| /* |
| * Copyright (c) 2017-2024 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. |
| */ |
| #pragma once |
| |
| #include <algorithm> |
| #include <cassert> |
| |
| #include "arm_gemm.hpp" |
| #include "bfloat.hpp" |
| #include "convolver.hpp" |
| #include "kernel_weight_format.hpp" |
| #include "kernel_traits.hpp" |
| #include "kernel_weight_format.hpp" |
| #include "mergeresults.hpp" |
| #include "performance_parameters.hpp" |
| #include "quantized.hpp" |
| #include "transform.hpp" |
| #include "utils.hpp" |
| |
| #ifdef CYCLE_PROFILING |
| #include "profiler.hpp" |
| #endif |
| |
| // Some macros used to decide how much working space to allocate. |
| // Round allocations up to the next cache line. |
| #define ALLOC_ROUND 64 |
| #define ROUND_UP(x) ((((x) + ALLOC_ROUND-1) / ALLOC_ROUND) * ALLOC_ROUND) |
| |
| // Implementation of the GemmCommon abstract class. |
| // |
| // This implementation interleaves the source matrices in blocks - good for |
| // larger matrices. |
| |
| namespace arm_gemm { |
| |
| namespace { |
| |
| // Some kernels output to a linear buffer and require a separate merge step. |
| // Others output directly to the matrix result. This helper class calls the |
| // appropriate functions, using templating to avoid calling non-existent |
| // functions. |
| template<bool MergeStep, bool FixedFormat, typename OutputStage> |
| class kernel_and_merge { |
| public: |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| static void run ( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t b_stride, Tri *c_panel, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, |
| unsigned int m_max, unsigned int n_0, unsigned int n_max, const Tr *biasptr, |
| const Activation &act, bool accumulate, const OutputStage &os, const int32_t *col_bias, |
| Tab *acc_buff); |
| }; |
| |
| // Run a kernel and call the separate merge step |
| template<> |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| void kernel_and_merge<true, false, Nothing>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t, Tri *c_panel, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, |
| unsigned int m_max, unsigned int n_0, unsigned int n_max, const Tr *biasptr, |
| const Activation &act, bool accumulate, const Nothing &, const int32_t *, Tab *) |
| { |
| const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_KERNEL, (strategy::out_height() * bblocks * strategy::out_width() * kern_k)); |
| #endif |
| |
| strat.kernel(a_ptr, b_panel, c_panel, 1, bblocks, kern_k); |
| } |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_MERGE, (strategy::out_height() * bblocks * strategy::out_width() * sizeof(Tr))); |
| #endif |
| strat.transforms.Merge(c_ptr, c_panel, ldc, m_0, m_max, n_0, n_max, biasptr, act, accumulate); |
| } |
| } |
| |
| // Run a fixed-format kernel and call the separate merge step |
| template<> |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| void kernel_and_merge<true, true, Nothing>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t b_stride, Tri *c_panel, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, |
| unsigned int m_max, unsigned int n_0, unsigned int n_max, const Tr *biasptr, |
| const Activation &act, bool accumulate, const Nothing &, const int32_t *, Tab *) |
| { |
| { |
| #ifdef CYCLE_PROFILING |
| const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); |
| auto p=prof.ScopedProfiler(PROFILE_KERNEL, (strategy::out_height() * bblocks * strategy::out_width() * kern_k)); |
| #endif |
| |
| strat.kernel(a_ptr, b_panel, b_stride, c_panel, 1, (n_max - n_0), kern_k); |
| } |
| |
| { |
| #ifdef CYCLE_PROFILING |
| const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); |
| auto p=prof.ScopedProfiler(PROFILE_MERGE, (strategy::out_height() * bblocks * strategy::out_width() * sizeof(Tr))); |
| #endif |
| strat.transforms.Merge(c_ptr, c_panel, ldc, m_0, m_max, n_0, n_max, biasptr, act, accumulate); |
| } |
| } |
| |
| // Run a kernel with integrated merge |
| template<> |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| void kernel_and_merge<false, false, Nothing>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t, Tri *, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, unsigned int m_max, |
| unsigned int n_0, unsigned int n_max, const Tr *biasptr, |
| const Activation &act, bool accumulate, const Nothing &, const int32_t *, |
| Tab *acc_buff) |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_KERNEL, (m_max - m_0) * (n_max - n_0) * kern_k); |
| #endif |
| |
| // We need to offset the C pointer, but as it might be NULL (requesting output to accumulation buffer) we need |
| // to be careful not to offset a null pointer. |
| Tri *offset_c_ptr; |
| |
| if (c_ptr == nullptr) { |
| offset_c_ptr = nullptr; |
| } else { |
| offset_c_ptr = c_ptr + m_0 * ldc + n_0; |
| } |
| |
| strat.kernel(// A and B pointers are just the packed panels. |
| a_ptr, b_panel, |
| // Provide relevant part of output array and row stride. |
| offset_c_ptr, ldc, |
| // M, N, K sizes |
| m_max-m_0, n_max - n_0, kern_k, |
| // Bias, activation, accumulation. Need to offset the bias as needed. |
| biasptr ? biasptr + n_0 : nullptr, act, accumulate, |
| // Accumulation buffer. |
| acc_buff ); |
| } |
| |
| // Run a kernel with integrated merge, quantizing |
| template<> |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| void kernel_and_merge<false, false, Requantize32>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t, Tri *, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, unsigned int m_max, |
| unsigned int n_0, unsigned int n_max, const Tr *, |
| const Activation &, bool accumulate, const Requantize32 &qp, const int32_t *col_bias, |
| Tab *acc_buff) |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_KERNEL, (m_max - m_0) * (n_max - n_0) * kern_k); |
| #endif |
| |
| strat.kernel(// A and B pointers are just the packed panels. |
| a_ptr, b_panel, |
| // Provide relevant part of output array and row stride. |
| c_ptr + m_0 * ldc + n_0, ldc, |
| // M, N, K sizes |
| m_max-m_0, n_max - n_0, kern_k, |
| // Bias, activation, accumulation. Need to offset the bias as needed. |
| col_bias + n_0, qp, n_0, accumulate, acc_buff); |
| } |
| |
| // Run a kernel and call the separate quantize step |
| template<> |
| template<typename strategy, typename To, typename Tr, typename Tri, typename Tab> |
| void kernel_and_merge<true, false, Requantize32>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| strategy &strat, const To *a_ptr, const To *b_panel, size_t, Tri *c_panel, |
| Tr *c_ptr, int ldc, int kern_k, unsigned int m_0, |
| unsigned int m_max, unsigned int n_0, unsigned int n_max, const Tr *, |
| const Activation &, bool, const Requantize32 &qp, const int32_t *col_bias, |
| Tab *) |
| { |
| const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_KERNEL, (strategy::out_height() * bblocks * strategy::out_width() * kern_k)); |
| #endif |
| |
| strat.kernel(a_ptr, b_panel, c_panel, 1, bblocks, kern_k); |
| } |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_QUANTIZE, ((m_max-m_0) * bblocks * strategy::out_width() * sizeof(Tr))); |
| #endif |
| // The interleaved kernel outputs in blocks - each block is a |
| // row-major matrix of size out_width * out_height. The merge |
| // kernels are designed to deal with this but the requantizer is |
| // not, so we need to requantize one block at a time. |
| for (int i=0; i<bblocks; i++) { |
| unsigned int n_start = n_0 + (strategy::out_width() * i); |
| unsigned int n_end = std::min(n_start + strategy::out_width(), n_max); |
| |
| // The row bias is interleaved with the transposed A data, get a pointer to it here. |
| const int32_t *row_bias = reinterpret_cast<const int32_t *>(a_ptr + strategy::out_height() * kern_k); |
| |
| requantize_block_32(qp, (n_end - n_start), (m_max-m_0), |
| c_panel + (i * strategy::out_width() * strategy::out_height()), strategy::out_width(), |
| c_ptr + m_0 * ldc + n_start, ldc, |
| row_bias, col_bias + n_start, n_start); |
| } |
| } |
| } |
| |
| // Integer GEMMs can be used in two contexts - "normal" where the full 32-bit output is required, or in |
| // "requantizing" context where the output will be requantized. |
| // |
| // These require different input transforms, as if we are requantizing we want to sum the rows of the A input, and |
| // if we are not we don't. |
| // |
| // This helper class allows the appropriate transforms to be found, without requiring kernels that don't support |
| // quantization to define useless "quantized" transforms. |
| template<typename strategy, bool quantized> |
| class transform_type { |
| public: |
| typedef decltype(strategy::transforms) type; |
| }; |
| |
| template<typename strategy> |
| class transform_type<strategy, true> { |
| public: |
| typedef decltype(strategy::transforms_quantized) type; |
| }; |
| |
| // We need a similar trick here to figure out what type the accumulator buffer should be. |
| template<typename strategy, typename OutputStage, bool ForceFloat> |
| class accumulate_buffer_type { |
| public: |
| typedef typename strategy::result_type type; |
| }; |
| |
| template<typename strategy> |
| class accumulate_buffer_type<strategy, Requantize32, false> { |
| public: |
| typedef int32_t type; |
| }; |
| |
| template<typename strategy, typename OutputStage> |
| class accumulate_buffer_type<strategy, OutputStage, true> { |
| public: |
| typedef float type; |
| }; |
| |
| // Stripe width is a concept only needed for FixedFormat kernels. Use an accessor to avoid issues in other scenarios. |
| template<typename strategy, bool FixedFormat> |
| struct get_stripe_width { |
| static unsigned int get() { |
| return 0; |
| } |
| }; |
| |
| template<typename strategy> |
| struct get_stripe_width<strategy, true> { |
| static unsigned int get() { |
| return strategy::stripe_width(); |
| } |
| }; |
| |
| // KernelWeightFormat is a similar story. |
| template<typename strategy, bool FixedFormat, typename To> |
| struct get_kernel_weight_format { |
| static KernelWeightFormat get() { |
| return KernelWeightFormat::NON_FIXED; |
| } |
| }; |
| |
| template<typename strategy, typename To> |
| struct get_kernel_weight_format<strategy, true, To> { |
| static KernelWeightFormat get() { |
| KernelWeightFormat kwf = strategy::kernel_weight_format(); |
| |
| // If we are using a BF16 kernel to do an FP32 problem (fast mode) then we need to set the BF16 flag on the |
| // weight format. |
| if (std::is_same<To, float>::value && std::is_same<typename strategy::operand_type, bfloat16>::value) { |
| uint32_t kwf_i = static_cast<uint32_t>(kwf); |
| kwf_i |= 0x10; |
| kwf = static_cast<KernelWeightFormat>(kwf_i); |
| } |
| |
| return kwf; |
| } |
| }; |
| |
| } // anonymous namespace |
| |
| template<typename strategy, typename To, typename Tr, typename OutputStage=Nothing, bool MergeStep=true, bool FixedFormat=false, bool ForceThreadColumns=false, bool ForceFloatAccumulate=false> |
| class GemmInterleaved : public GemmCommon<To, Tr> { |
| typedef typename strategy::operand_type Toi; |
| typedef typename strategy::result_type Tri; |
| typedef typename accumulate_buffer_type<strategy, OutputStage, ForceFloatAccumulate>::type Tab; |
| |
| /* const properties set by constructor */ |
| const CPUInfo * const _ci; |
| |
| const unsigned int _Msize; |
| const unsigned int _Nsize; |
| const unsigned int _Ksize; |
| const unsigned int _Ksections; |
| const unsigned int _Ktotal; |
| const unsigned int _rounded_Ksize; |
| |
| const unsigned int _nbatches; |
| const unsigned int _nmulti; |
| |
| const bool _thread_columns; |
| |
| const Activation _act; |
| const bool _accumulate; |
| |
| const int _maxthreads; |
| int _nthreads; |
| |
| /* Blocking info */ |
| unsigned int _k_block=0; |
| unsigned int _x_block=0; |
| unsigned int _Mround=0; |
| |
| /* Working space, pretransposed buffer, buffer manager */ |
| const Toi *_B_transposed=nullptr; |
| void *_working_space=nullptr; |
| |
| Tab *_accumulation_buffer=nullptr; |
| |
| /* Output stage */ |
| OutputStage _os; |
| |
| /* Quantized support (in addition to 'output stage' above */ |
| int32_t *col_bias = nullptr; |
| |
| /* Indirect parameters. _indirect_buf doubles as a flag to indicate that "indirect" transform should be used. */ |
| const To * const * const * _indirect_buf = nullptr; |
| |
| /* Convolver - only set up for convolution problems, so also doubles as a flag. */ |
| std::unique_ptr<convolver<To>> _convolver = nullptr; |
| |
| unsigned int get_col_sum_size() const { |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| return _Nsize * _nmulti * sizeof(int32_t); |
| } else { |
| return 0; |
| } |
| } |
| |
| /* We will need to walk through the blocks of B in a few contexts, so |
| * factor that out. */ |
| class blockwalker { |
| private: |
| /* Size loops, etc. based on our parent's configuration */ |
| const GemmInterleaved<strategy, To, Tr, OutputStage, MergeStep, FixedFormat, ForceThreadColumns, ForceFloatAccumulate> &_parent; |
| |
| /* K, X and multi parameters for current iteration. */ |
| unsigned int _k0=0, _x0=0, _multi=0; |
| |
| /* Range of X to iterate over - used in "ForceThreadColumns" cases */ |
| unsigned int _x_start=0; |
| unsigned int _x_end=_parent._Nsize; |
| |
| unsigned int _index=0; |
| bool _done=false; |
| bool _newkblock=true; |
| bool _newmulti=true; |
| |
| public: |
| blockwalker(const GemmInterleaved<strategy, To, Tr, OutputStage, MergeStep, FixedFormat, ForceThreadColumns, ForceFloatAccumulate> &parent) : _parent(parent) { } |
| |
| blockwalker(const GemmInterleaved<strategy, To, Tr, OutputStage, MergeStep, FixedFormat, ForceThreadColumns, ForceFloatAccumulate> &parent, |
| unsigned int x_start, unsigned int x_end) : _parent(parent), _x0 (_x_start), _x_start(x_start), _x_end(x_end) { } |
| |
| unsigned int xmax() { |
| return std::min(_x0 + _parent._x_block, _x_end); |
| } |
| |
| unsigned int kmax() { |
| return std::min(_k0 + _parent._k_block, _parent._Ktotal); |
| } |
| |
| /* Advance to the next block, return false at the end. */ |
| bool advance(void) { |
| if (_done) { |
| return false; |
| } |
| |
| _newkblock=false; |
| _x0 += _parent._x_block; |
| if (_x0 >= _x_end) { |
| _x0=_x_start; |
| _k0 += _parent._k_block; |
| if (_k0 >= _parent._Ktotal) { |
| _k0=0; |
| _multi++; |
| if (_multi >= _parent._nmulti) { |
| _done=true; |
| return false; |
| } |
| _newmulti=true; |
| } |
| _newkblock=true; |
| } |
| _index++; |
| |
| return true; |
| } |
| |
| unsigned int k0(void) { return _k0; } |
| unsigned int x0(void) { return _x0; } |
| unsigned int multi(void) { return _multi; } |
| unsigned int index(void) { return _index; } |
| bool done(void) { return _done; } |
| bool newkblock(void) { return _newkblock; } |
| }; |
| |
| // "k block" has two distinct uses: figuring out which iterations of K |
| // to actually process, but also various size/pointer computations. The |
| // latter needs to take account of the extra space needed for the row |
| // sums, if appropriate. |
| unsigned int get_total_k_depth() const { |
| unsigned int k_depth = _k_block; |
| |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| k_depth += sizeof(int32_t) / sizeof(Toi); |
| } |
| |
| return k_depth; |
| } |
| |
| // A working size. |
| size_t get_a_working_size() const { |
| if (_thread_columns) { |
| // For 2D threading: allocate a buffer of one block of rows per thread |
| return ROUND_UP(sizeof(Toi) * get_total_k_depth() * strategy::out_height() * _maxthreads); |
| } else { |
| // For 1D threaded: one of these needed, regardless of thread count. Divided according to window. |
| return ROUND_UP(sizeof(Toi) * get_total_k_depth() * _Mround * _nbatches); |
| } |
| } |
| |
| // C working size: One needed per thread. Not needed if there is no merge step. |
| size_t get_c_working_size() const { |
| if (MergeStep) { |
| return ROUND_UP(sizeof(Tri) * _x_block * strategy::out_height()); |
| } else { |
| return 0; |
| } |
| } |
| |
| // Accumulation buffer size |
| size_t get_accumulation_buffer_size() const { |
| // We only support an accumulation buffer for non-merge cases. |
| if (MergeStep) { |
| return 0; |
| } |
| |
| // Check if we are actually blocking |
| if (_k_block == _Ktotal) { |
| return 0; |
| } |
| |
| // We are no-merge, non-quantized with active blocking: accumulation buffer needed. |
| size_t size_per_buffer = sizeof(Tab) * strategy::out_height() * strategy::out_width(); |
| size_t num_buffers = iceildiv(_Msize, strategy::out_height()) * iceildiv(_Nsize, strategy::out_width()) * _nbatches * _nmulti; |
| |
| return num_buffers * size_per_buffer; |
| } |
| |
| // Get pointer into accumulation buffer |
| Tab *get_accumulation_buffer(unsigned int M, unsigned int N, unsigned int batch, unsigned int multi) const { |
| // Don't do anything if there's no buffer. |
| if (_accumulation_buffer == nullptr) { |
| return nullptr; |
| } |
| |
| // Here we are indexing an appropriately sized pointer, so no sizeof() needed to convert to bytes. |
| size_t size_per_buffer = strategy::out_height() * strategy::out_width(); |
| |
| size_t buffer_rows = iceildiv(_Msize, strategy::out_height()); |
| size_t buffer_cols = iceildiv(_Nsize, strategy::out_width()); |
| size_t buffers_per_batch = (buffer_rows * buffer_cols); |
| size_t buffers_per_multi = buffers_per_batch * _nbatches; |
| |
| // M/N must reference the top-left corner of a block. |
| size_t row = M / strategy::out_height(); |
| assert(M % strategy::out_height() == 0); |
| size_t col = N / strategy::out_width(); |
| assert(N % strategy::out_width() == 0); |
| |
| size_t buffer_index = multi * buffers_per_multi + batch * buffers_per_batch + row * buffer_cols + col; |
| |
| return _accumulation_buffer + (buffer_index * size_per_buffer); |
| } |
| |
| int32_t row_sum_multiplier() const { |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| const Requantize32 *qp = reinterpret_cast<const Requantize32 *>(&_os); |
| |
| return -qp->b_offset; |
| } |
| |
| return 0; |
| } |
| |
| // Heuristics to decide whether to use the 'thread columns' regime |
| static bool is_thread_columns(const GemmArgs &args) { |
| // For now, there is a templace parameter to force it. |
| if (ForceThreadColumns) { |
| return true; |
| } |
| |
| // Never do this for single threaded cases. |
| if (args._maxthreads == 1) { |
| return false; |
| } |
| |
| // How many blocks of work are available for threading on M? |
| int m_blocks = iceildiv(args._Msize, strategy::out_height()) * args._nbatches; |
| |
| // If we just can't share the work across threads with the row threading regime. |
| if (args._maxthreads > m_blocks) { |
| return true; |
| } |
| |
| // If the row threading regime is too wasteful (20% threshold) |
| if (((roundup(m_blocks, args._maxthreads) * 100) / m_blocks) > 120) { |
| return true; |
| } |
| |
| return false; |
| } |
| |
| static unsigned int get_ktotal(const GemmArgs &args) { |
| return args._Ksections * roundup(args._Ksize, strategy::k_unroll()); |
| } |
| |
| static unsigned int get_k_block_size(const GemmArgs &args) { |
| if (args._cfg && args._cfg->inner_block_size) { |
| return roundup(args._cfg->inner_block_size, strategy::k_unroll()); |
| } |
| |
| // K blocking not supported if we are requantizing. |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| return get_ktotal(args); |
| } |
| |
| // Special blocking for SME |
| if (is_sme<strategy>::value) { |
| // Don't bother to block below this size threshold, experimentally determined to be 320 for FP32 |
| unsigned int scaling_threshold = 1280 / sizeof(Toi); |
| |
| if (get_ktotal(args) <= scaling_threshold) { |
| return get_ktotal(args); |
| } |
| |
| // Once we are blocking, this (lower) threshold determines when we should use more blocks |
| // NOTE: Could be that some factor-based solution would work better here. |
| unsigned int max_block_size = 1024 / sizeof(Toi); |
| |
| unsigned int num_k_blocks = iceildiv(get_ktotal(args), max_block_size); |
| |
| unsigned int k_block = roundup(iceildiv(get_ktotal(args), num_k_blocks), strategy::k_unroll()); |
| |
| return k_block; |
| } |
| |
| const unsigned int L1_size = args._ci->get_L1_cache_size(); |
| unsigned int k_block; |
| |
| // k_block: Find out how much of the larger array can be loaded into half the cache. |
| // This should account for associative caches. |
| k_block = (L1_size / 2) / (sizeof(Toi) * (std::max(strategy::out_width(), strategy::out_height()))); |
| |
| // Needs to be (at least a single) multiple of the K unroll level. |
| k_block /= strategy::k_unroll(); |
| k_block = std::max(k_block, 1U) * strategy::k_unroll(); |
| |
| // Now tune to presented problem size; this is how many blocks we need. |
| unsigned int num_k_blocks = iceildiv(get_ktotal(args), k_block); |
| |
| // So divide the space equally into that many blocks. |
| k_block = iceildiv(get_ktotal(args), num_k_blocks); |
| |
| // And round UP to the K unroll level required. |
| k_block = roundup(k_block, strategy::k_unroll()); |
| |
| assert(k_block > 0); |
| |
| return k_block; |
| } |
| |
| static unsigned int get_x_block_size(const GemmArgs &args) { |
| if (is_thread_columns(args)) { |
| // In 2D mode, override X block, because we will process width first. |
| return roundup(args._Nsize, strategy::out_width()); |
| } |
| |
| if (args._cfg && args._cfg->outer_block_size) { |
| return roundup(args._cfg->outer_block_size, strategy::out_width()); |
| } |
| |
| unsigned int x_block; |
| const unsigned int L2_size = args._ci->get_L2_cache_size(); |
| const unsigned int k_block = get_k_block_size(args); |
| |
| // x_block: Work out how many rows (of length k_block) will fit in the L2 |
| // Don't allocate more than 90% of the L2 to allow for overheads, and subtract off the L1 contents. |
| const unsigned int scaled_l2_size = (L2_size * 9) / 10; |
| const unsigned int k_block_area = k_block * sizeof(Toi) * (strategy::out_width() + strategy::out_height()); |
| |
| // .. if the L1 contents is bigger than the L2, just return a minimal size block. |
| if (k_block_area > scaled_l2_size) { |
| return strategy::out_width(); |
| } |
| |
| x_block = (scaled_l2_size - k_block_area) / (sizeof(Toi) * k_block); |
| |
| // Needs to be (at least a single) multiple of the kernel output width. |
| x_block /= strategy::out_width(); |
| x_block = std::max(x_block, 1u) * strategy::out_width(); |
| |
| // And tune to the presented problem size. |
| unsigned int num_x_blocks = iceildiv(args._Nsize, x_block); |
| x_block = iceildiv(args._Nsize, num_x_blocks); |
| |
| x_block = roundup(x_block, strategy::out_width()); |
| |
| assert(x_block > 0); |
| |
| return x_block; |
| } |
| |
| public: |
| GemmInterleaved(GemmInterleaved &) = delete; |
| GemmInterleaved & operator= (GemmInterleaved &) = delete; |
| |
| /* Constructor */ |
| GemmInterleaved(const GemmArgs &args, const OutputStage &os) |
| : _ci(args._ci), _Msize(args._Msize), _Nsize(args._Nsize), _Ksize(args._Ksize), |
| _Ksections(args._Ksections), _Ktotal(get_ktotal(args)), |
| _rounded_Ksize(roundup(_Ksize, strategy::k_unroll())), |
| _nbatches(args._nbatches), _nmulti(args._nmulti), _thread_columns(is_thread_columns(args)), |
| _act(args._act), _accumulate(args._accumulate), _maxthreads(args._maxthreads), _nthreads(args._maxthreads), |
| _k_block(get_k_block_size(args)), _x_block(get_x_block_size(args)), _Mround(roundup(args._Msize, strategy::out_height())), |
| _os(os) { } |
| |
| /* Constructor without OutputStage */ |
| GemmInterleaved(const GemmArgs &args) |
| : _ci(args._ci), _Msize(args._Msize), _Nsize(args._Nsize), _Ksize(args._Ksize), |
| _Ksections(args._Ksections), _Ktotal(get_ktotal(args)), |
| _rounded_Ksize(roundup(_Ksize, strategy::k_unroll())), |
| _nbatches(args._nbatches), _nmulti(args._nmulti), _thread_columns(is_thread_columns(args)), |
| _act(args._act), _accumulate(args._accumulate), _maxthreads(args._maxthreads), _nthreads(args._maxthreads), |
| _k_block(get_k_block_size(args)), _x_block(get_x_block_size(args)), _Mround(roundup(args._Msize, strategy::out_height())), |
| _os() { } |
| |
| // Interface implementation - Compulsory functions |
| |
| // Window size: Only the last thread should do a ragged block, so dole |
| // out work in units of out_height. Factor batches into the window, but |
| // not multi for now (as this would cause problems with the buffer |
| // manager). |
| ndrange_t get_window_size() const override { |
| unsigned int row_blocks = (_Mround / strategy::out_height()) * _nbatches; |
| |
| if (_thread_columns) { |
| return { row_blocks, iceildiv(_Nsize, strategy::out_width()) }; |
| } else { |
| // _Mround is a multiple of out_height by definition. |
| return { row_blocks }; |
| } |
| } |
| |
| // set_nthreads: pass on to buffer manager to avoid it waiting for non-existant threads. |
| void set_nthreads(int nthreads) override { |
| _nthreads = std::min(nthreads, _maxthreads); |
| } |
| |
| // Execute |
| void execute(const ndcoord_t &work_range, const ndcoord_t &, int threadid) override { |
| #ifdef CYCLE_PROFILING |
| profiler prof; |
| #endif |
| |
| /* Make sure we've been set up correctly. */ |
| assert(FixedFormat || _B_transposed); |
| assert(_working_space); |
| int8_t *working_space_bytes = reinterpret_cast<int8_t *>(_working_space); |
| |
| /* Align if needed */ |
| intptr_t working_space_v = reinterpret_cast<intptr_t>(_working_space); |
| if (working_space_v & 0x3f) { |
| intptr_t alignment_offset = 0x40 - (working_space_v & 0x3f); |
| working_space_bytes += alignment_offset; |
| } |
| |
| strategy strat(_ci); |
| |
| const auto start = work_range.get_position(0); |
| const auto end = work_range.get_position_end(0); |
| |
| /* Translate 'start' and 'end' into a position within the batches and rows. */ |
| const unsigned int window_per_batch = _Mround / strategy::out_height(); |
| unsigned int batch_0 = start / window_per_batch; |
| unsigned int batch_end = end / window_per_batch; |
| |
| // In ThreadColumns mode, process work one horizontal strip at a time. |
| // Transpose the block of needed rows at the start, then do all the work on that block. |
| if (_thread_columns) { |
| const auto start_x = work_range.get_position(1) * strategy::out_width(); |
| const auto end_x = std::min(work_range.get_position_end(1) * strategy::out_width(), _Nsize); |
| |
| Tri * const c_panel = reinterpret_cast<Tri *>(working_space_bytes + (threadid * get_c_working_size())); |
| Toi * const a_panel = reinterpret_cast<Toi *>(working_space_bytes + (_maxthreads * get_c_working_size()) + |
| (threadid * sizeof(Toi) * get_total_k_depth() * strategy::out_height())); |
| |
| for (unsigned int multi=0; multi<_nmulti; multi++) { |
| for (unsigned int k0=0; k0<_Ktotal; k0+=_k_block) { |
| unsigned int kmax=std::min(k0+_k_block, _Ktotal); |
| |
| unsigned int rounded_width = roundup(_Nsize, strategy::out_width()); |
| |
| const bool first_pass = (k0==0); |
| const bool last_pass = (kmax==_Ktotal); |
| |
| // Figure out how many "K" the kernel will actually process. |
| unsigned int kern_k = roundup(kmax - k0, strategy::k_unroll()); |
| |
| const Toi *b_ptr = FixedFormat ? |
| reinterpret_cast<const Toi *>(this->_Bptr) + (multi * this->_B_multi_stride) + |
| ((start_x / get_stripe_width<strategy, FixedFormat>::get()) * this->_ldb) + |
| (k0 * get_stripe_width<strategy, FixedFormat>::get()) : |
| _B_transposed + (rounded_width * _Ktotal * multi) + (k0 * rounded_width) + (start_x * kern_k); |
| |
| unsigned int batch = batch_0; |
| unsigned int start_row = (start - (batch_0 * window_per_batch)) * strategy::out_height(); |
| |
| for (unsigned int p=start; p<end; p++) { |
| unsigned int end_row = std::min(start_row + strategy::out_height(), _Msize); |
| |
| // Set up transposed 'A' block |
| { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_PREPA, strategy::out_height() * (kmax-k0) * sizeof(Toi)); |
| #endif |
| // See comment above on transform_type<> class: this extracts either 'transforms' or |
| // 'transforms_quantized' as appropriate. |
| typename transform_type<strategy, MergeStep && std::is_same<OutputStage, Requantize32>::value>::type transforms; |
| |
| if (_indirect_buf != nullptr) { |
| transforms.PrepareA_indirect(a_panel, |
| _indirect_buf + (multi * _nbatches * _Ksections) + (batch * _Ksections), _Ksize, |
| _rounded_Ksize, start_row, end_row, k0, kmax, row_sum_multiplier()); |
| } else if (_convolver) { |
| transforms.PrepareA_convolution(a_panel, |
| this->_Aptr + (batch * this->_A_batch_stride) + (multi * this->_A_multi_stride), |
| this->_lda, *_convolver, _rounded_Ksize, start_row, end_row, k0, kmax, row_sum_multiplier()); |
| } else { |
| transforms.PrepareA(a_panel, |
| this->_Aptr + (batch * this->_A_batch_stride) + (multi * this->_A_multi_stride), |
| this->_lda, start_row, end_row, k0, std::min(kmax, _Ksize), row_sum_multiplier()); |
| } |
| } |
| |
| Tr *result_ptr = this->_Cptr + (batch * this->_C_batch_stride) + (multi * this->_C_multi_stride); |
| |
| // If we are using an accumulation buffer and this isn't the last pass, don't pass a result pointer. |
| if (_accumulation_buffer && !last_pass) { |
| result_ptr = nullptr; |
| } |
| |
| // Perform the kernel and merge step, either separately or together as required. |
| kernel_and_merge<MergeStep, FixedFormat, OutputStage>::run( |
| #ifdef CYCLE_PROFILING |
| prof, |
| #endif |
| // Strategy and panel pointers |
| strat, a_panel, b_ptr, this->_ldb, c_panel, |
| // Result buffer pointers |
| result_ptr, this->_ldc, |
| // K size, and M/N ranges |
| kern_k, start_row, end_row, start_x, end_x, |
| // Only do bias on the first pass |
| ((first_pass && this->_bias) ? this->_bias + (multi * this->_bias_multi_stride) : nullptr), |
| // Only do activation on the last pass, and accumulation on any non-first pass. |
| (last_pass ? _act : Activation()), (!first_pass || _accumulate), |
| // Pass in quantization parameters for requantizing kernels (others will ignore) |
| _os, col_bias + (multi * _Nsize), |
| // Accumulation buffer |
| get_accumulation_buffer(start_row, start_x, batch, multi)); |
| |
| /* Increment to the next block */ |
| start_row += strategy::out_height(); |
| if (start_row >= _Msize) { |
| start_row = 0; |
| batch++; |
| } |
| } |
| } |
| } |
| } else { |
| blockwalker current(*this); |
| |
| /* Compute the M values to operate on */ |
| unsigned int m_0 = (start - (batch_0 * window_per_batch)) * strategy::out_height(); |
| unsigned int m_max = (end - (batch_end * window_per_batch)) * strategy::out_height(); |
| |
| // Private buffers. Treat working_space as an array of C buffers |
| // (one per thread) first, followed by the (window-divided) A |
| // buffer. |
| // Set a_panel to the base of the A buffers - compute offsets into it based on M/batches later. |
| Toi * const a_panel = reinterpret_cast<Toi *>(working_space_bytes + (_maxthreads * get_c_working_size())); |
| Tri * const c_panel = reinterpret_cast<Tri *>(working_space_bytes + (threadid * get_c_working_size())); |
| |
| const Toi *b_panel; |
| b_panel = _B_transposed; |
| |
| // newkblock() is always true on the first iteration, so these will be set properly on the first loop. |
| |
| // kern_k tracks the accumulation depth for the CURRENT K block a_panel_stride similarly tracks the total |
| // stride of the A panel (i.e. with 4 added for cases with embedded row sums) |
| |
| // These are distinct from k_block and get_total_k_depth() which are based on the target K block size, and |
| // used for addressing inside a_panel. |
| |
| // In cases where K blocking is in use and the blocks are not all the same size, the (smaller) final block |
| // won't use all the memory allocated. |
| unsigned int kern_k = 0; |
| unsigned int a_panel_stride = 0; |
| |
| for (;!current.done();current.advance()) { |
| if (current.newkblock()) { |
| #ifdef CYCLE_PROFILING |
| auto p=prof.ScopedProfiler(PROFILE_PREPA, (end - start) * strategy::out_height() * (current.kmax()-current.k0()) * sizeof(Toi)); |
| #endif |
| // See comment above on transform_type<> class: this extracts either 'transforms' or |
| // 'transforms_quantized' as appropriate. |
| typename transform_type<strategy, MergeStep && std::is_same<OutputStage, Requantize32>::value>::type transforms; |
| |
| for (unsigned int batch = batch_0; batch <= batch_end; batch++) { |
| unsigned int first_m = (batch == batch_0) ? m_0 : 0; |
| unsigned int last_m = (batch == batch_end) ? m_max : _Msize; |
| |
| if (first_m >= last_m) |
| continue; |
| |
| if (_indirect_buf != nullptr) { |
| transforms.PrepareA_indirect(a_panel + ((batch * _Mround + first_m) * get_total_k_depth()), |
| _indirect_buf + (current.multi() * _nbatches * _Ksections) + (batch * _Ksections), _Ksize, |
| _rounded_Ksize, first_m, last_m, current.k0(), current.kmax(), row_sum_multiplier()); |
| } else if (_convolver) { |
| transforms.PrepareA_convolution(a_panel + ((batch * _Mround + first_m) * get_total_k_depth()), |
| this->_Aptr + (batch * this->_A_batch_stride) + (current.multi() * this->_A_multi_stride), |
| this->_lda, *_convolver, _rounded_Ksize, first_m, last_m, current.k0(), current.kmax(), row_sum_multiplier()); |
| } else { |
| transforms.PrepareA(a_panel + ((batch * _Mround + first_m) * get_total_k_depth()), |
| this->_Aptr + (batch * this->_A_batch_stride) + (current.multi() * this->_A_multi_stride), |
| this->_lda, first_m, last_m, current.k0(), std::min(_Ksize, current.kmax()), row_sum_multiplier()); |
| } |
| } |
| |
| // Figure out how many "K" the kernel will actually process. |
| kern_k = roundup(current.kmax() - current.k0(), strategy::k_unroll()); |
| |
| // Requantizing GEMMs have the row sums built in to the |
| // transposed data, so the stride between rows is 4 bytes |
| // larger than the (rounded) K value. |
| |
| if(std::is_same<OutputStage, Requantize32>::value) { |
| a_panel_stride = kern_k + (sizeof(int32_t) / sizeof(Toi)); |
| } else { |
| a_panel_stride = kern_k; |
| } |
| } |
| |
| // For FixedFormat cases, figure out the B pointer. The loop below moves through batches and vertically through the output so this will be the same throughout. |
| if (FixedFormat) { |
| b_panel = reinterpret_cast<const Toi *>(this->_Bptr) + (current.multi() * this->_B_multi_stride) + |
| ((current.x0() / get_stripe_width<strategy, FixedFormat>::get()) * this->_ldb) + |
| (current.k0() * get_stripe_width<strategy, FixedFormat>::get()); |
| } |
| |
| /* Do the actual work. */ |
| for (unsigned int batch = batch_0; batch <= batch_end; batch++) { |
| unsigned int first_m = (batch == batch_0) ? m_0 : 0; |
| unsigned int last_m = (batch == batch_end) ? m_max : _Msize; |
| |
| const Toi *a_ptr = a_panel + (batch * _Mround + first_m) * get_total_k_depth(); |
| |
| if (first_m >= last_m) |
| continue; |
| |
| // For the merge case we need to do this out_height() rows |
| // at a time, as that is the size of our intermediate |
| // buffer. If we are not doing that, we can do all the |
| // relevant rows in one go. |
| unsigned int m_step = MergeStep ? strategy::out_height() : (last_m - first_m); |
| |
| // But in the case where we have an accumulation buffer, we can't do that after all, unless |
| // there is no N blocking. |
| if (_accumulation_buffer && ((current.x0() != 0) || (current.xmax() < _Nsize))) { |
| m_step = strategy::out_height(); |
| } |
| |
| for (unsigned int y=first_m; y<last_m; y+=m_step) { |
| unsigned int ymax = std::min(_Msize, y + m_step); |
| |
| const bool first_pass = (current.k0() == 0); |
| const bool last_pass = (current.kmax() == _Ktotal); |
| |
| // Pointer to appropriate part of result array. |
| Tr *result_ptr = this->_Cptr + (batch * this->_C_batch_stride) + (current.multi() * this->_C_multi_stride); |
| |
| // If we are using an accumulation buffer, we don't pass the result buffer to ask the kernel |
| // to write things into the accumulation buffer instead, except on the last pass. |
| if (_accumulation_buffer && !last_pass) { |
| result_ptr = nullptr; |
| } |
| |
| // Perform the kernel and merge step, either separately or together as required. |
| kernel_and_merge<MergeStep, FixedFormat, OutputStage>::run( |
| #ifdef CYCLE_PROFILING |
| prof, |
| #endif |
| // Strategy and panel pointers |
| strat, a_ptr, b_panel, this->_ldb, c_panel, |
| // Result buffer pointers |
| result_ptr, this->_ldc, |
| // K size, and M/N ranges |
| kern_k, y, ymax, current.x0(), current.xmax(), |
| // Only do bias on the first pass |
| ((first_pass && this->_bias) ? this->_bias + (current.multi() * this->_bias_multi_stride) : nullptr), |
| // Only do activation on the last pass, and accumulation on any non-first pass. |
| (last_pass ? _act : Activation()), (!first_pass || _accumulate), |
| // Pass in quantization parameters for requantizing kernels (others will ignore) |
| _os, col_bias + (current.multi() * _Nsize), |
| // Accumulation buffer |
| get_accumulation_buffer(y, current.x0(), batch, current.multi()) ); |
| |
| a_ptr += (strategy::out_height() * a_panel_stride); |
| } |
| } |
| |
| if (FixedFormat == false) { |
| b_panel += (roundup(current.xmax() - current.x0(), strategy::out_width()) * kern_k); |
| } |
| } |
| } |
| } |
| |
| // Interface implementation - working space |
| size_t get_working_size() const override { |
| // In all cases, we need one A buffer plus a C buffer per thread, plus an accumulation buffer. |
| size_t size = get_a_working_size() + (get_c_working_size() * _maxthreads) + get_accumulation_buffer_size(); |
| |
| size += 128; // Add on two cache lines extra for alignment. |
| |
| return size; |
| } |
| |
| void set_working_space(void *working_space) override { |
| // Make sure everything ends up cache line aligned |
| int8_t *working_space_bytes = reinterpret_cast<int8_t *>(working_space); |
| intptr_t working_space_int = reinterpret_cast<intptr_t>(working_space); |
| |
| size_t diff=0; |
| |
| if (working_space_int & 0x3F) { |
| diff = 0x40 - (working_space_int & 0x3F); |
| } |
| |
| working_space_bytes += diff; |
| working_space_int += diff; |
| |
| // Pretransposed case: just set internal pointer to parameter value. |
| _working_space = reinterpret_cast<void *>(working_space_bytes); |
| |
| // Set up accumulation buffer |
| if (get_accumulation_buffer_size() > 0) { |
| intptr_t acc_buff_int = working_space_int + get_a_working_size() + (get_c_working_size() * _maxthreads); |
| // Make sure the accumulation buffer is aligned (needed if the other blocks are not a multiple of cache line length) |
| if (acc_buff_int & 0x3F) { |
| acc_buff_int += (0x40 - (acc_buff_int & 0x3F)); |
| } |
| _accumulation_buffer = reinterpret_cast<Tab *>(acc_buff_int); |
| } else { |
| _accumulation_buffer = nullptr; |
| } |
| } |
| |
| // Interface implementation - pretransposed |
| bool B_is_pretransposed() const override { |
| return (FixedFormat == false); |
| } |
| |
| bool B_pretranspose_required() const override { |
| return (FixedFormat == false) && (_B_transposed==nullptr); |
| } |
| |
| size_t get_B_pretransposed_array_size() const override { |
| if (FixedFormat) { |
| return 0; |
| } |
| |
| unsigned int x_size = roundup(_Nsize, strategy::out_width()); |
| |
| return (x_size * _Ktotal * _nmulti * sizeof(Toi)) + get_col_sum_size(); |
| } |
| |
| size_t get_B_pretranspose_window_size() const override { |
| size_t n_blocks = iceildiv(_Nsize, _x_block); |
| size_t k_blocks = iceildiv(_Ktotal, _k_block); |
| |
| return n_blocks * k_blocks * _nmulti; |
| } |
| |
| void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override { |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| col_bias = reinterpret_cast<int32_t *>(in_buffer); |
| |
| Requantize32 *qp_ptr = reinterpret_cast<Requantize32 *>(&_os); |
| |
| for (unsigned int i=0; i<_nmulti; i++) { |
| // The input is assumed not to have any padding between sections, so straightforward Ksize * Ksections computation gets the total size. |
| compute_col_sums(*qp_ptr, _Nsize, _Ksize * _Ksections, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize), _Ksize * _Ksections, i, 0); |
| } |
| } |
| } |
| |
| // Support for transposed B is a property of the strategy::transpose type |
| bool B_pretranspose_supports_transpose() const override { |
| typename transform_type<strategy, MergeStep && std::is_same<OutputStage, Requantize32>::value>::type transforms; |
| |
| return transforms.PrepareB_supports_transpose(); |
| } |
| |
| void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride, const bool transposed) override { |
| pretranspose_B_array_part(in_buffer, B, ldb, B_multi_stride, transposed, 0, get_B_pretranspose_window_size()); |
| } |
| |
| void pretranspose_B_array_part(void *in_buffer, const To *B, const int ldb, const int B_multi_stride, const bool transposed, size_t start, size_t end) override { |
| // Perform column sums etc as part of the last block. |
| if (end >= get_B_pretranspose_window_size()) { |
| requantize_bias(in_buffer, B, ldb, B_multi_stride); |
| } |
| |
| // Put the transposed data after the column sums - in non-quantized cases get_col_sum_size() == 0 |
| uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer); |
| Toi *buffer = reinterpret_cast<Toi *>(buffer_int + get_col_sum_size()); |
| _B_transposed = buffer; |
| |
| blockwalker current(*this); |
| strategy strat(_ci); |
| |
| // Skip over blocks we aren't doing |
| for(size_t i = 0; i < start; i++) { |
| buffer += roundup(current.xmax() - current.x0(), strategy::out_width()) * roundup(current.kmax() - current.k0(), strategy::k_unroll()); |
| current.advance(); |
| } |
| |
| size_t blocks_left = (end - start); |
| |
| // Double check that we haven't run out of work |
| if (current.done()) { |
| blocks_left = 0; |
| } |
| |
| for (/* blocks_left initialized above */; blocks_left > 0; blocks_left--) { |
| /* Figure out the size of each block. */ |
| unsigned int k_size = (current.kmax() - current.k0()); |
| |
| if (_Ksections > 1) { |
| // We need to insert padding at the end of each K section. |
| // The computation needed is a little delicate - the coordinates from the block walker are expressed in |
| // terms of the full, padded, _Ktotal. |
| // But we need to transform each section with reference to the original, unpadded, input, letting the |
| // transform pad each section as needed. |
| |
| // This is needed for computations below. |
| const unsigned int rounded_section_size = roundup(_Ksize, strategy::k_unroll()); |
| |
| // The expected output format is also an entire <out_width> columns interleaved, then the next set of |
| // columns, and so on. This means, as we are breaking it up vertically, we have to do it one column at |
| // a time. |
| for (unsigned int x0=current.x0(); x0 < current.xmax(); x0 += strategy::out_width() ) { |
| unsigned int xmax = std::min(x0 + strategy::out_width(), current.xmax()); |
| |
| // Track where we are and how much work is left. |
| unsigned int kpos = current.k0(); |
| unsigned int kleft = k_size; |
| |
| while (kleft) { |
| // Which section are we in? Based on the rounded-up section size. |
| unsigned int k_section_base = kpos / rounded_section_size; |
| // How far into the section are we? |
| unsigned int k_offset = kpos - (k_section_base * rounded_section_size); |
| |
| // We will either copy the rest of this section, or to the end of the requested length. |
| unsigned int k_length = std::min(_Ksize - k_offset, kleft); |
| |
| strat.transforms.PrepareB(buffer, B + (current.multi() * B_multi_stride), ldb, |
| x0, xmax, |
| (k_section_base * _Ksize) + k_offset, // K starting point - compute row to read based on our section and the true section length. |
| (k_section_base * _Ksize) + k_offset + k_length, // K end point - starting point plus length computed above. |
| transposed); |
| |
| // We need to modify our position based on the ROUNDED version of what we just did. |
| unsigned int padded_length = roundup(k_length, strategy::k_unroll()); |
| |
| buffer += strategy::out_width() * padded_length; |
| |
| kpos += padded_length; |
| kleft -= padded_length; |
| } |
| } |
| } else { |
| // In the single K section case, can process the whole lot in one go. |
| // Caution: 'blockwalker::kmax()' rounds up, so clamp to valid _Ksize. |
| strat.transforms.PrepareB(buffer, B + (current.multi() * B_multi_stride), ldb, |
| current.x0(), current.xmax(), current.k0(), std::min(current.kmax(), _Ksize), transposed); |
| buffer += roundup(current.xmax() - current.x0(), strategy::out_width()) * roundup(current.kmax() - current.k0(), strategy::k_unroll()); |
| } |
| |
| // Advance to the next block, break if we run off the end. |
| if (!current.advance()) { |
| break; |
| } |
| } |
| } |
| |
| void set_pretransposed_B_data(void *in_buffer) override { |
| // Put the transposed data after the column sums - in non-quantized cases get_col_sum_size() == 0 |
| uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer); |
| _B_transposed = reinterpret_cast<Toi *>(buffer_int + get_col_sum_size()); |
| col_bias = reinterpret_cast<int32_t *>(in_buffer); |
| } |
| |
| void set_quantized_bias(const int32_t *bias, size_t bias_multi_stride) override { |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| Requantize32 *qp = reinterpret_cast<Requantize32 *>(&_os); |
| |
| qp->bias = bias; |
| qp->bias_multi_stride = bias_multi_stride; |
| } |
| } |
| |
| void set_indirect_parameters(size_t string_len, const To * const * const *ptr) override { |
| assert(string_len == _Ksize); |
| _indirect_buf = ptr; |
| } |
| |
| void set_convolution_parameters(ConvolutionParameters parms) override { |
| assert(parms.input_channels == _Ksize); |
| _convolver = std::unique_ptr<convolver<To>>(new convolver<To>(parms)); |
| } |
| |
| // Estimate cycles for given problem given provided parameters |
| template<typename perf_type> |
| static uint64_t estimate_cycles(const GemmArgs &args) { |
| unsigned int k_blocks = iceildiv(args._Ksize, get_k_block_size(args)); |
| |
| const PerformanceParameters ¶ms = strategy::template get_performance_parameters<perf_type>(args._ci); |
| |
| uint64_t total_macs = static_cast<uint64_t>(args._nbatches) * args._nmulti * roundup(args._Msize, strategy::out_height()) * roundup(args._Nsize, strategy::out_width()) * get_ktotal(args); |
| uint64_t prepare_bytes = static_cast<uint64_t>(args._nbatches) * args._nmulti * roundup(args._Msize, strategy::out_height()) * get_ktotal(args) * sizeof(Toi); |
| uint64_t merge_bytes = static_cast<uint64_t>(args._nbatches) * args._nmulti * k_blocks * args._Msize * roundup(args._Nsize, strategy::out_width()) * sizeof(Tr); |
| |
| float mac_cycles = static_cast<float>(total_macs) / params.kernel_macs_cycle; |
| float prepare_cycles = static_cast<float>(prepare_bytes) / params.prepare_bytes_cycle; |
| float merge_cycles = static_cast<float>(merge_bytes) / params.merge_bytes_cycle; |
| |
| float total_cycles = mac_cycles + prepare_cycles + merge_cycles; |
| |
| // We can't thread over multis or width, which makes this a poor |
| // choice in many threaded cases. Penalize that here. |
| float parallelism_available = static_cast<float>(iceildiv(args._Msize, strategy::out_height()) * args._nbatches) * 0.9f; |
| |
| if (parallelism_available < args._maxthreads) { |
| total_cycles *= (static_cast<float>(args._maxthreads) / parallelism_available); |
| } |
| |
| return static_cast<uint64_t>(total_cycles); |
| } |
| |
| GemmConfig get_config() override { |
| GemmConfig c; |
| |
| c.method = GemmMethod::GEMM_INTERLEAVED; |
| c.inner_block_size = _k_block; |
| c.outer_block_size = _x_block; |
| c.filter = get_type_name<strategy>(); |
| c.weight_format = get_weight_format(get_kernel_weight_format<strategy, FixedFormat, To>::get(), sizeof(To)); |
| |
| return c; |
| } |
| }; |
| |
| // Aliases for the variations |
| template<typename strategy, typename To, typename Tr, typename OutputStage=Nothing> |
| using GemmInterleavedNoMerge = GemmInterleaved<strategy, To, Tr, OutputStage, false>; |
| |
| template<typename strategy, typename To, typename Tr, typename OutputStage=Nothing> |
| using GemmInterleavedFixedFormat = GemmInterleaved<strategy, To, Tr, OutputStage, true, true>; |
| |
| template<typename strategy, typename To, typename Tr> |
| using GemmInterleavedPretransposedNoMergeQuantizedInline = GemmInterleaved<strategy, To, Tr, Requantize32, false>; |
| |
| template<typename strategy, typename To, typename Tr> |
| using GemmInterleavedQuantized = GemmInterleaved<strategy, To, Tr, Requantize32>; |
| |
| } // namespace arm_gemm |