// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s
func.func @matmul(%lhs: memref<32x32xf32>, %rhs: memref<32x32xf32>, %out: memref<32x32xf32>) {
%c8 = arith.constant 8 : index
%c0 = arith.constant 0 : index
%cst = arith.constant dense<0.000000e+00> : vector<16x16xf32>
%c16 = arith.constant 16 : index
%c32 = arith.constant 32 : index
%cst_0 = arith.constant 0.000000e+00 : f32
%3 = gpu.thread_id x
%4 = gpu.thread_id y
%5 = affine.apply affine_map<()[s0] -> (s0 * 16)>()[%4]
%6 = affine.apply affine_map<()[s0] -> ((s0 floordiv 32) * 16)>()[%3]
// CHECK: scf.for {{.*}} -> (vector<16x16xf32>) {
// CHECK-COUNT-2: vector.transfer_read {{.*}} vector<16x8xf32>
// CHECK-COUNT-2: vector.transfer_read {{.*}} vector<8x16xf32>
// CHECK-COUNT-2: vector.contract {{.*}} vector<16x8xf32>, vector<8x16xf32> into vector<16x16xf32>
// CHECK: scf.yield {{.*}} : vector<16x16xf32>
// CHECK: }
%7 = scf.for %arg0 = %c0 to %c32 step %c16 iter_args(%arg1 = %cst) -> (vector<16x16xf32>) {
%10 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%5]
%11 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%arg0]
%12 = vector.transfer_read %lhs[%10, %11], %cst_0 {in_bounds = [true, true]} : memref<32x32xf32>, vector<16x16xf32>
%16 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%6]
%17 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%arg0]
%18 = vector.transfer_read %rhs[%17, %16], %cst_0 {in_bounds = [true, true]} : memref<32x32xf32>, vector<16x16xf32>
%22 = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %12, %18, %arg1 : vector<16x16xf32>, vector<16x16xf32> into vector<16x16xf32>
scf.yield %22 : vector<16x16xf32>
}
%8 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%5]
%9 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%6]
vector.transfer_write %7, %out[%8, %9] {in_bounds = [true, true]} : vector<16x16xf32>, memref<32x32xf32>
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%func_op = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.op<"func.func">
transform.apply_patterns to %func_op {
transform.apply_patterns.gpu.unroll_vectors_subgroup_mma [16, 16, 8]
} : !transform.op<"func.func">
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @gathered_matmul
func.func @gathered_matmul(%lhs: memref<32x32xf32>, %rhs: memref<32x32xf32>, %out: memref<32x32xf32>) {
%c8 = arith.constant 8 : index
%c0 = arith.constant 0 : index
%cst = arith.constant dense<0.000000e+00> : vector<16x16xf32>
%cst_mask = arith.constant dense<true> : vector<4x4xi1>
%cst_pt = arith.constant dense<0.000000e+00> : vector<4x4xf32>
%c16 = arith.constant 16 : index
%c32 = arith.constant 32 : index
%cst_0 = arith.constant 0.000000e+00 : f32
%cst_1 = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex>
%cst_2 = arith.constant dense<1> : vector<4x4xindex>
%alloc = memref.alloc() {alignment = 64 : i64} : memref<32x32xf32>
%3 = gpu.thread_id x
%4 = gpu.thread_id y
%5 = affine.apply affine_map<()[s0] -> (s0 * 16)>()[%4]
%6 = affine.apply affine_map<()[s0] -> ((s0 floordiv 32) * 16)>()[%3]
// CHECK: scf.for {{.*}} -> (vector<16x16xf32>) {
// CHECK: arith.addi {{.*}} : vector<4xindex>
// CHECK: vector.gather {{.*}} : memref<32x32xf32>, vector<4x4xindex>, vector<4x4xi1>, vector<4x4xf32> into vector<4x4xf32>
// CHECK-COUNT-8: vector.transfer_read {{.*}} vector<8x4xf32>
// CHECK-COUNT-4: vector.transfer_read {{.*}} vector<4x16xf32>
// CHECK-COUNT-8: vector.contract {{.*}} vector<8x4xf32>, vector<4x16xf32> into vector<8x16xf32>
// CHECK: scf.yield {{.*}} : vector<16x16xf32>
// CHECK: }
%7 = scf.for %arg0 = %c0 to %c32 step %c16 iter_args(%arg1 = %cst) -> (vector<16x16xf32>) {
%10 = vector.broadcast %arg0 : index to vector<4xindex>
%11 = arith.addi %10, %cst_1 : vector<4xindex>
%12 = vector.broadcast %11 : vector<4xindex> to vector<4x4xindex>
%13 = arith.addi %12, %cst_2 : vector<4x4xindex>
%14 = vector.gather %lhs[%c0, %c0] [%13], %cst_mask, %cst_pt : memref<32x32xf32>, vector<4x4xindex>, vector<4x4xi1>, vector<4x4xf32> into vector<4x4xf32>
vector.transfer_write %14, %alloc[%c0, %c0] {in_bounds = [true, true]} : vector<4x4xf32>, memref<32x32xf32>
gpu.barrier
%15 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%5]
%16 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%arg0]
%17 = vector.transfer_read %alloc[%15, %16], %cst_0 {in_bounds = [true, true]} : memref<32x32xf32>, vector<16x16xf32>
%18 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%6]
%19 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%arg0]
%20 = vector.transfer_read %rhs[%19, %18], %cst_0 {in_bounds = [true, true]} : memref<32x32xf32>, vector<16x16xf32>
%21 = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %17, %20, %arg1 : vector<16x16xf32>, vector<16x16xf32> into vector<16x16xf32>
scf.yield %21 : vector<16x16xf32>
}
%8 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%5]
%9 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%c0)[%6]
vector.transfer_write %7, %out[%8, %9] {in_bounds = [true, true]} : vector<16x16xf32>, memref<32x32xf32>
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%func_op = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.op<"func.func">
transform.apply_patterns to %func_op {
transform.apply_patterns.gpu.unroll_vectors_subgroup_mma [8, 16, 4]
} : !transform.op<"func.func">
transform.yield
}
}
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