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PiperOrigin-RevId: 410107766

PiperOrigin-RevId: 386140865

PiperOrigin-RevId: 362075728

PiperOrigin-RevId: 326286926

PiperOrigin-RevId: 326286926

PiperOrigin-RevId: 310658964

PiperOrigin-RevId: 308451074

Move movielens to recommendation

PiperOrigin-RevId: 269680664

* Merged commit includes the following changes:
259442882  by hongkuny<hongkuny@google.com>:

    Internal

--
259377621  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix NCF serialization/de-serialization logic in NCF input pipeline to use tf.FixedLenFeature instead of raw string/binary decoding.

--
259373183  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Create binary to generate NCF training/evaluation dataset offline.

--
259026454  by isaprykin<isaprykin@google.com>:

    Internal change

258871624  by hongkuny<hongkuny@google.com>:

    Internal change

257285772  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

256202287  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change.

--
254069984  by hongkuny<hongkuny@google.com>:
    Automated rollback of changelist 254060732.

254060732  by yifeif<yifeif@google.com>:
    Automated rollback of changelist 2540277...

* adding a new perf test for ncf, and changing some names

* Added change to make ncf use the data from the gcp bucket, and removed the need to re-download data >1day old. Reorganized the perf-zero tests

* 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

* from NCF_input import NCFDataset for line 181

The type __NCFDataset__ is used in the type declaration on line 81 but it is never imported.

[flake8](http://flake8.pycqa.org) testing of https://github.com/tensorflow/models on Python 3.7.1

$ __flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics__
```
./official/recommendation/data_preprocessing.py:180:3: F821 undefined name 'NCFDataset'
  # type: (str, str, dict, typing.Optional[str], bool, typing.Optional[str]) -> (NCFDataset, typing.Callable)
  ^
1    F821 undefined name 'NCFDataset'
1
```
__E901,E999,F821,F822,F823__ are the "_showstopper_" [flake8](http://flake8.pycqa.org) issues that can halt the runtime with a SyntaxError, NameError, etc. These 5 are different from most other flake8 issues which are merely "style violations" -- useful for readability but they do not effect runtime safety.
* F821: undefined name `name`
* F822: undefined name `name` in `__all__`
* F823: local variable name referenced before assignment
* E901: SyntaxError or IndentationError
* E999: SyntaxError -- failed to compile a file into an Abstract Syntax Tree

* int, int, data_pipeline.BaseDataConstructor

* Add support for TPUEstimator to data processing pipeline and add the ability to store epochs in user specified location.

Add bisection based producer for increased scalability, enable fully deterministic data production, and use the materialized and bisection producer to check each other (via expected output md5's)

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

This tag should match EVAL_HP_NUM_NEG.

I've noticed sometimes the async process's pool processes do not die when ncf_main.py ends and kills the async process. This commit fixes the issue.

The option is --nouse_estimator

--ml_perf now just changes the model to make it MLPerf compliant. --output_ml_perf_compliance_logging adds the MLPerf compliance logs.

* 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

* intermediate commit

finish replacing spillover with resampled padding

intermediate commit

* resolve merge conflict

* intermediate commit

* further consolidate the data pipeline

* complete first pass at data pipeline refactor

* remove some leftover code

* fix test

* remove resampling, and move train padding logic into neumf.py

* small tweaks

* fix weight bug

* address PR comments

* fix dict zip. (Reed led me astray)

* delint

* make data test deterministic and delint

* Reed didn't lead me astray. I just can't read.

* more delinting

* even more delinting

* use resampling for last batch padding

* pad last batch with unique data

* Revert "pad last batch with unique data"

This reverts commit cbdf46efcd5c7907038a24105b88d38e7f1d6da2.

* move padded batch to the beginning

* delint

* fix step check for synthetic data

* move flagfile into the cache_dir

* remove duplicate code

* delint

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Replace multiprocess pool with popen_helper.get_pool() in data_preprocessing.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

* Use data_dir instead of flags.FLAGS.data_dir in data_preprocessing.py.

Add comments, exit async process after waiting for flagfile for too long and make directory for data_dir in case it does not exist.

* Add --use_synthetic_data option to NCF.

* Add comment to _SYNTHETIC_BATCHES_PER_EPOCH

* Fix test

* Hopefully fix lint issue

* 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

Sometimes it takes longer than 15 seconds, and even longer than 1 minute, to spawn and create the alive file.

* Fix spurious "did not start correctly" error.

The error "Generation subprocess did not start correctly" would occur if the async process started up after the main process checked for the subproc_alive file.

* Add error message

* 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

This is done by using a higher Pickle protocol version, which the Python docs describe as being "slightly more efficient". This reduces the file write time at the beginning from 2 1/2 minutes to 5 seconds.