[WIP] Lightning glue example (#3290)

* ✨ Alter base pl transformer to use automodels

* 🐛 Add batch size env variable to function call

* 💄 Apply black code style from Makefile

* 🚚 Move lightning base out of ner directory

* ✨ Add lightning glue example

* 💄 self

* move _feature_file to base class

* ✨ Move eval logging to custom callback

* 💄 Apply black code style

* 🐛 Add parent to pythonpath, remove copy command

* 🐛 Add missing max_length kwarg

examples/glue/README.md

+# GLUE Benchmark
+
+Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py).
+
+#### Run PyTorch version using PyTorch-Lightning
+
+Run `bash run_pl.sh` from the `glue` directory. This will also install `pytorch-lightning` and the requirements in `examples/requirements.txt`. It is a shell pipeline that will automatically download, pre-process the data and run the specified models. Logs are saved in `lightning_logs` directory.
+
+Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
\ No newline at end of file

examples/glue/run_pl.sh

+# Install newest ptl.
+pip install -U git+http://github.com/PyTorchLightning/pytorch-lightning/
+# Install example requirements
+pip install -r ../requirements.txt
+
+# Download glue data
+python3 ../../utils/download_glue_data.py
+
+export TASK=mrpc
+export DATA_DIR=./glue_data/MRPC/
+export MAX_LENGTH=128
+export LEARNING_RATE=2e-5
+export BERT_MODEL=bert-base-cased
+export MODEL_TYPE=bert
+export BATCH_SIZE=32
+export NUM_EPOCHS=3
+export SEED=2
+export OUTPUT_DIR_NAME=mrpc-pl-bert
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+
+# Make output directory if it doesn't exist
+mkdir -p $OUTPUT_DIR
+# Add parent directory to python path to access transformer_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+python3 run_pl_glue.py --data_dir $DATA_DIR \
+--model_type $MODEL_TYPE \
+--task $TASK \
+--model_name_or_path $BERT_MODEL \
+--output_dir $OUTPUT_DIR \
+--max_seq_length  $MAX_LENGTH \
+--learning_rate $LEARNING_RATE \
+--num_train_epochs $NUM_EPOCHS \
+--train_batch_size $BATCH_SIZE \
+--seed $SEED \
+--do_train \
+--do_predict

examples/glue/run_pl_glue.py

+import argparse
+import glob
+import logging
+import os
+import time
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from transformer_base import BaseTransformer, add_generic_args, generic_train
+from transformers import glue_compute_metrics as compute_metrics
+from transformers import glue_convert_examples_to_features as convert_examples_to_features
+from transformers import glue_output_modes
+from transformers import glue_processors as processors
+from transformers import glue_tasks_num_labels
+
+
+logger = logging.getLogger(__name__)
+
+
+class GLUETransformer(BaseTransformer):
+
+    mode = "sequence-classification"
+
+    def __init__(self, hparams):
+        hparams.glue_output_mode = glue_output_modes[hparams.task]
+        num_labels = glue_tasks_num_labels[hparams.task]
+
+        super().__init__(hparams, num_labels, self.mode)
+
+    def forward(self, **inputs):
+        return self.model(**inputs)
+
+    def training_step(self, batch, batch_idx):
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = batch[2] if self.hparams.model_type in ["bert", "xlnet", "albert"] else None
+
+        outputs = self(**inputs)
+        loss = outputs[0]
+
+        tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
+        return {"loss": loss, "log": tensorboard_logs}
+
+    def prepare_data(self):
+        "Called to initialize data. Use the call to construct features"
+        args = self.hparams
+        processor = processors[args.task]()
+        self.labels = processor.get_labels()
+
+        for mode in ["train", "dev"]:
+            cached_features_file = self._feature_file(mode)
+            if not os.path.exists(cached_features_file) and not args.overwrite_cache:
+                logger.info("Creating features from dataset file at %s", args.data_dir)
+                examples = (
+                    processor.get_dev_examples(args.data_dir)
+                    if mode == "dev"
+                    else processor.get_train_examples(args.data_dir)
+                )
+                features = convert_examples_to_features(
+                    examples,
+                    self.tokenizer,
+                    max_length=args.max_seq_length,
+                    task=args.task,
+                    label_list=self.labels,
+                    output_mode=args.glue_output_mode,
+                    pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
+                    pad_token=self.tokenizer.convert_tokens_to_ids([self.tokenizer.pad_token])[0],
+                    pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
+                )
+                logger.info("Saving features into cached file %s", cached_features_file)
+                torch.save(features, cached_features_file)
+
+    def load_dataset(self, mode, batch_size):
+        "Load datasets. Called after prepare data."
+
+        # We test on dev set to compare to benchmarks without having to submit to GLUE server
+        mode = "dev" if mode == "test" else mode
+
+        cached_features_file = self._feature_file(mode)
+        logger.info("Loading features from cached file %s", cached_features_file)
+        features = torch.load(cached_features_file)
+        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+        all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
+        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
+        if self.hparams.glue_output_mode == "classification":
+            all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
+        elif self.hparams.glue_output_mode == "regression":
+            all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
+
+        return DataLoader(
+            TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels),
+            batch_size=batch_size,
+            shuffle=True,
+        )
+
+    def validation_step(self, batch, batch_idx):
+        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+
+        if self.hparams.model_type != "distilbert":
+            inputs["token_type_ids"] = batch[2] if self.hparams.model_type in ["bert", "xlnet", "albert"] else None
+
+        outputs = self(**inputs)
+        tmp_eval_loss, logits = outputs[:2]
+        preds = logits.detach().cpu().numpy()
+        out_label_ids = inputs["labels"].detach().cpu().numpy()
+
+        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
+
+    def _eval_end(self, outputs):
+        val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
+        preds = np.concatenate([x["pred"] for x in outputs], axis=0)
+
+        if self.hparams.glue_output_mode == "classification":
+            preds = np.argmax(preds, axis=1)
+        elif self.hparams.glue_output_mode == "regression":
+            preds = np.squeeze(preds)
+
+        out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
+        out_label_list = [[] for _ in range(out_label_ids.shape[0])]
+        preds_list = [[] for _ in range(out_label_ids.shape[0])]
+
+        results = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task, preds, out_label_ids)}
+
+        ret = {k: v for k, v in results.items()}
+        ret["log"] = results
+        return ret, preds_list, out_label_list
+
+    def validation_end(self, outputs: list) -> dict:
+        ret, preds, targets = self._eval_end(outputs)
+        logs = ret["log"]
+        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
+        ret, predictions, targets = self._eval_end(outputs)
+
+        # Converting to the dic required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
+        logs = ret["log"]
+        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
+        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        # Add NER specific options
+        BaseTransformer.add_model_specific_args(parser, root_dir)
+        parser.add_argument(
+            "--max_seq_length",
+            default=128,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+
+        parser.add_argument(
+            "--task", default="", type=str, required=True, help="The GLUE task to run",
+        )
+
+        parser.add_argument(
+            "--data_dir",
+            default=None,
+            type=str,
+            required=True,
+            help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
+        )
+
+        parser.add_argument(
+            "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+        )
+
+        return parser
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    add_generic_args(parser, os.getcwd())
+    parser = GLUETransformer.add_model_specific_args(parser, os.getcwd())
+    args = parser.parse_args()
+
+    # If output_dir not provided, a folder will be generated in pwd
+    if args.output_dir is None:
+        args.output_dir = os.path.join("./results", f"{args.task}_{args.model_type}_{time.strftime('%Y%m%d_%H%M%S')}",)
+        os.makedirs(args.output_dir)
+
+    model = GLUETransformer(args)
+    trainer = generic_train(model, args)
+
+    # Optionally, predict on dev set and write to output_dir
+    if args.do_predict:
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
+        GLUETransformer.load_from_checkpoint(checkpoints[-1])
+        trainer.test(model)

examples/ner/run_pl.sh

@@ -27,6 +27,9 @@ export CURRENT_DIR=${PWD}
 export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
 mkdir -p $OUTPUT_DIR
 
+# Add parent directory to python path to access transformer_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
 python3 run_pl_ner.py --data_dir ./ \
 --model_type bert \
 --labels ./labels.txt \
@@ -34,7 +37,7 @@ python3 run_pl_ner.py --data_dir ./ \
 --output_dir $OUTPUT_DIR \
 --max_seq_length  $MAX_LENGTH \
 --num_train_epochs $NUM_EPOCHS \
---train_batch_size 32 \
+--train_batch_size $BATCH_SIZE \
 --seed $SEED \
 --do_train \
 --do_predict
\ No newline at end of file

examples/ner/run_pl_ner.py

@@ -21,11 +21,13 @@ class NERTransformer(BaseTransformer):
     A training module for NER. See BaseTransformer for the core options.
     """
 
+    mode = "token-classification"
+
     def __init__(self, hparams):
         self.labels = get_labels(hparams.labels)
         num_labels = len(self.labels)
         self.pad_token_label_id = CrossEntropyLoss().ignore_index
-        super(NERTransformer, self).__init__(hparams, num_labels)
+        super(NERTransformer, self).__init__(hparams, num_labels, self.mode)
 
     def forward(self, **inputs):
         return self.model(**inputs)
@@ -38,21 +40,11 @@ class NERTransformer(BaseTransformer):
                 batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
             )  # XLM and RoBERTa don"t use segment_ids
 
-        outputs = self.forward(**inputs)
+        outputs = self(**inputs)
         loss = outputs[0]
         tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss, "log": tensorboard_logs}
 
-    def _feature_file(self, mode):
-        return os.path.join(
-            self.hparams.data_dir,
-            "cached_{}_{}_{}".format(
-                mode,
-                list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
-                str(self.hparams.max_seq_length),
-            ),
-        )
-
     def prepare_data(self):
         "Called to initialize data. Use the call to construct features"
         args = self.hparams
@@ -100,7 +92,7 @@ class NERTransformer(BaseTransformer):
             inputs["token_type_ids"] = (
                 batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
             )  # XLM and RoBERTa don"t use segment_ids
-        outputs = self.forward(**inputs)
+        outputs = self(**inputs)
         tmp_eval_loss, logits = outputs[:2]
         preds = logits.detach().cpu().numpy()
         out_label_ids = inputs["labels"].detach().cpu().numpy()
@@ -130,14 +122,8 @@ class NERTransformer(BaseTransformer):
             "f1": f1_score(out_label_list, preds_list),
         }
 
-        if self.is_logger():
-            logger.info("***** Eval results *****")
-            for key in sorted(results.keys()):
-                logger.info("  %s = %s", key, str(results[key]))
-
-        tensorboard_logs = results
         ret = {k: v for k, v in results.items()}
-        ret["log"] = tensorboard_logs
+        ret["log"] = results
         return ret, preds_list, out_label_list
 
     def validation_end(self, outputs):
@@ -151,32 +137,7 @@ class NERTransformer(BaseTransformer):
         # updating to test_epoch_end instead of deprecated test_end
         ret, predictions, targets = self._eval_end(outputs)
 
-        if self.is_logger():
-            # Write output to a file:
-            # Save results
-            output_test_results_file = os.path.join(self.hparams.output_dir, "test_results.txt")
-            with open(output_test_results_file, "w") as writer:
-                for key in sorted(ret.keys()):
-                    if key != "log":
-                        writer.write("{} = {}\n".format(key, str(ret[key])))
-            # Save predictions
-            output_test_predictions_file = os.path.join(self.hparams.output_dir, "test_predictions.txt")
-            with open(output_test_predictions_file, "w") as writer:
-                with open(os.path.join(self.hparams.data_dir, "test.txt"), "r") as f:
-                    example_id = 0
-                    for line in f:
-                        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                            writer.write(line)
-                            if not predictions[example_id]:
-                                example_id += 1
-                        elif predictions[example_id]:
-                            output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n"
-                            writer.write(output_line)
-                        else:
-                            logger.warning(
-                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
-                            )
-        # Converting to the dic required by pl
+        # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         logs = ret["log"]
@@ -230,6 +191,6 @@ if __name__ == "__main__":
         # pl use this format to create a checkpoint:
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
         # /pytorch_lightning/callbacks/model_checkpoint.py#L169
-        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpointepoch=*.ckpt", recursive=True)))
+        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
         NERTransformer.load_from_checkpoint(checkpoints[-1])
         trainer.test(model)

examples/ner/transformer_base.py → examples/transformer_base.py

@@ -7,43 +7,34 @@ import pytorch_lightning as pl
 import torch
 
 from transformers import (
+    ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
     AdamW,
-    BertConfig,
-    BertForTokenClassification,
-    BertTokenizer,
-    CamembertConfig,
-    CamembertForTokenClassification,
-    CamembertTokenizer,
-    DistilBertConfig,
-    DistilBertForTokenClassification,
-    DistilBertTokenizer,
-    RobertaConfig,
-    RobertaForTokenClassification,
-    RobertaTokenizer,
-    XLMRobertaConfig,
-    XLMRobertaForTokenClassification,
-    XLMRobertaTokenizer,
+    AutoConfig,
+    AutoModel,
+    AutoModelForPreTraining,
+    AutoModelForQuestionAnswering,
+    AutoModelForSequenceClassification,
+    AutoModelForTokenClassification,
+    AutoModelWithLMHead,
+    AutoTokenizer,
     get_linear_schedule_with_warmup,
 )
+from transformers.modeling_auto import MODEL_MAPPING
 
 
 logger = logging.getLogger(__name__)
 
 
-ALL_MODELS = sum(
-    (
-        tuple(conf.pretrained_config_archive_map.keys())
-        for conf in (BertConfig, RobertaConfig, DistilBertConfig, CamembertConfig, XLMRobertaConfig)
-    ),
-    (),
-)
+ALL_MODELS = tuple(ALL_PRETRAINED_MODEL_ARCHIVE_MAP)
+MODEL_CLASSES = tuple(m.model_type for m in MODEL_MAPPING)
 
-MODEL_CLASSES = {
-    "bert": (BertConfig, BertForTokenClassification, BertTokenizer),
-    "roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
-    "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
-    "xlmroberta": (XLMRobertaConfig, XLMRobertaForTokenClassification, XLMRobertaTokenizer),
+MODEL_MODES = {
+    "base": AutoModel,
+    "sequence-classification": AutoModelForSequenceClassification,
+    "question-answering": AutoModelForQuestionAnswering,
+    "pretraining": AutoModelForPreTraining,
+    "token-classification": AutoModelForTokenClassification,
+    "language-modeling": AutoModelWithLMHead,
 }
 
 
@@ -56,25 +47,24 @@ def set_seed(args):
 
 
 class BaseTransformer(pl.LightningModule):
-    def __init__(self, hparams, num_labels=None):
+    def __init__(self, hparams, num_labels=None, mode="base"):
         "Initialize a model."
 
         super(BaseTransformer, self).__init__()
         self.hparams = hparams
         self.hparams.model_type = self.hparams.model_type.lower()
 
-        config_class, model_class, tokenizer_class = MODEL_CLASSES[self.hparams.model_type]
-        config = config_class.from_pretrained(
+        config = AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
             num_labels=num_labels,
             cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,
         )
-        tokenizer = tokenizer_class.from_pretrained(
+        tokenizer = AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
             do_lower_case=self.hparams.do_lower_case,
             cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,
         )
-        model = model_class.from_pretrained(
+        model = MODEL_MODES[mode].from_pretrained(
             self.hparams.model_name_or_path,
             from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
             config=config,
@@ -144,6 +134,16 @@ class BaseTransformer(pl.LightningModule):
     def test_dataloader(self):
         return self.load_dataset("test", self.hparams.eval_batch_size)
 
+    def _feature_file(self, mode):
+        return os.path.join(
+            self.hparams.data_dir,
+            "cached_{}_{}_{}".format(
+                mode,
+                list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
+                str(self.hparams.max_seq_length),
+            ),
+        )
+
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         parser.add_argument(
@@ -151,7 +151,7 @@ class BaseTransformer(pl.LightningModule):
             default=None,
             type=str,
             required=True,
-            help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+            help="Model type selected in the list: " + ", ".join(MODEL_CLASSES),
         )
         parser.add_argument(
             "--model_name_or_path",
@@ -190,6 +190,31 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--eval_batch_size", default=32, type=int)
 
 
+class LoggingCallback(pl.Callback):
+    def on_validation_end(self, trainer, pl_module):
+        logger.info("***** Validation results *****")
+        if pl_module.is_logger():
+            metrics = trainer.callback_metrics
+            # Log results
+            for key in sorted(metrics):
+                if key not in ["log", "progress_bar"]:
+                    logger.info("{} = {}\n".format(key, str(metrics[key])))
+
+    def on_test_end(self, trainer, pl_module):
+        logger.info("***** Test results *****")
+
+        if pl_module.is_logger():
+            metrics = trainer.callback_metrics
+
+            # Log and save results to file
+            output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
+            with open(output_test_results_file, "w") as writer:
+                for key in sorted(metrics):
+                    if key not in ["log", "progress_bar"]:
+                        logger.info("{} = {}\n".format(key, str(metrics[key])))
+                        writer.write("{} = {}\n".format(key, str(metrics[key])))
+
+
 def add_generic_args(parser, root_dir):
     parser.add_argument(
         "--output_dir",
@@ -257,6 +282,7 @@ def generic_train(model, args):
         early_stop_callback=False,
         gradient_clip_val=args.max_grad_norm,
         checkpoint_callback=checkpoint_callback,
+        callbacks=[LoggingCallback()],
     )
 
     if args.fp16: