diff options
| author | Lester Solbakken <lesters@users.noreply.github.com> | 2020-09-04 11:58:59 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-09-04 11:58:59 +0200 |
| commit | 503b0ff5037f0db031b3410899e1f9cccb23bd0a (patch) | |
| tree | 17f3d7626d66e27239c0c4d9dde557d02b907889 | |
| parent | f0962f044ff9844fb8375d3cd0be265d165fa646 (diff) | |
| parent | 5f96bfdcb4dbf38743fd4c93bad9e3acabbbd3b0 (diff) | |
Merge pull request #14281 from vespa-engine/lesters/tf-via-onnx
Import TensorFlow models via ONNX conversion
61 files changed, 255 insertions, 75029 deletions
diff --git a/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx b/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx Binary files differnew file mode 100644 index 00000000000..0d4bffa5b57 --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt deleted file mode 100644 index 5528aa99401..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt +++ /dev/null @@ -1,8830 +0,0 @@ -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" - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -# Common imports -import numpy as np -import tensorflow as tf - -from tensorflow.examples.tutorials.mnist import input_data -from datetime import datetime - -now = datetime.utcnow().strftime("%Y%m%d%H%M%S") -root_logdir = "tf_logs" -logdir = "{}/run-{}/".format(root_logdir, now) - -mnist = input_data.read_data_sets("/tmp/data/") -X_train = mnist.train.images -X_test = mnist.test.images -y_train = mnist.train.labels.astype("int") -y_test = mnist.test.labels.astype("int") - -n_inputs = 28*28 # MNIST -n_hidden1 = 300 -n_hidden2 = 100 -n_hidden3 = 40 -n_outputs = 10 - -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -y = tf.placeholder(tf.int64, shape=(None), name="y") - - -def neuron_layer(X, n_neurons, name, activation=None): - with tf.name_scope(name): - n_inputs = int(X.get_shape()[1]) - stddev = 2 / np.sqrt(n_inputs) - init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) - W = tf.Variable(init, name="weights") - b = tf.Variable(tf.zeros([n_neurons]), name="bias") - Z = tf.matmul(X, W) + b - if activation is not None: - return activation(Z) - else: - return Z - - -def leaky_relu(z, name=None): - return tf.maximum(0.01 * z, z, name=name) - -def leaky_relu_with_small_constant(z, name=None): - return tf.maximum(tf.constant(0.01, shape=[1]) * z, z, name=name) - -with tf.name_scope("dnn"): - hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) - hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=leaky_relu_with_small_constant) - logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) - -with tf.name_scope("loss"): - xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - loss = tf.reduce_mean(xentropy, name="loss") - -with tf.name_scope("train"): - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - training_op = optimizer.minimize(loss) - -with tf.name_scope("eval"): - correct = tf.nn.in_top_k(logits, y, 1) - accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -accuracy_summary = tf.summary.scalar('Accuracy', accuracy) -file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) - -with tf.Session() as sess: - init.run() - for epoch in range(n_epochs): - for iteration in range(mnist.train.num_examples // batch_size): - X_batch, y_batch = mnist.train.next_batch(batch_size) - sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) - acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) - acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) - - # Save summary for tensorboard - summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - file_writer.add_summary(summary_str, epoch) - - 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':input}, outputs = {'y':logits}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -file_writer.close() diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py deleted file mode 100644 index 3f4f794d2ac..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -# 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]) - - with tf.name_scope("layer"): - 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/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt deleted file mode 100644 index 05b0e4e0f29..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt +++ /dev/null @@ -1,5039 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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key: "y" - value { - name: "layer/add:0" - dtype: DT_FLOAT - tensor_shape { - dim { - size: -1 - } - dim { - size: 10 - } - } - } - } - method_name: "tensorflow/serving/predict" - } - } -} diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 Binary files differdeleted file mode 100644 index 826b0280abf..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 +++ /dev/null diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index Binary files differdeleted file mode 100644 index d00fc5b06ed..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index +++ /dev/null diff --git a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java index 3d7eed1e729..369b2cbe42b 100644 --- a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java +++ b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java @@ -45,7 +45,7 @@ public class ContainerModelEvaluationTest { } private void assertLoadedModels(JDisc jdisc) { { - String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"tensorflow_mnist_softmax_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_softmax_saved\",\"tensorflow_mnist_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_saved\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}"; + String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"onnx_softmax_func\":\"http://localhost/model-evaluation/v1/onnx_softmax_func\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}"; assertResponse("http://localhost/model-evaluation/v1", expected, jdisc); } @@ -60,9 +60,8 @@ public class ContainerModelEvaluationTest { } { - // Note: The specific response value here has not been verified - String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\",\"d1\":\"0\"},\"value\":-0.5066885003407351},{\"address\":{\"d0\":\"0\",\"d1\":\"1\"},\"value\":0.3912837743150205},{\"address\":{\"d0\":\"0\",\"d1\":\"2\"},\"value\":-0.12401806321703948},{\"address\":{\"d0\":\"0\",\"d1\":\"3\"},\"value\":-0.7019029168606575},{\"address\":{\"d0\":\"0\",\"d1\":\"4\"},\"value\":0.13120114146441697},{\"address\":{\"d0\":\"0\",\"d1\":\"5\"},\"value\":0.6611923203384626},{\"address\":{\"d0\":\"0\",\"d1\":\"6\"},\"value\":-0.22365810810026446},{\"address\":{\"d0\":\"0\",\"d1\":\"7\"},\"value\":-0.0740018307465809},{\"address\":{\"d0\":\"0\",\"d1\":\"8\"},\"value\":0.056492490256153896},{\"address\":{\"d0\":\"0\",\"d1\":\"9\"},\"value\":-0.18422015072393733}]}"; - assertResponse("http://localhost/model-evaluation/v1/tensorflow_mnist_saved/serving_default.y/eval?input=" + inputTensor(), expected, jdisc); + String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\"},\"value\":0.3006095290184021},{\"address\":{\"d0\":\"1\"},\"value\":0.33222490549087524},{\"address\":{\"d0\":\"2\"},\"value\":0.36716532707214355}]}"; + assertResponse("http://localhost/model-evaluation/v1/onnx_softmax_func/default.output/eval?input=" + inputTensor(), expected, jdisc); } } @@ -79,9 +78,10 @@ public class ContainerModelEvaluationTest { } private String inputTensor() { - Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor(d0[],d1[784])")); - for (int i = 0; i < 784; i++) - b.cell(0.0, 0, i); + Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor<float>(d0[3])")); + b.cell(0.1, 0); + b.cell(0.2, 1); + b.cell(0.3, 2); return URLEncoder.encode(b.build().toString(), StandardCharsets.UTF_8); } diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java index ab143f77b6a..ec517768ea9 100644 --- a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java +++ b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java @@ -54,10 +54,10 @@ public class OnnxFeatureConverter extends ExpressionTransformer<RankProfileTrans private FeatureArguments asFeatureArguments(Arguments arguments) { if (arguments.isEmpty()) - throw new IllegalArgumentException("An onnx node must take an argument pointing to " + - "the onnx model directory under [application]/models"); + throw new IllegalArgumentException("An ONNX node must take an argument pointing to " + + "the ONNX model file under [application]/models"); if (arguments.expressions().size() > 3) - throw new IllegalArgumentException("An onnx feature can have at most 2 arguments"); + throw new IllegalArgumentException("An onnx feature can have at most 3 arguments"); return new FeatureArguments(arguments); } diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist/saved/saved_model.pbtxt b/config-model/src/test/cfg/application/ml_models/models/mnist/saved/saved_model.pbtxt deleted file mode 100644 index eb926836576..00000000000 --- a/config-model/src/test/cfg/application/ml_models/models/mnist/saved/saved_model.pbtxt +++ /dev/null @@ -1,7982 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - meta_info_def { - stripped_op_list { - op { - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -# Common imports -import numpy as np -import tensorflow as tf - -from tensorflow.examples.tutorials.mnist import input_data -from datetime import datetime - -now = datetime.utcnow().strftime("%Y%m%d%H%M%S") -root_logdir = "tf_logs" -logdir = "{}/run-{}/".format(root_logdir, now) - -mnist = input_data.read_data_sets("/tmp/data/") -X_train = mnist.train.images -X_test = mnist.test.images -y_train = mnist.train.labels.astype("int") -y_test = mnist.test.labels.astype("int") - -n_inputs = 28*28 # MNIST -n_hidden1 = 300 -n_hidden2 = 100 -n_hidden3 = 40 -n_outputs = 10 - -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -y = tf.placeholder(tf.int64, shape=(None), name="y") - - -def neuron_layer(X, n_neurons, name, activation=None): - with tf.name_scope(name): - n_inputs = int(X.get_shape()[1]) - stddev = 2 / np.sqrt(n_inputs) - init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) - W = tf.Variable(init, name="weights") - b = tf.Variable(tf.zeros([n_neurons]), name="bias") - Z = tf.matmul(X, W) + b - if activation is not None: - return activation(Z) - else: - return Z - - -def leaky_relu(z, name=None): - return tf.maximum(0.01 * z, z, name=name) - - -with tf.name_scope("dnn"): - hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) - hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu) - logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) - -with tf.name_scope("loss"): - xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - loss = tf.reduce_mean(xentropy, name="loss") - -with tf.name_scope("train"): - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - training_op = optimizer.minimize(loss) - -with tf.name_scope("eval"): - correct = tf.nn.in_top_k(logits, y, 1) - accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -accuracy_summary = tf.summary.scalar('Accuracy', accuracy) -file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) - -with tf.Session() as sess: - init.run() - for epoch in range(n_epochs): - for iteration in range(mnist.train.num_examples // batch_size): - X_batch, y_batch = mnist.train.next_batch(batch_size) - sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) - acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) - acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) - - # Save summary for tensorboard - summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - file_writer.add_summary(summary_str, epoch) - - 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':input}, outputs = {'y':logits}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -file_writer.close() diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py deleted file mode 100644 index 3f4f794d2ac..00000000000 --- a/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -# 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]) - - with tf.name_scope("layer"): - 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/cfg/application/ml_models/models/mnist_softmax/saved/saved_model.pbtxt b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/saved_model.pbtxt deleted file mode 100644 index 05b0e4e0f29..00000000000 --- a/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/saved_model.pbtxt +++ /dev/null @@ -1,5039 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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function mnist_tensorflow() { - expression: tensorflow("mnist/saved") - } - - function mnist_softmax_tensorflow() { - expression: tensorflow("mnist_softmax/saved") - } - function mnist_softmax_onnx() { expression: onnx("mnist_softmax") } @@ -38,7 +30,7 @@ search test { } first-phase { - expression: mnist_tensorflow + mnist_softmax_tensorflow + mnist_softmax_onnx + my_xgboost + my_lightgbm + expression: mnist_softmax_onnx + my_xgboost + my_lightgbm } } diff --git a/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/saved_model.pbtxt b/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/saved_model.pbtxt deleted file mode 100644 index eb926836576..00000000000 --- a/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/saved_model.pbtxt +++ /dev/null @@ -1,7982 +0,0 @@ -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" - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -# Common imports -import numpy as np -import tensorflow as tf - -from tensorflow.examples.tutorials.mnist import input_data -from datetime import datetime - -now = datetime.utcnow().strftime("%Y%m%d%H%M%S") -root_logdir = "tf_logs" -logdir = "{}/run-{}/".format(root_logdir, now) - -mnist = input_data.read_data_sets("/tmp/data/") -X_train = mnist.train.images -X_test = mnist.test.images -y_train = mnist.train.labels.astype("int") -y_test = mnist.test.labels.astype("int") - -n_inputs = 28*28 # MNIST -n_hidden1 = 300 -n_hidden2 = 100 -n_hidden3 = 40 -n_outputs = 10 - -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -y = tf.placeholder(tf.int64, shape=(None), name="y") - - -def neuron_layer(X, n_neurons, name, activation=None): - with tf.name_scope(name): - n_inputs = int(X.get_shape()[1]) - stddev = 2 / np.sqrt(n_inputs) - init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) - W = tf.Variable(init, name="weights") - b = tf.Variable(tf.zeros([n_neurons]), name="bias") - Z = tf.matmul(X, W) + b - if activation is not None: - return activation(Z) - else: - return Z - - -def leaky_relu(z, name=None): - return tf.maximum(0.01 * z, z, name=name) - - -with tf.name_scope("dnn"): - hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) - hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu) - logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) - -with tf.name_scope("loss"): - xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - loss = tf.reduce_mean(xentropy, name="loss") - -with tf.name_scope("train"): - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - training_op = optimizer.minimize(loss) - -with tf.name_scope("eval"): - correct = tf.nn.in_top_k(logits, y, 1) - accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -accuracy_summary = tf.summary.scalar('Accuracy', accuracy) -file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) - -with tf.Session() as sess: - init.run() - for epoch in range(n_epochs): - for iteration in range(mnist.train.num_examples // batch_size): - X_batch, y_batch = mnist.train.next_batch(batch_size) - sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) - acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) - acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) - - # Save summary for tensorboard - summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - file_writer.add_summary(summary_str, epoch) - - 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':input}, outputs = {'y':logits}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -file_writer.close() diff --git a/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/mnist_sftmax_with_saving.py b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/mnist_sftmax_with_saving.py deleted file mode 100644 index 3f4f794d2ac..00000000000 --- a/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/mnist_sftmax_with_saving.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -# 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]) - - with tf.name_scope("layer"): - 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/cfg/application/ml_serving/models/mnist_softmax/saved/saved_model.pbtxt b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/saved/saved_model.pbtxt deleted file mode 100644 index 05b0e4e0f29..00000000000 --- a/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/saved/saved_model.pbtxt +++ /dev/null @@ -1,5039 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. --> -<services version="1.0"> - - <container version="1.0"> - <model-evaluation/> - <nodes> - <node hostalias="node1" /> - </nodes> - - </container> - -</services> diff --git a/config-model/src/test/integration/onnx/models/mnist_softmax.onnx b/config-model/src/test/integration/onnx/models/mnist_softmax.onnx Binary files differindex a86019bf53a..395cd734444 100644 --- a/config-model/src/test/integration/onnx/models/mnist_softmax.onnx +++ b/config-model/src/test/integration/onnx/models/mnist_softmax.onnx diff --git a/config-model/src/test/integration/onnx/models/small_constants_and_functions.onnx b/config-model/src/test/integration/onnx/models/small_constants_and_functions.onnx Binary files differnew file mode 100644 index 00000000000..0d4bffa5b57 --- /dev/null +++ b/config-model/src/test/integration/onnx/models/small_constants_and_functions.onnx diff --git a/config-model/src/test/integration/onnx/models/small_constants_and_functions.py b/config-model/src/test/integration/onnx/models/small_constants_and_functions.py new file mode 100755 index 00000000000..7b04b93fee2 --- /dev/null +++ b/config-model/src/test/integration/onnx/models/small_constants_and_functions.py @@ -0,0 +1,51 @@ +# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +import onnx +from onnx import helper, TensorProto + +input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [3]) +output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [3]) + +initializers = [ + helper.make_tensor( + name='epsilon', # small constant: no dimensions + data_type=TensorProto.FLOAT, + dims=(), + vals=[1e-6] + ) +] + +nodes = [ + onnx.helper.make_node( + 'Exp', + inputs=['input'], + outputs=['exp_output'] + ), + onnx.helper.make_node( + 'ReduceSum', + inputs=['exp_output'], + outputs=['sum_exp_output'], + axes=[0] + ), + onnx.helper.make_node( + 'Add', + inputs=['sum_exp_output', 'epsilon'], + outputs=['add_output'] + ), + onnx.helper.make_node( + 'Div', + inputs=['exp_output', 'add_output'], + outputs=['output'] + ) +] + +graph_def = onnx.helper.make_graph( + nodes = nodes, + name = 'test', + inputs = [input], + outputs = [output], + initializer = initializers +) +model_def = helper.make_model(graph_def, producer_name='small_constants_and_functions.py') +onnx.checker.check_model(model_def) +onnx.save(model_def, 'small_constants_and_functions.onnx') diff --git a/config-model/src/test/integration/tensorflow/models/blog/saved/saved_model.pbtxt b/config-model/src/test/integration/tensorflow/models/blog/saved/saved_model.pbtxt deleted file mode 100644 index a669e69b709..00000000000 --- a/config-model/src/test/integration/tensorflow/models/blog/saved/saved_model.pbtxt +++ /dev/null @@ -1,14726 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -# Common imports -import numpy as np -import tensorflow as tf - -from tensorflow.examples.tutorials.mnist import input_data -from datetime import datetime - -now = datetime.utcnow().strftime("%Y%m%d%H%M%S") -root_logdir = "tf_logs" -logdir = "{}/run-{}/".format(root_logdir, now) - -mnist = input_data.read_data_sets("/tmp/data/") -X_train = mnist.train.images -X_test = mnist.test.images -y_train = mnist.train.labels.astype("int") -y_test = mnist.test.labels.astype("int") - -n_inputs = 28*28 # MNIST -n_hidden1 = 300 -n_hidden2 = 100 -n_hidden3 = 40 -n_outputs = 10 - -learning_rate = 0.01 -n_epochs = 20 -batch_size = 50 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -y = tf.placeholder(tf.int64, shape=(None), name="y") - - -def neuron_layer(X, n_neurons, name, activation=None): - with tf.name_scope(name): - n_inputs = int(X.get_shape()[1]) - stddev = 2 / np.sqrt(n_inputs) - init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) - W = tf.Variable(init, name="weights") - b = tf.Variable(tf.zeros([n_neurons]), name="bias") - Z = tf.matmul(X, W) + b - if activation is not None: - return activation(Z) - else: - return Z - - -def leaky_relu(z, name=None): - return tf.maximum(0.01 * z, z, name=name) - -def leaky_relu_with_small_constant(z, name=None): - return tf.maximum(tf.constant(0.01, shape=[1]) * z, z, name=name) - -with tf.name_scope("dnn"): - hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) - hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=leaky_relu_with_small_constant) - logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) - -with tf.name_scope("loss"): - xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) - loss = tf.reduce_mean(xentropy, name="loss") - -with tf.name_scope("train"): - optimizer = tf.train.GradientDescentOptimizer(learning_rate) - training_op = optimizer.minimize(loss) - -with tf.name_scope("eval"): - correct = tf.nn.in_top_k(logits, y, 1) - accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) - -init = tf.global_variables_initializer() -accuracy_summary = tf.summary.scalar('Accuracy', accuracy) -file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) - -with tf.Session() as sess: - init.run() - for epoch in range(n_epochs): - for iteration in range(mnist.train.num_examples // batch_size): - X_batch, y_batch = mnist.train.next_batch(batch_size) - sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) - acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) - acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) - - # Save summary for tensorboard - summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, - y: mnist.validation.labels}) - file_writer.add_summary(summary_str, epoch) - - 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':input}, outputs = {'y':logits}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - -file_writer.close() diff --git a/config-model/src/test/integration/tensorflow/models/mnist_softmax/mnist_sftmax_with_saving.py b/config-model/src/test/integration/tensorflow/models/mnist_softmax/mnist_sftmax_with_saving.py deleted file mode 100644 index 3f4f794d2ac..00000000000 --- a/config-model/src/test/integration/tensorflow/models/mnist_softmax/mnist_sftmax_with_saving.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -# 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]) - - with tf.name_scope("layer"): - 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/models/mnist_softmax/saved/saved_model.pbtxt b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/saved_model.pbtxt deleted file mode 100644 index 05b0e4e0f29..00000000000 --- a/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/saved_model.pbtxt +++ /dev/null @@ -1,5039 +0,0 @@ -saved_model_schema_version: 1 -meta_graphs { - 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key: "y" - value { - name: "layer/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/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 Binary files differdeleted file mode 100644 index 826b0280abf..00000000000 --- a/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 +++ /dev/null diff --git a/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.index b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.index Binary files differdeleted file mode 100644 index d00fc5b06ed..00000000000 --- a/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.index +++ /dev/null diff --git a/config-model/src/test/integration/tensorflow/services.xml b/config-model/src/test/integration/tensorflow/services.xml deleted file mode 100644 index aa1c0223bdf..00000000000 --- a/config-model/src/test/integration/tensorflow/services.xml +++ /dev/null @@ -1,6 +0,0 @@ -<!-- Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. --> -<services> - <container version="1.0"> - - </container> -</services> diff --git a/config-model/src/test/java/com/yahoo/config/model/ModelNameCollisionTest.java b/config-model/src/test/java/com/yahoo/config/model/ModelNameCollisionTest.java deleted file mode 100644 index 08f18331d1c..00000000000 --- a/config-model/src/test/java/com/yahoo/config/model/ModelNameCollisionTest.java +++ /dev/null @@ -1,43 +0,0 @@ -// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. -package com.yahoo.config.model; - -import com.yahoo.config.application.api.ApplicationPackage; -import com.yahoo.io.IOUtils; -import com.yahoo.path.Path; -import com.yahoo.vespa.model.VespaModel; -import org.junit.After; -import org.junit.Test; -import org.xml.sax.SAXException; - -import java.io.IOException; - -import static org.junit.Assert.assertEquals; - -/** - * @author bratseth - */ -public class ModelNameCollisionTest { - - private static final Path appDir = Path.fromString("src/test/cfg/application/ml_serving_name_collision"); - - @After - public void removeGeneratedModelFiles() { - IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - } - - @Test - public void testMl_ServingApplication() throws SAXException, IOException { - ApplicationPackageTester tester = ApplicationPackageTester.create(appDir.toString()); - try { - new VespaModel(tester.app()); - } - catch (IllegalArgumentException e) { - assertEquals("The models in " + - appDir + "/models/parent/mnist_softmax.onnx and " + - appDir + "/models/parent/mnist_softmax" + - " both resolve to the model name 'parent_mnist_softmax'", - e.getMessage()); - } - } - -} diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithLightGBMTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithLightGBMTestCase.java index 79d19371f1c..27fa07c7c37 100644 --- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithLightGBMTestCase.java +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithLightGBMTestCase.java @@ -48,7 +48,7 @@ public class RankingExpressionWithLightGBMTestCase { storedApplicationDirectory.toFile().mkdirs(); IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(storedApplicationDirectory); + RankingExpressionWithOnnxTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(storedApplicationDirectory); RankProfileSearchFixture searchFromStored = fixtureWith("lightgbm('regression.json')"); searchFromStored.assertFirstPhaseExpression(lightGBMExpression, "my_profile"); } @@ -59,13 +59,13 @@ public class RankingExpressionWithLightGBMTestCase { private RankProfileSearchFixture fixtureWith(String firstPhaseExpression) { return fixtureWith(firstPhaseExpression, null, null, - new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(applicationDir)); + new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(applicationDir)); } private RankProfileSearchFixture fixtureWith(String firstPhaseExpression, String constant, String field, - RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage application) { + RankingExpressionWithOnnxTestCase.StoringApplicationPackage application) { try { RankProfileSearchFixture fixture = new RankProfileSearchFixture( application, diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java index dffdc3b4a34..6bf69907609 100644 --- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java @@ -1,23 +1,33 @@ // Copyright 2018 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.config.application.api.ApplicationFile; import com.yahoo.config.application.api.ApplicationPackage; +import com.yahoo.config.model.test.MockApplicationPackage; import com.yahoo.io.IOUtils; +import com.yahoo.io.reader.NamedReader; import com.yahoo.path.Path; import com.yahoo.search.query.profile.QueryProfileRegistry; import com.yahoo.searchdefinition.parser.ParseException; +import com.yahoo.searchlib.rankingexpression.evaluation.Value; +import com.yahoo.tensor.TensorType; import com.yahoo.vespa.model.VespaModel; import com.yahoo.vespa.model.ml.ImportedModelTester; import com.yahoo.yolean.Exceptions; import org.junit.After; import org.junit.Test; +import java.io.File; +import java.io.FileReader; import java.io.IOException; +import java.io.UncheckedIOException; +import java.util.ArrayList; +import java.util.Collections; +import java.util.List; import java.util.Optional; -import com.yahoo.searchdefinition.processing.RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage; - import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; import static org.junit.Assert.assertNull; import static org.junit.Assert.fail; @@ -25,10 +35,8 @@ public class RankingExpressionWithOnnxTestCase { private final Path applicationDir = Path.fromString("src/test/integration/onnx/"); - /** The model name */ private final static String name = "mnist_softmax"; - - private final static String vespaExpression = "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(" + name + "_Variable), f(a,b)(a * b)), sum, d2), constant(" + name + "_Variable_1), f(a,b)(a + b))"; + private final static String vespaExpression = "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_layer_Variable), f(a,b)(a * b)), sum, d2) * 1.0, constant(mnist_softmax_layer_Variable_1) * 1.0, f(a,b)(a + b))"; @After public void removeGeneratedModelFiles() { @@ -39,8 +47,8 @@ public class RankingExpressionWithOnnxTestCase { public void testGlobalOnnxModel() throws IOException { ImportedModelTester tester = new ImportedModelTester(name, applicationDir); VespaModel model = tester.createVespaModel(); - tester.assertLargeConstant(name + "_Variable_1", model, Optional.of(10L)); - tester.assertLargeConstant(name + "_Variable", model, Optional.of(7840L)); + tester.assertLargeConstant(name + "_layer_Variable_1", model, Optional.of(10L)); + tester.assertLargeConstant(name + "_layer_Variable", model, Optional.of(7840L)); // At this point the expression is stored - copy application to another location which do not have a models dir Path storedAppDir = applicationDir.append("copy"); @@ -51,8 +59,8 @@ public class RankingExpressionWithOnnxTestCase { storedAppDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); ImportedModelTester storedTester = new ImportedModelTester(name, storedAppDir); VespaModel storedModel = storedTester.createVespaModel(); - tester.assertLargeConstant(name + "_Variable_1", storedModel, Optional.of(10L)); - tester.assertLargeConstant(name + "_Variable", storedModel, Optional.of(7840L)); + tester.assertLargeConstant(name + "_layer_Variable_1", storedModel, Optional.of(10L)); + tester.assertLargeConstant(name + "_layer_Variable", storedModel, Optional.of(7840L)); } finally { IOUtils.recursiveDeleteDir(storedAppDir.toFile()); @@ -63,7 +71,7 @@ public class RankingExpressionWithOnnxTestCase { public void testOnnxReferenceWithConstantFeature() { RankProfileSearchFixture search = fixtureWith("constant(mytensor)", "onnx('mnist_softmax.onnx')", - "constant mytensor { file: ignored\ntype: tensor<float>(d0[7],d1[784]) }", + "constant mytensor { file: ignored\ntype: tensor<float>(d0[1],d1[784]) }", null); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @@ -73,7 +81,7 @@ public class RankingExpressionWithOnnxTestCase { String queryProfile = "<query-profile id='default' type='root'/>"; String queryProfileType = "<query-profile-type id='root'>" + - " <field name='query(mytensor)' type='tensor<float>(d0[3],d1[784])'/>" + + " <field name='query(mytensor)' type='tensor<float>(d0[1],d1[784])'/>" + "</query-profile-type>"; StoringApplicationPackage application = new StoringApplicationPackage(applicationDir, queryProfile, @@ -120,22 +128,22 @@ public class RankingExpressionWithOnnxTestCase { @Test public void testNestedOnnxReference() { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", "5 + sum(onnx('mnist_softmax.onnx'))"); search.assertFirstPhaseExpression("5 + reduce(" + vespaExpression + ", sum)", "my_profile"); } @Test public void testOnnxReferenceWithSpecifiedOutput() { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", - "onnx('mnist_softmax.onnx', 'add')"); + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", + "onnx('mnist_softmax.onnx', 'layer_add')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @Test public void testOnnxReferenceWithSpecifiedOutputAndSignature() { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", - "onnx('mnist_softmax.onnx', 'default.add')"); + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", + "onnx('mnist_softmax.onnx', 'default.layer_add')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @@ -157,7 +165,7 @@ public class RankingExpressionWithOnnxTestCase { catch (IllegalArgumentException expected) { assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " + "onnx('mnist_softmax.onnx'): " + - "Model refers input 'Placeholder' of type tensor<float>(d0[],d1[784]) but this function is " + + "Model refers input 'Placeholder' of type tensor<float>(d0[1],d1[784]) but this function is " + "not present in rank profile 'my_profile'", Exceptions.toMessageString(expected)); } @@ -166,7 +174,7 @@ public class RankingExpressionWithOnnxTestCase { @Test public void testOnnxReferenceWithWrongFunctionType() { try { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d5[10])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d5[10])(0.0)", "onnx('mnist_softmax.onnx')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); fail("Expecting exception"); @@ -174,8 +182,8 @@ public class RankingExpressionWithOnnxTestCase { catch (IllegalArgumentException expected) { assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " + "onnx('mnist_softmax.onnx'): " + - "Model refers input 'Placeholder'. The required type of this is tensor<float>(d0[],d1[784]), " + - "but this function returns tensor(d0[2],d5[10])", + "Model refers input 'Placeholder'. The required type of this is tensor<float>(d0[1],d1[784]), " + + "but this function returns tensor(d0[1],d5[10])", Exceptions.toMessageString(expected)); } } @@ -191,14 +199,14 @@ public class RankingExpressionWithOnnxTestCase { catch (IllegalArgumentException expected) { assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " + "onnx('mnist_softmax.onnx','y'): " + - "No expressions named 'y' in model 'mnist_softmax.onnx'. Available expressions: default.add", + "No expressions named 'y' in model 'mnist_softmax.onnx'. Available expressions: default.layer_add", Exceptions.toMessageString(expected)); } } @Test public void testImportingFromStoredExpressions() throws IOException { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", "onnx('mnist_softmax.onnx')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); @@ -225,26 +233,29 @@ public class RankingExpressionWithOnnxTestCase { } @Test - public void testImportingFromStoredExpressionsWithFunctionOverridingConstant() throws IOException { + public void testImportingFromStoredExpressionsWithFunctionOverridingConstantAndInheritance() throws IOException { String rankProfile = " rank-profile my_profile {\n" + " function Placeholder() {\n" + - " expression: tensor<float>(d0[2],d1[784])(0.0)\n" + + " expression: tensor<float>(d0[1],d1[784])(0.0)\n" + " }\n" + - " function " + name + "_Variable() {\n" + + " function " + name + "_layer_Variable() {\n" + " expression: tensor<float>(d1[10],d2[784])(0.0)\n" + " }\n" + " first-phase {\n" + " expression: onnx('mnist_softmax.onnx')" + " }\n" + + " }" + + " rank-profile my_profile_child inherits my_profile {\n" + " }"; - String vespaExpressionWithoutConstant = - "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), " + name + "_Variable, f(a,b)(a * b)), sum, d2), constant(" + name + "_Variable_1), f(a,b)(a + b))"; + "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), " + name + "_layer_Variable, f(a,b)(a * b)), sum, d2) * 1.0, constant(" + name + "_layer_Variable_1) * 1.0, f(a,b)(a + b))"; RankProfileSearchFixture search = uncompiledFixtureWith(rankProfile, new StoringApplicationPackage(applicationDir)); search.compileRankProfile("my_profile", applicationDir.append("models")); + search.compileRankProfile("my_profile_child", applicationDir.append("models")); search.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile"); + search.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile_child"); assertNull("Constant overridden by function is not added", search.search().rankingConstants().get( name + "_Variable")); @@ -258,7 +269,9 @@ public class RankingExpressionWithOnnxTestCase { StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory); RankProfileSearchFixture searchFromStored = uncompiledFixtureWith(rankProfile, storedApplication); searchFromStored.compileRankProfile("my_profile", applicationDir.append("models")); + searchFromStored.compileRankProfile("my_profile_child", applicationDir.append("models")); searchFromStored.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile"); + searchFromStored.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile_child"); assertNull("Constant overridden by function is not added", searchFromStored.search().rankingConstants().get( name + "_Variable")); } finally { @@ -266,6 +279,82 @@ public class RankingExpressionWithOnnxTestCase { } } + @Test + public void testFunctionGeneration() { + final String name = "small_constants_and_functions"; + final String rankProfiles = + " rank-profile my_profile {\n" + + " function input() {\n" + + " expression: tensor<float>(d0[3])(0.0)\n" + + " }\n" + + " first-phase {\n" + + " expression: onnx('" + name + ".onnx')" + + " }\n" + + " }"; + final String functionName = "imported_ml_function_" + name + "_exp_output"; + final String expression = "join(" + functionName + ", reduce(join(join(reduce(" + functionName + ", sum, d0), tensor<float>(d0[1])(1.0), f(a,b)(a * b)), constant(" + name + "_epsilon), f(a,b)(a + b)), sum, d0), f(a,b)(a / b))"; + final String functionExpression = "map(input, f(a)(exp(a)))"; + + RankProfileSearchFixture search = uncompiledFixtureWith(rankProfiles, new StoringApplicationPackage(applicationDir)); + search.compileRankProfile("my_profile", applicationDir.append("models")); + search.assertFirstPhaseExpression(expression, "my_profile"); + search.assertFunction(functionExpression, functionName, "my_profile"); + } + + @Test + public void testImportingFromStoredExpressionsWithSmallConstantsAndInheritance() throws IOException { + final String name = "small_constants_and_functions"; + final String rankProfiles = + " rank-profile my_profile {\n" + + " function input() {\n" + + " expression: tensor<float>(d0[3])(0.0)\n" + + " }\n" + + " first-phase {\n" + + " expression: onnx('" + name + ".onnx')" + + " }\n" + + " }" + + " rank-profile my_profile_child inherits my_profile {\n" + + " }"; + final String functionName = "imported_ml_function_" + name + "_exp_output"; + final String expression = "join(" + functionName + ", reduce(join(join(reduce(" + functionName + ", sum, d0), tensor<float>(d0[1])(1.0), f(a,b)(a * b)), constant(" + name + "_epsilon), f(a,b)(a + b)), sum, d0), f(a,b)(a / b))"; + final String functionExpression = "map(input, f(a)(exp(a)))"; + + RankProfileSearchFixture search = uncompiledFixtureWith(rankProfiles, new StoringApplicationPackage(applicationDir)); + search.compileRankProfile("my_profile", applicationDir.append("models")); + search.compileRankProfile("my_profile_child", applicationDir.append("models")); + search.assertFirstPhaseExpression(expression, "my_profile"); + search.assertFirstPhaseExpression(expression, "my_profile_child"); + assertSmallConstant(name + "_epsilon", TensorType.fromSpec("tensor()"), search); + search.assertFunction(functionExpression, functionName, "my_profile"); + search.assertFunction(functionExpression, functionName, "my_profile_child"); + + // At this point the expression is stored - copy application to another location which do not have a models dir + Path storedApplicationDirectory = applicationDir.getParentPath().append("copy"); + try { + storedApplicationDirectory.toFile().mkdirs(); + IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), + storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); + StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory); + RankProfileSearchFixture searchFromStored = uncompiledFixtureWith(rankProfiles, storedApplication); + searchFromStored.compileRankProfile("my_profile", applicationDir.append("models")); + searchFromStored.compileRankProfile("my_profile_child", applicationDir.append("models")); + searchFromStored.assertFirstPhaseExpression(expression, "my_profile"); + searchFromStored.assertFirstPhaseExpression(expression, "my_profile_child"); + assertSmallConstant(name + "_epsilon", TensorType.fromSpec("tensor()"), search); + searchFromStored.assertFunction(functionExpression, functionName, "my_profile"); + searchFromStored.assertFunction(functionExpression, functionName, "my_profile_child"); + } + finally { + IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile()); + } + } + + private void assertSmallConstant(String name, TensorType type, RankProfileSearchFixture search) { + Value value = search.compiledRankProfile("my_profile").getConstants().get(name); + assertNotNull(value); + assertEquals(type, value.type()); + } + private RankProfileSearchFixture fixtureWith(String placeholderExpression, String firstPhaseExpression) { return fixtureWith(placeholderExpression, firstPhaseExpression, null, null, "Placeholder", new StoringApplicationPackage(applicationDir)); @@ -315,4 +404,39 @@ public class RankingExpressionWithOnnxTestCase { } } + static class StoringApplicationPackage extends MockApplicationPackage { + + StoringApplicationPackage(Path applicationPackageWritableRoot) { + this(applicationPackageWritableRoot, null, null); + } + + StoringApplicationPackage(Path applicationPackageWritableRoot, String queryProfile, String queryProfileType) { + super(new File(applicationPackageWritableRoot.toString()), + null, null, Collections.emptyList(), null, + null, null, false, queryProfile, queryProfileType); + } + + @Override + public ApplicationFile getFile(Path file) { + return new MockApplicationFile(file, Path.fromString(root().toString())); + } + + @Override + public List<NamedReader> getFiles(Path path, String suffix) { + List<NamedReader> readers = new ArrayList<>(); + for (File file : getFileReference(path).listFiles()) { + if ( ! file.getName().endsWith(suffix)) continue; + try { + readers.add(new NamedReader(file.getName(), new FileReader(file))); + } + catch (IOException e) { + throw new UncheckedIOException(e); + } + } + return readers; + } + + } + + } 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 deleted file mode 100644 index 610d92144ad..00000000000 --- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java +++ /dev/null @@ -1,497 +0,0 @@ -// Copyright 2018 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.config.application.api.ApplicationFile; -import com.yahoo.config.application.api.ApplicationPackage; -import com.yahoo.config.model.test.MockApplicationPackage; -import com.yahoo.io.GrowableByteBuffer; -import com.yahoo.io.IOUtils; -import com.yahoo.io.reader.NamedReader; -import com.yahoo.path.Path; -import com.yahoo.search.query.profile.QueryProfileRegistry; -import com.yahoo.searchdefinition.RankingConstant; -import com.yahoo.searchdefinition.parser.ParseException; -import com.yahoo.searchlib.rankingexpression.evaluation.Value; -import com.yahoo.tensor.Tensor; -import com.yahoo.tensor.TensorType; -import com.yahoo.tensor.serialization.TypedBinaryFormat; -import com.yahoo.vespa.model.VespaModel; -import com.yahoo.vespa.model.ml.ImportedModelTester; -import com.yahoo.yolean.Exceptions; -import org.junit.After; -import org.junit.Test; - -import java.io.File; -import java.io.FileReader; -import java.io.IOException; -import java.io.UncheckedIOException; -import java.util.ArrayList; -import java.util.Collections; -import java.util.List; -import java.util.Optional; - -import static junit.framework.TestCase.assertTrue; -import static org.junit.Assert.*; - -/** - * @author bratseth - */ -public class RankingExpressionWithTensorFlowTestCase { - - private final Path applicationDir = Path.fromString("src/test/integration/tensorflow/"); - - /** The model name */ - private final String name = "mnist_softmax_saved"; - - private final String vespaExpression = "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(" + name + "_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(" + name + "_layer_Variable_1_read), f(a,b)(a + b))"; - private final static String vespaExpressionWithBatchReduce = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))"; - - @After - public void removeGeneratedModelFiles() { - IOUtils.recursiveDeleteDir(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - } - - @Test - public void testGlobalTensorFlowModel() throws IOException { - ImportedModelTester tester = new ImportedModelTester(name, applicationDir); - VespaModel model = tester.createVespaModel(); - assertLargeConstant(name + "_layer_Variable_1_read", model, Optional.of(10L)); - assertLargeConstant(name + "_layer_Variable_read", model, Optional.of(7840L)); - - // At this point the expression is stored - copy application to another location which do not have a models dir - Path storedAppDir = applicationDir.append("copy"); - try { - storedAppDir.toFile().mkdirs(); - IOUtils.copy(applicationDir.append("services.xml").toString(), storedAppDir.append("services.xml").toString()); - IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), - storedAppDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - ImportedModelTester storedTester = new ImportedModelTester(name, storedAppDir); - VespaModel storedModel = storedTester.createVespaModel(); - tester.assertLargeConstant(name + "_layer_Variable_1_read", storedModel, Optional.of(10L)); - tester.assertLargeConstant(name + "_layer_Variable_read", storedModel, Optional.of(7840L)); - } - finally { - IOUtils.recursiveDeleteDir(storedAppDir.toFile()); - } - } - - @Test - public void testTensorFlowReference() { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceWithConstantFeature() { - RankProfileSearchFixture search = fixtureWith("constant(mytensor)", - "tensorflow('mnist_softmax/saved')", - "constant mytensor { file: ignored\ntype: tensor(d0[7],d1[784]) }", - null); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceWithQueryFeature() { - String queryProfile = "<query-profile id='default' type='root'/>"; - String queryProfileType = "<query-profile-type id='root'>" + - " <field name='query(mytensor)' type='tensor(d0[3],d1[784])'/>" + - "</query-profile-type>"; - StoringApplicationPackage application = new StoringApplicationPackage(applicationDir, - queryProfile, - queryProfileType); - RankProfileSearchFixture search = fixtureWith("query(mytensor)", - "tensorflow('mnist_softmax/saved')", - null, - null, - "Placeholder", - application); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceWithDocumentFeature() { - StoringApplicationPackage application = new StoringApplicationPackage(applicationDir); - RankProfileSearchFixture search = fixtureWith("attribute(mytensor)", - "tensorflow('mnist_softmax/saved')", - null, - "field mytensor type tensor(d0[1],d1[784]) { indexing: attribute }", - "Placeholder", - application); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceWithFeatureCombination() { - String queryProfile = "<query-profile id='default' type='root'/>"; - String queryProfileType = "<query-profile-type id='root'>" + - " <field name='query(mytensor)' type='tensor(d0[1],d1[784],d2[10])'/>" + - "</query-profile-type>"; - StoringApplicationPackage application = new StoringApplicationPackage(applicationDir, - queryProfile, - queryProfileType); - RankProfileSearchFixture search = fixtureWith("sum(query(mytensor) * attribute(mytensor) * constant(mytensor),d2)", - "tensorflow('mnist_softmax/saved')", - "constant mytensor { file: ignored\ntype: tensor(d0[1],d1[784]) }", - "field mytensor type tensor(d0[1],d1[784]) { indexing: attribute }", - "Placeholder", - application); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testNestedTensorFlowReference() { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "5 + sum(tensorflow('mnist_softmax/saved'))"); - search.assertFirstPhaseExpression("5 + reduce(" + vespaExpression + ", sum)", "my_profile"); - } - - @Test - public void testTensorFlowReferenceSpecifyingSignature() { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved', 'serving_default')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceSpecifyingSignatureAndOutput() { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved', 'serving_default', 'y')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - - @Test - public void testTensorFlowReferenceMissingFunction() throws ParseException { - try { - RankProfileSearchFixture search = new RankProfileSearchFixture( - new StoringApplicationPackage(applicationDir), - new QueryProfileRegistry(), - " rank-profile my_profile {\n" + - " first-phase {\n" + - " expression: tensorflow('mnist_softmax/saved')" + - " }\n" + - " }"); - search.compileRankProfile("my_profile", applicationDir.append("models")); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - fail("Expecting exception"); - } - catch (IllegalArgumentException expected) { - assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " + - "tensorflow('mnist_softmax/saved'): " + - "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this function is " + - "not present in rank profile 'my_profile'", - Exceptions.toMessageString(expected)); - } - } - - @Test - public void testTensorFlowReferenceWithWrongFunctionType() { - try { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d5[10])(0.0)", - "tensorflow('mnist_softmax/saved')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - fail("Expecting exception"); - } - catch (IllegalArgumentException expected) { - assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " + - "tensorflow('mnist_softmax/saved'): " + - "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " + - "but this function returns tensor(d0[2],d5[10])", - Exceptions.toMessageString(expected)); - } - } - - @Test - public void testTensorFlowReferenceSpecifyingNonExistingSignature() { - try { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved', 'serving_defaultz')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - fail("Expecting exception"); - } - catch (IllegalArgumentException expected) { - assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " + - "tensorflow('mnist_softmax/saved','serving_defaultz'): " + - "No expressions named 'serving_defaultz' in model 'mnist_softmax/saved'. "+ - "Available expressions: serving_default.y", - Exceptions.toMessageString(expected)); - } - } - - @Test - public void testTensorFlowReferenceSpecifyingNonExistingOutput() { - try { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved', 'serving_default', 'x')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - fail("Expecting exception"); - } - catch (IllegalArgumentException expected) { - assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " + - "tensorflow('mnist_softmax/saved','serving_default','x'): " + - "No expression 'serving_default.x' in model 'mnist_softmax/saved'. " + - "Available expressions: serving_default.y", - Exceptions.toMessageString(expected)); - } - } - - @Test - public void testImportingFromStoredExpressions() throws IOException { - RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved')"); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); - - // At this point the expression is stored - copy application to another location which do not have a models dir - Path storedApplicationDirectory = applicationDir.getParentPath().append("copy"); - try { - storedApplicationDirectory.toFile().mkdirs(); - IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), - storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory); - RankProfileSearchFixture searchFromStored = fixtureWith("tensor(d0[2],d1[784])(0.0)", - "tensorflow('mnist_softmax/saved')", - null, - null, - "Placeholder", - storedApplication); - searchFromStored.assertFirstPhaseExpression(vespaExpression, "my_profile"); - } - finally { - IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile()); - } - } - - @Test - public void testImportingFromStoredExpressionsWithFunctionOverridingConstantAndInheritance() throws IOException { - String rankProfiles = - " rank-profile my_profile {\n" + - " function Placeholder() {\n" + - " expression: tensor(d0[2],d1[784])(0.0)\n" + - " }\n" + - " function " + name + "_layer_Variable_read() {\n" + - " expression: tensor(d1[10],d2[784])(0.0)\n" + - " }\n" + - " first-phase {\n" + - " expression: tensorflow('mnist_softmax/saved')" + - " }\n" + - " }" + - " rank-profile my_profile_child inherits my_profile {\n" + - " }"; - - String vespaExpressionWithoutConstant = - "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), " + name + "_layer_Variable_read, f(a,b)(a * b)), sum, d2), constant(" + name + "_layer_Variable_1_read), f(a,b)(a + b))"; - RankProfileSearchFixture search = fixtureWithUncompiled(rankProfiles, new StoringApplicationPackage(applicationDir)); - search.compileRankProfile("my_profile", applicationDir.append("models")); - search.compileRankProfile("my_profile_child", applicationDir.append("models")); - search.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile"); - search.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile_child"); - - assertNull("Constant overridden by function is not added", - search.search().rankingConstants().get("mnist_softmax_saved_layer_Variable_read")); - - // At this point the expression is stored - copy application to another location which do not have a models dir - Path storedApplicationDirectory = applicationDir.getParentPath().append("copy"); - try { - storedApplicationDirectory.toFile().mkdirs(); - IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), - storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory); - RankProfileSearchFixture searchFromStored = fixtureWithUncompiled(rankProfiles, storedApplication); - searchFromStored.compileRankProfile("my_profile", applicationDir.append("models")); - searchFromStored.compileRankProfile("my_profile_child", applicationDir.append("models")); - searchFromStored.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile"); - searchFromStored.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile_child"); - assertNull("Constant overridden by function is not added", - searchFromStored.search().rankingConstants().get("mnist_softmax_saved_layer_Variable_read")); - } - finally { - IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile()); - } - } - - @Test - public void testFunctionGeneration() { - final String name = "mnist_saved"; - final String expression = "join(reduce(join(join(join(reduce(constant(" + name + "_dnn_hidden2_Const), sum, d2), imported_ml_function_" + name + "_dnn_hidden2_add, f(a,b)(a * b)), imported_ml_function_" + name + "_dnn_hidden2_add, f(a,b)(max(a,b))), constant(" + name + "_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(" + name + "_dnn_outputs_bias_read), f(a,b)(a + b))"; - final String functionExpression1 = "join(reduce(join(rename(input, (d0, d1), (d0, d4)), constant(" + name + "_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(" + name + "_dnn_hidden1_bias_read), f(a,b)(a + b))"; - final String functionExpression2 = "join(reduce(join(join(join(0.009999999776482582, imported_ml_function_" + name + "_dnn_hidden1_add, f(a,b)(a * b)), imported_ml_function_" + name + "_dnn_hidden1_add, f(a,b)(max(a,b))), constant(" + name + "_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(" + name + "_dnn_hidden2_bias_read), f(a,b)(a + b))"; - - RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)", - "tensorflow('mnist/saved')", - null, - null, - "input", - new StoringApplicationPackage(applicationDir)); - search.assertFirstPhaseExpression(expression, "my_profile"); - search.assertFunction(functionExpression1, "imported_ml_function_" + name + "_dnn_hidden1_add", "my_profile"); - search.assertFunction(functionExpression2, "imported_ml_function_" + name + "_dnn_hidden2_add", "my_profile"); - } - - @Test - public void testImportingFromStoredExpressionsWithSmallConstantsAndInheritance() throws IOException { - final String name = "mnist_saved"; - final String rankProfiles = - " rank-profile my_profile {\n" + - " function input() {\n" + - " expression: tensor(d0[1],d1[784])(0.0)\n" + - " }\n" + - " first-phase {\n" + - " expression: tensorflow('mnist/saved')" + - " }\n" + - " }" + - " rank-profile my_profile_child inherits my_profile {\n" + - " }"; - - final String expression = "join(reduce(join(join(join(reduce(constant(" + name + "_dnn_hidden2_Const), sum, d2), imported_ml_function_" + name + "_dnn_hidden2_add, f(a,b)(a * b)), imported_ml_function_" + name + "_dnn_hidden2_add, f(a,b)(max(a,b))), constant(" + name + "_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(" + name + "_dnn_outputs_bias_read), f(a,b)(a + b))"; - final String functionExpression1 = "join(reduce(join(rename(input, (d0, d1), (d0, d4)), constant(" + name + "_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(" + name + "_dnn_hidden1_bias_read), f(a,b)(a + b))"; - final String functionExpression2 = "join(reduce(join(join(join(0.009999999776482582, imported_ml_function_" + name + "_dnn_hidden1_add, f(a,b)(a * b)), imported_ml_function_" + name + "_dnn_hidden1_add, f(a,b)(max(a,b))), constant(" + name + "_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(" + name + "_dnn_hidden2_bias_read), f(a,b)(a + b))"; - - RankProfileSearchFixture search = fixtureWithUncompiled(rankProfiles, new StoringApplicationPackage(applicationDir)); - search.compileRankProfile("my_profile", applicationDir.append("models")); - search.compileRankProfile("my_profile_child", applicationDir.append("models")); - search.assertFirstPhaseExpression(expression, "my_profile"); - search.assertFirstPhaseExpression(expression, "my_profile_child"); - assertSmallConstant(name + "_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search); - search.assertFunction(functionExpression1, "imported_ml_function_" + name + "_dnn_hidden1_add", "my_profile"); - search.assertFunction(functionExpression1, "imported_ml_function_" + name + "_dnn_hidden1_add", "my_profile_child"); - search.assertFunction(functionExpression2, "imported_ml_function_" + name + "_dnn_hidden2_add", "my_profile"); - search.assertFunction(functionExpression2, "imported_ml_function_" + name + "_dnn_hidden2_add", "my_profile_child"); - - // At this point the expression is stored - copy application to another location which do not have a models dir - Path storedApplicationDirectory = applicationDir.getParentPath().append("copy"); - try { - storedApplicationDirectory.toFile().mkdirs(); - IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), - storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory); - RankProfileSearchFixture searchFromStored = fixtureWithUncompiled(rankProfiles, storedApplication); - searchFromStored.compileRankProfile("my_profile", applicationDir.append("models")); - searchFromStored.compileRankProfile("my_profile_child", applicationDir.append("models")); - searchFromStored.assertFirstPhaseExpression(expression, "my_profile"); - searchFromStored.assertFirstPhaseExpression(expression, "my_profile_child"); - assertSmallConstant(name + "_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search); - searchFromStored.assertFunction(functionExpression1, "imported_ml_function_" + name + "_dnn_hidden1_add", "my_profile"); - searchFromStored.assertFunction(functionExpression1, "imported_ml_function_" + name + "_dnn_hidden1_add", "my_profile_child"); - searchFromStored.assertFunction(functionExpression2, "imported_ml_function_" + name + "_dnn_hidden2_add", "my_profile"); - searchFromStored.assertFunction(functionExpression2, "imported_ml_function_" + name + "_dnn_hidden2_add", "my_profile_child"); - } - finally { - IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile()); - } - } - - private void assertSmallConstant(String name, TensorType type, RankProfileSearchFixture search) { - Value value = search.compiledRankProfile("my_profile").getConstants().get(name); - assertNotNull(value); - assertEquals(type, value.type()); - } - - /** - * Verifies that the constant with the given name exists, and - only if an expected size is given - - * that the content of the constant is available and has the expected size. - */ - private void assertLargeConstant(String constantName, VespaModel model, Optional<Long> expectedSize) { - try { - Path constantApplicationPackagePath = Path.fromString("models.generated/" + name + "/constants").append(constantName + ".tbf"); - RankingConstant rankingConstant = model.rankingConstants().get(constantName); - assertEquals(constantName, rankingConstant.getName()); - assertTrue(rankingConstant.getFileName().endsWith(constantApplicationPackagePath.toString())); - - if (expectedSize.isPresent()) { - Path constantPath = applicationDir.append(constantApplicationPackagePath); - assertTrue("Constant file '" + constantPath + "' has been written", - constantPath.toFile().exists()); - Tensor deserializedConstant = TypedBinaryFormat.decode(Optional.empty(), - GrowableByteBuffer.wrap(IOUtils.readFileBytes(constantPath.toFile()))); - assertEquals(expectedSize.get().longValue(), deserializedConstant.size()); - } - } - catch (IOException e) { - throw new UncheckedIOException(e); - } - } - - private RankProfileSearchFixture fixtureWith(String placeholderExpression, String firstPhaseExpression) { - return fixtureWith(placeholderExpression, firstPhaseExpression, null, null, "Placeholder", - new StoringApplicationPackage(applicationDir)); - } - - private RankProfileSearchFixture fixtureWith(String placeholderExpression, String firstPhaseExpression, - String constant, String field) { - return fixtureWith(placeholderExpression, firstPhaseExpression, constant, field, "Placeholder", - new StoringApplicationPackage(applicationDir)); - } - - private RankProfileSearchFixture fixtureWith(String functionExpression, - String firstPhaseExpression, - String constant, - String field, - String functionName, - StoringApplicationPackage application) { - try { - RankProfileSearchFixture fixture = new RankProfileSearchFixture( - application, - application.getQueryProfiles(), - " rank-profile my_profile {\n" + - " function " + functionName + "() {\n" + - " expression: " + functionExpression + - " }\n" + - " first-phase {\n" + - " expression: " + firstPhaseExpression + - " }\n" + - " }", - constant, - field); - fixture.compileRankProfile("my_profile", applicationDir.append("models")); - return fixture; - } - catch (ParseException e) { - throw new IllegalArgumentException(e); - } - } - - private RankProfileSearchFixture fixtureWithUncompiled(String rankProfile, StoringApplicationPackage application) { - try { - return new RankProfileSearchFixture(application, application.getQueryProfiles(), - rankProfile, null, null); - } - catch (ParseException e) { - throw new IllegalArgumentException(e); - } - } - - static class StoringApplicationPackage extends MockApplicationPackage { - - StoringApplicationPackage(Path applicationPackageWritableRoot) { - this(applicationPackageWritableRoot, null, null); - } - - StoringApplicationPackage(Path applicationPackageWritableRoot, String queryProfile, String queryProfileType) { - super(new File(applicationPackageWritableRoot.toString()), - null, null, Collections.emptyList(), null, - null, null, false, queryProfile, queryProfileType); - } - - @Override - public ApplicationFile getFile(Path file) { - return new MockApplicationFile(file, Path.fromString(root().toString())); - } - - @Override - public List<NamedReader> getFiles(Path path, String suffix) { - List<NamedReader> readers = new ArrayList<>(); - for (File file : getFileReference(path).listFiles()) { - if ( ! file.getName().endsWith(suffix)) continue; - try { - readers.add(new NamedReader(file.getName(), new FileReader(file))); - } - catch (IOException e) { - throw new UncheckedIOException(e); - } - } - return readers; - } - - } - -} diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithXGBoostTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithXGBoostTestCase.java index f73d1c823e2..1df6e5e5365 100644 --- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithXGBoostTestCase.java +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithXGBoostTestCase.java @@ -50,7 +50,7 @@ public class RankingExpressionWithXGBoostTestCase { storedApplicationDirectory.toFile().mkdirs(); IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(), storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); - RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(storedApplicationDirectory); + RankingExpressionWithOnnxTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(storedApplicationDirectory); RankProfileSearchFixture searchFromStored = fixtureWith("xgboost('xgboost.2.2.json')"); searchFromStored.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @@ -61,13 +61,13 @@ public class RankingExpressionWithXGBoostTestCase { private RankProfileSearchFixture fixtureWith(String firstPhaseExpression) { return fixtureWith(firstPhaseExpression, null, null, - new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(applicationDir)); + new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(applicationDir)); } private RankProfileSearchFixture fixtureWith(String firstPhaseExpression, String constant, String field, - RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage application) { + RankingExpressionWithOnnxTestCase.StoringApplicationPackage application) { try { RankProfileSearchFixture fixture = new RankProfileSearchFixture( application, diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java index cad0ad7bae0..854c4d32a64 100644 --- a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java +++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java @@ -44,8 +44,8 @@ public class MlModelsTest { } private void verify(VespaModel model) { - assertEquals("Global models are created (although not used directly here", - 5, model.rankProfileList().getRankProfiles().size()); + assertEquals("Global models are created (although not used directly here)", + 3, model.rankProfileList().getRankProfiles().size()); RankProfilesConfig.Builder builder = new RankProfilesConfig.Builder(); model.getSearchClusters().get(0).getConfig(builder); @@ -62,18 +62,15 @@ public class MlModelsTest { } private final String testProfile = - "rankingExpression(input).rankingScript: attribute(argument)\n" + - "rankingExpression(input).type: tensor<float>(d0[1],d1[784])\n" + - "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(rankingExpression(input), (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))\n" + - "rankingExpression(mnist_tensorflow).rankingScript: join(reduce(join(map(join(reduce(join(join(join(0.009999999776482582, rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(a * b)), rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.0507009873554805 * if (a >= 0, a, 1.6732632423543772 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))\n" + "rankingExpression(Placeholder).rankingScript: attribute(argument)\n" + "rankingExpression(Placeholder).type: tensor<float>(d0[1],d1[784])\n" + - "rankingExpression(mnist_softmax_tensorflow).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))\n" + "rankingExpression(mnist_softmax_onnx).rankingScript: join(reduce(join(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))\n" + "rankingExpression(my_xgboost).rankingScript: if (f29 < -0.1234567, if (!(f56 >= -0.242398), 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (!(f60 >= -0.482947), if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)\n" + "rankingExpression(my_lightgbm).rankingScript: if (!(numerical_2 >= 0.46643291586559305), 2.1594397038037663, if (categorical_2 in [\"k\", \"l\", \"m\"], 2.235297305276056, 2.1792953471546546)) + if (categorical_1 in [\"d\", \"e\"], 0.03070842919354316, if (!(numerical_1 >= 0.5102250691730842), -0.04439151147520909, 0.005117411709368601)) + if (!(numerical_2 >= 0.668665477622446), if (!(numerical_2 >= 0.008118820676863816), -0.15361238490967524, -0.01192330846157292), 0.03499044894987518) + if (!(numerical_1 >= 0.5201391072644542), -0.02141000620783247, if (categorical_1 in [\"a\", \"b\"], -0.004121485787596721, 0.04534090904886873)) + if (categorical_2 in [\"k\", \"l\", \"m\"], if (!(numerical_2 >= 0.27283279016959255), -0.01924803254356527, 0.03643772842347651), -0.02701711918923075)\n" + + "rankingExpression(input).rankingScript: attribute(argument)\n" + + "rankingExpression(input).type: tensor<float>(d0[1],d1[784])\n" + "vespa.rank.firstphase: rankingExpression(firstphase)\n" + - "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_tensorflow) + rankingExpression(mnist_softmax_tensorflow) + rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost) + rankingExpression(my_lightgbm)\n" + + "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost) + rankingExpression(my_lightgbm)\n" + "vespa.type.attribute.argument: tensor<float>(d0[1],d1[784])\n"; } diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java index 9a88b2a31f6..4c1c24c9790 100644 --- a/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java +++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java @@ -96,24 +96,23 @@ public class ModelEvaluationTest { cluster.getConfig(cb); RankingConstantsConfig constantsConfig = new RankingConstantsConfig(cb); - assertEquals(5, config.rankprofile().size()); + assertEquals(4, config.rankprofile().size()); Set<String> modelNames = config.rankprofile().stream().map(v -> v.name()).collect(Collectors.toSet()); assertTrue(modelNames.contains("xgboost_2_2")); assertTrue(modelNames.contains("lightgbm_regression")); - assertTrue(modelNames.contains("mnist_saved")); assertTrue(modelNames.contains("mnist_softmax")); - assertTrue(modelNames.contains("mnist_softmax_saved")); + assertTrue(modelNames.contains("small_constants_and_functions")); // Compare profile content in a denser format than config: StringBuilder sb = new StringBuilder(); - for (RankProfilesConfig.Rankprofile.Fef.Property p : findProfile("mnist_saved", config).property()) + for (RankProfilesConfig.Rankprofile.Fef.Property p : findProfile("small_constants_and_functions", config).property()) sb.append(p.name()).append(": ").append(p.value()).append("\n"); - assertEquals(mnistProfile, sb.toString()); + assertEquals(profile, sb.toString()); ModelsEvaluator evaluator = new ModelsEvaluator(new ToleratingMissingConstantFilesRankProfilesConfigImporter(MockFileAcquirer.returnFile(null)) .importFrom(config, constantsConfig)); - assertEquals(5, evaluator.models().size()); + assertEquals(4, evaluator.models().size()); Model xgboost = evaluator.models().get("xgboost_2_2"); assertNotNull(xgboost); @@ -125,16 +124,6 @@ public class ModelEvaluationTest { assertNotNull(lightgbm.evaluatorOf()); assertNotNull(lightgbm.evaluatorOf("lightgbm_regression")); - Model tensorflow_mnist = evaluator.models().get("mnist_saved"); - assertNotNull(tensorflow_mnist); - assertEquals(1, tensorflow_mnist.functions().size()); - assertNotNull(tensorflow_mnist.evaluatorOf("serving_default")); - assertNotNull(tensorflow_mnist.evaluatorOf("serving_default", "y")); - assertNotNull(tensorflow_mnist.evaluatorOf("serving_default.y")); - assertNotNull(evaluator.evaluatorOf("mnist_saved", "serving_default.y")); - assertNotNull(evaluator.evaluatorOf("mnist_saved", "serving_default", "y")); - assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), tensorflow_mnist.functions().get(0).argumentTypes().get("input")); - Model onnx_mnist_softmax = evaluator.models().get("mnist_softmax"); assertNotNull(onnx_mnist_softmax); assertEquals(1, onnx_mnist_softmax.functions().size()); @@ -152,22 +141,13 @@ public class ModelEvaluationTest { assertNotNull(evaluator.evaluatorOf("mnist_softmax", "serving_default.add")); assertNotNull(evaluator.evaluatorOf("mnist_softmax", "serving_default", "add")); assertEquals(TensorType.fromSpec("tensor<float>(d0[],d1[784])"), onnx_mnist_softmax.functions().get(0).argumentTypes().get("Placeholder")); - - Model tensorflow_mnist_softmax = evaluator.models().get("mnist_softmax_saved"); - assertNotNull(tensorflow_mnist_softmax); - assertEquals(1, tensorflow_mnist_softmax.functions().size()); - assertNotNull(tensorflow_mnist_softmax.evaluatorOf()); - assertNotNull(tensorflow_mnist_softmax.evaluatorOf("serving_default")); - assertNotNull(tensorflow_mnist_softmax.evaluatorOf("serving_default", "y")); - assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), tensorflow_mnist_softmax.functions().get(0).argumentTypes().get("Placeholder")); } - private final String mnistProfile = - "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(input, (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))\n" + - "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).type: tensor(d3[300])\n" + - "rankingExpression(serving_default.y).rankingScript: join(reduce(join(map(join(reduce(join(join(join(0.009999999776482582, rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(a * b)), rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.0507009873554805 * if (a >= 0, a, 1.6732632423543772 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))\n" + - "rankingExpression(serving_default.y).input.type: tensor(d0[],d1[784])\n" + - "rankingExpression(serving_default.y).type: tensor(d0[],d1[10])\n"; + private final String profile = + "rankingExpression(imported_ml_function_small_constants_and_functions_exp_output).rankingScript: map(input, f(a)(exp(a)))\n" + + "rankingExpression(default.output).rankingScript: join(rankingExpression(imported_ml_function_small_constants_and_functions_exp_output), reduce(join(join(reduce(rankingExpression(imported_ml_function_small_constants_and_functions_exp_output), sum, d0), tensor<float>(d0[1])(1.0), f(a,b)(a * b)), 9.999999974752427E-7, f(a,b)(a + b)), sum, d0), f(a,b)(a / b))\n" + + "rankingExpression(default.output).input.type: tensor<float>(d0[3])\n" + + "rankingExpression(default.output).type: tensor<float>(d0[3])\n"; private RankProfilesConfig.Rankprofile.Fef findProfile(String name, RankProfilesConfig config) { for (RankProfilesConfig.Rankprofile profile : config.rankprofile()) { diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/ModelImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/ModelImporter.java index a9d71b7d9d5..8f73cd02184 100644 --- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/ModelImporter.java +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/ModelImporter.java @@ -74,7 +74,7 @@ public abstract class ModelImporter implements MlModelImporter { signature.input(input.getKey(), input.getValue()); } for (Map.Entry<String, String> output : graph.outputs(signatureName).entrySet()) { - signature.output(output.getKey(), output.getValue()); + signature.output(IntermediateOperation.vespaName(output.getKey()), output.getValue()); } } } diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java index f8c7dc15857..c8d7392bb8d 100644 --- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java @@ -66,13 +66,13 @@ class TensorConverter { } private static class RawBoolValues extends RawValues { - private final IntBuffer values; + private final ByteString values; private final int size; RawBoolValues(Onnx.TensorProto tensorProto) { - values = bytes(tensorProto).asIntBuffer(); - size = values.remaining(); + values = tensorProto.getRawData(); + size = values.size(); } - @Override double get(int i) { return values.get(i); } + @Override double get(int i) { return values.byteAt(i) == 0 ? 0.0 : 1.0; } @Override int size() { return size; } } diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/IntermediateOperation.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/IntermediateOperation.java index 6711b999940..a5244125d5a 100644 --- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/IntermediateOperation.java +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/IntermediateOperation.java @@ -166,7 +166,7 @@ public abstract class IntermediateOperation { return vespaName(name); } - public String vespaName(String name) { + public static String vespaName(String name) { return name != null ? namePartOf(name).replace('/', '_').replace('.', '_') : null; } diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java index 34b9c847a12..a879c24b373 100644 --- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java @@ -26,7 +26,7 @@ public class TensorFlowImporter extends ModelImporter { private static final Logger log = Logger.getLogger(TensorFlowImporter.class.getName()); - private final static int defaultOnnxOpset = 8; + private final static int[] onnxOpsetsToTry = {8, 10, 12}; private final OnnxImporter onnxImporter = new OnnxImporter(); @@ -52,19 +52,10 @@ public class TensorFlowImporter extends ModelImporter { */ @Override public ImportedModel importModel(String modelName, String modelDir) { - // Temporary (for testing): if path contains "tf_2_onnx", convert to ONNX then import that model. - if (modelDir.contains("tf_2_onnx")) { - return convertToOnnxAndImport(modelName, modelDir); - } - try (SavedModelBundle model = SavedModelBundle.load(modelDir, "serve")) { - return importModel(modelName, modelDir, model); - } - catch (IllegalArgumentException e) { - throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e); - } + return convertToOnnxAndImport(modelName, modelDir); } - /** Imports a TensorFlow model */ + /** Imports a TensorFlow model - DEPRECATED */ public ImportedModel importModel(String modelName, String modelDir, SavedModelBundle model) { try { IntermediateGraph graph = GraphImporter.importGraph(modelName, model); @@ -78,15 +69,21 @@ public class TensorFlowImporter extends ModelImporter { private ImportedModel convertToOnnxAndImport(String modelName, String modelDir) { Path tempDir = null; try { - log.info("Converting TensorFlow model '" + modelDir + "' to ONNX..."); tempDir = Files.createTempDirectory("tf2onnx"); String convertedPath = tempDir.toString() + File.separatorChar + "converted.onnx"; - Pair<Integer, String> res = convertToOnnx(modelDir, convertedPath, defaultOnnxOpset); - if (res.getFirst() != 0) { - throw new IllegalArgumentException("Conversion from TensorFlow to ONNX failed for '" + modelDir + "'. " + - "Reason: " + res.getSecond()); + String outputOfLastConversionAttempt = ""; + for (int opset : onnxOpsetsToTry) { + log.info("Converting TensorFlow model '" + modelDir + "' to ONNX with opset " + opset + "..."); + Pair<Integer, String> res = convertToOnnx(modelDir, convertedPath, opset); + if (res.getFirst() == 0) { + log.info("Conversion to ONNX with opset " + opset + " successful."); + return onnxImporter.importModel(modelName, convertedPath); + } + log.fine("Conversion to ONNX with opset " + opset + " failed. Reason: " + res.getSecond()); + outputOfLastConversionAttempt = res.getSecond(); } - return onnxImporter.importModel(modelName, convertedPath); + throw new IllegalArgumentException("Unable to convert TensorFlow model in '" + modelDir + "' to ONNX. " + + "Reason: " + outputOfLastConversionAttempt); } catch (IOException e) { throw new IllegalArgumentException("Conversion from TensorFlow to ONNX failed for '" + modelDir + "'"); } finally { diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java index 35c853bd746..09455abc380 100644 --- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java @@ -55,47 +55,4 @@ public class OnnxMnistSoftmaxImportTestCase { assertEquals("{Placeholder=tensor<float>(d0[],d1[784])}", output.argumentTypes().toString()); } - @Test - public void testComparisonBetweenOnnxAndTensorflow() { - String tfModelPath = "src/test/models/tensorflow/mnist_softmax/saved"; - String onnxModelPath = "src/test/models/onnx/mnist_softmax/mnist_softmax.onnx"; - - Tensor argument = placeholderArgument(); - Tensor tensorFlowResult = evaluateTensorFlowModel(tfModelPath, argument, "Placeholder", "add"); - Tensor onnxResult = evaluateOnnxModel(onnxModelPath, argument, "Placeholder", "add"); - - assertEquals("Operation 'add' produces equal results", tensorFlowResult, onnxResult); - } - - private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) { - ImportedModel model = new TensorFlowImporter().importModel("test", path); - return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input); - } - - private Tensor evaluateOnnxModel(String path, Tensor argument, String input, String output) { - ImportedModel model = new OnnxImporter().importModel("test", path); - return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input); - } - - private Tensor evaluateExpression(RankingExpression expression, Context context, Tensor argument, String input) { - context.put(input, new TensorValue(argument)); - return expression.evaluate(context).asTensor(); - } - - private Context contextFrom(ImportedModel result) { - MapContext context = new MapContext(); - result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); - result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor)))); - return context; - } - - private Tensor placeholderArgument() { - Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 784).build()); - for (int d0 = 0; d0 < 1; d0++) - for (int d1 = 0; d1 < 784; d1++) - b.cell(d1 * 1.0 / 784, d0, d1); - return b.build(); - } - - } |