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
292 lines
11 KiB
Python
292 lines
11 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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"""Functional tests for SpaceToBatch and BatchToSpace 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 numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.platform import test
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def space_to_batch_direct(input_array, block_shape, paddings):
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"""Direct Python implementation of space-to-batch conversion.
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This is used for tests only.
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Args:
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input_array: N-D array
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block_shape: 1-D array of shape [num_block_dims].
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paddings: 2-D array of shape [num_block_dims, 2].
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Returns:
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Converted tensor.
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"""
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input_array = np.array(input_array)
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block_shape = np.array(block_shape)
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num_block_dims = len(block_shape)
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paddings = np.array(paddings).reshape((len(block_shape), 2))
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padded = np.pad(input_array,
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pad_width=([[0, 0]] + list(paddings) + [[0, 0]] *
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(input_array.ndim - 1 - num_block_dims)),
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mode="constant")
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reshaped_padded_shape = [input_array.shape[0]]
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output_shape = [input_array.shape[0] * np.prod(block_shape)]
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for block_dim, block_shape_value in enumerate(block_shape):
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reduced_size = padded.shape[block_dim + 1] // block_shape_value
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reshaped_padded_shape.append(reduced_size)
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output_shape.append(reduced_size)
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reshaped_padded_shape.append(block_shape_value)
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reshaped_padded_shape.extend(input_array.shape[num_block_dims + 1:])
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output_shape.extend(input_array.shape[num_block_dims + 1:])
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reshaped_padded = padded.reshape(reshaped_padded_shape)
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permuted_reshaped_padded = np.transpose(reshaped_padded, (
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list(np.arange(num_block_dims) * 2 + 2) + [0] +
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list(np.arange(num_block_dims) * 2 + 1) + list(
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np.arange(input_array.ndim - num_block_dims - 1) + 1 + num_block_dims
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* 2)))
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return permuted_reshaped_padded.reshape(output_shape)
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class SpaceToBatchTest(xla_test.XLATestCase):
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"""Tests input-output pairs for the SpaceToBatch and BatchToSpace ops."""
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def _testPad(self, inputs, paddings, block_size, outputs):
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with self.session() as sess, self.test_scope():
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for dtype in self.float_types:
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# outputs = space_to_batch(inputs)
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placeholder = array_ops.placeholder(dtype)
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x_tf = gen_array_ops.space_to_batch(
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placeholder, paddings, block_size=block_size)
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self.assertAllEqual(sess.run(x_tf, {placeholder: inputs}), outputs)
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# inputs = batch_to_space(outputs)
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x_tf = gen_array_ops.batch_to_space(
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placeholder, paddings, block_size=block_size)
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self.assertAllEqual(sess.run(x_tf, {placeholder: outputs}), inputs)
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def _testOne(self, inputs, block_size, outputs):
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paddings = np.zeros((2, 2), dtype=np.int32)
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self._testPad(inputs, paddings, block_size, outputs)
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# [1, 2, 2, 1] <-> [4, 1, 1, 1]
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def testSmallInput2x2(self):
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x_np = [[[[1], [2]], [[3], [4]]]]
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block_size = 2
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x_out = [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
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self._testOne(x_np, block_size, x_out)
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# [1, 2, 2, 1] <-> [1, 3, 3, 1] (padding) <-> [9, 1, 1, 1]
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def testSmallInput2x2Pad1x0(self):
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x_np = [[[[1], [2]], [[3], [4]]]]
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paddings = np.array([[1, 0], [1, 0]], dtype=np.int32)
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block_size = 3
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x_out = [[[[0]]], [[[0]]], [[[0]]], [[[0]]], [[[1]]], [[[2]]], [[[0]]],
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[[[3]]], [[[4]]]]
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self._testPad(x_np, paddings, block_size, x_out)
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# Test with depth larger than 1.
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# [1, 2, 2, 3] <-> [4, 1, 1, 3]
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def testDepthInput2x2(self):
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x_np = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
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block_size = 2
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x_out = [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
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self._testOne(x_np, block_size, x_out)
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# Test for larger input dimensions.
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# [1, 4, 4, 1] <-> [4, 2, 2, 1]
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def testLargerInput2x2(self):
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x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]],
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[[9], [10], [11], [12]], [[13], [14], [15], [16]]]]
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block_size = 2
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x_out = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]],
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[[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]]
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self._testOne(x_np, block_size, x_out)
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# Test with batch larger than 1.
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# [2, 2, 4, 1] <-> [8, 1, 2, 1]
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def testBatchInput2x2(self):
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x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
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[[[9], [10], [11], [12]], [[13], [14], [15], [16]]]]
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block_size = 2
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x_out = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
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[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
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self._testOne(x_np, block_size, x_out)
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# Tests for larger input spatial dimensions AND batch larger than 1, to ensure
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# that elements are correctly laid out spatially and properly interleaved
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# along the batch dimension.
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# [2, 4, 4, 1] <-> [8, 2, 2, 1]
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def testLargerInputBatch2x2(self):
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x_np = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]],
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[[9], [10], [11], [12]], [[13], [14], [15], [16]]],
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[[[17], [18], [19], [20]], [[21], [22], [23], [24]],
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[[25], [26], [27], [28]], [[29], [30], [31], [32]]]]
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x_out = [[[[1], [3]], [[9], [11]]], [[[17], [19]], [[25], [27]]],
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[[[2], [4]], [[10], [12]]], [[[18], [20]], [[26], [28]]],
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[[[5], [7]], [[13], [15]]], [[[21], [23]], [[29], [31]]],
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[[[6], [8]], [[14], [16]]], [[[22], [24]], [[30], [32]]]]
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block_size = 2
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self._testOne(x_np, block_size, x_out)
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class SpaceToBatchNDTest(xla_test.XLATestCase):
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"""Tests input-output pairs for the SpaceToBatchND and BatchToSpaceND ops."""
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def _testPad(self, inputs, block_shape, paddings, outputs):
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block_shape = np.array(block_shape)
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paddings = np.array(paddings).reshape((len(block_shape), 2))
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with self.session() as sess, self.test_scope():
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for dtype in self.float_types:
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# TODO(b/68813416): Skip bfloat16's as the input type for direct is
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# float32 and results in a mismatch, while making testDirect provide the
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# correctly typed input results in 'no fill-function for data-type'
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# error.
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if dtype == dtypes.bfloat16.as_numpy_dtype:
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continue
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if dtype == np.float16:
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actual_inputs = np.array(inputs).astype(dtype)
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actual_paddings = np.array(paddings).astype(dtype)
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expected_outputs = np.array(outputs).astype(dtype)
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else:
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actual_inputs = inputs
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actual_paddings = paddings
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expected_outputs = outputs
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placeholder = array_ops.placeholder(dtype)
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# outputs = space_to_batch(inputs)
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x_tf = array_ops.space_to_batch_nd(placeholder, block_shape,
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actual_paddings)
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self.assertAllEqual(
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sess.run(x_tf, {placeholder: actual_inputs}), expected_outputs)
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# inputs = batch_to_space(outputs)
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placeholder = array_ops.placeholder(dtype)
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x_tf = array_ops.batch_to_space_nd(placeholder, block_shape,
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actual_paddings)
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self.assertAllEqual(
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sess.run(x_tf, {placeholder: expected_outputs}), actual_inputs)
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def _testDirect(self, input_shape, block_shape, paddings):
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inputs = np.arange(np.prod(input_shape), dtype=np.float32)
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inputs = inputs.reshape(input_shape)
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self._testPad(inputs, block_shape, paddings,
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space_to_batch_direct(inputs, block_shape, paddings))
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def testZeroBlockDimsZeroRemainingDims(self):
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self._testPad(
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inputs=[1, 2],
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block_shape=[],
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paddings=[],
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outputs=[1, 2],)
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def testZeroBlockDimsOneRemainingDim(self):
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self._testPad(
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inputs=[[1, 2], [3, 4]],
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block_shape=[],
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paddings=[],
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outputs=[[1, 2], [3, 4]])
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# Same thing, but with a no-op block dim.
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self._testPad(
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inputs=[[1, 2], [3, 4]],
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block_shape=[1],
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paddings=[[0, 0]],
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outputs=[[1, 2], [3, 4]])
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def testZeroBlockDimsTwoRemainingDims(self):
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self._testPad(
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inputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
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block_shape=[],
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paddings=[],
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outputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
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# Same thing, but with a no-op block dim.
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self._testPad(
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inputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
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block_shape=[1],
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paddings=[[0, 0]],
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outputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
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# Same thing, but with two no-op block dims.
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self._testPad(
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inputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
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block_shape=[1, 1],
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paddings=[[0, 0], [0, 0]],
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outputs=[[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
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def testOneBlockDimZeroRemainingDims(self):
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self._testPad(
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inputs=[[1, 2, 3], [4, 5, 6]],
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block_shape=[2],
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paddings=[1, 0],
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outputs=[[0, 2], [0, 5], [1, 3], [4, 6]])
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def testOneBlockDimOneRemainingDim(self):
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self._testPad(
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inputs=[[[1, 11], [2, 21], [3, 31]], [[4, 41], [5, 51], [6, 61]]],
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block_shape=[2],
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paddings=[1, 0],
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outputs=[[[0, 0], [2, 21]], [[0, 0], [5, 51]], [[1, 11], [3, 31]],
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[[4, 41], [6, 61]]])
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def testDirect0(self):
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# Test with zero-size remaining dimension.
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self._testDirect(
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input_shape=[3, 1, 2, 0], block_shape=[3], paddings=[[0, 2]])
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def testDirect1(self):
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# Test with zero-size blocked dimension.
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self._testDirect(
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input_shape=[3, 0, 2, 5], block_shape=[3], paddings=[[0, 0]])
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def testDirect2(self):
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# Test with padding up from zero size.
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self._testDirect(
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input_shape=[3, 0, 2, 5], block_shape=[3], paddings=[[1, 2]])
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def testDirect3(self):
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self._testDirect(
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input_shape=[3, 3, 4, 5, 2],
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block_shape=[3, 4, 2],
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paddings=[[1, 2], [0, 0], [3, 0]])
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def testDirect4(self):
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self._testDirect(
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input_shape=[3, 3, 4, 5, 2],
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block_shape=[3, 4, 2, 2],
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paddings=[[1, 2], [0, 0], [3, 0], [0, 0]])
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def testDirect5(self):
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self._testDirect(
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input_shape=[3, 2, 2, 3, 4, 5, 2, 5],
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block_shape=[1, 1, 3, 4, 2, 2],
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paddings=[[0, 0], [0, 0], [1, 2], [0, 0], [3, 0], [0, 0]])
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def testDirect6(self):
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self._testDirect(
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input_shape=[3, 2, 2, 3, 4, 5, 2, 5],
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block_shape=[1, 1, 3, 4, 2, 2, 1],
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paddings=[[0, 0], [0, 0], [1, 2], [0, 0], [3, 0], [0, 0], [0, 0]])
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if __name__ == "__main__":
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test.main()
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