app.py

import gradio as gr
import transformers
from model_config import PragFormerConfig
from model import Pragformer
from simpletransformers.classification import ClassificationModel, ClassificationArgs
import torch
import json

# load all models
deep_scc_model_args = ClassificationArgs(num_train_epochs=10,max_seq_length=300,use_multiprocessing=False)
deep_scc_model = ClassificationModel("roberta", "NTUYG/DeepSCC-RoBERTa", num_labels=19, args=deep_scc_model_args, use_cuda=False)

pragformer_config = PragFormerConfig.from_pretrained("Pragformer/PragFormer")
pragformer = Pragformer.from_pretrained("Pragformer/PragFormer", config=pragformer_config)
pragformer_private = Pragformer.from_pretrained("Pragformer/PragFormer_private", config=pragformer_config)
pragformer_reduction = Pragformer.from_pretrained("Pragformer/PragFormer_reduction", config=pragformer_config)


#Event Listeners
with_omp_str = 'Should contain a parallel work-sharing loop construct'
without_omp_str = 'Should not contain a parallel work-sharing loop construct'
name_file = ['bash', 'c', 'c#', 'c++','css', 'haskell', 'java', 'javascript', 'lua', 'objective-c', 'perl', 'php', 'python','r','ruby', 'scala', 'sql', 'swift', 'vb.net']


tokenizer = transformers.AutoTokenizer.from_pretrained('NTUYG/DeepSCC-RoBERTa')

with open('./c_data.json', 'r') as f:
    data = json.load(f)

def fill_code(code_pth):
    pragma = data[code_pth]['pragma']
    code = data[code_pth]['code']
    return 'None' if len(pragma)==0 else pragma, code
    

def predict(code_txt):
    code = code_txt.lstrip().rstrip()
    tokenized = tokenizer.batch_encode_plus(
                [code],
                max_length = 150,
                pad_to_max_length = True,
                truncation = True
            )
    pred = pragformer(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))

    y_hat = torch.argmax(pred).item()
    return with_omp_str if y_hat==1 else without_omp_str, torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()


def is_private(code_txt):
    if predict(code_txt)[0] == without_omp_str:
        return gr.update(visible=False)

    code = code_txt.lstrip().rstrip()
    tokenized = tokenizer.batch_encode_plus(
                [code],
                max_length = 150,
                pad_to_max_length = True,
                truncation = True
            )
    pred = pragformer_private(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))

    y_hat = torch.argmax(pred).item()
    # if y_hat == 0:
    #     return gr.update(visible=False)
    # else:
    return gr.update(value=f"Should {'not' if y_hat==0 else ''} contain private with confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)


def is_reduction(code_txt):
    if predict(code_txt)[0] == without_omp_str:
        return gr.update(visible=False)

    code = code_txt.lstrip().rstrip()
    tokenized = tokenizer.batch_encode_plus(
                [code],
                max_length = 150,
                pad_to_max_length = True,
                truncation = True
            )
    pred = pragformer_reduction(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))

    y_hat = torch.argmax(pred).item()
    # if y_hat == 0:
    #     return gr.update(visible=False)
    # else:
    return gr.update(value=f"Should {'not' if y_hat==0 else ''} contain reduction with confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)


def lang_predict(code_txt):
    res = {}
    code = code_txt.replace('\n',' ').replace('\r',' ')
    predictions, raw_outputs = deep_scc_model.predict([code])
    # preds = [name_file[predictions[i]] for i in range(5)]
    softmax_vals = torch.nn.Softmax(dim=1)(torch.tensor(raw_outputs))
    top5 = torch.topk(softmax_vals, 5)

    for lang_idx, conf in zip(top5.indices.flatten(), top5.values.flatten()):
        res[name_file[lang_idx.item()]] = conf.item()

    return '\n'.join([f" {'V  ' if k=='c' else 'X'}{k}:   {v}" for k,v in res.items()])


# Define GUI
with gr.Blocks() as pragformer_gui:

    gr.Markdown(
        """
        # PragFormer Pragma Classifiction
        
        """)

    #with gr.Row(equal_height=True):
    with gr.Column():
        gr.Markdown("## Input")
        with gr.Row():
            with gr.Column():
                drop = gr.Dropdown(list(data.keys()), label="Mix of parallel and not-parallel code snippets", value="Minyoung-Kim1110/OpenMP/Excercise/atomic/0")
                sample_btn = gr.Button("Sample")
            
            pragma =  gr.Textbox(label="Original parallelization classification (if any)")
        with gr.Row():
            code_in = gr.Textbox(lines=5, label="Write some C code and see if it should contain a parallel work-sharing loop construct")
            lang_pred = gr.Textbox(lines=5, label="DeepScc programming language prediction")

        submit_btn = gr.Button("Submit")
    with gr.Column():
        gr.Markdown("## Results")

        with gr.Row():
            label_out = gr.Textbox(label="Label")
            confidence_out = gr.Textbox(label="Confidence")

        with gr.Row():
            private = gr.Textbox(label="Data-sharing attribute clause- private", visible=False)
            reduction = gr.Textbox(label="Data-sharing attribute clause- reduction", visible=False)

    code_in.change(fn=lang_predict, inputs=code_in, outputs=lang_pred)

    submit_btn.click(fn=predict, inputs=code_in, outputs=[label_out, confidence_out])
    submit_btn.click(fn=is_private, inputs=code_in, outputs=private)
    submit_btn.click(fn=is_reduction, inputs=code_in, outputs=reduction)
    sample_btn.click(fn=fill_code, inputs=drop, outputs=[pragma, code_in])

    gr.Markdown(
    """

    ## How it Works?

    To use the PragFormer tool, you will need to input a C language for-loop. You can either write your own code or use the samples
    provided in the dropdown menu, which have been gathered from GitHub. Once you submit the code, the PragFormer model will analyze
    it and predict whether the for-loop should be parallelized using OpenMP. If the PragFormer model determines that parallelization
    is necessary, two additional models will be used to determine if adding specific data-sharing attributes, such as ***private*** or ***reduction*** clauses, is needed.

    ***private***- Specifies that each thread should have its own instance of a variable. 

    ***reduction***- Specifies that one or more variables that are private to each thread are the subject of a reduction operation at 
    the end of the parallel region.


    ## Description
    
    In past years, the world has switched to many-core and multi-core shared memory architectures.
    As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes to software applications. 
    OpenMP is the most comprehensive API that implements such schemes, characterized by a readable interface. 
    Nevertheless, introducing OpenMP into code, especially legacy code, is challenging due to pervasive pitfalls in management of parallel shared memory. 
    To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into
     code automatically. 
    In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable
     code and generating appropriate directives.
    In this work, we propose leveraging recent advances in machine learning techniques, specifically in natural language processing (NLP), to replace S2S compilers altogether. 
    We create a database (corpus), OpenMP-OMP specifically for this goal.
    OpenMP-OMP contains over 28,000 code snippets, half of which contain OpenMP directives while the other half do not need parallelization at all with high probability. 
    We use the corpus to train systems to automatically classify code segments in need of parallelization, as well as suggest individual OpenMP clauses. 
    We train several transformer models, named PragFormer, for these tasks, and show that they outperform statistically-trained baselines and automatic S2S parallelization 
    compilers in both classifying the overall need for an OpenMP directive and the introduction of private and reduction clauses.

    ![](https://user-images.githubusercontent.com/104314626/165228036-d7fadd8d-768a-4e94-bd57-0a77e1330082.png)

    """)



pragformer_gui.launch()