| /* |
| * 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 |
| |
| #if !defined(_WIN64) && !defined(__OpenBSD__) |
| #include <alloca.h> |
| #endif /* !defined(_WIN64) && !defined(__OpenBSD__) */ |
| |
| #include <algorithm> |
| #include <cassert> |
| |
| #include "arm_gemm.hpp" |
| #include "bias_adder.hpp" |
| #include "convolver.hpp" |
| #include "kernel_weight_format.hpp" |
| #include "ndrange.hpp" |
| #include "performance_parameters.hpp" |
| #include "transform.hpp" |
| #include "utils.hpp" |
| |
| #ifdef CYCLE_PROFILING |
| #include "profiler.hpp" |
| #endif |
| |
| #ifndef UNUSED |
| #define __I_DEFINED_UNUSED |
| #define UNUSED(x) ((void)(x)) |
| #endif |
| |
| namespace arm_gemm { |
| |
| namespace { |
| |
| // We need to invoke the kernel differently for quantizing and non-quantizing cases, so here is a shim class to do |
| // that. |
| |
| template<typename OutputStage, bool SeparateQuantize, bool FixedFormat> |
| class run_hybrid_kernel { |
| public: |
| template<typename strategy, typename Tlo, typename Tro, typename Tr> |
| static inline void run ( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<Tlo> A_arg, unsigned int M, unsigned int N, |
| unsigned int kern_k, const Tro *b_ptr, size_t b_stride, IndirectOutputArg<Tr> output_arg, const Tr *bias_ptr, Activation act, bool accumulate, |
| const OutputStage &os, const int32_t *col_bias, unsigned int n_0 ); |
| }; |
| |
| template<> |
| template<typename strategy, typename Tlo, typename Tro, typename Tr> |
| inline void run_hybrid_kernel<Nothing, false, false>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<Tlo> A_arg, unsigned int M, unsigned int N, |
| unsigned int kern_k, const Tro *b_ptr, size_t, IndirectOutputArg<Tr> output_arg, const Tr *bias_ptr, Activation act, bool accumulate, |
| const Nothing &, const int32_t *, unsigned int) { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)M * kern_k * roundup(N, strategy::out_width())); |
| #endif |
| UNUSED(kern_k); |
| |
| /* Indirect hybrid kernels read the full width of the bias. So we need to detect the case where we are writing |
| * a partial block and pad the bias for that block. */ |
| if (bias_ptr && !accumulate && (N % strategy::out_width() != 0)) { |
| /* Break N into "N_bulk" (a multiple of output width) and "N_remainder" */ |
| unsigned int N_remainder = N % strategy::out_width(); |
| unsigned int N_bulk = N - N_remainder; |
| |
| /* Output argument to be used for the tail */ |
| IndirectOutputArg<Tr> offset_output = output_arg; |
| |
| /* If there is a "bulk" to be processed, handle that and update "offset_output" appropriately. */ |
| if (N_bulk > 0) { |
| strat.kernel(num_strings, string_ptr, A_arg, M, N_bulk, b_ptr, output_arg, bias_ptr, act, accumulate); |
| |
| if (output_arg.is_indirect) { |
| offset_output = IndirectOutputArg<Tr>(output_arg.indirect.ptr, output_arg.indirect.offset + N_bulk); |
| } else { |
| offset_output = IndirectOutputArg<Tr>(output_arg.direct.base + N_bulk, output_arg.direct.stride); |
| } |
| } |
| |
| /* Pad the bias buffer for the remainder */ |
| Tr *bias_pad_buffer = reinterpret_cast<Tr *>(alloca(strategy::out_width() * sizeof(Tr))); |
| memcpy(bias_pad_buffer, bias_ptr + N_bulk, N_remainder * sizeof(Tr)); |
| |
| /* Process the remainder, offsetting the B pointer as needed. */ |
| strat.kernel(num_strings, string_ptr, A_arg, M, N_remainder, b_ptr + (N_bulk * kern_k), offset_output, bias_pad_buffer, act, accumulate); |
| } else { |
| strat.kernel(num_strings, string_ptr, A_arg, M, N, b_ptr, output_arg, bias_ptr, act, accumulate); |
| } |
| } |
| |
| template<> |
| template<typename strategy, typename Tlo, typename Tro, typename Tr> |
| inline void run_hybrid_kernel<Nothing, false, true>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<Tlo> A_arg, unsigned int M, unsigned int N, |
| unsigned int kern_k, const Tro *b_ptr, size_t b_stride, IndirectOutputArg<Tr> output_arg, const Tr *bias_ptr, Activation act, bool accumulate, |
| const Nothing &, const int32_t *, unsigned int) { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)M * kern_k * roundup(N, strategy::out_width())); |
| #endif |
| UNUSED(kern_k); |
| |
| /* Indirect hybrid kernels read the full width of the bias. So we need to detect the case where we are writing |
| * a partial block and pad the bias for that block. */ |
| if (bias_ptr && !accumulate && (N % strategy::out_width() != 0)) { |
| /* Break N into "N_bulk" (a multiple of output width) and "N_remainder" */ |
| unsigned int N_remainder = N % strategy::out_width(); |
| unsigned int N_bulk = N - N_remainder; |
| |
| /* Output argument to be used for the tail */ |
| IndirectOutputArg<Tr> offset_output = output_arg; |
| |
| /* If there is a "bulk" to be processed, handle that and update "offset_output" appropriately. */ |
| if (N_bulk > 0) { |
| strat.kernel(num_strings, string_ptr, A_arg, M, N_bulk, b_ptr, b_stride, output_arg, bias_ptr, act, accumulate); |
| |
| if (output_arg.is_indirect) { |
| offset_output = IndirectOutputArg<Tr>(output_arg.indirect.ptr, output_arg.indirect.offset + N_bulk); |
| } else { |
| offset_output = IndirectOutputArg<Tr>(output_arg.direct.base + N_bulk, output_arg.direct.stride); |
| } |
| } |
| |
| /* Pad the bias buffer for the remainder */ |
| Tr *bias_pad_buffer = reinterpret_cast<Tr *>(alloca(strategy::out_width() * sizeof(Tr))); |
| memcpy(bias_pad_buffer, bias_ptr + N_bulk, N_remainder * sizeof(Tr)); |
| |
| /* Process the remainder, offsetting the B pointer as needed. */ |
| strat.kernel(num_strings, string_ptr, A_arg, M, N_remainder, |
| b_ptr + (N_bulk / strategy::stripe_width()) * b_stride, b_stride, offset_output, |
| bias_pad_buffer, act, accumulate); |
| } else { |
| strat.kernel(num_strings, string_ptr, A_arg, M, N, b_ptr, b_stride, output_arg, bias_ptr, act, accumulate); |
| } |
| } |
| |
| template<> |
| template<typename strategy, typename Tlo, typename Tro, typename Tr> |
| inline void run_hybrid_kernel<Requantize32, false, false>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<Tlo> A_arg, unsigned int M, unsigned int N, |
| unsigned int kern_k, const Tro *b_ptr, size_t, IndirectOutputArg<Tr> output_arg, const Tr *, Activation, bool, |
| const Requantize32 &os, const int32_t *col_bias, unsigned int n_0 ) { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)M * kern_k * roundup(N, strategy::out_width())); |
| #endif |
| UNUSED(kern_k); |
| |
| strat.kernel(num_strings, string_ptr, A_arg, M, N, b_ptr, output_arg, &os, col_bias + n_0, n_0); |
| } |
| |
| template<> |
| template<typename strategy, typename Tlo, typename Tro, typename Tr> |
| inline void run_hybrid_kernel<Requantize32, true, false>::run( |
| #ifdef CYCLE_PROFILING |
| profiler &prof, |
| #endif |
| const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<Tlo> A_arg, unsigned int M, unsigned int N, |
| unsigned int kern_k, const Tro *b_ptr, size_t, IndirectOutputArg<Tr> output_arg, const Tr *, Activation, bool, |
| const Requantize32 &os, const int32_t *col_bias, unsigned int n_0 ) { |
| UNUSED(kern_k); |
| // On this route we will only process one kernel height at a time and will make sure this happens in the driver loop. |
| assert(M <= strategy::out_height()); |
| // We don't yet support indirect output (as the quantizer can't do it). |
| assert(output_arg.is_indirect == false); |
| |
| // We need a row sum buffer and intermediate output buffer. |
| // These go on the stack as they are not too large, using an automatic array and alloca() respectively. |
| int32_t row_sums[strategy::out_height()]; |
| typename strategy::result_type *result_buffer; |
| |
| unsigned int output_width = roundup(N, strategy::out_width()); |
| |
| result_buffer = reinterpret_cast<typename strategy::result_type *>(alloca(output_width * strategy::out_height() * sizeof(typename strategy::result_type))); |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)M * kern_k * roundup(N, strategy::out_width())); |
| #endif |
| // Perform the GEMM, into the output buffer. |
| strat.kernel(num_strings, string_ptr, A_arg, M, N, b_ptr, IndirectOutputArg<typename strategy::result_type>(result_buffer, output_width), nullptr, Activation(), false); |
| } |
| |
| if (os.b_offset != 0) { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_ROWSUMS, (unsigned long)M * kern_k); |
| #endif |
| row_sums_indirect(num_strings, string_ptr, A_arg, M, row_sums, &os); |
| } else { |
| memset(row_sums, 0, sizeof(int32_t) * strategy::out_height()); |
| } |
| |
| { |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_QUANTIZE, (unsigned long)M * N); |
| #endif |
| // Quantize |
| requantize_block_32(os, N, M, result_buffer, output_width, output_arg.direct.base, output_arg.direct.stride, row_sums, col_bias + n_0, n_0); |
| } |
| } |
| |
| template<typename strategy, bool FixedFormat> |
| struct stripe_width { |
| static unsigned int get() { |
| return strategy::stripe_width(); |
| } |
| }; |
| |
| template<typename strategy> |
| struct stripe_width<strategy, false> { |
| static unsigned int get() { |
| return 0; |
| } |
| }; |
| |
| template<typename strategy, bool FixedFormat> |
| struct kernel_weight_format { |
| static KernelWeightFormat get() { |
| return strategy::kernel_weight_format(); |
| } |
| }; |
| |
| template<typename strategy> |
| struct kernel_weight_format<strategy, false> { |
| static KernelWeightFormat get() { |
| return KernelWeightFormat::NON_FIXED; |
| } |
| }; |
| |
| } // anonymous namespace |
| |
| // Implementation of the GemmCommon abstract class. |
| template<typename strategy, typename To, typename Tr, typename OutputStage=Nothing, bool SeparateQuantize=false, bool FixedFormat=false> |
| class GemmHybridIndirect : public GemmCommon<To, Tr> { |
| typedef typename strategy::lhs_operand_type Tloi; |
| typedef typename strategy::rhs_operand_type Troi; |
| typedef typename strategy::result_type Tri; |
| |
| GemmArgs _args; |
| OutputStage _os = {}; |
| |
| /* Quantized support (in addition to 'output stage' above) */ |
| int32_t *_col_bias = nullptr; |
| |
| const unsigned int _Ktotal; |
| const unsigned int _rounded_Ksize; |
| |
| /* Blocking info */ |
| const unsigned int _k_block; |
| const unsigned int _n_block; |
| const unsigned int _Mround; |
| |
| /* Pretransposed buffer. */ |
| const Troi *_B_transposed=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; |
| |
| // Array of pointers to output rows |
| // Tr * const * _output_ptrs; |
| |
| const NDRange<4> _window_range; |
| |
| unsigned int get_col_sum_size() const { |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| return _args._Nsize * _args._nmulti * sizeof(int32_t); |
| } else { |
| return 0; |
| } |
| } |
| |
| static unsigned int get_ktotal(const GemmArgs &args) { |
| return args._Ksections * roundup(args._Ksize, strategy::k_unroll()); |
| } |
| |
| static unsigned int compute_k_block(const GemmArgs &args) { |
| // Some kernels don't support accumulate mode - these can't do K blocking at all. |
| if (!strategy::supports_accumulate() || std::is_same<OutputStage, Requantize32>::value) { |
| return get_ktotal(args); |
| } |
| |
| if (args._cfg && args._cfg->inner_block_size) { |
| return roundup(args._cfg->inner_block_size, strategy::k_unroll()); |
| } |
| |
| // Experimental data suggests an optimal block size of 512 for FP32 (scaling accordingly for other |
| // datatypes); but don't divide into blocks until we hit 1.5X this size. |
| unsigned int target_block_size = 2048 / sizeof(To); |
| auto ktotal = get_ktotal(args); |
| |
| if (ktotal > ((target_block_size*3)/2)) { |
| unsigned int target_blocks = iceildiv(ktotal, target_block_size); |
| |
| unsigned int block_size = iceildiv(ktotal, target_blocks); |
| |
| block_size = roundup(block_size, strategy::k_unroll()); |
| |
| return block_size; |
| } |
| |
| return ktotal; |
| } |
| |
| // New N blocking strategy: if it's narrow, or much taller than it is wide, do the full width. Otherwise do a |
| // single block. |
| static unsigned int compute_n_block(const GemmArgs &args, const OutputStage os = {}) { |
| if (args._cfg && args._cfg->outer_block_size) { |
| return args._cfg->outer_block_size; |
| } |
| |
| if (args._Nsize <= 64) { |
| return args._Nsize; |
| } |
| |
| if ((args._Msize / args._Nsize) > 155) { |
| return args._Nsize; |
| } |
| |
| // "Asymmetric" quantizing GEMMs require a different approach - the tall skinny blocks we would otherwise |
| // use imply a great deal of repeated work performing the row sums. If row sums are involved, work out how |
| // much "column" parallelism is going to be required and set the block size accordingly. |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| const Requantize32 *qp = reinterpret_cast<const Requantize32 *>(&os); |
| |
| // Row sums only needed if b_offset isn't 0 |
| if (qp->b_offset != 0) { |
| // We can already parallelize across batches, multis and rows (in units of 'out_height') |
| int multi_row_parallelism = args._nmulti * args._nbatches * iceildiv(args._Msize, strategy::out_height()); |
| |
| // If this isn't enough, we will need to split up the columns too. |
| if (multi_row_parallelism < args._maxthreads) { |
| unsigned int columns_needed = iceildiv(args._maxthreads, multi_row_parallelism); |
| |
| unsigned int n_block = iceildiv(args._Nsize, columns_needed); |
| |
| return roundup(n_block, strategy::out_width()); |
| } |
| |
| // Multi/Batch/Row parallelism is enough - don't split up the columns. |
| return args._Nsize; |
| } |
| } |
| |
| if (args._Ksize <= 128 && args._maxthreads <= 16) { |
| return strategy::out_width() * 3; |
| } |
| |
| return strategy::out_width(); |
| } |
| |
| public: |
| GemmHybridIndirect(GemmHybridIndirect &) = delete; |
| GemmHybridIndirect & operator= (GemmHybridIndirect &) = delete; |
| |
| /* Constructor */ |
| GemmHybridIndirect(const GemmArgs &args, const OutputStage &os) |
| : _args(args), _os(os), _Ktotal(get_ktotal(args)), |
| _rounded_Ksize(roundup(args._Ksize, strategy::k_unroll())), |
| _k_block(compute_k_block(args)), _n_block(compute_n_block(args, os)), |
| _Mround(roundup(args._Msize, strategy::out_height())), |
| _window_range(iceildiv(args._Msize, strategy::out_height()), args._nbatches, |
| iceildiv(args._Nsize, _n_block), args._nmulti) |
| { |
| // We take a copy of the arguments (not a pointer or reference), but there is no lifetime requirement on the |
| // GemmConfig. Clear out the pointer to avoid accidents. |
| _args._cfg = nullptr; |
| } |
| |
| /* Constructor without OutputStage */ |
| GemmHybridIndirect(const GemmArgs &args) |
| : _args(args), _Ktotal(get_ktotal(args)), |
| _rounded_Ksize(roundup(args._Ksize, strategy::k_unroll())), |
| _k_block(compute_k_block(args)), _n_block(compute_n_block(args)), |
| _Mround(roundup(args._Msize, strategy::out_height())), |
| _window_range(iceildiv(args._Msize, strategy::out_height()), args._nbatches, |
| iceildiv(args._Nsize, _n_block), args._nmulti) |
| { |
| // We take a copy of the arguments (not a pointer or reference), but there is no lifetime requirement on the |
| // GemmConfig. Clear out the pointer to avoid accidents. |
| _args._cfg = nullptr; |
| } |
| |
| // Interface implementation - Compulsory functions |
| ndrange_t get_window_size() const override { |
| return { _window_range.total_size() }; |
| } |
| |
| // This kernel can always be dynamically scheduled. |
| bool supports_dynamic_scheduling() const override { |
| return true; |
| } |
| |
| // Execute |
| void execute(const ndcoord_t &work_range, const ndcoord_t &, int) override { |
| #ifdef CYCLE_PROFILING |
| profiler prof; |
| #endif |
| strategy strat(_args._ci); |
| |
| std::vector<const To *> in_row_ptrs; |
| std::vector<const To * const *> in_row_strings; |
| std::vector<unsigned int> string_lengths; |
| |
| // In convolution mode, we need input pointers. |
| if (_convolver) { |
| in_row_ptrs = std::vector<const To *>(strategy::out_height() * _args._Ksections, nullptr); |
| in_row_strings = std::vector<const To * const *>(_args._Ksections, nullptr); |
| |
| for (unsigned int i=0; i<_args._Ksections; i++) { |
| in_row_strings[i] = &(in_row_ptrs.data()[i * strategy::out_height()]); |
| } |
| } |
| |
| // In any indirect mode, we need the string lengths. |
| if (_args._indirect_input) { |
| string_lengths = std::vector<unsigned int>(_args._Ksections, 0); |
| } |
| |
| /* Make sure we've been set up correctly. */ |
| assert(FixedFormat || _B_transposed); |
| static_assert(std::is_same<To, Tloi>::value, "gemm_native: Operand types must be the same."); |
| // static_assert(std::is_same<Tr, Tri>::value, "gemm_native: Result types must be the same."); |
| |
| /* For now, each work item implies all the K for a given output |
| * pixel (so we don't need to synchronize access to the output |
| * array). So separate the loop over K blocks here. */ |
| for (unsigned int k0=0; k0<_Ktotal; k0+=_k_block) { |
| unsigned int kmax = std::min(k0 + _k_block, _Ktotal); |
| unsigned int kern_k = roundup(kmax-k0, strategy::k_unroll()); |
| |
| const bool first_pass = (k0 == 0); |
| const bool last_pass = (kmax == _Ktotal); |
| |
| unsigned int first_section = (k0 / _rounded_Ksize); |
| unsigned int first_offset = (k0 % _rounded_Ksize); |
| unsigned int kleft = kern_k; |
| unsigned int sections=0; |
| unsigned int offset = first_offset; |
| |
| if (_args._indirect_input) { |
| while (kleft) { |
| // When chopping into sections: the amount that goes into 'string_lengths' is the amount to be |
| // processed (excluding padding). But the amount we subtract from 'kleft' takes account of any |
| // padding applied. |
| string_lengths[sections] = std::min(kleft, _args._Ksize - offset); |
| kleft -= std::min(kleft, _rounded_Ksize - offset); |
| sections++; |
| offset=0; |
| } |
| } |
| |
| auto p = _window_range.iterator(work_range.get_position(0), work_range.get_position_end(0)); |
| |
| if (p.done()) { |
| return; |
| } |
| |
| // Process rows either 'out_height' rows at a time, or do all valid rows at once with a single kernel call. |
| // The separate quantizer path only handles one block of rows at a time (as it has to store sums and intermediate results). |
| // THe convolution path only generates the pointers for one block of rows at a time. |
| const bool process_all_rows = (!SeparateQuantize && !_convolver); |
| |
| do { |
| const unsigned int m_start = p.dim(0) * strategy::out_height(); |
| const unsigned int m_end = process_all_rows ? std::min(p.dim0_max() * strategy::out_height(), _args._Msize) : std::min(m_start + strategy::out_height(), _args._Msize); |
| // const unsigned int m_end = std::min(m_start + strategy::out_height(), _args._Msize); |
| const unsigned int batch = p.dim(1); |
| const unsigned int n0 = p.dim(2) * _n_block; |
| const unsigned int nmax = std::min(n0 + _n_block, _args._Nsize); |
| const unsigned int multi = p.dim(3); |
| |
| const Troi *b_panel; |
| if (FixedFormat) { |
| b_panel = reinterpret_cast<const Troi *>(this->_Bptr) + |
| (multi * this->_B_multi_stride) + |
| ((n0 / stripe_width<strategy, FixedFormat>::get()) * this->_ldb) + |
| (k0 * stripe_width<strategy, FixedFormat>::get()); |
| } else { |
| b_panel = _B_transposed + |
| (multi * roundup(_args._Nsize, strategy::out_width()) * _Ktotal) + |
| (k0 * roundup(_args._Nsize, strategy::out_width())) + |
| (n0 * kern_k); |
| } |
| |
| IndirectOutputArg<Tr> out_arg(this->_Cptr + (multi * this->_C_multi_stride) + (batch * this->_C_batch_stride) + (m_start * this->_ldc) + n0, this->_ldc); |
| |
| #ifdef CYCLE_PROFILING |
| auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)(m_end - m_start) * kern_k * roundup(nmax-n0, strategy::out_width())); |
| #endif |
| if (_indirect_buf) { |
| run_hybrid_kernel<OutputStage, SeparateQuantize, FixedFormat>::run( |
| #ifdef CYCLE_PROFILING |
| prof, |
| #endif |
| strat, sections, string_lengths.data(), |
| IndirectInputArg<To>(_indirect_buf + (multi * _args._nbatches * _args._Ksections) + (batch * _args._Ksections) + first_section, m_start, first_offset), |
| (m_end - m_start), (nmax - n0), kern_k, b_panel, this->_ldb, out_arg, |
| (this->_bias && first_pass) ? this->_bias + (multi * this->_bias_multi_stride) + n0 : nullptr, |
| last_pass ? _args._act : Activation(), |
| !first_pass || _args._accumulate, |
| // Quantization parameters |
| _os, _col_bias+(multi * _args._Nsize), n0); |
| } else if (_convolver) { |
| auto conv_cols = _convolver->process_columns(this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride), this->_lda, k0, kmax, _rounded_Ksize); |
| |
| unsigned int pos=0; |
| auto conv_rows = conv_cols.process_rows(m_start, m_end - m_start); |
| |
| while (!conv_rows.finished()) { |
| unsigned int width, conv_offset; |
| |
| assert(pos < sections); |
| |
| std::tie(width, conv_offset) = conv_rows.next_block(&(in_row_ptrs[pos * strategy::out_height()])); |
| |
| if (pos==0) { |
| assert(conv_offset == first_offset); |
| } |
| assert(width == string_lengths[pos]); |
| pos++; |
| } |
| assert(pos == sections); |
| |
| run_hybrid_kernel<OutputStage, SeparateQuantize, FixedFormat>::run( |
| #ifdef CYCLE_PROFILING |
| prof, |
| #endif |
| strat, sections, string_lengths.data(), |
| IndirectInputArg<To>(in_row_strings.data(), 0, first_offset), |
| (m_end - m_start), (nmax - n0), kern_k, b_panel, this->_ldb, out_arg, |
| (this->_bias && first_pass) ? this->_bias + (multi * this->_bias_multi_stride) + n0 : nullptr, |
| last_pass ? _args._act : Activation(), |
| !first_pass || _args._accumulate, |
| // Quantization parameters |
| _os, _col_bias+(multi * _args._Nsize), n0); |
| } else { |
| // Length to process. This needs to exclude padding, but 'kmax' potentially includes it. |
| const unsigned int len = (std::min(_args._Ksize, kmax) - k0); |
| |
| run_hybrid_kernel<OutputStage, SeparateQuantize, FixedFormat>::run( |
| #ifdef CYCLE_PROFILING |
| prof, |
| #endif |
| strat, 1, &len, |
| IndirectInputArg<To>(this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride) + m_start * this->_lda + k0, this->_lda), |
| (m_end - m_start), (nmax - n0), kern_k, b_panel, this->_ldb, out_arg, |
| (this->_bias && first_pass) ? this->_bias + (multi * this->_bias_multi_stride) + n0 : nullptr, |
| last_pass ? _args._act : Activation(), |
| !first_pass || _args._accumulate, |
| // Quantization parameters |
| _os, _col_bias+(multi * _args._Nsize), n0); |
| } |
| } while (process_all_rows ? p.next_dim1() : p.next_dim0()); |
| } |
| } |
| |
| // 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; |
| } |
| |
| // Start with actual pretransposed buffer... |
| size_t size = roundup(_args._Nsize, strategy::out_width()) * _Ktotal * _args._nmulti * sizeof(Troi); |
| |
| // Space for result row pointers (not strictly needed any more but retained for indirect output testing) |
| size += _args._Msize * _args._nbatches * _args._nmulti * sizeof(const Tr *); |
| |
| if (std::is_same<OutputStage, Requantize32>::value) { |
| size += get_col_sum_size(); |
| } |
| |
| return size; |
| } |
| |
| size_t get_B_pretranspose_window_size() const override { |
| return _args._nmulti * iceildiv(_args._Nsize, strategy::out_width()); |
| } |
| |
| 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<_args._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, _args._Nsize, _args._Ksize * _args._Ksections, B + (i * B_multi_stride), ldb, _col_bias + (i * _args._Nsize), _args._Ksize * _args._Ksections, i, 0); |
| } |
| } |
| } |
| |
| bool B_pretranspose_supports_transpose() const override { |
| strategy strat(_args._ci); |
| return strat.transforms.PrepareB_supports_transpose(); |
| } |
| |
| void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride, 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, bool transposed, size_t start, size_t end) override { |
| 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-transposing cases get_col_sum_size() == 0 |
| uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer); |
| Troi *buffer_base = reinterpret_cast<Troi *>(buffer_int + get_col_sum_size()); |
| _B_transposed = buffer_base; |
| |
| strategy strat(_args._ci); |
| size_t work_per_multi = iceildiv(_args._Nsize, strategy::out_width()); |
| |
| for (unsigned int multi=(start / work_per_multi); multi<_args._nmulti; multi++) { |
| // Work out which part of the window space this multi occupies, |
| // skip to the next multi or exit as needed. |
| size_t wk_start = multi * work_per_multi; |
| size_t wk_end = (multi + 1) * work_per_multi; |
| |
| assert(wk_end > start); |
| |
| if (wk_start >= end) { |
| break; |
| } |
| |
| for (unsigned int k0=0; k0<_Ktotal; k0+=_k_block) { |
| const unsigned int kmax=std::min(k0 + _k_block, _Ktotal); |
| |
| /* Figure out the size of each block. */ |
| unsigned int k_size = kmax - k0; |
| |
| // Correct the N range and buffer base if we are not processing the whole block. |
| size_t n_start = 0; |
| size_t n_end = _args._Nsize; |
| |
| // If we are not doing the first columns, update the buffer write position and starting N value. |
| if (start > wk_start) { |
| n_start = (start - wk_start) * strategy::out_width(); |
| } |
| |
| // If we are not doing the last items, update the final N value. |
| if (end < wk_end) { |
| n_end = (end - wk_start) * strategy::out_width(); |
| } |
| |
| // Set the buffer pointer |
| Troi *buffer = buffer_base + |
| (roundup(_args._Nsize, strategy::out_width()) * (multi * _Ktotal + k0)) + |
| (n_start * roundup(k_size, strategy::k_unroll())); |
| |
| if (_args._Ksections > 1) { |
| // We need to insert padding at the end of each K section. |
| // The computation needed is a little delicate - the k0/kmax coordinates 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(_args._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 = n_start; x0 < n_end; x0 += strategy::out_width()) { |
| unsigned int xmax = std::min(x0 + strategy::out_width(), _args._Nsize); |
| |
| // Track where we are and how much work is left. |
| unsigned int kpos = 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(_args._Ksize - k_offset, kleft); |
| |
| strat.transforms.PrepareB(buffer, B + (multi * B_multi_stride), ldb, |
| x0, xmax, |
| (k_section_base * _args._Ksize) + k_offset, // K starting point - compute row to read based on our section and the true section length. |
| (k_section_base * _args._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. |
| strat.transforms.PrepareB(buffer, B + (multi * B_multi_stride), ldb, |
| n_start, n_end, k0, std::min(kmax, _args._Ksize), transposed); |
| } |
| } |
| } |
| } |
| |
| void set_pretransposed_B_data(void *in_buffer) override { |
| // Put the transposed data after the column sums - in non-transposing cases get_col_sum_size() == 0 |
| uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer); |
| _B_transposed = reinterpret_cast<Troi *>(buffer_int + get_col_sum_size()); |
| _col_bias = reinterpret_cast<int32_t *>(in_buffer); |
| } |
| |
| // Estimate cycles for given problem given provided parameters. |
| // "perf_type" is a type to pass along to get_performance_parameters to get the right set of performance |
| // parameters - it's arbitrary but usually either the input or output type. |
| template <typename perf_type> |
| static uint64_t estimate_cycles(const GemmArgs &args, const OutputStage &os = {}) { |
| const PerformanceParameters params = strategy::template get_performance_parameters<perf_type>(args._ci); |
| |
| // Note: Current hybrid kernels don't actually round up height (they |
| // have paths for each possible height). Might need to make this |
| // configurable in future. |
| uint64_t total_macs = static_cast<uint64_t>(args._nbatches) * args._nmulti * args._Msize * roundup(args._Nsize, strategy::out_width()) * get_ktotal(args); |
| |
| float mac_cycles = static_cast<float>(total_macs) / params.kernel_macs_cycle; |
| |
| // TODO: A bit of a kludge here: current hybrid kernels incur extra |
| // overhead where the width is not a multiple of kernel width. It's |
| // most noticable where the overall width is quite low, so add 15% |
| // penalty for such widths. |
| if ((args._Nsize < strategy::out_width()) || (args._Nsize > strategy::out_width() && args._Nsize < 2*strategy::out_width())) { |
| mac_cycles *= 1.15f; |
| } |
| |
| uint64_t total_cycles = mac_cycles; |
| |
| // Quantizing kernels with separate quantize need to add in the extra stages. |
| if (std::is_same<OutputStage, Requantize32>::value && SeparateQuantize) { |
| const Requantize32 *qp = reinterpret_cast<const Requantize32 *>(&os); |
| |
| // Row sums: need to consider each value in A (batch * multi * M * K)... |
| uint64_t rowsum_bytes = static_cast<uint64_t>(args._nbatches) * args._nmulti * args._Msize * get_ktotal(args); |
| |
| // ... but row sums are skipped if B offset==0. |
| if (qp->b_offset == 0) { |
| rowsum_bytes = 0; |
| } |
| |
| // Use "prepare bytes per cycle" to store "row sum values per cycle". |
| float rowsum_cycles = static_cast<float>(rowsum_bytes) / params.prepare_bytes_cycle; |
| |
| // Requantize: need to consider each value in C (batch * multi * M * N) |
| uint64_t requantize_bytes = static_cast<uint64_t>(args._nbatches) * args._nmulti * args._Msize * args._Nsize; |
| |
| // Use "merge bytes per cycle" to store "requantize values per cycle". |
| float requantize_cycles = static_cast<float>(requantize_bytes) / params.merge_bytes_cycle; |
| |
| // Recalculate total_cycles with the extra components. |
| total_cycles = mac_cycles + rowsum_cycles + requantize_cycles; |
| } |
| |
| return total_cycles; |
| } |
| |
| 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 == _args._Ksize); |
| _indirect_buf = ptr; |
| } |
| |
| void set_convolution_parameters(ConvolutionParameters parms) override { |
| assert(parms.input_channels == _args._Ksize); |
| _convolver = std::unique_ptr<convolver<To>>(new convolver<To>(parms)); |
| } |
| |
| GemmConfig get_config() override { |
| GemmConfig c; |
| |
| c.method = GemmMethod::GEMM_HYBRID; |
| c.inner_block_size = _k_block; |
| c.outer_block_size = _n_block; |
| c.filter = get_type_name<strategy>(); |
| c.weight_format = get_weight_format(kernel_weight_format<strategy, FixedFormat>::get(), sizeof(To)); |
| |
| return c; |
| } |
| }; |
| |
| template<typename strategy, typename To, typename Tr, typename OutputStage=Nothing> |
| using GemmHybridIndirectFixedFormat = GemmHybridIndirect<strategy, To, Tr, OutputStage, false, true>; |
| |
| } // namespace arm_gemm |
| |
| #ifdef __I_DEFINED_UNUSED |
| #undef UNUSED |
| #endif |