Add map for Matmul
Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia
Kainan Cha
parent
3fa2bf519a
commit
21ecf5262e
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@ -0,0 +1,59 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#ifndef TIM_VX_OPS_MATMUL_H_
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#define TIM_VX_OPS_MATMUL_H_
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#include "tim/vx/operation.h"
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namespace tim {
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namespace vx {
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namespace ops {
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/**
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* ## Matmul
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*
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* Multiplies matrix a by matrix b, producing a * b.
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*
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* - transpose_a: If True, a is transposed before multiplication.
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* - transpose_b: If True, b is transposed before multiplication.
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* - adjoint_a: If True, a is conjugated and transposed before multiplication.
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* - adjoint_b: If True, b is conjugated and transposed before multiplication.
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*/
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class Matmul : public Operation {
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public:
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Matmul(Graph* graph, bool transpose_a = false, bool transpose_b = false,
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bool adjoint_a = false, bool adjoint_b = false);
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protected:
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bool transpose_a_;
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bool transpose_b_;
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bool adjoint_a_;
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bool adjoint_b_;
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};
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} // namespace ops
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} // namespace vx
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} // namespace tim
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#endif /* TIM_VX_OPS_MATMUL_H_ */
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@ -42,6 +42,7 @@ Elu|ELU|Mapped|[tf.nn.elu](https://tensorflow.google.cn/api_docs/python/tf/nn/el
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Batch2Space|BATCH2SPACE|Mapped|[tf.batch_to_space](https://tensorflow.google.cn/api_docs/python/tf/batch_to_space)
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Batch2Space|BATCH2SPACE|Mapped|[tf.batch_to_space](https://tensorflow.google.cn/api_docs/python/tf/batch_to_space)
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Space2Batch|SPACE2BATCH|Mapped|[tf.space_to_batch](https://tensorflow.google.cn/api_docs/python/tf/space_to_batch)
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Space2Batch|SPACE2BATCH|Mapped|[tf.space_to_batch](https://tensorflow.google.cn/api_docs/python/tf/space_to_batch)
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Pad|PAD|Mapped|[tf.pad](https://tensorflow.google.cn/api_docs/python/tf/pad)
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Pad|PAD|Mapped|[tf.pad](https://tensorflow.google.cn/api_docs/python/tf/pad)
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Matmul|MATRIXMUL|Mapped|[tf.linalg.matmul](https://www.tensorflow.org/api_docs/python/tf/linalg/matmul)
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LayerNormalization|LAYER_NORM|Mapped|[tf.keras.layers.LayerNormalization](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/LayerNormalization)
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LayerNormalization|LAYER_NORM|Mapped|[tf.keras.layers.LayerNormalization](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/LayerNormalization)
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ReduceMin|REDUCE_MIN|Mapped|[tf.math.reduce_min](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_min)
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ReduceMin|REDUCE_MIN|Mapped|[tf.math.reduce_min](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_min)
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ReduceMax|REDUCE_MAX|Mapped|[tf.math.reduce_max](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_max)
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ReduceMax|REDUCE_MAX|Mapped|[tf.math.reduce_max](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_max)
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@ -0,0 +1,46 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/ops/matmul.h"
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#include "operation_private.h"
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#include "vsi_nn_pub.h"
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#include "type_utils.h"
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namespace tim {
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namespace vx {
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namespace ops {
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Matmul::Matmul(Graph* graph, bool transpose_a, bool transpose_b,
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bool adjoint_a, bool adjoint_b)
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: Operation(graph, VSI_NN_OP_MATRIXMUL), transpose_a_(transpose_a),
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transpose_b_(transpose_b), adjoint_a_(adjoint_a), adjoint_b_(adjoint_b) {
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this->impl()->node()->nn_param.matrixmul.transpose[0] = ToVxBool(transpose_a_);
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this->impl()->node()->nn_param.matrixmul.transpose[1] = ToVxBool(transpose_b_);
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this->impl()->node()->nn_param.matrixmul.adjoint[0] = ToVxBool(adjoint_a_);
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this->impl()->node()->nn_param.matrixmul.adjoint[1] = ToVxBool(adjoint_b_);
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}
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} // namespace ops
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} // namespace vx
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} // namespace tim
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@ -0,0 +1,204 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/matmul.h"
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#include "gtest/gtest.h"
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namespace {
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template<typename T>
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::testing::AssertionResult ArraysMatch(const std::vector<T>& expected,
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const std::vector<T>& actual,
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T abs_error){
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for (size_t i = 0; i < expected.size(); ++i){
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EXPECT_NEAR(expected[i], actual[i], abs_error) << "at index:" << i;
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}
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return ::testing::AssertionSuccess();
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}
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}
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TEST(Matmul, shape_2_6_shape_6_2_float) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType a_shape({6, 2});
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tim::vx::ShapeType b_shape({2, 6});
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tim::vx::ShapeType out_shape({2, 2});
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tim::vx::TensorSpec a_spec(tim::vx::DataType::FLOAT32,
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a_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec b_spec(tim::vx::DataType::FLOAT32,
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b_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto a_tensor = graph->CreateTensor(a_spec);
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auto b_tensor = graph->CreateTensor(b_spec);
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auto out_tensor = graph->CreateTensor(out_spec);
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std::vector<float> a_data = {
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1, 2, 3, 4, 5, 6,
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-1, -2, -3, -4, -5, -6
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};
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std::vector<float> b_data = {
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6, 5,
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4, 3,
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2, 1,
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-6, -5,
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-4, -3,
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-2, -1
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};
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std::vector<float> golden = {
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-36, -27,
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36, 27
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};
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EXPECT_TRUE(a_tensor->CopyDataToTensor(a_data.data(), a_data.size() * sizeof(float)));
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EXPECT_TRUE(b_tensor->CopyDataToTensor(b_data.data(), b_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Matmul>();
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(*op).BindInputs({a_tensor, b_tensor}).BindOutputs({out_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(Matmul, shape_2_3_2_shape_2_3_2_float_transpose_b) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType a_shape({2, 3, 2});
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tim::vx::ShapeType b_shape({2, 3, 2});
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tim::vx::ShapeType out_shape({3, 3, 2});
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tim::vx::TensorSpec a_spec(tim::vx::DataType::FLOAT32,
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a_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec b_spec(tim::vx::DataType::FLOAT32,
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b_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto a_tensor = graph->CreateTensor(a_spec);
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auto b_tensor = graph->CreateTensor(b_spec);
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auto out_tensor = graph->CreateTensor(out_spec);
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std::vector<float> a_data = {
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1, 2,
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3, 4,
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5, 6,
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-1, -2,
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-3, -4,
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-5, -6
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};
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std::vector<float> b_data = {
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6, 5,
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4, 3,
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2, 1,
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-6, -5,
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-4, -3,
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-2, -1
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};
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std::vector<float> golden = {
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16, 10, 4,
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38, 24, 10,
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60, 38, 16,
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16, 10, 4,
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38, 24, 10,
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60, 38, 16,
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};
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EXPECT_TRUE(a_tensor->CopyDataToTensor(a_data.data(), a_data.size() * sizeof(float)));
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EXPECT_TRUE(b_tensor->CopyDataToTensor(b_data.data(), b_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Matmul>(false, true);
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(*op).BindInputs({a_tensor, b_tensor}).BindOutputs({out_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size() * sizeof(float));
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EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(Matmul, shape_2_3_2_shape_2_3_2_uint8_transpose_a) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType a_shape({2, 3, 2});
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tim::vx::ShapeType b_shape({2, 3, 2});
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tim::vx::ShapeType out_shape({2, 2, 2});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 6);
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tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
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tim::vx::TensorSpec a_spec(tim::vx::DataType::UINT8,
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a_shape, tim::vx::TensorAttribute::INPUT, input_quant);
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tim::vx::TensorSpec b_spec(tim::vx::DataType::UINT8,
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b_shape, tim::vx::TensorAttribute::INPUT, input_quant);
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tim::vx::TensorSpec out_spec(tim::vx::DataType::UINT8,
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out_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
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auto a_tensor = graph->CreateTensor(a_spec);
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auto b_tensor = graph->CreateTensor(b_spec);
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auto out_tensor = graph->CreateTensor(out_spec);
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std::vector<uint8_t> a_data = {
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7, 8,
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9, 10,
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11, 12,
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5, 4,
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3, 2,
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1, 0,
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};
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std::vector<uint8_t> b_data = {
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12, 11,
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10, 9,
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8, 7,
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0, 1,
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2, 3,
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4, 5,
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};
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std::vector<uint8_t> golden = {
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28, 19,
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40, 28,
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28, 19,
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40, 28,
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};
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EXPECT_TRUE(a_tensor->CopyDataToTensor(a_data.data(), a_data.size()));
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EXPECT_TRUE(b_tensor->CopyDataToTensor(b_data.data(), b_data.size()));
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auto op = graph->CreateOperation<tim::vx::ops::Matmul>(true);
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(*op).BindInputs({a_tensor, b_tensor}).BindOutputs({out_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output(golden.size());
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EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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