feat: open benchmark template and doc (#1465)

## What does this PR do?
- Provide a distro template to let developer easily run the open
benchmarks llama stack supports on llama and non-llama models.
- Provide doc on how to run open benchmark eval via CLI and open
benchmark contributing guide

[//]: # (If resolving an issue, uncomment and update the line below)
(Closes #1375 )

## Test Plan
open benchmark eval results on llama, gpt, gemini and clause
<img width="771" alt="Screenshot 2025-03-06 at 7 33 05 PM"
src="https://github.com/user-attachments/assets/1bd85456-b9b9-4b37-af76-4ce1d2bac00e"
/>

doc preview
<img width="944" alt="Screenshot 2025-03-06 at 7 33 58 PM"
src="https://github.com/user-attachments/assets/f4e5866d-b395-4c40-aa8b-080edeb5cdb6"
/>
<img width="955" alt="Screenshot 2025-03-06 at 7 34 04 PM"
src="https://github.com/user-attachments/assets/629defb6-d5e4-473c-aa03-308bce386fb4"
/>

<img width="965" alt="Screenshot 2025-03-06 at 7 35 29 PM"
src="https://github.com/user-attachments/assets/c21ff96c-9e8c-4c54-b6b8-25883125f4cf"
/>

<img width="957" alt="Screenshot 2025-03-06 at 7 35 37 PM"
src="https://github.com/user-attachments/assets/47571c90-1381-4e2c-bbed-c4f3a60578d0"
/>

distributions/dependencies.json

@@ -453,6 +453,42 @@
     "transformers",
     "uvicorn"
   ],
+  "open_benchmark": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "fastapi",
+    "fire",
+    "httpx",
+    "litellm",
+    "matplotlib",
+    "mcp",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pymongo",
+    "pypdf",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "sqlite-vec",
+    "together",
+    "tqdm",
+    "transformers",
+    "uvicorn",
+    "sentence-transformers --no-deps",
+    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
+  ],
   "remote-vllm": [
     "aiosqlite",
     "autoevals",

docs/source/concepts/evaluation_concepts.md

@@ -24,6 +24,56 @@ The Evaluation APIs are associated with a set of Resources as shown in the follo
   - Associated with `Benchmark` resource.
 
 
+## Open-benchmark Eval
+
+### List of open-benchmarks Llama Stack support
+
+Llama stack pre-registers several popular open-benchmarks to easily evaluate model perfomance via CLI.
+
+The list of open-benchmarks we currently support:
+- [MMLU-COT](https://arxiv.org/abs/2009.03300) (Measuring Massive Multitask Language Understanding): Benchmark designed to comprehensively evaluate the breadth and depth of a model's academic and professional understanding
+- [GPQA-COT](https://arxiv.org/abs/2311.12022) (A Graduate-Level Google-Proof Q&A Benchmark): A challenging benchmark of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
+- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions.
+- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.
+
+
+You can follow this contributing guidance to add more open-benchmarks to Llama Stack
+
+### Run evaluation on open-benchmarks via CLI
+
+We have built-in functionality to run the supported open-benckmarks using llama-stack-client CLI
+
+#### Spin up Llama Stack server
+
+Spin up llama stack server with 'open-benchmark' template
+```
+llama stack run llama_stack/templates/open-benchmark/run.yaml
+
+```
+
+#### Run eval CLI
+There are 3 necessary inputs to run a benchmark eval
+- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
+- `model-id`: The model id to evaluate on
+- `utput_dir`: Path to store the evaluate results
+```
+llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
+--model_id <model id to evaluate on> \
+--output_dir <directory to store the evaluate results> \
+```
+
+You can run
+```
+llama-stack-client eval run-benchmark help
+```
+to see the description of all the flags that eval run-benchmark has
+
+
+In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggrgate
+evaluation results over there.
+
+
+
 ## What's Next?
 
 - Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP).

docs/source/references/evals_reference/index.md

@@ -275,18 +275,25 @@ response = client.scoring.score(
 The following examples give the quick steps to start running evaluations using the llama-stack-client CLI.
 
 #### Benchmark Evaluation CLI
-Usage: There are 2 inputs necessary for running a benchmark eval
+There are 3 necessary input for running a benchmark eval
-- `eval-task-id`: the identifier associated with the eval task. Each `Benchmark` is parametrized by
+- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
-  - `dataset_id`: the identifier associated with the dataset.
+- `model-id`: The model id to evaluate on
-  - `List[scoring_function_id]`: list of scoring function identifiers.
+- `utput_dir`: Path to store the evaluate results
-- `eval-task-config`: specifies the configuration of the model / agent to evaluate on.
+```
+llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
+--model_id <model id to evaluate on> \
+--output_dir <directory to store the evaluate results> \
+```
+
+You can run
+```
+llama-stack-client eval run-benchmark help
+```
+to see the description of all the flags to run benckmark eval
 
 
-```
+In the output log, you can find the path to the file that has your evaluation results. Open that file and you can see you aggrgate
-llama-stack-client eval run_benchmark <eval-task-id> \
+evaluation results over there.
---eval-task-config ~/benchmark_config.json \
---visualize
-```
 
 
 #### Application Evaluation CLI
@@ -338,3 +345,52 @@ The `BenchmarkConfig` are user specified config to define:
     }
 }
 ```
+
+
+## Open-benchmark Contributing Guide
+
+### Create the new dataset for your new benchmark
+An eval open-benchmark essentially contains 2 parts:
+- `raw data`: The raw dataset associated with the benchmark. You typically need to search the original paper that introduces the benchmark and find the canonical dataset (usually hosted on huggingface)
+- `prompt template`: How to ask the candidate model to generate the answer (prompt template plays a critical role to the evaluation results). Tyically, you can find the reference prompt template associated with the benchmark in benchmarks author's repo ([exmaple](https://github.com/idavidrein/gpqa/blob/main/prompts/chain_of_thought.txt)) or some other popular open source repos ([example](https://github.com/openai/simple-evals/blob/0a6e8f62e52bc5ae915f752466be3af596caf392/common.py#L14))
+
+To create new open-benmark in llama stack, you need to combine the prompt template and the raw data into the `chat_completion_input` column in the evaluation dataset.
+
+Llama stack enforeces the evaluate dataset schema to contain at least 3 columns:
+- `chat_completion_input`: The actual input to the model to run the generation for eval
+- `input_query`: The raw input from the raw dataset without the prompt template
+- `expected_answer`: The ground truth for scoring functions to calcalate the score from.
+
+
+You need to write a script [example convert script](https://gist.github.com/yanxi0830/118e9c560227d27132a7fd10e2c92840) to convert the benchmark raw dataset to llama stack format eval dataset and update the dataset to huggingface [example benchmark dataset](https://huggingface.co/datasets/llamastack/mmmu)
+
+
+### Find scoring function for your new benchmark
+The purpose of scoring function is to calculate the score for each example based on candidate model generation result and expected_answer. It also aggregates the scores from all the examples and generate the final evaluate results.
+
+
+Firstly, you can see if the existing [llama stack scoring functions](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/scoring) can fulfill your need. If not, you need to write a new scoring function based on what benchmark author / other open source repo describe.
+
+### Add new benchmark into template
+Firstly, you need to add the evaluation dataset associated with your benchmark under `datasets` resource in templates/open-benchmark/run.yaml
+
+Secondly, you need to add the new benchmark you just created under the `benchmarks` resource in the same template. To add the new benchmark, you need to have
+- `benchmark_id`: identifier of the benchmark
+- `dataset_id`: identifier of the dataset associated with your benchmark
+- `scoring_functions`: scoring function to calculate the score based on generation results and expected_answer
+
+
+### Test the new benchmark
+
+Spin up llama stack server with 'open-benchmark' templates
+```
+llama stack run llama_stack/templates/open-benchmark/run.yaml
+
+```
+
+Run eval benchmark CLI with your new benchmark id
+```
+llama-stack-client eval run-benchmark <new_benchmark_id> \
+--model_id <model id to evaluate on> \
+--output_dir <directory to store the evaluate results> \
+```

llama_stack/templates/open-benchmark/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .open_benchmark import get_distribution_template  # noqa: F401

llama_stack/templates/open-benchmark/build.yaml

@@ -0,0 +1,36 @@
+version: '2'
+distribution_spec:
+  description: Distribution for running open benchmarks
+  providers:
+    inference:
+    - remote::openai
+    - remote::anthropic
+    - remote::gemini
+    - remote::groq
+    - remote::together
+    vector_io:
+    - inline::sqlite-vec
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+    eval:
+    - inline::meta-reference
+    datasetio:
+    - remote::huggingface
+    - inline::localfs
+    scoring:
+    - inline::basic
+    - inline::llm-as-judge
+    - inline::braintrust
+    tool_runtime:
+    - remote::brave-search
+    - remote::tavily-search
+    - inline::code-interpreter
+    - inline::rag-runtime
+    - remote::model-context-protocol
+image_type: conda

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -0,0 +1,178 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Tuple
+
+from llama_stack.distribution.datatypes import (
+    ModelInput,
+    Provider,
+    ShieldInput,
+    ToolGroupInput,
+)
+from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
+from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
+from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
+from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.groq.config import GroqConfig
+from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES as GROQ_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
+from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
+from llama_stack.providers.remote.inference.together.models import MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES
+from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
+from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
+
+
+def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
+    # in this template, we allow each API key to be optional
+    providers = [
+        (
+            "openai",
+            OPENAI_MODEL_ENTRIES,
+            OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
+        ),
+        (
+            "anthropic",
+            ANTHROPIC_MODEL_ENTRIES,
+            AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
+        ),
+        (
+            "gemini",
+            GEMINI_MODEL_ENTRIES,
+            GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
+        ),
+        (
+            "groq",
+            GROQ_MODEL_ENTRIES,
+            GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
+        ),
+        (
+            "together",
+            TOGETHER_MODEL_ENTRIES,
+            TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
+        ),
+    ]
+    inference_providers = []
+    available_models = {}
+    for provider_id, model_entries, config in providers:
+        inference_providers.append(
+            Provider(
+                provider_id=provider_id,
+                provider_type=f"remote::{provider_id}",
+                config=config,
+            )
+        )
+        available_models[provider_id] = model_entries
+    return inference_providers, available_models
+
+
+def get_distribution_template() -> DistributionTemplate:
+    inference_providers, available_models = get_inference_providers()
+    providers = {
+        "inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
+        "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+        "tool_runtime": [
+            "remote::brave-search",
+            "remote::tavily-search",
+            "inline::code-interpreter",
+            "inline::rag-runtime",
+            "remote::model-context-protocol",
+        ],
+    }
+    name = "open_benchmark"
+
+    vector_io_providers = [
+        Provider(
+            provider_id="sqlite-vec",
+            provider_type="inline::sqlite-vec",
+            config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_CHROMADB+chromadb}",
+            provider_type="remote::chromadb",
+            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_PGVECTOR+pgvector}",
+            provider_type="remote::pgvector",
+            config=PGVectorVectorIOConfig.sample_run_config(
+                db="${env.PGVECTOR_DB:}",
+                user="${env.PGVECTOR_USER:}",
+                password="${env.PGVECTOR_PASSWORD:}",
+            ),
+        ),
+    ]
+
+    default_tool_groups = [
+        ToolGroupInput(
+            toolgroup_id="builtin::websearch",
+            provider_id="tavily-search",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::rag",
+            provider_id="rag-runtime",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::code_interpreter",
+            provider_id="code-interpreter",
+        ),
+    ]
+
+    default_models = get_model_registry(available_models)
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Distribution for running open benchmarks",
+        container_image=None,
+        template_path=None,
+        providers=providers,
+        available_models_by_provider=available_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": inference_providers,
+                    "vector_io": vector_io_providers,
+                },
+                default_models=default_models,
+                default_tool_groups=default_tool_groups,
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+            ),
+        },
+        run_config_env_vars={
+            "LLAMA_STACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "OPENAI_API_KEY": (
+                "",
+                "OpenAI API Key",
+            ),
+            "GEMINI_API_KEY": (
+                "",
+                "Gemini API Key",
+            ),
+            "GROQ_API_KEY": (
+                "",
+                "Groq API Key",
+            ),
+            "ANTHROPIC_API_KEY": (
+                "",
+                "Anthropic API Key",
+            ),
+            "TOGETHER_API_KEY": (
+                "",
+                "Together API Key",
+            ),
+        },
+    )

llama_stack/templates/open-benchmark/run.yaml

@@ -0,0 +1,212 @@
+version: '2'
+image_name: dev
+apis:
+- agents
+- datasetio
+- eval
+- inference
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:}
+  - provider_id: groq
+    provider_type: remote::groq
+    config:
+      url: https://api.groq.com
+      api_key: ${env.GROQ_API_KEY:}
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+      api_key: ${env.TOGETHER_API_KEY}
+  vector_io:
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/sqlite_vec.db
+  - provider_id: ${env.ENABLE_CHROMADB+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:}
+  - provider_id: ${env.ENABLE_PGVECTOR+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:localhost}
+      port: ${env.PGVECTOR_PORT:5432}
+      db: ${env.PGVECTOR_DB:}
+      user: ${env.PGVECTOR_USER:}
+      password: ${env.PGVECTOR_PASSWORD:}
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db}
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config: {}
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config: {}
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+    config: {}
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+    config: {}
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:}
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: code-interpreter
+    provider_type: inline::code-interpreter
+    config: {}
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+    config: {}
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+    config: {}
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/registry.db
+models:
+- metadata: {}
+  model_id: openai/gpt-4o
+  provider_id: openai
+  provider_model_id: openai/gpt-4o
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct
+  provider_id: together
+  provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
+  model_type: llm
+- metadata: {}
+  model_id: anthropic/claude-3-5-sonnet-latest
+  provider_id: anthropic
+  provider_model_id: anthropic/claude-3-5-sonnet-latest
+  model_type: llm
+- metadata: {}
+  model_id: gemini/gemini-1.5-flash
+  provider_id: gemini
+  provider_model_id: gemini/gemini-1.5-flash
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: groq
+  provider_model_id: groq/llama-3.3-70b-versatile
+  model_type: llm
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+vector_dbs: []
+datasets:
+  - dataset_id: simpleqa
+    provider_id: huggingface
+    url:
+      uri: https://huggingface.co/datasets/llamastack/simpleqa
+    metadata:
+      path: llamastack/simpleqa
+      name:
+      split: train
+    dataset_schema:
+      input_query:
+        type: string
+      expected_answer:
+        type: string
+      chat_completion_input:
+        type: string
+  - dataset_id: mmlu_cot
+    provider_id: huggingface
+    url:
+      uri: https://huggingface.co/datasets/llamastack/mmlu_cot
+    metadata:
+      path: llamastack/mmlu_cot
+      name: all
+      split: test
+    dataset_schema:
+      input_query:
+        type: string
+      expected_answer:
+        type: string
+      chat_completion_input:
+        type: string
+  - dataset_id: gpqa_cot
+    provider_id: huggingface
+    url:
+      uri: https://huggingface.co/datasets/llamastack/gpqa_0shot_cot
+    metadata:
+      path: llamastack/gpqa_0shot_cot
+      name: gpqa_main
+      split: train
+    dataset_schema:
+      input_query:
+        type: string
+      expected_answer:
+        type: string
+      chat_completion_input:
+        type: string
+scoring_fns: []
+benchmarks:
+  - benchmark_id: meta-reference-simpleqa
+    dataset_id: simpleqa
+    scoring_functions: ["llm-as-judge::405b-simpleqa"]
+  - benchmark_id: meta-reference-mmlu-cot
+    dataset_id: mmlu_cot
+    scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
+  - benchmark_id: meta-reference-gpqa-cot
+    dataset_id: gpqa_cot
+    scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
+- toolgroup_id: builtin::code_interpreter
+  provider_id: code-interpreter
+server:
+  port: 8321