Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix for apply torchvision models #88

Merged
merged 4 commits into from
Feb 2, 2024
Merged

Fix for apply torchvision models #88

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
eea2249
fix(fronted): 为适配torchvision模型，修改前端从而接调用cpu kernel
bitzyz Jan 30, 2024
3f5b0ee
fix(fronted): 修复reduce算子对reducesum的opset特判bug
bitzyz Jan 31, 2024
7db54c1
fix(kernel):修复transpose info存在的bug
bitzyz Jan 31, 2024
b3e89a6
fix(kernel): 为 TransposeInfo 应对更多 coner case 并增加一个单元测试
YdrMaster Jan 31, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 7 additions & 1 deletion src/04kernel/src/attributes/transpose_info.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ namespace refactor::kernel {
}
}
}
if (rank == 0) {
if (rank <= 1) {
dims = {{1, 1}};
blockSize *= blockCount;
blockCount = 1;
Expand Down Expand Up @@ -73,6 +73,12 @@ namespace refactor::kernel {
}
perm.resize(rank);
}
if (rank <= 1) {
dims = {{1, 1}};
blockSize *= blockCount;
blockCount = 1;
return;
}
// 合并末尾连续访存
if (perm.back() == rank - 1) {
blockSize *= shape.back();
Expand Down
39 changes: 39 additions & 0 deletions src/04kernel/test/attributes/test_transpose_info.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,39 @@
#include "kernel/attributes/transpose_info.h"
#include <gtest/gtest.h>

using namespace refactor;
using namespace kernel;

TEST(kernel, TransposeInfo) {
{
TransposeInfo info(
DataType::F32,
{1, 2, 3, 2, 1},
{1, 2, 3, 0, 4});
EXPECT_EQ(info.blockSize, 48);
EXPECT_EQ(info.blockCount, 1);
EXPECT_EQ(info.dims.size(), 1);
}
{
TransposeInfo info(
DataType::F32,
{1, 1, 2, 1, 1},
{1, 2, 3, 0, 4});
EXPECT_EQ(info.blockSize, 8);
EXPECT_EQ(info.blockCount, 1);
EXPECT_EQ(info.dims.size(), 1);
}
{
TransposeInfo info(
DataType::F32,
{1, 2, 3, 4, 5},
{2, 3, 1, 0, 4});
EXPECT_EQ(info.blockSize, 20);
EXPECT_EQ(info.blockCount, 24);
EXPECT_EQ(info.dims.size(), 2);
EXPECT_EQ(info.dims[1].strideI, 12);
EXPECT_EQ(info.dims[1].strideO, 1);
EXPECT_EQ(info.dims[0].strideI, 1);
EXPECT_EQ(info.dims[0].strideO, 2);
}
}
2 changes: 1 addition & 1 deletion src/06frontend/src/graph.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@ namespace refactor::frontend {
for (auto i : range0_(inputs.size())) {
auto j = inputs[i];
auto const &input = _internal.edges[j].tensor;
ASSERT(input, "The {}th input of \"{}\" is nullptr", i, _internal.nodes[nodeIdx].name);
ASSERT(input, "The input[{}] of \"{}\" is nullptr", i, _internal.nodes[nodeIdx].name);
auto checked = edgeChanged[2 * j]; // NOTICE `std::vector<bool>::operator[]` 产生常引用！！！
auto changed = edgeChanged[2 * j + 1];// NOTICE `std::vector<bool>::operator[]` 产生常引用！！！
if (!checked) {
Expand Down
63 changes: 29 additions & 34 deletions src/07onnx/src/operators/gather.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
#include "computation/operators/gather.h"
#include "common.h"
#include "gather.hh"
#include "kernel/collectors/gather.h"
#include "runtime/resource.h"
#include <execution>

namespace refactor::onnx {
Expand Down Expand Up @@ -42,41 +44,34 @@ namespace refactor::onnx {
if (!options.shouldCalculate(inputs, {*ans})) {
return Ok(Tensors{std::move(ans)});
}
{
using Shape = kernel::Shape;
using Tensor = kernel::Tensor;
using LayoutType = kernel::LayoutType;

std::for_each_n(std::execution::unseq, natural_t(0), ans->elementsSize(),
[&data, &indices, &output,
axis_,
q = indices.shape.size(),
ssz = output.size(),
src = data.data->get<uint8_t>(),
dst = reinterpret_cast<uint8_t *>(ans->malloc()),
eleSize = data.dataType.size()](auto const i) {
auto indices_ = locateN(output, i);
int64_t k;
{
size_t ii = 0, mul = 1;
for (auto j : range0_(q).rev()) {
ii += indices_[j] * mul;
mul *= indices.shape[j].value();
}
k = indices.dataType == DataType::I64
? indices.data->get<int64_t>()[ii]
: indices.data->get<int32_t>()[ii];
}
{
size_t ii = 0, mul = 1;
for (auto j : range(static_cast<decltype(q)>(axis_) + q, ssz).rev()) {
ii += indices_[j] * mul;
mul *= data.shape[j - q + 1].value();
}
ii += k * mul;
for (auto j : range0_(axis_).rev()) {
ii += indices_[j] * mul;
mul *= data.shape[j].value();
}
std::memcpy(dst + i * eleSize, src + ii * eleSize, eleSize);
}
});
Shape t1Shape(data.shape.size(), 1);
Shape t2Shape(indices.shape.size(), 1);
Shape oShape(ans->shape.size(), 1);
std::transform(std::execution::unseq,
data.shape.begin(), data.shape.end(), t1Shape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
std::transform(std::execution::unseq,
indices.shape.begin(), indices.shape.end(), t2Shape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
auto t1 = Tensor::share(data.dataType, t1Shape, LayoutType::Others, data.data);
auto t2 = Tensor::share(indices.dataType, t2Shape, LayoutType::Others, indices.data);
std::transform(std::execution::unseq,
ans->shape.begin(), ans->shape.end(), oShape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
auto o = Tensor::share(data.dataType, oShape, LayoutType::Others);
runtime::Resources res;
const auto collector = kernel::GatherCollector(computation::Target::Cpu, axis_);
auto routine = std::move(collector.filter({*t1, *t2}, {*o}).at(0))->lower(res).routine;
void const *inputsCpu[]{*t1->data, *t2->data};
void *outputsCpu[]{o->malloc()};
routine(res, nullptr, inputsCpu, outputsCpu);
ans->data = o->data;
}

return Ok(Tensors{std::move(ans)});
}
Expand Down
18 changes: 14 additions & 4 deletions src/07onnx/src/operators/reduce.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,13 +20,23 @@ namespace refactor::onnx {

auto noopWithEmptyAxes = false;
decltype(Op::axes) axes = std::nullopt;
if (opsetVer >= 18) {
noopWithEmptyAxes = attributes.getOrInsert( "noop_with_empty_axes", {0}).int_() != 0;

// 针对ReduceSum做特判
if (opType == "onnx::ReduceSum") {
if (opsetVer >= 13) {
noopWithEmptyAxes = attributes.getOrInsert("noop_with_empty_axes", {0}).int_() != 0;
} else {
axes.emplace(attributes.getOrInsert("axes", {{}}).ints());
}
} else {
axes.emplace(attributes.getOrInsert( "axes", {{}}).ints());
if (opsetVer >= 18) {
noopWithEmptyAxes = attributes.getOrInsert("noop_with_empty_axes", {0}).int_() != 0;
} else {
axes.emplace(attributes.getOrInsert("axes", {{}}).ints());
}
}

auto keepDims = attributes.getOrInsert( "keepdims", {1}).int_();
auto keepDims = attributes.getOrInsert("keepdims", {1}).int_();
Ty ty;
if (opType == "onnx::ReduceMean") {
ty = Ty::Mean;
Expand Down
86 changes: 33 additions & 53 deletions src/07onnx/src/operators/simple_binary.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,9 @@
#include "simple_binary.hh"
#include "common.h"
#include "computation/operators/simple_binary.h"
#include "kernel/collectors/simple_binary.h"
#include "runtime/resource.h"
#include <execution>

namespace refactor::onnx {
using Op = SimpleBinary;
Expand All @@ -10,7 +13,7 @@ namespace refactor::onnx {
: Operator(), type(type_) {}

auto Op::build(ModelContext const &, std::string_view opType, Attributes attributes) -> OpBox {
auto fmod = attributes.getOrInsert( "fmod", {0}).int_();
auto fmod = attributes.getOrInsert("fmod", {0}).int_();
// clang-format off
auto type =
opType == "onnx::Add" ? Ty::Add :
Expand Down Expand Up @@ -93,30 +96,6 @@ namespace refactor::onnx {
// clang-format on
}

template<decltype(DataType::internal) T>
void calculate(Ty ty, void *dst, void const *a, void const *b) {
using T_ = typename primitive<T>::type;
auto a_ = *reinterpret_cast<T_ const *>(a);
auto b_ = *reinterpret_cast<T_ const *>(b);
auto dst_ = reinterpret_cast<T_ *>(dst);
switch (ty) {
case Ty::Add:
*dst_ = a_ + b_;
break;
case Ty::Sub:
*dst_ = a_ - b_;
break;
case Ty::Mul:
*dst_ = a_ * b_;
break;
case Ty::Div:
*dst_ = a_ / b_;
break;
default:
UNREACHABLE();
}
}

auto Op::infer(TensorRefs inputs, InferOptions const &options) const -> InferResult {
EXPECT_SIZE(2)

Expand All @@ -139,35 +118,36 @@ namespace refactor::onnx {
return Ok(Tensors{std::move(ans)});
}

auto eleSize = dataType.size();
auto dst = reinterpret_cast<uint8_t *>(ans->malloc());
for (auto i : range0_(ans->elementsSize())) {
auto indices = locateN(ans->shape, i);
auto a_ = locate1(a, indices),
b_ = locate1(b, indices);
auto dst_ = dst + i * eleSize;
//-------------------------------------
#define CASE(T) \
case DataType::T: \
calculate<DataType::T>(type, dst_, a_, b_); \
break
//-------------------------------------
switch (dataType.internal) {
CASE(F32);
CASE(F64);
CASE(I32);
CASE(I64);
CASE(I8);
CASE(I16);
CASE(U8);
CASE(U16);
CASE(U32);
CASE(U64);
default:
ans->free();
break;
}
{
using Shape = kernel::Shape;
using Tensor = kernel::Tensor;
using LayoutType = kernel::LayoutType;

Shape t1Shape(a.shape.size(), 1);
Shape t2Shape(b.shape.size(), 1);
Shape oShape(ans->shape.size(), 1);
std::transform(std::execution::unseq,
a.shape.begin(), a.shape.end(), t1Shape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
std::transform(std::execution::unseq,
b.shape.begin(), b.shape.end(), t2Shape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
auto t1 = Tensor::share(a.dataType, t1Shape, LayoutType::Others, a.data);
auto t2 = Tensor::share(b.dataType, t2Shape, LayoutType::Others, b.data);
std::transform(std::execution::unseq,
ans->shape.begin(), ans->shape.end(), oShape.begin(),
[](auto const &i) { return static_cast<dim_t>(i.value()); });
auto o = Tensor::share(a.dataType, oShape, LayoutType::Others);
runtime::Resources res;
auto type_ = static_cast<kernel::SimpleBinaryType>(type);
const auto collector = kernel::SimpleBinaryCollector(computation::Target::Cpu, type_);
auto routine = std::move(collector.filter({*t1, *t2}, {*o}).at(0))->lower(res).routine;
void const *inputsCpu[]{*t1->data, *t2->data};
void *outputsCpu[]{o->malloc()};
routine(res, nullptr, inputsCpu, outputsCpu);
ans->data = o->data;
}

return Ok(Tensors{std::move(ans)});
}

Expand Down
Loading