[test] fixed torchrec model test (#3167)

* [test] fixed torchrec model test

* polish code

* polish code

* polish code

* polish code

* polish code

* polish code

tests/kit/model_zoo/torchrec/torchrec.py

@@ -2,96 +2,95 @@ from collections import namedtuple
 from functools import partial
 
 import torch
-
+from torchrec.models import deepfm, dlrm
-try:
+from torchrec.modules.embedding_configs import EmbeddingBagConfig
-    from torchrec.models import deepfm, dlrm
+from torchrec.modules.embedding_modules import EmbeddingBagCollection
-    from torchrec.modules.embedding_configs import EmbeddingBagConfig
+from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
-    from torchrec.modules.embedding_modules import EmbeddingBagCollection
-    from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
-    NO_TORCHREC = False
-except ImportError:
-    NO_TORCHREC = True
 
 from ..registry import ModelAttribute, model_zoo
 
+BATCH = 2
+SHAPE = 10
+# KeyedTensor
+KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE)))
 
-def register_torchrec_models():
+# KeyedJaggedTensor
-    BATCH = 2
+KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"],
-    SHAPE = 10
+                                          values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]),
-    # KeyedTensor
+                                          offsets=torch.tensor([0, 2, 4, 6, 8]))
-    KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE)))
 
-    # KeyedJaggedTensor
+data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE)))
-    KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"],
-                                              values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]),
-                                              offsets=torch.tensor([0, 2, 4, 6, 8]))
 
-    data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE)))
+interaction_arch_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT)
 
-    interaction_arch_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT)
+simple_dfm_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT)
 
-    simple_dfm_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT)
+sparse_arch_data_gen_fn = lambda: dict(features=KJT)
-
-    sparse_arch_data_gen_fn = lambda: dict(features=KJT)
-
-    output_transform_fn = lambda x: dict(output=x)
-
-    def get_ebc():
-        # EmbeddingBagCollection
-        eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"])
-        eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"])
-        return EmbeddingBagCollection(tables=[eb1_config, eb2_config])
-
-    model_zoo.register(name='deepfm_densearch',
-                       model_fn=partial(deepfm.DenseArch, SHAPE, SHAPE, SHAPE),
-                       data_gen_fn=data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='deepfm_interactionarch',
-                       model_fn=partial(deepfm.FMInteractionArch, SHAPE * 3, ["f1", "f2"], SHAPE),
-                       data_gen_fn=interaction_arch_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='deepfm_overarch',
-                       model_fn=partial(deepfm.OverArch, SHAPE),
-                       data_gen_fn=data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='deepfm_simpledeepfmnn',
-                       model_fn=partial(deepfm.SimpleDeepFMNN, SHAPE, get_ebc(), SHAPE, SHAPE),
-                       data_gen_fn=simple_dfm_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='deepfm_sparsearch',
-                       model_fn=partial(deepfm.SparseArch, get_ebc()),
-                       data_gen_fn=sparse_arch_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='dlrm',
-                       model_fn=partial(dlrm.DLRM, get_ebc(), SHAPE, [SHAPE, SHAPE], [5, 1]),
-                       data_gen_fn=simple_dfm_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='dlrm_densearch',
-                       model_fn=partial(dlrm.DenseArch, SHAPE, [SHAPE, SHAPE]),
-                       data_gen_fn=data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='dlrm_interactionarch',
-                       model_fn=partial(dlrm.InteractionArch, 2),
-                       data_gen_fn=interaction_arch_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='dlrm_overarch',
-                       model_fn=partial(dlrm.OverArch, SHAPE, [5, 1]),
-                       data_gen_fn=data_gen_fn,
-                       output_transform_fn=output_transform_fn)
-
-    model_zoo.register(name='dlrm_sparsearch',
-                       model_fn=partial(dlrm.SparseArch, get_ebc()),
-                       data_gen_fn=sparse_arch_data_gen_fn,
-                       output_transform_fn=output_transform_fn)
 
 
-if not NO_TORCHREC:
+def output_transform_fn(x):
-    register_torchrec_models()
+    if isinstance(x, KeyedTensor):
+        output = dict()
+        for key in x.keys():
+            output[key] = x[key]
+        return output
+    else:
+        return dict(output=x)
+
+
+def get_ebc():
+    # EmbeddingBagCollection
+    eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"])
+    eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"])
+    return EmbeddingBagCollection(tables=[eb1_config, eb2_config])
+
+
+model_zoo.register(name='deepfm_densearch',
+                   model_fn=partial(deepfm.DenseArch, SHAPE, SHAPE, SHAPE),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='deepfm_interactionarch',
+                   model_fn=partial(deepfm.FMInteractionArch, SHAPE * 3, ["f1", "f2"], SHAPE),
+                   data_gen_fn=interaction_arch_data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='deepfm_overarch',
+                   model_fn=partial(deepfm.OverArch, SHAPE),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='deepfm_simpledeepfmnn',
+                   model_fn=partial(deepfm.SimpleDeepFMNN, SHAPE, get_ebc(), SHAPE, SHAPE),
+                   data_gen_fn=simple_dfm_data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='deepfm_sparsearch',
+                   model_fn=partial(deepfm.SparseArch, get_ebc()),
+                   data_gen_fn=sparse_arch_data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='dlrm',
+                   model_fn=partial(dlrm.DLRM, get_ebc(), SHAPE, [SHAPE, SHAPE], [5, 1]),
+                   data_gen_fn=simple_dfm_data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='dlrm_densearch',
+                   model_fn=partial(dlrm.DenseArch, SHAPE, [SHAPE, SHAPE]),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='dlrm_interactionarch',
+                   model_fn=partial(dlrm.InteractionArch, 2),
+                   data_gen_fn=interaction_arch_data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='dlrm_overarch',
+                   model_fn=partial(dlrm.OverArch, SHAPE, [5, 1]),
+                   data_gen_fn=data_gen_fn,
+                   output_transform_fn=output_transform_fn)
+
+model_zoo.register(name='dlrm_sparsearch',
+                   model_fn=partial(dlrm.SparseArch, get_ebc()),
+                   data_gen_fn=sparse_arch_data_gen_fn,
+                   output_transform_fn=output_transform_fn)

tests/test_booster/test_mixed_precision/test_fp16_torch.py

@@ -7,11 +7,17 @@ from tests.kit.model_zoo import model_zoo
 
 def test_torch_amp():
     for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items():
+        # dlrm_interactionarch has not parameters, so skip
+        if name == 'dlrm_interactionarch':
+            continue
+
         model = model_fn().cuda()
         optimizer = Adam(model.parameters(), lr=1e-3)
         criterion = lambda x: x.mean()
         data = data_gen_fn()
-        data = {k: v.cuda() if torch.is_tensor(v) else v for k, v in data.items()}
+        data = {
+            k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
+        }
         mixed_precision = FP16TorchMixedPrecision()
         model, optimizer, criterion = mixed_precision.configure(model, optimizer, criterion)
         output = model(**data)

tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py

@@ -7,11 +7,6 @@ from tests.kit.model_zoo import model_zoo
 BATCH = 2
 SHAPE = 10
 
-deepfm_models = model_zoo.get_sub_registry('deepfm')
-NOT_DFM = False
-if not deepfm_models:
-    NOT_DFM = True
-
 
 def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None):
     # trace
@@ -52,8 +47,9 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None):
                                  ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}'
 
 
-@pytest.mark.skipif(NOT_DFM, reason='torchrec is not installed')
+@pytest.mark.skip('unknown error')
-def test_torchrec_deepfm_models(deepfm_models):
+def test_torchrec_deepfm_models():
+    deepfm_models = model_zoo.get_sub_registry('deepfm')
     torch.backends.cudnn.deterministic = True
 
     for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in deepfm_models.items():
@@ -67,4 +63,4 @@ def test_torchrec_deepfm_models(deepfm_models):
 
 
 if __name__ == "__main__":
-    test_torchrec_deepfm_models(deepfm_models)
+    test_torchrec_deepfm_models()

tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py

@@ -7,11 +7,6 @@ from tests.kit.model_zoo import model_zoo
 BATCH = 2
 SHAPE = 10
 
-dlrm_models = model_zoo.get_sub_registry('dlrm')
-NOT_DLRM = False
-if not dlrm_models:
-    NOT_DLRM = True
-
 
 def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None):
     # trace
@@ -52,12 +47,18 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None):
                                  ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}'
 
 
-@pytest.mark.skipif(NOT_DLRM, reason='torchrec is not installed')
+@pytest.mark.skip('unknown error')
-def test_torchrec_dlrm_models(dlrm_models):
+def test_torchrec_dlrm_models():
     torch.backends.cudnn.deterministic = True
+    dlrm_models = model_zoo.get_sub_registry('dlrm')
 
     for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in dlrm_models.items():
         data = data_gen_fn()
+
+        # dlrm_interactionarch is not supported
+        if name == 'dlrm_interactionarch':
+            continue
+
         if attribute is not None and attribute.has_control_flow:
             meta_args = {k: v.to('meta') for k, v in data.items()}
         else:
@@ -67,4 +68,4 @@ def test_torchrec_dlrm_models(dlrm_models):
 
 
 if __name__ == "__main__":
-    test_torchrec_dlrm_models(dlrm_models)
+    test_torchrec_dlrm_models()

tests/test_gemini/update/test_fwd_bwd.py

@@ -34,17 +34,17 @@ def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
         assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5)
 
 
-@parameterize('init_device', [get_current_device()])
 @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
 @parameterize('keep_gather', [False, True])
 @parameterize('model_name', ['gpt2', 'bert', 'albert'])
 @parameterize('use_grad_checkpoint', [False, True])
-def exam_gpt_fwd_bwd(placement_policy,
+def exam_gpt_fwd_bwd(
-                     keep_gather,
+    placement_policy,
-                     model_name: str,
+    keep_gather,
-                     use_grad_checkpoint: bool = False,
+    model_name: str,
-                     init_device=get_current_device()):
+    use_grad_checkpoint: bool = False,
-
+):
+    init_device = get_current_device()
     get_components_func = non_distributed_component_funcs.get_callable(model_name)
     model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()