Add: Membound operator serialization

This commit is contained in:
Liyan Zheng 2023-04-18 21:53:48 +08:00
parent 2812900ea2
commit 537b3b4ea4
5 changed files with 87 additions and 29 deletions

View File

@ -36,10 +36,14 @@ class Serializer : public Functor<string()> {
* @param expr The expression to be serialized
* @param filePath The path of json file to be output
* @param msg Message of derivation
* @param inputs membound operator attributes
* @param exec_time membound operator attributes
* @param hint membound operator attributes
* @return bool Whether the serialization succeed
*/
bool serialize(const Expr &expr, const string &filePath,
const string &msg = "");
const string &msg = "", vector<Tensor> inputs = {},
double exec_time = -1e9, string hint = "");
/**
* @brief Deserialize the given json file to expression
@ -48,6 +52,9 @@ class Serializer : public Functor<string()> {
* @return Expression deserialized from the given json file
*/
Expr deserialize(const string &filePath);
tuple<Expr, vector<Tensor>, double, string>
deserializeAsMemobundOp(const string &filePath);
};
} // namespace nnet

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@ -6,14 +6,17 @@ namespace infini {
class MemBoundObj : public OperatorObj {
private:
nnet::Expr expr;
std::vector<nnet::Tensor>
nnetInputs; // The order of inputs in nnetInputs should be consistant
// with inputs in infinitensor
nnet::Expr expr, simplifiedExpr;
double exec_time;
std::string hint;
HashType hash, simplifiedHash;
int n, f, h, w;
// Generated attributes
HashType hash;
nnet::Expr simplifiedExpr;
HashType simplifiedHash;
public:
MemBoundObj(GraphObj *graph, const TensorVec &input,

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@ -82,13 +82,26 @@ string Serializer::visit_(const Func &c) {
}
bool Serializer::serialize(const Expr &expr, const string &filePath,
const string &msg) {
const string &msg, vector<Tensor> inputs,
double exec_time, string hint) {
// Metadata
j["Version"] = VERSION;
j["Msg"] = msg;
j["exec_time"] = exec_time;
j["hint"] = hint;
// Expressions and routines
id = 0;
dispatch(expr);
// Input tensors
vector<string> inputsIndices;
for (const auto &tensor : inputs) {
inputsIndices.emplace_back(std::to_string(id));
dispatch(tensor);
}
j["nnetInputs"] = inputsIndices;
// Write to file
std::ofstream fout(filePath);
fout << std::setw(4) << j << std::endl;
return true;
@ -254,4 +267,15 @@ Routine Serializer::buildRoutine(string key) {
return nullptr;
}
tuple<Expr, vector<Tensor>, double, string>
Serializer::deserializeAsMemobundOp(const string &filePath) {
std::ifstream fin(filePath);
fin >> j;
assert(j["Version"] == VERSION);
vector<Tensor> inputs;
for (const auto &input : j["nnetInputs"])
inputs.emplace_back(as<TensorNode>(buildExprTree(input)));
return {buildExprTree("0"), inputs, j["exec_time"], j["hint"]};
}
} // namespace nnet

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@ -10,8 +10,8 @@ MemBoundObj::MemBoundObj(GraphObj *graph, const TensorVec &input,
const TensorVec &output,
const std::vector<nnet::Tensor> &nnetInputs,
nnet::Expr expr, double exec_time, std::string hint)
: OperatorObj(OpType::MemBound, input, output), nnetInputs(nnetInputs),
expr(expr), exec_time(exec_time), hint(hint) {
: OperatorObj(OpType::MemBound, input, output), expr(expr),
nnetInputs(nnetInputs), exec_time(exec_time), hint(hint) {
IT_ASSERT(checkValid(graph));
IT_ASSERT(!checkOOB(expr));
hash = calcHash(expr);
@ -61,11 +61,18 @@ string MemBoundObj::toString() const {
optional<vector<Shape>> MemBoundObj::inferShape(const TensorVec &inputs) const {
// inputs have to match nnetInputs excatly
if (inputs.size() != nnetInputs.size())
if (inputs.size() != nnetInputs.size()) {
std::cout << "Num mismatch" << inputs.size() << " "
<< nnetInputs.size();
return {};
}
for (size_t i = 0; i < inputs.size(); ++i)
if (inputs[i]->getDims() != nnetInputs[i]->getShape())
if (inputs[i]->getDims() != nnetInputs[i]->getShape()) {
std::cout << "Shape mismatch " << inputs[i]
<< vecToString(inputs[i]->getDims()) << " "
<< vecToString(nnetInputs[i]->getShape());
return {};
}
return {{nnet::as<nnet::RangeOpNode>(expr)->getOutputShape()}};
}
@ -85,7 +92,8 @@ bool MemBoundObj::checkOOB(nnet::Expr expr) {
}
void MemBoundObj::saveAsJson(string path) const {
bool status = nnet::Serializer().serialize(expr, path);
bool status = nnet::Serializer().serialize(
expr, path, "MemBoundObj::saveAsJson", nnetInputs, exec_time, hint);
IT_ASSERT(status);
}

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@ -1,9 +1,11 @@
#include "core/graph.h"
#include "nnet/Visitor/FullPrinterVisitor.h"
#include "nnet/Visitor/Serializer.h"
#include "nnet/test.h"
#include "operators/membound.h"
#include "gtest/gtest.h"
using namespace nnet;
using namespace std;
#define DEFINE_VAR(name) auto name = make_ref<VarNode>(#name);
//{L<i3:0:2500><i4:0:4><b:0:8><w:0:65>Sum<k:0:512>
//{({A}[b, (i3 + (2500 * i4)), k] * {B<pad=0,128,0>}[b, ((i3 + (2500 * i4)) +
@ -12,11 +14,7 @@ using namespace std;
// ==> B : Input Tensor shape=[8,10000,512] pad=[0,128,0]
Expr buildSimpleExpr() {
DEFINE_VAR(b);
DEFINE_VAR(w);
DEFINE_VAR(k);
DEFINE_VAR(i3);
DEFINE_VAR(i4);
DEFINE_VAR(b, w, k, i3, i4);
auto A = makeTensor("A", {8, 10000, 512}, {0, 0, 0});
auto B = makeTensor("B", {8, 10000, 512}, {0, 128, 0});
auto subA = makeSubscript(A, {b, (i3 + (2500 * i4)), k});
@ -28,9 +26,7 @@ Expr buildSimpleExpr() {
}
Expr buildNestedExpr() {
DEFINE_VAR(j1);
DEFINE_VAR(j2);
DEFINE_VAR(j3);
DEFINE_VAR(j1, j2, j3);
// Build a Matmul to verify.
const int M = 10000, N = 512, K = 3;
auto C = make_ref<TensorNode>("C", vector<int>({M, K}));
@ -46,11 +42,7 @@ Expr buildNestedExpr() {
auto E = make_ref<TensorNode>("E", shapeE, shapeE, ele2);
auto ele1 = make_ref<ElementWiseNode>(expr, vector{E}, shapeE);
DEFINE_VAR(b);
DEFINE_VAR(w);
DEFINE_VAR(k);
DEFINE_VAR(i3);
DEFINE_VAR(i4);
DEFINE_VAR(b, w, k, i3, i4);
auto A = makeTensor("A", {8, 10000, 512}, {0, 0, 0}, matmul);
auto B = makeTensor("B", {8, 10000, 512}, {0, 128, 0}, ele1);
auto subA = makeSubscript(A, {b, (i3 + (2500 * i4)), k});
@ -68,11 +60,7 @@ TEST(Serializer, Serialization) {
}
TEST(Serializer, CompareTwoExprs) {
DEFINE_VAR(b);
DEFINE_VAR(w);
DEFINE_VAR(k);
DEFINE_VAR(i3);
DEFINE_VAR(i4);
DEFINE_VAR(b, w, k, i3, i4);
auto A = makeTensor("A", {8, 10000, 512}, {0, 0, 0});
auto B = makeTensor("B", {8, 10000, 512}, {0, 128, 0});
auto subA = makeSubscript(A, {b, (i3 + (2500 * i4)), k});
@ -97,4 +85,32 @@ TEST(Serializer, Serialization_NestedTensor) {
auto exprDeserialized = Serializer().deserialize("./test_serializer.json");
auto output = printer.print(exprDeserialized);
EXPECT_EQ(output, ans);
}
TEST(Serializer, Serialization_memboundOp) {
auto expr = buildSimpleExpr();
auto A = makeTensor("A", {8, 10000, 512}, {0, 0, 0});
auto B = makeTensor("B", {8, 10000, 512}, {0, 128, 0});
// using namespace infini;
auto runtime = infini::NativeCpuRuntimeObj::getInstance();
auto g = infini::make_ref<infini::GraphObj>(runtime);
auto AT = g->addTensor({8, 10000, 512});
auto BT = g->addTensor({8, 10000, 512});
auto CT = g->addTensor({2500, 4, 8, 65});
vector<Tensor> nnetInputs{A, B};
double execTime = 1;
string hint = "test";
infini::MemBoundObj memboundOp(nullptr, {AT, BT}, {CT}, nnetInputs, expr,
execTime, hint);
memboundOp.saveAsJson("./test_serializer.json");
auto [exprLoaded, nnetInputsLoaded, execTimeLoaded, hintLoaded] =
Serializer().deserializeAsMemobundOp("./test_serializer.json");
EXPECT_EQ(expr->toReadable(), exprLoaded->toReadable());
EXPECT_EQ(execTime, execTimeLoaded);
EXPECT_EQ(nnetInputs.size(), nnetInputsLoaded.size());
for (size_t i = 0; i < nnetInputs.size(); ++i)
EXPECT_EQ(nnetInputs[i]->toReadable(),
nnetInputsLoaded[i]->toReadable());
EXPECT_EQ(hint, hintLoaded);
}