Metadata-Version: 2.4
Name: parallel-llm
Version: 0.4.1
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
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 :: OS Independent
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: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
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Requires-Dist: transformers>=4.36.0
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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 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.

## 🚀 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

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

### Optional Dependencies

Install with specific features:

```bash
# GPU acceleration (Linux + CUDA only)
pip install parallel-llm[gpu]

# Distributed training
pip install parallel-llm[distributed]

# Multimodal models
pip install parallel-llm[multimodal]

# Inference optimization (Linux + CUDA)
pip install parallel-llm[inference]

# Logging and monitoring
pip install parallel-llm[logging]

# Development tools
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)
- CUDA >= 11.8 (for GPU features)
- 16GB+ GPU memory recommended (for full functionality)

## 🔥 Quick Start

### Training a Unimodal LLM

```python
import torch
from parallel_llm import DiffusionTransformer, ModelConfig, TrainingConfig, DistributedTrainer

# Configure model
model_config = ModelConfig(
    vocab_size=50257,
    hidden_size=2048,
    num_hidden_layers=24,
    num_attention_heads=16,
    use_flash_attention=True,
)

# Create model
model = DiffusionTransformer(model_config)

# Configure training
train_config = TrainingConfig(
    batch_size=8,
    learning_rate=3e-4,
    use_fsdp=True,
    fsdp_sharding_strategy="full",
    mixed_precision="bf16",
    use_torch_compile=True,
    torch_compile_mode="max-autotune",
)

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

# Train!
trainer.train()
```

### Parallel Generation (Inference)

```python
from parallel_llm import ParallelGenerator, GenerationConfig

# Configure generation
gen_config = GenerationConfig(
    max_new_tokens=512,
    temperature=1.0,
    num_refinement_steps=5,
    confidence_threshold=0.9,
)

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

# Generate (all 512 tokens in ~5 forward passes!)
prompt = torch.tensor([[1, 2, 3, 4, 5]])  # Your prompt tokens
generated = generator.generate(prompt)

print(f"Generated {generated.shape[1]} tokens")
```

### Multimodal Training

```python
from parallel_llm import MultimodalModel, MultimodalConfig

# Configure multimodal model
config = MultimodalConfig(
    # Text config
    vocab_size=50257,
    hidden_size=2048,
    num_hidden_layers=24,

    # Vision config
    vision_encoder="clip",
    image_size=224,
    patch_size=16,
    vision_hidden_size=1024,

    # Fusion
    fusion_type="cross_attention",
    use_contrastive=True,
)

# Create model
model = MultimodalModel(config)

# Train with image-text pairs
# ... (similar to unimodal training)
```

## 🏗️ 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

- [Training Guide](docs/TRAINING.md)
- [Inference Guide](docs/INFERENCE.md)
- [API Reference](docs/API.md)
- [Multimodal Models](docs/MULTIMODAL.md)
- [Performance Tuning](docs/PERFORMANCE.md)

## 🤝 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 from:
- FlashAttention (Dao et al.)
- Diffusion Language Models (various)
- DeepSpeed ZeRO (Microsoft)
- vLLM (UC Berkeley)
- PyTorch FSDP (Meta)

## 📞 Contact


- Email: furqan@lastappstanding.com


## 🌟 Star History

If you find this project useful, please give it a star! ⭐

## Citation

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