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
462 lines
15 KiB
Python
462 lines
15 KiB
Python
# 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.
|
|
# ==============================================================================
|
|
"""Functional tests for 3d pooling operations."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.compiler.tests import xla_test
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import gen_nn_ops
|
|
from tensorflow.python.ops import nn_ops
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
# Wrapper around AvgPoolGrad that ignores extra arguments needed by
|
|
# MaxPoolGrad.
|
|
def _AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding):
|
|
del outputs # Unused by average-pooling gradients.
|
|
return gen_nn_ops.avg_pool3d_grad(
|
|
inputs.get_shape().as_list(),
|
|
output_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding)
|
|
|
|
|
|
class Pooling3DTest(xla_test.XLATestCase):
|
|
|
|
def _VerifyValues(self, pool_func, input_sizes, window, strides, padding,
|
|
expected):
|
|
"""Verifies the output values of the pooling function.
|
|
|
|
Args:
|
|
pool_func: Function to be called: co.MaxPool, co.AvgPool.
|
|
input_sizes: Input tensor dimensions.
|
|
window: Tuple of kernel dims: planes, rows, cols.
|
|
strides: Tuple of strides for dims: planes, rows, cols.
|
|
padding: Padding type.
|
|
expected: An array containing the expected operation outputs.
|
|
"""
|
|
total_size = 1
|
|
for s in input_sizes:
|
|
total_size *= s
|
|
# Initializes the input tensor with array containing incrementing
|
|
# numbers from 1.
|
|
x = np.arange(1.0, total_size + 1, dtype=np.float32)
|
|
x = x.reshape(input_sizes)
|
|
with self.session() as sess, self.test_scope():
|
|
inputs = array_ops.placeholder(dtypes.float32)
|
|
t = pool_func(
|
|
inputs,
|
|
ksize=[1] + window + [1],
|
|
strides=[1] + strides + [1],
|
|
padding=padding)
|
|
vals = sess.run(t, {inputs: x})
|
|
# Verifies values.
|
|
actual = vals.flatten()
|
|
self.assertAllClose(expected, actual)
|
|
|
|
def testAvgPool3dValidPadding(self):
|
|
expected_output = [20.5, 21.5, 22.5]
|
|
self._VerifyValues(
|
|
nn_ops.avg_pool3d,
|
|
input_sizes=[1, 3, 3, 3, 3],
|
|
window=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="VALID",
|
|
expected=expected_output)
|
|
|
|
def testAvgPool3dSamePadding(self):
|
|
expected_output = [20.5, 21.5, 22.5, 26.5, 27.5, 28.5]
|
|
self._VerifyValues(
|
|
nn_ops.avg_pool3d,
|
|
input_sizes=[1, 2, 2, 4, 3],
|
|
window=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=expected_output)
|
|
|
|
def testAvgPool3dSamePaddingDifferentStrides(self):
|
|
expected_output = [1.5, 4.5, 7.5, 17.5, 20.5, 23.5, 33.5, 36.5, 39.5]
|
|
self._VerifyValues(
|
|
nn_ops.avg_pool3d,
|
|
input_sizes=[1, 5, 8, 1, 1],
|
|
window=[1, 2, 3],
|
|
strides=[2, 3, 1],
|
|
padding="SAME",
|
|
expected=expected_output)
|
|
|
|
def testMaxPool3dValidPadding(self):
|
|
expected_output = [40.0, 41.0, 42.0]
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 3, 3, 3, 3],
|
|
window=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="VALID",
|
|
expected=expected_output)
|
|
|
|
def testMaxPool3dSamePadding(self):
|
|
expected_output = [31., 32., 33., 34., 35., 36.]
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 2, 2, 3, 3],
|
|
window=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=expected_output)
|
|
|
|
def testMaxPool3dSamePaddingDifferentStrides(self):
|
|
expected_output = [2., 5., 8., 18., 21., 24., 34., 37., 40.]
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 5, 8, 1, 1],
|
|
window=[1, 2, 3],
|
|
strides=[2, 3, 1],
|
|
padding="SAME",
|
|
expected=expected_output)
|
|
|
|
# Test pooling on a larger input, with different stride and kernel
|
|
# size for the 'z' dimension.
|
|
|
|
# Simulate max pooling in numpy to get the expected output.
|
|
input_data = np.arange(1, 5 * 27 * 27 * 64 + 1).reshape((5, 27, 27, 64))
|
|
input_data = np.pad(input_data, [[0, 0], [0, 1], [0, 1], [0, 0]],
|
|
mode="constant")
|
|
expected_output = input_data[:, 1::2, 1::2, :]
|
|
expected_output[:, -1, :, :] = input_data[:, -2, 1::2, :]
|
|
expected_output[:, :, -1, :] = input_data[:, 1::2, -2, :]
|
|
expected_output[:, -1, -1, :] = input_data[:, -2, -2, :]
|
|
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 5, 27, 27, 64],
|
|
window=[1, 2, 2],
|
|
strides=[1, 2, 2],
|
|
padding="SAME",
|
|
expected=expected_output.flatten())
|
|
|
|
def testKernelSmallerThanStride(self):
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 3, 3, 3, 1],
|
|
window=[1, 1, 1],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=[1, 3, 7, 9, 19, 21, 25, 27])
|
|
|
|
self._VerifyValues(
|
|
nn_ops.max_pool3d,
|
|
input_sizes=[1, 7, 7, 7, 1],
|
|
window=[2, 2, 2],
|
|
strides=[3, 3, 3],
|
|
padding="VALID",
|
|
expected=[58, 61, 79, 82, 205, 208, 226, 229])
|
|
|
|
self._VerifyValues(
|
|
nn_ops.avg_pool3d,
|
|
input_sizes=[1, 3, 3, 3, 1],
|
|
window=[1, 1, 1],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=[1, 3, 7, 9, 19, 21, 25, 27])
|
|
|
|
self._VerifyValues(
|
|
nn_ops.avg_pool3d,
|
|
input_sizes=[1, 7, 7, 7, 1],
|
|
window=[2, 2, 2],
|
|
strides=[3, 3, 3],
|
|
padding="VALID",
|
|
expected=[29.5, 32.5, 50.5, 53.5, 176.5, 179.5, 197.5, 200.5])
|
|
|
|
def _VerifyGradient(self,
|
|
pool_func,
|
|
pool_grad_func,
|
|
input_sizes,
|
|
ksize,
|
|
strides,
|
|
padding,
|
|
pool_grad_grad_func=None):
|
|
"""Verifies the output values of the pooling gradient function.
|
|
|
|
Args:
|
|
pool_func: Forward pooling function
|
|
pool_grad_func: Pooling gradient function for pool_grad_func
|
|
input_sizes: Input tensor dimensions.
|
|
ksize: The kernel size dimensions
|
|
strides: The stride dimensions
|
|
padding: Padding type.
|
|
pool_grad_grad_func: Second-order gradient function, if available.
|
|
"""
|
|
ksize = [1] + ksize + [1]
|
|
strides = [1] + strides + [1]
|
|
total_size = np.prod(input_sizes)
|
|
x = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_sizes)
|
|
with self.session() as sess:
|
|
# Use the forward pool function to compute some corresponding outputs
|
|
# (needed for the CPU device, and we need the shape in both cases).
|
|
with ops.device("CPU"):
|
|
inputs = array_ops.placeholder(dtypes.float32, shape=input_sizes)
|
|
outputs = pool_func(
|
|
inputs,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding)
|
|
|
|
output_vals = np.array(sess.run(outputs, {inputs: x}))
|
|
output_gradient_vals = np.arange(
|
|
1, output_vals.size + 1, dtype=np.float32)
|
|
output_gradient_vals = output_gradient_vals.reshape(output_vals.shape)
|
|
output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32)
|
|
output_grad_grad_vals = output_grad_grad_vals.reshape(x.shape)
|
|
|
|
# Use the Tensorflow CPU pooling gradient to compute the expected input
|
|
# gradients.
|
|
with ops.device("CPU"):
|
|
output_gradients = array_ops.placeholder(
|
|
dtypes.float32, shape=output_vals.shape)
|
|
expected_input_gradients = pool_grad_func(
|
|
inputs,
|
|
outputs,
|
|
output_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding)
|
|
expected_input_gradient_vals = sess.run(
|
|
expected_input_gradients,
|
|
{inputs: x,
|
|
output_gradients: output_gradient_vals})
|
|
|
|
output_grad_gradients = array_ops.placeholder(
|
|
dtypes.float32, shape=expected_input_gradient_vals.shape)
|
|
if pool_grad_grad_func is not None:
|
|
expected_grad_gradients = pool_grad_grad_func(
|
|
inputs,
|
|
outputs,
|
|
output_grad_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format="NDHWC")
|
|
expected_grad_gradients_vals = sess.run(expected_grad_gradients, {
|
|
inputs: x,
|
|
output_grad_gradients: output_grad_grad_vals
|
|
})
|
|
|
|
# Run the gradient op on the XLA device
|
|
with self.test_scope():
|
|
outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape)
|
|
actual_input_gradients = pool_grad_func(
|
|
inputs,
|
|
outputs,
|
|
output_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding)
|
|
if pool_grad_grad_func is not None:
|
|
actual_grad_gradients = pool_grad_grad_func(
|
|
inputs,
|
|
outputs,
|
|
output_grad_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format="NDHWC")
|
|
|
|
actual = sess.run(actual_input_gradients, {
|
|
inputs: x,
|
|
outputs: output_vals,
|
|
output_gradients: output_gradient_vals
|
|
})
|
|
|
|
# Compare the Tensorflow and XLA results.
|
|
self.assertAllClose(
|
|
expected_input_gradient_vals.flatten(),
|
|
actual.flatten(),
|
|
rtol=1e-5,
|
|
atol=1e-6)
|
|
self.assertShapeEqual(actual, inputs)
|
|
|
|
if pool_grad_grad_func is not None:
|
|
actual_grad_gradients_vals = sess.run(
|
|
actual_grad_gradients, {
|
|
inputs: x,
|
|
outputs: output_vals,
|
|
output_grad_gradients: output_grad_grad_vals
|
|
})
|
|
|
|
# Compare the Tensorflow and XLA results.
|
|
self.assertAllClose(
|
|
expected_grad_gradients_vals,
|
|
actual_grad_gradients_vals,
|
|
rtol=1e-4,
|
|
atol=1e-6)
|
|
self.assertShapeEqual(actual_grad_gradients_vals, outputs)
|
|
|
|
def testMaxPoolGradValidPadding1_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[1, 3, 3, 3, 1],
|
|
ksize=[1, 1, 1],
|
|
strides=[1, 1, 1],
|
|
padding="VALID",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradValidPadding2_1_6_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 3, 3, 6, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
padding="VALID",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradValidPadding2_1_7_3d(self):
|
|
# TODO(b/73062247): the bfloat16 implementation of MaxPool3DGradGrad does
|
|
# not have enough precision for this test case to pass if
|
|
# pool_grad_grad_func is passed.
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 3, 5, 7, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
padding="VALID")
|
|
|
|
def testMaxPoolGradValidPadding2_2_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 2, 2, 2, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="VALID",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradSamePadding1_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 3, 2, 4, 1],
|
|
ksize=[1, 1, 1],
|
|
strides=[1, 1, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradSamePadding2_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 3, 2, 4, 1],
|
|
ksize=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradSamePadding2_2_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[2, 5, 2, 4, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testMaxPoolGradSamePadding3_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.max_pool3d,
|
|
gen_nn_ops.max_pool3d_grad,
|
|
input_sizes=[1, 3, 3, 7, 1],
|
|
ksize=[3, 3, 3],
|
|
strides=[1, 1, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=gen_nn_ops.max_pool3d_grad_grad)
|
|
|
|
def testAvgPoolGradValidPadding1_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[2, 3, 3, 3, 3],
|
|
ksize=[1, 1, 1],
|
|
strides=[1, 1, 1],
|
|
padding="VALID")
|
|
|
|
def testAvgPoolGradValidPadding2_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[2, 3, 3, 3, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
padding="VALID")
|
|
|
|
def testAvgPoolGradValidPadding2_2_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[2, 2, 2, 2, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="VALID")
|
|
|
|
def testAvgPoolGradSamePadding1_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[2, 3, 2, 4, 3],
|
|
ksize=[1, 1, 1],
|
|
strides=[1, 1, 1],
|
|
padding="SAME")
|
|
|
|
def testAvgPoolGradSamePadding2_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[1, 2, 2, 2, 1],
|
|
ksize=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
padding="SAME")
|
|
|
|
def testAvgPoolGradSamePadding2_2_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[2, 5, 2, 4, 3],
|
|
ksize=[2, 2, 2],
|
|
strides=[2, 2, 2],
|
|
padding="SAME")
|
|
|
|
def testAvgPoolGradSamePadding3_1_3d(self):
|
|
self._VerifyGradient(
|
|
nn_ops.avg_pool3d,
|
|
_AvgPoolGrad,
|
|
input_sizes=[1, 3, 6, 7, 1],
|
|
ksize=[3, 3, 3],
|
|
strides=[1, 1, 1],
|
|
padding="SAME")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|