!12084 fix Conv2DBackpropInputUnifyMindIR when it is a forward op in pynative

From: @yuchaojie
Reviewed-by: @kisnwang,@zhoufeng54
Signed-off-by: @zhoufeng54

mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc

@@ -37,6 +37,8 @@ constexpr auto kAttrPadList = "pad_list";
 constexpr auto kAttrMode = "mode";
 constexpr auto kAttrChannelMultiplier = "channel_multiplier";
 constexpr auto kAttrPerm = "perm";
+constexpr auto kAttrInputSizes = "input_sizes";
+constexpr auto kAttrInputSize = "input_size";
 
 bool NeedUpdate(const CNodePtr &conv2d, std::vector<size_t> in_shape, std::vector<size_t> out_shape) {
   MS_EXCEPTION_IF_NULL(conv2d);
@@ -144,14 +146,22 @@ CNodePtr CreateDepthwiseConv2DBackpropInput(const FuncGraphPtr &graph, const CNo
                                             const CNodePtr &transpose) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(conv2d_backin);
-  if (conv2d_backin->inputs().size() != kConv2DBackpropInputNum) {
-    MS_LOG(EXCEPTION) << "Conv2DBackpropInput's input number should be " << kConv2DBackpropInputNum - 1 << ", but got "
-                      << conv2d_backin->inputs().size() - 1;
+
+  CNodePtr depth_conv_backin = nullptr;
+  if (conv2d_backin->inputs().size() == kConv2DBackpropInputNum) {
+    std::vector<AnfNodePtr> depth_conv_backin_inputs = {
+      NewValueNode(std::make_shared<Primitive>(kDepthwiseConv2dNativeBackpropInputOpName)), conv2d_backin->input(3),
+      transpose, conv2d_backin->input(1)};
+    depth_conv_backin = graph->NewCNode(depth_conv_backin_inputs);
+  } else {
+    // In nn.Conv2DTranspose, Conv2DBackpropInput is a forward op and the input_sizes input will be convert to attr
+    // in pynative mode.
+    std::vector<AnfNodePtr> depth_conv_backin_inputs = {
+      NewValueNode(std::make_shared<Primitive>(kDepthwiseConv2dNativeBackpropInputOpName)), transpose,
+      conv2d_backin->input(1)};
+    depth_conv_backin = graph->NewCNode(depth_conv_backin_inputs);
+    AnfAlgo::CopyNodeAttr(kAttrInputSizes, kAttrInputSize, conv2d_backin, depth_conv_backin);
   }
-  std::vector<AnfNodePtr> depth_conv_backin_inputs = {
-    NewValueNode(std::make_shared<Primitive>(kDepthwiseConv2dNativeBackpropInputOpName)), conv2d_backin->input(3),
-    transpose, conv2d_backin->input(1)};
-  auto depth_conv_backin = graph->NewCNode(depth_conv_backin_inputs);
   MS_EXCEPTION_IF_NULL(depth_conv_backin);
   depth_conv_backin->set_abstract(conv2d_backin->abstract());
   depth_conv_backin->set_scope(conv2d_backin->scope());
@@ -265,10 +275,8 @@ const AnfNodePtr Conv2DUnifyMindIR::Process(const FuncGraphPtr &graph, const Anf
 }
 
 const BaseRef Conv2DBackpropInputUnifyMindIR::DefinePattern() const {
-  VarPtr dout = std::make_shared<Var>();
-  VarPtr weight = std::make_shared<Var>();
-  VarPtr input_size = std::make_shared<Var>();
-  VectorRef pattern({prim::kPrimConv2DBackpropInput, dout, weight, input_size});
+  VarPtr Xs = std::make_shared<SeqVar>();
+  VectorRef pattern({prim::kPrimConv2DBackpropInput, Xs});
   return pattern;
 }
 
@@ -285,9 +293,11 @@ const AnfNodePtr Conv2DBackpropInputUnifyMindIR::Process(const FuncGraphPtr &gra
     return nullptr;
   }
 
-  if (conv2d_backin->inputs().size() != kConv2DBackpropInputNum) {
-    MS_LOG(EXCEPTION) << "Conv2DBackpropInput's input number should be " << kConv2DBackpropInputNum - 1 << ", but got "
-                      << conv2d_backin->inputs().size() - 1;
+  auto input_size = conv2d_backin->inputs().size();
+  // In pynative mode, input_sizes input will be convert to attr if Conv2DBackpropInput is a forward op.
+  if (input_size != kConv2DBackpropInputNum && input_size != kConv2DBackpropInputNum - 1) {
+    MS_LOG(EXCEPTION) << "Conv2DBackpropInput's input number should be " << kConv2DBackpropInputNum - 1 << " or "
+                      << kConv2DBackpropInputNum - 2 << ", but got " << input_size - 1;
   }
   auto transpose = CreateTranspose(graph, conv2d_backin, conv2d_backin->input(2), true);
   auto depth_conv_backin = CreateDepthwiseConv2DBackpropInput(graph, conv2d_backin, transpose);

mindspore/ops/operations/nn_ops.py

@@ -803,8 +803,8 @@ class FusedBatchNorm(Primitive):
         mode (int): Mode of batch normalization, value is 0 or 1. Default: 0.
         epsilon (float): A small value added for numerical stability. Default: 1e-5.
         momentum (float): The hyper parameter to compute moving average for running_mean and running_var
-            (e.g. :math:`new\_running\_mean = momentum * running\_mean + (1 - momentum) * current\_mean`).
-            Momentum value must be [0, 1]. Default: 0.9.
+            (e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`).
+            Momentum value must be [0, 1]. Default: 0.1.
 
     Inputs:
         - **input_x** (Tensor) - Tensor of shape :math:`(N, C)`.
@@ -893,8 +893,8 @@ class FusedBatchNormEx(PrimitiveWithInfer):
         mode (int): Mode of batch normalization, value is 0 or 1. Default: 0.
         epsilon (float): A small value added for numerical stability. Default: 1e-5.
         momentum (float): The hyper parameter to compute moving average for running_mean and running_var
-            (e.g. :math:`new\_running\_mean = momentum * running\_mean + (1 - momentum) * current\_mean`).
-            Momentum value must be [0, 1]. Default: 0.9.
+            (e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`).
+            Momentum value must be [0, 1]. Default: 0.1.
         data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'.
             Default: "NCHW".
 
@@ -1262,6 +1262,9 @@ class BatchNorm(PrimitiveWithInfer):
         is_training (bool): If `is_training` is True, `mean` and `variance` are computed during training.
             If `is_training` is False, they're loaded from checkpoint during inference. Default: False.
         epsilon (float): A small value added for numerical stability. Default: 1e-5.
+        momentum (float): The hyper parameter to compute moving average for running_mean and running_var
+            (e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`).
+            Momentum value must be [0, 1]. Default: 0.1.
         data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'.
             Default: "NCHW".