# What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
GPTQ integration. # What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
""" Fine-tuning the library models for named entity recognition."""


import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


logger = logging.getLogger(__name__)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    task_type: Optional[str] = field(
        default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    data_dir: str = field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}
    )
    labels: Optional[str] = field(
        metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            " --overwrite_output_dir to overcome."
        )

    module = import_module("tasks")

    try:
        token_classification_task_clazz = getattr(module, model_args.task_type)
        token_classification_task: TokenClassificationTask = token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
            f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(
        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
        training_args.n_replicas,
        bool(training_args.n_replicas > 1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Prepare Token Classification task
    labels = token_classification_task.get_labels(data_args.labels)
    label_map: Dict[int, str] = dict(enumerate(labels))
    num_labels = len(labels)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
    )

    with training_args.strategy.scope():
        model = TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path,
            from_pt=bool(".bin" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )

    # Get datasets
    train_dataset = (
        TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )
    eval_dataset = (
        TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
        preds = np.argmax(predictions, axis=2)
        batch_size, seq_len = preds.shape
        out_label_list = [[] for _ in range(batch_size)]
        preds_list = [[] for _ in range(batch_size)]

        for i in range(batch_size):
            for j in range(seq_len):
                if label_ids[i, j] != -100:
                    out_label_list[i].append(label_map[label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        return preds_list, out_label_list

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)

        return {
            "precision": precision_score(out_label_list, preds_list),
            "recall": recall_score(out_label_list, preds_list),
            "f1": f1_score(out_label_list, preds_list),
        }

    # Initialize our Trainer
    trainer = TFTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset.get_dataset() if train_dataset else None,
        eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
        compute_metrics=compute_metrics,
    )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()
        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")

        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")

            for key, value in result.items():
                logger.info("  %s = %s", key, value)
                writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        test_dataset = TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        predictions, label_ids, metrics = trainer.predict(test_dataset.get_dataset())
        preds_list, labels_list = align_predictions(predictions, label_ids)
        report = classification_report(labels_list, preds_list)

        logger.info("\n%s", report)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")

        with open(output_test_results_file, "w") as writer:
            writer.write("%s\n" % report)

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")

        with open(output_test_predictions_file, "w") as writer:
            with open(os.path.join(data_args.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 preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"

                        writer.write(output_line)
                    else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])

    return results


if __name__ == "__main__":
    main()

#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
""" Fine-tuning the library models for named entity recognition."""


import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


logger = logging.getLogger(__name__)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    task_type: Optional[str] = field(
        default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    data_dir: str = field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}
    )
    labels: Optional[str] = field(
        metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            " --overwrite_output_dir to overcome."
        )

    module = import_module("tasks")

    try:
        token_classification_task_clazz = getattr(module, model_args.task_type)
        token_classification_task: TokenClassificationTask = token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
            f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(
        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
        training_args.n_replicas,
        bool(training_args.n_replicas > 1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Prepare Token Classification task
    labels = token_classification_task.get_labels(data_args.labels)
    label_map: Dict[int, str] = dict(enumerate(labels))
    num_labels = len(labels)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
    )

    with training_args.strategy.scope():
        model = TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path,
            from_pt=bool(".bin" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )

    # Get datasets
    train_dataset = (
        TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )
    eval_dataset = (
        TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
        preds = np.argmax(predictions, axis=2)
        batch_size, seq_len = preds.shape
        out_label_list = [[] for _ in range(batch_size)]
        preds_list = [[] for _ in range(batch_size)]

        for i in range(batch_size):
            for j in range(seq_len):
                if label_ids[i, j] != -100:
                    out_label_list[i].append(label_map[label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        return preds_list, out_label_list

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)

        return {
            "precision": precision_score(out_label_list, preds_list),
            "recall": recall_score(out_label_list, preds_list),
            "f1": f1_score(out_label_list, preds_list),
        }

    # Initialize our Trainer
    trainer = TFTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset.get_dataset() if train_dataset else None,
        eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
        compute_metrics=compute_metrics,
    )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()
        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")

        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")

            for key, value in result.items():
                logger.info("  %s = %s", key, value)
                writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        test_dataset = TFTokenClassificationDataset(
            token_classification_task=token_classification_task,
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        predictions, label_ids, metrics = trainer.predict(test_dataset.get_dataset())
        preds_list, labels_list = align_predictions(predictions, label_ids)
        report = classification_report(labels_list, preds_list)

        logger.info("\n%s", report)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")

        with open(output_test_results_file, "w") as writer:
            writer.write("%s\n" % report)

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")

        with open(output_test_predictions_file, "w") as writer:
            with open(os.path.join(data_args.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 preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"

                        writer.write(output_line)
                    else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])

    return results


if __name__ == "__main__":
    main()
