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Add support for FloorDiv 

Signed-off-by: Kainan Cha <kainan.zha@verisilicon.com>
This commit is contained in:
Kainan Cha 2021-05-25 01:19:44 +08:00
parent 804e068374
commit d0dadbc0fb
5 changed files with 141 additions and 3 deletions
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5
include/tim/vx/ops/elementwise.h
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@ -60,6 +60,10 @@ namespace ops {
* ## Maximum
*
* Maximum(x, y) : max(x, y). This operation supports broadcasting.
*
* ## FloorDiv
*
* FloorDiv(x, y): floor( x / y ). This operation supports broadcasting.
*/
#define DECLARE_ELEMENTWISE_OP(NAME) \
@ -74,6 +78,7 @@ DECLARE_ELEMENTWISE_OP(Add)
DECLARE_ELEMENTWISE_OP(Sub)
DECLARE_ELEMENTWISE_OP(Div)
DECLARE_ELEMENTWISE_OP(Pow)
DECLARE_ELEMENTWISE_OP(FloorDiv)
class Multiply : public Operation {
public:

2
src/tim/vx/ops/README.md
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@ -79,7 +79,7 @@ Equal|RELATIONAL_OPS_EQUAL|Mapped|[tf.math.equal](https://tensorflow.google.cn/a
NotEqual|RELATIONAL_OPS_NOT_EQUAL|Mapped|[tf.math.not_equal](https://tensorflow.google.cn/api_docs/python/tf/math/not_equal)
||SYNC_HOST|Unmapped
Pow|POW|Mapped|[tf.math.pow](https://tensorflow.google.cn/api_docs/python/tf/math/pow)
||FLOORDIV|Unmapped|[tf.math.floordiv](https://tensorflow.google.cn/api_docs/python/tf/math/floordiv)
FloorDiv|FLOORDIV|Mapped|[tf.math.floordiv](https://tensorflow.google.cn/api_docs/python/tf/math/floordiv)
Minimum|MINIMUM|Mapped|[tf.math.minimum](https://tensorflow.google.cn/api_docs/python/tf/math/minimum)
||SPATIAL_TRANSFORMER|Unmapped
And|LOGICAL_OPS|Mapped|[tf.math.logical_and](https://tensorflow.google.cn/api_docs/python/tf/math/logical_and)

1
src/tim/vx/ops/elementwise.cc
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@ -39,6 +39,7 @@ DEFINE_ELEMENTWISE_OP(Add, VSI_NN_OP_ADD)
DEFINE_ELEMENTWISE_OP(Sub, VSI_NN_OP_SUBTRACT)
DEFINE_ELEMENTWISE_OP(Div, VSI_NN_OP_DIVIDE)
DEFINE_ELEMENTWISE_OP(Pow, VSI_NN_OP_POW)
DEFINE_ELEMENTWISE_OP(FloorDiv, VSI_NN_OP_FLOORDIV)
#undef DEFINE_ELEMENTWISE_OP

132
src/tim/vx/ops/elementwise_test.cc Normal file
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@ -0,0 +1,132 @@
/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/elementwise.h"
#include "gtest/gtest.h"
TEST(FloorDiv, shape_1_fp32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor_x = graph->CreateTensor(input_spec);
auto input_tensor_y = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data_x = { 1 };
std::vector<float> in_data_y = { 0 };
std::vector<float> golden = { std::numeric_limits<float>::infinity() };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(1);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(FloorDiv, shape_5_1_broadcast_float32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape_x({5, 1});
tim::vx::ShapeType in_shape_y({1});
tim::vx::ShapeType out_shape({5, 1});
tim::vx::TensorSpec input_spec_x(tim::vx::DataType::FLOAT32,
in_shape_x, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec input_spec_y(tim::vx::DataType::FLOAT32,
in_shape_y, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor_x = graph->CreateTensor(input_spec_x);
auto input_tensor_y = graph->CreateTensor(input_spec_y);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data_x = { 1, 3, -2, 0, 99 };
std::vector<float> in_data_y = { 2 };
std::vector<float> golden = { 0, 1, -1, 0, 49 };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(FloorDiv, shape_5_1_broadcast_uint8) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape_x({1});
tim::vx::ShapeType in_shape_y({5, 1});
tim::vx::ShapeType out_shape({5, 1});
tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
tim::vx::Quantization quant_out(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8,
in_shape_x, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8,
in_shape_y, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT, quant_out);
auto input_tensor_x = graph->CreateTensor(input_spec_x);
auto input_tensor_y = graph->CreateTensor(input_spec_y);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data_x = { 255 };
std::vector<uint8_t> in_data_y = { 1, 3, 2, 0, 255 };
std::vector<uint8_t> golden = { 255, 170, 254, 255, 2 };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()));
auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}

4
src/tim/vx/ops/simple_operations_test.cc
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@ -27,7 +27,7 @@
#include "gtest/gtest.h"
TEST(OP, floor_shape_5_1_fp32) {
TEST(Floor, shape_5_1_fp32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
@ -55,7 +55,7 @@ TEST(OP, floor_shape_5_1_fp32) {
EXPECT_EQ(golden, output);
}
TEST(OP, cast_shape_5_1_fp32_to_int32) {
TEST(Cast, shape_5_1_fp32_to_int32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();