From 7582bff4b15dadbff73cdb32276f4d2dd040bafc Mon Sep 17 00:00:00 2001
From: NAUD Maxence <maxence.naud@cea.fr>
Date: Wed, 7 Aug 2024 12:43:24 +0000
Subject: [PATCH] Remove more inline warning from log and Tensor, fix metaop
names
---
include/aidge/data/Tensor.hpp | 17 +------
include/aidge/operator/Operator.hpp | 4 +-
include/aidge/utils/Log.hpp | 14 ++---
include/aidge/utils/Registrar.hpp | 8 ++-
src/data/Tensor.cpp | 51 +++++++++----------
src/operator/MetaOperatorDefs/PaddedConv.cpp | 21 +++++---
.../MetaOperatorDefs/PaddedConvDepthWise.cpp | 19 ++++---
7 files changed, 62 insertions(+), 72 deletions(-)
diff --git a/include/aidge/data/Tensor.hpp b/include/aidge/data/Tensor.hpp
index b4c5de2eb..3ee64ceca 100644
--- a/include/aidge/data/Tensor.hpp
+++ b/include/aidge/data/Tensor.hpp
@@ -346,22 +346,7 @@ public:
* @param copyFrom If true (default), move data from previous backend/device
* to the new one. Previous data is lost otherwise.
*/
- inline void setBackend(const std::string &name, DeviceIdx_t device = 0, bool copyFrom = true) {
- if (mImpl) {
- if (mImpl->device() != std::make_pair(name, device)) {
- // Backend change: create new impl, copy from old to new and replace
- // impl
- std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
- if (copyFrom) {
- newImpl->copyFrom(*mImpl, mImpl->size(), mImplOffset, 0);
- }
- setImpl(newImpl);
- }
- }
- else {
- mImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
- }
- }
+ void setBackend(const std::string &name, DeviceIdx_t device = 0, bool copyFrom = true);
/**
* @brief Get a list of available backends.
diff --git a/include/aidge/operator/Operator.hpp b/include/aidge/operator/Operator.hpp
index f1e25b7a1..c938fc362 100644
--- a/include/aidge/operator/Operator.hpp
+++ b/include/aidge/operator/Operator.hpp
@@ -196,8 +196,8 @@ public:
}
inline InputCategory inputCategory(IOIndex_t idx) const {
- AIDGE_ASSERT(idx < mInputsCategory.size(), "Input #{} out of range (number of inputs is {})", idx, mInputsCategory.size());
- return mInputsCategory[idx];
+ // AIDGE_ASSERT(idx < mInputsCategory.size(), "Input #{} out of range (number of inputs is {})", idx, mInputsCategory.size());
+ return mInputsCategory.at(idx);
}
virtual inline bool isAtomic() const noexcept { return true; }
diff --git a/include/aidge/utils/Log.hpp b/include/aidge/utils/Log.hpp
index f198e83fb..6b2ace1c6 100644
--- a/include/aidge/utils/Log.hpp
+++ b/include/aidge/utils/Log.hpp
@@ -74,7 +74,7 @@ public:
* inducing no runtime overhead for Release.
*/
template <typename... Args>
- constexpr static void debug(Args&&... args) {
+ static void debug(Args&&... args) {
#ifndef NDEBUG
// only when compiled in Debug
log(Debug, fmt::format(std::forward<Args>(args)...));
@@ -90,7 +90,7 @@ public:
* performed nominally.
*/
template <typename... Args>
- constexpr static void info(Args&&... args) {
+ static void info(Args&&... args) {
log(Info, fmt::format(std::forward<Args>(args)...));
}
@@ -101,7 +101,7 @@ public:
* performed normally.
*/
template <typename... Args>
- constexpr static void notice(Args&&... args) {
+ static void notice(Args&&... args) {
log(Notice, fmt::format(std::forward<Args>(args)...));
}
@@ -112,7 +112,7 @@ public:
* still provide an exploitable result.
*/
template <typename... Args>
- constexpr static void warn(Args&&... args) {
+ static void warn(Args&&... args) {
log(Warn, fmt::format(std::forward<Args>(args)...));
}
@@ -123,7 +123,7 @@ public:
* further operations.
*/
template <typename... Args>
- constexpr static void error(Args&&... args) {
+ static void error(Args&&... args) {
log(Error, fmt::format(std::forward<Args>(args)...));
}
@@ -134,14 +134,14 @@ public:
* impossible.
*/
template <typename... Args>
- constexpr static void fatal(Args&&... args) {
+ static void fatal(Args&&... args) {
log(Fatal, fmt::format(std::forward<Args>(args)...));
}
/**
* Set the minimum log level displayed in the console.
*/
- constexpr static void setConsoleLevel(Level level) {
+ static void setConsoleLevel(Level level) {
mConsoleLevel = level;
}
diff --git a/include/aidge/utils/Registrar.hpp b/include/aidge/utils/Registrar.hpp
index 47bb05ce7..872c3f6b5 100644
--- a/include/aidge/utils/Registrar.hpp
+++ b/include/aidge/utils/Registrar.hpp
@@ -75,11 +75,9 @@ struct Registrar {
return (C::registry().find(key) != C::registry().cend());
}
- static auto create(const registrar_key& key){
- const auto it = C::registry().find(key);
- AIDGE_ASSERT(it != C::registry().cend(), "missing or invalid registrar key: {} for registrable object {}\nDid you include/import the corresponding module?\nIf so, it is possible that the object is not yet supported.", key, typeid(C).name());
-
- return (*it).second;
+ static auto create(const registrar_key& key) {
+ AIDGE_ASSERT(exists(key), "missing or invalid registrar key: {} for registrable object {}\nDid you include/import the corresponding module?\nIf so, it is possible that the object is not yet supported.", key, typeid(C).name());
+ return C::registry()[key];
}
static std::vector<registrar_key> getKeys(){
std::vector<registrar_key> keys;
diff --git a/src/data/Tensor.cpp b/src/data/Tensor.cpp
index d1bf32594..20bf3fb78 100644
--- a/src/data/Tensor.cpp
+++ b/src/data/Tensor.cpp
@@ -25,15 +25,9 @@
#include "aidge/utils/Types.h"
-/**
- * @brief Element-wise addition operation for two ``Tensor``s.
- * @note ``Tensor``s should be stored on the same backend.
- * @todo If input ``Tensor``s have a different dataType, the output should
- * have the dataType of the ``Tensor`` with the highest precision.
- *
- * @param other
- * @return Tensor
- */
+Aidge::Tensor::~Tensor() noexcept = default;
+
+
Aidge::Tensor Aidge::Tensor::operator+(const Aidge::Tensor& other) const {
AIDGE_ASSERT(hasImpl() && other.hasImpl(), "At least one Tensor cannot perform any binary operation because it has no implementation.");
AIDGE_ASSERT(mImpl->backend() == other.mImpl->backend(), "Tensors must have the same backend");
@@ -50,15 +44,7 @@ Aidge::Tensor Aidge::Tensor::operator+(const Aidge::Tensor& other) const {
return add_.getOutput(0)->clone();
}
-/**
- * @brief Element-wise substraction operation for two ``Tensor``s.
- * @note ``Tensor``s should be stored on the same backend.
- * @todo If input ``Tensor``s have a different dataType, the output should
- * have the dataType of the ``Tensor`` with the highest precision.
- *
- * @param other
- * @return Tensor
- */
+
Aidge::Tensor Aidge::Tensor::operator-(const Aidge::Tensor& other) const {
AIDGE_ASSERT(hasImpl() && other.hasImpl(), "At least one Tensor cannot perform any binary operation because it has no implementation.");
AIDGE_ASSERT(mImpl->backend() == other.mImpl->backend(), "Tensors must have the same backend");
@@ -75,15 +61,7 @@ Aidge::Tensor Aidge::Tensor::operator-(const Aidge::Tensor& other) const {
return sub_.getOutput(0)->clone();
}
-/**
- * @brief Element-wise multiplication operation for two ``Tensor``s.
- * @note ``Tensor``s should be stored on the same backend.
- * @todo If input ``Tensor``s have a different dataType, the output should
- * have the dataType of the ``Tensor`` with the highest precision.
- *
- * @param other
- * @return Tensor
- */
+
Aidge::Tensor Aidge::Tensor::operator*(const Aidge::Tensor& other) const {
AIDGE_ASSERT(hasImpl() && other.hasImpl(), "At least one Tensor cannot perform any binary operation because it has no implementation.");
AIDGE_ASSERT(mImpl->backend() == other.mImpl->backend(), "Tensors must have the same backend");
@@ -100,6 +78,7 @@ Aidge::Tensor Aidge::Tensor::operator*(const Aidge::Tensor& other) const {
return mul_.getOutput(0)->clone();
}
+
Aidge::Tensor Aidge::Tensor::operator/(const Aidge::Tensor& other) const {
AIDGE_ASSERT(hasImpl() && other.hasImpl(), "At least one Tensor cannot perform any binary operation because it has no implementation.");
AIDGE_ASSERT(mImpl->backend() == other.mImpl->backend(), "Tensors must have the same backend");
@@ -146,7 +125,23 @@ Aidge::Tensor& Aidge::Tensor::operator=(const Aidge::Tensor& other) {
return *this;
}
-Aidge::Tensor::~Tensor() noexcept = default;
+
+void Aidge::Tensor::setBackend(const std::string &name, Aidge::DeviceIdx_t device, bool copyFrom) {
+ if (mImpl) {
+ if (mImpl->device() != std::make_pair(name, device)) {
+ // Backend change: create new impl, copy from old to new and replace
+ // impl
+ std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
+ if (copyFrom) {
+ newImpl->copyFrom(*mImpl, mImpl->size(), mImplOffset, 0);
+ }
+ setImpl(newImpl);
+ }
+ }
+ else {
+ mImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
+ }
+ }
void Aidge::Tensor::resize(const std::vector<Aidge::DimSize_t>& dims,
std::vector<Aidge::DimSize_t> strides) {
diff --git a/src/operator/MetaOperatorDefs/PaddedConv.cpp b/src/operator/MetaOperatorDefs/PaddedConv.cpp
index c70541f8b..31b1c675e 100644
--- a/src/operator/MetaOperatorDefs/PaddedConv.cpp
+++ b/src/operator/MetaOperatorDefs/PaddedConv.cpp
@@ -33,15 +33,22 @@ std::shared_ptr<Aidge::Node> Aidge::PaddedConv(Aidge::DimSize_t in_channels,
const std::array<Aidge::DimSize_t, DIM> &dilation_dims,
bool no_bias)
{
- auto metaOp = std::make_shared<Node>(PaddedConv_Op<DIM>(kernel_dims, stride_dims, padding_dims, dilation_dims), name);
- if (!name.empty()) {
- std::static_pointer_cast<MetaOperator_Op>(metaOp->getOperator())->getMicroGraph()->setNodesName();
- }
- addProducer(metaOp, 1, append(out_channels, append(in_channels, kernel_dims)), "w");
+ // auto metaOp = PaddedConv_Op<DIM>(kernel_dims, stride_dims, padding_dims, dilation_dims);
+ // if (!name.empty()) {
+ // metaOp->getMicroGraph()->setName(name);
+ // metaOp->getMicroGraph()->setNodesName();
+ // }
+ // auto metaOpNode = std::make_shared<Node>(metaOp, name);
+ auto graph = Sequential({
+ Pad<DIM>(padding_dims, (!name.empty()) ? name + "_pad" : ""),
+ std::make_shared<Node>(std::make_shared<Conv_Op<static_cast<DimIdx_t>(DIM)>>(kernel_dims, stride_dims, dilation_dims), (!name.empty()) ? name + "_conv" : "")
+ });
+ auto metaOpNode = MetaOperator("PaddedConv", graph, name);
+ addProducer(metaOpNode, 1, append(out_channels, append(in_channels, kernel_dims)), "w");
if (!no_bias) {
- addProducer(metaOp, 2, {out_channels}, "b");
+ addProducer(metaOpNode, 2, {out_channels}, "b");
}
- return metaOp;
+ return metaOpNode;
}
template std::shared_ptr<Aidge::Node> Aidge::PaddedConv<1>(const Aidge::DimSize_t, const Aidge::DimSize_t, const std::array<Aidge::DimSize_t,1>&, const std::string&, const std::array<Aidge::DimSize_t, 1>&, const std::array<Aidge::DimSize_t, 2>&, const std::array<Aidge::DimSize_t, 1>&, bool);
template std::shared_ptr<Aidge::Node> Aidge::PaddedConv<2>(const Aidge::DimSize_t, const Aidge::DimSize_t, const std::array<Aidge::DimSize_t,2>&, const std::string&, const std::array<Aidge::DimSize_t, 2>&, const std::array<Aidge::DimSize_t, 4>&, const std::array<Aidge::DimSize_t, 2>&, bool);
diff --git a/src/operator/MetaOperatorDefs/PaddedConvDepthWise.cpp b/src/operator/MetaOperatorDefs/PaddedConvDepthWise.cpp
index 0f686f22a..1c073b78a 100644
--- a/src/operator/MetaOperatorDefs/PaddedConvDepthWise.cpp
+++ b/src/operator/MetaOperatorDefs/PaddedConvDepthWise.cpp
@@ -32,15 +32,20 @@ std::shared_ptr<Aidge::Node> Aidge::PaddedConvDepthWise(const Aidge::DimSize_t n
const std::array<Aidge::DimSize_t, DIM> &dilation_dims,
bool no_bias)
{
- auto metaOp = std::make_shared<Node>(PaddedConvDepthWise_Op<DIM>(kernel_dims, stride_dims, padding_dims, dilation_dims), name);
- if (!name.empty()) {
- std::static_pointer_cast<MetaOperator_Op>(metaOp->getOperator())->getMicroGraph()->setNodesName();
- }
- addProducer(metaOp, 1, append(nb_channels, append(Aidge::DimSize_t(1), kernel_dims)), "w");
+ // auto metaOp = std::make_shared<Node>(PaddedConvDepthWise_Op<DIM>(kernel_dims, stride_dims, padding_dims, dilation_dims), name);
+ // if (!name.empty()) {
+ // std::static_pointer_cast<MetaOperator_Op>(metaOp->getOperator())->getMicroGraph()->setNodesName();
+ // }
+ auto graph = Sequential({
+ Pad<DIM>(padding_dims, (!name.empty()) ? name + "_pad" : ""),
+ std::make_shared<Node>(std::make_shared<ConvDepthWise_Op<static_cast<DimIdx_t>(DIM)>>(kernel_dims, stride_dims, dilation_dims), (!name.empty()) ? name + "_conv_depth_wise" : "")
+ });
+ auto metaOpNode = MetaOperator("PaddedConvDepthWise", graph, name);
+ addProducer(metaOpNode, 1, append(nb_channels, append(Aidge::DimSize_t(1), kernel_dims)), "w");
if (!no_bias) {
- addProducer(metaOp, 2, {nb_channels}, "b");
+ addProducer(metaOpNode, 2, {nb_channels}, "b");
}
- return metaOp;
+ return metaOpNode;
}
template std::shared_ptr<Aidge::Node> Aidge::PaddedConvDepthWise<1>(const Aidge::DimSize_t, const std::array<Aidge::DimSize_t, 1>&, const std::string&, const std::array<Aidge::DimSize_t, 1>&, const std::array<Aidge::DimSize_t, 2>&, const std::array<Aidge::DimSize_t, 1>&, bool);
template std::shared_ptr<Aidge::Node> Aidge::PaddedConvDepthWise<2>(const Aidge::DimSize_t, const std::array<Aidge::DimSize_t, 2>&, const std::string&, const std::array<Aidge::DimSize_t, 2>&, const std::array<Aidge::DimSize_t, 4>&, const std::array<Aidge::DimSize_t, 2>&, bool);
--
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