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a626b7bce3
# What does this PR do? create a new dataset BFCL_v3 from https://gorilla.cs.berkeley.edu/blogs/13_bfcl_v3_multi_turn.html overall each question asks the model to perform a task described in natural language, and additionally a set of available functions and their schema are given for the model to choose from. the model is required to write the function call form including function name and parameters , to achieve the stated purpose. the results are validated against provided ground truth, to make sure that the generated function call and the ground truth function call are syntactically and semantically equivalent, by checking their AST . ## Test Plan start server by ``` llama stack run ./llama_stack/templates/ollama/run.yaml ``` then send traffic ``` llama-stack-client eval run-benchmark "bfcl" --model-id meta-llama/Llama-3.2-3B-Instruct --output-dir /tmp/gpqa --num-examples 2 ``` [//]: # (## Documentation)
321 lines
11 KiB
Python
321 lines
11 KiB
Python
# 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 Dict, List, Tuple
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from llama_stack.apis.common.content_types import URL
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from llama_stack.apis.models.models import ModelType
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from llama_stack.distribution.datatypes import (
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BenchmarkInput,
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DatasetInput,
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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 (
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SQLiteVectorIOConfig,
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)
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from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
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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 (
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PGVectorVectorIOConfig,
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)
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from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
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from llama_stack.templates.template import (
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DistributionTemplate,
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RunConfigSettings,
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get_model_registry,
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)
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def get_inference_providers() -> Tuple[List[Provider], Dict[str, List[ProviderModelEntry]]]:
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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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[
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ProviderModelEntry(
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provider_model_id="openai/gpt-4o",
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model_type=ModelType.llm,
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)
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],
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"anthropic",
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[
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ProviderModelEntry(
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provider_model_id="anthropic/claude-3-5-sonnet-latest",
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model_type=ModelType.llm,
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)
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],
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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[
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ProviderModelEntry(
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provider_model_id="gemini/gemini-1.5-flash",
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model_type=ModelType.llm,
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)
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],
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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[],
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"together",
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[],
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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],
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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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ModelInput(
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model_id="meta-llama/Llama-3.3-70B-Instruct",
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provider_id="groq",
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provider_model_id="groq/llama-3.3-70b-versatile",
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model_type=ModelType.llm,
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),
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ModelInput(
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model_id="meta-llama/Llama-3.1-405B-Instruct",
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provider_id="together",
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provider_model_id="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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model_type=ModelType.llm,
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),
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]
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default_datasets = [
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DatasetInput(
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dataset_id="simpleqa",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/simpleqa"),
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metadata={
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"path": "llamastack/simpleqa",
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"split": "train",
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},
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dataset_schema={
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"input_query": {"type": "string"},
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"expected_answer": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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),
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DatasetInput(
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dataset_id="mmlu_cot",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/mmlu_cot"),
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metadata={
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"path": "llamastack/mmlu_cot",
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"name": "all",
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"split": "test",
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},
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dataset_schema={
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"input_query": {"type": "string"},
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"expected_answer": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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),
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DatasetInput(
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dataset_id="gpqa_cot",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/gpqa_0shot_cot"),
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metadata={
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"path": "llamastack/gpqa_0shot_cot",
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"name": "gpqa_main",
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"split": "train",
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},
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dataset_schema={
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"input_query": {"type": "string"},
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"expected_answer": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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),
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DatasetInput(
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dataset_id="math_500",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/math_500"),
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metadata={
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"path": "llamastack/math_500",
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"split": "test",
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},
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dataset_schema={
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"input_query": {"type": "string"},
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"expected_answer": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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),
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DatasetInput(
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dataset_id="bfcl",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/bfcl_v3"),
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metadata={
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"path": "llamastack/bfcl_v3",
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"split": "train",
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},
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dataset_schema={
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"function": {"type": "string"},
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"language": {"type": "string"},
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"ground_truth": {"type": "string"},
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"id": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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),
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]
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default_benchmarks = [
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BenchmarkInput(
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benchmark_id="meta-reference-simpleqa",
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dataset_id="simpleqa",
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scoring_functions=["llm-as-judge::405b-simpleqa"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-mmlu-cot",
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dataset_id="mmlu_cot",
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scoring_functions=["basic::regex_parser_multiple_choice_answer"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-gpqa-cot",
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dataset_id="gpqa_cot",
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scoring_functions=["basic::regex_parser_multiple_choice_answer"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-math-500",
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dataset_id="math_500",
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scoring_functions=["basic::regex_parser_math_response"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-bfcl",
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dataset_id="bfcl",
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scoring_functions=["basic::bfcl"],
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),
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]
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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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default_datasets=default_datasets,
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default_benchmarks=default_benchmarks,
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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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"TOGETHER_API_KEY": (
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"",
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"Together API Key",
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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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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic 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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},
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)
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