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Diffstat (limited to 'src/cpu/kernels/assembly/arm_gemm.hpp')
| -rw-r--r-- | src/cpu/kernels/assembly/arm_gemm.hpp | 302 |
1 files changed, 302 insertions, 0 deletions
diff --git a/src/cpu/kernels/assembly/arm_gemm.hpp b/src/cpu/kernels/assembly/arm_gemm.hpp new file mode 100644 index 0000000000..941fed0ba8 --- /dev/null +++ b/src/cpu/kernels/assembly/arm_gemm.hpp @@ -0,0 +1,302 @@ +/* + * Copyright (c) 2018-2022, 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_ASSEMBLY_ARM_GEMM_HPP +#define ACL_SRC_CPU_KERNELS_ASSEMBLY_ARM_GEMM_HPP + +#pragma once + +#include "arm_gemm_local.hpp" +#include "gemm_common.hpp" +#include <cstring> +#include <memory> +#include <vector> + +namespace arm_gemm +{ +enum class GemmMethod +{ + DEFAULT, + GEMV_BATCHED, + GEMV_PRETRANSPOSED, + GEMV_NATIVE_TRANSPOSED, + GEMM_NATIVE, + GEMM_HYBRID, + GEMM_INTERLEAVED, + GEMM_INTERLEAVED_2D, + QUANTIZE_WRAPPER, + QUANTIZE_WRAPPER_2D, + GEMM_HYBRID_QUANTIZED +}; + +enum class WeightFormat +{ + UNSPECIFIED = 0x1, + ANY = 0x2, + OHWI = 0x100100, + OHWIo2 = 0x100200, + OHWIo4 = 0x100400, + OHWIo8 = 0x100800, + OHWIo16 = 0x101000, + OHWIo32 = 0x102000, + OHWIo64 = 0x104000, + OHWIo128 = 0x108000, + OHWIo4i2 = 0x200400, + OHWIo4i2_bf16 = 0x200410, + OHWIo8i2 = 0x200800, + OHWIo8i2_bf16 = 0x200810, + OHWIo16i2 = 0x201000, + OHWIo16i2_bf16 = 0x201010, + OHWIo32i2 = 0x202000, + OHWIo32i2_bf16 = 0x202010, + OHWIo64i2 = 0x204000, + OHWIo64i2_bf16 = 0x204010, + OHWIo4i4 = 0x400400, + OHWIo4i4_bf16 = 0x400410, + OHWIo8i4 = 0x400800, + OHWIo8i4_bf16 = 0x400810, + OHWIo16i4 = 0x401000, + OHWIo16i4_bf16 = 0x401010, + OHWIo32i4 = 0x402000, + OHWIo32i4_bf16 = 0x402010, + OHWIo64i4 = 0x404000, + OHWIo64i4_bf16 = 0x404010, + OHWIo2i8 = 0x800200, + OHWIo4i8 = 0x800400, + OHWIo8i8 = 0x800800, + OHWIo16i8 = 0x801000, + OHWIo32i8 = 0x802000, + OHWIo64i8 = 0x804000 +}; + +struct KernelDescription +{ + GemmMethod method = GemmMethod::DEFAULT; + std::string name = ""; + bool is_default = false; + uint64_t cycle_estimate = 0; + + KernelDescription(GemmMethod m, std::string n, bool d = false, uint64_t c = 0) + : method(m), name(n), is_default(d), cycle_estimate(c) + { + } + KernelDescription() noexcept + { + } +}; + +struct GemmConfig +{ + GemmMethod method = GemmMethod::DEFAULT; + std::string filter = ""; + unsigned int inner_block_size = 0; + unsigned int outer_block_size = 0; + WeightFormat weight_format = WeightFormat::ANY; + + GemmConfig(GemmMethod method) : method(method) + { + } + GemmConfig() + { + } +}; + +struct Activation +{ + enum class Type + { + None, + ReLU, + BoundedReLU + }; + + Type type; + float param1; + float param2; + + Activation(Type type = Type::None, float p1 = 0.0f, float p2 = 0.0f) : type(type), param1(p1), param2(p2) + { + } +}; + +struct GemmArgs +{ +public: + const CPUInfo *_ci; + unsigned int _Msize; // num of tiles + unsigned int _Nsize; // output channels + unsigned int _Ksize; // input channels + unsigned int _Ksections; + unsigned int _nbatches; + unsigned int _nmulti; // n_gemms to be performed + bool _indirect_input; + Activation _act; + int _maxthreads; + bool _fixed_format; + bool _fast_mode; + bool _accumulate; + const GemmConfig *_cfg; + + GemmArgs(const CPUInfo *ci, + unsigned int M, + unsigned int N, + unsigned int K, + unsigned int Ksections, + unsigned int nbatches, + unsigned int nmulti, + bool indirect_input, + Activation act, + const int maxthreads, + bool fixed_format = false, + bool fast_mode = false, + bool accumulate = false, + const GemmConfig *cfg = nullptr) + : _ci(ci), + _Msize(M), + _Nsize(N), + _Ksize(K), + _Ksections(Ksections), + _nbatches(nbatches), + _nmulti(nmulti), + _indirect_input(indirect_input), + _act(act), + _maxthreads(maxthreads), + _fixed_format(fixed_format), + _fast_mode(fast_mode), + _accumulate(accumulate), + _cfg(cfg) + { + } +}; + +struct Requantize32 +{ +public: + const int32_t *bias = nullptr; + size_t bias_multi_stride = 0; + int32_t a_offset = 0; + int32_t b_offset = 0; + int32_t c_offset = 0; + bool per_channel_requant = false; + int32_t per_layer_left_shift = 0; + int32_t per_layer_right_shift = 0; + int32_t per_layer_mul = 0; + const int32_t *per_channel_left_shifts = nullptr; + const int32_t *per_channel_right_shifts = nullptr; + const int32_t *per_channel_muls = nullptr; + int32_t minval = 0; + int32_t maxval = 0; + + Requantize32() = default; + + // Constructor for per-tensor quantization + Requantize32(const int32_t *bias, + size_t bias_multi_stride, + int32_t a_offset, + int32_t b_offset, + int32_t c_offset, + int32_t requant_shift, + int32_t requant_mul, + int32_t minv, + int32_t maxv) + : bias(bias), + bias_multi_stride(bias_multi_stride), + a_offset(a_offset), + b_offset(b_offset), + c_offset(c_offset), + per_channel_requant(false), + per_layer_left_shift(std::max<int32_t>(requant_shift, 0)), + per_layer_right_shift(std::min<int32_t>(requant_shift, 0)), + per_layer_mul(requant_mul), + minval(minv), + maxval(maxv) + { + } + + // Constructor for per-channel quantization + Requantize32(const int32_t *bias, + size_t bias_multi_stride, + int32_t a_offset, + int32_t b_offset, + int32_t c_offset, + const int32_t *requant_left_shifts, + const int32_t *requant_right_shifts, + const int32_t *requant_muls, + int32_t minv, + int32_t maxv) + : bias(bias), + bias_multi_stride(bias_multi_stride), + a_offset(a_offset), + b_offset(b_offset), + c_offset(c_offset), + per_channel_requant(true), + per_channel_left_shifts(requant_left_shifts), + per_channel_right_shifts(requant_right_shifts), + per_channel_muls(requant_muls), + minval(minv), + maxval(maxv) + { + } +}; + +struct DequantizeFloat +{ +public: + float scale = 0; + + DequantizeFloat() = default; + + // Constructor + DequantizeFloat(const float scale) : scale(scale) + { + } +}; + +struct Nothing +{ +}; + +template <typename Top, typename Tret> +using UniqueGemmCommon = std::unique_ptr<GemmCommon<Top, Tret>>; + +/* Low level API calls. + * These are implemented as 'GemmArgs' versions, or with the arguments explicitly listed. */ + +/* get_gemm_method(): Given the templated types and provided parameters, + * which is the preferred method to implement this GEMM? */ +template <typename Top, typename Tret, class OutputStage = Nothing> +KernelDescription get_gemm_method(const GemmArgs &args, const OutputStage & = {}); + +template <typename Top, typename Tret, class OutputStage = Nothing> +UniqueGemmCommon<Top, Tret> gemm(const GemmArgs &args, const OutputStage & = {}); + +template <typename Top, typename Tret, class OutputStage = Nothing> +std::vector<KernelDescription> get_compatible_kernels(const GemmArgs &args, const OutputStage & = {}); + +template <typename Top, typename Tret, class OutputStage = Nothing> +bool has_opt_gemm(WeightFormat &weight_format, const GemmArgs &args, const OutputStage & = {}); + +} // namespace arm_gemm + +#endif // ACL_SRC_CPU_KERNELS_ASSEMBLY_ARM_GEMM_HPP |