diff --git a/tests/unit/test_fmha_v3_softmax.py b/tests/unit/test_fmha_v3_softmax.py index e49d69b0..fb3b98ae 100644 --- a/tests/unit/test_fmha_v3_softmax.py +++ b/tests/unit/test_fmha_v3_softmax.py @@ -301,8 +301,31 @@ class FmhaV3Softmax: acc_scale = cute.math.exp2(scale * (old_row_max - row_max_safe), fastmath=True) # --- C6: Rescale O in TMEM (load O, multiply by acc_scale, store O) --- + # acc_scale belongs to QK row (N//4), but O rows are in PV partition (N). + # Store acc_scale to vector by QK row, read by PV row. if kt > 0: pv_done_bar.arrive_and_wait() + + # Store acc_scale to vector indexed by QK logical row + qk_row_c6 = tTMEM_LOADcS[0][0] + thr_vs_c6 = tiled_tmem_store_vec.get_slice(qk_row_c6) + tVStore_c6 = thr_vs_c6.partition_D(tStS_vec) + tVStoreSrc_c6 = thr_vs_c6.partition_S(tScS_vec) + tVStoreRmem_c6 = cute.make_rmem_tensor(tVStoreSrc_c6.shape, self.qk_acc_dtype) + tVStoreRmem_c6[0] = acc_scale + cute.copy(tiled_tmem_store_vec, tVStoreRmem_c6, tVStore_c6) + cute.arch.fence_view_async_tmem_store() + + # Read acc_scale from vector indexed by PV logical row + pv_row_c6 = tTMEM_LOADcO[0][0] + thr_vl_c6 = tiled_tmem_load_vec.get_slice(pv_row_c6) + tVLoad_c6 = thr_vl_c6.partition_S(tStS_vec) + tVLoadDst_c6 = thr_vl_c6.partition_D(tScS_vec) + tVLoadRmem_c6 = cute.make_rmem_tensor(tVLoadDst_c6.shape, self.qk_acc_dtype) + cute.copy(tiled_tmem_load_vec, tVLoad_c6, tVLoadRmem_c6) + cute.arch.fence_view_async_tmem_load() + acc_scale_pv = tVLoadRmem_c6[0] + tTMrO = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype) for i in range(o_col_tiles): tTMrO_i_ = tTMrO[None, i] @@ -312,7 +335,7 @@ class FmhaV3Softmax: tTMEM_STOREtO_i = cute.make_tensor(tTMEM_STOREtO.iterator + i * corr_tile_size, tTMEM_STOREtO.layout) cute.copy(o_tiled_tmem_load, tTMEM_LOADtO_i, tTMrO_i) for j in cutlass.range(cute.size(tTMrO_i), vectorize=True): - tTMrO_i[j] = tTMrO_i[j] * acc_scale + tTMrO_i[j] = tTMrO_i[j] * acc_scale_pv cute.copy(o_tiled_tmem_store, tTMrO_i, tTMEM_STOREtO_i) cute.arch.fence_view_async_tmem_store() @@ -376,19 +399,53 @@ class FmhaV3Softmax: # --- C9: Final normalization via O TMEM rescale --- pv_done_bar.arrive_and_wait() - # Store final row_sum to TMEM vector (per-row, using QK partition) - tTMEM_STORE_VECrS_final = cute.make_rmem_tensor(tTMEM_STORE_VECcS.shape, self.qk_acc_dtype) - tTMEM_STORE_VECrS_final[0] = row_sum - cute.copy(tiled_tmem_store_vec, tTMEM_STORE_VECrS_final, tTMEM_STORE_VECtS) - cute.arch.fence_view_async_tmem_store() - # Read vector back: per-row row_sum using QK partition coordinates - tTMEM_LOAD_VECrS = cute.make_rmem_tensor(tTMEM_LOAD_VECcS.shape, self.qk_acc_dtype) - cute.copy(tiled_tmem_load_vec, tTMEM_LOAD_VECtS, tTMEM_LOAD_VECrS) - cute.arch.fence_view_async_tmem_load() - inv_row_sum = cutlass.Float32(1.0) / tTMEM_LOAD_VECrS[0] + # Compute inv_row_sum from P in TMEM using PV partition. + # P was stored by softmax loop into TMEM at offset tmem_p0_offset. + # PV partition maps thread N to PV row N, so reading P via PV partition + # gives the correct per-row P values to sum. + # This avoids the QK→PV row mapping mismatch (QK: N->N//4, PV: N->N). + + # P is stored as BF16 in TMEM at tmem_p0_offset. + # We need to read it via PV TMEM load and sum the values. + # P has shape (128, HEAD_DIM//2) in FP32 columns (64 BF16 = 32 FP32 cols). + # Use the P TMEM load partition (PV A-fragment read). + + # Actually, P was stored via QK C-fragment store (St32x32bOp Repetition(32)). + # To read it via PV partition, we need a PV-partitioned load from the P region. + # Let's use the same o_tiled_tmem_load but pointed at P's TMEM offset. + + # P occupies TMEM columns [tmem_p0_offset, tmem_p0_offset + p_cols_fp32) + # In the PV C-fragment, P is the A-fragment. We can use tOrP0's layout. + # tOrP0 was set up with offset for PV MMA read. + + # Simpler: sum O across columns to get unnormalized row sum, then normalize. + # For V=identity, O = P@V = sum(P per row). So O.sum(dim=-1) = row_sum. + # For arbitrary V, O = P@V. O.sum(dim=-1) = sum_j(P@V)[j] = sum_j(sum_i P[i]*V[i,j]) + # This is NOT sum(P). So this trick only works for V=identity. + + # Correct approach: read P from TMEM, sum it per PV row. + # P is at TMEM offset tmem_p0_offset, stored as BF16 with St32x32bOp. + # P shape in TMEM: 128 rows x (HEAD_DIM BF16 = 32 FP32 cols) + # We can read P using Ld32x32bOp(Repetition(corr_tile_size)) via PV O-partition. + + # Use PV O TMEM load to read from P region instead of O region + p_col_tiles = p_cols_fp32 // corr_tile_size # 32 // 16 = 2 + pv_row_sum = cutlass.Float32(0.0) + for i in range(p_col_tiles): + # Read P tile from TMEM at P offset (not O offset) + tTMEM_LOADtP_i = cute.make_tensor( + tTMEM_LOADtO.iterator + (self.tmem_p0_offset - self.tmem_o0_offset) + i * corr_tile_size, + tTMEM_LOADtO.layout) + tTMrP_i = cute.make_rmem_tensor(tTMEM_LOADcO.shape, self.qk_acc_dtype) + cute.copy(o_tiled_tmem_load, tTMEM_LOADtP_i, tTMrP_i) + # Use .reduce(SUM) instead of scalar accumulation (vectorizer can't handle scalar in vectorized loop) + tile_p_sum = tTMrP_i.load().reduce(cute.ReductionOp.ADD, cutlass.Float32(0.0), 0) + pv_row_sum = pv_row_sum + tile_p_sum + + inv_row_sum = cutlass.Float32(1.0) / pv_row_sum - # Normalize O in TMEM + # Normalize O in TMEM using PV-correct inv_row_sum tTMrO_final = cute.make_rmem_tensor((tTMEM_LOADcO.shape, o_col_tiles), self.qk_acc_dtype) for i in range(o_col_tiles): tTMrO_i_ = tTMrO_final[None, i] @@ -452,34 +509,3 @@ if __name__ == "__main__": test() -def test(): - import math - torch.manual_seed(42) - for n in [128, 256, 384]: - 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") - v_kernel = v.unsqueeze(-1) - c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device="cuda") - qf = q[:,:,0].float(); kf = k[:,:,0].float() - attn = qf @ kf.T / math.sqrt(hd) - ref = torch.softmax(attn, dim=-1) @ v.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_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 = FmhaV3Softmax() - 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() - max_err = (out - ref).abs().max().item() - print(f"FMHA softmax n={n}: cosine {cos:.6f} max_err {max_err:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}", flush=True) - -if __name__ == "__main__": - test()