Comprehensive README improvement with installation, usage examples, and documentation

[Copilot]

The README has been significantly enhanced to provide comprehensive documentation for users and contributors. The original README was functional but lacked critical information needed for users to get started with Flash-DMA.

What's New

Installation & Setup

• Prerequisites: Clear list of required dependencies (Python 3.7+, PyTorch 1.10+, CUDA 11.0+)
• Installation instructions: Step-by-step guide for installing from source
• CUDA environment setup: Instructions for configuring CUDA environment variables

Usage Examples

• Quick start guide: Practical code example showing basic usage
• API documentation: Complete function signatures with parameter descriptions
• Performance benchmarks: Concrete speedup numbers for different sequence lengths

Development Support

• Build instructions: How to build from source for development
• Testing guide: Commands to run tests and benchmarks
• Supported architectures: CUDA compute capabilities and GPU requirements

User Experience

• Troubleshooting: Solutions for common CUDA, compilation, and performance issues
• Compatibility matrix: Supported versions of PyTorch, CUDA, Python
• Contributing guidelines: Development workflow for contributors

Project Information

• License reference: Clear link to BSD 3-Clause license
• Citation format: BibTeX entry for academic use
• Enhanced acknowledgments: Preserved existing credits

Example Usage Added

import torch
from flash_dma_cpp import apply_dynamic_mask_attention

# Input tensors
batch_size, num_heads, seq_len, head_dim = 1, 8, 4096, 64
query = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)
key = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)
value = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)

# Dynamic mask parameters
dt_proj = torch.randn(num_heads, num_heads * head_dim, device='cuda', dtype=torch.float16)
A = torch.randn(num_heads, device='cuda', dtype=torch.float16)

# Apply Flash-DMA attention
output = apply_dynamic_mask_attention(
    query, key, value,
    dt_proj=dt_proj,
    A=A,
    keep_window_size=2048,
    is_causal=True
)

Impact

• 224 lines added with zero deletions - all existing content preserved
• README size increased from ~2KB to 7.7KB with comprehensive coverage
• Addresses all critical gaps: installation, usage, testing, troubleshooting
• Makes the project accessible to new users while supporting existing workflows

Fixes #18.

Warning

Firewall rules blocked me from connecting to one or more addresses

I tried to connect to the following addresses, but was blocked by firewall rules:

• http://168.63.129.16:80/machine/
  • Triggering command: /usr/bin/python3 -u bin/WALinuxAgent-2.13.1.1-py3.9.egg -collect-logs (http block)

If you need me to access, download, or install something from one of these locations, you can either:

• Configure Actions setup steps to set up my environment, which run before the firewall is enabled
• Add the appropriate URLs or hosts to my firewall allow list

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