213 lines
8.8 KiB
Python
213 lines
8.8 KiB
Python
# checkstyle: noqa
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import tensorflow.compat.v1 as tf
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from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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import tensorflow_hub as hub
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from datetime import datetime
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from tensorflow.compat.v1 import logging
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from twitter.deepbird.projects.timelines.configs import all_configs
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from twml.trainers import DataRecordTrainer
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from twml.contrib.calibrators.common_calibrators import build_percentile_discretizer_graph
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from twml.contrib.calibrators.common_calibrators import calibrate_discretizer_and_export
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from .metrics import get_multi_binary_class_metric_fn
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from .constants import TARGET_LABEL_IDX, PREDICTED_CLASSES
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from .example_weights import add_weight_arguments, make_weights_tensor
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from .lolly.data_helpers import get_lolly_logits
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from .lolly.tf_model_initializer_builder import TFModelInitializerBuilder
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from .lolly.reader import LollyModelReader
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from .tf_model.discretizer_builder import TFModelDiscretizerBuilder
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from .tf_model.weights_initializer_builder import TFModelWeightsInitializerBuilder
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import twml
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def get_feature_values(features_values, params):
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if params.lolly_model_tsv:
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# The default DBv2 HashingDiscretizer bin membership interval is (a, b]
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#
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# The Earlybird Lolly prediction engine discretizer bin membership interval is [a, b)
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#
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# TFModelInitializerBuilder converts (a, b] to [a, b) by inverting the bin boundaries.
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#
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# Thus, invert the feature values, so that HashingDiscretizer can to find the correct bucket.
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return tf.multiply(features_values, -1.0)
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else:
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return features_values
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def build_graph(features, label, mode, params, config=None):
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weights = None
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if "weights" in features:
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weights = make_weights_tensor(features["weights"], label, params)
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num_bits = params.input_size_bits
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if mode == "infer":
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indices = twml.limit_bits(features["input_sparse_tensor_indices"], num_bits)
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dense_shape = tf.stack([features["input_sparse_tensor_shape"][0], 1 << num_bits])
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sparse_tf = tf.SparseTensor(
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indices=indices,
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values=get_feature_values(features["input_sparse_tensor_values"], params),
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dense_shape=dense_shape
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)
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else:
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features["values"] = get_feature_values(features["values"], params)
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sparse_tf = twml.util.convert_to_sparse(features, num_bits)
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if params.lolly_model_tsv:
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tf_model_initializer = TFModelInitializerBuilder().build(LollyModelReader(params.lolly_model_tsv))
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bias_initializer, weight_initializer = TFModelWeightsInitializerBuilder(num_bits).build(tf_model_initializer)
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discretizer = TFModelDiscretizerBuilder(num_bits).build(tf_model_initializer)
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else:
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discretizer = hub.Module(params.discretizer_save_dir)
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bias_initializer, weight_initializer = None, None
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input_sparse = discretizer(sparse_tf, signature="hashing_discretizer_calibrator")
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logits = twml.layers.full_sparse(
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inputs=input_sparse,
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output_size=1,
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bias_initializer=bias_initializer,
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weight_initializer=weight_initializer,
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use_sparse_grads=(mode == "train"),
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use_binary_values=True,
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name="full_sparse_1"
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)
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loss = None
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if mode != "infer":
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lolly_activations = get_lolly_logits(label)
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if opt.print_data_examples:
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logits = print_data_example(logits, lolly_activations, features)
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if params.replicate_lolly:
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loss = tf.reduce_mean(tf.math.squared_difference(logits, lolly_activations))
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else:
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batch_size = tf.shape(label)[0]
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target_label = tf.reshape(tensor=label[:, TARGET_LABEL_IDX], shape=(batch_size, 1))
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loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=target_label, logits=logits)
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loss = twml.util.weighted_average(loss, weights)
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num_labels = tf.shape(label)[1]
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eb_scores = tf.tile(lolly_activations, [1, num_labels])
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logits = tf.tile(logits, [1, num_labels])
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logits = tf.concat([logits, eb_scores], axis=1)
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output = tf.nn.sigmoid(logits)
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return {"output": output, "loss": loss, "weights": weights}
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def print_data_example(logits, lolly_activations, features):
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return tf.Print(
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logits,
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[logits, lolly_activations, tf.reshape(features['keys'], (1, -1)), tf.reshape(tf.multiply(features['values'], -1.0), (1, -1))],
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message="DATA EXAMPLE = ",
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summarize=10000
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)
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def earlybird_output_fn(graph_output):
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export_outputs = {
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tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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tf.estimator.export.PredictOutput(
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{"prediction": tf.identity(graph_output["output"], name="output_scores")}
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)
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}
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return export_outputs
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if __name__ == "__main__":
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parser = DataRecordTrainer.add_parser_arguments()
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parser = twml.contrib.calibrators.add_discretizer_arguments(parser)
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parser.add_argument("--label", type=str, help="label for the engagement")
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parser.add_argument("--model.use_existing_discretizer", action="store_true",
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dest="model_use_existing_discretizer",
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help="Load a pre-trained calibration or train a new one")
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parser.add_argument("--input_size_bits", type=int)
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parser.add_argument("--export_module_name", type=str, default="base_mlp", dest="export_module_name")
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parser.add_argument("--feature_config", type=str)
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parser.add_argument("--replicate_lolly", type=bool, default=False, dest="replicate_lolly",
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help="Train a regression model with MSE loss and the logged Earlybird score as a label")
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parser.add_argument("--lolly_model_tsv", type=str, required=False, dest="lolly_model_tsv",
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help="Initialize with weights and discretizer bins available in the given Lolly model tsv file"
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"No discretizer gets trained or loaded if set.")
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parser.add_argument("--print_data_examples", type=bool, default=False, dest="print_data_examples",
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help="Prints 'DATA EXAMPLE = [[tf logit]][[logged lolly logit]][[feature ids][feature values]]'")
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add_weight_arguments(parser)
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opt = parser.parse_args()
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feature_config_module = all_configs.select_feature_config(opt.feature_config)
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feature_config = feature_config_module.get_feature_config(data_spec_path=opt.data_spec, label=opt.label)
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parse_fn = twml.parsers.get_sparse_parse_fn(
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feature_config,
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keep_fields=("ids", "keys", "values", "batch_size", "total_size", "codes"))
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if not opt.lolly_model_tsv:
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if opt.model_use_existing_discretizer:
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logging.info("Skipping discretizer calibration [model.use_existing_discretizer=True]")
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logging.info(f"Using calibration at {opt.discretizer_save_dir}")
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else:
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logging.info("Calibrating new discretizer [model.use_existing_discretizer=False]")
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calibrator = twml.contrib.calibrators.HashingDiscretizerCalibrator(
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opt.discretizer_num_bins,
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opt.discretizer_output_size_bits
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)
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calibrate_discretizer_and_export(name="recap_earlybird_hashing_discretizer",
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params=opt,
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calibrator=calibrator,
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build_graph_fn=build_percentile_discretizer_graph,
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feature_config=feature_config)
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trainer = DataRecordTrainer(
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name="earlybird",
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params=opt,
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build_graph_fn=build_graph,
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save_dir=opt.save_dir,
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feature_config=feature_config,
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metric_fn=get_multi_binary_class_metric_fn(
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metrics=["roc_auc"],
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classes=PREDICTED_CLASSES
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),
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warm_start_from=None
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)
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train_input_fn = trainer.get_train_input_fn(parse_fn=parse_fn)
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eval_input_fn = trainer.get_eval_input_fn(parse_fn=parse_fn)
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logging.info("Training and Evaluation ...")
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trainingStartTime = datetime.now()
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trainer.train_and_evaluate(train_input_fn=train_input_fn, eval_input_fn=eval_input_fn)
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trainingEndTime = datetime.now()
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logging.info("Training and Evaluation time: " + str(trainingEndTime - trainingStartTime))
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if trainer._estimator.config.is_chief:
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serving_input_in_earlybird = {
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"input_sparse_tensor_indices": array_ops.placeholder(
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name="input_sparse_tensor_indices",
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shape=[None, 2],
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dtype=dtypes.int64),
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"input_sparse_tensor_values": array_ops.placeholder(
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name="input_sparse_tensor_values",
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shape=[None],
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dtype=dtypes.float32),
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"input_sparse_tensor_shape": array_ops.placeholder(
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name="input_sparse_tensor_shape",
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shape=[2],
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dtype=dtypes.int64)
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}
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serving_input_receiver_fn = build_raw_serving_input_receiver_fn(serving_input_in_earlybird)
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twml.contrib.export.export_fn.export_all_models(
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trainer=trainer,
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export_dir=opt.export_dir,
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parse_fn=parse_fn,
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serving_input_receiver_fn=serving_input_receiver_fn,
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export_output_fn=earlybird_output_fn,
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feature_spec=feature_config.get_feature_spec()
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)
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logging.info("The export model path is: " + opt.export_dir)
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