[PHI]Add yaml and unittest for bmm op

paddle/phi/api/yaml/legacy_api.yaml

@@ -326,6 +326,16 @@
   kernel :
     func : bitwise_xor
 
+# bmm
+- api : bmm
+  args : (Tensor x, Tensor y)
+  output : Tensor
+  infer_meta :
+    func : BmmInferMeta
+  kernel :
+    func : bmm
+  backward : bmm_grad
+
 # box_coder
 - api : box_coder
   args : (Tensor prior_box, Tensor prior_box_var, Tensor target_box, str code_type, bool box_normalized, int axis, float[] variance)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -260,6 +260,15 @@
   kernel :
     func : bilinear_tensor_product_grad
 
+- backward_api : bmm_grad
+  forward : bmm (Tensor x, Tensor y) -> Tensor(out)
+  args : (Tensor x, Tensor y, Tensor out_grad)
+  output : Tensor(x_grad), Tensor(y_grad)
+  infer_meta :
+    func : BmmGradInferMeta
+  kernel :
+    func : bmm_grad
+
 - backward_api : brelu_grad
   forward : brelu (Tensor x, float t_min, float t_max) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, float t_min, float t_max)

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

@@ -27,17 +27,18 @@ class TestBmmOp(OpTest):
 
     def setUp(self):
         self.op_type = "bmm"
+        self.python_api = paddle.tensor.bmm
         X = np.random.random((10, 3, 4)).astype("float64")
         Y = np.random.random((10, 4, 5)).astype("float64")
         self.inputs = {'X': X, 'Y': Y}
         Out = np.matmul(X, Y)
         self.outputs = {'Out': Out}
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output(check_eager=True)
 
     def test_checkout_grad(self):
-        self.check_grad(['X', 'Y'], 'Out')
+        self.check_grad(['X', 'Y'], 'Out', check_eager=True)
 
 
 class API_TestBmm(unittest.TestCase):

python/paddle/tensor/linalg.py

@@ -1521,6 +1521,9 @@ def bmm(x, y, name=None):
             "x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}"
             .format(x_shape, y_shape))
 
+    if in_dygraph_mode():
+        return _C_ops.final_state_bmm(x, y)
+
     if paddle.in_dynamic_mode():
         return _C_ops.bmm(x, y)