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README.md

@@ -0,0 +1,18 @@
+# WaveDetect Inference
+
+Usage:
+
+```python
+from wavedetect_hf import WaveDetectPredictor
+
+predictor = WaveDetectPredictor(model_dir="hf_wavedetect")
+
+result = predictor.predict("This is a sample text to be detected.")
+print(result)
+```
+
+Run the example directly:
+
+```bash
+python hf_wavedetect/wavedetect_hf.py
+```

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

@@ -0,0 +1 @@
+{}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.37.0"
+}

requirements.txt

@@ -0,0 +1,3 @@
+torch
+transformers
+tqdm

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

wavedetect_all.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68d100bfa9f7a9081627b55e988963bd230628f8cc62e1913ef85d44fdbab096
+size 1010614711

wavedetect_hf.py

@@ -0,0 +1,158 @@
+import math
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    PreTrainedModel,
+    PretrainedConfig,
+    ResNetConfig,
+    ResNetModel,
+)
+
+
+class WaveDetectConfig(PretrainedConfig):
+    model_type = "wavedetect"
+
+    def __init__(self, base_model_path, num_scales=16, kernel_size=128, n_labels=2, **kwargs):
+        super().__init__(**kwargs)
+        self.base_model_path = base_model_path
+        self.num_scales = num_scales
+        self.kernel_size = kernel_size
+        self.n_labels = n_labels
+
+
+def build_resnet18_config():
+    return ResNetConfig(
+        num_channels=3,
+        embedding_size=64,
+        hidden_sizes=[64, 128, 256, 512],
+        depths=[2, 2, 2, 2],
+        layer_type="basic",
+        hidden_act="relu",
+        downsample_in_first_stage=False,
+    )
+
+
+class WaveletTransform(nn.Module):
+    def __init__(self, num_scales=16, kernel_size=128):
+        super().__init__()
+        self.num_scales = num_scales
+        self.kernel_size = kernel_size
+        self.sigma = 1.0
+        self.freq0 = 5.0
+        self.register_buffer("time", torch.linspace(-2, 2, steps=kernel_size))
+        self.register_buffer("scales", torch.linspace(1.0, num_scales, num_scales))
+
+    def morlet(self, scale):
+        t = self.time / scale
+        wave = torch.exp(-0.5 * (t / self.sigma) ** 2) * torch.cos(2 * math.pi * self.freq0 * t)
+        wave = wave / (wave.abs().sum() + 1e-6)
+        return wave.view(1, 1, -1)
+
+    def forward(self, token_probs, attention_mask):
+        batch_size, seq_len = token_probs.shape
+        spectra = []
+
+        for i in range(batch_size):
+            valid_len = attention_mask[i].sum().long().item()
+            x = token_probs[i, :valid_len].view(1, 1, -1)
+            rows = []
+            for scale in self.scales:
+                kernel = self.morlet(scale.to(x.device)).to(x.dtype)
+                rows.append(torch.nn.functional.conv1d(x, kernel, padding=self.kernel_size // 2))
+            spectrum = torch.stack(rows, dim=2)
+            if valid_len < seq_len:
+                spectrum = torch.nn.functional.pad(spectrum, (0, seq_len - valid_len))
+            spectra.append(spectrum)
+
+        return torch.cat(spectra, dim=0).abs()
+
+
+class WaveDetect(PreTrainedModel):
+    config_class = WaveDetectConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+        base_config = AutoConfig.from_pretrained(config.base_model_path, trust_remote_code=True)
+
+        self.qwen_model = AutoModelForCausalLM.from_config(base_config).to(dtype)
+        self.wavelet = WaveletTransform(config.num_scales, config.kernel_size)
+        self.cnn = ResNetModel(build_resnet18_config()).to(dtype)
+        self.classifier = nn.Linear(512, config.n_labels)
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.qwen_model(input_ids=input_ids, attention_mask=attention_mask)
+        logits = outputs.logits[:, :-1]
+        labels = input_ids[:, 1:]
+        mask = attention_mask[:, :-1]
+
+        log_probs = torch.log_softmax(logits, dim=-1)
+        token_probs = torch.exp(log_probs.gather(2, labels.unsqueeze(-1)).squeeze(-1)) * mask
+
+        spectrum = self.wavelet(token_probs, mask).repeat(1, 3, 1, 1)
+        cnn_out = self.cnn(spectrum, return_dict=True)
+        pooled = cnn_out.last_hidden_state.mean(dim=[2, 3]).to(self.classifier.weight.dtype)
+        return self.classifier(pooled)
+
+
+def clean_state_dict(state_dict):
+    if "state_dict" in state_dict:
+        state_dict = state_dict["state_dict"]
+    return {k.removeprefix("module.").removeprefix("_orig_mod."): v for k, v in state_dict.items()}
+
+
+class WaveDetectPredictor:
+    def __init__(self, model_dir=None, device=None, max_length=1024):
+        self.model_dir = Path(model_dir or Path(__file__).resolve().parent)
+        self.device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
+        self.max_length = max_length
+
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_dir, trust_remote_code=True)
+        if self.tokenizer.pad_token_id is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        config = WaveDetectConfig(base_model_path=str(self.model_dir))
+        self.model = WaveDetect(config).to(self.device)
+        state_dict = torch.load(self.model_dir / "wavedetect_all.bin", map_location="cpu")
+        self.model.load_state_dict(clean_state_dict(state_dict), strict=True)
+        self.model.eval()
+
+    @torch.no_grad()
+    def predict(self, text):
+        inputs = self.tokenizer(
+            text,
+            return_tensors="pt",
+            truncation=True,
+            max_length=self.max_length,
+            padding=True,
+        ).to(self.device)
+
+        logits = self.model(inputs["input_ids"], inputs["attention_mask"])
+        probs = torch.softmax(logits, dim=-1)[0]
+        ai_score = float(probs[1].detach().cpu())
+
+        return {
+            "label": int(ai_score >= 0.5),
+            "label_name": "ai_generated" if ai_score >= 0.5 else "human",
+            "ai_score": ai_score,
+        }
+
+
+def predict(text, model_dir=None):
+    return WaveDetectPredictor(model_dir=model_dir).predict(text)
+
+
+def main():
+    predictor = WaveDetectPredictor()
+    text = "Clara folded the letter and stood up. The hallway was dark, but a thin line of light shone from beneath the locked door upstairs."
+    print(predictor.predict(text))
+
+
+if __name__ == "__main__":
+    main()