fix bugs

mindspore/_extends/parse/__init__.py

@@ -22,7 +22,7 @@ from .parser import (Parser, create_obj_instance, generate_scope,
                      get_dataclass_attributes, get_dataclass_methods, get_obj_id,
                      get_module_namespace, get_obj_type, get_object_key,
                      get_parse_method_of_class, get_scope_name,
-                     is_class_member, parse_cb, resolve_symbol)
+                     is_class_member, parse_cb, resolve_symbol, convert_to_ms_tensor)
 from .serialize import *
 
 __all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class', 'resolve_symbol',
@@ -30,4 +30,4 @@ __all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class',
            'get_obj_type', 'get_obj_id', 'create_obj_instance', 'get_module_namespace',
            'get_class_member_namespace_symbol', 'get_obj_id', 'Parser', 'get_dataclass_attributes',
            'get_dataclass_methods', 'dump_obj', 'load_obj', 'get_dataclass_methods', 'get_scope_name',
-           'create_slice_obj']
+           'create_slice_obj', 'convert_to_ms_tensor']

mindspore/_extends/parse/parser.py

@@ -25,6 +25,7 @@ from dataclasses import is_dataclass
 import asttokens
 import mindspore.nn as nn
 from mindspore import log as logger
+from mindspore import Tensor as MsTensor
 from mindspore import ops
 from mindspore.common.dtype import pytype_to_dtype
 from mindspore.common.api import _MindSporeFunction
@@ -316,6 +317,11 @@ def get_dataclass_methods(cls):
     return methods
 
 
+def convert_to_ms_tensor(data):
+    """Convert C++ tensor to mindspore tensor."""
+    return MsTensor(data)
+
+
 class Parser:
     """
     Parser python code to ast tree.

mindspore/ccsrc/frontend/operator/composite/composite.cc

@@ -929,7 +929,7 @@ void GenerateTupleSliceParameter(const AbstractTuplePtr &tuple, const AbstractSl
 
   *step_value = CheckSliceMember(slice->step(), step_default, step_name);
   if (*step_value == 0) {
-    MS_LOG(EXCEPTION) << "TupleSlice require the step value could not be 0, but got 0.";
+    MS_EXCEPTION(ValueError) << "TupleSlice require the step value could not be 0, but got 0.";
   }
 
   if (*step_value < 0) {
@@ -941,7 +941,7 @@ void GenerateTupleSliceParameter(const AbstractTuplePtr &tuple, const AbstractSl
   *stop_index = CheckSliceMember(slice->stop(), stop_default, stop_name);
   if (!CheckIndexInRange(*start_index, -tuple_size, tuple_size - 1) ||
       !CheckIndexInRange(*stop_index, -tuple_size - 1, tuple_size)) {
-    MS_LOG(EXCEPTION) << "TupleSlice the start index " << *start_index << " or end end index " << *stop_index
+    MS_EXCEPTION(ValueError) << "TupleSlice the start index " << *start_index << " or end end index " << *stop_index
                              << " out of range, tuple size " << tuple_size << ".";
   }
 

mindspore/ccsrc/pipeline/jit/parse/parse_base.h

@@ -69,6 +69,7 @@ const char PYTHON_MOD_GET_MODULE_NAMESPACE[] = "get_module_namespace";
 const char PYTHON_MOD_GET_MEMBER_NAMESPACE_SYMBOL[] = "get_class_member_namespace_symbol";
 const char PYTHON_MOD_GET_PARSE_METHOD[] = "get_parse_method_of_class";
 const char PYTHON_MOD_GET_BPROP_METHOD[] = "get_bprop_method_of_class";
+const char PYTHON_MOD_CONVERT_TO_MS_TENSOR[] = "convert_to_ms_tensor";
 
 const char PYTHON_PARSE_GET_ARGS[] = "get_args";
 const char PYTHON_PARSE_GET_ARGS_DEFAULT_VALUES[] = "get_args_default_values";

mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc

@@ -226,11 +226,11 @@ static AbstractBasePtrList GetUnpackGraphSpecArgsList(AbstractBasePtrList args_s
     for (size_t index = 0; index < specialize_args_before_unpack.size(); index++) {
       MS_EXCEPTION_IF_NULL(specialize_args_before_unpack[index]);
       if (specialize_args_before_unpack[index]->isa<AbstractTuple>()) {
-        AbstractTuplePtr arg_tuple = specialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
+        auto arg_tuple = specialize_args_before_unpack[index]->cast<AbstractTuplePtr>();
         std::transform(arg_tuple->elements().begin(), arg_tuple->elements().end(),
                        std::back_inserter(graph_specialize_args), [](AbstractBasePtr abs) { return abs; });
       } else if (specialize_args_before_unpack[index]->isa<AbstractDictionary>()) {
-        AbstractDictionaryPtr arg_dict = specialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
+        auto arg_dict = specialize_args_before_unpack[index]->cast<AbstractDictionaryPtr>();
         auto dict_elems = arg_dict->elements();
         (void)std::transform(
           dict_elems.begin(), dict_elems.end(), std::back_inserter(graph_specialize_args),
@@ -353,7 +353,7 @@ EvalResultPtr MixedPrecisionCastEvaluator::Run(AnalysisEnginePtr engine, const C
   }
   auto out_node = out_conf->node()->cast<CNodePtr>();
   const auto &out_node_inputs = out_node->inputs();
-  if (out_node->inputs().size() == 0 || (out_node_inputs.size() - 1) != args_conf_list.size()) {
+  if (out_node->inputs().empty() || (out_node_inputs.size() - 1) != args_conf_list.size()) {
     MS_LOG(EXCEPTION) << "MixedPrecisionCast"
                       << " args size should equal to inputs size minus 1, but args size " << args_conf_list.size()
                       << ", inputs size " << out_node_inputs.size();

mindspore/ccsrc/pipeline/pynative/pynative_execute.cc

@@ -115,12 +115,12 @@ inline ValuePtr PyAttrValue(const py::object &obj) {
 static std::string GetId(const py::object &obj) {
   py::object to_process = obj;
   std::string prefix = "";
-  if (py::isinstance<py::tuple>(to_process)) {
+  if (py::isinstance<py::tuple>(to_process) || py::isinstance<py::list>(to_process)) {
     auto p_list = py::cast<py::tuple>(to_process);
-    if (p_list.size() == 0) {
+    if (p_list.empty()) {
       return "empty";
     }
-    prefix = "tuple:";
+    prefix = py::isinstance<py::tuple>(to_process) ? "tuple:" : "list";
     std::string key = "";
     for (size_t i = 0; i < p_list.size(); ++i) {
       key += std::string(py::str(GetId(p_list[i]))) + ":";
@@ -738,6 +738,21 @@ AnfNodePtr PynativeExecutor::GetObjNode(const py::object &obj) {
   return node;
 }
 
+std::string PynativeExecutor::GetCellId(const py::object &cell, const py::args &args) {
+  auto cell_id = GetId(cell);
+  for (size_t i = 0; i < args.size(); i++) {
+    std::string arg_id = GetId(args[i]);
+    if (node_abs_map_.find(arg_id) != node_abs_map_.end()) {
+      cell_id += node_abs_map_[arg_id]->ToString();
+    } else {
+      AbstractBasePtr abs = abstract::FromValueInside(PyAttrValue(args[i]), true);
+      cell_id += abs->ToString();
+      node_abs_map_[arg_id] = abs;
+    }
+  }
+  return cell_id;
+}
+
 py::tuple PynativeExecutor::RunOpInner(const OpExecInfoPtr &op_exec_info) {
   MS_LOG(INFO) << "RunOp start, op name is: " << op_exec_info->op_name;
   mindspore::parse::python_adapter::set_python_env_flag(true);
@@ -785,8 +800,8 @@ py::tuple PynativeExecutor::RunOpInner(const py::args &args) {
   }
   auto cnode = PynativeExecutor::GetInstance()->MakeCNode(op_exec_info, &op_masks, &args_spec_list);
   bool is_find = false;
-  if (prim_abs_list.find(prim->id()) != prim_abs_list.end()) {
-    auto abs_list = prim_abs_list[prim->id()];
+  if (prim_abs_list_.find(prim->id()) != prim_abs_list_.end()) {
+    auto abs_list = prim_abs_list_[prim->id()];
     MS_LOG(DEBUG) << "match prim input args " << op_exec_info->op_name << mindspore::ToString(args_spec_list);
     if (abs_list.find(args_spec_list) != abs_list.end()) {
       MS_LOG(DEBUG) << "match prim ok" << op_exec_info->op_name;
@@ -827,7 +842,7 @@ py::tuple PynativeExecutor::RunOpInner(const py::args &args) {
 
   if (!is_find) {
     // const_value need infer every step
-    auto &out = prim_abs_list[prim->id()];
+    auto &out = prim_abs_list_[prim->id()];
     out[args_spec_list].abs = op_exec_info->abstract;
     out[args_spec_list].attrs = prim->evaluate_added_attrs();
     MS_LOG(DEBUG) << "set prim " << op_exec_info->op_name << mindspore::ToString(args_spec_list);
@@ -890,7 +905,7 @@ PynativeExecutor::~PynativeExecutor() { ClearRes(); }
 PynativeExecutor::PynativeExecutor() { grad_flag_ = false; }
 
 void PynativeExecutor::NewGraphInner(const py::object &cell, const py::args &args) {
-  auto cell_id = GetId(cell);
+  auto cell_id = GetCellId(cell, args);
   if (cell_graph_map_.count(cell_id) != 0) {
     if (cell_resource_map_.find(cell_id) != cell_resource_map_.end()) {
       resource_ = cell_resource_map_[cell_id];
@@ -1016,7 +1031,7 @@ void PynativeExecutor::Popp() {
 }
 
 void PynativeExecutor::EndGraphInner(const py::object &cell, const py::object &out, const py::args &args) {
-  auto cell_id = GetId(cell);
+  auto cell_id = GetCellId(cell, args);
   if (cell_graph_map_.count(cell_id) != 0) {
     MS_LOG(DEBUG) << "Endgraph already compiled";
     return;
@@ -1078,7 +1093,7 @@ void PynativeExecutor::EndGraphByOutId(const std::string &out_id, const py::obje
       inputs.push_back(input);
     }
     auto out_cnode = curr_g_->NewCNode(inputs);
-    set_pyobj(curr_g_, GetId(cell));
+    set_pyobj(curr_g_, GetCellId(cell, args));
     if (py::isinstance<py::tuple>(out)) {
       auto out_list = py::cast<py::tuple>(out);
       auto out_size = static_cast<int>(out_list.size());
@@ -1169,7 +1184,7 @@ void PynativeExecutor::GradNetInner(const GradOperationPtr &grad, const py::obje
   MS_LOG(INFO) << "GradNet start" << args.size();
 
   std::size_t size = args.size();
-  auto cell_id = GetId(cell);
+  std::string cell_id = GetCellId(cell, args);
   if (graph_map_.count(cell_id) != 0) {
     MS_LOG(DEBUG) << "GradNet already compiled";
     return;

mindspore/ccsrc/pipeline/pynative/pynative_execute.h

@@ -92,6 +92,7 @@ class PynativeExecutor : public std::enable_shared_from_this<PynativeExecutor> {
   void set_grad_flag(bool flag) { grad_flag_ = flag; }
   AnfNodePtr GetInput(const py::object &obj, bool op_mask);
   AnfNodePtr GetObjNode(const py::object &obj);
+  std::string GetCellId(const py::object &obj, const py::args &args);
   FuncGraphPtr curr_g() { return curr_g_; }
   void set_pyobj(FuncGraphPtr g, const std::string obj) { graph_info_map_[g].objects.push_back(obj); }
   void set_obj_node_map(FuncGraphPtr g, const std::string obj, AnfNodePtr node) {
@@ -141,7 +142,7 @@ class PynativeExecutor : public std::enable_shared_from_this<PynativeExecutor> {
   FuncGraphPtr top_g_;
   FuncGraphPtr df_builder_;
   FuncGraphPtr curr_g_;
-  std::unordered_map<std::string, AbstractListMap> prim_abs_list;
+  std::unordered_map<std::string, AbstractListMap> prim_abs_list_;
 };
 
 using PynativeExecutorPtr = std::shared_ptr<PynativeExecutor>;

mindspore/ccsrc/utils/primitive_py.cc

@@ -78,12 +78,19 @@ py::function PrimitivePy::GetBpropFunction() {
 }
 
 BaseRef PrimitivePy::RunHookFunction(const VectorRef &args) const {
-  auto py_args = ConvertDatatoPyTuple(args);
+  py::tuple py_args = ConvertDatatoPyTuple(args);
   py::object obj;
   bool is_bprop = this->HasAttr(kBpropAttrName);
   if (is_bprop) {
     SyncData(py_args);
-    obj = hook_(*py_args);
+    py::tuple convert_args(py_args.size());
+    for (size_t i = 0; i < py_args.size(); i++) {
+      convert_args[i] = py::isinstance<tensor::Tensor>(py_args[i])
+                          ? parse::python_adapter::CallPyFn(parse::PYTHON_MOD_PARSE_MODULE,
+                                                            parse::PYTHON_MOD_CONVERT_TO_MS_TENSOR, py_args[i])
+                          : py_args[i];
+    }
+    obj = hook_(*convert_args);
     return std::make_shared<PyObjectRef>(obj);
   }
   SyncData(py_args[2]);

mindspore/common/tensor.py

@@ -210,12 +210,12 @@ class Tensor(Tensor_):
 
     @property
     def shape(self):
-        """The shape of tensor."""
+        """The shape of tensor is a tuple."""
         return self._shape
 
     @property
     def dtype(self):
-        """The dtype of tensor."""
+        """The dtype of tensor is a mindspore type."""
         return self._dtype
 
     @property
@@ -248,6 +248,8 @@ class Tensor(Tensor_):
             Tensor, has the same data type as x.
         """
 
+        if axis is None:
+            axis = ()
         return tensor_operator_registry.get('all')(keep_dims)(self, axis)
 
     def any(self, axis=(), keep_dims=False):
@@ -264,6 +266,8 @@ class Tensor(Tensor_):
             Tensor, has the same data type as x.
         """
 
+        if axis is None:
+            axis = ()
         return tensor_operator_registry.get('any')(keep_dims)(self, axis)
 
 

mindspore/ops/_grad/grad_array_ops.py

@@ -693,7 +693,7 @@ def get_bprop_unsorted_segment_min(self):
     select = P.Select()
 
     def bprop(x, segment_ids, num_segments, out, dout):
-        gathered_outputs, zero_clipped_indices, is_positive = _GatherDropNegatives(out, segment_ids)
+        gathered_outputs, zero_clipped_indices, is_positive = _GatherDropNegatives(out, segment_ids, None, None)
         is_selected = equal(x, gathered_outputs)
         is_selected = logical_and(is_selected, is_positive)
         num_selected = unsorted_segment_sum(cast(is_selected, get_dtype(dout)),