transformers_nlclassifier_quanteval.py

# =============================================================================
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#  Copyright (c) 2022 of Qualcomm Innovation Center, Inc. All rights reserved.
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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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# coding=utf-8
#python import 
import logging
import os
import random
import sys
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset, load_metric
import numpy as np
import urllib 
import progressbar 

#transformers import 
import transformers
from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PretrainedConfig,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process

#AIMET imports 
from aimet_torch import quantsim
from aimet_torch.quantsim import QuantizationSimModel
from aimet_torch.quantsim import load_checkpoint
from aimet_common.defs import QuantScheme
from aimet_torch.onnx_utils import OnnxExportApiArgs
# Utils imports 
from utils.utils_nlclassifier_dataclass import ModelArguments, DataTrainingArguments, AuxArguments

OFFICIAL_URL_HEAD="https://github.com/quic/aimet-model-zoo/releases/download/torch_dlv3_w8a8_pc/" # To be replaced once released 

os.environ['WANDB_DISABLED']="true"

logger = logging.getLogger(__name__)

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}


class DownloadProgressBar():
    def __init__(self):
        self.dpb = None

    def __call__(self, b_num, b_size, size):
        widgets = [
        '\x1b[33mDownloading weights \x1b[39m',
        progressbar.Percentage(),
        progressbar.Bar(marker='\x1b[32m#\x1b[39m'),
        ]
        if self.dpb is not None:
            self.dpb=progressbar.ProgressBar(widgets=widgets, maxval=size,redirect_stdout=True)
            self.dpb.start()

        processed = b_num * b_size
        if processed >= size:
            self.dpb.finish()
        else:
            self.dpb.update(processed)


def download_weights(model_args,data_args):
    # Download weights to cache directory 
    if not os.path.exists(".cache"):
        os.mkdir(".cache")
    url_checkpoint_test=f'{OFFICIAL_URL_HEAD}/{data_args.task_name}_fp.pth'
    urllib.request.urlretrieve(url_checkpoint_test,"./.cache/fp.pth",DownloadProgressBar())
    url_checkpoint_test=f'{OFFICIAL_URL_HEAD}/{data_args.task_name}_qat.pth'
    urllib.request.urlretrieve(url_checkpoint_test,"./.cache/qat.pth",DownloadProgressBar())


def main():

    # Parse arguments
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments, AuxArguments))
    model_args, data_args, training_args, aux_args = parser.parse_args_into_dataclasses()

    #download weights of original and quantized weight files 

    print ("===========download weights====================")
    # download_weights(model_args,data_args) # TODO after OFFICIAL_URL_HEAD is specified 

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
    )

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset("glue", data_args.task_name)
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                train_extension = data_args.train_file.split(".")[-1]
                test_extension = data_args.test_file.split(".")[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                data_files["test"] = data_args.test_file
            else:
                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")

        for key in data_files.keys():
            logger.info(f"load a local file for {key}: {data_files[key]}")

        if data_args.train_file.endswith(".csv"):
            # Loading a dataset from local csv files
            datasets = load_dataset("csv", data_files=data_files)
        else:
            # Loading a dataset from local json files
            datasets = load_dataset("json", data_files=data_files)

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = datasets["train"].features["label"].dtype in ["float32", "float64"]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            label_list = datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    # Load pretrained model and tokenizer
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    # ++++  
    config.return_dict = False
    config.classifier_dropout = None
    config.attention_probs_dropout_prob = model_args.attention_probs_dropout_prob

    # ++++ 
    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_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
        
    model = torch.load(aux_args.fmodel_path)

    # Preprocessing the datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [name for name in datasets["train"].column_names if name != "label"]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and data_args.task_name is not None
        and is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
        else:
            logger.warn(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None and not is_regression:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    def preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(*args, padding=padding, max_length=data_args.max_seq_length, truncation=True)

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [label_to_id[l] for l in examples["label"]]
        return result

    datasets = datasets.map(
        preprocess_function, 
        batched=True, 
        load_from_cache_file=not data_args.overwrite_cache
    )

    train_dataset = datasets["train"]
    eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
    if data_args.task_name is not None or data_args.test_file is not None:
        test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"]

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
        else:
            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}    

    # Load the Quantsim_modl object
    quantsim_model = load_checkpoint(aux_args.qmodel_path)
    
    # Initialize Trainer for evaluation
    model.cuda()
    model.eval()
    quantsim_model.model.cuda()
    quantsim_model.model.eval()
    ftrainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=default_data_collator if data_args.pad_to_max_length else None,
    )
    qtrainer = Trainer(
        model=quantsim_model.model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=default_data_collator if data_args.pad_to_max_length else None,
    )

    # Evaluation
    feval_results, qeval_results = {}, {}
    logger.info("*** Evaluate ***")

    # Loop to handle MNLI double evaluation (matched, mis-matched)
    tasks = [data_args.task_name]
    eval_datasets = [eval_dataset]
    if data_args.task_name == "mnli":
        tasks.append("mnli-mm")
        eval_datasets.append(datasets["validation_mismatched"])

    for eval_dataset, task in zip(eval_datasets, tasks):
        feval_result = ftrainer.evaluate(eval_dataset=eval_dataset)
        qeval_result = qtrainer.evaluate(eval_dataset=eval_dataset)
        # FP
        foutput_eval_file = os.path.join(training_args.output_dir, f"eval_results_fp.txt")
        if ftrainer.is_world_process_zero():
            with open(foutput_eval_file, "w") as writer:
                logger.info(f"***** FP Eval results *****")
                for key, value in sorted(feval_result.items()):
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")
                writer.write(f"Memory usage:\n{torch.cuda.memory_summary(device=0, abbreviated=False)}")
        feval_results.update(feval_result)
        # QAT
        qoutput_eval_file = os.path.join(training_args.output_dir, f"eval_results_qat.txt")
        if qtrainer.is_world_process_zero():
            with open(qoutput_eval_file, "w") as writer:
                logger.info(f"***** QAT Eval results *****")
                for key, value in sorted(qeval_result.items()):
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")
                writer.write(f"Memory usage:\n{torch.cuda.memory_summary(device=0, abbreviated=False)}")
        qeval_results.update(qeval_result)

    return None


if __name__ == "__main__":
    main()