aboutsummaryrefslogtreecommitdiff
path: root/src/cpu/kernels/pool2d/neon/fp16.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'src/cpu/kernels/pool2d/neon/fp16.cpp')
-rw-r--r--src/cpu/kernels/pool2d/neon/fp16.cpp659
1 files changed, 659 insertions, 0 deletions
diff --git a/src/cpu/kernels/pool2d/neon/fp16.cpp b/src/cpu/kernels/pool2d/neon/fp16.cpp
new file mode 100644
index 0000000000..9d24d79afb
--- /dev/null
+++ b/src/cpu/kernels/pool2d/neon/fp16.cpp
@@ -0,0 +1,659 @@
+/*
+ * Copyright (c) 2021-2023 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, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "src/cpu/kernels/pool2d/neon/impl.h"
+#include "src/cpu/kernels/pool2d/neon/list.h"
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+
+namespace arm_compute
+{
+namespace cpu
+{
+#ifdef ENABLE_NCHW_KERNELS
+
+namespace
+{
+float16x4_t
+read_4_boundary_aware_fp16(int srcw, int srch, int pad_l, int pad_t, int x, int y, const float16_t *ptr, float16_t fval)
+{
+ float16_t vec[4];
+ const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t)));
+ for (int i = 0; i < 4; i++)
+ {
+ if (row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l)))
+ {
+ vec[i] = *(ptr + i);
+ }
+ else
+ {
+ vec[i] = fval;
+ }
+ }
+ return wrapper::vload(vec);
+}
+} // namespace
+
+void pooling3_fp16_neon_nchw(const ITensor *src,
+ ITensor *dst0,
+ ITensor *dst1,
+ PoolingLayerInfo &pool_info,
+ const Window &window_src,
+ const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window);
+
+ constexpr const int pool_size = 3;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit);
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.f;
+ const unsigned char *const src_top_ptr =
+ src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const unsigned char *const src_middle_ptr =
+ src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+ const unsigned char *const src_bottom_ptr =
+ src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2));
+
+ execute_window_loop(
+ window,
+ [&](const Coordinates &id)
+ {
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ const auto y_val_2 = (id.y() * pool_stride_y) + 2;
+ float16x4_t top_data =
+ read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top, x_val, y_val_0,
+ reinterpret_cast<const float16_t *>(src_top_ptr + in.offset()), fill_value);
+ float16x4_t middle_data = read_4_boundary_aware_fp16(
+ src_w, src_h, pool_pad_left, pool_pad_top, x_val, y_val_1,
+ reinterpret_cast<const float16_t *>(src_middle_ptr + in.offset()), fill_value);
+ float16x4_t bottom_data = read_4_boundary_aware_fp16(
+ src_w, src_h, pool_pad_left, pool_pad_top, x_val, y_val_2,
+ reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset()), fill_value);
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ top_data = vmul_f16(top_data, top_data);
+ middle_data = vmul_f16(middle_data, middle_data);
+ bottom_data = vmul_f16(bottom_data, bottom_data);
+ }
+
+ if (pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale_pool2d(
+ pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h,
+ pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float16x4_t scale_v = vdup_n_f16(scale);
+ // Perform pooling
+ const float16x4_t sum_data = vadd_f16(vadd_f16(top_data, bottom_data), middle_data);
+ res = vpadd_f16(vset_lane_f16(0.f, sum_data, 3), sum_data);
+ res = vmul_f16(vpadd_f16(res, res), scale_v);
+ }
+ else
+ {
+ const float16x4_t max_data = vmax_f16(vmax_f16(top_data, bottom_data), middle_data);
+ res = vpmax_f16(vset_lane_f16(fp16_min, max_data, 3), max_data);
+ res = vpmax_f16(res, res);
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ res = vsqrt_f16(res);
+ }
+
+ *(reinterpret_cast<float16_t *>(out.ptr())) = vget_lane_f16(res, 0);
+ },
+ in, out);
+}
+#endif // ENABLE_NCHW_KERNELS
+
+void pooling2_f16_maxpool_indices(const ITensor *src,
+ ITensor *dst0,
+ ITensor *dst1,
+ PoolingLayerInfo &pool_info,
+ const Window &window_src,
+ const Window &window)
+{
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 8;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window_out);
+ Iterator indices(dst1, window_out);
+
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+
+ const int pad_right = src->info()->padding().right;
+ const int pad_left = src->info()->padding().left;
+ const int pad_horizontal = pad_right + pad_left;
+ const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y());
+ const int in_stride_z = static_cast<int>(src->info()->strides_in_bytes().z());
+
+ execute_window_loop(
+ window_out,
+ [&](const Coordinates &id)
+ {
+ const int idx_width = id.y() * pool_stride_x;
+ const int idx_height = id.z() * pool_stride_y;
+ const int pool_limit_y = pool_pad_top - idx_height;
+ const int pool_limit_x = pool_pad_left - idx_width;
+
+ const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
+ const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
+ const int in_x0_offset =
+ (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
+ const int in_x1_offset =
+ (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (pool_start_y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
+ const int in_x2_offset =
+ (pool_start_x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (pool_start_y + 1 - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
+ const int in_x3_offset =
+ (pool_start_x + 1 - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (pool_start_y + 1 - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z());
+
+ int x_off = window_start_x;
+ for (; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
+ {
+ const auto in_x0_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x0_offset) + x_off;
+ const auto in_x1_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x1_offset) + x_off;
+ const auto in_x2_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x2_offset) + x_off;
+ const auto in_x3_ptr = reinterpret_cast<const float16_t *>(in.ptr() + in_x3_offset) + x_off;
+ const auto v_x0 = vld1q_f16(in_x0_ptr);
+ const auto v_x1 = vld1q_f16(in_x1_ptr);
+ const auto v_x2 = vld1q_f16(in_x2_ptr);
+ const auto v_x3 = vld1q_f16(in_x3_ptr);
+ float16x8_t vres = vmaxq_f16(vmaxq_f16(v_x2, v_x3), vmaxq_f16(v_x0, v_x1));
+ // Store result
+ vst1q_f16(reinterpret_cast<float16_t *>(out.ptr()) + x_off, vres);
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(in.offset(), id, *src->info(), pool_stride_x,
+ pool_stride_y, DataLayout::NHWC);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) -
+ pad_horizontal * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32x4_t voffset_x0_0 = {offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3};
+ const uint32x4_t voffset_x0_1 = {offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7};
+ const uint16x8_t voffset_x0 = vcombine_u16(vmovn_u32(voffset_x0_0), vmovn_u32(voffset_x0_1));
+ const uint32x4_t voffset_x1_0 = {offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3};
+ const uint32x4_t voffset_x1_1 = {offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7};
+ const uint16x8_t voffset_x1 = vcombine_u16(vmovn_u32(voffset_x1_0), vmovn_u32(voffset_x1_1));
+ const uint32x4_t voffset_x2_0 = {offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3};
+ const uint32x4_t voffset_x2_1 = {offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7};
+ const uint16x8_t voffset_x2 = vcombine_u16(vmovn_u32(voffset_x2_0), vmovn_u32(voffset_x2_1));
+ const uint32x4_t voffset_x3_0 = {offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3};
+ const uint32x4_t voffset_x3_1 = {offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7};
+ const uint16x8_t voffset_x3 = vcombine_u16(vmovn_u32(voffset_x3_0), vmovn_u32(voffset_x3_1));
+ const uint16x8_t tmp_indices0 = vbslq_u16(vcgeq_f16(v_x0, v_x1), voffset_x0, voffset_x1);
+ const uint16x8_t tmp_indices1 = vbslq_u16(vcgeq_f16(v_x2, v_x3), voffset_x2, voffset_x3);
+ const uint16x8_t tmp_indices2 =
+ vbslq_u16(vcgeq_f16(vmaxq_f16(v_x0, v_x1), vmaxq_f16(v_x2, v_x3)), tmp_indices0, tmp_indices1);
+ const uint32x4_t tmp_indeces3_0 = vmovl_u16(vget_low_u16(tmp_indices2));
+ const uint32x4_t tmp_indeces3_1 = vmovl_u16(vget_high_u16(tmp_indices2));
+ // Store indicies
+ vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off, tmp_indeces3_0);
+ vst1q_u32(reinterpret_cast<uint32_t *>(indices.ptr() + 16) + x_off, tmp_indeces3_1);
+ }
+
+ // Left-overs loop
+ for (; x_off < window_end_x; ++x_off)
+ {
+ const auto x0 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x0_offset) + x_off);
+ const auto x1 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x1_offset) + x_off);
+ const auto x2 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x2_offset) + x_off);
+ const auto x3 = *(reinterpret_cast<const float16_t *>(in.ptr() + in_x3_offset) + x_off);
+ float16_t res = std::max(std::max(x2, x3), std::max(x0, x1));
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr()) + x_off) = res;
+
+ const uint32_t offset_base = offset_no_padding<float16_t>(in.offset(), id, *src->info(), pool_stride_x,
+ pool_stride_y, DataLayout::NHWC);
+ const uint32_t offset_x0 = (uint32_t)offset_base / sizeof(float16_t) + x_off;
+ const uint32_t offset_x1 = (uint32_t)offset_x0 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32_t offset_x2 = (uint32_t)offset_x0 + in_stride_z / sizeof(float16_t) -
+ pad_horizontal * src->info()->tensor_shape()[1];
+ const uint32_t offset_x3 = (uint32_t)offset_x2 + in_stride_y / sizeof(float16_t) - pad_horizontal;
+ const uint32_t tmp_idx0 = (x0 >= x1) ? offset_x0 : offset_x1;
+ const uint32_t tmp_idx1 = (x2 >= x3) ? offset_x2 : offset_x3;
+ const uint32_t tmp_idx2 = (std::max(x0, x1) >= std::max(x2, x3)) ? tmp_idx0 : tmp_idx1;
+
+ // Store indices
+ *(reinterpret_cast<uint32_t *>(indices.ptr()) + x_off) = tmp_idx2;
+ }
+ },
+ in, out, indices);
+}
+#ifdef ENABLE_NCHW_KERNELS
+
+void pooling2_fp16_neon_nchw(const ITensor *src,
+ ITensor *dst0,
+ ITensor *dst1,
+ PoolingLayerInfo &pool_info,
+ const Window &window_src,
+ const Window &window)
+{
+ if (pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_nchw_maxpool_indices<float16_t>(src, dst0, dst1, pool_info, window_src, window);
+ }
+ else
+ {
+ Iterator in(src, window_src);
+ Iterator out(dst0, window);
+ constexpr int pool_size = 2;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x, pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit);
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.0f;
+
+ const unsigned char *const src_top_ptr =
+ src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
+ const unsigned char *const src_bottom_ptr =
+ src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
+
+ execute_window_loop(
+ window,
+ [&](const Coordinates &id)
+ {
+ const auto in_top_ptr = reinterpret_cast<const float16_t *>(src_top_ptr + in.offset());
+ const auto in_bottom_ptr = reinterpret_cast<const float16_t *>(src_bottom_ptr + in.offset());
+
+ const auto x_val = id.x() * pool_stride_x;
+ const auto y_val_0 = id.y() * pool_stride_y;
+ const auto y_val_1 = (id.y() * pool_stride_y) + 1;
+ float16x4_t top_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top, x_val,
+ y_val_0, in_top_ptr, fill_value);
+ float16x4_t bottom_data = read_4_boundary_aware_fp16(src_w, src_h, pool_pad_left, pool_pad_top, x_val,
+ y_val_1, in_bottom_ptr, fill_value);
+ float16x4_t res = {};
+
+ // Get power of 2 in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ top_data = vmul_f16(top_data, top_data);
+ bottom_data = vmul_f16(bottom_data, bottom_data);
+ }
+
+ if (pool_info.pool_type != PoolingType::MAX)
+ {
+ const float scale = calculate_avg_scale_pool2d(
+ pool_info.exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w,
+ upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float16x4_t scale_v = vdup_n_f16(scale);
+
+ const float16x4_t sum_data = vadd_f16(top_data, bottom_data);
+ res = vmul_f16(vpadd_f16(sum_data, sum_data), scale_v);
+ }
+ else
+ {
+ const float16x4_t max_data = vmax_f16(top_data, bottom_data);
+ res = vpmax_f16(max_data, max_data);
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ res = vsqrt_f16(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr())) = vget_lane_f16(res, 0);
+ },
+ in, out);
+ }
+}
+
+void poolingMxN_fp16_neon_nchw(const ITensor *src,
+ ITensor *dst0,
+ ITensor *dst1,
+ PoolingLayerInfo &pool_info,
+ const Window &window_src,
+ const Window &window)
+{
+ ARM_COMPUTE_UNUSED(dst1);
+ Iterator in(src, window_src);
+ Iterator out(dst0, window);
+
+ const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().x() : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.height;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int src_w = src->info()->dimension(0);
+ const int src_h = src->info()->dimension(1);
+ const int upper_bound_w = src_w + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src_h + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float16_t fp16_min = get_initial_min<half_float::half>(pool_info.use_inf_as_limit);
+ const float16_t fill_value = (pool_info.pool_type == PoolingType::MAX) ? fp16_min : 0.0f;
+
+ execute_window_loop(
+ window,
+ [&](const Coordinates &id)
+ {
+ float16_t res = 0.0f;
+
+ if (pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float16_t scale = calculate_avg_scale_pool2d(
+ pool_info.exclude_padding, DataLayout::NCHW, id, pool_size_x, pool_size_y, upper_bound_w,
+ upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+
+ // Perform pooling
+ for (int y = 0; y < pool_size_y; ++y)
+ {
+ for (int x = 0; x < pool_size_x; ++x)
+ {
+ const auto ptr = reinterpret_cast<const float16_t *>(
+ in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().y()));
+
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float16_t data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
+
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ data *= data;
+ }
+
+ res += data;
+ }
+ }
+
+ // Divide by scale
+ res *= scale;
+ }
+ else // if max pooling
+ {
+ res = fp16_min;
+
+ for (int y = 0; y < pool_size_y; ++y)
+ {
+ for (int x = 0; x < pool_size_x; ++x)
+ {
+ const auto ptr = reinterpret_cast<const float16_t *>(
+ in.ptr() + (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().x()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().y()));
+
+ const int idx = x + id.x() * pool_stride_x - pool_pad_left;
+ const int idy = y + id.y() * pool_stride_y - pool_pad_top;
+ float16_t data = (idx < 0 || idy < 0 || idx >= src_w || idy >= src_h) ? fill_value : *ptr;
+ res = std::max(res, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr())) = res;
+ },
+ in, out);
+}
+#endif // ENABLE_NCHW_KERNELS
+
+void poolingMxN_fp16_neon_nhwc(const ITensor *src,
+ ITensor *dst0,
+ ITensor *dst1,
+ PoolingLayerInfo &pool_info,
+ const Window &window_src,
+ const Window &window)
+{
+ if (pool_info.pool_size == Size2D(2, 2) && pool_info.pool_type == PoolingType::MAX && dst1)
+ {
+ pooling2_f16_maxpool_indices(src, dst0, dst1, pool_info, window_src, window);
+ }
+ const int window_start_x = window.x().start();
+ const int window_end_x = window.x().end();
+ const int window_step_x = 8;
+
+ Window window_out = window;
+ window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, window_src);
+ Iterator out(dst0, window_out);
+
+ const int pool_size_x = pool_info.is_global_pooling ? src->info()->tensor_shape().y() : pool_info.pool_size.width;
+ const int pool_size_y = pool_info.is_global_pooling ? src->info()->tensor_shape().z() : pool_info.pool_size.height;
+ const int pool_pad_right = pool_info.pad_stride_info.pad_right();
+ const int pool_pad_top = pool_info.pad_stride_info.pad_top();
+ const int pool_pad_left = pool_info.pad_stride_info.pad_left();
+ const int pool_pad_bottom = pool_info.pad_stride_info.pad_bottom();
+ int pool_stride_x = 0;
+ int pool_stride_y = 0;
+ std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
+ const int upper_bound_w = src->info()->dimension(1) + (pool_info.exclude_padding ? 0 : pool_pad_right);
+ const int upper_bound_h = src->info()->dimension(2) + (pool_info.exclude_padding ? 0 : pool_pad_bottom);
+ const float16_t min_value = get_initial_min<half_float::half>(pool_info.use_inf_as_limit);
+ float16x8_t vres;
+
+ execute_window_loop(
+ window_out,
+ [&](const Coordinates &id)
+ {
+ const int idx_width = id.y() * pool_stride_x;
+ const int idx_height = id.z() * pool_stride_y;
+ const int pool_limit_y = pool_pad_top - idx_height;
+ const int pool_limit_x = pool_pad_left - idx_width;
+
+ const int pool_start_y = std::max(0, window_src.z().start() + pool_limit_y);
+ const int pool_end_y = std::min(pool_size_y, window_src.z().end() + pool_limit_y);
+ const int pool_start_x = std::max(0, window_src.y().start() + pool_limit_x);
+ const int pool_end_x = std::min(pool_size_x, window_src.y().end() + pool_limit_x);
+
+ int x_off = window_start_x;
+ for (; x_off <= (window_end_x - window_step_x); x_off += window_step_x)
+ {
+ if (pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float scale = calculate_avg_scale_pool2d(
+ pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w,
+ upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+ const float16x8_t scale_v = vdupq_n_f16(scale);
+
+ // Perform pooling
+ vres = vdupq_n_f16(0.0f);
+ for (int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for (int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float16x8_t data = vld1q_f16(
+ reinterpret_cast<const float16_t *>(
+ in.ptr() +
+ (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z())) +
+ x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ vres = vaddq_f16(vres, vmulq_f16(data, data));
+ }
+ else
+ {
+ vres = vaddq_f16(vres, data);
+ }
+ }
+ }
+ // Divide by scale
+ vres = vmulq_f16(vres, scale_v);
+ }
+ else
+ {
+ vres = vdupq_n_f16(min_value);
+
+ for (int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for (int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float16x8_t data = vld1q_f16(
+ reinterpret_cast<const float16_t *>(
+ in.ptr() +
+ (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z())) +
+ x_off);
+ vres = vmaxq_f16(vres, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ float16x8_t sqrt_reciprocal = vrsqrteq_f16(vres);
+ vres = vmulq_f16(vres, vmulq_f16(vrsqrtsq_f16(vmulq_f16(vres, sqrt_reciprocal), sqrt_reciprocal),
+ sqrt_reciprocal));
+ }
+
+ // Store result
+ vst1q_f16(reinterpret_cast<float16_t *>(out.ptr()) + x_off, vres);
+ }
+
+ // Left-overs loop
+ for (; x_off < window_end_x; ++x_off)
+ {
+ float16_t res = 0.0f;
+
+ if (pool_info.pool_type != PoolingType::MAX)
+ {
+ // Calculate scale
+ const float16_t scale = calculate_avg_scale_pool2d(
+ pool_info.exclude_padding, DataLayout::NHWC, id, pool_size_x, pool_size_y, upper_bound_w,
+ upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y);
+
+ for (int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for (int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float data =
+ *(reinterpret_cast<const float16_t *>(
+ in.ptr() +
+ (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z())) +
+ x_off);
+
+ // Get power of 2 in case of l2 pooling and accumulate
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ res += data * data;
+ }
+ else
+ {
+ res += data;
+ }
+ }
+ }
+
+ // Divide by scale
+ res *= scale;
+ }
+ else
+ {
+ res = min_value;
+ for (int y = pool_start_y; y < pool_end_y; ++y)
+ {
+ for (int x = pool_start_x; x < pool_end_x; ++x)
+ {
+ const float16_t data =
+ *(reinterpret_cast<const float16_t *>(
+ in.ptr() +
+ (x - pool_pad_left) * static_cast<int>(src->info()->strides_in_bytes().y()) +
+ (y - pool_pad_top) * static_cast<int>(src->info()->strides_in_bytes().z())) +
+ x_off);
+ res = std::max(res, data);
+ }
+ }
+ }
+
+ // Calculate square-root in case of l2 pooling
+ if (pool_info.pool_type == PoolingType::L2)
+ {
+ res = std::sqrt(res);
+ }
+
+ // Store result
+ *(reinterpret_cast<float16_t *>(out.ptr()) + x_off) = res;
+ }
+ },
+ in, out);
+}
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 20:36:52 +0000