diff options
Diffstat (limited to 'src/cpu/kernels/depthwiseconv2d/generic')
6 files changed, 1201 insertions, 0 deletions
diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/fp16.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/fp16.cpp new file mode 100644 index 0000000000..293e606d81 --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/fp16.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2022-2023 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. + */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#include "src/cpu/CpuTypes.h" +#include "src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h" + +namespace arm_compute +{ +namespace cpu +{ +void neon_fp16_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_float<float16_t, float16_t>(src, weights, bias, dst, window, has_biases, info); +} +} // namespace cpu +} // namespace arm_compute +#endif //__ARM_FEATURE_FP16_VECTOR_ARITHMETIC diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/fp32.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/fp32.cpp new file mode 100644 index 0000000000..c6fa4790b7 --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/fp32.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2022 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 "src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h" +namespace arm_compute +{ +namespace cpu +{ +void neon_fp32_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_float<float, float>(src, weights, bias, dst, window, has_biases, info); +} +} // namespace cpu +} // namespace arm_compute diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp new file mode 100644 index 0000000000..d08e973968 --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp @@ -0,0 +1,641 @@ +/* + * Copyright (c) 2019-2023 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 "src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h" + +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/function_info/ConvolutionInfo.h" + +#include "src/core/NEON/wrapper/wrapper.h" + +namespace arm_compute +{ +namespace cpu +{ +inline int32x4_t saturating_doubling_high_mul(const int32x4_t &a, const int32_t &b) +{ + return vqrdmulhq_n_s32(a, b); +} + +inline int32_t saturating_doubling_high_mul(const int32_t &a, const int32_t &b) +{ + return vget_lane_s32(vqrdmulh_n_s32(vdup_n_s32(a), b), 0); +} + +inline int32x4_t rounding_divide_by_exp2(const int32x4_t &x, const int exponent) +{ + const int32x4_t shift = vdupq_n_s32(-exponent); + const int32x4_t fixup = vshrq_n_s32(vandq_s32(x, shift), 31); + const int32x4_t fixed = vqaddq_s32(x, fixup); + return vrshlq_s32(fixed, shift); +} + +inline int32x2_t rounding_divide_by_exp2(const int32x2_t &x, const int exponent) +{ + const int32x2_t shift = vdup_n_s32(-exponent); + const int32x2_t fixup = vshr_n_s32(vand_s32(x, shift), 31); + const int32x2_t fixed = vqadd_s32(x, fixup); + return vrshl_s32(fixed, shift); +} + +inline int32_t rounding_divide_by_exp2(const int32_t &x, const int exponent) +{ + const int32x2_t xs = vdup_n_s32(x); + return vget_lane_s32(rounding_divide_by_exp2(xs, exponent), 0); +} + +namespace +{ +template <typename T, typename TW> +void depthwise_loop_multiplier1_quantized(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const PadStrideInfo &conv_info, + const Size2D &dilation, + std::vector<int> output_multiplier, + std::vector<int> output_shift, + const Window &window, + bool has_biases) // NOLINT +{ + ARM_COMPUTE_UNUSED(output_multiplier, output_shift); + constexpr auto element_per_vector = vector_size / sizeof(T); + using VectorType = typename wrapper::traits::neon_vector<T, element_per_vector>::type; + using TagType = typename wrapper::traits::neon_vector<T, element_per_vector>::tag_type; + using AccType = int32_t; + using AccArrayType = std::array<AccType, element_per_vector>; + + const auto out_of_bound_value = + PixelValue(static_cast<uint64_t>(0), src->info()->data_type(), src->info()->quantization_info()).get<T>(); + const auto out_of_bound_vector = wrapper::vdup_n(static_cast<T>(out_of_bound_value), TagType{}); + + const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window); + + const int32_t input_qoffset = src->info()->quantization_info().uniform().offset; + const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; + const int32_t output_qoffset = dst->info()->quantization_info().uniform().offset; + const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; + + Window execution_window = window; + execution_window.set(Window::DimX, dim_single_unit_step); + + Window win_input = window; + win_input.set(Window::DimX, dim_manual_loop); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); + + Window win_weights = win_input; + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set(Window::DimX, dim_manual_loop); + + Iterator input_it(src, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(dst, win_output); + Iterator biases_it{}; + + if (has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop( + execution_window, + [&](const Coordinates &id) + { + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + auto const base_weights_ptr = weights_it.ptr(); + size_t x = run_info.x_start; + + for (; x < run_info.x_leftover_start; x += run_info.x_step) + { + AccArrayType acc{}; + AccArrayType in_sum{}; + AccArrayType we_sum{}; + + auto weights_ptr = base_weights_ptr; + auto input_offset = base_input_offset; + + for (size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = + is_valid_region + ? wrapper::vload(reinterpret_cast<T *>( + input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) + : out_of_bound_vector; + const auto weights_vals = + wrapper::vload(reinterpret_cast<TW *>(weights_ptr + w * run_info.weights_stride_y) + x); + + for (size_t i = 0; i < element_per_vector; ++i) + { + acc.at(i) += input_vals[i] * weights_vals[i]; + in_sum.at(i) += input_vals[i]; + we_sum.at(i) += weights_vals[i]; + } + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + VectorType out_vals = wrapper::vdup_n(static_cast<T>(0), TagType{}); + for (size_t i = 0; i < element_per_vector; ++i) + { + acc.at(i) -= in_sum.at(i) * weights_qoffset; + acc.at(i) -= we_sum.at(i) * input_qoffset; + acc.at(i) += k_offset; + + if (has_biases) + { + acc.at(i) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + i * sizeof(int32_t)) + x); + } + + const int32_t out_mul = output_multiplier.at(x + i); + const int32_t out_shift = output_shift.at(x + i); + if (out_shift < 0) + { + acc.at(i) = + saturating_doubling_high_mul(acc.at(i) * (1 << (-out_shift)), out_mul) + output_qoffset; + } + else + { + acc.at(i) = + rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), out_mul), out_shift) + + output_qoffset; + } + out_vals[i] = static_cast<T>(utility::clamp<AccType, T>(acc.at(i))); + } + + wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()) + x, out_vals); + } + + // left-over + for (; x < run_info.x_end; ++x) + { + AccType acc = 0; + AccType in_sum = 0; + AccType we_sum = 0; + + auto weights_ptr = base_weights_ptr; + auto input_offset = base_input_offset; + + for (size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = + is_valid_region + ? *reinterpret_cast<T *>(input_it.ptr() + + std::min(static_cast<size_t>(offs), run_info.input_max_offset)) + : out_of_bound_value; + const auto weights_val = + *(reinterpret_cast<TW *>(weights_ptr + w * run_info.weights_stride_y) + x); + + acc += input_val * weights_val; + in_sum += input_val; + we_sum += weights_val; + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + T out_vals{0}; + + acc -= in_sum * weights_qoffset; + acc -= we_sum * input_qoffset; + acc += k_offset; + + if (has_biases) + { + acc += *(reinterpret_cast<int32_t *>(biases_it.ptr()) + x); + } + + const int32_t out_mul = output_multiplier.at(x); + const int32_t out_shift = output_shift.at(x); + + if (out_shift < 0) + { + acc = saturating_doubling_high_mul(acc * (1 << (-out_shift)), out_mul) + output_qoffset; + } + else + { + acc = + rounding_divide_by_exp2(saturating_doubling_high_mul(acc, out_mul), out_shift) + output_qoffset; + } + + out_vals = static_cast<T>(utility::clamp<AccType, T>(acc)); + *(reinterpret_cast<T *>(output_it.ptr()) + x) = out_vals; + } + }, + input_it, weights_it, biases_it, output_it); +} + +template <typename T, typename TW> +void depthwise_loop_generic_quantized(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const PadStrideInfo &conv_info, + const Size2D &dilation, + unsigned int depth_multiplier, + std::vector<int> output_multiplier, + std::vector<int> output_shift, + const Window &window, + bool has_biases) // NOLINT +{ + using AccType = int32_t; + + const auto run_info = + DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); + + const auto out_of_bound_value = + PixelValue(static_cast<uint64_t>(0), src->info()->data_type(), src->info()->quantization_info()).get<T>(); + + const int32_t input_qoffset = src->info()->quantization_info().uniform().offset; + const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; + const int32_t output_qoffset = dst->info()->quantization_info().uniform().offset; + const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; + + Window execution_window = window; + execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); + + Window win_input = execution_window; + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); + + Window win_weights = window; + win_weights.set_dimension_step(Window::DimX, run_info.x_step); + win_weights.set(Window::DimY, dim_manual_loop); + win_weights.set(Window::DimZ, dim_manual_loop); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set_dimension_step(Window::DimX, run_info.x_step); + + Iterator input_it(src, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(dst, win_output); + Iterator biases_it{}; + + if (has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop( + execution_window, + [&](const Coordinates &id) + { + std::vector<AccType> acc(depth_multiplier, 0); + std::vector<AccType> we_sum(depth_multiplier, 0); + AccType in_sum = 0; + + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + int64_t input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + + auto weights_ptr = weights_it.ptr(); + for (size_t h = 0; h < run_info.weights_height; ++h) + { + int offs = input_offset; + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = + is_valid_region ? *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), + run_info.input_max_offset))) + : out_of_bound_value; + + for (size_t m = 0; m < depth_multiplier; ++m) + { + const auto weights_val = + *(reinterpret_cast<TW *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); + acc.at(m) += input_val * weights_val; + + we_sum.at(m) += weights_val; + } + + offs += dilation.x() * run_info.input_stride_y; + in_sum += input_val; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + for (size_t m = 0; m < depth_multiplier; ++m) + { + acc.at(m) -= in_sum * weights_qoffset; + acc.at(m) -= we_sum.at(m) * input_qoffset; + acc.at(m) += k_offset; + + if (has_biases) + { + acc.at(m) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); + } + + const int32_t out_mul = output_multiplier.at(id.x() * depth_multiplier + m); + const int32_t out_shift = output_shift.at(id.x() * depth_multiplier + m); + if (out_shift < 0) + { + acc.at(m) = saturating_doubling_high_mul(acc.at(m) * (1 << (-out_shift)), out_mul) + output_qoffset; + } + else + { + acc.at(m) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), out_mul), out_shift) + + output_qoffset; + } + *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = + static_cast<T>(utility::clamp<AccType, T>(acc.at(m))); + } + }, + input_it, weights_it, biases_it, output_it); +} + +template <typename T, typename TW> +void depthwise_loop_pow2_quantized_per_tensor(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const PadStrideInfo &conv_info, + const Size2D &dilation, + unsigned int depth_multiplier, + std::vector<int> output_multiplier, + std::vector<int> output_shift, + const Window &window, + bool has_biases) // NOLINT +{ + constexpr int half_vec = vector_size / 2; + + using AccType = int32_t; + using AccVectorType = typename wrapper::traits::neon_vector<AccType, half_vec>::type; + using AccVectorTagType = typename wrapper::traits::neon_vector<AccType, half_vec>::tag_type; + using TagType = typename wrapper::traits::neon_vector<T, vector_size>::tag_type; + + const auto run_info = + DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); + + const auto input_qoffset_vec = wrapper::vreinterpret( + wrapper::vmovl(wrapper::vdup_n(static_cast<T>(src->info()->quantization_info().uniform().offset), TagType{}))); + const auto weights_qoffset_vec = wrapper::vreinterpret(wrapper::vmovl( + wrapper::vdup_n(static_cast<TW>(weights->info()->quantization_info().uniform().offset), TagType{}))); + const auto output_qoffset_vec = wrapper::vdup_n(dst->info()->quantization_info().uniform().offset, + arm_compute::wrapper::traits::vector_128_tag{}); + + const auto lower = wrapper::vdup_n(static_cast<AccType>(std::numeric_limits<T>::lowest()), AccVectorTagType{}); + const auto upper = wrapper::vdup_n(static_cast<AccType>(std::numeric_limits<T>::max()), AccVectorTagType{}); + const auto zero = wrapper::vdup_n(static_cast<AccType>(0), AccVectorTagType{}); + + const auto out_mul = output_multiplier.at(0); + const auto out_shift = output_shift.at(0); + + Window execution_window = window; + execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); + + Window win_input = execution_window; + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); + + Window win_weights = window; + win_weights.set_dimension_step(Window::DimX, run_info.x_step); + win_weights.set(Window::DimY, dim_manual_loop); + win_weights.set(Window::DimZ, dim_manual_loop); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set_dimension_step(Window::DimX, run_info.x_step); + + Iterator input_it(src, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(dst, win_output); + Iterator biases_it{}; + + if (has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + std::vector<AccVectorType> acc0(depth_multiplier / vector_size); + std::vector<AccVectorType> acc1(depth_multiplier / vector_size); + + execute_window_loop( + execution_window, + [&](const Coordinates &id) + { + std::fill(begin(acc0), end(acc0), zero); + std::fill(begin(acc1), end(acc1), zero); + + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + int64_t input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + + auto weights_ptr = weights_it.ptr(); + for (size_t h = 0; h < run_info.weights_height; ++h) + { + const int32_t current_h = input_z + h * dilation.y(); + if (current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height)) + { + int offs = input_offset; + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const int32_t current_w = input_y + w * dilation.x(); + if (current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width)) + { + const auto input_8x8 = wrapper::vdup_n( + *(reinterpret_cast<T *>( + input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))), + TagType{}); + const auto input_s16x8 = wrapper::vreinterpret(wrapper::vmovl(input_8x8)); + const auto input_no_offs = wrapper::vsub(input_s16x8, input_qoffset_vec); + + for (size_t m = 0, i = 0; m < depth_multiplier; m += vector_size, ++i) + { + const auto weights_8x8 = wrapper::vload(reinterpret_cast<TW *>( + weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); + const auto weights_s16x8 = wrapper::vreinterpret(wrapper::vmovl(weights_8x8)); + const auto weights_no_offs = wrapper::vsub(weights_s16x8, weights_qoffset_vec); + + acc0.at(i) = wrapper::vmlal(acc0.at(i), wrapper::vgetlow(input_no_offs), + wrapper::vgetlow(weights_no_offs)); + acc1.at(i) = wrapper::vmlal(acc1.at(i), wrapper::vgethigh(input_no_offs), + wrapper::vgethigh(weights_no_offs)); + } + } + + offs += dilation.x() * run_info.input_stride_y; + } + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + for (size_t m = 0, i = 0; m < depth_multiplier; m += vector_size, ++i) + { + if (has_biases) + { + const auto bias_val0 = + wrapper::vloadq(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); + const auto bias_val1 = wrapper::vloadq( + reinterpret_cast<int32_t *>(biases_it.ptr() + (m + half_vec) * sizeof(int32_t))); + + acc0.at(i) = wrapper::vadd(acc0.at(i), bias_val0); + acc1.at(i) = wrapper::vadd(acc1.at(i), bias_val1); + } + + if (out_shift < 0) + { + acc0.at(i) = wrapper::vadd(saturating_doubling_high_mul(acc0.at(i) * (1 << (-out_shift)), out_mul), + output_qoffset_vec); + acc1.at(i) = wrapper::vadd(saturating_doubling_high_mul(acc1.at(i) * (1 << (-out_shift)), out_mul), + output_qoffset_vec); + } + else + { + acc0.at(i) = wrapper::vadd( + rounding_divide_by_exp2(saturating_doubling_high_mul(acc0.at(i), out_mul), out_shift), + output_qoffset_vec); + acc1.at(i) = wrapper::vadd( + rounding_divide_by_exp2(saturating_doubling_high_mul(acc1.at(i), out_mul), out_shift), + output_qoffset_vec); + } + + acc0.at(i) = wrapper::vmin(wrapper::vmax(acc0.at(i), lower), upper); + acc1.at(i) = wrapper::vmin(wrapper::vmax(acc1.at(i), lower), upper); + + const auto out_val = wrapper::vcombine(wrapper::vmovn(acc0.at(i)), wrapper::vmovn(acc1.at(i))); + + if (std::is_same<T, uint8_t>::value) + { + wrapper::vstore(reinterpret_cast<uint8_t *>(output_it.ptr() + m * sizeof(uint8_t)), + wrapper::vqmovn(vreinterpretq_u16_s16(out_val))); + } + else + { + wrapper::vstore(reinterpret_cast<int8_t *>(output_it.ptr() + m * sizeof(int8_t)), + wrapper::vqmovn(out_val)); + } + } + }, + input_it, weights_it, biases_it, output_it); +} +} // namespace + +template <typename T, typename TW> +void run_depthwise_quanitized8bit(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + PadStrideInfo conv_info = info.pad_stride_info; + unsigned int depth_multiplier = info.depth_multiplier; + Size2D dilation = info.dilation; + std::vector<int> output_multiplier; + std::vector<int> output_shift; + + const auto input_scale = src->info()->quantization_info().uniform().scale; + const auto output_scale = dst->info()->quantization_info().uniform().scale; + auto weights_scale = weights->info()->quantization_info().scale(); + + if (!is_data_type_quantized_per_channel(weights->info()->data_type())) + { + for (size_t i = 1; i < weights->info()->dimension(channel_idx); ++i) + { + weights_scale.push_back(weights_scale.front()); + } + } + + for (const auto &s : weights_scale) + { + int32_t out_mult = 0; + int32_t out_shift = 0; + const float multiplier = input_scale * s / output_scale; + arm_compute::quantization::calculate_quantized_multiplier(multiplier, &out_mult, &out_shift); + + output_multiplier.push_back(out_mult); + output_shift.push_back(out_shift); + } + + if (depth_multiplier == 1) + { + depthwise_loop_multiplier1_quantized<T, TW>(src, weights, biases, dst, conv_info, dilation, output_multiplier, + output_shift, window, has_biases); + } + else + { + const bool is_pow2 = ((depth_multiplier & (depth_multiplier - 1)) == 0); + const bool is_quantized_per_tensor = !(is_data_type_quantized_per_channel(weights->info()->data_type())); + + if (is_pow2 && is_quantized_per_tensor && depth_multiplier >= 8) + { + depthwise_loop_pow2_quantized_per_tensor<T, TW>(src, weights, biases, dst, conv_info, dilation, + depth_multiplier, output_multiplier, output_shift, window, + has_biases); + } + else + { + depthwise_loop_generic_quantized<T, TW>(src, weights, biases, dst, conv_info, dilation, depth_multiplier, + output_multiplier, output_shift, window, has_biases); + } + } +} +template void run_depthwise_quanitized8bit<uint8_t, uint8_t>(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info); +template void run_depthwise_quanitized8bit<int8_t, int8_t>(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info); +template void run_depthwise_quanitized8bit<uint8_t, int8_t>(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info); +} // namespace cpu +} // namespace arm_compute diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h new file mode 100644 index 0000000000..3fa5c58c3c --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h @@ -0,0 +1,374 @@ +/* + * Copyright (c) 2022-2023 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. + */ +#ifndef SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H +#define SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H +#include "arm_compute/core/Helpers.h" + +#include "src/core/NEON/wrapper/wrapper.h" + +namespace arm_compute +{ +struct ConvolutionInfo; + +namespace cpu +{ +constexpr auto data_layout = DataLayout::NHWC; +const size_t width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); +const size_t height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); +const size_t channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + +constexpr auto dim_manual_loop = Window::Dimension(0, 0, 0); +constexpr auto dim_single_unit_step = Window::Dimension(0, 1, 1); +constexpr size_t vector_size = 8; + +struct DepthwiseConvolutionRunInfo +{ + const size_t num_read_elements_per_iteration; + const uint32_t x_start; + const uint32_t x_end; + const uint32_t x_step; + const uint32_t x_leftover_start; + const size_t input_stride_y; + const size_t input_stride_z; + const size_t input_max_offset; + const size_t weights_width; + const size_t weights_height; + const size_t weights_stride_y; + const size_t weights_stride_z; + const size_t conv_stride_x; + const size_t conv_stride_y; + const size_t conv_pad_left; + const size_t conv_pad_top; + const size_t input_height; + const size_t input_width; + const size_t input_depth; + + DepthwiseConvolutionRunInfo(const ITensorInfo &input, + const ITensorInfo &weights, + const PadStrideInfo &conv_info, + const Window &w, + uint32_t depth_multiplier = 1) // NOLINT + : num_read_elements_per_iteration( + (depth_multiplier == 1 ? (vector_size / element_size_from_data_type(input.data_type())) : 1)), + x_start(w.x().start()), + x_end(w.x().end()), + x_step(static_cast<uint32_t>(num_read_elements_per_iteration * depth_multiplier)), + x_leftover_start(std::max(static_cast<int32_t>(w.x().end() + 1) - static_cast<int32_t>(x_step), int32_t(0))), + input_stride_y(input.strides_in_bytes().y()), + input_stride_z(input.strides_in_bytes().z()), + input_max_offset(input.strides_in_bytes().z() * input.dimension(height_idx) - + (input.padding().bottom + input.padding().top) * input.strides_in_bytes().y()), + weights_width(weights.dimension(width_idx)), + weights_height(weights.dimension(height_idx)), + weights_stride_y(weights.strides_in_bytes().y()), + weights_stride_z(weights.strides_in_bytes().z()), + conv_stride_x(conv_info.stride().first), + conv_stride_y(conv_info.stride().second), + conv_pad_left(conv_info.pad_left()), + conv_pad_top(conv_info.pad_top()), + input_height(input.dimension(height_idx)), + input_width(input.dimension(width_idx)), + input_depth(input.dimension(channel_idx)) + { + } +}; + +inline bool is_valid_input_region(int32_t base_w, + uint32_t base_h, + uint32_t w, + uint32_t h, + const DepthwiseConvolutionRunInfo &run_info, + const Size2D &dilation) +{ + const int32_t current_h = base_h + h * dilation.y(); + const bool is_valid_h = current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height); + + const int32_t current_w = base_w + w * dilation.x(); + const bool is_valid_w = current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width); + + return is_valid_h && is_valid_w; +} + +template <typename T> +void depthwise_loop_multiplier1_fp(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const PadStrideInfo &conv_info, + const Size2D &dilation, + const Window &window, + bool has_biases) +{ + constexpr auto element_per_vector = vector_size / sizeof(T); + using VectorType = typename wrapper::traits::neon_vector<T, element_per_vector>::type; + using TagType = typename wrapper::traits::neon_vector<T, element_per_vector>::tag_type; + + const auto run_info = DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window); + + const VectorType zero_vector = wrapper::vdup_n(static_cast<T>(0), TagType{}); + + Window execution_window = window; + execution_window.set(Window::DimX, dim_single_unit_step); + + Window win_input = window; + win_input.set(Window::DimX, dim_manual_loop); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); + + Window win_weights = win_input; + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set(Window::DimX, dim_manual_loop); + + Iterator input_it(src, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(dst, win_output); + Iterator biases_it{}; + + if (has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop( + execution_window, + [&](const Coordinates &id) + { + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + + auto const base_weights_ptr = weights_it.ptr(); + uint32_t x = run_info.x_start; + + for (; x < run_info.x_leftover_start; x += run_info.x_step) + { + VectorType acc = zero_vector; + auto weights_ptr = base_weights_ptr; + int64_t input_offset = base_input_offset; + + for (uint32_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for (uint32_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = + is_valid_region + ? wrapper::vload(reinterpret_cast<T *>( + input_it.ptr() + std::min(static_cast<size_t>(offs), run_info.input_max_offset))) + : zero_vector; + const auto weights_vals = + wrapper::vload(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); + acc = wrapper::vmla(acc, weights_vals, input_vals); + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + if (has_biases) + { + const auto biases_vals = wrapper::vload(reinterpret_cast<T *>(biases_it.ptr()) + x); + acc = wrapper::vadd(acc, biases_vals); + } + + wrapper::vstore(reinterpret_cast<T *>(output_it.ptr()) + x, acc); + } + + for (; x < run_info.x_end; ++x) + { + auto acc_scalar = T{0}; + auto weights_ptr = base_weights_ptr; + int64_t input_offset = base_input_offset; + + for (size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = + is_valid_region + ? *reinterpret_cast<T *>(input_it.ptr() + + std::min(static_cast<size_t>(offs), run_info.input_max_offset)) + : 0; + const auto weights_vals = + *(reinterpret_cast<T *>(weights_ptr + w * run_info.weights_stride_y) + x); + + acc_scalar += (input_vals * weights_vals); + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + if (has_biases) + { + const auto biases_vals = *(reinterpret_cast<T *>(biases_it.ptr()) + x); + acc_scalar += biases_vals; + } + *(reinterpret_cast<T *>(output_it.ptr()) + x) = acc_scalar; + } + }, + input_it, weights_it, biases_it, output_it); +} + +template <typename T> +void depthwise_loop_generic_fp(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const PadStrideInfo &conv_info, + const Size2D &dilation, + unsigned int depth_multiplier, + const Window &window, + bool has_biases) +{ + const auto run_info = + DepthwiseConvolutionRunInfo(*src->info(), *weights->info(), conv_info, window, depth_multiplier); + + Window execution_window = window; + execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); + + Window win_input = execution_window; + win_input.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); + + Window win_weights = window; + win_weights.set_dimension_step(Window::DimX, run_info.x_step); + win_weights.set(Window::DimY, dim_manual_loop); + win_weights.set(Window::DimZ, dim_manual_loop); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set_dimension_step(Window::DimX, run_info.x_step); + + Iterator input_it(src, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(dst, win_output); + Iterator biases_it{}; + + if (has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop( + execution_window, + [&](const Coordinates &id) + { + std::vector<T> acc(depth_multiplier, static_cast<T>(0)); + + const int input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + int input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + + auto weights_ptr = weights_it.ptr(); + for (size_t h = 0; h < run_info.weights_height; ++h) + { + int offs = input_offset; + for (size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = + is_valid_region ? *(reinterpret_cast<T *>(input_it.ptr() + std::min(static_cast<size_t>(offs), + run_info.input_max_offset))) + : T(0); + + for (size_t m = 0; m < depth_multiplier; ++m) + { + const auto weights_val = + *(reinterpret_cast<T *>(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); + acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m)); + } + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + if (has_biases) + { + for (size_t m = 0; m < depth_multiplier; ++m) + { + const auto biases_val = *(reinterpret_cast<T *>(biases_it.ptr() + m * sizeof(T))); + *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val; + } + } + else + { + for (size_t m = 0; m < depth_multiplier; ++m) + { + *(reinterpret_cast<T *>(output_it.ptr() + m * sizeof(T))) = acc.at(m); + } + } + }, + input_it, weights_it, biases_it, output_it); +} + +template <typename T, typename TW> +void run_depthwise_float(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + PadStrideInfo conv_info = info.pad_stride_info; + unsigned int depth_multiplier = info.depth_multiplier; + Size2D dilation = info.dilation; + + if (depth_multiplier == 1) + { + depthwise_loop_multiplier1_fp<T>(src, weights, biases, dst, conv_info, dilation, window, has_biases); + } + else + { + depthwise_loop_generic_fp<T>(src, weights, biases, dst, conv_info, dilation, depth_multiplier, window, + has_biases); + } +} + +template <typename T, typename TW> +void run_depthwise_quanitized8bit(const ITensor *src, + const ITensor *weights, + const ITensor *biases, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info); + +} // namespace cpu +} // namespace arm_compute +#endif //define SRC_CORE_KERNELS_DEPTWISECONV2DNATIVE_IMPL_H diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8.cpp new file mode 100644 index 0000000000..d32847c1e8 --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2022 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 "src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h" +namespace arm_compute +{ +namespace cpu +{ +void neon_qu8_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_quanitized8bit<uint8_t, uint8_t>(src, weights, bias, dst, window, has_biases, info); +} + +void neon_qp8_qu8_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_quanitized8bit<uint8_t, int8_t>(src, weights, bias, dst, window, has_biases, info); +} +} // namespace cpu +} // namespace arm_compute diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8_signed.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8_signed.cpp new file mode 100644 index 0000000000..682fad0bda --- /dev/null +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/qasymm8_signed.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2022 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 "src/cpu/kernels/depthwiseconv2d/generic/neon/impl.h" +namespace arm_compute +{ +namespace cpu +{ +void neon_qs8_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_quanitized8bit<int8_t, int8_t>(src, weights, bias, dst, window, has_biases, info); +} + +void neon_qp8_qs8_deptwiseconv2dnative(const ITensor *src, + const ITensor *weights, + const ITensor *bias, + ITensor *dst, + const Window &window, + bool has_biases, + const ConvolutionInfo &info) +{ + return run_depthwise_quanitized8bit<int8_t, int8_t>(src, weights, bias, dst, window, has_biases, info); +} +} // namespace cpu +} // namespace arm_compute |