| # RUN: env SUPPORT_LIB=%mlir_cuda_runtime \ |
| # RUN: sh -c 'if [[ "%mlir_run_cuda_sm90_tests" == "1" ]]; \ |
| # RUN: then %PYTHON %s | FileCheck %s; \ |
| # RUN: else export MLIR_NVDSL_PRINT_IR=1; \ |
| # RUN: %PYTHON %s | FileCheck %s --check-prefix=DUMPIR; fi' |
| |
| |
| # ===----------------------------------------------------------------------===// |
| # Chapter 4 : Multistage GEMM with Tensor Core |
| # ===----------------------------------------------------------------------===// |
| # |
| # This program exemplifies a GEMM operation for `f32+=f16*f16`, utilizing the |
| # Multistage method with a tile size of 128x128x64. The code completely |
| # parallelizes the two outermost loops into thread blocks. It launches one Warp |
| # Groups (128 threads in total) and allocates multiple slots/stage in the |
| # shared memory. The program consists of three main parts: prologue, mainloop, |
| # and epilogue. In the prologue, thread0 requests for TMA to load data into |
| # shared memory slots. The mainloop executes MMA while simultaneously loading |
| # TMA for the utilized slots. This overlap of TMA and MMA operations enhances |
| # performance by maximizing computational throughput. |
| # |
| # Loops illustration: |
| # |
| # for s in range(num_stages): |
| # TMA_128x64_64x128... |
| # for ti in range(M//128): # -> blockIdx.x |
| # for tj in range(N//128): # -> blockIdx.y |
| # for tk in range(K//64): |
| # MMA_128x128x64... |
| # TMA_128x64_64x128... |
| # Epilogue... |
| # |
| # This chapter introduces demonstrates: |
| # 1. Partition shape based on block IDs |
| # 2. Prologue |
| # 2.1 Execute TMA Load for two input matrices for each stage |
| # 3. Main loop |
| # 3.1 Wait for completion of TMA load with mbarrier |
| # 3.2 Performs Tensor Core GEMM 64x128x64 by warpgroup |
| # 3.3 Load next stage if needed |
| # 4. Epilogue |
| # 4.1 Store fragmented registers to shared memory |
| # 4.2 Store shared memory to global |
| # |
| # ===----------------------------------------------------------------------===// |
| |
| |
| from mlir import ir |
| from mlir.dialects import gpu, scf, nvgpu, nvvm |
| from mlir.extras import types as T |
| from tools.nvdsl import * |
| import numpy as np |
| |
| |
| def partition_shape(): |
| """ |
| Calculate the partition shape based on the block IDs. |
| |
| It partitions the shape like below: |
| for(.. i < M ...) --> blockIdx.x |
| for(.. j < N ...) --> blockIdx.y |
| for(.. k < K ...) |
| |
| Returns: |
| dimX (int): Dimension along the x-axis. |
| dimY (int): Dimension along the y-axis. |
| """ |
| bidx = gpu.block_id(gpu.Dimension.x) |
| bidy = gpu.block_id(gpu.Dimension.y) |
| dimX = bidx * TILE_M |
| dimY = bidy * TILE_N |
| return dimX, dimY |
| |
| |
| def tma_load( |
| mbar_group: Mbarriers, |
| a_tma: TMA, |
| b_tma: TMA, |
| slot, |
| stage, |
| num_stages, |
| p=None, |
| ): |
| """ |
| TMA loads two input matrices from global memory to shared memory. It performs the following operations: |
| |
| - tma.load a_shared_memory[off_x] at coordinate [x, z] (Loads 128x64) |
| - tma.load b_shared_memory[off_y1] at coordinate [y, x] (Loads 64x64) |
| - tma.load b_shared_memory[off_y2] at coordinate [y + 64, x] (Loads 64x64) |
| |
| mbarrier.arrive ta_count = 128x64x2x4 |
| """ |
| dimX, dimY = partition_shape() |
| |
| tidx = gpu.thread_id(gpu.Dimension.x) |
| begin_b = num_stages * get_type_size(a_tma.tma_memref) |
| size_tma_a = get_type_size(a_tma.tma_memref) |
| size_tma_b = get_type_size(b_tma.tma_memref) |
| ta_count = size_tma_a + (size_tma_b * 2) |
| tidx = gpu.thread_id(gpu.Dimension.x) |
| |
| p = tidx == 0 if p is None else p |
| |
| off_a = slot * size_tma_a |
| off_b = (slot * size_tma_a) + begin_b |
| off_b2 = off_b + size_tma_b |
| a_elem_ty = a_tma.tma_memref.element_type |
| b_elem_ty = b_tma.tma_memref.element_type |
| a = get_dynamic_shared_memory(a_tma.tma_memref.shape, a_elem_ty, off_a) |
| b1 = get_dynamic_shared_memory(b_tma.tma_memref.shape, b_elem_ty, off_b) |
| b2 = get_dynamic_shared_memory(b_tma.tma_memref.shape, b_elem_ty, off_b2) |
| |
| mbar_group[slot].arrive(ta_count, predicate=p) |
| |
| c1 = stage * 64 |
| a_tma.load(a, mbar_group[slot], coords=[c1, dimX], predicate=p) |
| b_tma.load(b1, mbar_group[slot], coords=[dimY, c1], predicate=p) |
| b_tma.load(b2, mbar_group[slot], coords=[dimY + 64, c1], predicate=p) |
| |
| |
| def initialize(a_tma: TMA, b_tma: TMA, num_stages): |
| """ |
| Initialize mbarriers and prefetch TMA descriptors. |
| """ |
| tidx = gpu.thread_id(gpu.Dimension.x) |
| mbar_group = Mbarriers(number_of_barriers=num_stages) |
| isThread0 = tidx == const(0) |
| with ir.InsertionPoint(scf.IfOp(isThread0).then_block): |
| for i in scf.for_(0, num_stages, 1): |
| mbar_group[i].init(1) |
| scf.yield_([]) |
| a_tma.prefetch() |
| b_tma.prefetch() |
| scf.yield_([]) |
| |
| return mbar_group |
| |
| |
| def prologue(mbar_group: Mbarriers, a_tma: TMA, b_tma: TMA, num_stages): |
| """ |
| Prologue of the GEMM kernel. It loads 2 input matrices for each stage in loop like below: |
| |
| for stage in range(NUM_STAGES): |
| tma_load x, y, stage |
| |
| """ |
| ns = num_stages if num_stages == 1 else num_stages - 1 |
| for iv in scf.for_(0, ns, 1): |
| tma_load(mbar_group, a_tma, b_tma, iv, iv, num_stages) |
| scf.yield_([]) |
| |
| |
| def mainloop(mbar_group: Mbarriers, a_tma: TMA, b_tma: TMA, num_stages): |
| """ |
| Main loop of the Multistage GEMM kernel. It iterates through |
| stages and performs matrix multiplication, loading data by TMA to shared memory. It like following |
| |
| MatrixAccumulator D |
| for k in range(K // TILE_K): |
| |
| try_wait(stage, ...) # Wait TMA load |
| |
| Matrix A(stage, ...) # Find shared memory slot |
| Matrix B(stage, ...) # Find shared memory slot |
| D += A @ B # Multiply and accumulate |
| |
| if(needLoad) # Load next stage if needed |
| tma_load(x, y, nextSlot, nextStage) |
| |
| """ |
| ns = num_stages if num_stages == 1 else num_stages - 1 |
| |
| tidx = gpu.thread_id(gpu.Dimension.x) |
| begin_b = num_stages * get_type_size(a_tma.tma_memref) |
| |
| size_a = TILE_M * TILE_K * get_type_size(T.f16()) |
| |
| # Initialize A and B (input matrices) and C (accumulator) |
| A = WGMMAMatrix(WGMMAType.Descriptor, [TILE_M, TILE_K], desc=a_tma) |
| B = WGMMAMatrix(WGMMAType.Descriptor, [TILE_K, TILE_N], desc=b_tma) |
| D = WGMMAMatrix(WGMMAType.Accumulator, shape=[TILE_M, TILE_N], ty=T.f32()) |
| |
| phase = const(False, ty=T.bool()) |
| |
| # Main Loop |
| for_op = scf.ForOp(const(0), const(K // TILE_K), const(1), [D.acc_op, phase]) |
| with ir.InsertionPoint(for_op.body): |
| phase = for_op.inner_iter_args[1] |
| iv = for_op.induction_variable |
| stage = iv % num_stages |
| |
| # Wait for current stage |
| mbar_group[stage].try_wait(phase=phase) |
| |
| # Find shared memory slot |
| offset_a = stage * size_a |
| offset_b = offset_a + begin_b |
| a_smem = get_dynamic_shared_memory([TILE_M, TILE_K], T.f16(), offset_a) |
| b_smem = get_dynamic_shared_memory([TILE_K, TILE_N], T.f16(), offset_b) |
| |
| # Iterate input matrices, update accumulator |
| A.update_smem(a_smem) |
| B.update_smem(b_smem) |
| D.update_accumulator(for_op.inner_iter_args[0]) |
| |
| # Matrix Multiply |
| D += A @ B |
| |
| # Wait Tensor Core for single stage |
| if num_stages == 1: |
| nvvm.WgmmaWaitGroupSyncOp(0) |
| |
| # Load next stage |
| pred = ((iv + ns) < const(K // TILE_K)) & (tidx == 0) |
| nextStage = iv + ns |
| nextSlot = nextStage % num_stages |
| tma_load(mbar_group, a_tma, b_tma, nextSlot, nextStage, num_stages, pred) |
| |
| # Switch phase parity for the mbarrier |
| newPhase = arith.select( |
| stage == (num_stages - 1), |
| (phase ^ const(True, ty=T.bool())), |
| phase, |
| ) |
| scf.yield_([D.acc_op, newPhase]) |
| |
| nvvm.WgmmaWaitGroupSyncOp(0) |
| |
| D.update_accumulator(for_op.results[0]) |
| return D |
| |
| |
| def epilogue(D: WGMMAMatrix, d_dev): |
| """ |
| Epilogue of the GEMM kernel. It stores the fragmented registers to global memory. |
| |
| MatrixAccumulator D # Fragmented results |
| store D -> Shared Memory # Store Shared Memory |
| Shared Memory -> Z[dimX][dimY] # Store Shared Memory to Global Memory |
| |
| """ |
| tidx = gpu.thread_id(gpu.Dimension.x) |
| dimX, dimY = partition_shape() |
| |
| d_smem = get_dynamic_shared_memory([TILE_M, TILE_N], T.f32()) |
| d_gmem = memref.subview(d_dev, [dimX, dimY], [TILE_M, TILE_N], [1, 1]) |
| |
| # Store (registers -> shared memory) |
| D.store_accumulator(d_smem) |
| gpu.barrier() |
| |
| # Store (shared memory --> global memory) |
| for i in scf.for_(0, TILE_M, 1): |
| val = memref.load(d_smem, [i, tidx]) |
| memref.store(val, d_gmem, [i, tidx]) |
| scf.yield_([]) |
| |
| |
| # The decorator generates |
| # a -> memref<MxKxf16> |
| # b -> memref<NxKf16> |
| # d -> memref<MxNxf32> |
| @NVDSL.mlir_func |
| def gemm_multistage(a, b, d, num_stages): |
| token_ty = gpu.AsyncTokenType.get() |
| t1 = gpu.wait([]) |
| a_dev, t2 = gpu.alloc(a.type, token_ty, [t1], [], []) |
| b_dev, t3 = gpu.alloc(b.type, token_ty, [t2], [], []) |
| d_dev, t4 = gpu.alloc(d.type, token_ty, [t3], [], []) |
| t5 = gpu.memcpy(token_ty, [t4], a_dev, a) |
| t6 = gpu.memcpy(token_ty, [t5], b_dev, b) |
| t7 = gpu.wait([t6]) |
| |
| sw = nvgpu.TensorMapSwizzleKind.SWIZZLE_128B |
| a_tma = TMA([128, 64], a.type, swizzle=sw) |
| b_tma = TMA([64, 64], b.type, swizzle=sw) |
| a_tma.create_descriptor(a_dev) |
| b_tma.create_descriptor(b_dev) |
| |
| grid = [(M // TILE_M), (N // TILE_N), 1] |
| block = [128, 1, 1] |
| |
| size_a = get_type_size(a.type.element_type) * TILE_M * TILE_K |
| size_b = get_type_size(b.type.element_type) * TILE_N * TILE_K |
| smem_size_in_bytes = (size_a + size_b) * num_stages |
| |
| @NVDSL.mlir_gpu_launch(grid=grid, block=block, smem=smem_size_in_bytes) |
| def gemm_multistage_kernel(): |
| # Initialize mbarriers and prefetch TMA descriptors |
| mbar_group = initialize(a_tma, b_tma, num_stages) |
| |
| # Fill the pipeline stages |
| prologue(mbar_group, a_tma, b_tma, num_stages) |
| |
| # Main loop |
| D = mainloop(mbar_group, a_tma, b_tma, num_stages) |
| |
| # Store registers to global memory |
| epilogue(D, d_dev) |
| |
| gemm_multistage_kernel() |
| |
| t8 = gpu.memcpy(token_ty, [t7], d, d_dev) |
| gpu.wait([t8]) |
| |
| |
| # Python pass arguments to MLIR |
| N = 256 |
| M = 512 |
| K = 1024 |
| TILE_M = 128 |
| TILE_N = 128 |
| TILE_K = 64 |
| a = np.random.randn(M, K).astype(np.float16) |
| b = np.random.randn(K, N).astype(np.float16) |
| d = np.zeros((M, N), np.float32) |
| |
| gemm_multistage(a, b, d, num_stages=7) |
| |
| if os.getenv("MLIR_NVDSL_PRINT_IR") != "1": |
| # Verify MLIR with reference computation |
| ref_d = a.astype(np.float16) @ b.astype(np.float16) |
| np.testing.assert_allclose(d, ref_d, rtol=5e-03, atol=1e-01) |
| |
| print("PASS") |
| # CHECK-NOT: Mismatched elements |
| # CHECK: PASS |
| |
| # DUMPIR: func.func @gemm_multistage(%{{.*}}: memref<512x1024xf16>, %{{.*}}: memref<1024x256xf16>, %{{.*}}: memref<512x256xf32>) attributes {llvm.emit_c_interface} { |
| # DUMPIR: scf.if %{{.*}} { |
| # DUMPIR: %[[C0_INIT:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[C7:.*]] = arith.constant 7 : index |
| # DUMPIR: %[[C1_INIT:.*]] = arith.constant 1 : index |
| # DUMPIR: scf.for %arg15 = %[[C0_INIT]] to %[[C7]] step %[[C1_INIT]] { |
| # DUMPIR: %[[C1_MBAR:.*]] = arith.constant 1 : index |
| # DUMPIR: nvgpu.mbarrier.init %{{.*}}[%arg15], %[[C1_MBAR]] : <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> |
| # DUMPIR: } |
| # DUMPIR: nvgpu.tma.prefetch.descriptor %{{.*}} : <tensor = memref<128x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none> |
| # DUMPIR: nvgpu.tma.prefetch.descriptor %{{.*}} : <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none> |
| # DUMPIR: } |
| # DUMPIR: %[[C0_PROLOGUE:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[C6:.*]] = arith.constant 6 : index |
| # DUMPIR: %[[C1_PROLOGUE:.*]] = arith.constant 1 : index |
| # DUMPIR: scf.for %arg15 = %[[C0_PROLOGUE]] to %[[C6]] step %[[C1_PROLOGUE]] { |
| # DUMPIR: %[[BID_X_P:.*]] = gpu.block_id x |
| # DUMPIR: %[[BID_Y_P:.*]] = gpu.block_id y |
| # DUMPIR: %[[C128_P1:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMX_P:.*]] = arith.muli %[[BID_X_P]], %[[C128_P1]] : index |
| # DUMPIR: %[[C128_P2:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMY_P:.*]] = arith.muli %[[BID_Y_P]], %[[C128_P2]] : index |
| # DUMPIR: %{{.*}} = gpu.thread_id x |
| # DUMPIR: %[[TID_X_P:.*]] = gpu.thread_id x |
| # DUMPIR: %[[C0_P:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[PRED_P:.*]] = arith.cmpi eq, %[[TID_X_P]], %[[C0_P]] : index |
| # DUMPIR: %[[C16384_P1:.*]] = arith.constant 16384 : index |
| # DUMPIR: %[[OFF_A_P:.*]] = arith.muli %arg15, %[[C16384_P1]] : index |
| # DUMPIR: %[[C16384_P2:.*]] = arith.constant 16384 : index |
| # DUMPIR: %[[OFF_B_BASE_P:.*]] = arith.muli %arg15, %[[C16384_P2]] : index |
| # DUMPIR: %[[C114688:.*]] = arith.constant 114688 : index |
| # DUMPIR: %[[OFF_B1_P:.*]] = arith.addi %[[OFF_B_BASE_P]], %[[C114688]] : index |
| # DUMPIR: %[[C8192:.*]] = arith.constant 8192 : index |
| # DUMPIR: %[[OFF_B2_P:.*]] = arith.addi %[[OFF_B1_P]], %[[C8192]] : index |
| # DUMPIR: %[[SMEM_A_P:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_A_P:.*]] = memref.view %[[SMEM_A_P]][%[[OFF_A_P]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<128x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SMEM_B1_P:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_B1_P:.*]] = memref.view %[[SMEM_B1_P]][%[[OFF_B1_P]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SMEM_B2_P:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_B2_P:.*]] = memref.view %[[SMEM_B2_P]][%[[OFF_B2_P]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C32768:.*]] = arith.constant 32768 : index |
| # DUMPIR: nvgpu.mbarrier.arrive.expect_tx %{{.*}}[%arg15], %[[C32768]], predicate = %[[PRED_P]] : <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> |
| # DUMPIR: %[[C64_K_P:.*]] = arith.constant 64 : index |
| # DUMPIR: %[[K_COORD_P:.*]] = arith.muli %arg15, %[[C64_K_P]] : index |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[K_COORD_P]], %[[DIMX_P]]], %{{.*}}[%arg15] to %[[VIEW_A_P]], predicate = %[[PRED_P]] : <tensor = memref<128x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<128x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[DIMY_P]], %[[K_COORD_P]]], %{{.*}}[%arg15] to %[[VIEW_B1_P]], predicate = %[[PRED_P]] : <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C64_OFF:.*]] = arith.constant 64 : index |
| # DUMPIR: %[[DIMY_P_OFF:.*]] = arith.addi %[[DIMY_P]], %[[C64_OFF]] : index |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[DIMY_P_OFF]], %[[K_COORD_P]]], %{{.*}}[%arg15] to %[[VIEW_B2_P]], predicate = %[[PRED_P]] : <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: } |
| # DUMPIR: %[[TID_X_LOOP:.*]] = gpu.thread_id x |
| # DUMPIR: %[[ACC_INIT:.*]] = nvgpu.warpgroup.mma.init.accumulator -> <fragmented = vector<128x128xf32>> |
| # DUMPIR: %[[FALSE_LOOP:.*]] = arith.constant false |
| # DUMPIR: %[[C0_LOOP:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[C16_LOOP:.*]] = arith.constant 16 : index |
| # DUMPIR: %[[C1_LOOP:.*]] = arith.constant 1 : index |
| # DUMPIR: %[[LOOP_RES:.*]]:2 = scf.for %arg15 = %[[C0_LOOP]] to %[[C16_LOOP]] step %[[C1_LOOP]] iter_args(%arg16 = %[[ACC_INIT]], %arg17 = %[[FALSE_LOOP]]) -> (!nvgpu.warpgroup.accumulator<fragmented = vector<128x128xf32>>, i1) { |
| # DUMPIR: %[[C7_L:.*]] = arith.constant 7 : index |
| # DUMPIR: %[[STAGE_L:.*]] = arith.remui %arg15, %[[C7_L]] : index |
| # DUMPIR: %[[C10M:.*]] = arith.constant 10000000 : index |
| # DUMPIR: nvgpu.mbarrier.try_wait.parity %{{.*}}[%[[STAGE_L]]], %arg17, %[[C10M]] : <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> |
| # DUMPIR: %[[C16384_L:.*]] = arith.constant 16384 : index |
| # DUMPIR: %[[OFF_A_L:.*]] = arith.muli %[[STAGE_L]], %[[C16384_L]] : index |
| # DUMPIR: %[[C114688_L:.*]] = arith.constant 114688 : index |
| # DUMPIR: %[[OFF_B_L:.*]] = arith.addi %[[OFF_A_L]], %[[C114688_L]] : index |
| # DUMPIR: %[[SMEM_A_L:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_A_L:.*]] = memref.view %[[SMEM_A_L]][%[[OFF_A_L]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<128x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SMEM_B_L:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_B_L:.*]] = memref.view %[[SMEM_B_L]][%[[OFF_B_L]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<64x128xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[DESC_A_L:.*]] = nvgpu.warpgroup.generate.descriptor %[[VIEW_A_L]], %{{.*}} : memref<128x64xf16, #gpu.address_space<workgroup>>, <tensor = memref<128x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none> -> <tensor = memref<128x64xf16, #gpu.address_space<workgroup>>> |
| # DUMPIR: %[[DESC_B_L:.*]] = nvgpu.warpgroup.generate.descriptor %[[VIEW_B_L]], %{{.*}} : memref<64x128xf16, #gpu.address_space<workgroup>>, <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none> -> <tensor = memref<64x128xf16, #gpu.address_space<workgroup>>> |
| # DUMPIR: %[[ACC_L:.*]] = nvgpu.warpgroup.mma %[[DESC_A_L]], %[[DESC_B_L]], %arg16 {transposeB} : <tensor = memref<128x64xf16, #gpu.address_space<workgroup>>>, <tensor = memref<64x128xf16, #gpu.address_space<workgroup>>>, <fragmented = vector<128x128xf32>> -> <fragmented = vector<128x128xf32>> |
| # DUMPIR: %[[C6_NEXT:.*]] = arith.constant 6 : index |
| # DUMPIR: %[[ITER_NEXT:.*]] = arith.addi %arg15, %[[C6_NEXT]] : index |
| # DUMPIR: %[[C16_CMP:.*]] = arith.constant 16 : index |
| # DUMPIR: %[[IN_RANGE:.*]] = arith.cmpi ult, %[[ITER_NEXT]], %[[C16_CMP]] : index |
| # DUMPIR: %[[C0_CMP:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[IS_THREAD0_L:.*]] = arith.cmpi eq, %[[TID_X_LOOP]], %[[C0_CMP]] : index |
| # DUMPIR: %[[DO_LOAD:.*]] = arith.andi %[[IN_RANGE]], %[[IS_THREAD0_L]] : i1 |
| # DUMPIR: %[[C6_STAGE:.*]] = arith.constant 6 : index |
| # DUMPIR: %[[STAGE_NEXT_L:.*]] = arith.addi %arg15, %[[C6_STAGE]] : index |
| # DUMPIR: %[[C7_MOD:.*]] = arith.constant 7 : index |
| # DUMPIR: %[[STAGE_LOAD:.*]] = arith.remui %[[STAGE_NEXT_L]], %[[C7_MOD]] : index |
| # DUMPIR: %[[BID_X_L:.*]] = gpu.block_id x |
| # DUMPIR: %[[BID_Y_L:.*]] = gpu.block_id y |
| # DUMPIR: %[[C128_L1:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMX_L:.*]] = arith.muli %[[BID_X_L]], %[[C128_L1]] : index |
| # DUMPIR: %[[C128_L2:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMY_L:.*]] = arith.muli %[[BID_Y_L]], %[[C128_L2]] : index |
| # DUMPIR: %[[TID_X_L1:.*]] = gpu.thread_id x |
| # DUMPIR: %[[TID_X_L2:.*]] = gpu.thread_id x |
| # DUMPIR: %[[C16384_LA1:.*]] = arith.constant 16384 : index |
| # DUMPIR: %[[OFF_A_LOAD:.*]] = arith.muli %[[STAGE_LOAD]], %[[C16384_LA1]] : index |
| # DUMPIR: %[[C16384_LA2:.*]] = arith.constant 16384 : index |
| # DUMPIR: %[[OFF_B_BASE_LOAD:.*]] = arith.muli %[[STAGE_LOAD]], %[[C16384_LA2]] : index |
| # DUMPIR: %[[C114688_LOAD:.*]] = arith.constant 114688 : index |
| # DUMPIR: %[[OFF_B1_LOAD:.*]] = arith.addi %[[OFF_B_BASE_LOAD]], %[[C114688_LOAD]] : index |
| # DUMPIR: %[[C8192_LOAD:.*]] = arith.constant 8192 : index |
| # DUMPIR: %[[OFF_B2_LOAD:.*]] = arith.addi %[[OFF_B1_LOAD]], %[[C8192_LOAD]] : index |
| # DUMPIR: %[[SMEM_A_LOAD:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_A_LOAD:.*]] = memref.view %[[SMEM_A_LOAD]][%[[OFF_A_LOAD]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<128x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SMEM_B1_LOAD:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_B1_LOAD:.*]] = memref.view %[[SMEM_B1_LOAD]][%[[OFF_B1_LOAD]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SMEM_B2_LOAD:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[VIEW_B2_LOAD:.*]] = memref.view %[[SMEM_B2_LOAD]][%[[OFF_B2_LOAD]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C32768_LOAD:.*]] = arith.constant 32768 : index |
| # DUMPIR: nvgpu.mbarrier.arrive.expect_tx %{{.*}}[%[[STAGE_LOAD]]], %[[C32768_LOAD]], predicate = %[[DO_LOAD]] : <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> |
| # DUMPIR: %[[C64_K_LOAD:.*]] = arith.constant 64 : index |
| # DUMPIR: %[[K_COORD_LOAD:.*]] = arith.muli %[[STAGE_NEXT_L]], %[[C64_K_LOAD]] : index |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[K_COORD_LOAD]], %[[DIMX_L]]], %{{.*}}[%[[STAGE_LOAD]]] to %[[VIEW_A_LOAD]], predicate = %[[DO_LOAD]] : <tensor = memref<128x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<128x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[DIMY_L]], %[[K_COORD_LOAD]]], %{{.*}}[%[[STAGE_LOAD]]] to %[[VIEW_B1_LOAD]], predicate = %[[DO_LOAD]] : <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C64_OFF_LOAD:.*]] = arith.constant 64 : index |
| # DUMPIR: %[[DIMY_L_OFF:.*]] = arith.addi %[[DIMY_L]], %[[C64_OFF_LOAD]] : index |
| # DUMPIR: nvgpu.tma.async.load %{{.*}}[%[[DIMY_L_OFF]], %[[K_COORD_LOAD]]], %{{.*}}[%[[STAGE_LOAD]]] to %[[VIEW_B2_LOAD]], predicate = %[[DO_LOAD]] : <tensor = memref<64x64xf16, 3>, swizzle = swizzle_128b, l2promo = none, oob = zero, interleave = none>, <memorySpace = #gpu.address_space<workgroup>, num_barriers = 7> -> memref<64x64xf16, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C6_FLIP:.*]] = arith.constant 6 : index |
| # DUMPIR: %[[IS_STAGE6:.*]] = arith.cmpi eq, %[[STAGE_L]], %[[C6_FLIP]] : index |
| # DUMPIR: %[[TRUE:.*]] = arith.constant true |
| # DUMPIR: %[[PARITY_FLIP:.*]] = arith.xori %arg17, %[[TRUE]] : i1 |
| # DUMPIR: %[[NEW_PARITY:.*]] = arith.select %[[IS_STAGE6]], %[[PARITY_FLIP]], %arg17 : i1 |
| # DUMPIR: scf.yield %[[ACC_L]], %[[NEW_PARITY]] : !nvgpu.warpgroup.accumulator<fragmented = vector<128x128xf32>>, i1 |
| # DUMPIR: } |
| # DUMPIR: nvvm.wgmma.wait.group.sync.aligned 0 |
| # DUMPIR: %[[TID_X_EPI:.*]] = gpu.thread_id x |
| # DUMPIR: %[[BID_X_EPI:.*]] = gpu.block_id x |
| # DUMPIR: %[[BID_Y_EPI:.*]] = gpu.block_id y |
| # DUMPIR: %[[C128_EPI1:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMX_EPI:.*]] = arith.muli %[[BID_X_EPI]], %[[C128_EPI1]] : index |
| # DUMPIR: %[[C128_EPI2:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[DIMY_EPI:.*]] = arith.muli %[[BID_Y_EPI]], %[[C128_EPI2]] : index |
| # DUMPIR: %[[SMEM_EPI:.*]] = gpu.dynamic_shared_memory : memref<?xi8, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[C0_VIEW:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[VIEW_EPI:.*]] = memref.view %[[SMEM_EPI]][%[[C0_VIEW]]][] : memref<?xi8, #gpu.address_space<workgroup>> to memref<128x128xf32, #gpu.address_space<workgroup>> |
| # DUMPIR: %[[SUBVIEW_EPI:.*]] = memref.subview %{{.*}}[%[[DIMX_EPI]], %[[DIMY_EPI]]] [128, 128] [1, 1] : memref<512x256xf32> to memref<128x128xf32, strided<[256, 1], offset: ?>> |
| # DUMPIR: nvgpu.warpgroup.mma.store %[[LOOP_RES]]#0, %[[VIEW_EPI]] : <fragmented = vector<128x128xf32>> to memref<128x128xf32, #gpu.address_space<workgroup>> |
| # DUMPIR: gpu.barrier |
| # DUMPIR: %[[C0_STORE:.*]] = arith.constant 0 : index |
| # DUMPIR: %[[C128_STORE:.*]] = arith.constant 128 : index |
| # DUMPIR: %[[C1_STORE:.*]] = arith.constant 1 : index |
| # DUMPIR: scf.for %arg15 = %[[C0_STORE]] to %[[C128_STORE]] step %[[C1_STORE]] { |
| # DUMPIR: %[[VAL_LOAD:.*]] = memref.load %[[VIEW_EPI]][%arg15, %[[TID_X_EPI]]] : memref<128x128xf32, #gpu.address_space<workgroup>> |
| # DUMPIR: memref.store %[[VAL_LOAD]], %[[SUBVIEW_EPI]][%arg15, %[[TID_X_EPI]]] : memref<128x128xf32, strided<[256, 1], offset: ?>> |
