diff options
Diffstat (limited to 'src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp')
| -rw-r--r-- | src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp | 592 |
1 files changed, 344 insertions, 248 deletions
diff --git a/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp index a2ae5564e6..d08e973968 100644 --- a/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp +++ b/src/cpu/kernels/depthwiseconv2d/generic/neon/impl.cpp @@ -22,8 +22,10 @@ * 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 @@ -65,8 +67,16 @@ inline int32_t rounding_divide_by_exp2(const int32_t &x, const int exponent) 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 +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); @@ -75,7 +85,8 @@ void depthwise_loop_multiplier1_quantized(const ITensor *src, const ITensor *wei 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_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); @@ -104,152 +115,175 @@ void depthwise_loop_multiplier1_quantized(const ITensor *src, const ITensor *wei Iterator output_it(dst, win_output); Iterator biases_it{}; - if(has_biases) + 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) + execute_window_loop( + execution_window, + [&](const Coordinates &id) { - AccArrayType acc{}; - AccArrayType in_sum{}; - AccArrayType we_sum{}; + 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; - auto weights_ptr = base_weights_ptr; - auto input_offset = base_input_offset; - - for(size_t h = 0; h < run_info.weights_height; ++h) + for (; x < run_info.x_leftover_start; x += run_info.x_step) { - 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); + AccArrayType acc{}; + AccArrayType in_sum{}; + AccArrayType we_sum{}; + + auto weights_ptr = base_weights_ptr; + auto input_offset = base_input_offset; - for(size_t i = 0; i < element_per_vector; ++i) + 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) { - acc.at(i) += input_vals[i] * weights_vals[i]; - in_sum.at(i) += input_vals[i]; - we_sum.at(i) += weights_vals[i]; + 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; } - offs += dilation.x() * run_info.input_stride_y; + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; } - 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; - 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); + } - 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))); } - 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); } - 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; + // 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; + 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) + for (size_t h = 0; h < run_info.weights_height; ++h) { - 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; + 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; + } - offs += dilation.x() * run_info.input_stride_y; + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; } - weights_ptr += run_info.weights_stride_z; - input_offset += dilation.y() * run_info.input_stride_z; - } + T out_vals{0}; - T out_vals{ 0 }; + acc -= in_sum * weights_qoffset; + acc -= we_sum * input_qoffset; + acc += k_offset; - 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); + } - 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); - 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; + } - 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; } - - 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); + }, + 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 +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 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 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; @@ -277,76 +311,93 @@ void depthwise_loop_generic_quantized(const ITensor *src, const ITensor *weights Iterator output_it(dst, win_output); Iterator biases_it{}; - if(has_biases) + 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; + 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; + 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) + auto weights_ptr = weights_it.ptr(); + for (size_t h = 0; h < run_info.weights_height; ++h) { - 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) + int offs = input_offset; + for (size_t w = 0; w < run_info.weights_width; ++w) { - 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; - } + 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; - offs += dilation.x() * run_info.input_stride_y; - in_sum += input_val; - } + 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; - weights_ptr += run_info.weights_stride_z; - input_offset += dilation.y() * run_info.input_stride_z; - } + we_sum.at(m) += weights_val; + } - 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; + offs += dilation.x() * run_info.input_stride_y; + in_sum += input_val; + } - if(has_biases) - { - acc.at(m) += *(reinterpret_cast<int32_t *>(biases_it.ptr() + m * sizeof(int32_t))); + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; } - 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 + for (size_t m = 0; m < depth_multiplier; ++m) { - acc.at(m) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), out_mul), out_shift) + output_qoffset; + 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))); } - *(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); + }, + 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 +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; @@ -355,11 +406,15 @@ void depthwise_loop_pow2_quantized_per_tensor(const ITensor *src, const ITensor 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 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 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{}); @@ -389,7 +444,7 @@ void depthwise_loop_pow2_quantized_per_tensor(const ITensor *src, const ITensor Iterator output_it(dst, win_output); Iterator biases_it{}; - if(has_biases) + if (has_biases) { biases_it = Iterator(biases, win_weights); } @@ -397,95 +452,117 @@ void depthwise_loop_pow2_quantized_per_tensor(const ITensor *src, const ITensor 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); + 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; + 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)) + 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 int32_t current_h = input_z + h * dilation.y(); + if (current_h >= 0 && current_h < static_cast<int32_t>(run_info.input_height)) { - const int32_t current_w = input_y + w * dilation.x(); - if(current_w >= 0 && current_w < static_cast<int32_t>(run_info.input_width)) + int offs = input_offset; + for (size_t w = 0; w < run_info.weights_width; ++w) { - 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 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 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)); + 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; + offs += dilation.x() * run_info.input_stride_y; + } } - } - weights_ptr += run_info.weights_stride_z; - input_offset += dilation.y() * run_info.input_stride_z; - } + 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) + for (size_t m = 0, i = 0; m < depth_multiplier; m += vector_size, ++i) { - 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))); + 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); - } + 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); - } + 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); + 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))); + 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)); + 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); + }, + 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) +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; @@ -497,15 +574,15 @@ void run_depthwise_quanitized8bit(const ITensor *src, const ITensor *weights, co 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())) + if (!is_data_type_quantized_per_channel(weights->info()->data_type())) { - for(size_t i = 1; i < weights->info()->dimension(channel_idx); ++i) + 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) + for (const auto &s : weights_scale) { int32_t out_mult = 0; int32_t out_shift = 0; @@ -516,30 +593,49 @@ void run_depthwise_quanitized8bit(const ITensor *src, const ITensor *weights, co output_shift.push_back(out_shift); } - if(depth_multiplier == 1) + if (depth_multiplier == 1) { - depthwise_loop_multiplier1_quantized<T, TW>(src, weights, biases, dst, conv_info, dilation, output_multiplier, output_shift, window, has_biases); + 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) + 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); + 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); + 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); +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 |