From ca1fc70c02c049619ca5731c59270383785fece8 Mon Sep 17 00:00:00 2001 From: protonu Date: Tue, 22 Oct 2024 18:39:18 -0700 Subject: [PATCH 01/14] debugging --- csrc/device_lower/pass/index.cpp | 117 ++++++++++++++++++++ csrc/scheduler/mma_utils.cpp | 22 ++++ csrc/scheduler/mma_utils.h | 3 + tests/cpp/test_memory.cpp | 138 ++---------------------- tests/cpp/test_mma.cpp | 176 +++++++++++++++++++++++++++++++ 5 files changed, 324 insertions(+), 132 deletions(-) diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index 1ac49f5f2b4..913dc7e12b1 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1589,6 +1589,7 @@ namespace { // the 4 8x8 tiles are stores/accessed in a m/column-major fasion. When we // mulitple tiles (whether it be 8x8, 18x6 or 16x16), the tiles are stored in a // m/column-major fashion. +#if 0 Val* hardCodedIndexGenerationForStMatrix( const LoadStoreOp* ldst, const ForLoop* outer_loop, @@ -1686,6 +1687,122 @@ Val* hardCodedIndexGenerationForStMatrix( return out; } +#endif + +Val* hardCodedIndexGenerationForStMatrix( + const LoadStoreOp* ldst, + const ForLoop* outer_loop, + const int64_t m_tile, + const int64_t n_tile, + const int64_t m, + const int64_t n) { + NVF_ERROR( + (m_tile == 8 && n_tile == 8) || (m_tile == 16 && n_tile == 8) || + (m_tile == 16 && n_tile == 16), + "size not currently supported for stmatrix"); + Val* out_index = nullptr; + + // A tile_box can be 16x16, 16x8. 8x8 + // A warp group (128 threads) can work on a number of tile boxes. + int64_t max_tile_boxes_in_warp_group_box = 4; + + // These two variables gives the shape of the warp group in terms of tile + // boxes. Think of a tile box as a stmatrix call. + auto tiles_in_warp_group_m = + std::max(m / m_tile, max_tile_boxes_in_warp_group_box); + auto tiles_in_warp_group_n = + max_tile_boxes_in_warp_group_box / tiles_in_warp_group_m; + + auto warp_group_box_size_m = tiles_in_warp_group_m * m_tile; + + // each iteration of the for-loop compute a warp-group worth of stmatrix + auto warp_groups_m = m / warp_group_box_size_m; + + // With iteration we work on a different warp-group box. + auto warp_group_box_offset_m = IrBuilder::mulExpr( + IrBuilder::modExpr( + outer_loop->index(), + IrBuilder::create(warp_groups_m, DataType::Index)), + IrBuilder::create( + tiles_in_warp_group_m * m_tile * n * 2, DataType::Index)); + + auto warp_group_box_offset_n = IrBuilder::mulExpr( + IrBuilder::divExpr( + outer_loop->index(), + IrBuilder::create(warp_groups_m, DataType::Index)), + IrBuilder::create( + tiles_in_warp_group_n * n_tile * 2, DataType::Index)); + + auto warp_group_box_offset = + IrBuilder::addExpr(warp_group_box_offset_m, warp_group_box_offset_n); + + auto tile_box_id = IrBuilder::divExpr( + IrBuilder::create("threadIdx.x", DataType::Index), + IrBuilder::create(/* each warp gets a box */ 32, DataType::Index)); + + auto tile_box_id_m = IrBuilder::modExpr( + tile_box_id, + IrBuilder::create(tiles_in_warp_group_m, DataType::Index)); + + auto tile_box_id_n = IrBuilder::divExpr( + tile_box_id, + IrBuilder::create(tiles_in_warp_group_m, DataType::Index)); + + auto tile_box_id_m_offset = IrBuilder::mulExpr( + tile_box_id_m, IrBuilder::create(m_tile * n * 2, DataType::Index)); + + auto tile_box_id_n_offset = IrBuilder::mulExpr( + tile_box_id_n, IrBuilder::create(n_tile * 2, DataType::Index)); + + auto tile_box_offset = + IrBuilder::addExpr(tile_box_id_m_offset, tile_box_id_n_offset); + + auto cum_offset = IrBuilder::addExpr(warp_group_box_offset, tile_box_offset); + // For example M=64, N=8. There's no for-loop. + if (n == n_tile) { + cum_offset = tile_box_offset; + } + + // Offset inside the tile. + auto effective_tidx = IrBuilder::modExpr( + IrBuilder::create("threadIdx.x", DataType::Index), + IrBuilder::create(32, DataType::Index)); + + if (m_tile == 16 && n_tile == 16) { + // 2 is for dtype Half + // (tidx.x / (8 * 2)) * 16 + // This gives the offset in the n-dim + auto offset_in_tile_n = IrBuilder::mulExpr( + IrBuilder::create(16, DataType::Index), + IrBuilder::divExpr( + effective_tidx, IrBuilder::create(8 * 2, DataType::Index))); + + // (tidx.x%16) * n * 2 + // This gives the offset inside the 16x16 tile in the m-dim. + auto offset_in_tile_m = IrBuilder::mulExpr( + IrBuilder::modExpr( + effective_tidx, IrBuilder::create(16, DataType::Index)), + IrBuilder::create(n * 2, DataType::Index)); + + out_index = IrBuilder::addExpr( + IrBuilder::baseAddressExpr(ir_utils::getTvOutput(ldst)), + IrBuilder::addExpr( + cum_offset, + IrBuilder::addExpr(offset_in_tile_m, offset_in_tile_n))); + } else if (m_tile == 16 && n_tile == 8) { + auto offset_in_tile = IrBuilder::mulExpr( + IrBuilder::create(n * 2, DataType::Index), effective_tidx); + + out_index = IrBuilder::addExpr( + IrBuilder::baseAddressExpr(dynamic_cast(ldst->out())), + IrBuilder::addExpr(offset_in_tile, cum_offset)); + } + + Val* out = IrBuilder::create( + dynamic_cast(ldst->out()), out_index); + + return out; +} } // namespace diff --git a/csrc/scheduler/mma_utils.cpp b/csrc/scheduler/mma_utils.cpp index 2d43b61c393..0df24c7d55b 100644 --- a/csrc/scheduler/mma_utils.cpp +++ b/csrc/scheduler/mma_utils.cpp @@ -1280,6 +1280,28 @@ void scheduleTMAStoreForMmaOutput(TensorView* tv, int64_t m, int64_t n) { mma_utils::MmaSwizzler::parallelizeAsBulkSkippingFirstIDs(tv, 2); } +void scheduleStMatrixForMmaOutput( + TensorView* tv, + int64_t tile_m, + int64_t tile_n) { + NVF_ERROR( + ((tile_m == 16 && tile_n == 16) || (tile_m == 16 && tile_n == 8)), + "We only support 16x16 and 16x16 stmatrix now"); + auto s = + mma_utils::MmaSwizzler::scheduleMmaOutputAllocation(tv->getLoopDomain()); + tv->setLoopDomain(s.as()); + if (tile_m == 16 && tile_n == 16) { + tv->split(-3, 2); + tv->reorder({{-4, -5}}); + tv->merge(2); + tv->merge(2); + } else if (tile_m == 16 && tile_n == 8) { + tv->reorder({{-3, -4}}); + tv->merge(2); + } + tv->axis(-1)->parallelize(ParallelType::Vectorize); +} + MatmulOperandInnerDimsOpt getOperandInnerDims(Fusion* fusion) { const std::vector patterns = findMatmulPatterns(fusion); if (patterns.size() != 1) { diff --git a/csrc/scheduler/mma_utils.h b/csrc/scheduler/mma_utils.h index 1f7a82d6c99..5119f5a0015 100644 --- a/csrc/scheduler/mma_utils.h +++ b/csrc/scheduler/mma_utils.h @@ -249,6 +249,9 @@ class MmaSwizzler { //! marked parallel type bulk. void scheduleTMAStoreForMmaOutput(TensorView* tv, int64_t m, int64_t n); + +void scheduleStMatrixForMmaOutput(TensorView* tv, int64_t tile_m, int64_t tile_n); + void checkDimSize( TensorView* tv, std::vector axis, diff --git a/tests/cpp/test_memory.cpp b/tests/cpp/test_memory.cpp index f40267967b8..10122228adf 100644 --- a/tests/cpp/test_memory.cpp +++ b/tests/cpp/test_memory.cpp @@ -595,8 +595,7 @@ std::string testNameTMASimpleLdstTest( auto dtype = std::get<1>(info.param); auto dim = std::get<2>(info.param); std::stringstream ss; - ss << dim << "D" - << "_" << toString(swizzle) << "_" << dtype; + ss << dim << "D" << "_" << toString(swizzle) << "_" << dtype; return ss.str(); } @@ -2763,19 +2762,6 @@ TEST_P(LdMatrixTest, Regular) { testValidate(&fusion, cg_outputs, {t0}, __LINE__, __FILE__); } -// Test for Stmatrix instruction to store 8x8, 16x8 and 16x16 -// piece of memory with a single call (.x1, .x2 and .x4). -class StMatrixSingleTileTest - : public NVFuserFixtureParamTest> { - protected: - void SetUp() override { - if (cudaArchGuardShouldSkip(9, 0)) { - GTEST_SKIP() << "skipping tests on pre-Hopper GPUs"; - } - NVFuserTest::SetUp(); - } -}; - // We get shapes M and N from MmaMacrao. The vector of ints are // the tile_m and tile_n factors (8x8, 16x8 and 16x16). using StMatrixTestParams = std::tuple>; @@ -2790,105 +2776,6 @@ class StMatrixTest : public NVFuserFixtureParamTest { } }; -// This tile a larger piece of memory to 8x8, 16x8 or 16x16 tiles. This helps -// break down a store to multiple stmatrix calls. -void tileInnerStmatrixCall(TensorView* tv, int tile_m, int tile_n) { - // [M, N] -> [M, no, tile_n] - tv->split(-1, tile_n); - // [M, no, tile_n] -> [mo, tile_m, no, tile_n] - tv->split(0, tile_m); - // [mo, tile_m, no, tile_n] -> [no, mo, tile_m, tile_n] - tv->reorder({{-2, -4}}); - // [no, mo, tile_m, tile_n] -> [no*mo, tile_m, tile_n] - tv->merge(0); -}; - -void scheduleSwizzleHelper(TensorView* tv) { - // [no*mo, t_m, t_n] -> [no*mo, t_m/8, 8, t_n] - tv->split(-2, 8); - // [no*mo, t_m/8, 8, t_n] -> [no*mo, t_m/8, 8, t_n/2, 2] - tv->split(-1, 2); - // [no*mo, t_m/8, 8, t_n/2, 2] -> [no*mo, t_m/8, 8, (t_n/2)/4, 4, 2] - tv->split(-2, 4); - // [no*mo, t_m/8, 8, (t_n/2)/4, 4, 2] -> - // [no*mo, 8, 4, (t_n/2)/4, t_m/8, 2] - tv->reorder({{-4, -5}, {-5, -2}, {-2, -4}}); -} - -void scheduleStmatrixOutput(TensorView* tv, int tile_m, int tile_n) { - // We can run through this with an example shape: [32, 64] - // (8x8): [32, 64] -> [32, 8, 4, 1, 1, 2] (after swizzle) - // (16x8): [32, 64] -> [16, 8, 4, 1, 2, 2] - // (16x16): [32, 64] -> [8, 8, 4, 2, 2, 2] - scheduleSwizzleHelper(tv); - tv->merge(1); - // (8x8): [32, 32(TIDX), 1, 1, 2] - // (16x8): [16, 32(TIDX), 1, 2, 2] - // (16x16): [8, 32(TIDX), 2, 2, 2] - tv->axis(1)->parallelize(ParallelType::TIDx); - - // (8x8): [32, 32(TIDX), 1*1*2(vec)] - // (16x8): [16, 32(TIDX), 1*2*2(Vec)] - // (16x16): [8, 32(TIDX), 2*2*2(Vec)] - tv->merge(2); - tv->merge(2); - tv->axis(2)->parallelize(ParallelType::Vectorize); -} - -TEST_P(StMatrixSingleTileTest, Regular) { - Fusion fusion; - FusionGuard fg(&fusion); - - auto sizes = GetParam(); - int sizeM = sizes.at(0); - int sizeN = sizes.at(1); - - fusion.manage("st_matrix_m_tile", sizeM); - fusion.manage("st_matrix_n_tile", sizeN); - fusion.manage("st_matrix_m", sizeM); - fusion.manage("st_matrix_n", sizeN); - - auto tv0 = makeContigConcreteTensor({sizeM, sizeN}, DataType::Half); - fusion.addInput(tv0); - // tv0 (global) -> tv1 (registers) - auto tv1 = set(tv0); - // tv1 (registers) -> tv2 (shared) - auto tv2 = set(tv1); - tv2->definition()->as()->setOpType(LoadStoreOpType::StMatrix); - tv2->setMemoryType(MemoryType::Shared); - // tv2 (shared) -> tv3(global) - auto tv3 = set(tv2); - fusion.addOutput(tv3); - - tv0->merge(0); - tv0->split(0, 32); - tv0->axis(1)->parallelize(ParallelType::TIDx); - - tileInnerStmatrixCall(tv1, sizeM, sizeN); - tileInnerStmatrixCall(tv2, sizeM, sizeN); - - scheduleSwizzleHelper(tv1); - tv1->setAllocationDomain(tv1->getLoopDomain(), true); - // // Get 32 threads out to parallelize over. - tv1->merge(1); - tv1->axis(1)->parallelize(ParallelType::TIDx); - - scheduleStmatrixOutput(tv2, sizeM, sizeN); - - tv3->merge(0); - tv3->split(0, 32); - tv3->axis(1)->parallelize(ParallelType::TIDx); - - auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); - auto t0 = at::randn({sizeM, sizeN}, options); - - KernelExecutor ke; - ke.compile(&fusion, {t0}, LaunchParams(), matmul_cparams); - auto cg_outputs = ke.run({t0}); - - testValidate(&fusion, cg_outputs, {t0}, __LINE__, __FILE__); -} - TEST_P(StMatrixTest, Regular) { Fusion fusion; FusionGuard fg(&fusion); @@ -2925,16 +2812,12 @@ TEST_P(StMatrixTest, Regular) { tv0->split(0, 32); tv0->axis(1)->parallelize(ParallelType::TIDx); - tileInnerStmatrixCall(tv1, tile_m, tile_n); - tileInnerStmatrixCall(tv2, tile_m, tile_n); - - scheduleSwizzleHelper(tv1); - tv1->setAllocationDomain(tv1->getLoopDomain(), true); - // // Get 32 threads out to parallelize over. - tv1->merge(1); - tv1->axis(1)->parallelize(ParallelType::TIDx); + auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( + tv1->getLoopDomain()); + tv1->setLoopDomain(s.as()); + tv1->setAllocationDomain(s.as(), true); - scheduleStmatrixOutput(tv2, tile_m, tile_n); + mma_utils::scheduleStMatrixForMmaOutput(tv2, tile_m, tile_n); tv3->merge(0); tv3->split(0, 32); @@ -2950,14 +2833,6 @@ TEST_P(StMatrixTest, Regular) { testValidate(&fusion, cg_outputs, {t0}, __LINE__, __FILE__); } -INSTANTIATE_TEST_SUITE_P( - , - StMatrixSingleTileTest, - testing::Values( - // M, N - std::vector{8, 8}, - std::vector{16, 8}, - std::vector{16, 16})); std::string testNameStMatrixTest( const testing::TestParamInfo& info) { @@ -2981,7 +2856,6 @@ INSTANTIATE_TEST_SUITE_P( kAllHopperMacros, testing::Values( // tile_m, tile_n - std::vector{8, 8}, std::vector{16, 8}, std::vector{16, 16})), testNameStMatrixTest); diff --git a/tests/cpp/test_mma.cpp b/tests/cpp/test_mma.cpp index 5493b4a19d5..474d924827a 100644 --- a/tests/cpp/test_mma.cpp +++ b/tests/cpp/test_mma.cpp @@ -496,6 +496,169 @@ TEST_P(HopperRS, SingleTileWithTMALoadStore) { EXPECT_TRUE(at::allclose(cg_outputs[0], tref, 1e-5, 1e-5)); } +using HopperMmaRSStMatrixTestParams = std::tuple< + MmaMacro, + PrimDataType, + MmaLayout, + MmaInputSmemSwizzle, + std::vector>; + +class HopperRSStmatrix + : public HopperBase, + public ::testing::WithParamInterface { + protected: + MmaLayout layout; + MmaMacro macro; + PrimDataType dtype; + MmaInputSmemSwizzle swizzle_b; + std::vector tile_sizes; + + void SetUp() override { + HopperBase::SetUp(); + macro = std::get<0>(GetParam()); + dtype = std::get<1>(GetParam()); + layout = std::get<2>(GetParam()); + swizzle_b = std::get<3>(GetParam()); + tile_sizes = std::get<4>(GetParam()); + } +}; + +TEST_P(HopperRSStmatrix, SingleTileWithTMALoadStoreStMatrix) { + Fusion fusion; + FusionGuard fg(&fusion); + + auto shapes = matmulAtInputShape3DHopperRS( + getM(macro), getN(macro), getK(macro), layout); + + auto tile_m = tile_sizes.at(0); + auto tile_n = tile_sizes.at(1); + + if (getM(macro) % tile_m || getN(macro) % tile_n) { + GTEST_SKIP() << "skipping test as output is not divisible by tile size"; + } + + auto tv0 = makeContigConcreteTensor(shapes.first, dtype); + auto tv1 = makeContigConcreteTensor(shapes.second, dtype); + fusion.addInput(tv0); + fusion.addInput(tv1); + + // Just doing a gmem->register copy + tv0 = set(tv0); + // Just doing a gmem->smem copy + tv1 = set(tv1); + tv1->setMemoryType(MemoryType::Shared); + tv1->definition()->as()->setOpType( + LoadStoreOpType::CpAsyncBulkTensorTile); + + int axes = 0; + switch (layout) { + case MmaLayout::TT: + axes = 1; + break; + case MmaLayout::TN: + axes = 2; + break; + default: + NVF_ERROR("Invalid layout"); + } + + auto tv2 = fusedMultiplySum(tv0, tv1, {axes}); + + auto tv3 = castOp(dtype, tv2); + + auto tv4 = set(tv3); + tv3->setMemoryType(MemoryType::Shared); + tv4->definition()->as()->setOpType( + LoadStoreOpType::CpAsyncBulkTensorTile); + + fusion.addOutput(tv4); + + auto mma_ops = ir_utils::getOpsOfType(&fusion); + NVF_CHECK( + 1 == mma_ops.size(), + "Invalid number of MmaOp instances in fusion definition, expected 1, got ", + mma_ops.size()); + mma_ops.front()->setMacro(macro); + + auto tv3c = tv3->cacheBefore(); + + tv3->definition()->as()->setOpType(LoadStoreOpType::StMatrix); + + matmul_utils::moveInnerBroadcastLeft(tv0); + tv0->applyMmaSwizzle(MmaOperand::A); + + fusion.manage("st_matrix_m_tile", tile_m); + fusion.manage("st_matrix_n_tile", tile_n); + fusion.manage("st_matrix_m", getM(macro)); + fusion.manage("st_matrix_n", getN(macro)); + + tv0->merge(1); + tv0->merge(1); + tv0->axis(1)->parallelize(ParallelType::TIDx); + + matmul_utils::moveInnerBroadcastLeft(tv1); + tv1->applyMmaSwizzleForTMALoad(swizzle_b); + + if (layout == MmaLayout::TT) { + // [M, K, N] -> [M, N, K] + tv2->reorder({{-1, -2}}); + } + + EXPECT_TRUE(tv3c->getMemoryType() == MemoryType::Local); + EXPECT_TRUE(tv3->getMemoryType() == MemoryType::Shared); + EXPECT_TRUE(tv4->getMemoryType() == MemoryType::Global); + + { + auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( + tv3c->getLoopDomain()); + tv3c->setLoopDomain(s.as()); + tv3c->setAllocationDomain(s.as(), true); + // Note: according to internal doc "Representing ldmatrix", we need both a + // read domain and a write domain to correctly represent MmaOp. Without this + // new mechanism, there is no correct loop domain, and the only choices are + // either we want to represent the smem read well, or represent the register + // write well. We choose to represent the smem read well here. Likely, this + // means we will not be able to have multiple tiles in register, but we can + // workaround this by always inlining the MmaOp most. We should fix this + // after we implemented the new read/write domain mechanism. + } + { + auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( + tv2->getLoopDomain()); + tv2->setAllocationDomain(s.as(), true); + // Note: according to internal doc "Representing ldmatrix", we need both a + // read domain and a write domain to correctly represent MmaOp. Without this + // new mechanism, there is no correct loop domain, and the only choices are + // either we want to represent the smem read well, or represent the register + // write well. We choose to represent the smem read well here. Likely, this + // means we will not be able to have multiple tiles in register, but we can + // workaround this by always inlining the MmaOp most. We should fix this + // after we implemented the new read/write domain mechanism. + tv2->axis(-1)->parallelize(ParallelType::Mma); + tv2->axis(-2)->parallelize(ParallelType::Mma); + tv2->axis(-3)->parallelize(ParallelType::Mma); + } + + mma_utils::scheduleStMatrixForMmaOutput(tv3, tile_m, tile_n); + + mma_utils::scheduleTMAStoreForMmaOutput(tv4, getM(macro), getN(macro)); + + auto inputs = matmulAtInput3DHopperRS( + getM(macro), getN(macro), getK(macro), layout, data_type_to_aten(dtype)); + + FusionExecutor fe; + fe.compileFusion( + &fusion, {inputs.first, inputs.second}, LaunchParams(), matmul_cparams); + + auto cg_outputs = fe.runFusion({inputs.first, inputs.second}); + auto tref = atMatmul( + inputs.first.squeeze().to(at::kFloat), + inputs.second.squeeze().to(at::kFloat), + layout); + + EXPECT_TRUE(at::allclose(cg_outputs[0], tref.to(at::kHalf), 1e-1, 1e-1)); +} + std::string testNameHopperRS( const testing::TestParamInfo& info) { std::ostringstream os; @@ -508,6 +671,19 @@ std::string testNameHopperRS( return os.str(); } +INSTANTIATE_TEST_SUITE_P( + MmaTest, + HopperRSStmatrix, + testing::Combine( + kAllHopperMacros, + testing::Values(DataType::Half), + testing::Values(MmaLayout::TN, MmaLayout::TT), + kAllSmemSwizzleModes, + testing::Values( + // M, N + std::vector{16, 8}, + std::vector{16, 16}))); + INSTANTIATE_TEST_SUITE_P( MmaTest, HopperRS, From c8d1f6d5788c5c0feacf2363af34f6b77db0b1fd Mon Sep 17 00:00:00 2001 From: protonu Date: Thu, 14 Nov 2024 07:32:11 -0800 Subject: [PATCH 02/14] adding comments --- csrc/device_lower/pass/index.cpp | 176 ++++++++++--------------------- tests/cpp/test_mma.cpp | 22 +--- 2 files changed, 62 insertions(+), 136 deletions(-) diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index 913dc7e12b1..e7edb5090a4 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1581,15 +1581,31 @@ static inline DataType getMmaOutType(TensorView* mma_out) { } namespace { + // Helper function to compute the index of the output of stmatrix (in shared -// memory). We have two special cases (1) Handle tiles of size 8x8 and 16x8 and -// (2) tiles of size 16x16. Each of these two cases has further two special -// cases, (1) there is exactly one tile, (2) mulitple tiles thus multiple -// stmatrix calls are issued in a for-loop. Please note that in a 16x16 tile, -// the 4 8x8 tiles are stores/accessed in a m/column-major fasion. When we -// mulitple tiles (whether it be 8x8, 18x6 or 16x16), the tiles are stored in a -// m/column-major fashion. -#if 0 +// memory). We currently handle the cases: +// 1. stmatrix.x4 (we have 16x16 tiles) +// 2. stmatrix.x2 (we have 16x8 tiles) +// The assumption of the function is that we have 128-threads (warp group or 4 +// warps) The stmatrix call is inside a for-loop. We can think of 4-stmatrix (4 +// warps) calls being issued for every iteration of the for-loop. For example if +// we are storing [64, 16] piece of memory using stmatrix.x2 (16x8), there will +// be two iteration of the for-loop. In the first iteration we'll store [64 (0 +// ... 63), 8(0 ... 7)] and in the next iteration we'll store [64 (0 ... 63), +// 8(8 ... 15)]. + +// In each iteration, the 4-warps will be storing what we'll call +// a warp group box. Each warp group box will have (at most) 4 tile boxes, +// that's the memory stored by each stmatrix issued. This each tile-box is of +// shape: [16, 16], [16, 8]; to be supported: [8, 8]. + +// For example, if we are store [64, 16] using stmatrix.x2 [16, 8], then the +// warp group box will be [64, 8] (there will be 2 warp group boxes). If we are +// using stmatrix.x4 [16, 16] then the warp group box will be [64, 16]. + +// To compute the index/offset into shared memory, we sum the offset of the warp +// group box, the tile box inside the warp group box, and then finally the +// offset of the thread inside the tile box. Val* hardCodedIndexGenerationForStMatrix( const LoadStoreOp* ldst, const ForLoop* outer_loop, @@ -1601,138 +1617,50 @@ Val* hardCodedIndexGenerationForStMatrix( (m_tile == 8 && n_tile == 8) || (m_tile == 16 && n_tile == 8) || (m_tile == 16 && n_tile == 16), "size not currently supported for stmatrix"); - - auto m_tiles = m / m_tile; - Val* out_index = nullptr; - // For example: M=32, N=64, m_tile=16, n_tile=8. - // So m_tiles == 32/16 == 2; n_tiles == 8 - // This is computed by getting the loop index say L, then mod the - // number of tiles in the m-dimension, then multiplying by n (*2 for Half) - // into the size of the tile in the n-dimension. - // (L % m_tiles) * m_tile * n * 2 - auto tile_offset_m = IrBuilder::mulExpr( - IrBuilder::modExpr( - outer_loop->index(), - IrBuilder::create(m_tiles, DataType::Index)), - IrBuilder::create(m_tile * n * 2, DataType::Index)); - - // Offset of the tile in the n-dim. - // (L / m_tiles) * n_tile * 2 - auto tile_offset_n = IrBuilder::mulExpr( - IrBuilder::divExpr( - outer_loop->index(), - IrBuilder::create(m_tiles, DataType::Index)), - IrBuilder::create(n_tile * 2, DataType::Index)); - - // This will hanlde 8x8 and 16x8. - if (n_tile == 8) { - // tidx.x * n * 2(dtype = Half) - auto offset_in_tile = IrBuilder::mulExpr( - IrBuilder::create(n * 2, DataType::Index), - IrBuilder::create("threadIdx.x", DataType::Index)); - - // If it's a single tile we don't need to add the tile offsets. - if (m_tile == m && n_tile == n) { - out_index = IrBuilder::addExpr( - IrBuilder::baseAddressExpr(dynamic_cast(ldst->out())), - offset_in_tile); - - } else { - out_index = IrBuilder::addExpr( - IrBuilder::baseAddressExpr(dynamic_cast(ldst->out())), - IrBuilder::addExpr( - offset_in_tile, - IrBuilder::addExpr(tile_offset_m, tile_offset_n))); - } - - } else if (n_tile == 16) { - // This will hanlde 16x16 - - // 2 is for dtype Half - // (tidx.x / (8 * 2)) * 16 - // This gives the offset in the n-dim - auto offset_in_tile_n = IrBuilder::mulExpr( - IrBuilder::create(16, DataType::Index), - IrBuilder::divExpr( - IrBuilder::create("threadIdx.x", DataType::Index), - IrBuilder::create(8 * 2, DataType::Index))); - - // (tidx.x%16) * n * 2 - // This gives the offset inside the 16x16 tile in the m-dim. - auto offset_in_tile_m = IrBuilder::mulExpr( - IrBuilder::modExpr( - IrBuilder::create("threadIdx.x", DataType::Index), - IrBuilder::create(16, DataType::Index)), - IrBuilder::create(n * 2, DataType::Index)); - - // If it's a single tile we don't need to add the tile offsets. - if (m_tile == m && n_tile == n) { - out_index = IrBuilder::addExpr( - IrBuilder::baseAddressExpr(ir_utils::getTvOutput(ldst)), - IrBuilder::addExpr(offset_in_tile_n, offset_in_tile_m)); - } else { - out_index = IrBuilder::addExpr( - IrBuilder::baseAddressExpr(ir_utils::getTvOutput(ldst)), - IrBuilder::addExpr( - offset_in_tile_n, - IrBuilder::addExpr( - offset_in_tile_m, - IrBuilder::addExpr(tile_offset_m, tile_offset_n)))); - } - } - Val* out = IrBuilder::create( - dynamic_cast(ldst->out()), out_index); - - return out; -} -#endif - -Val* hardCodedIndexGenerationForStMatrix( - const LoadStoreOp* ldst, - const ForLoop* outer_loop, - const int64_t m_tile, - const int64_t n_tile, - const int64_t m, - const int64_t n) { NVF_ERROR( - (m_tile == 8 && n_tile == 8) || (m_tile == 16 && n_tile == 8) || - (m_tile == 16 && n_tile == 16), - "size not currently supported for stmatrix"); - Val* out_index = nullptr; + ldst->out()->dtype() == DataType::Half, + "we only support half type in stmatrix"); + + auto dtype_size = 2; - // A tile_box can be 16x16, 16x8. 8x8 + // A tile_box can be 16x16, 16x8 [to do: 8x8] // A warp group (128 threads) can work on a number of tile boxes. int64_t max_tile_boxes_in_warp_group_box = 4; - // These two variables gives the shape of the warp group in terms of tile + // These two variables gives the shape of the warp group box in terms of tile // boxes. Think of a tile box as a stmatrix call. auto tiles_in_warp_group_m = - std::max(m / m_tile, max_tile_boxes_in_warp_group_box); + std::min(m / m_tile, max_tile_boxes_in_warp_group_box); auto tiles_in_warp_group_n = max_tile_boxes_in_warp_group_box / tiles_in_warp_group_m; + // The size of a warp group box in the m dim. auto warp_group_box_size_m = tiles_in_warp_group_m * m_tile; - // each iteration of the for-loop compute a warp-group worth of stmatrix + // Each iteration of the for-loop compute a warp group box worth of stmatrix + // Compute how many warp group boxes are there in the m-dim auto warp_groups_m = m / warp_group_box_size_m; - // With iteration we work on a different warp-group box. + // Compute the offset of a warp group box. + // On each iteration we work on a different warp-group box. So the offset + // is a function of the trip-count and the shape of the warp group box. auto warp_group_box_offset_m = IrBuilder::mulExpr( IrBuilder::modExpr( outer_loop->index(), IrBuilder::create(warp_groups_m, DataType::Index)), IrBuilder::create( - tiles_in_warp_group_m * m_tile * n * 2, DataType::Index)); + tiles_in_warp_group_m * m_tile * n * dtype_size, DataType::Index)); auto warp_group_box_offset_n = IrBuilder::mulExpr( IrBuilder::divExpr( outer_loop->index(), IrBuilder::create(warp_groups_m, DataType::Index)), IrBuilder::create( - tiles_in_warp_group_n * n_tile * 2, DataType::Index)); + tiles_in_warp_group_n * n_tile * dtype_size, DataType::Index)); + // Offset of the warp group box. auto warp_group_box_offset = IrBuilder::addExpr(warp_group_box_offset_m, warp_group_box_offset_n); @@ -1740,6 +1668,9 @@ Val* hardCodedIndexGenerationForStMatrix( IrBuilder::create("threadIdx.x", DataType::Index), IrBuilder::create(/* each warp gets a box */ 32, DataType::Index)); + // We compute the offset the the tile box inside the warp group box. + // We identify the co-ord (m, n ) of the tile box in the warp group box. + // The compute the offset of the box in terms of the m and n-dim and sum. auto tile_box_id_m = IrBuilder::modExpr( tile_box_id, IrBuilder::create(tiles_in_warp_group_m, DataType::Index)); @@ -1749,25 +1680,32 @@ Val* hardCodedIndexGenerationForStMatrix( IrBuilder::create(tiles_in_warp_group_m, DataType::Index)); auto tile_box_id_m_offset = IrBuilder::mulExpr( - tile_box_id_m, IrBuilder::create(m_tile * n * 2, DataType::Index)); + tile_box_id_m, IrBuilder::create(m_tile * n * dtype_size, DataType::Index)); auto tile_box_id_n_offset = IrBuilder::mulExpr( - tile_box_id_n, IrBuilder::create(n_tile * 2, DataType::Index)); + tile_box_id_n, IrBuilder::create(n_tile * dtype_size, DataType::Index)); + // Offset of the tile box inside the warp group box. auto tile_box_offset = IrBuilder::addExpr(tile_box_id_m_offset, tile_box_id_n_offset); + // Cumulative offset of the the warp group box and tile box. auto cum_offset = IrBuilder::addExpr(warp_group_box_offset, tile_box_offset); - // For example M=64, N=8. There's no for-loop. + + // If there is only one warp group box, then the cumulative offset is just the + // tile box offset. if (n == n_tile) { cum_offset = tile_box_offset; } - // Offset inside the tile. + // Compute the offset of the thread inside the tile box. + // Since each warp works on a tile box, and there are 128-threads + // the effective tidx is threadIdx % 32 auto effective_tidx = IrBuilder::modExpr( IrBuilder::create("threadIdx.x", DataType::Index), IrBuilder::create(32, DataType::Index)); + // tidx.x is the effective threadIdx.x if (m_tile == 16 && n_tile == 16) { // 2 is for dtype Half // (tidx.x / (8 * 2)) * 16 @@ -1775,14 +1713,14 @@ Val* hardCodedIndexGenerationForStMatrix( auto offset_in_tile_n = IrBuilder::mulExpr( IrBuilder::create(16, DataType::Index), IrBuilder::divExpr( - effective_tidx, IrBuilder::create(8 * 2, DataType::Index))); + effective_tidx, IrBuilder::create(8 * dtype_size, DataType::Index))); // (tidx.x%16) * n * 2 // This gives the offset inside the 16x16 tile in the m-dim. auto offset_in_tile_m = IrBuilder::mulExpr( IrBuilder::modExpr( effective_tidx, IrBuilder::create(16, DataType::Index)), - IrBuilder::create(n * 2, DataType::Index)); + IrBuilder::create(n * dtype_size, DataType::Index)); out_index = IrBuilder::addExpr( IrBuilder::baseAddressExpr(ir_utils::getTvOutput(ldst)), @@ -1791,7 +1729,7 @@ Val* hardCodedIndexGenerationForStMatrix( IrBuilder::addExpr(offset_in_tile_m, offset_in_tile_n))); } else if (m_tile == 16 && n_tile == 8) { auto offset_in_tile = IrBuilder::mulExpr( - IrBuilder::create(n * 2, DataType::Index), effective_tidx); + IrBuilder::create(n * dtype_size, DataType::Index), effective_tidx); out_index = IrBuilder::addExpr( IrBuilder::baseAddressExpr(dynamic_cast(ldst->out())), diff --git a/tests/cpp/test_mma.cpp b/tests/cpp/test_mma.cpp index 474d924827a..00ed5a7356c 100644 --- a/tests/cpp/test_mma.cpp +++ b/tests/cpp/test_mma.cpp @@ -587,6 +587,9 @@ TEST_P(HopperRSStmatrix, SingleTileWithTMALoadStoreStMatrix) { matmul_utils::moveInnerBroadcastLeft(tv0); tv0->applyMmaSwizzle(MmaOperand::A); + // This is a temporary way to pass this information + // to the custom index generator for stmatrix. + // TODO: remove the need for fusion managed cache. fusion.manage("st_matrix_m_tile", tile_m); fusion.manage("st_matrix_n_tile", tile_n); fusion.manage("st_matrix_m", getM(macro)); @@ -602,7 +605,7 @@ TEST_P(HopperRSStmatrix, SingleTileWithTMALoadStoreStMatrix) { if (layout == MmaLayout::TT) { // [M, K, N] -> [M, N, K] tv2->reorder({{-1, -2}}); - } + } EXPECT_TRUE(tv3c->getMemoryType() == MemoryType::Local); EXPECT_TRUE(tv3->getMemoryType() == MemoryType::Shared); @@ -613,27 +616,12 @@ TEST_P(HopperRSStmatrix, SingleTileWithTMALoadStoreStMatrix) { tv3c->getLoopDomain()); tv3c->setLoopDomain(s.as()); tv3c->setAllocationDomain(s.as(), true); - // Note: according to internal doc "Representing ldmatrix", we need both a - // read domain and a write domain to correctly represent MmaOp. Without this - // new mechanism, there is no correct loop domain, and the only choices are - // either we want to represent the smem read well, or represent the register - // write well. We choose to represent the smem read well here. Likely, this - // means we will not be able to have multiple tiles in register, but we can - // workaround this by always inlining the MmaOp most. We should fix this - // after we implemented the new read/write domain mechanism. } { auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( tv2->getLoopDomain()); tv2->setAllocationDomain(s.as(), true); - // Note: according to internal doc "Representing ldmatrix", we need both a - // read domain and a write domain to correctly represent MmaOp. Without this - // new mechanism, there is no correct loop domain, and the only choices are - // either we want to represent the smem read well, or represent the register - // write well. We choose to represent the smem read well here. Likely, this - // means we will not be able to have multiple tiles in register, but we can - // workaround this by always inlining the MmaOp most. We should fix this - // after we implemented the new read/write domain mechanism. + tv2->axis(-1)->parallelize(ParallelType::Mma); tv2->axis(-2)->parallelize(ParallelType::Mma); tv2->axis(-3)->parallelize(ParallelType::Mma); From 5a12e4344a061bb3d061e8e68e65535894695f40 Mon Sep 17 00:00:00 2001 From: protonu Date: Thu, 14 Nov 2024 09:56:25 -0800 Subject: [PATCH 03/14] more comments --- csrc/scheduler/mma_utils.cpp | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/csrc/scheduler/mma_utils.cpp b/csrc/scheduler/mma_utils.cpp index 0df24c7d55b..ecf9d55e7b1 100644 --- a/csrc/scheduler/mma_utils.cpp +++ b/csrc/scheduler/mma_utils.cpp @@ -1287,16 +1287,31 @@ void scheduleStMatrixForMmaOutput( NVF_ERROR( ((tile_m == 16 && tile_n == 16) || (tile_m == 16 && tile_n == 8)), "We only support 16x16 and 16x16 stmatrix now"); + + NVF_ERROR( + tv->dtype() == DataType::Half, "we only support half type in stmatrix"); + + // [M, N] -> [128(TIDx), N/8 , 2 , 2] auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation(tv->getLoopDomain()); tv->setLoopDomain(s.as()); + if (tile_m == 16 && tile_n == 16) { + // Let [M, N] be [64, 32] + // After scheduleMmaOutputAllocation: [128(TIDx), 4, 2, 2] + // [128(TIDx), 4(n), 2, 2] -> [128(TIDx), 2(no), 2(ni), 2, 2] tv->split(-3, 2); + // [128(TIDx), 2(no), 2(ni), 2, 2] -> [2(no), 128(TIDx), 2(ni), 2, 2] tv->reorder({{-4, -5}}); + // [128(TIDx), 2(no), 2(ni), 2, 2] -> [2(no), 128(TIDx), 8 (vectorize)] tv->merge(2); tv->merge(2); } else if (tile_m == 16 && tile_n == 8) { + // Let [M, N] be [64, 16] + // After scheduleMmaOutputAllocation: [128(TIDx), 2, 2, 2] + // [128(TIDx), 2, 2, 2] -> [2, 128(TIDx), 2, 2] tv->reorder({{-3, -4}}); + // [2, 128(TIDx), 2, 2] -> [2, 128(TIDx), 4(vectorize)] tv->merge(2); } tv->axis(-1)->parallelize(ParallelType::Vectorize); From f92bd8bbdba7b5f3afce70ecb37edc6b266eb7b9 Mon Sep 17 00:00:00 2001 From: protonu Date: Thu, 14 Nov 2024 10:31:21 -0800 Subject: [PATCH 04/14] rebase and removing a test --- tests/cpp/test_mma.cpp | 102 ++--------------------------------------- 1 file changed, 3 insertions(+), 99 deletions(-) diff --git a/tests/cpp/test_mma.cpp b/tests/cpp/test_mma.cpp index 00ed5a7356c..83cda79a43e 100644 --- a/tests/cpp/test_mma.cpp +++ b/tests/cpp/test_mma.cpp @@ -400,102 +400,6 @@ TEST_P(HopperRS, SingleTile) { EXPECT_TRUE(at::allclose(cg_outputs[0], tref, 1e-5, 1e-5)); } -TEST_P(HopperRS, SingleTileWithTMALoadStore) { - Fusion fusion; - FusionGuard fg(&fusion); - - auto shapes = matmulAtInputShape3DHopperRS( - getM(macro), getN(macro), getK(macro), layout); - - auto tv0 = makeContigConcreteTensor(shapes.first, dtype); - auto tv1 = makeContigConcreteTensor(shapes.second, dtype); - fusion.addInput(tv0); - fusion.addInput(tv1); - - // Just doing a gmem->register copy - tv0 = set(tv0); - // Just doing a gmem->smem copy - tv1 = set(tv1); - tv1->setMemoryType(MemoryType::Shared); - tv1->definition()->as()->setOpType( - LoadStoreOpType::CpAsyncBulkTensorTile); - - auto tv2 = fusedMultiplySum(tv0, tv1, {layout == MmaLayout::TT ? 1 : 2}); - - auto tv3 = set(tv2); - tv2->setMemoryType(MemoryType::Shared); - tv3->definition()->as()->setOpType( - LoadStoreOpType::CpAsyncBulkTensorTile); - - fusion.addOutput(tv3); - - auto mma_ops = ir_utils::getOpsOfType(&fusion); - NVF_CHECK( - 1 == mma_ops.size(), - "Invalid number of MmaOp instances in fusion definition, expected 1, got ", - mma_ops.size()); - mma_ops.front()->setMacro(macro); - - auto tv2c = tv2->cacheBefore(); - - matmul_utils::moveInnerBroadcastLeft(tv0); - tv0->applyMmaSwizzle(MmaOperand::A); - - tv0->merge(1); - tv0->merge(1); - tv0->axis(1)->parallelize(ParallelType::TIDx); - - matmul_utils::moveInnerBroadcastLeft(tv1); - tv1->applyMmaSwizzleForTMALoad(swizzle_b); - - if (layout == MmaLayout::TT) { - // [M, K, N] -> [M, N, K] - tv2c->reorder({{-1, -2}}); - } - - EXPECT_TRUE(tv2c->getMemoryType() == MemoryType::Local); - EXPECT_TRUE(tv2->getMemoryType() == MemoryType::Shared); - EXPECT_TRUE(tv3->getMemoryType() == MemoryType::Global); - - { - auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( - tv2c->getLoopDomain()); - tv2c->setAllocationDomain(s.as(), true); - // Note: according to internal doc "Representing ldmatrix", we need both a - // read domain and a write domain to correctly represent MmaOp. Without this - // new mechanism, there is no correct loop domain, and the only choices are - // either we want to represent the smem read well, or represent the register - // write well. We choose to represent the smem read well here. Likely, this - // means we will not be able to have multiple tiles in register, but we can - // workaround this by always inlining the MmaOp most. We should fix this - // after we implemented the new read/write domain mechanism. - tv2c->axis(-1)->parallelize(ParallelType::Mma); - tv2c->axis(-2)->parallelize(ParallelType::Mma); - tv2c->axis(-3)->parallelize(ParallelType::Mma); - } - { - auto s = mma_utils::MmaSwizzler::scheduleMmaOutputAllocation( - tv2->getLoopDomain()); - tv2->setLoopDomain(s.as()); - } - - mma_utils::scheduleTMAStoreForMmaOutput(tv3, getM(macro), getN(macro)); - - auto inputs = matmulAtInput3DHopperRS( - getM(macro), getN(macro), getK(macro), layout, data_type_to_aten(dtype)); - - KernelExecutor ke; - ke.compile( - &fusion, {inputs.first, inputs.second}, LaunchParams(), matmul_cparams); - - auto cg_outputs = ke.run({inputs.first, inputs.second}); - auto tref = atMatmul( - inputs.first.squeeze().to(at::kFloat), - inputs.second.squeeze().to(at::kFloat), - layout); - EXPECT_TRUE(at::allclose(cg_outputs[0], tref, 1e-5, 1e-5)); -} - using HopperMmaRSStMatrixTestParams = std::tuple< MmaMacro, PrimDataType, @@ -634,11 +538,11 @@ TEST_P(HopperRSStmatrix, SingleTileWithTMALoadStoreStMatrix) { auto inputs = matmulAtInput3DHopperRS( getM(macro), getN(macro), getK(macro), layout, data_type_to_aten(dtype)); - FusionExecutor fe; - fe.compileFusion( + KernelExecutor ke; + ke.compile( &fusion, {inputs.first, inputs.second}, LaunchParams(), matmul_cparams); - auto cg_outputs = fe.runFusion({inputs.first, inputs.second}); + auto cg_outputs = ke.run({inputs.first, inputs.second}); auto tref = atMatmul( inputs.first.squeeze().to(at::kFloat), inputs.second.squeeze().to(at::kFloat), From 2ec25d074483285bfab4af341ff84bf836800e91 Mon Sep 17 00:00:00 2001 From: protonu Date: Thu, 14 Nov 2024 19:28:36 -0800 Subject: [PATCH 05/14] getting the offsets in the scheduler utility function ready for more complex cases --- csrc/scheduler/mma_utils.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/csrc/scheduler/mma_utils.cpp b/csrc/scheduler/mma_utils.cpp index ecf9d55e7b1..ad3272625b7 100644 --- a/csrc/scheduler/mma_utils.cpp +++ b/csrc/scheduler/mma_utils.cpp @@ -1302,17 +1302,17 @@ void scheduleStMatrixForMmaOutput( // [128(TIDx), 4(n), 2, 2] -> [128(TIDx), 2(no), 2(ni), 2, 2] tv->split(-3, 2); // [128(TIDx), 2(no), 2(ni), 2, 2] -> [2(no), 128(TIDx), 2(ni), 2, 2] - tv->reorder({{-4, -5}}); + tv->reorder({{-4, 0}}); // [128(TIDx), 2(no), 2(ni), 2, 2] -> [2(no), 128(TIDx), 8 (vectorize)] - tv->merge(2); - tv->merge(2); + tv->merge(-3); + tv->merge(-2); } else if (tile_m == 16 && tile_n == 8) { // Let [M, N] be [64, 16] // After scheduleMmaOutputAllocation: [128(TIDx), 2, 2, 2] // [128(TIDx), 2, 2, 2] -> [2, 128(TIDx), 2, 2] - tv->reorder({{-3, -4}}); + tv->reorder({{-3, 0}}); // [2, 128(TIDx), 2, 2] -> [2, 128(TIDx), 4(vectorize)] - tv->merge(2); + tv->merge(-2); } tv->axis(-1)->parallelize(ParallelType::Vectorize); } From aa1d7dbfa4ee3412bc52c4e7c9de2758c0316f1d Mon Sep 17 00:00:00 2001 From: protonu Date: Thu, 14 Nov 2024 20:18:03 -0800 Subject: [PATCH 06/14] more comments --- csrc/device_lower/pass/index.cpp | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index e7edb5090a4..f6d4aae9f0b 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1606,6 +1606,22 @@ namespace { // To compute the index/offset into shared memory, we sum the offset of the warp // group box, the tile box inside the warp group box, and then finally the // offset of the thread inside the tile box. + +// For an example of index computation assume we are storing [64, 32] using +// stmatrix using a single warp group, that is the cta has 128 threads/4-warps. +// We'll be using stmatrix.x4 (16x16 tiles). This will need two iterations of +// the for-loop (loop index I = [0,1]). After each iteration we'd have store +// [64, 16] piece of memory or a warp group box worth. Thus in this case the +// offset of warp group box is: + +// warp_group_box_size_m = 64; warp_group_box_size_n = 16 * 2 = 32 +// num_warps_groups_m = 64/ (warp_group_box_size_m = 64) = 1 +// warp_groups_box_offset_m = (I % num_warps_groups_m ) * warp_group_box_size_m = 0 +// warp_groups_box_offset_n = (I / num_warps_groups_m) * warp_group_box_size_n = 32 (when I = 1) +// warp_groups_offset = warp_groups_box_offset_m + warp_groups_box_offset_n + +// A warp group box of size [64, 16] has 4 tile boxes of size [16, 16] each. + Val* hardCodedIndexGenerationForStMatrix( const LoadStoreOp* ldst, const ForLoop* outer_loop, @@ -1680,10 +1696,12 @@ Val* hardCodedIndexGenerationForStMatrix( IrBuilder::create(tiles_in_warp_group_m, DataType::Index)); auto tile_box_id_m_offset = IrBuilder::mulExpr( - tile_box_id_m, IrBuilder::create(m_tile * n * dtype_size, DataType::Index)); + tile_box_id_m, + IrBuilder::create(m_tile * n * dtype_size, DataType::Index)); auto tile_box_id_n_offset = IrBuilder::mulExpr( - tile_box_id_n, IrBuilder::create(n_tile * dtype_size, DataType::Index)); + tile_box_id_n, + IrBuilder::create(n_tile * dtype_size, DataType::Index)); // Offset of the tile box inside the warp group box. auto tile_box_offset = @@ -1713,7 +1731,8 @@ Val* hardCodedIndexGenerationForStMatrix( auto offset_in_tile_n = IrBuilder::mulExpr( IrBuilder::create(16, DataType::Index), IrBuilder::divExpr( - effective_tidx, IrBuilder::create(8 * dtype_size, DataType::Index))); + effective_tidx, + IrBuilder::create(8 * dtype_size, DataType::Index))); // (tidx.x%16) * n * 2 // This gives the offset inside the 16x16 tile in the m-dim. @@ -1729,7 +1748,8 @@ Val* hardCodedIndexGenerationForStMatrix( IrBuilder::addExpr(offset_in_tile_m, offset_in_tile_n))); } else if (m_tile == 16 && n_tile == 8) { auto offset_in_tile = IrBuilder::mulExpr( - IrBuilder::create(n * dtype_size, DataType::Index), effective_tidx); + IrBuilder::create(n * dtype_size, DataType::Index), + effective_tidx); out_index = IrBuilder::addExpr( IrBuilder::baseAddressExpr(dynamic_cast(ldst->out())), From 929a03ad9d1f09e01fc7211f5559ec6e06d509c5 Mon Sep 17 00:00:00 2001 From: protonu Date: Fri, 15 Nov 2024 09:24:42 -0800 Subject: [PATCH 07/14] more comments --- csrc/device_lower/pass/index.cpp | 54 ++++++++++++++++++++++++++------ 1 file changed, 45 insertions(+), 9 deletions(-) diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index f6d4aae9f0b..484bdb7e4e9 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1616,12 +1616,49 @@ namespace { // warp_group_box_size_m = 64; warp_group_box_size_n = 16 * 2 = 32 // num_warps_groups_m = 64/ (warp_group_box_size_m = 64) = 1 -// warp_groups_box_offset_m = (I % num_warps_groups_m ) * warp_group_box_size_m = 0 -// warp_groups_box_offset_n = (I / num_warps_groups_m) * warp_group_box_size_n = 32 (when I = 1) -// warp_groups_offset = warp_groups_box_offset_m + warp_groups_box_offset_n - -// A warp group box of size [64, 16] has 4 tile boxes of size [16, 16] each. - +// warp_groups_box_offset_m = +// (I % num_warps_groups_m ) * warp_group_box_size_m = 0 +// warp_groups_box_offset_n = +// (I / num_warps_groups_m) * warp_group_box_size_n = 32 (when I = 1) +// warp_groups_offset = +// warp_groups_box_offset_m + warp_groups_box_offset_n + +// A warp group box of size [64(M), 16(N)] has 4 tile boxes of size [16(tile_m), +// 16(tile_n)] each. +// Since each warp computes a tile box, we find tile_box_id as: +// tile_box_id = threadIdx.x/32 +// tile_box_id will have two components tile_box_id_m, tile_box_id_n +// But first we compute the number of tile boxes in the m and n dim . +// tile_boxes_in_warp_group_box_m = +// min (4, m/ tile_m(16)) +// tile_boxes_in_warp_group_box_n = 4 / tile_boxes_in_warp_group_box_m +// Now tile_box_id_m = tile_box_id % tile_boxes_in_warp_group_box_m +// tile_box_id_n = tile_box_id / tile_boxes_in_warp_group_box_m +// tile_box_offset_m = tile_box_id_m * tile_m(16) * N * 2 (half) +// tile_box_offset_n = tile_box_id_m * tile_n (16) * N * 2 (half) +// tile_box_offset = tile_box_offet_m + tile_box_offset_m + +// Inside the tile box [16, 16], we can think of it as 4 8x8 tiles +// ***************** +// * * * +// * * * +// * T0 * T2 * +// * * * +// * * * +// ***************** +// * * * +// * * * +// * T1 * T3 * +// * * * +// * * * +// ***************** +// Since there aree 128 threads working on 4 tile boxes. +// Effective threadIdx effective_tidx = tidx.x % 32 +// offset_in_tile_box_m = +// (effective_tidx % 16 ) * N * 2 (half) +// offset_in_tile_box_n = +// (effective_tidx/ 16 ) * 8 * 2 (half) +// offset_in_tile = offset_in_tile_box_m + offset_in_tile_box_n Val* hardCodedIndexGenerationForStMatrix( const LoadStoreOp* ldst, const ForLoop* outer_loop, @@ -1729,10 +1766,9 @@ Val* hardCodedIndexGenerationForStMatrix( // (tidx.x / (8 * 2)) * 16 // This gives the offset in the n-dim auto offset_in_tile_n = IrBuilder::mulExpr( - IrBuilder::create(16, DataType::Index), + IrBuilder::create(8 * dtype_size, DataType::Index), IrBuilder::divExpr( - effective_tidx, - IrBuilder::create(8 * dtype_size, DataType::Index))); + effective_tidx, IrBuilder::create(16, DataType::Index))); // (tidx.x%16) * n * 2 // This gives the offset inside the 16x16 tile in the m-dim. From e617b65ed22b4de74639c5e7a7060c52a4579bb2 Mon Sep 17 00:00:00 2001 From: protonu Date: Fri, 15 Nov 2024 11:44:28 -0800 Subject: [PATCH 08/14] add offset from threadIdx.y --- csrc/device_lower/pass/index.cpp | 30 ++++++++++++++++++++++++------ 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index 484bdb7e4e9..71eea65dd36 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1659,6 +1659,18 @@ namespace { // offset_in_tile_box_n = // (effective_tidx/ 16 ) * 8 * 2 (half) // offset_in_tile = offset_in_tile_box_m + offset_in_tile_box_n +// cumulative_offset = warp_groups_offset + tile_box_offset + offset_in_tile + +// In the above comment, M and N are instruction tiles. +// We can mulitples of 4-warps (warp groups) working to execute stmatrix. +// threadIdx.x will range from [0 ... 127] and will be the 4 warps to store +// an instruction tile work of data. +// threadIdx.y will provide the multiples of warp groups. + +// To account for the threadIdx.y we have to add it to the offest: +// offset_from_threadIdx.y = threadIdx.y * M * N * 2 (half) + +// Final offset: cumulative_offset + offset_from_threadIdx.y Val* hardCodedIndexGenerationForStMatrix( const LoadStoreOp* ldst, const ForLoop* outer_loop, @@ -1744,14 +1756,20 @@ Val* hardCodedIndexGenerationForStMatrix( auto tile_box_offset = IrBuilder::addExpr(tile_box_id_m_offset, tile_box_id_n_offset); - // Cumulative offset of the the warp group box and tile box. - auto cum_offset = IrBuilder::addExpr(warp_group_box_offset, tile_box_offset); - // If there is only one warp group box, then the cumulative offset is just the // tile box offset. - if (n == n_tile) { - cum_offset = tile_box_offset; - } + auto warp_box_tile_box_offset_sum = (n == n_tile) + ? tile_box_offset + : IrBuilder::addExpr(warp_group_box_offset, tile_box_offset); + + auto threadIdx_y_offset = IrBuilder::mulExpr( + IrBuilder::create("threadIdx.y", DataType::Index), + IrBuilder::create(m * n * 2)); + + // Cumulative offset of the the warp group box and tile box and the product of + // the threadIx.y and the instruction tile. + auto cum_offset = + IrBuilder::addExpr(threadIdx_y_offset, warp_box_tile_box_offset_sum); // Compute the offset of the thread inside the tile box. // Since each warp works on a tile box, and there are 128-threads From eb43ef3d24d65bdd1765fa64e69f60ae844e03d6 Mon Sep 17 00:00:00 2001 From: protonu Date: Tue, 19 Nov 2024 09:46:05 -0800 Subject: [PATCH 09/14] addressing reviewer comments --- csrc/device_lower/pass/index.cpp | 4 ++-- report.ncu-rep | Bin 0 -> 40716133 bytes tests/cpp/test_memory.cpp | 5 ++--- 3 files changed, 4 insertions(+), 5 deletions(-) create mode 100644 report.ncu-rep diff --git a/csrc/device_lower/pass/index.cpp b/csrc/device_lower/pass/index.cpp index 71eea65dd36..80e75938389 100644 --- a/csrc/device_lower/pass/index.cpp +++ b/csrc/device_lower/pass/index.cpp @@ -1635,8 +1635,8 @@ namespace { // Now tile_box_id_m = tile_box_id % tile_boxes_in_warp_group_box_m // tile_box_id_n = tile_box_id / tile_boxes_in_warp_group_box_m // tile_box_offset_m = tile_box_id_m * tile_m(16) * N * 2 (half) -// tile_box_offset_n = tile_box_id_m * tile_n (16) * N * 2 (half) -// tile_box_offset = tile_box_offet_m + tile_box_offset_m +// tile_box_offset_n = tile_box_id_n * tile_n (16) * N * 2 (half) +// tile_box_offset = tile_box_offet_m + tile_box_offset_n // Inside the tile box [16, 16], we can think of it as 4 8x8 tiles // ***************** diff --git a/report.ncu-rep b/report.ncu-rep new file mode 100644 index 0000000000000000000000000000000000000000..b673a64ccad3116a4a1c3d82f6f2a884fb09d287 GIT binary patch literal 40716133 zcmeFa4SZeKRVR9UE!$WAkgl#itPk6|lCEqyPOJ~hvQyI7P2)HrY3#-^boyX!WJ^x` zkY#gpon9yUC8`=a4rNGQ9@CcsQ_>Jh2+&`E4zGqzqn5OU6532BFnP4hxWR-_`eA6N zQ<#F@|G)OxXYX@#@70klTb6Tn8|&`xwbxpE?X}n5XYD(G>@FwaI8JiKXZf#_a+)7X 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