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[mxfp8 moe training] update readme with kernel microbenchmarks for dsv3 #3521

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45 changes: 45 additions & 0 deletions torchao/prototype/moe_training/README.md
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Expand Up @@ -222,6 +222,51 @@ cd benchmarks/prototype/moe_training/mxfp8
python roofline_unified.py --K=7168 --N=2048 --G=8 --power_limit_percent=100 --breakdown_M=131072 --plot_file=dsv3_rooflines.png
```

### MXFP8 Kernel Breakdown by Pass

The following table provides a detailed breakdown of all MXFP8 kernels used in the forward and backward passes, with shapes representative of **DeepSeekV3 671B** (dim=7168, hidden_dim=2048, total_tokens=131072, groups=8, block_size=32).

**Environment:**
- torch: `2.11.0.dev20251216+cu128`
- torchao: `0.15.0+gitd1305bc78`
- NVIDIA B200

| Pass | Kernel | Purpose | Input Shape | Time (µs) | Efficiency |
|------|--------|---------|-------------|-----------|------------|
| **Forward** | `triton_to_mxfp8_dim0` | Quantize A (activations) along dim0 | (131072, 7168) | 580.6 | 83.3% peak BW |
| **Forward** | `mxfp8_quantize_cuda_3d` | Quantize B (weights) along dim0 | (8, 2048, 7168) | 76.8 | 78.8% peak BW |
| **Forward** | `triton_mx_block_rearrange_2d_M_groups` | Convert A scales to blocked format | (131072, 224) | 198.7 | — |
| **Forward** | `triton_mx_block_rearrange_per_group_3d` | Convert B scales to blocked format | (8, 2048, 224) | 11.4 | — |
| **Forward** | `torch._scaled_grouped_mm` | 2D-3D scaled grouped GEMM | (131072, 7168) @ (8, 7168, 2048) | 1838.1 | 74.6% peak TFLOPS |
| **Backward (dA)** | `triton_to_mxfp8_dim0` | Quantize grad_out along dim0 | (131072, 2048) | 166.0 | 83.3% peak BW |
| **Backward (dA)** | `mxfp8_quantize_cuda_3d` | Quantize B along dim1 (N dimension) | (8, 2048, 7168) | 76.8 | 78.8% peak BW |
| **Backward (dA)** | `triton_mx_block_rearrange_2d_M_groups` | Convert grad_out scales to blocked format | (131072, 64) | 192.5 | — |
| **Backward (dA)** | `triton_mx_block_rearrange_per_group_3d` | Convert B scales to blocked format | (8, 7168, 64) | 11.0 | — |
| **Backward (dA)** | `torch._scaled_grouped_mm` | 2D-3D scaled grouped GEMM | (131072, 2048) @ (8, 2048, 7168) | 1838.1 | 74.6% peak TFLOPS |
| **Backward (dB)** | `mxfp8_quantize_cuda` | Quantize grad_out along dim1 (colwise) | (131072, 2048) | 191.7 | 72.1% peak BW |
| **Backward (dB)** | `mxfp8_quantize_cuda` | Quantize A along dim1 (colwise) | (131072, 7168) | 670.7 | 72.1% peak BW |
| **Backward (dB)** | `mx_block_rearrange_2d_K_groups_cuda` | Convert grad_out_t scales to blocked format | (2048, 4096) | 17.4 | — |
| **Backward (dB)** | `mx_block_rearrange_2d_K_groups_cuda` | Convert A_t scales to blocked format | (7168, 4096) | 31.6 | — |
| **Backward (dB)** | `torch._scaled_grouped_mm` | 2D-2D scaled grouped GEMM | (2048, 131072) @ (131072, 7168) | 2412.4 | 56.9% peak TFLOPS |

**Notes:**
- **Efficiency** is reported as percentage of peak achievable bandwidth (for memory-bound quantization kernels) or percentage of peak TFLOPS (for compute-bound GEMM kernels)
- Scale rearrangement kernels are not conventional memory bandwidth bound or compute bound kernels, so we report absolute runtime only
- Scale tensor shapes are derived from input shapes divided by `block_size=32` along the scaling dimension
- Detailed kernel breakdown with timing for all kernels is available in the roofline plots above (generated by `roofline_unified.py`)
- All kernels can be benchmarked individually using the scripts in `benchmarks/prototype/moe_training/mxfp8/`

**Benchmark Scripts:**
| Kernel Type | Benchmark Script |
|-------------|------------------|
| 2D Quantization (dim0/dim1) | `benchmarks/mx_formats/cast_bench.py` |
| 3D Quantization | `benchmarks/prototype/moe_training/mxfp8/bench_quantize_3d.py` |
| 2D M-groups Scale Rearrange | `benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_2d_M_groups.py` |
| 2D K-groups Scale Rearrange | `benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_2d_K_groups.py` |
| 3D Per-group Scale Rearrange | `benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_per_group_3d.py` |
| Grouped GEMM (2D-3D, 2D-2D) | `benchmarks/prototype/moe_training/bench_2d_3d_grouped_gemm.py` |
| Unified Roofline Analysis | `benchmarks/prototype/moe_training/mxfp8/roofline_unified.py` |

## Benchmark: single MoE layer forward + backward pass

| Model | total_M | N | K | bf16 time (ms) | mxfp8 time (ms) | speedup |
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