init

README.md

@@ -1,3 +1,175 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- decompile
+- binary
+---
+
+### 1. Introduction of LLM4Decompile
+
+LLM4Decompile aims to decompile x86 assembly instructions into C. The newly released V1.5 series are trained with a larger dataset (15B tokens) and a maximum token length of 4,096, with remarkable performance (up to 100% improvement) compared to the previous model.
+
+- **Github Repository:** [LLM4Decompile](https://github.com/albertan017/LLM4Decompile)
+
+
+### 2. Evaluation Results
+
+|         Metrics         | Re-executability Rate |         |         |         |         | Edit Similarity |         |         |         |         |
+|:-----------------------:|:---------------------:|:-------:|:-------:|:-------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:-------:|
+|    Optimization Level   |           O0          |    O1   |    O2   |    O3   |   AVG   |        O0       |    O1   |    O2   |    O3   |   AVG   |
+|  LLM4Decompile-End-6.7B |        0.6805         | 0.3951  | 0.3671  | 0.3720  | 0.4537  |     0.1557      | 0.1292  | 0.1293  | 0.1269  | 0.1353  |
+|          Ghidra         |        0.3476         | 0.1646  | 0.1524  | 0.1402  | 0.2012  |     0.0699      | 0.0613  | 0.0619  | 0.0547  | 0.0620  |
+|         +GPT-4o         |        0.4695         | 0.3415  | 0.2866  | 0.3110  | 0.3522  |     0.0660      | 0.0563  | 0.0567  | 0.0499  | 0.0572  |
+| +LLM4Decompile-Ref-1.3B |        0.6890         | 0.3720  | 0.4085  | 0.3720  | 0.4604  |     0.1517      | 0.1325  | 0.1292  | 0.1267  | 0.1350  |
+| +LLM4Decompile-Ref-6.7B |        0.7439         | 0.4695  | 0.4756  | 0.4207  | 0.5274  |     0.1559      | 0.1353  | 0.1342  | 0.1273  | 0.1382  |
+|  +LLM4Decompile-Ref-33B |        0.7073         | 0.4756  | 0.4390  | 0.4146  | 0.5091  |     0.1540      | 0.1379  | 0.1363  | 0.1307  | 0.1397  |
+
+### 3. How to Use
+Here is an example of how to use our model (Only for V2. For previous models, please check the corresponding model page at HF).
+
+1. Install Ghidra
+Download [Ghidra](https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip) to the current folder. You can also check the [page](https://github.com/NationalSecurityAgency/ghidra/releases) for other versions. Unzip the package to the current folder.
+In bash, you can use the following:
+```bash
+cd LLM4Decompile/ghidra
+wget https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip
+unzip ghidra_11.0.3_PUBLIC_20240410.zip
+```
+2. Install Java-SDK-17
+Ghidra 11 is dependent on Java-SDK-17, a simple way to install the SDK on Ubuntu:
+```bash
+apt-get update
+apt-get upgrade
+apt install openjdk-17-jdk openjdk-17-jre
+```
+Please check [Ghidra install guide](https://htmlpreview.github.io/?https://github.com/NationalSecurityAgency/ghidra/blob/Ghidra_11.1.1_build/GhidraDocs/InstallationGuide.html) for other platforms. 
+
+3. Use Ghidra Headless to decompile binary (demo.py)
+
+Note: **Replace** func0 with the function name you want to decompile.
+
+**Preprocessing:** Compile the C code into binary, and disassemble the binary into assembly instructions.
+```python
+import os
+import subprocess
+from tqdm import tqdm,trange
+
+OPT = ["O0", "O1", "O2", "O3"]
+timeout_duration = 10
+
+ghidra_path = "./ghidra_11.0.3_PUBLIC/support/analyzeHeadless"#path to the headless analyzer, change the path accordingly
+postscript = "./decompile.py"#path to the decompiler helper function, change the path accordingly
+project_path = "."#path to temp folder for analysis, change the path accordingly
+project_name = "tmp_ghidra_proj"
+func_path = "../samples/sample.c"#path to c code for compiling and decompiling, change the path accordingly
+fileName = "sample"
+
+with tempfile.TemporaryDirectory() as temp_dir:
+    pid = os.getpid()
+    asm_all = {}
+    for opt in [OPT[0]]:
+        executable_path = os.path.join(temp_dir, f"{pid}_{opt}.o")
+        cmd = f'gcc -{opt} -o {executable_path} {func_path} -lm'
+        subprocess.run(
+        cmd.split(' '),
+        check=True,
+        stdout=subprocess.DEVNULL,  # Suppress stdout
+        stderr=subprocess.DEVNULL,  # Suppress stderr
+        timeout=timeout_duration,
+        )
+
+        output_path = os.path.join(temp_dir, f"{pid}_{opt}.c")
+        command = [
+            ghidra_path,
+            temp_dir,
+            project_name,
+            "-import", executable_path,
+            "-postScript", postscript, output_path,
+            "-deleteProject",  # WARNING: This will delete the project after analysis
+        ]
+        result = subprocess.run(command, text=True, capture_output=True, check=True)
+        with open(output_path,'r') as f:
+            c_decompile = f.read()
+        c_func = []
+        flag = 0
+        for line in c_decompile.split('\n'):
+            if "Function: func0" in line:#**Replace** func0 with the function name you want to decompile.
+                flag = 1
+                c_func.append(line)
+                continue
+            if flag:
+                if '// Function:' in line:
+                    if len(c_func) > 1:
+                        break
+                c_func.append(line)
+        if flag == 0:
+            raise ValueError('bad case no function found')
+        for idx_tmp in range(1,len(c_func)):##########remove the comments
+            if 'func0' in c_func[idx_tmp]:
+                break
+        c_func = c_func[idx_tmp:]
+        input_asm = '\n'.join(c_func).strip()
+
+        before = f"# This is the assembly code:\n"#prompt
+        after = "\n# What is the source code?\n"#prompt
+        input_asm_prompt = before+input_asm.strip()+after
+        with open(fileName +'_' + opt +'.pseudo','w',encoding='utf-8') as f:
+            f.write(input_asm_prompt)
+```
+
+Ghidra pseudo-code may look like this:
+```c
+undefined4 func0(float param_1,long param_2,int param_3)
+{
+  int local_28;
+  int local_24;
+  
+  local_24 = 0;
+  do {
+    local_28 = local_24;
+    if (param_3 <= local_24) {
+      return 0;
+    }
+    while (local_28 = local_28 + 1, local_28 < param_3) {
+      if ((double)((ulong)(double)(*(float *)(param_2 + (long)local_24 * 4) -
+                                  *(float *)(param_2 + (long)local_28 * 4)) &
+                  SUB168(_DAT_00402010,0)) < (double)param_1) {
+        return 1;
+      }
+    }
+    local_24 = local_24 + 1;
+  } while( true );
+}
+```
+4. Refine pseudo-code using LLM4Decompile (demo.py)
+
+**Decompilation:** Use LLM4Decompile-Ref to refine the Ghidra pseudo-code into C:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_path = 'LLM4Binary/llm4decompile-6.7b-v2' # V2 Model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()
+
+with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#optimization level O0
+    asm_func = f.read()
+inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
+c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
+
+with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#original file
+    func = f.read()
+
+print(f'pseudo function:\n{func}')# Note we only decompile one function, where the original file may contain multiple functions
+print(f'refined function:\n{c_func_decompile}')
+
+```
+
+### 4. License
+This code repository is licensed under the MIT License.
+
+### 5. Contact
+
+If you have any questions, please raise an issue.

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 6144,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 48,
+  "num_hidden_layers": 56,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.2",
+  "use_cache": true,
+  "vocab_size": 32768
+}

convert_mistral_weights_to_hf-22B.py

@@ -0,0 +1,290 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import json
+import os
+import shutil
+import warnings
+
+import torch
+from safetensors.torch import load_file as safe_load_file
+
+from transformers import (
+    LlamaTokenizer,
+    MistralConfig,
+    MistralForCausalLM,
+)
+
+
+try:
+    from transformers import LlamaTokenizerFast
+
+    tokenizer_class = LlamaTokenizerFast
+except ImportError as e:
+    warnings.warn(e)
+    warnings.warn(
+        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
+    )
+    tokenizer_class = LlamaTokenizer
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
+    --input_dir /path/to/downloaded/mistral/weights --model_size 22B --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import MistralForCausalLM, LlamaTokenizer
+
+model = MistralForCausalLM.from_pretrained("/output/path")
+tokenizer = LlamaTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+NUM_SHARDS = {"22B": 1}
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def write_json(text, path):
+    with open(path, "w") as f:
+        json.dump(text, f)
+
+
+def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True, is_v3=False):
+    # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
+    if not os.path.isfile(os.path.join(input_base_path, "params.json")):
+        input_base_path = os.path.join(input_base_path, model_size)
+
+    os.makedirs(model_path, exist_ok=True)
+    tmp_model_path = os.path.join(model_path, "tmp")
+    os.makedirs(tmp_model_path, exist_ok=True)
+
+    params = read_json(os.path.join(input_base_path, "params.json"))
+    num_shards = NUM_SHARDS[model_size]
+
+    sliding_window = params.get("sliding_window", None)
+
+    # For some reason this is a string in the params.json
+    if sliding_window is not None:
+        sliding_window = int(sliding_window)
+
+    n_layers = params["n_layers"]
+    n_heads = params["n_heads"]
+    n_heads_per_shard = n_heads // num_shards
+    dim = params["dim"]
+    dims_per_head = dim // n_heads
+    base = params.get("rope_theta", 10000.0)
+    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+    max_position_embeddings = 4096 * 8
+
+    if tokenizer_path is not None:
+        tokenizer = tokenizer_class(tokenizer_path + ".v3" if is_v3 else "")
+        tokenizer.save_pretrained(model_path)
+    vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
+
+    if "n_kv_heads" in params:
+        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
+        num_local_key_value_heads = num_key_value_heads // num_shards
+        key_value_dim = dims_per_head * num_local_key_value_heads
+    else:  # compatibility with other checkpoints
+        num_key_value_heads = n_heads
+        num_local_key_value_heads = n_heads_per_shard
+        key_value_dim = dim
+
+    # permute for sliced rotary
+    def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
+        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+    # Load weights - for v3 models the consolidated weights are in a single file format in safetensors
+    if is_v3:
+        loaded = [safe_load_file(os.path.join(input_base_path, "consolidated.safetensors"))]
+    else:
+        loaded = [
+            torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
+            for i in range(num_shards)
+        ]
+    param_count = 0
+    index_dict = {"weight_map": {}}
+    for layer_i in range(n_layers):
+        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+
+        # Sharded
+        # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
+        # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
+        # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
+
+        state_dict = {
+            f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
+                f"layers.{layer_i}.attention_norm.weight"
+            ].clone(),
+            f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
+                f"layers.{layer_i}.ffn_norm.weight"
+            ].clone(),
+        }
+        state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
+            torch.cat(
+                [
+                    loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
+                    for i in range(num_shards)
+                ],
+                dim=0,
+            ).reshape(dim, dim)
+        )
+        state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
+            torch.cat(
+                [
+                    loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
+                        num_local_key_value_heads, dims_per_head, dim
+                    )
+                    for i in range(num_shards)
+                ],
+                dim=0,
+            ).reshape(key_value_dim, dim),
+            num_key_value_heads,
+            key_value_dim,
+            dim,
+        )
+        state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
+            [
+                loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_local_key_value_heads, dims_per_head, dim)
+                for i in range(num_shards)
+            ],
+            dim=0,
+        ).reshape(key_value_dim, dim)
+
+        state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
+        )
+
+        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+        for k, v in state_dict.items():
+            index_dict["weight_map"][k] = filename
+            param_count += v.numel()
+        torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+    state_dict = {
+        "model.norm.weight": loaded[0]["norm.weight"],
+        "model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1),
+        "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
+    }
+
+    for k, v in state_dict.items():
+        index_dict["weight_map"][k] = filename
+        param_count += v.numel()
+    torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    # Write configs
+    index_dict["metadata"] = {"total_size": param_count * 2}
+    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+    config = MistralConfig(
+        hidden_size=dim,
+        intermediate_size=params["hidden_dim"],
+        num_attention_heads=params["n_heads"],
+        num_hidden_layers=params["n_layers"],
+        rms_norm_eps=params["norm_eps"],
+        num_key_value_heads=num_key_value_heads,
+        vocab_size=vocab_size,
+        rope_theta=base,
+        max_position_embeddings=max_position_embeddings,
+        sliding_window=sliding_window,
+    )
+    config.save_pretrained(tmp_model_path)
+
+    # Make space so we can load the model properly now.
+    del state_dict
+    del loaded
+    gc.collect()
+
+    print("Loading the checkpoint in a Mistral model.")
+    model = MistralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
+    # Avoid saving this as part of the config.
+    del model.config._name_or_path
+    model.config.torch_dtype = torch.float16
+    print("Saving in the Transformers format.")
+
+    model.save_pretrained(model_path, safe_serialization=safe_serialization)
+    shutil.rmtree(tmp_model_path)
+
+
+def write_tokenizer(tokenizer_path, input_tokenizer_path):
+    # Initialize the tokenizer based on the `spm` model
+    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
+    tokenizer = tokenizer_class(input_tokenizer_path)
+    tokenizer.save_pretrained(tokenizer_path)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_dir",
+        help="Location of Mistral weights, which contains tokenizer.model and model folders",
+    )
+    parser.add_argument(
+        "--model_size",
+        choices=["22B", "tokenizer_only"],
+        help="'f' models correspond to the finetuned versions, and are specific to the Mistral2 official release. For more details on Mistral2, checkout the original repo: https://huggingface.co/meta-mistral",
+    )
+    parser.add_argument(
+        "--output_dir",
+        help="Location to write HF model and tokenizer",
+    )
+    parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+    parser.add_argument(
+        "--is_v3", action="store_true", help="Whether the checkpoints correspond to the 3rd version or not."
+    )
+    args = parser.parse_args()
+    spm_path = os.path.join(args.input_dir, "tokenizer.model")
+    if args.model_size != "tokenizer_only":
+        write_model(
+            model_path=args.output_dir,
+            input_base_path=args.input_dir,
+            model_size=args.model_size,
+            safe_serialization=args.safe_serialization,
+            tokenizer_path=spm_path,
+            is_v3=args.is_v3,
+        )
+    else:
+        write_tokenizer(args.output_dir, spm_path)
+
+
+if __name__ == "__main__":
+    main()

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.2"
+}

model-00001-of-00009.safetensors

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