1b920224d0
We change the tf2xla compiler for EinsumOp from accepting two Tensors 'a' and 'b' to accepting a list of Tensors. This list must have length 1 or 2 just like the EinsumOp kernel for CPU/GPU. PiperOrigin-RevId: 272360882
181 lines
6.2 KiB
Python
181 lines
6.2 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 tpu_function helpers."""
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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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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 importer
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.layers import convolutional
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import control_flow_util
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import special_math_ops
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.platform import test
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from tensorflow.python.tpu import tpu
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from tensorflow.python.tpu import tpu_feed
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from tensorflow.python.tpu import training_loop
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class TPUContextTest(test.TestCase):
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@test_util.deprecated_graph_mode_only
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def testIsInContext(self):
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"""Test that control_flow_util can check that we're in a TPU context."""
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z1 = array_ops.identity(1)
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pivot = control_flow_ops.no_op()
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context = tpu.TPUReplicateContext(b"context", 1, pivot=pivot)
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context.Enter()
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z2 = array_ops.identity(1)
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context.Exit()
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self.assertFalse(control_flow_util.IsInXLAContext(z1.op))
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self.assertTrue(control_flow_util.IsInXLAContext(z2.op))
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class TPULayerRewriteTest(test.TestCase):
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@test_util.deprecated_graph_mode_only
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def testUsingInfeedQueueWithRegularizer(self):
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"""Test that Layer regularizers can reference data created in loops."""
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def make_regularizer(scale):
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return lambda inputs: scale * math_ops.reduce_sum(math_ops.square(inputs))
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def training_step(inputs, scale):
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outputs = convolutional.conv2d(
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inputs,
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filters=16,
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kernel_size=(3, 3),
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data_format="channels_first",
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kernel_regularizer=make_regularizer(scale))
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loss = math_ops.reduce_mean(math_ops.square(outputs))
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return loss.op
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inputs = array_ops.zeros(shape=(128, 32, 32, 16))
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scale = array_ops.ones(shape=())
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infeed = tpu_feed.InfeedQueue(
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tuple_types=[dtypes.float32, dtypes.float32],
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tuple_shapes=[inputs.shape, scale.shape])
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def loop():
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return training_loop.repeat(5, training_step, infeed_queue=infeed)
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# This should not throw an error.
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tpu.rewrite(loop)
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class TPUGraphPruneTest(test.TestCase):
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def test_prune_unconnected_ops(self):
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with ops.Graph().as_default():
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a = array_ops.placeholder(dtype=dtypes.float32, name="a")
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b = array_ops.placeholder(dtype=dtypes.float32, name="b")
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constant_op.constant(1.0, name="constant")
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x = variable_scope.get_variable(
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name="x",
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dtype=dtypes.float32,
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shape=[],
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use_resource=True,
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initializer=init_ops.constant_initializer(2.0))
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y = variable_scope.get_variable(
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name="y",
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dtype=dtypes.float32,
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shape=[],
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use_resource=True,
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initializer=init_ops.constant_initializer(3.0))
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math_ops.add(a, b)
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math_ops.add(x, y)
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graph_def = ops.get_default_graph().as_graph_def()
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for node in graph_def.node:
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# Attach a TPU_REPLICATE_ATTR to each node.
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node.attr[tpu._TPU_REPLICATE_ATTR].s = b"0"
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# Rewire placeholder "a" and variable "y" leaving them unconnected.
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for (input_index, node_input) in enumerate(node.input):
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if node_input == "b":
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node.input[input_index] = "constant"
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if node_input == "y":
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node.input[input_index] = "x"
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with ops.Graph().as_default() as graph:
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# Reimport the graph and prune unconnected ops.
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importer.import_graph_def(graph_def)
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tpu.prune_unconnected_ops_from_xla(ops.get_default_graph())
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# Verify that ops "a" and "x" still have TPU_REPLICATE_ATTR.
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a = graph.get_operation_by_name("import/a").get_attr(
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tpu._TPU_REPLICATE_ATTR)
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self.assertEqual(b"0", a)
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x = graph.get_operation_by_name("import/x").get_attr(
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tpu._TPU_REPLICATE_ATTR)
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self.assertEqual(b"0", x)
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# Verify that ops "b" and "y" have TPU_REPLICATE_ATTR removed.
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with self.assertRaisesRegexp(
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ValueError,
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"Operation \'import/b\' has no attr named \'_tpu_replicate\'"):
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graph.get_operation_by_name("import/b").get_attr(
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tpu._TPU_REPLICATE_ATTR)
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with self.assertRaisesRegexp(
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ValueError,
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"Operation \'import/y\' has no attr named \'_tpu_replicate\'"):
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graph.get_operation_by_name("import/y").get_attr(
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tpu._TPU_REPLICATE_ATTR)
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def do_einsum():
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a = array_ops.placeholder(dtype=dtypes.float32, name="a", shape=[2, 3, 4])
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b = array_ops.placeholder(dtype=dtypes.float32, name="b", shape=[2, 4, 5])
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return special_math_ops.einsum("abc,acd->abd", a, b)
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def find_einsum(g):
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graph_def = g.as_graph_def()
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for node in graph_def.node:
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if node.op == "Einsum":
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return True
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return False
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def find_xla_einsum(g):
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graph_def = g.as_graph_def()
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for node in graph_def.node:
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if node.op == "XlaEinsum":
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return True
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return False
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class TPUXlaEinsumTest(test.TestCase):
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def test_tpu_rewrite_uses_xla_einsum(self):
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with ops.Graph().as_default() as g:
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tpu.rewrite(do_einsum)
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self.assertTrue(find_einsum(g) or find_xla_einsum(g))
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def test_default_does_not_use_xla_einsum(self):
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with ops.Graph().as_default() as g:
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do_einsum()
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self.assertFalse(find_xla_einsum(g))
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if __name__ == "__main__":
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test.main()
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