app.py

import gradio as gr
import json
import os
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

import huggingface_hub

import prep_decompiled

description = """# ReSym Test Space

This is a test space of the models from the [ReSym
artifacts](https://github.com/lt-asset/resym).  Sadly, at the time I am writing
this, not all of ReSym is publicly available; specifically, the Prolog component
is [not available](https://github.com/lt-asset/resym/issues/2).

This space simply performs inference on the two pretrained models available as
part of the ReSym artifacts. It takes a variable name and some decompiled code
as input, and outputs the variable type and other information.

## Todo

* Add support for FieldDecoder model

"""

hf_key = os.environ["HF_TOKEN"]
huggingface_hub.login(token=hf_key)

tokenizer = AutoTokenizer.from_pretrained(
    "bigcode/starcoderbase-3b"
)
vardecoder_model = AutoModelForCausalLM.from_pretrained(
    "ejschwartz/resym-vardecoder", torch_dtype=torch.bfloat16, device_map="auto"
)

example = """{
  "input": "What are the original name and data type of variables `a1`, `a2`, `k`, `j`, `i`?\n```\n_BYTE *__fastcall sub_4022CD(_BYTE *a1, __int64 a2)\n{\n_BYTE *result; // rax\n__int16 v4; // [rsp+1Ch] [rbp-14h]\nunsigned __int16 v5; // [rsp+1Eh] [rbp-12h]\nunsigned __int16 v6; // [rsp+20h] [rbp-10h]\nunsigned __int16 v7; // [rsp+22h] [rbp-Eh]\nunsigned int k; // [rsp+24h] [rbp-Ch]\nunsigned int j; // [rsp+28h] [rbp-8h]\nunsigned int i; // [rsp+2Ch] [rbp-4h]\n\nfor ( i = 0; i <= 2; ++i )\n{\nfor ( j = 0; j <= 0x3F; ++j )\n{\nfor ( k = 0; k <= 3; ++k )\n{\n*(&v4 + k) = *(_WORD *)(a2 + 2 * (k + 4 * j + ((unsigned __int64)i << 8)));\n*(&v4 + k) += (*(&v4 + k) >> 15) & 0xD01;\n*(&v4 + k) = ((((unsigned __int16)*(&v4 + k) << 10) + 1664) / 0xD01u) & 0x3FF;\n}\n*a1 = v4;\na1[1] = (4 * v5) | HIBYTE(v4);\na1[2] = (16 * v6) | (v5 >> 6);\na1[3] = ((_BYTE)v7 << 6) | (v6 >> 4);\nresult = a1 + 4;\na1[4] = v7 >> 2;\na1 += 5;\n}\n}\nreturn result;\n}\n```",
  "output": "a1: r, uint8_t*\na2: a, const polyvec*\nk: t, uint16_t\nj: -, -\ni: k, unsigned int",
  "funname": "pqcrystals_kyber768_ref_polyvec_compress",
  "bin": "6ea440a6c772bc0d6a6089c9ff33ae31da13daf3b72acbe175674b0bb21987ed",
  "proj": "pq-crystals/kyber",
  "cluster_var": {
    "array": [
      [
        "k",
        "j"
      ]
    ]
  }
}"""


@spaces.GPU
def infer(var_name, code):

    splitcode = code.splitlines()
    comments = prep_decompiled.extract_comments(splitcode)
    sig = prep_decompiled.parse_signature(splitcode)
    print(f"comments={comments} sig={sig}")

    #line = json.loads(input)
    #first_token = line["output"].split(":")[0]
    prompt = code + var_name + ":"

    input_ids = tokenizer.encode(prompt, return_tensors="pt").cuda()[:, : 8192 - 1024]
    output = vardecoder_model.generate(
        input_ids=input_ids,
        max_new_tokens=1024,
        num_beams=4,
        num_return_sequences=1,
        do_sample=False,
        early_stopping=False,
        pad_token_id=0,
        eos_token_id=0,
    )[0]
    output = tokenizer.decode(
        output[input_ids.size(1) :],
        skip_special_tokens=True,
        clean_up_tokenization_spaces=True,
    )

    output = var_name + ":" + output
    return output


demo = gr.Interface(
    fn=infer,
    inputs=[
        gr.Text(label="First Token", value="a1"),
        gr.Textbox(lines=10, value=json.loads(example)['input']),
    ],
    outputs=gr.Text(label="Var Decoder Output"),
    description=description
)
demo.launch()