Implement head_mask for Flax BERT and other models copied from BERT (#14620)

* Implement head_mask for Flax BERT and other models copied from BERT

* Remove `from jax._src.nn.functions import sigmoid`

Remove `from jax._src.nn.functions import sigmoid` unintentionally added by IDE

* Remove no more valid copy statement

* Apply patil-suraj's suggestions from code review

* Apply suggestions from the code review

* Update Flax template

* Fix a typo

* Also update template for CausalLM modules

tests/test_modeling_flax_common.py

@@ -111,6 +111,7 @@ class FlaxModelTesterMixin:
     all_model_classes = ()
     test_mismatched_shapes = True
     is_encoder_decoder = False
+    test_head_masking = False
 
     def _prepare_for_class(self, inputs_dict, model_class):
         inputs_dict = copy.deepcopy(inputs_dict)
@@ -777,6 +778,53 @@ class FlaxModelTesterMixin:
             for name, type_ in types.items():
                 self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")
 
+    def test_headmasking(self):
+        if not self.test_head_masking:
+            return
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        def _prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
+            if i == 0:
+                return np.concatenate([np.zeros(1, dtype=jnp.int32), np.ones(attention_heads - 1, dtype=jnp.int32)])
+            if i == num_hidden_layers - 1:
+                return np.concatenate([np.zeros(attention_heads - 1, dtype=jnp.int32), np.ones(1, dtype=jnp.int32)])
+            return np.ones(attention_heads, dtype=jnp.int32)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
+            # Prepare head mask
+            inputs["head_mask"] = np.stack(
+                [
+                    _prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
+                    for i in range(config.num_hidden_layers)
+                ]
+            )
+            outputs = model(**inputs)
+
+            def _check_attentions_validity(attentions):
+                # Remove NaN
+                for t in attentions:
+                    # Check we don't have more than 25% nans (arbitrary)
+                    self.assertLess(np.isnan(t).sum(), t.size / 4)
+                attentions = [np.where(np.isnan(t), 0.0, t) for t in attentions]
+
+                self.assertAlmostEqual(attentions[0][..., 0, :, :].sum(), 0.0)
+                self.assertNotEqual(attentions[0][..., -1, :, :].sum(), 0.0)
+                if len(attentions) > 2:  # encoder-decodere models have only 2 layers in each modules
+                    self.assertNotEqual(attentions[1][..., 0, :, :].sum(), 0.0)
+                self.assertAlmostEqual(attentions[-1][..., -2, :, :].sum(), 0.0)
+                self.assertNotEqual(attentions[-1][..., -1, :, :].sum(), 0.0)
+
+            if model.config.is_encoder_decoder:
+                raise NotImplementedError("The test has not been implemented for encoder-decoder models yet.")
+            else:
+                _check_attentions_validity(outputs.attentions)
+
 
 @require_flax
 @is_staging_test