| # Copyright 2020 The IREE Authors |
| # |
| # Licensed under the Apache License v2.0 with LLVM Exceptions. |
| # See https://llvm.org/LICENSE.txt for license information. |
| # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| |
| import collections |
| import os |
| from typing import Any, Dict, Sequence, Type, Union |
| |
| from absl import app |
| from absl import flags |
| from iree.tf.support import tf_test_utils |
| from iree.tf.support import tf_utils |
| import numpy as np |
| import tensorflow.compat.v2 as tf |
| |
| FLAGS = flags.FLAGS |
| |
| # As high as tf goes without breaking. |
| RANK_7_SHAPE = [2] * 7 |
| UNARY_SIGNATURE_SHAPES = [[RANK_7_SHAPE]] |
| BINARY_SIGNATURE_SHAPES = [[RANK_7_SHAPE] * 2] |
| TERNARY_SIGNATURE_SHAPES = [[RANK_7_SHAPE] * 3] |
| |
| # Reused UnitTestSpecs. |
| SEGMENT_UNIT_TEST_SPECS = tf_test_utils.unit_test_specs_from_args( |
| names_to_input_args={ |
| "tf_doc_example": [ |
| tf.constant([ |
| [1, 2, 3, 4], |
| [4, 3, 2, 1], |
| [5, 6, 7, 8], |
| ], np.float32), |
| np.array([0, 0, 1], np.int32), |
| ] |
| }) |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS = tf_test_utils.unit_test_specs_from_args( |
| names_to_input_args={ |
| "tf_doc_example": [ |
| tf.constant([ |
| [1, 2, 3, 4], |
| [4, 3, 2, 1], |
| [5, 6, 7, 8], |
| ], np.float32), |
| np.array([0, 0, 1], np.int32), |
| 2, |
| ] |
| }) |
| |
| REDUCE_KWARGS_TO_VALUES = { |
| "axis": [None, 1], |
| "keepdims": [False, True], |
| } |
| |
| # A dictionary mapping tf.math function names to lists of UnitTestSpecs. |
| # Each unit_test_name will have the tf.math function name prepended to it. |
| FUNCTIONS_TO_UNIT_TEST_SPECS = { |
| "abs": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "accumulate_n": [ |
| tf_test_utils.UnitTestSpec( |
| unit_test_name='f32', |
| input_signature=[[tf.TensorSpec(RANK_7_SHAPE, tf.float32)] * 5]), |
| tf_test_utils.UnitTestSpec( |
| unit_test_name='i32', |
| input_signature=[[tf.TensorSpec(RANK_7_SHAPE, tf.int32)] * 5]), |
| ], |
| "acos": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "acosh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32], |
| input_generators=[tf_utils.ndarange]), |
| "add": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "add_n": [ |
| tf_test_utils.UnitTestSpec( |
| unit_test_name='f32', |
| input_signature=[[tf.TensorSpec(RANK_7_SHAPE, tf.float32)] * 5]), |
| tf_test_utils.UnitTestSpec( |
| unit_test_name='i32', |
| input_signature=[[tf.TensorSpec(RANK_7_SHAPE, tf.int32)] * 5]), |
| ], |
| "angle": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "argmax": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "argmin": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "asin": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "asinh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "atan": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "atan2": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "atanh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "bessel_i0": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "bessel_i0e": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "bessel_i1": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "bessel_i1e": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "betainc": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=TERNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "bincount": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.int32], |
| input_generators=[tf_utils.ndarange]), |
| "ceil": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "confusion_matrix": |
| tf_test_utils.unit_test_specs_from_args(names_to_input_args={ |
| "five_classes": [tf.constant([1, 2, 4]), |
| tf.constant([2, 2, 4])] |
| }), |
| "conj": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "cos": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "cosh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "count_nonzero": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64], |
| input_generators=[tf_utils.ndarange]), |
| "cumprod": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "cumsum": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "cumulative_logsumexp": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "digamma": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "divide": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "divide_no_nan": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "equal": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "erf": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "erfc": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "erfinv": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "exp": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "expm1": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "floor": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "floordiv": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| # Avoid integer division by 0. |
| input_generators={ |
| "uniform_1_3": |
| lambda *args: tf_utils.uniform(*args, low=1.0, high=3.0) |
| }), |
| "floormod": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| # Avoid integer division by 0. |
| input_generators={ |
| "uniform_1_3": |
| lambda *args: tf_utils.uniform(*args, low=1.0, high=3.0) |
| }), |
| "greater": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "greater_equal": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "igamma": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "igammac": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "imag": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "in_top_k": [ |
| tf_test_utils.UnitTestSpec( |
| unit_test_name="k_3", |
| input_signature=[ |
| tf.TensorSpec([8], tf.int32), |
| tf.TensorSpec([8, 3]) |
| ], |
| input_generator=tf_utils.ndarange, |
| kwargs=dict(k=3), |
| ) |
| ], |
| "invert_permutation": [ |
| tf_test_utils.UnitTestSpec( |
| unit_test_name="random", |
| input_signature=[tf.TensorSpec([8], tf.int32)], |
| input_generator=tf_utils.random_permutation, |
| ) |
| ], |
| "is_finite": |
| tf_test_utils.unit_test_specs_from_args(names_to_input_args={ |
| "nan_and_inf": [tf.constant([[1., np.nan], [np.inf, 2.]])] |
| }), |
| "is_inf": |
| tf_test_utils.unit_test_specs_from_args(names_to_input_args={ |
| "nan_and_inf": [tf.constant([[1., np.nan], [np.inf, 2.]])] |
| }), |
| "is_nan": |
| tf_test_utils.unit_test_specs_from_args(names_to_input_args={ |
| "nan_and_inf": [tf.constant([[1., np.nan], [np.inf, 2.]])] |
| }), |
| "is_non_decreasing": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "is_strictly_increasing": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "l2_normalize": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "lbeta": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "less": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "less_equal": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "lgamma": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "log": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "log1p": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "log_sigmoid": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "log_softmax": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "logical_and": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool]), |
| "logical_not": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool]), |
| "logical_or": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool]), |
| "logical_xor": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool]), |
| "maximum": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "minimum": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "mod": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| input_generators={ |
| "positive_ndarange": lambda *args: tf_utils.ndarange(*args) + 1 |
| }), |
| "multiply": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "multiply_no_nan": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "ndtri": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "negative": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "nextafter": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES), |
| "not_equal": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32]), |
| "polygamma": |
| tf_test_utils.unit_test_specs_from_args(names_to_input_args={ |
| "nan_and_inf": [tf.ones(16), tf.linspace(0.5, 4, 16)] |
| }), |
| "polyval": [ |
| tf_test_utils.UnitTestSpec( |
| unit_test_name="three_coeffs", |
| input_signature=[[tf.TensorSpec(RANK_7_SHAPE)] * 3, |
| tf.TensorSpec([])], |
| ) |
| ], |
| "pow": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=[[[1, 2, 2, 2], [1, 2, 2, 2]]], |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64], |
| input_generators={ |
| "positive_ndarange": lambda *args: tf_utils.ndarange(*args) + 1 |
| }), |
| "real": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "reciprocal": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "reciprocal_no_nan": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "reduce_all": [ |
| # Explicitly test all True inputs to be absolutely sure that some |
| # reduction axes return True. |
| *tf_test_utils.unit_test_specs_from_args( |
| names_to_input_args={ |
| "all_true": [np.ones(RANK_7_SHAPE, np.bool)], |
| }, |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| *tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| ], |
| "reduce_any": [ |
| # Explicitly test all False inputs to be absolutely sure that some |
| # reduction axes return False. |
| *tf_test_utils.unit_test_specs_from_args( |
| names_to_input_args={ |
| "all_false": [np.zeros(RANK_7_SHAPE, np.bool)], |
| }, |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| *tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.bool], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| ], |
| "reduce_euclidean_norm": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_logsumexp": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_max": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_mean": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_min": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_prod": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_std": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_sum": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "reduce_variance": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64], |
| kwargs_to_values=REDUCE_KWARGS_TO_VALUES), |
| "rint": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "round": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "rsqrt": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "scalar_mul": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=[[[], [8]]]), |
| "segment_max": |
| SEGMENT_UNIT_TEST_SPECS, |
| "segment_mean": |
| SEGMENT_UNIT_TEST_SPECS, |
| "segment_min": |
| SEGMENT_UNIT_TEST_SPECS, |
| "segment_prod": |
| SEGMENT_UNIT_TEST_SPECS, |
| "segment_sum": |
| SEGMENT_UNIT_TEST_SPECS, |
| "sigmoid": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "sign": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "sin": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "sinh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "sobol_sample": |
| tf_test_utils.unit_test_specs_from_args( |
| names_to_input_args={"simple": [4, 3]}), |
| "softmax": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "softplus": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "softsign": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32]), |
| "sqrt": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "square": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "squared_difference": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "subtract": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "tan": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "tanh": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "top_k": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=[[[2, 2]]], |
| signature_dtypes=[tf.float32], |
| kwargs_to_values={"k": [1, 2]}), |
| "truediv": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "unsorted_segment_max": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "unsorted_segment_mean": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "unsorted_segment_min": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "unsorted_segment_prod": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "unsorted_segment_sqrt_n": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "unsorted_segment_sum": |
| UNSORTED_SEGMENT_UNIT_TEST_SPECS, |
| "xdivy": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "xlog1py": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "xlogy": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.complex64]), |
| "zero_fraction": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=UNARY_SIGNATURE_SHAPES, |
| signature_dtypes=[tf.float32, tf.int32, tf.complex64]), |
| "zeta": |
| tf_test_utils.unit_test_specs_from_signatures( |
| signature_shapes=BINARY_SIGNATURE_SHAPES, |
| # The function is poorly behaved near zero, so we test this range |
| # to avoid outputing all nans. |
| input_generators={ |
| "uniform_3_4": |
| lambda *args: tf_utils.uniform(*args, low=3.0, high=4.0) |
| }, |
| ) |
| } |
| |
| for function, specs in FUNCTIONS_TO_UNIT_TEST_SPECS.items(): |
| # Update using 'with_name' to avoid updating shared UnitTestSpecs. |
| specs = [ |
| spec.with_name(f"{function}__{spec.unit_test_name}") for spec in specs |
| ] |
| FUNCTIONS_TO_UNIT_TEST_SPECS[function] = specs |
| |
| # Validate that there are not multiple UnitTestSpecs with the same name. |
| seen_unit_test_names = set() |
| for spec in specs: |
| if spec.unit_test_name in seen_unit_test_names: |
| raise ValueError( |
| f"Found multiple UnitTestSpecs with the name '{spec.unit_test_name}'") |
| seen_unit_test_names.add(spec.unit_test_name) |
| |
| flags.DEFINE_list( |
| "functions", None, |
| f"Any of {list(FUNCTIONS_TO_UNIT_TEST_SPECS.keys())}. If more than one " |
| "function is provided then len(--target_backends) must be one.") |
| flags.DEFINE_bool( |
| "dynamic_dims", False, |
| "Whether or not to compile the layer with dynamic dimensions.") |
| flags.DEFINE_bool( |
| "test_complex", False, |
| "Whether or not to test or ignore function signatures with complex types.") |
| flags.DEFINE_bool( |
| 'list_functions_with_complex_tests', False, |
| 'Whether or not to print out all functions with complex inputs ' |
| '(and skip running the tests).') |
| |
| |
| def _wrap_top_k(top_k): |
| # top_k returns a tensorflow.python.ops.gen_nn_ops.TopKV2. Wrap it in a tuple |
| # so we don't have to. (The lambda is wrapped to avoid a recursive capture). |
| return lambda *args, **kwargs: tuple(top_k(*args, **kwargs)) |
| |
| |
| def create_function_unit_test( |
| function_name: str, |
| unit_test_spec: tf_test_utils.UnitTestSpec) -> tf.function: |
| """Creates a tf_function_unit_test from the provided UnitTestSpec.""" |
| function = getattr(tf.math, function_name) |
| signature = unit_test_spec.input_signature |
| |
| if tf_utils.is_complex(signature): |
| function, signature = tf_utils.rewrite_complex_signature( |
| function, signature) |
| if function_name == "top_k": |
| function = _wrap_top_k(function) |
| wrapped_function = lambda *args: function(*args, **unit_test_spec.kwargs) |
| |
| if FLAGS.dynamic_dims: |
| signature = tf_utils.apply_function(signature, tf_utils.make_dims_dynamic) |
| |
| return tf_test_utils.tf_function_unit_test( |
| input_signature=signature, |
| input_generator=unit_test_spec.input_generator, |
| input_args=unit_test_spec.input_args, |
| name=unit_test_spec.unit_test_name, |
| rtol=1e-5, |
| atol=1e-5)(wrapped_function) |
| |
| |
| class TfMathModule(tf_test_utils.TestModule): |
| |
| def __init__(self): |
| super().__init__() |
| for function in FLAGS.functions: |
| for unit_test_spec in FUNCTIONS_TO_UNIT_TEST_SPECS[function]: |
| if not FLAGS.test_complex and tf_utils.is_complex( |
| unit_test_spec.input_signature): |
| continue |
| function_unit_test = create_function_unit_test(function, unit_test_spec) |
| setattr(self, unit_test_spec.unit_test_name, function_unit_test) |
| |
| |
| def get_relative_artifacts_dir() -> str: |
| if len(FLAGS.functions) > 1: |
| # We only allow testing multiple functions with a single target backend |
| # so that we can store the artifacts under: |
| # 'artifacts_dir/multiple_functions__backend/...' |
| # We specialize the 'multiple_functions' dir by backend to avoid overwriting |
| # tf_input.mlir and iree_input.mlir. These are typically identical across |
| # backends, but are not when the functions to compile change per-backend. |
| if len(FLAGS.target_backends) != 1: |
| raise flags.IllegalFlagValueError( |
| "Expected len(target_backends) == 1 when len(functions) > 1, but got " |
| f"the following values for target_backends: {FLAGS.target_backends}.") |
| function_str = f"multiple_functions__{FLAGS.target_backends[0]}" |
| else: |
| function_str = FLAGS.functions[0] |
| dim_str = "dynamic_dims" if FLAGS.dynamic_dims else "static_dims" |
| complex_str = "complex" if FLAGS.test_complex else "non_complex" |
| return os.path.join("tf", "math", function_str, f"{dim_str}_{complex_str}") |
| |
| |
| class TfMathTest(tf_test_utils.TracedModuleTestCase): |
| |
| def __init__(self, *args, **kwargs): |
| super().__init__(*args, **kwargs) |
| self._modules = tf_test_utils.compile_tf_module( |
| TfMathModule, |
| exported_names=TfMathModule.get_tf_function_unit_tests(), |
| relative_artifacts_dir=get_relative_artifacts_dir()) |
| |
| |
| def main(argv): |
| del argv # Unused. |
| if hasattr(tf, "enable_v2_behavior"): |
| tf.enable_v2_behavior() |
| |
| if FLAGS.list_functions_with_complex_tests: |
| for function_name, unit_test_specs in FUNCTIONS_TO_UNIT_TEST_SPECS.items(): |
| for spec in unit_test_specs: |
| if tf_utils.is_complex(spec.input_signature): |
| print(f' "{function_name}",') |
| return |
| |
| if FLAGS.functions is None: |
| raise flags.IllegalFlagValueError( |
| "'--functions' must be specified if " |
| "'--list_functions_with_complex_tests' isn't") |
| |
| TfMathTest.generate_unit_tests(TfMathModule) |
| tf.test.main() |
| |
| |
| if __name__ == "__main__": |
| app.run(main) |
