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#pragma once
#include <string>
#include <vector>
#include <c10/util/irange.h>
#include "torch/csrc/jit/tensorexpr/eval.h"
namespace torch {
namespace jit {
namespace tensorexpr {
template <typename T>
struct DefaultPaddedValue;
template <>
struct DefaultPaddedValue<int> {
static const int kValue = static_cast<int>(0xDEADBEEF);
};
template <>
struct DefaultPaddedValue<int8_t> {
static const int8_t kValue = static_cast<int8_t>(0xBE);
};
template <>
struct DefaultPaddedValue<uint8_t> {
static const uint8_t kValue = static_cast<uint8_t>(0xBE);
};
template <>
struct DefaultPaddedValue<int16_t> {
static const int16_t kValue = static_cast<int16_t>(0xBEEF);
};
template <>
struct DefaultPaddedValue<int64_t> {
static const int64_t kValue = static_cast<int64_t>(0xDEADBEEF);
};
template <>
struct DefaultPaddedValue<float> {
static constexpr float kValue = 0.1357;
};
template <>
struct DefaultPaddedValue<at::Half> {
// at::Half ctor isn't constexpr, so just fill it with bits.
static constexpr uint16_t kValue = 1357;
};
template <>
struct DefaultPaddedValue<double> {
static constexpr double kValue = 0.1357;
};
// A concrete base to be used in PaddedBase.
class PaddedBufferBase {
public:
const std::string& name() const {
return name_;
}
int size() const {
return total_size_;
}
int raw_size() const {
return total_size_ + 2 * kPaddingSize;
}
virtual ~PaddedBufferBase() {}
protected:
explicit PaddedBufferBase(
const std::vector<int>& dims,
const std::string& name);
int Index(const std::vector<int>& indices) const;
std::vector<int> dims_;
std::string name_;
std::vector<int> strides_;
int total_size_; // total number of useful element, does not include the
// paddings
static constexpr int kPaddingSize = 64;
};
// A padded buffer with wartermarks for testing.
// The buffer carries padded watermarks on both sides to catch potential
// out-of-bounds writes. For read-only data that are not supposed to change, it
// can also make a backup and be compared later.
template <typename T>
class PaddedBuffer : public PaddedBufferBase {
public:
PaddedBuffer(int d0, const std::string& name = "")
: PaddedBuffer(std::vector<int>({d0}), name) {}
PaddedBuffer(int d0, int d1, const std::string& name = "")
: PaddedBuffer(std::vector<int>({d0, d1}), name) {}
PaddedBuffer(int d0, int d1, int d2, const std::string& name = "")
: PaddedBuffer(std::vector<int>({d0, d1, d2}), name) {}
PaddedBuffer(int d0, int d1, int d2, int d3, const std::string& name = "")
: PaddedBuffer(std::vector<int>({d0, d1, d2, d3}), name) {}
PaddedBuffer(const std::vector<int>& dims, const std::string& name = "")
: PaddedBufferBase(dims, name) {
data_.resize(total_size_ + 2 * kPaddingSize, kPaddingValue);
}
PaddedBuffer(const PaddedBuffer& other, const std::string& name)
: PaddedBuffer(other) {
this->name_ = name;
}
T* data() {
return data_.data() + kPaddingSize;
}
const T* data() const {
return const_cast<PaddedBuffer*>(this)->data();
}
T* raw_data() {
return data_.data();
}
const T* raw_data() const {
return const_cast<PaddedBuffer*>(this)->raw_data();
}
T& operator()(int i0) {
// There is a bit performance impact with forming a vector here. But this
// data structure is for testing only, and not performance critical.
return this->operator()(std::vector<int>({i0}));
}
const T& operator()(int i0) const {
return const_cast<PaddedBuffer*>(this)->operator()(i0);
}
T& operator()(int i0, int i1) {
return this->operator()(std::vector<int>({i0, i1}));
}
const T& operator()(int i0, int i1) const {
return const_cast<PaddedBuffer*>(this)->operator()(i0, i1);
}
T& operator()(int i0, int i1, int i2) {
return this->operator()(std::vector<int>({i0, i1, i2}));
}
const T& operator()(int i0, int i1, int i2) const {
return const_cast<PaddedBuffer*>(this)->operator()(i0, i1, i2);
}
T& operator()(int i0, int i1, int i2, int i3) {
return this->operator()(std::vector<int>({i0, i1, i2, i3}));
}
const T& operator()(int i0, int i1, int i2, int i3) const {
return const_cast<PaddedBuffer*>(this)->operator()(i0, i1, i2, i3);
}
T& operator()(const std::vector<int>& indices) {
return data_[kPaddingSize + Index(indices)];
}
const T& operator()(const std::vector<int>& indices) const {
return const_cast<PaddedBuffer*>(this)->operator()(indices);
}
template <typename U>
friend void ExpectAllNear(
const PaddedBuffer<U>& v1,
const PaddedBuffer<U>& v2,
float abs_error);
template <typename U>
friend void ExpectAllEqual(
const PaddedBuffer<U>& v1,
const PaddedBuffer<U>& v2);
void Backup() {
backup_data_ = data_;
}
// Verify the watermarks in the paddings are intact.
void ValidateWatermark() const {
for (const auto i : c10::irange(kPaddingSize)) {
ASSERT_EQ(data_[i], kPaddingValue);
ASSERT_EQ(data_[i + total_size_ + kPaddingSize], kPaddingValue);
}
}
void CheckBackup() const {
ValidateWatermark();
DCHECK(backup_data_.size() == data_.size())
<< "Please make sure you have call Backup() before calling CheckBackup()";
for (const auto i : c10::irange(total_size_)) {
ASSERT_EQ(data_[i + kPaddingSize], backup_data_[i + kPaddingSize]);
}
}
private:
std::vector<T> data_;
std::vector<T> backup_data_;
T kPaddingValue = DefaultPaddedValue<T>::kValue;
};
template <typename T>
inline CodeGen::CallArg::CallArg(const PaddedBuffer<T>& buffer)
: data_(const_cast<T*>(buffer.data())) {}
template <typename T>
std::string CompareErrorMsg(
const PaddedBuffer<T>& v1,
const PaddedBuffer<T>& v2,
int index) {
std::ostringstream oss;
oss << "index: " << index << ", v1: (" << v1.name() << ", " << v1(index)
<< ")"
<< ", v2: (" << v2.name() << ", " << v2(index) << ")";
return oss.str();
}
template <typename T>
void ExpectAllEqual(const PaddedBuffer<T>& f1, const PaddedBuffer<T>& f2) {
const std::vector<T>& v1 = f1.data_;
const std::vector<T>& v2 = f2.data_;
const int kPaddingSize = f1.kPaddingSize;
const int total_size = f1.total_size_;
ASSERT_EQ(v1.size(), v2.size());
f1.ValidateWatermark();
f2.ValidateWatermark();
for (const auto i : c10::irange(total_size)) {
ASSERT_EQ(v1[kPaddingSize + i], v2[kPaddingSize + i]);
}
}
template <typename T>
void ExpectAllNear(
const PaddedBuffer<T>& f1,
const PaddedBuffer<T>& f2,
float abs_error) {
const std::vector<T>& v1 = f1.data_;
const std::vector<T>& v2 = f2.data_;
const int kPaddingSize = f1.kPaddingSize;
const int total_size = f1.total_size_;
ASSERT_EQ(v1.size(), v2.size());
f1.ValidateWatermark();
f2.ValidateWatermark();
for (const auto i : c10::irange(total_size)) {
ASSERT_NEAR(v1[kPaddingSize + i], v2[kPaddingSize + i], abs_error);
}
}
} // namespace tensorexpr
} // namespace jit
} // namespace torch
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