Description

[fix] self._default_scores was not tracked by tensorflow graph when it's as a class property, which caused fail saving. Sinc _default_scores was always tf.constant([], dtypes.int64), so replace it totally instead of _track_trackable it.

[fix] Inherit abc.ABC may cause metaclass conflict with other Tensorflow class.

[fix] hkv_hashtable_ops_test has duplicate variable name when test both CPU and GPU device.

[fix] Compatible with HKV asserting when init_capacity is smaller than max_bucket_size

Type of change

[x] Bug fix
[ ] New Tutorial
[ ] Updated or additional documentation
[ ] Additional Testing
[ ] New Feature

Checklist:

[x] I've properly formatted my code according to the guidelines
- [x] By running yapf
- [x] By running clang-format
[x] This PR addresses an already submitted issue for TensorFlow Recommenders-Addons
[x] I have made corresponding changes to the documentation
[x] I have added tests that prove my fix is effective or that my feature works

How Has This Been Tested?

Run this code to simulate training

# -*- coding: utf-8 -*-
import json
import os
import psutil
import gc
import time

import tensorflow as tf
from tensorflow.keras.layers import (Layer, Input, Embedding, Reshape,
                                     Concatenate, Dense, Lambda)
import tensorflow_datasets as tfds
import tensorflow_recommenders_addons as tfra
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import Callback
from tensorflow.python.platform import tf_logging as logging

import horovod.tensorflow.keras as hvd

# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'

tf.random.set_seed(12345)

os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
# os.environ["TF_GPU_THREAD_MODE"] = "gpu_private"
os.environ["TF_DETERMINISTIC_OPS"] = "1"

value_dtype_impl = {
    "bfloat16": tf.bfloat16,
    "float16": tf.float16,
    "float32": tf.float16,
}

try:
  from keras.src import mixed_precision
except:
  from keras import mixed_precision
policy = mixed_precision.policy.Policy('mixed_bfloat16')
# mixed_precision.policy.set_global_policy(policy)
print("compute_dtype:", mixed_precision.policy.global_policy().compute_dtype)

hvd.init()

gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
  tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
  tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')

def input_fn():
  # 公开数据集，其内部继承tf.datset
  # todo：tfrecord
  ids = tfds.load("movielens/100k-ratings",
                  split="train",
                  data_dir=".",
                  download=False)
  ids = ids.map(
      lambda x: {
          "movie_id": tf.strings.to_number(x["movie_id"], tf.int64),
          "user_id": tf.strings.to_number(x["user_id"], tf.int64),
      })
  ratings = tfds.load("movielens/100k-ratings",
                      split="train",
                      data_dir=".",
                      download=False)
  ratings = ratings.map(lambda x: {"user_rating": x["user_rating"]})
  dataset = tf.data.Dataset.zip((ids, ratings))
  # dataset = dataset.shuffle(1_000_000,
  #                           seed=2021,
  #                           reshuffle_each_iteration=False)
  dataset = dataset.take(1_000_000*4096).cache().repeat()
  dataset = dataset.prefetch(tf.data.AUTOTUNE).batch(4096)
  return dataset

class DeepLayer(Layer):

  def __init__(self, hidden_dim, layer_num, out_dim):
    self.layers = []
    for i in range(layer_num):
      self.layers.append(Dense(hidden_dim, "relu"))
    self.layers.append(Dense(out_dim, "sigmoid"))
    super(DeepLayer, self).__init__()

  def call(self, inputs):
    output = inputs
    for layer in self.layers:
      output = layer(output)
    return output  # (batch, out_dim)

export_dir = "hvd_test_export/"
saved_options = tf.saved_model.SaveOptions(
    namespace_whitelist=['TFRA'],
    experimental_variable_policy=tf.saved_model.experimental.VariablePolicy.
    EXPAND_DISTRIBUTED_VARIABLES)

device = "/job:localhost/replica:0/task:0/GPU:0"
# device = ["/job:localhost/replica:0/task:0/GPU:0", "/job:localhost/replica:0/task:0/GPU:1"]
# device = "/job:localhost/replica:0/task:0/CPU:0"

class EmbLayer(Layer):
  def __init__(self, input_name, is_training, hkv):
    super(EmbLayer, self).__init__()
    saver=None
    saver=tfra.dynamic_embedding.FileSystemSaver(proc_size=hvd.size(), proc_rank=hvd.rank())
    if hkv:
      kv_creator=tfra.dynamic_embedding.HkvHashTableCreator(saver=saver)
    else:
      kv_creator=tfra.dynamic_embedding.CuckooHashTableCreator(saver=saver)
    self.emb = tfra.dynamic_embedding.keras.layers.HvdAllToAllEmbedding(
        embedding_size=8,
        combiner='mean',
        key_dtype=tf.int64,
        value_dtype=value_dtype_impl[mixed_precision.global_policy().compute_dtype],
        initializer=tf.keras.initializers.RandomNormal() if is_training else 0,
        devices=device,
        name=input_name + '_DELayer',
        # kv_creator=None,
        kv_creator=kv_creator
    )

  def call(self, input_tensor):
    return self.emb(input_tensor)

def build_model(is_training=True, hkv=True):

  # 输入层
  input_list = ["movie_id", "user_id"]
  # input_list = ["movie_id"]

  inputs = dict()
  embedding_outs = []

  for input_name in input_list:
    input_tensor = Input(shape=(1,), dtype=tf.int64, name=input_name)
    inputs[input_name] = input_tensor

    #      自定义keras Embedding层（通过继承tfra.dynamic_embedding.layers.Embedding修改）
    embedding_out = EmbLayer(input_name, is_training, hkv)(input_tensor)
    # embedding_out = Embedding(
    # 1000,
    # 16,
    # embeddings_initializer=tf.keras.initializers.Ones(),
    # name=input_name+'_DELayer'
    # )(input_tensor)
    ####################################################################################
    embedding_out = tf.cast(embedding_out, tf.float32)
    embedding_out = Reshape((-1,))(embedding_out)
    embedding_outs.append(embedding_out)

  embeddings_concat = Concatenate(axis=1)(embedding_outs)

  outs = DeepLayer(8, 1, 1)(embeddings_concat)
  # outs = DeepLayer(4096*2, 5, 1)(embeddings_concat)
  outs = Lambda(lambda x: x, name="user_rating")(outs)

  model = tf.keras.Model(inputs=inputs, outputs=outs)
  # model.summary()

  # optimizer = tf.keras.optimizers.Adam(learning_rate=1E-4, amsgrad=False, jit_compile=True)
  optimizer = tf.keras.optimizers.legacy.Adam(learning_rate=1E-4, amsgrad=False)

  # optimizer = hvd.DistributedOptimizer(optimizer)

  optimizer = tfra.dynamic_embedding.DynamicEmbeddingOptimizer(optimizer,
                                                               synchronous=True)

  model.compile(
      optimizer=optimizer,
      loss="binary_crossentropy",
      metrics=tf.keras.metrics.AUC(num_thresholds=1000,
                                   summation_method='minoring'),
      # jit_compile=True,
      steps_per_execution = 10
  )
  return model

from tensorflow.python.eager import context
from tensorflow.python.framework import config
context.enable_jit_compile_rewrite()
config.set_soft_device_placement(True)

model = build_model(True)
data = input_fn()

callbacks = []

# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
hvd_callback = tfra.dynamic_embedding.keras.callbacks.DEHvdBroadcastGlobalVariablesCallback(0)
callbacks.append(hvd_callback)

tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir=f'{export_dir}/TFtensorboard',
    profile_batch=(0,10),
    )
# callbacks.append(tensorboard_callback)

# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
callbacks.append(tfra.dynamic_embedding.keras.callbacks.DEHvdModelCheckpoint('./checkpoint-{epoch}'))

# if hvd.rank() == 0:
#   tf.profiler.experimental.start(f'{export_dir}/TFtensorboard', options=None)
model.fit(
    data,
    batch_size=4096,
    epochs=1,
    # steps_per_epoch=10 // hvd.size(),
    steps_per_epoch=10,
    callbacks=callbacks,
    verbose=1 if hvd.rank() == 0 else 0)
# if hvd.rank() == 0:    
#   tf.profiler.experimental.stop()
tfra.dynamic_embedding.keras.models.de_save_model(model, export_dir)

tfra.dynamic_embedding.enable_inference_mode()
export_model = build_model(is_training=False)
# Modify the inference graph to a stand-alone version
from tensorflow.python.saved_model import save as tf_save
# The save_and_return_nodes function is used to overwrite the saved_model.pb file generated by the save_model function and rewrite the inference graph.
tf_save.save_and_return_nodes(obj=export_model,
                              export_dir=export_dir,
                              options=saved_options,
                              experimental_skip_checkpoint=True)

# time.sleep(10)
# print("\n==================Start to Reload and Train==================\n")

tf.keras.backend.clear_session()
model = build_model(hkv=False)
#Unsupport# model = tf.keras.models.load_model(export_dir, compile=False)
model.load_weights(export_dir)
data = input_fn()
model.fit(
        data,
        epochs=1,
        steps_per_epoch=1,
    )
tfra.dynamic_embedding.keras.models.de_save_model(model, export_dir)

tensorflow / recommenders-addons

[Fix] Untracked constant tensor cause fail to save Embedding and other bugs #441

Description

Type of change

Checklist:

How Has This Been Tested?