Transformer_handler_generalized.py

from abc import ABC
import json
import logging
import os
import ast
import torch
import transformers
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    AutoModelForQuestionAnswering,
    AutoModelForTokenClassification,
)
from ts.torch_handler.base_handler import BaseHandler
from captum.attr import LayerIntegratedGradients

logger = logging.getLogger(__name__)
logger.info("Transformers version %s",transformers.__version__)
class TransformersSeqClassifierHandler(BaseHandler, ABC):
    """
    Transformers handler class for sequence, token classification and question answering.
    """

    def __init__(self):
        super(TransformersSeqClassifierHandler, self).__init__()
        self.initialized = False

    def initialize(self, ctx):
        """In this initialize function, the BERT model is loaded and
        the Layer Integrated Gradients Algorithm for Captum Explanations
        is initialized here.
        Args:
            ctx (context): It is a JSON Object containing information
            pertaining to the model artefacts parameters.
        """
        self.manifest = ctx.manifest
        properties = ctx.system_properties
        model_dir = properties.get("model_dir")
        serialized_file = self.manifest["model"]["serializedFile"]
        model_pt_path = os.path.join(model_dir, serialized_file)

        self.device = torch.device(
            "cuda:" + str(properties.get("gpu_id"))
            if torch.cuda.is_available() and properties.get("gpu_id") is not None
            else "cpu"
        )
        # read configs for the mode, model_name, etc. from setup_config.json
        setup_config_path = os.path.join(model_dir, "setup_config.json")
        if os.path.isfile(setup_config_path):
            with open(setup_config_path) as setup_config_file:
                self.setup_config = json.load(setup_config_file)
        else:
            logger.warning("Missing the setup_config.json file.")

        # Loading the shared object of compiled Faster Transformer Library if Faster Transformer is set
        if self.setup_config["FasterTransformer"]:
            faster_transformer_complied_path = os.path.join(model_dir, "libpyt_fastertransformer.so")
            torch.classes.load_library(faster_transformer_complied_path)

        # Loading the model and tokenizer from checkpoint and config files based on the user's choice of mode
        # further setup config can be added.
        if self.setup_config["save_mode"] == "torchscript":
            self.model = torch.jit.load(model_pt_path, map_location=self.device)
        elif self.setup_config["save_mode"] == "pretrained":
            if self.setup_config["mode"] == "sequence_classification":
                self.model = AutoModelForSequenceClassification.from_pretrained(
                    model_dir
                )
            elif self.setup_config["mode"] == "question_answering":
                self.model = AutoModelForQuestionAnswering.from_pretrained(model_dir)
            elif self.setup_config["mode"] == "token_classification":
                self.model = AutoModelForTokenClassification.from_pretrained(model_dir)
            else:
                logger.warning("Missing the operation mode.")
            self.model.to(self.device)
            
        else:
            logger.warning("Missing the checkpoint or state_dict.")

        if any(fname for fname in os.listdir(model_dir) if fname.startswith("vocab.") and os.path.isfile(fname)):
            self.tokenizer = AutoTokenizer.from_pretrained(
                model_dir, do_lower_case=self.setup_config["do_lower_case"]
            )
        else:
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.setup_config["model_name"],
                do_lower_case=self.setup_config["do_lower_case"],
            )

        self.model.eval()

        logger.info(
            "Transformer model from path %s loaded successfully", model_dir
        )

        # Read the mapping file, index to object name
        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
        # Question answering does not need the index_to_name.json file.
        if not self.setup_config["mode"] == "question_answering":
            if os.path.isfile(mapping_file_path):
                with open(mapping_file_path) as f:
                    self.mapping = json.load(f)
            else:
                logger.warning("Missing the index_to_name.json file.")
        self.initialized = True

    def preprocess(self, requests):
        """Basic text preprocessing, based on the user's chocie of application mode.
        Args:
            requests (str): The Input data in the form of text is passed on to the preprocess
            function.
        Returns:
            list : The preprocess function returns a list of Tensor for the size of the word tokens.
        """
        input_ids_batch = None
        attention_mask_batch = None
        for idx, data in enumerate(requests):
            input_text = data.get("data")
            if input_text is None:
                input_text = data.get("body")
            if isinstance(input_text, (bytes, bytearray)):
                input_text = input_text.decode('utf-8')
            if self.setup_config["captum_explanation"] and not self.setup_config["mode"] == "question_answering":
                input_text_target = ast.literal_eval(input_text)
                input_text = input_text_target["text"]
            max_length = self.setup_config["max_length"]
            logger.info("Received text: '%s'", input_text)
            # preprocessing text for sequence_classification and token_classification.
            if self.setup_config["mode"] == "sequence_classification" or self.setup_config["mode"] == "token_classification":
                inputs = self.tokenizer.encode_plus(input_text, max_length=int(max_length), pad_to_max_length=True, add_special_tokens=True, return_tensors='pt')
            # preprocessing text for question_answering.
            elif self.setup_config["mode"] == "question_answering":
                # TODO Reading the context from a pickeled file or other fromats that
                # fits the requirements of the task in hand. If this is done then need to
                # modify the following preprocessing accordingly.

                # the sample text for question_answering in the current version
                # should be formated as dictionary with question and text as keys
                # and related text as values.
                # we use this format here seperate question and text for encoding.

                question_context = ast.literal_eval(input_text)
                question = question_context["question"]
                context = question_context["context"]
                inputs = self.tokenizer.encode_plus(question, context, max_length=int(max_length), pad_to_max_length=True, add_special_tokens=True, return_tensors="pt")
            input_ids = inputs["input_ids"].to(self.device)
            attention_mask = inputs["attention_mask"].to(self.device)
            # making a batch out of the recieved requests
            # attention masks are passed for cases where input tokens are padded.
            if input_ids.shape is not None:
                if input_ids_batch is None:
                    input_ids_batch = input_ids
                    attention_mask_batch = attention_mask
                else:
                    input_ids_batch = torch.cat((input_ids_batch, input_ids), 0)
                    attention_mask_batch = torch.cat((attention_mask_batch, attention_mask), 0)
        return (input_ids_batch, attention_mask_batch)

    def inference(self, input_batch):
        """Predict the class (or classes) of the received text using the
        serialized transformers checkpoint.
        Args:
            input_batch (list): List of Text Tensors from the pre-process function is passed here
        Returns:
            list : It returns a list of the predicted value for the input text
        """
        input_ids_batch, attention_mask_batch = input_batch
        inferences = []
        # Handling inference for sequence_classification.
        if self.setup_config["mode"] == "sequence_classification":
            predictions = self.model(input_ids_batch, attention_mask_batch)
            print("This the output size from the Seq classification model", predictions[0].size())
            print("This the output from the Seq classification model", predictions)

            num_rows, num_cols = predictions[0].shape
            for i in range(num_rows):
                out = predictions[0][i].unsqueeze(0)
                y_hat = out.argmax(1).item()
                predicted_idx = str(y_hat)
                inferences.append(self.mapping[predicted_idx])
        # Handling inference for question_answering.
        elif self.setup_config["mode"] == "question_answering":
            # the output should be only answer_start and answer_end
            # we are outputing the words just for demonstration.
            if self.setup_config["save_mode"]=="pretrained":
                outputs = self.model(input_ids_batch,attention_mask_batch)
                answer_start_scores = outputs.start_logits
                answer_end_scores = outputs.end_logits
            else:
                answer_start_scores, answer_end_scores = self.model(input_ids_batch, attention_mask_batch)
            print("This the output size for answer start scores from the question answering model", answer_start_scores.size())
            print("This the output for answer start scores from the question answering model", answer_start_scores)
            print("This the output size for answer end scores from the question answering model", answer_end_scores.size())
            print("This the output for answer end scores from the question answering model", answer_end_scores)

            num_rows, num_cols = answer_start_scores.shape
            # inferences = []
            for i in range(num_rows):
                answer_start_scores_one_seq = answer_start_scores[i].unsqueeze(0)
                answer_start = torch.argmax(answer_start_scores_one_seq)
                answer_end_scores_one_seq = answer_end_scores[i].unsqueeze(0)
                answer_end = torch.argmax(answer_end_scores_one_seq) + 1
                prediction = self.tokenizer.convert_tokens_to_string(self.tokenizer.convert_ids_to_tokens(input_ids_batch[i].tolist()[answer_start:answer_end]))
                inferences.append(prediction)
            logger.info("Model predicted: '%s'", prediction)
        # Handling inference for token_classification.
        elif self.setup_config["mode"]== "token_classification":
            outputs = self.model(input_ids_batch, attention_mask_batch)[0]
            print("This the output size from the token classification model", outputs.size())
            print("This the output from the token classification model",outputs)
            num_rows = outputs.shape[0]
            for i in range(num_rows):
                output = outputs[i].unsqueeze(0)
                predictions = torch.argmax(output, dim=2)
                tokens = self.tokenizer.tokenize(self.tokenizer.decode(input_ids_batch[i]))
                if self.mapping:
                    label_list = self.mapping["label_list"]
                label_list = label_list.strip('][').split(', ')
                prediction = [(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())]
                inferences.append(prediction)
            logger.info("Model predicted: '%s'", prediction)

        return inferences

    def postprocess(self, inference_output):
        """Post Process Function converts the predicted response into Torchserve readable format.
        Args:
            inference_output (list): It contains the predicted response of the input text.
        Returns:
            (list): Returns a list of the Predictions and Explanations.
        """
        return inference_output

    def get_insights(self, input_batch, text, target):
        """This function initialize and calls the layer integrated gradient to get word importance
        of the input text if captum explanation has been selected through setup_config
        Args:
            input_batch (int): Batches of tokens IDs of text
            text (str): The Text specified in the input request
            target (int): The Target can be set to any acceptable label under the user's discretion.
        Returns:
            (list): Returns a list of importances and words.
        """
        
        if self.setup_config["captum_explanation"]:
            embedding_layer = getattr(self.model,self.setup_config["embedding_name"])
            embeddings = embedding_layer.embeddings
            self.lig = LayerIntegratedGradients(
                captum_sequence_forward, embeddings
            )
        else:
            logger.warning("Captum Explanation is not chosen and will not be available")
        
        if isinstance(text, (bytes, bytearray)):
            text = text.decode('utf-8')
        text_target = ast.literal_eval(text)
        
        if not self.setup_config["mode"]=="question_answering":
            text = text_target["text"]
        self.target = text_target["target"]
        
        input_ids, ref_input_ids, attention_mask = construct_input_ref(
            text, self.tokenizer, self.device,self.setup_config["mode"] 
        )
        all_tokens = get_word_token(input_ids, self.tokenizer)
        response = {}
        response["words"] = all_tokens
        if self.setup_config["mode"] == "sequence_classification" or self.setup_config["mode"] == "token_classification":
            
            attributions, delta = self.lig.attribute(
                inputs=input_ids,
                baselines=ref_input_ids,
                target=self.target,
                additional_forward_args=(attention_mask, 0, self.model),
                return_convergence_delta=True,
            )

            attributions_sum = summarize_attributions(attributions)
            response["importances"] = attributions_sum.tolist()
            response["delta"] = delta[0].tolist()
        
        elif self.setup_config["mode"] == "question_answering":
            attributions_start, delta_start = self.lig.attribute(
                inputs=input_ids,
                baselines=ref_input_ids,
                target=self.target,
                additional_forward_args=( attention_mask, 0, self.model),
                return_convergence_delta=True,
            )
            attributions_end, delta_end = self.lig.attribute(
                inputs=input_ids,
                baselines=ref_input_ids,
                target=self.target,
                additional_forward_args=(attention_mask, 1, self.model),
                return_convergence_delta=True,
            )
            attributions_sum_start = summarize_attributions(attributions_start)
            attributions_sum_end = summarize_attributions(attributions_end)
            response["importances_answer_start"] = attributions_sum_start.tolist()
            response["importances_answer_end"] = attributions_sum_end.tolist()
            response["delta_start"] = delta_start[0].tolist()
            response["delta_end"] = delta_end[0].tolist()
            
            
        
        return [response]


def construct_input_ref(text, tokenizer, device, mode):
    """For a given text, this function creates token id, reference id and
    attention mask based on encode which is faster for captum insights
    Args:
        text (str): The text specified in the input request
        tokenizer (AutoTokenizer Class Object): To word tokenize the input text
        device (cpu or gpu): Type of the Environment the server runs on.
    Returns:
        input_id(Tensor): It attributes to the tensor of the input tokenized words
        ref_input_ids(Tensor): Ref Input IDs are used as baseline for the attributions
        attention mask() :  The attention mask is a binary tensor indicating the position
         of the padded indices so that the model does not attend to them.
    """
    if mode == "question_answering":
        question_context = ast.literal_eval(text)
        question = question_context["question"]
        context = question_context["context"]
        text_ids = tokenizer.encode(question,context, add_special_tokens=False)
       
    text_ids = tokenizer.encode(text, add_special_tokens=False)
    # construct input token ids
    logger.info("text_ids %s", text_ids)
    logger.info("[tokenizer.cls_token_id] %s", [tokenizer.cls_token_id])
    input_ids = [tokenizer.cls_token_id] + text_ids + [tokenizer.sep_token_id]
    logger.info("input_ids %s", input_ids)

    input_ids = torch.tensor([input_ids], device=device)
    # construct reference token ids
    ref_input_ids = (
        [tokenizer.cls_token_id]
        + [tokenizer.pad_token_id] * len(text_ids)
        + [tokenizer.sep_token_id]
    )
    ref_input_ids = torch.tensor([ref_input_ids], device=device)
    # construct attention mask
    attention_mask = torch.ones_like(input_ids)
    return input_ids, ref_input_ids, attention_mask


def captum_sequence_forward(inputs, attention_mask=None, position=0, model=None):
    """This function is used to get the predictions from the model and this function 
    can be used independent of the type of the BERT Task. 
    Args:
        inputs (list): Input for Predictions
        attention_mask (list, optional): The attention mask is a binary tensor indicating the position
         of the padded indices so that the model does not attend to them, it defaults to None.
        position (int, optional): Position depends on the BERT Task. 
        model ([type], optional): Name of the model, it defaults to None.
    Returns:
        list: Prediction Outcome
    """
    model.eval()
    model.zero_grad()
    pred = model(inputs, attention_mask=attention_mask)
    pred = pred[position]
    return pred


def summarize_attributions(attributions):
    """Summarises the attribution across multiple runs
    Args:
        attributions ([list): attributions from the Layer Integrated Gradients
    Returns:
        list : Returns the attributions after normalizing them. 
    """
    attributions = attributions.sum(dim=-1).squeeze(0)
    attributions = attributions / torch.norm(attributions)
    return attributions


def get_word_token(input_ids, tokenizer):
    """constructs word tokens from token id using the BERT's
    Auto Tokenizer
    Args:
        input_ids (list): Input IDs from construct_input_ref method
        tokenizer (class): The Auto Tokenizer Pre-Trained model object
    Returns:
        (list): Returns the word tokens
    """
    indices = input_ids[0].detach().tolist()
    tokens = tokenizer.convert_ids_to_tokens(indices)
    # Remove unicode space character from BPE Tokeniser
    tokens = [token.replace("Ġ", "") for token in tokens]
    return tokens