!13989 Fix float16 support problem and add raises for Batch Dot ops

From: @anrui-wang
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @wuxuejian

mindspore/ops/composite/math_ops.py

@@ -336,6 +336,17 @@ def _get_batch_size(x1_shape, x2_shape):
     return x1_shape[0], x2_shape[0]
 
 
+@constexpr
+def _typecheck_input_batch_dot(x1_type, x2_type):
+    """
+    Check input tensor types to be valid and confirm they are the same type for batch dot ops.
+    """
+    const_utils.check_type_valid(x1_type, [mstype.float32], 'x1')
+    const_utils.check_type_valid(x2_type, [mstype.float32], 'x2')
+    if x1_type != x2_type:
+        raise TypeError(f'Both Inputs must be the same Type. x1 is \'{x1_type}\' and x2 is \'{x2_type}\' ')
+
+
 @constexpr
 def _check_axes_for_batch_dot(x1_shape, x2_shape, axes):
     """
@@ -419,15 +430,29 @@ def batch_dot(x1, x2, axes=None):
     Computation of batch dot product between samples in two tensors containing batch dims.
 
     Inputs:
-        - **x1** (Tensor) - First tensor in Batch Dot op with datatype float16 or float32
-        - **x2** (Tensor) - Second tensor in Batch Dot op with datatype float16 or float32. x2's datatype should
+        - **x1** (Tensor) - First tensor in Batch Dot op with datatype float32
+        - **x2** (Tensor) - Second tensor in Batch Dot op with datatype float32. x2's datatype should
           be same as x1's.
         - **axes** (Union[int, tuple(int), list(int)]) - Single value or tuple/list of length 2 with dimensions
           specified for `a` and `b` each. If single value `N` passed, automatically picks up last N dims from
           `a` input shape and last N dims from `b` input shape in order as axes for each respectively.
 
     Outputs:
-        Tensor, batch dot product of x1 and x2.
+        Tensor, batch dot product of x1 and x2. The Shape of output for input shapes (batch, d1, axes, d2) and
+          (batch, d3, axes, d4) is (batch, d1, d2, d3, d4)
+
+    .. math::
+        output = x1[batch, :] * x2[batch, :]
+
+    Raises:
+        TypeError: If shapes of x1 and x2 are not the same.
+        ValueError: If rank of x1 or x2 less than 2.
+        ValueError: If batch dim used in axes.
+        ValueError: If dtype of x1 or x2 is not float32.
+        ValueError: If len(axes) less than 2.
+        ValueError: If axes is not one of those: None, int, (int, int).
+        ValueError: If axes value is too high for dimensions of input arrays.
+        ValueError: If batch size of x1 and x2 are not the same.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -458,7 +483,7 @@ def batch_dot(x1, x2, axes=None):
 
     x1_batch_size, x2_batch_size = _get_batch_size(x1_shape, x2_shape)
 
-    _typecheck_input(x1_type, x2_type)
+    _typecheck_input_batch_dot(x1_type, x2_type)
     _check_batch_size(x1_batch_size, x2_batch_size)
     axes = _check_axes_for_batch_dot(x1_shape, x2_shape, axes)
 

tests/st/ops/cpu/test_batchdot_op.py

@@ -211,17 +211,3 @@ def test_batch_dot_fp32():
     ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
     tf_result = _reference_batch_dot(x1, x2, axes)
     assert np.allclose(ms_result_np, tf_result)
-
-    # case 10
-    shape_x1 = (4, 3, 2, 1, 7, 5)
-    shape_x2 = (4, 5, 7, 1)
-    axes = -2
-    x1 = np.ones(shape=shape_x1).astype(np.float16)
-    x2 = np.ones(shape=shape_x2).astype(np.float16)
-    x1_tensor = Tensor(x1, dtype=mindspore.float16)
-    x2_tensor = Tensor(x2, dtype=mindspore.float16)
-
-    network = NetBatchDot(axes)
-    ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
-    tf_result = _reference_batch_dot(x1, x2, axes)
-    assert np.allclose(ms_result_np, tf_result)