使用 TPU SparseCore 和 TensorFlow 进行分布式嵌入

!pip install -U -q tensorflow-tpu==2.19.1
!pip install -q keras-rs

import os
import libtpu

os.environ["PJRT_DEVICE"] = "TPU"
os.environ["NEXT_PLUGGABLE_DEVICE_USE_C_API"] = "true"
os.environ["TF_PLUGGABLE_DEVICE_LIBRARY_PATH"] = libtpu.get_library_path()
os.environ["TF_XLA_FLAGS"] = (
    "--tf_mlir_enable_mlir_bridge=true "
    "--tf_mlir_enable_convert_control_to_data_outputs_pass=true "
    "--tf_mlir_enable_merge_control_flow_pass=true"
)

import tensorflow as tf

os.environ["KERAS_BACKEND"] = "tensorflow"

import keras
import keras_rs
import tensorflow_datasets as tfds

resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu="local")
topology = tf.tpu.experimental.initialize_tpu_system(resolver)
tpu_metadata = resolver.get_tpu_system_metadata()

device_assignment = tf.tpu.experimental.DeviceAssignment.build(
    topology, num_replicas=tpu_metadata.num_cores
)
strategy = tf.distribute.TPUStrategy(
    resolver, experimental_device_assignment=device_assignment
)

PER_REPLICA_BATCH_SIZE = 256
BATCH_SIZE = PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync

# Ratings data.
ratings = tfds.load("movielens/100k-ratings", split="train")
# Features of all the available movies.
movies = tfds.load("movielens/100k-movies", split="train")

users_count = int(
    ratings.map(lambda x: tf.strings.to_number(x["user_id"], out_type=tf.int32))
    .reduce(tf.constant(0, tf.int32), tf.maximum)
    .numpy()
)

movies_count = int(movies.cardinality().numpy())

def preprocess_rating(x):
    return (
        # Inputs are user IDs and movie IDs
        {
            "user_id": tf.strings.to_number(x["user_id"], out_type=tf.int32),
            "movie_id": tf.strings.to_number(x["movie_id"], out_type=tf.int32),
        },
        # Labels are ratings between 0 and 1.
        (x["user_rating"] - 1.0) / 4.0,
    )

shuffled_ratings = ratings.map(preprocess_rating).shuffle(
    100_000, seed=42, reshuffle_each_iteration=False
)
train_ratings = (
    shuffled_ratings.take(80_000).batch(BATCH_SIZE, drop_remainder=True).cache()
)
test_ratings = (
    shuffled_ratings.skip(80_000)
    .take(20_000)
    .batch(BATCH_SIZE, drop_remainder=True)
    .cache()
)

EMBEDDING_DIMENSION = 32

movie_table = keras_rs.layers.TableConfig(
    name="movie_table",
    vocabulary_size=movies_count + 1,  # +1 for movie ID 0, which is not used
    embedding_dim=EMBEDDING_DIMENSION,
    optimizer="adam",
    placement="sparsecore",
)
user_table = keras_rs.layers.TableConfig(
    name="user_table",
    vocabulary_size=users_count + 1,  # +1 for user ID 0, which is not used
    embedding_dim=EMBEDDING_DIMENSION,
    optimizer="adam",
    placement="sparsecore",
)

FEATURE_CONFIGS = {
    "movie_id": keras_rs.layers.FeatureConfig(
        name="movie",
        table=movie_table,
        input_shape=(BATCH_SIZE,),
        output_shape=(BATCH_SIZE, EMBEDDING_DIMENSION),
    ),
    "user_id": keras_rs.layers.FeatureConfig(
        name="user",
        table=user_table,
        input_shape=(BATCH_SIZE,),
        output_shape=(BATCH_SIZE, EMBEDDING_DIMENSION),
    ),
}

class EmbeddingModel(keras.Model):
    """Create the model with the embedding configuration.

    Args:
        feature_configs: the configuration for `DistributedEmbedding`.
    """

    def __init__(self, feature_configs):
        super().__init__()

        self.embedding_layer = keras_rs.layers.DistributedEmbedding(
            feature_configs=feature_configs
        )
        self.ratings = keras.Sequential(
            [
                # Learn multiple dense layers.
                keras.layers.Dense(256, activation="relu"),
                keras.layers.Dense(64, activation="relu"),
                # Make rating predictions in the final layer.
                keras.layers.Dense(1),
            ]
        )

    def call(self, features):
        # Embedding lookup. Outputs have the same structure as the inputs.
        embedding = self.embedding_layer(features)
        return self.ratings(
            keras.ops.concatenate(
                [embedding["user_id"], embedding["movie_id"]],
                axis=1,
            )
        )

with strategy.scope():
    model = EmbeddingModel(FEATURE_CONFIGS)

    model.compile(
        loss=keras.losses.MeanSquaredError(),
        metrics=[keras.metrics.RootMeanSquaredError()],
        optimizer="adagrad",
    )

with strategy.scope():
    model.fit(train_ratings, epochs=5)

with strategy.scope():
    model.evaluate(test_ratings, return_dict=True)