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Allow models to be registered as long as llama model is provided

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This commit is contained in:
Dinesh Yeduguru 2024-11-18 11:58:32 -08:00
parent f1b9578f8d
commit ccb5445d2a
2 changed files with 30 additions and 21 deletions
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33
llama_stack/providers/tests/inference/test_model_registration.py
View file

@ -8,6 +8,7 @@ import pytest
from llama_models.datatypes import CoreModelId from llama_models.datatypes import CoreModelId
# How to run this test: # How to run this test:
# #
# pytest -v -s llama_stack/providers/tests/inference/test_model_registration.py # pytest -v -s llama_stack/providers/tests/inference/test_model_registration.py
@ -17,8 +18,17 @@ from llama_models.datatypes import CoreModelId
class TestModelRegistration: class TestModelRegistration:
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_register_unsupported_model(self, inference_stack): async def test_register_unsupported_model(self, inference_stack, inference_model):
_, models_impl = inference_stack inference_impl, models_impl = inference_stack
provider = inference_impl.routing_table.get_provider_impl(inference_model)
if provider.__provider_spec__.provider_type not in (
"meta-reference",
"remote::ollama",
"remote::vllm",
"remote::tgi",
):
pytest.skip("70B instruct is too big only for local inference providers")
# Try to register a model that's too large for local inference # Try to register a model that's too large for local inference
with pytest.raises(Exception) as exc_info: with pytest.raises(Exception) as exc_info:
@ -37,21 +47,10 @@ class TestModelRegistration:
) )
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_update_model(self, inference_stack): async def test_register_with_llama_model(self, inference_stack):
_, models_impl = inference_stack _, models_impl = inference_stack
# Register a model to update _ = await models_impl.register_model(
model_id = CoreModelId.llama3_1_8b_instruct.value model_id="custom-model",
old_model = await models_impl.register_model(model_id=model_id) metadata={"llama_model": CoreModelId.llama3_1_8b_instruct.value},
# Update the model
new_model_id = CoreModelId.llama3_2_3b_instruct.value
updated_model = await models_impl.update_model(
model_id=model_id, provider_model_id=new_model_id
) )
# Retrieve the updated model to verify changes
assert updated_model.provider_resource_id != old_model.provider_resource_id
# Cleanup
await models_impl.unregister_model(model_id=model_id)

18
llama_stack/providers/utils/inference/model_registry.py
View file

@ -51,7 +51,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
if identifier in self.alias_to_provider_id_map: if identifier in self.alias_to_provider_id_map:
return self.alias_to_provider_id_map[identifier] return self.alias_to_provider_id_map[identifier]
else: else:
raise ValueError(f"Unknown model: `{identifier}`") return None
def get_llama_model(self, provider_model_id: str) -> str: def get_llama_model(self, provider_model_id: str) -> str:
if provider_model_id in self.provider_id_to_llama_model_map: if provider_model_id in self.provider_id_to_llama_model_map:
@ -60,8 +60,18 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
return None return None
async def register_model(self, model: Model) -> Model: async def register_model(self, model: Model) -> Model:
model.provider_resource_id = self.get_provider_model_id( provider_resource_id = self.get_provider_model_id(model.provider_resource_id)
model.provider_resource_id if provider_resource_id:
) model.provider_resource_id = provider_resource_id
else:
if model.metadata.get("llama_model") is None:
raise ValueError(
f"Model '{model.provider_resource_id}' is not available and no llama_model was specified in metadata. "
"Please specify a llama_model in metadata or use a supported model identifier"
)
# Register the mapping from provider model id to llama model for future lookups
self.provider_id_to_llama_model_map[model.provider_resource_id] = (
model.metadata["llama_model"]
)
return model return model