PLbart support

docs/classes/models/plbart.rst

@@ -0,0 +1,20 @@
+PLBART
+=====
+
+The PLBART model was proposed in [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
+This is a BART-like model which can be used to perform code-summarization, code-generation, and code-translation tasks. The pre-trained model `plbart-base` has been trained using multilingual denoising task
+on Java, Python and English.
+
+According to the abstract,
+
+- PLBART is a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks
+- PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding.
+- PLBART learns program syntax, style (e.g., identifier naming convention) and logical flow.
+
+
+PLBartAdapterModel
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: adapters.PLBartAdapterModel
+    :members:
+    :inherited-members: PLBartPretrainedModel

docs/index.rst

@@ -82,6 +82,7 @@ Currently, we support the PyTorch versions of all models as listed on the `Model
    classes/models/llama
    classes/models/mbart
    classes/models/mt5
+   classes/models/plbart
    classes/models/roberta
    classes/models/t5
    classes/models/vit

docs/model_overview.md

@@ -28,6 +28,7 @@ The table below further shows which model architectures support which adaptation
 | [Llama](classes/models/llama.html)       | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ |
 | [MBart](classes/models/mbart.html)      | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ |
 | [MT5](classes/models/mt5.html)          | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ |
+| [PLBart](classes/models/plbart.html)      | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ |
 | [RoBERTa](classes/models/roberta.html)  | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | [T5](classes/models/t5.html)            | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ |
 | [ViT](classes/models/vit.html)            | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |

examples/pytorch/adapterfusion/run_fusion_glue.py

@@ -13,7 +13,7 @@
 # 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.
-""" Finetuning the library models for sequence classification on
+"""Finetuning the library models for sequence classification on
 GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
 
 

examples/pytorch/dependency-parsing/preprocessing.py

@@ -3,6 +3,7 @@
 Credits: "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" (Rust et al., 2021)
 https://arxiv.org/abs/2012.15613
 """
+
 from collections import defaultdict
 from typing import List
 

examples/pytorch/dependency-parsing/run_udp.py

@@ -3,6 +3,7 @@
 Credits: "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" (Rust et al., 2021)
 https://arxiv.org/abs/2012.15613
 """
+
 import logging
 import os
 import sys
@@ -156,9 +157,11 @@ def main():
         use_fast=model_args.use_fast,
         do_lower_case=model_args.do_lower_case,
         add_prefix_space=True,  # Used e.g. for RoBERTa
-        mecab_kwargs={"mecab_option": f"-r {model_args.mecab_dir} -d {model_args.mecab_dic_dir}"}
-        if model_args.is_japanese
-        else None,
+        mecab_kwargs=(
+            {"mecab_option": f"-r {model_args.mecab_dir} -d {model_args.mecab_dic_dir}"}
+            if model_args.is_japanese
+            else None
+        ),
     )
 
     # The task name (with prefix)
@@ -244,19 +247,23 @@ def main():
             if adapter_args.train_adapter:
                 adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs)
                 model.load_adapter(
-                    os.path.join(training_args.output_dir, "best_model", task_name)
-                    if training_args.do_train
-                    else adapter_args.load_adapter,
+                    (
+                        os.path.join(training_args.output_dir, "best_model", task_name)
+                        if training_args.do_train
+                        else adapter_args.load_adapter
+                    ),
                     config=adapter_config,
                     load_as=task_name,
                     **adapter_load_kwargs,
                 )
                 if adapter_args.load_lang_adapter:
                     lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs)
                     lang_adapter_name = model.load_adapter(
-                        os.path.join(training_args.output_dir, "best_model", lang_adapter_name)
-                        if training_args.do_train
-                        else adapter_args.load_lang_adapter,
+                        (
+                            os.path.join(training_args.output_dir, "best_model", lang_adapter_name)
+                            if training_args.do_train
+                            else adapter_args.load_lang_adapter
+                        ),
                         config=lang_adapter_config,
                         load_as=lang_adapter_name,
                         **adapter_load_kwargs,

examples/pytorch/dependency-parsing/utils_udp.py

@@ -3,6 +3,7 @@
 Credits: "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" (Rust et al., 2021)
 https://arxiv.org/abs/2012.15613
 """
+
 import collections
 import logging
 import os

examples/pytorch/language-modeling/run_clm.py

@@ -541,9 +541,9 @@ def compute_metrics(eval_preds):
         # Data collator will default to DataCollatorWithPadding, so we change it.
         data_collator=default_data_collator,
         compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
-        preprocess_logits_for_metrics=preprocess_logits_for_metrics
-        if training_args.do_eval and not is_torch_tpu_available()
-        else None,
+        preprocess_logits_for_metrics=(
+            preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None
+        ),
     )
 
     # Training

examples/pytorch/language-modeling/run_mlm.py

@@ -557,9 +557,9 @@ def compute_metrics(eval_preds):
         tokenizer=tokenizer,
         data_collator=data_collator,
         compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
-        preprocess_logits_for_metrics=preprocess_logits_for_metrics
-        if training_args.do_eval and not is_torch_tpu_available()
-        else None,
+        preprocess_logits_for_metrics=(
+            preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None
+        ),
     )
 
     # Training

examples/pytorch/text-classification/run_glue.py

@@ -13,7 +13,7 @@
 # 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.
-""" Finetuning the library models for sequence classification on GLUE."""
+"""Finetuning the library models for sequence classification on GLUE."""
 # You can also adapt this script on your own text classification task. Pointers for this are left as comments.
 
 import logging

examples/pytorch/text-generation/run_generation.py

@@ -14,8 +14,7 @@
 # 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.
-""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
-"""
+"""Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)"""
 
 
 import argparse

src/adapters/__init__.py

@@ -110,6 +110,7 @@
     "models.llama": ["LlamaAdapterModel"],
     "models.mbart": ["MBartAdapterModel"],
     "models.mt5": ["MT5AdapterModel"],
+    "models.plbart": ["PLBartAdapterModel"],
     "models.roberta": ["RobertaAdapterModel"],
     "models.t5": ["T5AdapterModel"],
     "models.vit": ["ViTAdapterModel"],
@@ -217,6 +218,7 @@
     from .models.llama import LlamaAdapterModel
     from .models.mbart import MBartAdapterModel
     from .models.mt5 import MT5AdapterModel
+    from .models.plbart import PLBartAdapterModel
     from .models.roberta import RobertaAdapterModel
     from .models.t5 import T5AdapterModel
     from .models.vit import ViTAdapterModel

src/adapters/composition.py

@@ -129,6 +129,7 @@ def __init__(
         "bart",
         "mbart",
         "mt5",
+        "plbart",
         "gpt2",
         "gptj",
         "t5",

src/adapters/head_utils.py

@@ -369,6 +369,27 @@
         },
         "layers": ["lm_head"],
     },
+    # PLBART
+    "PLBartForSequenceClassification": {
+        "config": {
+            "head_type": "classification",
+            "layers": 2,
+            "activation_function": "tanh",
+        },
+        "layers": [
+            None,
+            "classification_head.dense",
+            None,
+            None,
+            "classification_head.out_proj",
+        ],
+    },
+    "PLBartForConditionalGeneration": {
+        "config": {
+            "head_type": "seq2seq_lm",
+        },
+        "layers": ["lm_head"],
+    },
     # MT5
     "MT5ForConditionalGeneration": {
         "config": {

src/adapters/heads/dependency_parsing.py

@@ -2,6 +2,7 @@
 Code taken and modified from: https://github.com/Adapter-Hub/hgiyt. Credits: "How Good is Your Tokenizer? On the
 Monolingual Performance of Multilingual Language Models" (Rust et al., 2021) https://arxiv.org/abs/2012.15613
 """
+
 from dataclasses import dataclass
 from typing import Optional, Tuple
 

src/adapters/methods/bottleneck.py

@@ -195,9 +195,11 @@ def pad_and_concat(self, states: List[BottleneckState]) -> BottleneckState:
             torch.cat([state.input_tensor for state in states], dim=0),
             torch.cat([state.adapter_residual for state in states], dim=0),
             states[0].layer_norm,
-            torch.cat([state.bottleneck_up for state in states], dim=0)
-            if states[0].bottleneck_up is not None
-            else None,
+            (
+                torch.cat([state.bottleneck_up for state in states], dim=0)
+                if states[0].bottleneck_up is not None
+                else None
+            ),
             states[-1].last,
         )
 

src/adapters/methods/lora.py

@@ -408,9 +408,11 @@ def repeat(self, state: LoRAState, channels: int) -> LoRAState:
     def mean(self, states: List[LoRAState], weights: torch.Tensor) -> LoRAState:
         return LoRAState(
             states[0].layer_input,
-            torch.mean(torch.stack([s.hidden_states for s in states], dim=0) * weights, dim=0)
-            if states[0].hidden_states is not None
-            else None,
+            (
+                torch.mean(torch.stack([s.hidden_states for s in states], dim=0) * weights, dim=0)
+                if states[0].hidden_states is not None
+                else None
+            ),
             states[0].layer_output,
             states[-1].last,
         )

src/adapters/models/__init__.py

@@ -18,6 +18,12 @@
 from .gpt2.mixin_gpt2 import GPT2ModelAdapterMixin
 from .gptj.mixin_gptj import GPTJMLPAdaptersMixin, GPTJModelAdapterMixin
 from .llama.mixin_llama import LlamaForQuestionAnsweringAdapterMixin, LlamaModelAdapterMixin
+from .plbart.mixin_plbart import (
+    PLBartDecoderAdaptersMixin,
+    PLBartDecoderWrapperAdaptersMixin,
+    PLBartEncoderAdaptersMixin,
+    PLBartModelAdaptersMixin,
+)
 from .t5.mixin_t5 import (
     T5BlockAdaptersMixin,
     T5ForCondiditionalGenerationWithHeadsMixin,
@@ -33,8 +39,8 @@
     "AlbertModel": AlbertModelAdaptersMixin,
     "BartEncoder": BartEncoderAdaptersMixin,
     "BartDecoder": BartDecoderAdaptersMixin,
-    "BartModel": BartModelAdaptersMixin,
     "BartDecoderWrapper": BartDecoderWrapperAdaptersMixin,
+    "BartModel": BartModelAdaptersMixin,
     "BeitIntermediate": BeitIntermediateAdaptersMixin,
     "BeitOutput": BeitOutputAdaptersMixin,
     "BeitModel": BeitModelAdaptersMixin,
@@ -60,6 +66,10 @@
     "MT5ForConditionalGeneration": T5ForCondiditionalGenerationWithHeadsMixin,
     "MT5ForQuestionAnswering": T5ForQuestionAnsweringWithHeadsMixin,
     "MT5EncoderModel": T5ModelAdaptersMixin,
+    "PLBartEncoder": PLBartEncoderAdaptersMixin,
+    "PLBartDecoder": PLBartDecoderAdaptersMixin,
+    "PLBartModel": PLBartModelAdaptersMixin,
+    "PLBartDecoderWrapper": PLBartDecoderWrapperAdaptersMixin,
     "GPT2Model": GPT2ModelAdapterMixin,
     "GPTJMLP": GPTJMLPAdaptersMixin,
     "GPTJModel": GPTJModelAdapterMixin,

src/adapters/models/auto/adapter_model.py

@@ -24,6 +24,7 @@
         ("llama", "LlamaAdapterModel"),
         ("mbart", "MBartAdapterModel"),
         ("mt5", "MT5AdapterModel"),
+        ("plbart", "PLBartAdapterModel"),
         ("roberta", "RobertaAdapterModel"),
         ("t5", "T5AdapterModel"),
         ("vit", "ViTAdapterModel"),

src/adapters/models/bart/modeling_bart.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch BART model."""
+"""PyTorch BART model."""
 from typing import Optional, Tuple
 
 import torch

src/adapters/models/beit/modeling_beit.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch BEiT model."""
+"""PyTorch BEiT model."""
 
 
 import math

src/adapters/models/clip/modeling_clip.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch CLIP model."""
+"""PyTorch CLIP model."""
 
 
 from typing import Optional, Tuple

src/adapters/models/deberta/modeling_deberta.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch DeBERTa model."""
+"""PyTorch DeBERTa model."""
 
 import torch
 import torch.utils.checkpoint

src/adapters/models/deberta_v2/modeling_deberta_v2.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch DeBERTa-v2 model."""
+"""PyTorch DeBERTa-v2 model."""
 
 import torch
 import torch.utils.checkpoint

src/adapters/models/distilbert/modeling_distilbert.py

@@ -14,8 +14,8 @@
 # limitations under the License.
 
 """
- PyTorch DistilBERT model adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) and in
- part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
+PyTorch DistilBERT model adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) and in
+part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
 """
 
 

src/adapters/models/encoder_decoder/modeling_encoder_decoder.py

@@ -12,7 +12,7 @@
 # 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.
-""" Classes to support Encoder-Decoder architectures"""
+"""Classes to support Encoder-Decoder architectures"""
 
 from transformers.models.encoder_decoder.modeling_encoder_decoder import EncoderDecoderModel
 

src/adapters/models/gptj/modeling_gptj.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch GPT-J model."""
+"""PyTorch GPT-J model."""
 
 from typing import Optional, Tuple, Union
 

src/adapters/models/llama/modeling_llama.py

@@ -17,7 +17,7 @@
 # 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.
-""" PyTorch LLaMA model."""
+"""PyTorch LLaMA model."""
 import math
 import warnings
 from typing import Optional, Tuple

src/adapters/models/mbart/modeling_mbart.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch MBART model."""
+"""PyTorch MBART model."""
 from typing import Optional, Tuple
 
 import torch

src/adapters/models/mt5/modeling_mt5.py

@@ -12,7 +12,7 @@
 # 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.
-""" PyTorch MT5 model."""
+"""PyTorch MT5 model."""
 
 import torch
 from torch import nn