GitLab

PiperOrigin-RevId: 264468498

Only the V1 resnet model uses --max_train_steps. This unexposes the flag in the keras_application_models, mnist, keras resnet, CTL resnet Models. Before this change, such models allowed the flag to be specified, but ignored it.

I also removed the "max_train" argument from the run_synthetic function, since this only had any meaning for the V1 resnet model. Instead, the V1 resnet model now directly passes --max_train_steps=1 to run_synthetic.

PiperOrigin-RevId: 264269836

PiperOrigin-RevId: 263401952

PiperOrigin-RevId: 263257133

PiperOrigin-RevId: 263204353

PiperOrigin-RevId: 263217684

PiperOrigin-RevId: 263158478

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

    Enable NCF TF 2.0 model to run on TPUStrategy.

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

    Internal change

262967691  by hongkuny<hongkuny@google.com>:

    Internal

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PiperOrigin-RevId: 262988559

* Add run_eagerly for ctl.

* fix test name and do not set "default".

* 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.

* Merged commit includes the following changes:
260800447  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix tf function signature errors for NCF model.

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260767064  by psv<psv@google.com>:

    Renames the `run_distributed` flag to `experimental_run_tf_function` and modifies the tests generated by the keras_all_modes decorator to better reflect that.

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PiperOrigin-RevId: 260800447

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

260622966  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add absl_app import.

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PiperOrigin-RevId: 260622966

260228553  by priyag<priyag@google.com>:

    Enable transformer and NCF official model tests. Also fix some minor issues so that all tests pass with TF 1 + enable_v2_behavior.

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260043210  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add logic to train NCF model using offline generated data.

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259778607  by priyag<priyag@google.com>:

    Internal change

259656389  by hongkuny<hongkuny@google.com>:

    Internal change

PiperOrigin-RevId: 260228553

* top_1 to hr_at_10_max.

* Call self._run_and_report_benchmark not Super

* 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:
259442882  by hongkuny<hongkuny@google.com>:

    Internal

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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.

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

    Create binary to generate NCF training/evaluation dataset offline.

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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.

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

254060732  by yifeif<yifeif@google.com>:
    Automated rollback of changelist 254027750.

254027750  by hongkuny<hongkuny@google.com>:

    Internal change

253118910  by hongkuny<hongkuny@google.com>:

    Internal change

251906769  by hongkuny<hongkuny@google.com>:

    Internal change

251303452  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

PiperOrigin-RevId: 259442882

* Update ncf_keras_main.py

This combination does not yet work. Fail early with an explicit message instead of throwing error later on.

* Ncf perf changes 1)exclude metric layer from CTL train step 2)dataset optimization to fix size of the sample_weights, preventing a costly broadcast during loss calculation for multi-gpu case

* reduce iterations from 20 to 12.

* add fp16 dynamic batch accuracy check.

* fix existing lint issue.

* Fix unit tests failures.

* 96% of TF 2.0 tests on GPU are passing.

* Currently all passing GPU and CPU TF 2.0

* Address code comments.

* use tf 2.0 cast.

* Comment about working on TF 2.0 CPU

* Uses contrib turn off for TF 2.0.

* Fix wide_deep and add keras_common_tests.

* use context to get num_gpus.

* Switch to tf.keras.metrics

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

* cleanup after code review

* 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.

* 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