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mindspore/lite/internal/CMakeLists.txt

@@ -8,8 +8,10 @@ file(GLOB_RECURSE C_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cc)
 file(GLOB KERNEL_SRC
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/*.c
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32/*.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32_grad/*.c
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/int8/*.c
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/quantization/*.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/src/kernel/fp32/*.cc
         )
 list(REMOVE_ITEM KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/opt_op_handler.c)
 

mindspore/lite/internal/include/lite_session.h

@@ -84,7 +84,7 @@ typedef struct LiteSession {
   /// \param[in] inputs Define the new inputs shape.
   ///
   /// \return STATUS as an error code of resize inputs, STATUS is defined in errorcode.h.
-  int Resize(const TensorPtrVector &inputs);
+  int Resize(const TensorPtrVector &inputs, Int32VectorVector dims);
 } LiteSession;
 
 #endif  // MINDSPORE_LITE_INCLUDE_LITE_SESSION_H

mindspore/lite/internal/include/lite_utils.h

@@ -27,5 +27,6 @@ using String = std::string;
 using StringVector = std::vector<std::string>;
 using ShapeVector = std::vector<int>;
 using NodePtrVector = std::vector<struct Node *>;
-
+using Int32Vector = std::vector<int32_t>;
+using Int32VectorVector = std::vector<Int32Vector>;
 #endif  // MINDSPORE_LITE_INCLUDE_LITE_UTILS_H_

mindspore/lite/internal/include/model.h

@@ -27,6 +27,183 @@ enum NodeType {
   NodeType_MAX = NodeType_CNode
 };
 
+enum KernelType {
+    Concat,
+    SoftMax,
+    Activation,
+    Conv2D,
+    FusedBatchNorm,
+    BatchNorm,
+    BiasAdd,
+    Pooling,
+    ROIPooling,
+    DepthwiseConv2D,
+    DeDepthwiseConv2D,
+    Resize,
+    DetectionPostProcess,
+    FullConnection,
+    Mean,
+    DeConv2D,
+    Scale,
+    Reshape,
+    Eltwise,
+    NetOutput,
+    Add,
+    Sub,
+    MatMul,
+    StridedSlice,
+    Power,
+    Slice,
+    Stack,
+    Mul,
+    RealDiv,
+    Pad,
+    Maximum,
+    Minimum,
+    PReLU,
+    LeakyReLU,
+    ArgMax,
+    ArgMin,
+    Exp,
+    Crop,
+    Range,
+    Rsqrt,
+    ExpandDims,
+    Tile,
+    Cast,
+    Shape,
+    Nchw2Nhwc,
+    Nhwc2Nchw,
+    QuantDTypeCast,
+    Split,
+    Permute,
+    FakeQuantWithMinMaxVars,
+    Equal,
+    Less,
+    Greater,
+    NotEqual,
+    LessEqual,
+    GreaterEqual,
+    Min,
+    Floor,
+    Abs,
+    Neg,
+    Cos,
+    Sin,
+    Sqrt,
+    Square,
+    Constant,
+    Log,
+    Tan,
+    Atan,
+    Asin,
+    Clip,
+    Transpose,
+    Squeeze,
+    Unsqueeze,
+    Upsample,
+    Dropout,
+    Broadcast,
+    BroadcastTo,
+    Lrn,
+    ZerosLike,
+    TopK,
+    SpaceToDepth,
+    SpaceToBatch,
+    SparseToDense,
+    ReverseSequence,
+    Rank,
+    Gather,
+    GatherNd,
+    Fill,
+    Elu,
+    DepthToSpace,
+    BatchToSpace,
+    AddN,
+    Ceil,
+    EmbeddingLookup,
+    EmbeddingLookupSparse,
+    FloorDiv,
+    FloorMod,
+    L2Norm,
+    LocalResponseNormalization,
+    MatrixDiag,
+    Reduce,
+    Reverse,
+    Round,
+    Select,
+    Scatter,
+    ScatterND,
+    ConstantOfShape,
+    Unique,
+    Unstack,
+    LogicalAnd,
+    LogicalOr,
+    LogicalXor,
+    LogicalNot,
+    OnnxInt8Quantize,
+    OnnxInt8Dequantize,
+    FakeQuantWithMinMax,
+    FakeQuantWithMinMaxPerChannel,
+    BatchNormFold,
+    MulFold,
+    AddFold,
+    SquaredDifference,
+    Flatten,
+    FlattenGrad,
+    TupleGetItem,
+    Div,
+    Where,
+    OneHot,
+    Lstm,
+    Conv2DGradFilter,
+    Conv2DGradInput,
+    PoolingGrad,
+    BNGrad,
+    BNGradInput,
+    ApplyMomentum,
+    BiasGrad,
+    SoftmaxCrossEntropy,
+    AddGrad,
+    SubGrad,
+    MulGrad,
+    DivGrad,
+    PowerGrad,
+    ActivationGrad,
+    PriorBox,
+    SpaceToBatchND,
+    Depend,
+    Return,
+    MakeTuple,
+    ToFormat,
+    Proposal,
+    Custom,
+    BlackBox,
+    NegGrad,
+    LogGrad,
+    BatchToSpaceND,
+};
+
+enum ActivationType {
+    NO_ACTIVATION = 0,
+    RELU = 1,
+    SIGMOID = 2,
+    RELU6 = 3,
+    ELU = 4,
+    LEAKY_RELU = 5,
+    ABS = 6,
+    RELU1 = 7,
+    SOFTSIGN = 8,
+    SOFTPLUS = 9,
+    TANH = 10,
+    SELU = 11,
+    HSWISH = 12,
+    HSIGMOID = 13,
+    THRESHOLDRELU = 14,
+    LINEAR = 15,
+    UNKNOW = 16
+};
+
 typedef struct Node {
   String name_;
   NodeType node_type_;

mindspore/lite/internal/src/kernel/fp32/activation.cc

@@ -0,0 +1,49 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "internal/src/kernel/fp32/activation.h"
+#include "internal/include/errorcode.h"
+#include "internal/include/ms_tensor.h"
+#include "nnacl/fp32/activation.h"
+#include "utils/log_adapter.h"
+#include "nnacl/errorcode.h"
+
+int DoActivation(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                 mindspore::lite::Allocator *allocator) {
+  ActivationParameter *param = (ActivationParameter *)node->primitive_;
+  int ret = RET_OK;
+  size_t length = in_tensors[0]->ElementsNum();
+  float *input_addr = (float *)in_tensors[0]->data_;
+  float *output_addr = (float *)out_tensors[0]->data_;
+  if (param->type_ == ActivationType::RELU) {
+    ret = Fp32Relu(input_addr, length, output_addr);
+  } else if (param->type_ == ActivationType::SIGMOID) {
+    ret = Sigmoid(input_addr, length, output_addr);
+  } else if (param->type_ == ActivationType::RELU6) {
+    ret = Fp32Relu6(input_addr, length, output_addr);
+  } else if (param->type_ == ActivationType::LEAKY_RELU) {
+    float alpha = param->alpha_;
+    ret = LRelu(input_addr, length, output_addr, alpha);
+  } else {
+    MS_LOG(ERROR) << "Unsupport activation type " << param->type_;
+    return RET_PARAM_INVALID;
+  }
+  if (ret != NNACL_OK) {
+    MS_LOG(ERROR) << "do activation(" << param->type_ << ") fail!ret: " << ret;
+    return RET_ERROR;
+  }
+  return RET_OK;
+}

mindspore/lite/internal/src/kernel/fp32/activation.h

@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ACTIVATION_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ACTIVATION_H_
+
+#include "internal/include/model.h"
+#include "src/runtime/allocator.h"
+
+int DoActivation(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                 mindspore::lite::Allocator *allocator);
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ACTIVATION_H_

mindspore/lite/internal/src/kernel/fp32/arithmetic_self.cc

@@ -0,0 +1,41 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "internal/src/kernel/fp32/arithmetic_self.h"
+#include "internal/include/errorcode.h"
+#include "internal/include/ms_tensor.h"
+#include "utils/log_adapter.h"
+#include "nnacl/fp32/arithmetic_self.h"
+
+int DoArithmeticSelf(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                     mindspore::lite::Allocator *allocator) {
+  size_t data_size = in_tensors[0]->ElementsNum();
+  OpParameter *param = node->primitive_;
+  int ret;
+  if (param->type_ == KernelType::Log) {
+    ret = ElementLog((float *)in_tensors[0]->data_, (float *)out_tensors[0]->data_, data_size);
+  } else if (param->type_ == KernelType::Neg) {
+    ret = ElementNegative((float *)in_tensors[0]->data_, (float *)out_tensors[0]->data_, data_size);
+  } else {
+    MS_LOG(ERROR) << "Unsupport kernel type: " << param->type_;
+    return RET_PARAM_INVALID;
+  }
+  if (ret != NNACL_OK) {
+    MS_LOG(ERROR) << "do arithmetic " << param->type_ << " fail!ret: " << ret;
+    return RET_ERROR;
+  }
+  return RET_OK;
+}

mindspore/lite/internal/src/kernel/fp32/arithmetic_self.h

@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ARITHMETIC_SELF_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ARITHMETIC_SELF_H_
+
+#include "internal/include/model.h"
+#include "src/runtime/allocator.h"
+
+int DoArithmeticSelf(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                     mindspore::lite::Allocator *allocator);
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_ARITHMETIC_SELF_H_

mindspore/lite/internal/src/kernel/fp32/matmul.cc

@@ -0,0 +1,145 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "internal/src/kernel/fp32/matmul.h"
+#include "nnacl/fp32/matmul.h"
+#include "internal/include/errorcode.h"
+#include "internal/include/ms_tensor.h"
+#include "utils/log_adapter.h"
+
+typedef struct MatMulCPUKernelData {
+  float *a_c12_ptr_;
+  float *b_r8_ptr_;
+  float *bias_ptr_;
+} MatMulCPUKernelData;
+
+void MatMulInitMatrixA(float *src_ptr, float *dst_ptr, MatMulParameter *params) {
+  for (int i = 0; i < params->batch; i++) {
+    float *src = src_ptr + i * params->deep_ * params->row_;
+    float *dst = dst_ptr + i * params->deep_ * params->row_12_;
+    if (params->a_transpose_) {
+      RowMajor2Row12Major(src, dst, params->deep_, params->row_);
+    } else {
+      RowMajor2Col12Major(src, dst, params->row_, params->deep_);
+    }
+  }
+}
+
+void MatMulInitMatrixB(float *src_ptr, float *dst_ptr, MatMulParameter *params) {
+  for (int i = 0; i < params->batch; i++) {
+    float *src = src_ptr + i * params->deep_ * params->col_;
+    float *dst = dst_ptr + i * params->deep_ * params->col_8_;
+    if (params->b_transpose_) {
+      RowMajor2Col8Major(src, dst, params->col_, params->deep_);
+    } else {
+      RowMajor2Row8Major(src, dst, params->deep_, params->col_);
+    }
+  }
+}
+
+void FreeMatMulKernelData(MatMulCPUKernelData *kernel_data, mindspore::lite::Allocator *allocator) {
+  if (kernel_data == NULL) {
+    return;
+  }
+  if (kernel_data->a_c12_ptr_ != NULL) {
+    allocator->Free(kernel_data->a_c12_ptr_);
+    kernel_data->a_c12_ptr_ = NULL;
+  }
+
+  if (kernel_data->b_r8_ptr_ != NULL) {
+    allocator->Free(kernel_data->b_r8_ptr_);
+    kernel_data->b_r8_ptr_ = NULL;
+  }
+
+  if (kernel_data->bias_ptr_ != NULL) {
+    allocator->Free(kernel_data->bias_ptr_);
+    kernel_data->bias_ptr_ = NULL;
+  }
+  free(kernel_data);
+}
+
+int DoMatMul(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+             mindspore::lite::Allocator *allocator) {
+  if (in_tensors[0]->data_ == NULL || in_tensors[1]->data_ ==NULL) {
+    MS_LOG(ERROR) << "input data is NULL!";
+    return RET_PARAM_INVALID;
+  }
+  if (allocator == NULL) {
+    MS_LOG(ERROR) << "input allocator is NULL!";
+    return RET_PARAM_INVALID;
+  }
+  int batch = 1;
+  std::vector<int> a_shape = in_tensors[0]->shape_;
+  std::vector<int> c_shape = out_tensors[0]->shape_;
+  if (in_tensors.size() == 3) {
+    std::vector<int> bias_shape = in_tensors[2]->shape_;
+    if (bias_shape[bias_shape.size() - 1] != c_shape[c_shape.size() - 1]) {
+      MS_LOG(ERROR) << "The bias' dimension is not equal with column";
+      return RET_INPUT_TENSOR_ERROR;
+    }
+  }
+  for (size_t i = 0; i < a_shape.size() - 2; ++i) {
+    batch *= a_shape[i];
+  }
+
+  MatMulParameter *params = (MatMulParameter *)node->primitive_;
+  params->batch = batch;
+  params->row_ = c_shape[c_shape.size() - 2];
+  params->col_ = c_shape[c_shape.size() - 1];
+  params->deep_ = params->a_transpose_ ? a_shape[a_shape.size() - 2] : a_shape[a_shape.size() - 1];
+  params->row_12_ = UP_ROUND(params->row_, C12NUM);
+  params->col_8_ = UP_ROUND(params->col_, 8);
+
+  MatMulCPUKernelData *kernel_data = (MatMulCPUKernelData *)malloc(sizeof(MatMulCPUKernelData));
+  kernel_data->a_c12_ptr_
+    = reinterpret_cast<float *>(allocator->Malloc(params->batch * params->row_12_ * params->deep_ * sizeof(float)));
+  if (kernel_data->a_c12_ptr_ == NULL) {
+    return RET_MEMORY_FAILED;
+  }
+  memset(kernel_data->a_c12_ptr_, 0, params->row_12_ * params->deep_ * sizeof(float));
+
+  kernel_data->b_r8_ptr_
+    = reinterpret_cast<float *>(allocator->Malloc(params->batch * params->col_8_ * params->deep_ * sizeof(float)));
+  if (kernel_data->b_r8_ptr_ == NULL) {
+    FreeMatMulKernelData(kernel_data, allocator);
+    return RET_MEMORY_FAILED;
+  }
+  memset(kernel_data->b_r8_ptr_, 0, params->col_8_ * params->deep_ * sizeof(float));
+
+  MatMulInitMatrixA((float *)in_tensors[0]->data_, kernel_data->a_c12_ptr_, params);
+  MatMulInitMatrixB((float *)in_tensors[1]->data_, kernel_data->b_r8_ptr_, params);
+  kernel_data->bias_ptr_ = (float *)(allocator->Malloc(params->col_8_ * sizeof(float)));
+  if (kernel_data->bias_ptr_ == NULL) {
+    FreeMatMulKernelData(kernel_data, allocator);
+    return RET_MEMORY_FAILED;
+  }
+  memset(kernel_data->bias_ptr_, 0, params->col_8_ * sizeof(float));
+
+  if (in_tensors.size() == 3) {
+    memcpy(kernel_data->bias_ptr_, in_tensors[2]->data_, params->col_ * sizeof(float));
+  }
+  auto c_src = (float *)out_tensors[0]->data_;
+  for (int i = 0; i < params->batch; ++i) {
+    float *a_ptr = kernel_data->a_c12_ptr_ + i * params->row_12_ * params->deep_;
+    float *b_ptr = kernel_data->b_r8_ptr_ + i * params->deep_ * params->col_8_;
+    float *c_ptr = c_src + i * params->row_ * params->col_;
+    MatMulOpt(a_ptr, b_ptr, c_ptr, kernel_data->bias_ptr_, ActType_No, params->deep_, params->row_, params->col_,
+              params->col_, OutType_Nhwc);
+  }
+
+  return RET_OK;
+}
+

mindspore/lite/internal/src/kernel/fp32/matmul.h

@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_MATMUL_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_MATMUL_H_
+
+#include "internal/include/model.h"
+#include "src/runtime/allocator.h"
+
+int DoMatMul(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+             mindspore::lite::Allocator *allocator);
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_MATMUL_H_

mindspore/lite/internal/src/kernel/fp32_grad/activation_grad.cc

@@ -0,0 +1,50 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "internal/src/kernel/fp32_grad/activation_grad.h"
+#include "internal/include/errorcode.h"
+#include "internal/include/ms_tensor.h"
+#include "nnacl/fp32_grad/activation_grad.h"
+#include "utils/log_adapter.h"
+#include "nnacl/errorcode.h"
+
+int DoActivationGrad(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                     mindspore::lite::Allocator *allocator) {
+  ActivationGradParameter *param = (ActivationGradParameter *)node->primitive_;
+  int ret = RET_OK;
+  size_t length = in_tensors[0]->ElementsNum();
+  float *dy_data = (float *)in_tensors[0]->data_;
+  float *x_data = (float *)in_tensors[1]->data_;
+  float *dx_data = (float *)(float *)out_tensors[0]->data_;
+  if (param->type_ == ActivationType::RELU) {
+    ret = ReluGrad(dy_data, x_data, length, dx_data);
+  } else if (param->type_ == ActivationType::SIGMOID) {
+    ret = SigmoidGrad(dy_data, x_data, length, dx_data);
+  } else if (param->type_ == ActivationType::RELU6) {
+    ret = Relu6Grad(dy_data, x_data, length, dx_data);
+  } else if (param->type_ == ActivationType::LEAKY_RELU) {
+    float alpha = param->alpha_;
+    ret = LReluGrad(dy_data, x_data, length, dx_data, alpha);
+  } else {
+    MS_LOG(ERROR) << "Unsupport activation type " << param->type_;
+    return RET_PARAM_INVALID;
+  }
+  if (ret != NNACL_OK) {
+    MS_LOG(ERROR) << "do activation(" << param->type_ << ") fail!ret: " << ret;
+    return RET_ERROR;
+  }
+  return RET_OK;
+}

mindspore/lite/internal/src/kernel/fp32_grad/activation_grad.h

@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ACTIVATION_GRAD_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ACTIVATION_GRAD_H_
+
+#include "internal/include/model.h"
+#include "src/runtime/allocator.h"
+
+int DoActivationGrad(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                     mindspore::lite::Allocator *allocator);
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ACTIVATION_GRAD_H_

mindspore/lite/internal/src/kernel/fp32_grad/arithmetic_self_grad.cc

@@ -0,0 +1,45 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "internal/src/kernel/fp32_grad/arithmetic_self_grad.h"
+#include "internal/include/errorcode.h"
+#include "internal/include/ms_tensor.h"
+#include "utils/log_adapter.h"
+#include "nnacl/fp32/arithmetic_self.h"
+#include "nnacl/fp32/arithmetic.h"
+
+int DoArithmeticGradSelf(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                         mindspore::lite::Allocator *allocator) {
+  size_t data_size = in_tensors[0]->ElementsNum();
+  OpParameter *param = node->primitive_;
+  float *dy_data = (float *)in_tensors[0]->data_;
+  float *x_data = (float *)in_tensors[1]->data_;
+  float *dx_data = (float *)(float *)out_tensors[0]->data_;
+  int ret;
+  if (param->type_ == KernelType::LogGrad) {
+    ret = ElementDiv(dy_data, x_data, dx_data, data_size);
+  } else if (param->type_ == KernelType::NegGrad) {
+    ret = ElementNegative(dy_data, dx_data, data_size);
+  } else {
+    MS_LOG(ERROR) << "Unsupport kernel type: " << param->type_;
+    return RET_PARAM_INVALID;
+  }
+  if (ret != NNACL_OK) {
+    MS_LOG(ERROR) << "do arithmetic " << param->type_ << " fail!ret: " << ret;
+    return RET_ERROR;
+  }
+  return RET_OK;
+}

mindspore/lite/internal/src/kernel/fp32_grad/arithmetic_self_grad.h

@@ -0,0 +1,26 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_
+
+#include "internal/include/model.h"
+#include "src/runtime/allocator.h"
+
+int DoArithmeticGradSelf(TensorPtrVector in_tensors, TensorPtrVector out_tensors, Node *node,
+                         mindspore::lite::Allocator *allocator);
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_KERNEL_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_

mindspore/lite/internal/src/lite_session.cc

@@ -17,6 +17,13 @@
 #include "internal/include/model.h"
 #include "internal/include/ms_tensor.h"
 #include "src/runtime/allocator.h"
+#include "internal/include/errorcode.h"
+#include "utils/log_adapter.h"
+#include "internal/src/kernel/fp32/activation.h"
+#include "internal/src/kernel/fp32/arithmetic_self.h"
+#include "internal/src/kernel/fp32/matmul.h"
+#include "internal/src/kernel/fp32_grad/arithmetic_self_grad.h"
+#include "internal/src/kernel/fp32_grad/activation_grad.h"
 
 static Context *g_Ctx;
 static Model *g_Model;
@@ -58,11 +65,56 @@ TensorPtrVector LiteSession::GetOutputs() const {
 
 int LiteSession::RunGraph() {
   // invoke nnacl kernel
-  return 0;
+  NodePtrVector nodes = g_Model->nodes_;
+  size_t nodes_size = nodes.size();
+  for (size_t i = 0; i < nodes_size; ++i) {
+    auto node = nodes[i];
+    if (node->primitive_ == nullptr) {
+      MS_LOG(ERROR) << "node's primitive is NULL!";
+      return RET_ERROR;
+    }
+    TensorPtrVector in_tensors;
+    for (size_t j = 0; j < node->input_indices_.size(); ++j) {
+      in_tensors.push_back(g_Model->all_tensors_[node->input_indices_[j]]);
+    }
+    TensorPtrVector out_tensors;
+    for (size_t j = 0; j < node->output_indices_.size(); ++j) {
+      out_tensors.push_back(g_Model->all_tensors_[node->output_indices_[j]]);
+    }
+    int type = node->primitive_->type_;
+    int ret = RET_ERROR;
+    switch (type) {
+      case KernelType::MatMul:
+        ret = DoMatMul(in_tensors, out_tensors, node, &allocator);
+        break;
+      case KernelType::Activation:
+        ret = DoActivation(in_tensors, out_tensors, node, &allocator);
+        break;
+      case KernelType::Log:
+      case KernelType::Neg:
+        ret = DoArithmeticSelf(in_tensors, out_tensors, node, &allocator);
+        break;
+      case KernelType::LogGrad:
+      case KernelType::NegGrad:
+        ret = DoArithmeticGradSelf(in_tensors, out_tensors, node, &allocator);
+        break;
+      case KernelType::ActivationGrad:
+        ret = DoActivationGrad(in_tensors, out_tensors, node, &allocator);
+        break;
+      default:
+        MS_LOG(ERROR) << "Unsupport kernel type: " << type;
+        return RET_PARAM_INVALID;
+    }
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "run kernel fail!ret: " << ret;
+      return ret;
+    }
+  }
+  return RET_OK;
 }
 
 StringVector LiteSession::GetOutputTensorNames() const { return StringVector(); }
 
 MSTensor *LiteSession::GetOutputByTensorName(const String &tensor_name) const { return NULL; }
 
-int LiteSession::Resize(const TensorPtrVector &inputs) { return 0; }
+int LiteSession::Resize(const TensorPtrVector &inputs, Int32VectorVector dims) { return 0; }