diff options
61 files changed, 75038 insertions, 262 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 differdeleted file mode 100644 index 0d4bffa5b57..00000000000 --- a/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx +++ /dev/null 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 new file mode 100644 index 00000000000..5528aa99401 --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt @@ -0,0 +1,8830 @@ +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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--- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..c877b02b42a --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py new file mode 100644 index 00000000000..7494e93fa71 --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py @@ -0,0 +1,100 @@ +# Copyright 2018 Yahoo Holdings. 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 new file mode 100644 index 00000000000..3f4f794d2ac --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,93 @@ +# 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 new file mode 100644 index 00000000000..05b0e4e0f29 --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + 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value { + inputs { + key: "x" + value { + name: "Placeholder:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 784 + } + } + } + } + outputs { + 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 differnew file mode 100644 index 00000000000..826b0280abf --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 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 differnew file mode 100644 index 00000000000..d00fc5b06ed --- /dev/null +++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index 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 369b2cbe42b..3d7eed1e729 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\",\"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\"}"; + 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\"}"; assertResponse("http://localhost/model-evaluation/v1", expected, jdisc); } @@ -60,8 +60,9 @@ public class ContainerModelEvaluationTest { } { - 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); + // 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); } } @@ -78,10 +79,9 @@ public class ContainerModelEvaluationTest { } private String inputTensor() { - 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); + 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); 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 ec517768ea9..ab143f77b6a 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 file under [application]/models"); + throw new IllegalArgumentException("An onnx node must take an argument pointing to " + + "the onnx model directory under [application]/models"); if (arguments.expressions().size() > 3) - throw new IllegalArgumentException("An onnx feature can have at most 3 arguments"); + throw new IllegalArgumentException("An onnx feature can have at most 2 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 new file mode 100644 index 00000000000..eb926836576 --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist/saved/saved_model.pbtxt @@ -0,0 +1,7982 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + 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b/config-model/src/test/cfg/application/ml_models/models/mnist/saved/variables/variables.data-00000-of-00001 diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist/saved/variables/variables.index b/config-model/src/test/cfg/application/ml_models/models/mnist/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..7989c109a3a --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist/saved/variables/variables.index diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist/simple_mnist.py b/config-model/src/test/cfg/application/ml_models/models/mnist/simple_mnist.py new file mode 100644 index 00000000000..86a17e81f8f --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist/simple_mnist.py @@ -0,0 +1,98 @@ +# Copyright 2018 Yahoo Holdings. 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 new file mode 100644 index 00000000000..3f4f794d2ac --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,93 @@ +# 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 new file mode 100644 index 00000000000..05b0e4e0f29 --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + 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value { + inputs { + key: "x" + value { + name: "Placeholder:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 784 + } + } + } + } + outputs { + 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/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 Binary files differnew file mode 100644 index 00000000000..826b0280abf --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.index b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..d00fc5b06ed --- /dev/null +++ b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/saved/variables/variables.index diff --git a/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd b/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd index e9575af6010..6d10c50e80a 100644 --- a/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd +++ b/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd @@ -17,6 +17,14 @@ search test { expression: attribute(argument) } + function mnist_tensorflow() { + expression: tensorflow("mnist/saved") + } + + function mnist_softmax_tensorflow() { + expression: tensorflow("mnist_softmax/saved") + } + function mnist_softmax_onnx() { expression: onnx("mnist_softmax") } @@ -30,7 +38,7 @@ search test { } 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a/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/variables/variables.index b/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..7989c109a3a --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving/models/mnist/saved/variables/variables.index diff --git a/config-model/src/test/cfg/application/ml_serving/models/mnist/simple_mnist.py b/config-model/src/test/cfg/application/ml_serving/models/mnist/simple_mnist.py new file mode 100644 index 00000000000..86a17e81f8f --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving/models/mnist/simple_mnist.py @@ -0,0 +1,98 @@ +# Copyright 2018 Yahoo Holdings. 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 new file mode 100644 index 00000000000..3f4f794d2ac --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,93 @@ +# 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 new file mode 100644 index 00000000000..05b0e4e0f29 --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 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b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..d00fc5b06ed --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving/models/mnist_softmax/saved/variables/variables.index diff --git a/config-model/src/test/cfg/application/ml_serving/models/small_constants_and_functions.onnx b/config-model/src/test/cfg/application/ml_serving/models/small_constants_and_functions.onnx Binary files differdeleted file mode 100644 index 0d4bffa5b57..00000000000 --- a/config-model/src/test/cfg/application/ml_serving/models/small_constants_and_functions.onnx +++ /dev/null diff --git a/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax.onnx b/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax.onnx Binary files differnew file mode 100644 index 00000000000..a86019bf53a --- /dev/null +++ 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value { + inputs { + key: "x" + value { + name: "Placeholder:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 784 + } + } + } + } + outputs { + 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/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.data-00000-of-00001 b/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.data-00000-of-00001 Binary files differnew file mode 100644 index 00000000000..826b0280abf --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.data-00000-of-00001 diff --git a/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.index b/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.index Binary files differnew file mode 100644 index 00000000000..d00fc5b06ed --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving_name_collision/models/parent/mnist_softmax/variables/variables.index diff --git a/config-model/src/test/cfg/application/ml_serving_name_collision/services.xml b/config-model/src/test/cfg/application/ml_serving_name_collision/services.xml new file mode 100644 index 00000000000..41f44e04c99 --- /dev/null +++ b/config-model/src/test/cfg/application/ml_serving_name_collision/services.xml @@ -0,0 +1,13 @@ +<?xml version="1.0" encoding="utf-8" ?> +<!-- Copyright 2018 Yahoo Holdings. 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 395cd734444..a86019bf53a 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 differdeleted file mode 100644 index 0d4bffa5b57..00000000000 --- a/config-model/src/test/integration/onnx/models/small_constants_and_functions.onnx +++ /dev/null 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 deleted file mode 100755 index 7b04b93fee2..00000000000 --- a/config-model/src/test/integration/onnx/models/small_constants_and_functions.py +++ /dev/null @@ -1,51 +0,0 @@ -# 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, 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b/config-model/src/test/integration/tensorflow/models/mnist/saved/variables/variables.index diff --git a/config-model/src/test/integration/tensorflow/models/mnist/simple_mnist.py b/config-model/src/test/integration/tensorflow/models/mnist/simple_mnist.py new file mode 100644 index 00000000000..7494e93fa71 --- /dev/null +++ b/config-model/src/test/integration/tensorflow/models/mnist/simple_mnist.py @@ -0,0 +1,100 @@ +# Copyright 2018 Yahoo Holdings. 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 new file mode 100644 index 00000000000..3f4f794d2ac --- /dev/null +++ b/config-model/src/test/integration/tensorflow/models/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,93 @@ +# 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 new file mode 100644 index 00000000000..05b0e4e0f29 --- /dev/null +++ b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + type: 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{ + name: "save/RestoreV2_1" + op: "RestoreV2" + input: "save/Const" + input: "save/RestoreV2_1/tensor_names" + input: "save/RestoreV2_1/shape_and_slices" + attr { + key: "_output_shapes" + value { + list { + shape { + unknown_rank: true + } + } + } + } + attr { + key: "dtypes" + value { + list { + type: DT_FLOAT + } + } + } + } + node { + name: "save/Assign_1" + op: "Assign" + input: "layer/Variable_1" + input: "save/RestoreV2_1" + attr { + key: "T" + value { + type: DT_FLOAT + } + } + attr { + key: "_class" + value { + list { + s: "loc:@layer/Variable_1" + } + } + } + attr { + key: "_output_shapes" + value { + list { + shape { + dim { + size: 10 + } + } + } + } + } + attr { + key: "use_locking" + value { + b: true + } + } + attr { + key: "validate_shape" + value { + b: true + } + } + } + node { + name: "save/restore_shard" + op: "NoOp" + input: "^save/Assign" + input: "^save/Assign_1" + } + node { + name: "save/restore_all" + op: "NoOp" + input: "^save/restore_shard" + } + versions { + producer: 24 + } + } + saver_def { + filename_tensor_name: "save/Const:0" + save_tensor_name: "save/Identity:0" + restore_op_name: "save/restore_all" + max_to_keep: 5 + sharded: true + keep_checkpoint_every_n_hours: 10000.0 + version: V2 + } + collection_def { + key: "train_op" + value { + node_list { + value: "GradientDescent" + } + } + } + collection_def { + key: "trainable_variables" + value { + bytes_list { + value: "\n\020layer/Variable:0\022\025layer/Variable/Assign\032\025layer/Variable/read:02\rlayer/zeros:0" + value: "\n\022layer/Variable_1:0\022\027layer/Variable_1/Assign\032\027layer/Variable_1/read:02\017layer/zeros_1:0" + } + } + } + collection_def { + key: "variables" + value { + bytes_list { + value: "\n\020layer/Variable:0\022\025layer/Variable/Assign\032\025layer/Variable/read:02\rlayer/zeros:0" + value: "\n\022layer/Variable_1:0\022\027layer/Variable_1/Assign\032\027layer/Variable_1/read:02\017layer/zeros_1:0" + } + } + } + signature_def { + key: "serving_default" + value { + inputs { + key: "x" + value { + name: "Placeholder:0" + dtype: DT_FLOAT + tensor_shape { + dim { + size: -1 + } + dim { + size: 784 + } + } + } + } + outputs { + key: "y" + value { + name: "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 differnew file mode 100644 index 00000000000..826b0280abf --- /dev/null +++ b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.data-00000-of-00001 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 differnew file mode 100644 index 00000000000..d00fc5b06ed --- /dev/null +++ b/config-model/src/test/integration/tensorflow/models/mnist_softmax/saved/variables/variables.index diff --git a/config-model/src/test/integration/tensorflow/services.xml b/config-model/src/test/integration/tensorflow/services.xml new file mode 100644 index 00000000000..aa1c0223bdf --- /dev/null +++ b/config-model/src/test/integration/tensorflow/services.xml @@ -0,0 +1,6 @@ +<!-- 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 new file mode 100644 index 00000000000..08f18331d1c --- /dev/null +++ b/config-model/src/test/java/com/yahoo/config/model/ModelNameCollisionTest.java @@ -0,0 +1,43 @@ +// 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 27fa07c7c37..79d19371f1c 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()); - RankingExpressionWithOnnxTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(storedApplicationDirectory); + RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithTensorFlowTestCase.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 RankingExpressionWithOnnxTestCase.StoringApplicationPackage(applicationDir)); + new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(applicationDir)); } private RankProfileSearchFixture fixtureWith(String firstPhaseExpression, String constant, String field, - RankingExpressionWithOnnxTestCase.StoringApplicationPackage application) { + RankingExpressionWithTensorFlowTestCase.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 132cf936054..1fe1ebf2bb3 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 @@ -1,33 +1,23 @@ // 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; @@ -38,7 +28,8 @@ public class RankingExpressionWithOnnxTestCase { /** The model name */ private final static String name = "mnist_softmax"; - private final static String vespaExpression = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(" + name + "_layer_Variable), f(a,b)(a * b)), sum, d2) * 1.0, constant(" + name + "_layer_Variable_1) * 1.0, f(a,b)(a + b)), tensor<float>(d0[1])(1.0), f(a,b)(a * b))"; + 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 vespaExpressionWithBatchReduce = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b)), tensor<float>(d0[1])(1.0), f(a,b)(a * b))"; @After public void removeGeneratedModelFiles() { @@ -49,8 +40,8 @@ public class RankingExpressionWithOnnxTestCase { public void testGlobalOnnxModel() throws IOException { ImportedModelTester tester = new ImportedModelTester(name, applicationDir); VespaModel model = tester.createVespaModel(); - tester.assertLargeConstant(name + "_layer_Variable_1", model, Optional.of(10L)); - tester.assertLargeConstant(name + "_layer_Variable", model, Optional.of(7840L)); + tester.assertLargeConstant(name + "_Variable_1", model, Optional.of(10L)); + tester.assertLargeConstant(name + "_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"); @@ -61,8 +52,8 @@ public class RankingExpressionWithOnnxTestCase { storedAppDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile()); ImportedModelTester storedTester = new ImportedModelTester(name, storedAppDir); VespaModel storedModel = storedTester.createVespaModel(); - tester.assertLargeConstant(name + "_layer_Variable_1", storedModel, Optional.of(10L)); - tester.assertLargeConstant(name + "_layer_Variable", storedModel, Optional.of(7840L)); + tester.assertLargeConstant(name + "_Variable_1", storedModel, Optional.of(10L)); + tester.assertLargeConstant(name + "_Variable", storedModel, Optional.of(7840L)); } finally { IOUtils.recursiveDeleteDir(storedAppDir.toFile()); @@ -73,7 +64,7 @@ public class RankingExpressionWithOnnxTestCase { public void testOnnxReferenceWithConstantFeature() { RankProfileSearchFixture search = fixtureWith("constant(mytensor)", "onnx('mnist_softmax.onnx')", - "constant mytensor { file: ignored\ntype: tensor<float>(d0[1],d1[784]) }", + "constant mytensor { file: ignored\ntype: tensor<float>(d0[7],d1[784]) }", null); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @@ -83,7 +74,7 @@ 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[1],d1[784])'/>" + + " <field name='query(mytensor)' type='tensor<float>(d0[3],d1[784])'/>" + "</query-profile-type>"; StoringApplicationPackage application = new StoringApplicationPackage(applicationDir, queryProfile, @@ -106,7 +97,7 @@ public class RankingExpressionWithOnnxTestCase { "field mytensor type tensor<float>(d0[1],d1[784]) { indexing: attribute }", "Placeholder", application); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + search.assertFirstPhaseExpression(vespaExpressionWithBatchReduce, "my_profile"); } @@ -124,28 +115,28 @@ public class RankingExpressionWithOnnxTestCase { "field mytensor type tensor<float>(d0[1],d1[784]) { indexing: attribute }", "Placeholder", application); - search.assertFirstPhaseExpression(vespaExpression, "my_profile"); + search.assertFirstPhaseExpression(vespaExpressionWithBatchReduce, "my_profile"); } @Test public void testNestedOnnxReference() { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],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[1],d1[784])(0.0)", - "onnx('mnist_softmax.onnx', 'layer_add')"); + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", + "onnx('mnist_softmax.onnx', 'add')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @Test public void testOnnxReferenceWithSpecifiedOutputAndSignature() { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", - "onnx('mnist_softmax.onnx', 'default.layer_add')"); + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", + "onnx('mnist_softmax.onnx', 'default.add')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); } @@ -167,7 +158,7 @@ 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[1],d1[784]) but this function is " + + "Model refers input 'Placeholder' of type tensor<float>(d0[],d1[784]) but this function is " + "not present in rank profile 'my_profile'", Exceptions.toMessageString(expected)); } @@ -176,7 +167,7 @@ public class RankingExpressionWithOnnxTestCase { @Test public void testOnnxReferenceWithWrongFunctionType() { try { - RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d5[10])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d5[10])(0.0)", "onnx('mnist_softmax.onnx')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); fail("Expecting exception"); @@ -184,8 +175,8 @@ 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[1],d1[784]), " + - "but this function returns tensor(d0[1],d5[10])", + "Model refers input 'Placeholder'. The required type of this is tensor<float>(d0[],d1[784]), " + + "but this function returns tensor(d0[2],d5[10])", Exceptions.toMessageString(expected)); } } @@ -201,14 +192,14 @@ 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.layer_add", + "No expressions named 'y' in model 'mnist_softmax.onnx'. Available expressions: default.add", Exceptions.toMessageString(expected)); } } @Test public void testImportingFromStoredExpressions() throws IOException { - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", + RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)", "onnx('mnist_softmax.onnx')"); search.assertFirstPhaseExpression(vespaExpression, "my_profile"); @@ -235,29 +226,26 @@ public class RankingExpressionWithOnnxTestCase { } @Test - public void testImportingFromStoredExpressionsWithFunctionOverridingConstantAndInheritance() throws IOException { + public void testImportingFromStoredExpressionsWithFunctionOverridingConstant() throws IOException { String rankProfile = " rank-profile my_profile {\n" + " function Placeholder() {\n" + - " expression: tensor<float>(d0[1],d1[784])(0.0)\n" + + " expression: tensor<float>(d0[2],d1[784])(0.0)\n" + " }\n" + - " function " + name + "_layer_Variable() {\n" + + " function " + name + "_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(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), " + name + "_layer_Variable, f(a,b)(a * b)), sum, d2) * 1.0, constant(" + name + "_layer_Variable_1) * 1.0, f(a,b)(a + b)), tensor<float>(d0[1])(1.0), f(a,b)(a * b))"; + "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))"; 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")); @@ -271,9 +259,7 @@ 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 { @@ -281,90 +267,6 @@ public class RankingExpressionWithOnnxTestCase { } } - @Test - public void testReduceBatchDimension() { - final String expression = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(" + name + "_layer_Variable), f(a,b)(a * b)), sum, d2) * 1.0, constant(" + name + "_layer_Variable_1) * 1.0, f(a,b)(a + b)), tensor<float>(d0[1])(1.0), f(a,b)(a * b))"; - RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[1],d1[784])(0.0)", - "onnx('mnist_softmax.onnx')"); - search.assertFirstPhaseExpression(expression, "my_profile"); - } - - @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)); @@ -414,39 +316,4 @@ 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 new file mode 100644 index 00000000000..126a41e14ad --- /dev/null +++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java @@ -0,0 +1,505 @@ +// 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(vespaExpressionWithBatchReduce, "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(vespaExpressionWithBatchReduce, "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 testTensorFlowReduceBatchDimension() { + final String expression = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(" + name + "_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(" + name + "_layer_Variable_1_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))"; + RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)", + "tensorflow('mnist_softmax/saved')"); + search.assertFirstPhaseExpression(expression, "my_profile"); + } + + @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(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), 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(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), 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 1df6e5e5365..f73d1c823e2 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()); - RankingExpressionWithOnnxTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithOnnxTestCase.StoringApplicationPackage(storedApplicationDirectory); + RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage storedApplication = new RankingExpressionWithTensorFlowTestCase.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 RankingExpressionWithOnnxTestCase.StoringApplicationPackage(applicationDir)); + new RankingExpressionWithTensorFlowTestCase.StoringApplicationPackage(applicationDir)); } private RankProfileSearchFixture fixtureWith(String firstPhaseExpression, String constant, String field, - RankingExpressionWithOnnxTestCase.StoringApplicationPackage application) { + RankingExpressionWithTensorFlowTestCase.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 3907cf04870..5b38e09537d 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)", - 3, model.rankProfileList().getRankProfiles().size()); + assertEquals("Global models are created (although not used directly here", + 5, model.rankProfileList().getRankProfiles().size()); RankProfilesConfig.Builder builder = new RankProfilesConfig.Builder(); model.getSearchClusters().get(0).getConfig(builder); @@ -62,15 +62,18 @@ 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(reduce(rename(rankingExpression(input), (d0, d1), (d0, d4)), sum, d0), 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(join(reduce(join(reduce(rename(rankingExpression(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))\n" + "rankingExpression(mnist_softmax_onnx).rankingScript: join(join(reduce(join(reduce(rename(rankingExpression(Placeholder), (d0, d1), (d0, d2)), sum, d0), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b)), tensor<float>(d0[1])(1.0), 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_softmax_onnx) + rankingExpression(my_xgboost) + rankingExpression(my_lightgbm)\n" + + "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_tensorflow) + rankingExpression(mnist_softmax_tensorflow) + 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 731991fa18d..36b11cee067 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 @@ -96,23 +96,24 @@ public class ModelEvaluationTest { cluster.getConfig(cb); RankingConstantsConfig constantsConfig = new RankingConstantsConfig(cb); - assertEquals(4, config.rankprofile().size()); + assertEquals(5, 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("small_constants_and_functions")); + assertTrue(modelNames.contains("mnist_softmax_saved")); // Compare profile content in a denser format than config: StringBuilder sb = new StringBuilder(); - for (RankProfilesConfig.Rankprofile.Fef.Property p : findProfile("small_constants_and_functions", config).property()) + for (RankProfilesConfig.Rankprofile.Fef.Property p : findProfile("mnist_saved", config).property()) sb.append(p.name()).append(": ").append(p.value()).append("\n"); - assertEquals(profile, sb.toString()); + assertEquals(mnistProfile, sb.toString()); ModelsEvaluator evaluator = new ModelsEvaluator(new ToleratingMissingConstantFilesRankProfilesConfigImporter(MockFileAcquirer.returnFile(null)) .importFrom(config, constantsConfig)); - assertEquals(4, evaluator.models().size()); + assertEquals(5, evaluator.models().size()); Model xgboost = evaluator.models().get("xgboost_2_2"); assertNotNull(xgboost); @@ -124,6 +125,16 @@ 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()); @@ -134,13 +145,22 @@ public class ModelEvaluationTest { assertNotNull(evaluator.evaluatorOf("mnist_softmax", "default.add")); assertNotNull(evaluator.evaluatorOf("mnist_softmax", "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 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 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 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 8f73cd02184..a9d71b7d9d5 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(IntermediateOperation.vespaName(output.getKey()), output.getValue()); + signature.output(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 c8d7392bb8d..f8c7dc15857 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 ByteString values; + private final IntBuffer values; private final int size; RawBoolValues(Onnx.TensorProto tensorProto) { - values = tensorProto.getRawData(); - size = values.size(); + values = bytes(tensorProto).asIntBuffer(); + size = values.remaining(); } - @Override double get(int i) { return values.byteAt(i) == 0 ? 0.0 : 1.0; } + @Override double get(int i) { return values.get(i); } @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 7647161db16..6e637c72d0f 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 static String vespaName(String name) { + public 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 5bf11ed8cf6..96ea58edc61 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[] onnxOpsetsToTry = {8, 10, 12}; + private final static int defaultOnnxOpset = 8; private final OnnxImporter onnxImporter = new OnnxImporter(); @@ -52,10 +52,19 @@ public class TensorFlowImporter extends ModelImporter { */ @Override public ImportedModel importModel(String modelName, String modelDir) { - return convertToOnnxAndImport(modelName, 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); + } } - /** Imports a TensorFlow model - DEPRECATED */ + /** Imports a TensorFlow model */ public ImportedModel importModel(String modelName, String modelDir, SavedModelBundle model) { try { IntermediateGraph graph = GraphImporter.importGraph(modelName, model); @@ -69,18 +78,15 @@ 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"; - 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.info("Conversion to ONNX with opset " + opset + " failed. Reason: " + res.getSecond()); + 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()); } - throw new IllegalArgumentException("Unable to convert TensorFlow model in '" + modelDir + "' to ONNX."); + return onnxImporter.importModel(modelName, convertedPath); } 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 09455abc380..35c853bd746 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 @@ -55,4 +55,47 @@ 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(); + } + + } |