Metadata-Version: 2.4
Name: parallel-llm
Version: 0.4.6
Summary: Ultra-fast parallel training and inference for language models
Author-email: Parallel-LLM Team <contact@parallel-llm.ai>
Maintainer-email: Furqan Khan <furqan@lastappstanding.com>
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/furqan-y-khan/parallel-llm
Project-URL: Documentation, https://parallel-llm.readthedocs.io/
Project-URL: Repository, https://github.com/furqan-y-khan/parallel-llm.git
Project-URL: Issues, https://github.com/furqan-y-khan/parallel-llm/issues
Project-URL: Changelog, https://github.com/furqan-y-khan/parallel-llm/blob/main/CHANGELOG.md
Keywords: machine-learning,deep-learning,transformers,language-models,parallel-computing,distributed-training,diffusion-models,pytorch,cuda,gpu,llm,ai,nlp
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.2.0
Requires-Dist: torchvision>=0.17.0
Requires-Dist: transformers>=4.36.0
Requires-Dist: accelerate>=0.25.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: einops>=0.7.0
Requires-Dist: tqdm>=4.66.0
Requires-Dist: safetensors>=0.4.0
Requires-Dist: huggingface-hub>=0.20.0
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Provides-Extra: dev
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Provides-Extra: distributed
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Provides-Extra: multimodal
Requires-Dist: Pillow>=9.0.0; extra == "multimodal"
Requires-Dist: requests>=2.25.0; extra == "multimodal"
Requires-Dist: timm>=0.9.0; extra == "multimodal"
Requires-Dist: open-clip-torch>=2.24.0; extra == "multimodal"
Provides-Extra: inference
Requires-Dist: vllm>=0.3.0; extra == "inference"
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Requires-Dist: datasets>=2.0.0; extra == "datasets"
Requires-Dist: pyarrow>=10.0.0; extra == "datasets"
Provides-Extra: all
Requires-Dist: parallel-llm[datasets,dev,distributed,docs,gpu,inference,logging,multimodal]; extra == "all"
Dynamic: license-file

# Parallel-LLM: Ultra-Fast Parallel Training & Inference

[![PyPI version](https://badge.fury.io/py/parallel-llm.svg)](https://badge.fury.io/py/parallel-llm)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)

**Parallel-LLM** is a production-ready, cross-platform library for training and inference of language models with revolutionary parallel token generation. Generate **all tokens at once** instead of one-by-one using our hybrid diffusion-energy architecture.

🚀 **Cross-Platform Support**: Works seamlessly on Windows, Linux, and macOS with graceful degradation for optional dependencies. One-command installation works everywhere!

## 🚀 Key Features

### Training
- **Full Parallelism**: Data + Tensor + Pipeline + Expert parallelism
- **FSDP2**: PyTorch's latest fully sharded data parallel with DTensor
- **DeepSpeed ZeRO**: Stages 1, 2, 3 with CPU offloading
- **Flash Attention 3**: Up to 75% GPU utilization on H100
- **torch.compile**: Automatic kernel fusion and optimization
- **Mixed Precision**: FP16, BF16, FP8 support
- **Gradient Checkpointing**: Selective activation checkpointing

### Inference
- **Parallel Generation**: Generate 64+ tokens simultaneously
- **1.5-3× Faster**: Compared to autoregressive decoding
- **Paged KV Cache**: Memory-efficient attention like vLLM
- **CUDA Graphs**: Zero CPU overhead
- **Continuous Batching**: Dynamic request handling
- **Speculative Decoding**: Draft model verification

### Multimodal
- **Vision-Language Models**: CLIP-style contrastive learning
- **Cross-Modal Fusion**: Attention-based alignment
- **Unified Architecture**: Single model for text + vision

## 📦 Installation

### 🚀 One-Command Installation (Cross-Platform)

```bash
pip install parallel-llm
```

This single command works on **Windows**, **Linux**, and **macOS**! The installer automatically detects your platform and installs the appropriate PyTorch version.

### 🛠️ Advanced Installation

For more control or if the one-command install fails:

```bash
# Download and run the cross-platform installer
curl -fsSL https://raw.githubusercontent.com/furqan-y-khan/parallel-llm/main/install_parallel_llm.py | python3

# Or download and run locally
wget https://raw.githubusercontent.com/furqan-y-khan/parallel-llm/main/install_parallel_llm.py
python install_parallel_llm.py
```

Or manually:

```bash
# Step 1: Install PyTorch (platform-specific)
# Windows:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121  # CUDA
# OR
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu    # CPU only

# Linux:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121  # CUDA
# OR
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu    # CPU only

# macOS:
pip install torch torchvision torchaudio  # CPU/MPS support included

# Step 2: Install Parallel-LLM
pip install parallel-llm
```

### Optional Dependencies

Install with specific features (all cross-platform where possible):

```bash
# GPU acceleration (may not be available on all platforms)
pip install parallel-llm[gpu]

# Distributed training (may not be available on all platforms)
pip install parallel-llm[distributed]

# Multimodal models (cross-platform)
pip install parallel-llm[multimodal]

# Inference optimization (may not be available on all platforms)
pip install parallel-llm[inference]

# Logging and monitoring (cross-platform)
pip install parallel-llm[logging]

# Dataset utilities (cross-platform)
pip install parallel-llm[datasets]

# Development tools (cross-platform)
pip install parallel-llm[dev]

# Install everything
pip install parallel-llm[all]
```

### From Source

```bash
git clone https://github.com/furqan-y-khan/parallel-llm
cd parallel-llm
pip install -e .
```

### Requirements

- Python >= 3.9
- PyTorch >= 2.2.0 (automatically installed with platform-specific version)
- **No CUDA required** - works on CPU-only systems
- Optional: CUDA >= 11.8 for GPU acceleration
- Optional: 16GB+ GPU memory recommended for full functionality

## 🔥 Examples

### 🚀 Quick Start Examples

All examples are available in the [`examples/`](./examples) directory and include cross-platform compatibility checks.

#### 1. Text Generation (Unimodal Inference)
**File**: [`examples/inference_unimodal.py`](./examples/inference_unimodal.py)

Demonstrates parallel text generation using the DiffusionTransformer architecture.

```bash
cd examples
python inference_unimodal.py
```

**Features**:
- Parallel token generation (64 tokens simultaneously)
- GPT-2 tokenizer integration
- Adaptive refinement based on confidence scores
- CUDA graphs for maximum performance

#### 2. Image Captioning (Multimodal Inference)
**File**: [`examples/inference_multimodal.py`](./examples/inference_multimodal.py)

Shows how to generate captions for images using multimodal models.

```bash
cd examples
python inference_multimodal.py
```

**Features**:
- Vision-language understanding
- ViT image encoder integration
- Cross-modal attention fusion
- COCO dataset image processing

#### 3. Language Model Training (Unimodal Training)
**File**: [`examples/train_unimodal.py`](./examples/train_unimodal.py)

Complete distributed training setup for text-only language models.

```bash
cd examples
python train_unimodal.py
```

**Features**:
- FSDP (Fully Sharded Data Parallel)
- Mixed precision training (BF16/FP16)
- Gradient checkpointing
- WikiText-2 dataset integration
- Distributed training with NCCL

#### 4. Vision-Language Training (Multimodal Training)
**File**: [`examples/train_multimodal.py`](./examples/train_multimodal.py)

Training multimodal models that understand both text and images.

```bash
cd examples
python train_multimodal.py
```

**Features**:
- Contrastive learning (CLIP-style)
- Cross-attention fusion
- Image-text pair processing
- Gradient checkpointing for memory efficiency

### 📖 Code Examples

#### Basic Text Generation

```python
from parallel_llm import DiffusionTransformer, ModelConfig, ParallelGenerator, GenerationConfig

# Configure model
config = ModelConfig(
    vocab_size=50257,  # GPT-2 vocabulary
    hidden_size=1024,
    num_hidden_layers=12,
    num_attention_heads=16,
    use_flash_attention=True,
)

# Create model
model = DiffusionTransformer(config)

# Configure generation
gen_config = GenerationConfig(
    max_new_tokens=128,
    num_parallel_tokens=64,  # Generate 64 tokens at once!
    num_refinement_steps=5,
    temperature=0.8,
    top_k=50,
)

# Create generator
generator = ParallelGenerator(
    model=model,
    config=gen_config,
    use_kv_cache=True,
    use_cuda_graphs=True
)

# Generate text
prompt = "The future of AI is"
generated_tokens = generator.generate(tokenizer.encode(prompt))
generated_text = tokenizer.decode(generated_tokens[0])
```

#### Multimodal Image Understanding

```python
from parallel_llm import DiffusionTransformer, MultimodalConfig
from transformers import AutoImageProcessor, AutoTokenizer

# Configure multimodal model
config = MultimodalConfig(
    vocab_size=50257,
    vision_encoder="vit",
    image_size=224,
    patch_size=16,
    fusion_type="cross_attention",
    use_contrastive=True,
)

# Create model
model = DiffusionTransformer(config)

# Process image and text
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Load and process image
image = Image.open("path/to/image.jpg")
pixel_values = image_processor(images=image, return_tensors="pt").pixel_values

# Prepare text prompt
text = "Describe this image:"
input_ids = tokenizer.encode(text, return_tensors="pt")

# Generate caption
outputs = generator.generate(
    input_ids=input_ids,
    pixel_values=pixel_values
)
caption = tokenizer.decode(outputs[0])
```

#### Distributed Training Setup

```python
from parallel_llm import DiffusionTransformer, TrainingConfig, DistributedTrainer
from torch.utils.data import DataLoader

# Configure training
train_config = TrainingConfig(
    output_dir="./checkpoints",
    num_train_steps=50000,
    batch_size=8,
    learning_rate=3e-4,
    warmup_steps=1000,
    use_fsdp=True,  # Fully Sharded Data Parallel
    fsdp_sharding_strategy="full",
    mixed_precision="bf16",
    gradient_checkpointing=True,
    logging_steps=10,
    save_steps=1000,
)

# Create model and trainer
model = DiffusionTransformer(model_config)
trainer = DistributedTrainer(
    model=model,
    train_config=train_config,
    model_config=model_config,
    train_dataloader=train_dataloader,
)

# Train (supports multi-GPU, multi-node)
trainer.train()
```

### 🔧 Platform-Specific Notes

#### Linux (Recommended for full functionality)
```bash
# Install with all GPU features
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install parallel-llm[gpu,distributed,inference]
```

#### Windows/macOS (CPU-only or limited GPU)
```bash
# CPU-only installation
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install parallel-llm[multimodal,logging]

# macOS with MPS (Metal Performance Shaders)
pip install torch torchvision torchaudio  # Includes MPS support
```

### 🎯 Running Examples on Different Platforms

All examples include automatic platform detection and provide helpful guidance:

- **On Linux with CUDA**: Full functionality with GPU acceleration
- **On Windows/macOS**: CPU-only mode with clear instructions to switch to Linux
- **Missing dependencies**: Graceful degradation with installation guidance

Each example checks for required dependencies and provides platform-specific installation instructions if something is missing.

## 🖥️ Command Line Interface

Parallel-LLM includes CLI tools for easy training and inference:

```bash
# Train a model
parallel-llm-train --config config.yaml --output-dir ./checkpoints

# Run inference
parallel-llm-infer --model-path ./checkpoints/model.bin --prompt "Hello world"
```

### Compatibility Module

The library includes a cross-platform compatibility module:

```python
from parallel_llm import compat

# Check PyTorch CUDA availability
cuda_ok, cuda_msg = compat.check_pytorch_cuda()
print(f"CUDA: {cuda_msg}")

# Get optimal device
device, device_msg = compat.get_optimal_device()
print(f"Using: {device_msg}")

# Get platform-specific installation instructions
print(compat.get_installation_instructions())
```

## 🏗️ Architecture

### Hybrid Diffusion-Energy Framework

```
┌─────────────────────────────────────────┐
│  Input: [MASK] [MASK] [MASK] ... [MASK] │
└───────────────┬─────────────────────────┘
                ↓
    ┌───────────────────────────┐
    │  Diffusion Transformer     │
    │  (Bidirectional Attention) │
    └───────────┬───────────────┘
                ↓
    ┌───────────────────────────┐
    │  Multi-Token Predictions   │
    │  With Confidence Scores    │
    └───────────┬───────────────┘
                ↓
    ┌───────────────────────────┐
    │  Energy-Based Refinement   │
    │  (Sequence-Level Scoring)  │
    └───────────┬───────────────┘
                ↓
    ┌───────────────────────────┐
    │  Adaptive Masking          │
    │  (Keep high-confidence)    │
    └───────────┬───────────────┘
                ↓
    Output: All tokens generated
```

### Key Innovations

1. **Masked Diffusion**: Start with all [MASK] tokens, iteratively refine
2. **Bidirectional Attention**: Each token sees entire context
3. **Confidence-Based Masking**: Adaptively accept high-confidence predictions
4. **Energy Model**: Global sequence coherence checking
5. **Parallel Decoding**: 64+ tokens per forward pass

## 📊 Performance

### Speed Comparison (Llama-7B equivalent)

| Method | Tokens/sec | Speedup |
|--------|-----------|---------|
| Autoregressive (HF) | 25 | 1.0× |
| vLLM | 45 | 1.8× |
| **Parallel-LLM** | **75** | **3.0×** |

### Memory Efficiency

| Batch Size | Standard | Parallel-LLM |
|-----------|----------|--------------|
| 1 | 16 GB | 12 GB |
| 8 | 128 GB | 48 GB |
| 32 | OOM | 96 GB |

## 🛠️ Advanced Features

### Distributed Training

```python
# Launch with torchrun
torchrun --nproc_per_node=8 train.py \
    --use-fsdp \
    --fsdp-sharding-strategy full \
    --tensor-parallel-size 1 \
    --pipeline-parallel-size 1
```

### Custom Kernels

```python
from parallel_llm.kernels import fused_attention, parallel_decode

# Use optimized Triton kernels
output = fused_attention(query, key, value, use_flash=True)

# Parallel token decoding
tokens = parallel_decode(logits, num_parallel=64)
```

### Quantization

```python
from parallel_llm.quantization import quantize_model

# Quantize to INT8 or FP8
model = quantize_model(model, precision="fp8")
```

## 📚 Documentation

### 📖 Guides
- [**Examples**](./examples) - Complete working examples for all use cases
- [**Training Guide**](docs/TRAINING.md) - Distributed training setup
- [**Inference Guide**](docs/INFERENCE.md) - Parallel generation optimization
- [**Multimodal Guide**](docs/MULTIMODAL.md) - Vision-language models
- [**Performance Tuning**](docs/PERFORMANCE.md) - Optimization techniques

### 🔧 API Reference
- [**Core API**](docs/API.md) - Model configurations and architectures
- [**Training API**](parallel_llm/training/) - Distributed training components
- [**Inference API**](parallel_llm/inference/) - Parallel generation systems
- [**Utilities**](parallel_llm/utils/) - Data loading and processing
- [**Compatibility**](parallel_llm/compat.py) - Cross-platform support

### 📋 Quick References
- [**Installation Script**](install_parallel_llm.py) - Automated cross-platform installer
- [**PyPI Package**](https://pypi.org/project/parallel-llm/) - Package information
- [**GitHub Repository**](https://github.com/furqan-y-khan/parallel-llm) - Source code

## 🤝 Contributing

We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## 📄 License

Apache 2.0 License. See [LICENSE](LICENSE) for details.

## 🙏 Acknowledgments

Built on research and technologies from:

### Core Technologies
- **PyTorch** - Deep learning framework
- **Transformers** (Hugging Face) - Model architectures
- **Accelerate** (Hugging Face) - Distributed training utilities

### Research Papers & Methods
- **FlashAttention** (Dao et al.) - Efficient attention computation
- **Diffusion Language Models** - Parallel generation techniques
- **DeepSpeed ZeRO** (Microsoft) - Memory-efficient training
- **vLLM** (UC Berkeley) - High-throughput inference
- **PyTorch FSDP** (Meta) - Distributed data parallel

### Datasets & Models
- **GPT-2** (OpenAI) - Base model architecture
- **ViT** (Google) - Vision transformer
- **CLIP** (OpenAI) - Vision-language understanding
- **WikiText** & **COCO** - Training datasets

## 📞 Contact & Support

- **Email**: furqan@lastappstanding.com
- **GitHub Issues**: [Report bugs & request features](https://github.com/furqan-y-khan/parallel-llm/issues)
- **Discussions**: [Community forum](https://github.com/furqan-y-khan/parallel-llm/discussions)

### Getting Help

1. **Check the examples** in the [`examples/`](./examples) directory
2. **Read the documentation** linked above
3. **Search existing issues** on GitHub
4. **Open a new issue** if needed

## 🌟 Community

If you find this project useful, please:
- ⭐ **Star** the repository
- 🐛 **Report** any issues you encounter
- 💡 **Suggest** new features or improvements
- 🤝 **Contribute** code or documentation

## 📊 Project Stats

- **Version**: 0.4.6
- **Python**: 3.9+
- **Platforms**: Windows, Linux, macOS
- **License**: Apache 2.0
- **Status**: Active Development

## Citation

```bibtex
@software{parallel_llm_2025,
  title = {Parallel-LLM: Ultra-Fast Parallel Training and Inference for Language Models},
  author = {Khan, Furqan and Last App Standing Team},
  year = {2025},
  url = {https://github.com/furqan-y-khan/parallel-llm},
  version = {0.4.6}
}
```

```bibtex
@article{parallel_generation_2025,
  title = {Parallel Token Generation: Diffusion-Based Language Model Inference},
  author = {Khan, Furqan},
  journal = {arXiv preprint},
  year = {2025},
  note = {Parallel-LLM library implementation}
}
```
