!1950 [front]add `has_effect` direct to target construct method not cell object

Merge pull request !1950 from vlne-v1/move-flags-to-construct

.gitignore

@@ -26,6 +26,7 @@ cmake-build-debug
 *_pb2.py
 *.pb.h
 *.pb.cc
+*.pb
 
 # Object files
 *.o

mindspore/ccsrc/ir/primitive.cc

@@ -86,7 +86,7 @@ void PrimitivePy::AddPyAttr(const py::str &name, const py::object &obj) {
   }
   bool converted = parse::ConvertData(obj, &converted_ret);
   if (!converted) {
-    MS_LOG(EXCEPTION) << "Attribute convert error with type:" << std::string(py::str(obj));
+    MS_LOG(EXCEPTION) << "Attribute convert error with type: " << std::string(py::str(obj));
   }
   (void)this->AddAttr(attr_name, converted_ret);
 }

mindspore/ccsrc/ir/tensor.cc

@@ -345,14 +345,14 @@ abstract::AbstractBasePtr Tensor::ToAbstract() {
 
 std::string Tensor::GetShapeAndDataTypeInfo() const {
   std::ostringstream buf;
-  buf << "Tensor \nshape:[" << shape() << "]" << this->Dtype()->ToString();
+  buf << "Tensor shape:[" << shape() << "]" << this->Dtype()->ToString();
   return buf.str();
 }
 
 std::string Tensor::ToString() const {
   const int small_tensor_size = 30;
   std::ostringstream buf;
-  buf << "Tensor \nshape:[" << shape() << "]" << this->Dtype()->ToString();
+  buf << "Tensor shape:[" << shape() << "]" << this->Dtype()->ToString();
   // only print small tensor
   if (DataSize() < small_tensor_size) {
     buf << "val:" << std::string(py::str(data()));

mindspore/ccsrc/pipeline/parse/parse.cc

@@ -234,7 +234,11 @@ FunctionBlockPtr Parser::ParseFunction(const py::object &node, const FunctionBlo
     current_fg->debug_info()->set_deco_location(GetLocation(deco_list));
   }
 
-  bool set_flag = ast_->UpdateFuncGraphFlags(current_fg);
+  bool set_flag = UpdateFuncGraphFlags(ast_->function(), current_fg);
+  if (ast_->obj() != ast_->function()) {
+    set_flag = set_flag && UpdateFuncGraphFlags(ast_->obj(), current_fg);
+  }
+
   if (!set_flag) {
     MS_LOG(ERROR) << "Set flags failed";
     return nullptr;
@@ -1436,17 +1440,17 @@ bool ParseAst::IsClassMember(const py::object &node) {
   return ret.cast<bool>();
 }
 
-bool ParseAst::UpdateFuncGraphFlags(const FuncGraphPtr &func_graph) {
+bool UpdateFuncGraphFlags(py::object obj, const FuncGraphPtr &func_graph) {
   if (func_graph == nullptr) {
     MS_LOG(ERROR) << "FuncGraph is null";
     return false;
   }
 
-  if (!py::hasattr(obj_, PYTHON_EXTERN_MINDSPORE_FLAG)) {
+  if (!py::hasattr(obj, PYTHON_EXTERN_MINDSPORE_FLAG)) {
     MS_LOG(DEBUG) << "No flags";
     return true;
   }
-  py::dict flags = python_adapter::GetPyObjAttr(obj_, PYTHON_EXTERN_MINDSPORE_FLAG);
+  py::dict flags = python_adapter::GetPyObjAttr(obj, PYTHON_EXTERN_MINDSPORE_FLAG);
   for (auto &item : flags) {
     if (!py::isinstance<py::str>(item.first)) {
       MS_LOG(ERROR) << "Type error in flags dict convert";
@@ -1466,7 +1470,6 @@ bool ParseAst::UpdateFuncGraphFlags(const FuncGraphPtr &func_graph) {
       return false;
     }
   }
-
   return true;
 }
 

mindspore/ccsrc/pipeline/parse/parse.h

@@ -327,9 +327,6 @@ class ParseAst {
 
   bool IsClassMember(const py::object &node);
 
-  // update the graph flags
-  bool UpdateFuncGraphFlags(const FuncGraphPtr &func_graph);
-
  private:
   // save obj,eg: class instance or function
   py::object obj_;
@@ -350,6 +347,9 @@ class ParseAst {
   int function_line_offset_;
 };
 
+// update the graph flags
+bool UpdateFuncGraphFlags(py::object obj, const FuncGraphPtr &func_graph);
+
 AnfNodePtr GetMixedPrecisionCastHelp(const FuncGraphPtr &func_graph, const AnfNodePtr &param);
 
 }  // namespace parse

mindspore/nn/layer/basic.py

@@ -284,7 +284,6 @@ class ClipByNorm(Cell):
         self.reduce_sum = P.ReduceSum(keep_dims=True)
         self.select_ = P.Select()
         self.greater_ = P.Greater()
-        self.axis = ()
         self.cast = P.Cast()
         self.zero = Tensor(np.array([0.0]).astype(np.float32))
         self.sqrt = P.Sqrt()
@@ -299,7 +298,7 @@ class ClipByNorm(Cell):
     def construct(self, x, clip_norm):
         """add ms_function decorator for pynative mode"""
         mul_x = F.square(x)
-        l2sum = self.cast(self.reduce_sum(mul_x, self.axis), mstype.float32)
+        l2sum = self.cast(self.reduce_sum(mul_x), mstype.float32)
         cond = self.greater_(l2sum, self.zero)
         ones_ = self.fill(self.dtype(cond), self.shape(cond), 1.0)
 

mindspore/nn/wrap/loss_scale.py

@@ -234,8 +234,8 @@ class TrainOneStepWithLossScaleCell(Cell):
         if scale_update_cell:
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
-        self.add_flags(has_effect=True)
 
+    @C.add_flags(has_effect=True)
     def construct(self, data, label, sens=None):
         weights = self.weights
         loss = self.network(data, label)

mindspore/ops/composite/base.py

@@ -30,16 +30,16 @@ from ...common.parameter import Parameter
 __all__ = [EnvInstance_, TupleAdd_, TupleSlice_, UnpackCall_, TupleGetItemTensor_]
 
 
-def add_flags(fn, **flags):
+def add_flags(fn=None, **flags):
     """
-    An interface to add flag for a function.
+    An decorator to add flag for a function.
 
     Note:
         Only supports bool value.
 
     Args:
-        fn (Function): Function or cell to add flag.
-        flags (bool): Flags use kwargs.
+        fn (Function): Function or cell to add flag. Default: None.
+        flags (dict): Flags use kwargs. Default: None.
 
     Returns:
         Function, the fn added flags.
@@ -47,11 +47,17 @@ def add_flags(fn, **flags):
     Examples:
         >>> add_flags(net, predit=True)
     """
-    # need set the attr and access on c++
-    if not hasattr(fn, "_mindspore_flags"):
-        fn._mindspore_flags = {}
-    fn._mindspore_flags.update({**flags})
-    return fn
+    def deco(fn):
+        # need set the attr and access on c++
+        if not hasattr(fn, "_mindspore_flags"):
+            fn._mindspore_flags = {}
+
+        fn._mindspore_flags.update({**flags})
+        return fn
+    ret = deco
+    if fn is not None:
+        ret = deco(fn)
+    return ret
 
 
 def core(fn=None, **flags):

model_zoo/Transformer/src/transformer_for_train.py

@@ -277,8 +277,8 @@ class TransformerTrainOneStepWithLossScaleCell(nn.Cell):
         if scale_update_cell:
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
-        self.add_flags(has_effect=True)
 
+    @C.add_flags(has_effect=True)
     def construct(self,
                   source_eos_ids,
                   source_eos_mask,

model_zoo/bert/src/bert_for_pre_training.py

@@ -132,9 +132,9 @@ class GetNextSentenceOutput(nn.Cell):
     def __init__(self, config):
         super(GetNextSentenceOutput, self).__init__()
         self.log_softmax = _selected_ops.LogSoftmax()
-        self.weight_init = TruncatedNormal(config.initializer_range)
+        weight_init = TruncatedNormal(config.initializer_range)
         self.dense = nn.Dense(config.hidden_size, 2,
-                              weight_init=self.weight_init, has_bias=True).to_float(config.compute_type)
+                              weight_init=weight_init, has_bias=True).to_float(config.compute_type)
         self.dtype = config.dtype
         self.cast = P.Cast()
 
@@ -321,7 +321,6 @@ class BertTrainOneStepCell(nn.Cell):
         if self.reducer_flag:
             # apply grad reducer on grads
             grads = self.grad_reducer(grads)
-
         succ = self.optimizer(grads)
         return F.depend(loss, succ)
 
@@ -380,8 +379,8 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         if scale_update_cell:
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
-        self.add_flags(has_effect=True)
 
+    @C.add_flags(has_effect=True)
     def construct(self,
                   input_ids,
                   input_mask,

model_zoo/deeplabv3/src/deeplabv3.py

@@ -17,6 +17,7 @@
 import numpy as np
 import mindspore.nn as nn
 from mindspore.ops import operations as P
+from mindspore.ops.composite import add_flags
 from .backbone.resnet_deeplab import _conv_bn_relu, resnet50_dl, _deep_conv_bn_relu, \
     DepthwiseConv2dNative, SpaceToBatch, BatchToSpace
 
@@ -121,6 +122,7 @@ class ASPP(nn.Cell):
         self.feature_shape = feature_shape
         self.concat = P.Concat(axis=1)
 
+    @add_flags(loop_can_unroll=True)
     def construct(self, x, scale_index=0):
         aspp0 = self.aspp0(x)
         aspp1 = self.global_poolings[scale_index](x)
@@ -276,7 +278,7 @@ class SingleDeepLabV3(nn.Cell):
                          atrous_rates=atrous_rates,
                          output_stride=output_stride,
                          fine_tune_batch_norm=fine_tune_batch_norm)
-        self.aspp.add_flags(loop_can_unroll=True)
+
         atrous_rates_len = 0
         if atrous_rates is not None:
             atrous_rates_len = len(atrous_rates)

tests/st/networks/models/bert/src/bert_for_pre_training.py

@@ -379,8 +379,8 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         if scale_update_cell:
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
-        self.add_flags(has_effect=True)
 
+    @C.add_flags(has_effect=True)
     def construct(self,
                   input_ids,
                   input_mask,

tests/ut/python/keep_order/test_keep_order.py

@@ -133,8 +133,8 @@ def test_keep_order_io_effect_exception_return_dtype():
             self.dtype = P.DType()
             self.sub = P.Sub()
             self.neg = P.Neg()
-            self.add_flags(has_effect=True)
 
+        @C.add_flags(has_effect=True)
         def construct(self, x):
             init = self.alloc_status()
             self.clear_status(init)

tests/ut/python/ops/test_math_ops.py

@@ -268,8 +268,8 @@ class NpuFloatNet(nn.Cell):
         self.reduce_sum = P.ReduceSum(keep_dims=True)
         self.sub = P.Sub()
         self.neg = P.Neg()
-        self.add_flags(has_effect=True)
 
+    @C.add_flags(has_effect=True)
     def construct(self, x):
         init = self.alloc_status()
         self.clear_status(init)

tests/ut/python/pynative_mode/ge/model/test_lenet_model.py

@@ -14,13 +14,13 @@
 # ============================================================================
 """ test_lenet_model """
 import numpy as np
+import pytest
 
 import mindspore.nn as nn
 from mindspore.common.tensor import Tensor
 from mindspore.nn import WithGradCell, WithLossCell
 from mindspore.nn.optim import Momentum
 from mindspore.ops import operations as P
-from ....ut_filter import non_graph_engine
 
 
 class LeNet5(nn.Cell):
@@ -47,7 +47,7 @@ class LeNet5(nn.Cell):
         return x
 
 
-@non_graph_engine
+@pytest.mark.skip(reason="need ge backend")
 def test_lenet_pynative_train_net():
     """ test_lenet_pynative_train_net """
     data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)