[!NOTE] LITETORCH IS COMPLETE AND PRODUCTION-READY. Officially released on PyPI: pip install litetorch.

LiteTorch

LiteTorch is a lightweight, high-performance deep learning and large language model (LLM) training engine built in native C++14 with Python bindings via pybind11.

LiteTorch delivers the core training capabilities of PyTorch + Megatron-LM + DeepSpeed ZeRO-3 within a standalone, lean C++ runtime that eliminates Python GIL latency and external dependency bloat.

Key Capabilities

1. Modern Transformer & LLM Primitives

• Rotary Position Embedding (RoPE): Native rotary embeddings as used in modern architectures like LLaMA 3 and Qwen.
• FlashAttention & GQA: Integrated FlashAttention kernel with causal masking and Grouped-Query Attention (GQA). Automatic dynamic probe for external FlashAttention-3 (libflash_attn.so) on Hopper/Blackwell.
• RMSNorm & LayerNorm: High-performance normalization layers with fused backward passes.
• SwiGLU & Activation Functions: SiLU, GELU, ReLU, LeakyReLU, Sigmoid, Tanh with warp-level GPU implementations.
• Mixture of Experts (MoE): Top-K routing with native GPU expert execution.

2. 4D Distributed Parallelism & Large-Scale Scaling

• Fully Sharded Data Parallel (FSDP / ZeRO-3): Automatic parameter, gradient, and optimizer state sharding across arbitrary cluster sizes with batched ncclGroupStart/ncclGroupEnd all-gather pipelines.
• Tensor Parallelism (TP): Megatron-LM style ColumnParallelLinear and RowParallelLinear with overlapped inter-GPU reductions.
• Pipeline Parallelism (PP): 1F1B (One-Forward-One-Backward) schedule to minimize pipeline bubbles.
• Context Parallelism (CP) & Ring Attention: Sequence splitting across GPUs with ring-based Key-Value communication.
• Rendezvous System: Dual initialization via Shared FileStore (LITETORCH_RENDEZVOUS_FILE) and TCP sockets for massive GPU scale (1000+ GPUs).

3. Dual-Platform Hardware Acceleration

• NVIDIA CUDA: cuBLAS, cuDNN, Blackwell B200 and Rubin R100 support (sm_100/sm_105+), auto-architecture discovery (-arch=native), TensorFloat-32 (TF32) acceleration, and native FP8/FP4 precision.
• AMD ROCm / HIP: Full hipcc compilation with rocBLAS and MIOpen support.
• OpenCL & CPU Fallback: Automatic hardware detection falling back to OpenCL or multi-threaded CPU execution.

4. Advanced Memory Management

• Activation Checkpointing: Recomputes intermediate layer activations on the backward pass to reduce activation VRAM by 60% to 70%.
• LRU Memory Eviction & Caching Allocator: Smart block caching with automatic LRU swapping between Host RAM and GPU VRAM.
• Mixed Precision (AMP) & Quantization: Automatic FP16/BF16/FP8/FP4 training with dynamic loss scaling via GradScaler and TF32 execution.
• CUDA Graph Capture: Stream recording to eliminate host-device launch latency.

5. JIT Kernel Fusion & Autograd Engine

• Full JIT Autograd Support: Automatic symbolic differentiation (Tracer::derivative) and gradient backpropagation through JITNode across training and inference.
• Multi-Operator Fusion: Fused elementwise, activation, and normalization kernels (lt.jit.fused_bias_relu, lt.jit.fused_bias_gelu, lt.jit.fused_residual_add, lt.jit.fused_residual_gelu) eliminating intermediate VRAM allocations.

Installation & Setup Guide

LiteTorch contains native C++ extensions for maximum runtime performance. Follow the platform-specific instructions below:

1. Linux & Google Colab

Step 1: Install Prerequisites

LiteTorch requires a C++ compiler (g++ >= 7.0) and Python development headers.

• Ubuntu / Debian / Google Colab:

  sudo apt-get update
  sudo apt-get install -y build-essential python3-dev
  

• Fedora / RHEL / CentOS:

  sudo dnf groupinstall "Development Tools" -y
  sudo dnf install python3-devel -y
  

Step 2: Install via pip

pip install --upgrade litetorch

Step 3: (Optional) GPU Acceleration

• NVIDIA GPU: Ensure NVIDIA CUDA Toolkit (nvcc) is in your PATH. LiteTorch will automatically detect and engage native CUDA acceleration.
• AMD GPU: Ensure ROCm / HIP (hipcc) is installed.

2. Windows

Step 1: Install C++ Build Tools

On Windows, Python requires a C++ compiler to build extensions. Choose one of the two options:

• Option A: Microsoft Visual C++ Build Tools (Recommended)

  1. Download the official installer: vs_BuildTools.exe
  2. Run the installer, select Desktop development with C++, and click Install.
  3. Alternatively, install automatically via PowerShell (Admin):

     winget install --id Microsoft.VisualStudio.2022.BuildTools --exact --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;includeRecommended"
     

• Option B: MinGW-w64 GCC

  1. Install MinGW-w64 (GCC 10+).
  2. Add the mingw64\bin folder to your Windows User/System PATH environment variable.

Step 2: Install via pip

Open a new Terminal / Command Prompt and run:

pip install --upgrade litetorch

3. Verify Installation

Run the following Python command to verify that LiteTorch and the hardware compute backend are initialized:

import litetorch as lt

device = lt.auto_device()
print(f"LiteTorch ready! Active compute device: {device}")

# Run a quick test tensor calculation
a = lt.Tensor.from_vector([1.0, 2.0, 3.0, 4.0], [2, 2], device, requires_grad=True)
b = a * 2.0 + 1.0
print("Output:", b.to_vector())

4. Install from Source (Developers)

git clone https://github.com/nguyenminh20000/Litetorch-.git
cd Litetorch-
pip install -r requirements.txt
pip install -e .

Architecture Overview

graph TD
    A["Python API (import litetorch as lt)"] --> B["C++ Binding Layer (pybind11)"]
    B --> C["Core Tensor & Autograd DAG Engine"]
    C --> D["Memory Management (LRU Eviction, Caching Allocator, Checkpointing)"]
    C --> E["Distributed Engine (FSDP, ZeRO-3, TP, PP, CP, NCCL/RCCL)"]
    D --> F["Compute Backends"]
    E --> F
    F --> G1["NVIDIA CUDA Backend (cuBLAS, cuLt, FlashAttention, Blackwell)"]
    F --> G2["AMD ROCm Backend (rocBLAS, MIOpen)"]
    F --> G3["OpenCL GPU Backend"]
    F --> G4["Multi-Threaded CPU Engine"]

Code Examples

Step 1: Install Build Tools

Install Microsoft C++ Build Tools or MinGW-w64 (g++ >= 7.0).

Step 2: Install LiteTorch via pip

pip install litetorch

Quick Start Examples

1. Simple Linear Regression

import litetorch as lt

device = lt.auto_device()
x = lt.Tensor.from_vector([1.0, 2.0, 3.0, 4.0], [4, 1], device, False)
y = lt.Tensor.from_vector([2.0, 4.0, 6.0, 8.0], [4, 1], device, False)

linear = lt.nn.Linear(1, 1, True)
linear.to(device)
optimizer = lt.optim.SGD(linear.parameters(), lr=0.01)

for epoch in range(100):
    optimizer.zero_grad()
    pred = linear.forward(x)
    loss = lt.Ops.mse_loss(pred, y)
    loss.backward()
    optimizer.step()

2. Transformer Decoder Block

import litetorch as lt

class TransformerDecoderBlock(lt.nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.norm1 = lt.nn.LayerNorm([embed_dim])
        self.norm2 = lt.nn.LayerNorm([embed_dim])
        self.q_proj = lt.nn.Linear(embed_dim, embed_dim, False)
        self.k_proj = lt.nn.Linear(embed_dim, embed_dim, False)
        self.v_proj = lt.nn.Linear(embed_dim, embed_dim, False)
        self.out_proj = lt.nn.Linear(embed_dim, embed_dim, False)
        self.fc1 = lt.nn.Linear(embed_dim, embed_dim * 4, True)
        self.fc2 = lt.nn.Linear(embed_dim * 4, embed_dim, True)

    def forward(self, x):
        norm_x = self.norm1.forward(x)
        q = self.q_proj.forward(norm_x)
        k = self.k_proj.forward(norm_x)
        v = self.v_proj.forward(norm_x)
        attn_out = lt.Ops.flash_attention(q, k, v, is_causal=True)
        attn_out = self.out_proj.forward(attn_out)
        x = lt.Ops.add(x, attn_out)

        norm_x2 = self.norm2.forward(x)
        mlp_h = lt.Ops.gelu(self.fc1.forward(norm_x2))
        mlp_out = self.fc2.forward(mlp_h)
        out = lt.Ops.add(x, mlp_out)
        return out

    def parameters(self):
        return (
            self.norm1.parameters() + self.norm2.parameters() +
            self.q_proj.parameters() + self.k_proj.parameters() +
            self.v_proj.parameters() + self.out_proj.parameters() +
            self.fc1.parameters() + self.fc2.parameters()
        )

3. Mixed Precision Training with GradScaler & AdamW

import litetorch as lt

device = lt.auto_device()
model = TransformerDecoderBlock(128, 4)
model.to(device)

optimizer = lt.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01)
scaler = lt.amp.GradScaler(init_scale=65536.0)

x = lt.Tensor.from_vector([0.1] * (2 * 16 * 128), [2, 16, 128], device, False)
target = lt.Tensor.from_vector([0.0] * (2 * 16 * 128), [2, 16, 128], device, False)

for step in range(50):
    optimizer.zero_grad()
    with lt.amp.AutocastGuard(True):
        logits = model.forward(x)
        loss = lt.Ops.mse_loss(logits, target)
    
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    
    if step % 10 == 0:
        print(f"Step {step} | Loss: {loss.item():.6f}")

4. Fully Sharded Data Parallel (FSDP) Training

import litetorch as lt

class LargeModel(lt.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = lt.nn.Linear(1024, 4096, True)
        self.layer2 = lt.nn.Linear(4096, 1024, True)

    def forward(self, x):
        h = lt.Ops.relu(self.layer1.forward(x))
        return self.layer2.forward(h)

model = LargeModel()
lt.distributed.FSDP.fully_shard(model)

5. JIT Operator Fusion & Memory Management

import litetorch as lt

device = lt.auto_device()

# Define and trace fused activation function
def fused_block(args):
    return lt.Ops.gelu(lt.Ops.add(args[0], args[1]))

t1 = lt.Tensor.from_vector([1.0, -2.0, 3.0], [3], device, False)
t2 = lt.Tensor.from_vector([0.5, 1.5, -2.0], [3], device, False)

jitted_fn = lt.jit.Tracer.trace([t1, t2], fused_block, "fused_gelu_add")
out = jitted_fn([t1, t2])
print("JIT Fused Output:", out.to_vector())

# Reclaim host and device cached memory
lt.empty_cache()

Verification & Benchmarks

License

LiteTorch is released under the MIT License.