fix kunlun2 softmax unitest bug (#39274)

* fix kunlun2 softmax unitest bug
*test=kunlun

* minor

paddle/fluid/platform/device/xpu/xpu2_op_list.h

@@ -307,8 +307,8 @@ XPUOpMap& get_kl2_ops() {
       {"slice", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                               pOpKernelType(vartype::FP16, XPUPlace()),
                               pOpKernelType(vartype::INT32, XPUPlace())})},
-      {"softmax_grad",
-       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"softmax", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
+                                pOpKernelType(vartype::FP16, XPUPlace())})},
       {"softmax_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
@@ -317,9 +317,6 @@ XPUOpMap& get_kl2_ops() {
                      pOpKernelType(vartype::FP16, XPUPlace())})},
       {"softmax_with_cross_entropy",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
-      {"softmax", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
-      {"softmax", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
-                                pOpKernelType(vartype::FP16, XPUPlace())})},
       {"split", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                               pOpKernelType(vartype::INT32, XPUPlace())})},
       {"squeeze2_grad",

python/paddle/fluid/tests/unittests/xpu/test_softmax_op_xpu.py

@@ -18,6 +18,8 @@ import sys
 import unittest
 sys.path.append("..")
 from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
+
 paddle.enable_static()
 np.random.seed(10)
 
@@ -40,63 +42,49 @@ def ref_softmax(x, axis=None, dtype=None):
     return np.apply_along_axis(stable_softmax, axis, x_t)
 
 
-class TestXPUSoftmaxOp(XPUOpTest):
-    def setUp(self):
-        self.op_type = "softmax"
-        self.shape = [2, 3, 4, 5]
-        self.axis = -1
-        self.set_attrs()
-        self.init_type()
-
-        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
-        out = np.apply_along_axis(stable_softmax, self.axis, x)
-
-        self.inputs = {'X': x}
-        self.outputs = {'Out': out}
-        self.attrs = {'axis': self.axis, 'use_xpu': True}
-
-    def init_type(self):
-        self.dtype = np.float16
-
-    def set_attrs(self):
-        pass
-
-    def test_check_output(self):
-        self.check_output_with_place(paddle.XPUPlace(0), atol=1e-4)
-
-    def test_check_grad(self):
-        self.check_grad_with_place(paddle.XPUPlace(0), ['X'], 'Out')
-
-
-# class TestXPUSoftmaxAxis3(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.axis = 3
-
-# class TestXPUSoftmax2D(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.shape = [10, 12]
-
-# class TestXPUSoftmax3D(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.shape = [4, 5, 6]
-
-# class TestXPUSoftmaxAxis3FP16(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.axis = 3
-#     def init_type(self):
-#         self.dtype = np.float16
-
-# class TestXPUSoftmax2DFP16(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.shape = [10, 12]
-#     def init_type(self):
-#         self.dtype = np.float16
-
-# class TestXPUSoftmax3DFP16(TestXPUSoftmaxOp):
-#     def set_attrs(self):
-#         self.shape = [4, 5, 6]
-#     def init_type(self):
-#         self.dtype = np.float16
+class XPUTestSoftmaxOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'softmax'
+        self.use_dynamic_create_class = True
+
+    def dynamic_create_class(self):
+        base_class = self.TestSoftmaxOp
+        classes = []
+        shapes = [[2, 3, 4, 5], [7, 1], [63, 18], [2, 38512], [3, 4095]]
+        axis = [-1, 0, 1]
+        for shape in shapes:
+            for axi in axis:
+                class_name = 'XPUTestSoftmax_' + \
+                       str(shape) + "_" + str(axi)
+                attr_dict = {'shape': shape, 'axis': axi}
+                classes.append([class_name, attr_dict])
+        return base_class, classes
+
+    class TestSoftmaxOp(XPUOpTest):
+        def setUp(self):
+            self.op_type = "softmax"
+            if not hasattr(self, 'shape'):
+                self.shape = [1, 7]
+                self.axis = -1
+            self.dtype = np.float32
+
+            x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+            out = np.apply_along_axis(stable_softmax, self.axis, x)
+
+            self.inputs = {'X': x}
+            self.outputs = {'Out': out}
+            self.attrs = {'axis': self.axis, 'use_xpu': True}
+
+        def test_check_output(self):
+            self.check_output_with_place(paddle.XPUPlace(0), atol=1e-4)
+
+        def test_check_grad(self):
+            self.check_grad_with_place(paddle.XPUPlace(0), ['X'], 'Out')
+
+
+support_types = get_xpu_op_support_types('softmax')
+for stype in support_types:
+    create_test_class(globals(), XPUTestSoftmaxOp, stype)
 
 if __name__ == "__main__":
     unittest.main()

tools/check_file_diff_approvals.sh

@@ -336,8 +336,8 @@ if [ "${NEW_OP_TEST_ADDED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then
     CHECK_WHOLE=$CHECK_OUTPUT$CHECK_OUTPUT_WITH_PLACE$CHECK_GRAD$CHECK_GRAD_CHECK
     if [ "${CHECK_WHOLE}" != "" ] ; then
         CHECK_OP=${CHECK_WHOLE//+/'\n+'}       
-        echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), fuyinno4 (Recommend for kunlun), zhiqiu or qili93 (Recommend for NPU) , luotao1, lanxianghit or phlrain) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n"
-        check_approval 1 6836917 47554610 12538138 43953930 35824027 6888866 16605440
+        echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), fuyinno4, QingshuChen(Recommend for kunlun), zhiqiu or qili93 (Recommend for NPU) , luotao1, lanxianghit or phlrain) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n"
+        check_approval 1 6836917 47554610 12538138 43953930 35824027 6888866 16605440 2002279
     fi
 fi