364 lines
16 KiB
C++
364 lines
16 KiB
C++
/*
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* Copyright (c) 2015, PHILIPPE TILLET. All rights reserved.
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*
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* This file is part of ISAAC.
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*
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* ISAAC is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with this library; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
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* MA 02110-1301 USA
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*/
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#include <cstring>
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#include <iostream>
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#include "isaac/jit/syntax/engine/process.h"
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#include "isaac/jit/generation/engine/keywords.h"
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#include "isaac/jit/generation/engine/stream.h"
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#include "isaac/jit/generation/reduce_2d.h"
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#include "tools/arguments.hpp"
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#include "tools/loop.hpp"
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#include "tools/reductions.hpp"
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#include "tools/vector_types.hpp"
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#include <string>
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namespace isaac
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{
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namespace templates
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{
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reduce_2d_parameters::reduce_2d_parameters(unsigned int _simd_width,
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unsigned int _local_size_0, unsigned int _local_size_1,
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unsigned int _num_groups_0, unsigned int _num_groups_1, fetching_policy_type _fetch_policy): base::parameters_type(_simd_width, _local_size_0, _local_size_1, 1),
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num_groups_0(_num_groups_0), num_groups_1(_num_groups_1), fetch_policy(_fetch_policy) { }
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int reduce_2d::is_invalid_impl(driver::Device const &, expression_tree const &) const
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{
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if (p_.fetch_policy==FETCH_FROM_LOCAL)
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return TEMPLATE_INVALID_FETCHING_POLICY_TYPE;
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return TEMPLATE_VALID;
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}
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unsigned int reduce_2d::lmem_usage(const expression_tree&) const
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{
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return (p_.local_size_0+1)*p_.local_size_1;
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}
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unsigned int reduce_2d::temporary_workspace(expression_tree const & expressions) const
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{
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std::vector<int_t> MN = input_sizes(expressions);
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int_t M = MN[0];
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if(p_.num_groups_0 > 1)
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return M*p_.num_groups_0;
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return 0;
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}
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std::string reduce_2d::generate_impl(std::string const & suffix, expression_tree const & tree, driver::Device const & device, symbolic::symbols_table const & symbols) const
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{
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using tools::to_string;
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std::vector<symbolic::reduce_2d*> reductions = symbolic::extract<symbolic::reduce_2d>(tree, symbols);
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std::vector<std::size_t> assignments = symbolic::assignments(tree);
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driver::backend_type backend = device.backend();
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kernel_generation_stream stream(backend);
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std::string name[2] = {"prod", "reduce"};
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name[0] += suffix;
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name[1] += suffix;
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int col_simd_width = (reduction_type_ == REDUCE_COLUMNS) ? 1 : p_.simd_width;
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unsigned int local_size_0_ld = p_.local_size_0;
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std::string ls0ldstr = to_string(local_size_0_ld);
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auto unroll_tmp = [&]()
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{
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unsigned int offset = 0;
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for (symbolic::reduce_2d* rd : reductions)
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{
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numeric_type dtype = tree.dtype();
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std::string sdtype = to_string(dtype);
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if (is_indexing(rd->op().type))
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{
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stream << rd->process("$GLOBAL uint* #name_temp = ($GLOBAL uint*)(tmp + " + tools::to_string(offset) + "*M);");
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offset += 4*p_.num_groups_0;
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stream << rd->process("$GLOBAL " + sdtype + "* #name_temp_value = ($GLOBAL " + sdtype + "*)(tmp + " + tools::to_string(offset) + "*M);");
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offset += size_of(dtype)*p_.num_groups_0;
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}
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else{
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stream << rd->process("$GLOBAL " + sdtype + "* #name_temp = ($GLOBAL " + sdtype + "*)(tmp + " + tools::to_string(offset) + "*M);");
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offset += size_of(dtype)*p_.num_groups_0;
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}
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}
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};
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/* ------------------------
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* Kernel 1
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* -----------------------*/
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switch(backend)
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{
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case driver::CUDA:
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stream << "#include \"vector.h\"" << std::endl;
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break;
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case driver::OPENCL:
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stream << " __attribute__((reqd_work_group_size(" << p_.local_size_0 << "," << p_.local_size_1 << ",1)))" << std::endl;
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break;
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}
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stream << "$KERNEL void " << name[0] << "($SIZE_T M, $SIZE_T N, $GLOBAL char* tmp, " << tools::join(kernel_arguments(device, symbols, tree), ", ") << ")" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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//Unroll
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unroll_tmp();
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//Declare Buffers
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for (symbolic::reduce_2d* rd : reductions)
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stream << rd->process("$LOCAL " + append_width("#scalartype", col_simd_width) + " #name_buf[" + to_string(p_.local_size_1*local_size_0_ld) + "];") << std::endl;
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//Loop r
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stream << "for($SIZE_T r = $GLOBAL_IDX_1*" << col_simd_width << "; r < (M +" << p_.local_size_1 - 1 << ")/" << p_.local_size_1 << "*" << p_.local_size_1*col_simd_width << "; r += $GLOBAL_SIZE_1*" << col_simd_width << ")" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "$SIZE_T lidx = $LOCAL_IDX_0;" << std::endl;
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stream << "$SIZE_T lidy = $LOCAL_IDX_1;" << std::endl;
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//Accumulators
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for (symbolic::reduce_2d* rd : reductions){
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std::string data_type = append_width("#scalartype",col_simd_width);
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stream << rd->process(data_type + " #name_acc = " + InitPrefix(backend, data_type).get() + "(" + neutral_element((rd)->op(), backend, "#scalartype") + ");") << std::endl;
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}
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//Loop c
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stream << "if (r < M)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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element_wise_loop_1D(stream, p_.fetch_policy, (reduction_type_==REDUCE_COLUMNS)?p_.simd_width:1, "c", "N", "$GLOBAL_IDX_0", "$GLOBAL_SIZE_0", device, [&](unsigned int row_simd_width)
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{
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std::string rdtype = append_width("#scalartype", row_simd_width);
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std::string cdtype = append_width("#scalartype", col_simd_width);
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//Fetch
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std::set<std::string> fetched;
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for (symbolic::reduce_2d* rd : reductions)
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for(symbolic::leaf* sym: symbolic::extract<symbolic::leaf>(tree, symbols, rd->root(), false))
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if(fetched.insert(sym->process("#name")).second){
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if(reduction_type_==REDUCE_COLUMNS)
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stream << sym->process(rdtype + " #name = " + append_width("loadv",row_simd_width) + "(c,r);") << std::endl;
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else
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stream << sym->process(cdtype + " #name = " + append_width("loadv",col_simd_width) + "(r,c);") << std::endl;
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}
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//Compute
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for (symbolic::reduce_2d* rd : reductions)
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for (unsigned int s = 0; s < row_simd_width; ++s){
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std::string value = rd->lhs()->evaluate({{"leaf", access_vector_type("#name", s, row_simd_width)}});
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if (is_indexing(rd->op().type))
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compute_index_reduce_1d(stream, rd->process("#name_acc"), "c*"+to_string(row_simd_width) + to_string(s), rd->process("#name_acc_value"), value, rd->op());
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else
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compute_reduce_1d(stream, rd->process("#name_acc"), value,rd->op());
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}
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});
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stream.dec_tab();
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stream << "}" << std::endl;
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//Copy to local memory
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for (symbolic::reduce_2d* rd : reductions)
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stream << rd->process("#name_buf[lidy*" + ls0ldstr + "+ lidx] = #name_acc;") << std::endl;
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//Reduce local memory
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stream << "#pragma unroll" << std::endl;
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stream << "for($SIZE_T stride = " << p_.local_size_0/2 << "; stride >0; stride /=2)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "$LOCAL_BARRIER;" << std::endl;
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stream << "if (lidx < stride)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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for (symbolic::reduce_2d* rd : reductions)
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if (is_indexing(rd->op().type))
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compute_index_reduce_1d(stream, rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx + stride]")
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, rd->process("#name_buf_value[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf_value[lidy*" + ls0ldstr + " + lidx + stride]")
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, rd->op());
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else
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compute_reduce_1d(stream,rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx + stride]"), rd->op());
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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//Write result/temporary
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stream << "if (lidx == 0 && r < M)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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if(p_.num_groups_0==1)
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for(size_t idx: assignments)
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for(int s = 0 ; s < col_simd_width ; ++s)
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stream << symbols.at(idx)->evaluate({{"leaf", "at(r+" + to_string(s) + ")"},
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{"reduce_2d", access_vector_type("#name_buf[lidy*" + ls0ldstr + "]", s, col_simd_width)}}) << ";" << std::endl;
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else
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{
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for (symbolic::reduction const * rd : reductions)
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{
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if(col_simd_width > 1)
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stream << "if(M - r > " << col_simd_width << "){" << std::endl;
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if (is_indexing(rd->op().type))
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stream << rd->process(vstore(col_simd_width,"uint", "#name_buf_value[lidy*" + ls0ldstr + "]", "0", "#name_temp_value + r + M*$GROUP_IDX_0", "1", backend, false)) << ";" << std::endl;
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stream << rd->process(vstore(col_simd_width,"#scalartype", "#name_buf[lidy*" + ls0ldstr + "]", "0", "#name_temp + r + M*$GROUP_IDX_0", "1", backend, false)) << ";" << std::endl;
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if(col_simd_width > 1)
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{
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stream << "}" << std::endl;
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stream << "else{" << std::endl;
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stream.inc_tab();
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for(int s = 0 ; s < col_simd_width ; ++s){
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if (is_indexing(rd->op().type))
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stream << "if(r + " << s << "< M) " << rd->process("#name_temp_value[r + " + to_string(s) + " + M*$GROUP_IDX_0] = " + access_vector_type("#name_buf_value[lidy*" + ls0ldstr + "]", s)) << ";" << std::endl;
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stream << "if(r + " << s << "< M) " << rd->process("#name_temp[r + " + to_string(s) + " + M*$GROUP_IDX_0] = " + access_vector_type("#name_buf[lidy*" + ls0ldstr + "]", s)) << ";" << std::endl;
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}
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stream.dec_tab();
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stream << "}" << std::endl;
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}
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}
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}
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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/* ------------------------
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* Kernel 2
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* -----------------------*/
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if(p_.num_groups_0>1)
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{
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if(backend==driver::OPENCL)
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stream << " __attribute__((reqd_work_group_size(" << p_.local_size_0 << "," << p_.local_size_1 << ",1)))" << std::endl;
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stream << "$KERNEL void " << name[1] << "($SIZE_T M, $SIZE_T N , $GLOBAL char* tmp, " << tools::join(kernel_arguments(device, symbols, tree), ", ") << ")" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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unroll_tmp();
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for (symbolic::reduce_2d* rd : reductions)
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stream << rd->process("$LOCAL #scalartype #name_buf[" + to_string(p_.local_size_1*local_size_0_ld) + "];") << std::endl;
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stream << "for($SIZE_T r = $GLOBAL_IDX_1; r < (M +" << p_.local_size_1 - 1 << ")/" << p_.local_size_1 << "*" << p_.local_size_1 << "; r += " << GlobalSize1(backend) << "){" << std::endl;
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stream.inc_tab();
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stream << "$SIZE_T lidx = $LOCAL_IDX_0;" << std::endl;
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stream << "$SIZE_T lidy = $LOCAL_IDX_1;" << std::endl;
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for (symbolic::reduce_2d* rd : reductions)
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stream << rd->process("#scalartype #name_acc = " + neutral_element((rd)->op(), backend, "#scalartype") + ";") << std::endl;
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stream << "if (r < M)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "for($SIZE_T c = lidx; c < " << p_.num_groups_0 << "; c += $LOCAL_SIZE_0){" << std::endl;
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stream.inc_tab();
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for (symbolic::reduce_2d* rd: reductions)
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compute_reduce_1d(stream, rd->process("#name_acc"), rd->process("#name_temp[r + M*c]"), rd->op());
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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for (symbolic::reduce_2d* rd : reductions)
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stream << rd->process("#name_buf[lidy*" + ls0ldstr + "+ lidx] = #name_acc;") << std::endl;
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stream << "#pragma unroll" << std::endl;
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stream << "for($SIZE_T stride = " << p_.local_size_0/2 << "; stride >0; stride /=2)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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stream << "$LOCAL_BARRIER;" << std::endl;
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stream << "if (lidx < stride)" << std::endl;
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stream << "{" << std::endl;
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stream.inc_tab();
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for (symbolic::reduce_2d* rd : reductions)
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if (is_indexing(rd->op().type))
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compute_index_reduce_1d(stream, rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx + stride]")
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, rd->process("#name_buf_value[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf_value[lidy*" + ls0ldstr + " + lidx + stride]")
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, rd->op());
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else
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compute_reduce_1d(stream,rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx]"), rd->process("#name_buf[lidy*" + ls0ldstr + " + lidx + stride]"), rd->op());
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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stream << "if (lidx == 0 && r < M)";
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stream << "{" << std::endl;
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stream.inc_tab();
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for(size_t idx: assignments)
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stream << symbols.at(idx)->evaluate({{"leaf", "at(r)"}, {"reduce_2d", "#name_buf[lidy*" + ls0ldstr + "]"}}) << ";" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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stream.dec_tab();
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stream << "}" << std::endl;
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}
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// std::cout << stream.str() << std::endl;
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return stream.str();
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}
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reduce_2d::reduce_2d(reduce_2d::parameters_type const & parameters,
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operation_type_family rtype,
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fusion_policy_t fusion_policy) :
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base_impl<reduce_2d, reduce_2d_parameters>(parameters, fusion_policy),
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reduction_type_(rtype){ }
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std::vector<int_t> reduce_2d::input_sizes(expression_tree const & tree) const
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{
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std::vector<size_t> idx = symbolic::find(tree, [this](expression_tree::node const & x){return x.binary_operator.op.type_family==reduction_type_;});
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std::vector<int_t> shape = tree[tree[idx[0]].binary_operator.lhs].shape;
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if(reduction_type_==REDUCE_COLUMNS)
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return {shape[1], shape[0]};
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return {shape[0], shape[1]};
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}
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void reduce_2d::enqueue(driver::CommandQueue & queue, driver::Program const & program, std::string const & suffix, runtime::execution_handler const & control)
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{
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expression_tree const & tree = control.x();
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std::vector<int_t> MN = input_sizes(tree);
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//Kernel
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std::string name[2] = {"prod", "reduce"};
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name[0] += suffix;
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name[1] += suffix;
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unsigned int nk = (p_.num_groups_0==1)?1:2;
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std::vector<driver::Kernel> kernels;
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for(unsigned int k = 0 ; k < nk ; ++k)
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kernels.push_back(driver::Kernel(program, name[k].c_str()));
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for(unsigned int k = 0 ; k < nk ; ++k)
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{
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driver::Kernel & kernel = kernels[k];
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unsigned int n_arg = 0;
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int_t M = MN[0];
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int_t N = MN[1];
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kernel.setSizeArg(n_arg++, M);
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kernel.setSizeArg(n_arg++, N);
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kernel.setArg(n_arg++, driver::backend::workspaces::get(queue)); //Temporary buffers
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symbolic::set_arguments(tree, kernel, n_arg, fusion_policy_);
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}
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//NDRange
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driver::NDRange global[2] = { driver::NDRange(p_.local_size_0*p_.num_groups_0, p_.local_size_1*p_.num_groups_1), driver::NDRange(p_.local_size_0, p_.local_size_1*p_.num_groups_1) };
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driver::NDRange local[2] = { driver::NDRange(p_.local_size_0, p_.local_size_1), driver::NDRange(p_.local_size_0, p_.local_size_1) };
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for(unsigned int i = 0 ; i < nk ; ++i)
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control.execution_options().enqueue(program.context(), kernels[i], global[i], local[i]);
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}
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reduce_2d_rows::reduce_2d_rows(reduce_2d_parameters const & parameters,fusion_policy_t fusion_policy): reduce_2d(parameters, REDUCE_ROWS, fusion_policy){}
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reduce_2d_rows::reduce_2d_rows(unsigned int simd, unsigned int ls1, unsigned int ls2, unsigned int ng1, unsigned int ng2,
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fetching_policy_type fetch, fusion_policy_t bind): reduce_2d(reduce_2d_parameters(simd, ls1, ls2, ng1, ng2, fetch), REDUCE_ROWS, bind) {}
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reduce_2d_cols::reduce_2d_cols(reduce_2d::parameters_type const & parameters, fusion_policy_t fusion_policy): reduce_2d(parameters, REDUCE_COLUMNS, fusion_policy){}
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reduce_2d_cols::reduce_2d_cols(unsigned int simd, unsigned int ls1, unsigned int ls2, unsigned int ng1, unsigned int ng2,
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fetching_policy_type fetch, fusion_policy_t bind): reduce_2d(reduce_2d_parameters(simd, ls1, ls2, ng1, ng2, fetch), REDUCE_COLUMNS, bind) {}
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}
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}
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