diff options
Diffstat (limited to 'src/cpu/kernels/softmax/generic/neon/impl.h')
| -rw-r--r-- | src/cpu/kernels/softmax/generic/neon/impl.h | 248 |
1 files changed, 133 insertions, 115 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.h b/src/cpu/kernels/softmax/generic/neon/impl.h index 206d36a2e0..4d9b789297 100644 --- a/src/cpu/kernels/softmax/generic/neon/impl.h +++ b/src/cpu/kernels/softmax/generic/neon/impl.h @@ -25,6 +25,7 @@ #define SRC_CORE_NEON_KERNELS_SOFTMAX_IMPL_H #include "arm_compute/core/Helpers.h" + #include "src/core/NEON/NEMath.h" #include "src/core/NEON/wrapper/wrapper.h" @@ -42,53 +43,65 @@ void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) const auto window_start_x = static_cast<int>(window.x().start()); const auto window_end_x = static_cast<int>(window.x().end()); - Window win{ window }; + Window win{window}; win.set(Window::DimX, Window::Dimension(0, 1, 1)); Iterator input(in, win); Iterator output(out, win); const int sum_stages = log2(window_step_x / 2); - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast<const T *>(input.ptr()); - const auto out_ptr = reinterpret_cast<T *>(output.ptr()); + execute_window_loop( + win, + [&](const Coordinates &) + { + // Get pointers + const auto in_ptr = reinterpret_cast<const T *>(input.ptr()); + const auto out_ptr = reinterpret_cast<T *>(output.ptr()); - // Init max value - auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{}); - int x = window_start_x; + // Init max value + auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{}); + int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto current_value = wrapper::vloadq(in_ptr + x); - vec_max = wrapper::vmax(vec_max, current_value); - } - auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max)); + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + const auto current_value = wrapper::vloadq(in_ptr + x); + vec_max = wrapper::vmax(vec_max, current_value); + } + auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max)); - for(int i = 0; i < sum_stages; ++i) - { - carry_max = wrapper::vpmax(carry_max, carry_max); - } - T max_val = wrapper::vgetlane(carry_max, 0); + for (int i = 0; i < sum_stages; ++i) + { + carry_max = wrapper::vpmax(carry_max, carry_max); + } + T max_val = wrapper::vgetlane(carry_max, 0); - // Compute left-over elements - for(; x < window_end_x; ++x) - { - max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val; - } + // Compute left-over elements + for (; x < window_end_x; ++x) + { + max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val; + } - *out_ptr = max_val; - }, - input, output); + *out_ptr = max_val; + }, + input, output); } template <typename T> -void neon_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, float beta, bool is_log, const Window &window); +void neon_softmax_logits_1d_quantized(const ITensor *in, + const ITensor *max, + void *const tmp, + ITensor *out, + float beta, + bool is_log, + const Window &window); template <typename T> -void neon_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, const float beta, bool is_log, const Window &window) +void neon_softmax_logits_1d_float(const ITensor *in, + const ITensor *max, + void *const tmp, + ITensor *out, + const float beta, + bool is_log, + const Window &window) { const int start_x = in->info()->valid_region().anchor.x(); const int input_width = in->info()->valid_region().shape.x(); @@ -103,113 +116,118 @@ void neon_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *c constexpr int vec_size = 16 / sizeof(T); const int sum_stages = log2(vec_size / 2); - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast<T *>(tmp); - - T sum{}; - T sum_inversed{}; - - /* Compute exponentials and sum */ + execute_window_loop( + window, + [&](const Coordinates &) { - /* Get max value */ - const auto max_val = *reinterpret_cast<const T *>(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{}); - - /* Init sum to zero */ - auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); + /* Get pointers */ + const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x; + const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x; + const auto tmp_ptr = reinterpret_cast<T *>(tmp); - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vsub(vec_elements, vec_max); - if(is_log) - { - vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})); - vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); - } - else - { - vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}))); - vec_sum = wrapper::vadd(vec_sum, vec_elements); - } - wrapper::vstore(tmp_ptr + x, vec_elements); - } + T sum{}; + T sum_inversed{}; - /* Reduce sum */ - auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); - for(int i = 0; i < sum_stages; ++i) + /* Compute exponentials and sum */ { - sum_res = wrapper::vpadd(sum_res, sum_res); - } - sum = wrapper::vgetlane(sum_res, 0); + /* Get max value */ + const auto max_val = *reinterpret_cast<const T *>(max_it.ptr()); + const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{}); - /* Run remaining elements */ - for(; x < input_width; ++x) - { - T element{}; + /* Init sum to zero */ + auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); - if(is_log) + /* Loop over row and compute exponentials and sum */ + int x = 0; + for (; x <= (input_width - vec_size); x += vec_size) { - element = (in_ptr[x] - max_val) * beta; - sum += std::exp(element); + auto vec_elements = wrapper::vloadq(in_ptr + x); + vec_elements = wrapper::vsub(vec_elements, vec_max); + if (is_log) + { + vec_elements = + wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})); + vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); + } + else + { + vec_elements = wrapper::vexpq( + wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}))); + vec_sum = wrapper::vadd(vec_sum, vec_elements); + } + wrapper::vstore(tmp_ptr + x, vec_elements); } - else + + /* Reduce sum */ + auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); + for (int i = 0; i < sum_stages; ++i) { - element = std::exp((in_ptr[x] - max_val) * beta); - sum += element; + sum_res = wrapper::vpadd(sum_res, sum_res); } - tmp_ptr[x] = element; - } + sum = wrapper::vgetlane(sum_res, 0); - if(!is_log) - { - sum_inversed = T(1) / sum; - } - else - { - sum = static_cast<T>(std::log(sum)); - } - } + /* Run remaining elements */ + for (; x < input_width; ++x) + { + T element{}; + + if (is_log) + { + element = (in_ptr[x] - max_val) * beta; + sum += std::exp(element); + } + else + { + element = std::exp((in_ptr[x] - max_val) * beta); + sum += element; + } + tmp_ptr[x] = element; + } - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_in = wrapper::vloadq(tmp_ptr + x); - auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); - if(is_log) + if (!is_log) { - normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{})); + sum_inversed = T(1) / sum; } else { - normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{})); + sum = static_cast<T>(std::log(sum)); } - wrapper::vstore(out_ptr + x, normalized_value); } - /* Run remaining elements */ - for(; x < input_width; ++x) + + /* Normalize exponentials */ { - if(is_log) + /* Loop over row and compute softmax */ + int x = 0; + for (; x <= (input_width - vec_size); x += vec_size) { - out_ptr[x] = tmp_ptr[x] - sum; + auto vec_in = wrapper::vloadq(tmp_ptr + x); + auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); + if (is_log) + { + normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{})); + } + else + { + normalized_value = + wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{})); + } + wrapper::vstore(out_ptr + x, normalized_value); } - else + /* Run remaining elements */ + for (; x < input_width; ++x) { - out_ptr[x] = tmp_ptr[x] * sum_inversed; + if (is_log) + { + out_ptr[x] = tmp_ptr[x] - sum; + } + else + { + out_ptr[x] = tmp_ptr[x] * sum_inversed; + } } } - } - }, - in_it, max_it, out_it); + }, + in_it, max_it, out_it); } } // namespace cpu } // namespace arm_compute |