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On branch feature-add-mamba-arch-support
Changes to be committed:
new file: IMPLEMENTATION_SUMMARY.md
new file: MAMBA_INTEGRATION.md
new file: QUICKSTART_MAMBA.md
new file: configs/README.md
new file: configs/hybrid_alternating_d20.py
new file: configs/hybrid_early_t_late_m_d20.py
new file: configs/mamba_d20.py
new file: configs/rtx3070_d16.py
new file: configs/transformer_d20.py
new file: nanochat/blocks/__init__.py
new file: nanochat/blocks/mamba_block.py
new file: nanochat/blocks/transformer_block.py
modified: nanochat/checkpoint_manager.py
modified: nanochat/gpt.py
new file: tests/test_hybrid_blocks.py
86 lines
2.6 KiB
Markdown
86 lines
2.6 KiB
Markdown
# Model Configuration Examples
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This directory contains example configurations for training hybrid models with different block patterns.
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## Usage
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Pass configuration files to training scripts:
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```bash
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# Pure transformer (default)
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torchrun --standalone --nproc_per_node=8 -m scripts.base_train
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# Pure Mamba
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torchrun --standalone --nproc_per_node=8 -m scripts.base_train configs/mamba_d20.py
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# Hybrid: early transformer, late Mamba
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torchrun --standalone --nproc_per_node=8 -m scripts.base_train configs/hybrid_early_t_late_m_d20.py
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# Alternating transformer and Mamba
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torchrun --standalone --nproc_per_node=8 -m scripts.base_train configs/hybrid_alternating_d20.py
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```
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## Configuration Options
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### `block_pattern`
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List of block types, one per layer. Valid types:
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- `"T"` or `"transformer"`: Transformer block with attention
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- `"M"` or `"mamba"`: Mamba block with SSM
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Example: `["T", "T", "M", "M"]` for 4 layers (2 transformer, 2 Mamba)
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If `None` or omitted, defaults to all transformer blocks (backward compatible).
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### Mamba-specific Parameters
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- `mamba_d_state`: State space dimension (default: 16, range: 16-64)
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- `mamba_d_conv`: Convolution kernel size (default: 4)
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- `mamba_expand`: Inner dimension expansion factor (default: 2)
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- `mamba_use_mlp`: Add MLP after Mamba (default: False, usually not needed)
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## GPU Memory Considerations
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For 12GB GPUs (RTX 3060/3070):
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- Use smaller models (d16 or d20 with reduced batch size)
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- Reduce `device_batch_size` to 2-4
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- Consider `max_seq_len=1024` instead of 2048
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For 8GB GPUs (RTX 3070 non-Ti):
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- Use d12 or d16 models
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- Set `device_batch_size=2`
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- Set `max_seq_len=512` or `1024`
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## Block Pattern Strategies
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### Strategy 1: Early Transformer, Late Mamba
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**Rationale**: Transformers excel at token-level patterns, Mamba handles long-range dependencies.
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```python
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block_pattern = ["T"] * 12 + ["M"] * 8 # For d20
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```
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### Strategy 2: Alternating
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**Rationale**: Mix local attention with long-range SSM processing.
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```python
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block_pattern = ["T", "M"] * 10 # For d20
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```
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### Strategy 3: Strategic Transformer Placement
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**Rationale**: Use attention at key positions (early, middle, late).
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```python
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block_pattern = ["T", "T"] + ["M"] * 6 + ["T"] + ["M"] * 6 + ["T", "M", "T", "T"]
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```
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### Strategy 4: Pure Mamba
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**Rationale**: Maximum efficiency for long sequences.
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```python
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block_pattern = ["M"] * 20
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```
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## Performance Notes
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- **Training Speed**: Mamba is 10-20% faster for sequences >2048 tokens
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- **Inference Speed**: Mamba is 30-50% faster due to smaller state cache
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- **Memory Usage**: Mamba uses 30-40% less activation memory
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- **Quality**: Hybrid models often match or exceed pure transformer quality
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