src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized.hpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2020 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 <assert.h>

 #include <algorithm>

 #include "arm_gemm.hpp"
 #include "utils.hpp"

 #include "arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp"

 #include "mergeresults.hpp"
 #include "transform.hpp"

 #ifdef CYCLE_PROFILING
 #include "profiler.hpp"
 #endif

 namespace arm_gemm {

 // Implementation of the GemmCommon abstract class.
 template<typename strategy, typename To, typename Tr>
 class GemmHybridQuantized : public GemmCommon<To, Tr> {
     typedef typename strategy::operand_type Toi;
     typedef typename strategy::result_type Tri;

     /* const properties set by constructor */
     const CPUInfo * const _ci;

     const unsigned int _Msize;
     const unsigned int _Nsize;
     const unsigned int _Ksize;

     const unsigned int _nbatches;
     const unsigned int _nmulti;

     const bool _trB;

     /* Blocking info */
     const unsigned int _k_block;
     const unsigned int _n_block;
     const unsigned int _Mround;

     /* Pretransposed buffer. */
     const Toi *_B_transposed=nullptr;

     const NDRange<4> _window_range;

     Requantize32  _qp;
     int32_t *row_bias = nullptr;
     int32_t *col_bias = nullptr;

     void *working_space = nullptr;

     unsigned int _nthreads;

     unsigned int get_col_sum_size() const {
         return _Nsize * _nmulti * sizeof(int32_t);
     }

     static unsigned int compute_k_block(const GemmArgs &args) {
         // We don't support K blocks as we only temporarily store 32 bit results.
         return args._Ksize;

         if (args._cfg && args._cfg->inner_block_size) {
             return args._cfg->inner_block_size;
         }

         const unsigned int L1_size = args._ci->get_L1_cache_size();

         // k_block: Find out how much of the larger array can be loaded into half the cache.
         // This should account for associative caches.
         unsigned int 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 numk_blocks = iceildiv(args._Ksize, k_block);

         // So divide the space equally into that many blocks.
         k_block = iceildiv(args._Ksize, numk_blocks);

         // And round UP to the K unroll level required.
         k_block = roundup(k_block, strategy::k_unroll());

         return k_block;
     }

     static unsigned int compute_n_block(const GemmArgs &args) {
         if (args._cfg && args._cfg->outer_block_size) {
             return args._cfg->outer_block_size;
         }

         const unsigned int k_block = compute_k_block(args);
         const unsigned int L2_size = args._ci->get_L2_cache_size();

         // n_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.
         unsigned int n_block = (((L2_size * 9) / 10) - (k_block * sizeof(Toi) * (strategy::out_width() + strategy::out_height()))) /
                                  (sizeof(Toi) * k_block);

         // Needs to be (at least a single) multiple of the kernel output width.
         n_block /= strategy::out_width();
         n_block = std::max(n_block, 1U) * strategy::out_width();

         // And tune to the presented problem size.
         unsigned int numblocks = iceildiv(args._Nsize, n_block);
         n_block = iceildiv(args._Nsize, numblocks);
         n_block = roundup(n_block, strategy::out_width());

         return n_block;
     }

 public:
     GemmHybridQuantized(GemmHybridQuantized &) = delete;
     GemmHybridQuantized & operator= (GemmHybridQuantized &) = delete;

     /* Constructor */
     GemmHybridQuantized(const GemmArgs &args, const Requantize32 &qp)
               : _ci(args._ci), _Msize(args._Msize), _Nsize(args._Nsize), _Ksize(args._Ksize),
                 _nbatches(args._nbatches), _nmulti(args._nmulti), _trB(args._trB),
                 _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()), _nbatches, iceildiv(_Nsize, _n_block), _nmulti),
                 _qp (qp), _nthreads(args._maxthreads) { }

     // Interface implementation - Compulsory functions
     ndrange_t get_window_size() const override {
         return { _window_range.total_size(), 1u, 1u, 1u, 1u, 1u };
     }

     // This kernel can always be dynamically scheduled.
     bool supports_dynamic_scheduling() const override {
         return true;
     }

     void execute_1d(unsigned int start, unsigned int end, int threadid) {
 #ifdef CYCLE_PROFILING
         profiler prof;
 #endif
         strategy strat(_ci);

         uintptr_t working_int = reinterpret_cast<uintptr_t>(working_space);

         Tri *result_buffer = reinterpret_cast<Tri *>(working_int + (threadid * strategy::out_height() * _Nsize * sizeof(Tri)));

         /* Make sure we've been set up correctly. */
         assert(_B_transposed);
         static_assert(std::is_same<To, Toi>::value, "gemm_native: Operand 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<_Ksize; k0+=_k_block) {
             unsigned int kmax   = std::min(k0 + _k_block, _Ksize);
             unsigned int kern_k = roundup(kmax-k0, strategy::k_unroll());

             auto p = _window_range.iterator(start, end);

             if (p.done()) {
                 return;
             }

             do {
                 const unsigned int m_start = p.dim(0) * strategy::out_height();
                 const unsigned int m_end   = std::min((p.dim(0) + 1) * strategy::out_height(), _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, _Nsize);
                 const unsigned int multi   = p.dim(3);

                 int32_t local_row_sums[strategy::out_height()];

                 const Toi *b_panel = _B_transposed +
                                      (multi * roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll())) +
                                      (k0 * roundup(_Nsize, strategy::out_width())) +
                                      (n0 * kern_k);

                 {
 #ifdef CYCLE_PROFILING
                     auto p = prof.ScopedProfiler(PROFILE_KERNEL, (m_end - m_start) * kern_k * roundup(nmax-n0, strategy::out_width()));
 #endif
                     strat.kernel(this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride) + (m_start * this->_lda) + k0, this->_lda,
                                  b_panel,
                                  result_buffer, (nmax-n0),
                                  (m_end - m_start), (nmax - n0), kern_k,
                                  nullptr, Activation(), false);
                 }

                 {
 #ifdef CYCLE_PROFILING
                     auto p = prof.ScopedProfiler(PROFILE_ROWSUMS, (m_end - m_start) * _Ksize);
 #endif
                     compute_row_sums(_qp, _Ksize, (m_end - m_start),
                                      this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride) + (m_start * this->_lda), this->_lda,
                                      local_row_sums);
                 }

                 {
 #ifdef CYCLE_PROFILING
                     auto p = prof.ScopedProfiler(PROFILE_QUANTIZE, (m_end - m_start) * _Nsize);
 #endif

                     requantize_block_32(_qp, (nmax - n0), (m_end - m_start), result_buffer, (nmax - n0),
                                         this->_Cptr + (multi * this->_C_multi_stride) + (batch * this->_C_batch_stride) + (m_start * this->_ldc) + n0, this->_ldc,
                                         local_row_sums, col_bias + (multi * _Nsize) + n0);
                 }
             } while (p.next_dim0());
         }
     }

     // Execute
     void execute(const ndcoord_t& work_range, const ndcoord_t& thread_locator, int threadid) override {
         UNUSED(thread_locator);

         const auto start = work_range.get_position(0);
         const auto size  = work_range.get_size(0);
         const auto stop  = start + size;

         execute_1d(start, stop, threadid);
     }

     // Working space needed for intermediate result buffers.
     size_t get_working_size() const override {
         return (_nthreads * strategy::out_height() * _Nsize * sizeof(Tri));
     }

     void set_working_space(void *buffer) override {
         working_space = buffer;
     }

     // Interface implementation - pretransposed
     bool B_is_pretransposed() const override {
         return true;
     }

     bool B_pretranspose_required() const override {
         return (_B_transposed==nullptr);
     }

     size_t get_B_pretransposed_array_size() const override {
         return get_col_sum_size() + (roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll()) * _nmulti * sizeof(Toi));
     }

     void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         col_bias = reinterpret_cast<int32_t *>(in_buffer);

         for (unsigned int i=0; i<_nmulti; i++) {
             compute_col_sums(_qp, _Nsize, _Ksize, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize),  _Ksize, i, 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;
         strategy strat(_ci);

         for (unsigned int multi=0; multi<_nmulti; multi++) {
             for (unsigned int k0=0; k0<_Ksize; k0+=_k_block) {
                 const unsigned int kmax = std::min(k0 + _k_block, _Ksize);
                 const unsigned int k_size = roundup(kmax-k0, strategy::k_unroll());

                 for (unsigned int x0=0; x0<_Nsize; x0+=_n_block) {
                     const unsigned int xmax = std::min(x0+_n_block, _Nsize);

                     const unsigned int size = roundup(xmax-x0, strategy::out_width()) * k_size;

                     strat.transforms.PrepareB( buffer, B + (multi * B_multi_stride), ldb,
                                                x0, xmax, k0, kmax, _trB);

                     buffer += size;
                 }
             }
         }
     }

     void set_pretransposed_B_data(void *in_buffer) override {
         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 {
         _qp.bias = bias;
         _qp.bias_multi_stride = bias_multi_stride;
     }
 };

 } // namespace arm_gemm
	/*
	* Copyright (c) 2017-2020 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 <assert.h>

	#include <algorithm>

	#include "arm_gemm.hpp"
	#include "utils.hpp"

	#include "arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp"

	#include "mergeresults.hpp"
	#include "transform.hpp"

	#ifdef CYCLE_PROFILING
	#include "profiler.hpp"
	#endif

	namespace arm_gemm {

	// Implementation of the GemmCommon abstract class.
	template<typename strategy, typename To, typename Tr>
	class GemmHybridQuantized : public GemmCommon<To, Tr> {
	typedef typename strategy::operand_type Toi;
	typedef typename strategy::result_type Tri;

	/* const properties set by constructor */
	const CPUInfo * const _ci;

	const unsigned int _Msize;
	const unsigned int _Nsize;
	const unsigned int _Ksize;

	const unsigned int _nbatches;
	const unsigned int _nmulti;

	const bool _trB;

	/* Blocking info */
	const unsigned int _k_block;
	const unsigned int _n_block;
	const unsigned int _Mround;

	/* Pretransposed buffer. */
	const Toi *_B_transposed=nullptr;

	const NDRange<4> _window_range;

	Requantize32 _qp;
	int32_t *row_bias = nullptr;
	int32_t *col_bias = nullptr;

	void *working_space = nullptr;

	unsigned int _nthreads;

	unsigned int get_col_sum_size() const {
	return _Nsize * _nmulti * sizeof(int32_t);
	}

	static unsigned int compute_k_block(const GemmArgs &args) {
	// We don't support K blocks as we only temporarily store 32 bit results.
	return args._Ksize;

	if (args._cfg && args._cfg->inner_block_size) {
	return args._cfg->inner_block_size;
	}

	const unsigned int L1_size = args._ci->get_L1_cache_size();

	// k_block: Find out how much of the larger array can be loaded into half the cache.
	// This should account for associative caches.
	unsigned int 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 numk_blocks = iceildiv(args._Ksize, k_block);

	// So divide the space equally into that many blocks.
	k_block = iceildiv(args._Ksize, numk_blocks);

	// And round UP to the K unroll level required.
	k_block = roundup(k_block, strategy::k_unroll());

	return k_block;
	}

	static unsigned int compute_n_block(const GemmArgs &args) {
	if (args._cfg && args._cfg->outer_block_size) {
	return args._cfg->outer_block_size;
	}

	const unsigned int k_block = compute_k_block(args);
	const unsigned int L2_size = args._ci->get_L2_cache_size();

	// n_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.
	unsigned int n_block = (((L2_size * 9) / 10) - (k_block * sizeof(Toi) * (strategy::out_width() + strategy::out_height()))) /
	(sizeof(Toi) * k_block);

	// Needs to be (at least a single) multiple of the kernel output width.
	n_block /= strategy::out_width();
	n_block = std::max(n_block, 1U) * strategy::out_width();

	// And tune to the presented problem size.
	unsigned int numblocks = iceildiv(args._Nsize, n_block);
	n_block = iceildiv(args._Nsize, numblocks);
	n_block = roundup(n_block, strategy::out_width());

	return n_block;
	}

	public:
	GemmHybridQuantized(GemmHybridQuantized &) = delete;
	GemmHybridQuantized & operator= (GemmHybridQuantized &) = delete;

	/* Constructor */
	GemmHybridQuantized(const GemmArgs &args, const Requantize32 &qp)
	: _ci(args._ci), _Msize(args._Msize), _Nsize(args._Nsize), _Ksize(args._Ksize),
	_nbatches(args._nbatches), _nmulti(args._nmulti), _trB(args._trB),
	_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()), _nbatches, iceildiv(_Nsize, _n_block), _nmulti),
	_qp (qp), _nthreads(args._maxthreads) { }

	// Interface implementation - Compulsory functions
	ndrange_t get_window_size() const override {
	return { _window_range.total_size(), 1u, 1u, 1u, 1u, 1u };
	}

	// This kernel can always be dynamically scheduled.
	bool supports_dynamic_scheduling() const override {
	return true;
	}

	void execute_1d(unsigned int start, unsigned int end, int threadid) {
	#ifdef CYCLE_PROFILING
	profiler prof;
	#endif
	strategy strat(_ci);

	uintptr_t working_int = reinterpret_cast<uintptr_t>(working_space);

	Tri result_buffer = reinterpret_cast<Tri >(working_int + (threadid * strategy::out_height() * _Nsize * sizeof(Tri)));

	/* Make sure we've been set up correctly. */
	assert(_B_transposed);
	static_assert(std::is_same<To, Toi>::value, "gemm_native: Operand 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<_Ksize; k0+=_k_block) {
	unsigned int kmax = std::min(k0 + _k_block, _Ksize);
	unsigned int kern_k = roundup(kmax-k0, strategy::k_unroll());

	auto p = _window_range.iterator(start, end);

	if (p.done()) {
	return;
	}

	do {
	const unsigned int m_start = p.dim(0) * strategy::out_height();
	const unsigned int m_end = std::min((p.dim(0) + 1) * strategy::out_height(), _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, _Nsize);
	const unsigned int multi = p.dim(3);

	int32_t local_row_sums[strategy::out_height()];

	const Toi *b_panel = _B_transposed +
	(multi * roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll())) +
	(k0 * roundup(_Nsize, strategy::out_width())) +
	(n0 * kern_k);

	{
	#ifdef CYCLE_PROFILING
	auto p = prof.ScopedProfiler(PROFILE_KERNEL, (m_end - m_start) * kern_k * roundup(nmax-n0, strategy::out_width()));
	#endif
	strat.kernel(this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride) + (m_start * this->_lda) + k0, this->_lda,
	b_panel,
	result_buffer, (nmax-n0),
	(m_end - m_start), (nmax - n0), kern_k,
	nullptr, Activation(), false);
	}

	{
	#ifdef CYCLE_PROFILING
	auto p = prof.ScopedProfiler(PROFILE_ROWSUMS, (m_end - m_start) * _Ksize);
	#endif
	compute_row_sums(_qp, _Ksize, (m_end - m_start),
	this->_Aptr + (multi * this->_A_multi_stride) + (batch * this->_A_batch_stride) + (m_start * this->_lda), this->_lda,
	local_row_sums);
	}

	{
	#ifdef CYCLE_PROFILING
	auto p = prof.ScopedProfiler(PROFILE_QUANTIZE, (m_end - m_start) * _Nsize);
	#endif

	requantize_block_32(_qp, (nmax - n0), (m_end - m_start), result_buffer, (nmax - n0),
	this->_Cptr + (multi * this->_C_multi_stride) + (batch * this->_C_batch_stride) + (m_start * this->_ldc) + n0, this->_ldc,
	local_row_sums, col_bias + (multi * _Nsize) + n0);
	}
	} while (p.next_dim0());
	}
	}

	// Execute
	void execute(const ndcoord_t& work_range, const ndcoord_t& thread_locator, int threadid) override {
	UNUSED(thread_locator);

	const auto start = work_range.get_position(0);
	const auto size = work_range.get_size(0);
	const auto stop = start + size;

	execute_1d(start, stop, threadid);
	}

	// Working space needed for intermediate result buffers.
	size_t get_working_size() const override {
	return (_nthreads * strategy::out_height() * _Nsize * sizeof(Tri));
	}

	void set_working_space(void *buffer) override {
	working_space = buffer;
	}

	// Interface implementation - pretransposed
	bool B_is_pretransposed() const override {
	return true;
	}

	bool B_pretranspose_required() const override {
	return (_B_transposed==nullptr);
	}

	size_t get_B_pretransposed_array_size() const override {
	return get_col_sum_size() + (roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll()) * _nmulti * sizeof(Toi));
	}

	void pretranspose_B_array(void in_buffer, const To B, const int ldb, const int B_multi_stride) override {
	col_bias = reinterpret_cast<int32_t *>(in_buffer);

	for (unsigned int i=0; i<_nmulti; i++) {
	compute_col_sums(_qp, _Nsize, _Ksize, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize), _Ksize, i, 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;
	strategy strat(_ci);

	for (unsigned int multi=0; multi<_nmulti; multi++) {
	for (unsigned int k0=0; k0<_Ksize; k0+=_k_block) {
	const unsigned int kmax = std::min(k0 + _k_block, _Ksize);
	const unsigned int k_size = roundup(kmax-k0, strategy::k_unroll());

	for (unsigned int x0=0; x0<_Nsize; x0+=_n_block) {
	const unsigned int xmax = std::min(x0+_n_block, _Nsize);

	const unsigned int size = roundup(xmax-x0, strategy::out_width()) * k_size;

	strat.transforms.PrepareB( buffer, B + (multi * B_multi_stride), ldb,
	x0, xmax, k0, kmax, _trB);

	buffer += size;
	}
	}
	}
	}

	void set_pretransposed_B_data(void *in_buffer) override {
	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 {
	_qp.bias = bias;
	_qp.bias_multi_stride = bias_multi_stride;
	}
	};

	} // namespace arm_gemm