# trustformers-training TODO List

## Overview

The `trustformers-training` crate provides comprehensive training infrastructure for the TrustformeRS ecosystem.
It implements distributed training strategies, mixed precision training, quantization-aware training (QAT),
hyperparameter tuning, and advanced training methods like RLHF and continual learning.

**Key Responsibilities:**
- Trainer API for high-level training orchestration
- Distributed training (Data Parallelism, Model Parallelism, Pipeline Parallelism)
- ZeRO optimization (Stages 1/2/3)
- Mixed precision training (FP16, BF16, AMP)
- Quantization-Aware Training (QAT)
- Hyperparameter tuning (Grid search, Random search, Bayesian optimization)
- Advanced training methods (RLHF, Continual learning, Curriculum learning)
- Multi-node training with MPI
- Checkpoint management and resumption

---

## Current Status

### Implementation Status
✅ **PRODUCTION-READY** - Complete training infrastructure
✅ **DISTRIBUTED TRAINING** - MPI, NCCL, Gloo backends
✅ **ZERO OPTIMIZATION** - Stages 1/2/3 implemented
✅ **MIXED PRECISION** - FP16, BF16, AMP support
✅ **QAT** - Quantization-aware training infrastructure
✅ **ZERO COMPILATION ERRORS** - Clean compilation

### Feature Coverage
- **Distributed Training:** Data/Model/Pipeline Parallelism, ZeRO (Stages 1/2/3)
- **Precision:** Mixed precision (FP16/BF16), Automatic Mixed Precision (AMP)
- **Advanced Methods:** RLHF, Continual learning, Curriculum learning, Meta-learning
- **Hyperparameter Tuning:** Grid/Random/Bayesian/Hyperband/Population-based
- **Multi-Node:** Complete MPI communicator with all collective operations

---

## Completed Features

### Core Training Infrastructure

#### Trainer API

**High-level training orchestration**

- ✅ **Features**
  - Simple API for training transformers
  - Automatic device management
  - Gradient accumulation
  - Checkpoint saving/loading
  - Early stopping
  - Metrics logging (TensorBoard, W&B, Neptune, ClearML)

- ✅ **Training Loop**
  - Forward pass
  - Backward pass
  - Optimizer step
  - Learning rate scheduling
  - Gradient clipping
  - Validation

**Example:**
```rust
use trustformers_training::Trainer;

let trainer = Trainer::new(
    model,
    train_dataset,
    val_dataset,
    optimizer,
    args,
)?;

trainer.train()?;
```

---

#### Training Arguments

**Comprehensive configuration**

- ✅ **Basic Settings**
  - Number of epochs
  - Batch size (train/eval)
  - Learning rate
  - Weight decay
  - Warmup steps

- ✅ **Advanced Settings**
  - Gradient accumulation steps
  - Max gradient norm (clipping)
  - Logging frequency
  - Evaluation frequency
  - Save frequency

- ✅ **Distributed Settings**
  - Local rank, world size
  - Backend (nccl, gloo, mpi)
  - Gradient sync frequency

**Example:**
```rust
let args = TrainingArguments {
    num_epochs: 3,
    train_batch_size: 32,
    eval_batch_size: 64,
    learning_rate: 1e-4,
    warmup_steps: 1000,
    gradient_accumulation_steps: 4,
    max_grad_norm: 1.0,
    logging_steps: 100,
    eval_steps: 500,
    save_steps: 1000,
    ..Default::default()
};
```

---

### Distributed Training

#### Data Parallelism

**Replicate model across GPUs, partition data**

- ✅ **Implementation**
  - Distribute data across devices
  - Synchronize gradients with AllReduce
  - Average gradients across devices

- ✅ **Features**
  - Linear scalability with number of GPUs
  - Automatic data distribution
  - Gradient synchronization

**Example:**
```rust
let trainer = Trainer::new(model, data, optimizer, args)?
    .with_data_parallel()?;
```

---

#### Model Parallelism

**Split model across devices**

- ✅ **Tensor Parallelism**
  - Split individual layers across devices
  - Column parallelism (split columns of weight matrices)
  - Row parallelism (split rows of weight matrices)
  - Optimal for large layers (attention, FFN)

- ✅ **Pipeline Parallelism**
  - Split model vertically (layers across devices)
  - Microbatching for pipeline efficiency
  - GPipe-style pipeline scheduling
  - Reduces bubble time

**Example:**
```rust
let config = ModelParallelConfig {
    tensor_parallel_size: 4,
    pipeline_parallel_size: 2,
    ..Default::default()
};

let trainer = Trainer::new(model, data, optimizer, args)?
    .with_model_parallel(config)?;
```

---

#### ZeRO Optimization

**Zero Redundancy Optimizer - memory-efficient distributed training**

- ✅ **Stage 1: Optimizer State Partitioning**
  - Partition optimizer state (momentum, variance) across devices
  - Reduces memory by N (number of devices)
  - No communication overhead during forward/backward

- ✅ **Stage 2: Gradient Partitioning**
  - Partition gradients across devices
  - Further memory reduction
  - Communication during gradient reduction

- ✅ **Stage 3: Parameter Partitioning**
  - Partition model parameters across devices
  - Maximum memory reduction
  - Communication during forward/backward

**Example:**
```rust
let zero_config = ZeROConfig {
    stage: 3,  // Stage 1, 2, or 3
    offload_optimizer: true,  // CPU offload for optimizer
    offload_params: false,
    ..Default::default()
};

let trainer = Trainer::new(model, data, optimizer, args)?
    .with_zero(zero_config)?;
```

---

#### Communication Backends

- ✅ **NCCL** - NVIDIA Collective Communications Library
  - Optimized for NVIDIA GPUs
  - Best performance for multi-GPU training
  - All-reduce, broadcast, reduce, all-gather

- ✅ **Gloo** - Facebook's collective communications
  - CPU and GPU support
  - Cross-platform (Linux, macOS, Windows)
  - Good for heterogeneous clusters

- ✅ **MPI** - Message Passing Interface
  - Multi-node training
  - Complete collective operations
  - Standard for HPC

---

### Mixed Precision Training

#### FP16 (Half Precision)

**16-bit floating point training**

- ✅ **Features**
  - 2x memory reduction
  - 2-3x faster training (on modern GPUs)
  - Loss scaling to prevent underflow

- ✅ **Implementation**
  - Forward/backward in FP16
  - Optimizer state in FP32
  - Dynamic or static loss scaling

**Example:**
```rust
let args = TrainingArguments {
    fp16: true,
    fp16_opt_level: "O2",  // Optimization level
    fp16_loss_scale: 128.0,  // Static loss scale
    ..Default::default()
};
```

---

#### BF16 (Brain Float 16)

**16-bit format with FP32 range**

- ✅ **Features**
  - Same dynamic range as FP32
  - No loss scaling needed
  - Better numerical stability than FP16

- ✅ **Use Cases**
  - TPU training
  - Modern GPUs (Ampere, Ada)
  - More stable than FP16

---

#### Automatic Mixed Precision (AMP)

**Dynamic precision management**

- ✅ **Features**
  - Automatic loss scaling (dynamic)
  - Per-operation precision selection
  - Gradient scaler for numerical stability

- ✅ **Implementation**
  - Monitor gradient magnitudes
  - Adjust loss scale dynamically
  - Skip updates on overflow/underflow

---

### Quantization-Aware Training (QAT)

**Train with quantization in the loop**

- ✅ **Fake Quantization**
  - Simulate quantization during training
  - Quantize then dequantize (fake quantize)
  - Model learns to be robust to quantization

- ✅ **Observer System**
  - Collect activation/weight statistics
  - Calibrate quantization parameters
  - Min/max tracking

- ✅ **Quantization Schemes**
  - Per-tensor quantization
  - Per-channel quantization
  - Symmetric and asymmetric

**Example:**
```rust
let qat_config = QATConfig {
    observer_type: ObserverType::MinMaxObserver,
    quantization_scheme: QuantizationScheme::PerChannel,
    fake_quantize: true,
    ..Default::default()
};

let trainer = Trainer::new(model, data, optimizer, args)?
    .with_qat(qat_config)?;
```

---

### Advanced Training Methods

#### RLHF (Reinforcement Learning from Human Feedback)

**Align models with human preferences**

- ✅ **Components**
  - Reward model training
  - Proximal Policy Optimization (PPO)
  - KL divergence constraint
  - Reference model for stability

- ✅ **Pipeline**
  1. Supervised fine-tuning (SFT)
  2. Reward model training from human preferences
  3. RL optimization with PPO
  4. KL penalty to prevent drift

**Example:**
```rust
let rlhf_config = RLHFConfig {
    reward_model_path: "path/to/reward_model",
    kl_coef: 0.1,
    gamma: 0.99,
    lam: 0.95,
    ..Default::default()
};

let trainer = Trainer::new(model, data, optimizer, args)?
    .with_rlhf(rlhf_config)?;
```

---

#### Continual Learning

**Learn incrementally without forgetting**

- ✅ **Strategies**
  - Elastic Weight Consolidation (EWC)
  - Progressive Neural Networks
  - Learning without Forgetting (LwF)
  - Replay buffers

- ✅ **Features**
  - Task boundaries
  - Importance weights for parameters
  - Regularization to prevent forgetting

---

#### Curriculum Learning

**Train with progressively harder examples**

- ✅ **Strategies**
  - Length-based curriculum (short → long sequences)
  - Difficulty-based curriculum
  - Self-paced learning

- ✅ **Implementation**
  - Data ordering based on curriculum
  - Dynamic difficulty adjustment
  - Performance-based progression

---

#### Meta-Learning

**Learn to learn**

- ✅ **Algorithms**
  - Model-Agnostic Meta-Learning (MAML)
  - First-order MAML (FOMAML)
  - Reptile

- ✅ **Use Cases**
  - Few-shot learning
  - Fast adaptation to new tasks
  - Domain adaptation

---

### Hyperparameter Tuning

#### Grid Search

- ✅ **Features**
  - Exhaustive search over hyperparameter grid
  - Parallel execution
  - Best parameters selection

**Example:**
```rust
let param_grid = ParamGrid {
    learning_rate: vec![1e-5, 1e-4, 1e-3],
    batch_size: vec![16, 32, 64],
    warmup_steps: vec![500, 1000, 2000],
};

let best_params = grid_search(model, data, param_grid)?;
```

---

#### Random Search

- ✅ **Features**
  - Random sampling from hyperparameter distributions
  - More efficient than grid search
  - Configurable number of trials

---

#### Bayesian Optimization

- ✅ **Features**
  - Gaussian Process surrogate model
  - Acquisition function (Expected Improvement, UCB)
  - Sequential optimization
  - Sample-efficient

**Example:**
```rust
let bayes_config = BayesianOptConfig {
    n_trials: 50,
    acquisition_fn: AcquisitionFunction::ExpectedImprovement,
    ..Default::default()
};

let best_params = bayesian_optimization(model, data, bayes_config)?;
```

---

#### Hyperband

- ✅ **Features**
  - Successive halving with multiple brackets
  - Early stopping for poor configurations
  - Resource-efficient

---

#### Population-Based Training (PBT)

- ✅ **Features**
  - Evolution-based hyperparameter search
  - Online hyperparameter adaptation
  - Exploit and explore

---

### Multi-Node Training

#### MPI Communicator

**Complete MPI integration**

- ✅ **Collective Operations**
  - All-Reduce (sum gradients across nodes)
  - All-Gather (collect tensors from all nodes)
  - Reduce-Scatter (reduce then scatter results)
  - Send/Recv (point-to-point communication)
  - Broadcast (send tensor from root to all)

- ✅ **Process Management**
  - Rank and world size tracking
  - Process group creation
  - Barrier synchronization

- ✅ **Fault Tolerance**
  - Checkpoint recovery
  - Graceful degradation

**Example:**
```rust
let mpi_config = MPIConfig {
    backend: MPIBackend::MPICH,
    init_method: "tcp://node0:23456",
    world_size: 8,
    rank: 0,
};

let trainer = Trainer::new(model, data, optimizer, args)?
    .with_mpi(mpi_config)?;
```

---

### Checkpoint Management

- ✅ **Save Checkpoints**
  - Model state
  - Optimizer state
  - Scheduler state
  - Training metadata (epoch, step, best metric)

- ✅ **Load Checkpoints**
  - Resume training from checkpoint
  - Load for inference
  - Load for fine-tuning

- ✅ **Checkpoint Strategy**
  - Save best model (based on validation metric)
  - Save every N steps
  - Keep last K checkpoints
  - Automatic cleanup of old checkpoints

**Example:**
```rust
trainer.save_checkpoint("checkpoint-1000.pt")?;
trainer.load_checkpoint("checkpoint-1000.pt")?;
```

---

### Metrics and Logging

#### Experiment Trackers

- ✅ **TensorBoard** - Visualization dashboard
- ✅ **Weights & Biases (W&B)** - Experiment tracking
- ✅ **Neptune.ai** - ML metadata store
- ✅ **ClearML** - Experiment management
- ✅ **MLflow** - ML lifecycle platform

#### Logged Metrics

- ✅ Training loss, validation loss
- ✅ Learning rate
- ✅ Gradient norm
- ✅ Task-specific metrics (accuracy, F1, BLEU, etc.)
- ✅ System metrics (GPU utilization, memory usage)

---

## Testing & Documentation

- ✅ Comprehensive test suite
- ✅ Multi-node training guide
- ✅ RLHF documentation
- ✅ QAT best practices
- ✅ Distributed training tutorial

---

## Known Limitations

- Some very recent training techniques not yet implemented
- Fault tolerance could be enhanced

---

## Future Enhancements

### High Priority
- Enhanced fault tolerance (auto-resume on failure)
- More advanced RLHF techniques (DPO, RLAIF)
- Additional meta-learning algorithms

### Performance
- Communication optimization (gradient compression)
- Faster checkpoint saving/loading
- Better pipeline scheduling

### Features
- Automatic hyperparameter tuning based on hardware
- Multi-objective hyperparameter optimization
- Enhanced curriculum learning strategies

---

## Development Guidelines

### Code Standards
- **Use trustformers-core abstractions only**
- **File size limit:** <2000 lines per file
- **Error handling:** Use `Result<T, TrustformersError>`
- **Testing:** Integration tests for distributed training
- **Naming:** snake_case for all identifiers

### Build & Test Commands

```bash
# Run all tests
cargo nextest run -p trustformers-training --all-features

# Test distributed training (requires multiple GPUs)
cargo test -p trustformers-training --features distributed

# Test multi-node (requires MPI)
mpirun -np 4 cargo test -p trustformers-training test_mpi

# Check compilation
cargo check -p trustformers-training --all-features
```

---

**Last Updated:** Refactored for alpha.1 release
**Status:** Production-ready training infrastructure
**Multi-Node:** Full MPI support with all collective operations
