[T5] allow config.decoder_layers to control decoder size (#7409)

* Working assymmetrical T5

* rename decoder_layers -> num_decoder_layers

* Fix docstring

* Allow creation of asymmetric t5 students

examples/seq2seq/make_student.py

@@ -116,12 +116,14 @@ def create_student_by_copying_alternating_layers(
             d = teacher_d
         init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
     except AttributeError:  # T5
-        teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_hidden_layers
+        teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_decoder_layers
-        assert e == d, "T5 Students must be symmetric"
+        if e is None:
-        init_kwargs["num_layers"] = e
+            e = teacher_e
-
+        if d is None:
-    # Kwargs to instantiate student = teacher kwargs with updated layer numbers + **extra_config_kwargs
+            d = teacher_d
+        init_kwargs.update({"num_layers": e, "num_decoder_layers": d})
 
+    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
     init_kwargs.update(extra_config_kwargs)
 
     # Copy weights

examples/seq2seq/test_make_student.py

@@ -21,10 +21,8 @@ class MakeStudentTester(unittest.TestCase):
         student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=1)
         self.assertEqual(student.config.num_hidden_layers, 1)
 
-    def test_invalid_t5(self):
+    def test_asymmetric_t5(self):
-        # T5 students must have the same e==d because there is only one config property
+        student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=None)
-        with self.assertRaises(AssertionError):
-            student, *_ = create_student_by_copying_alternating_layers(TINY_T5, tempfile.mkdtemp(), e=1, d=None)
 
     def test_same_decoder_small_encoder(self):
         student, *_ = create_student_by_copying_alternating_layers(TINY_BART, tempfile.mkdtemp(), e=1, d=None)

src/transformers/configuration_t5.py

@@ -57,6 +57,8 @@ class T5Config(PretrainedConfig):
             Size of the intermediate feed forward layer in each :obj:`T5Block`.
         num_layers (:obj:`int`, `optional`, defaults to 6):
             Number of hidden layers in the Transformer encoder.
+        num_decoder_layers (:obj:`int`, `optional`):
+            Number of hidden layers in the Transformer decoder. Will use the same value as :obj:`num_layers` if not set.
         num_heads (:obj:`int`, `optional`, defaults to 8):
             Number of attention heads for each attention layer in
             the Transformer encoder.
@@ -80,6 +82,7 @@ class T5Config(PretrainedConfig):
         d_kv=64,
         d_ff=2048,
         num_layers=6,
+        num_decoder_layers=None,
         num_heads=8,
         relative_attention_num_buckets=32,
         dropout_rate=0.1,
@@ -102,6 +105,9 @@ class T5Config(PretrainedConfig):
         self.d_kv = d_kv
         self.d_ff = d_ff
         self.num_layers = num_layers
+        self.num_decoder_layers = (
+            num_decoder_layers if num_decoder_layers is not None else self.num_layers
+        )  # default = symmetry
         self.num_heads = num_heads
         self.relative_attention_num_buckets = relative_attention_num_buckets
         self.dropout_rate = dropout_rate

src/transformers/modeling_t5.py

@@ -907,7 +907,7 @@ T5_INPUTS_DOCSTRING = r"""
     T5_START_DOCSTRING,
 )
 class T5Model(T5PreTrainedModel):
-    def __init__(self, config):
+    def __init__(self, config: T5Config):
         super().__init__(config)
         self.shared = nn.Embedding(config.vocab_size, config.d_model)
 
@@ -919,6 +919,7 @@ class T5Model(T5PreTrainedModel):
         decoder_config = copy.deepcopy(config)
         decoder_config.is_decoder = True
         decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
         self.decoder = T5Stack(decoder_config, self.shared)
 
         self.init_weights()
@@ -1077,6 +1078,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         decoder_config = copy.deepcopy(config)
         decoder_config.is_decoder = True
         decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
         self.decoder = T5Stack(decoder_config, self.shared)
 
         self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

tests/test_modeling_t5.py

@@ -59,6 +59,7 @@ class T5ModelTester:
         pad_token_id=0,
         decoder_start_token_id=0,
         scope=None,
+        decoder_layers=None,
     ):
 
         self.parent = parent
@@ -83,6 +84,7 @@ class T5ModelTester:
         self.pad_token_id = pad_token_id
         self.decoder_start_token_id = decoder_start_token_id
         self.scope = None
+        self.decoder_layers = decoder_layers
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
@@ -105,6 +107,7 @@ class T5ModelTester:
             d_ff=self.d_ff,
             d_kv=self.hidden_size // self.num_attention_heads,
             num_layers=self.num_hidden_layers,
+            num_decoder_layers=self.decoder_layers,
             num_heads=self.num_attention_heads,
             relative_attention_num_buckets=self.relative_attention_num_buckets,
             dropout_rate=self.dropout_rate,
@@ -623,3 +626,40 @@ class T5ModelIntegrationTests(unittest.TestCase):
         output = model.generate(**inputs)
         translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
         self.assertEqual(translation, expected_translation)
+
+
+@require_torch
+class TestAsymmetricT5(unittest.TestCase):
+    def build_model_and_check_forward_pass(self, **kwargs):
+        tester = T5ModelTester(self, **kwargs)
+        config, *inputs = tester.prepare_config_and_inputs()
+        (
+            input_ids,
+            decoder_input_ids,
+            attention_mask,
+            decoder_attention_mask,
+            lm_labels,
+        ) = inputs
+        model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
+        outputs = model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            labels=lm_labels,
+        )
+        # outputs = model(*inputs)
+        assert len(outputs) == 4
+        assert outputs["logits"].size() == (tester.batch_size, tester.decoder_seq_length, tester.vocab_size)
+        assert outputs["loss"].size() == ()
+        return model
+
+    def test_small_decoder(self):
+        # num_hidden_layers is passed to T5Config as num_layers
+        model = self.build_model_and_check_forward_pass(decoder_layers=1, num_hidden_layers=2)
+        assert len(model.encoder.block) == 2
+        assert len(model.decoder.block) == 1
+
+    def test_defaulting_to_symmetry(self):
+        # num_hidden_layers is passed to T5Config as num_layers
+        model = self.build_model_and_check_forward_pass(num_hidden_layers=2)
+        assert len(model.decoder.block) == len(model.encoder.block) == 2