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[Codegen][GPU] Add pass for fallback distribution (#18726)
If any operation fails to tile or fuse in earlier pipeline stages we need to have a fallback distribution step. This adds a pass for fallback distribution that looks for all tilable ops not inside `scf.forall` regions mapped to threads and greedily tiles + fuses starting from the base of the dispatch.
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compiler/src/iree/compiler/Codegen/Common/GPU/GPUGreedilyDistributeToThreads.cpp
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| Original file line number | Diff line number | Diff line change |
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| // Copyright 2024 The IREE Authors | ||
| // | ||
| // Licensed under the Apache License v2.0 with LLVM Exceptions. | ||
| // See https://llvm.org/LICENSE.txt for license information. | ||
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | ||
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||
| #include "iree/compiler/Codegen/Common/GPU/Passes.h" | ||
| #include "iree/compiler/Codegen/Dialect/GPU/IR/IREEGPUAttrs.h" | ||
| #include "iree/compiler/Codegen/Dialect/GPU/IR/IREEGPUEnums.h" | ||
| #include "iree/compiler/Codegen/Dialect/GPU/Transforms/Transforms.h" | ||
| #include "iree/compiler/Codegen/Utils/GPUUtils.h" | ||
| #include "llvm/ADT/STLForwardCompat.h" | ||
| #include "llvm/ADT/SmallVectorExtras.h" | ||
| #include "mlir/Dialect/Affine/IR/AffineOps.h" | ||
| #include "mlir/Dialect/Arith/IR/Arith.h" | ||
| #include "mlir/Dialect/GPU/IR/GPUDialect.h" | ||
| #include "mlir/Dialect/SCF/IR/SCF.h" | ||
| #include "mlir/Interfaces/TilingInterface.h" | ||
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|
||
| namespace mlir::iree_compiler { | ||
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| #define GEN_PASS_DEF_GPUGREEDILYDISTRIBUTETOTHREADSPASS | ||
| #include "iree/compiler/Codegen/Common/GPU/Passes.h.inc" | ||
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| namespace { | ||
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| struct GPUGreedilyDistributeToThreadsPass final | ||
| : impl::GPUGreedilyDistributeToThreadsPassBase< | ||
| GPUGreedilyDistributeToThreadsPass> { | ||
| void runOnOperation() override; | ||
| }; | ||
| } // namespace | ||
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| /// Helper to tile and greedily fuse the given operation to threads. This uses | ||
| /// the iree_gpu.derived_thread_config logic internally to determine tile sizes | ||
| /// to use. This does not yield any fused operation and only replaces the tiling | ||
| /// root. | ||
| /// | ||
| /// If tiling fails this returns silently (tiling is best effort). Later | ||
| /// verification steps will throw an error if distribution does not occur. | ||
| static void tileToThreads(RewriterBase &rewriter, | ||
| TilingInterface tilingInterfaceOp) { | ||
| rewriter.setInsertionPoint(tilingInterfaceOp); | ||
| auto configAttr = | ||
| IREE::GPU::DerivedThreadConfigAttr::get(rewriter.getContext()); | ||
| SmallVector<OpFoldResult> tileSizes = configAttr.getTilingLevelSizes( | ||
| rewriter, llvm::to_underlying(IREE::GPU::TilingLevel::Thread), | ||
| tilingInterfaceOp); | ||
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| // Pad the tile sizes with zero. | ||
| auto zero = rewriter.getIndexAttr(0); | ||
| int64_t numLoops = tilingInterfaceOp.getLoopIteratorTypes().size(); | ||
| if (tileSizes.size() > numLoops) { | ||
| return; | ||
| } | ||
| while (tileSizes.size() < numLoops) { | ||
| tileSizes.push_back(zero); | ||
| } | ||
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| scf::SCFTilingOptions tilingOptions; | ||
| tilingOptions.setTileSizes(tileSizes); | ||
| tilingOptions.setLoopType(scf::SCFTilingOptions::LoopType::ForallOp); | ||
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| // Use a "descending" relative thread mapping (ordered from slowest to | ||
| // fastest varying ID). For example, [linear_dim_n, ..., linear_dim_0]. | ||
| SmallVector<Attribute> mapping; | ||
| int idx = 0; | ||
| for (auto size : tileSizes) { | ||
| if (!isConstantIntValue(size, 0)) { | ||
| unsigned mappingId = | ||
| static_cast<unsigned>(gpu::MappingId::LinearDim0) + idx++; | ||
| mapping.push_back(gpu::GPUThreadMappingAttr::get( | ||
| rewriter.getContext(), static_cast<gpu::MappingId>(mappingId))); | ||
| } | ||
| } | ||
| tilingOptions.setMapping(llvm::to_vector(llvm::reverse(mapping))); | ||
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| scf::SCFTileAndFuseOptions tileAndFuseOptions; | ||
| tileAndFuseOptions.setTilingOptions(tilingOptions); | ||
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| scf::SCFTileAndFuseOptions::ControlFnTy controlFn = | ||
| [&](tensor::ExtractSliceOp candidateSliceOp, OpResult originalProducer, | ||
| bool isDestinationOperand) | ||
| -> std::optional<scf::SCFTileAndFuseOptions::ControlFnResult> { | ||
| // Always fuse but never yield a replacement. | ||
| return scf::SCFTileAndFuseOptions::ControlFnResult{ | ||
| /*yieldProducerReplacement=*/false}; | ||
| }; | ||
| tileAndFuseOptions.setFusionControlFn(controlFn); | ||
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| FailureOr<scf::SCFTileAndFuseResult> tiledResults = | ||
| scf::tileConsumerAndFuseProducersUsingSCF(rewriter, tilingInterfaceOp, | ||
| tileAndFuseOptions); | ||
| if (failed(tiledResults)) { | ||
| return; | ||
| } | ||
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| // Perform the replacement of the tiling root. | ||
| for (OpResult res : tilingInterfaceOp->getResults()) { | ||
| if (auto replacement = tiledResults->replacements.lookup(res)) { | ||
| rewriter.replaceAllUsesWith(res, replacement); | ||
| } | ||
| } | ||
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| if (tilingInterfaceOp->use_empty()) { | ||
| rewriter.eraseOp(tilingInterfaceOp); | ||
| } | ||
| } | ||
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| /// Recursively process the given region and tile all tilable operations not | ||
| /// present within an `scf.forall` with thread/warp/lane mapping. | ||
| static void processRegion(RewriterBase &rewriter, Region *region) { | ||
| // Process the region blocks in reverse. | ||
| for (Block &block : llvm::reverse(region->getBlocks())) { | ||
| // Save a reversed list of operations within the block. Ops will be | ||
| // greedily tiled + fused in reverse so that if a producer can be fused | ||
| // with a consumer we only distribute the producer once via fusion. | ||
| SmallVector<Operation *> targetOps = | ||
| llvm::map_to_vector(llvm::reverse(block.getOperations()), | ||
| [](Operation &op) { return &op; }); | ||
| // Skip all unused ops (possibly from tiling). | ||
| for (Operation *op : targetOps) { | ||
| if (op->use_empty()) { | ||
| continue; | ||
| } | ||
| // Skip all operations contained within an `scf.forall` mapped to threads | ||
| // warps or lanes. These are already distributed and fine to leave as is. | ||
| if (auto forall = dyn_cast<scf::ForallOp>(op)) { | ||
| if (forallOpHasMappingType<gpu::GPUThreadMappingAttr, | ||
| gpu::GPUWarpMappingAttr, | ||
| IREE::GPU::LaneIdAttr>(forall)) { | ||
| continue; | ||
| } | ||
| } | ||
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| // If an op implements the tiling interface, try to greedily tile + fuse. | ||
| if (auto tilableOp = dyn_cast<TilingInterface>(op)) { | ||
| tileToThreads(rewriter, tilableOp); | ||
| continue; | ||
| } | ||
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| // Else recursively process all nested operations. | ||
| for (auto ®ion : op->getRegions()) { | ||
| processRegion(rewriter, ®ion); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| void GPUGreedilyDistributeToThreadsPass::runOnOperation() { | ||
| auto funcOp = getOperation(); | ||
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| IRRewriter rewriter(funcOp->getContext()); | ||
| for (auto ®ion : funcOp->getRegions()) { | ||
| processRegion(rewriter, ®ion); | ||
| } | ||
| } | ||
|
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||
| } // namespace mlir::iree_compiler |
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162 changes: 162 additions & 0 deletions
162
compiler/src/iree/compiler/Codegen/Common/GPU/test/gpu_greedily_distribute_to_threads.mlir
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,162 @@ | ||
| // RUN: iree-opt %s --split-input-file --mlir-print-local-scope \ | ||
| // RUN: --pass-pipeline="builtin.module(func.func(iree-codegen-gpu-greedily-distribute-to-threads, canonicalize, cse))" | \ | ||
| // RUN: FileCheck %s | ||
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| #map = affine_map<(d0, d1) -> (d0, d1)> | ||
| func.func @simple_generic(%3: tensor<64x256xf32>, %4: tensor<64x256xf32>, %5: tensor<64x256xf32>) -> tensor<64x256xf32> | ||
| attributes { | ||
| translation_info = #iree_codegen.translation_info<LLVMGPUTileAndFuse workgroup_size = [64, 1, 1] subgroup_size = 64, {}> | ||
| } { | ||
| %6 = linalg.generic { | ||
| indexing_maps = [#map, #map, #map], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%3, %4 : tensor<64x256xf32>, tensor<64x256xf32>) outs(%5 : tensor<64x256xf32>) { | ||
| ^bb0(%in: f32, %in_0: f32, %out: f32): | ||
| %7 = arith.addf %in, %in_0 : f32 | ||
| linalg.yield %7 : f32 | ||
| } -> tensor<64x256xf32> | ||
| return %6 : tensor<64x256xf32> | ||
| } | ||
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| // CHECK-LABEL: func.func @simple_generic | ||
| // CHECK: scf.forall | ||
| // CHECK: linalg.generic {{.*}} outs({{.*}}: tensor<1x4xf32>) | ||
| // CHECK: scf.forall.in_parallel | ||
| // CHECK: mapping = [#gpu.thread<linear_dim_1>, #gpu.thread<linear_dim_0>] | ||
|
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| // ----- | ||
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| #map = affine_map<(d0, d1) -> (d0, d1)> | ||
| func.func @fuse_destination(%3: tensor<64x64xf32>, %4: tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| attributes { | ||
| translation_info = #iree_codegen.translation_info<LLVMGPUTileAndFuse workgroup_size = [64, 1, 1] subgroup_size = 64, {}> | ||
| } { | ||
| %empty = tensor.empty() : tensor<64x64xf32> | ||
| %cst = arith.constant 0.0 : f32 | ||
| %5 = linalg.fill ins(%cst : f32) outs(%empty : tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| %7 = linalg.matmul ins(%3, %4 : tensor<64x64xf32>, tensor<64x64xf32>) outs(%5 : tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| return %7 : tensor<64x64xf32> | ||
| } | ||
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| // CHECK-LABEL: func.func @fuse_destination | ||
| // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<64x64xf32> | ||
| // CHECK: scf.forall {{.*}} shared_outs(%[[INIT:.+]] = %[[EMPTY]] | ||
| // CHECK: linalg.fill {{.*}} -> tensor<1x1xf32> | ||
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| // Additionally verify that reduction dimensions do not get tiled. | ||
| // CHECK: linalg.matmul ins({{.*}}: tensor<1x64xf32>, tensor<64x1xf32>) | ||
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| // ----- | ||
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| func.func @in_nested_region(%3: tensor<64x64xf32>, %4: tensor<64x64xf32>, %5: tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| attributes { | ||
| translation_info = #iree_codegen.translation_info<LLVMGPUTileAndFuse workgroup_size = [64, 1, 1] subgroup_size = 64, {}> | ||
| } { | ||
| %c8 = arith.constant 8 : index | ||
| %c64 = arith.constant 64 : index | ||
| %c0 = arith.constant 0 : index | ||
| %6 = scf.for %arg0 = %c0 to %c64 step %c8 iter_args(%arg1 = %5) -> (tensor<64x64xf32>) { | ||
| %extracted_slice = tensor.extract_slice %3[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %extracted_slice_0 = tensor.extract_slice %4[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %extracted_slice_1 = tensor.extract_slice %arg1[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %7 = linalg.add | ||
| ins(%extracted_slice, %extracted_slice_0 : tensor<64x8xf32>, tensor<64x8xf32>) | ||
| outs(%extracted_slice_1 : tensor<64x8xf32>) -> tensor<64x8xf32> | ||
| %insert = tensor.insert_slice %7 into %arg1[0, %arg0] [64, 8] [1, 1] : tensor<64x8xf32> into tensor<64x64xf32> | ||
| scf.yield %insert : tensor<64x64xf32> | ||
| } | ||
| return %6 : tensor<64x64xf32> | ||
| } | ||
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| // CHECK-LABEL: func.func @in_nested_region | ||
| // CHECK: scf.for | ||
| // CHECK: scf.forall | ||
| // CHECK: linalg.add {{.*}} -> tensor<1x4xf32> | ||
|
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| // ----- | ||
|
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| func.func @do_not_redistribute_in_forall(%3: tensor<64x64xf32>, %4: tensor<64x64xf32>, %5: tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| attributes { | ||
| translation_info = #iree_codegen.translation_info<LLVMGPUTileAndFuse workgroup_size = [64, 1, 1] subgroup_size = 64, {}> | ||
| } { | ||
| %c8 = arith.constant 8 : index | ||
| %c64 = arith.constant 64 : index | ||
| %c0 = arith.constant 0 : index | ||
| %6 = scf.for %arg0 = %c0 to %c64 step %c8 iter_args(%arg1 = %5) -> (tensor<64x64xf32>) { | ||
| %extracted_slice = tensor.extract_slice %3[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %extracted_slice_0 = tensor.extract_slice %4[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %extracted_slice_1 = tensor.extract_slice %arg1[0, %arg0] [64, 8] [1, 1] : tensor<64x64xf32> to tensor<64x8xf32> | ||
| %1 = scf.forall (%arg5, %arg6) = (0, 0) to (64, 8) step (1, 4) shared_outs(%arg7 = %extracted_slice_1) -> (tensor<64x8xf32>) { | ||
| %extracted_slice_2 = tensor.extract_slice %extracted_slice[%arg5, %arg6] [1, 4] [1, 1] : tensor<64x8xf32> to tensor<1x4xf32> | ||
| %extracted_slice_3 = tensor.extract_slice %extracted_slice_0[%arg5, %arg6] [1, 4] [1, 1] : tensor<64x8xf32> to tensor<1x4xf32> | ||
| %extracted_slice_4 = tensor.extract_slice %arg7[%arg5, %arg6] [1, 4] [1, 1] : tensor<64x8xf32> to tensor<1x4xf32> | ||
| %2 = linalg.add ins(%extracted_slice_2, %extracted_slice_3 : tensor<1x4xf32>, tensor<1x4xf32>) outs(%extracted_slice_4 : tensor<1x4xf32>) -> tensor<1x4xf32> | ||
| scf.forall.in_parallel { | ||
| tensor.parallel_insert_slice %2 into %arg7[%arg5, %arg6] [1, 4] [1, 1] : tensor<1x4xf32> into tensor<64x8xf32> | ||
| } | ||
| } {mapping = [#gpu.thread<linear_dim_1>, #gpu.thread<linear_dim_0>]} | ||
| %7 = linalg.add | ||
| ins(%1, %1 : tensor<64x8xf32>, tensor<64x8xf32>) | ||
| outs(%1 : tensor<64x8xf32>) -> tensor<64x8xf32> | ||
| %insert = tensor.insert_slice %7 into %arg1[0, %arg0] [64, 8] [1, 1] : tensor<64x8xf32> into tensor<64x64xf32> | ||
| scf.yield %insert : tensor<64x64xf32> | ||
| } | ||
| %8 = linalg.add | ||
| ins(%6, %6 : tensor<64x64xf32>, tensor<64x64xf32>) | ||
| outs(%6 : tensor<64x64xf32>) -> tensor<64x64xf32> | ||
| return %8 : tensor<64x64xf32> | ||
| } | ||
|
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| // CHECK-LABEL: func.func @do_not_redistribute_in_forall | ||
| // CHECK-SAME: %[[ARG0:[A-Za-z0-9]+]]: tensor<64x64xf32> | ||
| // CHECK-SAME: %[[ARG1:[A-Za-z0-9]+]]: tensor<64x64xf32> | ||
| // CHECK-SAME: %[[ARG2:[A-Za-z0-9]+]]: tensor<64x64xf32> | ||
| // CHECK: scf.for {{.*}} iter_args(%[[FOR_ITER:.+]] = %[[ARG2]]) | ||
| // CHECK: %[[INIT:.+]] = tensor.extract_slice %[[FOR_ITER]] | ||
|
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| // Verify that the existing forall stays the same. | ||
| // CHECK: scf.forall {{.*}} shared_outs(%[[ITER:.+]] = %[[INIT]]) | ||
| // CHECK: %[[DEST:.+]] = tensor.extract_slice %[[ITER]] | ||
| // CHECK: linalg.add {{.*}} outs(%[[DEST]] : tensor<1x4xf32> | ||
| // CHECK: scf.forall.in_parallel | ||
|
|
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| // CHECK: %[[DIST_ADD:.+]] = scf.forall | ||
| // CHECK: linalg.add | ||
| // CHECK: scf.forall.in_parallel | ||
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[DIST_ADD]] | ||
| // CHECK: scf.yield %[[INSERT]] | ||
| // CHECK: %[[RES_ADD:.+]] = scf.forall | ||
| // CHECK: linalg.add | ||
| // CHECK: scf.forall.in_parallel | ||
| // CHECK: return %[[RES_ADD]] | ||
|
|
||
| // ----- | ||
|
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| func.func @multiple_use_tilable_op(%3: tensor<64x256xf32>, %4: tensor<64x256xf32>) -> (tensor<64x256xf32>, tensor<256x64xf32>) | ||
| attributes { | ||
| translation_info = #iree_codegen.translation_info<LLVMGPUTileAndFuse workgroup_size = [64, 1, 1] subgroup_size = 64, {}> | ||
| } { | ||
| %add_empty = tensor.empty() : tensor<64x256xf32> | ||
| %6 = linalg.add | ||
| ins(%3, %4 : tensor<64x256xf32>, tensor<64x256xf32>) | ||
| outs(%add_empty : tensor<64x256xf32>) -> tensor<64x256xf32> | ||
| %transpose_empty = tensor.empty() : tensor<256x64xf32> | ||
| %7 = linalg.transpose | ||
| ins(%6 : tensor<64x256xf32>) | ||
| outs(%transpose_empty : tensor<256x64xf32>) permutation = [1, 0] | ||
| return %6, %7 : tensor<64x256xf32>, tensor<256x64xf32> | ||
| } | ||
|
|
||
| // CHECK-LABEL: func.func @multiple_use_tilable_op | ||
| // CHECK: %[[ADD_DIST:.+]] = scf.forall | ||
| // CHECK: %[[ADD:.+]] = linalg.add {{.*}} -> tensor<1x4xf32> | ||
| // CHECK: scf.forall.in_parallel | ||
| // CHECK: tensor.parallel_insert_slice %[[ADD]] | ||
| // CHECK: mapping = [#gpu.thread<linear_dim_1>, #gpu.thread<linear_dim_0>] | ||
| // CHECK: %[[T_DIST:.+]] = scf.forall | ||
| // CHECK: %[[FUSED_ADD:.+]] = linalg.add {{.*}} -> tensor<4x1xf32> | ||
| // CHECK: %[[T:.+]] = linalg.transpose ins(%[[FUSED_ADD]] | ||
| // CHECK: scf.forall.in_parallel | ||
| // CHECK: tensor.parallel_insert_slice %[[T]] | ||
| // CHECK: mapping = [#gpu.thread<linear_dim_1>, #gpu.thread<linear_dim_0>] | ||
| // CHECK: return %[[ADD_DIST]], %[[T_DIST]] |
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