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[cker] Make Mul support bool type. (Samsung#12145)
This commit makes the kernel for Mul support boolean type too. - Add mul op unit tests - Support bool type ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>
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| /* | ||
| * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| #include <cker/operation/BinaryArithmeticOps.h> | ||
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| #include <gtest/gtest.h> | ||
| #include <vector> | ||
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| TEST(CKer_Operation, Mul) | ||
| { | ||
| // Simple | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<int32_t> input1 = {10, 9, 11, 3}; | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<int32_t> input2 = {2, 2, 3, 4}; | ||
| std::vector<int32_t> expected_output = {20, 18, 33, 12}; | ||
| std::vector<int32_t> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.quantized_activation_min = std::numeric_limits<int32_t>::lowest(); | ||
| param.quantized_activation_max = std::numeric_limits<int32_t>::max(); | ||
| nnfw::cker::Shape shape{1, 2, 2, 1}; | ||
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| nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, shape, input1.data(), shape, input2.data(), shape, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_EQ(output[i], expected_output[i]); | ||
| } | ||
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| // Negative Value | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<int32_t> input1 = {10, -9, -11, 7}; | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<int32_t> input2 = {2, 2, -3, -4}; | ||
| std::vector<int32_t> expected_output = {20, -18, 33, -28}; | ||
| std::vector<int32_t> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.quantized_activation_min = std::numeric_limits<int32_t>::lowest(); | ||
| param.quantized_activation_max = std::numeric_limits<int32_t>::max(); | ||
| nnfw::cker::Shape shape{1, 2, 2, 1}; | ||
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| nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, shape, input1.data(), shape, input2.data(), shape, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_EQ(output[i], expected_output[i]); | ||
| } | ||
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| // Broadcast | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<int32_t> input1 = {10, -9, -11, 7}; | ||
| // Shape: {1} | ||
| std::vector<int32_t> input2 = {-3}; | ||
| std::vector<int32_t> expected_output = {-30, 27, 33, -21}; | ||
| std::vector<int32_t> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.broadcast_category = nnfw::cker::BroadcastableOpCategory::kGenericBroadcast; | ||
| param.quantized_activation_min = std::numeric_limits<int32_t>::lowest(); | ||
| param.quantized_activation_max = std::numeric_limits<int32_t>::max(); | ||
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| nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, nnfw::cker::Shape{1, 2, 2, 1}, input1.data(), nnfw::cker::Shape{1}, input2.data(), | ||
| nnfw::cker::Shape{1, 2, 2, 1}, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_EQ(output[i], expected_output[i]); | ||
| } | ||
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| // Simple Float | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<float> input1 = {10, 9, -11.1, 3}; | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<float> input2 = {2, -2.2, -3.3, 4}; | ||
| std::vector<float> expected_output = {20, -19.8, 36.63, 12}; | ||
| std::vector<float> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
| param.float_activation_max = std::numeric_limits<float>::max(); | ||
| nnfw::cker::Shape shape{1, 2, 2, 1}; | ||
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| nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, shape, input1.data(), shape, input2.data(), shape, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_NEAR(output[i], expected_output[i], 1e-5f); | ||
| } | ||
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| // Float Relu | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<float> input1 = {10, 9, -11.1, 3}; | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<float> input2 = {2, -2.2, -3.3, 4}; | ||
| std::vector<float> expected_output = {20, 0, 36.63, 12}; | ||
| std::vector<float> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.float_activation_min = 0; | ||
| param.float_activation_max = std::numeric_limits<float>::max(); | ||
| nnfw::cker::Shape shape{1, 2, 2, 1}; | ||
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| nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, shape, input1.data(), shape, input2.data(), shape, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_NEAR(output[i], expected_output[i], 1e-5f); | ||
| } | ||
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| // Broadcast | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| std::vector<float> input1 = {10, 9, -11.1, 3}; | ||
| // Shape: {1} | ||
| std::vector<float> input2 = {-3}; | ||
| std::vector<float> expected_output = {-30, -27, 33.3, -9}; | ||
| std::vector<float> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| param.broadcast_category = nnfw::cker::BroadcastableOpCategory::kGenericBroadcast; | ||
| param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
| param.float_activation_max = std::numeric_limits<float>::max(); | ||
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| nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, nnfw::cker::Shape{1, 2, 2, 1}, input1.data(), nnfw::cker::Shape{1}, input2.data(), | ||
| nnfw::cker::Shape{1, 2, 2, 1}, output.data()); | ||
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| for (size_t i = 0; i < expected_output.size(); ++i) | ||
| EXPECT_NEAR(output[i], expected_output[i], 1e-5f); | ||
| } | ||
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| // Bool8 | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| bool input1[4] = {true, true, false, false}; | ||
| // Shape: {1, 2, 2, 1} | ||
| bool input2[4] = {true, false, true, false}; | ||
| bool expected_output[4] = {true, false, false, false}; | ||
| bool output[4]; | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
| nnfw::cker::Shape shape{1, 2, 2, 1}; | ||
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| nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL, bool>( | ||
| param, shape, input1, shape, input2, shape, output); | ||
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| for (size_t i = 0; i < 4; ++i) | ||
| EXPECT_EQ(output[i], expected_output[i]); | ||
| } | ||
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| // Broadcast Bool8 | ||
| { | ||
| // Shape: {1, 2, 2, 1} | ||
| bool input1[4] = {true, true, false, false}; | ||
| // Shape: {1, 2, 2, 1} | ||
| bool input2[1] = {true}; | ||
| bool expected_output[4] = {true, true, false, false}; | ||
| bool output[4]; | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
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| nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL, bool>( | ||
| param, nnfw::cker::Shape{1, 2, 2, 1}, input1, nnfw::cker::Shape{1}, input2, | ||
| nnfw::cker::Shape{1, 2, 2, 1}, output); | ||
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| for (size_t i = 0; i < 4; ++i) | ||
| EXPECT_EQ(output[i], expected_output[i]); | ||
| } | ||
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| // TODO Add other types | ||
| } | ||
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| TEST(CKer_Operation, neg_MulUnsupportedBroadcastRank) | ||
| { | ||
| // Unsupported rank | ||
| { | ||
| // Shape: {1, 2, 2, 1, 1} | ||
| std::vector<float> input1 = {10, -9, -11, 7}; | ||
| // Shape: {1} | ||
| std::vector<float> input2 = {-3}; | ||
| std::vector<float> output(4); | ||
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| nnfw::cker::BinaryArithmeticOpParam param; | ||
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| EXPECT_ANY_THROW( | ||
| nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>( | ||
| param, nnfw::cker::Shape{1, 2, 2, 1, 1}, input1.data(), nnfw::cker::Shape{1}, input2.data(), | ||
| nnfw::cker::Shape{1, 2, 2, 1, 1}, output.data())); | ||
| } | ||
| } |