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Ported SparseLengthsSumTest to EE2 #3309

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Ported SparseLengthsSumTest to EE2 #3309

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2 changes: 1 addition & 1 deletion tests/stress/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ add_executable(SparseLengthsSumTest
target_link_libraries(SparseLengthsSumTest
PRIVATE
Backends
ExecutionEngine
ExecutionEngine2
gtest
glog::glog)

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93 changes: 73 additions & 20 deletions tests/stress/SparseLengthsSumTest.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -14,94 +14,148 @@
* limitations under the License.
*/

#include "BackendTestUtils.h"
#include "BackendTestUtils2.h"

#include "glow/ExecutionEngine/ExecutionEngine.h"
#include "glow/ExecutionEngine/ExecutionEngine2.h"

#include <glog/logging.h>
#include <gtest/gtest.h>

using namespace glow;

class SparseLengthsSum : public BackendTest {};
class SparseLengthsSum : public BackendTest {
public:
~SparseLengthsSum() override{};
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dtor in the base class needs to be virtual.

};

TEST_P(SparseLengthsSum, Big) {
ENABLED_BACKENDS(CPU, Habana);

ExecutionEngine2 interp{};
interp.setDeviceMemory(10000000000);
EE_.setDeviceMemory(10000000000);
auto *mod = &EE_.getModule();
F_ = mod->createFunction("main");
auto *interpMod = &interp.getModule();
auto *G = interp.getModule().createFunction("main");
std::array<size_t, 13> dataRows = {
5000000, 5000000, 6000000, 8000000, 8000000, 8000000, 3000000,
3000000, 1000000, 5000000, 8000000, 5000000, 1000000,
};

std::vector<Constant *> data;
std::vector<Constant *> dataI;
std::vector<Placeholder *> indices;
std::vector<Placeholder *> indicesI;
std::vector<Placeholder *> lengths;
std::vector<Placeholder *> lengthsI;
std::vector<Placeholder *> weights;
std::vector<Placeholder *> weightsI;
std::vector<Placeholder *> results;
std::vector<Placeholder *> resultsI;
for (int i = 0; i < 13; i++) {
LOG(INFO) << "Creating data " << i;
Tensor fData(ElemKind::FloatTy, {dataRows[i], 72});
fData.getHandle<float>().randomize(-1.0, 1.0, mod_.getPRNG());
auto *C = mod_.createConstant(ElemKind::UInt8FusedQTy, {dataRows[i], 80},
fData.getHandle<float>().randomize(-1.0, 1.0, mod->getPRNG());
auto *C = mod->createConstant(ElemKind::UInt8FusedQTy, {dataRows[i], 80},
0.0, 0, "data");
auto *CI = interpMod->createConstant(ElemKind::UInt8FusedQTy,
{dataRows[i], 80}, 0.0, 0, "data");
quantization::tensorFusedRowwiseQuantization<float>(fData,
C->getPayloadMutable());
quantization::tensorFusedRowwiseQuantization<float>(
fData, CI->getPayloadMutable());
data.push_back(C);
dataI.push_back(CI);
}
for (int i = 0; i < 13; i++) {
LOG(INFO) << "Creating indices " << i;
indices.push_back(
mod_.createPlaceholder(ElemKind::Int64ITy, {3000}, "indices", false));
mod->createPlaceholder(ElemKind::Int64ITy, {3000}, "indices", false));
indicesI.push_back(interpMod->createPlaceholder(ElemKind::Int64ITy, {3000},
"indices", false));
}
for (int i = 0; i < 13; i++) {
LOG(INFO) << "Creating lengths " << i;
lengths.push_back(
mod_.createPlaceholder(ElemKind::Int32ITy, {10}, "lengths", false));
mod->createPlaceholder(ElemKind::Int32ITy, {10}, "lengths", false));
lengthsI.push_back(interpMod->createPlaceholder(ElemKind::Int32ITy, {10},
"lengths", false));
}
for (int i = 0; i < 13; i++) {
LOG(INFO) << "Creating weights " << i;
weights.push_back(
mod_.createPlaceholder(ElemKind::FloatTy, {3000}, "weights", false));
mod->createPlaceholder(ElemKind::FloatTy, {3000}, "weights", false));
weightsI.push_back(interpMod->createPlaceholder(ElemKind::FloatTy, {3000},
"weights", false));
}
for (int i = 0; i < 10; i++) {
auto *sls = F_->createFusedRowwiseQuantizedSparseLengthsWeightedSum(
"sls", data[i], weights[i], indices[i], lengths[i]);
auto *save = F_->createSave("save", sls);
results.push_back(save->getPlaceholder());

auto *slsI = G->createFusedRowwiseQuantizedSparseLengthsWeightedSum(
"sls", dataI[i], weightsI[i], indicesI[i], lengthsI[i]);
auto *saveI = G->createSave("save", slsI);
results.push_back(saveI->getPlaceholder());
}
for (int i = 10; i < 11; i++) {
auto *sls = F_->createFusedRowwiseQuantizedSparseLengthsWeightedSum(
"sls", data[i], weights[i], indices[3], lengths[3]);
auto *save = F_->createSave("save", sls);
results.push_back(save->getPlaceholder());

auto *slsI = G->createFusedRowwiseQuantizedSparseLengthsWeightedSum(
"sls", dataI[i], weightsI[i], indicesI[3], lengthsI[3]);
auto *saveI = G->createSave("save", slsI);
results.push_back(saveI->getPlaceholder());
}
for (int i = 11; i < 13; i++) {
auto *sls = F_->createFusedRowwiseQuantizedSparseLengthsSum(
"sls", data[i], indices[i], lengths[i]);
auto *save = F_->createSave("save", sls);
results.push_back(save->getPlaceholder());

auto *slsI = G->createFusedRowwiseQuantizedSparseLengthsSum(
"sls", dataI[i], indicesI[i], lengthsI[i]);
auto *saveI = G->createSave("save", slsI);
results.push_back(saveI->getPlaceholder());
}

PlaceholderBindings bindings;
PlaceholderBindings bindings, interpBindings;
for (size_t i = 0; i < indices.size(); i++) {
auto *index = indices[i];
auto *I = bindings.allocate(index);
I->getHandle<int64_t>().randomize(0, dataRows[i] - 1, mod_.getPRNG());
I->getHandle<int64_t>().randomize(0, dataRows[i] - 1, mod->getPRNG());

auto *indexI = indicesI[i];
auto *II = interpBindings.allocate(indexI);
II->assign(I);
}
for (auto *length : lengths) {
auto *L = bindings.allocate(length);
L->getHandle<int32_t>().randomize(0, 100, mod_.getPRNG());
L->getHandle<int32_t>().randomize(0, 100, mod->getPRNG());

auto *lengthI = interpMod->getPlaceholderByName(length->getName());
auto *LI = interpBindings.allocate(lengthI);
LI->assign(L);
}
for (auto *weight : weights) {
auto *W = bindings.allocate(weight);
W->getHandle<float>().randomize(-1.0, 1.0, mod_.getPRNG());
W->getHandle<float>().randomize(-1.0, 1.0, mod->getPRNG());

auto *weightI = interpMod->getPlaceholderByName(weight->getName());
auto *WI = interpBindings.allocate(weightI);
WI->assign(W);
}
for (auto *result : results) {
bindings.allocate(result);
}
for (auto *result : resultsI) {
interpBindings.allocate(result);
}

auto *G = F_->clone("clone");
EE_.compile(CompilationMode::Infer, G);
EE_.compile(CompilationMode::Infer);
EE_.run(bindings);
std::vector<Tensor> test;
for (auto *result : results) {
Expand All @@ -110,12 +164,11 @@ TEST_P(SparseLengthsSum, Big) {
T->getHandle<float>().clear(0);
}

ExecutionEngine interp{};
interp.compile(CompilationMode::Infer, F_);
interp.run(bindings);
interp.compile(CompilationMode::Infer);
interp.run(interpBindings);
std::vector<Tensor *> base;
for (auto *result : results) {
auto *T = bindings.get(result);
for (auto *result : resultsI) {
auto *T = interpBindings.get(result);
base.push_back(T);
}

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