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

Signed-off-by: Chen Yang <2082464740@qq.com>
Signed-off-by: erfgss <97771661+erfgss@users.noreply.github.com>

docs/contributing/metrics.md

@@ -69,8 +69,20 @@ 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
 
-These logs include:
+| 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 |
 
 - **Overall summary**: total requests, wall time, average tokens/sec, etc.
 
@@ -80,6 +92,9 @@ These logs include:
 
 - **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.
 
 
@@ -130,6 +145,11 @@ 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.                                                              |
+
 
 ---
 
@@ -143,6 +163,8 @@ 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:**
@@ -171,3 +193,14 @@ 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**