[test] added timm models to test model zoo (#3129)

* [test] added timm models to test model zoo

* polish code

* polish code

* polish code

* polish code

* polish code

tests/kit/model_zoo/__init__.py

+from . import timm
+from .registry import model_zoo
+
+__all__ = ['model_zoo']

tests/kit/model_zoo/registry.py

+#!/usr/bin/env python
+from dataclasses import dataclass
+from typing import Callable
+
+__all__ = ['ModelZooRegistry', 'ModelAttributem', 'model_zoo']
+
+
+@dataclass
+class ModelAttribute:
+    """
+    Attributes of a model.
+    """
+    has_control_flow: bool = False
+
+
+class ModelZooRegistry(dict):
+    """
+    A registry to map model names to model and data generation functions.
+    """
+
+    def register(self,
+                 name: str,
+                 model_fn: Callable,
+                 data_gen_fn: Callable,
+                 output_transform_fn: Callable,
+                 model_attribute: ModelAttribute = None):
+        """
+        Register a model and data generation function.
+
+        Examples:
+        >>> # Register
+        >>> model_zoo = ModelZooRegistry()
+        >>> model_zoo.register('resnet18', resnet18, resnet18_data_gen)
+        >>> # Run the model
+        >>> data = resnresnet18_data_gen() # do not input any argument
+        >>> model = resnet18() # do not input any argument
+        >>> out = model(**data)
+
+        Args:
+            name (str): Name of the model.
+            model_fn (callable): A function that returns a model. **It must not contain any arguments.**
+            output_transform_fn (callable): A function that transforms the output of the model into Dict.
+            data_gen_fn (callable): A function that returns a data sample in the form of Dict. **It must not contain any arguments.**
+            model_attribute (ModelAttribute): Attributes of the model. Defaults to None.
+        """
+        self[name] = (model_fn, data_gen_fn, output_transform_fn, model_attribute)
+
+    def get_sub_registry(self, keyword: str):
+        """
+        Get a sub registry with models that contain the keyword.
+
+        Args:
+            keyword (str): Keyword to filter models.
+        """
+        new_dict = dict()
+
+        for k, v in self.items():
+            if keyword in k:
+                new_dict[k] = v
+        return new_dict
+
+
+model_zoo = ModelZooRegistry()

tests/kit/model_zoo/timm/__init__.py

+from .timm import *

tests/kit/model_zoo/timm/timm.py

+import timm.models as tm
+import torch
+
+from ..registry import ModelAttribute, model_zoo
+
+## ==============
+# Register models without control flow
+## ==============
+data_gen_fn = lambda: dict(x=torch.rand(2, 3, 224, 224))
+output_transform_fn = lambda x: dict(output=x)
+
+model_zoo.register(name='timm_resnet',
+                   model_fn=tm.resnest.resnest50d,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_beit',
+                   model_fn=tm.beit.beit_base_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_cait',
+                   model_fn=tm.cait.cait_s24_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_convmixer',
+                   model_fn=tm.convmixer.convmixer_768_32,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_efficientnetv2',
+                   model_fn=tm.efficientnet.efficientnetv2_m,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_resmlp',
+                   model_fn=tm.resmlp_12_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_vision_transformer',
+                   model_fn=tm.vision_transformer.vit_base_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_deit',
+                   model_fn=tm.deit_base_distilled_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_beitv2',
+                   model_fn=tm.beitv2_base_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_coat',
+                   model_fn=tm.coat.coat_lite_mini,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='timm_deit3',
+                   model_fn=tm.deit3_base_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='timm_eca_nfnet',
+                   model_fn=tm.eca_nfnet_l0,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_efficientformer',
+                   model_fn=tm.efficientformer_l1,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_ese_vovnet19b_dw',
+                   model_fn=tm.ese_vovnet19b_dw,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_gmixer_12_224',
+                   model_fn=tm.gmixer_12_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_gmlp_b16_224',
+                   model_fn=tm.gmlp_b16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_hardcorenas_a',
+                   model_fn=tm.hardcorenas_a,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_hrnet_w18_small',
+                   model_fn=tm.hrnet_w18_small,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_inception_v3',
+                   model_fn=tm.inception_v3,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_mixer_b16_224',
+                   model_fn=tm.mixer_b16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_nf_ecaresnet101',
+                   model_fn=tm.nf_ecaresnet101,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_nf_regnet_b0',
+                   model_fn=tm.nf_regnet_b0,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_regnetv_040',
+                   model_fn=tm.regnetv_040,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_skresnet18',
+                   model_fn=tm.skresnet18,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_tnt_b_patch16_224',
+                   model_fn=tm.tnt_b_patch16_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_wide_resnet50_2',
+                   model_fn=tm.wide_resnet50_2,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_convit',
+                   model_fn=tm.convit_base,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+model_zoo.register(name='timm_dm_nfnet',
+                   model_fn=tm.dm_nfnet_f0,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+# ==============
+# Register models with control flow
+# ==============
+model_zoo.register(name='timm_convnext',
+                   model_fn=tm.convnext.convnext_base,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='timm_vgg',
+                   model_fn=tm.vgg.vgg11,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='timm_dpn',
+                   model_fn=tm.dpn.dpn68,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='timm_densenet',
+                   model_fn=tm.densenet.densenet121,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='timm_rexnet',
+                   model_fn=tm.rexnet.rexnet_100,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+model_zoo.register(name='timm_swin_transformer',
+                   model_fn=tm.swin_transformer.swin_base_patch4_window7_224,
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))

tests/test_fx/test_tracer/test_timm_model/test_timm_model.py

@@ -3,9 +3,10 @@ import timm.models as tm
 import torch
 
 from colossalai.fx import symbolic_trace
+from tests.kit.model_zoo import model_zoo
 
 
-def trace_and_compare(model_cls, data, meta_args=None):
+def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None):
     # trace
     model = model_cls()
 
@@ -14,60 +15,47 @@ def trace_and_compare(model_cls, data, meta_args=None):
     # without this statement, the torch.nn.functional.batch_norm will always be in training mode
     model.eval()
 
+    # TODO: support the following models
+    # 1. ConViT
+    # 2. NormFreeNet
+    # as they are not supported, let's skip them
+    if model.__class__.__name__ in ['ConViT', 'NormFreeNet']:
+        return
+
     gm = symbolic_trace(model, meta_args=meta_args)
 
     # run forward
     with torch.no_grad():
-        fx_out = gm(data)
-        non_fx_out = model(data)
+        fx_out = gm(**data)
+        non_fx_out = model(**data)
 
     # compare output
-    if isinstance(fx_out, tuple):
-        # some models produce tuple as output
-        for v1, v2 in zip(fx_out, non_fx_out):
-            assert torch.allclose(v1, v2), f'{model.__class__.__name__} has inconsistent outputs, {v1} vs {v2}'
-    else:
-        assert torch.allclose(
-            fx_out, non_fx_out,
-            atol=1e-5), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}'
-
-
-def test_timm_models_without_control_flow():
-    torch.backends.cudnn.deterministic = True
-
-    MODEL_LIST = [
-        tm.resnest.resnest50d,
-        tm.beit.beit_base_patch16_224,
-        tm.cait.cait_s24_224,
-        tm.convmixer.convmixer_768_32,
-        tm.efficientnet.efficientnetv2_m,
-        tm.resmlp_12_224,
-        tm.vision_transformer.vit_base_patch16_224,
-        tm.deit_base_distilled_patch16_224,
-    ]
+    transformed_fx_out = output_transform_fn(fx_out)
+    transformed_non_fx_out = output_transform_fn(non_fx_out)
 
-    data = torch.rand(2, 3, 224, 224)
+    assert len(transformed_fx_out) == len(transformed_non_fx_out)
 
-    for model_cls in MODEL_LIST:
-        trace_and_compare(model_cls, data)
+    for key in transformed_fx_out.keys():
+        fx_output_val = transformed_fx_out[key]
+        non_fx_output_val = transformed_non_fx_out[key]
+        assert torch.allclose(fx_output_val, non_fx_output_val, atol=1e-5), \
+            f'{model.__class__.__name__} has inconsistent outputs, {fx_output_val} vs {non_fx_output_val}'
 
 
-def test_timm_models_with_control_flow():
+def test_timm_models():
     torch.backends.cudnn.deterministic = True
 
-    MODEL_LIST_WITH_CONTROL_FLOW = [
-        tm.convnext.convnext_base, tm.vgg.vgg11, tm.dpn.dpn68, tm.densenet.densenet121, tm.rexnet.rexnet_100,
-        tm.swin_transformer.swin_base_patch4_window7_224
-    ]
-
-    data = torch.rand(2, 3, 224, 224)
+    sub_model_zoo = model_zoo.get_sub_registry('timm')
 
-    meta_args = {'x': data.to('meta')}
+    for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items():
+        data = data_gen_fn()
+        if attribute is not None and attribute.has_control_flow:
+            meta_args = {k: v.to('meta') for k, v in data.items()}
+        else:
+            meta_args = None
 
-    for model_cls in MODEL_LIST_WITH_CONTROL_FLOW:
-        trace_and_compare(model_cls, data, meta_args)
+        trace_and_compare(model_fn, data, output_transform_fn, meta_args)
 
 
 if __name__ == '__main__':
-    test_timm_models_with_control_flow()
-    test_timm_models_without_control_flow()
+    test_timm_models()