Add dist norm yamls (#41424) (#41615)

* add dist erfinv gumbel softmax

* fix test gumbel softmax op bug

* try to fix gumbel softmax error

* add label smooth backlist

python/paddle/fluid/tests/unittests/test_dist_op.py

@@ -37,6 +37,7 @@ def dist(x, y, p):
 class TestDistOp(OpTest):
     def setUp(self):
         self.op_type = 'dist'
+        self.python_api = paddle.dist
         self.attrs = {}
         self.init_case()
         self.init_data_type()
@@ -106,10 +107,14 @@ class TestDistOp(OpTest):
         return x_grad, y_grad
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
-        self.check_grad(["X", "Y"], "Out", user_defined_grads=self.gradient)
+        self.check_grad(
+            ["X", "Y"],
+            "Out",
+            user_defined_grads=self.gradient,
+            check_eager=True)
 
 
 class TestDistOpCase1(TestDistOp):
@@ -174,4 +179,5 @@ class TestDistAPI(unittest.TestCase):
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/test_erfinv_op.py

@@ -28,6 +28,7 @@ np.random.seed(0)
 class TestErfinv(OpTest):
     def setUp(self):
         self.op_type = "erfinv"
+        self.python_api = paddle.erfinv
         self.init_dtype()
         self.shape = [11, 17]
         self.x = np.random.uniform(-1, 1, size=self.shape).astype(self.dtype)
@@ -42,7 +43,7 @@ class TestErfinv(OpTest):
         self.dtype = np.float64
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_check_grad(self):
         self.check_grad(

python/paddle/fluid/tests/unittests/test_expand_v2_op.py

@@ -27,6 +27,7 @@ class TestExpandV2OpRank1(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
         self.init_data()
+        self.python_api = paddle.expand
 
         self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
         self.attrs = {'shape': self.shape}

python/paddle/fluid/tests/unittests/test_gumbel_softmax_op.py

@@ -17,6 +17,7 @@ import paddle.fluid.core as core
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
+from paddle.fluid.framework import _test_eager_guard
 paddle.enable_static()
 
 
@@ -177,12 +178,17 @@ class TestGumbelSoftmaxAPI(unittest.TestCase):
         self.assertEqual(out_np.sum(), self.count_expected)
 
         # test dygrapg api
-        paddle.disable_static()
+        with paddle.fluid.dygraph.base.guard():
+            x = paddle.to_tensor(self.x)
+            y = paddle.nn.functional.gumbel_softmax(x, hard=True)
+            out_np = np.array(y)
+            self.assertEqual(out_np.sum(), self.count_expected)
+
+            with _test_eager_guard():
                 x = paddle.to_tensor(self.x)
                 y = paddle.nn.functional.gumbel_softmax(x, hard=True)
                 out_np = np.array(y)
                 self.assertEqual(out_np.sum(), self.count_expected)
-        paddle.enable_static()
 
 
 class TestGumbelSoftmaxOpError(unittest.TestCase):

python/paddle/nn/functional/activation.py

@@ -1522,6 +1522,9 @@ def gumbel_softmax(x, temperature=1.0, hard=False, axis=-1, name=None):
             # [0.00000000, 0.00000000, 0.00000000, 0.00001258, 0.99998736, 0.00000000]]
         
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_gumbel_softmax(x, temperature, hard, axis)
+
     if in_dynamic_mode():
         return _C_ops.gumbel_softmax(x, 'temperature', temperature, 'hard',
                                      hard, 'axis', axis)

python/paddle/tensor/linalg.py

@@ -551,6 +551,9 @@ def dist(x, y, p=2, name=None):
             out = paddle.dist(x, y, float("-inf"))
             print(out) # out = [0.]
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_dist(x, y, p)
+
     check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')
     check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')
     check_type(p, 'p', (float, int), 'dist')

python/paddle/tensor/math.py

@@ -3634,6 +3634,9 @@ def erfinv(x, name=None):
             # out: [0, 0.4769, -inf]
 
     """
+    if in_dygraph_mode():
+        return _C_ops.final_state_erfinv( x )
+
     check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'erfinv')
 
     if paddle.in_dynamic_mode():

python/paddle/utils/code_gen/api.yaml

@@ -772,7 +772,7 @@
     func : GumbelSoftmaxInferMeta
   kernel :
     func : gumbel_softmax
-  # backward : gumbel_softmax_grad
+  backward : gumbel_softmax_grad
 
 # hard_shrink
 - api : hard_shrink

python/paddle/utils/code_gen/backward.yaml

-# - backward_api : gumbel_softmax_grad
-#   forward : gumbel_softmax (Tensor x, float temperature, bool hard, int axis) -> Tensor(out)
-#   args : (Tensor out, Tensor out_grad, int axis)
-#   output : Tensor(x_grad)
-#   infer_meta :
-#     func : GumbelSoftmaxGradInferMeta
-#     param : [out, out_grad, axis]
-#   kernel :
-#     func : gumbel_softmax_grad
-
 - backward_api : abs_grad
   forward : abs (Tensor x) -> Tensor(out)
   args : (Tensor x, Tensor out_grad)
@@ -517,6 +507,27 @@
   kernel :
     func : gelu_grad
 
+- backward_api : graph_send_recv_grad
+  forward : graph_send_recv (Tensor x, Tensor src_index, Tensor dst_index, str pool_type = "SUM", int64_t out_size = 0) -> Tensor(out), Tensor(dst_count)
+  args : (Tensor x, Tensor src_index, Tensor dst_index, Tensor out, Tensor dst_count, Tensor out_grad, str pool_type = "SUM")
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GeneralUnaryGradInferMeta
+    param : [x]
+  kernel :
+    func : graph_send_recv_grad
+  optional: out, dst_count
+
+- backward_api : gumbel_softmax_grad
+  forward : gumbel_softmax (Tensor x, float temperature, bool hard, int axis) -> Tensor(out)
+  args : (Tensor out, Tensor out_grad, int axis)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GumbelSoftmaxGradInferMeta
+    param : [out, out_grad, axis]
+  kernel :
+    func : gumbel_softmax_grad
+
 - backward_api : hard_shrink_grad
   forward : hard_shrink (Tensor x, float threshold) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, float threshold)