From 147e1059a832aaf1bfbe7d530152cba13adf0514 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Tue, 23 Nov 2021 14:32:32 -0800 Subject: [PATCH] Remove FX backward test from GitHub actions runs for now. --- tests/test_models.py | 60 +++++++++++++++++++++++--------------------- 1 file changed, 32 insertions(+), 28 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index c2b151c3..18162431 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -386,37 +386,41 @@ def test_model_forward_fx(model_name, batch_size): assert not torch.isnan(outputs).any(), 'Output included NaNs' -@pytest.mark.timeout(120) -@pytest.mark.parametrize('model_name', list_models( - exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True)) -@pytest.mark.parametrize('batch_size', [2]) -def test_model_backward_fx(model_name, batch_size): - """Symbolically trace each model and run single backward pass through the resulting GraphModule""" - if not has_fx_feature_extraction: - pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.") - - input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE) - if max(input_size) > MAX_BWD_FX_SIZE: - pytest.skip("Fixed input size model > limit.") +if 'GITHUB_ACTIONS' not in os.environ: + # FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process - model = create_model(model_name, pretrained=False, num_classes=42) - model.train() - num_params = sum([x.numel() for x in model.parameters()]) - if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6: - pytest.skip("Skipping FX backward test on model with more than 100M params.") + @pytest.mark.timeout(120) + @pytest.mark.parametrize('model_name', list_models( + exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True)) + @pytest.mark.parametrize('batch_size', [2]) + def test_model_backward_fx(model_name, batch_size): + """Symbolically trace each model and run single backward pass through the resulting GraphModule""" + if not has_fx_feature_extraction: + pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.") + + input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE) + if max(input_size) > MAX_BWD_FX_SIZE: + pytest.skip("Fixed input size model > limit.") + + model = create_model(model_name, pretrained=False, num_classes=42) + model.train() + num_params = sum([x.numel() for x in model.parameters()]) + if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6: + pytest.skip("Skipping FX backward test on model with more than 100M params.") + + model = _create_fx_model(model, train=True) + outputs = tuple(model(torch.randn((batch_size, *input_size))).values()) + if isinstance(outputs, tuple): + outputs = torch.cat(outputs) + outputs.mean().backward() + for n, x in model.named_parameters(): + assert x.grad is not None, f'No gradient for {n}' + num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None]) - model = _create_fx_model(model, train=True) - outputs = tuple(model(torch.randn((batch_size, *input_size))).values()) - if isinstance(outputs, tuple): - outputs = torch.cat(outputs) - outputs.mean().backward() - for n, x in model.named_parameters(): - assert x.grad is not None, f'No gradient for {n}' - num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None]) + assert outputs.shape[-1] == 42 + assert num_params == num_grad, 'Some parameters are missing gradients' + assert not torch.isnan(outputs).any(), 'Output included NaNs' - assert outputs.shape[-1] == 42 - assert num_params == num_grad, 'Some parameters are missing gradients' - assert not torch.isnan(outputs).any(), 'Output included NaNs' # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow EXCLUDE_FX_JIT_FILTERS = [