mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 12:06:04 +00:00
chore(package): migrate to src/ layout
Moved package code from llama_stack/ to src/llama_stack/ following Python packaging best practices. Updated pyproject.toml, MANIFEST.in, and tool configurations accordingly. Public API and import paths remain unchanged. Developers will need to reinstall in editable mode after pulling this change. Also updated paths in pre-commit config, scripts, and GitHub workflows.
This commit is contained in:
Ashwin Bharambe
parent
98a5047f9d
commit
8e5ed739ec
790 changed files with 2947 additions and 447 deletions
|
|
@ -1,7 +0,0 @@
|
|||
# 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
|
||||
|
|
@ -1,303 +0,0 @@
|
|||
# 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 llama_stack.apis.datasets import DatasetPurpose, URIDataSource
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.core.datatypes import (
|
||||
BenchmarkInput,
|
||||
BuildProvider,
|
||||
DatasetInput,
|
||||
ModelInput,
|
||||
Provider,
|
||||
ShieldInput,
|
||||
ToolGroupInput,
|
||||
)
|
||||
from llama_stack.distributions.template import (
|
||||
DistributionTemplate,
|
||||
RunConfigSettings,
|
||||
get_model_registry,
|
||||
)
|
||||
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.gemini.config import GeminiConfig
|
||||
from llama_stack.providers.remote.inference.groq.config import GroqConfig
|
||||
from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
|
||||
from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
|
||||
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.providers.utils.inference.model_registry import ProviderModelEntry
|
||||
|
||||
|
||||
def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]:
|
||||
# in this template, we allow each API key to be optional
|
||||
providers = [
|
||||
(
|
||||
"openai",
|
||||
[
|
||||
ProviderModelEntry(
|
||||
provider_model_id="gpt-4o",
|
||||
model_type=ModelType.llm,
|
||||
)
|
||||
],
|
||||
OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:=}"),
|
||||
),
|
||||
(
|
||||
"anthropic",
|
||||
[
|
||||
ProviderModelEntry(
|
||||
provider_model_id="claude-3-5-sonnet-latest",
|
||||
model_type=ModelType.llm,
|
||||
)
|
||||
],
|
||||
AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:=}"),
|
||||
),
|
||||
(
|
||||
"gemini",
|
||||
[
|
||||
ProviderModelEntry(
|
||||
provider_model_id="gemini/gemini-1.5-flash",
|
||||
model_type=ModelType.llm,
|
||||
)
|
||||
],
|
||||
GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:=}"),
|
||||
),
|
||||
(
|
||||
"groq",
|
||||
[],
|
||||
GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:=}"),
|
||||
),
|
||||
(
|
||||
"together",
|
||||
[],
|
||||
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": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in inference_providers],
|
||||
"vector_io": [
|
||||
BuildProvider(provider_type="inline::sqlite-vec"),
|
||||
BuildProvider(provider_type="remote::chromadb"),
|
||||
BuildProvider(provider_type="remote::pgvector"),
|
||||
],
|
||||
"safety": [BuildProvider(provider_type="inline::llama-guard")],
|
||||
"agents": [BuildProvider(provider_type="inline::meta-reference")],
|
||||
"eval": [BuildProvider(provider_type="inline::meta-reference")],
|
||||
"datasetio": [
|
||||
BuildProvider(provider_type="remote::huggingface"),
|
||||
BuildProvider(provider_type="inline::localfs"),
|
||||
],
|
||||
"scoring": [
|
||||
BuildProvider(provider_type="inline::basic"),
|
||||
BuildProvider(provider_type="inline::llm-as-judge"),
|
||||
BuildProvider(provider_type="inline::braintrust"),
|
||||
],
|
||||
"tool_runtime": [
|
||||
BuildProvider(provider_type="remote::brave-search"),
|
||||
BuildProvider(provider_type="remote::tavily-search"),
|
||||
BuildProvider(provider_type="inline::rag-runtime"),
|
||||
BuildProvider(provider_type="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(
|
||||
f"~/.llama/distributions/{name}", url="${env.CHROMADB_URL:=}"
|
||||
),
|
||||
),
|
||||
Provider(
|
||||
provider_id="${env.ENABLE_PGVECTOR:+pgvector}",
|
||||
provider_type="remote::pgvector",
|
||||
config=PGVectorVectorIOConfig.sample_run_config(
|
||||
f"~/.llama/distributions/{name}",
|
||||
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",
|
||||
),
|
||||
]
|
||||
|
||||
models, _ = get_model_registry(available_models)
|
||||
default_models = models + [
|
||||
ModelInput(
|
||||
model_id="meta-llama/Llama-3.3-70B-Instruct",
|
||||
provider_id="groq",
|
||||
provider_model_id="groq/llama-3.3-70b-versatile",
|
||||
model_type=ModelType.llm,
|
||||
),
|
||||
ModelInput(
|
||||
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=ModelType.llm,
|
||||
),
|
||||
]
|
||||
|
||||
default_datasets = [
|
||||
DatasetInput(
|
||||
dataset_id="simpleqa",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/simpleqa?split=train",
|
||||
),
|
||||
),
|
||||
DatasetInput(
|
||||
dataset_id="mmlu_cot",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/mmlu_cot?split=test&name=all",
|
||||
),
|
||||
),
|
||||
DatasetInput(
|
||||
dataset_id="gpqa_cot",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main",
|
||||
),
|
||||
),
|
||||
DatasetInput(
|
||||
dataset_id="math_500",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/math_500?split=test",
|
||||
),
|
||||
),
|
||||
DatasetInput(
|
||||
dataset_id="ifeval",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/IfEval?split=train",
|
||||
),
|
||||
),
|
||||
DatasetInput(
|
||||
dataset_id="docvqa",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/docvqa?split=val",
|
||||
),
|
||||
),
|
||||
]
|
||||
|
||||
default_benchmarks = [
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-simpleqa",
|
||||
dataset_id="simpleqa",
|
||||
scoring_functions=["llm-as-judge::405b-simpleqa"],
|
||||
),
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-mmlu-cot",
|
||||
dataset_id="mmlu_cot",
|
||||
scoring_functions=["basic::regex_parser_multiple_choice_answer"],
|
||||
),
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-gpqa-cot",
|
||||
dataset_id="gpqa_cot",
|
||||
scoring_functions=["basic::regex_parser_multiple_choice_answer"],
|
||||
),
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-math-500",
|
||||
dataset_id="math_500",
|
||||
scoring_functions=["basic::regex_parser_math_response"],
|
||||
),
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-ifeval",
|
||||
dataset_id="ifeval",
|
||||
scoring_functions=["basic::ifeval"],
|
||||
),
|
||||
BenchmarkInput(
|
||||
benchmark_id="meta-reference-docvqa",
|
||||
dataset_id="docvqa",
|
||||
scoring_functions=["basic::docvqa"],
|
||||
),
|
||||
]
|
||||
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")],
|
||||
default_datasets=default_datasets,
|
||||
default_benchmarks=default_benchmarks,
|
||||
),
|
||||
},
|
||||
run_config_env_vars={
|
||||
"LLAMA_STACK_PORT": (
|
||||
"8321",
|
||||
"Port for the Llama Stack distribution server",
|
||||
),
|
||||
"TOGETHER_API_KEY": (
|
||||
"",
|
||||
"Together API Key",
|
||||
),
|
||||
"OPENAI_API_KEY": (
|
||||
"",
|
||||
"OpenAI API Key",
|
||||
),
|
||||
"GEMINI_API_KEY": (
|
||||
"",
|
||||
"Gemini API Key",
|
||||
),
|
||||
"ANTHROPIC_API_KEY": (
|
||||
"",
|
||||
"Anthropic API Key",
|
||||
),
|
||||
"GROQ_API_KEY": (
|
||||
"",
|
||||
"Groq API Key",
|
||||
),
|
||||
},
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue