expand gelu and gelugrad op

mindspore/_extends/graph_kernel/expanders/__init__.py

@@ -15,6 +15,7 @@
 """expanders init"""
 
 from .gelu import expand_gelu
+from .gelu_grad import expand_gelugrad
 from .layernorm import expand_layernorm
 from .softmax import expand_softmax
 from .square import expand_square

mindspore/_extends/graph_kernel/expanders/gelu.py

@@ -16,11 +16,16 @@
 from mindspore._extends.graph_kernel.model import model_builder as builder
 
 CSVALUE = 0.044715
-CSVALUE_A = 1.5957691  # 2*np.sqrt(2/np.pi)
+CSVALUE_SQRT_TWO_DIV_PI = 0.7978845608028564  # np.sqrt(2/np.pi)
+ONE = 1.0
+HALF = 0.5
 
 
 def expand_gelu(expand_info):
     """Gelu expander"""
+    # cal formula are:
+    # gelu(x) = 0.5 * x * (1.0 + tanh(y))
+    # y = sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
 
     # get op info.
     input_desc = expand_info['input_desc'][0]
@@ -30,35 +35,29 @@ def expand_gelu(expand_info):
     with graph_builder.graph_scope('main') as graph_scope:
         # create tensor input.
         input_x = graph_builder.tensor(input_desc['shape'], input_desc['data_type'], input_desc['format'])
+        graph_scope.set_input(input_x)
         dtype = input_x.dtype
         if dtype == 'float16':
             input_x = graph_builder.emit('Cast', [input_x], attrs={'dst_type': 'float32'})
 
-        # cal tanh.
+        # cal y
         mul_0 = graph_builder.emit('Mul', [input_x, input_x])
         pow_0 = graph_builder.emit('Mul', [mul_0, input_x])
         const_csvalue = graph_builder.value(pow_0.dtype, CSVALUE, input_desc['format'])
         mul_1 = graph_builder.emit('Mul', [pow_0, const_csvalue])
         tanh_res = graph_builder.emit('TensorAdd', [input_x, mul_1])
+        const_csvalue_sqrt_two_div_pi = graph_builder.value(
+            tanh_res.dtype, CSVALUE_SQRT_TWO_DIV_PI, input_desc['format'])
+        y = graph_builder.emit('Mul', [tanh_res, const_csvalue_sqrt_two_div_pi])
 
-        const_csvalue_a = graph_builder.value(tanh_res.dtype, CSVALUE_A, input_desc['format'])
+        # cal gelu(x)
-        mul_0 = graph_builder.emit('Mul', [tanh_res, const_csvalue_a])
+        tanh_y = graph_builder.emit('Tanh', [y])
+        const_one = graph_builder.value(tanh_y.dtype, ONE, input_desc['format'])
+        const_half = graph_builder.value(tanh_y.dtype, HALF, input_desc['format'])
+        tanh_y_add_one = graph_builder.emit('TensorAdd', [tanh_y, const_one])
+        mul_x = graph_builder.emit('Mul', [input_x, tanh_y_add_one])
+        result = graph_builder.emit('Mul', [const_half, mul_x])
 
-        const_zero = graph_builder.value(mul_0.dtype, 0.0, input_desc['format'])
-        mul_0_min = graph_builder.emit('Minimum', [mul_0, const_zero])
-        right_mul = graph_builder.emit('Exp', [mul_0_min])
-
-        mul_0_abs = graph_builder.emit('Abs', [mul_0])
-        const_neg_one = graph_builder.value(mul_0_abs.dtype, -1.0, input_desc['format'])
-        mul_0_abs_neg = graph_builder.emit('Mul', [mul_0_abs, const_neg_one])
-
-        mul_0_abs_neg_exp = graph_builder.emit('Exp', [mul_0_abs_neg])
-
-        const_one = graph_builder.value(mul_0_abs_neg_exp.dtype, 1.0, input_desc['format'])
-        mul_0_abs_neg_exp_add = graph_builder.emit('TensorAdd', [mul_0_abs_neg_exp, const_one])
-        left_mul = graph_builder.emit('RealDiv', [input_x, mul_0_abs_neg_exp_add])
-
-        result = graph_builder.emit('Mul', [left_mul, right_mul])
         if dtype == 'float16':
             result = graph_builder.emit('Cast', [result], attrs={'dst_type': 'float16'})
         # set graph output.

mindspore/_extends/graph_kernel/expanders/gelu_grad.py

@@ -0,0 +1,92 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===========================================================================
+"""generate json desc for gelugrad"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+CSVALUE = 0.044715
+CSVALUE_SQRT_TWO_DIV_PI = 0.7978845608028564  # np.sqrt(2/np.pi)
+CSVALUE_TRI = 0.134141  # CSVALUE * 3
+ONE = 1.0
+HALF = 0.5
+
+
+def expand_gelugrad(expand_info):
+    """GeluGrad expander"""
+    # cal formula are:
+    # gelu_grad(dy, x) = 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)
+
+    # get op info.
+    input_desc_0 = expand_info['input_desc'][0]
+    input_desc_1 = expand_info['input_desc'][1]
+    input_desc_2 = expand_info['input_desc'][2]
+    graph_builder = builder.GraphBuilder()
+
+    # generate a graph.
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_dy = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
+        input_x = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
+        input_y = graph_builder.tensor(input_desc_2['shape'], input_desc_2['data_type'], input_desc_2['format'])
+        graph_scope.set_input(input_dy, input_x, input_y)
+        dtype = input_dy.dtype
+        if dtype == 'float16':
+            input_dy = graph_builder.emit('Cast', [input_dy], attrs={'dst_type': 'float32'})
+
+        # create some const var
+        const_csvalue = graph_builder.value(input_dy.dtype, CSVALUE, input_desc_0['format'])
+        const_csvalue_sqrt_two_div_pi = graph_builder.value(
+            input_dy.dtype, CSVALUE_SQRT_TWO_DIV_PI, input_desc_0['format'])
+        const_csvalue_tri = graph_builder.value(input_dy.dtype, CSVALUE_TRI, input_desc_0['format'])
+        const_one = graph_builder.value(input_dy.dtype, ONE, input_desc_0['format'])
+        const_half = graph_builder.value(input_dy.dtype, HALF, input_desc_0['format'])
+
+        # cal mul_right
+        mul_double = graph_builder.emit('Mul', [input_x, input_x])
+        mul_double_mul_tri = graph_builder.emit('Mul', [const_csvalue_tri, mul_double])
+        mul_add_one = graph_builder.emit('TensorAdd', [const_one, mul_double_mul_tri])
+        mul_right = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_one])
+
+        # cal tanh_para
+        mul_triple = graph_builder.emit('Mul', [input_x, mul_double])
+        mul_triple_mul_csvalue = graph_builder.emit('Mul', [const_csvalue, mul_triple])
+        mul_add_x = graph_builder.emit('TensorAdd', [input_x, mul_triple_mul_csvalue])
+        tanh_para = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_x])
+
+        # cal 0.5 * (1.0 + tanh(tahn_para))
+        tanh_res = graph_builder.emit('Tanh', [tanh_para])
+        tanh_res_add_one = graph_builder.emit('TensorAdd', [const_one, tanh_res])
+        half_mul_tanh_res_add_one = graph_builder.emit('Mul', [const_half, tanh_res_add_one])
+
+        # cal 0.5 * x * (1.0 - tanh(tanh_para) * tanh(tanh_para)) * mul_right
+        tan_res_double = graph_builder.emit('Mul', [tanh_res, tanh_res])
+        one_sub_tan_res_double = graph_builder.emit('Sub', [const_one, tan_res_double])
+        half_mul_x = graph_builder.emit('Mul', [const_half, input_x])
+        mul_tmp = graph_builder.emit('Mul', [half_mul_x, one_sub_tan_res_double])
+        mul_final = graph_builder.emit('Mul', [mul_tmp, mul_right])
+
+        # cal result
+        result_tmp = graph_builder.emit('TensorAdd', [half_mul_tanh_res_add_one, mul_final])
+        result = graph_builder.emit('Mul', [input_dy, result_tmp])
+
+        if dtype == 'float16':
+            result = graph_builder.emit('Cast', [result], attrs={'dst_type': 'float16'})
+        # set graph output.
+        graph_scope.set_output(result)
+
+    graph = graph_builder.get()[0]
+    return graph

mindspore/_extends/graph_kernel/model/model.py

@@ -153,6 +153,7 @@ class PrimLib:
         'make_tuple': Prim(CONTROL),
         'ControlDepend': Prim(CONTROL),
         'Assign': Prim(ELEMWISE),
+        'Tanh': Prim(ELEMWISE),
         '@ReduceInit': Prim(ELEMWISE),
     }
 

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc

@@ -705,6 +705,7 @@ std::unordered_set<PrimitivePtr> GetExpandOps() {
     prim::kPrimSquare,
     prim::kPrimBiasAdd,
     prim::kPrimBiasAddGrad,
+    prim::kPrimGelu,
   };
   return expand_ops;
 }
@@ -731,7 +732,7 @@ std::vector<PrimitivePtr> GetFusibleOpList() {
     prim::kPrimRealDiv, prim::kPrimMul,    prim::kPrimMinimum,    prim::kPrimMaximum,   prim::kPrimLog,
     prim::kPrimPow,     prim::kPrimSub,    prim::kPrimRsqrt,      prim::kPrimSqrt,      prim::kPrimCast,
     prim::kPrimAddN,    prim::kPrimEqual,  prim::kPrimReciprocal, prim::KPrimTransData, prim::kPrimSelect,
-    prim::kPrimGreater, prim::kPrimAssign, prim::kPrimReduceSum};
+    prim::kPrimGreater, prim::kPrimAssign, prim::kPrimReduceSum,  prim::kPrimTanh};
   return fusible_basic_ops;
 }