Refactor: Support auto transformation and net optimization.

1. Auto transformation between data format, data type and memory type.
2. Add device placement optimization strategy.

mace/core/arg_helper.cc

@@ -96,6 +96,44 @@ MACE_GET_REPEATED_ARGUMENT_FUNC(int, ints, true)
 MACE_GET_REPEATED_ARGUMENT_FUNC(int64_t, ints, true)
 #undef MACE_GET_REPEATED_ARGUMENT_FUNC
 
+#define MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, T, fieldname)                     \
+  template<>                                                                   \
+  void SetProtoArg<T>(Def *def,                                                \
+                      const std::string &arg_name,                             \
+                      const T &value) {                                        \
+    int size = def->arg_size();                                                \
+    for (int i = 0; i < size; ++i) {                                           \
+      auto arg = def->mutable_arg(i);                                          \
+      if (arg->name() == arg_name) {                                           \
+        VLOG(3) << "Update old argument value from "                           \
+                << arg->fieldname() << " to "                                  \
+                << value << " for " << arg_name;                               \
+        arg->set_##fieldname(value);                                           \
+        return;                                                                \
+      }                                                                        \
+    }                                                                          \
+    VLOG(3) << "Add new argument " << arg_name << "(name: "                    \
+            << arg_name << ", value: " << value << ")";                        \
+    auto arg = def->add_arg();                                                 \
+    arg->set_name(arg_name);                                                   \
+    arg->set_##fieldname(value);                                               \
+  }
+
+#define MACE_SET_OPTIONAL_ARGUMENT_FUNC_MACRO(Def)     \
+  MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, float, f)       \
+  MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, bool, i)        \
+  MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, int, i)         \
+  MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, int64_t, i)     \
+  MACE_SET_OPTIONAL_ARGUMENT_FUNC(Def, std::string, s)
+
+MACE_SET_OPTIONAL_ARGUMENT_FUNC_MACRO(OperatorDef)
+MACE_SET_OPTIONAL_ARGUMENT_FUNC_MACRO(NetDef)
+#undef MACE_SET_OPTIONAL_ARGUMENT_FUNC
+
+std::string OutputMemoryTypeTagName() {
+  static const char *kOutputMemTypeArgName = "output_mem_type";
+  return kOutputMemTypeArgName;
+}
 
 bool IsQuantizedModel(const NetDef &net_def) {
   return

mace/core/arg_helper.h

@@ -55,6 +55,18 @@ class ProtoArgHelper {
   std::map<std::string, Argument> arg_map_;
 };
 
+template <typename T>
+void SetProtoArg(OperatorDef *op_def,
+                 const std::string &arg_name,
+                 const T&value);
+
+template <typename T>
+void SetProtoArg(NetDef *op_def,
+                 const std::string &arg_name,
+                 const T&value);
+
+std::string OutputMemoryTypeTagName();
+
 bool IsQuantizedModel(const NetDef &def);
 
 }  // namespace mace

mace/core/memory_optimizer.cc

@@ -33,7 +33,7 @@ namespace mace {
 
 bool MemoryOptimizer::IsMemoryReuseOp(const std::string &op_type) {
   static const std::unordered_set<std::string> kReuseOp = {
-      "Reshape", "Identity", "Squeeze"
+      "Reshape", "Identity", "Squeeze", "ExpandDims"
   };
   return kReuseOp.count(op_type) == 1;
 }
@@ -124,8 +124,9 @@ void MemoryOptimizer::Optimize(
       op_def->output_type_size());
   DataType dt;
 
-  bool has_data_format = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
-      *op_def, "has_data_format", 0) != 0;
+  DataFormat data_format = static_cast<DataFormat>(
+      ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+          *op_def, "data_format", DataFormat::DF_NONE));
   int output_size = op_def->output_size();
   for (int i = 0; i < output_size; ++i) {
     if (i < op_def->output_type_size()) {
@@ -209,7 +210,7 @@ void MemoryOptimizer::Optimize(
         mem_ref_count_[best_mem_id] = 1;
       }
       tensor_mem_map_.emplace(op_def->output(i), TensorMemInfo(best_mem_id,
-          dt, has_data_format));
+          dt, data_format));
     }
   }
 

mace/core/memory_optimizer.h

@@ -22,6 +22,7 @@
 #include <vector>
 
 #include "mace/proto/mace.pb.h"
+#include "mace/port/port.h"
 #include "mace/core/types.h"
 
 namespace mace {
@@ -81,10 +82,10 @@ class MemoryOptimizer {
   struct TensorMemInfo {
     int mem_id;
     DataType data_type;
-    bool has_data_format;
+    DataFormat data_format;
 
-    TensorMemInfo(int mem_id, DataType data_type, bool has_data_format) :
-        mem_id(mem_id), data_type(data_type), has_data_format(has_data_format)
+    TensorMemInfo(int mem_id, DataType data_type, DataFormat data_format) :
+        mem_id(mem_id), data_type(data_type), data_format(data_format)
     {}
   };
 

mace/core/net.cc

@@ -31,99 +31,8 @@
 #include "mace/utils/memory.h"
 #include "mace/utils/timer.h"
 
-#ifdef MACE_ENABLE_OPENCL
-#include "mace/core/runtime/opencl/opencl_util.h"
-#endif  // MACE_ENABLE_OPENCL
-
 namespace mace {
 
-namespace {
-struct InternalOutputInfo {
-  InternalOutputInfo(const MemoryType mem_type,
-                     const DataType dtype,
-                     const DataFormat data_format,
-                     const std::vector<index_t> &shape,
-                     int op_idx)
-      : mem_type(mem_type), dtype(dtype), data_format(data_format),
-        shape(shape), op_idx(op_idx) {}
-
-  MemoryType mem_type;  // transformed memory type
-  DataType dtype;
-  DataFormat data_format;
-  std::vector<index_t> shape;  // tensor shape
-  int op_idx;  // operation which generate the tensor
-};
-
-#ifdef MACE_ENABLE_OPENCL
-std::string TransformedName(const std::string &input_name,
-                            const mace::MemoryType mem_type) {
-  std::stringstream ss;
-  ss << input_name << "_mem_type_" << mem_type;
-  return ss.str();
-}
-
-bool TransformRequiredOp(const std::string &op_type) {
-  static const std::unordered_set<std::string> kNoTransformOp = {
-      "Shape", "InferConv2dShape"
-  };
-  return kNoTransformOp.count(op_type) == 0;
-}
-#endif  // MACE_ENABLE_OPENCL
-
-}  // namespace
-
-std::unique_ptr<Operation> SerialNet::CreateOperation(
-    const OpRegistryBase *op_registry,
-    OpConstructContext *construct_context,
-    std::shared_ptr<OperatorDef> op_def,
-    bool has_data_format,
-    bool is_quantize_model) {
-  // Create the Operation
-  DeviceType target_device_type = target_device_->device_type();
-  DeviceType device_type = DeviceType::CPU;
-  construct_context->set_device(cpu_device_.get());
-  construct_context->set_operator_def(op_def);
-  construct_context->set_output_mem_type(MemoryType::CPU_BUFFER);
-  // Get available devices
-  auto available_devices =
-      op_registry->AvailableDevices(op_def->type(), construct_context);
-  // Find the device type to run the op.
-  // If the target_device_type in available devices, use target_device_type,
-  // otherwise, fallback to CPU device.
-  for (auto device : available_devices) {
-    if (device == target_device_type) {
-      device_type = target_device_type;
-      construct_context->set_device(target_device_);
-      if (target_device_->device_type() == DeviceType::GPU) {
-        construct_context->set_output_mem_type(MemoryType::GPU_IMAGE);
-      }
-      break;
-    }
-  }
-  op_def->set_device_type(device_type);
-
-  // transpose output shape if run on CPU (default format is NHWC)
-  if (!is_quantize_model && device_type == DeviceType::CPU &&
-      op_def->output_shape_size() == op_def->output_size()) {
-    for (int out_idx = 0; out_idx < op_def->output_size(); ++out_idx) {
-      if (has_data_format && op_def->output_shape(out_idx).dims_size() == 4) {
-        //  NHWC -> NCHW
-        std::vector<index_t> output_shape =
-            TransposeShape<index_t, index_t>(
-                std::vector<index_t>(
-                    op_def->output_shape(out_idx).dims().begin(),
-                    op_def->output_shape(out_idx).dims().end()),
-                {0, 3, 1, 2});
-        for (int i = 0; i < 4; ++i) {
-          op_def->mutable_output_shape(out_idx)->set_dims(i, output_shape[i]);
-        }
-      }
-    }
-  }
-
-  return op_registry->CreateOperation(construct_context, device_type);
-}
-
 SerialNet::SerialNet(const OpRegistryBase *op_registry,
                      const NetDef *net_def,
                      Workspace *ws,
@@ -138,237 +47,47 @@ SerialNet::SerialNet(const OpRegistryBase *op_registry,
               target_device->cpu_runtime()->policy(),
               &target_device->cpu_runtime()->thread_pool())) {
   MACE_LATENCY_LOGGER(1, "Constructing SerialNet");
-  // quantize model flag
-  bool is_quantize_model = IsQuantizedModel(*net_def);
-  // Tensor Shape map
-  std::unordered_map<std::string, std::vector<index_t>> tensor_shape_map;
-  for (auto &op : net_def->op()) {
-    if (op.output_size() != op.output_shape_size()) {
-      continue;
-    }
-    for (int i = 0; i < op.output_size(); ++i) {
-      tensor_shape_map[op.output(i)] = std::vector<index_t>(
-          op.output_shape(i).dims().begin(),
-          op.output_shape(i).dims().end());
-    }
-  }
-  for (auto &tensor : net_def->tensors()) {
-    tensor_shape_map[tensor.name()] =
-        std::vector<index_t>(tensor.dims().begin(), tensor.dims().end());
-  }
 
-  bool has_data_format = false;
-  if (target_device_->device_type() == DeviceType::CPU) {
-    for (auto &input_info : net_def->input_info()) {
-      std::vector<index_t> input_shape =
-          std::vector<index_t>(input_info.dims().begin(),
-                               input_info.dims().end());
-      // update tensor shape map
-      tensor_shape_map[input_info.name()] = input_shape;
-      // Only could be NONE or NHWC
-      DataFormat input_data_format = static_cast<DataFormat>(
-          input_info.data_format());
-      has_data_format = has_data_format ||
-          (input_data_format != DataFormat::DF_NONE);
-      if (!is_quantize_model && input_data_format == DataFormat::NHWC &&
-          input_info.dims_size() == 4) {
-        // NHWC -> NCHW
-        input_shape =
-            TransposeShape<index_t, index_t>(input_shape, {0, 3, 1, 2});
-      }
-    }
-  }
 #ifdef MACE_ENABLE_OPENCL
-  // output tensor : related information
-  std::unordered_map<std::string, InternalOutputInfo> output_map;
   // used for memory optimization
   std::unordered_map<std::string, MemoryType> output_mem_map;
-  std::unordered_set<std::string> transformed_set;
-  // add input information
-  MemoryType target_mem_type;
-  // default data format of output tensor
-  DataFormat default_output_df = DataFormat::DF_NONE;
-  if (target_device_->device_type() == DeviceType::GPU) {
-    target_mem_type = MemoryType::GPU_BUFFER;
-    for (auto &input_info : net_def->input_info()) {
-      DataFormat input_data_format = static_cast<DataFormat>(
-          input_info.data_format());
-      has_data_format = input_data_format != DataFormat::DF_NONE;
-      std::vector<index_t> input_shape =
-          std::vector<index_t>(input_info.dims().begin(),
-                               input_info.dims().end());
-      // update tensor shape map
-      tensor_shape_map[input_info.name()] = input_shape;
-      output_map.emplace(input_info.name(), InternalOutputInfo(
-          target_mem_type, DataType::DT_FLOAT, input_data_format,
-          input_shape, -1));
-    }
-    default_output_df =
-        has_data_format ? DataFormat::NHWC : DataFormat::DF_NONE;
-  }
 #endif  // MACE_ENABLE_OPENCL
 
-  OpConstructContext construct_context(ws_, &tensor_shape_map);
+  OpConstructContext construct_context(ws_);
   for (int idx = 0; idx < net_def->op_size(); ++idx) {
     std::shared_ptr<OperatorDef> op_def(new OperatorDef(net_def->op(idx)));
     // Create operation
-    auto op = CreateOperation(op_registry,
-                              &construct_context,
-                              op_def,
-                              has_data_format,
-                              is_quantize_model);
-#ifdef MACE_ENABLE_OPENCL
-    // Add input transform operation if necessary
-    if (target_device_->device_type() == DeviceType::GPU) {
-      // the outputs' memory type of the operation
-      MemoryType out_mem_type = construct_context.output_mem_type();
-      int input_size = op_def->input_size();
-      // if op is memory-unused op, no transformation
-      if (TransformRequiredOp(op_def->type())) {
-        for (int i = 0; i < input_size; ++i) {
-          if (output_map.count(op_def->input(i)) == 1) {
-            // if op is memory-reuse op, no transformation
-            if (MemoryOptimizer::IsMemoryReuseOp(op_def->type())) {
-              out_mem_type = output_map.at(op_def->input(i)).mem_type;
-              break;
-            }
-            // check whether to do transform
-            MemoryType wanted_in_mem_type =
-                construct_context.GetInputMemType(i);
-            DataType wanted_in_dt = construct_context.GetInputDataType(i);
-            if (output_map.at(op_def->input(i)).mem_type != wanted_in_mem_type
-                || output_map.at(op_def->input(i)).dtype != wanted_in_dt) {
-              auto t_input_name = TransformedName(op_def->input(i),
-                                                  wanted_in_mem_type);
-              auto &output_info = output_map.at(op_def->input(i));
-              // check whether the tensor has been transformed
-              if (transformed_set.count(t_input_name) == 0) {
-                VLOG(1) << "Add Transform operation " << op_def->name()
-                        << " to transform tensor "
-                        << op_def->input(i) << "', from memory type "
-                        << output_info.mem_type << " to "
-                        << wanted_in_mem_type
-                        << ", from Data Type " << output_info.dtype << " to "
-                        << wanted_in_dt << ". with data format "
-                        << output_info.data_format;
-                std::string input_name = op_def->input(i);
-                op_def->set_input(i, t_input_name);
-                auto input_shape = output_info.shape;
-                if (output_info.mem_type == MemoryType::CPU_BUFFER &&
-                    output_info.data_format == DataFormat::NCHW &&
-                    input_shape.size() == 4) {
-                  // NCHW -> NHWC
-                  input_shape =
-                      TransposeShape<index_t, index_t>(input_shape,
-                                                       {0, 2, 3, 1});
-                }
-                auto transform_op_def = OpenCLUtil::CreateTransformOpDef(
-                    input_name, input_shape, t_input_name, wanted_in_dt,
-                    construct_context.GetInputOpenCLBufferType(i),
-                    wanted_in_mem_type, has_data_format);
-                OpConstructContext t_construct_context(ws_);
-                auto transform_op = CreateOperation(
-                    op_registry,
-                    &t_construct_context,
-                    transform_op_def,
-                    has_data_format);
-                operators_.emplace_back(std::move(transform_op));
-                transformed_set.insert(t_input_name);
-                output_mem_map[t_input_name] = wanted_in_mem_type;
-                // where to do graph reference count.
-                mem_optimizer->UpdateTensorRef(transform_op_def.get());
+    auto op_device_type = static_cast<DeviceType>(op_def->device_type());
+    if (op_device_type == target_device_->device_type()) {
+      construct_context.set_device(target_device_);
+    } else if (op_device_type == DeviceType::CPU) {
+      construct_context.set_device(cpu_device_.get());
     } else {
-                op_def->set_input(i, t_input_name);
-              }
-            }
-          } else {
-            MACE_CHECK(ws_->GetTensor(op_def->input(i)) != nullptr
-                           && ws_->GetTensor(op_def->input(i))->is_weight(),
-                       "Tensor ", op_def->input(i), " of ",
-                       op_def->name(), " not allocated");
-          }
-        }
-      }
-      // update the map : output_tensor -> Operation
-      for (int out_idx = 0; out_idx < op_def->output_size(); ++out_idx) {
-        DataType dt;
-        if (op_def->output_type_size() == op_def->output_size()) {
-          dt = op_def->output_type(out_idx);
-        } else {
-          dt = static_cast<DataType>(
-              ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
-                  *op_def, "T", static_cast<int>(DataType::DT_FLOAT)));
+      LOG(FATAL) << "Encounter unexpected error: "
+                 << op_device_type << " vs " << target_device_->device_type();
     }
-        output_mem_map[op_def->output(out_idx)] = out_mem_type;
-        output_map.emplace(
-            op_def->output(out_idx),
-            InternalOutputInfo(
-                out_mem_type,
-                dt,
-                default_output_df,
-                op_def->output_shape().empty() ?
-                std::vector<index_t>() :
-                std::vector<index_t>(
-                    op_def->output_shape(out_idx).dims().begin(),
-                    op_def->output_shape(out_idx).dims().end()),
-                static_cast<int>(operators_.size())));
-      }
-    }
-#endif  // MACE_ENABLE_OPENCL
+    construct_context.set_operator_def(op_def);
+
+    auto op = op_registry->CreateOperation(&construct_context,
+                                           op_device_type);
     operators_.emplace_back(std::move(op));
     // where to do graph reference count.
     mem_optimizer->UpdateTensorRef(op_def.get());
-  }
 
 #ifdef MACE_ENABLE_OPENCL
-  // Transform the output tensor if necessary
     if (target_device_->device_type() == DeviceType::GPU) {
-    for (auto &output_info : net_def->output_info()) {
-      auto &internal_output_info = output_map.at(output_info.name());
-      if ((internal_output_info.mem_type != target_mem_type &&
-          internal_output_info.mem_type != MemoryType::CPU_BUFFER) ||
-          internal_output_info.dtype != output_info.data_type()) {
-        VLOG(1) << "Add Transform operation to transform output tensor '"
-                << output_info.name() << "', from memory type "
-                << internal_output_info.mem_type
-                << " to " << target_mem_type
-                << ", from Data Type " << internal_output_info.dtype
-                << " to " << output_info.data_type();
-        std::string t_output_name = TransformedName(output_info.name(),
-            target_mem_type);
-        auto output_op_def =
-            operators_[internal_output_info.op_idx]->operator_def();
-        int output_size = output_op_def->output_size();
-        for (int i = 0; i < output_size; ++i) {
-          if (output_op_def->output(i) == output_info.name()) {
-            output_op_def->set_output(i, t_output_name);
-            // update the output : mem_type map
-            output_mem_map[t_output_name] = output_mem_map[output_info.name()];
-            output_mem_map[output_info.name()] = target_mem_type;
-          }
-        }
-        bool output_has_data_format =
-            static_cast<DataFormat>(output_info.data_format());
-        auto transform_op_def = OpenCLUtil::CreateTransformOpDef(
-            t_output_name,
-            internal_output_info.shape,
-            output_info.name(),
-            output_info.data_type(),
-            OpenCLBufferType::IN_OUT_CHANNEL,
-            target_mem_type,
-            output_has_data_format);
-        auto transform_op = CreateOperation(
-            op_registry,
-            &construct_context,
-            transform_op_def,
-            output_has_data_format);
-        operators_.emplace_back(std::move(transform_op));
-        // where to do graph reference count.
-        mem_optimizer->UpdateTensorRef(transform_op_def.get());
-      }
+      // update the map : output_tensor -> Operation
+      MemoryType out_mem_type =
+          static_cast<MemoryType>(
+              ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                  net_def->op(idx), OutputMemoryTypeTagName(),
+                  static_cast<int>(MemoryType::CPU_BUFFER)));
+      for (int out_idx = 0; out_idx < op_def->output_size(); ++out_idx) {
+        output_mem_map[op_def->output(out_idx)] = out_mem_type;
       }
     }
 #endif  // MACE_ENABLE_OPENCL
+  }
   // Update output tensor reference
   for (auto &output_info : net_def->output_info()) {
     mem_optimizer->UpdateTensorRef(output_info.name());

mace/core/net.h

@@ -54,14 +54,6 @@ class SerialNet : public NetBase {
 
   MaceStatus Run(RunMetadata *run_metadata = nullptr) override;
 
- private:
-  std::unique_ptr<Operation> CreateOperation(
-      const OpRegistryBase *op_registry,
-      OpConstructContext *construct_context,
-      std::shared_ptr<OperatorDef> op_def,
-      bool has_data_format,
-      bool is_quantize_model = false);
-
  protected:
   Workspace *ws_;
   Device *target_device_;

mace/core/net_def_adapter.h

+// Copyright 2019 The MACE Authors. All Rights Reserved.
+//
+// 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 MACE_CORE_NET_DEF_ADAPTER_H_
+#define MACE_CORE_NET_DEF_ADAPTER_H_
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "mace/core/types.h"
+#include "mace/proto/mace.pb.h"
+#include "mace/port/port.h"
+#include "mace/core/operator.h"
+#include "mace/core/net_optimizer.h"
+
+namespace mace {
+
+class OpRegistryBase;
+class Workspace;
+class Device;
+
+/**
+ * Conventions:
+ * 1. DataFormat::DT_AUTO stands for formatted (NHWC or NCHW)
+ * 2. if Op with DataFormat::DT_AUTO, the arguments of this op
+ *    is formatted to NHWC
+ */
+class NetDefAdapter {
+ public:
+  NetDefAdapter(const OpRegistryBase *op_registry,
+                const Workspace *ws);
+  MaceStatus AdaptNetDef(
+      const NetDef *net_def,
+      Device *target_device,
+      NetDef *target_net_def);
+
+ public:
+  NetDefAdapter(const NetDefAdapter&) = delete;
+  NetDefAdapter(const NetDefAdapter&&) = delete;
+  NetDefAdapter &operator=(const NetDefAdapter &) = delete;
+  NetDefAdapter &operator=(const NetDefAdapter &&) = delete;
+
+ private:
+  struct InternalOutputInfo {
+    InternalOutputInfo(const MemoryType mem_type,
+                       const DataType dtype,
+                       const DataFormat data_format,
+                       const std::vector<index_t> &shape,
+                       int op_idx)
+        : mem_type(mem_type), dtype(dtype), data_format(data_format),
+          shape(shape), op_idx(op_idx) {}
+
+    MemoryType mem_type;
+    DataType dtype;
+    DataFormat data_format;
+    std::vector<index_t> shape;  // tensor shape
+    int op_idx;  // operation which generate the tensor
+  };
+
+  typedef std::unordered_map<std::string, InternalOutputInfo> TensorInfoMap;
+
+ private:
+  MaceStatus AdaptDevice(OpConditionContext *context,
+                         Device *target_device,
+                         Device *cpu_device,
+                         const TensorInfoMap &output_map,
+                         const NetDef *net_def,
+                         OperatorDef *op);
+  MaceStatus AdaptDataType(OpConditionContext *context,
+                           OperatorDef *op);
+  MaceStatus AdaptDataFormat(
+      OpConditionContext *context,
+      OperatorDef *op,
+      bool is_quantized_model,
+      TensorInfoMap *output_map,
+      std::unordered_set<std::string> *transformed_set,
+      DataFormat *op_output_df,
+      NetDef *target_net_def);
+
+  MaceStatus AdaptMemoryType(
+      mace::OpConditionContext *context,
+      mace::OperatorDef *op_def,
+      TensorInfoMap *output_map,
+      std::unordered_set<std::string> *transformed_set,
+      MemoryType *op_output_mem_types,
+      mace::NetDef *target_net_def);
+
+  std::string DebugString(const NetDef *net_def);
+
+ private:
+  const OpRegistryBase *op_registry_;
+  const Workspace *ws_;
+  NetOptimizer net_optimizer_;
+};
+
+}  // namespace mace
+#endif  // MACE_CORE_NET_DEF_ADAPTER_H_

mace/core/net_optimizer.cc

+//  Copyright 2019 The MACE Authors. All Rights Reserved.
+//
+// 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 "mace/core/net_optimizer.h"
+
+#include <string>
+
+namespace mace {
+
+DeviceType NetOptimizer::SelectBestDevice(
+    const mace::OperatorDef *op_def,
+    DeviceType target_device_type,
+    const std::set<mace::DeviceType> &available_devices,
+    const std::vector<mace::DeviceType> &inputs_op_devices) {
+  static const std::set<std::string> kComputeIntensiveOps = {
+      "Conv2D", "DepthwiseConv2d", "Deconv2D", "DepthwiseDeconv2d",
+      "FullyConnected"
+  };
+  // CPU is the device to fall back
+  DeviceType best_device = DeviceType::CPU;
+  if (available_devices.count(target_device_type) == 1) {
+    best_device = target_device_type;
+  }
+  if (best_device == DeviceType::CPU) {
+    return best_device;
+  }
+  // Put compute-intensive ops in target device
+  if (kComputeIntensiveOps.count(op_def->type()) == 1) {
+    return best_device;
+  }
+  // Greedy strategy: Use input op's device type as current op's device
+  for (auto device_type : inputs_op_devices) {
+    if (device_type != best_device) {
+      best_device = device_type;
+    }
+  }
+  return best_device;
+}
+}  // namespace mace

mace/core/net_optimizer.h

+//  Copyright 2019 The MACE Authors. All Rights Reserved.
+//
+// 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 MACE_CORE_NET_OPTIMIZER_H_
+#define MACE_CORE_NET_OPTIMIZER_H_
+
+#include <set>
+#include <vector>
+
+#include "mace/port/port.h"
+#include "mace/proto/mace.pb.h"
+
+namespace mace {
+
+class NetOptimizer {
+ public:
+  DeviceType SelectBestDevice(const OperatorDef *op_def,
+                              DeviceType target_device,
+                              const std::set<DeviceType> &available_devices,
+                              const std::vector<DeviceType> &inputs_op_devices);
+};
+
+}  // namespace mace
+#endif  // MACE_CORE_NET_OPTIMIZER_H_

mace/core/operator.cc

@@ -20,34 +20,21 @@
 #include "mace/core/operator.h"
 
 namespace mace {
-
-OpConstructContext::OpConstructContext(Workspace *ws)
-    : operator_def_(nullptr),
-      ws_(ws),
-      device_(nullptr),
-      tensor_shape_info_(nullptr) {}
-
-OpConstructContext::OpConstructContext(
-    mace::Workspace *ws,
-    mace::OpConstructContext::TensorShapeMap *info)
+OpConditionContext::OpConditionContext(
+    const mace::Workspace *ws,
+    mace::OpConditionContext::TensorShapeMap *info)
     : operator_def_(nullptr),
       ws_(ws),
       device_(nullptr),
       tensor_shape_info_(info) {}
 
-void OpConstructContext::set_operator_def(
-    std::shared_ptr<mace::OperatorDef> operator_def) {
+void OpConditionContext::set_operator_def(
+    const mace::OperatorDef *operator_def) {
   operator_def_ = operator_def;
   input_data_types_.clear();
 }
 
-void OpConstructContext::set_output_mem_type(mace::MemoryType type) {
-  MACE_CHECK(operator_def_ != nullptr);
-  output_mem_type_ = type;
-  input_mem_types_.clear();
-}
-
-void OpConstructContext::SetInputInfo(size_t idx,
+void OpConditionContext::SetInputInfo(size_t idx,
                                       mace::MemoryType mem_type,
                                       mace::DataType dt) {
   if (input_mem_types_.empty()) {
@@ -66,7 +53,13 @@ void OpConstructContext::SetInputInfo(size_t idx,
   input_data_types_[idx] = dt;
 }
 
-MemoryType OpConstructContext::GetInputMemType(size_t idx) const {
+void OpConditionContext::set_output_mem_type(mace::MemoryType type) {
+  MACE_CHECK(operator_def_ != nullptr);
+  output_mem_type_ = type;
+  input_mem_types_.clear();
+}
+
+MemoryType OpConditionContext::GetInputMemType(size_t idx) const {
   if (input_mem_types_.empty()) {
     return output_mem_type_;
   }
@@ -75,7 +68,7 @@ MemoryType OpConstructContext::GetInputMemType(size_t idx) const {
   return input_mem_types_[idx];
 }
 
-DataType OpConstructContext::GetInputDataType(size_t idx) const {
+DataType OpConditionContext::GetInputDataType(size_t idx) const {
   if (input_data_types_.empty()) {
     // the default inputs' data types are same as operation's data type.
     return static_cast<DataType>(
@@ -87,7 +80,7 @@ DataType OpConstructContext::GetInputDataType(size_t idx) const {
 }
 
 #ifdef MACE_ENABLE_OPENCL
-void OpConstructContext::SetInputOpenCLBufferType(
+void OpConditionContext::SetInputOpenCLBufferType(
     size_t idx, OpenCLBufferType buffer_type) {
   if (input_opencl_buffer_types_.empty()) {
     // the default inputs' memory types are same as output memory type.
@@ -97,7 +90,7 @@ void OpConstructContext::SetInputOpenCLBufferType(
   MACE_CHECK(idx < input_opencl_buffer_types_.size());
   input_opencl_buffer_types_[idx] = buffer_type;
 }
-OpenCLBufferType OpConstructContext::GetInputOpenCLBufferType(
+OpenCLBufferType OpConditionContext::GetInputOpenCLBufferType(
     size_t idx) const {
   if (input_opencl_buffer_types_.empty()) {
     return OpenCLBufferType::IN_OUT_CHANNEL;
@@ -107,6 +100,16 @@ OpenCLBufferType OpConstructContext::GetInputOpenCLBufferType(
 }
 #endif  // MACE_ENABLE_OPENCL
 
+OpConstructContext::OpConstructContext(Workspace *ws)
+    : operator_def_(nullptr),
+      ws_(ws),
+      device_(nullptr) {}
+
+void OpConstructContext::set_operator_def(
+    std::shared_ptr<mace::OperatorDef> operator_def) {
+  operator_def_ = operator_def;
+}
+
 OpInitContext::OpInitContext(Workspace *ws, Device *device)
     : ws_(ws), device_(device) {}
 
@@ -202,16 +205,26 @@ const std::string OpKeyBuilder::Build() {
 }  // namespace
 
 OpRegistrationInfo::OpRegistrationInfo() {
-  device_placer = [this](OpConstructContext *context) -> std::set<DeviceType> {
-    auto op = context->operator_def();
-    // The GPU ops only support 4D In/Out tensor by default
-    if (this->devices.count(DeviceType::CPU) == 1 &&
-        op->output_shape_size() == op->output_size() &&
-        op->output_shape(0).dims_size() != 4) {
-      return { DeviceType::CPU };
-    }
+  // default device type placer
+  device_placer = [this](OpConditionContext *context) -> std::set<DeviceType> {
+    MACE_UNUSED(context);
     return this->devices;
   };
+
+  // default input and output memory type setter
+  memory_type_setter = [](OpConditionContext *context) -> void {
+    if (context->device()->device_type() == DeviceType::GPU) {
+#ifdef MACE_ENABLE_OPENCL
+      if (context->device()->gpu_runtime()->UseImageMemory()) {
+        context->set_output_mem_type(MemoryType::GPU_IMAGE);
+      } else {
+        context->set_output_mem_type(MemoryType::GPU_BUFFER);
+      }
+#endif  // MACE_ENABLE_OPENCL
+    } else {
+      context->set_output_mem_type(MemoryType::CPU_BUFFER);
+    }
+  };
 }
 
 void OpRegistrationInfo::AddDevice(mace::DeviceType device) {
@@ -255,13 +268,21 @@ MaceStatus OpRegistryBase::Register(
 }
 
 const std::set<DeviceType> OpRegistryBase::AvailableDevices(
-    const std::string &op_type, OpConstructContext *context) const {
+    const std::string &op_type, OpConditionContext *context) const {
   MACE_CHECK(registry_.count(op_type) != 0,
              op_type, " operation is not registered.");
 
   return registry_.at(op_type)->device_placer(context);
 }
 
+void OpRegistryBase::GetInOutMemoryTypes(
+    const std::string &op_type,
+    mace::OpConditionContext *context) const {
+  MACE_CHECK(registry_.count(op_type) != 0,
+             op_type, " operation is not registered.");
+  return registry_.at(op_type)->memory_type_setter(context);
+}
+
 std::unique_ptr<Operation> OpRegistryBase::CreateOperation(
     OpConstructContext *context,
     DeviceType device_type) const {
@@ -269,15 +290,6 @@ std::unique_ptr<Operation> OpRegistryBase::CreateOperation(
   DataType dtype = static_cast<DataType>(
       ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
           *operator_def, "T", static_cast<int>(DT_FLOAT)));
-  if (device_type == DeviceType::CPU && dtype == DT_HALF) {
-    int arg_size = operator_def->arg_size();
-    for (int i = 0; i < arg_size; ++i) {
-      if (operator_def->arg(i).name() == "T") {
-        operator_def->mutable_arg(i)->set_i(DT_FLOAT);
-      }
-    }
-    dtype = DT_FLOAT;
-  }
   VLOG(1) << "Creating operator " << operator_def->name() << "("
           << operator_def->type() << "<" << dtype << ">" << ") on "
           << device_type;
@@ -308,10 +320,21 @@ OpConditionBuilder &OpConditionBuilder::SetDevicePlacerFunc(
   return *this;
 }
 
+OpConditionBuilder& OpConditionBuilder::SetInputMemoryTypeSetter(
+    mace::OpRegistrationInfo::MemoryTypeSetter setter) {
+  memory_type_setter_ = setter;
+  return *this;
+}
+
 void OpConditionBuilder::Finalize(OpRegistrationInfo *info) const {
-  if (info != nullptr && placer_) {
+  if (info != nullptr) {
+    if (placer_) {
       info->device_placer = placer_;
     }
+    if (memory_type_setter_) {
+      info->memory_type_setter = memory_type_setter_;
+    }
+  }
 }
 
 }  // namespace mace

mace/core/operator.h

@@ -32,22 +32,20 @@
 
 namespace mace {
 
-// memory_optimizer, device
-class OpConstructContext {
-  typedef std::unordered_map<std::string, std::vector<index_t>> TensorShapeMap;
-
+// OpConditionContext has all information used for choosing proper Op
+class OpConditionContext {
  public:
-  explicit OpConstructContext(Workspace *ws);
-  OpConstructContext(Workspace *ws, TensorShapeMap *info);
-  ~OpConstructContext() = default;
+  typedef std::unordered_map<std::string, std::vector<index_t>> TensorShapeMap;
+  OpConditionContext(const Workspace *ws, TensorShapeMap *info);
+  ~OpConditionContext() = default;
 
-  void set_operator_def(std::shared_ptr<OperatorDef> operator_def);
+  void set_operator_def(const OperatorDef* operator_def);
 
-  inline std::shared_ptr<OperatorDef> operator_def() const {
+  inline const OperatorDef *operator_def() const {
     return operator_def_;
   }
 
-  inline Workspace *workspace() const {
+  inline const Workspace *workspace() const {
     return ws_;
   }
 
@@ -81,8 +79,8 @@ class OpConstructContext {
 #endif  // MACE_ENABLE_OPENCL
 
  private:
-  std::shared_ptr<OperatorDef> operator_def_;
-  Workspace *ws_;
+  const OperatorDef *operator_def_;
+  const Workspace *ws_;
   Device *device_;
   TensorShapeMap *tensor_shape_info_;
   // used for memory transform
@@ -94,6 +92,38 @@ class OpConstructContext {
 #endif  // MACE_ENABLE_OPENCL
 };
 
+// memory_optimizer, device
+class OpConstructContext {
+  typedef std::unordered_map<std::string, std::vector<index_t>> TensorShapeMap;
+
+ public:
+  explicit OpConstructContext(Workspace *ws);
+  ~OpConstructContext() = default;
+
+  void set_operator_def(std::shared_ptr<OperatorDef> operator_def);
+
+  inline std::shared_ptr<OperatorDef> operator_def() const {
+    return operator_def_;
+  }
+
+  inline Workspace *workspace() const {
+    return ws_;
+  }
+
+  inline void set_device(Device* device) {
+    device_ = device;
+  }
+
+  inline Device *device() const {
+    return device_;
+  }
+
+ private:
+  std::shared_ptr<OperatorDef> operator_def_;
+  Workspace *ws_;
+  Device *device_;
+};
+
 // memory_optimizer, device
 class OpInitContext {
  public:
@@ -207,8 +237,11 @@ struct OpRegistrationInfo {
  public:
   typedef std::function<std::unique_ptr<Operation>(OpConstructContext *)>
       OpCreator;
-  typedef std::function<std::set<DeviceType>(OpConstructContext *)>
+  typedef std::function<std::set<DeviceType>(OpConditionContext *)>
       DevicePlacer;
+  typedef std::function<void(OpConditionContext *)> MemoryTypeSetter;
+  typedef std::function<std::vector<DataFormat>(OpConditionContext *)>
+      DataFormatSelector;
 
   OpRegistrationInfo();
 
@@ -219,6 +252,8 @@ struct OpRegistrationInfo {
   std::set<DeviceType> devices;
   std::unordered_map<std::string, OpCreator> creators;
   DevicePlacer device_placer;
+  MemoryTypeSetter memory_type_setter;
+  DataFormatSelector data_format_selector;
 };
 
 class OpConditionBuilder {
@@ -230,11 +265,18 @@ class OpConditionBuilder {
   OpConditionBuilder &SetDevicePlacerFunc(
       OpRegistrationInfo::DevicePlacer placer);
 
+  // If you set input memory type for specified Op,
+  // you must call OpConditionContext::set_output_mem_type
+  OpConditionBuilder &SetInputMemoryTypeSetter(
+      OpRegistrationInfo::MemoryTypeSetter setter);
+
   void Finalize(OpRegistrationInfo *info) const;
 
  private:
   std::string type_;
   OpRegistrationInfo::DevicePlacer placer_;
+  OpRegistrationInfo::MemoryTypeSetter memory_type_setter_;
+  OpRegistrationInfo::DataFormatSelector data_format_selector_;
 };
 
 
@@ -250,7 +292,10 @@ class OpRegistryBase {
   MaceStatus Register(const OpConditionBuilder &builder);
 
   const std::set<DeviceType> AvailableDevices(
-      const std::string &op_type, OpConstructContext *context) const;
+      const std::string &op_type, OpConditionContext *context) const;
+
+  void GetInOutMemoryTypes(
+      const std::string &op_type, OpConditionContext *context) const;
 
   std::unique_ptr<Operation> CreateOperation(
       OpConstructContext *context,

mace/core/runtime/opencl/opencl_util.cc

@@ -147,38 +147,38 @@ void OpenCLUtil::CalImage2DShape(const std::vector<index_t> &shape, /* NHWC */
   }
 }
 
-std::shared_ptr<OperatorDef> OpenCLUtil::CreateTransformOpDef(
+void OpenCLUtil::BuildTransformOpDef(
     const std::string &input_name,
     const std::vector<mace::index_t> &input_shape,
     const std::string &output_name,
     const mace::DataType dt,
     const OpenCLBufferType buffer_type,
     const mace::MemoryType mem_type,
-    bool has_data_format) {
-  std::unique_ptr<OperatorDef> op(new OperatorDef);
+    DataFormat data_format,
+    OperatorDef *op_def) {
   std::string op_name = "mace_node_" + output_name;
-  op->set_name(op_name);
-  op->set_type("BufferTransform");
-  op->add_input(input_name);
-  op->add_output(output_name);
-  Argument *arg = op->add_arg();
+  op_def->set_name(op_name);
+  op_def->set_type("BufferTransform");
+  op_def->add_input(input_name);
+  op_def->add_output(output_name);
+  op_def->set_device_type(DeviceType::GPU);
+  Argument *arg = op_def->add_arg();
   arg->set_name("buffer_type");
   arg->set_i(static_cast<int32_t>(buffer_type));
-  arg = op->add_arg();
+  arg = op_def->add_arg();
   arg->set_name("mem_type");
   arg->set_i(static_cast<int32_t>(mem_type));
-  arg = op->add_arg();
+  arg = op_def->add_arg();
   arg->set_name("T");
   arg->set_i(static_cast<int32_t>(dt));
-  arg = op->add_arg();
-  arg->set_name("has_data_format");
-  arg->set_i(has_data_format);
+  arg = op_def->add_arg();
+  arg->set_name("data_format");
+  arg->set_i(data_format);
   if (!input_shape.empty()) {
-    OutputShape *shape = op->add_output_shape();
+    OutputShape *shape = op_def->add_output_shape();
     for (auto value : input_shape) {
       shape->add_dims(value);
     }
   }
-  return std::move(op);
 }
 }  // namespace mace

mace/core/runtime/opencl/opencl_util.h

@@ -43,14 +43,15 @@ class OpenCLUtil {
                               std::vector<size_t> *image_shape,
                               const int wino_blk_size = 2);
 
-  static std::shared_ptr<OperatorDef> CreateTransformOpDef(
+  static void BuildTransformOpDef(
       const std::string &input_name,
       const std::vector<mace::index_t> &input_shape,
       const std::string &output_name,
       const mace::DataType dt,
       const OpenCLBufferType buffer_type,
       const MemoryType mem_type,
-      bool has_data_format);
+      DataFormat data_format,
+      OperatorDef *op_def);
 };
 
 }  // namespace mace

mace/core/workspace.cc

@@ -263,13 +263,13 @@ MaceStatus Workspace::PreallocateOutputTensor(
     }
   }
   VLOG(1) << "Preallocate buffer to tensors";
-  bool is_quantize_model = IsQuantizedModel(net_def);
   for (auto &tensor_mem : mem_optimizer->tensor_mem_map()) {
     std::unique_ptr<Tensor> tensor
         (new Tensor(preallocated_allocator_.GetBuffer(tensor_mem.second.mem_id),
                     tensor_mem.second.data_type,
                     false, tensor_mem.first));
-    if (tensor_mem.second.has_data_format) {
+    tensor->set_data_format(tensor_mem.second.data_format);
+    if (tensor_mem.second.data_format != DataFormat::DF_NONE) {
       if (mem_blocks[tensor_mem.second.mem_id].mem_type()
           == MemoryType::GPU_IMAGE) {
         VLOG(1) << "Tensor: " << tensor_mem.first
@@ -279,23 +279,13 @@ MaceStatus Workspace::PreallocateOutputTensor(
                 << tensor->UnderlyingBuffer()->shape()[0]
                 << ", "
                 << tensor->UnderlyingBuffer()->shape()[1];
-        tensor->set_data_format(DataFormat::NHWC);
       } else {
         VLOG(1) << "Tensor: " << tensor_mem.first
                 << " Mem: " << tensor_mem.second.mem_id
                 << " Data type: " << tensor->dtype()
                 << ", Buffer size: " << tensor->UnderlyingBuffer()->size();
-        if (mem_blocks[tensor_mem.second.mem_id].mem_type()
-            == MemoryType::GPU_BUFFER ||
-            is_quantize_model) {
-          tensor->set_data_format(DataFormat::NHWC);
-        } else {
-          tensor->set_data_format(DataFormat::NCHW);
       }
     }
-    } else {
-      tensor->set_data_format(DataFormat::DF_NONE);
-    }
     tensor_map_[tensor_mem.first] = std::move(tensor);
   }
 

mace/libmace/mace.cc

@@ -27,6 +27,7 @@
 #include "mace/public/mace.h"
 #include "mace/port/env.h"
 #include "mace/port/file_system.h"
+#include "mace/core/net_def_adapter.h"
 
 #ifdef MACE_ENABLE_OPENCL
 #include "mace/core/runtime/opencl/gpu_device.h"
@@ -516,20 +517,26 @@ MaceStatus MaceEngine::Impl::Init(
                                               device_.get(),
                                               model_data));
 
+    NetDef adapted_net_def;
+    NetDefAdapter net_def_adapter(op_registry_.get(), ws_.get());
+    net_def_adapter.AdaptNetDef(net_def, device_.get(), &adapted_net_def);
+
     MemoryOptimizer mem_optimizer;
     // Init model
     net_ = std::unique_ptr<NetBase>(new SerialNet(op_registry_.get(),
-                                                net_def,
+                                                  &adapted_net_def,
                                                   ws_.get(),
                                                   device_.get(),
                                                   &mem_optimizer));
 
     // Preallocate all output tensors of ops
-  MACE_RETURN_IF_ERROR(ws_->PreallocateOutputTensor(*net_def,
+    MACE_RETURN_IF_ERROR(ws_->PreallocateOutputTensor(adapted_net_def,
                                                       &mem_optimizer,
                                                       device_.get()));
     if (device_type_ == DeviceType::GPU) {
-    ws_->RemoveAndReloadBuffer(*net_def, model_data, device_->allocator());
+      ws_->RemoveAndReloadBuffer(adapted_net_def,
+                                 model_data,
+                                 device_->allocator());
     }
     MACE_RETURN_IF_ERROR(net_->Init());
 #if defined(MACE_ENABLE_HEXAGON) || defined(MACE_ENABLE_HTA)

mace/ops/activation.cc

@@ -15,6 +15,8 @@
 #include "mace/ops/activation.h"
 
 #include <memory>
+#include <set>
+
 #include "mace/core/operator.h"
 
 #if defined(MACE_ENABLE_NEON)
@@ -132,6 +134,22 @@ void RegisterActivation(OpRegistryBase *op_registry) {
   MACE_REGISTER_OP(op_registry, "Activation", ActivationOp,
                    DeviceType::GPU, half);
 #endif  // MACE_ENABLE_OPENCL
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("Activation")
+          .SetDevicePlacerFunc(
+              [](OpConditionContext *context) -> std::set<DeviceType> {
+                auto op = context->operator_def();
+                int has_data_format =
+                    ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                        *op, "has_data_format", 0);
+                if (!has_data_format ||
+                    (op->output_shape_size() != op->output_size()) ||
+                    op->output_shape(0).dims_size() != 4) {
+                  return { DeviceType::CPU };
+                }
+                return { DeviceType::CPU, DeviceType::GPU };
+              }));
 }
 
 }  // namespace ops

mace/ops/addn.cc

@@ -103,6 +103,22 @@ void RegisterAddN(OpRegistryBase *op_registry) {
 
   MACE_REGISTER_OP(op_registry, "AddN", AddNOp, DeviceType::GPU, half);
 #endif  // MACE_ENABLE_OPENCL
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("AddN")
+          .SetDevicePlacerFunc(
+              [](OpConditionContext *context) -> std::set<DeviceType> {
+                auto op = context->operator_def();
+                int has_data_format =
+                    ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                        *op, "has_data_format", 0);
+                if (!has_data_format ||
+                    (op->output_shape_size() != op->output_size()) ||
+                    op->output_shape(0).dims_size() != 4) {
+                  return { DeviceType::CPU };
+                }
+                return { DeviceType::CPU, DeviceType::GPU };
+              }));
 }
 
 }  // namespace ops

mace/ops/bias_add.cc

@@ -145,6 +145,22 @@ void RegisterBiasAdd(OpRegistryBase *op_registry) {
   MACE_REGISTER_OP(op_registry, "BiasAdd", BiasAddOp,
                    DeviceType::GPU, half);
 #endif  // MACE_ENABLE_OPENCL
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("BiasAdd")
+          .SetDevicePlacerFunc(
+              [](OpConditionContext *context) -> std::set<DeviceType> {
+                auto op = context->operator_def();
+                int has_data_format =
+                    ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                        *op, "has_data_format", 0);
+                if (!has_data_format ||
+                    (op->output_shape_size() != op->output_size()) ||
+                    op->output_shape(0).dims_size() != 4) {
+                  return { DeviceType::CPU };
+                }
+                return { DeviceType::CPU, DeviceType::GPU };
+              }));
 }
 
 }  // namespace ops

mace/ops/buffer_transform.cc

@@ -39,14 +39,14 @@ class BufferTransformOp<DeviceType::GPU, T> : public Operation {
     auto type =
         static_cast<OpenCLBufferType>(Operation::GetOptionalArg<int>(
             "buffer_type", static_cast<int>(CONV2D_FILTER)));
-    bool has_data_format = Operation::GetOptionalArg<int>("has_data_format", 0)
-        != 0;
+    DataFormat data_format = static_cast<DataFormat>(
+        Operation::GetOptionalArg<int>("data_format", DataFormat::DF_NONE));
 
     MemoryType in_mem_type = context->workspace()->GetTensor(
         operator_def_->input(0))->memory_type();
     return OpenCLBufferTransformer<T>(in_mem_type, out_mem_type_).Transform(
         context, input, type, out_mem_type_, wino_blk_size_,
-        has_data_format, output);
+        data_format, output);
   }
 
  private:

mace/ops/channel_shuffle.cc

@@ -116,10 +116,10 @@ void RegisterChannelShuffle(OpRegistryBase *op_registry) {
       op_registry,
       OpConditionBuilder("ChannelShuffle")
           .SetDevicePlacerFunc(
-              [](OpConstructContext *context) -> std::set<DeviceType> {
+              [](OpConditionContext *context) -> std::set<DeviceType> {
                 auto op = context->operator_def();
                 if (op->output_shape_size() != op->output_size()) {
-                  return { DeviceType::CPU, DeviceType::GPU };
+                  return { DeviceType::CPU };
                 }
                 int groups = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
                     *op, "group", 1);

mace/ops/concat.cc

@@ -241,13 +241,11 @@ void RegisterConcat(OpRegistryBase *op_registry) {
       op_registry,
       OpConditionBuilder("Concat")
           .SetDevicePlacerFunc(
-            [](OpConstructContext *context) -> std::set<DeviceType> {
+            [](OpConditionContext *context) -> std::set<DeviceType> {
               auto op = context->operator_def();
               auto tensor_shape_info = context->tensor_shape_info();
-              if (op->output_shape_size() != op->output_size()) {
-                return { DeviceType::CPU, DeviceType::GPU };
-              }
-              if (op->output_shape(0).dims_size() != 4) {
+              if (op->output_shape_size() != op->output_size() ||
+                  op->output_shape(0).dims_size() != 4) {
                 return { DeviceType::CPU };
               } else {
                 int has_data_format =

mace/ops/conv_2d.cc

@@ -466,7 +466,6 @@ class Conv2dOp<DeviceType::GPU, T> : public ConvPool2dOpBase {
       mem_type = MemoryType::GPU_BUFFER;
       kernel_ = make_unique<opencl::buffer::Conv2dKernel<T>>();
     }
-    context->set_output_mem_type(mem_type);
     // Transform filter tensor to target format
     if ((wino_block_size_ == 2 || wino_block_size_ == 4) &&
         (kernel_->CheckUseWinograd(

mace/ops/crop.cc

@@ -145,6 +145,22 @@ void RegisterCrop(OpRegistryBase *op_registry) {
   MACE_REGISTER_OP(op_registry, "Crop", CropOp,
                    DeviceType::GPU, half);
 #endif  // MACE_ENABLE_OPENCL
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("Crop")
+          .SetDevicePlacerFunc(
+              [](OpConditionContext *context) -> std::set<DeviceType> {
+                auto op = context->operator_def();
+                int has_data_format =
+                    ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                        *op, "has_data_format", 0);
+                if (!has_data_format ||
+                    (op->output_shape_size() != op->output_size()) ||
+                    op->output_shape(0).dims_size() != 4) {
+                  return { DeviceType::CPU };
+                }
+                return { DeviceType::CPU, DeviceType::GPU };
+              }));
 }
 
 }  // namespace ops

mace/ops/deconv_2d.cc

@@ -197,7 +197,6 @@ class Deconv2dOp<DeviceType::GPU, T> : public Deconv2dOpBase {
             OpenCLBufferType::ARGUMENT,
             mem_type) == MaceStatus::MACE_SUCCESS);
       }
-      context->SetInputInfo(2, MemoryType::CPU_BUFFER, DataType::DT_INT32);
     }
   }
   MaceStatus Run(OpContext *context) override {
@@ -264,6 +263,30 @@ void RegisterDeconv2D(OpRegistryBase *op_registry) {
 
   MACE_REGISTER_OP(op_registry, "Deconv2D", Deconv2dOp,
                    DeviceType::GPU, half);
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("Deconv2D")
+          .SetInputMemoryTypeSetter(
+              [](OpConditionContext *context) -> void {
+                MemoryType mem_type = MemoryType::CPU_BUFFER;
+                if (context->device()->device_type() == DeviceType::GPU) {
+                  if (context->device()->gpu_runtime()->UseImageMemory()) {
+                    mem_type = MemoryType::GPU_IMAGE;
+                  } else {
+                    MACE_NOT_IMPLEMENTED;
+                  }
+                  FrameworkType framework_type =
+                      static_cast<ops::FrameworkType>(
+                        ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
+                            *(context->operator_def()), "framework_type",
+                            FrameworkType::TENSORFLOW));
+                  if (framework_type == FrameworkType::TENSORFLOW) {
+                    context->SetInputInfo(2, MemoryType::CPU_BUFFER,
+                                          DataType::DT_INT32);
+                  }
+                }
+                context->set_output_mem_type(mem_type);
+              }));
 #endif  // MACE_ENABLE_OPENCL
 }
 

mace/ops/depthwise_conv2d.cc

@@ -382,7 +382,6 @@ class DepthwiseConv2dOp<DeviceType::GPU, T> : public DepthwiseConv2dOpBase {
       mem_type = MemoryType::GPU_BUFFER;
       kernel_ = make_unique<opencl::buffer::DepthwiseConv2dKernel<T>>();
     }
-    context->set_output_mem_type(mem_type);
     Tensor *filter_tensor = context->workspace()->GetTensor(
         operator_def_->input(1));
     if (filter_tensor != nullptr && filter_tensor->is_weight()) {
@@ -393,8 +392,6 @@ class DepthwiseConv2dOp<DeviceType::GPU, T> : public DepthwiseConv2dOpBase {
           1,
           OpenCLBufferType::DW_CONV2D_FILTER,
           mem_type) == MaceStatus::MACE_SUCCESS);
-    } else {
-      context->SetInputOpenCLBufferType(1, OpenCLBufferType::DW_CONV2D_FILTER);
     }
     if (operator_def_->input_size() > 2) {
       MACE_CHECK(TransformFilter<T>(
@@ -440,6 +437,27 @@ void RegisterDepthwiseConv2d(OpRegistryBase *op_registry) {
 
   MACE_REGISTER_OP(op_registry, "DepthwiseConv2d",
                    DepthwiseConv2dOp, DeviceType::GPU, half);
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("DepthwiseConv2d")
+          .SetInputMemoryTypeSetter(
+              [](OpConditionContext *context) -> void {
+                MemoryType mem_type = MemoryType::CPU_BUFFER;
+                if (context->device()->device_type() == DeviceType::GPU) {
+                  if (context->device()->gpu_runtime()->UseImageMemory()) {
+                    mem_type = MemoryType::GPU_IMAGE;
+                  } else {
+                    mem_type = MemoryType::GPU_BUFFER;
+                  }
+                  auto filter_tensor = context->workspace()->GetTensor(
+                      context->operator_def()->input(1));
+                  if (filter_tensor == nullptr || !filter_tensor->is_weight()) {
+                    context->SetInputOpenCLBufferType(
+                        1, OpenCLBufferType::DW_CONV2D_FILTER);
+                  }
+                }
+                context->set_output_mem_type(mem_type);
+              }));
 #endif  // MACE_ENABLE_OPENCL
 }
 

mace/ops/expand_dims.cc

@@ -14,7 +14,6 @@
 
 
 #include "mace/core/operator.h"
-#include "mace/ops/common/transpose.h"
 #include "mace/utils/math.h"
 
 namespace mace {
@@ -44,27 +43,8 @@ class ExpandDimsOp<DeviceType::CPU, T> : public Operation {
     std::vector<index_t> output_shape(input_shape);
     output_shape.insert(output_shape.begin() + axis_, 1);
 
-    bool has_data_format = Operation::GetOptionalArg<int>(
-        "has_data_format", 0) == 1;
-    if (has_data_format && output_shape.size() == 4) {
-      // only tensorflow support expand dim, so the default format is NHWC
-      // transform NHWC to NCHW
-      auto t_output_shape = TransposeShape<int64_t, int64_t>(output_shape,
-                                                             {0, 3, 1, 2});
-      output->Resize(t_output_shape);
-      Tensor::MappingGuard input_guard(input);
-      Tensor::MappingGuard output_guard(output);
-      auto input_data = input->data<T>();
-      auto output_data = output->mutable_data<T>();
-
-      Transpose(&context->device()->cpu_runtime()->thread_pool(),
-                input_data, output_shape, {0, 3, 1, 2}, output_data);
-    } else {
-      output->Resize(output_shape);
-      Tensor::MappingGuard input_guard(input);
-      auto input_data = input->data<T>();
-      output->Copy<T>(input_data, input->size());
-    }
+    output->ReuseTensorBuffer(*input);
+    output->Reshape(output_shape);
 
     return MaceStatus::MACE_SUCCESS;
   }

mace/ops/matmul.cc

@@ -518,14 +518,6 @@ void RegisterMatMul(OpRegistryBase *op_registry) {
   MACE_REGISTER_OP(op_registry, "MatMul", MatMulOp,
                    DeviceType::CPU, uint8_t);
 #endif  // MACE_ENABLE_QUANTIZE
-
-#ifdef MACE_ENABLE_OPENCL
-  MACE_REGISTER_OP(op_registry, "MatMul", MatMulOp,
-                   DeviceType::GPU, float);
-
-  MACE_REGISTER_OP(op_registry, "MatMul", MatMulOp,
-                   DeviceType::GPU, half);
-#endif  // MACE_ENABLE_OPENCL
 }
 
 }  // namespace ops

mace/ops/opencl/buffer_transformer.h

@@ -23,7 +23,6 @@
 #include "mace/ops/opencl/image/buffer_to_image.h"
 #include "mace/ops/opencl/image/image_to_buffer.h"
 #include "mace/ops/opencl/buffer/buffer_transform.h"
-#include "mace/ops/common/transpose.h"
 #include "mace/utils/memory.h"
 
 namespace mace {
@@ -48,7 +47,7 @@ class OpenCLBufferTransformer {
                        const OpenCLBufferType type,
                        const MemoryType out_mem_type,
                        const int wino_blk_size,
-                       bool has_data_format,
+                       DataFormat data_format,
                        Tensor *output) {
     Workspace *ws = context->workspace();
     DataType dt = DataTypeToEnum<T>::value;
@@ -67,31 +66,12 @@ class OpenCLBufferTransformer {
         VLOG(2) << "Transform CPU Buffer " << input->name()
                 << " to GPU Buffer " << internal_tensor->name()
                 << " with data type " << dt;
-        if (has_data_format && input->shape().size() == 4) {
-          // 1. (NCHW -> NHWC)
-          std::vector<int> dst_dims = {0, 2, 3, 1};
-          std::vector<index_t> output_shape =
-              TransposeShape<index_t, index_t>(input->shape(),
-                                               dst_dims);
-          internal_tensor->Resize(output_shape);
-          internal_tensor->set_data_format(DataFormat::NHWC);
-          // TODO(liuqi): Only support float now
-          const float *input_ptr = input->data<float>();
-          Tensor::MappingGuard guard(internal_tensor);
-          float *internal_ptr = internal_tensor->mutable_data<float>();
-          MACE_RETURN_IF_ERROR(ops::Transpose(
-              &context->device()->cpu_runtime()->thread_pool(),
-              input_ptr,
-              input->shape(),
-              dst_dims,
-              internal_ptr));
-        } else {
+        MACE_CHECK(data_format == DataFormat::NHWC);
         internal_tensor->Resize(input->shape());
         const uint8_t *input_ptr = input->data<uint8_t>();
         Tensor::MappingGuard guard(internal_tensor);
         uint8_t *internal_ptr = internal_tensor->mutable_data<uint8_t>();
         memcpy(internal_ptr, input_ptr, input->raw_size());
-        }
         // 2. convert the internal GPU Buffer to output.
         return kernel_->Compute(
             context, internal_tensor, type, wino_blk_size, output);
@@ -108,30 +88,13 @@ class OpenCLBufferTransformer {
       VLOG(2) << "Transform GPU Buffer " << internal_tensor.name()
               << " to CPU Buffer " << output->name()
               << " with data type " << dt;
-      if (has_data_format && internal_tensor.shape().size() == 4) {
-        // NHWC -> NCHW
-        std::vector<int> dst_dims = {0, 3, 1, 2};
-        std::vector<index_t> output_shape =
-            TransposeShape<index_t, index_t>(internal_tensor.shape(),
-                                             dst_dims);
-        output->set_data_format(DataFormat::NCHW);
-        Tensor::MappingGuard guard(&internal_tensor);
-        const float *internal_ptr = internal_tensor.data<float>();
-        output->Resize(output_shape);
-        float *output_ptr = output->mutable_data<float>();
-        return ops::Transpose(&context->device()->cpu_runtime()->thread_pool(),
-                              internal_ptr,
-                              internal_tensor.shape(),
-                              dst_dims,
-                              output_ptr);
-      } else {
+      MACE_CHECK(data_format == DataFormat::NHWC);
       Tensor::MappingGuard guard(&internal_tensor);
       const T *internal_ptr = internal_tensor.data<T>();
       output->Resize(internal_tensor.shape());
       T *output_ptr = output->mutable_data<T>();
       memcpy(output_ptr, internal_ptr, internal_tensor.size() * sizeof(T));
       return MaceStatus::MACE_SUCCESS;
-      }
     } else {
       LOG(FATAL) << "Unexpected error: " << out_mem_type;
       return MaceStatus::MACE_SUCCESS;

mace/ops/opencl/image/eltwise.h

@@ -71,14 +71,17 @@ MaceStatus EltwiseKernel<T>::Compute(
   if (input1 == nullptr) {
     input1_type = "INPUT_SCALAR";
   } else {
-    MACE_CHECK(input0->dim_size() == input1->dim_size() ||
+    MACE_CHECK((input0->dim_size() == input1->dim_size()
+        && input0->dim_size() == 4) ||
         input0->dim_size() == 1 || input1->dim_size() == 1)
-      << "Inputs of Eltwise op must be same shape";
+      << "Inputs of Eltwise op must be same shape or fulfill broadcast logic";
     MACE_CHECK(type_ != EltwiseType::EQUAL)
       << "Eltwise op on GPU does not support EQUAL";
     // broadcast
-    if (input0->size() != input1->size()) {
-      if (input0->size() < input1->size()) {
+    if (input0->size() != input1->size() ||
+        input0->dim_size() != input1->dim_size()) {
+      if (input0->size() < input1->size()
+          || input0->dim_size() < input1->dim_size()) {
         std::swap(input0, input1);
         swapped = true;
       }

mace/ops/opencl/image/reduce.h

@@ -59,11 +59,6 @@ MaceStatus ReduceKernel<T>::Compute(
     const Tensor *input,
     Tensor *output) {
   MACE_CHECK_NOTNULL(input);
-  MACE_CHECK(keep_dims_, "reduce mean gpu only support keep dims.");
-  MACE_CHECK(input->dim_size() == 4,
-             "reduce gpu only support 4-dim input");
-  MACE_CHECK(axis_.size() == 2 && axis_[0] == 1 && axis_[1] == 2,
-             "reduce gpu only support 1,2-axis reduce");
   index_t batch = input->dim(0);
   const index_t in_height = input->dim(1);
   const index_t in_width = input->dim(2);

mace/ops/pooling.cc

@@ -480,7 +480,6 @@ class PoolingOp<DeviceType::GPU, T> : public PoolingOpBase {
     if (context->device()->gpu_runtime()->UseImageMemory()) {
       kernel_ = make_unique<opencl::image::PoolingKernel<T>>();
     } else {
-      context->set_output_mem_type(MemoryType::GPU_BUFFER);
       kernel_ = make_unique<opencl::buffer::PoolingKernel<T>>();
     }
   }

mace/ops/reduce.cc

@@ -16,6 +16,7 @@
 
 #include <algorithm>
 #include <memory>
+#include <set>
 #include <vector>
 
 #include "mace/core/future.h"
@@ -907,6 +908,31 @@ void RegisterReduce(OpRegistryBase *op_registry) {
   MACE_REGISTER_OP(op_registry, "Reduce", ReduceOp,
                    DeviceType::GPU, half);
 #endif  // MACE_ENABLE_OPENCL
+  MACE_REGISTER_OP_CONDITION(
+      op_registry,
+      OpConditionBuilder("Reduce")
+          .SetDevicePlacerFunc(
+              [](OpConditionContext *context) -> std::set<DeviceType> {
+                auto op = context->operator_def();
+                bool keep_dims =
+                    ProtoArgHelper::GetOptionalArg<OperatorDef, bool>(
+                        *op, "keepdims", false);
+                if (!keep_dims) {
+                  return { DeviceType::CPU };
+                }
+                auto axis =
+                    ProtoArgHelper::GetRepeatedArgs<OperatorDef, int>(
+                        *op, "axis");
+                if (axis.size() != 2 || axis[0] != 1 || axis[1] == 2) {
+                  return { DeviceType::CPU };
+                }
+                auto tensor_shape_info = context->tensor_shape_info();
+                if (tensor_shape_info->count(op->input(0)) == 0
+                    || tensor_shape_info->at(op->input(0)).size() != 4) {
+                  return { DeviceType::CPU };
+                }
+                return { DeviceType::CPU, DeviceType::GPU };
+              }));
 }
 
 }  // namespace ops

mace/ops/scalar_math.cc

@@ -100,11 +100,7 @@ class ScalarMathOp : public Operation {
         coeff_(Operation::GetRepeatedArgs<float>("coeff")),
         scalar_input_(Operation::GetOptionalArg<float>("scalar_input", 1.0)),
         scalar_input_index_(Operation::GetOptionalArg<int32_t>(
-            "scalar_input_index", 1)) {
-    if (D == DeviceType::GPU) {
-      context->set_output_mem_type(MemoryType::GPU_BUFFER);
-    }
-  }
+            "scalar_input_index", 1)) {}
 
   MaceStatus Run(OpContext *context) override {
     MACE_UNUSED(context);

mace/ops/softmax.cc

@@ -417,7 +417,6 @@ class SoftmaxOp<DeviceType::GPU, T> : public Operation {
     if (context->device()->gpu_runtime()->UseImageMemory()) {
       kernel_ = make_unique<opencl::image::SoftmaxKernel<T>>(use_log);
     } else {
-      context->set_output_mem_type(MemoryType::GPU_BUFFER);
       kernel_ = make_unique<opencl::buffer::SoftmaxKernel<T>>(use_log);
     }
   }
@@ -456,7 +455,7 @@ void RegisterSoftmax(OpRegistryBase *op_registry) {
       op_registry,
       OpConditionBuilder("Softmax")
           .SetDevicePlacerFunc(
-              [](OpConstructContext *context) -> std::set<DeviceType> {
+              [](OpConditionContext *context) -> std::set<DeviceType> {
                 auto op = context->operator_def();
                 if (op->output_shape_size() != op->output_size()) {
                   return { DeviceType::CPU, DeviceType::GPU };

mace/ops/split.cc

@@ -144,10 +144,10 @@ void RegisterSplit(OpRegistryBase *op_registry) {
       op_registry,
       OpConditionBuilder("Split")
           .SetDevicePlacerFunc(
-              [](OpConstructContext *context) -> std::set<DeviceType> {
+              [](OpConditionContext *context) -> std::set<DeviceType> {
                 auto op = context->operator_def();
                 if (op->output_shape_size() != op->output_size()) {
-                  return {DeviceType::CPU, DeviceType::GPU};
+                  return { DeviceType::CPU };
                 }
                 int axis = ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
                     *op, "axis", 3);

mace/ops/squeeze.cc

@@ -77,7 +77,7 @@ void RegisterSqueeze(OpRegistryBase *op_registry) {
       op_registry,
       OpConditionBuilder("Squeeze")
           .SetDevicePlacerFunc(
-              [](OpConstructContext *context) -> std::set<DeviceType> {
+              [](OpConditionContext *context) -> std::set<DeviceType> {
                 auto op = context->operator_def();
                 if (op->output_shape_size() != op->output_size()) {
                   return { DeviceType::CPU, DeviceType::GPU };

mace/public/mace.h

@@ -36,7 +36,8 @@ enum DeviceType { CPU = 0, GPU = 2, HEXAGON = 3, HTA = 4 };
 
 enum DataFormat {
   DF_NONE = 0, NHWC = 1, NCHW = 2,
-  HWOI = 100, OIHW = 101, HWIO = 102, OHWI = 103
+  HWOI = 100, OIHW = 101, HWIO = 102, OHWI = 103,
+  DF_AUTO = 1000,
 };
 
 enum GPUPerfHint {

mace/python/tools/converter_tool/base_converter.py

@@ -33,6 +33,7 @@ class DataFormat(Enum):
     OIHW = 101
     HWOI = 102
     OHWI = 103
+    DF_AUTO = 1000
 
 
 # SAME_LOWER: if the amount of paddings to be added is odd,
@@ -161,13 +162,39 @@ MaceSupportedOps = [
     'SumGroup',
     'TargetRMSNorm',
     'Transpose',
-    'WinogradInverseTransform',
-    'WinogradTransform',
     'Cumsum',
 ]
 
 MaceOp = Enum('MaceOp', [(op, op) for op in MaceSupportedOps], type=str)
 
+MaceHasDataFormatOps = [MaceOp.BatchNorm,
+                        MaceOp.BatchToSpaceND,
+                        MaceOp.Conv2D,
+                        MaceOp.Deconv2D,
+                        MaceOp.DepthToSpace,
+                        MaceOp.DepthwiseConv2d,
+                        MaceOp.DepthwiseDeconv2d,
+                        MaceOp.FullyConnected,
+                        MaceOp.Pooling,
+                        MaceOp.ResizeBicubic,
+                        MaceOp.ResizeBilinear,
+                        MaceOp.ResizeNearestNeighbor,
+                        MaceOp.SpaceToBatchND,
+                        MaceOp.SpaceToDepth]
+
+MaceMayHasDataFormatOps = [MaceOp.Activation,
+                           MaceOp.AddN,
+                           MaceOp.BiasAdd,
+                           MaceOp.ChannelShuffle,
+                           MaceOp.Concat,
+                           MaceOp.Crop,
+                           MaceOp.Eltwise,
+                           MaceOp.Pad,
+                           MaceOp.Reduce,
+                           MaceOp.Softmax,
+                           MaceOp.Split,
+                           MaceOp.SqrDiffMean]
+
 
 class MaceKeyword(object):
     # node related str
@@ -505,12 +532,11 @@ class ConverterOption(object):
                 TransformerRule.TRANSFORM_CHANNEL_SHUFFLE,
                 # Model data format related transformation
                 TransformerRule.TRANSPOSE_FILTERS,
-                TransformerRule.TRANSPOSE_DATA_FORMAT,
+                # Mace model structure related transformation
+                TransformerRule.ADD_IN_OUT_TENSOR_INFO,
                 TransformerRule.TRANSPOSE_MATMUL_WEIGHT,
                 # Add winograd argument
                 TransformerRule.ADD_WINOGRAD_ARG,
-                # Mace model structure related transformation
-                TransformerRule.ADD_IN_OUT_TENSOR_INFO,
                 # Data type related transformation
                 TransformerRule.UPDATE_FLOAT_OP_DATA_TYPE,
                 # Transform finalization
@@ -519,6 +545,7 @@ class ConverterOption(object):
                 TransformerRule.SORT_BY_EXECUTION,
                 # update the data format of ops
                 TransformerRule.UPDATE_DATA_FORMAT,
+                TransformerRule.TRANSPOSE_DATA_FORMAT,
                 # Need to be put after SORT_BY_EXECUTION
                 TransformerRule.ADD_QUANTIZE_TENSOR_RANGE,
             ]
@@ -571,6 +598,8 @@ class ConverterUtil(object):
             return DataFormat.NHWC
         elif arg.i == DataFormat.NCHW.value:
             return DataFormat.NCHW
+        elif arg.i == DataFormat.DF_AUTO.value:
+            return DataFormat.DF_AUTO
         else:
             return None
 

mace/python/tools/converter_tool/caffe_converter.py

@@ -195,6 +195,7 @@ class CaffeConverter(base_converter.ConverterInterface):
         self._option = option
         self._mace_net_def = mace_pb2.NetDef()
         ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.OIHW)
+        ConverterUtil.add_data_format_arg(self._mace_net_def, DataFormat.NCHW)
         self._caffe_net = CaffeNet()
         self._caffe_layers = caffe_pb2.NetParameter()
         caffe_weights = caffe_pb2.NetParameter()

mace/python/tools/converter_tool/onnx_converter.py

@@ -387,6 +387,7 @@ class OnnxConverter(base_converter.ConverterInterface):
         self._mace_net_def = mace_pb2.NetDef()
         self._data_format = DataFormat.NCHW
         ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.OIHW)
+        ConverterUtil.add_data_format_arg(self._mace_net_def, self._data_format)
         onnx_model = onnx.load(src_model_file)
 
         ir_version = onnx_model.ir_version

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -270,6 +270,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
         self._option = option
         self._mace_net_def = mace_pb2.NetDef()
         ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.HWIO)
+        ConverterUtil.add_data_format_arg(self._mace_net_def, DataFormat.NHWC)
 
         # import tensorflow graph
         tf_graph_def = tf.GraphDef()

mace/python/tools/converter_tool/transformer.py

@@ -27,6 +27,8 @@ from mace.python.tools.converter_tool.base_converter import EltwiseType
 from mace.python.tools.converter_tool.base_converter import FrameworkType
 from mace.python.tools.converter_tool.base_converter import MaceKeyword
 from mace.python.tools.converter_tool.base_converter import MaceOp
+from mace.python.tools.converter_tool.base_converter import MaceHasDataFormatOps
+from mace.python.tools.converter_tool.base_converter import MaceMayHasDataFormatOps  # noqa
 from mace.python.tools.converter_tool.base_converter import PaddingMode
 from mace.python.tools.converter_tool.base_converter import ReduceType
 from mace.python.tools.converter_tool.base_converter import TransformerRule
@@ -77,10 +79,9 @@ class Transformer(base_converter.ConverterInterface):
                 self.transpose_matmul_weight,
             TransformerRule.FOLD_FC_RESHAPE:
                 self.fold_fc_reshape,
-            TransformerRule.TRANSPOSE_DATA_FORMAT: self.transpose_data_format,
-            TransformerRule.ADD_WINOGRAD_ARG: self.add_winograd_arg,
             TransformerRule.ADD_IN_OUT_TENSOR_INFO:
                 self.add_in_out_tensor_info,
+            TransformerRule.ADD_WINOGRAD_ARG: self.add_winograd_arg,
             TransformerRule.TRANSFORM_GLOBAL_CONV_TO_FC:
                 self.transform_global_conv_to_fc,
             TransformerRule.RESHAPE_FC_WEIGHT: self.reshape_fc_weight,
@@ -96,6 +97,7 @@ class Transformer(base_converter.ConverterInterface):
                 self.add_opencl_informations,
             TransformerRule.SORT_BY_EXECUTION: self.sort_by_execution,
             TransformerRule.UPDATE_DATA_FORMAT: self.update_data_format,
+            TransformerRule.TRANSPOSE_DATA_FORMAT: self.transpose_data_format,
             TransformerRule.CHECK_QUANTIZE_INFO:
                 self.check_quantize_info,
             TransformerRule.TRANSPOSE_CAFFE_RESHAPE_AND_FLATTEN:
@@ -194,21 +196,19 @@ class Transformer(base_converter.ConverterInterface):
                     op.type = "Input"
                     data_type_arg = op.arg.add()
                     data_type_arg.name = MaceKeyword.mace_op_data_type_str
-                    data_type_arg.i = mace_pb2.DT_FLOAT
+                    data_type_arg.i = input_node.data_type
                     op.output.extend([input_node.name])
                     output_shape = op.output_shape.add()
                     output_shape.dims.extend(input_node.shape)
-                    if input_node.name in self._consumers:
-                        if ConverterUtil.data_format(
-                                self._consumers[input_node.name][0]) \
-                                == DataFormat.NCHW:
+                    if input_node.data_format != DataFormat.DF_NONE:
+                        if input_node.data_format == DataFormat.NCHW:
                             self.transpose_shape(output_shape.dims,
                                                  [0, 3, 1, 2])
                         ConverterUtil.add_data_format_arg(op,
-                                                              DataFormat.NCHW)
+                                                          DataFormat.DF_AUTO)
                     else:
                         ConverterUtil.add_data_format_arg(op,
-                                                              DataFormat.NHWC)
+                                                          DataFormat.DF_NONE)
                     self._producer[op.output[0]] = op
 
     @staticmethod
@@ -256,6 +256,13 @@ class Transformer(base_converter.ConverterInterface):
         else:
             return None
 
+    def get_tensor_data_format(self, tensor):
+        if tensor in self._producer:
+            producer = self._producer[tensor]
+            return ConverterUtil.data_format(producer)
+        else:
+            return DataFormat.DF_NONE
+
     def consumer_count(self, tensor_name):
         return len(self._consumers.get(tensor_name, []))
 
@@ -838,8 +845,6 @@ class Transformer(base_converter.ConverterInterface):
                   or op.type == MaceOp.DepthwiseConv2d.name
                   or op.type == MaceOp.FullyConnected.name)
                  and len(op.input) == 2)
-                or (op.type == MaceOp.WinogradInverseTransform.name
-                    and len(op.input) == 1)
                 or (op.type == MaceOp.Deconv2D.name
                     and ((ConverterUtil.get_arg(
                                 op,
@@ -930,8 +935,7 @@ class Transformer(base_converter.ConverterInterface):
                 or op.type == MaceOp.Deconv2D.name
                 or op.type == MaceOp.DepthwiseConv2d.name
                 or op.type == MaceOp.FullyConnected.name
-                or op.type == MaceOp.BatchNorm.name
-                or op.type == MaceOp.WinogradInverseTransform.name) \
+                or op.type == MaceOp.BatchNorm.name) \
                     and len(self._consumers.get(op.output[0], [])) == 1:
                 consumer_op = self._consumers[op.output[0]][0]
                 if consumer_op.type == MaceOp.Activation.name \
@@ -1017,96 +1021,6 @@ class Transformer(base_converter.ConverterInterface):
                                        filter_format.name)
         return False
 
-    def transpose_data_format(self):
-        net = self._model
-
-        for op in net.op:
-            # transpose args
-            if op.type == MaceOp.Pad.name:
-                for arg in op.arg:
-                    if arg.name == MaceKeyword.mace_paddings_str:
-                        mace_check(len(arg.ints) == 8,
-                                   "pad dim rank should be 8.")
-                        if ConverterUtil.data_format(op) == DataFormat.NCHW:
-                            print("Transpose pad args: %s(%s)"
-                                  % (op.name, op.type))
-                            self.transpose_shape(arg.ints,
-                                                 [0, 1, 4, 5, 6, 7, 2, 3])
-            elif op.type == MaceOp.Concat.name or op.type == MaceOp.Split.name:
-                for arg in op.arg:
-                    if arg.name == MaceKeyword.mace_axis_str:
-                        if (ConverterUtil.data_format(op) == DataFormat.NCHW
-                                and len(op.output_shape[0].dims) == 4):
-                            print("Transpose concat/split args: %s(%s)"
-                                  % (op.name, op.type))
-                            if arg.i == 1:
-                                arg.i = 3
-                            elif arg.i == 2:
-                                arg.i = 1
-                            elif arg.i == 3:
-                                arg.i = 2
-
-                        producer = self._producer[op.input[0]]
-                        input_shape = producer.output_shape[0].dims
-                        if producer.type == MaceOp.FullyConnected.name and \
-                                len(input_shape) == 2:
-                            axis_arg = ConverterUtil.get_arg(
-                                op, MaceKeyword.mace_axis_str)
-                            if axis_arg.i == 1:
-                                axis_arg.i = 3
-
-            elif op.type == MaceOp.Squeeze.name:
-                for arg in op.arg:
-                    if arg.name == MaceKeyword.mace_axis_str:
-                        if ConverterUtil.data_format(op) == DataFormat.NCHW:
-                            print("Transpose squeeze args: %s(%s)"
-                                  % (op.name, op.type))
-                            mace_check(list(arg.ints) == [2, 3],
-                                       'only support squeeze at at [2, 3]')
-                            arg.ints[:] = [1, 2]
-
-            elif op.type == MaceOp.Reduce.name:
-                for arg in op.arg:
-                    if arg.name == MaceKeyword.mace_axis_str:
-                        if ConverterUtil.data_format(
-                                op) == DataFormat.NCHW:
-                            print("Transpose reduce args: %s(%s)"
-                                  % (op.name, op.type))
-                            reduce_axises = list(arg.ints)
-                            new_axises = []
-                            for i in range(len(reduce_axises)):
-                                idx = reduce_axises[i]
-                                if idx == 2 or idx == 3:
-                                    new_axises.append(idx - 1)
-                                elif idx == 1:
-                                    new_axises.append(3)
-                                else:
-                                    new_axises.append(idx)
-                            new_axises.sort()
-                            arg.ints[:] = []
-                            arg.ints.extend(new_axises)
-            elif op.type == MaceOp.Crop.name:
-                offset_arg = ConverterUtil.get_arg(op,
-                                                   MaceKeyword.mace_offset_str)
-                mace_check(offset_arg and
-                           ConverterUtil.data_format(op) == DataFormat.NCHW and
-                           len(op.output_shape[0].dims) == 4,
-                           "MACE only support crop with NCHW format")
-                print("Transpose crop args: %s(%s)"
-                      % (op.name, op.type))
-                self.transpose_shape(offset_arg.ints, [0, 2, 3, 1])
-
-            # transpose op output shape
-            data_format = ConverterUtil.data_format(op)
-            if data_format is not None \
-                    and data_format != DataFormat.NHWC:
-                print("Transpose output shapes: %s(%s)" % (op.name, op.type))
-                for output_shape in op.output_shape:
-                    if len(output_shape.dims) == 4:
-                        self.transpose_shape(output_shape.dims,
-                                             [0, 2, 3, 1])
-
-        return False
 
     def add_winograd_arg(self):
         if self._wino_arg == 0:
@@ -1428,17 +1342,121 @@ class Transformer(base_converter.ConverterInterface):
 
     def update_data_format(self):
         print("update data format")
-        data_format_flag = 1
-        for input_node in self._option.input_nodes.values():
-            if input_node.data_format.value == DataFormat.DF_NONE.value:
-                data_format_flag = 0
         net = self._model
         for op in net.op:
-            ConverterUtil.del_arg(
+            df_arg = ConverterUtil.get_arg(
                 op, MaceKeyword.mace_data_format_str)
+            if not df_arg:
+                df_arg = op.arg.add()
+                df_arg.name = MaceKeyword.mace_data_format_str
+            if op.type in MaceHasDataFormatOps:
+                df_arg.i = DataFormat.DF_AUTO.value
+            elif op.type in MaceMayHasDataFormatOps:
+                input_df = DataFormat.DF_AUTO.value
+                for input_tensor in op.input:
+                    if input_tensor in self._consts:
+                        continue
+                    mace_check(input_tensor in self._producer,
+                               "Input tensor %s not in producer" % input_tensor)
+                    father_op = self._producer[input_tensor]
+                    temp_input_df = ConverterUtil.get_arg(
+                        father_op, MaceKeyword.mace_data_format_str)
+                    if temp_input_df.i != DataFormat.DF_AUTO.value:
+                        input_df = temp_input_df.i
+                if input_df == DataFormat.DF_AUTO.value:
+                    df_arg.i = input_df
+                    # add flag to mark the ops may has data format
                     has_data_format_arg = op.arg.add()
-            has_data_format_arg.name = MaceKeyword.mace_has_data_format_str
-            has_data_format_arg.i = data_format_flag
+                    has_data_format_arg.name = \
+                        MaceKeyword.mace_has_data_format_str
+                    has_data_format_arg.i = 1
+        return False
+
+    def transpose_data_format(self):
+        print("Transpose arguments based on data format")
+        net = self._model
+
+        src_data_format = ConverterUtil.data_format(net)
+        for op in net.op:
+            has_data_format = ConverterUtil.data_format(op) == \
+                              DataFormat.DF_AUTO
+            # transpose args
+            if op.type == MaceOp.Pad.name:
+                for arg in op.arg:
+                    if arg.name == MaceKeyword.mace_paddings_str:
+                        mace_check(len(arg.ints) == 8,
+                                   "pad dim rank should be 8.")
+                        if src_data_format == DataFormat.NCHW and \
+                                has_data_format:
+                            print("Transpose pad args: %s(%s)"
+                                  % (op.name, op.type))
+                            self.transpose_shape(arg.ints,
+                                                 [0, 1, 4, 5, 6, 7, 2, 3])
+            elif op.type == MaceOp.Concat.name or op.type == MaceOp.Split.name:
+                for arg in op.arg:
+                    if arg.name == MaceKeyword.mace_axis_str:
+                        if (src_data_format == DataFormat.NCHW
+                                and has_data_format
+                                and len(op.output_shape[0].dims) == 4):
+                            print("Transpose concat/split args: %s(%s)"
+                                  % (op.name, op.type))
+                            if arg.i == 1:
+                                arg.i = 3
+                            elif arg.i == 2:
+                                arg.i = 1
+                            elif arg.i == 3:
+                                arg.i = 2
+
+                        producer = self._producer[op.input[0]]
+                        input_shape = producer.output_shape[0].dims
+                        if producer.type == MaceOp.FullyConnected.name and \
+                                len(input_shape) == 2:
+                            axis_arg = ConverterUtil.get_arg(
+                                op, MaceKeyword.mace_axis_str)
+                            if axis_arg.i == 1:
+                                axis_arg.i = 3
+
+            elif op.type == MaceOp.Reduce.name:
+                for arg in op.arg:
+                    if arg.name == MaceKeyword.mace_axis_str:
+                        if src_data_format == DataFormat.NCHW and \
+                                has_data_format:
+                            print("Transpose reduce args: %s(%s)"
+                                  % (op.name, op.type))
+                            reduce_axises = list(arg.ints)
+                            new_axises = []
+                            for i in range(len(reduce_axises)):
+                                idx = reduce_axises[i]
+                                if idx == 2 or idx == 3:
+                                    new_axises.append(idx - 1)
+                                elif idx == 1:
+                                    new_axises.append(3)
+                                else:
+                                    new_axises.append(idx)
+                            new_axises.sort()
+                            arg.ints[:] = []
+                            arg.ints.extend(new_axises)
+            elif op.type == MaceOp.Crop.name:
+                offset_arg = ConverterUtil.get_arg(op,
+                                                   MaceKeyword.mace_offset_str)
+                mace_check(offset_arg and
+                           src_data_format == DataFormat.NCHW
+                           and has_data_format
+                           and len(op.output_shape[0].dims) == 4,
+                           "MACE only support crop with NCHW format")
+                print("Transpose crop args: %s(%s)"
+                      % (op.name, op.type))
+                self.transpose_shape(offset_arg.ints, [0, 2, 3, 1])
+
+            # transpose op output shape
+            if src_data_format == DataFormat.NCHW and \
+                    has_data_format:
+                print("Transpose output shapes: %s(%s)" % (op.name, op.type))
+                for output_shape in op.output_shape:
+                    if len(output_shape.dims) == 4:
+                        self.transpose_shape(output_shape.dims,
+                                             [0, 2, 3, 1])
+
         return False
 
     def quantize_nodes(self):
@@ -1493,7 +1511,7 @@ class Transformer(base_converter.ConverterInterface):
             self._model.input_info[i].zero_point = quantize_info.zero_point
 
             ConverterUtil.add_data_type_arg(op_def, mace_pb2.DT_UINT8)
-            ConverterUtil.add_data_format_arg(op_def, DataFormat.NHWC)
+            ConverterUtil.add_data_format_arg(op_def, input_node.data_format)
             # use actual ranges for model input quantize
             find_range_every_time_arg = op_def.arg.add()
             find_range_every_time_arg.name = \
@@ -1516,6 +1534,7 @@ class Transformer(base_converter.ConverterInterface):
             self._model.output_info[i].zero_point = quantize_info.zero_point
 
             ConverterUtil.add_data_type_arg(op_def, mace_pb2.DT_UINT8)
+            ConverterUtil.add_data_format_arg(op_def, output_node.data_format)
 
         quantize_flag_arg = self._model.arg.add()
         quantize_flag_arg.name = MaceKeyword.mace_quantize_flag_arg_str
@@ -1886,9 +1905,6 @@ class Transformer(base_converter.ConverterInterface):
                     shape_tensor.data_type = mace_pb2.DT_INT32
                 else:
                     mace_check(False, "Only support reshape and flatten")
-                # NCHW -> NHWC
-                if len(dims) == 4:
-                    self.transpose_shape(dims, [0, 2, 3, 1])
                 shape_tensor.int32_data.extend(dims)
                 op.input.append(shape_tensor.name)
 
@@ -2030,6 +2046,9 @@ class Transformer(base_converter.ConverterInterface):
                     data_type_arg = quantize_op.arg.add()
                     data_type_arg.name = MaceKeyword.mace_op_data_type_str
                     data_type_arg.i = mace_pb2.DT_UINT8
+                    ConverterUtil.add_data_format_arg(
+                        quantize_op,
+                        self.get_tensor_data_format(input_tensor))
 
                     data_type_arg = quantize_op.arg.add()
                     data_type_arg.name = MaceKeyword.mace_non_zero
@@ -2050,8 +2069,8 @@ class Transformer(base_converter.ConverterInterface):
             del op.input[:]
             op.input.extend(quantized_inputs_names)
 
-            orginal_output_name = op.output[0]
-            op.output[0] = orginal_output_name + "_quant"
+            original_output_name = op.output[0]
+            op.output[0] = original_output_name + "_quant"
             op.output_type.extend([to_quantize_ops_output_type[op.type]])
             data_type_arg = ConverterUtil.get_arg(op,
                                                   MaceKeyword.mace_op_data_type_str)  # noqa
@@ -2064,13 +2083,15 @@ class Transformer(base_converter.ConverterInterface):
             dequantize_op.name = op.name + "_dequant"
             dequantize_op.type = MaceOp.Dequantize.name
             dequantize_op.input.extend([op.output[0]])
-            dequantize_op.output.extend([orginal_output_name])
+            dequantize_op.output.extend([original_output_name])
             dequantize_op.output_shape.extend(op.output_shape)
             dequantize_op.output_type.extend([mace_pb2.DT_FLOAT])
             data_type_arg = dequantize_op.arg.add()
             data_type_arg.name = MaceKeyword.mace_op_data_type_str
             data_type_arg.i = to_quantize_ops_output_type[op.type]
-
+            ConverterUtil.add_data_format_arg(
+                dequantize_op,
+                self.get_tensor_data_format(original_output_name))
             quantize_flag_arg = ConverterUtil.get_arg(self._model,
                                                       MaceKeyword.mace_quantize_flag_arg_str)  # noqa
             if quantize_flag_arg is None: