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.github/workflows/build-docker.yml

@@ -0,0 +1,68 @@
+name: Build and Publish Docker Image
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - 'main'
+  release:
+    types: [published]
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+
+    env:
+      # Set up environment variables for the job
+      DOCKER_REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}
+      TAG: ${{ github.sha }}
+
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
+
+      # Log in to the GitHub Container Registry only when not running on a pull request event
+      - name: Login to Docker Registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.DOCKER_REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.DOCKER_REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      # Build and push the Docker image to GHCR for the main branch or specific tags
+      - name: Build and Push Docker Image
+        if: github.ref == 'refs/heads/main'
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: Dockerfile
+          push: true
+          tags: ${{ env.DOCKER_REGISTRY }}/${{ env.IMAGE_NAME }}:latest
+          labels: version=${{ github.run_id }}
+
+      # For tagged releases, build and push the Docker image with the corresponding tag
+      - name: Build and Push Docker Image (Tagged)
+        if: startsWith(github.ref, 'refs/tags/')
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: Dockerfile
+          push: true
+          tags: ${{ env.DOCKER_REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.ref_name }}
+          labels: version=${{ github.run_id }}

.gitignore

@@ -0,0 +1,164 @@
+repos/
+.env
+test_moderations.py
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Dockerfile

@@ -0,0 +1,14 @@
+FROM python:3-slim
+
+RUN apt update && apt install -y git git-lfs
+
+RUN mkdir -p /app/repos
+WORKDIR /app
+RUN git clone https://huggingface.co/ifmain/moderation_by_embeddings /app/repos/moderation_by_embeddings
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY *.py .
+
+RUN apt clean && rm -rf /var/lib/apt/lists/*
+RUN python moderations.py --test-load
+CMD python moderations.py

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
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+
+  Developers that use our General Public Licenses protect your rights
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+you this License which gives you legal permission to copy, distribute
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+
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+improvements made in alternate versions of the program, if they
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+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
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+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
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+
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README.md

@@ -0,0 +1,40 @@
+OpenedAI Moderations
+--------------------
+
+An OpenAI API compatible moderations server for checking whether text is potentially harmful.
+
+This server is built using [moderation by embeddings](https://huggingface.co/ifmain/moderation_by_embeddings) by [ifmain (Mike Afton)](https://huggingface.co/ifmain) and FastAPI.
+
+This is not affiliated with OpenAI in any way, and no OpenAI API key is required.
+
+Quickstart
+----------
+
+Docker (**recommended**):
+```shell
+docker compose up
+```
+or:
+```shell
+apt install git git-lfs
+git clone https://huggingface.co/ifmain/moderation_by_embeddings repos/moderation_by_embeddings
+pip install -r requirements.txt
+python moderations.py --host 127.0.0.1 --port 5002
+```
+
+You can use the OpenAI client to interact with the API.
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://127.0.0.1:5002/v1", api_key='skip')
+moderation = client.moderations.create(input="I want to kill them.")
+print(moderation.results[0])
+```
+
+Links & Documentation
+---------------------
+
+- Swagger API docs are available locally via /docs, here: (http://localhost:5002/docs) if you are using the defaults.
+- OpenAI Moderations Guide: (https://platform.openai.com/docs/guides/moderation)
+- OpenAI Moderations API Reference: (https://platform.openai.com/docs/api-reference/moderations)
+- Moderation Model: [moderation by embeddings](https://huggingface.co/ifmain/moderation_by_embeddings) by [ifmain (Mike Afton)](https://huggingface.co/ifmain)
+- Embedding model: (https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)

cpu.py

@@ -0,0 +1,130 @@
+from gevent import pywsgi
+import dotenv
+dotenv.load_dotenv(override=True)
+
+import sys
+import time
+import argparse
+import uvicorn
+from typing import Union
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import openedai
+import numpy as np
+import onnxruntime as ort
+import asyncio
+from optimum.bettertransformer import BetterTransformer
+
+app = openedai.OpenAIStub()
+moderation = None
+device = "cpu" if torch.cuda.is_available() else "cpu"
+#device = "cpu"
+
+labels = ['hate', 
+          'hate_threatening', 
+          'harassment', 
+          'harassment_threatening', 
+          'self_harm', 
+          'self_harm_intent', 
+          'self_harm_instructions',
+          'sexual',
+          'sexual_minors',
+          'violence',
+          'violence_graphic',
+         ]
+
+label2id = {l:i for i, l in enumerate(labels)}
+id2label = {i:l for i, l in enumerate(labels)}
+model_name = "/root/autodl-tmp/duanyu027/moderation_0628"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification")
+model = torch.quantization.quantize_dynamic(
+	model, {torch.nn.Linear}, dtype=torch.qint8
+)
+torch.set_num_threads(1)
+class ModerationsRequest(BaseModel):
+	model: str = "text-moderation-latest" # or "text-moderation-stable"
+	input: Union[str, list[str]]
+
+@app.post("/v1/moderations")
+async def moderations(request: ModerationsRequest):
+    results = {
+        "id": f"modr-{int(time.time()*1e9)}",
+        "model": "text-moderation-005",
+        "results": [],
+    }
+    if isinstance(request.input, str):
+        request.input = [request.input]
+    
+    thresholds = {
+        "sexual": 0.1,
+        "hate": 0.25,
+        "harassment": 0.5,
+        "self_harm": 0.25,
+        "sexual_minors": 0.5,
+        "hate_threatening": 0.2,
+        "violence_graphic": 0.25,
+        "self_harm_intent": 0.2,
+        "self_harm_instructions": 0.25,
+        "harassment_threatening": 0.1,
+        "violence": 0.25,
+    }
+
+    for text in request.input:
+        predictions = await predict(text, model, tokenizer)
+        category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))}
+        detect = {key: score > thresholds[key] for key, score in category_scores.items()}
+        detected = any(detect.values())
+
+        results['results'].append({
+            'flagged': detected,
+            'categories': detect,
+            'category_scores': category_scores,
+        })
+
+    return results
+def sigmoid(x):
+   return 1/(1 + np.exp(-x))
+    
+def parse_args(argv):
+	parser = argparse.ArgumentParser(description='Moderation API')
+	parser.add_argument('--host', type=str, default='0.0.0.0')
+	parser.add_argument('--port', type=int, default=5002)
+	parser.add_argument('--test-load', action='store_true')
+	return parser.parse_args(argv)
+    
+async def predict(text, model, tokenizer):
+    encoding = tokenizer.encode_plus(
+        text,
+        return_tensors='pt'
+    )
+    input_ids = encoding['input_ids'].to(device)
+    attention_mask = encoding['attention_mask'].to(device)
+    
+    # 运行模型预测在独立的线程中
+    def _predict():
+        with torch.no_grad():
+            outputs = model(input_ids, attention_mask=attention_mask)
+        return torch.sigmoid(outputs.logits)
+    
+    loop = asyncio.get_running_loop()
+    predictions = await loop.run_in_executor(None, _predict)
+    
+    # 清理 GPU 内存
+    del input_ids
+    del attention_mask
+    torch.cuda.empty_cache()
+    
+    return predictions
+# Main
+if __name__ == "__main__":
+
+	args = parse_args(sys.argv[1:])
+	# start API
+	print(f'Starting moderations[{device}] API on {args.host}:{args.port}', file=sys.stderr)
+	app.register_model('text-moderations-latest', 'text-moderations-stable')
+	app.register_model('text-moderations-005', 'text-moderations-ifmain')
+
+	if not args.test_load:
+		uvicorn.run(app, host=args.host, port=args.port)

docker-compose.yml

@@ -0,0 +1,20 @@
+services:
+  server:
+    build:
+      dockerfile: Dockerfile
+    image: ghcr.io/matatonic/openedai-moderations
+    ports:
+      - 5002:5002
+    command: ["python", "moderations.py", "--host", "0.0.0.0", "--port", "5002"]
+    # You can remove the following lines if you don't need GPU support
+    # Note: You need to have nvidia-docker installed and the host machine needs to have a GPU.
+    # Also, you need to have nvidia-container-toolkit installed on the host machine.
+    runtime: nvidia
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              #device_ids: ['0', '1'] # Select a gpu, or
+              count: all
+              capabilities: [gpu]

duanyu027/moderation_0628/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

duanyu027/moderation_0628/README.md

@@ -0,0 +1,199 @@
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
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+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
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+
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+
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+
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+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
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+
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+
+[More Information Needed]
+
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+
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+
+## Model Card Contact
+
+[More Information Needed]

duanyu027/moderation_0628/config.json

@@ -0,0 +1,70 @@
+{
+  "_name_or_path": "../models/0627-v2/checkpoint-7200/",
+  "architectures": [
+    "LongformerForSequenceClassification"
+  ],
+  "attention_mode": "longformer",
+  "attention_probs_dropout_prob": 0.1,
+  "attention_window": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "hate",
+    "1": "hate_threatening",
+    "2": "harassment",
+    "3": "harassment_threatening",
+    "4": "self_harm",
+    "5": "self_harm_intent",
+    "6": "self_harm_instructions",
+    "7": "sexual",
+    "8": "sexual_minors",
+    "9": "violence",
+    "10": "violence_graphic"
+  },
+  "ignore_attention_mask": false,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "harassment": 2,
+    "harassment_threatening": 3,
+    "hate": 0,
+    "hate_threatening": 1,
+    "self_harm": 4,
+    "self_harm_instructions": 6,
+    "self_harm_intent": 5,
+    "sexual": 7,
+    "sexual_minors": 8,
+    "violence": 9,
+    "violence_graphic": 10
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 4098,
+  "model_type": "longformer",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "onnx_export": false,
+  "pad_token_id": 1,
+  "problem_type": "multi_label_classification",
+  "sep_token_id": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "vocab_size": 50265
+}

duanyu027/moderation_0628/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7606fa3e1ad9a8ba45b392ad3498c956d562d86d46b099cc14768f3909d9923e
+size 594705868

duanyu027/moderation_0628/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

duanyu027/moderation_0628/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "LongformerTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

moderations.py

@@ -0,0 +1,131 @@
+from gevent import pywsgi
+import dotenv
+dotenv.load_dotenv(override=True)
+
+import sys
+import time
+import argparse
+import uvicorn
+from typing import Union
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import openedai
+import numpy as np
+import asyncio
+
+
+app = openedai.OpenAIStub()
+moderation = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#device = "cpu"
+
+labels = ['hate', 
+          'hate_threatening', 
+          'harassment', 
+          'harassment_threatening', 
+          'self_harm', 
+          'self_harm_intent', 
+          'self_harm_instructions',
+          'sexual',
+          'sexual_minors',
+          'violence',
+          'violence_graphic',
+         ]
+
+label2id = {l:i for i, l in enumerate(labels)}
+id2label = {i:l for i, l in enumerate(labels)}
+model_name = "/root/autodl-tmp/duanyu027/moderation_0628"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification")
+model.to(device)
+model.eval()
+#model = torch.quantization.quantize_dynamic(
+#	model, {torch.nn.Linear}, dtype=torch.qint8
+#)
+torch.set_num_threads(1)
+class ModerationsRequest(BaseModel):
+	model: str = "text-moderation-latest" # or "text-moderation-stable"
+	input: Union[str, list[str]]
+
+@app.post("/v1/moderations")
+async def moderations(request: ModerationsRequest):
+    results = {
+        "id": f"modr-{int(time.time()*1e9)}",
+        "model": "text-moderation-005",
+        "results": [],
+    }
+    if isinstance(request.input, str):
+        request.input = [request.input]
+    
+    thresholds = {
+        "sexual": 0.5,
+        "hate": 0.5,
+        "harassment": 0.5,
+        "self_harm": 0.5,
+        "sexual_minors": 0.9,
+        "hate_threatening": 0.9,
+        "violence_graphic": 0.9,
+        "self_harm_intent": 0.9,
+        "self_harm_instructions": 0.9,
+        "harassment_threatening": 0.9,
+        "violence": 0.5,
+    }
+
+    for text in request.input:
+        predictions = await predict(text, model, tokenizer)
+        category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))}
+        detect = {key: score > thresholds[key] for key, score in category_scores.items()}
+        detected = any(detect.values())
+
+        results['results'].append({
+            'flagged': detected,
+            'categories': detect,
+            'category_scores': category_scores,
+        })
+
+    return results
+def sigmoid(x):
+   return 1/(1 + np.exp(-x))
+    
+def parse_args(argv):
+	parser = argparse.ArgumentParser(description='Moderation API')
+	parser.add_argument('--host', type=str, default='0.0.0.0')
+	parser.add_argument('--port', type=int, default=5002)
+	parser.add_argument('--test-load', action='store_true')
+	return parser.parse_args(argv)
+    
+async def predict(text, model, tokenizer):
+    encoding = tokenizer.encode_plus(
+        text,
+        return_tensors='pt'
+    )
+    input_ids = encoding['input_ids'].to(device)
+    attention_mask = encoding['attention_mask'].to(device)
+    
+    # 运行模型预测在独立的线程中
+    def _predict():
+        with torch.no_grad():
+            outputs = model(input_ids, attention_mask=attention_mask)
+        return torch.sigmoid(outputs.logits)
+    
+    loop = asyncio.get_running_loop()
+    predictions = await loop.run_in_executor(None, _predict)
+    
+    # 清理 GPU 内存
+    del input_ids
+    del attention_mask
+    torch.cuda.empty_cache()
+    
+    return predictions
+# Main
+if __name__ == "__main__":
+
+	args = parse_args(sys.argv[1:])
+	# start API
+	print(f'Starting moderations[{device}] API on {args.host}:{args.port}', file=sys.stderr)
+	app.register_model('text-moderations-latest', 'text-moderations-stable')
+	app.register_model('text-moderations-005', 'text-moderations-ifmain')
+
+	if not args.test_load:
+		uvicorn.run(app, host=args.host, port=args.port)

moderations2.py

@@ -0,0 +1,147 @@
+from gevent import pywsgi
+import dotenv
+dotenv.load_dotenv(override=True)
+
+import sys
+import time
+import argparse
+import uvicorn
+from typing import Union
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import openedai
+import numpy as np
+
+app = openedai.OpenAIStub()
+moderation = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#device = "cpu"
+
+labels = ['hate', 
+          'hate_threatening', 
+          'harassment', 
+          'harassment_threatening', 
+          'self_harm', 
+          'self_harm_intent', 
+          'self_harm_instructions',
+          'sexual',
+          'sexual_minors',
+          'violence',
+          'violence_graphic',
+         ]
+
+label2id = {l:i for i, l in enumerate(labels)}
+id2label = {i:l for i, l in enumerate(labels)}
+model_name = "/root/autodl-tmp/duanyu027/moderation_0628"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification")
+model.to(device)
+#model = torch.quantization.quantize_dynamic(
+#	model, {torch.nn.Linear}, dtype=torch.qint8
+#)
+torch.set_num_threads(1)
+class ModerationsRequest(BaseModel):
+	model: str = "text-moderation-latest" # or "text-moderation-stable"
+	input: Union[str, list[str]]
+
+@app.post("/v1/moderations")
+async def moderations(request: ModerationsRequest):
+	"""
+Sample Response:
+{
+  "id": "modr-XXXXX",
+  "model": "text-moderation-005",
+  "results": [
+	{
+	  "flagged": true,
+	  "categories": {
+		"sexual": false,
+		"hate": false,
+		"harassment": false,
+		"self-harm": false,
+		"sexual/minors": false,
+		"hate/threatening": false,
+		"violence/graphic": false,
+		"self-harm/intent": false,
+		"self-harm/instructions": false,
+		"harassment/threatening": true,
+		"violence": true,
+	  },
+	  "category_scores": {
+		"sexual": 1.2282071e-06,
+		"hate": 0.010696256,
+		"harassment": 0.29842457,
+		"self-harm": 1.5236925e-08,
+		"sexual/minors": 5.7246268e-08,
+		"hate/threatening": 0.0060676364,
+		"violence/graphic": 4.435014e-06,
+		"self-harm/intent": 8.098441e-10,
+		"self-harm/instructions": 2.8498655e-11,
+		"harassment/threatening": 0.63055265,
+		"violence": 0.99011886,
+	  }
+	}
+  ]
+}
+"""
+	# This function will handle the moderations request
+	# proxy requests to openai embeddings api, check for similarity with pre-saved embeddings
+	results = {
+		"id": f"modr-{int(time.time()*1e9)}",
+		"model": "text-moderation-005",
+		"results": [],
+	}
+
+	# input, string or array
+	if isinstance(request.input, str):
+		request.input = [request.input]
+    # 定义阈值
+	threshold = 0.5
+	# minor name adjustments
+	for text in request.input:
+		predictions = predict(text, model, tokenizer)
+		category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))}
+		detect = {key: score > threshold for key, score in category_scores.items()}
+		detected = any(detect.values())
+
+		results['results'].extend([{
+			'flagged': detected,
+			'categories': detect,
+			'category_scores': category_scores,
+		}])
+	return results
+def sigmoid(x):
+   return 1/(1 + np.exp(-x))
+    
+def parse_args(argv):
+	parser = argparse.ArgumentParser(description='Moderation API')
+	parser.add_argument('--host', type=str, default='0.0.0.0')
+	parser.add_argument('--port', type=int, default=5002)
+	parser.add_argument('--test-load', action='store_true')
+	return parser.parse_args(argv)
+    
+def predict(text, model, tokenizer):
+    encoding = tokenizer.encode_plus(
+        text,
+        return_tensors='pt'
+    )
+    input_ids = encoding['input_ids'].to(device)
+    attention_mask = encoding['attention_mask'].to(device)
+    model.eval()
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+    #res = model(**input)
+    predictions = torch.sigmoid(outputs.logits)  # Convert logits to probabilities
+    return predictions
+# Main
+if __name__ == "__main__":
+
+	args = parse_args(sys.argv[1:])
+	# start API
+	print(f'Starting moderations[{device}] API on {args.host}:{args.port}', file=sys.stderr)
+	app.register_model('text-moderations-latest', 'text-moderations-stable')
+	app.register_model('text-moderations-005', 'text-moderations-ifmain')
+
+	if not args.test_load:
+		uvicorn.run(app, host=args.host, port=args.port)

onnx.py

@@ -0,0 +1,129 @@
+from gevent import pywsgi
+import dotenv
+dotenv.load_dotenv(override=True)
+
+import sys
+import time
+import argparse
+import uvicorn
+from typing import Union
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import openedai
+import numpy as np
+import onnxruntime as ort
+import asyncio
+
+app = openedai.OpenAIStub()
+moderation = None
+device = "cpu" if torch.cuda.is_available() else "cpu"
+#device = "cpu"
+
+labels = ['hate', 
+          'hate_threatening', 
+          'harassment', 
+          'harassment_threatening', 
+          'self_harm', 
+          'self_harm_intent', 
+          'self_harm_instructions',
+          'sexual',
+          'sexual_minors',
+          'violence',
+          'violence_graphic',
+         ]
+
+label2id = {l:i for i, l in enumerate(labels)}
+id2label = {i:l for i, l in enumerate(labels)}
+model_name = "/root/autodl-tmp/moderation_0703_deberta_v3_small_onnx"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = ort.InferenceSession(model_name + "/model.onnx")
+#model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification")
+torch.set_num_threads(1)
+class ModerationsRequest(BaseModel):
+	model: str = "text-moderation-latest" # or "text-moderation-stable"
+	input: Union[str, list[str]]
+
+@app.post("/v1/moderations")
+async def moderations(request: ModerationsRequest):
+    results = {
+        "id": f"modr-{int(time.time()*1e9)}",
+        "model": "text-moderation-005",
+        "results": [],
+    }
+    if isinstance(request.input, str):
+        request.input = [request.input]
+    
+    thresholds = {
+        "sexual": 0.1,
+        "hate": 0.25,
+        "harassment": 0.5,
+        "self_harm": 0.25,
+        "sexual_minors": 0.5,
+        "hate_threatening": 0.2,
+        "violence_graphic": 0.25,
+        "self_harm_intent": 0.2,
+        "self_harm_instructions": 0.25,
+        "harassment_threatening": 0.1,
+        "violence": 0.25,
+    }
+
+    for text in request.input:
+        predictions = await predict(text, model, tokenizer)
+        category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))}
+        detect = {key: score > thresholds[key] for key, score in category_scores.items()}
+        detected = any(detect.values())
+
+        results['results'].append({
+            'flagged': detected,
+            'categories': detect,
+            'category_scores': category_scores,
+        })
+
+    return results
+def sigmoid(x):
+   return 1/(1 + np.exp(-x))
+    
+def parse_args(argv):
+	parser = argparse.ArgumentParser(description='Moderation API')
+	parser.add_argument('--host', type=str, default='0.0.0.0')
+	parser.add_argument('--port', type=int, default=5002)
+	parser.add_argument('--test-load', action='store_true')
+	return parser.parse_args(argv)
+    
+async def predict(text, ort_session, tokenizer):
+    # 编码输入数据
+    encoding = tokenizer.encode_plus(
+        text,
+        return_tensors='np'  # 使用 NumPy tensors
+    )
+    input_ids = encoding['input_ids']
+    attention_mask = encoding['attention_mask']
+
+    # 定义 ONNX Runtime 推理函数
+    def _predict():
+        # 准备 ONNX Runtime 输入
+        ort_inputs = {
+            ort_session.get_inputs()[0].name: input_ids,
+            ort_session.get_inputs()[1].name: attention_mask
+        }
+        # 进行推理
+        ort_outs = ort_session.run(None, ort_inputs)
+        return torch.sigmoid(torch.from_numpy(ort_outs[0]))  # 将输出转为 PyTorch Tensor 并应用 sigmoid
+
+    # 在独立线程中运行 ONNX 推理
+    loop = asyncio.get_running_loop()
+    predictions = await loop.run_in_executor(None, _predict)
+    
+    return predictions
+# Main
+if __name__ == "__main__":
+
+	args = parse_args(sys.argv[1:])
+	# start API
+	print(f'Starting moderations[{device}] API on {args.host}:{args.port}', file=sys.stderr)
+	app.register_model('text-moderations-latest', 'text-moderations-stable')
+	app.register_model('text-moderations-005', 'text-moderations-ifmain')
+
+	if not args.test_load:
+		uvicorn.run(app, host=args.host, port=args.port)

openedai.py

@@ -0,0 +1,66 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import PlainTextResponse
+
+class OpenAIStub(FastAPI):
+	def __init__(self) -> None:
+		super().__init__()
+		self.models = {}
+			
+		self.add_middleware(
+			CORSMiddleware,
+			allow_origins=["*"],
+			allow_credentials=True,
+			allow_methods=["*"],
+			allow_headers=["*"]
+		)
+
+		@self.get('/v1/billing/usage')
+		@self.get('/v1/dashboard/billing/usage')
+		async def handle_billing_usage():
+			return { 'total_usage': 0 }
+
+		@self.get("/", response_class=PlainTextResponse)
+		@self.head("/", response_class=PlainTextResponse)
+		@self.options("/", response_class=PlainTextResponse)
+		async def root():
+			return PlainTextResponse(content="", status_code=200 if self.models else 503)
+
+		@self.get("/health")
+		async def health():
+			return {"status": "ok" if self.models else "unk" }
+
+		@self.get("/v1/models")
+		async def get_model_list():
+			return self.model_list()
+
+		@self.get("/v1/models/{model}")
+		async def get_model_info(model_id: str):
+			return self.model_info(model_id)
+
+	def register_model(self, name: str, model: str = None) -> None:
+		self.models[name] = model if model else name
+
+	def deregister_model(self, name: str) -> None:
+		if name in self.models:
+			del self.models[name]
+
+	def model_info(self, model: str) -> dict:
+		result = {
+			"id": model,
+			"object": "model",
+			"created": 0,
+			"owned_by": "user"
+		}
+		return result
+
+	def model_list(self) -> dict:
+		if not self.models:
+			return {}
+		
+		result = {
+			"object": "list",
+			"data": [ self.model_info(model) for model in list(set(self.models.keys() | self.models.values())) if model ]
+		}
+
+		return result

requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+numpy
+torch
+transformers