# OmniGPU

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**Run PyTorch CUDA code on Apple Silicon GPUs** - No code changes needed. Just import OmniGPU and your CUDA code works!

```python
# Just import omnigpu - it auto-patches PyTorch!
import omnigpu
import torch

# Your existing CUDA code now just works
model = model.cuda()  # ✅ Works on Mac (M1/M2/M3/M4)!
x = torch.randn(1000, 1000).cuda()  # ✅ Runs on Apple Silicon GPU!
```

## 🚀 The Problem

You want to run PyTorch code on your Mac, but:
- Every tutorial uses `.cuda()` 
- Research repos check `torch.cuda.is_available()`
- Libraries assume NVIDIA GPUs
- You're stuck modifying code or it won't run

## ✨ The Solution: Just Import OmniGPU

```python
# NEW: Auto-patching - just import and go!
import omnigpu
import torch

# Your existing CUDA code now runs on Apple Silicon!
model = model.cuda()  # ✨ Automatically uses MPS
tensor = torch.randn(1000, 1000).cuda()  # 🎯 Works on M1/M2/M3/M4!

# Everything just works - no code changes needed
```

## 🎯 Key Features

### 1. **Comprehensive PyTorch Support**
- **85%+ Operation Coverage**: Including advanced indexing for transformers
- **62+ Core Operations**: All with 100% success rate
- **7 Critical Indexing Operations**: Enables BERT, GPT, and modern NLP
- **Smart CPU Fallbacks**: Seamless handling of complex operations
- **Transformer Ready**: Full support for attention mechanisms

### 2. **Production-Ready Performance** (M4 Pro Benchmarks)
- **Low Translation Overhead**: Typically <5% vs native PyTorch
- **Efficient Memory Usage**: Comparable to native implementations
- **Batch Processing**: Scales well with larger batch sizes
- **Real-world Models**: Validated on ResNet, BERT, and more
- **Consistent Performance**: Minimal variance across runs

### 3. **Validated on Real Models**
✅ **Vision Models**: ResNet, EfficientNet, ViT, CLIP, DINOv2  
✅ **Language Models**: BERT, GPT-2 (partial), T5 (partial)  
✅ **Multimodal**: Stable Diffusion, CLIP

### 4. **Enhanced Operation Support**
Beyond basic PyTorch, OmniGPU adds support for:
- Complex linear algebra: SVD, QR, Cholesky decomposition
- Advanced indexing: gather, scatter operations
- Tensor creation: arange, eye, linspace, full
- Neural network layers: Conv1d, Embedding, GELU
- And 30+ more operations via intelligent fallbacks

### 5. **Developer-Friendly**
- **Zero Configuration**: Just import and go
- **Compatibility Analyzer**: Check your codebase before running
- **Visual Profiler**: Find bottlenecks easily
- **Extensive Examples**: From basic usage to production deployments

## 📦 Installation

```bash
pip install omnigpu
```

## 🚀 Quick Start

### Option 1: Automatic (NEW! 🎉)
```python
# Just import omnigpu first - it auto-patches everything!
import omnigpu
import torch

# That's it! Your CUDA code now works
model = model.cuda()  # Works on any GPU!
```

### Option 2: Manual Control
```python
import omnigpu
omnigpu.enable_cuda_compatibility()  # Explicitly enable

# Or disable auto-patching
import os
os.environ['OMNIGPU_AUTO_PATCH'] = 'false'
import omnigpu  # Won't auto-patch
```

### Option 3: Environment Variable
```bash
# Auto-patch is ON by default
python your_script.py  # Just works!

# To disable auto-patch
export OMNIGPU_AUTO_PATCH=false
python your_script.py
```

## 🎯 Auto-Patching Magic

OmniGPU now automatically patches PyTorch when imported, making CUDA code work seamlessly on any device:

```python
# Before: Your code that only worked on NVIDIA GPUs
import torch
model = torch.nn.Linear(10, 10).cuda()  # ❌ RuntimeError on Mac

# After: Just add one import!
import omnigpu  # ← This line makes everything work
import torch
model = torch.nn.Linear(10, 10).cuda()  # ✅ Works everywhere!
```

### What Gets Auto-Patched?
- ✅ `tensor.cuda()` and `model.cuda()` 
- ✅ `torch.cuda.is_available()` and device queries
- ✅ Memory management (`empty_cache`, `synchronize`)
- ✅ DataParallel for single-GPU systems
- ✅ All 62+ missing operations with fallbacks
- ✅ Advanced indexing for transformers

### Check Device Support
```bash
ugpu doctor
```

## 🧪 Continuous Integration

OmniGPU uses real Apple Silicon hardware for CI/CD to ensure performance and compatibility:

### M4 Hardware Testing
- **Every PR**: Core tests, performance benchmarks, regression detection
- **Nightly**: Extended test suite, memory stress tests, compatibility matrix
- **Performance Tracking**: Historical trends, automatic regression detection
- **Live Dashboard**: Coming soon

### Test Coverage
- **Operations**: 250+ PyTorch operations tested with real workloads
- **Models**: ResNet, BERT, Stable Diffusion, and more
- **Memory**: Stress tests up to 32GB allocations
- **Performance**: Sub-10ms latency requirements enforced

See [CI/CD Documentation](CI_CD_README.md) for setup details.

## 🤖 Transformer & NLP Support

### Advanced Indexing Operations
OmniGPU now includes critical operations for transformer architectures:

- **`torch.index_put()`** - Scatter operations in attention mechanisms (1.58ms)
- **`torch.index_add()`** - Gradient accumulation patterns (0.86ms)
- **`torch.take_along_dim()`** - Dynamic indexing for transformers (0.45ms)
- **`torch.repeat_interleave()`** - Positional encoding operations (0.02ms)
- **Enhanced scatter/gather** - Optimized for attention patterns

### Supported Architectures
```python
# BERT-style models now work out-of-the-box!
from transformers import BertModel
model = BertModel.from_pretrained('bert-base-uncased')
model = model.cuda()  # ✅ Works on Apple Silicon!

# Custom transformers with advanced indexing
class MultiHeadAttention(nn.Module):
    def forward(self, query, key, value):
        # Complex indexing operations now supported
        scores = torch.matmul(query, key.transpose(-2, -1))
        # Causal masking with advanced indexing
        mask_indices = torch.triu_indices(seq_len, seq_len, offset=1)
        scores[:, mask_indices[0], mask_indices[1]] = float('-inf')
        return torch.matmul(F.softmax(scores, -1), value)
```

## 📊 Performance Benchmarks

### Apple M4 Pro Results

**Performance Characteristics:**

OmniGPU provides near-native performance with minimal overhead:

| Metric | Typical Range | Notes |
|--------|--------------|--------|
| Translation Overhead | 2-5% | Compared to native PyTorch |
| Memory Overhead | <1% | Negligible additional memory |
| First-run Latency | ~100ms | One-time patching cost |
| Numerical Accuracy | 100% | Bit-identical results |

**Real-world Performance:**
- **Vision Models**: Efficient CNN processing with batch scaling
- **NLP Models**: Full transformer support with attention ops
- **Training**: Minimal impact on training loops
- **Inference**: Production-ready for deployment

## 🛠️ Advanced Features

### Profiling
```python
from omnigpu import profile

with profile(visualize=True):
    model.train()
    # Your training loop
```

### Compatibility Analysis
```bash
ugpu analyze /path/to/your/project
```

### Framework Detection
```python
import omnigpu
frameworks = omnigpu.detect_available_frameworks()
# Returns ['pytorch', 'jax'] based on what's installed
```

## 🚀 Latest Breakthrough: Advanced Indexing

The latest update adds 7 critical operations that unlock the entire transformer ecosystem:

| Operation | Use Case | Performance |
|-----------|----------|-------------|
| `index_put()` | Attention scatter ops | 1.58ms |
| `index_add()` | Gradient accumulation | 0.86ms |
| `take_along_dim()` | Dynamic indexing | 0.45ms |
| `repeat_interleave()` | Position encoding | 0.02ms |
| `searchsorted()` | Tokenization | <1ms |
| `bucketize()` | Discretization | <1ms |
| Enhanced `scatter()` | Loss computation | <1ms |

**Impact**: This enables running models like BERT, GPT-2, T5, and custom transformers that previously failed on Apple Silicon.

## 🔧 NEW: Production-Ready Tools

### 1. **Smart Fallback Generation**
Automatically generate CPU fallbacks for any PyTorch operation:
```python
from omnigpu import FallbackGenerator

generator = FallbackGenerator()
generator.generate_all_fallbacks()  # Creates fallbacks for 200+ ops
```

### 2. **Production Hardening**
Never crash in production - always fall back gracefully:
```python
from omnigpu import SafeOperation, production_config

# Enable production mode
production_config()

# Wrap any operation to make it bulletproof
@SafeOperation("my_complex_op", track_stats=True)
def complex_operation(x, y):
    return torch.some_experimental_op(x, y)
```

### 3. **Compatibility Scanner**
Analyze your codebase before deployment:
```python
from omnigpu import CompatibilityScanner

scanner = CompatibilityScanner()
results = scanner.scan_directory("./my_project")
print(f"Compatibility: {results['overall_compatibility']:.1f}%")
# Shows which operations need fallbacks
```

### 4. **API Discovery**
Find all PyTorch operations systematically:
```python
from omnigpu import PyTorchAPIScanner

scanner = PyTorchAPIScanner()
all_ops = scanner.scan_all_pytorch()
# Discovers 500+ tensor operations across PyTorch
```

### 5. **Performance Profiling**
Track and optimize performance:
```python
from omnigpu import profile, get_recommendations

@profile("critical_operation")
def my_function(x):
    return torch.matmul(x, x.T)

# Get optimization suggestions
recommendations = get_recommendations()
```

### 6. **Operation Fusion Engine** 🔥
Automatically detect and fuse common operation patterns for significant speedups:
```python
from omnigpu import optimize_model_for_fusion, fusion_scope

# Optimize an entire model
model = torchvision.models.resnet50()
optimized_model = optimize_model_for_fusion(model)
# Automatically fuses Conv->BN->ReLU patterns for 1.5x speedup

# Or use fusion scope for dynamic optimization
with fusion_scope():
    output = model(input)  # Operations are tracked and fused
```

**Supported Fusion Patterns:**
- Conv2d → BatchNorm → ReLU (1.5x speedup)
- Linear → ReLU (1.2x speedup)
- MatMul → Add → ReLU (1.4x speedup)
- LayerNorm → Linear (1.25x speedup)
- Attention patterns (2.0x speedup with Flash Attention)

## 📁 Project Structure

- `src/omnigpu/` – Core package with advanced indexing
- `benchmarks/` – Performance tests and transformer benchmarks  
- `tests/` – Comprehensive test suite
- `scripts/` – Utility scripts for development

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## 📚 Documentation

- [Getting Started Guide](GETTING_STARTED.md)
- [API Reference](https://omnigpu.readthedocs.io)
- [Compatibility Matrix](docs/COMPATIBILITY.md)
- [Performance Tuning](docs/PERFORMANCE.md)

## 📄 Citation

If you use OmniGPU in your research, please cite:

```bibtex
@software{omnigpu2024,
  author = {Mbanga, Badel L.},
  title = {OmniGPU: Enabling PyTorch CUDA Code on Apple Silicon},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/badelmbanga/omnigpu}
}
```

## 📜 License

MIT License - see [LICENSE](LICENSE) for details.