6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
# Copyright 2017 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 Local Response Normalization ops."""
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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 copy
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import constant_op
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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.ops import array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.platform import googletest
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CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
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# Local response normalization tests. The forward tests are copied from
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# tensorflow/python/kernel_tests/lrn_op_test.py
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class LRNTest(xla_test.XLATestCase):
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def _LRN(self, input_image, lrn_depth_radius=5, bias=1.0, alpha=1.0,
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beta=0.5):
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"""Compute expected result."""
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output = copy.deepcopy(input_image)
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batch_size = input_image.shape[0]
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rows = input_image.shape[1]
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cols = input_image.shape[2]
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depth = input_image.shape[3]
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for b in range(batch_size):
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for r in range(rows):
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for c in range(cols):
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for d in range(depth):
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begin = max(0, d - lrn_depth_radius)
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end = min(depth, d + lrn_depth_radius + 1)
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patch = input_image[b, r, c, begin:end]
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output[b, r, c, d] /= (
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np.power(bias + alpha * np.sum(patch * patch), beta))
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return output
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def _RunAndVerify(self, dtype):
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with self.session():
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# random shape
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shape = np.random.randint(1, 16, size=4)
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# Make depth at least 2 to make it meaningful
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shape[3] += 1
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p = array_ops.placeholder(dtype, shape=shape)
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# random depth_radius, bias, alpha, beta
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lrn_depth_radius = np.random.randint(1, shape[3])
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bias = 1.0 + np.random.rand()
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alpha = 2.0 * np.random.rand()
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beta = 2.0 * np.random.rand()
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with self.test_scope():
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lrn_t = nn.local_response_normalization(
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p,
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name="lrn",
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depth_radius=lrn_depth_radius,
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bias=bias,
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alpha=alpha,
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beta=beta)
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params = {p: np.random.rand(*shape).astype("f")}
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result = lrn_t.eval(feed_dict=params)
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expected = self._LRN(
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params[p],
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lrn_depth_radius=lrn_depth_radius,
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bias=bias,
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alpha=alpha,
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beta=beta)
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err = np.amax(np.abs(result - expected))
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print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta, " is ",
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err)
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if dtype == dtypes.float32:
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self.assertTrue(err < 1e-4)
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else:
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self.assertTrue(err < 1e-2)
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self.assertShapeEqual(expected, lrn_t)
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def testCompute(self):
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for _ in range(2):
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self._RunAndVerify(dtypes.float32)
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def testLrnGrad(self):
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# Test for LRNGrad that compares against the CPU implementation.
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shape = [1, 2, 3, 4]
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total_size = np.prod(shape)
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in_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
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out_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
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out_grads_vals = np.arange(1, total_size + 1, dtype=np.float32)
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depth_radius = np.random.randint(1, shape[3])
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bias = 1.0 + np.random.rand()
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alpha = 1.0 * np.random.rand()
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beta = 1.0 * np.random.rand()
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with self.session():
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in_image = constant_op.constant(in_image_vals, shape=shape)
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out_image = constant_op.constant(out_image_vals, shape=shape)
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out_grads = constant_op.constant(out_grads_vals, shape=shape)
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with ops.device(CPU_DEVICE):
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expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
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depth_radius, bias, alpha, beta)
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with self.test_scope():
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actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
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depth_radius, bias, alpha, beta)
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expected_val = self.evaluate(expected)
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actual_val = self.evaluate(actual)
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self.assertAllClose(actual_val, expected_val, rtol=1e-3)
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if __name__ == "__main__":
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googletest.main()
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