wehub-resource-sync
137 lines
5.4 KiB
MLIR
137 lines
5.4 KiB
MLIR
// Copyright 2026 The TensorFlow Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ==============================================================================
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// RUN: litert-opt %s -canonicalize | FILECHECK_OPTS="" FileCheck %s
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// RUN: litert-opt %s --tfl-dense-to-dense-resource-elements -canonicalize | litert-opt --tfl-dense-resource-to-dense-elements | FILECHECK_OPTS="" FileCheck %s
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// CHECK-LABEL: @test_logical_not_i1
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func.func @test_logical_not_i1() -> tensor<4xi1> {
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// CHECK: arith.constant dense<[false, true, false, true]> : tensor<4xi1>
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%0 = arith.constant dense<[true, false, true, false]> : tensor<4xi1>
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%1 = "tfl.logical_not"(%0) : (tensor<4xi1>) -> tensor<4xi1>
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func.return %1 : tensor<4xi1>
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}
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// CHECK-LABEL: @test_relu_i32
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func.func @test_relu_i32() -> tensor<4xi32> {
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// CHECK: arith.constant dense<[0, 2, 0, 4]> : tensor<4xi32>
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%0 = arith.constant dense<[-1, 2, -3, 4]> : tensor<4xi32>
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%1 = "tfl.relu"(%0) : (tensor<4xi32>) -> tensor<4xi32>
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func.return %1 : tensor<4xi32>
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}
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// CHECK-LABEL: @test_relu_f32
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func.func @test_relu_f32() -> tensor<4xf32> {
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// CHECK: arith.constant dense<[0.000000e+00, 2.000000e+00, 0.000000e+00, 4.000000e+00]> : tensor<4xf32>
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%0 = arith.constant dense<[-1.0, 2.0, -3.0, 4.0]> : tensor<4xf32>
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%1 = "tfl.relu"(%0) : (tensor<4xf32>) -> tensor<4xf32>
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func.return %1 : tensor<4xf32>
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}
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// CHECK-LABEL: @test_abs_f32
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func.func @test_abs_f32() -> tensor<4xf32> {
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// CHECK: arith.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
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%0 = arith.constant dense<[-1.0, 2.0, -3.0, 4.0]> : tensor<4xf32>
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%1 = "tfl.abs"(%0) : (tensor<4xf32>) -> tensor<4xf32>
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func.return %1 : tensor<4xf32>
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}
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// CHECK-LABEL: @test_ceil_f32
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func.func @test_ceil_f32() -> tensor<4xf32> {
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// CHECK: arith.constant dense<[-1.000000e+00, 2.000000e+00, -3.000000e+00, 4.000000e+00]> : tensor<4xf32>
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%0 = arith.constant dense<[-1.5, 1.5, -3.5, 3.5]> : tensor<4xf32>
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%1 = "tfl.ceil"(%0) : (tensor<4xf32>) -> tensor<4xf32>
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func.return %1 : tensor<4xf32>
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}
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// CHECK-LABEL: @test_floor_f32
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func.func @test_floor_f32() -> tensor<4xf32> {
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// CHECK: arith.constant dense<[-2.000000e+00, 1.000000e+00, -4.000000e+00, 3.000000e+00]> : tensor<4xf32>
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%0 = arith.constant dense<[-1.5, 1.5, -3.5, 3.5]> : tensor<4xf32>
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%1 = "tfl.floor"(%0) : (tensor<4xf32>) -> tensor<4xf32>
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func.return %1 : tensor<4xf32>
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}
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// CHECK-LABEL: @test_neg_f32
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func.func @test_neg_f32() -> tensor<4xf32> {
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// CHECK: arith.constant dense<[1.000000e+00, -2.000000e+00, 3.000000e+00, -4.000000e+00]> : tensor<4xf32>
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%0 = arith.constant dense<[-1.0, 2.0, -3.0, 4.0]> : tensor<4xf32>
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%1 = "tfl.neg"(%0) : (tensor<4xf32>) -> tensor<4xf32>
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func.return %1 : tensor<4xf32>
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}
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// CHECK-LABEL: @test_sin_f32
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func.func @test_sin_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<0.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[0.0]> : tensor<1xf32>
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%1 = "tfl.sin"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_cos_f32
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func.func @test_cos_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<1.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[0.0]> : tensor<1xf32>
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%1 = "tfl.cos"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_log_f32
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func.func @test_log_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<0.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[1.0]> : tensor<1xf32>
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%1 = "tfl.log"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_sqrt_f32
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func.func @test_sqrt_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<2.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[4.0]> : tensor<1xf32>
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%1 = "tfl.sqrt"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_rsqrt_f32
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func.func @test_rsqrt_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<5.000000e-01> : tensor<1xf32>
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%0 = arith.constant dense<[4.0]> : tensor<1xf32>
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%1 = "tfl.rsqrt"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_square_f32
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func.func @test_square_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<1.600000e+01> : tensor<1xf32>
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%0 = arith.constant dense<[4.0]> : tensor<1xf32>
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%1 = "tfl.square"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_tanh_f32
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func.func @test_tanh_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<0.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[0.0]> : tensor<1xf32>
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%1 = "tfl.tanh"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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// CHECK-LABEL: @test_exp_f32
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func.func @test_exp_f32() -> tensor<1xf32> {
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// CHECK: arith.constant dense<1.000000e+00> : tensor<1xf32>
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%0 = arith.constant dense<[0.0]> : tensor<1xf32>
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%1 = "tfl.exp"(%0) : (tensor<1xf32>) -> tensor<1xf32>
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func.return %1 : tensor<1xf32>
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}
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