bc27e470f3
We were missing a bfloat16 kernel for LogSoftMax and a double kernel on the GPU. PiperOrigin-RevId: 314646942 Change-Id: Ifb235609c129f373d4ba30b698f8d906596627fe
287 lines
11 KiB
Python
287 lines
11 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for SoftmaxOp and LogSoftmaxOp."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import numpy as np
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors_impl
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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 math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging as logging
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class SoftmaxTest(test.TestCase):
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def _npSoftmax(self, features, dim=-1, log=False):
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if dim == -1:
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dim = len(features.shape) - 1
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one_only_on_dim = list(features.shape)
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one_only_on_dim[dim] = 1
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is_fp16 = features.dtype == np.float16
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if is_fp16:
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# Do the compute in fp32 and cast the input back to fp32.
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features = features.astype(np.float32)
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e = np.exp(features - np.reshape(
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np.amax(
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features, axis=dim), one_only_on_dim))
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softmax = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim)
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if log:
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res = np.log(softmax)
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else:
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res = softmax
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if is_fp16:
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res = res.astype(np.float16)
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return res
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def _testSoftmax(self,
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np_features,
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dim=-1,
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log=False,
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dtype=None,
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use_gpu=False):
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# A previous version of the code checked the op name rather than the op type
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# to distinguish between log and non-log. Use an arbitrary name to catch
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# this bug in future.
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name = "arbitrary"
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np_softmax = self._npSoftmax(np_features, dim=dim, log=log)
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with self.cached_session(use_gpu=use_gpu):
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if dtype is not None:
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np_features = math_ops.cast(np_features, dtype=dtype)
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if log:
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tf_softmax = nn_ops.log_softmax(np_features, axis=dim, name=name)
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else:
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tf_softmax = nn_ops.softmax(np_features, axis=dim, name=name)
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out = self.evaluate(tf_softmax)
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self.assertAllCloseAccordingToType(np_softmax, out)
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self.assertShapeEqual(np_softmax, tf_softmax)
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if not log and dtype is None:
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# Bonus check: the softmaxes should add to one in dimension dim.
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sum_along_dim = np.sum(out, axis=dim)
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self.assertAllCloseAccordingToType(
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np.ones(sum_along_dim.shape), sum_along_dim)
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def _testAll(self, features, dtype=None):
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self._testSoftmax(features, dtype=dtype, use_gpu=True)
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self._testSoftmax(features, dtype=dtype, log=True, use_gpu=True)
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self._testOverflow(use_gpu=True)
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def testNpSoftmax(self):
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features = [[1., 1., 1., 1.], [1., 2., 3., 4.]]
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# Batch 0: All exps are 1. The expected result is
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# Softmaxes = [0.25, 0.25, 0.25, 0.25]
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# LogSoftmaxes = [-1.386294, -1.386294, -1.386294, -1.386294]
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#
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# Batch 1:
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# exps = [1., 2.718, 7.389, 20.085]
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# sum = 31.192
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# Softmaxes = exps / sum = [0.0320586, 0.08714432, 0.23688282, 0.64391426]
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# LogSoftmaxes = [-3.44019 , -2.44019 , -1.44019 , -0.44019]
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np_sm = self._npSoftmax(np.array(features))
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self.assertAllClose(
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np.array([[0.25, 0.25, 0.25, 0.25],
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[0.0320586, 0.08714432, 0.23688282, 0.64391426]]),
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np_sm,
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rtol=1.e-5,
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atol=1.e-5)
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np_lsm = self._npSoftmax(np.array(features), log=True)
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self.assertAllClose(
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np.array([[-1.386294, -1.386294, -1.386294, -1.386294],
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[-3.4401897, -2.4401897, -1.4401897, -0.4401897]]),
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np_lsm,
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rtol=1.e-5,
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atol=1.e-5)
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def _testOverflow(self, use_gpu=False):
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if use_gpu:
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type = np.float32 # pylint: disable=redefined-builtin
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else:
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type = np.float64 # pylint: disable=redefined-builtin
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max = np.finfo(type).max # pylint: disable=redefined-builtin
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features = np.array([[1., 1., 1., 1.], [max, 1., 2., 3.]]).astype(type)
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with self.cached_session(use_gpu=use_gpu):
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tf_log_softmax = nn_ops.log_softmax(features)
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out = self.evaluate(tf_log_softmax)
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self.assertAllClose(
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np.array([[-1.386294, -1.386294, -1.386294, -1.386294],
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[0, -max, -max, -max]]),
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out,
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rtol=1.e-5,
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atol=1.e-5)
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def testFloat(self):
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self._testAll(
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np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32))
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@unittest.skipUnless(test.is_built_with_gpu_support(),
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"Test only applicable when running on GPUs")
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def testFloatGPU(self):
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if test.is_gpu_available(cuda_only=True):
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rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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for row, col in zip(rows, cols):
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logging.info("Testing softmax float dtype in shape [%d, %d]", row, col)
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data = np.random.rand(row, col)
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self._testAll(data.astype(np.float32))
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def testHalf(self):
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self._testAll(
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np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16))
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@unittest.skipUnless(test.is_built_with_gpu_support(),
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"Test only applicable when running on GPUs")
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def testHalfGPU(self):
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if test.is_gpu_available(cuda_only=True):
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rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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for row, col in zip(rows, cols):
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logging.info("Testing softmax half dtype in shape [%d, %d]", row, col)
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data = np.random.rand(row, col)
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self._testAll(data.astype(np.float16))
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def testDouble(self):
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self._testSoftmax(
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np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64))
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self._testOverflow()
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@unittest.skipUnless(test.is_built_with_gpu_support(),
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"Test only applicable when running on GPUs")
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def testDoubleGPU(self):
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if test.is_gpu_available(cuda_only=True):
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rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
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for row, col in zip(rows, cols):
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logging.info("Testing softmax float dtype in shape [%d, %d]", row, col)
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data = np.random.rand(row, col)
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self._testAll(data.astype(np.float64))
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def testBfloat16(self):
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self._testAll(
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np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32),
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dtype=dtypes.bfloat16)
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def test1DTensorAsInput(self):
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self._testSoftmax(
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np.array([3., 2., 3., 9.]).astype(np.float64), use_gpu=False)
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self._testOverflow(use_gpu=False)
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def test1DTensorAsInputNoReshape(self):
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self._testSoftmax(
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np.array([3., 2., 3., 9.]).astype(np.float64), use_gpu=False)
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self._testOverflow(use_gpu=False)
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def test3DTensorAsInput(self):
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self._testSoftmax(
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np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
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use_gpu=False)
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self._testOverflow(use_gpu=False)
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def test3DTensorAsInputNoReshape(self):
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self._testSoftmax(
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np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
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use_gpu=False)
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self._testOverflow(use_gpu=False)
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def testAlongFirstDimension(self):
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self._testSoftmax(
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np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
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dim=0,
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use_gpu=False)
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self._testOverflow(use_gpu=False)
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def testAlongSecondDimension(self):
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self._testSoftmax(
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np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
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dim=1,
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use_gpu=False)
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self._testOverflow(use_gpu=False)
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def testAlongNegativeDimension(self):
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self._testSoftmax(
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np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32),
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dim=-2,
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use_gpu=False)
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self._testOverflow(use_gpu=False)
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def testShapeInference(self):
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op = nn_ops.softmax([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
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[[2., 3., 4., 5.], [6., 7., 8., 9.]],
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[[5., 4., 3., 2.], [1., 2., 3., 4.]]])
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self.assertEqual([3, 2, 4], op.get_shape())
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@test_util.run_deprecated_v1
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def testEmptyInput(self):
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with self.cached_session():
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x = array_ops.placeholder(dtypes.float32, shape=[0, 3])
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self.assertEqual(0, array_ops.size(x).eval())
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# reshape would raise if logits is empty
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with self.assertRaises(errors_impl.InvalidArgumentError):
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nn_ops.softmax(x, axis=0).eval()
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def testDimTooLarge(self):
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with self.cached_session():
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# Use placeholder to make sure we get runtime error instead of shape
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# inference error.
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dim = array_ops.placeholder_with_default(100, shape=[])
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with self.assertRaises(errors_impl.InvalidArgumentError):
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nn_ops.softmax([1., 2., 3., 4.], axis=dim).eval()
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def testInvalidAxis(self):
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# Test case for GitHub issue 22793.
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with self.cached_session():
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ones = array_ops.ones(shape=[2, 3])
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with self.assertRaises(errors_impl.InvalidArgumentError):
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nn_ops.softmax(ones, axis=2).eval()
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@test_util.run_deprecated_v1
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def testLargeDims(self):
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# Make sure that we properly handle large inputs. See
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# https://github.com/tensorflow/tensorflow/issues/4425 for details
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for dims in [129, 256]:
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ones = np.random.rand(dims, dims).astype(np.float32)
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np_softmax = self._npSoftmax(ones)
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for use_gpu in [True, False]:
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with self.cached_session(use_gpu=use_gpu) as sess:
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x = array_ops.placeholder(dtypes.float32)
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y = nn_ops.softmax(x)
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tf_softmax = sess.run(y, feed_dict={x: ones})
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self.assertAllClose(tf_softmax, np_softmax)
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if __name__ == "__main__":
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test.main()
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