr = 64*m + (k%16) + 16*((k//16)%4) + 8192*(k//64) + + # This is a 16384-element lookup table. We can build it at Python time. + + # Let me build the lookup table + print(f"\n=== Building (tid, vid) → sP_addr mapping ===") + + # Get the TV layout + tv = dst_tv + tv_shape = tv.shape + tv_stride = tv.stride + + # We need to evaluate the TV layout for all (tid, vid) pairs + # and then map the resulting tStS0 address to sP address + + # First, let's understand the TV layout structure + # It should have shape (num_threads, values_per_thread) + # num_threads = 128 (4 softmax warps) + # values_per_thread = 128 (32*4 from the coordinate shape) + + print(f" TV shape: {tv_shape}") + print(f" TV stride: {tv_stride}") + print(f" Total elements: {cute.size(tv)} (should be 16384)") + + # Evaluate the TV layout to get all (tid, vid) → tStS0_addr values + # We can do this by iterating over (tid, vid) and computing the layout value + + # CuTe layout evaluation: layout(idx) = sum(idx_i * stride_i) + # For a 2D layout (tid, vid) → addr: + # addr = tid * stride[0] + vid * stride[1] (simplified) + + # But the TV layout might have nested shapes/strides + # Let me just try to evaluate it + + # Actually, for make_cotiled_copy, I don't need to manually compute the mapping. + # I can use CuTe's composition operation: + # atom_layout_tv = composition(sP_flat_layout, dst_tv) + # This would compose dst_tv: (tid, vid) → tStS0_addr + # with a mapping tStS0_addr → sP_addr + + # But I need to build the tStS0_addr → sP_addr mapping first + # Let me build it as a CuTe layout + + # tStS0 has layout ((128,128),1,1):((65536,1),0,0) + # Coalescing: (128,128):(65536,1) — so tStS0_addr = m*65536 + k + # The inverse is: m = addr // 65536, k = addr % 65536 + + # sP has layout ((128,16),1,(4,2),1):((64,1),0,(16,8192),0) (before swizzle) + # Coalescing: ((128,16,4,2),1,1,1):((64,1,16,8192),0,0,0) → flat + # sP_addr = 64*m + 1*k0 + 16*k1 + 8192*k2 + # = 64*m + (k%16) + 16*((k//16)%4) + 8192*(k//64) + + # To build tStS0_addr → sP_addr as a CuTe layout: + # We need a layout with shape matching tStS0's domain (16384 elements) + # But CuTe layouts are affine, and the mapping is NOT affine + # (due to the modular decomposition of k) + + # So we CAN'T use composition directly. We need to build a custom layout. + + # Alternative: build the sP layout as a (128, 128) → addr layout directly + # sP_addr(m, k) = 64*m + (k%16) + 16*((k//16)%4) + 8192*(k//64) + # This can be expressed as: + # sP_addr = 64*m + f(k) where f(k) = (k%16) + 16*((k//16)%4) + 8192*(k//64) + + # f(k) IS representable as a CuTe layout because 16*4*2 = 128 + # k can be decomposed as (k0, k1, k2) where k0=k%16, k1=(k//16)%4, k2=k//64 + # f(k) = k0 + 16*k1 + 8192*k2 = (k0, k1, k2) → (1, 16, 8192) → addr + + # So the full sP layout in (m, k) coordinates: + # (m, k) → (m, k0, k1, k2) → (64*m + k0 + 16*k1 + 8192*k2) + # = (m, k0, k1, k2) → (64, 1, 16, 8192) → sP_addr + + # And the tStS0 layout: + # (m, k) → (65536*m + k) → tStS0_addr + + # The mapping tStS0_addr → (m, k) is the INVERSE of the tStS0 layout: + # (65536, 1) → (m, k) — this is what left_inverse gives + + # Then (m, k) → sP_addr via the sP layout + + # So the full chain \ No newline at end of file