diff options
Diffstat (limited to 'src/cpu/kernels/softmax/generic/neon/impl.h')
| -rw-r--r-- | src/cpu/kernels/softmax/generic/neon/impl.h | 197 |
1 files changed, 194 insertions, 3 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.h b/src/cpu/kernels/softmax/generic/neon/impl.h index 60380cd233..e417271d0e 100644 --- a/src/cpu/kernels/softmax/generic/neon/impl.h +++ b/src/cpu/kernels/softmax/generic/neon/impl.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2023 Arm Limited. + * Copyright (c) 2021-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -62,8 +62,9 @@ inline float wrapper_vaddv(const float32x4_t &a, int sum_stages) // The template implementation for float data types is stored in the header file because // we need all fp16 instantiated code to live in fp16.cpp files. template <typename T, bool IS_LOG> -void neon_softmax_float(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window) +void neon_softmax_x_float(const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window) { + ARM_COMPUTE_UNUSED(axis); ARM_COMPUTE_UNUSED(tmp); const int input_width = in->info()->valid_region().shape.x(); @@ -228,9 +229,199 @@ void neon_softmax_float(const ITensor *in, void *const tmp, ITensor *out, float }, in_it, out_it); } +template <typename T, bool IS_LOG> +void neon_softmax_non_x_float( + const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window) +{ + ARM_COMPUTE_UNUSED(tmp); + + Iterator in_it(in, window); + Iterator out_it(out, window); + + /** SIMD vector tag type. */ + using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; + + const auto beta_vec = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}); + constexpr int vec_size = 16 / sizeof(T); + const ITensorInfo *in_info = in->info(); + const ITensorInfo *out_info = out->info(); + const int x_width = in_info->valid_region().shape.x(); + const unsigned int in_axis_stride = in_info->strides_in_bytes()[axis]; + const unsigned int out_axis_stride = out_info->strides_in_bytes()[axis]; + const int axis_width = in_info->dimension(axis); + + execute_window_loop( + window, + [&](const Coordinates &winCoords) + { + const bool vector_exceeds_bounds = (winCoords[0] + vec_size) > x_width; + + /* Get pointers */ + const uint8_t *in_ptr = in_it.ptr(); + uint8_t *out_ptr = out_it.ptr(); + + // Init max value + auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{}); + + /* Compute Max */ + { + if (!vector_exceeds_bounds) + { + int i = 0; + for (; i < axis_width; ++i) + { + const auto current_value = + wrapper::vloadq(reinterpret_cast<const T *>((i * in_axis_stride) + in_ptr)); + vec_max = wrapper::vmax(vec_max, current_value); + } + } + else + { + int i = 0; + for (; i < axis_width; ++i) + { + const T *const base_ptr_in = reinterpret_cast<const T *>((i * in_axis_stride) + in_ptr); + int j = 0; + for (; j < (x_width - winCoords[0]); ++j) + { + const auto current_value = *(base_ptr_in + j); + vec_max[j] = std::max(vec_max[j], current_value); + } + } + } + } // compute max + + auto vec_sum_transformed = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); + + auto vec_elements = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); + /* Init sum to zero */ + auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); + + /* Compute exponentials and sum */ + { + if (!vector_exceeds_bounds) + { + const auto vec_one = wrapper::vdup_n(static_cast<T>(1), ExactTagType{}); + /* Loop over row and compute exponentials and sum */ + int i = 0; + for (; i < axis_width; ++i) + { + vec_elements = wrapper::vloadq(reinterpret_cast<const T *>((i * in_axis_stride) + in_ptr)); + vec_elements = wrapper::vsub(vec_elements, vec_max); + if (IS_LOG) + { + vec_elements = wrapper::vmul(vec_elements, beta_vec); + vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); + } + else + { + vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, beta_vec)); + vec_sum = wrapper::vadd(vec_sum, vec_elements); + } + + wrapper::vstore(reinterpret_cast<T *>((i * out_axis_stride) + out_ptr), vec_elements); + } + + if (!IS_LOG) + { + vec_sum_transformed = wrapper::vdiv(vec_one, vec_sum); + } + else + { + vec_sum_transformed = wrapper::vlog(vec_sum); + } + } + else + { + int i = 0; + for (; i < axis_width; ++i) + { + const T *const base_ptr_in = reinterpret_cast<const T *>((i * in_axis_stride) + in_ptr); + T *const base_ptr_out = reinterpret_cast<T *>((i * out_axis_stride) + out_ptr); + int j = 0; + for (; j < (x_width - winCoords[0]); ++j) + { + vec_elements[j] = *(base_ptr_in + j); + vec_elements[j] -= vec_max[j]; + if (IS_LOG) + { + vec_elements[j] *= beta; + vec_sum[j] += std::exp(vec_elements[j]); + } + else + { + vec_elements[j] = std::exp(vec_elements[j] * beta); + vec_sum[j] += vec_elements[j]; + } + *(base_ptr_out + j) = vec_elements[j]; + } + } + int j = 0; + for (; j < (x_width - winCoords[0]); ++j) + { + if (!IS_LOG) + { + vec_sum_transformed[j] = 1 / vec_sum[j]; + } + else + { + vec_sum_transformed[j] = std::log(vec_sum[j]); + } + } + } + } // Compute exponentials and sum + + /* Normalize exponentials */ + { + if (!vector_exceeds_bounds) + { + /* Loop over row and compute softmax */ + int i = 0; + for (; i < axis_width; ++i) + { + T *const base_ptr_out = reinterpret_cast<T *>((i * out_axis_stride) + out_ptr); + auto vec_in = wrapper::vloadq(base_ptr_out); + if (IS_LOG) + { + wrapper::vstore(base_ptr_out, wrapper::vsub(vec_in, vec_sum_transformed)); + } + else + { + wrapper::vstore(base_ptr_out, wrapper::vmul(vec_in, vec_sum_transformed)); + } + } + } + else + { + int i = 0; + for (; i < axis_width; ++i) + { + T *const base_ptr_out = reinterpret_cast<T *>((i * out_axis_stride) + out_ptr); + int j = 0; + for (; j < (x_width - winCoords[0]); ++j) + { + if (IS_LOG) + { + *(base_ptr_out + j) -= vec_sum_transformed[j]; + } + else + { + *(base_ptr_out + j) *= vec_sum_transformed[j]; + } + } + } + } + } // Normalize exponentials + }, + in_it, out_it); +} +template <typename T, bool IS_LOG> +void neon_softmax_x_quantized( + const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window); template <typename T, bool IS_LOG> -void neon_softmax_quantized(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window); +void neon_softmax_non_x_quantized( + const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window); } // namespace cpu } // namespace arm_compute |