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operator.h
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#ifndef CAFFE2_CORE_OPERATOR_H_
#define CAFFE2_CORE_OPERATOR_H_
#include <array>
#include <cfenv>
#include <climits>
#include <cstddef>
#include <exception>
#include <functional>
#include <set>
#include <string>
#include <typeinfo>
#include <vector>
#include <c10/macros/Macros.h>
#include <c10/util/Registry.h>
#include <c10/util/typeid.h>
#include "caffe2/core/blob.h"
#include "caffe2/core/common.h"
#include "caffe2/core/net.h"
#include "caffe2/core/observer.h"
#include "caffe2/core/operator_gradient.h"
#include "caffe2/core/operator_schema.h"
#include "caffe2/core/tensor.h"
#include "caffe2/core/tensor_int8.h"
#include "caffe2/core/types.h"
#include "caffe2/core/workspace.h"
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/utils/proto_utils.h"
#if !defined(CAFFE2_IS_XPLAT_BUILD)
#include <ATen/core/Tensor.h>
#include <ATen/core/ivalue.h>
#endif
C10_DECLARE_bool(caffe2_operator_throw_if_fp_exceptions);
C10_DECLARE_bool(caffe2_operator_throw_if_fp_overflow_exceptions);
#ifdef __GNU_LIBRARY__
C10_DECLARE_bool(caffe2_operator_throw_on_first_occurrence_if_fp_exceptions);
#endif
namespace c10 {
struct FunctionSchema;
}
namespace caffe2 {
class CAFFE2_API OperatorBase;
typedef ObserverBase<OperatorBase> OperatorObserver;
class CAFFE2_API OperatorBase : public Observable<OperatorBase> {
public:
explicit OperatorBase(const OperatorDef& operator_def, Workspace* ws);
/*
* Notes: All outputs ivalues must be tensors. Input ivalue list must start
* with all tensors ("inputs" in caffe2 terminology),
* followed by non-tensors ("arguments" in caffe2 terminology).
* Alternatively, inputs can be one tensor list ivalue followed by non-tensors
* to represent operators with a variable number of inputs.
*/
#if !defined(CAFFE2_IS_XPLAT_BUILD)
explicit OperatorBase(
const c10::FunctionSchema& schema,
std::vector<c10::IValue> inputs,
c10::List<at::Tensor> outputs);
#endif
virtual ~OperatorBase() noexcept;
/** @brief Return true if the operator was instantiated with OperatorDef
* New operators should be instantiated with FunctionSchema
*/
bool isLegacyOperator() const {
#if !defined(CAFFE2_IS_XPLAT_BUILD)
return !fn_schema_;
#else
return true;
#endif
}
const c10::FunctionSchema& getFunctionSchema() const {
CAFFE_ENFORCE(!isLegacyOperator());
#if !defined(CAFFE2_IS_XPLAT_BUILD)
return *fn_schema_.get();
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
/** @brief Checks if the operator has an argument of the given name.
*/
inline bool HasArgument(const string& name) const {
if (isLegacyOperator()) {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::HasArgument(*operator_def_, name);
}
return argumentIndexWithName(name).has_value();
}
// Functions that deal with arguments. Basically, this allows us to map an
// argument name to a specific type of argument that we are trying to access.
template <typename T>
inline T GetSingleArgument(const string& name, const T& default_value) const {
if (isLegacyOperator()) {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::GetSingleArgument<OperatorDef, T>(
*operator_def_, name, default_value);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
auto index = argumentIndexWithName(name);
CAFFE_ENFORCE(index.has_value(), "Couldn't get index for argument!", name);
const auto& value = newstyle_inputs_[index.value()];
return value.template to<T>();
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
template <typename T>
inline bool HasSingleArgumentOfType(const string& name) const {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::HasSingleArgumentOfType<OperatorDef, T>(
*operator_def_, name);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
template <typename T>
inline vector<T> GetVectorFromIValueList(const c10::IValue& value) const {
return c10::impl::toVector(value.template to<List<T>>());
}
#endif
template <typename T>
inline vector<T> GetRepeatedArgument(
const string& name,
const vector<T>& default_value = {}) const;
// Get the inputs and outputs as specific types.
template <typename T>
inline const T& Input(int idx) {
static_assert(
!std::is_same<T, Tensor>::value,
"You should use Input<Tensor>(int, DeviceType) for "
"Tensor.");
DCHECK_LT((size_t)idx, inputs_.size());
try {
return inputs_.at(idx)->template Get<T>();
} catch (::caffe2::EnforceNotMet& enf) {
if (has_debug_def()) {
enf.AppendMessage(".\nOffending Blob name: ");
enf.AppendMessage(debug_def().input(idx));
enf.AppendMessage(".\n");
}
throw enf;
}
}
// TODO(jerryzh): Remove template
// and the type argument?
// This is to keep the API changes minimal and make refactoring
// a bit easier
template <typename T>
inline const T& Input(int idx, DeviceType type) {
if (isLegacyOperator()) {
static_assert(
std::is_same<T, Tensor>::value,
"Input(int, DeviceType) is only available for Tensor");
DCHECK_LT((size_t)idx, inputs_.size());
try {
// TODO(jerryzh): We'll need to check device type in Get<T>() later
// Get<T>() -> Get<T>(type)
const auto& tensor = inputs_.at(idx)->template Get<T>();
return tensor;
} catch (::caffe2::EnforceNotMet& enf) {
if (has_debug_def()) {
enf.AppendMessage(".\nOffending Blob name: ");
enf.AppendMessage(debug_def().input(idx));
enf.AppendMessage(".\n");
}
throw enf;
}
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
DCHECK_LT(0, newstyle_inputs_.size());
IValue ival;
if (newstyle_inputs_[0].isTensorList()) {
// if the first input is a tensor list, we get input tensors by indexing into that list.
// currently, this means that only tensors from that list are accessible as inputs.
// any hypothetical input tensors that come after the list are not accessible.
auto tensorList = newstyle_inputs_[0].toTensorListRef();
DCHECK_LT((size_t)idx, tensorList.size());
ival = tensorList[idx];
} else {
// if the first input is not a tensor list, we get input tensors by indexing into the inputs.
DCHECK_LT((size_t)idx, newstyle_inputs_.size());
ival = newstyle_inputs_[idx];
}
CAFFE_ENFORCE(
ival.isTensor(),
"Input(int, DeviceType) is only available for IValues that store Tensors");
Tensor tensor = caffe2::Tensor(ival.toTensor());
CAFFE_ENFORCE_EQ(tensor.GetDeviceType(), type);
input_tensors_[idx] = std::move(tensor);
return input_tensors_[idx];
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
template <typename T>
inline T* Output(int idx) {
CAFFE_ENFORCE(
isLegacyOperator(),
"Output(idx) not supported for operators exported to c10. Please use XOutput instead.");
static_assert(
!std::is_same<T, Tensor>::value,
"You should use Output<Tensor>(int, DeviceType) for "
"Tensor.");
return outputs_.at(idx)->template GetMutable<T>();
}
// TODO(jerryzh): Remove this template
template <typename T>
inline T* Output(int idx, DeviceType type) {
if (isLegacyOperator()) {
static_assert(
std::is_same<T, Tensor>::value,
"Output(int, DeviceType) is only available for Tensor");
// When you get a Tensor here it is not fully initialized
return BlobGetMutableTensor(outputs_.at(idx), type);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
at::Tensor output = newstyle_outputs_[idx];
Tensor tensor = caffe2::Tensor(output);
if (!tensor.defined() || tensor.GetDeviceType() != type) {
// Fix tensor type
tensor = Tensor(type);
output = at::Tensor(std::move(tensor.getIntrusivePtr()));
}
output_tensors_[idx] = caffe2::Tensor(output);
newstyle_outputs_[idx] = std::move(output);
return &output_tensors_[idx];
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
inline Tensor
XOutputTensor(int idx, at::IntArrayRef dims, at::TensorOptions options) {
CAFFE_ENFORCE_WITH_CALLER(
options.device_opt() != c10::nullopt,
"device must be provided in option.");
if (isLegacyOperator()) {
return XBlobGetMutableTensor(outputs_.at(idx), dims, options);
}
return OutputTensor(idx, dims, options)->UnsafeSharedInstance();
}
void SetOutputTensor(int idx, Tensor tensor) {
if (!isLegacyOperator()) {
#if !defined(CAFFE2_IS_XPLAT_BUILD)
newstyle_outputs_[idx] = at::Tensor(tensor);
// also update the tensor in the hack
output_tensors_[idx] = std::move(tensor);
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
} else {
// update the tensor in the workspace
BlobSetTensor(outputs_.at(idx), std::move(tensor));
}
}
Tensor OutputTensorOrUndefined(int idx) {
if (isLegacyOperator()) {
return BlobGetTensorOrUndefined(*outputs_.at(idx));
}
return output_tensors_[idx].UnsafeSharedInstance();
}
inline Tensor*
OutputTensor(int idx, at::IntArrayRef dims, at::TensorOptions options) {
if (isLegacyOperator()) {
CAFFE_ENFORCE_WITH_CALLER(
options.device_opt() != c10::nullopt,
"device must be provided in options.");
return BlobGetMutableTensor(outputs_.at(idx), dims, options);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
at::Tensor output = newstyle_outputs_[idx];
Tensor tensor =
GetSizedTensorWithOptions(caffe2::Tensor(output), dims, options);
// assign it back in case it changed
output = at::Tensor(std::move(tensor.getIntrusivePtr()));
output_tensors_[idx] = caffe2::Tensor(output);
newstyle_outputs_[idx] = std::move(output);
return &output_tensors_[idx];
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
// Get output Tensor of the operator and CopyFrom the given Tensor
Tensor* OutputTensorCopyFrom(
int idx,
at::TensorOptions options,
const Tensor& src,
bool async = false) {
CAFFE_ENFORCE_WITH_CALLER(
options.device_opt() != c10::nullopt,
"device must be provided in options.");
// Ouptut Tensor will always have the same data type as `src`
if (!options.has_dtype()) {
options = options.dtype(src.dtype());
}
CAFFE_ENFORCE_WITH_CALLER(
options.dtype() == src.dtype(),
"We don't allow change of src data type in OutputTensorCopyFrom");
Tensor* t = OutputTensor(idx, src.sizes(), options);
t->CopyFrom(src, async);
return t;
}
Tensor* OutputTensorAlias(int idx, const Tensor& src) {
CAFFE_ENFORCE(
isLegacyOperator(),
"OutputTensorAlias(idx, src) not (yet) supported for operators exported to c10.");
return BlobSetTensor(OutputBlob(idx),
src.Alias());
}
template <typename T>
inline T* Output(int idx, T* allocated) {
CAFFE_ENFORCE(
isLegacyOperator(),
"Output(idx, allocated) not supported for operators exported to c10. Please use XOutput.");
outputs_.at(idx)->Reset(allocated);
return allocated;
}
inline const Blob& InputBlob(int idx) {
CAFFE_ENFORCE(
isLegacyOperator(),
"InputBlob(idx) not (yet) supported for operators exported to c10.");
return *inputs_.at(idx);
}
inline Blob* OutputBlob(int idx) {
CAFFE_ENFORCE(
isLegacyOperator(),
"OutputBlob(idx) not (yet) supported for operators exported to c10.");
return outputs_.at(idx);
}
// Check whether output j is an alias of input i by comparing Blob pointers,
// note this does not check if the two Blobs points to the same Tensor, or if
// the Tensor pointers point to the same TensorImpl, or if the Storages alias
inline bool IsInputOutputAlias(int i, int j) {
CAFFE_ENFORCE(
isLegacyOperator(),
"IsInputOutputAlias(i, j) not (yet) supported for operators exported to c10.");
return inputs_.at(i) == outputs_.at(j);
}
template <typename T>
inline bool InputIsType(int idx) {
CAFFE_ENFORCE(
isLegacyOperator(),
"InputIsType(idx) not (yet) supported for operators exported to c10.");
static_assert(
!std::is_same<T, Tensor>::value,
"You should use InputIsTensorType(int, DeviceType) for "
"Tensor.");
return inputs_.at(idx)->template IsType<T>();
}
inline bool InputIsTensorType(int idx, DeviceType device_type) {
CAFFE_ENFORCE(
isLegacyOperator(),
"InputIsTensorType(idx, device_type) not (yet) supported for operators exported to c10.");
return BlobIsTensorType(*inputs_.at(idx), device_type);
}
template <typename T>
inline bool OutputIsType(int idx) {
CAFFE_ENFORCE(
isLegacyOperator(),
"OutputIsType(idx) not (yet) supported for operators exported to c10.");
static_assert(
!std::is_same<T, Tensor>::value,
"You should use OutputIsTensorType(int, DeviceType) for "
"Tensor.");
return outputs_.at(idx)->template IsType<T>();
}
inline bool OutputIsTensorType(int idx, DeviceType type) {
CAFFE_ENFORCE(
isLegacyOperator(),
"OutputIsTensorType(idx, type) not (yet) supported for operators exported to c10.");
return BlobIsTensorType(*outputs_.at(idx), type);
}
inline int InputSize() const {
return input_size_;
}
inline int OutputSize() const {
if (isLegacyOperator()) {
return outputs_.size();
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
return newstyle_outputs_.size();
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
inline const vector<const Blob*>& Inputs() const {
CAFFE_ENFORCE(
isLegacyOperator(),
"Inputs() not supported for operators exported to c10.");
return inputs_;
}
inline const vector<Blob*>& Outputs() {
CAFFE_ENFORCE(
isLegacyOperator(),
"Outputs() not supported for operators exported to c10.");
return outputs_;
}
vector<TensorShape> InputTensorShapes() const;
virtual void WaitEvent(const Event& ev, int /*stream_id */ = -1) {
ev.Finish();
}
inline void Wait(const OperatorBase& other, int stream_id = -1) {
if (!other.IsEventDisabled()) {
WaitEvent(other.event(), stream_id);
}
}
virtual void WaitEvents(
const std::vector<const Event*>& events,
int /*stream_id*/ = -1) {
for (const auto& ev : events) {
ev->Finish();
}
}
virtual void Finish() {
if (event_) {
event_->Finish();
}
}
virtual bool Run(int /* unused */ /*stream_id*/ = 0) {
CAFFE_NOT_IMPLEMENTED;
}
virtual bool HasAsyncPart() const {
return false;
}
virtual bool SupportsAsyncScheduling() const {
return false;
}
virtual void CancelAsyncCallback() {}
// RunAsync, if implemenented by the specific operators, will schedule the
// computation on the corresponding context and record the event in its
// event_ member object. If the specific operator does not support RunAsync,
// it will simply be synchronous as a fallback.
virtual bool RunAsync(int stream_id = 0) {
try {
auto result = Run(stream_id);
if (result) {
if (HasAsyncPart()) {
RecordEvent();
} else {
SetEventFinished();
}
} else {
SetEventFinished(getErrorMsg().c_str());
}
return result;
} catch (EnforceNotMet& err) {
SetEventFinishedWithException(err.what());
throw;
} catch (const std::exception& err) {
SetEventFinishedWithException(err.what());
throw;
} catch (...) {
SetEventFinishedWithException(getErrorMsg().c_str());
throw;
}
}
virtual void AddRelatedBlobInfo(EnforceNotMet* err) {
CAFFE_ENFORCE(
isLegacyOperator(),
"AddRelatedBlobInfo(err) not supported for operators exported to c10.");
if (!has_debug_def()) {
return;
}
bool found_input;
if (err->caller() != nullptr) {
for (size_t i = 0; i < inputs_.size(); i++) {
if (inputs_[i]->GetRaw() == err->caller()) {
found_input = true;
err->AppendMessage(
"\n** while accessing input: " + debug_def().input(i));
break;
}
}
for (size_t i = 0; i < outputs_.size(); i++) {
if (outputs_[i]->GetRaw() == err->caller()) {
if (found_input) {
err->AppendMessage("\n OR ");
}
err->AppendMessage(
"\n** while accessing output: " + debug_def().output(i));
break;
}
}
}
}
virtual std::string debug_info_string() const {
return "";
}
inline const OperatorDef& debug_def() const {
CAFFE_ENFORCE(has_debug_def(), "operator_def was null!");
return *operator_def_;
}
inline void set_debug_def(
const std::shared_ptr<const OperatorDef>& operator_def) {
operator_def_ = operator_def;
}
inline bool has_debug_def() const {
return operator_def_ != nullptr;
}
public:
void RecordLastFailedOpNetPosition() {
if (net_position_ != kNoNetPositionSet) {
VLOG(1) << "Operator with id " << net_position_ << " failed";
operator_ws_->last_failed_op_net_position = net_position_;
} else {
VLOG(1) << "Failed operator doesn't have id set";
}
}
int net_position() const {
return net_position_;
}
void set_net_position(int idx) {
net_position_ = idx;
}
const DeviceOption& device_option() const {
return device_option_;
}
const Event& event() const {
CAFFE_ENFORCE(event_, "Event is disabled");
return *event_;
}
Event& event() {
CAFFE_ENFORCE(event_, "Event is disabled");
return *event_;
}
void ResetEvent() {
if (event_) {
event_->Reset();
}
}
void DisableEvent() {
event_ = nullptr;
}
bool IsEventDisabled() const {
return !event_;
}
// Internal API invoked by observers. Normal callers shouldn't invoke it.
virtual void SyncDeviceBarrierForObservers() {
CAFFE_NOT_IMPLEMENTED;
}
// Checks whether stream is ready to execute new computation,
// used in stream allocation optimization to skip stream that is currently
// busy. Depends on context and operator's device, returns true by default
virtual bool IsStreamFree(int /* unused */) const {
return true;
}
const std::string& type() const {
return type_;
}
void annotate_engine(const std::string& engine) {
engine_ = engine;
}
const std::string& engine() const {
return engine_;
}
void SetExecutorHelper(ExecutorHelper* helper) {
helper_ = helper;
}
ExecutorHelper* GetExecutorHelper() const {
return helper_;
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
c10::List<at::Tensor> move_newstyle_outputs() && {
return std::move(newstyle_outputs_);
}
#endif
public:
static const int kNoNetPositionSet = -1;
private:
Workspace* operator_ws_;
std::shared_ptr<const OperatorDef> operator_def_;
DeviceOption device_option_;
std::string engine_;
std::string type_;
vector<const Blob*> inputs_;
vector<Blob*> outputs_;
// Preferrably use c10::optional, but nvcc doesn't work
#if !defined(CAFFE2_IS_XPLAT_BUILD)
std::unique_ptr<const c10::FunctionSchema> fn_schema_;
vector<c10::IValue> newstyle_inputs_;
c10::List<at::Tensor> newstyle_outputs_;
#endif
// HACK
// We preserve the fact that Output() returns Tensor*
// by storing Tensor in a vector owned by the
// operator.
vector<caffe2::Tensor> input_tensors_;
vector<caffe2::Tensor> output_tensors_;
int input_size_;
int net_position_{kNoNetPositionSet};
ExecutorHelper* helper_ = nullptr;
protected:
virtual void RecordEvent(const char* /*err_msg*/ = nullptr) {
CAFFE_NOT_IMPLEMENTED;
}
void SetEventFinished(const char* err_msg = nullptr) {
if (event_) {
event_->SetFinished(err_msg);
}
}
void SetEventFinishedWithException(const char* err_msg = nullptr) {
if (event_) {
event_->SetFinishedWithException(err_msg);
}
}
std::string getErrorMsg() {
if (has_debug_def()) {
return "Error from operator: " + ProtoDebugString(debug_def());
} else {
return "Error from operator: no op def";
}
}
c10::optional<int> argumentIndexWithName(const std::string& name) const;
// An event used by asynchronous execution.
std::unique_ptr<Event> event_;
C10_DISABLE_COPY_AND_ASSIGN(OperatorBase);
};
template <>
inline NetDef OperatorBase::GetSingleArgument<NetDef>(
const std::string& name,
const NetDef& default_value) const {
if (isLegacyOperator()) {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::GetSingleArgument<OperatorDef, NetDef>(
*operator_def_, name, default_value);
}
CAFFE_THROW("Cannot get NetDefs from IValue");
return NetDef();
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
template <>
inline vector<int> OperatorBase::GetVectorFromIValueList<int>(
const c10::IValue& value) const {
auto vs = value.toIntListRef();
vector<int> out;
out.reserve(vs.size());
for (int64_t v : vs) {
out.emplace_back(v);
}
return out;
}
template <>
inline vector<float> OperatorBase::GetVectorFromIValueList<float>(
const c10::IValue& value) const {
const auto& vs = value.toDoubleListRef();
vector<float> out;
out.reserve(vs.size());
for (double v : vs) {
out.emplace_back(v);
}
return out;
}
template <>
inline vector<string> OperatorBase::GetVectorFromIValueList<string>(
const c10::IValue& value) const {
CAFFE_THROW("Cannot extract vector<string> from ivalue.");
vector<string> out;
return out;
}
// We need this specialisation because IValue based lists don't support
// int16_t. We need to load it as List<int64_t> and transform to int16_t.
template <>
inline vector<int16_t> OperatorBase::GetVectorFromIValueList<int16_t>(
const c10::IValue& value) const {
auto list = value.template to<c10::List<int64_t>>();
std::vector<int16_t> result;
result.reserve(list.size());
for (int64_t elem : list) {
result.push_back(static_cast<int16_t>(elem));
}
return result;
}
#endif
// OP_SINGLE_ARG provides a shorter initialization choice for initialization of
// member variables for the class constructors.
// This is a workaround for CUDA9.2 and GCC7
#if defined(CUDART_VERSION) && CUDART_VERSION >= 9020 && __GNUC__ >= 7
#define OP_SINGLE_ARG(type, name, variable, default) \
variable(this->template GetSingleArgument<type>(name, (default)))
#else
#define OP_SINGLE_ARG(type, name, variable, default) \
variable(OperatorBase::GetSingleArgument<type>(name, (default)))
#endif
// INPUT_TAGS and OUTPUT_TAGS are optional features to name the indices of the
// operator's inputs and outputs, in order to avoid confusion. For example, for
// a fully convolution layer that has input, weight and bias, you can define its
// input tags as:
// INPUT_TAGS(INPUT, WEIGHT, BIAS);
// And in the code, instead of doing
// auto& weight = Input(1);
// you can now do
// auto& weight = Input(WEIGHT);
// to make it more clear.
#define INPUT_TAGS(first_input, ...) \
enum _InputTags { first_input = 0, __VA_ARGS__ }
#define OUTPUT_TAGS(first_input, ...) \
enum _OutputTags { first_input = 0, __VA_ARGS__ }
template <typename T>
inline vector<T> OperatorBase::GetRepeatedArgument(
const string& name,
const vector<T>& default_value) const {
if (isLegacyOperator()) {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::GetRepeatedArgument<OperatorDef, T>(
*operator_def_, name, default_value);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
auto index = argumentIndexWithName(name);
CAFFE_ENFORCE(index.has_value(), "Couldn't get index for argument!", name);
const auto& value = newstyle_inputs_[index.value()];
return GetVectorFromIValueList<T>(value);
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
// We need this specialisation because IValue based lists don't support
// int16_t. We need to load it as List<int64_t> and transform to int16_t.
template <>
inline vector<int16_t> OperatorBase::GetRepeatedArgument<int16_t>(
const string& name,
const vector<int16_t>& default_value) const {
if (isLegacyOperator()) {
CAFFE_ENFORCE(operator_def_, "operator_def was null!");
return ArgumentHelper::GetRepeatedArgument<OperatorDef, int16_t>(
*operator_def_, name, default_value);
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
auto index = argumentIndexWithName(name);
CAFFE_ENFORCE(index.has_value(), "Couldn't get index for argument!", name);
const auto& value = newstyle_inputs_[index.value()];
auto vec = GetVectorFromIValueList<int64_t>(value);
std::vector<int16_t> result;
result.reserve(vec.size());
for (int64_t elem : vec) {
result.push_back(static_cast<int16_t>(elem));
}
return result;
#else
CAFFE_THROW("Non-legacy operators are not legal in xplat/caffe2");
#endif
}
// Operator is the class that you usually want to derive, if your operator will
// run on different devices. You should then implement the RunOnDevice()
// function.
template <class Context>
class Operator : public OperatorBase {
public:
explicit Operator(const OperatorDef& operator_def, Workspace* ws)
: OperatorBase(operator_def, ws), context_(operator_def.device_option()) {
// In the constructor, we switch to the device so that the child class
// constructors will run on that device.
context_.SwitchToDevice();
}
#if !defined(CAFFE2_IS_XPLAT_BUILD)
explicit Operator(
const c10::FunctionSchema& fn_schema,
std::vector<c10::IValue> inputs,
c10::List<at::Tensor> outputs)
: OperatorBase(fn_schema, std::move(inputs), std::move(outputs)) {
// In the constructor, we switch to the device so that the child class
// constructors will run on that device.
context_.SwitchToDevice();
}
#endif
~Operator() noexcept override {}
/// Retrieve a non-owning reference to the input at position 'idx' for this
/// operator. The returned reference is valid for the duration of the
/// RunOnDevice call. The optional 'type' parameter can be used to assert a
/// required device type for the input (by default, we assert that the tensor
/// is consistent with the device type implied by the Context parameter of an
/// Operator.)
inline const Tensor& Input(
int idx,
DeviceType type = Context::GetDeviceType()) {
return OperatorBase::template Input<Tensor>(idx, type);
}
/// XOutput is a modernized version of Output which returns a Tensor
/// rather than a Tensor* (the raw pointer in the latter case is
/// useless, as Tensor is a pointer type.)
Tensor XOutput(int idx, at::IntArrayRef dims, at::TensorOptions options) {
// We'll default device to the device of the current Operator Context
if (options.device_opt() == c10::nullopt) {
return OperatorBase::XOutputTensor(
idx, dims, options.device(context_.device()));
}
return OperatorBase::XOutputTensor(idx, dims, options);
}
/// Retrieve a non-owning pointer to the output at position 'idx',
/// initializing it to have size 'dims' and properties 'options' if
/// there is no pre-existing output or the pre-existing output does
/// not have the correct options. The returned pointer is valid for
/// the duration of the RunOnDevice call. If device is not explicitly
/// specified in options, we default to allocating output on the
/// current device of the device type implied by the Context parameter
/// of this Operator.
///
/// Note [Operator::Output what?]
/// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// The contract of Operator::Output is somewhat complex; it is perhaps better
/// understood in terms of what was historically an idiomatic Caffe2 operator
/// implementation:
///
/// void RunOnDevice() override {
/// auto* output = Output(0, output_size, dtype<float>());
/// float* output_ptr = output->data<float>();
/// // write into output_ptr
/// }
///
/// In the simple case, this code does the following things:
///
/// 1. Allocates a new tensor with size 'output_size' and dtype 'float'
/// (and device type whatever the Operator's device type is)
/// 2. "Registers" this tensor as the 0th output tensor of this operator
/// (Caffe2 operators don't "return" outputs; instead, outputs
/// are shoved into an output vector which the executor reads out.)
/// 3. Returns the tensor, so the operator implementation can write
/// the actual output data into the tensor.
///
/// So what's this business with "pre-existing" outputs? Caffe2
/// commonly applies an optimization whereby it reuses tensors on
/// subsequent runs of operators in a graph. It doesn't know ahead
/// of time what intermediate tensors it will need, so the first
/// time it runs a graph it has all of the operators create the outputs
/// necessary (as described above). However, the second time around,
/// it will reuse all of the tensors created from the first time.
/// If they are lucky, this time the Output() call is a no-op and
/// just returns the old tensor.
///
/// However, we cannot /guarantee/ that the output size will be the
/// same the next time the Operator is called; for example, output
/// size may be data dependent and vary between runs. In this case,
/// we have to resize it to the correct size. Resizing is still
/// helpful, as we may be able to fit the output in the same
/// space that was previously used.
///
Tensor* Output(int idx, at::IntArrayRef dims, at::TensorOptions options) {
// We'll default device to the device of the current Operator Context
if (options.device_opt() == c10::nullopt) {
return OperatorBase::OutputTensor(
idx, dims, options.device(context_.device()));
}
return OperatorBase::OutputTensor(idx, dims, options);
}
/// Legacy: please consider using the version of Output() which also takes
/// dtype and size as arguments.
inline Tensor* Output(int idx, DeviceType type = Context::GetDeviceType()) {
return OperatorBase::template Output<Tensor>(idx, type);
}
/// Get the output Tensor of an operator (allocating it if it is not
/// already initialized), and copy the contents of src into it.
/// You probably don't actually want to use this function (the fact
/// that you have a Tensor to copy from is probably a mistake:
/// you should have written the output into the output tensor,
/// from Output, directly in the first place), but this method
/// is situationally useful.
Tensor* OutputTensorCopyFrom(
int idx,
at::TensorOptions options,
const Tensor& src,
bool async = false) {
if (options.device_opt() == c10::nullopt) {
return OperatorBase::OutputTensorCopyFrom(
idx, options.device(context_.device()), src, async);
}
return OperatorBase::OutputTensorCopyFrom(idx, options, src, async);
}
void WaitEvent(const Event& ev, int stream_id = -1) final {
if (stream_id >= 0) {
context_.SwitchToDevice(stream_id);
}
context_.WaitEvent(ev);
}
void WaitEvents(const std::vector<const Event*>& events, int stream_id = -1)
final {
if (stream_id >= 0) {
context_.SwitchToDevice(stream_id);
}
for (const auto& ev : events) {
context_.WaitEvent(*ev);
}
}
// The run function of Operator switches to the device, and then carries out
// the actual computation with RunOnDevice(). You should implement RunOnDevice
// instead of Run().
// Note: Run does not update operator's event and can be used only with
// non-async executors that do not rely on events
bool Run(int stream_id = 0) final {
try {
StartAllObservers();
context_.SwitchToDevice(stream_id);
// Clear floating point exception flags before RunOnDevice. We will test
// exception flags afterwards, and raise an error if an exception has
// happend.
if (FLAGS_caffe2_operator_throw_if_fp_exceptions ||
FLAGS_caffe2_operator_throw_if_fp_overflow_exceptions) {
std::feclearexcept(FE_ALL_EXCEPT);
}