Added Initial version of DocSum support for benchmarking scripts for OPEA (#1840)
Signed-off-by: Valtteri Rantala <valtteri.rantala@intel.com> Co-authored-by: Liang Lv <liang1.lv@intel.com> Co-authored-by: ZePan110 <ze.pan@intel.com>
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vrantala
87
DocSum/benchmark_docsum.yaml
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87
DocSum/benchmark_docsum.yaml
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@@ -0,0 +1,87 @@
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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deploy:
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device: gaudi
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version: 1.2.0
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modelUseHostPath: /mnt/models
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HUGGINGFACEHUB_API_TOKEN: "" # mandatory
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node: [1]
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namespace: ""
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node_name: []
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timeout: 1000 # timeout in seconds for services to be ready, default 30 minutes
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interval: 5 # interval in seconds between service ready checks, default 5 seconds
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services:
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backend:
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resources:
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enabled: False
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cores_per_instance: "16"
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memory_capacity: "8000Mi"
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replicaCount: [1]
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teirerank:
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enabled: False
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llm:
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engine: vllm # or tgi
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model_id: "meta-llama/Llama-3.2-3B-Instruct" # mandatory
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replicaCount:
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without_teirerank: [1] # When teirerank.enabled is False
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resources:
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enabled: False
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cards_per_instance: 1
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model_params:
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vllm: # VLLM specific parameters
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batch_params:
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enabled: True
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max_num_seqs: "8" # Each value triggers an LLM service upgrade
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token_params:
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enabled: True
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max_input_length: ""
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max_total_tokens: ""
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max_batch_total_tokens: ""
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max_batch_prefill_tokens: ""
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tgi: # TGI specific parameters
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batch_params:
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enabled: True
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max_batch_size: [1] # Each value triggers an LLM service upgrade
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token_params:
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enabled: False
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max_input_length: "1280"
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max_total_tokens: "2048"
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max_batch_total_tokens: "65536"
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max_batch_prefill_tokens: "4096"
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docsum-ui:
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replicaCount: [1]
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whisper:
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replicaCount: [1]
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llm-uservice:
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model_id: "meta-llama/Llama-3.2-3B-Instruct" # mandatory
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replicaCount: [1]
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nginx:
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replicaCount: [1]
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benchmark:
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# http request behavior related fields
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user_queries: [16]
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concurrency: [4]
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load_shape_type: "constant" # "constant" or "poisson"
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poisson_arrival_rate: 1.0 # only used when load_shape_type is "poisson"
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warmup_iterations: 10
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seed: 1024
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collect_service_metric: True
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# workload, all of the test cases will run for benchmark
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bench_target: ["docsumfixed"] # specify the bench_target for benchmark
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dataset: "/home/sdp/upload.txt" # specify the absolute path to the dataset file
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summary_type: "stuff"
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stream: True
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llm:
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# specify the llm output token size
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max_token_size: [1024]
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33
benchmark.py
33
benchmark.py
@@ -12,6 +12,7 @@ from kubernetes import client, config
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# only support chatqna for now
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service_endpoints = {
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"chatqna": "/v1/chatqna",
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"docsum": "/v1/docsum",
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}
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@@ -35,6 +36,9 @@ def construct_benchmark_config(test_suite_config):
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"dataset": test_suite_config.get("dataset", ""),
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"prompt": test_suite_config.get("prompt", [10]),
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"llm_max_token_size": test_suite_config.get("llm", {}).get("max_token_size", [128]),
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"collect_service_metric": test_suite_config.get("collect_service_metric", False),
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"summary_type": test_suite_config.get("summary_type", "auto"),
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"stream": test_suite_config.get("stream", "auto"),
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}
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@@ -144,6 +148,8 @@ def _create_yaml_content(service, base_url, bench_target, test_phase, num_querie
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"llm-model": test_params["llm_model"],
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"deployment-type": test_params["deployment_type"],
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"load-shape": load_shape,
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"summary_type": test_params.get("summary_type", "auto"),
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"stream": test_params.get("stream", True),
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},
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"runs": [{"name": test_phase, "users": concurrency, "max-request": num_queries}],
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}
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@@ -373,7 +379,9 @@ def run_benchmark(benchmark_config, chart_name, namespace, node_num=1, llm_model
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"user_queries": parsed_data["user_queries"], # num of user queries
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"random_prompt": False, # whether to use random prompt, set to False by default
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"run_time": "30m", # The max total run time for the test suite, set to 60m by default
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"collect_service_metric": False, # whether to collect service metrics, set to False by default
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"collect_service_metric": (
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parsed_data["collect_service_metric"] if parsed_data["collect_service_metric"] else False
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), # Metrics collection set to False by default
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"llm_model": llm_model, # The LLM model used for the test
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"deployment_type": "k8s", # Default is "k8s", can also be "docker"
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"service_ip": None, # Leave as None for k8s, specify for Docker
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@@ -398,9 +406,15 @@ def run_benchmark(benchmark_config, chart_name, namespace, node_num=1, llm_model
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"dataset": parsed_data["dataset"],
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"prompt": parsed_data["prompt"],
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"llm_max_token_size": parsed_data["llm_max_token_size"],
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"summary_type": parsed_data["summary_type"],
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"stream": parsed_data["stream"],
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}
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dataset = None
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if parsed_data["dataset"]: # This checks if user provided dataset/document for DocSum service
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dataset = parsed_data["dataset"]
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else:
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dataset = None
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query_data = None
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os.environ["MODEL_NAME"] = test_suite_config.get("llm_model", "meta-llama/Meta-Llama-3-8B-Instruct")
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# Do benchmark in for-loop for different llm_max_token_size
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@@ -428,6 +442,21 @@ def run_benchmark(benchmark_config, chart_name, namespace, node_num=1, llm_model
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"max_output": llm_max_token, # max number of output tokens
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"k": 1, # number of retrieved documents
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}
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if chart_name == "docsum":
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case_data = {
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"run_test": True,
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"service_name": "docsum",
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"service_list": [
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"docsum",
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"docsum-llm-uservice",
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"docsum-vllm",
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],
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"stream": parsed_data["stream"],
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"max_output": llm_max_token, # max number of output tokens
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"summary_type": parsed_data["summary_type"], # Summary_type for DocSum
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"dataset": dataset, # Dataset used for document summary
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}
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output_folder = _run_service_test(chart_name, case_data, test_suite_config, namespace)
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print(f"[OPEA BENCHMARK] 🚀 Test Finished. Output saved in {output_folder}.")
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@@ -177,6 +177,8 @@ def configure_models(values, deploy_config):
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values[service_name]["EMBEDDING_MODEL_ID"] = model_id
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elif service_name == "teirerank":
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values[service_name]["RERANK_MODEL_ID"] = model_id
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elif service_name == "llm-uservice":
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values[service_name]["LLM_MODEL_ID"] = model_id
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return values
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