# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for XLA JIT compiler.""" import platform import unittest import numpy as np import six from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops_stack # pylint: disable=g-direct-tensorflow-import from tensorflow.python.ops import bitwise_ops from tensorflow.python.ops import gen_functional_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.platform import googletest def nhwc_to_format(x, data_format): """Converts a numpy array from NHWC format to `data_format`.""" rank = len(x.shape) if data_format == "NCHW": return np.transpose(x, [0, rank - 1] + list(range(1, rank - 1))) elif data_format == "NHWC": return x else: raise ValueError("Unknown format {}".format(data_format)) class UnaryOpsTest(xla_test.XLATestCase): """Test cases for unary operators.""" def _assertOpOutputMatchesExpected(self, op, inp, expected, equality_test=None, rtol=1e-3, atol=1e-5): """Verifies that 'op' produces 'expected' when fed input 'inp' . Args: op: operator to test inp: numpy input array to use as input to 'op'. expected: numpy array representing the expected output of 'op'. equality_test: either None, or a function that tests two numpy arrays for equality. If None, self.assertAllClose is used. rtol: relative tolerance for equality test. atol: absolute tolerance for equality test. """ with self.session() as session: with self.test_scope(): pinp = array_ops.placeholder( dtypes.as_dtype(inp.dtype), inp.shape, name="a") output = op(pinp) result = session.run(output, {pinp: inp}) if equality_test is None: self.assertEqual(output.dtype, expected.dtype) self.assertAllCloseAccordingToType( expected, result, rtol=rtol, atol=atol, bfloat16_rtol=0.03) else: equality_test(result, expected, rtol=rtol, atol=atol) def ListsAreClose(self, result, expected, rtol, atol): """Tests closeness of two lists of floats.""" self.assertEqual(len(result), len(expected)) for i in range(len(result)): self.assertAllClose(result[i], expected[i], rtol, atol) def AssertCloseAndSorted(self, result, expected, rtol, atol): """Tests that result and expected are both close and sorted.""" self.assertAllClose(result, expected, rtol, atol) self.assertAllEqual(np.sort(result), result) def AssertAllEqual(self, result, expected, rtol, atol): """Tests that result and expected are exactly equal.""" self.assertAllEqual(result, expected) def testAllTypeOps(self): for dtype in self.numeric_types - {np.int8, np.uint8}: self._assertOpOutputMatchesExpected( array_ops.diag, np.array([1, 2, 3, 4], dtype=dtype), np.array( [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.diag_part, np.arange(36).reshape([2, 3, 2, 3]).astype(dtype), np.array([[0, 7, 14], [21, 28, 35]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.diag, np.array([[1, 2], [3, 4]], dtype=dtype), np.array( [[[[1, 0], [0, 0]], [[0, 2], [0, 0]]], [[[0, 0], [3, 0]], [[0, 0], [0, 4]]]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.identity, np.array([[-1, 1]], dtype=dtype), expected=np.array([[-1, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.prevent_gradient, np.array([[-1, 1]], dtype=dtype), expected=np.array([[-1, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.squeeze, np.array([[[[[]]]]], dtype=dtype), expected=np.array([], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.squeeze, np.array([[[1], [2]]], dtype=dtype), expected=np.array([1, 2], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.squeeze, np.array([[[1]], [[2]]], dtype=dtype), expected=np.array([1, 2], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.squeeze, np.array([[[1, 2], [3, 4]]], dtype=dtype), expected=np.array([[1, 2], [3, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.stop_gradient, np.array([[-1, 1]], dtype=dtype), expected=np.array([[-1, 1]], dtype=dtype)) def testLog(self): for dtype in self.float_types - {dtypes.bfloat16.as_numpy_dtype}: tol = 1e-4 if dtype == np.float32 else 1e-9 # pylint: disable=invalid-unary-operand-type x = np.linspace(-np.e, np.e, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.log, x, expected=np.log(x), atol=tol, rtol=tol) x = np.linspace(0., np.e * 1e-30, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.log, x, expected=np.log(x), atol=tol, rtol=tol) x = np.linspace(0., np.pi * 1e30, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.log, x, expected=np.log(x), atol=tol, rtol=tol) def testSin(self): for dtype in self.float_types - {dtypes.bfloat16.as_numpy_dtype}: tol = 1e-6 if dtype == np.float32 else 1e-12 x = np.linspace(-4 * np.e, 4 * np.e, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.sin, x, expected=np.sin(x), rtol=tol, atol=tol) x = np.linspace(0., np.e * 1e-30, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.sin, x, expected=np.sin(x), rtol=tol, atol=tol) if dtype == np.float64: x = np.linspace(0., np.e * 1e8, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.sin, x, expected=np.sin(x), rtol=tol, atol=1e-5) def testCos(self): for dtype in self.float_types - {dtypes.bfloat16.as_numpy_dtype}: tol = 1e-6 if dtype == np.float32 else 1e-12 x = np.linspace(-4 * np.e, 4 * np.e, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.cos, x, expected=np.cos(x), rtol=tol, atol=tol) x = np.linspace(0., np.e * 1e-30, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.cos, x, expected=np.cos(x), rtol=tol, atol=tol) if dtype == np.float64: x = np.linspace(0., np.e * 1e8, num=1000, dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.cos, x, expected=np.cos(x), rtol=tol, atol=1e-5) def testFloatOps(self): for dtype in self.float_types: x = np.arange(-0.90, 0.90, 0.25) self._assertOpOutputMatchesExpected( math_ops.acos, x.astype(dtype), expected=np.arccos(x).astype(dtype)) self._assertOpOutputMatchesExpected( math_ops.asin, x.astype(dtype), expected=np.arcsin(x).astype(dtype)) x = np.arange(-3, 3).reshape(1, 3, 2) self._assertOpOutputMatchesExpected( math_ops.atan, x.astype(dtype), expected=np.arctan(x).astype(dtype)) self._assertOpOutputMatchesExpected( math_ops.acosh, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array( [0, 1.3169579, 1.76274717, 2.06343707], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.asinh, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array( [0.88137359, 1.44363548, 1.81844646, 2.09471255], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.atanh, np.array([0.1, 0.2, 0.3, 0.4], dtype=dtype), expected=np.array( [0.10033535, 0.20273255, 0.3095196, 0.42364893], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.ceil, np.array([[-1.7, 1.2]], dtype=dtype), expected=np.array([[-1, 2]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.cosh, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array( [1.54308063, 3.76219569, 10.067662, 27.30823284], dtype=dtype)) # Disable float16 testing for now if dtype != np.float16: x = np.arange(-10, 10, 1).astype(dtype) with self.session() as session: erf_x = session.run(math_ops.erf(x)) erfc_x = session.run(math_ops.erfc(x)) self._assertOpOutputMatchesExpected(math_ops.erf, x, expected=erf_x) self._assertOpOutputMatchesExpected(math_ops.erfc, x, expected=erfc_x) self._assertOpOutputMatchesExpected( math_ops.exp, np.array([[-1, 1]], dtype=dtype), expected=np.array([[0.36787945, 2.7182817]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.expm1, np.array([[-1, 1]], dtype=dtype), expected=np.array([[-0.63212056, 1.71828183]], dtype=dtype), rtol=1e-5) self._assertOpOutputMatchesExpected( math_ops.floor, np.array([[-1.7, 1.2]], dtype=dtype), expected=np.array([[-2, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.is_finite, np.array([[-np.inf, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[0, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=np.bool_)) # Tests for tf.nn ops. self._assertOpOutputMatchesExpected( nn_ops.l2_loss, np.array([[[]]], dtype=dtype), expected=dtype(0)) self._assertOpOutputMatchesExpected(nn_ops.l2_loss, dtype(4), dtype(8)) self._assertOpOutputMatchesExpected( nn_ops.l2_loss, np.array([[-2, 4]], dtype=dtype), expected=dtype(10)) self._assertOpOutputMatchesExpected( math_ops.reciprocal, np.array([[1, 2]], dtype=dtype), expected=np.array([[1, 0.5]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.log, np.array([[1, 2]], dtype=dtype), expected=np.array([[0, 0.69314718]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sin, np.array([[1, 2]], dtype=dtype), expected=np.array([[0.841478, 0.909302]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.cos, np.array([[1, 2]], dtype=dtype), expected=np.array([[0.540297, -0.41614]], dtype=dtype)) # Confirm that log1p will remain precise across a range of small values. self._assertOpOutputMatchesExpected( math_ops.log1p, np.array([[1e-14, 1e-15, 0.6, 2] + [x * 1e-5 for x in range(1, 20)]], dtype=dtype), expected=np.log1p( np.array( [[1e-14, 1e-15, 0.6, 2] + [x * 1e-5 for x in range(1, 20)]], dtype=dtype)).astype(dtype), rtol=1e-15 if dtype == np.float64 else 1e-4, atol=1e-15 if dtype == np.float64 else 1e-4) self._assertOpOutputMatchesExpected( math_ops.rint, np.array( [[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], [0.5, 1.5, 2.5, 3.5]], dtype=dtype), expected=np.array( [[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.round, np.array( [[-1.7, 1.2, 4.0, 0.0], [-3.5, -2.5, -1.5, -0.5], [0.5, 1.5, 2.5, 3.5]], dtype=dtype), expected=np.array( [[-2, 1, 4, 0], [-4, -2, -2, 0], [0, 2, 2, 4]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.rsqrt, np.array([[4, 16]], dtype=dtype), expected=np.array([[0.5, 0.25]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sigmoid, np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[0.7310586, 0.7310586, 0.7310586, 0.7310586], [0.7310586, 0.880797, 0.95257413, 0.98201376]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sigmoid, np.array([-300, -150, 0, 150, 300], dtype=dtype), expected=np.array([0, 0, 0.5, 1, 1], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sinh, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array( [1.17520119, 3.62686041, 10.01787493, 27.2899172], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sqrt, np.array([[4, 9]], dtype=dtype), expected=np.array([[2, 3]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.tan, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array( [1.55740772, -2.18503986, -0.14254654, 1.15782128], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.tanh, np.array([[1, 2, 3, 4], [np.inf, -np.inf, np.nan, 20], [19, -19, 22, -22]], dtype=dtype), expected=np.array( [[0.76159418, 0.96402758, 0.99505478, 0.99932933], [1.0, -1.0, np.nan, 1.0], [1.0, -1.0, 1.0, -1.0]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.log_softmax, np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[-1.3862944, -1.3862944, -1.3862944, -1.3862944], [-3.4401896, -2.4401896, -1.4401897, -0.44018969]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.elu, np.array([[-1, 0, 1, -1e-6]], dtype=dtype), expected=np.array([[-0.63212056, 0, 1, -9.999995e-07]], dtype=dtype), rtol=1e-5, atol=1e-6) self._assertOpOutputMatchesExpected( nn_ops.selu, np.array([[-1, 0, 1, -1e-5]], dtype=dtype), expected=np.array( [[-1.11133074, 0., 1.05070099, -1.758090550379974e-05]], dtype=dtype), rtol=1e-5, atol=1e-6) self._assertOpOutputMatchesExpected( nn_ops.relu, np.array([[-1, 1]], dtype=dtype), expected=np.array([[0, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.relu6, np.array([[-0.05, 6.05, 5]], dtype=dtype), expected=np.array([[0, 6, 5]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.leaky_relu, np.array([[-2, -1, 0, 1, 2]], dtype=dtype), expected=np.array([[-0.4, -0.2, 0.0, 1.0, 2.0]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.softmax, np.array([1, 2, 3, 4], dtype=dtype), expected=np.array([0.032058604, 0.087144323, 0.23688284, 0.64391428], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.softmax, np.array([[1, 1, 1, 1], [1, 2, 3, 4]], dtype=dtype), expected=np.array( [[0.25, 0.25, 0.25, 0.25], [0.032058604, 0.087144323, 0.23688284, 0.64391428]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.softmax, np.array([[[1, 1], [1, 1]], [[1, 2], [3, 4]]], dtype=dtype), expected=np.array( [[[0.5, 0.5], [0.5, 0.5]], [[0.26894142, 0.73105858], [0.26894142, 0.73105858]]], dtype=dtype)) self._assertOpOutputMatchesExpected( nn_ops.softsign, np.array([[-2, -1, 0, 1, 2]], dtype=dtype), expected=np.array( [[-0.66666669, -0.5, 0, 0.5, 0.66666669]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.sign, np.array([[-2.0, -1.0, -0.0, +0.0, 1.0, 2.0, float("nan")]], dtype=dtype), expected=np.array([[-1.0, -1.0, -0.0, +0.0, 1.0, 1.0, float("nan")]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.is_finite, np.array([[42, float("inf"), -123], [float("nan"), 0, -0.0]], dtype=dtype), expected=np.array([[True, False, True], [False, True, True]], dtype=np.bool_)) self._assertOpOutputMatchesExpected( math_ops.lgamma, np.array(0.5, dtype=dtype), expected=np.array(np.log(np.pi) / 2, dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.lgamma, np.array( [[1, 2, 3], [4, 5, 6], [1 / 2, 3 / 2, 5 / 2], [-3 / 2, -7 / 2, -11 / 2]], dtype=dtype), expected=np.array( [ [0, 0, np.log(2.0)], [np.log(6.0), np.log(24.0), np.log(120)], [ np.log(np.pi) / 2, np.log(np.pi) / 2 - np.log(2), np.log(np.pi) / 2 - np.log(4) + np.log(3) ], [ np.log(np.pi) / 2 - np.log(3) + np.log(4), np.log(np.pi) / 2 - np.log(105) + np.log(16), np.log(np.pi) / 2 - np.log(10395) + np.log(64), ], ], dtype=dtype)) # The actual result is complex. Take the real part. self._assertOpOutputMatchesExpected( math_ops.lgamma, np.array([-1 / 2, -5 / 2, -9 / 2], dtype=dtype), expected=np.array( [ np.log(np.pi) / 2 + np.log(2), np.log(np.pi) / 2 - np.log(15) + np.log(8), np.log(np.pi) / 2 - np.log(945) + np.log(32), ], dtype=dtype), atol=1e-4) self._assertOpOutputMatchesExpected( math_ops.digamma, np.array( [[1.0, 0.5, 1 / 3.0], [0.25, 1 / 6.0, 0.125], [2.0, 3.0, 4.0], [6.0, 8.0, 9.0]], dtype=dtype), expected=np.array( [ [ -np.euler_gamma, -2 * np.log(2) - np.euler_gamma, -np.pi / 2 / np.sqrt(3) - 3 * np.log(3) / 2 - np.euler_gamma ], [ -np.pi / 2 - 3 * np.log(2) - np.euler_gamma, -np.pi * np.sqrt(3) / 2 - 2 * np.log(2) - 3 * np.log(3) / 2 - np.euler_gamma, -np.pi / 2 - 4 * np.log(2) - (np.pi + np.log(2 + np.sqrt(2)) - np.log(2 - np.sqrt(2))) / np.sqrt(2) - np.euler_gamma ], [ 1 - np.euler_gamma, 1.5 - np.euler_gamma, 11 / 6.0 - np.euler_gamma ], [ 137 / 60.0 - np.euler_gamma, 363 / 140.0 - np.euler_gamma, 761 / 280.0 - np.euler_gamma ], ], dtype=dtype)) def testSigmoidNumericalStability(self): for dtype in self.float_types: if dtype != np.float16: self._assertOpOutputMatchesExpected( lambda x: math_ops.sigmoid(x) / math_ops.log1p(math_ops.exp(x)), np.array([-40, 40], dtype=dtype), expected=np.array([1.0, 0.025], dtype=dtype)) def testQuantizeAndDequantize(self): for dtype in self.float_types: def quantize_and_dequantize_v2(x): return array_ops.quantize_and_dequantize( x, -127, 127, signed_input=True, num_bits=8) def quantize_and_dequantize_v3(x): return array_ops.quantize_and_dequantize_v3( x, -127, 127, num_bits=8, signed_input=True, range_given=False) def quantize_and_dequantize_v4(x): return array_ops.quantize_and_dequantize_v2( x, -127, 127, signed_input=True, num_bits=8) test_fns = (quantize_and_dequantize_v2, quantize_and_dequantize_v3, quantize_and_dequantize_v4) for test_fn in test_fns: self._assertOpOutputMatchesExpected( test_fn, np.array([-1, -0.5, 0, 0.3], dtype=dtype), expected=np.array([-1., -0.5, 0., 0.296875], dtype=dtype)) def quantize_and_dequantize_v2_round_half_up(x): return array_ops.quantize_and_dequantize( x, -1.0, 1.0, signed_input=True, num_bits=8, range_given=True, round_mode="HALF_UP") self._assertOpOutputMatchesExpected( quantize_and_dequantize_v2_round_half_up, np.array([-0.8, -0.4, 0, 0.3, 0.8, -2, 33], dtype=dtype), expected=np.array([ -102.0 / 127, -51.0 / 127, 0, 38.0 / 127, 102.0 / 127, -128.0 / 127, 1, ], dtype=dtype)) def quantize_and_dequantize_v2_round_half_to_even(x): return array_ops.quantize_and_dequantize( x, -1.0, 1.0, signed_input=True, num_bits=8, range_given=True, round_mode="HALF_TO_EVEN") self._assertOpOutputMatchesExpected( quantize_and_dequantize_v2_round_half_to_even, np.array([-0.8, -0.4, 0, 0.3, 0.8, -2, 33], dtype=dtype), expected=np.array([ -102.0 / 127, -51.0 / 127, 0, 38.0 / 127, 102.0 / 127, -128.0 / 127, 1, ], dtype=dtype)) def testComplexOps(self): for dtype in self.complex_types: self._assertOpOutputMatchesExpected( math_ops.acosh, np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype), expected=np.arccosh( np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.asinh, np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype), expected=np.arcsinh( np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.atanh, np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype), expected=np.arctanh( np.array([0.1, 0.2j, 0.3 - 0.1j, 0.4 + 0.5j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.cosh, np.array([1j, 2 - 3j, 3, 4 + 2j], dtype=dtype), expected=np.cosh(np.array([1j, 2 - 3j, 3, 4 + 2j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.sinh, np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype), expected=np.sinh(np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.exp, np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype), expected=np.exp(np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.expm1, np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype), expected=np.expm1(np.array([[-1 + 2j, 3j, 2 - 3j]], dtype=dtype)), rtol=1e-6, atol=1e-6) # For real part close to zero, or imaginary part close to a multiple of # pi. self._assertOpOutputMatchesExpected( math_ops.expm1, np.array( [[ 1e-11 + 1j, -1e-11 - 1j, 1.0 + 1e-11j, -1.0 - 1e-11j, 1e-13j + 1e-13j, ]], dtype=dtype, ), expected=np.expm1( np.array( [[ 1e-11 + 1j, -1e-11 - 1j, 1.0 + 1e-11j, -1.0 - 1e-11j, 1e-13j + 1e-13j, ]], dtype=dtype, ), dtype=dtype, ), rtol=1e-09, atol=1e-20, ) self._assertOpOutputMatchesExpected( math_ops.reciprocal, np.array([[1, 2j, 2 + 3j]], dtype=dtype), expected=1.0 / np.array([[1, 2j, 2 + 3j]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.log, np.array([[5j, 3 - 2j]], dtype=dtype), expected=np.log(np.array([[5j, 3 - 2j]], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.sin, np.array([[5j, 3 - 2j]], dtype=dtype), expected=np.sin(np.array([[5j, 3 - 2j]], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.cos, np.array([[5j, 3 - 2j]], dtype=dtype), expected=np.cos(np.array([[5j, 3 - 2j]], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.log1p, np.array([[1e-14, 1e-15j, 0.6 - 0.3j]], dtype=dtype), expected=np.log1p( np.array([[1e-14, 1e-15j, 0.6 - 0.3j]], dtype=dtype)), rtol=1e-4, atol=1e-6) val = np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype) self._assertOpOutputMatchesExpected( math_ops.rsqrt, val, expected=1 / np.sqrt(val)) self._assertOpOutputMatchesExpected( math_ops.sigmoid, val, expected=1 / (1 + np.exp(-val))) self._assertOpOutputMatchesExpected( math_ops.sqrt, val, expected=np.sqrt(val)) self._assertOpOutputMatchesExpected( math_ops.tanh, np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype), expected=np.tanh(np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.tan, np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype), expected=np.tan(np.array([1, 2j, 2 - 3j, 4 + 5j], dtype=dtype))) ctypes = {np.complex64: np.float32, np.complex128: np.float64} self._assertOpOutputMatchesExpected( math_ops.abs, np.array([[3 - 4j, -1j, np.inf]], dtype=dtype), expected=np.array([[5, 1, np.inf]], dtype=ctypes[dtype])) self._assertOpOutputMatchesExpected( math_ops.negative, np.array([[-1 + 2j, -3j]], dtype=dtype), expected=np.array([[1 - 2j, 3j]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.square, np.array([[-2 - 3j, 3 + 4j, 5j]], dtype=dtype), expected=np.array([[-2 - 3j, 3 + 4j, 5j]], dtype=dtype)**2) self._assertOpOutputMatchesExpected( array_ops.zeros_like, np.array([[4j, 3 - 2j], [2, -1j]], dtype=dtype), expected=np.array([[0, 0], [0, 0]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.ones_like, np.array([[-4j, 3 + 2j], [2, -1j]], dtype=dtype), expected=np.array([[1, 1], [1, 1]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.angle, np.array([1 + 3j, -4 + 7j, 2.7, -3j], dtype=dtype), expected=np.angle(np.array([1 + 3j, -4 + 7j, 2.7, -3j], dtype=dtype))) self._assertOpOutputMatchesExpected( math_ops.conj, np.array([1 + 3j, -4 + 7j, 2.7, -3j], dtype=dtype), expected=np.array([1 - 3j, -4 - 7j, 2.7, 3j], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.imag, np.array([1 + 3j, -4 + 7j, 2.7, -3j], dtype=dtype), expected=np.array([3, 7, 0, -3], dtype=ctypes[dtype])) self._assertOpOutputMatchesExpected( math_ops.real, np.array([1 + 3j, -4 + 7j, 2.7, -3j], dtype=dtype), expected=np.array([1, -4, 2.7, 0], dtype=ctypes[dtype])) def testIntOps(self): for dtype in self.int_types - self.unsigned_int_types: self._assertOpOutputMatchesExpected( bitwise_ops.invert, np.array([0, -1, 1, 16, 42], dtype=dtype), expected=np.array([-1, 0, -2, -17, -43], dtype=dtype)) # Test population_count for array inputs. raw_inputs = [ 0, 1, -1, 3, -3, 5, -5, 14, -14, 127, 128, 255, 256, 65535, 65536, 2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32, -2**63 + 1, 2**63 - 1 ] # Only choose inputs which fit in the int dtype. raw_inputs = list( filter(lambda x: np.iinfo(dtype).min <= x <= np.iinfo(dtype).max, raw_inputs)) inputs = np.array(raw_inputs, dtype=dtype) def count_bits(x): return sum(bin(z).count("1") for z in six.iterbytes(x.tobytes())) truth = [count_bits(x) for x in inputs] self._assertOpOutputMatchesExpected( bitwise_ops.population_count, inputs, expected=np.array(truth, dtype=np.uint8), equality_test=self.AssertAllEqual) # Test population_count for scalar inputs. for raw_inp in raw_inputs: inp = dtype(raw_inp) truth = count_bits(inp) self._assertOpOutputMatchesExpected( bitwise_ops.population_count, inp, expected=np.uint8(truth), equality_test=self.AssertAllEqual) def testNumericOps(self): for dtype in self.numeric_types - {np.int8, np.uint8}: self._assertOpOutputMatchesExpected( math_ops.abs, np.array([[2, -1]], dtype=dtype), expected=np.array([[2, 1]], dtype=np.real(dtype(0)).dtype)) self._assertOpOutputMatchesExpected( math_ops.negative, np.array([[-1, 1]], dtype=dtype), expected=np.array([[1, -1]], dtype=dtype)) self._assertOpOutputMatchesExpected( math_ops.square, np.array([[-2, 3]], dtype=dtype), expected=np.array([[4, 9]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.zeros_like, np.array([[4, 3], [2, 1]], dtype=dtype), expected=np.array([[0, 0], [0, 0]], dtype=dtype)) self._assertOpOutputMatchesExpected( array_ops.ones_like, np.array([[4, 3], [2, 1]], dtype=dtype), expected=np.array([[1, 1], [1, 1]], dtype=dtype)) # TODO(phawkins): these tests fail unless fastmath optimizations # are disabled. Use more robust IsInf/IsNaN detection and enable these # tests. @unittest.skip("test case fails in fast-math mode") def testIsInfAndIsNan(self): for dtype in self.float_types: self._assertOpOutputMatchesExpected( math_ops.is_inf, np.array([[-np.inf, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[1, 0, 0, 0, 0, 0, 0, 1, 0]], dtype=np.bool_)) self._assertOpOutputMatchesExpected( math_ops.is_nan, np.array([[-np.inf, -2, -1, 0, 0.5, 1, 2, np.inf, np.nan]], dtype=dtype), expected=np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=np.bool_)) self._assertOpOutputMatchesExpected( math_ops.sign, np.array([[np.nan]], dtype=dtype), expected=np.array([[0.0]], dtype=dtype)) def testLogicalOps(self): self._assertOpOutputMatchesExpected( math_ops.logical_not, np.array([[True, False], [False, True]], dtype=np.bool_), expected=np.array([[False, True], [True, False]], dtype=np.bool_)) def testBiasAddGrad(self): self._assertOpOutputMatchesExpected( gen_nn_ops.bias_add_grad, np.array([[1., 2.], [3., 4.]], dtype=np.float32), expected=np.array([4., 6.], dtype=np.float32)) self._assertOpOutputMatchesExpected( lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"), np.array( [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]], dtype=np.float32), expected=np.array([14., 22.], dtype=np.float32)) def testCast(self): types = { dtypes.bool, dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.int32, dtypes.int64, dtypes.uint32, dtypes.uint64 } for src_type in types: for dst_type in types: self._testCast(src_type, dst_type) def testCastFp8(self): if platform.system() == "Darwin": # TODO(b/271327511): Fix issue where casts to FP8 very rarely result in # NaN on Mac self.skipTest("Casts to FP8 sometimes result in NaN on Mac") fp8_types = { dtypes.float8_e5m2, dtypes.float8_e4m3fn, dtypes.float8_e4m3fnuz, dtypes.float8_e4m3b11fnuz, dtypes.float8_e5m2fnuz, } other_types = { dtypes.bool, dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.int32, dtypes.int64, dtypes.uint32, dtypes.uint64 } for fp8_type in fp8_types: for other_type in other_types | fp8_types: self._testCast(fp8_type, other_type) self._testCast(other_type, fp8_type) def _testCast(self, src_type, dst_type): with self.subTest(src_type=src_type, dst_type=dst_type): shapes = [[], [4], [2, 3], [2, 0, 4]] src_np_dtype = src_type.as_numpy_dtype dst_np_dtype = dst_type.as_numpy_dtype for shape in shapes: src = np.arange(np.prod(shape)).astype(src_np_dtype) if src_type in self.complex_tf_types: src += (np.arange(np.prod(shape)) * 2j).astype(src_np_dtype) src = src.reshape(shape) dst = src.astype(dst_np_dtype) self._assertOpOutputMatchesExpected( lambda x, dst_type=dst_type: math_ops.cast(x, dst_type), src, expected=dst) # Check special values. if src_type.is_integer: imin = np.iinfo(src_np_dtype).min imax = np.iinfo(src_np_dtype).max if src_type.is_unsigned: src = np.array([imin, imax, 0, 1], dtype=src_np_dtype) else: src = np.array([imin, imax, 0, 1, -1], dtype=src_np_dtype) elif src_type in self.float_tf_types: if dst_type.is_integer: imin = np.iinfo(dst_np_dtype).min imax = np.iinfo(dst_np_dtype).max // 2 src = np.array([imin, imax, 0, 1], dtype=src_np_dtype) elif dst_type in self.float_tf_types: fmin = np.finfo(dst_np_dtype).min fmax = np.finfo(dst_np_dtype).max tiny = np.finfo(dst_np_dtype).tiny eps = np.finfo(dst_np_dtype).eps src = np.array( [fmin, fmax, np.nan, eps, -eps, tiny, -tiny, np.inf, -np.inf], dtype=src_np_dtype, ) dst = src.astype(dst_np_dtype) self._assertOpOutputMatchesExpected( lambda x, dst_type=dst_type: math_ops.cast(x, dst_type), src, expected=dst, ) def testBitcast(self): self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.int32), np.array([1, 0x3F800000], np.int32), expected=np.array([1, 0x3F800000], np.int32), ) self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.float32), np.array([1, 0x3F800000], np.int32), expected=np.array([1e-45, 1.0], np.float32), ) self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.int32), np.array([1e-45, 1.0], np.float32), expected=np.array([1, 0x3F800000], np.int32), ) if np.int64 in self.numeric_types: self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.int64), np.array([1, 0x100000003F800000], np.uint64), expected=np.array([1, 0x100000003F800000], np.int64), ) self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.uint64), np.array([1, 0x100000003F800000], np.int64), expected=np.array([1, 0x100000003F800000], np.uint64), ) self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.float64), np.array( [0, 0x3FF0000000000000, 0xC3AF161421C8E000, 0x4032000000000007], np.uint64, ), expected=np.array( [0, 1.0, -1.12e+18, 18.000000000000024869], np.float64 ), atol=0, ) def testBitcastInt8ToFloat(self): self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.float32), np.array([[1, 0, 0, 0], [0xD0, 0x0F, 0x49, 0x40]]).astype(np.int8), expected=np.array([1e-45, 3.14159], np.float32), ) self._assertOpOutputMatchesExpected( lambda x: array_ops.bitcast(x, dtypes.np.int8), np.array([1e-45, 3.14159], np.float32), expected=np.array([[1, 0, 0, 0], [0xD0, 0x0F, 0x49, 0x40]]).astype( np.int8 ), ) def testInvertPermutation(self): for np_dtype in [np.int32, np.int64]: self._assertOpOutputMatchesExpected( array_ops.invert_permutation, np.array([1, 2, 0], np_dtype), expected=np.array([2, 0, 1], dtype=np_dtype), ) def testInvertPermutationTwiceIsNoop(self): def invert_twice(x): return array_ops.invert_permutation(array_ops.invert_permutation(x)) for np_dtype in [np.int32, np.int64]: self._assertOpOutputMatchesExpected( invert_twice, np.array([1, 2, 0], np_dtype), expected=np.array([1, 2, 0], dtype=np_dtype)) def testRank(self): rank_op = lambda x: array_ops.rank_internal(x, optimize=False) for dtype in self.numeric_types: self._assertOpOutputMatchesExpected( rank_op, dtype(7), expected=np.int32(0)) self._assertOpOutputMatchesExpected( rank_op, np.array([[], []], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( rank_op, np.array([0, 1], dtype=dtype), expected=np.int32(1)) self._assertOpOutputMatchesExpected( rank_op, np.array([[0, 1]], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( rank_op, np.array([[0], [1], [4]], dtype=dtype), expected=np.int32(2)) def testShape(self): shape_op = lambda x: array_ops.shape_internal(x, optimize=False) for dtype in self.numeric_types: self._assertOpOutputMatchesExpected( shape_op, dtype(7), expected=np.array([], dtype=np.int32)) self._assertOpOutputMatchesExpected( shape_op, np.array([[], []], dtype=dtype), expected=np.array([2, 0], dtype=np.int32)) self._assertOpOutputMatchesExpected( shape_op, np.array([0, 1], dtype=dtype), expected=np.array([2], dtype=np.int32)) self._assertOpOutputMatchesExpected( shape_op, np.array([[0, 1]], dtype=dtype), expected=np.array([1, 2], dtype=np.int32)) self._assertOpOutputMatchesExpected( shape_op, np.array([[0], [1], [4]], dtype=dtype), expected=np.array([3, 1], dtype=np.int32)) def testSize(self): size_op = lambda x: array_ops.size_internal(x, optimize=False) for dtype in self.numeric_types: self._assertOpOutputMatchesExpected( size_op, dtype(7), expected=np.int32(1)) self._assertOpOutputMatchesExpected( size_op, np.array([[], []], dtype=dtype), expected=np.int32(0)) self._assertOpOutputMatchesExpected( size_op, np.array([0, 1], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( size_op, np.array([[0, 1]], dtype=dtype), expected=np.int32(2)) self._assertOpOutputMatchesExpected( size_op, np.array([[0], [1], [4]], dtype=dtype), expected=np.int32(3)) def testSizeWithInt64OutType(self): def size_op(x): return array_ops.size_internal(x, optimize=False, out_type=np.int64) for dtype in self.numeric_types: self._assertOpOutputMatchesExpected( size_op, np.array([[0], [1], [4]], dtype=dtype), expected=np.int64(3)) def testUnpack(self): self._assertOpOutputMatchesExpected( array_ops_stack.unstack, np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=np.float32), expected=[ np.array([1., 2.], dtype=np.float32), np.array([3., 4.], dtype=np.float32), np.array([5., 6.], dtype=np.float32), ], equality_test=self.ListsAreClose) self._assertOpOutputMatchesExpected( lambda x: array_ops_stack.unstack(x, axis=1), np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=np.float32), expected=[ np.array([1., 3., 5.], dtype=np.float32), np.array([2., 4., 6.], dtype=np.float32), ], equality_test=self.ListsAreClose) def testDepthToSpace(self): def make_op(data_format): def op(x): return array_ops.depth_to_space( x, block_size=2, data_format=data_format) return op for dtype in self.numeric_types: for data_format in ["NCHW", "NHWC"]: self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array([[[[1, 2, 3, 4]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array([[[[1], [2]], [[3], [4]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array( [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array( [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array( [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array( [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9], [10], [13], [14]], [[11], [12], [15], [16]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op("NCHW_VECT_C"), np.arange(32, dtype=dtype).reshape((1, 8, 1, 1, 4)), expected=np.array([[[[[0, 1, 2, 3], [8, 9, 10, 11]], [[16, 17, 18, 19], [24, 25, 26, 27]]], [[[4, 5, 6, 7], [12, 13, 14, 15]], [[20, 21, 22, 23], [28, 29, 30, 31]]]]], dtype=dtype)) def testSpaceToDepth(self): def make_op(data_format): def op(x): return array_ops.space_to_depth( x, block_size=2, data_format=data_format) return op for dtype in self.numeric_types: for data_format in ["NCHW", "NHWC"]: self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array([[[[1], [2]], [[3], [4]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array([[[[1, 2, 3, 4]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array( [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array( [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op(data_format), nhwc_to_format( np.array( [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9], [10], [13], [14]], [[11], [12], [15], [16]]]], dtype=dtype), data_format), expected=nhwc_to_format( np.array( [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]], dtype=dtype), data_format)) self._assertOpOutputMatchesExpected( make_op("NCHW_VECT_C"), np.arange(32, dtype=dtype).reshape((1, 2, 2, 2, 4)), expected=np.array( [[[[[0, 1, 2, 3]]], [[[16, 17, 18, 19]]], [[[4, 5, 6, 7]]], [[[20, 21, 22, 23]]], [[[8, 9, 10, 11]]], [[[24, 25, 26, 27]]], [[[12, 13, 14, 15]]], [[[28, 29, 30, 31]]]]], dtype=dtype)) def _assertSoftplusMatchesExpected(self, features, dtype, equality_test=None, rtol=1e-6, atol=9.1e-6): features = np.array(features, dtype=dtype) zero = np.asarray(0).astype(dtype) expected = np.logaddexp(zero, features).astype(dtype) self._assertOpOutputMatchesExpected( nn_ops.softplus, features, expected=expected, equality_test=equality_test, rtol=rtol, atol=atol) def testSoftplus(self): for dtype in self.float_types & {dtypes.float32, dtypes.float64}: self._assertSoftplusMatchesExpected([[-2, 0, 8]], dtype) self._assertSoftplusMatchesExpected( [[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]], dtype) if dtype == dtypes.bfloat16.as_numpy_dtype: log_eps = np.log(np.finfo(np.float32).eps) else: log_eps = np.log(np.finfo(dtype).eps) one = dtype(1) ten = dtype(10) self._assertSoftplusMatchesExpected([ log_eps, log_eps - one, log_eps + one, log_eps - ten, log_eps + ten, -log_eps, -log_eps - one, -log_eps + one, -log_eps - ten, -log_eps + ten ], dtype) self._assertSoftplusMatchesExpected( [0.69302183, 0.69324386], dtype, equality_test=self.AssertCloseAndSorted, rtol=9e-5, atol=9e-5) def testToBool(self): for dtype in self.numeric_types - self.complex_types: self._assertOpOutputMatchesExpected( gen_functional_ops.to_bool, np.array(5, dtype=dtype), expected=np.array(True)) self._assertOpOutputMatchesExpected( gen_functional_ops.to_bool, np.array(0, dtype=dtype), expected=np.array(False)) self._assertOpOutputMatchesExpected( gen_functional_ops.to_bool, np.array([], dtype=dtype), expected=np.array(False)) self._assertOpOutputMatchesExpected( gen_functional_ops.to_bool, np.array([1, 2, 3], dtype=dtype), expected=np.array(True)) if __name__ == "__main__": googletest.main()