!6193 [MS][LITE][Develop]lite/internal support log with C style

Merge pull request !6193 from chenjianping/lite_dev2

mindspore/lite/internal/CMakeLists.txt

@@ -10,19 +10,25 @@ file(GLOB KERNEL_SRC
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32/arithmetic_self.c
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32/arithmetic.c
         ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32/matmul.c
-        ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32_grad/activation_grad.c
         ${CMAKE_CURRENT_SOURCE_DIR}/src/kernel/fp32/*.cc
-        ${CMAKE_CURRENT_SOURCE_DIR}/src/kernel/fp32_grad/*.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/src/kernel/common/*.cc
         )
+if (SUPPORT_TRAIN)
+    file(GLOB TRAIN_KERNEL_SRC
+            ${KERNEL_SRC}
+            ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/fp32_grad/activation_grad.c
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/kernel/fp32_grad/*.cc
+            )
+endif ()
+
 list(REMOVE_ITEM KERNEL_SRC ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/opt_op_handler.c)
 
 set(CCSRC
         ${CMAKE_CURRENT_SOURCE_DIR}/src/lite_session.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/src/ms_tensor.cc
         ${TOP_DIR}/src/common/log_adapter.cc
-        ${TOP_DIR}/src/runtime/allocator.cc
         ${CMAKE_CURRENT_SOURCE_DIR}/../../core/gvar/logging_level.cc
+        ${TOP_DIR}/src/runtime/allocator.cc
         )
 
 if (PLATFORM_ARM64)
@@ -32,7 +38,7 @@ if (PLATFORM_ARM64)
             ${CMAKE_CURRENT_SOURCE_DIR}/../nnacl/assembly/arm64/MatmulFp32Opt.S)
     set_property(SOURCE ${ASSEMBLY_SRC} PROPERTY LANGUAGE C)
     set(KERNEL_SRC ${KERNEL_SRC} ${ASSEMBLY_SRC})
-    add_library(mslite_internal SHARED ${CCSRC} ${KERNEL_SRC})
+    add_library(mslite_internal SHARED ${CCSRC} ${KERNEL_SRC} ${TRAIN_KERNEL_SRC})
     target_link_libraries(mslite_internal log)
 endif()
 

mindspore/lite/internal/include/model.h

@@ -27,8 +27,8 @@ enum NodeType {
   NodeType_MAX = NodeType_CNode
 };
 
-enum KernelType {
-    Concat,
+enum KernelType : int {
+    Concat = 0,
     SoftMax,
     Activation,
     Conv2D,

mindspore/lite/internal/src/kernel/common/common_infershape.cc

@@ -17,7 +17,7 @@
 #include "internal/src/kernel/common/common_infershape.h"
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 
 int DoCommonInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tensors) {
   TensorPtr input = in_tensors.at(0);

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

@@ -19,7 +19,7 @@
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
 #include "nnacl/fp32/activation.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 #include "nnacl/errorcode.h"
 
 int DoActivationInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tensors, OpParameter *param) {
@@ -43,11 +43,11 @@ int DoActivation(const TensorPtrVector &in_tensors, const TensorPtrVector &out_t
     float alpha = param->alpha_;
     ret = LRelu(input_addr, length, output_addr, alpha);
   } else {
-    MS_LOG(ERROR) << "Unsupport activation type " << param->type_;
+    LITE_ERROR_LOG("Unsupport activation type: %d", param->type_);
     return RET_PARAM_INVALID;
   }
   if (ret != NNACL_OK) {
-    MS_LOG(ERROR) << "do activation(" << param->type_ << ") fail!ret: " << ret;
+    LITE_ERROR_LOG("do activation(%d) fail!ret: %d", param->type_, ret);
     return RET_ERROR;
   }
   return RET_OK;

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

@@ -18,7 +18,7 @@
 #include "internal/src/kernel/common/common_infershape.h"
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 #include "nnacl/fp32/arithmetic_self.h"
 
 int DoArithmeticSelfInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tensors,
@@ -36,11 +36,11 @@ int DoArithmeticSelf(const TensorPtrVector &in_tensors, const TensorPtrVector &o
   } 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_;
+    LITE_ERROR_LOG("Unsupport kernel type: %d", param->type_);
     return RET_PARAM_INVALID;
   }
   if (ret != NNACL_OK) {
-    MS_LOG(ERROR) << "do arithmetic " << param->type_ << " fail!ret: " << ret;
+    LITE_ERROR_LOG("do arithmetic %d fail!ret: %d", param->type_, ret);
     return RET_ERROR;
   }
   return RET_OK;

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

@@ -18,7 +18,7 @@
 #include "nnacl/fp32/matmul.h"
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 
 typedef struct MatMulCPUKernelData {
   float *a_c12_ptr_;
@@ -92,12 +92,12 @@ int DoMatMulInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector
   Int32Vector a_shape = input0->shape_;
   Int32Vector b_shape = input1->shape_;
   if (a_shape.size() < 2 || b_shape.size() < 2) {
-    MS_LOG(ERROR) << "inputs shape is invalid";
+    LITE_ERROR_LOG("inputs shape is invalid");
     return RET_INPUT_TENSOR_ERROR;
   }
   for (size_t i = 0; i < a_shape.size() - 2; ++i) {
     if (a_shape[i] != b_shape[i]) {
-      MS_LOG(ERROR) << "Op MatMul's dimensions must be equal";
+      LITE_ERROR_LOG("Op MatMul's dimensions must be equal");
       return RET_INPUT_TENSOR_ERROR;
     }
   }
@@ -117,11 +117,11 @@ int DoMatMulInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector
 int DoMatMul(const TensorPtrVector &in_tensors, const 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!";
+    LITE_LOG_ERROR("input data is NULL!");
     return RET_PARAM_INVALID;
   }
   if (allocator == NULL) {
-    MS_LOG(ERROR) << "input allocator is NULL!";
+    LITE_LOG_ERROR("input allocator is NULL!");
     return RET_PARAM_INVALID;
   }
   int batch = 1;
@@ -130,7 +130,8 @@ int DoMatMul(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tenso
   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";
+      LITE_ERROR_LOG("The bias' dimension %d is not equal with column %d", bias_shape[bias_shape.size() - 1],
+                     c_shape[c_shape.size() - 1]);
       return RET_INPUT_TENSOR_ERROR;
     }
   }

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

@@ -19,7 +19,7 @@
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
 #include "nnacl/fp32_grad/activation_grad.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 #include "nnacl/errorcode.h"
 
 int DoActivationGradInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tensors,
@@ -45,11 +45,11 @@ int DoActivationGrad(const TensorPtrVector &in_tensors, const TensorPtrVector &o
     float alpha = param->alpha_;
     ret = LReluGrad(dy_data, x_data, length, dx_data, alpha);
   } else {
-    MS_LOG(ERROR) << "Unsupport activation type " << param->type_;
+    LITE_ERROR_LOG("Unsupport activation type %d", param->type_);
     return RET_PARAM_INVALID;
   }
   if (ret != NNACL_OK) {
-    MS_LOG(ERROR) << "do activation(" << param->type_ << ") fail!ret: " << ret;
+    LITE_ERROR_LOG("do activation(%d) fail!ret: %d", param->type_, ret);
     return RET_ERROR;
   }
   return RET_OK;

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

@@ -18,7 +18,7 @@
 #include "internal/src/kernel/common/common_infershape.h"
 #include "internal/include/errorcode.h"
 #include "internal/include/ms_tensor.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 #include "nnacl/fp32/arithmetic_self.h"
 #include "nnacl/fp32/arithmetic.h"
 
@@ -40,11 +40,11 @@ int DoArithmeticSelfGrad(const TensorPtrVector &in_tensors, const TensorPtrVecto
   } else if (param->type_ == KernelType::NegGrad) {
     ret = ElementNegative(dy_data, dx_data, data_size);
   } else {
-    MS_LOG(ERROR) << "Unsupport kernel type: " << param->type_;
+    LITE_ERROR_LOG("Unsupport kernel type: %d", param->type_);
     return RET_PARAM_INVALID;
   }
   if (ret != NNACL_OK) {
-    MS_LOG(ERROR) << "do arithmetic " << param->type_ << " fail!ret: " << ret;
+    LITE_ERROR_LOG("do arithmetic %d fail!ret: %d", param->type_, ret);
     return RET_ERROR;
   }
   return RET_OK;

mindspore/lite/internal/src/lite_log.h

@@ -0,0 +1,44 @@
+/**
+ * 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_LITE_LOG_H_
+#define MINDSPORE_LITE_INTERNAL_SRC_LITE_LOG_H_
+#ifdef DEBUG
+#include <assert.h>
+#endif
+
+#ifdef DEBUG
+#define LITE_DEBUG_LOG(format, ...) \
+  printf("[DEBUG] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
+#define LITE_INFO_LOG(format, ...) \
+  printf("[INFO] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
+#define LITE_WARNING_LOG(format, ...) \
+  printf("[WARNING] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
+#define LITE_ERROR_LOG(format, ...) \
+  printf("[ERROR] [%s %s] [%s] [%d] " format "\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
+#define LITE_LOG_ERROR(...) \
+  printf("[ERROR] [%s %s] [%s] [%d] %s\n", __DATE__, __TIME__, __FILE__, __LINE__, __VA_ARGS__)
+#define MS_ASSERT(f) assert(f)
+#else
+#define LITE_DEBUG_LOG(...)
+#define LITE_INFO_LOG(...)
+#define LITE_WARNING_LOG(...)
+#define LITE_ERROR_LOG(...)
+#define LITE_LOG_ERROR(...)
+#define MS_ASSERT(f) ((void)0)
+#endif
+
+#endif  // MINDSPORE_LITE_INTERNAL_SRC_LITE_LOG_H_

mindspore/lite/internal/src/lite_session.cc

@@ -18,7 +18,7 @@
 #include "internal/include/ms_tensor.h"
 #include "src/runtime/allocator.h"
 #include "internal/include/errorcode.h"
-#include "utils/log_adapter.h"
+#include "internal/src/lite_log.h"
 #include "internal/src/kernel/fp32/activation.h"
 #include "internal/src/kernel/fp32/arithmetic_self.h"
 #include "internal/src/kernel/fp32/matmul.h"
@@ -42,8 +42,8 @@ static int ModelInferShape() {
   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!";
+    if (node->primitive_ == NULL) {
+      LITE_ERROR_LOG("node's primitive is NULL!");
       return RET_ERROR;
     }
     TensorPtrVector in_tensors;
@@ -57,16 +57,16 @@ static int ModelInferShape() {
     int type = node->primitive_->type_;
     InferShape infershape = g_infershape_funcs[type];
     if (infershape == NULL) {
-      MS_LOG(ERROR) << "Unsupport kernel type: " << type;
+      LITE_ERROR_LOG("Unsupport kernel type: %d", type);
       return RET_PARAM_INVALID;
     }
     int ret = (*infershape)(in_tensors, out_tensors, node->primitive_);
     if (ret == RET_INFER_INVALID) {
       g_infershape_interrupt = true;
-      MS_LOG(INFO) << node->name_ << "inferShape shouldn't be done before runtime, inferShape interrupt!";
+      LITE_INFO_LOG("%s inferShape shouldn't be done before runtime, inferShape interrupt!", node->name_.c_str());
     }
     if (ret != RET_OK) {
-      MS_LOG(ERROR) << "Infer shape fail!ret: " << ret;
+      LITE_ERROR_LOG("Infer shape fail!ret: %d", ret);
       return ret;
     }
   }
@@ -79,15 +79,19 @@ static void InitFuncs() {
     g_infershape_funcs[KernelType::Activation] = DoActivationInferShape;
     g_infershape_funcs[KernelType::Log] = DoArithmeticSelfInferShape;
     g_infershape_funcs[KernelType::Neg] = DoArithmeticSelfInferShape;
-    g_infershape_funcs[KernelType::ActivationGrad] = DoActivationGradInferShape;
 
     g_runkernel_funcs[KernelType::MatMul] = DoMatMul;
     g_runkernel_funcs[KernelType::Activation] = DoActivation;
     g_runkernel_funcs[KernelType::Log] = DoArithmeticSelf;
-    g_runkernel_funcs[KernelType::LogGrad] = DoArithmeticSelfGrad;
     g_runkernel_funcs[KernelType::Neg] = DoArithmeticSelf;
+
+#ifdef SUPPORT_TRAIN
+    g_infershape_funcs[KernelType::ActivationGrad] = DoActivationGradInferShape;
+
     g_runkernel_funcs[KernelType::NegGrad] = DoArithmeticSelfGrad;
     g_runkernel_funcs[KernelType::ActivationGrad] = DoActivationGrad;
+    g_runkernel_funcs[KernelType::LogGrad] = DoArithmeticSelfGrad;
+#endif
     g_first_load = false;
   }
 }
@@ -114,7 +118,7 @@ int LiteSession::CompileGraph(Model *model) {
 TensorPtrVector LiteSession::GetInputs() const {
   TensorPtrVector in(g_model->input_indices_.size());
   for (size_t i = 0; i < g_model->input_indices_.size(); ++i) {
-    in.at(i) = g_model->all_tensors_[i];
+    in.at(i) = g_model->all_tensors_[g_model->input_indices_[i]];
   }
   return in;
 }
@@ -126,7 +130,7 @@ TensorPtrVector LiteSession::GetOutputsByNodeName(const String &node_name) const
 TensorPtrVector LiteSession::GetOutputs() const {
   TensorPtrVector out(g_model->output_indices_.size());
   for (size_t i = 0; i < g_model->output_indices_.size(); ++i) {
-    out.at(i) = g_model->all_tensors_[i];
+    out.at(i) = g_model->all_tensors_[g_model->output_indices_[i]];
   }
   return out;
 }
@@ -137,7 +141,7 @@ int LiteSession::RunGraph() {
   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!";
+      LITE_ERROR_LOG("node's primitive is NULL!");
       return RET_ERROR;
     }
     TensorPtrVector in_tensors;
@@ -152,31 +156,31 @@ int LiteSession::RunGraph() {
     if (g_infershape_interrupt) {
       InferShape infershape = g_infershape_funcs[type];
       if (infershape == NULL) {
-        MS_LOG(ERROR) << "Unsupport kernel type: " << type;
+        LITE_ERROR_LOG("Unsupport kernel type: %d", type);
         return RET_PARAM_INVALID;
       }
       int ret = (*infershape)(in_tensors, out_tensors, node->primitive_);
       if (ret != RET_OK) {
-        MS_LOG(ERROR) << "InferShape fail!ret: " << ret;
+        LITE_ERROR_LOG("InferShape fail!ret: %d", ret);
         return ret;
       }
     }
     for (size_t j = 0; j < out_tensors.size(); ++j) {
       out_tensors[j]->data_ = g_allocator.Malloc(out_tensors[j]->Size());
       if (out_tensors[j]->data_ == NULL) {
-        MS_LOG(ERROR) << "Malloc data for out tensor fail!";
+        LITE_ERROR_LOG("Malloc data for out tensor fail!");
         return RET_NULL_PTR;
       }
     }
     RunKernel run_kernel = g_runkernel_funcs[type];
     if (run_kernel == NULL) {
-      MS_LOG(ERROR) << "Unsupport kernel type: " << type;
+      LITE_ERROR_LOG("Unsupport kernel type: %d", type);
       return RET_PARAM_INVALID;
     }
 
     int ret = (*run_kernel)(in_tensors, out_tensors, node, &g_allocator);
     if (ret != RET_OK) {
-      MS_LOG(ERROR) << "run kernel fail!ret: " << ret;
+      LITE_ERROR_LOG("run kernel fail!ret: ", ret);
       return ret;
     }
   }

mindspore/lite/internal/src/ms_tensor.cc

@@ -15,11 +15,10 @@
  */
 #include <iostream>
 #include <vector>
-#include <numeric>
 #include <string>
 #include "internal/include/ms_tensor.h"
 MSTensor *CreateTensor(TypeId data_type, const ShapeVector &shape) {
-  MSTensor *tensor = (MSTensor *)malloc(sizeof(MSTensor));
+  MSTensor *tensor = new (std::nothrow) MSTensor();
   if (tensor == NULL) {
     return NULL;
   }

mindspore/lite/test/ut/internal/infer_test.cc

@@ -32,22 +32,23 @@ class InferTest : public mindspore::CommonTest {
 
 TEST_F(InferTest, TestSession) {
   Model model;
-  Node *node = reinterpret_cast<Node *>(malloc(sizeof(Node)));
+  Node node;
+  model.nodes_.push_back(&node);
 
-  node->name_ = "Neg";
-  node->node_type_ = NodeType::NodeType_CNode;
-  PrimitiveC *prim = reinterpret_cast<PrimitiveC *>(malloc(sizeof(PrimitiveC)));
-  prim->type_ = KernelType::Neg;
-  node->input_indices_.push_back(0);
-  node->output_indices_.push_back(1);
-
-  MSTensor *in = CreateTensor(kNumberTypeFloat32, {1, 1, 1, 10});
+  node.node_type_ = NodeType::NodeType_CNode;
+  PrimitiveC prim;
+  prim.type_ = KernelType::Neg;
+  node.primitive_ = &prim;
+  node.input_indices_.push_back(0);
+  node.output_indices_.push_back(1);
+  ShapeVector shape = {1, 1, 1, 10};
+  MSTensor *in = CreateTensor(kNumberTypeFloat32, shape);
   model.all_tensors_.push_back(in);
   model.input_indices_.push_back(0);
 
-  MSTensor *out = CreateTensor(kNumberTypeFloat32, {1, 1, 1, 10});
+  MSTensor *out = CreateTensor(kNumberTypeFloat32, shape);
   model.all_tensors_.emplace_back(out);
-  node->output_indices_.push_back(1);
+  model.output_indices_.push_back(1);
 
   LiteSession session;
   session.CompileGraph(&model);