// RUN: mlir-opt %s --test-transform-dialect-interpreter \ // RUN: --test-transform-dialect-erase-schedule \ // RUN: --math-uplift-to-fma \ // RUN: --convert-bufferization-to-memref \ // RUN: --test-lower-to-llvm |\ // RUN: FileCheck %s // Fixed-size tensor types to be used in convolution. // Named sizes are: N=5 OH=80 OW=100 F=C=128 KH=KW=3. // Input is NHWC. // Filter is CHWF. // Ouptut is NHWF. !tinput = tensor<5x82x102x128xf32> !tfilter = tensor<128x3x3x128xf32> !tbias = tensor<128xf32> !toutput = tensor<5x80x100x128xf32> // Function containing the convolution. Note that its arguments and results are // tensors annotated with attributes from the `bufferization` dialect. These // attributes hint the bufferization pass to assume buffers can be directly // used for these tensors without reshaping. func.func @conv( %input: !tinput {bufferization.writable = false, bufferization.access = "read", bufferization.buffer_layout = affine_map<(d0,d1,d2,d3)->(d0,d1,d2,d3)>}, %filter: !tfilter {bufferization.writable = false, bufferization.access = "read", bufferization.buffer_layout = affine_map<(d0,d1,d2,d3)->(d0,d1,d2,d3)>}, %bias: !tbias {bufferization.writable = false, bufferization.access = "read", bufferization.buffer_layout = affine_map<(d0)->(d0)>}, %output: !toutput {bufferization.writable = true, bufferization.buffer_layout = affine_map<(d0,d1,d2,d3)->(d0,d1,d2,d3)>, bufferization.access = "write"}) -> !toutput // This requests a C-compatible interface to be emitted for the function // when translating to LLVM IR. attributes { llvm.emit_c_interface } { // Bias. Using a named Linalg operation for brevity. %bias_init = tensor.empty() : !toutput %biased = linalg.broadcast ins(%bias : !tbias) outs(%bias_init : !toutput) dimensions = [0, 1, 2] // Convolution proper. While Linalg has named operations for 2D convolutions, // the one in the Halide example has an uncommon order of filter dimensions // and is not supported. It also takes the fitler as first argument. This // code recreates it faithfully using the generic form. %convolved = linalg.generic { iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"], indexing_maps = [ affine_map<(n, y, x, c, rz, ry, rx) -> (rx, rz, ry, c)>, affine_map<(n, y, x, c, rz, ry, rx) -> (n, y+rz, x+ry, rx)>, affine_map<(n, y, x, c, rz, ry, rx) -> (n, y, x, c)> ] } ins(%filter, %input: !tfilter, !tinput) outs(%biased : !toutput) { ^bb0(%in: f32, %f: f32, %b: f32): // Note the fastmath attributes that allow operations to be recombined into // %0 = math.fma %in, %f, %b : f32 // later on and to reorder reductions. %m1 = arith.mulf %in, %f {fastmath = #arith.fastmath} : f32 %0 = arith.addf %b, %m1 {fastmath = #arith.fastmath} : f32 linalg.yield %0 : f32 } -> !toutput // ReLU is just a max(0, x). %c0 = arith.constant 0.0 : f32 %relued = linalg.generic { iterator_types = ["parallel", "parallel", "parallel", "parallel"], indexing_maps = [ affine_map<(d0, d1, d2, d3) -> ()>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> ] } ins(%c0, %convolved : f32, !toutput) outs(%output : !toutput) { ^bb0(%cst: f32, %in: f32, %out: f32): %0 = llvm.intr.maxnum(%cst, %in) : (f32, f32) -> f32 linalg.yield %0 : f32 } -> !toutput return %relued : !toutput } // Module containing the transformation script to be applied. The attribute // is required to correctly verify the use of named (macro-like) sequences. module attributes { transform.with_named_sequence } { // Apply transformations in a sequence to recreate the following Halide // schedule: // // Var co, ci, xo, xi; // relu.split(c, co, ci, vec * tile_w) // .split(x, xo, xi, tile_h) // .reorder(ci, xi, xo, y, n, co) // .vectorize(ci, vec) // .unroll(ci) // .unroll(xi); // conv.compute_at(relu, xo) // .vectorize(c, vec) // .unroll(c) // .unroll(x) // .unroll(y) // .update() // .reorder(c, x, y, r.x, r.y, r.z, n) // .vectorize(c, vec) // .unroll(c) // .unroll(x) // .unroll(y) // .unroll(r.x, 2); // // where tile_w = 4, tile_h = 5, vec = 16. Note that unroll(y) and unroll(r.x) // have no effect on the Halide IR as of 294f80c49bf3bb8582446613c25fcce03b82. // Also note that the order of dimensions in Halide is inverted, e.g., co and // n are the outermost loops in the respective reorder directives. transform.sequence failures(propagate) { // This argument will point to the top-level module. ^bb0(%arg0: !transform.any_op): // 1. Find the operations we are going to transform usnig their names. This // is a simplistic approach that works when there are few operations in the // IR to be transformed. More complex scenarios should rely on operations // with `transform.match` prefix that are out of scope for this chapter. %bias = transform.structured.match ops{["linalg.broadcast"]} in %arg0 : (!transform.any_op) -> !transform.any_op %generics = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op %conv, %relu = transform.split_handle %generics : (!transform.any_op) -> (!transform.any_op, !transform.any_op) // 2. Initial tiling to start producing the loop structure. Note that the // linalg.generic operation has the implicit loop order (n, y, x, c). Since // the desired order of dimensions is (co, n, y, xo, xi, ci), we first tile // only the c dimension to materialize the outermost co loop, and then tile // the other dimensions since they are already in the expected order. Tiling // by 1 produces the loop that iterates along the entire dimension. Tiling // by 0 does not produce a loop. The size 64 is chosen as tiling by 4*16 // where 16 is the AVX512 vector length. Note that structured tiling doesn't // remove the dimensions that became trivial (unit size) so the resulting // sturucture is technically (co, no=n, yo=y, xo, [ni=1, yi=1, xi, ci]) // where brackets indicate implicit loops of the `linalg.generic` operation // inside the loops produced by tiling. // // [n y x c] %relu2, %co = transform.structured.tile_using_forall %relu tile_sizes [0, 0, 0, 64] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) %relu3, %n_y_xo = transform.structured.tile_using_forall %relu2 tile_sizes [1, 1, 5, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) // Compute_at is actually fusion into the given loop (given that we start // with totally fissioned form, Halide starts with a fused form by reusing // the loop iterators). %conv2, %co2 = transform.structured.fuse_into_containing_op %conv into %co : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op) %conv3, %n_y_xo2 = transform.structured.fuse_into_containing_op %conv2 into %n_y_xo : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op) // Also fuse the bias that we represent as a separate operation and Halide // represents as the "pure" (as opposed to "update") part of the conv // expression. Note that fusion consumes both handles and produces new // handles for chaining purposes. %bias2, %co3 = transform.structured.fuse_into_containing_op %bias into %co2 : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op) %bias3, %n_y_xo3 = transform.structured.fuse_into_containing_op %bias2 into %n_y_xo2 : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op) // Clean up the result of fusion, which mechanically duplicates the producer // operation in the consumer loop without removing the original operation. // The original operation is now "dead": it has no uses and no side effects // so it can be removed by dead-code elimination (DCE) that runs as part of // pattern rewriting. The transform dialect allows to apply a combination // of named pattern sets, exposed as operations, in one sweep to an // isolated-from-above container payload operation. Note that we don't // actually need any patterns for DCE to run, just trigger the rewriting. // // This step is optional. The transformation can continue without it and // produce the same final IR, but makes it easier to manually examine the // intermediate stages. %f00 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op transform.apply_patterns to %f00 { } : !transform.any_op // The loop reordering requested for the convolution operation requires // putting reduction loops (r.z, r.y. r.x) before the "inner" loops xi, ci. // The "inner" loops are still implicit as part of the linalg.generic // operation, and we need to materialize reduction loops around it by tiling // with size 1. Since we are producing reduction loops, we indicate that we // are tiling a reduction and request a sequential `scf.for` loops (parallel // reductions are supported by `scf.forall`, but we don't need those here). // // This transform operation is more capable than merely producing // (reduction) loops: the transformed code performs `tile_size` partial // reductions of `N / tile_size` elements, potentially in parallel by // changing the dimension kind of the structured operation inside the loop, // and then performs a final reduction of these partial results by producing // a new “combiner” structured operation after the loops. In our case, // tile_size = 1 along all dimensions, so the reduction is entirely // performed by the generated loops. The combiner structured operation is // still produced and adds up the reduction result with the initial value. %red_fill, %conv4, %combining, %rz_ry_rx = transform.structured.tile_reduction_using_for %conv3 by // n y x c rz ry rx tile_sizes=[0, 0, 0, 0, 1, 1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) // At this point, the inner Linalg operations have implicit iteration spaces // of 5x64 size, with some additional unit-size dimensions. Completely // replicating Halide schedule would require materializing the loops with // 5 and 4 iterations, respectively, unrolling those loops and marking the // remaining 16-point iteration space for vectorization. // // This is unnecessary in MLIR that supports multi-dimensional vectors, // which will be decomposed into target-specific sizes during the lowering. // Therefore, this schedule stops here. // Transform the named broadcast operation used for bias into the generic // form before vectorization to prevent special cases from kicking in. transform.structured.generalize %bias3 : (!transform.any_op) -> !transform.any_op // Use the named macro to perform most of the lowering. transform.include @lower failures(propagate) (%arg0) : (!transform.any_op) -> () transform.yield } // Named sequence of transformations is a macro-like object that can be // included from another place in the transform dialect, but doesn't allow for // recursion. This can be reused in other scenarios. transform.named_sequence @lower( %arg0: !transform.any_op {transform.consumed}) { %f00 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op // Simplify the code as tiling and fusion may have produced a lot of // operations computing tensor subsets and loop ranges, some of which may be // duplicated or excessively complex. Simplification involving // canonicalization, common subexpression elimination, loop invariant code // motion and various rewrite patterns can be applied directly from the // transform dialect. Furthermore, an arbitrary combination of rewrite // patterns can be applied in one sweep to a given scope, a functionality // that cannot be achieved with conventional compiler passes that apply each // group of patterns separately (at least without creating a new pass for // each combination of pattern groups). transform.apply_patterns to %f00 { transform.apply_patterns.canonicalization transform.apply_patterns.linalg.tiling_canonicalization } : !transform.any_op transform.apply_cse to %f00 : !transform.any_op %all_loops = transform.structured.match interface{LoopLikeInterface} in %arg0 : (!transform.any_op) -> !transform.any_op transform.apply_licm to %all_loops : !transform.any_op // Tiling-by-one as a way of materializing loops produced operations // processing 4+D types where only a handful of dimension isn’t unit-sized, // e.g., tensor<1x1x1x5x64xf32> where 5 and 64 are tile sizes. Remove such // unit dimensions before vectorization, for clarity. transform.apply_patterns to %f00 { transform.apply_patterns.linalg.fold_unit_extent_dims_via_reshapes } : !transform.any_op // Vectorize the remaining non-unit dimensions in structured operations. // This essentially rewrites operations on `tensor<5x64xf32>` into // opreations on `vector<5x64xf32>`. Further lowering in MLIR and LLVM will // decompose this into a sequence of operations on single-dimensional // vectors of the platform-relevant size, e.g., `vector<16xf32>` for AVX512. // High-level vector primitives, such as `vector.transpose` and // `vector.broadcast` can be introduced at this stage. They will be later // lowered to sequences of lower-level primitives such as `vector.shuffle` // depending on the selected lowering strategy. %fv = transform.structured.vectorize_children_and_apply_patterns %f00 : (!transform.any_op) -> !transform.any_op // Vectorization may have created new opportunities for cleanups. In // particular, tensor subsetting operations can be composed with vector // operations, and vector transfer (multi-dimensional load/store) operations // can be recombined and hoisted out of loops. transform.apply_patterns to %fv { transform.apply_patterns.canonicalization transform.apply_patterns.tensor.fold_tensor_subset_ops_into_vector_transfers } : !transform.any_op transform.apply_cse to %fv : !transform.any_op transform.structured.hoist_redundant_vector_transfers %fv : (!transform.any_op) -> !transform.any_op // Apply bufferization that rewrites the remaining operations on tensors // as operations on structured buffer (memref) types, including the function // API. MLIR bufferization uses destination-passing style meaning that a // buffer is shared between one of the operation's operands and its result. // // Since bufferization rewrites function signatures, it is applied as a // module-wise transformation. Therefore, it invalidates all previously // defined handles. Bufferization is usually a late step in the // transformation process, so invalidation is not an issue. However, if // other transformations, such as loop unrolling, are required after // bufferization, new handles should be produced using the match operations. // // One-shot bufferization itself does not produce buffer deallocations, // which may lead to leaks. So we have to run the buffer deallocation pass // pipeline to avoid them. Note that the transform dialect seamlessly runs // named passes and pass pipelines: if desired, one could replace complex // --pass-pipeline expressions with operations. Note that we apply the // pipeline to functions rather than entire module to avoid running it // on the transform IR that is contained in the module. %arg1 = transform.bufferization.one_shot_bufferize %arg0 { bufferize_function_boundaries = true, function_boundary_type_conversion = 1 : i32 } : (!transform.any_op) -> !transform.any_op %f = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.any_op transform.apply_registered_pass "buffer-deallocation-pipeline" to %f : (!transform.any_op) -> !transform.any_op // Apply general canonicalization and CSE to each function after // bufferization as new simplification opportunities may have appeared. %fb = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.any_op transform.apply_patterns to %fb { transform.apply_patterns.canonicalization } : !transform.any_op transform.apply_cse to %fb : !transform.any_op // Lower complex, multidimensional vector operations into simpler // primitives. This particular selection of the pattern groups corresponds // to vector dialect operations present in the payload IR at this stage. // Many of these groups can be parameterized to use different strategies or // lower-level primitives offering performance trade-offs. In this case, we // are selecting the simplest strategies. transform.apply_patterns to %fb { transform.apply_patterns.vector.lower_contraction lowering_strategy = parallelarith transform.apply_patterns.vector.lower_transfer max_transfer_rank = 1 transform.apply_patterns.vector.lower_transpose lowering_strategy = eltwise transform.apply_patterns.vector.lower_shape_cast } : !transform.any_op // These patterns apply in a separate sweep to avoid transfer-to-scf // patterns overlap with lower-transfer patterns as they apply to the same // kind of operations. These patterns may produce local allocations to act // as temporary caches deep inside loops, which could lead to catastrophic // performance. Such allocations are moved onto the stack and hoisted from // all the surrounding loops. transform.apply_patterns to %fb { transform.apply_patterns.vector.transfer_to_scf transform.apply_patterns.memref.alloc_to_alloca } : !transform.any_op transform.bufferization.buffer_loop_hoisting %fb : !transform.any_op // A final round of cleanups additionally includes patterns to simplify // buffer aliasing operations that may have been introduced during // bufferization and could result in excessively complex address // computation. transform.apply_patterns to %fb { transform.apply_patterns.memref.fold_memref_alias_ops transform.apply_patterns.canonicalization } : !transform.any_op transform.apply_cse to %fb : !transform.any_op transform.yield } } // The core computation, at the LLVM dialect level, must correspond to five // immediately adjacent fma on vector<64xf32>. // CHECK: %[[R0:.+]] = llvm.mlir.undef : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[LINE0:.+]] = llvm.extractvalue %[[V:.+]][0] : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[FMA0:.+]] = llvm.intr.fma(%{{.*}}, %{{.*}}, %[[LINE0]]) // CHECK-SAME: -> vector<64xf32> // CHECK-NEXT: %[[R1:.+]] = llvm.insertvalue %[[FMA0]], %[[R0]][0] // CHECK-NEXT: %[[LINE1:.+]] = llvm.extractvalue %[[V:.+]][1] : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[FMA1:.+]] = llvm.intr.fma(%{{.*}}, %{{.*}}, %[[LINE1]]) // CHECK-SAME: -> vector<64xf32> // CHECK-NEXT: %[[R2:.+]] = llvm.insertvalue %[[FMA1]], %[[R1]][1] // CHECK-NEXT: %[[LINE2:.+]] = llvm.extractvalue %[[V:.+]][2] : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[FMA2:.+]] = llvm.intr.fma(%{{.*}}, %{{.*}}, %[[LINE2]]) // CHECK-SAME: -> vector<64xf32> // CHECK-NEXT: %[[R3:.+]] = llvm.insertvalue %[[FMA2]], %[[R2]][2] // CHECK-NEXT: %[[LINE3:.+]] = llvm.extractvalue %[[V:.+]][3] : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[FMA3:.+]] = llvm.intr.fma(%{{.*}}, %{{.*}}, %[[LINE3]]) // CHECK-SAME: -> vector<64xf32> // CHECK-NEXT: %[[R4:.+]] = llvm.insertvalue %[[FMA3]], %[[R3]][3] // CHECK-NEXT: %[[LINE4:.+]] = llvm.extractvalue %[[V:.+]][4] : !llvm.array<5 x vector<64xf32>> // CHECK-NEXT: %[[FMA4:.+]] = llvm.intr.fma(%{{.*}}, %{{.*}}, %[[LINE4]]) // CHECK-SAME: -> vector<64xf32> // CHECK-NEXT: %[[R5:.+]] = llvm.insertvalue %[[FMA4]], %[[R4]][4]