diff options
Diffstat (limited to 'arm_compute/core/NEON/kernels')
3 files changed, 0 insertions, 1450 deletions
diff --git a/arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h b/arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h deleted file mode 100644 index 067a18cb62..0000000000 --- a/arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h +++ /dev/null @@ -1,315 +0,0 @@ -/* - * Copyright (c) 2018-2020 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. - */ -#ifndef ARM_COMPUTE_DETAIL_NEACTIVATION_FUNCTION_DETAIL_H -#define ARM_COMPUTE_DETAIL_NEACTIVATION_FUNCTION_DETAIL_H - -#include "arm_compute/core/NEON/wrapper/wrapper.h" - -namespace arm_compute -{ -namespace detail -{ -/** Dummy activation object */ -template <typename T, int S> -struct dummy -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - - /** Construct a dummy activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit dummy(ActivationLayerInfo act_info) - { - ARM_COMPUTE_UNUSED(act_info); - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - ARM_COMPUTE_UNUSED(vval); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - ARM_COMPUTE_UNUSED(val); - } -}; -/** Linear activation object */ -template <typename T, int S> -struct linear -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a Linear activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit linear(ActivationLayerInfo act_info) - : alpha(act_info.a()), - beta(act_info.b()), - valpha(wrapper::vdup_n(static_cast<T>(alpha), ExactTagType{})), - vbeta(wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})) - { - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vmla(vbeta, vval, valpha); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = alpha * val + beta; - } - - const T alpha; /**< Scalar alpha */ - const T beta; /**< Scalar alpha */ - const ExactType valpha; /**< Vector of alphas. */ - const ExactType vbeta; /**< Vector of betas. */ -}; -/** Square activation object */ -template <typename T, int S> -struct square -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a Square activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit square(ActivationLayerInfo act_info) - { - ARM_COMPUTE_UNUSED(act_info); - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vmul(vval, vval); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = val * val; - } -}; -/** Logistic activation object */ -template <typename T, int S> -struct logistic -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a Logistic activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit logistic(ActivationLayerInfo act_info) - : vone(wrapper::vdup_n(static_cast<T>(1), ExactTagType{})) - { - ARM_COMPUTE_UNUSED(act_info); - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vinv(wrapper::vadd(vone, wrapper::vexpq(wrapper::vneg(vval)))); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = 1 / (1 + std::exp(-val)); - } - - /** Vector of ones. */ - const ExactType vone; -}; -/** RELU activation object */ -template <typename T, int S> -struct relu -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a RELU activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit relu(ActivationLayerInfo act_info) - : vzero(wrapper::vdup_n(static_cast<T>(0), ExactTagType{})) - { - ARM_COMPUTE_UNUSED(act_info); - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vmax(vzero, vval); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = std::max(static_cast<T>(0), val); - } - - /** Vector of zeroes. */ - const ExactType vzero; -}; -/** Bounded RELU activation object */ -template <typename T, int S> -struct brelu -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a bounded RELU activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit brelu(ActivationLayerInfo act_info) - : alpha(act_info.a()), - vzero(wrapper::vdup_n(static_cast<T>(0), ExactTagType{})), - valpha(wrapper::vdup_n(static_cast<T>(act_info.a()), ExactTagType{})) - { - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vmin(valpha, wrapper::vmax(vzero, vval)); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = std::min(alpha, std::max(static_cast<T>(0), val)); - } - - const T alpha; /** Scalar alpha */ - const ExactType vzero; /** Vector of zeroes. */ - const ExactType valpha; /** Vector of alphas. */ -}; -/** Lower-Upper Bounded RELU activation object */ -template <typename T, int S> -struct lubrelu -{ - /** NEON vector type. */ - using ExactType = typename wrapper::traits::neon_vector<T, S>::type; - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; - - /** Construct a lower-upper bounded RELU activation object. - * - * @param[in] act_info Activation layer information. - */ - explicit lubrelu(ActivationLayerInfo act_info) - : alpha(act_info.a()), - beta(act_info.b()), - valpha(wrapper::vdup_n(static_cast<T>(act_info.a()), ExactTagType{})), - vbeta(wrapper::vdup_n(static_cast<T>(act_info.b()), ExactTagType{})) - { - } - - /** Run activation function. - * - * @param[in] vval Vector of values. - */ - void operator()(ExactType &vval) - { - vval = wrapper::vmin(valpha, wrapper::vmax(vbeta, vval)); - } - - /** Run activation function. - * - * @param[in] val Scalar value. - */ - void operator()(T &val) - { - val = std::min(alpha, std::max(beta, val)); - } - - const T alpha; /**< Scalar alpha */ - const T beta; /**< Scalar alpha */ - const ExactType valpha; /** Vector of alphas. */ - const ExactType vbeta; /** Vector of betas. */ -}; -} // namespace detail -} // namespace arm_compute -#endif /* ARM_COMPUTE_DETAIL_NEACTIVATION_FUNCTION_DETAIL_H */ diff --git a/arm_compute/core/NEON/kernels/detail/NEDirectConvolution3x3.h b/arm_compute/core/NEON/kernels/detail/NEDirectConvolution3x3.h deleted file mode 100644 index 41ad8fc706..0000000000 --- a/arm_compute/core/NEON/kernels/detail/NEDirectConvolution3x3.h +++ /dev/null @@ -1,170 +0,0 @@ -/* - * Copyright (c) 2017-2019 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. - */ - -#ifndef ARM_COMPUTE_NECONVOLUTIONKERNEL3x3_H -#define ARM_COMPUTE_NECONVOLUTIONKERNEL3x3_H - -#include <arm_neon.h> - -namespace arm_compute -{ -namespace detail -{ -inline float32x4x3_t load_matrix_row(const float *ptr) -{ - const float32x4x3_t r = - { - { - vld1q_dup_f32(ptr), - vld1q_dup_f32(1 + ptr), - vld1q_dup_f32(2 + ptr) - } - }; - return r; -} - -template <unsigned int stridex> -float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2); - -template <> -inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2) -{ - const float32x4x3_t vtop = - { - { - vld1q_f32(in_top), - vld1q_f32(in_top + 4), - vld1q_f32(in_top + 8) - } - }; - const float32x4x3_t vmid = - { - { - vld1q_f32(in_mid), - vld1q_f32(in_mid + 4), - vld1q_f32(in_mid + 8) - } - }; - const float32x4x3_t vlow = - { - { - vld1q_f32(in_low), - vld1q_f32(in_low + 4), - vld1q_f32(in_low + 8) - } - }; - float32x4x2_t out = - { - { - vmulq_f32(vtop.val[0], m0.val[0]), - vmulq_f32(vtop.val[1], m0.val[0]) - } - }; - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]); - return out; -} - -template <> -inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2) -{ - float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3); - return out; -} - -template <> -inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2) -{ - float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); - return out; -} - -template <unsigned int stridex> -void store_results(float *buffer, const float32x4x2_t &values); - -template <> -void store_results<1>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, values.val[0]); - vst1q_f32(buffer + 4, values.val[1]); -} - -template <> -void store_results<2>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, values.val[0]); -} - -template <> -void store_results<3>(float *buffer, const float32x4x2_t &values) -{ - vst1_f32(buffer, vget_low_f32(values.val[0])); -} - -template <unsigned int stridex> -int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration); - -template <> -int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration) -{ - return num_elems_written_per_iteration; -} - -template <> -int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration) -{ - return num_elems_written_per_iteration << 1; -} - -template <> -int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration) -{ - return num_elems_written_per_iteration * 3; -} -} -} // namespace arm_compute -#endif /* ARM_COMPUTE_NECONVOLUTIONKERNEL3x3_H */
\ No newline at end of file diff --git a/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h b/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h deleted file mode 100644 index 78f08fdca6..0000000000 --- a/arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h +++ /dev/null @@ -1,965 +0,0 @@ -/* - * Copyright (c) 2017-2020 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. - */ - -#ifndef ARM_COMPUTE_NEDIRECTCONVOLUTIONDETAIL_H -#define ARM_COMPUTE_NEDIRECTCONVOLUTIONDETAIL_H - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/NEON/NEFixedPoint.h" -#include "arm_compute/core/NEON/wrapper/wrapper.h" -#include "arm_compute/core/utils/misc/Requires.h" - -#include <arm_neon.h> - -namespace arm_compute -{ -namespace detail -{ -/** Loads a 3x3 matrix as a row (float). - * - * @param[in] ptr Pointer to a float 3x3 matrix. - * @param[in] weights_offset (Optional) Weights quantization offset. - * - * @return The loaded matrix. - */ -inline float32x4x3_t load_matrix_row(const float *ptr, int weights_offset = 0) -{ - ARM_COMPUTE_UNUSED(weights_offset); - const float32x4x3_t r = - { - { - vld1q_dup_f32(ptr), - vld1q_dup_f32(1 + ptr), - vld1q_dup_f32(2 + ptr) - } - }; - return r; -} - -/** Loads a 3x3 matrix as a row (uint8_t/int8_t). - * - * @param[in] ptr Pointer to a uint8_t/int8_t 3x3 matrix. - * @param[in] weights_offset (Optional) Weights quantization offset. - * - * @return The loaded matrix. - */ -template < typename T, REQUIRES_TA(std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) > -inline int32x4x3_t load_matrix_row(const T *ptr, int weights_offset = 0) -{ - const int32x4_t v_weights_offset = vdupq_n_s32(weights_offset); - - /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes: - r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */ - int32x4x3_t r = - { - { - vaddq_s32(v_weights_offset, vdupq_n_s32(*ptr)), - vaddq_s32(v_weights_offset, vdupq_n_s32(*(ptr + 1))), - vaddq_s32(v_weights_offset, vdupq_n_s32(*(ptr + 2))) - } - }; - return r; -} - -/** Stores a float32x4x2_t array into a memory location. - * - * @param[in] buffer Pointer to the memory location where the values will be stored. - * @param[in] values Values that will be stored. - * - */ -template <unsigned int stridex> -void store_results(float *buffer, const float32x4x2_t &values); - -template <> -inline void store_results<1>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, values.val[0]); - vst1q_f32(buffer + 4, values.val[1]); -} - -template <> -inline void store_results<2>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, values.val[0]); -} - -template <> -inline void store_results<3>(float *buffer, const float32x4x2_t &values) -{ - vst1_f32(buffer, vget_low_f32(values.val[0])); -} - -/** Stores a uint32_t array into a memory location. - * - * @param[in] buffer Pointer to the memory location where the values will be stored. - * @param[in] values Values that will be stored. - * - */ -template <unsigned int stridex> -void store_results(int32_t *buffer, const int32x4x2_t &values); - -template <> -inline void store_results<1>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1q_s32(buffer, values.val[0]); - vst1q_s32(buffer + 4, values.val[1]); -} - -template <> -inline void store_results<2>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1q_s32(buffer, values.val[0]); -} - -template <> -inline void store_results<3>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1_s32(buffer, vget_low_s32(values.val[0])); -} - -template <unsigned int stridex> -inline void accumulate_results(float *buffer, const float32x4x2_t &values); - -template <> -inline void accumulate_results<1>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); - vst1q_f32(buffer + 4, vaddq_f32(vld1q_f32(buffer + 4), values.val[1])); -} - -template <> -inline void accumulate_results<2>(float *buffer, const float32x4x2_t &values) -{ - vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0])); -} - -template <> -inline void accumulate_results<3>(float *buffer, const float32x4x2_t &values) -{ - vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0]))); -} - -template <unsigned int stridex> -void accumulate_results(int32_t *buffer, const int32x4x2_t &values); - -template <> -inline void accumulate_results<1>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1q_s32(buffer, vaddq_s32(vld1q_s32(buffer), values.val[0])); - vst1q_s32(buffer + 4, vaddq_s32(vld1q_s32(buffer + 4), values.val[1])); -} - -template <> -inline void accumulate_results<2>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1q_s32(buffer, vaddq_s32(vld1q_s32(buffer), values.val[0])); -} - -template <> -inline void accumulate_results<3>(int32_t *buffer, const int32x4x2_t &values) -{ - vst1_s32(buffer, vadd_s32(vld1_s32(buffer), vget_low_s32(values.val[0]))); -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -/** Stores a float16x8x2_t array into a memory location. - * - * @param[in] buffer Pointer to the memory location where the values will be stored. - * @param[in] values Values that will be stored. - * - */ -template <unsigned int stridex> -void store_results(float16_t *buffer, const float16x8x2_t &values); - -template <> -inline void store_results<1>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, values.val[0]); - vst1q_f16(buffer + 8, values.val[1]); -} - -template <> -inline void store_results<2>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, values.val[0]); -} - -template <> -inline void store_results<3>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1_f16(buffer, vget_low_f16(values.val[0])); -} - -template <unsigned int stridex> -inline void accumulate_results(float16_t *buffer, const float16x8x2_t &values); - -template <> -inline void accumulate_results<1>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, vaddq_f16(vld1q_f16(buffer), values.val[0])); - vst1q_f16(buffer + 8, vaddq_f16(vld1q_f16(buffer + 8), values.val[1])); -} - -template <> -inline void accumulate_results<2>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1q_f16(buffer, vaddq_f16(vld1q_f16(buffer), values.val[0])); -} - -template <> -inline void accumulate_results<3>(float16_t *buffer, const float16x8x2_t &values) -{ - vst1_f16(buffer, vadd_f16(vld1_f16(buffer), vget_low_f16(values.val[0]))); -} -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - -/** Perform a 3x3 convolution for 4 consecutive elements on float32 when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] input_offset (Optional) Input quantization offset. - * - */ -inline float32x4_t single_convolve_3x3_dilation(const float *in_top, const float *in_mid, const float *in_low, - const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, - const size_t dilation_x, int input_offset) -{ - ARM_COMPUTE_UNUSED(input_offset); - - const float32x4x3_t vtop = - { - { - vld1q_f32(in_top), - vld1q_f32(in_top + dilation_x), - vld1q_f32(in_top + 2 * dilation_x) - } - }; - const float32x4x3_t vmid = - { - { - vld1q_f32(in_mid), - vld1q_f32(in_mid + dilation_x), - vld1q_f32(in_mid + 2 * dilation_x) - } - }; - const float32x4x3_t vlow = - { - { - vld1q_f32(in_low), - vld1q_f32(in_low + dilation_x), - vld1q_f32(in_low + 2 * dilation_x) - } - }; - float32x4_t out = vmulq_f32(vtop.val[0], m0.val[0]); - out = vmlaq_f32(out, vtop.val[1], m0.val[1]); - out = vmlaq_f32(out, vtop.val[2], m0.val[2]); - - out = vmlaq_f32(out, vmid.val[0], m1.val[0]); - out = vmlaq_f32(out, vmid.val[1], m1.val[1]); - out = vmlaq_f32(out, vmid.val[2], m1.val[2]); - - out = vmlaq_f32(out, vlow.val[0], m2.val[0]); - out = vmlaq_f32(out, vlow.val[1], m2.val[1]); - out = vmlaq_f32(out, vlow.val[2], m2.val[2]); - - return out; -} - -/** Perform a 3x3 convolution for 8 consecutive elements on float32 when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset (Optional) Input quantization offset. - * - */ -inline float32x4x2_t convolve_3x3_dilation(const float *in_top, const float *in_mid, const float *in_low, - const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, - const size_t dilation_x, unsigned int stridex, int input_offset = 0) -{ - ARM_COMPUTE_ERROR_ON(stridex > 3); - float32x4x2_t out = - { - { - single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset), - single_convolve_3x3_dilation(in_top + 4, in_mid + 4, in_low + 4, m0, m1, m2, dilation_x, input_offset) - } - }; - - if(stridex == 2) - { - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2); - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3); - } - else if(stridex == 3) - { - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); - } - - return out; -} - -/** Perform a convolve3x3 on float32. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[out] out_ptr Pointer to the output. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset (Optional) Input quantization offset. - * - */ -template <bool accumulate> -void convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, float *out_ptr, - const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, - unsigned int stridex, int input_offset = 0); - -template <bool accumulate> -inline void convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, float *out_ptr, - const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, - unsigned int stridex, int input_offset) -{ - ARM_COMPUTE_UNUSED(input_offset); - ARM_COMPUTE_ERROR_ON(stridex > 3); - - float32x4x2_t out = - { - { - vdupq_n_f32(0.f), - vdupq_n_f32(0.f) - } - }; - if(stridex == 2) - { - const float32x4x2_t vtop = vld2q_f32(in_top); - const float32x4x2_t vmid = vld2q_f32(in_mid); - const float32x4x2_t vlow = vld2q_f32(in_low); - const float32x4_t vtop_end = vld1q_f32(in_top + 8); - const float32x4_t vmid_end = vld1q_f32(in_mid + 8); - const float32x4_t vlow_end = vld1q_f32(in_low + 8); - - out.val[0] = vmulq_f32(vtop.val[0], m0.val[0]); - - out.val[0] = vmlaq_f32(out.val[0], vtop.val[1], m0.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop_end, 1), m0.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vmid.val[1], m1.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid_end, 1), m1.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vlow.val[1], m2.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow_end, 1), m2.val[2]); - - accumulate ? accumulate_results<2>(out_ptr, out) : store_results<2>(out_ptr, out); - } - else - { - const float32x4x3_t vtop = - { - { - vld1q_f32(in_top), - vld1q_f32(in_top + 4), - vld1q_f32(in_top + 8) - } - }; - const float32x4x3_t vmid = - { - { - vld1q_f32(in_mid), - vld1q_f32(in_mid + 4), - vld1q_f32(in_mid + 8) - } - }; - const float32x4x3_t vlow = - { - { - vld1q_f32(in_low), - vld1q_f32(in_low + 4), - vld1q_f32(in_low + 8) - } - }; - out.val[0] = vmulq_f32(vtop.val[0], m0.val[0]); - out.val[1] = vmulq_f32(vtop.val[1], m0.val[0]); - - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]); - - out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]); - out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]); - - out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]); - out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]); - - if(stridex == 3) - { - out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1); - accumulate ? accumulate_results<3>(out_ptr, out) : store_results<3>(out_ptr, out); - } - else - { - accumulate ? accumulate_results<1>(out_ptr, out) : store_results<1>(out_ptr, out); - } - } -} - -/** Perform a 3x3 convolution for 4 consecutive 8-bit elements when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] input_offset Input quantization offset. - * - */ -template < typename T, REQUIRES_TA(std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) > -inline int32x4_t single_convolve_3x3_dilation(const T *in_top, const T *in_mid, const T *in_low, - const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2, - size_t dilation_x, int32_t input_offset) -{ - using VectorType = typename std::conditional<std::is_same<T, uint8_t>::value, uint8x8x3_t, int8x8x3_t>::type; - using OutputTagType = typename wrapper::traits::neon_bitvector_tag_t<int32_t, wrapper::traits::BitWidth::W128>; - - const int32x4_t v_input_offset = wrapper::vdup_n(input_offset, OutputTagType{}); - - const VectorType vtop = - { - { - wrapper::vload(in_top), - wrapper::vload(in_top + dilation_x), - wrapper::vload(in_top + 2 * dilation_x) - } - }; - const VectorType vmid = - { - { - wrapper::vload(in_mid), - wrapper::vload(in_mid + dilation_x), - wrapper::vload(in_mid + 2 * dilation_x) - } - }; - const VectorType vlow = - { - { - wrapper::vload(in_low), - wrapper::vload(in_low + dilation_x), - wrapper::vload(in_low + 2 * dilation_x) - } - }; - - const int32x4x3_t vtop_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vtop.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vtop.val[1])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vtop.val[2])))), - } - }; - const int32x4x3_t vmid_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vmid.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vmid.val[1])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vmid.val[2])))), - } - }; - const int32x4x3_t vlow_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vlow.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vlow.val[1])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vlow.val[2])))), - } - }; - - int32x4_t out = wrapper::vmul(vtop_s32.val[0], m0.val[0]); - out = wrapper::vmla(out, vtop_s32.val[1], m0.val[1]); - out = wrapper::vmla(out, vtop_s32.val[2], m0.val[2]); - - out = wrapper::vmla(out, vmid_s32.val[0], m1.val[0]); - out = wrapper::vmla(out, vmid_s32.val[1], m1.val[1]); - out = wrapper::vmla(out, vmid_s32.val[2], m1.val[2]); - - out = wrapper::vmla(out, vlow_s32.val[0], m2.val[0]); - out = wrapper::vmla(out, vlow_s32.val[1], m2.val[1]); - out = wrapper::vmla(out, vlow_s32.val[2], m2.val[2]); - - return out; -} - -/** Perform a 3x3 convolution for 4 consecutive 8-bit elements when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset Input quantization offset. - * - */ -template < typename T, REQUIRES_TA(std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value) > -inline int32x4x2_t convolve_3x3_dilation(const T *in_top, const T *in_mid, const T *in_low, const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2, - const size_t dilation_x, unsigned int stridex, int input_offset) -{ - ARM_COMPUTE_ERROR_ON(stridex > 3); - int32x4x2_t out = - { - { - single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset), - single_convolve_3x3_dilation(in_top + 4, in_mid + 4, in_low + 4, m0, m1, m2, dilation_x, input_offset) - } - }; - - if(stridex == 2) - { - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[0], 2), out.val[0], 1); - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[1], 0), out.val[0], 2); - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[1], 2), out.val[0], 3); - } - else if(stridex == 3) - { - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[0], 3), out.val[0], 1); - } - return out; -} - -/** Perform a convolve3x3 on 8-bit elements - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[out] out_ptr Pointer to the output. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset Input quantization offset. - * - */ -template < bool accumulate, typename T1, typename T2, REQUIRES_TA(std::is_same<T1, uint8_t>::value || std::is_same<T1, int8_t>::value) > -void convolve_3x3(const T1 *in_top, const T1 *in_mid, const T1 *in_low, T2 *out_ptr, - const int32x4x3_t &m0, const int32x4x3_t &m1, const int32x4x3_t &m2, - unsigned int stridex, int32_t input_offset) -{ - ARM_COMPUTE_ERROR_ON(stridex > 3); - using VectorType = typename std::conditional<std::is_same<T1, uint8_t>::value, uint8x8x2_t, int8x8x2_t>::type; - using OutputTagType = typename wrapper::traits::neon_bitvector_tag_t<int32_t, wrapper::traits::BitWidth::W128>; - - const int32x4_t v_input_offset = wrapper::vdup_n(input_offset, OutputTagType{}); - - const VectorType vtop = - { - { - wrapper::vload(in_top), - wrapper::vload(in_top + 8) - } - }; - const VectorType vmid = - { - { - wrapper::vload(in_mid), - wrapper::vload(in_mid + 8) - } - }; - const VectorType vlow = - { - { - wrapper::vload(in_low), - wrapper::vload(in_low + 8) - } - }; - - const int32x4x3_t vtop_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vtop.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgethigh(wrapper::vmovl(vtop.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vtop.val[1])))), - } - }; - const int32x4x3_t vmid_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vmid.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgethigh(wrapper::vmovl(vmid.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vmid.val[1])))), - } - }; - const int32x4x3_t vlow_s32 = - { - { - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vlow.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgethigh(wrapper::vmovl(vlow.val[0])))), - wrapper::vaddw(v_input_offset, wrapper::vreinterpret(wrapper::vgetlow(wrapper::vmovl(vlow.val[1])))), - } - }; - - int32x4x2_t out - { - { - wrapper::vdup_n(static_cast<int32_t>(0), OutputTagType{}), - wrapper::vdup_n(static_cast<int32_t>(0), OutputTagType{}), - } - }; - - // 0 - out.val[0] = wrapper::vmla(out.val[0], vtop_s32.val[0], m0.val[0]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_1(vtop_s32.val[0], vtop_s32.val[1]), m0.val[1]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_2(vtop_s32.val[0], vtop_s32.val[1]), m0.val[2]); - - out.val[0] = wrapper::vmla(out.val[0], vmid_s32.val[0], m1.val[0]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_1(vmid_s32.val[0], vmid_s32.val[1]), m1.val[1]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_2(vmid_s32.val[0], vmid_s32.val[1]), m1.val[2]); - - out.val[0] = wrapper::vmla(out.val[0], vlow_s32.val[0], m2.val[0]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_1(vlow_s32.val[0], vlow_s32.val[1]), m2.val[1]); - out.val[0] = wrapper::vmla(out.val[0], wrapper::vext_2(vlow_s32.val[0], vlow_s32.val[1]), m2.val[2]); - - // 1 - out.val[1] = wrapper::vmla(out.val[1], vtop_s32.val[1], m0.val[0]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_1(vtop_s32.val[1], vtop_s32.val[2]), m0.val[1]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_2(vtop_s32.val[1], vtop_s32.val[2]), m0.val[2]); - - out.val[1] = wrapper::vmla(out.val[1], vmid_s32.val[1], m1.val[0]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_1(vmid_s32.val[1], vmid_s32.val[2]), m1.val[1]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_2(vmid_s32.val[1], vmid_s32.val[2]), m1.val[2]); - - out.val[1] = wrapper::vmla(out.val[1], vlow_s32.val[1], m2.val[0]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_1(vlow_s32.val[1], vlow_s32.val[2]), m2.val[1]); - out.val[1] = wrapper::vmla(out.val[1], wrapper::vext_2(vlow_s32.val[1], vlow_s32.val[2]), m2.val[2]); - - if(stridex == 1) - { - accumulate ? accumulate_results<1>(out_ptr, out) : store_results<1>(out_ptr, out); - } - else if(stridex == 2) - { - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[0], 2), out.val[0], 1); - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[1], 0), out.val[0], 2); - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[1], 2), out.val[0], 3); - - accumulate ? accumulate_results<2>(out_ptr, out) : store_results<2>(out_ptr, out); - } - else if(stridex == 3) - { - out.val[0] = wrapper::vsetlane(wrapper::vgetlane(out.val[0], 3), out.val[0], 1); - accumulate ? accumulate_results<3>(out_ptr, out) : store_results<3>(out_ptr, out); - } -} - -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -/** Loads a 3x3 matrix as a row (float16_t). - * - * @param[in] ptr Pointer to a float 3x3 matrix. - * - * @return The loaded matrix. - */ -inline float16x8x3_t load_matrix_row(const float16_t *ptr, int weights_offset = 0) -{ - ARM_COMPUTE_UNUSED(weights_offset); - /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes: - r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */ - const float16x8x3_t r = - { - { - vld1q_dup_f16(ptr), - vld1q_dup_f16(1 + ptr), - vld1q_dup_f16(2 + ptr) - } - }; - return r; -} - -/** Perform a 3x3 convolution for 8 consecutive elements on float16 when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] input_offset (Optional)Input quantization offset. - * - */ -inline float16x8_t single_convolve_3x3_dilation(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, - const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - const size_t dilation_x, int input_offset = 0) -{ - ARM_COMPUTE_UNUSED(input_offset); - const float16x8x3_t vtop = - { - { - vld1q_f16(in_top), - vld1q_f16(in_top + dilation_x), - vld1q_f16(in_top + 2 * dilation_x) - } - }; - const float16x8x3_t vmid = - { - { - vld1q_f16(in_mid), - vld1q_f16(in_mid + dilation_x), - vld1q_f16(in_mid + 2 * dilation_x) - } - }; - const float16x8x3_t vlow = - { - { - vld1q_f16(in_low), - vld1q_f16(in_low + dilation_x), - vld1q_f16(in_low + 2 * dilation_x) - } - }; - float16x8_t out = vmulq_f16(vtop.val[0], m0.val[0]); - out = vaddq_f16(out, vmulq_f16(vtop.val[1], m0.val[1])); - out = vaddq_f16(out, vmulq_f16(vtop.val[2], m0.val[2])); - - out = vaddq_f16(out, vmulq_f16(vmid.val[0], m1.val[0])); - out = vaddq_f16(out, vmulq_f16(vmid.val[1], m1.val[1])); - out = vaddq_f16(out, vmulq_f16(vmid.val[2], m1.val[2])); - - out = vaddq_f16(out, vmulq_f16(vlow.val[0], m2.val[0])); - out = vaddq_f16(out, vmulq_f16(vlow.val[1], m2.val[1])); - out = vaddq_f16(out, vmulq_f16(vlow.val[2], m2.val[2])); - - return out; -} - -/** Perform a 3x3 convolution for 16 consecutive elements on float16 when dilation.x() or dilation.y() is not 1. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] dilation_x Dilation, in elements across x. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset (Optional) Input quantization offset. - * - */ -inline float16x8x2_t convolve_3x3_dilation(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, - const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - const size_t dilation_x, unsigned int stridex, int input_offset = 0) -{ - float16x8x2_t out = - { - { - single_convolve_3x3_dilation(in_top, in_mid, in_low, m0, m1, m2, dilation_x, input_offset), - single_convolve_3x3_dilation(in_top + 8, in_mid + 8, in_low + 8, m0, m1, m2, dilation_x, input_offset) - } - }; - - if(stridex == 2) - { - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 2), out.val[0], 1); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 4), out.val[0], 2); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 6), out.val[0], 3); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 0), out.val[0], 4); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 2), out.val[0], 5); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 4), out.val[0], 6); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 6), out.val[0], 7); - } - else if(stridex == 3) - { - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 6), out.val[0], 2); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 1), out.val[0], 3); - } - - return out; -} - -/** Perform a convolve3x3 on float16. - * - * @param[in] in_top Pointer to the first row of the input. - * @param[in] in_mid Pointer to the second row of the input. - * @param[in] in_low Pointer to the third row of the input. - * @param[out] out_ptr Pointer to the output. - * @param[in] m0 First row of the filter. - * @param[in] m1 Second row of the filter. - * @param[in] m2 Third row of the filter. - * @param[in] stridex Stride value in elements across x. - * @param[in] input_offset (Optional) Input quantization offset. - * - */ -template <bool accumulate> -inline void convolve_3x3(const float16_t *in_top, const float16_t *in_mid, const float16_t *in_low, float16_t *out_ptr, - const float16x8x3_t &m0, const float16x8x3_t &m1, const float16x8x3_t &m2, - unsigned int stridex, int input_offset = 0) -{ - ARM_COMPUTE_UNUSED(input_offset); - - float16x8x2_t out = - { - { - vdupq_n_f16(0), - vdupq_n_f16(0) - } - }; - if(stridex == 2) - { - const float16x8x2_t vtop = vld2q_f16(in_top); - const float16x8x2_t vmid = vld2q_f16(in_mid); - const float16x8x2_t vlow = vld2q_f16(in_low); - const float16x8_t vtop_end = vld1q_f16(in_top + 16); - const float16x8_t vmid_end = vld1q_f16(in_mid + 16); - const float16x8_t vlow_end = vld1q_f16(in_low + 16); - - out.val[0] = vmulq_f16(vtop.val[0], m0.val[0]); - - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vtop.val[1], m0.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop_end, 1), m0.val[2])); - - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[0], m1.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[1], m1.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid_end, 1), m1.val[2])); - - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[0], m2.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[1], m2.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow_end, 1), m2.val[2])); - - accumulate ? accumulate_results<2>(out_ptr, out) : store_results<2>(out_ptr, out); - } - else - { - const float16x8x3_t vtop = - { - { - vld1q_f16(in_top), - vld1q_f16(in_top + 8), - vld1q_f16(in_top + 16) - } - }; - const float16x8x3_t vmid = - { - { - vld1q_f16(in_mid), - vld1q_f16(in_mid + 8), - vld1q_f16(in_mid + 16) - } - }; - const float16x8x3_t vlow = - { - { - vld1q_f16(in_low), - vld1q_f16(in_low + 8), - vld1q_f16(in_low + 16) - } - }; - out.val[0] = vmulq_f16(vtop.val[0], m0.val[0]); - out.val[1] = vmulq_f16(vtop.val[1], m0.val[0]); - - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 1), m0.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vtop.val[0], vtop.val[1], 2), m0.val[2])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vmid.val[0], m1.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 1), m1.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vmid.val[0], vmid.val[1], 2), m1.val[2])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vlow.val[0], m2.val[0])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 1), m2.val[1])); - out.val[0] = vaddq_f16(out.val[0], vmulq_f16(vextq_f16(vlow.val[0], vlow.val[1], 2), m2.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 1), m0.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vtop.val[1], vtop.val[2], 2), m0.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vmid.val[1], m1.val[0])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 1), m1.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vmid.val[1], vmid.val[2], 2), m1.val[2])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vlow.val[1], m2.val[0])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 1), m2.val[1])); - out.val[1] = vaddq_f16(out.val[1], vmulq_f16(vextq_f16(vlow.val[1], vlow.val[2], 2), m2.val[2])); - - if(stridex == 3) - { - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 3), out.val[0], 1); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[0], 6), out.val[0], 2); - out.val[0] = vsetq_lane_f16(vgetq_lane_f16(out.val[1], 1), out.val[0], 3); - - accumulate ? accumulate_results<3>(out_ptr, out) : store_results<3>(out_ptr, out); - } - else - { - accumulate ? accumulate_results<1>(out_ptr, out) : store_results<1>(out_ptr, out); - } - } -} -#endif /** __ARM_FEATURE_FP16_VECTOR_ARITHMETIC **/ - -/** Get the number of elements processed on 3x3 convolution. - * - * @param[in] num_elems_written_per_iteration Number of elements written per iteration on 3x3 convolution. - * @param[in] stridex Stride value in elements across x. - * - * @return The number of elements processed. - */ -inline int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration, unsigned int stridex) -{ - switch(stridex) - { - case 1: - return num_elems_written_per_iteration; - case 2: - return num_elems_written_per_iteration << 1; - case 3: - return num_elems_written_per_iteration * 3; - default: - ARM_COMPUTE_ERROR("stridex not supported"); - return 0; - } -} -} -} // namespace arm_compute -#endif /* ARM_COMPUTE_NEDIRECTCONVOLUTIONDETAIL_H */ |