conv2d bp filter input primitive stride 2 and adapt to 4

mindspore/ccsrc/transform/op_adapter.h

@@ -736,7 +736,7 @@ class OpAdapter : public BaseOpAdapter {
     return static_cast<int64_t>(GetValue<int>(value));
   }
 
-  // specialization for int to Vector
+  // specialization for int or tuple broadcast to Vector
   static std::vector<int64_t> ConvertAny(const ValuePtr &value, const std::string &name,
                                          const AnyTraits<std::vector<int64_t>> anyTraitsInt) {
     return ConvertAnyUtil(value, name, anyTraitsInt);

mindspore/ccsrc/transform/op_adapter_util.cc

@@ -35,14 +35,20 @@ GeTensor ConvertAnyUtil(const ValuePtr &value, const AnyTraits<mindspore::tensor
 
 std::vector<int64_t> ConvertAnyUtil(const ValuePtr &value, const std::string &name,
                                     const AnyTraits<std::vector<int64_t>>) {
+  MS_EXCEPTION_IF_NULL(value);
   int64_t data = GetValue<int>(value);
   std::vector<int64_t> list;
   int size = 2;  // 2 int in list
   if (name == "pad") {
-    size = 4;  // 4 int in list
-    list = TransformUtil::ConvertIntToList(data, size);
-    list[0] = 1;
-    list[1] = 1;
+    if (!value->isa<ValueSequeue>()) {
+      MS_LOG(EXCEPTION) << "Value should be ValueTuple, but got" << value->type_name();
+    }
+    auto vec = value->cast<ValueSequeuePtr>();
+    list.push_back(1);
+    list.push_back(1);
+    for (auto &it : vec->value()) {
+      list.push_back(static_cast<int64_t>(GetValue<int>(it)));
+    }
   } else {
     list = TransformUtil::ConvertIntToList(data, size);
   }

mindspore/ccsrc/transform/op_declare.cc

@@ -733,7 +733,7 @@ INPUT_ATTR_MAP(Conv2DBackpropInputD) = {
   {3, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropInputD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
   {"group", ATTR_DESC(groups, AnyTraits<int>())},
@@ -746,7 +746,7 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = {
   {3, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
 ATTR_MAP(Conv2DBackpropFilterD) = {
   {"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
-  {"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
+  {"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
   {"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
   {"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
   {"group", ATTR_DESC(groups, AnyTraits<int>())},

mindspore/ops/operations/_grad_ops.py

@@ -174,9 +174,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
         pad_mode = pad_mode.upper()
         self.add_prim_attr('pad_mode', pad_mode)
         self.pad = pad
-        if isinstance(stride, tuple) and len(stride) == 2:
-            self.stride = stride
-            self.add_prim_attr('stride', (1, 1, self.stride[0], self.stride[1]))
+        if isinstance(stride, tuple) and len(stride) == 4:
+            self.stride = (stride[2], stride[3])
+            self.add_prim_attr('stride', self.stride)
         self.dilation = dilation
         self.group = group
         self.add_prim_attr('data_format', "NCHW")

mindspore/ops/operations/nn_ops.py

@@ -1084,7 +1084,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output'])
         self.out_channel = validator.check_integer('out_channel', out_channel, 0, Rel.GT, self.name)
         self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name)
-        self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True)
+        self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=False)
         self.add_prim_attr('stride', self.stride)
         self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True)
         self.add_prim_attr('dilation', self.dilation)