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Diffstat (limited to 'src/cpu/kernels/softmax/generic/neon/impl.h')
| -rw-r--r-- | src/cpu/kernels/softmax/generic/neon/impl.h | 428 |
1 files changed, 428 insertions, 0 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.h b/src/cpu/kernels/softmax/generic/neon/impl.h new file mode 100644 index 0000000000..e417271d0e --- /dev/null +++ b/src/cpu/kernels/softmax/generic/neon/impl.h @@ -0,0 +1,428 @@ +/* + * Copyright (c) 2021-2024 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 ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H +#define ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H + +#include "arm_compute/core/Helpers.h" + +#include "src/core/NEON/NEMath.h" +#include "src/core/NEON/wrapper/wrapper.h" + +namespace arm_compute +{ +namespace cpu +{ + +#ifdef __aarch64__ +namespace +{ +// These helper functions are added because vaddv does not exist for fp16, +// and, therefore, is not part of the wrapper::vaddv interface. +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +inline float16_t wrapper_vaddv(const float16x8_t &a, int sum_stages) +{ + auto sum_res = wrapper::vpadd(wrapper::vgethigh(a), wrapper::vgetlow(a)); + for (int i = 0; i < sum_stages; ++i) + { + sum_res = wrapper::vpadd(sum_res, sum_res); + } + return wrapper::vgetlane(sum_res, 0); +} +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +inline float wrapper_vaddv(const float32x4_t &a, int sum_stages) +{ + ARM_COMPUTE_UNUSED(sum_stages); + return wrapper::vaddv(a); +} +} // namespace +#endif // __aarch64__ + +// 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_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(); + + 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>; + + constexpr int vec_size = 16 / sizeof(T); + + const int sum_stages = log2(vec_size >> 1); + + const auto beta_vec = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}); + + execute_window_loop( + window, + [&](const Coordinates &) + { + /* Get pointers */ + const T *in_ptr = reinterpret_cast<const T *>(in_it.ptr()); + T *out_ptr = reinterpret_cast<T *>(out_it.ptr()); + + T max_val; + + /* Compute Max */ + { + // Init max value + auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{}); + int x = 0; + + for (; x <= (input_width - vec_size); x += vec_size) + { + const auto current_value = wrapper::vloadq(in_ptr + x); + vec_max = wrapper::vmax(vec_max, current_value); + } + +#ifdef __aarch64__ + max_val = wrapper::vmaxv(vec_max); +#else // __aarch64__ + 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); + } + + max_val = wrapper::vgetlane(carry_max, 0); +#endif // __aarch64__ + + // Compute left-over elements + for (; x < input_width; ++x) + { + max_val = std::max(*(in_ptr + x), max_val); + } + } // compute max + + T sum_transformed{}; + + /* Compute exponentials and sum */ + { + /* Get max value */ + 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{}); + + /* 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, 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(out_ptr + x, vec_elements); + } + + /* Reduce sum */ + T sum{}; +#ifdef __aarch64__ + sum = wrapper_vaddv(vec_sum, sum_stages); +#else // __aarch64__ + auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); + for (int i = 0; i < sum_stages; ++i) + { + sum_res = wrapper::vpadd(sum_res, sum_res); + } + sum = wrapper::vgetlane(sum_res, 0); +#endif // __aarch64__ + + /* 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; + } + + out_ptr[x] = element; + } + + if (!IS_LOG) + { + sum_transformed = T(1) / sum; + } + else + { + sum_transformed = static_cast<T>(std::log(sum)); + } + } // Compute exponentials and sum + + /* Normalize exponentials */ + { + const auto sum_vec = wrapper::vdup_n(static_cast<T>(sum_transformed), ExactTagType{}); + + /* Loop over row and compute softmax */ + int x = 0; + for (; x <= (input_width - vec_size); x += vec_size) + { + const auto vec_in = wrapper::vloadq(out_ptr + x); + if (IS_LOG) + { + wrapper::vstore(out_ptr + x, wrapper::vsub(vec_in, sum_vec)); + } + else + { + wrapper::vstore(out_ptr + x, wrapper::vmul(vec_in, sum_vec)); + } + } + + /* Run remaining elements */ + for (; x < input_width; ++x) + { + if (IS_LOG) + { + out_ptr[x] = out_ptr[x] - sum_transformed; + } + else + { + out_ptr[x] = out_ptr[x] * sum_transformed; + } + } + } // Normalize exponentials + }, + 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_non_x_quantized( + const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window); +} // namespace cpu +} // namespace arm_compute + +#endif // ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H |