modify adam optimizer and script of bert to match the patterns of fusion rule

mindspore/nn/optim/adam.py

@@ -67,9 +67,9 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
     next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
                                             - beta2, op_square(gradient_fp32))
 
-    update = next_m / (op_sqrt(next_v) + eps)
+    update = next_m / (eps + op_sqrt(next_v))
     if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param_fp32)
+        update = op_mul(weight_decay_tensor, param_fp32) + update
 
     update_with_lr = op_mul(lr, update)
     next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))

mindspore/ops/_op_impl/tbe/bias_add_grad.py

@@ -26,6 +26,8 @@ bias_add_grad_op_info = TBERegOp("BiasAddGrad") \
     .attr("data_format", "required", "str", "all") \
     .input(0, "output_backprop", False, "required", "all") \
     .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.F32_FracNZ, DataType.F32_Default) \
     .get_op_info()

model_zoo/bert/src/bert_model.py

@@ -261,7 +261,7 @@ class BertOutput(nn.Cell):
     def construct(self, hidden_status, input_tensor):
         output = self.dense(hidden_status)
         output = self.dropout(output)
-        output = self.add(output, input_tensor)
+        output = self.add(input_tensor, output)
         output = self.layernorm(output)
         return output
 
@@ -832,8 +832,7 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
         if not self.input_mask_from_dataset:
             input_mask = self.input_mask
 
-        input_mask = self.cast(self.reshape(input_mask, self.shape), mstype.float32)
-        attention_mask = self.batch_matmul(self.broadcast_ones, input_mask)
+        attention_mask = self.cast(self.reshape(input_mask, self.shape), mstype.float32)
         return attention_mask