Supporting seq2seq models for `bitsandbytes` integration (#18579)

* Supporting seq2seq models for `bitsandbytes` integration

- `bitsandbytes` integration supports now seq2seq models
- check if a model has tied weights as an additional check

* small modification

- tie the weights before looking at tied weights!

src/transformers/utils/bitsandbytes.py

+from copy import deepcopy
+
 from transformers.utils import is_accelerate_available, is_bitsandbytes_available
 
 
@@ -9,6 +11,7 @@ if is_bitsandbytes_available():
 
 if is_accelerate_available():
     from accelerate import init_empty_weights
+    from accelerate.utils import find_tied_parameters
 
 
 def set_module_8bit_tensor_to_device(module, tensor_name, device, value=None):
@@ -132,8 +135,17 @@ def get_key_to_not_convert(model):
     model (`torch.nn.Module`):
         Input model
     """
+    # Create a copy of the model and tie the weights, then
+    # check if it contains tied weights
+    tied_model = deepcopy(model)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
+    tied_model.tie_weights()
+    has_tied_params = len(find_tied_parameters(tied_model)) > 0
+
+    # Check if it is a base model
+    is_base_model = not hasattr(model, model.base_model_prefix)
+
     # Ignore this for base models (BertModel, GPT2Model, etc.)
-    if not hasattr(model, model.base_model_prefix):
+    if (not has_tied_params) and is_base_model:
         return ""
 
     # otherwise they have an attached head

tests/mixed_int8/test_mixed_int8.py

@@ -15,7 +15,14 @@
 import gc
 import unittest
 
-from transformers import AutoModel, AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from transformers import (
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    pipeline,
+)
 from transformers.testing_utils import (
     is_torch_available,
     require_accelerate,
@@ -106,12 +113,21 @@ class MixedInt8ModelClassesTest(BaseMixedInt8Test):
         super().setUp()
         # model_name
         self.model_name = "bigscience/bloom-560m"
-        # Models and tokenizer
+        self.seq_to_seq_name = "t5-small"
+
+        # Different types of model
+
         self.base_model = AutoModel.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
+        # Sequence classification model
         self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
             self.model_name, load_in_8bit=True, device_map="auto"
         )
+        # CausalLM model
         self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
+        # Seq2seq model
+        self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.seq_to_seq_name, load_in_8bit=True, device_map="auto"
+        )
 
     def tearDown(self):
         r"""
@@ -121,6 +137,7 @@ class MixedInt8ModelClassesTest(BaseMixedInt8Test):
         del self.base_model
         del self.sequence_model
         del self.model_8bit
+        del self.seq_to_seq_model
 
         gc.collect()
         torch.cuda.empty_cache()
@@ -138,6 +155,7 @@ class MixedInt8ModelClassesTest(BaseMixedInt8Test):
         # Other heads should be nn.Parameter
         self.assertTrue(self.model_8bit.lm_head.weight.__class__ == torch.nn.Parameter)
         self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
+        self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
 
 
 class MixedInt8TestPipeline(BaseMixedInt8Test):