[Benchmark Suite] improve cpu Benchmark Suite tests and comparison report for 0.12.0 (#30994)

Signed-off-by: Tsai, Louie <louie.tsai@intel.com>

.buildkite/performance-benchmarks/README.md

@@ -176,19 +176,6 @@ If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
 
-The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
-When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
-If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
-
-Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
-
-|   | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
-| 0  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334                             | 294.018783                             | 1.216863 |
-
-A comparison diagram will be generated below the table.
-Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
-<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
+#### Performance Results Comparison  
+
+Follow the instructions in [performance results comparison](https://docs.vllm.ai/en/latest/benchmarking/dashboard/#performance-results-comparison) to analyze performance results and the sizing guide.

.buildkite/performance-benchmarks/tests/serving-tests-cpu.json

@@ -19,10 +19,8 @@
       "block_size": 128,
       "trust_remote_code": "",
       "disable_log_stats": "",
-      "enforce_eager": "",
       "max_num_batched_tokens": 2048,
-      "max_num_seqs": 256,
-      "load_format": "dummy"
+      "max_num_seqs": 256
     },
     "client_parameters": {
       "model": "meta-llama/Llama-3.1-8B-Instruct",
@@ -151,6 +149,45 @@
         "random-output-len": 128
       }
     },
+    {
+      "test_name": "serving_llama8B_int4_tp1_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp2_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp4_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
     {
       "test_name": "serving_llama3B_tp1_random_128_128",
       "server_parameters": {

docs/benchmarking/dashboard.md

@@ -40,7 +40,58 @@ When run, benchmark script generates results under **benchmark/results** folder,
 - `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
 - `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
 
-For more results visualization, check the [visualizing the results](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md#visualizing-the-results).
+### Visualization
+
+The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table with real benchmarking results.
+You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
+If you do not see the table, please wait till the benchmark finish running.
+The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
+The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
+
+#### Performance Results Comparison
+
+The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
+When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
+`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
+If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
+
+Here is an example using the script to compare result_a and result_b with max concurrency and qps for same Model, Dataset name, input/output length.
+`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
+
+***Output Tput (tok/s) — Model : [ meta-llama/Llama-3.1-8B-Instruct ] , Dataset Name : [ random ] , Input Len : [ 2048.0 ] , Output Len : [ 2048.0 ]***
+
+|    | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
+|----|------|-----|-----------|----------|----------|
+| 0  | 12 | inf | 24.98   | 186.03 |  7.45 |
+| 1  | 16 | inf|  25.49  | 246.92 | 9.69 |
+| 2  | 24 | inf| 27.74  | 293.34 |  10.57 |
+| 3  | 32 | inf| 28.61  |306.69 | 10.72 |
+
+***compare-json-results.py – Command-Line Parameters***  
+
+compare-json-results.py provides configurable parameters to compare one or more benchmark_results.json files and generate summary tables and plots.  
+In most cases, users only need to specify --file to parse the desired benchmark results.
+
+| Parameter              | Type               | Default Value           | Description                                                                                           |
+| ---------------------- | ------------------ | ----------------------- | ----------------------------------------------------------------------------------------------------- |
+| `--file`               | `str` (appendable) | *None*                  | Input JSON result file(s). Can be specified multiple times to compare multiple benchmark outputs.     |
+| `--debug`              | `bool`             | `False`                 | Enables debug mode. When set, prints all available information to aid troubleshooting and validation. |
+| `--plot` / `--no-plot` | `bool`             | `True`                  | Controls whether performance plots are generated. Use `--no-plot` to disable graph generation.        |
+| `--xaxis`              | `str`              | `# of max concurrency.` | Column name used as the X-axis in comparison plots (for example, concurrency or batch size).          |
+| `--latency`            | `str`              | `p99`                   | Latency aggregation method used for TTFT/TPOT. Supported values: `median` or `p99`.                   |
+| `--ttft-max-ms`        | `float`            | `3000.0`                | Reference upper bound (milliseconds) for TTFT plots, typically used to visualize SLA thresholds.      |
+| `--tpot-max-ms`        | `float`            | `100.0`                 | Reference upper bound (milliseconds) for TPOT plots, typically used to visualize SLA thresholds.      |
+
+***Valid Max Concurrency Summary***  
+
+Based on the configured TTFT and TPOT SLA thresholds, compare-json-results.py computes the maximum valid concurrency for each benchmark result.  
+The “Max # of max concurrency. (Both)” column represents the highest concurrency level that satisfies both TTFT and TPOT constraints simultaneously.  
+This value is typically used in capacity planning and sizing guides.  
+
+| # | Configuration  | Max # of max concurrency. (TTFT ≤ 10000 ms) | Max # of max concurrency. (TPOT ≤ 100 ms) | Max # of max concurrency. (Both) | Output Tput @ Both (tok/s) | TTFT @ Both (ms) | TPOT @ Both (ms) |
+| - | -------------- | ------------------------------------------- | ----------------------------------------- | -------------------------------- | -------------------------- | ---------------- | ---------------- |
+| 0 | results-a      | 128.00                                      | 12.00                                     | 12.00                            | 127.76                     | 3000.82          | 93.24            |
+| 1 | results-b      | 128.00                                      | 32.00                                     | 32.00                            | 371.42                     | 2261.53          | 81.74            |
 
 More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](../../.buildkite/performance-benchmarks/performance-benchmarks-descriptions.md).