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Merge pull request #1916 from RenaLu/main
Add support for Vertex AI custom models deployed on private endpoint
This commit is contained in:
Krish Dholakia
GitHub
commit
9b60ef9a3c
2 changed files with 128 additions and 42 deletions
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@ -343,24 +343,31 @@ def completion(
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llm_model = CodeChatModel.from_pretrained(model)
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mode = "chat"
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request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n"
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else: # assume vertex model garden
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client = aiplatform.gapic.PredictionServiceClient(
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client_options=client_options
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elif model == "private":
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mode = "private"
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model = optional_params.pop("model_id", None)
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# private endpoint requires a dict instead of JSON
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instances = [optional_params.copy()]
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instances[0]["prompt"] = prompt
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llm_model = aiplatform.PrivateEndpoint(
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endpoint_name=model,
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project=vertex_project,
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location=vertex_location,
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)
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request_str += f"llm_model = aiplatform.PrivateEndpoint(endpoint_name={model}, project={vertex_project}, location={vertex_location})\n"
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else: # assume vertex model garden on public endpoint
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mode = "custom"
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instances = [optional_params]
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instances = [optional_params.copy()]
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instances[0]["prompt"] = prompt
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instances = [
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json_format.ParseDict(instance_dict, Value())
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for instance_dict in instances
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]
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llm_model = client.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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mode = "custom"
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request_str += f"llm_model = client.endpoint_path(project={vertex_project}, location={vertex_location}, endpoint={model})\n"
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# Will determine the API used based on async parameter
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llm_model = None
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# NOTE: async prediction and streaming under "private" mode isn't supported by aiplatform right now
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if acompletion == True:
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data = {
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"llm_model": llm_model,
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@ -532,9 +539,6 @@ def completion(
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"""
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Vertex AI Model Garden
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"""
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request_str += (
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f"client.predict(endpoint={llm_model}, instances={instances})\n"
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)
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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@ -544,11 +548,21 @@ def completion(
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"request_str": request_str,
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},
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)
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response = client.predict(
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endpoint=llm_model,
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instances=instances,
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llm_model = aiplatform.gapic.PredictionServiceClient(
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client_options=client_options
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)
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request_str += f"llm_model = aiplatform.gapic.PredictionServiceClient(client_options={client_options})\n"
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endpoint_path = llm_model.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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request_str += (
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f"llm_model.predict(endpoint={endpoint_path}, instances={instances})\n"
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)
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response = llm_model.predict(
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endpoint=endpoint_path,
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instances=instances
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).predictions
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completion_response = response[0]
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if (
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isinstance(completion_response, str)
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@ -558,6 +572,36 @@ def completion(
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if "stream" in optional_params and optional_params["stream"] == True:
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response = TextStreamer(completion_response)
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return response
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elif mode == "private":
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"""
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Vertex AI Model Garden deployed on private endpoint
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"""
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=None,
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additional_args={
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"complete_input_dict": optional_params,
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"request_str": request_str,
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},
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)
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request_str += (
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f"llm_model.predict(instances={instances})\n"
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)
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response = llm_model.predict(
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instances=instances
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).predictions
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completion_response = response[0]
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if (
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isinstance(completion_response, str)
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and "\nOutput:\n" in completion_response
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):
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completion_response = completion_response.split("\nOutput:\n", 1)[1]
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if "stream" in optional_params and optional_params["stream"] == True:
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response = TextStreamer(completion_response)
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return response
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## LOGGING
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logging_obj.post_call(
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input=prompt, api_key=None, original_response=completion_response
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@ -722,17 +766,6 @@ async def async_completion(
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Vertex AI Model Garden
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"""
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from google.cloud import aiplatform
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async_client = aiplatform.gapic.PredictionServiceAsyncClient(
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client_options=client_options
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)
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llm_model = async_client.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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request_str += (
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f"client.predict(endpoint={llm_model}, instances={instances})\n"
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)
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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@ -743,8 +776,18 @@ async def async_completion(
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},
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)
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response_obj = await async_client.predict(
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endpoint=llm_model,
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llm_model = aiplatform.gapic.PredictionServiceAsyncClient(
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client_options=client_options
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)
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request_str += f"llm_model = aiplatform.gapic.PredictionServiceAsyncClient(client_options={client_options})\n"
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endpoint_path = llm_model.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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request_str += (
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f"llm_model.predict(endpoint={endpoint_path}, instances={instances})\n"
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)
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response_obj = await llm_model.predict(
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endpoint=endpoint_path,
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instances=instances,
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)
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response = response_obj.predictions
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@ -754,6 +797,23 @@ async def async_completion(
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and "\nOutput:\n" in completion_response
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):
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completion_response = completion_response.split("\nOutput:\n", 1)[1]
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elif mode == "private":
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request_str += (
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f"llm_model.predict_async(instances={instances})\n"
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)
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response_obj = await llm_model.predict_async(
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instances=instances,
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)
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response = response_obj.predictions
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completion_response = response[0]
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if (
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isinstance(completion_response, str)
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and "\nOutput:\n" in completion_response
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):
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completion_response = completion_response.split("\nOutput:\n", 1)[1]
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## LOGGING
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logging_obj.post_call(
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input=prompt, api_key=None, original_response=completion_response
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@ -894,15 +954,8 @@ async def async_streaming(
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response = llm_model.predict_streaming_async(prompt, **optional_params)
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elif mode == "custom":
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from google.cloud import aiplatform
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stream = optional_params.pop("stream", None)
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async_client = aiplatform.gapic.PredictionServiceAsyncClient(
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client_options=client_options
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)
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llm_model = async_client.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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request_str += f"client.predict(endpoint={llm_model}, instances={instances})\n"
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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@ -912,9 +965,34 @@ async def async_streaming(
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"request_str": request_str,
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},
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)
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llm_model = aiplatform.gapic.PredictionServiceAsyncClient(
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client_options=client_options
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)
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request_str += f"llm_model = aiplatform.gapic.PredictionServiceAsyncClient(client_options={client_options})\n"
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endpoint_path = llm_model.endpoint_path(
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project=vertex_project, location=vertex_location, endpoint=model
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)
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request_str += f"client.predict(endpoint={endpoint_path}, instances={instances})\n"
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response_obj = await llm_model.predict(
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endpoint=endpoint_path,
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instances=instances,
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)
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response_obj = await async_client.predict(
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endpoint=llm_model,
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response = response_obj.predictions
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completion_response = response[0]
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if (
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isinstance(completion_response, str)
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and "\nOutput:\n" in completion_response
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):
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completion_response = completion_response.split("\nOutput:\n", 1)[1]
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if stream:
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response = TextStreamer(completion_response)
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elif mode == "private":
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stream = optional_params.pop("stream", None)
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_ = instances[0].pop("stream", None)
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request_str += f"llm_model.predict_async(instances={instances})\n"
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response_obj = await llm_model.predict_async(
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instances=instances,
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)
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response = response_obj.predictions
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@ -924,8 +1002,9 @@ async def async_streaming(
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and "\nOutput:\n" in completion_response
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):
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completion_response = completion_response.split("\nOutput:\n", 1)[1]
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if "stream" in optional_params and optional_params["stream"] == True:
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if stream:
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response = TextStreamer(completion_response)
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streamwrapper = CustomStreamWrapper(
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completion_stream=response,
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model=model,
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@ -4256,7 +4256,14 @@ def get_optional_params(
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optional_params["stop_sequences"] = stop
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if max_tokens is not None:
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optional_params["max_output_tokens"] = max_tokens
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elif custom_llm_provider == "vertex_ai":
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elif custom_llm_provider == "vertex_ai" and model in (
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litellm.vertex_chat_models
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or model in litellm.vertex_code_chat_models
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or model in litellm.vertex_text_models
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or model in litellm.vertex_code_text_models
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or model in litellm.vertex_language_models
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or model in litellm.vertex_embedding_models
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):
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## check if unsupported param passed in
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supported_params = [
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"temperature",
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