/usr/include/nntrainer/layer_devel.h
/usr/include/nntrainer/neuralnet.h
/usr/include/nntrainer/tensor.h
+/usr/include/nntrainer/tensor_wrap_specs.h
/usr/include/nntrainer/optimizer_devel.h
/usr/include/nntrainer/optimizer_impl.h
/usr/include/nntrainer/profiler.h
$(NNTRAINER_ROOT)/nntrainer/tensor/tensor_dim.cpp \
$(NNTRAINER_ROOT)/nntrainer/tensor/blas_interface.cpp \
$(NNTRAINER_ROOT)/nntrainer/layers/layer_node.cpp \
+ $(NNTRAINER_ROOT)/nntrainer/layers/layer_context.cpp \
$(NNTRAINER_ROOT)/nntrainer/layers/input_layer.cpp \
$(NNTRAINER_ROOT)/nntrainer/layers/multiout_layer.cpp \
$(NNTRAINER_ROOT)/nntrainer/layers/fc_layer.cpp \
$(NNTRAINER_ROOT)/nntrainer/optimizers/optimizer_impl.cpp \
$(NNTRAINER_ROOT)/nntrainer/optimizers/adam.cpp \
$(NNTRAINER_ROOT)/nntrainer/optimizers/sgd.cpp \
- $(NNTRAINER_ROOT)/nntrainer/optimizers/optimizer_factory.cpp \
$(NNTRAINER_ROOT)/nntrainer/utils/util_func.cpp \
$(NNTRAINER_ROOT)/nntrainer/utils/ini_wrapper.cpp \
$(NNTRAINER_ROOT)/nntrainer/utils/parse_util.cpp \
--- /dev/null
+// SPDX-License-Identifier: Apache-2.0
+/**
+ * Copyright (C) 2021 Parichay Kapoor <pk.kapoor@samsung.com>
+ *
+ * @file layer_context.cpp
+ * @date 26 July 2021
+ * @see https://github.com/nnstreamer/nntrainer
+ * @author Parichay Kapoor <pk.kapoor@samsung.com>
+ * @bug No known bugs except for NYI items
+ * @brief This is the layer context for each layer
+ */
+
+#include <layer_context.h>
+#include <var_grad.h>
+#include <weight.h>
+
+namespace nntrainer {
+
+/**
+ * @brief Get the Weight tensor object
+ *
+ * @param idx Identifier of the weight
+ * @return Tensor& Reference to the weight tensor
+ */
+Tensor &RunLayerContext::getWeight(unsigned int idx) const {
+ return weights[idx]->getVariableRef();
+}
+
+/**
+ * @brief Get the Weight Gradient tensor object
+ *
+ * @param idx Identifier of the weight
+ * @return Tensor& Reference to the weight grad tensor
+ */
+Tensor &RunLayerContext::getWeightGrad(unsigned int idx) const {
+ if (!weights[idx]->hasGradient())
+ throw std::invalid_argument(
+ "Requesting gradient for a non-trainable weight.");
+ return weights[idx]->getGradientRef();
+}
+
+/**
+ * @brief Get regularization loss for the weight
+ *
+ * @param idx Identifier of the weight
+ * @return float Value of the loss
+ */
+float RunLayerContext::getWeightRegularizationLoss(unsigned int idx) const {
+ return weights[idx]->getRegularizationLoss();
+}
+
+/**
+ * @brief Get the Weight name
+ *
+ * @param idx Identifier of the weight
+ * @return name of the weight
+ */
+const std::string &RunLayerContext::getWeightName(unsigned int idx) const {
+ return weights[idx]->getName();
+}
+
+/**
+ * @brief check if the weight has gradient
+ *
+ * @param idx Identifier of the weight
+ * @return true if weight has gradient, else false
+ */
+bool RunLayerContext::weightHasGradient(unsigned int idx) const {
+ return weights[idx]->hasGradient();
+}
+
+/**
+ * @brief Get the Output tensor object
+ *
+ * @param idx Identifier of the output
+ * @return Tensor& Reference to the output tensor
+ */
+Tensor &RunLayerContext::getOutput(unsigned int idx) {
+ return outputs[idx]->getVariableRef();
+}
+
+/**
+ * @brief Get the Output Grad tensor object
+ *
+ * @param idx Identifier of the output
+ * @return Tensor& Reference to the output grad tensor
+ */
+Tensor &RunLayerContext::getOutputGrad(unsigned int idx) {
+ if (!outputs[idx]->hasGradient())
+ throw std::invalid_argument(
+ "Requesting gradient for a non-trainable tensor.");
+ return getOutputGradUnsafe(idx);
+}
+
+/**
+ * @brief Get the Output Grad tensor object
+ *
+ * @param idx Identifier of the output
+ * @return Tensor& Reference to the output grad tensor
+ *
+ * @note recommended to NOT use this function as a layer developer but rather
+ * use getOutputGrad().
+ */
+Tensor &RunLayerContext::getOutputGradUnsafe(unsigned int idx) {
+ return outputs[idx]->getGradientRef();
+}
+
+/**
+ * @brief Get the incoming Derivative tensor object
+ *
+ * @param idx Identifier of the output
+ * @return Tensor& Reference to the output derivative tensor
+ */
+Tensor &RunLayerContext::getIncomingDerivative(unsigned int idx) {
+ return getOutputGrad(idx);
+}
+
+/**
+ * @brief Get the Input tensor object
+ *
+ * @param idx Identifier of the input
+ * @return Tensor& Reference to the input grad tensor
+ */
+Tensor &RunLayerContext::getInput(unsigned int idx) {
+ return inputs[idx]->getVariableRef();
+}
+
+/**
+ * @brief Get the Input Grad tensor object
+ *
+ * @param idx Identifier of the input
+ * @return Tensor& Reference to the input grad tensor
+ */
+Tensor &RunLayerContext::getInputGrad(unsigned int idx) {
+ if (!inputs[idx]->hasGradient())
+ throw std::invalid_argument(
+ "Requesting gradient for a non-trainable tensor.");
+ return inputs[idx]->getGradientRef();
+}
+
+/**
+ * @brief Get the outgoing Derivative tensor object
+ *
+ * @param idx Identifier of the input
+ * @return Tensor& Reference to the input derivative tensor
+ */
+Tensor &RunLayerContext::getOutgoingDerivative(unsigned int idx) {
+ return getInputGrad(idx);
+}
+
+/**
+ * @brief Get the Tensor object
+ *
+ * @param idx Identifier of the tensor
+ * @return Tensor& Reference to the tensor
+ */
+Tensor &RunLayerContext::getTensor(unsigned int idx) {
+ return tensors[idx]->getVariableRef();
+}
+
+/**
+ * @brief Get the Tensor Grad object
+ *
+ * @param idx Identifier of the tensor
+ * @return Tensor& Reference to the tensor grad tensor
+ */
+Tensor &RunLayerContext::getTensorGrad(unsigned int idx) {
+ if (!tensors[idx]->hasGradient())
+ throw std::invalid_argument(
+ "Requesting gradient for a non-trainable tensor.");
+ return tensors[idx]->getGradientRef();
+}
+
+/**
+ * @brief check if the tensor has gradient
+ *
+ * @param idx Identifier of the tensor
+ * @return true if tensor has gradient, else false
+ */
+bool RunLayerContext::tensorHasGradient(unsigned int idx) const {
+ return tensors[idx]->hasGradient();
+}
+
+/**
+ * @brief Get the tensor name
+ *
+ * @param idx Identifier of the tensor
+ * @return name of the tensor
+ */
+const std::string &RunLayerContext::getTensorName(unsigned int idx) const {
+ return tensors[idx]->getName();
+}
+
+/**
+ * @brief Set the batch for the run context
+ *
+ * @param batch Update batch size
+ */
+void RunLayerContext::setBatch(unsigned int batch) {
+ for (auto &vg : inputs)
+ vg->setBatchSize(batch);
+ for (auto &vg : outputs)
+ vg->setBatchSize(batch);
+}
+
+/**
+ * @brief Update the dimensions for a requested tensor
+ *
+ * @param idx index of the tensor (identifier)
+ * @param batch Updated batch size
+ */
+void RunLayerContext::updateTensor(unsigned int idx, unsigned int batch) {
+ tensors[idx]->setBatchSize(batch);
+}
+
+/**
+ * @brief Get weight object for the weights
+ *
+ * @param idx index of the weight (identifier)
+ * @return weight object
+ */
+Weight &RunLayerContext::getWeightObject(unsigned int idx) {
+ return *weights[idx];
+}
+
+/**
+ * @brief check if the label is available
+ *
+ * @param idx Identifier of the input
+ * @return true if label is available else false
+ */
+bool RunLayerContext::isLabelAvailable(unsigned int idx) const {
+ return !outputs[idx]->getGradientRef().uninitialized();
+}
+
+/**
+ * @brief Get label tensor
+ *
+ * @param idx Identifier of the input
+ * @return Tensor& Reference to the label tensor
+ */
+Tensor &RunLayerContext::getLabel(unsigned int idx) {
+ if (isLabelAvailable(idx))
+ return outputs[idx]->getGradientRef();
+ else
+ throw std::invalid_argument("Request tensor which does not exist");
+}
+
+/**
+ * @brief check if run context is set and is ready to use
+ *
+ * @return true if ready, else false
+ */
+bool RunLayerContext::readyToUse() const {
+ /**
+ * assumption:
+ * 1. there must be atleast 1 input
+ * 2. the setter set everything at once
+ */
+ if (inputs.empty())
+ return false;
+ return !inputs[0]->getVariable().uninitialized();
+}
+
+} // namespace nntrainer
#include <common_properties.h>
#include <tensor.h>
#include <tensor_dim.h>
-#include <var_grad.h>
-#include <weight.h>
+#include <tensor_wrap_specs.h>
namespace nntrainer {
+
+class Weight;
+class Var_Grad;
+
/**
* @brief define the lifespan of the given tensor to reduce peak memory
*
* @todo Consider providing a guarantee that the returned indices will always
* start from 0 and will always be incremental.
*/
- unsigned int requestWeight(const Weight::Spec &spec) {
+ unsigned int requestWeight(const WeightSpec &spec) {
weights_spec.emplace_back(spec);
return weights_spec.size() - 1;
}
* @brief Specification of the tensors
*
*/
- typedef Var_Grad::Spec TensorSpec;
+ typedef VarGradSpec TensorSpec;
/**
* @brief Request a new tensor for the layer
*
* @return The current weights spec
*/
- const std::vector<Weight::Spec> &getWeightsSpec() const {
- return weights_spec;
- }
+ const std::vector<WeightSpec> &getWeightsSpec() const { return weights_spec; }
/**
* @brief Get the number of requested weights
std::vector<TensorDim> input_dim; /**< Input dimensions for the layer */
std::vector<TensorDim> output_dim; /**< Output dimensions for the layer */
- std::vector<Weight::Spec> weights_spec; /**< Specification for the weights */
+ std::vector<WeightSpec> weights_spec; /**< Specification for the weights */
std::vector<TensorSpec>
tensors_spec; /**< Specification for the var_grad (trainable/non-trainable
variables) */
* @param idx Identifier of the weight
* @return Tensor& Reference to the weight tensor
*/
- Tensor &getWeight(unsigned int idx) const {
- return weights[idx]->getVariableRef();
- }
+ Tensor &getWeight(unsigned int idx) const;
/**
* @brief Get the Weight Gradient tensor object
* @param idx Identifier of the weight
* @return Tensor& Reference to the weight grad tensor
*/
- Tensor &getWeightGrad(unsigned int idx) const {
- if (!weights[idx]->hasGradient())
- throw std::invalid_argument(
- "Requesting gradient for a non-trainable weight.");
- return weights[idx]->getGradientRef();
- }
+ Tensor &getWeightGrad(unsigned int idx) const;
/**
* @brief Get the Weight name
* @param idx Identifier of the weight
* @return name of the weight
*/
- const std::string &getWeightName(unsigned int idx) const {
- return weights[idx]->getName();
- }
+ const std::string &getWeightName(unsigned int idx) const;
/**
* @brief check if the weight has gradient
* @param idx Identifier of the weight
* @return true if weight has gradient, else false
*/
- bool weightHasGradient(unsigned int idx) const {
- return weights[idx]->hasGradient();
- }
+ bool weightHasGradient(unsigned int idx) const;
/**
* @brief Get the Output tensor object
* @param idx Identifier of the output
* @return Tensor& Reference to the output tensor
*/
- Tensor &getOutput(unsigned int idx) { return outputs[idx]->getVariableRef(); }
+ Tensor &getOutput(unsigned int idx);
/**
* @brief Get the Output Grad tensor object
* @param idx Identifier of the output
* @return Tensor& Reference to the output grad tensor
*/
- Tensor &getOutputGrad(unsigned int idx) {
- if (!outputs[idx]->hasGradient())
- throw std::invalid_argument(
- "Requesting gradient for a non-trainable tensor.");
- return getOutputGradUnsafe(idx);
- }
+ Tensor &getOutputGrad(unsigned int idx);
/**
* @brief Get the Output Grad tensor object
* @note recommended to NOT use this function as a layer developer but rather
* use getOutputGrad().
*/
- Tensor &getOutputGradUnsafe(unsigned int idx) {
- return outputs[idx]->getGradientRef();
- }
+ Tensor &getOutputGradUnsafe(unsigned int idx);
/**
* @brief Get the incoming Derivative tensor object
* @param idx Identifier of the output
* @return Tensor& Reference to the output derivative tensor
*/
- Tensor &getIncomingDerivative(unsigned int idx) { return getOutputGrad(idx); }
+ Tensor &getIncomingDerivative(unsigned int idx);
/**
* @brief Get the Input tensor object
* @param idx Identifier of the input
* @return Tensor& Reference to the input grad tensor
*/
- Tensor &getInput(unsigned int idx) { return inputs[idx]->getVariableRef(); }
+ Tensor &getInput(unsigned int idx);
/**
* @brief Get the Input Grad tensor object
* @param idx Identifier of the input
* @return Tensor& Reference to the input grad tensor
*/
- Tensor &getInputGrad(unsigned int idx) {
- if (!inputs[idx]->hasGradient())
- throw std::invalid_argument(
- "Requesting gradient for a non-trainable tensor.");
- return inputs[idx]->getGradientRef();
- }
+ Tensor &getInputGrad(unsigned int idx);
/**
* @brief Get the outgoing Derivative tensor object
* @param idx Identifier of the input
* @return Tensor& Reference to the input derivative tensor
*/
- Tensor &getOutgoingDerivative(unsigned int idx) { return getInputGrad(idx); }
+ Tensor &getOutgoingDerivative(unsigned int idx);
/**
* @brief Get the Tensor object
* @param idx Identifier of the tensor
* @return Tensor& Reference to the tensor
*/
- Tensor &getTensor(unsigned int idx) { return tensors[idx]->getVariableRef(); }
+ Tensor &getTensor(unsigned int idx);
/**
* @brief Get the Tensor Grad object
* @param idx Identifier of the tensor
* @return Tensor& Reference to the tensor grad tensor
*/
- Tensor &getTensorGrad(unsigned int idx) {
- if (!tensors[idx]->hasGradient())
- throw std::invalid_argument(
- "Requesting gradient for a non-trainable tensor.");
- return tensors[idx]->getGradientRef();
- }
+ Tensor &getTensorGrad(unsigned int idx);
/**
* @brief check if the tensor has gradient
* @param idx Identifier of the tensor
* @return true if tensor has gradient, else false
*/
- bool tensorHasGradient(unsigned int idx) const {
- return tensors[idx]->hasGradient();
- }
+ bool tensorHasGradient(unsigned int idx) const;
/**
* @brief Get the tensor name
* @param idx Identifier of the tensor
* @return name of the tensor
*/
- const std::string &getTensorName(unsigned int idx) const {
- return tensors[idx]->getName();
- }
+ const std::string &getTensorName(unsigned int idx) const;
/**
* @brief Get the number of Outputs tensor objects
*
* @param batch Update batch size
*/
- void setBatch(unsigned int batch) {
- for (auto &vg : inputs)
- vg->setBatchSize(batch);
- for (auto &vg : outputs)
- vg->setBatchSize(batch);
- }
+ void setBatch(unsigned int batch);
/**
* @brief Update the dimensions for a requested tensor
* @param idx index of the tensor (identifier)
* @param batch Updated batch size
*/
- void updateTensor(unsigned int idx, unsigned int batch) {
- tensors[idx]->setBatchSize(batch);
- }
+ void updateTensor(unsigned int idx, unsigned int batch);
/**
* @brief Get weight object for the weights
* @param idx index of the weight (identifier)
* @return weight object
*/
- Weight &getWeightObject(unsigned int idx) { return *weights[idx]; }
+ Weight &getWeightObject(unsigned int idx);
/**
* @brief check if the label is available
* @param idx Identifier of the input
* @return true if label is available else false
*/
- bool isLabelAvailable(unsigned int idx) const {
- return !outputs[idx]->getGradientRef().uninitialized();
- }
+ bool isLabelAvailable(unsigned int idx) const;
/**
* @brief Get label tensor
* @param idx Identifier of the input
* @return Tensor& Reference to the label tensor
*/
- Tensor &getLabel(unsigned int idx) {
- if (isLabelAvailable(idx))
- return outputs[idx]->getGradientRef();
- else
- throw std::invalid_argument("Request tensor which does not exist");
- }
+ Tensor &getLabel(unsigned int idx);
/**
* @brief update loss by the layer
*
* @return true if ready, else false
*/
- bool readyToUse() const {
- /**
- * assumption:
- * 1. there must be atleast 1 input
- * 2. the setter set everything at once
- */
- if (inputs.empty())
- return false;
- return !inputs[0]->getVariable().uninitialized();
- }
+ bool readyToUse() const;
private:
std::tuple<props::Name> props; /**< props of the layer */
* @param idx Identifier of the weight
* @return float Value of the loss
*/
- float getWeightRegularizationLoss(unsigned int idx) const {
- if (weights[idx]->hasGradient())
- return weights[idx]->getRegularizationLoss();
-
- return 0;
- }
+ float getWeightRegularizationLoss(unsigned int idx) const;
};
} // namespace nntrainer
#include <layer.h>
#include <layer_context.h>
#include <layer_devel.h>
+#include <weight.h>
namespace nntrainer {
'layer_impl.cpp',
'gru.cpp',
'dropout.cpp',
+ 'layer_context.cpp'
]
layer_headers = [
#include <parse_util.h>
#include <time_dist.h>
#include <util_func.h>
+#include <weight.h>
namespace nntrainer {
'manager.h',
'tensor.h',
'weight.h',
- 'var_grad.h'
+ 'var_grad.h',
+ 'tensor_wrap_specs.h'
]
foreach s : tensor_sources
--- /dev/null
+// SPDX-License-Identifier: Apache-2.0
+/**
+ * Copyright (C) 2021 Parichay Kapoor <pk.kapoor@samsung.com>
+ *
+ * @file tensor_wrap_specs.h
+ * @date 26 July 2021
+ * @see https://github.com/nnstreamer/nntrainer
+ * @author Parichay Kapoor <pk.kapoor@samsung.com>
+ * @bug No known bugs except for NYI items
+ * @brief This is specs for various tensor wrappers
+ *
+ */
+
+#ifndef __TENSOR_WRAP_SPECS_H__
+#define __TENSOR_WRAP_SPECS_H__
+
+#include <tuple>
+
+#include <tensor.h>
+
+namespace nntrainer {
+
+/**
+ * @brief Enumeration of Weight Regularizer
+ * @todo Update to TensorRegularizer
+ */
+enum class WeightRegularizer {
+ L2NORM, /**< L2 norm regularization */
+ NONE, /**< no regularization */
+ UNKNOWN /**< Unknown */
+};
+
+/**
+ * @brief Enumeration of Weight Initialization Type
+ * @todo Update to TensorInitializer
+ */
+enum class WeightInitializer {
+ WEIGHT_ZEROS, /** Zero initialization */
+ WEIGHT_ONES, /** One initialization */
+ WEIGHT_LECUN_NORMAL, /** LeCun normal initialization */
+ WEIGHT_LECUN_UNIFORM, /** uniform initialization */
+ WEIGHT_XAVIER_NORMAL, /** Xavier normal initialization */
+ WEIGHT_XAVIER_UNIFORM, /** Xavier uniform initialization */
+ WEIGHT_HE_NORMAL, /** He normal initialization */
+ WEIGHT_HE_UNIFORM, /** He uniform initialization */
+ WEIGHT_UNKNOWN /** Unknown */
+};
+
+/**
+ * @brief Specification of the Weight as a tensor wrapper
+ *
+ * @details The tuple values are dimension, initializer, regularizer,
+ * regularizer_constant, need_gradient property amd name of the Weight object.
+ */
+typedef std::tuple<TensorDim, WeightInitializer, WeightRegularizer, float, bool,
+ const std::string>
+ WeightSpec;
+
+/**
+ * @brief Specification of the Var_Grad (trainable tensor) as a tensor wrapper
+ *
+ * @details The tuple values are dimension, need_gradient property, and the
+ * name of the Var_Grad object.
+ */
+typedef std::tuple<TensorDim, bool, const std::string> VarGradSpec;
+
+} // namespace nntrainer
+
+#endif /** __TENSOR_WRAP_SPECS_H__ */
#include <tuple>
#include <tensor.h>
+#include <tensor_wrap_specs.h>
namespace nntrainer {
* @details The tuple values are dimension, need_gradient property, and the
* name of the Var_Grad object.
*/
- typedef std::tuple<TensorDim, bool, const std::string> Spec;
+ typedef VarGradSpec Spec;
/**
* @brief Var_Grad default constructor
#include <tuple>
#include <tensor.h>
+#include <tensor_wrap_specs.h>
#include <var_grad.h>
namespace nntrainer {
/**
- * @brief Enumeration of Weight Regularizer
- */
-enum class WeightRegularizer {
- L2NORM, /**< L2 norm regularization */
- NONE, /**< no regularization */
- UNKNOWN /**< Unknown */
-};
-
-/**
- * @brief Enumeration of Weight Initialization Type
- */
-enum class WeightInitializer {
- WEIGHT_ZEROS, /** Zero initialization */
- WEIGHT_ONES, /** One initialization */
- WEIGHT_LECUN_NORMAL, /** LeCun normal initialization */
- WEIGHT_LECUN_UNIFORM, /** uniform initialization */
- WEIGHT_XAVIER_NORMAL, /** Xavier normal initialization */
- WEIGHT_XAVIER_UNIFORM, /** Xavier uniform initialization */
- WEIGHT_HE_NORMAL, /** He normal initialization */
- WEIGHT_HE_UNIFORM, /** He uniform initialization */
- WEIGHT_UNKNOWN /** Unknown */
-};
-
-/**
* @class Weight
* @brief Weight with gradient, and its corresponding need_gradient property
*/
* @details The tuple values are dimension, initializer, regularizer,
* regularizer_constant, need_gradient property amd name of the Weight object.
*/
- typedef std::tuple<TensorDim, WeightInitializer, WeightRegularizer, float,
- bool, const std::string>
- Spec;
+ typedef WeightSpec Spec;
/**
* @brief Weight default constructor
* @brief Get loss from the regularization of the weight
*/
float getRegularizationLoss() {
- if (isWeightRegularizerL2Norm())
+ if (hasGradient() && isWeightRegularizerL2Norm())
return regularizer_constant * 0.5f * var->l2norm();
return 0;
%{_includedir}/nntrainer/layer_devel.h
%{_includedir}/nntrainer/neuralnet.h
%{_includedir}/nntrainer/tensor.h
+%{_includedir}/nntrainer/tensor_wrap_specs.h
%{_includedir}/nntrainer/optimizer_devel.h
%{_includedir}/nntrainer/optimizer_impl.h
%{_includedir}/nntrainer/profiler.h
projects / platform / core / ml / nntrainer.git / commitdiff