# Spider-FLEXITOKENS FP8 Training

FP8 training pipeline for [Spider-FLEXITOKENS](https://huggingface.co/CLIWorks/Spider-FLEXITOKENS) on NVIDIA Blackwell GPUs (sm_120) using **torchao Float8Linear** and optional **TileKernels** fused MoE routing.

## Architecture

Spider is a Recurrent-Depth Transformer (RDT) with:
- **1B parameters** (996M), hidden_size=2048
- **Byte-level vocab**: 272 tokens (256 UTF-8 bytes + 16 specials: BOS=257, EOS=258, PAD=256)
- **6 recurrent layers** with MoE (32 experts, top-2 routing) + MLA attention
- **2 prelude + 2 coda** dense layers
- **Engram conditional memory** at recurrent layers 1 and 4
- **FlexiTokens** via BoundaryPredictor + downsample/upsample
- **ACT halting** with LTI injection + LoRA adapter per loop iteration
- Gradient checkpointing on recurrent layers

## Training Scripts

| File | Description |
|------|-------------|
| `fp8_train.py` | Pure torchao FP8 training (Python MoE loop) |
| `tk-train.py` | torchao FP8 + TileKernels fused MoE routing |
| `fp8-ready-spider.py` | Model definition (Python MoE loop) — used by `fp8_train.py` |
| `tk-spider.py` | Model definition (TileKernels MoE) — used by `tk-train.py` |

### fp8_train.py

Standard torchao FP8 training. Uses Python loops for MoE expert routing (top-2 gating, expert dispatch, weighted reduction). All linear layers (except embeddings, norms, routers, predictors) are converted to `Float8Linear` with the **tensorwise** recipe.

### tk-train.py

Same as `fp8_train.py` but replaces the Python MoE loop with **TileKernels** fused CUDA kernels:
- `topk_gate` — fused top-k gating
- `normalize_weight` — gate weight normalization
- `get_fused_mapping` — expert-token mapping computation
- `expand_to_fused` — token-to-expert expansion
- `reduce_fused` — expert output reduction
- `aux_fi` — auxiliary load-balancing loss

Falls back to Python MoE loop if TileKernels is unavailable.

## Requirements

- **GPU**: NVIDIA Blackwell (sm_120) or Hopper (sm_90)
- **CUDA**: 13.2+
- **PyTorch**: 2.11.0+ (with CUDA 13.x support)
- **torchao**: `pip install torchao` (for Float8Linear FP8 training)
- **TileKernels** (optional, for tk-train.py): included in `tile_kernels/` directory
- **bitsandbytes** (optional): for 8-bit AdamW optimizer
- **loguru** (optional): logging; falls back to stdlib logging
- **numpy**: for memory-mapped dataset loading

## Data

### FineWeb-Edu Byte-Level Dataset

Pre-tokenized FineWeb-Edu at byte level (~17B tokens, 14 shards):
- Format: uint16 `.bin` files, no header
- Vocab: BOS=257, EOS=258, PAD=256, raw bytes 0-255
- Each shard: ~2.5GB, ~1.2B tokens
- `metadata.json` with shard info

```python
from torch.utils.data import IterableDataset, DataLoader, get_worker_info
import numpy as np, os, torch

class LocalByteLevelDataset(IterableDataset):
    def __init__(self, data_dir, seq_len=2048, rank=0, world_size=1):
        self.seq_len = seq_len
        self.data_dir = data_dir
        self.rank = rank
        self.world_size = world_size
        self._files = self._discover_files()

    def _discover_files(self):
        import glob as _glob
        files = sorted(_glob.glob(os.path.join(self.data_dir, "**/*.bin"), recursive=True))
        return [f for i, f in enumerate(files) if i % self.world_size == self.rank]

    def __iter__(self):
        worker = get_worker_info()
        num_workers = worker.num_workers if worker else 1
        worker_id = worker.id if worker else 0
        files = [f for i, f in enumerate(self._files) if i % num_workers == worker_id]
        for filepath in files:
            arr = np.memmap(filepath, dtype=np.uint16, mode='r')
            pos = 0
            while pos + self.seq_len + 1 <= len(arr):
                chunk = arr[pos:pos + self.seq_len + 1]
                pos += self.seq_len + 1
                x = torch.tensor(chunk[:-1], dtype=torch.long)
                y = torch.tensor(chunk[1:], dtype=torch.long)
                y[y == 256] = -100  # PAD_ID = -100 for loss
                yield x, y
```

## Usage

### FP8 Training (torchao only)

```bash
python3 fp8_train.py \
    --seq_len 256 --micro_batch 112 --grad_accum 4 \
    --precision fp8 --n_loops 6
```

### FP8 + TileKernels Training

```bash
# Make TileKernels importable
export PYTHONPATH="$HOME/TileKernels:$PYTHONPATH"

python3 tk-train.py \
    --seq_len 256 --micro_batch 112 --grad_accum 4 \
    --precision fp8 --n_loops 6
```

### Resume from Checkpoint

```bash
python3 fp8_train.py --resume checkpoints-fp8/spider-step2650.pt
```

### Fresh Start (Load Weights, Reset Optimizer)

```bash
python3 fp8_train.py --resume checkpoints-fp8/spider-step2650.pt --reset_steps
```

### With torch.compile

```bash
python3 fp8_train.py --compile --precision fp8
```

### Mock Data (Quick Test)

```bash
python3 fp8_train.py --mock_data --max_steps 20 --seq_len 128 --micro_batch 2
```

## CLI Flags

| Flag | Default | Description |
|------|---------|-------------|
| `--seq_len` | 2048 | Sequence length |
| `--micro_batch` | 64 | Micro batch size per GPU |
| `--grad_accum` | 1 (env: `GRAD_ACCUM`) | Gradient accumulation steps |
| `--n_loops` | 6 (env: `N_LOOPS`) | RDT loop iterations |
| `--lr` | 3e-4 (env: `LR`) | Peak learning rate |
| `--precision` | auto | `auto`, `fp8`, or `bf16` |
| `--compile` | off | Enable `torch.compile(mode="default")` |
| `--resume` | auto-detect | Path to checkpoint |
| `--reset_steps` | off | Load weights, zero optimizer, reset step=0 |
| `--ckpt_dir` | `checkpoints-fp8` / `checkpoints-tk` | Checkpoint directory |
| `--data_dir` | `/home/lamcodealong/fineweb_bytelevel` | Local .bin data directory |
| `--mock_data` | off | Use synthetic data for testing |
| `--max_steps` | 0 (unlimited) | Max training steps |
| `--no_gradient_checkpointing` | off | Disable grad checkpointing |

## Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `SEQ_LEN` | 2048 | Override `--seq_len` |
| `MICRO_BATCH` | 64 | Override `--micro_batch` |
| `GRAD_ACCUM` | 1 | Override `--grad_accum` |
| `N_LOOPS` | 6 | Override `--n_loops` |
| `LR` | 3e-4 | Override `--lr` |
| `TARGET_TOKENS` | 10B | Total training tokens |
| `CKPT_EVERY` | 50 | Steps between checkpoints |

## FP8 Configuration

- **Recipe**: `tensorwise` (axiswise incompatible with grad checkpointing + reshape on sm_120)
- **Module filter**: Skips `boundary_predictor`, `loop_embedding`, `engram`, `layernorm`, `norm`, `embed_tokens`, `lm_head`, `halt_predictor`, `router`
- **pad_inner_dim=True**: Pads linear layer inner dims for FP8 alignment
- **Warmup**: 2 fwd+bwd passes with dummy data before training starts (kernel compilation)

## Performance

Benchmarked on RTX PRO 6000 (Blackwell sm_120, 96GB VRAM):

| Config | Throughput | Peak VRAM |
|--------|-----------|-----------|
| FP8, mb=112, seq=256, ga=4 | ~11.8k tok/s | 25.3 GB |
| FP8, mb=64, seq=2048, ga=1 | ~2.2k tok/s | ~42 GB |

## Checkpoints

Latest checkpoint: `spider-step2650.pt` (3.8GB)
- Contains: `model_state_dict`, `optimizer_state_dict`, `step`, `epoch`, `cfg`, `best_loss`
- Step 2650, loss ~1.9

## TileKernels

TileKernels provides fused CUDA/Triton kernels for MoE operations. Only the MoE routing kernels are used here (no weight dtype changes):

- `tile_kernels.moe.topk_gate` — fused top-k gating
- `tile_kernels.moe.normalize_weight` — gate weight L2 normalization
- `tile_kernels.moe.get_fused_mapping` — compute expert-token dispatch mapping
- `tile_kernels.moe.expand_to_fused` — expand tokens to expert slots
- `tile_kernels.moe.reduce_fused` — reduce expert outputs back to tokens
- `tile_kernels.moe.aux_fi` — auxiliary load-balancing loss

Install from the included `tile_kernels/` directory:
```bash
cd tile_kernels && pip install -e .
```

## Tokenizer

Byte-level encoding (no learned tokenizer needed):

```python
def encode(text: str) -> list[int]:
    return [257] + list(text.encode('utf-8')) + [258]  # BOS + bytes + EOS

def decode(ids: list[int]) -> str:
    return bytes(i for i in ids if 0 <= i <= 255).decode('utf-8', errors='replace')
```

Special tokens: `PAD=256`, `BOS=257`, `EOS=258`, `IMG_START=0`, `IMG_END=1`, `IMG_PATCH=2`, `IMG_NEWLINE=3`, plus boundary and loop sentinels.

## Known Limitations

- **DeepGEMM**: Blocked on sm_120 (`Unknown recipe arch_major: 12`)
- **FlashMLA**: sm_100f kernels produce CUTLASS errors on sm_120
- **TransformerEngine**: `cublasLtGroupedMatrixLayoutInit_internal` symbol error
- **torchao axiswise recipe**: Incompatible with gradient checkpointing + reshape on sm_120
- **torch.compile reduce-overhead mode**: Has issues; use `mode="default"` only

## License

Same as Spider-FLEXITOKENS.