feat(src/opr): add api of training of cpp and related test

GitOrigin-RevId: befb85fd43

src/opr/impl/training/dataview.cpp

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+/**
+ * \file src/opr/impl/training/dataview.cpp
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#include "megbrain/opr/training/dataview.h"
+
+#include "megbrain/exception.h"
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/tensor.h"
+
+#include <random>
+
+namespace mgb {
+DataLoader::DataLoader(
+        std::shared_ptr<IDataView> dataview, mgb::CompNode comp_node,
+        unsigned long batchsize, bool shuffle, bool drop_last)
+        : m_dataview(dataview),
+          m_comp_node(comp_node),
+          m_batchsize(batchsize),
+          m_shuffle(shuffle),
+          m_drop_last(drop_last),
+          m_idx(0) {
+    if (!m_comp_node.valid()) {
+        m_comp_node = CompNode::load("xpu0");
+    }
+    for (size_t i = 0; i < m_dataview->size(); i++) {
+        m_index_collection.push_back(i);
+    }
+
+    if (m_dataview->size() > 0) {
+        auto data_sample = m_dataview->get_item(0);
+        SmallVector<size_t> dshape;
+        dshape.push_back(static_cast<size_t>(batchsize));
+        for (size_t i = 0; i < data_sample.first->layout().ndim; i++) {
+            dshape.push_back(data_sample.first->shape()[i]);
+        }
+        m_data_shape = dshape;
+        SmallVector<size_t> lshape;
+        lshape.push_back(m_batchsize);
+        for (size_t i = 1; i < data_sample.second->layout().ndim; i++) {
+            lshape.push_back(data_sample.second->shape()[i]);
+        }
+        m_label_shape = lshape;
+
+        m_data_type = data_sample.first->dtype();
+        m_label_type = data_sample.second->dtype();
+    } else {
+        mgb_throw(AssertionError, "The dataset is empty.");
+    }
+}
+
+size_t DataLoader::size() {
+    return m_dataview->size() / m_batchsize;
+}
+
+DataPair DataLoader::next() {
+    if (m_idx == 0 && m_shuffle) {
+        std::shuffle(
+                m_index_collection.begin(), m_index_collection.end(),
+                std::default_random_engine());
+    }
+    if (m_idx >= m_index_collection.size() - m_batchsize) {
+        m_idx = 0;
+    }
+
+    auto data = std::make_shared<HostTensorND>(m_comp_node, m_data_shape, m_data_type);
+    auto label =
+            std::make_shared<HostTensorND>(m_comp_node, m_label_shape, m_label_type);
+    size_t data_bytes = m_dataview->get_item(m_index_collection.at(m_idx))
+                                .first->layout()
+                                .access_bytes();
+    size_t label_bytes = m_dataview->get_item(m_index_collection.at(m_idx))
+                                 .second->layout()
+                                 .access_bytes();
+
+    auto data_ptr = data->raw_ptr();
+    auto label_ptr = label->raw_ptr();
+    for (unsigned int i = 0; i < m_batchsize; i++) {
+        auto item = m_dataview->get_item(m_index_collection.at(m_idx));
+        auto pre_data = item.first;
+        auto pre_label = item.second;
+        auto pre_data_ptr = pre_data->raw_ptr();
+        auto pre_label_ptr = pre_label->raw_ptr();
+
+        memcpy(data_ptr + data_bytes * i, pre_data_ptr,
+               sizeof(megdnn::dt_byte) * data_bytes);
+        memcpy(label_ptr + label_bytes * i, pre_label_ptr,
+               sizeof(megdnn::dt_byte) * label_bytes);
+        m_idx++;
+    }
+    return {data, label};
+}
+}  // namespace mgb

src/opr/impl/training/loss.cpp

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+/**
+ * \file src/opr/impl/training/loss.cpp
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#include "megbrain/opr/training/loss.h"
+#include "megbrain/exception.h"
+#include "megbrain/opr/indexing.h"
+
+namespace mgb {
+namespace loss {
+CrossEntropyLoss::CrossEntropyLoss(
+        bool with_logits, float label_smooth, ReduceMode reduce_mode, int axis)
+        : m_with_logits(with_logits),
+          m_label_smooth(label_smooth),
+          m_reduce_mode(reduce_mode),
+          m_axis(axis) {}
+
+SymbolVar CrossEntropyLoss::operator()(
+        mgb::SymbolVar symbol_pred, mgb::SymbolVar symbol_label) {
+    mgb_assert(
+            symbol_pred.shape().ndim >= symbol_label.shape().ndim,
+            "The label must have less dimensions than the pred.");
+    for (size_t i = 0; i < symbol_label.shape().ndim; i++) {
+        mgb_assert(
+                symbol_pred.shape()[i] == symbol_label.shape()[i] || (int)i == m_axis,
+                "Unmatched shape for pred and label.");
+    }
+    mgb_assert(m_label_smooth >= .0f, "The label_smmoth must be positive value");
+
+    SymbolVar symbol_loss;
+    SymbolVar symbol_middle;
+
+    SymbolVar symbol_max = opr::reduce_ax_max(symbol_pred, m_axis);
+    SymbolVar symbol_primary_item =
+            opr::IndexingOneHot::make(symbol_pred, symbol_label, {m_axis});
+    if (m_with_logits) {
+        symbol_middle = opr::reduce_ax_sum(symbol_pred, m_axis) /
+                        opr::GetVarShape::make(symbol_pred, {m_axis});
+        SymbolVar symbol_logits =
+                symbol_max + opr::log(opr::reduce_ax_sum(
+                                     opr::exp(symbol_pred - symbol_max), m_axis));
+
+        symbol_loss = symbol_logits;
+    } else {
+        symbol_middle = opr::reduce_ax_sum(opr::log(symbol_pred), m_axis) /
+                        opr::GetVarShape::make(symbol_pred, {m_axis});
+        symbol_primary_item = opr::log(symbol_primary_item);
+    }
+
+    if (m_label_smooth > .0f) {
+        symbol_loss = symbol_loss - m_label_smooth * symbol_middle -
+                      (1 - m_label_smooth) * symbol_primary_item;
+    } else {
+        symbol_loss = symbol_loss - symbol_primary_item;
+    }
+
+    if (m_reduce_mode == ReduceMode::MEAN) {
+        symbol_loss =
+                opr::reduce_sum(symbol_loss.flatten(), symbol_loss.make_scalar(1)) /
+                (float)(symbol_loss.shape().total_nr_elems());
+    } else if (m_reduce_mode == ReduceMode::SUM) {
+        symbol_loss =
+                opr::reduce_sum(symbol_loss.flatten(), symbol_loss.make_scalar(1));
+    }
+
+    return symbol_loss;
+}
+
+MSELoss::MSELoss(ReduceMode reduce_mode) : m_reduce_mode(reduce_mode){};
+
+mgb::SymbolVar MSELoss::operator()(
+        mgb::SymbolVar symbol_pred, mgb::SymbolVar symol_label) {
+    return opr::pow(symbol_pred - symol_label, symbol_pred.make_scalar(2));
+}
+}  // namespace loss
+
+}  // namespace mgb

src/opr/impl/training/optimizer.cpp

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+/**
+ * \file src/opr/impl/training/optimizer.cpp
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#include "megbrain/opr/training/optimizer.h"
+#include "megbrain/exception.h"
+#include "megbrain/opr/training/utils.h"
+
+namespace mgb {
+namespace optimizer {
+SymbolVarArray Optimizer::make_multiple(
+        SymbolVarArray symbol_weights, SymbolVarArray symbol_grads,
+        std::shared_ptr<mgb::cg::ComputingGraph> graph) {
+    if (symbol_weights.size() != symbol_grads.size()) {
+        mgb_throw(AssertionError, "The count of weights differs with that of grads.");
+    }
+
+    SymbolVarArray r;
+    for (size_t i = 0; i < symbol_weights.size(); i++) {
+        r.push_back(make(symbol_weights[i], symbol_grads[i], graph));
+    }
+    return r;
+}
+
+SGD::SGD(float lr, float weight_decay, float momentum)
+        : m_lr(lr), m_weight_decay(weight_decay), m_momentum(momentum) {
+    if (m_lr <= 0) {
+        mgb_throw(AssertionError, "Invalid learning rate: negative value.");
+    }
+    if (m_weight_decay < 0) {
+        mgb_throw(AssertionError, "Invalid weight_decay value: negative value.");
+    }
+    if (m_momentum < 0) {
+        mgb_throw(AssertionError, "Invalid momentum value: negative value.");
+    }
+}
+
+SymbolVar SGD::make(
+        SymbolVar symbol_weight, SymbolVar symbol_grad,
+        std::shared_ptr<cg::ComputingGraph> graph) {
+    SymbolVar symbol_pre_grad;
+    auto pre_grad = TensorGen::zeros<dtype::Float32>(
+            symbol_grad.shape(), symbol_grad.node()->comp_node());
+    m_pre_grads.push_back(pre_grad);
+    symbol_pre_grad = opr::SharedDeviceTensor::make(*graph, *pre_grad);
+
+    if (m_weight_decay != .0f) {
+        symbol_grad = symbol_grad + m_weight_decay * symbol_weight;
+    }
+
+    if (m_momentum != .0f) {
+        symbol_pre_grad =
+                opr::AddUpdate::make(symbol_pre_grad, symbol_grad, {m_momentum, 1.0f});
+        return opr::AddUpdate::make(symbol_weight, -symbol_pre_grad, {1.f, m_lr});
+    } else {
+        return opr::AddUpdate::make(symbol_weight, -symbol_grad, {1.f, m_lr});
+    }
+}
+
+Adam::Adam(
+        float lr, float weight_decay, std::pair<float, float> betas, float eps,
+        bool amsgrad)
+        : m_lr(lr),
+          m_weight_decay(weight_decay),
+          m_betas(betas),
+          m_eps(eps),
+          m_amsgrad(amsgrad) {
+    mgb_assert(m_lr > 0, "Invalid learning rate: negative value.");
+    mgb_assert(m_weight_decay >= 0, "Invalid weight_decay value: negative value.");
+    mgb_assert(
+            m_betas.first >= 0 && m_betas.second >= 0 && m_betas.first < 1 &&
+                    m_betas.second < 1,
+            "Invalid betas value: negative value or larger than 1.");
+}
+
+SymbolVar Adam::make(
+        SymbolVar symbol_weight, SymbolVar symbol_grad,
+        std::shared_ptr<cg::ComputingGraph> graph) {
+    CompNode comp_node = symbol_grad.node()->comp_node();
+    DType dt = symbol_grad.dtype();
+    m_correction1 = TensorGen::ones<dtype::Float32>({1}, comp_node);
+    m_correction2 = TensorGen::ones<dtype::Float32>({1}, comp_node);
+    std::shared_ptr<DeviceTensorND> exp_avg =
+            std::make_shared<DeviceTensorND>(comp_node, symbol_grad.shape(), dt);
+    mgb::fill_zero_dev_tensor(*exp_avg);
+    std::shared_ptr<DeviceTensorND> exp_avg_sq =
+            std::make_shared<DeviceTensorND>(comp_node, symbol_grad.shape(), dt);
+    mgb::fill_zero_dev_tensor(*exp_avg_sq);
+    m_exp_avg.push_back(exp_avg);
+    m_exp_avg_sq.push_back(exp_avg_sq);
+
+    SymbolVar symbol_correction1 =
+            opr::SharedDeviceTensor::make(*graph, *m_correction1);
+    SymbolVar symbol_correction2 =
+            opr::SharedDeviceTensor::make(*graph, *m_correction2);
+    SymbolVar symbol_exp_avg = opr::SharedDeviceTensor::make(*graph, exp_avg);
+    SymbolVar symbol_exp_avg_sq = opr::SharedDeviceTensor::make(*graph, exp_avg_sq);
+
+    symbol_correction1 = opr::AddUpdate::make(
+            symbol_correction1, symbol_correction1, {m_betas.first, .0f});
+    symbol_correction2 = opr::AddUpdate::make(
+            symbol_correction2, symbol_correction2, {m_betas.second, .0f});
+
+    if (m_weight_decay != .0f) {
+        symbol_grad = symbol_grad + m_weight_decay * symbol_weight;
+    }
+    symbol_exp_avg = opr::AddUpdate::make(
+            symbol_exp_avg, symbol_grad, {m_betas.first, 1.f - m_betas.first});
+    symbol_exp_avg_sq = opr::AddUpdate::make(
+            symbol_exp_avg_sq, symbol_grad * symbol_grad,
+            {m_betas.second, 1.f - m_betas.second});
+
+    SymbolVar delta;
+    if (m_amsgrad) {
+        std::shared_ptr<DeviceTensorND> max_exp_avg_sq =
+                std::make_shared<DeviceTensorND>(comp_node, symbol_grad.shape(), dt);
+        mgb::fill_zero_dev_tensor(*max_exp_avg_sq);
+        SymbolVar symbol_max_exp_avg_sq =
+                opr::SharedDeviceTensor::make(*graph, max_exp_avg_sq);
+
+        symbol_max_exp_avg_sq = opr::AddUpdate::make(
+                symbol_exp_avg_sq, opr::max(symbol_max_exp_avg_sq, symbol_exp_avg_sq),
+                {1.0f, 1.0f});
+        delta = (symbol_exp_avg / (1.f - symbol_correction1)) /
+                (opr::powf(symbol_max_exp_avg_sq / (1.f - symbol_correction2), 0.5f) +
+                 m_eps);
+    } else {
+        delta = (symbol_exp_avg / (1.f - symbol_correction1)) /
+                (opr::pow(
+                         symbol_exp_avg_sq / (1.f - symbol_correction2),
+                         symbol_exp_avg.make_scalar(0.5f)) +
+                 m_eps);
+    }
+
+    return opr::AddUpdate::make(symbol_weight, -delta, {1.0f, m_lr});
+}
+}  // namespace optimizer
+}  // namespace mgb

src/opr/include/megbrain/opr/training/dataview.h

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+/**
+ * \file src/opr/include/training/dataview.h
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#pragma once
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/tensor_manip.h"
+
+#include <type_traits>
+
+namespace mgb {
+using DataPair = std::pair<
+        std::shared_ptr<mgb::HostTensorND>, std::shared_ptr<mgb::HostTensorND>>;
+
+//! The interface of the dataset.
+class IDataView {
+public:
+    /*!
+     * The method to get an item in dataset with index.
+     */
+    virtual DataPair get_item(int idx) = 0;
+
+    /*!
+     * The method to get the size of the dataset.
+     */
+    virtual size_t size() = 0;
+
+    virtual ~IDataView() = default;
+};
+
+//! The definition of dataloader, which is corresponding to the <DataLoader> of
+//! Python API of MegEngine.
+class DataLoader {
+public:
+    DataLoader(
+            std::shared_ptr<IDataView> dataview, mgb::CompNode compnode,
+            unsigned long batchsize = 1U, bool shuffle = false, bool drop_last = true);
+    /*!
+     * Get the next pair of data of the dataset.
+     */
+    DataPair next();
+    /*!
+     * Get the size of the dataloader.
+     */
+    size_t size();
+
+private:
+    std::shared_ptr<IDataView> m_dataview;
+    mgb::CompNode m_comp_node;
+    unsigned long m_batchsize;
+    bool m_shuffle;
+    bool m_drop_last;
+    size_t m_idx;
+    mgb::TensorShape m_data_shape;
+    mgb::TensorShape m_label_shape;
+    mgb::DType m_data_type;
+    mgb::DType m_label_type;
+
+    // Only used in the temp solution for shuffle
+    std::vector<int> m_index_collection;
+};
+
+}  // namespace mgb

src/opr/include/megbrain/opr/training/loss.h

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+/**
+ * \file src/opr/include/training/loss.h
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#pragma once
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/tensor.h"
+
+namespace mgb {
+namespace loss {
+//! The interface of losses which should be inherited by each loss class.
+class ILoss {
+public:
+    /*!
+     * The reduce mode of loss to convert output to scalar.
+     */
+    enum ReduceMode { SUM = 0, MEAN = 1 };
+    /*!
+     * The calculation of the loss, in which the output is a scalar symbolvar
+     */
+    virtual mgb::SymbolVar operator()(
+            mgb::SymbolVar symbol_pred, mgb::SymbolVar symol_label) = 0;
+    virtual ~ILoss() = default;
+};
+
+/*!
+ * The cross entropy loss. The definition could be found here:
+ * https://en.wikipedia.org/wiki/Cross_entropy
+ *
+ * It's corresponding to the <CrossEntropy> of Python API of MegEngine.
+ */
+class CrossEntropyLoss : public ILoss {
+public:
+    CrossEntropyLoss(
+            bool with_logits = true, float label_smooth = .0f,
+            ReduceMode reduce_mode = ReduceMode::MEAN, int axis = 1);
+    mgb::SymbolVar operator()(mgb::SymbolVar symbol_pred, mgb::SymbolVar symol_label);
+
+protected:
+    bool m_with_logits;
+    float m_label_smooth;
+    ReduceMode m_reduce_mode;
+    int m_axis;
+};
+
+/*!
+ * The MSE(Mean Square Error) loss. The definition could be found here:
+ * https://en.wikipedia.org/wiki/Mean_squared_error
+ *
+ * It's corresponding to the <MSE> of Python API of MegEngine.
+ */
+class MSELoss : public ILoss {
+public:
+    MSELoss(ReduceMode reduce_mode = ReduceMode::MEAN);
+    mgb::SymbolVar operator()(mgb::SymbolVar symbol_pred, mgb::SymbolVar symol_label);
+
+protected:
+    ReduceMode m_reduce_mode;
+};
+}  // namespace loss
+
+}  // namespace mgb

src/opr/include/megbrain/opr/training/optimizer.h

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+/**
+ * \file src/opr/include/training/optimizer.h
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#pragma once
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/tensor.h"
+
+namespace mgb {
+namespace optimizer {
+//! The interface of optimizers which should be inherited by each optimizer.
+class IOptimizer {
+public:
+    /*!
+     * The method to add manipulations to the graph to update the weight when the
+     * input is SymbolvarArrays.
+     */
+    virtual mgb::SymbolVarArray make_multiple(
+            mgb::SymbolVarArray symbol_weights, mgb::SymbolVarArray symbol_grads,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph) = 0;
+    /*!
+     * The method to add manipulations to the graph to update the weight with a
+     * certain strategy.
+     * The output is expected to be the symbolvar after updating the weight.
+     */
+    virtual mgb::SymbolVar make(
+            mgb::SymbolVar symbol_weight, mgb::SymbolVar symbol_grad,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph) = 0;
+    virtual ~IOptimizer() = default;
+};
+
+/*!
+ * An abstract class which helps to simplify the implemention of optimizers.
+ * It gives a default implemention of method <make_multiple> based on the method
+ * <make> defined by its derived class.
+ */
+class Optimizer : public IOptimizer {
+public:
+    mgb::SymbolVarArray make_multiple(
+            mgb::SymbolVarArray symbol_weights, mgb::SymbolVarArray symbol_grads,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph);
+    virtual mgb::SymbolVar make(
+            mgb::SymbolVar symbol_weight, mgb::SymbolVar symbol_grad,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph) = 0;
+    virtual ~Optimizer() = default;
+};
+
+/*!
+ * The SGD(Stochastic gradient descent) optimizer.
+ * The definition could be found here:
+ * https://en.wikipedia.org/wiki/Stochastic_gradient_descent
+ * It is corresponding to the <SGD> of Python API of MegEngine.
+ */
+class SGD : public Optimizer {
+public:
+    SGD() = default;
+    SGD(float lr, float weight_decay = .0f, float momentum = .0f);
+
+    SGD(const SGD& that) {
+        m_lr = that.m_lr;
+        m_momentum = that.m_momentum;
+        m_weight_decay = that.m_weight_decay;
+    }
+    mgb::SymbolVar make(
+            mgb::SymbolVar symbol_weight, mgb::SymbolVar symbol_grad,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph);
+
+    const SGD& operator=(const SGD& that) {
+        m_lr = that.m_lr;
+        m_momentum = that.m_momentum;
+        m_weight_decay = that.m_weight_decay;
+        return *this;
+    }
+
+protected:
+    float m_lr;
+    float m_weight_decay;
+    float m_momentum;
+    std::vector<std::shared_ptr<mgb::HostTensorND>> m_pre_grads;
+};
+
+/*!
+ * The Adam optimizer. The definition could be found here:
+ * https://en.wikipedia.org/wiki/Stochastic_gradient_descent#:~:text=full%2Dbatches.%5B26%5D-,Adam,-%5Bedit%5D
+ * It is corresponding to the <Adam> of Python API of MegEngine.
+ */
+class Adam : public Optimizer {
+public:
+    Adam() = default;
+    Adam(float lr, float weight_decay = .0f,
+         std::pair<float, float> betas = {0.9f, 0.999f}, float eps = 1e-8f,
+         bool amsgrad = false);
+
+    Adam(const Adam& that) {
+        m_lr = that.m_lr;
+        m_betas = that.m_betas;
+        m_eps = that.m_eps;
+        m_weight_decay = that.m_weight_decay;
+        m_amsgrad = that.m_amsgrad;
+    }
+    mgb::SymbolVar make(
+            mgb::SymbolVar symbol_weight, mgb::SymbolVar symbol_grad,
+            std::shared_ptr<mgb::cg::ComputingGraph> graph);
+
+    const Adam& operator=(const Adam& that) {
+        m_lr = that.m_lr;
+        m_betas = that.m_betas;
+        m_eps = that.m_eps;
+        m_weight_decay = that.m_weight_decay;
+        m_amsgrad = that.m_amsgrad;
+        return *this;
+    }
+
+protected:
+    float m_lr;
+    float m_weight_decay;
+    std::pair<float, float> m_betas;
+    float m_eps;
+    bool m_amsgrad;
+    std::vector<std::shared_ptr<mgb::DeviceTensorND>> m_exp_avg;
+    std::vector<std::shared_ptr<mgb::DeviceTensorND>> m_exp_avg_sq;
+    std::vector<std::shared_ptr<mgb::DeviceTensorND>> m_max_exp_avg_sq;
+    std::shared_ptr<mgb::HostTensorND> m_correction1;
+    std::shared_ptr<mgb::HostTensorND> m_correction2;
+};
+}  // namespace optimizer
+}  // namespace mgb

src/opr/include/megbrain/opr/training/utils.h

@@ -0,0 +1,81 @@
+/**
+ * \file src/opr/include/tensor_gen.h
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#pragma once
+
+#include "megbrain/tensor.h"
+
+namespace mgb {
+/*!
+ * A static class including methods to generate host tensors.
+ */
+class TensorGen {
+public:
+    /*!
+     * \brief Generate a tensor with all the elements equal to the given value
+     */
+    template <typename ctype, typename = typename mgb::ctype_enable_if<ctype>::type>
+    static std::shared_ptr<mgb::HostTensorND> constant(
+            mgb::TensorShape shape, ctype value,
+            mgb::CompNode comp_node = mgb::CompNode::load("xpu0")) {
+        std::shared_ptr<mgb::HostTensorND> r = std::make_shared<mgb::HostTensorND>(
+                comp_node, shape, typename mgb::DTypeTrait<ctype>::dtype());
+        auto ptr = r->ptr<ctype>();
+        for (size_t i = 0, it = r->layout().total_nr_elems(); i < it; i++) {
+            ptr[i] = value;
+        }
+
+        return r;
+    }
+
+    /*!
+     * \brief Generate a tensor with all the elements equal to 0
+     */
+    template <typename T>
+    static std::shared_ptr<mgb::HostTensorND> zeros(
+            mgb::TensorShape shape,
+            mgb::CompNode comp_node = mgb::CompNode::load("xpu0")) {
+        static_assert(
+                std::is_base_of<mgb::DType, T>(),
+                "Please use the dtype in namespace mgb or use "
+                "Tensor::constant.");
+        using ctype = typename mgb::DTypeTrait<T>::ctype;
+        return constant(shape, (ctype)0, comp_node);
+    }
+
+    /*!
+     * \brief Generate a tensor with all the elements equal to 0. In this method
+     * typename is not required.
+     */
+    static std::shared_ptr<mgb::HostTensorND> zeros(
+            mgb::TensorShape shape, mgb::DType dtype = mgb::dtype::Float32(),
+            mgb::CompNode comp_node = mgb::CompNode::load("xpu0")) {
+        std::shared_ptr<mgb::HostTensorND> r =
+                std::make_shared<mgb::HostTensorND>(comp_node, shape, dtype);
+        auto ptr = r->raw_ptr();
+        memset(ptr, 0, sizeof(megdnn::dt_byte));
+        return r;
+    }
+
+    /*!
+     * \brief Generate a tensor with all the elements equal to 1
+     */
+    template <typename T>
+    static std::shared_ptr<mgb::HostTensorND> ones(
+            mgb::TensorShape shape,
+            mgb::CompNode comp_node = mgb::CompNode::load("xpu0")) {
+        static_assert(
+                std::is_base_of<mgb::DType, T>(),
+                "Please use the dtype in namespace mgb or use "
+                "Tensor::constant.");
+        using ctype = typename mgb::DTypeTrait<T>::ctype;
+        return constant(shape, (ctype)1, comp_node);
+    }
+};
+}  // namespace mgb

src/opr/test/training/loss.cpp

@@ -0,0 +1,106 @@
+/**
+ * \file src/opr/test/training/loss.cpp
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/indexing.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/tensor.h"
+#include "megbrain/test/helper.h"
+
+#include "megbrain/opr/training/loss.h"
+
+using namespace mgb;
+using namespace loss;
+
+namespace {
+class Device2HostCallback {
+public:
+    Device2HostCallback(std::shared_ptr<HostTensorND> host) : m_host{host} {}
+    void operator()(const DeviceTensorND& device) { m_host->copy_from(device).sync(); }
+
+private:
+    std::shared_ptr<HostTensorND> m_host;
+};
+
+class CrossEntropyTest : public ::testing::Test {
+private:
+    /* data */
+    std::shared_ptr<HostTensorND> pred, label, truth, loss;
+    TensorShape pred_shape = {2, 10};
+    TensorShape label_shape = {2};
+    TensorShape truth_shape = {1};
+    std::vector<float> pred_values = {
+            -0.22847f, -0.65020f, -0.42470f, 1.32903f,  -0.58377f, -0.15881f, -0.23134f,
+            -0.36147f, -1.05848f, -0.23285f, 0.32360f,  -0.36430f, -0.03172f, 1.18970f,
+            -0.23465f, -0.16139f, -0.22942f, -0.22538f, -0.68029f, -0.41004f};
+    std::vector<int> label_values = {5, 3};
+    std::vector<float> truth_values = {1.8120441};
+
+    CompNode node = CompNode::load("cpu0");
+
+    std::shared_ptr<cg::ComputingGraph> graph;
+
+    CrossEntropyLoss cross_entropy_loss;
+
+public:
+    std::unique_ptr<cg::AsyncExecutable> func;
+
+    void setup();
+    void build_model(float label_smooth = .0f);
+    void verify();
+    template <typename T>
+    void assign_value(std::shared_ptr<HostTensorND> tensor, std::vector<T> value);
+};
+}  // namespace
+
+void CrossEntropyTest::setup() {
+    pred = std::make_shared<HostTensorND>(node, pred_shape, dtype::Float32());
+    label = std::make_shared<HostTensorND>(node, label_shape, dtype::Int32());
+    truth = std::make_shared<HostTensorND>(node, truth_shape, dtype::Float32());
+    loss = std::make_shared<HostTensorND>(node, truth_shape, dtype::Float32());
+
+    assign_value<float>(pred, pred_values);
+    assign_value<int>(label, label_values);
+    assign_value<float>(truth, truth_values);
+}
+
+template <typename T>
+void CrossEntropyTest::assign_value(
+        std::shared_ptr<HostTensorND> tensor, std::vector<T> values) {
+    ASSERT_EQ(values.size(), tensor->shape().total_nr_elems());
+    auto ptr = tensor->ptr<T>();
+    for (size_t i = 0, it = tensor->shape().total_nr_elems(); i < it; i += 1) {
+        ptr[i] = values.at(i);
+    }
+}
+
+void CrossEntropyTest::build_model(float label_smooth) {
+    graph = cg::ComputingGraph::make();
+
+    SymbolVar symbol_pred = opr::SharedDeviceTensor::make(*graph, *pred);
+    SymbolVar symbol_label = opr::SharedDeviceTensor::make(*graph, *label);
+
+    SymbolVar symbol_loss = cross_entropy_loss(symbol_pred, symbol_label);
+
+    cg::ComputingGraph::OutputSpec spec;
+    spec.push_back({symbol_loss, Device2HostCallback(loss)});
+    func = graph->compile(spec);
+}
+
+void CrossEntropyTest::verify() {
+    func->execute().wait();
+    ASSERT_NEAR(loss->ptr<float>()[0], truth->ptr<float>()[0], 0.001f);
+}
+
+TEST_F(CrossEntropyTest, CrossEntropy) {
+    setup();
+    build_model();
+    verify();
+}

src/opr/test/training/optimizer.cpp

@@ -0,0 +1,98 @@
+/**
+ * \file src/opr/test/training/optimizer.cpp
+ *
+ * This file is part of MegBrain, a deep learning framework developed by Megvii.
+ *
+ * \copyright Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ */
+
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/indexing.h"
+#include "megbrain/opr/io.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/tensor.h"
+#include "megbrain/test/helper.h"
+
+#include "megbrain/opr/training/optimizer.h"
+#include "megbrain/opr/training/utils.h"
+
+using namespace mgb;
+using namespace optimizer;
+
+namespace {
+
+class Device2HostCallback {
+public:
+    Device2HostCallback(std::shared_ptr<HostTensorND> host) : m_host{host} {}
+    void operator()(const DeviceTensorND& device) { m_host->copy_from(device).sync(); }
+
+private:
+    std::shared_ptr<HostTensorND> m_host;
+};
+
+template <typename T>
+void assign_value(std::shared_ptr<HostTensorND>& tensor, std::vector<T>& values) {
+    ASSERT_EQ(values.size(), tensor->layout().total_nr_elems());
+    auto ptr = tensor->ptr<T>();
+    for (size_t i = 0, it = tensor->layout().total_nr_elems(); i < it; i += 1) {
+        ptr[i] = values.at(i);
+    }
+}
+
+class OptimizerTest : public ::testing::Test {
+public:
+    void verify(
+            std::shared_ptr<IOptimizer> optimizer, std::shared_ptr<HostTensorND> weight,
+            std::shared_ptr<HostTensorND> grad, std::shared_ptr<HostTensorND> truth,
+            int execute_times);
+
+protected:
+    std::shared_ptr<IOptimizer> optimizer;
+    std::shared_ptr<cg::ComputingGraph> graph;
+};
+
+void OptimizerTest::verify(
+        std::shared_ptr<IOptimizer> optimizer, std::shared_ptr<HostTensorND> weight,
+        std::shared_ptr<HostTensorND> grad, std::shared_ptr<HostTensorND> truth,
+        int execute_times) {
+    graph = cg::ComputingGraph::make();
+    SymbolVar symbol_weight = opr::SharedDeviceTensor::make(*graph, *weight);
+    SymbolVar symbol_grad = opr::SharedDeviceTensor::make(*graph, *grad);
+
+    cg::ComputingGraph::OutputSpec spec;
+    spec.push_back(
+            {optimizer->make(symbol_weight, symbol_grad, graph),
+             Device2HostCallback(weight)});
+    auto func = graph->compile(spec);
+    for (int i = 0; i < execute_times; i++) {
+        func->execute();
+    }
+    auto weight_ptr = weight->ptr<float>();
+    auto truth_ptr = truth->ptr<float>();
+    for (size_t i = 0, it = weight->shape().total_nr_elems(); i < it; i += 1) {
+        ASSERT_NEAR(weight_ptr[i], truth_ptr[i], 0.001f);
+    }
+}
+
+}  // namespace
+
+TEST_F(OptimizerTest, SGD) {
+    auto weight = TensorGen::constant({1}, 0.30542f);
+    auto grad = TensorGen::constant({1}, -1.81453f);
+    auto truth = TensorGen::constant({1}, 1.04673f);
+    int execute_times = 10;
+    std::shared_ptr<SGD> sgd = std::make_shared<SGD>(0.01f, 5e-2f, 0.9f);
+
+    verify(sgd, weight, grad, truth, execute_times);
+}
+
+TEST_F(OptimizerTest, AdamTest) {
+    auto weight = TensorGen::constant({1}, 1.62957f);
+    auto grad = TensorGen::constant({1}, 1.02605f);
+    auto truth = TensorGen::constant({1}, 1.52969f);
+    int execute_times = 10;
+    std::shared_ptr<Adam> adam = std::make_shared<Adam>(0.01f, 0.9f);
+
+    verify(adam, weight, grad, truth, execute_times);
+}