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Remove not maintained code path.

PiperOrigin-RevId: 285869559

PiperOrigin-RevId: 285618209

PiperOrigin-RevId: 285533511

PiperOrigin-RevId: 285503670

To test, I did 50 fp32 runs and 50 fp16 runs. I used the following command:

python ncf_keras_main.py --dataset=ml-20m --num_gpus=1 --train_epochs=10 --clean --batch_size=99000 --learning_rate=0.00382059 --beta1=0.783529 --beta2=0.909003 --epsilon=1.45439e-7 --layers=256,256,128,64 --num_factors=64 --hr_threshold=0.635 --ml_perf --nouse_synthetic_data --data_dir ~/ncf_data_dir_python3 --model_dir ~/tmp_model_dir --keras_use_ctl

For the fp16 runs, I added --dtype=fp16. The average hit-rate for both fp16 and fp32 was 0.6365. I also did 50 runs with the mixed precision graph rewrite, and the average hit-rate was 0.6363. The difference is likely due to noise.

PiperOrigin-RevId: 275059871

Move movielens to recommendation

PiperOrigin-RevId: 269680664

262988559  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Enable NCF TF 2.0 model to run on TPUStrategy.

--
262971756  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change

262967691  by hongkuny<hongkuny@google.com>:

    Internal

--

PiperOrigin-RevId: 262988559

* borrowing a tf1.x optimization which converts gradients from sparse to dense for better perf

* cleanup after code review

* Add a custom training loop for NCF model with TF2.0

* Fix long line in ncf_keras_main.py

* Remove dataset repeat when using custom training loop.

* Remove contrib imports, or move them inline

* Use exposed API for FixedLenFeature

* Replace tf.logging with absl logging

* Change GFile to v2 APIs

* replace tf.logging with absl loggin in movielens

* Fixing an import bug

* Change gfile to v2 APIs in code

* Swap to keras optimizer v2

* Bug fix for optimizer

* Change tf.log to tf.keras.backend.log

* Change the loss function to keras loss

* convert another loss to keras loss

* Resolve comments and fix lint

* Add a doc string

* Fix existing tests and add new tests for DS

* Added tests for multi-replica

* Fix lint

* resolve comments

* make estimator run in tf2.0

* use compat v1 loss

* fix lint issue

* tmp commit

* tmp commit

* first attempt (without eval)

* Bug fixes

* bug fixes

* training done

* Loss NAN, no eval

* Loss weight problem solved

* resolve the NAN loss problem

* Problem solved. Clean up needed

* Added a todo

* Remove debug prints

* Extract get_optimizer to ncf_common

* Move metrics computation back to neumf; use DS.scope api

* Extract DS.scope code to utils

* lint fixes

* Move obtaining DS above producer.start to avoid race condition

* move pt 1

* move pt 2

* Update the run script

* Wrap keras_model related code into functions

* Update the doc for softmax_logitfy and change the method name

* Resolve PR comments

* working version with: eager, DS, batch and no masks

* Remove git conflict indicator

* move reshape to neumf_model

* working version, not converge

* converged

* fix a test

* more lint fix

* more lint fix

* more lint fixes

* more lint fix

* Removed unused imports

* fix test

* dummy commit for kicking of checks

* fix lint issue

* dummy input to kick off checks

* dummy input to kick off checks

* add collective to dist strat

* addressed review comments

* add a doc string

2nd half of rough replacement pass

fix dataset map functions

reduce bias in sample selection

cache pandas work on a daily basis

cleanup and fix batch check for multi gpu

multi device fix

fix treatment of eval data padding

print data producer

replace epoch overlap with padding and masking

move type and shape info into the producer class and update run.sh with larger batch size hyperparams

remove xla for multi GPU

more cleanup

remove model runner altogether

bug fixes

address subtle pipeline hang and improve producer __repr__

fix crash

fix assert

use popen_helper to create pools

add StreamingFilesDataset and abstract data storage to a separate class

bug fix

fix wait bug and add manual stack trace print

more bug fixes and refactor valid point mask to work with TPU sharding

misc bug fixes and adjust dtypes

address crash from decoding bools

fix remaining dtypes and change record writer pattern since it does not append

fix synthetic data

use TPUStrategy instead of TPUEstimator

minor tweaks around moving to TPUStrategy

cleanup some old code

delint and simplify permutation generation

remove low level tf layer definition, use single table with slice for keras, and misc fixes

missed minor point on removing tf layer definition

fix several bugs from recombinging layer definitions

delint and add docstrings

Update ncf_test.py. Section for identical inputs and different outputs was removed.

update data test to run against the new producer class

* Keras-ify TPU embedding lookup

* delint

* pull get_variable() out of keras lambda

* delint

* move get_variable under variable scope

* Merges TPU-TC optimizations into HEAD.

* Split a line that went over 80 from a tab.

* Remove trailing whitespace.

The option is --nouse_estimator

* first pass at __getattr__ abuse logger

* first pass at adding tags to NCF

* minor formatting updates

* fix tag name

* convert metrics to python floats

* getting closer...

* direct mlperf logs to a file

* small tweaks and add stitching

* update tags

* fix tag and add a sudo call

* tweak format of run.sh

* delint

* use distribution strategies for evaluation

* address PR comments

* delint and fix test

* adjust flag validation for xla

* add prefix to distinguish log stitching

* fix index bug

* fix clear cache for root user

* dockerize cache drop

* TIL some regex magic

* move evaluation from numpy to tensorflow

fix syntax error

don't use sigmoid to convert logits. there is too much precision loss.

WIP: add logit metrics

continue refactor of NCF evaluation

fix syntax error

fix bugs in eval loss calculation

fix eval loss reweighting

remove numpy based metric calculations

fix logging hooks

fix sigmoid to softmax bug

fix comment

catch rare PIPE error and address some PR comments

* fix metric test and address PR comments

* delint and fix python2

* fix test and address PR comments

* extend eval to TPUs

* bug fixes and add seed

* more random corrections

* make cleanup more robust

* return cleanup fn

* delint and address PR comments.

* delint and fix tests

* delinting is never done

* add pipeline hashing

* delint

* Fix convergence issues for MLPerf.

Thank you to @robieta for helping me find these issues, and for providng an algorithm for the `get_hit_rate_and_ndcg_mlperf` function.

This change causes every forked process to set a new seed, so that forked processes do not generate the same set of random numbers. This improves evaluation hit rates.

Additionally, it adds a flag, --ml_perf, that makes further changes so that the evaluation hit rate can match the MLPerf reference implementation.

I ran 4 times with --ml_perf and 4 times without. Without --ml_perf, the highest hit rates achieved by each run were 0.6278, 0.6287, 0.6289, and 0.6241. With --ml_perf, the highest hit rates were 0.6353, 0.6356, 0.6367, and 0.6353.

* fix lint error

* Fix failing test

* Address @robieta's feedback

* Address more feedback

The output of an embeddding layer is already flattened, so the Flatten layers acted as no-ops.

* add indirection file

* remove unused imports

* fix import

* intermediate commit

* ncf now working

* reorder pipeline

* allow batched decode for file backed dataset

* fix bug

* more tweaks

* parallize false negative generation

* shared pool hack

* workers ignore sigint

* intermediate commit

* simplify buffer backed dataset creation to fixed length record approach only. (more cleanup needed)

* more tweaks

* simplify pipeline

* fix misplaced cleanup() calls. (validation works\!)

* more tweaks

* sixify memoryview usage

* more sixification

* fix bug

* add future imports

* break up training input pipeline

* more pipeline tuning

* first pass at moving negative generation to async

* refactor async pipeline to use files instead of ipc

* refactor async pipeline

* move expansion and concatenation from reduce worker to generation workers

* abandon complete async due to interactions with the tensorflow threadpool

* cleanup

* remove performance_comparison.py

* experiment with rough generator + interleave pipeline

* yet more pipeline tuning

* update on-the-fly pipeline

* refactor preprocessing, and move train generation behind a GRPC server

* fix leftover call

* intermediate commit

* intermediate commit

* fix index error in data pipeline, and add logging to train data server

* make sharding more robust to imbalance

* correctly sample with replacement

* file buffers are no longer needed for this branch

* tweak sampling methods

* add README for data pipeline

* fix eval sampling, and vectorize eval metrics

* add spillover and static training batch sizes

* clean up cruft from earlier iterations

* rough delint

* delint 2 / n

* add type annotations

* update run script

* make run.sh a bit nicer

* change embedding initializer to match reference

* rough pass at pure estimator model_fn

* impose static shape hack (revisit later)

* refinements

* fix dir error in run.sh

* add documentation

* add more docs and fix an assert

* old data test is no longer valid. Keeping it around as reference for the new one

* rough draft of data pipeline validation script

* don't rely on shuffle default

* tweaks and documentation

* add separate eval batch size for performance

* initial commit

* terrible hacking

* mini hacks

* missed a bug

* messing about trying to get TPU running

* TFRecords based TPU attempt

* bug fixes

* don't log remotely

* more bug fixes

* TPU tweaks and bug fixes

* more tweaks

* more adjustments

* rework model definition

* tweak data pipeline

* refactor async TFRecords generation

* temp commit to run.sh

* update log behavior

* fix logging bug

* add check for subprocess start to avoid cryptic hangs

* unify deserialize and make it TPU compliant

* delint

* remove gRPC pipeline code

* fix logging bug

* delint and remove old test files

* add unit tests for NCF pipeline

* delint

* clean up run.sh, and add run_tpu.sh

* forgot the most important line

* fix run.sh bugs

* yet more bash debugging

* small tweak to add keras summaries to model_fn

* Clean up sixification issues

* address PR comments

* delinting is never over

* begin branch

* finish download script

* rename download to dataset

* intermediate commit

* intermediate commit

* misc tweaks

* intermediate commit

* intermediate commit

* intermediate commit

* delint and update census test.

* add movie tests

* delint

* fix py2 issue

* address PR comments

* intermediate commit

* intermediate commit

* intermediate commit

* finish wide deep transition to vanilla movielens

* delint

* intermediate commit

* intermediate commit

* intermediate commit

* intermediate commit

* fix import

* add default ncf csv construction

* change default on download_if_missing

* shard and vectorize example serialization

* fix import

* update ncf data unittests

* delint

* delint

* more delinting

* fix wide-deep movielens serialization

* address PR comments

* add file_io tests

* investigate wide-deep test failure

* remove hard coded path and properly use flags.

* address file_io test PR comments

* missed a hash_bucked_size

* Add unit test, official flags, and benchmark logs

* Fix checking errors

* Reorder imports to fix lints

* Address comments and correct model layers

* Add dataset checking

* Add recommendation model

* Fix pylints check error

* Rename file

* Address comments, update input pipeline, and add distribution strategy

* Fix import error

* Address more comments

* Fix lints