Enable tf.linalg.matrix_solve tests in eager mode.
PiperOrigin-RevId: 311829192 Change-Id: I8d8c0fb2e28c6dd497a99724d4e2bcd78f2d2ed6
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A. Unique TensorFlower
TensorFlower Gardener
parent
a3746cc77a
commit
c26ac449e0
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@ -21,14 +21,16 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.python.client import session
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import errors_impl
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import linalg_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import stateless_random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import benchmark
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from tensorflow.python.platform import test
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@ -56,19 +58,19 @@ class MatrixSolveOpTest(test.TestCase):
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a_np = np.tile(a_np, batch_dims + [1, 1])
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b = np.tile(b, batch_dims + [1, 1])
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np_ans = np.linalg.solve(a_np, b)
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for use_placeholder in False, True:
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with self.cached_session(use_gpu=True) as sess:
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if use_placeholder:
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a_ph = array_ops.placeholder(dtypes.as_dtype(np_type))
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b_ph = array_ops.placeholder(dtypes.as_dtype(np_type))
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tf_ans = linalg_ops.matrix_solve(a_ph, b_ph, adjoint=adjoint)
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for use_placeholder in set((False, not context.executing_eagerly())):
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if use_placeholder:
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a_ph = array_ops.placeholder(dtypes.as_dtype(np_type))
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b_ph = array_ops.placeholder(dtypes.as_dtype(np_type))
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tf_ans = linalg_ops.matrix_solve(a_ph, b_ph, adjoint=adjoint)
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with self.cached_session(use_gpu=True) as sess:
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out = sess.run(tf_ans, {a_ph: a, b_ph: b})
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else:
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tf_ans = linalg_ops.matrix_solve(a, b, adjoint=adjoint)
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out = self.evaluate(tf_ans)
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self.assertEqual(tf_ans.get_shape(), out.shape)
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self.assertEqual(np_ans.shape, out.shape)
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self.assertAllClose(np_ans, out, atol=tol, rtol=tol)
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else:
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tf_ans = linalg_ops.matrix_solve(a, b, adjoint=adjoint)
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out = self.evaluate(tf_ans)
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self.assertEqual(tf_ans.get_shape(), out.shape)
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self.assertEqual(np_ans.shape, out.shape)
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self.assertAllClose(np_ans, out, atol=tol, rtol=tol)
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def _generateMatrix(self, m, n):
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matrix = (np.random.normal(-5, 5,
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@ -77,7 +79,7 @@ class MatrixSolveOpTest(test.TestCase):
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[m, n]))
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return matrix
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@test_util.run_deprecated_v1
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testSolve(self):
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for n in 1, 2, 4, 9:
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matrix = self._generateMatrix(n, n)
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@ -85,7 +87,7 @@ class MatrixSolveOpTest(test.TestCase):
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rhs = self._generateMatrix(n, nrhs)
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self._verifySolve(matrix, rhs)
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@test_util.run_deprecated_v1
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testSolveBatch(self):
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for n in 2, 5:
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matrix = self._generateMatrix(n, n)
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@ -94,48 +96,50 @@ class MatrixSolveOpTest(test.TestCase):
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for batch_dims in [[2], [2, 2], [7, 4]]:
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self._verifySolve(matrix, rhs, batch_dims=batch_dims)
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@test_util.run_deprecated_v1
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testNonSquareMatrix(self):
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# When the solve of a non-square matrix is attempted we should return
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# an error
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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matrix = constant_op.constant([[1., 2., 3.], [3., 4., 5.]])
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linalg_ops.matrix_solve(matrix, matrix)
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with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
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matrix = constant_op.constant([[1., 2., 3.], [3., 4., 5.]])
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self.evaluate(linalg_ops.matrix_solve(matrix, matrix))
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@test_util.run_deprecated_v1
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testWrongDimensions(self):
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# The matrix and right-hand sides should have the same number of rows.
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with self.session(use_gpu=True):
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matrix = constant_op.constant([[1., 0.], [0., 1.]])
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rhs = constant_op.constant([[1., 0.]])
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with self.assertRaises(ValueError):
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linalg_ops.matrix_solve(matrix, rhs)
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matrix = constant_op.constant([[1., 0.], [0., 1.]])
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rhs = constant_op.constant([[1., 0.]])
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with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
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self.evaluate(linalg_ops.matrix_solve(matrix, rhs))
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def testNotInvertible(self):
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# The input should be invertible.
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with self.session(use_gpu=True):
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with self.assertRaisesOpError("Input matrix is not invertible."):
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# All rows of the matrix below add to zero
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matrix = constant_op.constant([[1., 0., -1.], [-1., 1., 0.],
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[0., -1., 1.]])
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linalg_ops.matrix_solve(matrix, matrix).eval()
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with self.assertRaisesOpError("Input matrix is not invertible."):
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# All rows of the matrix below add to zero
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matrix = constant_op.constant([[1., 0., -1.], [-1., 1., 0.],
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[0., -1., 1.]])
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self.evaluate(linalg_ops.matrix_solve(matrix, matrix))
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@test_util.run_deprecated_v1
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testConcurrent(self):
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with self.session(use_gpu=True) as sess:
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all_ops = []
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for adjoint_ in False, True:
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lhs1 = random_ops.random_normal([3, 3], seed=42)
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lhs2 = random_ops.random_normal([3, 3], seed=42)
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rhs1 = random_ops.random_normal([3, 3], seed=42)
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rhs2 = random_ops.random_normal([3, 3], seed=42)
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s1 = linalg_ops.matrix_solve(lhs1, rhs1, adjoint=adjoint_)
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s2 = linalg_ops.matrix_solve(lhs2, rhs2, adjoint=adjoint_)
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all_ops += [s1, s2]
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val = self.evaluate(all_ops)
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self.assertAllEqual(val[0], val[1])
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self.assertAllEqual(val[2], val[3])
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seed = [42, 24]
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matrix_shape = [3, 3]
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all_ops = []
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for adjoint_ in False, True:
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lhs1 = stateless_random_ops.stateless_random_normal(
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matrix_shape, seed=seed)
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lhs2 = stateless_random_ops.stateless_random_normal(
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matrix_shape, seed=seed)
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rhs1 = stateless_random_ops.stateless_random_normal(
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matrix_shape, seed=seed)
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rhs2 = stateless_random_ops.stateless_random_normal(
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matrix_shape, seed=seed)
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s1 = linalg_ops.matrix_solve(lhs1, rhs1, adjoint=adjoint_)
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s2 = linalg_ops.matrix_solve(lhs2, rhs2, adjoint=adjoint_)
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all_ops += [s1, s2]
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val = self.evaluate(all_ops)
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for i in range(0, len(all_ops), 2):
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self.assertAllEqual(val[i], val[i + 1])
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class MatrixSolveBenchmark(test.Benchmark):
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