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license: apache-2.0
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# Virtual Compiler Is All You Need For Assembly Code Search
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## Introduction
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This repo contains the models and the corresponding evaluation datasets of ACL 2024 paper "Virtual Compiler Is All You Need For Assembly Code Search".
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A virtual compiler is a LLM that is capable of compiling any programming language into underlying assembly code. The virtual compiler model is available at [elsagranger/VirtualCompiler](https://huggingface.co/elsagranger/VirtualCompiler), based on 34B CodeLlama.
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We evaluate the similiarity of the virtual assembly code generated by the virtual compiler and the real assembly code using force execution by script [force-exec.py](./force_exec.py), the corresponding evaluation dataset is avaiable at [virtual_assembly_and_ground_truth](./virtual_assembly_and_ground_truth).
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We evaluate the effective of the virtual compiler throught downstream task -- assembly code search, the evaluation dataset is avaiable at [elsagranger/
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## Usage
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We use FastChat and vllm worker to host the model. Run these following commands in seperate terminals, such as `tmux`.
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```shell
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LOGDIR="" python3 -m fastchat.serve.openai_api_server \
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--host 0.0.0.0 --port 8080 \
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--controller-address http://localhost:21000
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LOGDIR="" python3 -m fastchat.serve.controller \
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--host 0.0.0.0 --port 21000
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LOGDIR="" RAY_LOG_TO_STDERR=1 \
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python3 -m fastchat.serve.vllm_worker \
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--model-path ./VirtualCompiler \
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--num-gpus 8 \
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--controller http://localhost:21000 \
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--max-num-batched-tokens 40960 \
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--disable-log-requests \
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--host 0.0.0.0 --port 22000 \
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--worker-address http://localhost:22000 \
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--model-names "VirtualCompiler"
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```
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Then with the model hosted, use `do_request.py` to make request to the model.
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```shell
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~/C/VirtualCompiler (main)> python3 do_request.py
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test rdx, rdx
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setz al
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movzx eax, al
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neg eax
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retn
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```
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---
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license: apache-2.0
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---
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# Virtual Compiler Is All You Need For Assembly Code Search
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## Introduction
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This repo contains the models and the corresponding evaluation datasets of ACL 2024 paper "Virtual Compiler Is All You Need For Assembly Code Search".
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A virtual compiler is a LLM that is capable of compiling any programming language into underlying assembly code. The virtual compiler model is available at [elsagranger/VirtualCompiler](https://huggingface.co/elsagranger/VirtualCompiler), based on 34B CodeLlama.
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We evaluate the similiarity of the virtual assembly code generated by the virtual compiler and the real assembly code using force execution by script [force-exec.py](./force_exec.py), the corresponding evaluation dataset is avaiable at [virtual_assembly_and_ground_truth](./virtual_assembly_and_ground_truth).
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We evaluate the effective of the virtual compiler throught downstream task -- assembly code search, the evaluation dataset is avaiable at [elsagranger/AssemblyCodeSearchEval](https://huggingface.co/datasets/elsagranger/AssemblyCodeSearchEval).
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## Usage
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We use FastChat and vllm worker to host the model. Run these following commands in seperate terminals, such as `tmux`.
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```shell
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LOGDIR="" python3 -m fastchat.serve.openai_api_server \
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--host 0.0.0.0 --port 8080 \
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--controller-address http://localhost:21000
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LOGDIR="" python3 -m fastchat.serve.controller \
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--host 0.0.0.0 --port 21000
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LOGDIR="" RAY_LOG_TO_STDERR=1 \
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python3 -m fastchat.serve.vllm_worker \
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--model-path ./VirtualCompiler \
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--num-gpus 8 \
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--controller http://localhost:21000 \
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--max-num-batched-tokens 40960 \
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--disable-log-requests \
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--host 0.0.0.0 --port 22000 \
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--worker-address http://localhost:22000 \
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--model-names "VirtualCompiler"
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```
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Then with the model hosted, use `do_request.py` to make request to the model.
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```shell
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~/C/VirtualCompiler (main)> python3 do_request.py
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test rdx, rdx
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setz al
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movzx eax, al
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neg eax
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retn
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```
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## Assembly Code Search Encoder
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As huggingface does not support load a remote model inside a folder, we host the model trained on the assembly code search dataset augmented by the Virtual Compiler in [vic-encoder](https://cloud.vul337.team:9443/s/t5Ltt8gy7kPfyw8). You can use the `model.py` to test the custom model loading.
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Here is a example on text encoder and asm encoder. Please refer to this script on how to extract the assembly code from the binary: [process_asm.py](https://github.com/Hustcw/CLAP/blob/main/scripts/process_asm.py).
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```python
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def calc_map_at_k(logits, pos_cnt, ks=[10,]):
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_, indices = torch.sort(logits, dim=1, descending=True)
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# [batch_size, pos_cnt]
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ranks = torch.nonzero(
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indices < pos_cnt,
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as_tuple=False
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)[:, 1].reshape(logits.shape[0], -1)
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# [batch_size, pos_cnt]
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mrr = torch.mean(1 / (ranks + 1), dim=1)
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res = {}
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for k in ks:
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res[k] = (
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torch.sum((ranks < k).float(), dim=1) / min(k, pos_cnt)
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).cpu().numpy()
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return ranks.cpu().numpy(), res, mrr.cpu().numpy()
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pos_asm_cnt = 1
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query = ["List all files in a directory"]
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anchor_asm = [...]
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neg_anchor_asm = [...]
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query_embs = text_encoder(**text_tokenizer(query))
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asm_embs = asm_encoder(**asm_tokenizer(anchor_asm))
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asm_neg_emb = asm_encoder(**asm_tokenizer(neg_anchor_asm))
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# query_embs: [query_cnt, emb_dim]
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# asm_embs: [pos_asm_cnt, emb_dim]
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# logits_pos: [query_cnt, pos_asm_cnt]
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logits_pos = torch.einsum(
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"ic,jc->ij", [query_embs, asm_embs])
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# logits_neg: [query_cnt, neg_asm_cnt]
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logits_neg = torch.einsum(
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"ic,jc->ij", [query_embs, asm_neg_emb[pos_asm_cnt:]]
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)
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logits = torch.cat([logits_pos, logits_neg], dim=1)
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ranks, map_at_k, mrr = calc_map_at_k(
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logits, pos_asm_cnt, [1, 5, 10, 20, 50, 100])
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```
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