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Revert "[Profile] Adding metrics for Diffusion/DiT Single diffusion Pipeline (#668)" (#1724)

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Signed-off-by: gcanlin <canlinguosdu@gmail.com>
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Canlin Guo authored and GitHub committed 2026-03-08 00:11:47 +13:00
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docs/contributing/metrics.md
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@@ -69,20 +69,8 @@ With `--log-stats` enabled, the server will output detailed metrics logs after e
| rx_decode_time_ms | 111.865 | 31.706 |
| in_flight_time_ms | 2.015 | 2.819 |
### DiffusionStats
| Field | Value |
|---------------------------------|------------|
| batch_id | 1 |
| batch_size | 1 |
| diffusion_engine_exec_time_ms | 16,363.419 |
| diffusion_engine_total_time_ms | 16,234.849 |
| image_num | 1.000 |
| num_inference_steps | 50.000 |
| postprocess_time_ms | 67.685 |
| preprocess_time_ms | 60.106 |
| resolution | 640.000 |
| stage_gen_time_ms | 16,363.714 |
These logs include:
- **Overall summary**: total requests, wall time, average tokens/sec, etc.
@@ -92,9 +80,6 @@ With `--log-stats` enabled, the server will output detailed metrics logs after e
- **Transfer table**: data transfer and timing for each edge.
- **DiffusionStats**:preprocessing time, model execution time, postprocessing time, generated images, resolution, and inference steps.
You can use these logs to monitor system health, debug performance, and analyze request-level metrics as described above.
@@ -145,11 +130,6 @@ For **online inference** (serving mode), the summary is always per-request. `e2e
| `image_num` | Number of images generated (for diffusion/image stages). |
| `resolution` | Image resolution (for diffusion/image stages). |
| `postprocess_time_ms` | Diffusion/image: post-processing time in ms. |
| `preprocess_time_ms` | Diffusion/image: pre-processing time in ms (before model execution). |
| `diffusion_engine_exec_time_ms` | Diffusion/image: model execution time in ms, excluding pre-processing and post-processing. |
| `diffusion_engine_total_time_ms` | Diffusion/image: total diffusion engine time in ms, including pre-processing, model execution, and post-processing. |
| `num_inference_steps` | Number of inference steps for diffusion generation. |
---
@@ -163,8 +143,6 @@ For **online inference** (serving mode), the summary is always per-request. `e2e
| `in_flight_time_ms` | In-flight time in ms. |
### Expectation of the Numbers (Verification)
**Formulas:**
@@ -193,14 +171,3 @@ For each edge:
- 1->2: tx_time_ms (**18.790**) + rx_decode_time_ms (**31.706**) + in_flight_time_ms (**2.819**) = **53.315**
192.581 + 53.315 = **245.896** = transfers_total_time_ms, which matches the calculation (difference is due to rounding)
**diffusion_engine_total_time_ms**
- preprocess_time_ms: Time spent on input preprocessing
- diffusion_engine_exec_time_ms: Pure model execution time
- postprocess_time_ms: Time spent on output postprocessing
- diffusion_engine_total_time_ms: Total time spent in the diffusion engine
so `diffusion_engine_total_time_ms` = `diffusion_engine_exec_time_ms` + `preprocess_time_ms` + `postprocess_time_ms`
= **16,363.419** + **60.106** + **67.685**
= **16,491.210**