GitLab

Also add `worker_hosts` and `task_index` flags.  These flags enable running the
model over multiple hosts by passing the cluster information via command line.

Setting `TF_CONFIG` will continue to work.

PiperOrigin-RevId: 263825245

* force_v2_in_keras_compile FLAG default to None and added seperate temp path.

* switch to force testing 1v path not force v2 path.

* Rename function force_v1_path.

261339941  by haoyuzhang<haoyuzhang@google.com>:

    Own library functions in Keras ResNet models, and remove dependencies on v1 Estimator version of ResNet models.

    Most dependencies that the Keras version has are related to data input pipelines. Created dedicated files (cifar_preprocessing.py, imagenet_preprocessing.py) to collect all logic handling Cifar and ImageNet data input function.

--
261339166  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

261317601  by akuegel<akuegel@google.com>:

    Internal change

261218818  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change

PiperOrigin-RevId: 261339941

261171038  by gjn<gjn@google.com>:

    Remove weight_decay_rate 0 early exit check

    Removing this code path should be fine since this was actually not doing
    what it meant to do. Since weight_decay_rate is actually a tensor, the
    equality check was only looking at the id of the object and comparing to
    0. This should never be true. Evaluating a tensor is also not what we
    want to do at this point of the code. Thus it should be fine to simply
    remove this code.

--
261169862  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

261153520  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

261140302  by hongkuny<hongkuny@google.com>:

    Clean up

--

PiperOrigin-RevId: 261171038

* Add force_run_distributed tests.

* Added enable_eager

* r/force_run_distributed/force_v2_in_keras_compile

* Adding force_v2 tests and FLAGs.

* Rename method to avoid conflict.

* Add cpu force_v2 tests.

* fix lint, wrap line.

* change to force_v2_in_keras_compile

* Update method name.

* Lower mlperf target to 0.736.

* Merged commit includes the following changes:
258867180  by jingli<jingli@google.com>:

    Add new folders for upcoming reorg in model garden.

--
258893811  by hongkuny<hongkuny@google.com>:

    Adds summaries for metrics, allowing metrics inside keras.model.

--
258893048  by isaprykin<isaprykin@google.com>:

    Remove the `cloning` argument to `compile()`.

    Keras models are distributed by cloning in graph mode and without cloning in eager mode as of the change # 258652546.

--
258881002  by hongkuny<hongkuny@google.com>:

    Fix lint.

--
258874998  by hongkuny<hongkuny@google.com>:

    Internal

--
258872662  by hongkuny<hongkuny@google.com>:

    Fix doc

--

PiperOrigin-RevId: 258867180

* Create __init__.py

* Update __init__.py

* Update __init__.py

* Update __init__.py

* XLA FP32 and first test

* More XLA benchmarks FP32.

* Add eager to NCF and refactor resnet.

* fix v2_0 calls and more flag refactor.

* Remove extra flag args.

* 90 epoch default

* add return

* remove xla not used by estimator.

* Remove duplicate run_eagerly.

* fix flag defaults.

* Remove fp16_implementation flag option.

* Remove stop early on mlperf test.

* remove unneeded args.

* load flags from keras mains.

* Do not set learning phase when skipping eval

* Do not set learning phase in no dist strat case

* Added device placement, tweaked benchmarks

* Added tweaked benchmarks for Cifar

* Fix device scope

* Fix lint

* Add explicit GPU placement flag

* Also run accuracy test with explicit GPU placement

* Added doc string

* set default steps to 300K.

* Log flags to perfzero.

* Add XLA support to transformer

- Moved config logic to keras_utils
- Added enable_xla flag to _performance flags
- Did not refactor enable_xla flag from keras resnet due to
  reliance on calling FLAGs in estimator keras and that is
  a needed refactor for another time.

* fix g3 lint complaint.

* Refactor set config into keras_utils.

* Move flags out of main.

* pipe through enable_xla

* Update official/transformer/v2/misc.py
Co-Authored-By: Reed <reedwm@google.com>

Before, there was a global default loss scale for all models. Currently, only resnet uses loss scaling, but this will be useful once more models support it.

* Support pure eager execution in ResNet50

* Use smaller batch size

* Add enable_get_next_as_optional flag.

* Set enable_get_next_as_optional to strategy.

* Add comments to explain the flag.

* Remove trailing whitespace.

* Remove trailing space.

* Added 'tfdata_exp' version of all benchmarks which set
FLAGS.tf_data_experimental_slack = True. Renamed
`data_prefetch_with_slack` to `data_delay_prefetch` (haoyu's change)
to make the names more distinct.

* Add flag to resnet input pipeline and surface through
keras_imagenet_main.py

* Fix trivial model to work properly with fp16

* Add comment on manual casting

* Do not report metrics in performance benchmarks

* Rename flag

* Add learning rate tensor. This makes training slower

* Improve LearningRateSchedule with better efficiency

* Fix lint error

* Replace constant definition with existing one

* Enable CuDNN BatchNorm spatial persistent by default; Remove 2nd zero padding layer

* Apply scale=False and fused=True consistently to BatchNorm layers

* Undo remove padding layer

* Replace zero padding with padding attribute in max pooling for better performance

* Resolve comments

* Revert "Replace zero padding with padding attribute in max pooling for better performance"

This reverts commit ad49db057c800ecac008eec1057005bd2c08ac73.

* Add benchmarks with the --cloning flag to Resnet and NFC.

* Renamed cloning to clone_model_in_keras_dist_strat. Dropped a few tests that aren't essential.

* Fixed up the formatting after re-naming the flag to a much longer  name.  Thanks, lint.
* Fixed the lint error in nfc_common.py

Reason: test failures because contrib is not available in V2

This reverts commit 325dd761.

* Introduce a short sleep before ds.prefetch in tf.data.

* Further limit dataset threads to reduce CPU contention

* Tuned dataset sleep time

* Rename dataset sleep flag; enable it only for Keras Graph mode

* Revert "Set input layer `batch_size` in multi-replica mode (#6578)"

This reverts commit f1a59682.

* Rename variables.

* Revert "Revert " Ensure static shapes when enabling XLA in Resnet Keras model (#6508)" (#6517)"

This reverts commit cc9eef76.

* Set `batch_size` to keras.Input in non-eager mode.

Eager mode currently has OOM problem.

* Add comments for enable_eager flag.

* Always set drop_remainder=True.

* Only set drop_remainder=True for XLA.

* add ds support for ncf

* remove comments for in_top_k

* avoid expanding the input layers

* resolve comments and fix lint

* Added some comments in code and fix lint

* fix lint

* add some documentation

* add tensorflow imports

* Add profiler callback for Keras models

* Update build stats to identify time callback by type

* Add warning message when both TensorBoard and profiler callbacks are used

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

Don't pass `batch_size` to keras.layers.Input in DS multi-replica case. There is currently a bug in Keras side which will cause a batch size incompatible error.

* Update resnet_model.py

* Ensure static shapes when enabling XLA.

* Define `drop_remainder` as a variable.

* Handles per_replica_batch_size in non-XLA mode

* Remove trailing whitespace.

required by multi-node collective ops in eager mode.