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Added general Float16 support (#631) 

Added Float16 type definition from third-party
Refine float16 bias handlling in conv2d
Refine float16 case in conv2d
Caution: Headers of float16 only be included when build unit_test

Type: New Feature

Signed-off-by: Feiyue Chen <Feiyue.Chen@verisilicon.com>
This commit is contained in:
Chen Feiyue 2023-08-12 10:04:16 +08:00 committed by GitHub
parent 35e50d7692
commit af50cc5e3f
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GPG Key ID: 4AEE18F83AFDEB23
12 changed files with 3771 additions and 196 deletions
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2
BUILD
View File

@ -134,8 +134,10 @@ cc_binary(
cc_test ( cc_test (
name = "unit_test", name = "unit_test",
copts = ["-std=c++14", "-Werror"], copts = ["-std=c++14", "-Werror"],
includes = ["third_party/half"],
srcs = [ srcs = [
"src/tim/vx/test_utils.h", "src/tim/vx/test_utils.h",
"third_party/half/half.hpp"
] + glob(["src/tim/**/*_test.cc"]), ] + glob(["src/tim/**/*_test.cc"]),
deps = [ deps = [
"@gtest//:gtest", "@gtest//:gtest",

2
CMakeLists.txt
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@ -98,6 +98,8 @@ if(TIM_VX_ENABLE_TEST)
FetchContent_Populate(googletest) FetchContent_Populate(googletest)
add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR}) add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR})
endif() endif()
include_directories(third_party/half)
endif() endif()
if(TIM_VX_ENABLE_GRPC) if(TIM_VX_ENABLE_GRPC)

3
include/tim/vx/ops/conv2d.h
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@ -99,12 +99,9 @@ class Conv2d : public BuiltinOp {
const int32_t multiplier_; const int32_t multiplier_;
const DataLayout kernel_layout_; const DataLayout kernel_layout_;
#if defined(__clang__) && (__clang_major__ >= 15)
#define TIM_VX_OPS_CONV2D_WITH_F16BIAS 1
private: private:
void OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor, void OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor,
int32_t input_idx) override; int32_t input_idx) override;
#endif
}; };
} // namespace ops } // namespace ops

28
src/tim/vx/ops/conv2d.cc
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@ -42,8 +42,8 @@ Conv2d::Conv2d(Graph* graph, const std::array<uint32_t, 4> pad,
const std::array<uint32_t, 2>& stride, const std::array<uint32_t, 2>& stride,
const std::array<uint32_t, 2>& dilation, int32_t multiplier, const std::array<uint32_t, 2>& dilation, int32_t multiplier,
DataLayout input_layout, DataLayout kernel_layout) DataLayout input_layout, DataLayout kernel_layout)
: Conv2d(graph, 0, PadType::AUTO, {0, 0}, stride, dilation, pad, : Conv2d(graph, 0, PadType::AUTO, {0, 0}, stride, dilation, pad, multiplier,
multiplier, input_layout, kernel_layout) {} input_layout, kernel_layout) {}
Conv2d::Conv2d(Graph* graph, int32_t weights, PadType padding, Conv2d::Conv2d(Graph* graph, int32_t weights, PadType padding,
const std::array<uint32_t, 2>& ksize, const std::array<uint32_t, 2>& ksize,
@ -88,41 +88,33 @@ std::shared_ptr<Operation> Conv2d::Clone(std::shared_ptr<Graph>& graph) const {
this->kernel_layout_); this->kernel_layout_);
} }
const std::vector<std::shared_ptr<Tensor>> Conv2d::ConstantInputsTensor() const { const std::vector<std::shared_ptr<Tensor>> Conv2d::ConstantInputsTensor()
if (this->IsAllInputsConst()) { const {
if (this->IsAllInputsConst()) {
return {this->impl_->inputs_tensor_[0]}; return {this->impl_->inputs_tensor_[0]};
} else { } else {
return {}; return {};
} }
} }
// Handle float16 bias if clang compiler is no less than 15.0.0 version // Handle float16 bias
#ifdef TIM_VX_OPS_CONV2D_WITH_F16BIAS
void Conv2d::OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor, void Conv2d::OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor,
int32_t input_idx) { int32_t input_idx) {
if (tensor->GetDataType() == vx::DataType::FLOAT16 && if (tensor->GetDataType() == vx::DataType::FLOAT16 &&
tensor->IsConstTensor() && impl_->inputs_tensor_.size() == 3) { tensor->IsConstTensor() && impl_->inputs_tensor_.size() == 3) {
uint32_t bias_size = 1; float* float32_bias = tensor->ConvertTensorToFloat32Data();
for (auto i : tensor->GetShape()) {
bias_size *= i;
}
std::vector<_Float16> in(bias_size);
tensor->CopyDataFromTensor(in.data());
std::vector<float> out(bias_size);
for (uint i = 0; i < bias_size; i++) {
out[i] = static_cast<float>(in[i]);
}
TensorSpec fp32bias_spec(tim::vx::DataType::FLOAT32, tensor->GetShape(), TensorSpec fp32bias_spec(tim::vx::DataType::FLOAT32, tensor->GetShape(),
tim::vx::TensorAttribute::CONSTANT); tim::vx::TensorAttribute::CONSTANT);
auto out_tensor = impl_->graph_->CreateTensor(fp32bias_spec, out.data());
auto out_tensor = impl_->graph_->CreateTensor(fp32bias_spec, float32_bias);
vsi_nn_Free(float32_bias);
impl_->inputs_tensor_[2] = out_tensor; impl_->inputs_tensor_[2] = out_tensor;
impl_->node()->input.tensors[input_idx] = out_tensor->GetId(); impl_->node()->input.tensors[input_idx] = out_tensor->GetId();
impl_->graph_->RenewTensorConsumersMap(tensor, out_tensor, this); impl_->graph_->RenewTensorConsumersMap(tensor, out_tensor, this);
} }
} }
#endif
} // namespace ops } // namespace ops
} // namespace vx } // namespace vx

354
src/tim/vx/ops/conv2d_test.cc
View File

@ -28,11 +28,12 @@
#include "tim/vx/context.h" #include "tim/vx/context.h"
#include "tim/vx/graph.h" #include "tim/vx/graph.h"
#include "tim/vx/types.h" #include "tim/vx/types.h"
#include "third_party/half/half.hpp"
#ifdef TIM_VX_OPS_CONV2D_WITH_F16BIAS
TEST(Conv2d, shape_4_2_1_1_float16_PaddingTest) { TEST(Conv2d, shape_4_2_1_1_float16_PaddingTest) {
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph(); auto graph = ctx->CreateGraph();
using namespace half_float::literal;
tim::vx::ShapeType input_shape({4, 2, 1, 1}); //whcn tim::vx::ShapeType input_shape({4, 2, 1, 1}); //whcn
tim::vx::ShapeType weight_shape({2, 2, 1, 3}); //whio tim::vx::ShapeType weight_shape({2, 2, 1, 3}); //whio
@ -50,26 +51,29 @@ TEST(Conv2d, shape_4_2_1_1_float16_PaddingTest) {
tim::vx::TensorAttribute::OUTPUT); tim::vx::TensorAttribute::OUTPUT);
// Input data nchw // Input data nchw
std::vector<_Float16> input_data = {
1, 1, 1, 1, // row = 1 std::vector<half_float::half> input_data = {
2, 2, 3, 2 // row = 2 1.0_h, 1.0_h, 1.0_h, 1.0_h, // row = 1
2.0_h, 2.0_h, 3.0_h, 2.0_h // row = 2
}; };
// weight data oihw // weight data oihw
std::vector<_Float16> weight_data = { std::vector<half_float::half> weight_data = {
1, 2, 3, 4, //first 2x2 filter 1.0_h, 2.0_h, 3.0_h, 4.0_h, //first 2x2 filter
-1, 1, -1, 1, // second 2x2 filter -1.0_h, 1.0_h, -1.0_h, 1.0_h, // second 2x2 filter
-1, -1, 1, 1, // third 2x2 filter -1.0_h, -1.0_h, 1.0_h, 1.0_h, // third 2x2 filter
}; };
// bias data // bias data
std::vector<_Float16> bias_data = {1, 2, 3}; std::vector<half_float::half> bias_data = {1.0_h, 2.0_h, 3.0_h};
// nchw std::vector<half_float::half> golden = {
std::vector<_Float16> golden = {// first channel // first channel
18, 22, 21, 8, 7, 9, 8, 3, 2, 3, 1, -1, 18.0_h, 22.0_h, 21.0_h, 8.0_h, 7.0_h, 9.0_h, 8.0_h, 3.0_h, 2.0_h, 3.0_h,
// second channel 1.0_h, -1.0_h,
2, 3, 1, 0, 5, 6, 6, 4, -1, -2, -2, 1}; // second channel
2.0_h, 3.0_h, 1.0_h, 0.0_h, 5.0_h, 6.0_h, 6.0_h, 4.0_h, -1.0_h, -2.0_h,
-2.0_h, 1.0_h};
auto input_tensor = graph->CreateTensor(input_spec); auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data()); auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
@ -80,8 +84,8 @@ TEST(Conv2d, shape_4_2_1_1_float16_PaddingTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -98,11 +102,10 @@ TEST(Conv2d, shape_4_2_1_1_float16_PaddingTest) {
for (auto i : output_tensor->GetShape()) { for (auto i : output_tensor->GetShape()) {
output_size *= i; output_size *= i;
} }
std::vector<_Float16> output(output_size); std::vector<half_float::half> output(output_size);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, (_Float16)0.1)); EXPECT_TRUE(ArraysMatch(golden, output, (half_float::half)0.1));
} }
#endif
TEST(Conv2d, shape_4_2_1_1_float32_PaddingTest) { TEST(Conv2d, shape_4_2_1_1_float32_PaddingTest) {
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
@ -155,8 +158,8 @@ TEST(Conv2d, shape_4_2_1_1_float32_PaddingTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -224,8 +227,8 @@ TEST(Conv2d, shape_4_2_2_2_float32_PointwiseTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -295,8 +298,8 @@ TEST(Conv2d, shape_4_2_1_2_float32_SimpleTest) {
std::array<uint32_t, 2> stride({2, 2}); std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -361,8 +364,8 @@ TEST(Conv2d, shape_4_2_2_2_float32_SimpleChannelsTest) {
std::array<uint32_t, 2> stride({2, 2}); std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -432,8 +435,8 @@ TEST(Conv2d, shape_6_3_1_1_float32_SimpleAnisotropicStridesTest) {
std::array<uint32_t, 2> stride({3, 1}); std::array<uint32_t, 2> stride({3, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -497,8 +500,8 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -562,8 +565,8 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedConstFilterTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -627,8 +630,8 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedBiasTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -691,8 +694,8 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedValidTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -759,8 +762,8 @@ TEST(Conv2d, DISABLED_shape_4_2_2_2_float32_DisabledPointwiseMultifilterTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -827,8 +830,8 @@ TEST(Conv2d, shape_9_9_1_1_float32_SimpleDilationTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({3, 3}); std::array<uint32_t, 2> dilation({3, 3});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -893,8 +896,8 @@ TEST(Conv2d, shape_4_2_1_2_float32_StrideTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -958,8 +961,8 @@ TEST(Conv2d, shape_4_2_1_2_float32_InputAndFilterSameWidthHeightTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1011,13 +1014,13 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest1) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2,
scales_weight, zero_point_weight); scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2, scales_bias, tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2,
zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1047,8 +1050,8 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest1) {
std::vector<u_int8_t> input_data = std::vector<u_int8_t> input_data =
Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]); Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]);
std::vector<u_int8_t> weight_data = std::vector<u_int8_t> weight_data = Quantize<uint8_t>(
Quantize<uint8_t>(weight_data_float, scales_weight[0], zero_point_input[0]); weight_data_float, scales_weight[0], zero_point_input[0]);
std::vector<int32_t> bias_data = std::vector<int32_t> bias_data =
Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]); Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]);
std::vector<u_int8_t> golden = std::vector<u_int8_t> golden =
@ -1062,8 +1065,8 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest1) {
std::array<uint32_t, 2> stride({2, 2}); std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({1, 1}); std::array<uint32_t, 2> dilation({1, 1});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1094,8 +1097,8 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest2) {
tim::vx::ShapeType output_shape( tim::vx::ShapeType output_shape(
{2, 1, weight_shape[3], input_shape[3]}); //whcn {2, 1, weight_shape[3], input_shape[3]}); //whcn
float input_min = -128.5, input_max = 128, weight_min = -128.5, weight_max = 128, float input_min = -128.5, input_max = 128, weight_min = -128.5,
output_min = -127, output_max = 128; weight_max = 128, output_min = -127, output_max = 128;
std::pair<float, int32_t> scales_zp; std::pair<float, int32_t> scales_zp;
@ -1115,13 +1118,13 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest2) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2,
scales_weight, zero_point_weight); scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2, scales_bias, tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2,
zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1151,8 +1154,8 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest2) {
std::vector<u_int8_t> input_data = std::vector<u_int8_t> input_data =
Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]); Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]);
std::vector<u_int8_t> weight_data = std::vector<u_int8_t> weight_data = Quantize<uint8_t>(
Quantize<uint8_t>(weight_data_float, scales_weight[0], zero_point_input[0]); weight_data_float, scales_weight[0], zero_point_input[0]);
std::vector<int32_t> bias_data = std::vector<int32_t> bias_data =
Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]); Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]);
std::vector<u_int8_t> golden = std::vector<u_int8_t> golden =
@ -1167,8 +1170,8 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest2) {
std::array<uint32_t, 2> stride({2, 2}); std::array<uint32_t, 2> stride({2, 2});
std::array<uint32_t, 2> dilation({1, 1}); std::array<uint32_t, 2> dilation({1, 1});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1220,13 +1223,13 @@ TEST(Conv2d, shape_6_3_1_1_uint8_AnisotropicStridesQuantizedTest) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2,
scales_weight, zero_point_weight); scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2, scales_bias, tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2,
zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1255,8 +1258,8 @@ TEST(Conv2d, shape_6_3_1_1_uint8_AnisotropicStridesQuantizedTest) {
std::vector<u_int8_t> input_data = std::vector<u_int8_t> input_data =
Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]); Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]);
std::vector<u_int8_t> weight_data = std::vector<u_int8_t> weight_data = Quantize<uint8_t>(
Quantize<uint8_t>(weight_data_float, scales_weight[0], zero_point_input[0]); weight_data_float, scales_weight[0], zero_point_input[0]);
std::vector<int32_t> bias_data = std::vector<int32_t> bias_data =
Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]); Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]);
std::vector<u_int8_t> golden = std::vector<u_int8_t> golden =
@ -1271,8 +1274,8 @@ TEST(Conv2d, shape_6_3_1_1_uint8_AnisotropicStridesQuantizedTest) {
std::array<uint32_t, 2> stride({3, 1}); std::array<uint32_t, 2> stride({3, 1});
std::array<uint32_t, 2> dilation({1, 1}); std::array<uint32_t, 2> dilation({1, 1});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1324,13 +1327,13 @@ TEST(Conv2d, shape_9_9_1_1_uint8_DilationQuantizedTest) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2,
scales_weight, zero_point_weight); scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2, scales_bias, tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2,
zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1362,8 +1365,8 @@ TEST(Conv2d, shape_9_9_1_1_uint8_DilationQuantizedTest) {
std::vector<u_int8_t> input_data = std::vector<u_int8_t> input_data =
Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]); Quantize<uint8_t>(input_data_float, scales_input[0], zero_point_input[0]);
std::vector<u_int8_t> weight_data = std::vector<u_int8_t> weight_data = Quantize<uint8_t>(
Quantize<uint8_t>(weight_data_float, scales_weight[0], zero_point_input[0]); weight_data_float, scales_weight[0], zero_point_input[0]);
std::vector<int32_t> bias_data = std::vector<int32_t> bias_data =
Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]); Quantize<int32_t>(bias_data_float, scales_bias[0], zero_point_bias[0]);
std::vector<u_int8_t> golden = std::vector<u_int8_t> golden =
@ -1378,8 +1381,8 @@ TEST(Conv2d, shape_9_9_1_1_uint8_DilationQuantizedTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({3, 3}); std::array<uint32_t, 2> dilation({3, 3});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1431,13 +1434,13 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerTensorTest) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_weight(tim::vx::QuantType::ASYMMETRIC, 2,
scales_weight, zero_point_weight); scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2, scales_bias, tim::vx::Quantization quant_bias(tim::vx::QuantType::ASYMMETRIC, 2,
zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1481,8 +1484,8 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerTensorTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({1, 1}); std::array<uint32_t, 2> dilation({1, 1});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1527,7 +1530,7 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
std::vector<int32_t> zero_point_weight = {0, 0}; std::vector<int32_t> zero_point_weight = {0, 0};
std::vector<float> scales_bias = {scales_input[0] * scales_weight[0], std::vector<float> scales_bias = {scales_input[0] * scales_weight[0],
scales_input[0] * scales_weight[1]}; scales_input[0] * scales_weight[1]};
std::vector<int32_t> zero_point_bias = {0, 0}; std::vector<int32_t> zero_point_bias = {0, 0};
scales_zp = QuantizationParams<int8_t>(output_min, output_max); scales_zp = QuantizationParams<int8_t>(output_min, output_max);
@ -1535,13 +1538,13 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
std::vector<int32_t> zero_point_output = {scales_zp.second}; std::vector<int32_t> zero_point_output = {scales_zp.second};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, tim::vx::Quantization quant_weight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
3, scales_weight, zero_point_weight); 3, scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0, tim::vx::Quantization quant_bias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
scales_bias, zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
tim::vx::TensorAttribute::INPUT, quant_input); tim::vx::TensorAttribute::INPUT, quant_input);
@ -1583,8 +1586,8 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({1, 1}); std::array<uint32_t, 2> dilation({1, 1});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1609,12 +1612,12 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
TEST(Conv2d, shape_w_h_128_1_ksize_1_1_stride_2_int8_QuantizedPerChannelTest) { TEST(Conv2d, shape_w_h_128_1_ksize_1_1_stride_2_int8_QuantizedPerChannelTest) {
std::map<uint32_t, std::vector<uint32_t>> input_shape_list; std::map<uint32_t, std::vector<uint32_t>> input_shape_list;
input_shape_list[32] = {18, 20, 22, 26, 28, 30, 34, 36, 38, input_shape_list[32] = {18, 20, 22, 26, 28, 30, 34, 36, 38,
42, 44, 46, 50, 52, 54, 58, 60, 62}; 42, 44, 46, 50, 52, 54, 58, 60, 62};
input_shape_list[63] = {18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62}; input_shape_list[63] = {18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62};
input_shape_list[95] = {18, 20, 22, 26, 28, 30, 34, 36, 38, input_shape_list[95] = {18, 20, 22, 26, 28, 30, 34, 36, 38,
42, 44, 46, 50, 52, 54, 58, 60, 62}; 42, 44, 46, 50, 52, 54, 58, 60, 62};
input_shape_list[96] = {18, 20, 22, 26, 28, 30, 34, 36, 38, input_shape_list[96] = {18, 20, 22, 26, 28, 30, 34, 36, 38,
42, 44, 46, 50, 52, 54, 58, 60, 62}; 42, 44, 46, 50, 52, 54, 58, 60, 62};
tim::vx::ShapeType input_shape({2, 2, 128, 1}); //whcn tim::vx::ShapeType input_shape({2, 2, 128, 1}); //whcn
tim::vx::ShapeType weight_shape({1, 1, 128, 256}); //whio tim::vx::ShapeType weight_shape({1, 1, 128, 256}); //whio
tim::vx::ShapeType bias_shape({weight_shape[3]}); tim::vx::ShapeType bias_shape({weight_shape[3]});
@ -1642,13 +1645,13 @@ TEST(Conv2d, shape_w_h_128_1_ksize_1_1_stride_2_int8_QuantizedPerChannelTest) {
std::vector<int32_t> zero_point_output = {-1}; std::vector<int32_t> zero_point_output = {-1};
tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_input(tim::vx::QuantType::ASYMMETRIC, 2,
scales_input, zero_point_input); scales_input, zero_point_input);
tim::vx::Quantization quant_weight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, tim::vx::Quantization quant_weight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
3, scales_weight, zero_point_weight); 3, scales_weight, zero_point_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0, tim::vx::Quantization quant_bias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
scales_bias, zero_point_bias); scales_bias, zero_point_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2, tim::vx::Quantization quant_output(tim::vx::QuantType::ASYMMETRIC, 2,
scales_output, zero_point_output); scales_output, zero_point_output);
uint32_t weight_size = uint32_t weight_size =
weight_shape[0] * weight_shape[1] * weight_shape[2] * weight_shape[3]; weight_shape[0] * weight_shape[1] * weight_shape[2] * weight_shape[3];
@ -1699,8 +1702,8 @@ TEST(Conv2d, shape_w_h_128_1_ksize_1_1_stride_2_int8_QuantizedPerChannelTest) {
for (uint32_t i = 0; i < golden_size; i++) { for (uint32_t i = 0; i < golden_size; i++) {
golden_float[i] = 129; golden_float[i] = 129;
} }
std::vector<int8_t> golden = std::vector<int8_t> golden = Quantize<int8_t>(
Quantize<int8_t>(golden_float, scales_output[0], zero_point_output[0]); golden_float, scales_output[0], zero_point_output[0]);
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph(); auto graph = ctx->CreateGraph();
@ -1738,28 +1741,30 @@ TEST(Conv2d, shape_w_h_128_1_ksize_1_1_stride_2_int8_QuantizedPerChannelTest) {
} }
} }
TEST(Conv2d, shape_4_2_2_2_int16_DFPQuantizedTest){ TEST(Conv2d, shape_4_2_2_2_int16_DFPQuantizedTest) {
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
if(ctx->isClOnly()) GTEST_SKIP(); if (ctx->isClOnly()) GTEST_SKIP();
auto graph = ctx->CreateGraph(); auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({4, 2, 2, 2}); //whcn tim::vx::ShapeType input_shape({4, 2, 2, 2}); //whcn
tim::vx::ShapeType weight_shape({1, 1, 2, 1}); //whio tim::vx::ShapeType weight_shape({1, 1, 2, 1}); //whio
tim::vx::ShapeType bias_shape({weight_shape[3]}); tim::vx::ShapeType bias_shape({weight_shape[3]});
tim::vx::ShapeType output_shape( tim::vx::ShapeType output_shape(
{4, 2, weight_shape[3], input_shape[3]}); //whcn {4, 2, weight_shape[3], input_shape[3]}); //whcn
int8_t fl_input = 9, fl_weight= 8, fl_output = 8; int8_t fl_input = 9, fl_weight = 8, fl_output = 8;
tim::vx::Quantization quant_input(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_input); tim::vx::Quantization quant_input(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
tim::vx::Quantization quant_weight(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_weight); fl_input);
tim::vx::Quantization quant_output(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_output); tim::vx::Quantization quant_weight(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
fl_weight);
tim::vx::Quantization quant_output(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
fl_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT16, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::INT16, input_shape,
tim::vx::TensorAttribute::INPUT, tim::vx::TensorAttribute::INPUT, quant_input);
quant_input);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT16, weight_shape, tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT16, weight_shape,
tim::vx::TensorAttribute::CONSTANT, tim::vx::TensorAttribute::CONSTANT,
quant_weight); quant_weight);
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT16, output_shape, tim::vx::TensorSpec output_spec(tim::vx::DataType::INT16, output_shape,
tim::vx::TensorAttribute::OUTPUT, tim::vx::TensorAttribute::OUTPUT,
quant_output); quant_output);
// Input data float // Input data float
std::vector<float> input_data_float = { std::vector<float> input_data_float = {
@ -1767,25 +1772,23 @@ TEST(Conv2d, shape_4_2_2_2_int16_DFPQuantizedTest){
0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2}; 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2, 0.5, 1, 1.5, 2};
// weight data float // weight data float
std::vector<float> weight_data_float= { std::vector<float> weight_data_float = {
1, 2 // first filter 1, 2 // first filter
}; };
//input data(dfp16) //input data(dfp16)
std::vector<int16_t> input_data = { std::vector<int16_t> input_data = {256, 256, 256, 256, 512, 512, 512, 512,
256,256,256,256, 512,512,512,512, 256,256,256,256,512,512,512,512, 256, 256, 256, 256, 512, 512, 512, 512,
256,512,768,1024,256,512,768,1024,256,512,768,1024,256,512,768,1024 256, 512, 768, 1024, 256, 512, 768, 1024,
}; 256, 512, 768, 1024, 256, 512, 768, 1024};
//weight data(dfp16) //weight data(dfp16)
std::vector<int16_t> weight_data = { std::vector<int16_t> weight_data = {256, 512};
256,512
};
// bias data // bias data
std::vector<int64_t> bias_data = {0}; std::vector<int64_t> bias_data = {0};
//golden //golden
std::vector<float> golden = {1.5, 1.5, 1.5, 1.5, 3, 3, 3, 3, std::vector<float> golden = {1.5, 1.5, 1.5, 1.5, 3, 3, 3, 3,
1.5, 3, 4.5, 6, 1.5, 3, 4.5, 6}; 1.5, 3, 4.5, 6, 1.5, 3, 4.5, 6};
auto input_tensor = graph->CreateTensor(input_spec,input_data.data()); auto input_tensor = graph->CreateTensor(input_spec, input_data.data());
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data()); auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
auto output_tensor = graph->CreateTensor(output_spec); auto output_tensor = graph->CreateTensor(output_spec);
@ -1793,8 +1796,8 @@ TEST(Conv2d, shape_4_2_2_2_int16_DFPQuantizedTest){
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1812,16 +1815,16 @@ TEST(Conv2d, shape_4_2_2_2_int16_DFPQuantizedTest){
} }
std::vector<int16_t> output(output_size); std::vector<int16_t> output(output_size);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
//transform output(int16) to fp //transform output(int16) to fp
std::vector<float> f; std::vector<float> f;
for(const auto& q : output){ for (const auto& q : output) {
f.push_back( q / (float)((int64_t)1 << fl_output)); f.push_back(q / (float)((int64_t)1 << fl_output));
} }
EXPECT_EQ(golden, f); EXPECT_EQ(golden, f);
} }
TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) { TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) {
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
if(ctx->isClOnly()) GTEST_SKIP(); if (ctx->isClOnly()) GTEST_SKIP();
auto graph = ctx->CreateGraph(); auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({4, 2, 1, 1}); //whcn tim::vx::ShapeType input_shape({4, 2, 1, 1}); //whcn
@ -1829,32 +1832,34 @@ TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) {
tim::vx::ShapeType bias_shape({weight_shape[3]}); tim::vx::ShapeType bias_shape({weight_shape[3]});
tim::vx::ShapeType output_shape( tim::vx::ShapeType output_shape(
{4, 2, weight_shape[3], input_shape[3]}); //whcn {4, 2, weight_shape[3], input_shape[3]}); //whcn
int8_t fl_input = 9, fl_weight = 8, fl_bias = 17,fl_output = 8; int8_t fl_input = 9, fl_weight = 8, fl_bias = 17, fl_output = 8;
tim::vx::Quantization quant_input(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_input); tim::vx::Quantization quant_input(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
tim::vx::Quantization quant_weight(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_weight); fl_input);
tim::vx::Quantization quant_bias(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_bias); tim::vx::Quantization quant_weight(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
tim::vx::Quantization quant_output(tim::vx::QuantType::DYNAMIC_FIXED_POINT, fl_output); fl_weight);
tim::vx::Quantization quant_bias(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
fl_bias);
tim::vx::Quantization quant_output(tim::vx::QuantType::DYNAMIC_FIXED_POINT,
fl_output);
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT16, input_shape, tim::vx::TensorSpec input_spec(tim::vx::DataType::INT16, input_shape,
tim::vx::TensorAttribute::INPUT, tim::vx::TensorAttribute::INPUT, quant_input);
quant_input);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT16, weight_shape, tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT16, weight_shape,
tim::vx::TensorAttribute::CONSTANT, tim::vx::TensorAttribute::CONSTANT,
quant_weight); quant_weight);
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT64, bias_shape, tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT64, bias_shape,
tim::vx::TensorAttribute::CONSTANT, tim::vx::TensorAttribute::CONSTANT, quant_bias);
quant_bias);
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT16, output_shape, tim::vx::TensorSpec output_spec(tim::vx::DataType::INT16, output_shape,
tim::vx::TensorAttribute::OUTPUT, tim::vx::TensorAttribute::OUTPUT,
quant_output); quant_output);
// Input data nchw // Input data nchw
std::vector<float> input_data_float= { std::vector<float> input_data_float = {
1, 1, 1, 1, // row = 1 1, 1, 1, 1, // row = 1
2, 2, 3, 2 // row = 2 2, 2, 3, 2 // row = 2
}; };
// weight data oihw // weight data oihw
std::vector<float> weight_data_float= { std::vector<float> weight_data_float = {
1, 2, 3, 4, //first 2x2 filter 1, 2, 3, 4, //first 2x2 filter
-1, 1, -1, 1, // second 2x2 filter -1, 1, -1, 1, // second 2x2 filter
-1, -1, 1, 1, // third 2x2 filter -1, -1, 1, 1, // third 2x2 filter
@ -1865,24 +1870,18 @@ TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) {
// nchw // nchw
std::vector<float> golden = {// first channel std::vector<float> golden = {// first channel
18, 22, 21, 8, 7, 9, 8, 3, 18, 22, 21, 8, 7, 9, 8, 3,
// second channel // second channel
2, 3, 1, -1, 2, 3, 1, 0, 2, 3, 1, -1, 2, 3, 1, 0,
// third channel // third channel
5, 6, 6, 4, -1, -2, -2, 1}; 5, 6, 6, 4, -1, -2, -2, 1};
std::vector<int16_t> input_data = { std::vector<int16_t> input_data = {512, 512, 512, 512,
512, 512, 512, 512, 1024, 1024, 1536, 1024};
1024,1024,1536,1024 std::vector<int16_t> weight_data = {256, 512, 768, 1024, -256, 256,
}; -256, 256, -256, -256, 256, 256};
std::vector<int16_t> weight_data = { std::vector<int64_t> bias_data = {1 << fl_bias, 2 * (1 << fl_bias),
256,512,768,1024, 3 * (1 << fl_bias)};
-256,256,-256,256,
-256,-256,256,256
};
std::vector<int64_t> bias_data = {
1<<fl_bias, 2*(1<<fl_bias),3*(1<<fl_bias)
};
auto input_tensor = graph->CreateTensor(input_spec, input_data.data()); auto input_tensor = graph->CreateTensor(input_spec, input_data.data());
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data()); auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
@ -1894,8 +1893,8 @@ TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) {
std::array<uint32_t, 2> stride({1, 1}); std::array<uint32_t, 2> stride({1, 1});
std::array<uint32_t, 2> dilation({0, 0}); std::array<uint32_t, 2> dilation({0, 0});
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>( auto conv2d =
padding, stride, dilation); graph->CreateOperation<tim::vx::ops::Conv2d>(padding, stride, dilation);
(*conv2d) (*conv2d)
.BindInput(input_tensor) .BindInput(input_tensor)
.BindInput(weight_tensor) .BindInput(weight_tensor)
@ -1916,8 +1915,8 @@ TEST(Conv2d, shape_4_2_1_1_int16_DFPQuantizedTest) {
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
//transform output(int16) to fp //transform output(int16) to fp
std::vector<float> f; std::vector<float> f;
for(const auto& q : output){ for (const auto& q : output) {
f.push_back( q / (float)((int64_t)1 << fl_output)); f.push_back(q / (float)((int64_t)1 << fl_output));
} }
EXPECT_EQ(golden, f); EXPECT_EQ(golden, f);
} }
@ -1926,7 +1925,7 @@ TEST(Conv2d, kernel_bigger_than_input_SAME) {
auto ctx = tim::vx::Context::Create(); auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph(); auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({2, 3, 1, 1}); //whcn tim::vx::ShapeType input_shape({2, 3, 1, 1}); //whcn
tim::vx::ShapeType kernel_shape({3, 2, 1, 1}); //whio tim::vx::ShapeType kernel_shape({3, 2, 1, 1}); //whio
tim::vx::ShapeType bias_shape({1}); tim::vx::ShapeType bias_shape({1});
tim::vx::ShapeType output_shape({2, 3, 1, 1}); tim::vx::ShapeType output_shape({2, 3, 1, 1});
@ -1939,13 +1938,16 @@ TEST(Conv2d, kernel_bigger_than_input_SAME) {
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape, tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
tim::vx::TensorAttribute::OUTPUT); tim::vx::TensorAttribute::OUTPUT);
std::vector<float> input_data = {1.0f, 3.0f, 4.0f, 2.0f, 2.0f, 3.0f, std::vector<float> input_data = {
}; 1.0f, 3.0f, 4.0f, 2.0f, 2.0f, 3.0f,
std::vector<float> weight = {100.0f, 20.0f, 1.0f, 200.0f, 10.0f, 2.0f, };
}; std::vector<float> weight = {
100.0f, 20.0f, 1.0f, 200.0f, 10.0f, 2.0f,
};
std::vector<float> bias = {500.0f}; std::vector<float> bias = {500.0f};
std::vector<float> golden = {567.0f, 1480.0f, 608.0f, 1370.0f, std::vector<float> golden = {
543.0f, 760.0f, }; 567.0f, 1480.0f, 608.0f, 1370.0f, 543.0f, 760.0f,
};
auto input_tensor = graph->CreateTensor(input_spec); auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(kernel_spec, weight.data()); auto weight_tensor = graph->CreateTensor(kernel_spec, weight.data());
auto bias_tensor = graph->CreateTensor(bias_spec, bias.data()); auto bias_tensor = graph->CreateTensor(bias_spec, bias.data());
@ -1956,7 +1958,9 @@ TEST(Conv2d, kernel_bigger_than_input_SAME) {
auto op = graph->CreateOperation<tim::vx::ops::Conv2d>( auto op = graph->CreateOperation<tim::vx::ops::Conv2d>(
tim::vx::PadType::SAME, strides, dilations, 0, tim::vx::DataLayout::WHCN, tim::vx::PadType::SAME, strides, dilations, 0, tim::vx::DataLayout::WHCN,
tim::vx::DataLayout::IcWHOc); tim::vx::DataLayout::IcWHOc);
(*op).BindInputs({input_tensor, weight_tensor, bias_tensor}).BindOutputs({output_tensor}); (*op)
.BindInputs({input_tensor, weight_tensor, bias_tensor})
.BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Compile());

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Release Notes {#changelog}
=============
1.12.0 release (2017-03-06):
----------------------------
- Changed behaviour of `half_cast` to perform conversions to/from `double`
and `long double` directly according to specified rounding mode, without an
intermediate `float` conversion.
- Added `noexcept` specifiers to constructors.
- Fixed minor portability problem with `logb` and `ilogb`.
- Tested for *VC++ 2015*.
1.11.0 release (2013-11-16):
----------------------------
- Made tie-breaking behaviour in round to nearest configurable by
`HALF_ROUND_TIES_TO_EVEN` macro.
- Completed support for all C++11 mathematical functions even if single-
precision versions from `<cmath>` are unsupported.
- Fixed inability to disable support for C++11 mathematical functions on
*VC++ 2013*.
1.10.0 release (2013-11-09):
----------------------------
- Made default rounding mode configurable by `HALF_ROUND_STYLE` macro.
- Added support for non-IEEE single-precision implementations.
- Added `HALF_ENABLE_CPP11_TYPE_TRAITS` preprocessor flag for checking
support for C++11 type traits and TMP features.
- Restricted `half_cast` to support built-in arithmetic types only.
- Changed behaviour of `half_cast` to respect rounding mode when casting
to/from integer types.
1.9.2 release (2013-11-01):
---------------------------
- Tested for *gcc 4.8*.
- Tested and fixed for *VC++ 2013*.
- Removed unnecessary warnings in *MSVC*.
1.9.1 release (2013-08-08):
---------------------------
- Fixed problems with older gcc and MSVC versions.
- Small fix to non-C++11 implementations of `remainder` and `remquo`.
1.9.0 release (2013-08-07):
---------------------------
- Changed behaviour of `nearbyint`, `rint`, `lrint` and `llrint` to use
rounding mode of half-precision implementation (which is
truncating/indeterminate) instead of single-precision rounding mode.
- Added support for more C++11 mathematical functions even if single-
precision versions from `<cmath>` are unsupported, in particular
`remainder`, `remquo` and `cbrt`.
- Minor implementation changes.
1.8.1 release (2013-01-22):
---------------------------
- Fixed bug resulting in multiple definitions of the `nanh` function due to
a missing `inline` specification.
1.8.0 release (2013-01-19):
---------------------------
- Added support for more C++11 mathematical functions even if single-
precision versions from `<cmath>` are unsupported, in particular
exponential and logarithm functions, hyperbolic area functions and the
hypotenuse function.
- Made `fma` function use default implementation if single-precision version
from `<cmath>` is not faster and thus `FP_FAST_FMAH` to be defined always.
- Fixed overload resolution issues when invoking certain mathematical
functions by unqualified calls.
1.7.0 release (2012-10-26):
---------------------------
- Added support for C++11 `noexcept` specifiers.
- Changed C++11 `long long` to be supported on *VC++ 2003* and up.
1.6.1 release (2012-09-13):
---------------------------
- Made `fma` and `fdim` functions available even if corresponding
single-precision functions are not.
1.6.0 release (2012-09-12):
---------------------------
- Added `HALF_ENABLE_CPP11_LONG_LONG` to control support for `long long`
integers and corresponding mathematical functions.
- Fixed C++98 compatibility on non-VC compilers.
1.5.1 release (2012-08-17):
---------------------------
- Recorrected `std::numeric_limits::round_style` to always return
`std::round_indeterminate`, due to overflow-handling deviating from
correct round-toward-zero behaviour.
1.5.0 release (2012-08-16):
---------------------------
- Added `half_cast` for explicitly casting between half and any type
convertible to/from `float` and allowing the explicit specification of
the rounding mode to use.
1.4.0 release (2012-08-12):
---------------------------
- Added support for C++11 generalized constant expressions (`constexpr`).
1.3.1 release (2012-08-11):
---------------------------
- Fixed requirement for `std::signbit` and `std::isnan` (even if C++11
`<cmath>` functions disabled) on non-VC compilers.
1.3.0 release (2012-08-10):
---------------------------
- Made requirement for `<cstdint>` and `static_assert` optional and thus
made the library C++98-compatible.
- Made support for C++11 features user-overridable through explicit
definition of corresponding preprocessor symbols to either 0 or 1.
- Renamed `HALF_ENABLE_HASH` to `HALF_ENABLE_CPP11_HASH` in correspondence
with other C++11 preprocessor symbols.
1.2.0 release (2012-08-07):
---------------------------
- Added proper preprocessor definitions for `HUGE_VALH` and `FP_FAST_FMAH`
in correspondence with their single-precision counterparts from `<cmath>`.
- Fixed internal preprocessor macros to be properly undefined after use.
1.1.2 release (2012-08-07):
---------------------------
- Revised `std::numeric_limits::round_style` to return
`std::round_toward_zero` if the `float` version also does and
`std::round_indeterminate` otherwise.
- Fixed `std::numeric_limits::round_error` to reflect worst-case round
toward zero behaviour.
1.1.1 release (2012-08-06):
---------------------------
- Fixed `std::numeric_limits::min` to return smallest positive normal
number, instead of subnormal number.
- Fixed `std::numeric_limits::round_style` to return
`std::round_indeterminate` due to mixture of separately rounded
single-precision arithmetics with truncating single-to-half conversions.
1.1.0 release (2012-08-06):
---------------------------
- Added half-precision literals.
1.0.0 release (2012-08-05):
---------------------------
- First release.

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@ -0,0 +1,4 @@
#
# Copyright (c) 2012-2017 Christian Rau
# SPDX-License-Identifier: MIT
#

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@ -0,0 +1,7 @@
PackageName: half
SPDXID: SPDXRef-half
FilesAnalyzed: true
PackageLicenseConcluded: MIT
PackageLicenseInfoFromFiles: MIT
PackageLicenseDeclared: MIT
PackageCopyrightText:<text>Copyright (c) 2012-2017 Christian Rau <rauy@users.sourceforge.net></text>

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@ -0,0 +1,21 @@
The MIT License
Copyright (c) 2012-2017 Christian Rau
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.

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HALF-PRECISION FLOATING POINT LIBRARY (Version 1.12.0)
------------------------------------------------------
This is a C++ header-only library to provide an IEEE 754 conformant 16-bit
half-precision floating point type along with corresponding arithmetic
operators, type conversions and common mathematical functions. It aims for both
efficiency and ease of use, trying to accurately mimic the behaviour of the
builtin floating point types at the best performance possible.
INSTALLATION AND REQUIREMENTS
-----------------------------
Comfortably enough, the library consists of just a single header file
containing all the functionality, which can be directly included by your
projects, without the neccessity to build anything or link to anything.
Whereas this library is fully C++98-compatible, it can profit from certain
C++11 features. Support for those features is checked automatically at compile
(or rather preprocessing) time, but can be explicitly enabled or disabled by
defining the corresponding preprocessor symbols to either 1 or 0 yourself. This
is useful when the automatic detection fails (for more exotic implementations)
or when a feature should be explicitly disabled:
- 'long long' integer type for mathematical functions returning 'long long'
results (enabled for VC++ 2003 and newer, gcc and clang, overridable with
'HALF_ENABLE_CPP11_LONG_LONG').
- Static assertions for extended compile-time checks (enabled for VC++ 2010,
gcc 4.3, clang 2.9 and newer, overridable with 'HALF_ENABLE_CPP11_STATIC_ASSERT').
- Generalized constant expressions (enabled for VC++ 2015, gcc 4.6, clang 3.1
and newer, overridable with 'HALF_ENABLE_CPP11_CONSTEXPR').
- noexcept exception specifications (enabled for VC++ 2015, gcc 4.6, clang 3.0
and newer, overridable with 'HALF_ENABLE_CPP11_NOEXCEPT').
- User-defined literals for half-precision literals to work (enabled for
VC++ 2015, gcc 4.7, clang 3.1 and newer, overridable with
'HALF_ENABLE_CPP11_USER_LITERALS').
- Type traits and template meta-programming features from <type_traits>
(enabled for VC++ 2010, libstdc++ 4.3, libc++ and newer, overridable with
'HALF_ENABLE_CPP11_TYPE_TRAITS').
- Special integer types from <cstdint> (enabled for VC++ 2010, libstdc++ 4.3,
libc++ and newer, overridable with 'HALF_ENABLE_CPP11_CSTDINT').
- Certain C++11 single-precision mathematical functions from <cmath> for
an improved implementation of their half-precision counterparts to work
(enabled for VC++ 2013, libstdc++ 4.3, libc++ and newer, overridable with
'HALF_ENABLE_CPP11_CMATH').
- Hash functor 'std::hash' from <functional> (enabled for VC++ 2010,
libstdc++ 4.3, libc++ and newer, overridable with 'HALF_ENABLE_CPP11_HASH').
The library has been tested successfully with Visual C++ 2005-2015, gcc 4.4-4.8
and clang 3.1. Please contact me if you have any problems, suggestions or even
just success testing it on other platforms.
DOCUMENTATION
-------------
Here follow some general words about the usage of the library and its
implementation. For a complete documentation of its iterface look at the
corresponding website http://half.sourceforge.net. You may also generate the
complete developer documentation from the library's only include file's doxygen
comments, but this is more relevant to developers rather than mere users (for
reasons described below).
BASIC USAGE
To make use of the library just include its only header file half.hpp, which
defines all half-precision functionality inside the 'half_float' namespace. The
actual 16-bit half-precision data type is represented by the 'half' type. This
type behaves like the builtin floating point types as much as possible,
supporting the usual arithmetic, comparison and streaming operators, which
makes its use pretty straight-forward:
using half_float::half;
half a(3.4), b(5);
half c = a * b;
c += 3;
if(c > a)
std::cout << c << std::endl;
Additionally the 'half_float' namespace also defines half-precision versions
for all mathematical functions of the C++ standard library, which can be used
directly through ADL:
half a(-3.14159);
half s = sin(abs(a));
long l = lround(s);
You may also specify explicit half-precision literals, since the library
provides a user-defined literal inside the 'half_float::literal' namespace,
which you just need to import (assuming support for C++11 user-defined literals):
using namespace half_float::literal;
half x = 1.0_h;
Furthermore the library provides proper specializations for
'std::numeric_limits', defining various implementation properties, and
'std::hash' for hashing half-precision numbers (assuming support for C++11
'std::hash'). Similar to the corresponding preprocessor symbols from <cmath>
the library also defines the 'HUGE_VALH' constant and maybe the 'FP_FAST_FMAH'
symbol.
CONVERSIONS AND ROUNDING
The half is explicitly constructible/convertible from a single-precision float
argument. Thus it is also explicitly constructible/convertible from any type
implicitly convertible to float, but constructing it from types like double or
int will involve the usual warnings arising when implicitly converting those to
float because of the lost precision. On the one hand those warnings are
intentional, because converting those types to half neccessarily also reduces
precision. But on the other hand they are raised for explicit conversions from
those types, when the user knows what he is doing. So if those warnings keep
bugging you, then you won't get around first explicitly converting to float
before converting to half, or use the 'half_cast' described below. In addition
you can also directly assign float values to halfs.
In contrast to the float-to-half conversion, which reduces precision, the
conversion from half to float (and thus to any other type implicitly
convertible from float) is implicit, because all values represetable with
half-precision are also representable with single-precision. This way the
half-to-float conversion behaves similar to the builtin float-to-double
conversion and all arithmetic expressions involving both half-precision and
single-precision arguments will be of single-precision type. This way you can
also directly use the mathematical functions of the C++ standard library,
though in this case you will invoke the single-precision versions which will
also return single-precision values, which is (even if maybe performing the
exact same computation, see below) not as conceptually clean when working in a
half-precision environment.
The default rounding mode for conversions from float to half uses truncation
(round toward zero, but mapping overflows to infinity) for rounding values not
representable exactly in half-precision. This is the fastest rounding possible
and is usually sufficient. But by redefining the 'HALF_ROUND_STYLE'
preprocessor symbol (before including half.hpp) this default can be overridden
with one of the other standard rounding modes using their respective constants
or the equivalent values of 'std::float_round_style' (it can even be
synchronized with the underlying single-precision implementation by defining it
to 'std::numeric_limits<float>::round_style'):
- 'std::round_indeterminate' or -1 for the fastest rounding (default).
- 'std::round_toward_zero' or 0 for rounding toward zero.
- std::round_to_nearest' or 1 for rounding to the nearest value.
- std::round_toward_infinity' or 2 for rounding toward positive infinity.
- std::round_toward_neg_infinity' or 3 for rounding toward negative infinity.
In addition to changing the overall default rounding mode one can also use the
'half_cast'. This converts between half and any built-in arithmetic type using
a configurable rounding mode (or the default rounding mode if none is
specified). In addition to a configurable rounding mode, 'half_cast' has
another big difference to a mere 'static_cast': Any conversions are performed
directly using the given rounding mode, without any intermediate conversion
to/from 'float'. This is especially relevant for conversions to integer types,
which don't necessarily truncate anymore. But also for conversions from
'double' or 'long double' this may produce more precise results than a
pre-conversion to 'float' using the single-precision implementation's current
rounding mode would.
half a = half_cast<half>(4.2);
half b = half_cast<half,std::numeric_limits<float>::round_style>(4.2f);
assert( half_cast<int, std::round_to_nearest>( 0.7_h ) == 1 );
assert( half_cast<half,std::round_toward_zero>( 4097 ) == 4096.0_h );
assert( half_cast<half,std::round_toward_infinity>( 4097 ) == 4100.0_h );
assert( half_cast<half,std::round_toward_infinity>( std::numeric_limits<double>::min() ) > 0.0_h );
When using round to nearest (either as default or through 'half_cast') ties are
by default resolved by rounding them away from zero (and thus equal to the
behaviour of the 'round' function). But by redefining the
'HALF_ROUND_TIES_TO_EVEN' preprocessor symbol to 1 (before including half.hpp)
this default can be changed to the slightly slower but less biased and more
IEEE-conformant behaviour of rounding half-way cases to the nearest even value.
#define HALF_ROUND_TIES_TO_EVEN 1
#include <half.hpp>
...
assert( half_cast<int,std::round_to_nearest>(3.5_h)
== half_cast<int,std::round_to_nearest>(4.5_h) );
IMPLEMENTATION
For performance reasons (and ease of implementation) many of the mathematical
functions provided by the library as well as all arithmetic operations are
actually carried out in single-precision under the hood, calling to the C++
standard library implementations of those functions whenever appropriate,
meaning the arguments are converted to floats and the result back to half. But
to reduce the conversion overhead as much as possible any temporary values
inside of lengthy expressions are kept in single-precision as long as possible,
while still maintaining a strong half-precision type to the outside world. Only
when finally assigning the value to a half or calling a function that works
directly on halfs is the actual conversion done (or never, when further
converting the result to float.
This approach has two implications. First of all you have to treat the
library's documentation at http://half.sourceforge.net as a simplified version,
describing the behaviour of the library as if implemented this way. The actual
argument and return types of functions and operators may involve other internal
types (feel free to generate the exact developer documentation from the Doxygen
comments in the library's header file if you really need to). But nevertheless
the behaviour is exactly like specified in the documentation. The other
implication is, that in the presence of rounding errors or over-/underflows
arithmetic expressions may produce different results when compared to
converting to half-precision after each individual operation:
half a = std::numeric_limits<half>::max() * 2.0_h / 2.0_h; // a = MAX
half b = half(std::numeric_limits<half>::max() * 2.0_h) / 2.0_h; // b = INF
assert( a != b );
But this should only be a problem in very few cases. One last word has to be
said when talking about performance. Even with its efforts in reducing
conversion overhead as much as possible, the software half-precision
implementation can most probably not beat the direct use of single-precision
computations. Usually using actual float values for all computations and
temproraries and using halfs only for storage is the recommended way. On the
one hand this somehow makes the provided mathematical functions obsolete
(especially in light of the implicit conversion from half to float), but
nevertheless the goal of this library was to provide a complete and
conceptually clean half-precision implementation, to which the standard
mathematical functions belong, even if usually not needed.
IEEE CONFORMANCE
The half type uses the standard IEEE representation with 1 sign bit, 5 exponent
bits and 10 mantissa bits (11 when counting the hidden bit). It supports all
types of special values, like subnormal values, infinity and NaNs. But there
are some limitations to the complete conformance to the IEEE 754 standard:
- The implementation does not differentiate between signalling and quiet
NaNs, this means operations on halfs are not specified to trap on
signalling NaNs (though they may, see last point).
- Though arithmetic operations are internally rounded to single-precision
using the underlying single-precision implementation's current rounding
mode, those values are then converted to half-precision using the default
half-precision rounding mode (changed by defining 'HALF_ROUND_STYLE'
accordingly). This mixture of rounding modes is also the reason why
'std::numeric_limits<half>::round_style' may actually return
'std::round_indeterminate' when half- and single-precision rounding modes
don't match.
- Because of internal truncation it may also be that certain single-precision
NaNs will be wrongly converted to half-precision infinity, though this is
very unlikely to happen, since most single-precision implementations don't
tend to only set the lowest bits of a NaN mantissa.
- The implementation does not provide any floating point exceptions, thus
arithmetic operations or mathematical functions are not specified to invoke
proper floating point exceptions. But due to many functions implemented in
single-precision, those may still invoke floating point exceptions of the
underlying single-precision implementation.
Some of those points could have been circumvented by controlling the floating
point environment using <cfenv> or implementing a similar exception mechanism.
But this would have required excessive runtime checks giving two high an impact
on performance for something that is rarely ever needed. If you really need to
rely on proper floating point exceptions, it is recommended to explicitly
perform computations using the built-in floating point types to be on the safe
side. In the same way, if you really need to rely on a particular rounding
behaviour, it is recommended to either use single-precision computations and
explicitly convert the result to half-precision using 'half_cast' and
specifying the desired rounding mode, or synchronize the default half-precision
rounding mode to the rounding mode of the single-precision implementation (most
likely 'HALF_ROUND_STYLE=1', 'HALF_ROUND_TIES_TO_EVEN=1'). But this is really
considered an expert-scenario that should be used only when necessary, since
actually working with half-precision usually comes with a certain
tolerance/ignorance of exactness considerations and proper rounding comes with
a certain performance cost.
CREDITS AND CONTACT
-------------------
This library is developed by CHRISTIAN RAU and released under the MIT License
(see LICENSE.txt). If you have any questions or problems with it, feel free to
contact me at rauy@users.sourceforge.net.
Additional credit goes to JEROEN VAN DER ZIJP for his paper on "Fast Half Float
Conversions", whose algorithms have been used in the library for converting
between half-precision and single-precision values.

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#
# Copyright (c) 2012-2017 Christian Rau
# SPDX-License-Identifier: MIT
#

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