diff options
Diffstat (limited to 'mlir/test/Conversion/TosaToLinalg')
| -rw-r--r-- | mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir | 54 | ||||
| -rw-r--r-- | mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir | 12 |
2 files changed, 62 insertions, 4 deletions
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir index e64903671e59..b4049000c50d 100644 --- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir +++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir @@ -1,5 +1,6 @@ // RUN: mlir-opt --split-input-file -pass-pipeline="builtin.module(func.func(tosa-to-linalg-named))" %s -verify-diagnostics -o -| FileCheck %s // RUN: mlir-opt --split-input-file -pass-pipeline="builtin.module(func.func(tosa-to-linalg-named{prefer-conv2d-kernel-layout-hwcf=true}))" %s -verify-diagnostics -o -| FileCheck --check-prefix="HWCF" %s +// RUN: mlir-opt --split-input-file -pass-pipeline="builtin.module(func.func(tosa-to-linalg-named,cse))" %s -verify-diagnostics -o -| FileCheck --check-prefix="CHECK-CSE" %s // CHECK-LABEL: @matmul func.func @matmul(%arg0: tensor<1x5x3xf32>, %arg1: tensor<1x3x6xf32>) -> (tensor<1x5x6xf32>) { @@ -215,6 +216,59 @@ func.func @max_pool_i32(%arg0: tensor<1x6x34x62xi32>) -> () { return } +// CHECK-CSE-LABEL: @max_pool_all_dynamic +func.func @max_pool_all_dynamic(%arg0: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> { + // Batch size + // CHECK-CSE: %[[C0:.+]] = arith.constant 0 : index + // CHECK-CSE: %[[BATCH:.+]] = tensor.dim %arg0, %[[C0]] : tensor<?x?x?x?xf32> + + // Compute output height + // CHECK-CSE: %[[C1:.+]] = arith.constant 1 : index + // CHECK-CSE: %[[IH:.+]] = tensor.dim %arg0, %[[C1]] : tensor<?x?x?x?xf32> + // CHECK-CSE: %[[C2:.+]] = arith.constant 2 : index + // CHECK-CSE: %[[PADDED_BEFORE:.+]] = arith.addi %[[IH]], %[[C0]] : index + // CHECK-CSE: %[[PADDED_AFTER:.+]] = arith.addi %[[PADDED_BEFORE]], %[[C0]] : index + // CHECK-CSE: %[[SUB_ONE:.+]] = arith.subi %[[C2]], %[[C1]] : index + // CHECK-CSE: %[[DILATED:.+]] = arith.muli %[[C1]], %[[SUB_ONE]] : index + // CHECK-CSE: %[[ADD_ONE:.+]] = arith.addi %[[DILATED]], %[[C1]] : index + // CHECK-CSE: %[[SUBTRACT:.+]] = arith.subi %[[PADDED_AFTER]], %[[ADD_ONE]] : index + // CHECK-CSE: %[[DIVIDE:.+]] = arith.divui %[[SUBTRACT]], %[[C1]] : index + // CHECK-CSE: %[[HEIGHT:.+]] = arith.addi %[[DIVIDE]], %[[C1]] : index + + // Compute output width + // CHECK-CSE: %[[IW:.+]] = tensor.dim %arg0, %[[C2]] : tensor<?x?x?x?xf32> + // CHECK-CSE: %[[C5:.+]] = arith.constant 5 : index + // CHECK-CSE: %[[PADDED_BEFORE:.+]] = arith.addi %[[IW]], %[[C2]] : index + // CHECK-CSE: %[[PADDED_AFTER:.+]] = arith.addi %[[PADDED_BEFORE]], %[[C2]] : index + // CHECK-CSE: %[[SUB_ONE:.+]] = arith.subi %[[C5]], %[[C1]] : index + // CHECK-CSE: %[[DILATED:.+]] = arith.muli %[[C1]], %[[SUB_ONE]] : index + // CHECK-CSE: %[[ADD_ONE:.+]] = arith.addi %[[DILATED]], %[[C1]] : index + // CHECK-CSE: %[[SUBTRACT:.+]] = arith.subi %[[PADDED_AFTER]], %[[ADD_ONE]] : index + // CHECK-CSE: %[[DIVIDE:.+]] = arith.divui %[[SUBTRACT]], %[[C1]] : index + // CHECK-CSE: %[[WIDTH:.+]] = arith.addi %14, %[[C1]] : index + + // Channel size + // CHECK-CSE: %[[C3:.+]] = arith.constant 3 : index + // CHECK-CSE: %[[CHANNEL:.+]] = tensor.dim %arg0, %[[C3]] : tensor<?x?x?x?xf32> + + // Pad the input + // CHECK-CSE: %[[FLOAT_MIN:.+]] = arith.constant -3.40282347E+38 : f32 + // CHECK-CSE: %[[PADDED:.+]] = tensor.pad %arg0 low[0, 0, 2, 0] high[0, 0, 2, 0] { + // CHECK-CSE: tensor.yield %[[FLOAT_MIN]] : f32 + + // Allocate the output and fill with minimum value + // CHECK-CSE: %[[INIT:.+]] = tensor.empty(%[[BATCH]], %[[HEIGHT]], %[[WIDTH]], %[[CHANNEL]]) : tensor<?x?x?x?xf32> + // CHECK-CSE: %[[FILL:.+]] = linalg.fill ins(%[[FLOAT_MIN]] : f32) outs(%[[INIT]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> + // CHECK-CSE: %[[FAKE_WINDOW:.+]] = tensor.empty() : tensor<2x5xf32> + + // Compute max pool + // CHECK-CSE: %[[OUT:.+]] = linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%[[PADDED]], %[[FAKE_WINDOW]] : tensor<?x?x?x?xf32>, tensor<2x5xf32>) outs(%[[FILL]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> + // CHECK-CSE: return %[[OUT]] + + %0 = tosa.max_pool2d %arg0 {kernel = array<i64: 2, 5>, pad = array<i64: 0, 0, 2, 2>, stride = array<i64: 1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> + return %0 : tensor<?x?x?x?xf32> +} + // ----- // CHECK-LABEL: @avg_pool_f32 diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir index 1fa783f05f04..445e8be47678 100644 --- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir +++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir @@ -270,7 +270,8 @@ func.func @test_add_2d_all_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32 // CHECK: %[[VAL_0:.*]] = tensor.dim %[[ARG0]], %[[CONST0]] : tensor<?x?xf32> // CHECK: %[[VAL_1:.*]] = arith.cmpi eq, %[[VAL_0]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM0_BROADCAST:.*]] = scf.if %[[VAL_1]] -> (tensor<?x?xf32>) { - // CHECK: %[[VAL_2:.*]] = tensor.dim %[[ARG0]], %[[CONST1]] : tensor<?x?xf32> + // CHECK: %[[LOCAL_CONST1:.*]] = arith.constant 1 : index + // CHECK: %[[VAL_2:.*]] = tensor.dim %[[ARG0]], %[[LOCAL_CONST1]] : tensor<?x?xf32> // CHECK: %[[VAL_3:.*]] = tensor.empty(%[[MAX_DIM0]], %[[VAL_2]]) : tensor<?x?xf32> // CHECK: %[[VAL_4:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x?xf32>) outs(%[[VAL_3]] : tensor<?x?xf32>) { // CHECK: ^bb0(%[[VAL_5:.*]]: f32, %[[VAL_6:.*]]: f32): @@ -284,7 +285,8 @@ func.func @test_add_2d_all_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32 // CHECK: %[[VAL_7:.*]] = tensor.dim %[[ARG0_DIM0_BROADCAST]], %[[CONST1]] : tensor<?x?xf32> // CHECK: %[[VAL_8:.*]] = arith.cmpi eq, %[[VAL_7]], %[[CONST1]] : index // CHECK: %[[ARG0_DIM1_BROADCAST:.*]] = scf.if %[[VAL_8]] -> (tensor<?x?xf32>) { - // CHECK: %[[VAL_9:.*]] = tensor.dim %[[ARG0_DIM0_BROADCAST]], %[[CONST0]] : tensor<?x?xf32> + // CHECK: %[[LOCAL_CONST0:.*]] = arith.constant 0 : index + // CHECK: %[[VAL_9:.*]] = tensor.dim %[[ARG0_DIM0_BROADCAST]], %[[LOCAL_CONST0]] : tensor<?x?xf32> // CHECK: %[[VAL_10:.*]] = tensor.empty(%[[VAL_9]], %[[MAX_DIM1]]) : tensor<?x?xf32> // CHECK: %[[VAL_11:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0_DIM0_BROADCAST]] : tensor<?x?xf32>) outs(%[[VAL_10]] : tensor<?x?xf32>) { // CHECK: ^bb0(%[[VAL_12:.*]]: f32, %[[VAL_13:.*]]: f32): @@ -298,7 +300,8 @@ func.func @test_add_2d_all_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32 // CHECK: %[[VAL_14:.*]] = tensor.dim %[[ARG1]], %[[CONST0]] : tensor<?x?xf32> // CHECK: %[[VAL_15:.*]] = arith.cmpi eq, %[[VAL_14]], %[[CONST1]] : index // CHECK: %[[ARG1_DIM0_BROADCAST:.*]] = scf.if %[[VAL_15]] -> (tensor<?x?xf32>) { - // CHECK: %[[VAL_16:.*]] = tensor.dim %[[ARG1]], %[[CONST1]] : tensor<?x?xf32> + // CHECK: %[[LOCAL_CONST1:.*]] = arith.constant 1 : index + // CHECK: %[[VAL_16:.*]] = tensor.dim %[[ARG1]], %[[LOCAL_CONST1]] : tensor<?x?xf32> // CHECK: %[[VAL_17:.*]] = tensor.empty(%[[MAX_DIM0]], %[[VAL_16]]) : tensor<?x?xf32> // CHECK: %[[VAL_18:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG1]] : tensor<?x?xf32>) outs(%[[VAL_17]] : tensor<?x?xf32>) { // CHECK: ^bb0(%[[VAL_19:.*]]: f32, %[[VAL_20:.*]]: f32): @@ -312,7 +315,8 @@ func.func @test_add_2d_all_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32 // CHECK: %[[VAL_21:.*]] = tensor.dim %[[ARG1_DIM0_BROADCAST]], %[[CONST1]] : tensor<?x?xf32> // CHECK: %[[VAL_22:.*]] = arith.cmpi eq, %[[VAL_21]], %[[CONST1]] : index // CHECK: %[[ARG1_DIM1_BROADCAST:.*]] = scf.if %[[VAL_22]] -> (tensor<?x?xf32>) { - // CHECK: %[[VAL_23:.*]] = tensor.dim %[[ARG1_DIM0_BROADCAST]], %[[CONST0]] : tensor<?x?xf32> + // CHECK: %[[LOCAL_CONST0:.*]] = arith.constant 0 : index + // CHECK: %[[VAL_23:.*]] = tensor.dim %[[ARG1_DIM0_BROADCAST]], %[[LOCAL_CONST0]] : tensor<?x?xf32> // CHECK: %[[VAL_24:.*]] = tensor.empty(%[[VAL_23]], %[[MAX_DIM1]]) : tensor<?x?xf32> // CHECK: %[[VAL_25:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG1_DIM0_BROADCAST]] : tensor<?x?xf32>) outs(%[[VAL_24]] : tensor<?x?xf32>) { // CHECK: ^bb0(%[[VAL_26:.*]]: f32, %[[VAL_27:.*]]: f32): |