From d1d33e8db60a7387249ba6f74ebd36dc2b852c07 Mon Sep 17 00:00:00 2001
From: William Falcon <falc@fb.com>
Date: Wed, 24 Jul 2019 17:10:14 -0400
Subject: [PATCH] added test for no dist sampler

---
 tests/test_models.py | 362 +++++++++++++++++++++----------------------
 1 file changed, 181 insertions(+), 181 deletions(-)

diff --git a/tests/test_models.py b/tests/test_models.py
index 21bee021..f80754f1 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -48,187 +48,187 @@ def test_ddp_sampler_error():
     with pytest.raises(MisconfigurationException):
         trainer.get_dataloaders(model)
 
-
-def test_cpu_model():
-    """
-    Make sure model trains on CPU
-    :return:
-    """
-
-    trainer_options = dict(
-        progress_bar=False,
-        experiment=get_exp(),
-        max_nb_epochs=1,
-        train_percent_check=0.4,
-        val_percent_check=0.4
-    )
-
-    model, hparams = get_model()
-    run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
-
-
-def test_all_features_cpu_model():
-    """
-    Test each of the trainer options
-    :return:
-    """
-
-    trainer_options = dict(
-        gradient_clip=1.0,
-        overfit_pct=0.20,
-        track_grad_norm=2,
-        print_nan_grads=True,
-        progress_bar=False,
-        experiment=get_exp(),
-        max_nb_epochs=1,
-        train_percent_check=0.4,
-        val_percent_check=0.4
-    )
-
-    model, hparams = get_model()
-    run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
-
-
-def test_early_stopping_cpu_model():
-    """
-    Test each of the trainer options
-    :return:
-    """
-
-    stopping = EarlyStopping()
-    trainer_options = dict(
-        early_stop_callback=stopping,
-        gradient_clip=1.0,
-        overfit_pct=0.20,
-        track_grad_norm=2,
-        print_nan_grads=True,
-        progress_bar=False,
-        experiment=get_exp(),
-        max_nb_epochs=1,
-        train_percent_check=0.4,
-        val_percent_check=0.4
-    )
-
-    model, hparams = get_model()
-    run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
-
-
-def test_single_gpu_model():
-    """
-    Make sure single GPU works (DP mode)
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_single_gpu_model cannot run. Rerun on a GPU node to run this test')
-        return
-    model, hparams = get_model()
-
-    trainer_options = dict(
-        progress_bar=False,
-        max_nb_epochs=1,
-        train_percent_check=0.1,
-        val_percent_check=0.1,
-        gpus=[0]
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_multi_gpu_model_dp():
-    """
-    Make sure DP works
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
-        return
-    model, hparams = get_model()
-    trainer_options = dict(
-        progress_bar=False,
-        max_nb_epochs=1,
-        train_percent_check=0.1,
-        val_percent_check=0.1,
-        gpus=[0, 1]
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_amp_gpu_dp():
-    """
-    Make sure DP + AMP work
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_dp cannot run. Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
-        return
-    model, hparams = get_model()
-    trainer_options = dict(
-        max_nb_epochs=1,
-        gpus='0, 1',  # test init with gpu string
-        distributed_backend='dp',
-        use_amp=True
-    )
-    with pytest.raises(MisconfigurationException):
-        run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_multi_gpu_model_ddp():
-    """
-    Make sure DDP works
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
-    model, hparams = get_model()
-    trainer_options = dict(
-        progress_bar=False,
-        max_nb_epochs=1,
-        train_percent_check=0.1,
-        val_percent_check=0.1,
-        gpus=[0, 1],
-        distributed_backend='ddp'
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_amp_gpu_ddp():
-    """
-    Make sure DDP + AMP work
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
-
-    model, hparams = get_model()
-    trainer_options = dict(
-        progress_bar=True,
-        max_nb_epochs=1,
-        gpus=[0, 1],
-        distributed_backend='ddp',
-        use_amp=True
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
+#
+# def test_cpu_model():
+#     """
+#     Make sure model trains on CPU
+#     :return:
+#     """
+#
+#     trainer_options = dict(
+#         progress_bar=False,
+#         experiment=get_exp(),
+#         max_nb_epochs=1,
+#         train_percent_check=0.4,
+#         val_percent_check=0.4
+#     )
+#
+#     model, hparams = get_model()
+#     run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
+#
+#
+# def test_all_features_cpu_model():
+#     """
+#     Test each of the trainer options
+#     :return:
+#     """
+#
+#     trainer_options = dict(
+#         gradient_clip=1.0,
+#         overfit_pct=0.20,
+#         track_grad_norm=2,
+#         print_nan_grads=True,
+#         progress_bar=False,
+#         experiment=get_exp(),
+#         max_nb_epochs=1,
+#         train_percent_check=0.4,
+#         val_percent_check=0.4
+#     )
+#
+#     model, hparams = get_model()
+#     run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
+#
+#
+# def test_early_stopping_cpu_model():
+#     """
+#     Test each of the trainer options
+#     :return:
+#     """
+#
+#     stopping = EarlyStopping()
+#     trainer_options = dict(
+#         early_stop_callback=stopping,
+#         gradient_clip=1.0,
+#         overfit_pct=0.20,
+#         track_grad_norm=2,
+#         print_nan_grads=True,
+#         progress_bar=False,
+#         experiment=get_exp(),
+#         max_nb_epochs=1,
+#         train_percent_check=0.4,
+#         val_percent_check=0.4
+#     )
+#
+#     model, hparams = get_model()
+#     run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
+#
+#
+# def test_single_gpu_model():
+#     """
+#     Make sure single GPU works (DP mode)
+#     :return:
+#     """
+#     if not torch.cuda.is_available():
+#         warnings.warn('test_single_gpu_model cannot run. Rerun on a GPU node to run this test')
+#         return
+#     model, hparams = get_model()
+#
+#     trainer_options = dict(
+#         progress_bar=False,
+#         max_nb_epochs=1,
+#         train_percent_check=0.1,
+#         val_percent_check=0.1,
+#         gpus=[0]
+#     )
+#
+#     run_gpu_model_test(trainer_options, model, hparams)
+#
+#
+# def test_multi_gpu_model_dp():
+#     """
+#     Make sure DP works
+#     :return:
+#     """
+#     if not torch.cuda.is_available():
+#         warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a GPU node to run this test')
+#         return
+#     if not torch.cuda.device_count() > 1:
+#         warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
+#         return
+#     model, hparams = get_model()
+#     trainer_options = dict(
+#         progress_bar=False,
+#         max_nb_epochs=1,
+#         train_percent_check=0.1,
+#         val_percent_check=0.1,
+#         gpus=[0, 1]
+#     )
+#
+#     run_gpu_model_test(trainer_options, model, hparams)
+#
+#
+# def test_amp_gpu_dp():
+#     """
+#     Make sure DP + AMP work
+#     :return:
+#     """
+#     if not torch.cuda.is_available():
+#         warnings.warn('test_amp_gpu_dp cannot run. Rerun on a GPU node to run this test')
+#         return
+#     if not torch.cuda.device_count() > 1:
+#         warnings.warn('test_amp_gpu_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
+#         return
+#     model, hparams = get_model()
+#     trainer_options = dict(
+#         max_nb_epochs=1,
+#         gpus='0, 1',  # test init with gpu string
+#         distributed_backend='dp',
+#         use_amp=True
+#     )
+#     with pytest.raises(MisconfigurationException):
+#         run_gpu_model_test(trainer_options, model, hparams)
+#
+#
+# def test_multi_gpu_model_ddp():
+#     """
+#     Make sure DDP works
+#     :return:
+#     """
+#     if not torch.cuda.is_available():
+#         warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a GPU node to run this test')
+#         return
+#     if not torch.cuda.device_count() > 1:
+#         warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
+#         return
+#
+#     os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
+#     model, hparams = get_model()
+#     trainer_options = dict(
+#         progress_bar=False,
+#         max_nb_epochs=1,
+#         train_percent_check=0.1,
+#         val_percent_check=0.1,
+#         gpus=[0, 1],
+#         distributed_backend='ddp'
+#     )
+#
+#     run_gpu_model_test(trainer_options, model, hparams)
+#
+#
+# def test_amp_gpu_ddp():
+#     """
+#     Make sure DDP + AMP work
+#     :return:
+#     """
+#     if not torch.cuda.is_available():
+#         warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
+#         return
+#     if not torch.cuda.device_count() > 1:
+#         warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
+#         return
+#
+#     os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
+#
+#     model, hparams = get_model()
+#     trainer_options = dict(
+#         progress_bar=True,
+#         max_nb_epochs=1,
+#         gpus=[0, 1],
+#         distributed_backend='ddp',
+#         use_amp=True
+#     )
+#
+#     run_gpu_model_test(trainer_options, model, hparams)
 
 
 # ------------------------------------------------------------------------