Tests to enforce all models FX traceable

tests/test_models.py

@@ -7,6 +7,7 @@ import fnmatch
 import timm
 from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
     get_model_default_value
+from timm.models.fx_features import NodePathTracer
 
 if hasattr(torch._C, '_jit_set_profiling_executor'):
     # legacy executor is too slow to compile large models for unit tests
@@ -297,3 +298,82 @@ def test_model_forward_features(model_name, batch_size):
         assert e == o.shape[1]
         assert o.shape[0] == batch_size
         assert not torch.isnan(o).any()
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward_fx(model_name, batch_size):
+    """Symbolically trace each model and run single forward pass through the resulting GraphModule"""
+    model = create_model(model_name, pretrained=False)
+    model.eval()
+
+    input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
+    if max(input_size) > MAX_FWD_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    tracer = NodePathTracer()
+    graph = tracer.trace(model)
+    model = torch.fx.GraphModule(model, graph)
+
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = model(inputs)
+
+    assert outputs.shape[0] == 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, 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"""
+    input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
+    if max(input_size) > MAX_BWD_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()])
+
+    tracer = NodePathTracer()
+    graph = tracer.trace(model)
+    model = torch.fx.GraphModule(model, graph)
+
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = model(inputs)
+    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'
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize(
+    'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward_fx_torchscript(model_name, batch_size):
+    """Symbolically trace each model, script it, and run single forward pass"""
+    input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
+    if max(input_size) > MAX_JIT_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    with set_scriptable(True):
+        model = create_model(model_name, pretrained=False)
+    model.eval()
+
+    tracer = NodePathTracer()
+    graph = tracer.trace(model)
+    model = torch.fx.GraphModule(model, graph)
+
+    model = torch.jit.script(model)
+    outputs = model(torch.randn((batch_size, *input_size)))
+
+    assert outputs.shape[0] == batch_size
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'