Adding MT5 support

docs/classes/models/mt5.rst

@@ -0,0 +1,24 @@
+MT5
+=====
+
+The mT5 model was presented in `mT5: A massively multilingual pre-trained text-to-text transformer
+<https://arxiv.org/pdf/2010.11934.pdf>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou,
+Aditya Siddhant, Aditya Barua, Colin Raffel.
+
+The abstract from the paper is the following,
+
+
+- The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain
+  state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a
+  multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail
+  the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual
+  benchmarks. We also describe a simple technique to prevent "accidental translation" in the zero-shot setting, where a
+  generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model
+  checkpoints used in this work are publicly available.
+
+MT5AdapterModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: adapters.MT5AdapterModel
+    :members:
+    :inherited-members: MT5PreTrainedModel

docs/model_overview.md

@@ -27,6 +27,7 @@ The table below further shows which model architectures support which adaptation
 | [GPT-J](classes/models/gptj.html)       | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
 | [Llama](classes/models/llama.html)       | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
 | [MBart](classes/models/mbart.html)      | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
+| [MT5](classes/models/mt5.html)          | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
 | [RoBERTa](classes/models/roberta.html)  | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | [T5](classes/models/t5.html)            | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
 | [ViT](classes/models/vit.html)            | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |

src/adapters/__init__.py

@@ -105,6 +105,7 @@
     "models.gptj": ["GPTJAdapterModel"],
     "models.llama": ["LlamaAdapterModel"],
     "models.mbart": ["MBartAdapterModel"],
+    "models.mt5": ["MT5AdapterModel"],
     "models.roberta": ["RobertaAdapterModel"],
     "models.t5": ["T5AdapterModel"],
     "models.vit": ["ViTAdapterModel"],
@@ -207,6 +208,7 @@
     from .models.gptj import GPTJAdapterModel
     from .models.llama import LlamaAdapterModel
     from .models.mbart import MBartAdapterModel
+    from .models.mt5 import MT5AdapterModel
     from .models.roberta import RobertaAdapterModel
     from .models.t5 import T5AdapterModel
     from .models.vit import ViTAdapterModel

src/adapters/composition.py

@@ -127,6 +127,7 @@ def __init__(
         "deberta",
         "bart",
         "mbart",
+        "mt5",
         "gpt2",
         "gptj",
         "t5",

src/adapters/head_utils.py

@@ -369,6 +369,35 @@
         },
         "layers": ["lm_head"],
     },
+    # MT5
+    "MT5ForConditionalGeneration": {
+        "config": {
+            "head_type": "seq2seq_lm",
+        },
+        "layers": ["lm_head"],
+    },
+    "MT5ForQuestionAnswering": {
+        "config": {
+            "head_type": "question_answering",
+            "activation_function": None,
+            "layers": 1,
+        },
+        "layers": [None, "qa_outputs"],
+    },
+    "MT5ForSequenceClassification": {
+        "config": {
+            "head_type": "classification",
+            "layers": 2,
+            "activation_function": "tanh",
+        },
+        "layers": [
+            None,
+            "classification_head.dense",
+            None,
+            None,
+            "classification_head.out_proj",
+        ],
+    },
     # DistilBERT
     "DistilBertForSequenceClassification": {
         "config": {

src/adapters/models/__init__.py

@@ -55,6 +55,11 @@
     "MBartDecoder": BartDecoderAdaptersMixin,
     "MBartDecoderWrapper": BartDecoderWrapperAdaptersMixin,
     "MBartModel": BartModelAdaptersMixin,
+    "MT5Block": T5BlockAdaptersMixin,
+    "MT5Model": T5ModelAdaptersMixin,
+    "MT5ForConditionalGeneration": T5ForCondiditionalGenerationWithHeadsMixin,
+    "MT5ForQuestionAnswering": T5ForQuestionAnsweringWithHeadsMixin,
+    "MT5EncoderModel": T5ModelAdaptersMixin,
     "GPT2Model": GPT2ModelAdapterMixin,
     "GPTJMLP": GPTJMLPAdaptersMixin,
     "GPTJModel": GPTJModelAdapterMixin,

src/adapters/models/auto/adapter_model.py

@@ -23,6 +23,7 @@
         ("gptj", "GPTJAdapterModel"),
         ("llama", "LlamaAdapterModel"),
         ("mbart", "MBartAdapterModel"),
+        ("mt5", "MT5AdapterModel"),
         ("roberta", "RobertaAdapterModel"),
         ("t5", "T5AdapterModel"),
         ("vit", "ViTAdapterModel"),

src/adapters/models/mt5/__init__.py

@@ -0,0 +1,39 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2020 The Adapter-Hub Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from transformers.utils import _LazyModule
+
+
+_import_structure = {
+    "adapter_model": ["MT5AdapterModel"],
+}
+
+
+if TYPE_CHECKING:
+    from .adapter_model import MT5AdapterModel
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+    )