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-rw-r--r--src/core/NEON/kernels/NEReductionOperationKernel.cpp481
-rw-r--r--src/runtime/NEON/functions/NEReduceMean.cpp117
-rw-r--r--src/runtime/NEON/functions/NEReductionOperation.cpp32
3 files changed, 598 insertions, 32 deletions
diff --git a/src/core/NEON/kernels/NEReductionOperationKernel.cpp b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
index 30f21bbf33..b77219cd79 100644
--- a/src/core/NEON/kernels/NEReductionOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
@@ -32,10 +32,11 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include <arm_neon.h>
-using namespace arm_compute;
-
+namespace arm_compute
+{
namespace
{
template <class F>
@@ -57,31 +58,281 @@ public:
Iterator in(input, in_slice);
Iterator out(output, out_slice);
- f(in, out, in_slice, out_slice);
+ f(in, out, in_slice, out_slice, *input->info());
+ }
+ while(window.slide_window_slice_1D(in_slice) && out_window.slide_window_slice_1D(out_slice));
+ }
+ static void reduceY(const Window &window, const ITensor *input, ITensor *output, F f)
+ {
+ // Set in window
+ Window in_window(window);
+
+ in_window.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ // Get first input and output slices
+ Window in_slice = in_window.first_slice_window_2D();
+ Window out_slice = window.first_slice_window_2D();
+
+ do
+ {
+ Iterator in(input, in_slice);
+ Iterator out(output, out_slice);
+
+ f(in, out, in_slice, out_slice, *input->info(), 1);
+ }
+ while(in_window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
+ }
+ static void reduceZ(const Window &window, const ITensor *input, ITensor *output, F f)
+ {
+ // Set in window
+ Window in_window(window);
+
+ in_window.set(Window::DimZ, Window::Dimension(0, 1, 1));
+
+ // Get first input and output slices
+ Window in_slice = in_window.first_slice_window_3D();
+ Window out_slice = window.first_slice_window_3D();
+
+ do
+ {
+ Iterator in(input, in_slice);
+ Iterator out(output, out_slice);
+
+ f(in, out, in_slice, out_slice, *input->info(), 2);
}
- while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(out_slice));
+ while(in_window.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice));
+ }
+ static void reduceW(const Window &window, const ITensor *input, ITensor *output, F f)
+ {
+ // Set in/out window
+ Window in_window(window);
+ Window out_window(window);
+
+ in_window.set(3, Window::Dimension(0, 1, 1));
+ out_window.set(3, Window::Dimension(0, 1, 1));
+
+ // Get first input and output slices
+ Window in_slice = in_window.first_slice_window_4D();
+ Window out_slice = out_window.first_slice_window_4D();
+
+ do
+ {
+ Iterator in(input, in_slice);
+ Iterator out(output, out_slice);
+
+ f(in, out, in_slice, out_slice, *input->info(), 3);
+ }
+ while(in_window.slide_window_slice_4D(in_slice) && out_window.slide_window_slice_4D(out_slice));
}
};
-struct SumsqOpX
+template <typename T, int S, ReductionOperation op>
+struct RedOpX
{
- inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice)
+ /** NEON vector tag type. */
+ using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
+
+ inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info)
{
ARM_COMPUTE_UNUSED(out_slice);
- float32x4_t vec_sum_value = vdupq_n_f32(0.f);
+ auto vec_sum_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
execute_window_loop(in_slice, [&](const Coordinates & id)
{
- const auto in_ptr = reinterpret_cast<const float *>(input.ptr());
- const float32x4_t vec_elements = vld1q_f32(in_ptr);
- vec_sum_value = vaddq_f32(vmulq_f32(vec_elements, vec_elements), vec_sum_value);
+ const auto in_ptr = reinterpret_cast<const T *>(input.ptr());
+ const auto vec_elements = wrapper::vloadq(in_ptr);
+
+ if(op == ReductionOperation::SUM_SQUARE)
+ {
+ vec_sum_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_sum_value);
+ }
+ else
+ {
+ vec_sum_value = wrapper::vadd(vec_elements, vec_sum_value);
+ }
},
input);
- float32x2_t carry_addition = vpadd_f32(vget_high_f32(vec_sum_value), vget_low_f32(vec_sum_value));
- carry_addition = vpadd_f32(carry_addition, carry_addition);
+ auto carry_addition = wrapper::vpadd(wrapper::vgethigh(vec_sum_value), wrapper::vgetlow(vec_sum_value));
+ carry_addition = wrapper::vpadd(carry_addition, carry_addition);
+
+ auto res = wrapper::vgetlane(carry_addition, 0);
+ if(op == ReductionOperation::MEAN_SUM)
+ {
+ res /= in_info.dimension(0);
+ }
- *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(carry_addition, 0);
+ *(reinterpret_cast<T *>(output.ptr())) = res;
+ }
+};
+
+template <ReductionOperation op>
+struct RedOpX_qasymm8
+{
+ inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info)
+ {
+ ARM_COMPUTE_UNUSED(out_slice);
+ auto vec_sum_value1 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value2 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value3 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value4 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+
+ execute_window_loop(in_slice, [&](const Coordinates & id)
+ {
+ const auto vec_elements = wrapper::vloadq(input.ptr());
+
+ const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
+ const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
+
+ const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
+ const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
+ const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
+ const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
+
+ vec_sum_value1 = wrapper::vadd(temp32x4t_1, vec_sum_value1);
+ vec_sum_value2 = wrapper::vadd(temp32x4t_2, vec_sum_value2);
+ vec_sum_value3 = wrapper::vadd(temp32x4t_3, vec_sum_value3);
+ vec_sum_value4 = wrapper::vadd(temp32x4t_4, vec_sum_value4);
+ },
+ input);
+
+ auto carry_addition = wrapper::vadd(vec_sum_value1, vec_sum_value2);
+ carry_addition = wrapper::vadd(carry_addition, vec_sum_value3);
+ carry_addition = wrapper::vadd(carry_addition, vec_sum_value4);
+
+ auto carry_paddition = wrapper::vpadd(wrapper::vgethigh(carry_addition), wrapper::vgetlow(carry_addition));
+ carry_paddition = wrapper::vpadd(carry_paddition, carry_paddition);
+ auto res = wrapper::vgetlane(carry_paddition, 0);
+
+ if(op == ReductionOperation::MEAN_SUM)
+ {
+ res /= in_info.dimension(0);
+ }
+
+ *(output.ptr()) = static_cast<uint8_t>(res);
+ }
+};
+
+template <typename T, int S, ReductionOperation op>
+struct RedOpYZW
+{
+ /** NEON vector tag type. */
+ using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
+
+ inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis)
+ {
+ ARM_COMPUTE_UNUSED(out_slice);
+
+ execute_window_loop(in_slice, [&](const Coordinates & id)
+ {
+ auto vec_sum_value = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
+ for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
+ {
+ T *in_ptr;
+ switch(axis)
+ {
+ case 1:
+ in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, dim)));
+ break;
+ case 2:
+ in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, dim)));
+ break;
+ case 3:
+ in_ptr = reinterpret_cast<T *>(input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, 0, dim)));
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ const auto vec_elements = wrapper::vloadq(in_ptr);
+
+ if(op == ReductionOperation::SUM_SQUARE)
+ {
+ vec_sum_value = wrapper::vadd(wrapper::vmul(vec_elements, vec_elements), vec_sum_value);
+ }
+ else
+ {
+ vec_sum_value = wrapper::vadd(vec_elements, vec_sum_value);
+ }
+ }
+
+ if(op == ReductionOperation::MEAN_SUM)
+ {
+ auto vec_width_inv = wrapper::vinv(wrapper::vdup_n(static_cast<T>(in_info.dimension(axis)), ExactTagType{}));
+ vec_sum_value = wrapper::vmul(vec_sum_value, vec_width_inv);
+ }
+
+ wrapper::vstore(reinterpret_cast<T *>(output.ptr()), vec_sum_value);
+ },
+ input, output);
+ }
+};
+
+template <ReductionOperation op>
+struct RedOpYZW_qasymm8
+{
+ inline void operator()(Iterator &input, Iterator &output, Window &in_slice, Window &out_slice, const TensorInfo &in_info, int axis)
+ {
+ ARM_COMPUTE_UNUSED(out_slice);
+
+ execute_window_loop(in_slice, [&](const Coordinates & id)
+ {
+ auto vec_sum_value1 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value2 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value3 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ auto vec_sum_value4 = vdupq_n_u32(static_cast<uint32_t>(0.f));
+ for(unsigned int dim = 0; dim < in_info.dimension(axis); ++dim)
+ {
+ uint8_t *in_ptr;
+ switch(axis)
+ {
+ case 1:
+ in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, dim));
+ break;
+ case 2:
+ in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, dim));
+ break;
+ case 3:
+ in_ptr = input.ptr() + in_info.offset_element_in_bytes(Coordinates(0, 0, 0, dim));
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ const auto vec_elements = wrapper::vloadq(in_ptr);
+
+ const auto temp16x8t_1 = wrapper::vmovl(wrapper::vgetlow(vec_elements));
+ const auto temp16x8t_2 = wrapper::vmovl(wrapper::vgethigh(vec_elements));
+
+ const auto temp32x4t_1 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_1));
+ const auto temp32x4t_2 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_1));
+ const auto temp32x4t_3 = wrapper::vmovl(wrapper::vgetlow(temp16x8t_2));
+ const auto temp32x4t_4 = wrapper::vmovl(wrapper::vgethigh(temp16x8t_2));
+
+ vec_sum_value1 = wrapper::vadd(temp32x4t_1, vec_sum_value1);
+ vec_sum_value2 = wrapper::vadd(temp32x4t_2, vec_sum_value2);
+ vec_sum_value3 = wrapper::vadd(temp32x4t_3, vec_sum_value3);
+ vec_sum_value4 = wrapper::vadd(temp32x4t_4, vec_sum_value4);
+ }
+
+ if(op == ReductionOperation::MEAN_SUM)
+ {
+ const auto vec_width_inv = wrapper::vinv(vdupq_n_f32(in_info.dimension(axis)));
+ const auto vec_sum_value1_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value1), vec_width_inv);
+ const auto vec_sum_value2_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value2), vec_width_inv);
+ const auto vec_sum_value3_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value3), vec_width_inv);
+ const auto vec_sum_value4_f = wrapper::vmul(vcvtq_f32_u32(vec_sum_value4), vec_width_inv);
+
+ vec_sum_value1 = vcvtq_u32_f32(vec_sum_value1_f);
+ vec_sum_value2 = vcvtq_u32_f32(vec_sum_value2_f);
+ vec_sum_value3 = vcvtq_u32_f32(vec_sum_value3_f);
+ vec_sum_value4 = vcvtq_u32_f32(vec_sum_value4_f);
+ }
+
+ const auto temp16x8t_1 = vcombine_u16(wrapper::vqmovn(vec_sum_value1), wrapper::vqmovn(vec_sum_value2));
+ const auto temp16x8t_2 = vcombine_u16(wrapper::vqmovn(vec_sum_value3), wrapper::vqmovn(vec_sum_value4));
+ auto res = vcombine_u8(wrapper::vqmovn(temp16x8t_1), wrapper::vqmovn(temp16x8t_2));
+ wrapper::vstore(output.ptr(), res);
+ },
+ input, output);
}
};
@@ -90,7 +341,186 @@ void reduce_sumsq(const Window &window, const ITensor *input, ITensor *output, u
switch(axis)
{
case 0:
- return Reducer<SumsqOpX>::reduceX(window, input, output, SumsqOpX());
+ switch(input->info()->data_type())
+ {
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpX<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::SUM_SQUARE>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpX<float, 4, ReductionOperation::SUM_SQUARE>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::SUM_SQUARE>());
+ case DataType::QASYMM8:
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 1:
+ switch(input->info()->data_type())
+ {
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>());
+ case DataType::QASYMM8:
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 2:
+ switch(input->info()->data_type())
+ {
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>());
+ case DataType::QASYMM8:
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 3:
+ switch(input->info()->data_type())
+ {
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM_SQUARE>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM_SQUARE>());
+ case DataType::QASYMM8:
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ default:
+ ARM_COMPUTE_ERROR("Unsupported reduction axis");
+ }
+}
+
+void reduce_sum(const Window &window, const ITensor *input, ITensor *output, unsigned int axis)
+{
+ switch(axis)
+ {
+ case 0:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpX_qasymm8<ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX_qasymm8<ReductionOperation::SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpX<float16_t, 8, ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpX<float, 4, ReductionOperation::SUM>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 1:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 2:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 3:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW_qasymm8<ReductionOperation::SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::SUM>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ default:
+ ARM_COMPUTE_ERROR("Unsupported reduction axis");
+ }
+}
+void reduce_mean_sum(const Window &window, const ITensor *input, ITensor *output, unsigned int axis)
+{
+ switch(axis)
+ {
+ case 0:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpX_qasymm8<ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX_qasymm8<ReductionOperation::MEAN_SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpX<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX<float16_t, 8, ReductionOperation::MEAN_SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpX<float, 4, ReductionOperation::MEAN_SUM>>::reduceX(window, input, output, RedOpX<float, 4, ReductionOperation::MEAN_SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 1:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceY(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 2:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceZ(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
+ case 3:
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ return Reducer<RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW_qasymm8<ReductionOperation::MEAN_SUM>());
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ return Reducer<RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW<float16_t, 8, ReductionOperation::MEAN_SUM>());
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ return Reducer<RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>>::reduceW(window, input, output, RedOpYZW<float, 4, ReductionOperation::MEAN_SUM>());
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ }
default:
ARM_COMPUTE_ERROR("Unsupported reduction axis");
}
@@ -109,16 +539,15 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u
ARM_COMPUTE_UNUSED(op);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 0, "Unsupported reduction axis, Supported axis is 0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis");
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON(output->data_layout() != DataLayout::NCHW);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
const TensorShape output_shape = calculate_output_shape(input->tensor_shape(), axis);
const TensorInfo tensor_info_reshaped = input->clone()->set_tensor_shape(output_shape);
@@ -170,10 +599,11 @@ void NEReductionOperationKernel::configure(const ITensor *input, ITensor *output
unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->info()->data_type());
- _input = input;
- _output = output;
- _border_size = (axis == 0) ? BorderSize(0, num_elems_processed_per_iteration - (input->info()->dimension(0) % num_elems_processed_per_iteration), 0, 0) : BorderSize();
- _op = op;
+ _input = input;
+ _output = output;
+ _border_size = (axis == 0) ? BorderSize(0, num_elems_processed_per_iteration - (input->info()->dimension(0) % num_elems_processed_per_iteration), 0, 0) : BorderSize();
+ _op = op;
+ _reduction_axis = axis;
// Configure kernel window
auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis);
@@ -202,7 +632,14 @@ void NEReductionOperationKernel::run(const Window &window, const ThreadInfo &inf
case ReductionOperation::SUM_SQUARE:
reduce_sumsq(window, _input, _output, _reduction_axis);
break;
+ case ReductionOperation::MEAN_SUM:
+ reduce_mean_sum(window, _input, _output, _reduction_axis);
+ break;
+ case ReductionOperation::SUM:
+ reduce_sum(window, _input, _output, _reduction_axis);
+ break;
default:
ARM_COMPUTE_ERROR("Unsupported reduction operation.");
}
}
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp
new file mode 100644
index 0000000000..0b022df729
--- /dev/null
+++ b/src/runtime/NEON/functions/NEReduceMean.cpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * 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, INNEUDING 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 NEAIM, 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 "arm_compute/runtime/NEON/functions/NEReduceMean.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+using namespace arm_compute;
+
+NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims()
+{
+}
+
+Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
+{
+ ARM_COMPUTE_UNUSED(keep_dims);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+ ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
+
+ for(unsigned int i = 0; i < reduction_axis.num_dimensions(); ++i)
+ {
+ if(output->total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(reduction_axis[i]) != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(reduction_axis[i]) > input->num_dimensions() - 1);
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, reduction_axis[i], ReductionOperation::MEAN_SUM));
+ }
+
+ return Status{};
+}
+
+void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input);
+
+ _reduction_ops = reduction_axis.num_dimensions();
+ _reduction_kernels = arm_compute::support::cpp14::make_unique<NEReductionOperation[]>(_reduction_ops);
+ _reduced_outs = arm_compute::support::cpp14::make_unique<Tensor[]>(_reduction_ops - (keep_dims ? 1 : 0));
+ _keep_dims = keep_dims;
+
+ // Perform reduction for every axis
+ for(unsigned int i = 0; i < _reduction_ops; ++i)
+ {
+ TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (_reduced_outs.get() + i - 1)->info()->tensor_shape();
+ out_shape.set(reduction_axis[i], 1);
+ auto in = (i == 0) ? input : (_reduced_outs.get() + i - 1);
+
+ if(i == _reduction_ops - 1 && keep_dims)
+ {
+ _reduction_kernels[i].configure(in, output, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ }
+ else
+ {
+ _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type()));
+ _memory_group.manage(_reduced_outs.get() + i);
+ _reduction_kernels[i].configure(in, _reduced_outs.get() + i, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ }
+ }
+
+ // Allocate intermediate tensors
+ for(unsigned int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
+ {
+ _reduced_outs[i].allocator()->allocate();
+ }
+
+ // Configure reshape layer if we want to drop the dimensions
+ if(!keep_dims)
+ {
+ TensorShape out_shape = input->info()->tensor_shape();
+ for(unsigned int i = 0; i < _reduction_ops; ++i)
+ {
+ out_shape.remove_dimension(reduction_axis[i]);
+ }
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
+ _reshape.configure(_reduced_outs.get() + _reduction_ops - 1, output);
+ }
+}
+
+void NEReduceMean::run()
+{
+ _memory_group.acquire();
+
+ for(unsigned int i = 0; i < _reduction_ops; ++i)
+ {
+ _reduction_kernels[i].run();
+ }
+
+ if(!_keep_dims)
+ {
+ _reshape.run();
+ }
+ _memory_group.release();
+}
diff --git a/src/runtime/NEON/functions/NEReductionOperation.cpp b/src/runtime/NEON/functions/NEReductionOperation.cpp
index cd0b42fbe3..188c2bbb18 100644
--- a/src/runtime/NEON/functions/NEReductionOperation.cpp
+++ b/src/runtime/NEON/functions/NEReductionOperation.cpp
@@ -26,8 +26,8 @@
#include "arm_compute/core/Helpers.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
-using namespace arm_compute;
-
+namespace arm_compute
+{
namespace
{
/** Define dimension to split the window
@@ -42,6 +42,10 @@ size_t reduction_window_split_dimension(unsigned int axis)
{
case 0:
return Window::DimY;
+ case 1:
+ case 2:
+ case 3:
+ return Window::DimX;
default:
ARM_COMPUTE_ERROR("Unsupported reduction axis");
}
@@ -59,7 +63,7 @@ BorderMode reduction_operation_border_mode(ReductionOperation op)
} // namespace
NEReductionOperation::NEReductionOperation()
- : _reduction_kernel(), _fill_border_kernel(), _window_split(0)
+ : _reduction_kernel(), _fill_border_kernel(), _window_split(0), _reduction_axis()
{
}
@@ -72,20 +76,28 @@ Status NEReductionOperation::validate(const ITensorInfo *input, const ITensorInf
void NEReductionOperation::configure(ITensor *input, ITensor *output, unsigned int axis, ReductionOperation op)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
// Configure reduction kernel
_reduction_kernel.configure(input, output, axis, op);
- _window_split = reduction_window_split_dimension(axis);
+ _window_split = reduction_window_split_dimension(axis);
+ _reduction_axis = axis;
- // Configure fill border kernel
- BorderSize fill_border_size = (axis == 0) ? _reduction_kernel.border_size() : BorderSize();
- BorderMode fill_border_mode = reduction_operation_border_mode(op);
- _fill_border_kernel.configure(input, fill_border_size, fill_border_mode, PixelValue(static_cast<float>(0.f)));
+ if(axis == 0)
+ {
+ // Configure fill border kernel
+ BorderSize fill_border_size = (axis == 0) ? _reduction_kernel.border_size() : BorderSize();
+ BorderMode fill_border_mode = reduction_operation_border_mode(op);
+ _fill_border_kernel.configure(input, fill_border_size, fill_border_mode, PixelValue(static_cast<float>(0.f)));
+ }
}
void NEReductionOperation::run()
{
- NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
+ if(_reduction_axis == 0)
+ {
+ NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
+ }
NEScheduler::get().schedule(&_reduction_kernel, _window_split);
}
+} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-04-27 14:36:22 +0000