message: Enable gradient tests for tf.linalg.qr in eager mode.
diff: @@ -28,7 +28,7 @@ from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  -from tensorflow.python.ops import gradient_checker
  +from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateless_random_ops
  @@ -175,13 +175,16 @@ class QrGradOpTest(test.TestCase):
  def _GetQrGradOpTest(dtype_, shape_, full_matrices_):
  - @test_util.run_v1_only("b/120545219")
  - def Test(self):
  - np.random.seed(42)
  + def RandomInput():
  a = np.random.uniform(low=-1.0, high=1.0, size=shape_).astype(dtype_)
  if dtype_ in [np.complex64, np.complex128]:
  a += 1j * np.random.uniform(
  low=-1.0, high=1.0, size=shape_).astype(dtype_)
  + return a
  +
  + @test_util.run_in_graph_and_eager_modes(use_gpu=True)
  + def Test(self):
  + np.random.seed(42)
  # Optimal stepsize for central difference is O(epsilon^{1/3}).
  epsilon = np.finfo(dtype_).eps
  delta = 0.1 * epsilon**(1.0 / 3.0)
  @@ -189,22 +192,15 @@ def _GetQrGradOpTest(dtype_, shape_, full_matrices_):
  tol = 3e-2
  else:
  tol = 1e-6
  - with self.session(use_gpu=True):
  - tf_a = constant_op.constant(a)
  - tf_b = linalg_ops.qr(tf_a, full_matrices=full_matrices_)
  - for b in tf_b:
  - x_init = np.random.uniform(
  - low=-1.0, high=1.0, size=shape_).astype(dtype_)
  - if dtype_ in [np.complex64, np.complex128]:
  - x_init += 1j * np.random.uniform(
  - low=-1.0, high=1.0, size=shape_).astype(dtype_)
  - theoretical, numerical = gradient_checker.compute_gradient(
  - tf_a,
  - tf_a.get_shape().as_list(),
  - b,
  - b.get_shape().as_list(),
  - x_init_value=x_init,
  - delta=delta)
  + # TODO(b/157171666): Sadly we have to double the computation because
  + # gradient_checker_v2.compute_gradient expects a list of functions.
  + funcs = [
  + lambda a: linalg_ops.qr(a, full_matrices=full_matrices_)[0],
  + lambda a: linalg_ops.qr(a, full_matrices=full_matrices_)[1]
  + ]
  + for f in funcs:
  + theoretical, numerical = gradient_checker_v2.compute_gradient(
  + f, [RandomInput()], delta=delta)
  self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
  return Test
  