def testBatchMatMulV3OutputType(self):
# TODO(shivaniagrawal): uint8 is not supported for mixed matmul type in XLA.
for (a_dtype, b_dtype) in [(np.int8, np.int8), (np.uint8, np.uint8)]:
a = np.array([[1, 2], [3, 4]], dtype=a_dtype)
b = np.array([[1, 2], [3, 4]], dtype=b_dtype)
c = math_ops.batch_mat_mul_v3(a, b, adj_y=True, Tout=np.int32)
self.assertAllEqual((2, 2), c.shape)
self.assertAllEqual([[5, 11], [11, 25]], c)
def testBatchMatMulV3MixedPrec(self): # TODO(shivaniagrawal): uint8 is not supported for mixed matmul type in XLA. np_bf16 = dtypes.bfloat16.as_numpy_dtype a = np.array([[1, 2], [3, 4]], dtype=np.int8) b = np.array([[1, 2], [3, 4]], dtype=np_bf16) c = math_ops.batch_mat_mul_v3(a, b, adj_y=True, Tout=np_bf16) self.assertAllEqual((2, 2), c.shape) self.assertAllEqual([[5, 11], [11, 25]], c)
def testBatchMatMulV3OutputType(self):
a = np.array([[1, 2], [3, 4]], dtype=np.int8)
b = np.array([[1, 2], [3, 4]], dtype=np.int8)
c = math_ops.batch_mat_mul_v3(a, b, adj_y=True, Tout=np.int32)
self.assertAllEqual((2, 2), c.shape)
self.assertAllEqual([[5, 11], [11, 25]], c)
