!13336 Fix optimizer and conv2d infer.

From: @linqingke
Reviewed-by: @xu-yfei,@chujinjin
Signed-off-by: @xu-yfei

mindspore/ops/operations/inner_ops.py

@@ -247,6 +247,7 @@ class LambApplyOptimizerAssign(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize LambApplyOptimizerAssign"""
+        self.add_prim_attr('side_effect_mem', True)
 
     def infer_shape(self, grad_shape, v_shape, m_shape, var_shape, beta1_shape, sub1_shape,
                     beta2_shape, sub2_shape, eps_shape, steps_shape, use_weight_shape, weight_decay_shape):
@@ -308,6 +309,7 @@ class LambApplyWeightAssign(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize LambApplyWeightAssign"""
+        self.add_prim_attr('side_effect_mem', True)
 
     def infer_shape(self, w_norm_shape, g_norm_shape, lr_shape, update_shape, var_shape):
         validator.check("var_shape", var_shape, "update_shape", update_shape, Rel.EQ, self.name)

mindspore/ops/operations/nn_ops.py

@@ -78,6 +78,17 @@ def _check_shape(arg_name, arg_value, prim_name):
     return arg_value
 
 
+def _update_attr_by_format(arg_value, arg_format):
+    """
+    If the format is NHWC, should modify the strides or dilation shape.
+    """
+    ret = arg_value
+    if len(arg_value) == 4 and arg_format == "NHWC":
+        ret = arg_value[1:] + (1,)
+
+    return ret
+
+
 class Flatten(PrimitiveWithInfer):
     r"""
     Flattens a tensor without changing its batch size on the 0-th axis.
@@ -2157,9 +2168,15 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['out_backprop', 'filter', 'input_sizes'], outputs=['output'])
         self.out_channel = validator.check_positive_int(out_channel, 'out_channel', self.name)
         self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name)
+        self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
+        if context.get_context("device_target") != "GPU" and self.format == "NHWC":
+            raise ValueError("NHWC format only support in GPU target.")
+        self.add_prim_attr('data_format', self.format)
         self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True)
+        self.stride = _update_attr_by_format(self.stride, self.format)
         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.dilation = _update_attr_by_format(self.dilation, self.format)
         self.add_prim_attr('dilation', self.dilation)
         validator.check_value_type('pad', pad, (int, tuple), self.name)
         if isinstance(pad, int):
@@ -2180,10 +2197,6 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
         self.mode = validator.check_equal_int(mode, 1, 'mode', self.name)
         self.group = validator.check_positive_int(group, 'group', self.name)
         self.add_prim_attr('groups', self.group)
-        self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
-        if context.get_context("device_target") != "GPU" and self.format == "NHWC":
-            raise ValueError("NHWC format only support in GPU target.")
-        self.add_prim_attr('data_format', self.format)
         if pad_list:
             for x in pad_list:
                 validator.check_non_negative_int(x, 'element of pad_list', self.name)