!2002 Add dump ir function in binary format

Merge pull request !2002 from leopz/test_dump

mindspore/ccsrc/debug/anf_ir_utils.cc

@@ -1566,7 +1566,7 @@ class IrParser {
       return lexer_.GetNextToken();
     } else if (type == "Tuple") {
       return ParseTypeVector(func_graph, lexer_.GetNextToken(), type, ptr);
-    } else if (type == "Array") {
+    } else if (type == "Tensor") {
       return ParseTypeArray(func_graph, lexer_.GetNextToken(), ptr);
     } else if (type == "List") {
       return ParseTypeVector(func_graph, lexer_.GetNextToken(), type, ptr);

mindspore/ccsrc/debug/anf_ir_utils.h

@@ -118,6 +118,8 @@ std::string GetFuncGraphProtoString(const FuncGraphPtr &func_graph);
 void DumpIRProto(const FuncGraphPtr &func_graph, const std::string &suffix);
 
 std::string GetOnnxProtoString(const FuncGraphPtr &func_graph);
+
+std::string GetBinaryProtoString(const FuncGraphPtr &func_graph);
 }  // namespace mindspore
 
 #endif  // MINDSPORE_CCSRC_DEBUG_ANF_IR_UTILS_H_

mindspore/ccsrc/pipeline/pipeline.cc

@@ -59,6 +59,7 @@ using mindspore::abstract::AbstractTuplePtr;
 
 const char IR_TYPE_ANF[] = "anf_ir";
 const char IR_TYPE_ONNX[] = "onnx_ir";
+const char IR_TYPE_BINARY[] = "binary_ir";
 
 ExecutorPyPtr ExecutorPy::executor_ = nullptr;
 std::mutex ExecutorPy::instance_lock_;
@@ -212,6 +213,14 @@ py::bytes ExecutorPy::GetFuncGraphProto(const std::string &phase, const std::str
     return proto_str;
   }
 
+  if (ir_type == IR_TYPE_BINARY) {
+    std::string proto_str = GetBinaryProtoString(fg_ptr);
+    if (proto_str.empty()) {
+      MS_LOG(EXCEPTION) << "Graph proto is empty.";
+    }
+    return proto_str;
+  }
+
   MS_LOG(EXCEPTION) << "Unknown ir type: " << ir_type;
 }
 
@@ -506,7 +515,6 @@ void RunPipelineAction(const ActionItem &action, pipeline::ResourcePtr resource,
 
   // when in loading anf ir mode, action `parse` do nothing
   if (action.first == "parse") {
-    parse::PythonAdapter::SetPythonEnvFlag(true);
     return;
   }
 
@@ -566,6 +574,7 @@ void Pipeline::Run() {
           draw::Draw(base_name + ".dot", graph);
           // generate IR file in human readable format
           DumpIR(base_name + ".ir", graph);
+
           // generate IR file in a heavily commented format, which can also be reloaded
           if (action.first != "parse") {
             ExportIR(base_name + ".dat", std::to_string(i), graph);

mindspore/train/serialization.py

@@ -398,17 +398,18 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         net (Cell): MindSpore network.
         inputs (Tensor): Inputs of the `net`.
         file_name (str): File name of model to export.
-        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'LITE' format for exported model.
+        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' 'LITE' and 'BINARY' format for exported model.
 
             - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
               Ascend model.
             - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
             - LITE: Huawei model format for mobile. A lite model only for the MindSpore Lite
+            - BINARY: Binary format for model. An intermidiate representation format for models.
     """
     logger.info("exporting model file:%s format:%s.", file_name, file_format)
     check_input_data(*inputs, data_class=Tensor)
 
-    supported_formats = ['GEIR', 'ONNX', 'LITE']
+    supported_formats = ['GEIR', 'ONNX', 'LITE', 'BINARY']
     if file_format not in supported_formats:
         raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
     # switch network mode to infer when it is training
@@ -428,6 +429,13 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         with open(file_name, 'wb') as f:
             os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
             f.write(onnx_stream)
+    elif file_format == 'BINARY':  # file_format is 'BINARY'
+        phase_name = 'export_binary'
+        graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
+        onnx_stream = _executor._get_func_graph_proto(graph_id, 'binary_ir')
+        with open(file_name, 'wb') as f:
+            os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
+            f.write(onnx_stream)
     elif file_format == 'LITE':  # file_format is 'LITE'
         context.set_context(save_ms_model=True, save_ms_model_path=file_name)
         net(*inputs)

tests/ut/cpp/stub/anf_ir/dump_proto_stub.cc

@@ -17,8 +17,9 @@
 
 namespace mindspore {
 
-std::string GetFuncGraphProtoString(const FuncGraphPtr& func_graph) { return ""; }
+std::string GetFuncGraphProtoString(const FuncGraphPtr &func_graph) { return ""; }
 
-std::string GetOnnxProtoString(const FuncGraphPtr& func_graph) { return ""; }
+std::string GetOnnxProtoString(const FuncGraphPtr &func_graph) { return ""; }
 
+std::string GetBinaryProtoString(const FuncGraphPtr &func_graph) { return ""; }
 }  // namespace mindspore