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80 changes: 80 additions & 0 deletions .github/configs/CONFIGS.md
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# Search Space Configuration

This file defines which benchmark configurations to run for each model, GPU, and precision combination.

## Quick Start

Add a new configuration by following this pattern:
```yaml
model_name:
precision:
gpu_type:
sequence_length:
- { tp: [1, 2, 4], conc: { start: 4, end: 64 } }
```

## Field Definitions

### Required Fields

- **`tp`**: Tensor Parallelism (number of GPUs)
- Single value: `tp: 4`
- Multiple values: `tp: [2, 4, 8]`

- **`conc`**: Concurrency (number of simultaneous requests)
- `start`: First value to test
- `end`: Last value to test
- `step`: Multiplier (default: 2)
- Example: `{start: 4, end: 64}` → tests [4, 8, 16, 32, 64]

### Optional Fields

- **`ep`**: Expert Parallelism for MoE models (default: 1)

- **`dp_attention`**: Data Parallel Attention (default: `"false"`)

## Examples

### Basic configuration
```yaml
gptoss:
fp4:
h100:
1k1k: # 1024 input, 1024 output
- { tp: [2, 4, 8], conc: { start: 4, end: 64 } }
```
This tests 15 combinations: 3 TP values × 5 concurrency values

### Configuration with optional fields
```yaml
dsr1:
fp4:
b200-trt:
1k1k:
- { tp: 4, ep: 4, dp_attention: "true", conc: { start: 256, end: 256 } }
```

### Custom step factor
```yaml
llama:
fp8:
b200:
1k8k:
- { tp: 2, conc: { start: 4, end: 64, step: 4 } }
```
This tests [4, 16, 64] (multiplies by 4 instead of default 2)

## Key Points

1. **Models**: `gptoss`, `llama`, `dsr1`
2. **Precisions**: `fp4`, `fp8`
3. **Sequence lengths**: `1k1k`, `1k8k`, `8k1k` (input×output)
4. Each entry expands to test all combinations of TP and concurrency values
5. There are comments throughout the yaml that were ported over from bash scripts describing what parallelism settings should be set depending on concurrency -- keep an eye out for those.

## Testing Your Changes

Run the flattening script to validate:
```bash
python utils/flatten_matrix.py
```
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