feat(ai_dynamo): add aiperf workload support

conf/experimental/ai_dynamo/test/sglang.toml

@@ -21,7 +21,7 @@ extra_container_mounts = ["/run/udev:/run/udev", "/tmp:/tmp"]
 
 [cmd_args]
 docker_image_url = "nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.9.0"
-workloads = "genai_perf.sh"
+workloads = "aiperf.sh"
 
   [cmd_args.dynamo]
   backend = "sglang"
@@ -93,6 +93,14 @@ workloads = "genai_perf.sh"
     warmup-request-count = 5
     concurrency = 2
 
+  [cmd_args.aiperf]
+
+    [cmd_args.aiperf.args]
+    concurrency = 2
+    request-count = 50
+    synthetic-input-tokens-mean = 300
+    output-tokens-mean = 500
+
 [extra_env_vars]
 UCX_LOG_LEVEL = "warn"
 HF_HUB_OFFLINE = "1"

conf/experimental/ai_dynamo/test/vllm.toml

@@ -21,7 +21,7 @@ extra_container_mounts = ["/run/udev:/run/udev", "/tmp:/tmp"]
 
 [cmd_args]
 docker_image_url = "nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.1"
-workloads = "genai_perf.sh"
+workloads = "aiperf.sh"
 
   [cmd_args.dynamo]
   backend = "vllm"
@@ -85,6 +85,14 @@ workloads = "genai_perf.sh"
     warmup-request-count = 5
     concurrency = 2
 
+  [cmd_args.aiperf]
+
+    [cmd_args.aiperf.args]
+    concurrency = 2
+    request-count = 50
+    synthetic-input-tokens-mean = 300
+    output-tokens-mean = 500
+
 [extra_env_vars]
 UCX_LOG_LEVEL = "warn"
 HF_HUB_OFFLINE = "1"

conf/experimental/ai_dynamo/test_scenario/sglang_slurm.toml

@@ -18,7 +18,7 @@ name = "dynamo_sglang"
 
 [[Tests]]
 id = "sglang-Qwen3-0.6B"
-test_name = "sglang-Qwen3-0.6B"
+test_name = "sglang"
 time_limit = "00:20:00"
 
   [Tests.cmd_args]

conf/experimental/ai_dynamo/test_scenario/vllm_slurm.toml

@@ -19,7 +19,7 @@ job_status_check = false
 
 [[Tests]]
 id = "test.disagg.single-node"
-test_name = "vLLM-Qwen3-0.6B"
+test_name = "vLLM"
 time_limit = "00:10:00"
 
   [Tests.cmd_args]
@@ -38,7 +38,7 @@ time_limit = "00:10:00"
 
 [[Tests]]
 id = "test.disagg.multinode"
-test_name = "vLLM-Qwen3-0.6B"
+test_name = "vLLM"
 time_limit = "00:10:00"
 
   [Tests.cmd_args]

doc/workloads/ai_dynamo.rst

@@ -47,7 +47,7 @@ Node Configuration for AI Dynamo
 
 AI Dynamo jobs use three distinct types of nodes:
 
-- **Frontend node**: Hosts the coordination services (`etcd`, `nats`), the **frontend server**, the **request generator** (`genai-perf`), and the first decode worker
+- **Frontend node**: Hosts the coordination services (`etcd`, `nats`), the **frontend server**, the **request generator** (`aiperf` by default, configurable via ``workloads`` in the test TOML), and the first decode worker
 - **Prefill node(s)**: Handle the prefill stage of inference
 - **Decode node(s)**: Handle the decode stage of inference (optional, depending on model and setup)
 
@@ -82,32 +82,71 @@ The job progress monitoring can be done using either of the following options:
 
    watch tail -n 4 ./results/<scenario name>/*.txt
 
-The frontend node will initially wait to allow weight loading on all nodes. Once ready, it will launch ``genai-perf``, which begins generating requests to the frontend server. All servers cooperate to complete inference, and the output will appear in ``stdout.txt``.
+The frontend node will initially wait to allow weight loading on all nodes. Once ready, it will launch the configured benchmark tool (``aiperf`` by default), which begins generating requests to the frontend server. All servers cooperate to complete inference, and the output will appear in ``stdout.txt``.
 
-Review genai-perf Benchmark Results
------------------------------------
+Choosing a Benchmark Tool
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
-After job completion, CloudAI will place the output logs and result files in the designated results directory. To analyze performance metrics and validate inference outcomes:
+The benchmark tool is controlled by the ``workloads`` field in the test TOML. The default is ``aiperf.sh``:
 
-- Navigate to the results directory (e.g., ``./results/...``)
-- Most importantly, open the ``profile_genai_perf.csv`` file to examine the final benchmarking results
+.. code-block:: toml
 
-This CSV file includes detailed metrics collected by genai-perf, such as request latency, throughput, and system utilization statistics. Use this data to evaluate the model's performance and identify potential bottlenecks or optimization opportunities.
+   [cmd_args]
+   workloads = "aiperf.sh"   # default — uses aiperf, writes aiperf_report.csv
+
+To use genai-perf instead, set:
+
+.. code-block:: toml
+
+   [cmd_args]
+   workloads = "genai_perf.sh"   # uses genai-perf, writes genai_perf_report.csv
+
+   [cmd_args.genai_perf]
+   cmd = "genai-perf profile"
+   extra-args = "--streaming --verbose -- -v --async"
+
+     [cmd_args.genai_perf.args]
+     endpoint-type = "chat"
+     output-tokens-mean = 500
+     request-count = 50
+
+Review Benchmark Results
+------------------------
+
+After job completion, CloudAI places output logs and result files in the designated results directory. The result file name depends on the configured ``workloads`` field:
+
+- ``aiperf.sh`` (default) → ``aiperf_report.csv``
+- ``genai_perf.sh`` → ``genai_perf_report.csv``
+
+Navigate to ``./results/<scenario>/<test-id>/0/`` and open the CSV to examine performance metrics.
+
+Example ``aiperf_report.csv`` (default):
 
 ::
 
-   Metric,avg,min,max,p99,p95,p90,p75,p50,p25
-   Time To First Token (ms),"1,146.31",249.48,"3,485.23","3,457.97","3,349.56","3,215.06","1,330.93",640.07,286.52
-   Time To Second Token (ms),26.05,0.00,133.51,96.12,36.56,34.88,34.35,33.55,1.78
-   Request Latency (ms),"6,406.20","5,371.47","9,608.72","9,436.13","9,046.58","9,028.16","6,549.60","5,690.23","5,493.63"
-   Inter Token Latency (ms),30.35,27.59,35.60,35.23,33.88,32.53,31.05,30.13,29.04
-   Output Sequence Length (tokens),174.45,164.00,187.00,186.22,183.10,180.10,177.00,174.00,171.75
-   Input Sequence Length (tokens),"3,000.05","2,999.00","3,001.00","3,001.00","3,001.00","3,000.00","3,000.00","3,000.00","3,000.00"
+   Metric,avg,min,max,p25,p50,p75,p99,std
+   Inter Token Latency (ms),2.81,2.66,2.88,2.79,2.83,2.84,2.87,0.04
+   Time to First Token (ms),49.87,17.15,99.91,49.35,49.87,50.52,92.31,9.20
+   Time to Second Token (ms),0.50,0.03,4.05,0.03,0.04,0.04,3.47,1.08
+   Request Latency (ms),1652.30,1203.61,6433.87,1453.19,1462.99,1466.72,6431.16,976.18
+   Output Sequence Length (tokens),498.06,410.00,501.00,500.00,500.00,500.00,501.00,12.62
+   Input Sequence Length (tokens),300.00,300.00,300.00,300.00,300.00,300.00,300.00,0.00
 
    Metric,Value
-   Output Token Throughput (per sec),261.25
-   Request Throughput (per sec),1.50
-   Request Count (count),40.00
+   Output Token Throughput (tokens/sec),598.78
+   Total Token Throughput (tokens/sec),962.32
+   Request Throughput (requests/sec),1.20
+   Request Count,50.00
+
+Supported Backends
+------------------
+
+The following backends are available via the ``conf/experimental/ai_dynamo/test/`` directory:
+
+- **vLLM** (``vllm.toml``) — use with ``test_scenario/vllm_slurm.toml``
+- **sglang** (``sglang.toml``) — use with ``test_scenario/sglang_slurm.toml``
+
+Both backends use ``aiperf`` as the default benchmark tool and support disaggregated prefill/decode.
 
 
 API Documentation

src/cloudai/workloads/ai_dynamo/__init__.py

@@ -18,6 +18,7 @@
     AIDynamoArgs,
     AIDynamoCmdArgs,
     AIDynamoTestDefinition,
+    AIPerf,
     GenAIPerf,
     LMCache,
     LMCacheArgs,
@@ -35,6 +36,7 @@
     "AIDynamoReportGenerationStrategy",
     "AIDynamoSlurmCommandGenStrategy",
     "AIDynamoTestDefinition",
+    "AIPerf",
     "GenAIPerf",
     "LMCache",
     "LMCacheArgs",

src/cloudai/workloads/ai_dynamo/ai_dynamo.py

@@ -282,6 +282,25 @@ def installables(self) -> list[Installable]:
         return [self.script]
 
 
+class AIPerf(Workload):
+    """Workload configuration for aiperf benchmarking."""
+
+    model_config = ConfigDict(extra="allow")
+
+    name: str = "aiperf"
+    cmd: str = "aiperf profile"
+    script: File = File(Path(__file__).parent.parent / "ai_dynamo/aiperf.sh")
+    report_name: str = Field(
+        default="aiperf_report.csv",
+        serialization_alias="report-name",
+        validation_alias=AliasChoices("report-name", "report_name"),
+    )
+
+    @property
+    def installables(self) -> list[Installable]:
+        return [self.script]
+
+
 class Constraints(BaseModel):
     """Constraints for validation of AI Dynamo configurations when using DSE."""
 
@@ -301,12 +320,13 @@ class AIDynamoCmdArgs(CmdArgs):
     dynamo: AIDynamoArgs
     lmcache: LMCache = Field(default_factory=LMCache)
     genai_perf: GenAIPerf = Field(default_factory=GenAIPerf)
+    aiperf: AIPerf = Field(default_factory=AIPerf)
     workloads: str = "genai_perf.sh"
 
     @field_validator("workloads", mode="before")
     @classmethod
     def validate_workloads(cls, v: str) -> str:
-        allowed_workloads = ["genai_perf.sh"]
+        allowed_workloads = ["genai_perf.sh", "aiperf.sh"]
         values = [w.strip() for w in v.split(",")]
         for workload in values:
             if workload not in allowed_workloads:
@@ -322,6 +342,7 @@ def installables(self) -> list[Installable]:
         return [
             *self.lmcache.installables,
             *self.genai_perf.installables,
+            *self.aiperf.installables,
         ]
 
 
@@ -356,6 +377,7 @@ def get_workload_map(self) -> dict[str, Workload]:
         """Get a map of workload scripts to workload objects."""
         return {
             self.cmd_args.genai_perf.script.src.name: self.cmd_args.genai_perf,
+            self.cmd_args.aiperf.script.src.name: self.cmd_args.aiperf,
         }
 
     @property

src/cloudai/workloads/ai_dynamo/ai_dynamo.sh

@@ -35,6 +35,8 @@ declare -A lmcache_args
 declare -A lmcache_config
 declare -A genai_perf_args
 declare -A genai_perf_config
+declare -A aiperf_args
+declare -A aiperf_config
 
 declare -A dynamo_args
 dynamo_args["backend"]="vllm"
@@ -163,6 +165,10 @@ _parse_cli_pairs() {
         genai_perf_args["--${key#--genai_perf-args-}"]="$2" ;;
       --genai_perf-*)
         genai_perf_config["--${key#--genai_perf-}"]="$2" ;;
+      --aiperf-args-*)
+        aiperf_args["--${key#--aiperf-args-}"]="$2" ;;
+      --aiperf-*)
+        aiperf_config["--${key#--aiperf-}"]="$2" ;;
       --hf-home)
         HUGGINGFACE_HOME="$2" ;;
       --storage-cache-dir)
@@ -353,6 +359,8 @@ _dump_args() {
   log "LMCache args:\n$(arg_array_to_string lmcache_args)"
   log "GenAI config params:\n$(arg_array_to_string genai_perf_config)"
   log "GenAI-Perf args:\n$(arg_array_to_string genai_perf_args)"
+  log "AIPerf config params:\n$(arg_array_to_string aiperf_config)"
+  log "AIPerf args:\n$(arg_array_to_string aiperf_args)"
   log "--------------------------------"
 }
 
@@ -505,6 +513,10 @@ _is_genai_perf_workload() {
   [[ "${dynamo_args["workloads"]}" == *"genai_perf.sh"* ]]
 }
 
+_is_aiperf_workload() {
+  [[ "${dynamo_args["workloads"]}" == *"aiperf.sh"* ]]
+}
+
 _init_runtime_env() {
   if _is_vllm || _is_sglang; then
     export HF_HOME="${HUGGINGFACE_HOME}"
@@ -1026,6 +1038,10 @@ function launch_workloads()
     launch_workload genai_perf_config genai_perf_args
   fi
 
+  if _is_aiperf_workload; then
+    launch_workload aiperf_config aiperf_args
+  fi
+
   mark_done
 }