diff options
Diffstat (limited to 'config-model/src/test')
7 files changed, 5312 insertions, 49 deletions
diff --git a/config-model/src/test/integration/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/config-model/src/test/integration/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py new file mode 100644 index 00000000000..a1861a1c981 --- /dev/null +++ b/config-model/src/test/integration/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,89 @@ +# Copyright 2015 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. +# ============================================================================== + +"""A very simple MNIST classifier. + +See extensive documentation at +https://www.tensorflow.org/get_started/mnist/beginners +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +from tensorflow.examples.tutorials.mnist import input_data + +import tensorflow as tf + +FLAGS = None + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + W = tf.Variable(tf.zeros([784, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for _ in range(1000): + batch_xs, batch_ys = mnist.train.next_batch(100) + sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run(accuracy, feed_dict={x: mnist.test.images, + y_: mnist.test.labels})) + + # Save the model + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = tf.saved_model.builder.SavedModelBuilder(export_path) + signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y}) + builder.add_meta_graph_and_variables(sess, + [tf.saved_model.tag_constants.SERVING], + signature_def_map={'serving_default':signature}) + builder.save(as_text=True) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/config-model/src/test/integration/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/saved_model.pbtxt new file mode 100644 index 00000000000..8100dfd594d --- /dev/null +++ b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + type: DT_STRING + } + } + } + } + op { + name: 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{ + dim { + size: 10 + } + } + } + } + } + attr { + key: "use_locking" + value { + b: true + } + } + attr { + key: "validate_shape" + value { + b: true + } + } + } + node { + name: "save/restore_shard" + op: "NoOp" + input: "^save/Assign" + input: "^save/Assign_1" + } + node { + name: "save/restore_all" + op: "NoOp" + input: "^save/restore_shard" + } + versions { + producer: 24 + } + } + saver_def { + filename_tensor_name: "save/Const:0" + save_tensor_name: "save/Identity:0" + restore_op_name: "save/restore_all" + max_to_keep: 5 + sharded: true + keep_checkpoint_every_n_hours: 10000.0 + version: V2 + } + collection_def { + key: "train_op" + value { + node_list { + value: "GradientDescent" + } + } + } + collection_def { + key: "trainable_variables" + value { + bytes_list { + value: "\n\nVariable:0\022\017Variable/Assign\032\017Variable/read:02\007zeros:0" + value: "\n\014Variable_1:0\022\021Variable_1/Assign\032\021Variable_1/read:02\tzeros_1:0" + } + } + } + collection_def { + key: "variables" + value { + bytes_list { + value: "\n\nVariable:0\022\017Variable/Assign\032\017Variable/read:02\007zeros:0" + value: "\n\014Variable_1:0\022\021Variable_1/Assign\032\021Variable_1/read:02\tzeros_1:0" + } + } + } + signature_def { + key: "serving_default" + value { + inputs { + key: "x" + value { + name: "Placeholder:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 784 + } + } + } + } + outputs { + key: "y" + value { + name: "add:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 10 + } + } + } + } + method_name: "tensorflow/serving/predict" + } + } +} diff --git a/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 Binary files differnew file mode 100644 index 00000000000..8474aa0a04c --- /dev/null +++ b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 diff --git a/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.index b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..cfcdac20409 --- /dev/null +++ b/config-model/src/test/integration/tensorflow/mnist_softmax/saved/variables/variables.index diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankProfileSearchFixture.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankProfileSearchFixture.java new file mode 100644 index 00000000000..e71a627d7db --- /dev/null +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankProfileSearchFixture.java @@ -0,0 +1,58 @@ +package com.yahoo.searchdefinition.processing; + +import com.yahoo.searchdefinition.RankProfile; +import com.yahoo.searchdefinition.RankProfileRegistry; +import com.yahoo.searchdefinition.Search; +import com.yahoo.searchdefinition.SearchBuilder; +import com.yahoo.searchdefinition.parser.ParseException; + +import java.util.List; + +import static org.junit.Assert.assertEquals; + +/** + * Helper class for setting up and asserting over a Search instance with a rank profile given literally + * in the search definition language. + * + * @author geirst + */ +class RankProfileSearchFixture { + + private RankProfileRegistry rankProfileRegistry = new RankProfileRegistry(); + private Search search; + + RankProfileSearchFixture(String rankProfiles) throws ParseException { + SearchBuilder builder = new SearchBuilder(rankProfileRegistry); + String sdContent = "search test {\n" + + " document test {\n" + + " }\n" + + rankProfiles + + "\n" + + "}"; + builder.importString(sdContent); + builder.build(); + search = builder.getSearch(); + } + + public void assertFirstPhaseExpression(String expExpression, String rankProfile) { + assertEquals(expExpression, rankProfile(rankProfile).getFirstPhaseRanking().getRoot().toString()); + } + + public void assertSecondPhaseExpression(String expExpression, String rankProfile) { + assertEquals(expExpression, rankProfile(rankProfile).getSecondPhaseRanking().getRoot().toString()); + } + + public void assertRankProperty(String expValue, String name, String rankProfile) { + List<RankProfile.RankProperty> rankPropertyList = rankProfile(rankProfile).getRankPropertyMap().get(name); + assertEquals(1, rankPropertyList.size()); + assertEquals(expValue, rankPropertyList.get(0).getValue()); + } + + public void assertMacro(String expExpression, String macroName, String rankProfile) { + assertEquals(expExpression, rankProfile(rankProfile).getMacros().get(macroName).getRankingExpression().getRoot().toString()); + } + + public RankProfile rankProfile(String rankProfile) { + return rankProfileRegistry.getRankProfile(search, rankProfile).compile(); + } +} diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java new file mode 100644 index 00000000000..5ad85ac872c --- /dev/null +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java @@ -0,0 +1,119 @@ +// Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +package com.yahoo.searchdefinition.processing; + +import com.yahoo.searchdefinition.parser.ParseException; +import com.yahoo.tensor.Tensor; +import com.yahoo.yolean.Exceptions; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.fail; + +/** + * @author bratseth + */ +public class RankingExpressionWithTensorFlowTestCase { + + private final String modelDirectory = "src/test/integration/tensorflow/mnist_softmax/saved"; + private final String vespaExpression = "join(rename(reduce(join(Placeholder, rename(constant(Variable), (d0, d1), (d1, d3)), f(a,b)(a * b)), sum, d1), d3, d1), rename(constant(Variable_1), d0, d1), f(a,b)(a + b))"; + + @Test + public void testMinimalTensorFlowReference() throws ParseException { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: tensorflow('" + modelDirectory + "')" + + " }\n" + + " }"); + search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + + Tensor variable_1 = search.rankProfile("my_profile").getConstants().get("Variable_1").asTensor(); + assertNotNull("Variable_1 is imported", variable_1); + assertEquals(10, variable_1.size()); + + Tensor variable = search.rankProfile("my_profile").getConstants().get("Variable").asTensor(); + assertNotNull("Variable is imported", variable); + assertEquals(7840, variable.size()); + } + + @Test + public void testNestedTensorFlowReference() throws ParseException { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: 5 + sum(tensorflow('" + modelDirectory + "'))" + + " }\n" + + " }"); + search.assertFirstPhaseExpression("5 + reduce(" + vespaExpression + ", sum)", "my_profile"); + + Tensor variable_1 = search.rankProfile("my_profile").getConstants().get("Variable_1").asTensor(); + assertNotNull("Variable_1 is imported", variable_1); + assertEquals(10, variable_1.size()); + + Tensor variable = search.rankProfile("my_profile").getConstants().get("Variable").asTensor(); + assertNotNull("Variable is imported", variable); + assertEquals(7840, variable.size()); + } + + @Test + public void testTensorFlowReferenceSpecifyingSignature() throws ParseException { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: tensorflow('" + modelDirectory + "', 'serving_default')" + + " }\n" + + " }"); + search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + } + + @Test + public void testTensorFlowReferenceSpecifyingSignatureAndOutput() throws ParseException { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: tensorflow('" + modelDirectory + "', 'serving_default', 'y')" + + " }\n" + + " }"); + search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + } + + @Test + public void testTensorFlowReferenceSpecifyingNonExistingSignature() throws ParseException { + try { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: tensorflow('" + modelDirectory + "', 'serving_defaultz')" + + " }\n" + + " }"); + search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + fail("Expecting exception"); + } + catch (IllegalArgumentException expected) { + assertEquals("Rank profile 'my_profile' is invalid: Could not import tensorflow model from tensorflow('" + + modelDirectory + "','serving_defaultz'): Model does not have the specified signatures 'serving_defaultz'", + Exceptions.toMessageString(expected)); + } + } + + @Test + public void testTensorFlowReferenceSpecifyingNonExistingOutput() throws ParseException { + try { + RankProfileSearchFixture search = new RankProfileSearchFixture( + " rank-profile my_profile {\n" + + " first-phase {\n" + + " expression: tensorflow('" + modelDirectory + "', 'serving_default', 'x')" + + " }\n" + + " }"); + search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + fail("Expecting exception"); + } + catch (IllegalArgumentException expected) { + assertEquals("Rank profile 'my_profile' is invalid: Could not import tensorflow model from tensorflow('" + + modelDirectory + "','serving_default','x'): Model does not have the specified outputs 'x'", + Exceptions.toMessageString(expected)); + } + } + +} diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorTestCase.java index 4dcf7523fd0..dba2bdbfbbf 100644 --- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorTestCase.java +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorTestCase.java @@ -1,61 +1,19 @@ // Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. package com.yahoo.searchdefinition.processing; -import com.yahoo.searchdefinition.RankProfile; -import com.yahoo.searchdefinition.RankProfileRegistry; -import com.yahoo.searchdefinition.Search; -import com.yahoo.searchdefinition.SearchBuilder; import com.yahoo.searchdefinition.parser.ParseException; import org.junit.Rule; import org.junit.Test; import org.junit.rules.ExpectedException; -import java.util.List; - -import static org.junit.Assert.assertEquals; - /** * @author geirst */ public class RankingExpressionWithTensorTestCase { - private static class SearchFixture { - RankProfileRegistry rankProfileRegistry = new RankProfileRegistry(); - Search search; - SearchFixture(String rankProfiles) throws ParseException { - SearchBuilder builder = new SearchBuilder(rankProfileRegistry); - String sdContent = "search test {\n" + - " document test {\n" + - " }\n" + - rankProfiles + - "\n" + - "}"; - builder.importString(sdContent); - builder.build(); - search = builder.getSearch(); - } - public void assertFirstPhaseExpression(String expExpression, String rankProfile) { - assertEquals(expExpression, getRankProfile(rankProfile).getFirstPhaseRanking().getRoot().toString()); - } - public void assertSecondPhaseExpression(String expExpression, String rankProfile) { - assertEquals(expExpression, getRankProfile(rankProfile).getSecondPhaseRanking().getRoot().toString()); - } - public void assertRankProperty(String expValue, String name, String rankProfile) { - List<RankProfile.RankProperty> rankPropertyList = getRankProfile(rankProfile).getRankPropertyMap().get(name); - assertEquals(1, rankPropertyList.size()); - assertEquals(expValue, rankPropertyList.get(0).getValue()); - } - public void assertMacro(String expExpression, String macroName, String rankProfile) { - assertEquals(expExpression, getRankProfile(rankProfile).getMacros().get(macroName).getRankingExpression().getRoot().toString()); - } - private RankProfile getRankProfile(String rankProfile) { - return rankProfileRegistry.getRankProfile(search, rankProfile).compile(); - } - } - @Test public void requireThatSingleLineConstantTensorAndTypeCanBeParsed() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile my_profile {\n" + " first-phase {\n" + " expression: sum(my_tensor)\n" + @@ -74,7 +32,7 @@ public class RankingExpressionWithTensorTestCase { @Test public void requireThatMultiLineConstantTensorAndTypeCanBeParsed() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile my_profile {\n" + " first-phase {\n" + " expression: sum(my_tensor)\n" + @@ -96,7 +54,7 @@ public class RankingExpressionWithTensorTestCase { @Test public void requireThatConstantTensorsCanBeUsedInSecondPhaseExpression() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile my_profile {\n" + " second-phase {\n" + " expression: sum(my_tensor)\n" + @@ -114,7 +72,7 @@ public class RankingExpressionWithTensorTestCase { @Test public void requireThatConstantTensorsCanBeUsedInInheritedRankProfile() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile parent {\n" + " constants {\n" + " my_tensor {\n" + @@ -134,7 +92,7 @@ public class RankingExpressionWithTensorTestCase { @Test public void requireThatConstantTensorsCanBeUsedInMacro() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile my_profile {\n" + " macro my_macro() {\n" + " expression: sum(my_tensor)\n" + @@ -156,7 +114,7 @@ public class RankingExpressionWithTensorTestCase { @Test public void requireThatCombinationOfConstantTensorsAndConstantValuesCanBeUsed() throws ParseException { - SearchFixture f = new SearchFixture( + RankProfileSearchFixture f = new RankProfileSearchFixture( " rank-profile my_profile {\n" + " first-phase {\n" + " expression: my_number_1 + sum(my_tensor) + my_number_2\n" + @@ -181,7 +139,7 @@ public class RankingExpressionWithTensorTestCase { public void requireThatInvalidTensorTypeSpecThrowsException() throws ParseException { exception.expect(IllegalArgumentException.class); exception.expectMessage("For constant tensor 'my_tensor' in rank profile 'my_profile': Illegal tensor type spec: Failed parsing element 'x' in type spec 'tensor(x)'"); - new SearchFixture( + new RankProfileSearchFixture( " rank-profile my_profile {\n" + " constants {\n" + " my_tensor {\n" + |