wehub-resource-sync
792 lines
36 KiB
C++
792 lines
36 KiB
C++
//
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// LoopBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2023/04/23.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifndef MNN_OPENCL_BUFFER_CLOSED
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#include "backend/opencl/execution/buffer/LoopBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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static std::string getComputeOption(MNN::BinaryOpOperation type){
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std::string compute;
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switch (type) {
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case BinaryOpOperation_MUL:
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compute = "in0*in1";break;
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case BinaryOpOperation_ADD:
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compute = "in0+in1";break;
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case BinaryOpOperation_SUB:
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compute = "in0-in1";break;
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case BinaryOpOperation_REALDIV:
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compute = "sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001))";break;
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case BinaryOpOperation_MINIMUM:
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compute = "in0>in1?in1:in0";break;
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case BinaryOpOperation_MAXIMUM:
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compute = "in0>in1?in0:in1";break;
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case BinaryOpOperation_GREATER:
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compute = "(float)(isgreater(in0,in1))";break;
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case BinaryOpOperation_LESS:
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compute = "(float)(isless(in0,in1))";break;
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case BinaryOpOperation_LESS_EQUAL:
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compute = "(float)(islessequal(in0,in1))";break;
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case BinaryOpOperation_GREATER_EQUAL:
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compute = "(float)(isgreaterequal(in0,in1))";break;
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case BinaryOpOperation_EQUAL:
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compute = "(float)(isequal(in0,in1))";break;
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case BinaryOpOperation_FLOORDIV:
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compute = "floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))";break;
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case BinaryOpOperation_FLOORMOD:
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compute = "in0-floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))*in1";break;
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case BinaryOpOperation_POW:
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compute = "pow(in0,in1)";break;
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case BinaryOpOperation_SquaredDifference:
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compute = "(in0-in1)*(in0-in1)";break;
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case BinaryOpOperation_ATAN2:
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compute = "(in1==(float)0?(sign(in0)*(float)(PI/2)):(atan(in0/in1)+(in1>(float)0?(float)0:sign(in0)*(float)PI)))";break;
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case BinaryOpOperation_NOTEQUAL:
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compute = "(float)(isnotequal(in0,in1))";break;
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case BinaryOpOperation_MOD:
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compute = "in0-floor(sign(in1)*in0/(fabs(in1)>(float)((float)0.0000001)?fabs(in1):(float)((float)0.0000001)))*in1";break;
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default:
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break;
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}
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return compute;
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}
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static std::string getUnaryComputeOption(MNN::UnaryOpOperation type){
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std::string compute;
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switch (type) {
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case UnaryOpOperation_ABS:
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compute = "fabs((float)(in))"; break;
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case UnaryOpOperation_SQUARE:
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compute = "in*in"; break;
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case UnaryOpOperation_RSQRT:
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compute = "rsqrt((float))(in)>(float)(0.000001)?(float))(in):(float)(0.000001))"; break;
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case UnaryOpOperation_NEG:
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compute = "-(in)"; break;
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case UnaryOpOperation_EXP:
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compute = "exp((float))(in))"; break;
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case UnaryOpOperation_COS:
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compute = "cos((float)(in))"; break;
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case UnaryOpOperation_SIN:
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compute = "sin((float)(in))"; break;
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case UnaryOpOperation_TAN:
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compute = "tan((float)(in))"; break;
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case UnaryOpOperation_ATAN:
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compute = "atan((float)(in))"; break;
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case UnaryOpOperation_SQRT:
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compute = "sqrt((float)(in))"; break;
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case UnaryOpOperation_CEIL:
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compute = "ceil((float)(in))"; break;
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case UnaryOpOperation_RECIPROCAL:
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compute = "native_recip((float)(in))"; break;
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case UnaryOpOperation_LOG1P:
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compute = "log1p((float)(in))"; break;
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case UnaryOpOperation_LOG:
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compute = "native_log((float)(in)>(float)(0.0000001)?(float)(in):(float)(0.0000001))"; break;
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case UnaryOpOperation_FLOOR:
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compute = "floor((float)(in))"; break;
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case UnaryOpOperation_BNLL:
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compute = "in>(float)((float)0)?(in+native_log(exp((float)(-(in)))+(float)(1.0))):(native_log(exp((float)(in))+(float)(1.0)))"; break;
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case UnaryOpOperation_ACOSH:
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compute = "acosh((float)(in))"; break;
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case UnaryOpOperation_SINH:
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compute = "sinh((float)(in))"; break;
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case UnaryOpOperation_ASINH:
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compute = "asinh((float)(in))"; break;
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case UnaryOpOperation_ATANH:
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compute = "atanh((float)(in))"; break;
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case UnaryOpOperation_SIGN:
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compute = "sign((float)(in))"; break;
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case UnaryOpOperation_ROUND:
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compute = "round((float)(in))"; break;
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case UnaryOpOperation_COSH:
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compute = "cosh((float)(in))"; break;
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case UnaryOpOperation_ERF:
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compute = "erf((float)(in))"; break;
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case UnaryOpOperation_ERFC:
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compute = "erfc((float)(in))"; break;
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case UnaryOpOperation_EXPM1:
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compute = "expm1((float)(in))"; break;
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case UnaryOpOperation_SIGMOID:
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compute = "native_recip((float)1+native_exp((float)(-in)))"; break;
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case UnaryOpOperation_SILU:
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compute = "((float)(in)*native_recip((float)1+native_exp((float)(-in))))"; break;
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case UnaryOpOperation_TANH:
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compute = "tanh((float)(in))"; break;
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case UnaryOpOperation_HARDSWISH:
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compute = "(float)(in)>(float)(-3.0f)?((float)(in)<(float)(3.0f)?(((float)(in)*((float)(in)+(float)3.0f))/(float)6.0f):(float)(in)):(float)(0.0f)"; break;
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case UnaryOpOperation_GELU:
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compute = "gelu((float)(in))"; break;
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case UnaryOpOperation_GELU_STANDARD:
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compute = "(erf((float)(in)*(float)0.7071067932881648)+(float)1.0)*(float)(in)*(float)0.5"; break;
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default:
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break;
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}
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return compute;
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}
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static void _setTensorStack(std::vector<Tensor *> &result, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs, const LoopParam *loop) {
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if (loop->inputIndexes() != nullptr) {
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for (int i = 0; i < loop->inputIndexes()->size(); ++i) {
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result[loop->inputIndexes()->data()[i]] = inputs[i];
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}
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}
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for (int i = 0; i < loop->outputIndexes()->size(); ++i) {
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result[loop->outputIndexes()->data()[i]] = outputs[i];
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}
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}
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LoopBufExecution::LoopBufExecution(const LoopParam *loop, const MNN::Op *op, Backend *bn)
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: CommonExecution(bn, op) {
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mLoop = loop;
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mTensors.resize(mLoop->tensorNumber());
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}
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ErrorCode LoopBufExecution::InitCommandOnEncode(){
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for (int i=0; i<mLoop->initCommand()->size(); ++i) {
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auto cmd = mLoop->initCommand()->GetAs<RegionCommand>(i);
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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int mStride_src[4];
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int mStride_dst[4];
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int mStep[2];
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int mIter[2];
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if (cmd->op() == nullptr){
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Unit unit;
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auto output = mTensors[cmd->indexes()->data()[0]];
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auto outputShape = tensorShapeFormat(output);
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auto outputDes = TensorUtils::getDescribe(output);
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int region[] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//nchw
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if(MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat){
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region[1] = ROUND_UP(outputShape[3], 4);
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}
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unit.kernel = runTime->buildKernel("loop", "set_zero", {}, mOpenCLBackend->getPrecision(), output, output);
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unit.localWorkSize = {8, 8};
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unit.globalWorkSize = {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8};
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int global_dim0 = region[2] * region[3];
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int global_dim1 = region[0] * region[1];
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, global_dim0);
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ret |= unit.kernel->get().setArg(idx++, global_dim1);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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MNN_CHECK_CL_SUCCESS(ret, "setArg set_zero buffer");
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mOpenCLBackend->recordKernel2d(unit.kernel, {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8}, {8, 8});
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mUnits.emplace_back(unit);
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return NO_ERROR;
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}
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int x = cmd->size()->data()[0];
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int y = cmd->size()->data()[1];
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int z = cmd->size()->data()[2];
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int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
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int outputSize = mTensors[cmd->indexes()->data()[0]]->elementSize();
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auto srcStride = cmd->view()->GetAs<View>(1)->stride()->data();
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auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
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for (int i = 0; i < 3; ++i) {
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mStride_src[i] = srcStride[i];
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mStride_dst[i] = dstStride[i];
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}
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mStride_src[3] = 0;
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mStride_dst[3] = 0;
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::memset(mStep, 0, 2 * sizeof(int));
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// gather
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{
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Unit unit;
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auto input = mTensors[cmd->indexes()->data()[1]];
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auto output = mTensors[cmd->indexes()->data()[0]];
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std::set<std::string> buildOptions;
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unit.kernel = runTime->buildKernel("loop", "batch_gather", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x * y), (uint32_t)(z), (uint32_t)(1)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input));
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ret |= unit.kernel->get().setArg(index++, x);
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ret |= unit.kernel->get().setArg(index++, 0);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_src), mStride_src);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_dst), mStride_dst);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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ret |= unit.kernel->get().setArg(index++, inputSize);
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ret |= unit.kernel->get().setArg(index++, outputSize);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopInitGatherBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "batch_gather", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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mUnits.emplace_back(unit);
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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}
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}
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return NO_ERROR;
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}
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ErrorCode LoopBufExecution::LoopGather(const Tensor *output, int cmdIndex, int iter) {
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auto cmd = mLoop->commands()->GetAs<RegionCommand>(cmdIndex);
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auto op = cmd->op();
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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int x = cmd->size()->data()[0];
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int y = cmd->size()->data()[1];
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int z = cmd->size()->data()[2];
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int n = mLoop->parallel() ? mLoop->loopNumber() : 1;
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if(mLoop->commands()->size() == 1 && OpType_UnaryOp == op->type() && nullptr == op->main() && cmd->fuse() < 0){
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// only one gather
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n = mLoop->loopNumber();
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}
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int mStride_src[4];
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int mStride_dst[4];
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int mStep[2];
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int mIter[2];
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int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
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int outputSize = output->elementSize();
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auto srcStride = cmd->view()->GetAs<View>(1)->stride()->data();
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auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
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for (int i = 0; i < 3; ++i) {
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mStride_src[i] = srcStride[i];
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mStride_dst[i] = dstStride[i];
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}
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if(cmd->fuse() >= 0){
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mStride_dst[0] = y * z;
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mStride_dst[1] = z;
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mStride_dst[2] = 1;
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}
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mStride_src[3] = cmd->view()->GetAs<View>(1)->offset();
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mStride_dst[3] = cmd->view()->GetAs<View>(0)->offset();
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::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
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::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
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// gather
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Unit unit;
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auto input = mTensors[cmd->indexes()->data()[1]];
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std::set<std::string> buildOptions;
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if(op->main() != nullptr){
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std::string compute = getUnaryComputeOption(cmd->op()->main_as_UnaryOp()->opType());
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buildOptions.emplace("-DUNARY_OPERATOR=" + compute);
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}
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if (mIter[0] >= 0) {
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buildOptions.emplace("-DOFFSET_DST");
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}
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if (mIter[1] >= 0) {
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buildOptions.emplace("-DOFFSET_SRC");
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}
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unit.kernel = runTime->buildKernel("loop", "batch_gather", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x * y), (uint32_t)(z), (uint32_t)(n)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(input));
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for (int i = 0; i < cmd->iterIndexes()->size(); ++i) {
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if (mIter[i] >= 0) {
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[i]]));
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}
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}
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ret |= unit.kernel->get().setArg(index++, x);
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ret |= unit.kernel->get().setArg(index++, iter);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_src), mStride_src);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStride_dst), mStride_dst);
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ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
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ret |= unit.kernel->get().setArg(index++, inputSize);
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ret |= unit.kernel->get().setArg(index++, outputSize);
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MNN_CHECK_CL_SUCCESS(ret, "setArg LoopGatherBufExecution");
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std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "batch_gather", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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mUnits.emplace_back(unit);
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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if(cmd->fuse() >= 0){
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FuseOutput(cmdIndex, mStride_dst, cmd->size()->data()[0], cmd->size()->data()[1], cmd->size()->data()[2], n, iter);
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}
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return NO_ERROR;
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}
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ErrorCode LoopBufExecution::LoopBatchMatMul(const Tensor *output, int cmdIndex, int iter) {
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auto cmd = mLoop->commands()->GetAs<RegionCommand>(cmdIndex);
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bool mHasBias = cmd->indexes()->size() > 3;
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OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
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auto runTime = mOpenCLBackend->getOpenCLRuntime();
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int mOffset[4];
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int mStep[4];
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int mIter[4];
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mOffset[0] = cmd->view()->GetAs<View>(0)->offset();
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mOffset[1] = cmd->view()->GetAs<View>(1)->offset();
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mOffset[2] = cmd->view()->GetAs<View>(2)->offset();
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if (mHasBias) {
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mOffset[3] = cmd->view()->GetAs<View>(3)->offset();
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}
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::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
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::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
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int e = cmd->size()->data()[0];
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int l = cmd->size()->data()[1];
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int h = cmd->size()->data()[2];
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int n = mLoop->parallel() ? mLoop->loopNumber() : 1;
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// matmul
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Unit unit;
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std::string KernelName = "batch_matmul";
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std::set<std::string> buildOptions;
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if (mHasBias) {
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buildOptions.emplace("-DBIAS");
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}
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if (cmd->op()->main_as_MatMul()->transposeA()) {
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buildOptions.emplace("-DTRANSPOSE_A");
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}
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if (cmd->op()->main_as_MatMul()->transposeB()) {
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buildOptions.emplace("-DTRANSPOSE_B");
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}
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buildOptions.emplace("-DH_LEAVES=" + std::to_string(h % 4));
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unit.kernel = runTime->buildKernel("loop", KernelName, buildOptions, mOpenCLBackend->getPrecision(), mTensors[cmd->indexes()->data()[1]], mTensors[cmd->indexes()->data()[0]]);
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(UP_DIV(h, 4)), (uint32_t)(UP_DIV(e, 4)),(uint32_t)(n)};
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[1]]));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[2]]));
|
|
if (mHasBias) {
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[3]]));
|
|
}
|
|
for (int i = 0; i < cmd->iterIndexes()->size(); ++i) {
|
|
if (mIter[i] >= 0) {
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[i]]));
|
|
} else {
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->indexes()->data()[1]]));
|
|
}
|
|
}
|
|
ret |= unit.kernel->get().setArg(index++, e);
|
|
ret |= unit.kernel->get().setArg(index++, l);
|
|
ret |= unit.kernel->get().setArg(index++, h);
|
|
ret |= unit.kernel->get().setArg(index++, iter);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mIter), mIter);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBatchMatMulBufExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, KernelName, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
mUnits.emplace_back(unit);
|
|
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
|
|
|
|
if(cmd->fuse() >= 0){
|
|
int mStride_dst[4];
|
|
mStride_dst[0] = h * e;
|
|
mStride_dst[1] = h;
|
|
mStride_dst[2] = 1;
|
|
mStride_dst[3] = 1;
|
|
FuseOutput(cmdIndex, mStride_dst, 1, e, h, n, iter);
|
|
}
|
|
|
|
return NO_ERROR;
|
|
}
|
|
|
|
ErrorCode LoopBufExecution::LoopBinary(const Tensor *output, int cmdIndex, int iter) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(cmdIndex);
|
|
std::string compute = getComputeOption(cmd->op()->main_as_BinaryOp()->opType());
|
|
std::set<std::string> buildOptions;
|
|
buildOptions.emplace("-DOPERATOR=" + compute);
|
|
if(cmd->op()->main_as_BinaryOp()->opType() == BinaryOpOperation_MOD && (output->getType().code == halide_type_int || output->getType().code == halide_type_uint)){
|
|
buildOptions.emplace("-DINT_COMPUTE_MOD");
|
|
}
|
|
OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
|
|
auto runTime = mOpenCLBackend->getOpenCLRuntime();
|
|
|
|
int mOffset[4];
|
|
int mStep[4];
|
|
int mIter[4];
|
|
int mStride_src0[3];
|
|
int mStride_src1[3];
|
|
int mStride_dst[3];
|
|
|
|
Unit unit;
|
|
int z = cmd->size()->data()[0];
|
|
int y = cmd->size()->data()[1];
|
|
int x = cmd->size()->data()[2];
|
|
int n = mLoop->parallel() ? mLoop->loopNumber() : 1;
|
|
int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
|
|
int outputSize = output->elementSize();
|
|
|
|
auto src0Stride = cmd->view()->GetAs<View>(1)->stride()->data();
|
|
auto src1Stride = cmd->view()->GetAs<View>(2)->stride()->data();
|
|
auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
|
|
for (int i = 0; i < 3; ++i) {
|
|
mStride_src0[i] = src0Stride[i];
|
|
mStride_src1[i] = src1Stride[i];
|
|
mStride_dst[i] = dstStride[i];
|
|
}
|
|
if(cmd->fuse() >= 0){
|
|
mStride_dst[0] = y * x;
|
|
mStride_dst[1] = x;
|
|
mStride_dst[2] = 1;
|
|
}
|
|
|
|
auto input0 = mTensors[cmd->indexes()->data()[1]];
|
|
auto input1 = mTensors[cmd->indexes()->data()[2]];
|
|
|
|
::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
|
|
::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
|
|
mOffset[0] = cmd->view()->GetAs<View>(0)->offset();
|
|
mOffset[1] = cmd->view()->GetAs<View>(1)->offset();
|
|
mOffset[2] = cmd->view()->GetAs<View>(2)->offset();
|
|
|
|
if (mIter[0] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_DST");
|
|
}
|
|
if (mIter[1] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_SRC0");
|
|
}
|
|
if (mIter[2] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_SRC1");
|
|
}
|
|
unit.kernel = runTime->buildKernel("loop", "loop_binary", buildOptions, mOpenCLBackend->getPrecision(), input0, output);
|
|
uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
|
|
|
|
std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x), (uint32_t)(y), (uint32_t)(z*n)};
|
|
uint32_t index = 0;
|
|
cl_int ret = CL_SUCCESS;
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input0));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input1));
|
|
for (int i = 0; i < cmd->iterIndexes()->size(); ++i) {
|
|
if (mIter[i] >= 0) {
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[i]]));
|
|
}
|
|
}
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[2]);
|
|
ret |= unit.kernel->get().setArg(index++, iter);
|
|
ret |= unit.kernel->get().setArg(index++, z);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
|
|
ret |= unit.kernel->get().setArg(index++, outputSize);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBinaryBufExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "loop_binary", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
mUnits.emplace_back(unit);
|
|
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
|
|
|
|
if(cmd->fuse() >= 0){
|
|
FuseOutput(cmdIndex, mStride_dst, cmd->size()->data()[0], cmd->size()->data()[1], cmd->size()->data()[2], n, iter);
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
|
|
ErrorCode LoopBufExecution::LoopCumsum(const Tensor *output) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
|
|
std::string compute = getComputeOption(cmd->op()->main_as_BinaryOp()->opType());
|
|
std::set<std::string> buildOptions;
|
|
buildOptions.emplace("-DOPERATOR=" + compute);
|
|
if(cmd->op()->main_as_BinaryOp()->opType() == BinaryOpOperation_MOD && (output->getType().code == halide_type_int || output->getType().code == halide_type_uint)){
|
|
buildOptions.emplace("-DINT_COMPUTE_MOD");
|
|
}
|
|
OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
|
|
auto runTime = mOpenCLBackend->getOpenCLRuntime();
|
|
|
|
int mOffset[4];
|
|
int mStep[4];
|
|
int mIter[4];
|
|
int mStride_src0[3];
|
|
int mStride_src1[3];
|
|
int mStride_dst[3];
|
|
|
|
Unit unit;
|
|
int z = cmd->size()->data()[0];
|
|
int y = cmd->size()->data()[1];
|
|
int x = cmd->size()->data()[2];
|
|
int n = mLoop->parallel() ? mLoop->loopNumber() : 1;
|
|
int inputSize = mTensors[cmd->indexes()->data()[1]]->elementSize();
|
|
int outputSize = output->elementSize();
|
|
|
|
auto src0Stride = cmd->view()->GetAs<View>(1)->stride()->data();
|
|
auto src1Stride = cmd->view()->GetAs<View>(2)->stride()->data();
|
|
auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
|
|
for (int i = 0; i < 3; ++i) {
|
|
mStride_src0[i] = src0Stride[i];
|
|
mStride_src1[i] = src1Stride[i];
|
|
mStride_dst[i] = dstStride[i];
|
|
}
|
|
if(cmd->fuse() >= 0){
|
|
mStride_dst[0] = y * x;
|
|
mStride_dst[1] = x;
|
|
mStride_dst[2] = 1;
|
|
}
|
|
|
|
auto input0 = mTensors[cmd->indexes()->data()[1]];
|
|
auto input1 = mTensors[cmd->indexes()->data()[2]];
|
|
|
|
// cumsum
|
|
// mTensors cmd->indexes()->data() = {2, 0, 1} -> {output, input0, input1}, output = input0
|
|
int loopNumber = mLoop->loopNumber();
|
|
|
|
::memcpy(mStep, cmd->steps()->data(), cmd->steps()->size() * sizeof(int));
|
|
mOffset[0] = cmd->view()->GetAs<View>(0)->offset();
|
|
mOffset[1] = cmd->view()->GetAs<View>(1)->offset();
|
|
mOffset[2] = cmd->view()->GetAs<View>(2)->offset();
|
|
unit.kernel = runTime->buildKernel("loop", "loop_cumsum", buildOptions, mOpenCLBackend->getPrecision(), input0, output);
|
|
uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
|
|
|
|
std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x), (uint32_t)(y), (uint32_t)(z)};
|
|
uint32_t index = 0;
|
|
cl_int ret = CL_SUCCESS;
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input0));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input1));
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[2]);
|
|
ret |= unit.kernel->get().setArg(index++, loopNumber);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
|
|
ret |= unit.kernel->get().setArg(index++, outputSize);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopCumsumBufExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "loop_cumsum", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
|
|
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
mUnits.emplace_back(unit);
|
|
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
|
|
|
|
return NO_ERROR;
|
|
}
|
|
|
|
ErrorCode LoopBufExecution::FuseOutput(int iter, int* inputStride, int sizeZ, int sizeY, int SizeX, int n, int n_offset) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(iter);
|
|
std::string compute = getComputeOption(MNN::BinaryOpOperation(cmd->fuse()));
|
|
std::set<std::string> buildOptions;
|
|
buildOptions.emplace("-DOPERATOR=" + compute);
|
|
OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
|
|
auto runTime = mOpenCLBackend->getOpenCLRuntime();
|
|
|
|
int mOffset[4];
|
|
int mStep[4];
|
|
int mIter[4];
|
|
int mStride_src0[3];
|
|
int mStride_src1[3];
|
|
int mStride_dst[3];
|
|
auto input = mFuseTensor.get();
|
|
auto output = mTensors[cmd->indexes()->data()[0]];
|
|
int outputSize = output->elementSize();
|
|
|
|
Unit unit;
|
|
int z = sizeZ;
|
|
int y = sizeY;
|
|
int x = SizeX;
|
|
|
|
auto dstStride = cmd->view()->GetAs<View>(0)->stride()->data();
|
|
for (int i = 0; i < 3; ++i) {
|
|
mStride_src0[i] = dstStride[i];
|
|
mStride_src1[i] = inputStride[i];
|
|
mStride_dst[i] = dstStride[i];
|
|
}
|
|
|
|
for(int i = 0; i < 4; ++i){
|
|
mStep[i] = cmd->steps()->data()[0];
|
|
}
|
|
::memcpy(mIter, cmd->iterIndexes()->data(), cmd->iterIndexes()->size() * sizeof(int));
|
|
mOffset[0] = cmd->view()->GetAs<View>(0)->offset();
|
|
mOffset[1] = cmd->view()->GetAs<View>(0)->offset();
|
|
mOffset[2] = cmd->view()->GetAs<View>(0)->offset();
|
|
|
|
if (mIter[0] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_DST");
|
|
}
|
|
if (mIter[0] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_SRC0");
|
|
}
|
|
if (mIter[0] >= 0) {
|
|
buildOptions.emplace("-DOFFSET_SRC1");
|
|
}
|
|
unit.kernel = runTime->buildKernel("loop", "loop_binary", buildOptions, mOpenCLBackend->getPrecision(), input, output);
|
|
uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
|
|
|
|
std::vector<uint32_t> mGlobalWorkSize = {(uint32_t)(x), (uint32_t)(y), (uint32_t)(z*n)};
|
|
|
|
uint32_t index = 0;
|
|
cl_int ret = CL_SUCCESS;
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mGlobalWorkSize[2]);
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(input));
|
|
if (mIter[0] >= 0) {
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[0]]));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[0]]));
|
|
ret |= unit.kernel->get().setArg(index++, openCLBuffer(mTensors[cmd->iterIndexes()->data()[0]]));
|
|
}
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src0[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_src1[2]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[0]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[1]);
|
|
ret |= unit.kernel->get().setArg(index++, mStride_dst[2]);
|
|
ret |= unit.kernel->get().setArg(index++, n_offset);
|
|
ret |= unit.kernel->get().setArg(index++, z);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mOffset), mOffset);
|
|
ret |= unit.kernel->get().setArg(index++, sizeof(mStep), mStep);
|
|
ret |= unit.kernel->get().setArg(index++, outputSize);
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg LoopBinaryBufExecution");
|
|
|
|
std::vector<uint32_t> mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runTime, "loop_binary", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "loop").first;
|
|
|
|
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
|
|
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
|
|
mUnits.emplace_back(unit);
|
|
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
|
|
return NO_ERROR;
|
|
}
|
|
|
|
ErrorCode LoopBufExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs){
|
|
OpenCLBackend *mOpenCLBackend = (OpenCLBackend *)backend();
|
|
auto runTime = mOpenCLBackend->getOpenCLRuntime();
|
|
_setTensorStack(mTensors, inputs, outputs, mLoop);
|
|
// Make Temp output buffer
|
|
int bufferUnitSize = mOpenCLBackend->getPrecision() != BackendConfig::Precision_High ? sizeof(half_float::half) : sizeof(float);
|
|
int mMaxFuseBufferSize = 0;
|
|
int loopNumber = mLoop->parallel() ? 1 : mLoop->loopNumber();
|
|
for (int i=0; i<mLoop->commands()->size(); ++i) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(i);
|
|
auto op = cmd->op();
|
|
if (cmd->fuse() >= 0) {
|
|
// Make Temp output buffer
|
|
auto size = cmd->size()->data();
|
|
if (cmd->op()->type() == OpType_MatMul) {
|
|
mMaxFuseBufferSize = std::max(mMaxFuseBufferSize, bufferUnitSize * size[0] * size[2]);
|
|
} else {
|
|
mMaxFuseBufferSize = std::max(mMaxFuseBufferSize, bufferUnitSize * size[0] * size[1] * size[2]);
|
|
}
|
|
}
|
|
}
|
|
if(mMaxFuseBufferSize != 0){
|
|
mFuseTensor.reset(Tensor::createDevice<float>({loopNumber * mMaxFuseBufferSize}));
|
|
mOpenCLBackend->onAcquireBuffer(mFuseTensor.get(), Backend::DYNAMIC);
|
|
mOpenCLBackend->onReleaseBuffer(mFuseTensor.get(), Backend::DYNAMIC);
|
|
}
|
|
mUnits.clear();
|
|
if(mLoop->initCommand() != nullptr){
|
|
InitCommandOnEncode();
|
|
}
|
|
if (1 == mLoop->commands()->size()) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
|
|
auto op = cmd->op();
|
|
if (OpType_UnaryOp == op->type() && nullptr == op->main() && cmd->fuse() < 0) {
|
|
return LoopGather(mTensors[cmd->indexes()->data()[0]], 0, 0);
|
|
}
|
|
if(OpType_BinaryOp == op->type() && mLoop->parallel() == false && cmd->fuse() < 0){
|
|
return LoopCumsum(mTensors[cmd->indexes()->data()[0]]);
|
|
}
|
|
}
|
|
for(int iter = 0; iter < loopNumber; ++iter){
|
|
for (int index = 0; index<mLoop->commands()->size(); ++index) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(index);
|
|
auto op = cmd->op();
|
|
Tensor *originOutput = mTensors[cmd->indexes()->data()[0]];
|
|
Tensor *output = originOutput;
|
|
if(cmd->fuse() >= 0){
|
|
output = mFuseTensor.get();
|
|
}
|
|
if (OpType_UnaryOp == op->type()){
|
|
LoopGather(output, index, iter);
|
|
}else if (OpType_MatMul == op->type()){
|
|
LoopBatchMatMul(output, index, iter);
|
|
}else if(OpType_BinaryOp == op->type()){
|
|
LoopBinary(output, index, iter);
|
|
}
|
|
}
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
|
|
class LoopBufCreator : public OpenCLBackend::Creator {
|
|
public:
|
|
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
|
|
const MNN::Op *op, Backend *backend) const override {
|
|
for (int i = 0; i < inputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(inputs[i], false);
|
|
}
|
|
for (int i = 0; i < outputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(outputs[i], false);
|
|
}
|
|
auto loop = op->main_as_LoopParam();
|
|
if (nullptr == loop || loop->commands() == nullptr) {
|
|
return nullptr;
|
|
}
|
|
OPENCL_CREATOR_CHECK(new LoopBufExecution(loop, op, backend));
|
|
}
|
|
};
|
|
|
|
REGISTER_OPENCL_OP_CREATOR(LoopBufCreator, OpType_While, BUFFER);
|
|
|
|
} // namespace OpenCL
|
|
} // namespace MNN
|
|
#endif /* MNN_OPENCL_BUFFER_CLOSED */
|