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cavis/libnd4j/include/performance/benchmarking/impl/LightBenchmarkSuit.cpp

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <ops/declarable/CustomOperations.h>
#include "performance/benchmarking/LightBenchmarkSuit.h"
#ifdef _RELEASE
#define WARMUP 3
#define NUM_ITER 10
#else
#define WARMUP 0
#define NUM_ITER 1
#endif
namespace nd4j {
template <typename T>
static std::string transformBenchmark() {
std::string output;
output += "transformBenchmark " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
IntPowerParameters length("length", 2, 8, 20, 4); //2^8, 2^12, 2^16, 2^20 - 4MB
BoolParameters inplace("inplace");
ParametersBatch batch({&length, &inplace});
auto generator = PARAMETRIC_XZ() {
auto arr = NDArrayFactory::create_<T>('c', {p.getIntParam("length")});
arr->assign(1.0);
x.push_back(arr);
if(p.getIntParam("inplace") == 1){
z.push_back(arr);
} else {
z.push_back(NDArrayFactory::create_<T>('c', {p.getIntParam("length")}));
}
};
ScalarBenchmark sbRelu(scalar::Ops::RELU, "RELU");
sbRelu.setY(NDArrayFactory::create_<T>(0.0));
TransformBenchmark tbSigmoid(transform::StrictOps::Sigmoid, "sigmoid");
//TransformBenchmark tbSoftmax(transform::StrictOps::SoftMax, "softmax");
output += helper.runOperationSuit(&sbRelu, generator, batch, "RELU");
output += helper.runOperationSuit(&tbSigmoid, generator, batch, "Sigmoid");
//output += helper.runOperationSuit(&tbSigmoid, generator, batch, "Softmax");
return output;
}
template <typename T>
static std::string scalarBenchmark() {
std::string output;
output += "scalarBenchmark " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
IntPowerParameters length("length", 2, 8, 20, 4); //2^8, 2^12, 2^16, 2^20
BoolParameters inplace("inplace");
ParametersBatch batch({&length, &inplace});
auto generator = PARAMETRIC_XZ() {
auto arr = NDArrayFactory::create_<T>('c', {p.getIntParam("length")});
arr->assign(1.0);
x.push_back(arr);
if(p.getIntParam("inplace") == 1){
z.push_back(arr);
} else {
z.push_back(NDArrayFactory::create_<T>('c', {p.getIntParam("length")}));
}
};
ScalarBenchmark sbAdd(scalar::Ops::Add, "sAdd");
ScalarBenchmark sbDiv(scalar::Ops::Divide, "sDiv");
ScalarBenchmark sbPow(scalar::Ops::Pow, "sPow");
sbAdd.setY(NDArrayFactory::create_<T>(3.14159265359));
sbDiv.setY(NDArrayFactory::create_<T>(3.14159265359));
sbPow.setY(NDArrayFactory::create_<T>(3.14159265359));
output += helper.runOperationSuit(&sbAdd, generator, batch, "Scalar Addition - x.add(3.14159265359)");
output += helper.runOperationSuit(&sbDiv, generator, batch, "Scalar Division - x.div(3.14159265359)");
output += helper.runOperationSuit(&sbPow, generator, batch, "Scalar Power - x.pow(3.14159265359)");
return output;
}
template <typename T>
static std::string pairwiseBenchmark() {
std::string output;
output += "pairwiseBenchmark " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
IntPowerParameters length("length", 2, 8, 20, 4); //2^4 to 2^20 in steps of 4 - 2^4, 2^8, 2^16, 2^20
BoolParameters inplace("inplace");
ParametersBatch batch({&length, &inplace});
auto generator = PARAMETRIC_XYZ() {
auto arr1 = NDArrayFactory::create_<T>('c', {p.getIntParam("length")});
auto arr2 = NDArrayFactory::create_<T>('c', {p.getIntParam("length")});
x.push_back(arr1);
y.push_back(arr2);
if(p.getIntParam("inplace") == 1){
z.push_back(arr1);
} else {
z.push_back(NDArrayFactory::create_<T>('c', {p.getIntParam("length")}));
}
};
PairwiseBenchmark pb1(pairwise::Ops::Add, "Add");
output += helper.runOperationSuit(&pb1, generator, batch, "Pairwise Add");
PairwiseBenchmark pb2(pairwise::Ops::Divide, "Divide");
output += helper.runOperationSuit(&pb2, generator, batch, "Pairwise Divide");
return output;
}
static std::string mismatchedOrderAssign() {
std::string output;
BenchmarkHelper helper(WARMUP, NUM_ITER);
IntPowerParameters rows("rows", 2, 8, 20, 4); //2^8, 2^12, 2^16, 2^20
BoolParameters cf("cf");
ParametersBatch batch({&rows, &cf});
auto generator = PARAMETRIC_XZ() {
int numElements = 4194304; //2^24
int rows = p.getIntParam("rows");
int cols = numElements / rows;
bool c = p.getIntParam("cf");
auto arr = NDArrayFactory::create_<float>(c ? 'c' : 'f', {rows, cols});
auto arr2 = NDArrayFactory::create_<float>(c ? 'f' : 'c', {rows, cols});
x.push_back(arr);
z.push_back(arr2);
};
TransformBenchmark tb(transform::AnyOps::Assign, "assign");
output += helper.runOperationSuit(&tb, generator, batch, "C->F and F->C Assign F32");
//Also test: NCHW to NHWC and back
BoolParameters nchw("nchw");
int mb = 8;
int hw = 64;
int c = 3;
ParametersBatch batch2({&nchw});
auto generator2 = PARAMETRIC_XZ() {
bool nchw = p.getIntParam("nchw");
if(nchw) {
auto orig = NDArrayFactory::create_<float>('c', {mb, c, hw, hw});
orig->permutei({0,2,3,1});
x.push_back(orig);
z.push_back(NDArrayFactory::create_<float>('c', {mb, hw, hw, c}));
} else {
auto orig = NDArrayFactory::create_<float>('c', {mb, hw, hw, c});
orig->permutei({0,3,1,2});
x.push_back(orig);
z.push_back(NDArrayFactory::create_<float>('c', {mb, c, hw, hw}));
}
};
TransformBenchmark tb2(transform::AnyOps::Assign, "assign_nchw");
output += helper.runOperationSuit(&tb2, generator2, batch2, "nchw->nhwc and nhwc->nchw Assign FP32");
return output;
}
template <typename T>
static std::string gemmBenchmark() {
std::string output;
output += "gemm " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
for (int o = 0; o <= 1; o++) {
char resultOrder = (o == 0 ? 'f' : 'c');
IntPowerParameters sz("sz", 2, 4, 10, 2); //2^4=16, ..., 2^10=1024 -> 4 elements
ParametersBatch b({&sz});
auto generator = PARAMETRIC_XYZ() {
auto a = p.getIntParam("sz");
auto b = p.getIntParam("sz");
auto c = p.getIntParam("sz");
std::vector<Nd4jLong> shapeA;
std::vector<Nd4jLong> shapeB;
shapeA = {a, b};
shapeB = {b, c};
auto A = NDArrayFactory::create_<T>('c', shapeA);
auto B = NDArrayFactory::create_<T>('c', shapeB);
auto C = NDArrayFactory::create_<T>(resultOrder, {a, c});
x.push_back(A);
y.push_back(B);
z.push_back(C);
};
std::string n;
n += "Gemm - cOrder=";
n += resultOrder;
MatrixBenchmark mb(1.0, 0.0, false, false, n);
output += helper.runOperationSuit(&mb, generator, b, n.c_str());
}
return output;
}
template <typename T>
static std::string reduceFullBenchmark() {
std::string output;
output += "reduceFullBenchmark " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
IntPowerParameters length("length", 2, 8, 20, 4); //2^8, 2^12, 2^16, 2^20
ParametersBatch batch({&length});
auto generator = PARAMETRIC_XYZ() {
auto arr = NDArrayFactory::create_<T>('c', {p.getIntParam("length")});
x.push_back(arr);
y.push_back(nullptr);
z.push_back(NDArrayFactory::create_<T>(0.0f));
};
ReductionBenchmark rbSum(reduce::SameOps::Sum, "sum");
ReductionBenchmark rbProd(reduce::SameOps::Prod, "prod");
ReductionBenchmark rbMax(reduce::SameOps::Max, "max");
output += helper.runOperationSuit(&rbSum, (const std::function<void (Parameters &, ResultSet &, ResultSet &, ResultSet &)>)(generator), batch, "Sum - Full Array Reduction");
output += helper.runOperationSuit(&rbProd, (const std::function<void (Parameters &, ResultSet &, ResultSet &, ResultSet &)>)(generator), batch, "Product - Full Array Reduction");
output += helper.runOperationSuit(&rbMax, (const std::function<void (Parameters &, ResultSet &, ResultSet &, ResultSet &)>)(generator), batch, "Maximum - Full Array Reduction");
//Index reduction
nd4j::ops::argmax opArgmax;
DeclarableBenchmark dbArgmax(opArgmax, "Argmax");
auto generator3 = PARAMETRIC_D(){
auto ctx = new Context(1);
ctx->setInputArray(0, NDArrayFactory::create_<T>('c', {p.getIntParam("length")}), true);
ctx->setInputArray(1, NDArrayFactory::create_<Nd4jLong>((Nd4jLong)0), true);
ctx->setOutputArray(0, NDArrayFactory::create_<Nd4jLong>(0), true);
return ctx;
};
output += helper.runOperationSuit(&dbArgmax, generator3, batch, "Argmax Full Array Reduction");
return output;
}
template <typename T>
static std::string reduceDimBenchmark(){
std::string output;
output += "reduceDimBenchmark " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
int length[] = {1024*1024};
int pow[] = {10};
for( int i=0; i<1; i++ ){
IntPowerParameters rows("rows", 2, 0, pow[i], 2);
BoolParameters dim("dim");
ParametersBatch batch({&rows, &dim});
auto generator = PARAMETRIC_XYZ() {
int rows = p.getIntParam("rows");
int cols = length[i] / rows;
int dim = p.getIntParam("dim");
auto arr = NDArrayFactory::create_<T>('c', {rows, cols});
x.push_back(arr);
y.push_back(NDArrayFactory::create_<Nd4jLong>(dim));
NDArray* result;
if(dim == 0){
result = NDArrayFactory::create_<T>('c', {cols});
} else {
result = NDArrayFactory::create_<T>('c', {rows});
}
z.push_back(result);
};
ReductionBenchmark rbSum(reduce::SameOps::Sum, "sum");
ReductionBenchmark rbMax(reduce::SameOps::Max, "max");
std::string s1("Sum Along Dimension - ");
s1 += std::to_string(length[i]);
std::string s3("Maximum Along Dimension - ");
s3 += std::to_string(length[i]);
output += helper.runOperationSuit(&rbSum, (const std::function<void (Parameters &, ResultSet &, ResultSet &, ResultSet &)>)(generator), batch, s1.c_str());
output += helper.runOperationSuit(&rbMax, (const std::function<void (Parameters &, ResultSet &, ResultSet &, ResultSet &)>)(generator), batch, s3.c_str());
auto generator3 = PARAMETRIC_D(){
auto ctx = new Context(1);
int rows = p.getIntParam("rows");
int cols = length[i] / rows;
int dim = p.getIntParam("dim");
auto arr = NDArrayFactory::create_<T>('c', {rows, cols});
auto dimArg = new Nd4jLong[1];
dimArg[0] = dim;
ctx->setIArguments(dimArg, 1);
delete[] dimArg;
ctx->setInputArray(0, arr, true);
NDArray* result;
if(dim == 0){
result = NDArrayFactory::create_<Nd4jLong>('c', {cols});
} else {
result = NDArrayFactory::create_<Nd4jLong>('c', {rows});
}
ctx->setOutputArray(0, result, true);
return ctx;
};
std::string s5("Argmax Along Dimension - ");
s5 += std::to_string(length[i]);
nd4j::ops::argmax opArgmax;
DeclarableBenchmark dbArgmax(opArgmax, "Argmax");
output += helper.runOperationSuit(&dbArgmax, generator3, batch, s5.c_str());
}
return output;
}
template <typename T>
static std::string conv2d(){
std::string output;
output += "conv2d " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
//Convolution2D op
BoolParameters nhwc("nhwc");
PredefinedParameters k("k", {2, 3});
ParametersBatch batch({&nhwc, &k});
nd4j::ops::conv2d conv2d;
DeclarableBenchmark benchmark(conv2d, "conv2d");
int hw = 64;
auto generator = PARAMETRIC_D() {
auto ctx = new Context(1);
int n = p.getIntParam("nhwc");
int khw = p.getIntParam("k");
if (n == 0) {
auto input = NDArrayFactory::create_<T>('c', {8, 3, hw, hw});
auto output = NDArrayFactory::create_<T>('c', {8, 3, hw, hw});
ctx->setInputArray(0, input, true);
ctx->setOutputArray(0, output, true);
} else {
auto input = NDArrayFactory::create_<T>('c', {8, hw, hw, 3});
auto output = NDArrayFactory::create_<T>('c', {8, hw, hw, 3});
ctx->setInputArray(0, input, true);
ctx->setOutputArray(0, output, true);
}
auto b = NDArrayFactory::create_<T>('c', {3});
auto w = NDArrayFactory::create_<T>('c', {khw, khw, 3, 3}); // [kH, kW, iC, oC] always
ctx->setInputArray(1, w, true);
ctx->setInputArray(2, b, true);
auto args = new Nd4jLong[10];
args[0] = args[1] = khw; //Kernel
args[2] = args[3] = 1;//Stride
args[4] = args[5] = 0; //Pad
args[6] = args[7] = 1; //Dilation
args[8] = 1; //SAME
args[9] = n;//0-nchw, 1=nhwc
ctx->setIArguments(args, 10);
delete[] args;
return ctx;
};
output += helper.runOperationSuit(&benchmark, generator, batch, "Conv2d");
return output;
}
template <typename T>
static std::string pool2d() {
std::string output;
output += "pool2d " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
//Convolution2D op
BoolParameters nhwc("nhwc");
PredefinedParameters k("k", {2, 3});
ParametersBatch batch({&nhwc, &k});
int c = 3;
int hw = 64;
auto generator = PARAMETRIC_D() {
auto ctx = new Context(1);
int n = p.getIntParam("nhwc");
int khw = p.getIntParam("k");
if (n == 0) {
auto input = NDArrayFactory::create_<T>('c', {8, c, hw, hw});
auto output = NDArrayFactory::create_<T>('c', {8, c, hw, hw});
ctx->setInputArray(0, input, true);
ctx->setOutputArray(0, output, true);
} else {
auto input = NDArrayFactory::create_<T>('c', {8, hw, hw, c});
auto output = NDArrayFactory::create_<T>('c', {8, hw, hw, c});
ctx->setInputArray(0, input, true);
ctx->setOutputArray(0, output, true);
}
auto args = new Nd4jLong[11];
args[0] = args[1] = khw; //Kernel
args[2] = args[3] = 1;//Stride
args[4] = args[5] = 0; //Pad
args[6] = args[7] = 1; //Dilation
args[8] = 1; //SAME
args[9] = 0; //Divisor mode - 0 = exclude padding in divisor
args[10] = n;//0-nchw, 1=nhwc
ctx->setIArguments(args, 11);
delete[] args;
return ctx;
};
nd4j::ops::avgpool2d avgpool2d;
DeclarableBenchmark benchmark1(avgpool2d, "avgpool");
output += helper.runOperationSuit(&benchmark1, generator, batch, "Average Pool 2d");
nd4j::ops::maxpool2d maxpool2d;
DeclarableBenchmark benchmark2(maxpool2d, "maxpool");
output += helper.runOperationSuit(&benchmark2, generator, batch, "Max Pool 2d");
return output;
}
template <typename T>
static std::string lstmBenchmark() {
std::string output;
output += "lstm " + DataTypeUtils::asString(DataTypeUtils::fromT<T>());
BenchmarkHelper helper(WARMUP, NUM_ITER);
BoolParameters format("format"); //0=TNS=[seqLen,mb,size]; 1=NST=[mb,size,seqLen]
PredefinedParameters mb("mb", {1, 8});
int n = 128;
ParametersBatch batch({&format, &mb});
nd4j::ops::lstmBlock lstmBlock;
DeclarableBenchmark benchmark(lstmBlock, "lstm");
int seqLength = 8;
auto generator = PARAMETRIC_D() {
auto ctx = new Context(1);
int f = p.getIntParam("format");
int m = p.getIntParam("mb");
Nd4jLong l = 0;
ctx->setInputArray(0, NDArrayFactory::create_<Nd4jLong>(l), true); //Max TS length (unused)
if (f == 0) {
//TNS format
ctx->setInputArray(1, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //x
ctx->setOutputArray(0, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //i
ctx->setOutputArray(1, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //c
ctx->setOutputArray(2, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //f
ctx->setOutputArray(3, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //o
ctx->setOutputArray(4, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //z
ctx->setOutputArray(5, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //h
ctx->setOutputArray(6, NDArrayFactory::create_<T>('c', {seqLength, m, n}), true); //y
} else {
//NST format
ctx->setInputArray(1, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //x
ctx->setOutputArray(0, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //i
ctx->setOutputArray(1, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //c
ctx->setOutputArray(2, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //f
ctx->setOutputArray(3, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //o
ctx->setOutputArray(4, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //z
ctx->setOutputArray(5, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //h
ctx->setOutputArray(6, NDArrayFactory::create_<T>('f', {m, n, seqLength}), true); //y
}
auto cLast = NDArrayFactory::create_<T>('c', {m, n});
auto yLast = NDArrayFactory::create_<T>('c', {m, n});
auto W = NDArrayFactory::create_<T>('c', {2 * n, 4 * n});
auto Wci = NDArrayFactory::create_<T>('c', {n});
auto Wcf = NDArrayFactory::create_<T>('c', {n});
auto Wco = NDArrayFactory::create_<T>('c', {n});
auto b = NDArrayFactory::create_<T>('c', {4 * n});
ctx->setInputArray(2, cLast, true);
ctx->setInputArray(3, yLast, true);
ctx->setInputArray(4, W, true);
ctx->setInputArray(5, Wci, true);
ctx->setInputArray(6, Wcf, true);
ctx->setInputArray(7, Wco, true);
ctx->setInputArray(8, b, true);
auto iargs = new Nd4jLong[2];
iargs[0] = 0; //No peephole
iargs[1] = f;
ctx->setIArguments(iargs, 2);
delete[] iargs;
auto targs = new double[2];
targs[0] = 1.0; //forget bias
targs[1] = 0.0; //cell clipping value
ctx->setTArguments(targs, 2);
delete[] targs;
return ctx;
};
output += helper.runOperationSuit(&benchmark, generator, batch, "LSTMBlock");
return output;
}
static std::string broadcast2d() {
std::string output;
BenchmarkHelper helper(WARMUP, NUM_ITER);
int rows = 65536;
IntPowerParameters cols("cols", 2, 2, 12, 4); //2^2 to 2^12 in steps of 2 - 2^1=2, ..., 2^10=1024
BoolParameters axis("axis");
BoolParameters inplace("inplace");
ParametersBatch batch({&cols, &axis, &inplace});
auto generator = PARAMETRIC_D() {
auto a = p.getIntParam("axis");
auto arr = NDArrayFactory::create_<float>('c', {rows, p.getIntParam("cols")});
auto ctx = new Context(1);
ctx->setInputArray(0, arr, true);
if(a == 0){
ctx->setInputArray(1, NDArrayFactory::create_<float>('c', {rows, 1}), true);
} else {
ctx->setInputArray(1, NDArrayFactory::create_<float>('c', {1, p.getIntParam("cols")}), true);
}
if (p.getIntParam("inplace") == 1) {
ctx->setOutputArray(0, arr);
ctx->markInplace(true);
} else {
ctx->setOutputArray(0, NDArrayFactory::create_<float>('c', {rows, p.getIntParam("cols")}), true);
}
return ctx;
};
std::string s("add");
nd4j::ops::add op;
DeclarableBenchmark benchmark(op, "add");
output += helper.runOperationSuit(&benchmark, generator, batch, "Broadcast (Custom) Add - 2d");
return output;
}
std::string LightBenchmarkSuit::runSuit() {
#ifdef _RELEASE
std::vector<nd4j::DataType> dtypes({nd4j::DataType::FLOAT32, nd4j::DataType::HALF});
#else
std::vector<nd4j::DataType> dtypes({nd4j::DataType::FLOAT32});
#endif
std::string result;
for (auto t:dtypes) {
nd4j_printf("Running LightBenchmarkSuite.transformBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += transformBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.scalarBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += scalarBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.pairwiseBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += pairwiseBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.reduceFullBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += reduceFullBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.reduceDimBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += reduceDimBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.gemmBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += gemmBenchmark, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.conv2d [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += conv2d, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.pool2d [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += pool2d, (), LIBND4J_TYPES);
nd4j_printf("Running LightBenchmarkSuite.lstmBenchmark [%s]\n", DataTypeUtils::asString(t).c_str());
BUILD_SINGLE_SELECTOR(t, result += lstmBenchmark, (), LIBND4J_TYPES);
}
nd4j_printf("Running LightBenchmarkSuite.broadcast2d\n", "");
result += broadcast2d();
nd4j_printf("Running LightBenchmarkSuite.mismatchedOrderAssign\n", "");
result += mismatchedOrderAssign();
return result;
}
}
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