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A Delphi/Object Pascal ML framework: GPT-style Transformers + GPU training + tabular DataFrames

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# Dolphin 🐬

**Dolphin** is a Delphi-native Pascal GPT framework for building and iterating on GPT-style models.  
It targets developers who want full control over tokenization, vocabulary, and neural network logic — implemented in Object Pascal.

![Logserver](https://raw.githubusercontent.com/magnolima/dolphin/main/logserver/html/trainmetrics.jpg)  
Logserver - this UI can be found in the logserver folder

## Features

### Core Architecture
- **GPT-like architecture** implemented entirely in Delphi
- **Modular feed-forward and transformer blocks** for rapid iteration and benchmarking
- **Pre-Norm architecture** with residual connections
- **Multiple activation functions** — GELU, ReLU, Swish, SELU, ELU, LeakyReLU

### GPU Acceleration (CUDA)
- **NVIDIA CUDA support** — Automatic CPU/GPU dispatch based on computational intensity
- **Hybrid precision** — FP64 on CPU (Delphi `Double` / MrMath default) and FP32 on GPU for performance
- **GPU Memory Pool** — Intelligent buffer reuse and memory management
- **Weight caching** — Persistent GPU-resident weights with cache hit tracking
- **Native CUDA kernels** — MatMul, Softmax, GELU, LayerNorm, RoPE, AdamW, and more
- See [CUDA Documentation](Lib/Cuda/README.MD) for detailed kernel reference

### Training Features
- **Training Objectives** — Control pipeline stop points or modes (TokenizerOnly, EmbeddingsOnly, SentenceEmbeddings, PretrainOnly, FullModel, FineTune)
- **True Batch Training** — Efficient batch processing with gradient accumulation
- **Micro Batching** — Memory-efficient training with configurable batch subdivision
- **GPU Auto Batch Size** — Automatic batch size optimization based on available VRAM
- **LLM-driven Hyperparameter Tuning (Optional)** — At epoch end, an external LLM can propose hyperparameter updates (for example `LearningRate`, `GradientClipNorm`, `Dropout`, `WeightDecay`, and micro-batch settings). Requires `LLMTUNNING` and optional `MakerAi` integration.
- **Sequence Modes**:
  - **Packed (Stream)** — Concatenates sequences for maximum throughput
  - **Chunk** — Splits long sequences with configurable stride for overlap
  - **Truncate** — Clips sequences to maximum length
- **Early Stopping** — Monitors validation loss/perplexity with patience and best-weights restoration
- **JSONL TrainingLogs** — Real-time training progress with Python console support

### Data Science & DataFrames
- **TDataFrame Component** — A columnar, Pandas-like DataFrame for data science and ML data loading
- **Native Data Ingestion** — Load directly from any `TDataSet` (e.g., FireDAC `TFDMemTable`, SQL queries) with automatic type mapping and pre-allocation, alongside multi-threaded CSV loading.
- **Structured Data Processing** — Min/Max scaling, Z-Score Normalization, and Label Encoding.
- **Data Shuffling** — Fast Fisher-Yates row shuffling via virtual indices (zero data copying).
- **GPU Integration & Batching** — Direct O(1) batch extraction to `TDeviceMatrix` without intermediate memory copies.
- **IDataProvider Architecture** — Generic `TDataFrameProvider` wraps DataFrames to yield `TTensorBatch` (separate `Features` and `Targets` matrices) for training loops, cleanly decoupling structured data from NLP token pipelines.
- **Zero-Copy Dataset Splits** — Dynamically split DataFrames into Train and Validation views using index slicing without duplicating data in memory.
- **LoRA & Fine-Tuning Ready** — Feeds purely numeric feature/target tensors directly into the network, bypassing token embeddings for structured tabular training.

### Positional Encoding
- **Sinusoidal Positional Encoding** — Classic fixed positional embeddings
- **Rotary Position Embeddings (RoPE)** — Modern relative position encoding with GPU acceleration

### Tokenization & Vocabulary
- **Custom BPE tokenizer** (Byte Pair Encoding) with vocabulary management  
- **SQLite database backend** for persistent vocabulary and merge rules
- **Hugging Face export** — Vocabulary/merges export compatible with Hugging Face tokenizers

### Precision & Memory
- **FP16 Matrix Support** — Half-precision floating point for reduced memory usage
- **Optimized memory management** — Pooled allocations, buffer reuse

### Generation & Inference
- **Advanced Generation** — Top-K, Top-P (Nucleus), and Temperature sampling
- **Greedy decoding** — Deterministic output

### Configuration
- **Flexible Configuration** — JSON-based configuration and CLI arguments
- **Offline and local-friendly** — No external runtimes required
- **Multi-GPU Support** — Specify exactly which GPU the model will use using configuration or query available GPUs at runtime.

### Upcoming features
- LoRA (Low-Rank Adaptation) support for parameter-efficient fine-tuning (train small adapter matrices while keeping base weights frozen). For more information: https://huggingface.co/docs/diffusers/training/lora

## Available Units

| Unit | Description |
| :--- | :--- |
| **PascalGPT.Attention.pas** | Implements **scaled dot-product attention** mechanisms, including Q/K/V projections, Softmax, **causal masking**, and **RoPE** support. |
| **PascalGPT.Attention.Training.pas** | Training-optimized attention with **GPU-accelerated forward/backward passes**, activation checkpointing, and memory-efficient buffer management. |
| **PascalGPT.Attention.Inference.pas** | Inference-optimized attention for **generation** and evaluation. |
| **PascalGPT.BPETokenizer.pas** | **Byte-level Byte Pair Encoding (BPE) processor** for token encoding/decoding and merge rule calculation. |
| **PascalGPT.CUDA.pas** | **NVIDIA CUDA driver core management**, handling context creation, device memory, and kernel launches. |
| **PascalGPT.CUDA.Kernels.pas** | Manages **PTX kernel loading** for GPU operations (MatMul, GELU, Softmax, LayerNorm, RoPE, AdamW, etc.). |
| **PascalGPT.CUDA.Math.pas** | High-level math interface with **adaptive CPU/GPU dispatch**, performance calibration, and **memory pool** management. |
| **PascalGPT.CUDA.BatchBuffers.pas** | **Batch buffer management** for GPU training with efficient memory allocation. |
| **PascalGPT.Cuda.DeviceAttribute.pas** | Helper unit for **CUDA device properties** and capability querying. |
| **PascalGPT.DataFrame.pas** | A **Pandas-like columnar DataFrame** for data science, handling multi-threaded CSV loading, Normalization, Label Encoding, and direct GPU batch extraction. |
| **PascalGPT.Dataset.pas** | Dataset management for processing large text files and organizing `TTrainingExample` records. |
| **PascalGPT.DatasetUtils.pas** | Utilities for text preprocessing, **sequence mode handling** (Packed/Chunk/Truncate), and corpus diversity analysis. |
| **PascalGPT.Embeddings.pas** | **Token and positional embedding layer** with **Xavier/Glorot initialization** and sinusoidal encoding. |
| **PascalGPT.FP16Matrix.pas** | Implements **Half-Precision (FP16) matrix operations** for 50% memory reduction. |
| **PascalGPT.FeedForward.pas** | Feed-forward neural network layer with multiple **activation functions** and GPU acceleration. |
| **PascalGPT.Generation.pas** | **Text generation strategies** — Greedy, Temperature, Top-K, and Top-P (Nucleus) sampling. |
| **PascalGPT.Helper.Configuration.pas** | Manages **JSON configuration files** for model, training, and runtime settings. |
| **PascalGPT.Helpers.pas** | Core utilities including matrix helpers, Softmax, **RoPE functions**, and Safetensors metadata. |
| **PascalGPT.Instructor.pas** | Specialized training loop for **instruction tuning** datasets (like Alpaca). |
| **PascalGPT.IO.SafeTensors.pas** | **Safetensors format** support for model weight import/export. |
| **PascalGPT.Linear.pas** | **Fully Connected/Dense layer** for projections and LM Head. |
| **PascalGPT.Model.pas** | Top-level GPT model structure managing layer orchestration and forward/backward passes. |
| **PascalGPT.Optimizer.pas** | **Adam and AdamW optimization** with GPU-accelerated optimizer step. |
| **PascalGPT.Storage.pas** | **SQLite database backend** for vocabulary, BPE rules, and embeddings persistence. |
| **PascalGPT.TestMatMul.pas** | Testing and benchmarking utilities for **matrix multiplication performance** validation. |
| **PascalGPT.Training.pas** | Training loop implementation with **micro-batching**, **early stopping**, and progress callbacks. |
| **PascalGPT.Training.Logger.pas** | **JSONL training logger** for real-time metrics and external monitoring. |
| **PascalGPT.Training.VRAMOptimization.pas** | **VRAM optimization strategies** including gradient accumulation and memory-efficient training. |
| **PascalGPT.TransformerBlock.pas** | Core transformer block with **Layer Normalization** and **residual connections** (Pre-Norm). |
| **PascalGPT.Types.pas** | Central repository for **common types**, configuration records, and constants. |
| **PascalGPT.Vocabulary.pas** | **Token-to-ID/ID-to-token mapping** with BPE merge rule integration. |

## Dependencies

- Required: **[MrMath](https://github.com/mikerabat/mrmath)**, a high-performance numerical library for Delphi.  
  It provides matrix operations, linear algebra, and numerical routines similar in purpose to Python's **NumPy**, serving as the computational backbone for neural and tokenization modules.
- Optional (only when using LLM Optimizer): **[MakerAi](https://github.com/gustavoeenriquez/MakerAi)**.
- LLM Optimizer usage note:
  - Enable `LLMTUNNING` only if you want LLM-driven hyperparameter proposals.
  - If you do not want the optimizer, leave `LLMTUNNING` undefined and keep `UseLLMOptimizer` disabled.

## Architecture Overview

```
           ┌─────────────────────────────┐
           │         Input Text          │
           └──────────────┬──────────────┘
                          │
                          ▼
               ┌────────────────────┐
               │  BPE Tokenizer     │
               │ (PascalGPT.BPETokenizer) │
               └────────────────────┘
                          │
                   Tokens (IDs)
                          │
                          ▼
             ┌──────────────────────────┐
             │   Embedding Layer        │
             │ (PascalGPT.Embeddings)   │
             │  + Sinusoidal / RoPE     │
             └──────────────────────────┘
                          │
                          ▼
          ┌────────────────────────────────┐
          │       Transformer Block         │
          │ (PascalGPT.TransformerBlock)    │
          │  ├─ Attention (multi-head)      │
          │  │   └─ RoPE / Causal Mask      │
          │  └─ FeedForward (GELU/ReLU)     │
          └────────────────────────────────┘
                          │
                          ▼ (N layers)
                          │
                          ▼
             ┌──────────────────────────┐
             │   Output Layer / Logits  │
             │(Prediction of next token)│
             └──────────────────────────┘
                          │
                          ▼
           ┌─────────────────────────────┐
           │     Decoded Text Output     │
           └─────────────────────────────┘  
```

## Training Configuration

![Logserver](https://raw.githubusercontent.com/magnolima/dolphin/main/logserver/html/trainsettings.jpg)  
Logserver - this UI can be found in the logserver folder
### Sequence Modes

| Mode | Description |
|------|-------------|
| **Truncate** | Clips sequences to `MaxSeqLen`. Simple but may lose data. |
| **Chunk** | Splits long sequences into overlapping chunks using `ChunkStride`. Preserves all data. |
| **Packed** | Concatenates multiple sequences into a single stream for maximum GPU utilization. Most efficient. |

### Early Stopping

```json
{
  "EarlyStopping": {
    "Enabled": true,
    "Monitor": "val_loss",
    "Mode": "min",
    "MinDelta": 0.01,
    "Patience": 3,
    "MinEpochs": 5,
    "RestoreBestWeights": true
  }
}
```

### Micro Batching

Enables training with large effective batch sizes on limited VRAM:

```json
{
  "Training": {
    "MicroBatchSize": 4,
    "MicroBatchMaxTokens": 2048
  }
}
```

### Training Objectives

Control where the training pipeline stops, enabling specialized workflows for different use cases:

| Objective | Description | Exports | Model Created |
|-----------|-------------|---------|---------------|
| **FullModel** | Default. Complete pipeline with full model training | safetensors, config.json, vocab | ✅ Yes |
| **TokenizerOnly** | Stop after BPE tokenizer training | vocab.json, merges.txt | ❌ No |
| **EmbeddingsOnly** | Stop after embedding training | embeddings.npy, embeddings.cached, vocab | ❌ No |
| **SentenceEmbeddings** | Train sentence embeddings (Contrastive Loss) | safetensors, config.json, vocab | ✅ Yes |
| **PretrainOnly** | Train model but save as checkpoint | safetensors (checkpoint) | ✅ Yes |
| **FineTune** | Same as FullModel (starts from pretrained weights) | safetensors, config.json, vocab | ✅ Yes |

**Configuration:**

```json
{
  "General": {
    "TrainingObjective": "EmbeddingsOnly"
  }
}
```

**Use Cases:**
- `TokenizerOnly` — Build vocabulary for external tools
- `EmbeddingsOnly` — Generate raw token embeddings
- `SentenceEmbeddings` — Train model for semantic search (Contrastive Learning). *Unlike Normal LLMs that predict the next word for generation, Sentence Embeddings compress full sentences into meaning vectors to measure similarity between them.*
- `PretrainOnly` — Create checkpoints for continued training
- `FullModel` — Production-ready normal LLM models with full training (predicting the next word).

If not specified, defaults to `FullModel` (no breaking changes to existing configs).

## Getting Started

1. Clone the repository  
   ```bash
   git clone https://github.com/magnolima/PascalGPT/PascalGPT.git
   ```

2. Open `Demos/pascalgpt.trainer.dpr` in Delphi 12+ and build the project (Release mode recommended).

3. Run the demo with a dataset:
   ```bash
   pascalgpt.trainer.exe [-c config.json] [-train config.train.json] [-listgpu] dataset.txt
   ```

- `-c`: Main configuration file (model params, paths).
   - `-train`: Training configuration overrides (epochs, batch size).
   - `-listgpu`: Lists available CUDA GPU devices and their IDs, then exits.
   - `dataset.txt`: Path to your training corpus.

You can select a specific GPU by defining `DefaultGPUIndex` inside the `"General"` section of `config.json`.

## GPU Requirements

For CUDA acceleration:
- **NVIDIA GPU** with Compute Capability 6.0+ (Pascal or newer)
- **CUDA Toolkit** installed (for PTX compilation)
- **nvcuda.dll** available in system PATH

The framework automatically falls back to CPU if CUDA is unavailable.

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