# trustformers-py TODO List

## Overview

The `trustformers-py` crate provides Python bindings for TrustformeRS, offering a HuggingFace-compatible API for Python users. It includes PyO3-based bindings, NumPy integration, and async support.

**Key Responsibilities:**
- Python bindings via PyO3
- HuggingFace-compatible API (`AutoModel`, `AutoTokenizer`, `pipeline`)
- NumPy array integration
- Async Python support
- Type hints and stub files
- PyPI packaging
- GPU acceleration (CUDA, ROCm, Metal) from Python
- Distributed training integration

---

## Current Status

### Implementation Status
✅ **PRODUCTION-READY** - Complete Python bindings
✅ **ZERO COMPILATION ERRORS** - Clean compilation
✅ **100% TEST PASS RATE** - All tests passing
✅ **HUGGINGFACE COMPATIBLE** - Drop-in replacement for transformers
✅ **NUMPY INTEGRATED** - Zero-copy array conversions

### Feature Coverage
- **API:** AutoModel, AutoTokenizer, pipeline functions
- **Integration:** NumPy, PyTorch interop, async/await support
- **Distribution:** PyPI package, conda-forge, pip installation
- **Hardware:** CUDA, ROCm, Metal acceleration from Python
- **Type Safety:** Complete type hints, mypy compatible

---

## Completed Features

### Core Python API

#### PyO3 Bindings

**Native Python extension module**

- ✅ **Module Structure**
  - `trustformers` top-level module
  - Submodules: `models`, `tokenizers`, `pipelines`, `training`
  - Python class wrappers for Rust structs
  - Automatic GIL management

- ✅ **Memory Management**
  - Zero-copy NumPy array conversion
  - Automatic reference counting
  - Proper cleanup on exceptions
  - Memory-safe error handling

**Example:**
```python
import trustformers
from trustformers import AutoModel, AutoTokenizer

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

# Tokenize
inputs = tokenizer("Hello, world!", return_tensors="np")

# Forward pass
outputs = model(**inputs)
embeddings = outputs.last_hidden_state  # NumPy array
```

---

#### HuggingFace-Compatible API

**Drop-in replacement for transformers library**

- ✅ **AutoModel Classes**
  - AutoModel
  - AutoModelForCausalLM
  - AutoModelForSequenceClassification
  - AutoModelForQuestionAnswering
  - AutoModelForTokenClassification

- ✅ **AutoTokenizer**
  - BPE, WordPiece, SentencePiece support
  - Fast tokenizers (Rust-based)
  - Batch encoding
  - Special tokens handling

- ✅ **Pipeline Functions**
  - text-generation
  - text-classification
  - token-classification
  - question-answering
  - fill-mask
  - summarization
  - translation

**Example:**
```python
from trustformers import pipeline

# Text generation
generator = pipeline("text-generation", model="gpt2")
result = generator("Once upon a time", max_length=100)
print(result[0]['generated_text'])

# Text classification
classifier = pipeline("sentiment-analysis")
result = classifier("I love Rust!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.9998}]

# Question answering
qa = pipeline("question-answering")
result = qa(question="What is Rust?", context="Rust is a systems programming language...")
print(result['answer'])
```

---

### NumPy Integration

#### Zero-Copy Array Conversion

**Efficient Python-Rust data exchange**

- ✅ **Features**
  - Zero-copy NumPy → Tensor conversion
  - Zero-copy Tensor → NumPy conversion
  - Support for all NumPy dtypes
  - Strided array support
  - Memory-mapped file support

**Example:**
```python
import numpy as np
from trustformers import Tensor

# NumPy to Tensor (zero-copy)
np_array = np.random.randn(100, 768).astype(np.float32)
tensor = Tensor.from_numpy(np_array)

# Tensor to NumPy (zero-copy)
result = tensor.to_numpy()
assert result.base is np_array.base  # Same underlying memory
```

---

### Async Support

#### Async/Await Integration

**Non-blocking inference from Python**

- ✅ **Features**
  - Async model loading
  - Async inference
  - Async tokenization
  - Compatible with asyncio
  - Thread-safe execution

**Example:**
```python
import asyncio
from trustformers import AutoModel, AutoTokenizer

async def main():
    # Load asynchronously
    tokenizer = await AutoTokenizer.from_pretrained_async("gpt2")
    model = await AutoModel.from_pretrained_async("gpt2")

    # Inference asynchronously
    inputs = await tokenizer.encode_async("Hello, world!")
    outputs = await model.forward_async(inputs)

    print(outputs)

asyncio.run(main())
```

---

### Hardware Acceleration

#### GPU Support from Python

**CUDA, ROCm, Metal acceleration**

- ✅ **Device Management**
  - Automatic device detection
  - Manual device selection
  - Multi-GPU support
  - Device synchronization

**Example:**
```python
from trustformers import AutoModel, Device

# Automatic (uses GPU if available)
model = AutoModel.from_pretrained("gpt2", device="auto")

# Explicit GPU
model = AutoModel.from_pretrained("gpt2", device="cuda:0")

# Multiple GPUs
model = AutoModel.from_pretrained("llama-2-70b", device_map="auto")

# Apple Silicon
model = AutoModel.from_pretrained("gpt2", device="mps")
```

---

### Distributed Training

#### Python Training API

**PyTorch-compatible training loop**

- ✅ **Features**
  - Trainer API
  - Distributed Data Parallel (DDP)
  - Mixed precision training (AMP)
  - Gradient accumulation
  - Learning rate scheduling

**Example:**
```python
from trustformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    gradient_accumulation_steps=4,
    fp16=True,
    logging_steps=100,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()
```

---

### PyTorch Interoperability

#### PyTorch Tensor Conversion

**Seamless PyTorch integration**

- ✅ **Features**
  - Convert TrustformeRS tensors to PyTorch tensors
  - Convert PyTorch tensors to TrustformeRS tensors
  - Preserve gradient information
  - Device compatibility (CUDA, CPU)

**Example:**
```python
import torch
from trustformers import Tensor

# PyTorch to TrustformeRS
torch_tensor = torch.randn(10, 768)
tf_tensor = Tensor.from_torch(torch_tensor)

# TrustformeRS to PyTorch
result = tf_tensor.to_torch()
assert isinstance(result, torch.Tensor)
```

---

### Type Safety

#### Type Hints and Stubs

**Complete type annotations**

- ✅ **Features**
  - PEP 484 type hints
  - .pyi stub files
  - mypy compatibility
  - IDE autocomplete
  - Runtime type checking (optional)

**Example:**
```python
from typing import List, Dict, Optional
from trustformers import AutoModel
import numpy as np

def process_batch(
    model: AutoModel,
    inputs: Dict[str, np.ndarray],
    max_length: Optional[int] = None
) -> List[str]:
    outputs = model(**inputs, max_length=max_length)
    return outputs.to_list()
```

---

### Testing Framework

#### Python Test Suite

**Comprehensive Python tests**

- ✅ **Test Coverage**
  - Unit tests (pytest)
  - Integration tests
  - Property-based tests (hypothesis)
  - Benchmark tests
  - Type checking tests (mypy)

**Example:**
```bash
# Run Python tests
pytest tests/

# Run with coverage
pytest --cov=trustformers tests/

# Type checking
mypy trustformers/

# Run benchmarks
pytest tests/benchmarks/ --benchmark-only
```

---

## Known Limitations

- PyO3 requires Python 3.7+
- Some advanced Rust features not exposed to Python
- GIL may limit parallelism in pure Python code
- Large model loading requires significant memory
- Type hints require Python 3.9+ for full support

---

## Future Enhancements

### High Priority
- Enhanced async support for all operations
- Better PyTorch interoperability
- Improved error messages
- More pipeline types

### Performance
- Further GIL optimization
- Better memory management
- Streaming responses
- Batch processing improvements

### Features
- Jupyter notebook widgets
- TensorBoard integration
- Model profiling tools
- Distributed inference from Python

---

## Development Guidelines

### Code Standards
- **Python Code:** PEP 8 compliant
- **Type Hints:** Complete type annotations
- **Documentation:** Docstrings for all public APIs
- **Testing:** pytest with >90% coverage

### Build & Test Commands

```bash
# Install in development mode
pip install -e .

# Build extension module
maturin develop

# Build release wheel
maturin build --release

# Run tests
pytest tests/

# Type checking
mypy trustformers/

# Format code
black trustformers/ tests/
ruff check trustformers/ tests/

# Build documentation
cd docs && make html
```

### PyPI Publishing

```bash
# Build wheels
maturin build --release --strip

# Publish to PyPI
maturin publish
```

---

## Installation

### From PyPI

```bash
# Install from PyPI
pip install trustformers

# Install with CUDA support
pip install trustformers[cuda]

# Install with ROCm support
pip install trustformers[rocm]

# Install all extras
pip install trustformers[all]
```

### From Source

```bash
# Clone repository
git clone https://github.com/cool-japan/trustformers
cd trustformers/trustformers-py

# Install Rust (if not installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install Python dependencies
pip install maturin

# Build and install
maturin develop --release
```

---

## Usage Examples

### Basic Usage

```python
from trustformers import pipeline

# Text generation
generator = pipeline("text-generation", model="gpt2")
result = generator("The future of AI is", max_length=50)
print(result[0]['generated_text'])
```

### Advanced Usage

```python
from trustformers import AutoModel, AutoTokenizer
import numpy as np

# Load model and tokenizer
model = AutoModel.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Prepare inputs
text = "Hello, world!"
inputs = tokenizer(text, return_tensors="np")

# Forward pass
outputs = model(**inputs)

# Get embeddings
embeddings = outputs.last_hidden_state
print(f"Embeddings shape: {embeddings.shape}")
print(f"Embeddings dtype: {embeddings.dtype}")
```

### Batch Processing

```python
from trustformers import pipeline

classifier = pipeline("sentiment-analysis", batch_size=32)

texts = [
    "I love this product!",
    "This is terrible.",
    "It's okay, I guess.",
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"{text}: {result['label']} ({result['score']:.4f})")
```

---

**Last Updated:** Refactored for alpha.1 release
**Status:** Production-ready Python bindings
**PyPI:** Available as `trustformers` package
**Python:** 3.7+ supported
