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fixed VertexAIModelGarden class #13917

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Nov 27, 2023
Merged

fixed VertexAIModelGarden class #13917

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109 changes: 57 additions & 52 deletions libs/langchain/langchain/llms/vertexai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,8 @@
PredictionServiceAsyncClient,
PredictionServiceClient,
)
from google.cloud.aiplatform.models import Prediction
from google.protobuf.struct_pb2 import Value
from vertexai.language_models._language_models import (
TextGenerationResponse,
_LanguageModel,
Expand Down Expand Up @@ -370,9 +372,11 @@ class VertexAIModelGarden(_VertexAIBase, BaseLLM):
endpoint_id: str
"A name of an endpoint where the model has been deployed."
allowed_model_args: Optional[List[str]] = None
"""Allowed optional args to be passed to the model."""
"Allowed optional args to be passed to the model."
prompt_arg: str = "prompt"
result_arg: str = "generated_text"
result_arg: Optional[str] = "generated_text"
"Set result_arg to None if output of the model is expected to be a string."
"Otherwise, if it's a dict, provided an argument that contains the result."

@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
Expand All @@ -386,7 +390,7 @@ def validate_environment(cls, values: Dict) -> Dict:
except ImportError:
raise_vertex_import_error()

if values["project"] is None:
if not values["project"]:
raise ValueError(
"A GCP project should be provided to run inference on Model Garden!"
)
Expand All @@ -401,20 +405,18 @@ def validate_environment(cls, values: Dict) -> Dict:
values["async_client"] = PredictionServiceAsyncClient(
client_options=client_options, client_info=client_info
)
values["endpoint_path"] = values["client"].endpoint_path(
project=values["project"],
location=values["location"],
endpoint=values["endpoint_id"],
)
return values

@property
def _llm_type(self) -> str:
return "vertexai_model_garden"

def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
def _prepare_request(self, prompts: List[str], **kwargs: Any) -> List["Value"]:
try:
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
Expand All @@ -423,7 +425,6 @@ def _generate(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)

instances = []
for prompt in prompts:
if self.allowed_model_args:
Expand All @@ -438,18 +439,53 @@ def _generate(
predict_instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]
return predict_instances

endpoint = self.client.endpoint_path(
project=self.project, location=self.location, endpoint=self.endpoint_id
)
response = self.client.predict(endpoint=endpoint, instances=predict_instances)
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
instances = self._prepare_request(prompts, **kwargs)
response = self.client.predict(endpoint=self.endpoint_path, instances=instances)
return self._parse_response(response)

def _parse_response(self, predictions: "Prediction") -> LLMResult:
generations: List[List[Generation]] = []
for result in response.predictions:
for result in predictions.predictions:
generations.append(
[Generation(text=prediction[self.result_arg]) for prediction in result]
[
Generation(text=self._parse_prediction(prediction))
for prediction in result
]
)
return LLMResult(generations=generations)

def _parse_prediction(self, prediction: Any) -> str:
if isinstance(prediction, str):
return prediction

if self.result_arg:
try:
return prediction[self.result_arg]
except KeyError:
if isinstance(prediction, str):
error_desc = (
"Provided non-None `result_arg` (result_arg="
f"{self.result_arg}). But got prediction of type "
f"{type(prediction)} instead of dict. Most probably, you"
"need to set `result_arg=None` during VertexAIModelGarden "
"initialization."
)
raise ValueError(error_desc)
else:
raise ValueError(f"{self.result_arg} key not found in prediction!")

return prediction

async def _agenerate(
self,
prompts: List[str],
Expand All @@ -458,39 +494,8 @@ async def _agenerate(
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
try:
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
except ImportError:
raise ImportError(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)

instances = []
for prompt in prompts:
if self.allowed_model_args:
instance = {
k: v for k, v in kwargs.items() if k in self.allowed_model_args
}
else:
instance = {}
instance[self.prompt_arg] = prompt
instances.append(instance)

predict_instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]

endpoint = self.async_client.endpoint_path(
project=self.project, location=self.location, endpoint=self.endpoint_id
)
instances = self._prepare_request(prompts, **kwargs)
response = await self.async_client.predict(
endpoint=endpoint, instances=predict_instances
endpoint=self.endpoint_path, instances=instances
)
generations: List[List[Generation]] = []
for result in response.predictions:
generations.append(
[Generation(text=prediction[self.result_arg]) for prediction in result]
)
return LLMResult(generations=generations)
return self._parse_response(response)
66 changes: 53 additions & 13 deletions libs/langchain/tests/integration_tests/llms/test_vertexai.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
`gcloud auth login` first).
"""
import os
from typing import Optional

import pytest
from langchain_core.outputs import LLMResult
Expand Down Expand Up @@ -71,40 +72,79 @@ async def test_vertex_consistency() -> None:
assert output.generations[0][0].text == async_output.generations[0][0].text


def test_model_garden() -> None:
"""In order to run this test, you should provide an endpoint name.
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
def test_model_garden(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
"""In order to run this test, you should provide endpoint names.

Example:
export ENDPOINT_ID=...
export FALCON_ENDPOINT_ID=...
export LLAMA_ENDPOINT_ID=...
export PROJECT=...
"""
endpoint_id = os.environ["ENDPOINT_ID"]
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
llm = VertexAIModelGarden(endpoint_id=endpoint_id, project=project)
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = llm("What is the meaning of life?")
assert isinstance(output, str)
assert llm._llm_type == "vertexai_model_garden"


def test_model_garden_generate() -> None:
"""In order to run this test, you should provide an endpoint name.
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
def test_model_garden_generate(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
"""In order to run this test, you should provide endpoint names.

Example:
export ENDPOINT_ID=...
export FALCON_ENDPOINT_ID=...
export LLAMA_ENDPOINT_ID=...
export PROJECT=...
"""
endpoint_id = os.environ["ENDPOINT_ID"]
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
llm = VertexAIModelGarden(endpoint_id=endpoint_id, project=project)
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = llm.generate(["What is the meaning of life?", "How much is 2+2"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 2


async def test_model_garden_agenerate() -> None:
endpoint_id = os.environ["ENDPOINT_ID"]
@pytest.mark.asyncio
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
async def test_model_garden_agenerate(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
llm = VertexAIModelGarden(endpoint_id=endpoint_id, project=project)
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 2
Expand Down
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