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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" />
This commit is contained in:
Botao Chen
GitHub
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4dccf916d1
7 changed files with 585 additions and 10 deletions
178
llama_stack/templates/open-benchmark/open_benchmark.py
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llama_stack/templates/open-benchmark/open_benchmark.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import List, Tuple
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from llama_stack.distribution.datatypes import (
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
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from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES as GROQ_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
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from llama_stack.providers.remote.inference.together.models import MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
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def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
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# in this template, we allow each API key to be optional
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providers = [
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(
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"openai",
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OPENAI_MODEL_ENTRIES,
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"anthropic",
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ANTHROPIC_MODEL_ENTRIES,
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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GEMINI_MODEL_ENTRIES,
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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GROQ_MODEL_ENTRIES,
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"together",
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TOGETHER_MODEL_ENTRIES,
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TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
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),
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]
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inference_providers = []
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available_models = {}
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for provider_id, model_entries, config in providers:
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_id}",
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config=config,
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)
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)
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available_models[provider_id] = model_entries
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return inference_providers, available_models
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def get_distribution_template() -> DistributionTemplate:
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inference_providers, available_models = get_inference_providers()
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providers = {
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"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
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"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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"eval": ["inline::meta-reference"],
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"datasetio": ["remote::huggingface", "inline::localfs"],
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"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
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"tool_runtime": [
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"remote::brave-search",
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"remote::tavily-search",
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"inline::code-interpreter",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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],
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}
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name = "open_benchmark"
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vector_io_providers = [
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.ENABLE_CHROMADB+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
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),
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Provider(
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provider_id="${env.ENABLE_PGVECTOR+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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db="${env.PGVECTOR_DB:}",
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user="${env.PGVECTOR_USER:}",
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password="${env.PGVECTOR_PASSWORD:}",
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),
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),
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]
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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ToolGroupInput(
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toolgroup_id="builtin::code_interpreter",
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provider_id="code-interpreter",
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),
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]
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default_models = get_model_registry(available_models)
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Distribution for running open benchmarks",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider=available_models,
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": inference_providers,
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"vector_io": vector_io_providers,
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},
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default_models=default_models,
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default_tool_groups=default_tool_groups,
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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"TOGETHER_API_KEY": (
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"",
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"Together API Key",
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),
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},
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)
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