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Model: XXXiong/ChatHLS-HLSFixer Source: Original Platform
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README.md
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README.md
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- chat
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- llm
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- safetensors
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base_model: Qwen/Qwen2.5-14B-Instruct
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language:
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- en
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---
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# ChatHLS-HLSFixer
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## Model Details
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- **Finetuned from model:** `Qwen/Qwen2.5-14B-Instruct`
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- **Model source paper:** [ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis](https://arxiv.org/abs/2507.00642)
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- **Project repository:** [GEAR-SEU/ChatHLS-ACL-26](https://github.com/GEAR-SEU/ChatHLS-ACL-26)
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## Description
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**ChatHLS-HLSFixer** is a specialized Large Language Model fine-tuned for High-Level Synthesis (HLS) C/C++ error correction. It acts as the core analysis agent within the ChatHLS framework.
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## Quickstart
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### Prerequisites
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```bash
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pip install transformers torch accelerate
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```
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### Example
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Here is a simple example of how to format your prompt and generate debugging instructions for buggy HLS code:
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````python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "XXXiong/ChatHLS-HLSFixer"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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buggy_code = """
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#include "ap_fixed.h"
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void vector_mul(ap_fixed<32,16> a[8], ap_fixed<32,16> b[8], ap_fixed<32,16> c[8]) {
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#pragma HLS ARRAY_PARTITION variable=a complete dim=1
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#pragma HLS ARRAY_PARTITION variable=b complete dim=1
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#pragma HLS ARRAY_PARTITION variable=c complete dim=1
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for (int i = 0; i < 8; ++i) {
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#pragma HLS UNROLL
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#pragma HLS PIPELINE II=1
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c[i] = a[i] * b[i];
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}
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}
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"""
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error_log = """
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"ERROR: [HLS 214-274] In 'VITIS_LOOP_7_1', Pragma conflict happens on 'UNROLL' and 'PIPELINE' pragmas: Complete unroll will break the target of other loop pragmas (vector_mul.cpp:7:18)",
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"ERROR: [HLS 200-1715] Encountered problem during source synthesis"
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"""
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prompt = f"""You are given a piece of HLS code that contains bugs, the compiler error log, and the description of the buggy code.
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Your task is to debug the buggy code by analyzing the differences between it and the original code, using the error log to guide you.
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Generate the COT debugging strategy through the buggy code, the code's description and error log.
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**Instructions:**
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1. **Debug the Code**:
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- **Identify Errors**: List the erroneous code line in the buggy code.
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- **Analyze Reasons**: Explain why each bug occurred, referencing the error log and the differences from the original code.
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- **Propose Fixes**: Specify the exact changes needed to fix each bug, including code modifications.
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2. **Construct a Chain of Thought (CoT)**:
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- **Sequential Reasoning**: Address one error at a time, starting with syntax errors from the compiler.
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- **Hypothesis Formation**: Hypothesize why logical errors occur based on the error log and code comparison.
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- **Testing and Verification**: Validate hypotheses through analysis.
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- **Reflection**: Reflect on the findings after each step and plan the next steps.
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3. **Output**:
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- **Chain of Thought (CoT)**: Write a detailed explanation of your debugging process.
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- **Bug Analysis in JSON Format**: Provide a structured summary as per the format below.
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- **No other explanations or conclusions**
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**Inputs:**
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Buggy Code:
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```cpp
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{buggy_code}
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```
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Vitis HLS Compiler Error Log:
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```text
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{error_log}
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```
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**Expected Output Format:**
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Bug Analysis:
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```json
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{{
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"bug_analysis": [
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{{
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"erroneous_code_line": "[code line]",
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"reason": "[Explanation of why the bug occurred]",
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"modification_actions": [
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{{
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"action": "[Specific modification to be made]",
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"reason": "[Reason for the modification]"
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}}
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]
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}}
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]
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}}
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```
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Please ensure that the JSON is properly formatted and valid.
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"""
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messages = [
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{"role": "system",
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"content": """You are an expert in High-Level Synthesis (HLS) programming.
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Your primary task is to identify and debug HLS-incompatible errors in regular C/C++ programs. You will be provided with:
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- **Buggy Code**: A C/C++ program containing HLS-incompatible errors.
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- **Compiler Error Log**: The error messages generated by the Vitis HLS compiler."""},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048,
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temperature=0.7
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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````
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