_extends\graph_kernel\expanders\gelu_grad.py

src/mindspore2022/mindspore/python/mindspore/_extends/graph_kernel/expanders/gelu_grad.py

@@ -19,11 +19,29 @@ from ._utils import Expander, ExpanderInfoValidator as VLD
 @VLD.check_all_formats_same
 class GeLUGrad(Expander):
     """GeLUGrad expander"""
-    CSVALUE = 0.044715
+
+    # CSVALUE = 0.044715
+    CSVALUE = 0.044715  # CSVALUE的值为0.044715
     CSVALUE_SQRT_TWO_DIV_PI = 0.7978845608028564  # np.sqrt(2/np.pi)
     CSVALUE_TRI = 0.134141  # CSVALUE * 3
 
     def _expand(self, graph_builder):
+        """
+        计算GELU函数的梯度。
+
+        Args:
+            graph_builder (GraphBuilder): 图构建器对象，用于生成计算图。
+
+        Returns:
+            Tensor: GELU函数的梯度。
+
+        计算公式如下：
+        GELU的梯度dy和x是dy * y'
+        y' = 0.5 * (1.0 + tanh(tanh_para)) + 0.5 * x * (1.0 - tanh(tanh_para) * tanh(para)) * mul_right
+        tanh_para = sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
+        mul_right = sqrt(2.0 / pi) * (1 + 3 * 0.044715 * x * x)
+
+        """
         # cal formula are:
         # gelu_grad of dy and x is dy * y'
         # y' is 0.5 * (1.0 + tanh(tanh_para)) + 0.5 * x * (1.0 - tanh(tanh_para) * tanh(para)) * mul_right
@@ -33,21 +51,33 @@ class GeLUGrad(Expander):
         input_dy, input_x, _ = self.inputs
 
         # create some const var
+        # 创建一个常量，值为self.CSVALUE，数据类型为input_dy.dtype
         const_csvalue = graph_builder.value(input_dy.dtype, self.CSVALUE)
+        # 创建一个常量，值为self.CSVALUE_SQRT_TWO_DIV_PI，数据类型为input_dy.dtype
         const_csvalue_sqrt_two_div_pi = graph_builder.value(input_dy.dtype, self.CSVALUE_SQRT_TWO_DIV_PI)
+        # 创建一个常量，值为self.CSVALUE_TRI，数据类型为input_dy.dtype
         const_csvalue_tri = graph_builder.value(input_dy.dtype, self.CSVALUE_TRI)
+        # 创建一个常量，值为1，数据类型为input_dy.dtype
         const_one = graph_builder.value(input_dy.dtype, 1)
+        # 创建一个常量，值为0.5，数据类型为input_dy.dtype
         const_half = graph_builder.value(input_dy.dtype, 0.5)
 
         # cal mul_right
+        # 计算input_x的平方
         mul_double = graph_builder.emit('Mul', [input_x, input_x])
+        # 将const_csvalue_tri与mul_double相乘
         mul_double_mul_tri = graph_builder.emit('Mul', [const_csvalue_tri, mul_double])
+        # 将const_one与mul_double_mul_tri相加
         mul_add_one = graph_builder.emit('Add', [const_one, mul_double_mul_tri])
+        # 将const_csvalue_sqrt_two_div_pi与mul_add_one相乘
         mul_right = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_one])
 
         # cal tanh_para
+        # 计算input_x和mul_double的乘积
         mul_triple = graph_builder.emit('Mul', [input_x, mul_double])
+        # 计算const_csvalue和mul_triple的乘积
         mul_triple_mul_csvalue = graph_builder.emit('Mul', [const_csvalue, mul_triple])
+        # 计算input_x和mul_triple_mul_csvalue的和
         mul_add_x = graph_builder.emit('Add', [input_x, mul_triple_mul_csvalue])
         tanh_para = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_x])