[feat] MMFT MLP head output key; mix model losses and MMFLosses

mmf/models/base_model.py

@@ -272,23 +272,27 @@ def __call__(self, sample_list, *args, **kwargs):
             model_output, collections.abc.Mapping
         ), "A dict must be returned from the forward of the model."
 
+        final_output = {"losses": {}}
+        final_output.update(model_output)
+
         if "losses" in model_output:
-            if not self._logged_warning["losses_present"]:
+            assert isinstance(
+                model_output["losses"], collections.abc.Mapping
+            ), "'losses' returned from the model must be a dict."
+
+        if hasattr(self, "losses"):
+            if "losses" in model_output and not self._logged_warning["losses_present"]:
                 warnings.warn(
-                    "'losses' already present in model output. "
-                    "No calculation will be done in base model."
+                    "'losses' already present in model output and 'loss' key "
+                    "was specified. Results from the two will be merged. "
+                    "If this is not expected, either (i) assign unique keys to "
+                    "losses returned from your model (ii) remove 'loss' key from "
+                    "your model output"
                 )
                 self._logged_warning["losses_present"] = True
+            final_output["losses"].update(self.losses(sample_list, model_output))
 
-            assert isinstance(
-                model_output["losses"], collections.abc.Mapping
-            ), "'losses' must be a dict."
-        elif hasattr(self, "losses"):
-            model_output["losses"] = self.losses(sample_list, model_output)
-        else:
-            model_output["losses"] = {}
-
-        return model_output
+        return final_output
 
     def load_requirements(self, *args, **kwargs):
         requirements = self.config.get("zoo_requirements", [])

mmf/models/transformers/heads/mlp.py

@@ -20,6 +20,7 @@ class Config(BaseTransformerHead.Config):
         hidden_dropout_prob: float = 0.1
         layer_norm_eps: float = 1e-6
         hidden_act: str = "gelu"
+        output_key: str = "scores"
 
     def __init__(self, config: Config, *args, **kwargs):
         super().__init__(config, *args, **kwargs)
@@ -33,6 +34,7 @@ def __init__(self, config: Config, *args, **kwargs):
         )
         self.num_labels = self.config.num_labels
         self.hidden_size = self.config.hidden_size
+        self.output_key = self.config.get("output_key", "scores")
 
     def forward(
         self,
@@ -46,5 +48,5 @@ def forward(
         output_dict = {}
         pooled_output = self.pooler(sequence_output)
         prediction = self.classifier(pooled_output)
-        output_dict["scores"] = prediction.view(-1, self.num_labels)
+        output_dict[self.output_key] = prediction.view(-1, self.num_labels)
         return output_dict

tests/models/test_base_model.py

@@ -0,0 +1,50 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import unittest
+
+import torch
+from mmf.common.sample import SampleList
+from mmf.models.base_model import BaseModel
+from tests.test_utils import compare_tensors
+
+
+class LocalTestModelWithForwardLoss(BaseModel):
+    def forward(self, *args, **kwargs):
+        return {"losses": {"x": torch.tensor(1.0)}}
+
+
+class LocalTestModelWithNoLoss(BaseModel):
+    def forward(self, *args, **kwargs):
+        return {}
+
+
+class LocalTestModelWithLossAttribute(BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.losses = lambda x, y: {"x": torch.tensor(2.0)}
+
+    def forward(self, *args, **kwargs):
+        return {}
+
+
+class TestBaseModel(unittest.TestCase):
+    def test_forward_loss(self):
+        sample_list = SampleList()
+        sample_list.add_field("x", torch.tensor(1))
+        model = LocalTestModelWithForwardLoss({})
+        with torch.no_grad():
+            output = model(sample_list)
+        self.assertTrue("losses" in output)
+        self.assertTrue(compare_tensors(output["losses"]["x"], torch.tensor(1.0)))
+
+        model = LocalTestModelWithLossAttribute({})
+        with torch.no_grad():
+            output = model(sample_list)
+        self.assertTrue("losses" in output)
+        self.assertTrue(compare_tensors(output["losses"]["x"], torch.tensor(2.0)))
+
+        model = LocalTestModelWithNoLoss({})
+        with torch.no_grad():
+            output = model(sample_list)
+        self.assertTrue("losses" in output)
+        self.assertEqual(output["losses"], {})