409 lines
22 KiB
MLIR
409 lines
22 KiB
MLIR
// RUN: mlir-opt --split-input-file --transform-interpreter %s | FileCheck %s
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func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
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%0 = linalg.matmul ins(%A, %B: tensor<16x256xf32>, tensor<256x32xf32>)
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outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
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return %0: tensor<16x32xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3, d1)>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: @matmul_split
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// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x4x64xf32>
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// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<4x64x32xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
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// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
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// CHECK-SAME: , iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
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// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<16x4x64xf32>, tensor<4x64x32xf32>) outs(%[[F]] : tensor<16x32x4xf32>) {
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// CHECK: arith.mulf
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// CHECK: arith.addf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<16x32x4xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]} ins(%[[G]] : tensor<16x32x4xf32>) outs(%{{.*}} : tensor<16x32xf32>) {
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// CHECK: arith.addf
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// CHECK: linalg.yield %{{.*}} : f32
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// CHECK: } -> tensor<16x32xf32>
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// CHECK: return %[[R]] : tensor<16x32xf32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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// -----
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func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: tensor<f32>) -> tensor<f32> {
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%red = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
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affine_map<(d0) -> ()>,
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affine_map<(d0) -> ()>],
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iterator_types = ["reduction"]}
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ins(%arg0, %arg1 : tensor<32xf32>, tensor<f32>)
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outs(%out : tensor<f32>) {
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^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
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%40 = arith.subf %arg7, %arg8 : f32
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%41 = math.exp %40 : f32
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%42 = arith.mulf %41, %arg9 : f32
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linalg.yield %42 : f32
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} -> tensor<f32>
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return %red : tensor<f32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> ()>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> (d0)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
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//CHECK-LABEL: @generic_split_1d
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// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<4x8xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
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// CHECK: %[[G:.*]] = linalg.generic
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// CHECK: {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]],
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// CHECK: iterator_types = ["parallel", "reduction"]} ins(%[[I1]], %{{.*}} : tensor<4x8xf32>, tensor<f32>) outs(%[[F]] : tensor<4xf32>) {
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// CHECK: arith.subf
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// CHECK: math.exp
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// CHECK: arith.mulf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<4xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["reduction"]} ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
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// CHECK: arith.mulf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<f32>
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// CHECK: return %[[R]] : tensor<f32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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// -----
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func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
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-> tensor<5x2xf32>
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{
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%0 = linalg.generic {
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indexing_maps = [
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affine_map<(d0, d1, d2) -> (d1, d0)>,
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affine_map<(d0, d1, d2) -> (d2, d1)>,
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affine_map<(d0, d1, d2) -> (d2, d0)>
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],
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iterator_types = ["parallel", "reduction", "parallel"]
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} ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
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^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
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%3 = arith.addf %arg0, %arg1 : f32
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%4 = arith.maximumf %3, %arg2 : f32
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linalg.yield %4 : f32
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} -> tensor<5x2xf32>
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return %0 : tensor<5x2xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d0)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d2, d1)>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: func @generic_split_3d
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// CHECK-DAG: %[[ID:.*]] = arith.constant 0xFF800000 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<4x8x2xf32>
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// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x4x8xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
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// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
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// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<4x8x2xf32>, tensor<5x4x8xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
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// CHECK: arith.addf
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// CHECK: arith.maximumf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<5x2x4xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
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// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
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// CHECK: arith.maximumf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<5x2xf32>
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// CHECK: return %[[R]] : tensor<5x2xf32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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// -----
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// Check that we don't use -inf as the neutral element for maxf when maxf has
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// ninf. Instead check that we use the smallest finite floating point value.
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// Also check that the fastmath flags are set on the created maxf
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// instructions.
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func.func @generic_split_3d_ninf(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
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-> tensor<5x2xf32>
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{
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%0 = linalg.generic {
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indexing_maps = [
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affine_map<(d0, d1, d2) -> (d1, d0)>,
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affine_map<(d0, d1, d2) -> (d2, d1)>,
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affine_map<(d0, d1, d2) -> (d2, d0)>
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],
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iterator_types = ["parallel", "reduction", "parallel"]
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} ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
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^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
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%3 = arith.addf %arg0, %arg1 : f32
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%4 = arith.maximumf %3, %arg2 fastmath<nnan,ninf> : f32
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linalg.yield %4 : f32
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} -> tensor<5x2xf32>
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return %0 : tensor<5x2xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d0)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d2, d1)>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: func @generic_split_3d_ninf
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// CHECK-DAG: %[[ID:.*]] = arith.constant -3.40282347E+38 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<4x8x2xf32>
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// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x4x8xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
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// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
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// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<4x8x2xf32>, tensor<5x4x8xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
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// CHECK: arith.addf
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// CHECK: arith.maximumf {{.*}} fastmath<nnan,ninf>
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// CHECK: linalg.yield
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// CHECK: } -> tensor<5x2x4xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
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// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
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// CHECK: arith.maximumf {{.*}} fastmath<nnan,ninf>
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// CHECK: linalg.yield
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// CHECK: } -> tensor<5x2xf32>
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// CHECK: return %[[R]] : tensor<5x2xf32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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// -----
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func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
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%0 = linalg.matmul ins(%A, %B: tensor<16x256xf32>, tensor<256x32xf32>)
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outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
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return %0: tensor<16x32xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3, d1)>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: @matmul_split
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// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x64x4xf32>
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// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<64x4x32xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
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// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
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// CHECK-SAME: , iterator_types = ["parallel", "parallel", "reduction", "parallel"]}
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// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<16x64x4xf32>, tensor<64x4x32xf32>) outs(%[[F]] : tensor<16x32x4xf32>) {
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// CHECK: arith.mulf
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// CHECK: arith.addf
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// CHECK: linalg.yield
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// CHECK: } -> tensor<16x32x4xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]} ins(%[[G]] : tensor<16x32x4xf32>) outs(%{{.*}} : tensor<16x32xf32>) {
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// CHECK: arith.addf
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// CHECK: linalg.yield %{{.*}} : f32
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// CHECK: } -> tensor<16x32xf32>
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// CHECK: return %[[R]] : tensor<16x32xf32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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// -----
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func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: tensor<f32>) -> tensor<f32> {
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%red = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
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affine_map<(d0) -> ()>,
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affine_map<(d0) -> ()>],
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iterator_types = ["reduction"]}
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ins(%arg0, %arg1 : tensor<32xf32>, tensor<f32>)
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outs(%out : tensor<f32>) {
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^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
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%40 = arith.subf %arg7, %arg8 : f32
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%41 = math.exp %40 : f32
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%42 = arith.mulf %41, %arg9 : f32
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linalg.yield %42 : f32
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} -> tensor<f32>
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return %red : tensor<f32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> ()>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d1)>
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// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> (d0)>
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// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
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//CHECK-LABEL: @generic_split_1d
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// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
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// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<8x4xf32>
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// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
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// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
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// CHECK: %[[G:.*]] = linalg.generic
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// CHECK: {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]],
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// CHECK: iterator_types = ["reduction", "parallel"]} ins(%[[I1]], %{{.*}} : tensor<8x4xf32>, tensor<f32>) outs(%[[F]] : tensor<4xf32>) {
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// CHECK: arith.subf
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// CHECK: math.exp
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// CHECK: arith.mulf
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// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<4xf32>
|
|
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["reduction"]} ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
|
|
// CHECK: arith.mulf
|
|
// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<f32>
|
|
// CHECK: return %[[R]] : tensor<f32>
|
|
|
|
module attributes {transform.with_named_sequence} {
|
|
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
|
|
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
|
|
%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0, inner_parallel}
|
|
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
|
|
transform.yield
|
|
}
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
|
|
-> tensor<5x2xf32>
|
|
{
|
|
%0 = linalg.generic {
|
|
indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d1, d0)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d0)>
|
|
],
|
|
iterator_types = ["parallel", "reduction", "parallel"]
|
|
} ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
|
|
^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
|
|
%3 = arith.addf %arg0, %arg1 : f32
|
|
%4 = arith.minimumf %3, %arg2 : f32
|
|
linalg.yield %4 : f32
|
|
} -> tensor<5x2xf32>
|
|
return %0 : tensor<5x2xf32>
|
|
}
|
|
|
|
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d0)>
|
|
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d1, d2)>
|
|
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
|
|
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
|
|
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
// CHECK-LABEL: func @generic_split_3d
|
|
// CHECK-DAG: %[[ID:.*]] = arith.constant 0x7F800000 : f32
|
|
// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<8x4x2xf32>
|
|
// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x8x4xf32>
|
|
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
|
|
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
|
|
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
|
|
// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<8x4x2xf32>, tensor<5x8x4xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
|
|
// CHECK: arith.addf
|
|
// CHECK: arith.minimumf
|
|
// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<5x2x4xf32>
|
|
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
|
|
// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
|
|
// CHECK: arith.minimumf
|
|
// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<5x2xf32>
|
|
// CHECK: return %[[R]] : tensor<5x2xf32>
|
|
|
|
module attributes {transform.with_named_sequence} {
|
|
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
|
|
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
|
|
%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
|
|
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
|
|
transform.yield
|
|
}
|
|
}
|
|
|
|
// -----
|
|
|
|
// Check that we don't use +inf as the neutral element for minf when minf has
|
|
// ninf. Instead check that we use the largest finite floating point value.
|
|
// Also check that the fastmath flags are set on the created minf
|
|
// instructions.
|
|
func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
|
|
-> tensor<5x2xf32>
|
|
{
|
|
%0 = linalg.generic {
|
|
indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d1, d0)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d1)>,
|
|
affine_map<(d0, d1, d2) -> (d2, d0)>
|
|
],
|
|
iterator_types = ["parallel", "reduction", "parallel"]
|
|
} ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
|
|
^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
|
|
%3 = arith.addf %arg0, %arg1 : f32
|
|
%4 = arith.minimumf %3, %arg2 fastmath<ninf> : f32
|
|
linalg.yield %4 : f32
|
|
} -> tensor<5x2xf32>
|
|
return %0 : tensor<5x2xf32>
|
|
}
|
|
|
|
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d0)>
|
|
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d1, d2)>
|
|
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
|
|
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
|
|
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
// CHECK-LABEL: func @generic_split_3d
|
|
// CHECK-DAG: %[[ID:.*]] = arith.constant 3.40282347E+38 : f32
|
|
// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<8x4x2xf32>
|
|
// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x8x4xf32>
|
|
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
|
|
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
|
|
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
|
|
// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<8x4x2xf32>, tensor<5x8x4xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
|
|
// CHECK: arith.addf
|
|
// CHECK: arith.minimumf {{.*}} fastmath<ninf>
|
|
// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<5x2x4xf32>
|
|
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
|
|
// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
|
|
// CHECK: arith.minimumf {{.*}} fastmath<ninf>
|
|
// CHECK: linalg.yield
|
|
// CHECK: } -> tensor<5x2xf32>
|
|
// CHECK: return %[[R]] : tensor<5x2xf32>
|
|
|
|
module attributes {transform.with_named_sequence} {
|
|
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
|
|
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
|
|
%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
|
|
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
|
|
transform.yield
|
|
}
|
|
}
|