forked from phoenix-oss/llama-stack-mirror
feat: Add watsonx inference adapter (#1895)
# What does this PR do? IBM watsonx ai added as the inference [#1741 ](https://github.com/meta-llama/llama-stack/issues/1741) [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) --------- Co-authored-by: Sajikumar JS <sajikumar.js@ibm.com>
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Sajikumar JS
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llama_stack/providers/remote/inference/watsonx/watsonx.py
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llama_stack/providers/remote/inference/watsonx/watsonx.py
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# 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 AsyncGenerator, List, Optional, Union
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from ibm_watson_machine_learning.foundation_models import Model
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from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
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from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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CompletionRequest,
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EmbeddingsResponse,
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EmbeddingTaskType,
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Inference,
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LogProbConfig,
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Message,
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ResponseFormat,
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SamplingParams,
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TextTruncation,
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ToolChoice,
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ToolConfig,
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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OpenAICompatCompletionChoice,
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OpenAICompatCompletionResponse,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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process_completion_response,
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process_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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completion_request_to_prompt,
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request_has_media,
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)
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from . import WatsonXConfig
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from .models import MODEL_ENTRIES
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class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
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def __init__(self, config: WatsonXConfig) -> None:
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ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
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print(f"Initializing watsonx InferenceAdapter({config.url})...")
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self._config = config
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self._project_id = self._config.project_id
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def completion(
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self,
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model_id: str,
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content: InterleavedContent,
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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request = CompletionRequest(
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model=model.provider_resource_id,
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content=content,
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sampling_params=sampling_params,
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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)
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if stream:
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return self._stream_completion(request)
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else:
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return await self._nonstream_completion(request)
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def _get_client(self, model_id) -> Model:
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config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None
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config_url = self._config.url
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project_id = self._config.project_id
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credentials = {"url": config_url, "apikey": config_api_key}
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return Model(model_id=model_id, credentials=credentials, project_id=project_id)
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async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
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params = await self._get_params(request)
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r = self._get_client(request.model).generate(**params)
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choices = []
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if "results" in r:
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for result in r["results"]:
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choice = OpenAICompatCompletionChoice(
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finish_reason=result["stop_reason"] if result["stop_reason"] else None,
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text=result["generated_text"],
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)
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choices.append(choice)
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response = OpenAICompatCompletionResponse(
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choices=choices,
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)
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return process_completion_response(response)
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async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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async def _generate_and_convert_to_openai_compat():
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s = self._get_client(request.model).generate_text_stream(**params)
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for chunk in s:
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choice = OpenAICompatCompletionChoice(
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finish_reason=None,
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text=chunk,
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_completion_stream_response(stream):
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yield chunk
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async def chat_completion(
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self,
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model_id: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = None,
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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tool_config: Optional[ToolConfig] = None,
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) -> AsyncGenerator:
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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request = ChatCompletionRequest(
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model=model.provider_resource_id,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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tool_config=tool_config,
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)
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if stream:
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return self._stream_chat_completion(request)
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else:
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return await self._nonstream_chat_completion(request)
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async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
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params = await self._get_params(request)
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r = self._get_client(request.model).generate(**params)
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choices = []
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if "results" in r:
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for result in r["results"]:
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choice = OpenAICompatCompletionChoice(
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finish_reason=result["stop_reason"] if result["stop_reason"] else None,
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text=result["generated_text"],
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)
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choices.append(choice)
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response = OpenAICompatCompletionResponse(
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choices=choices,
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)
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return process_chat_completion_response(response, request)
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async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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model_id = request.model
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# if we shift to TogetherAsyncClient, we won't need this wrapper
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async def _to_async_generator():
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s = self._get_client(model_id).generate_text_stream(**params)
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for chunk in s:
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choice = OpenAICompatCompletionChoice(
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finish_reason=None,
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text=chunk,
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _to_async_generator()
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async for chunk in process_chat_completion_stream_response(stream, request):
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yield chunk
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async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict:
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input_dict = {"params": {}}
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media_present = request_has_media(request)
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llama_model = self.get_llama_model(request.model)
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if isinstance(request, ChatCompletionRequest):
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input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
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else:
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assert not media_present, "Together does not support media for Completion requests"
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input_dict["prompt"] = await completion_request_to_prompt(request)
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if request.sampling_params:
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if request.sampling_params.strategy:
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input_dict["params"][GenParams.DECODING_METHOD] = request.sampling_params.strategy.type
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if request.sampling_params.max_tokens:
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input_dict["params"][GenParams.MAX_NEW_TOKENS] = request.sampling_params.max_tokens
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if request.sampling_params.repetition_penalty:
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input_dict["params"][GenParams.REPETITION_PENALTY] = request.sampling_params.repetition_penalty
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if request.sampling_params.additional_params.get("top_p"):
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input_dict["params"][GenParams.TOP_P] = request.sampling_params.additional_params["top_p"]
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if request.sampling_params.additional_params.get("top_k"):
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input_dict["params"][GenParams.TOP_K] = request.sampling_params.additional_params["top_k"]
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if request.sampling_params.additional_params.get("temperature"):
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input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.additional_params["temperature"]
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if request.sampling_params.additional_params.get("length_penalty"):
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input_dict["params"][GenParams.LENGTH_PENALTY] = request.sampling_params.additional_params[
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"length_penalty"
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]
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if request.sampling_params.additional_params.get("random_seed"):
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input_dict["params"][GenParams.RANDOM_SEED] = request.sampling_params.additional_params["random_seed"]
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if request.sampling_params.additional_params.get("min_new_tokens"):
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input_dict["params"][GenParams.MIN_NEW_TOKENS] = request.sampling_params.additional_params[
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"min_new_tokens"
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]
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if request.sampling_params.additional_params.get("stop_sequences"):
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input_dict["params"][GenParams.STOP_SEQUENCES] = request.sampling_params.additional_params[
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"stop_sequences"
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]
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if request.sampling_params.additional_params.get("time_limit"):
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input_dict["params"][GenParams.TIME_LIMIT] = request.sampling_params.additional_params["time_limit"]
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if request.sampling_params.additional_params.get("truncate_input_tokens"):
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input_dict["params"][GenParams.TRUNCATE_INPUT_TOKENS] = request.sampling_params.additional_params[
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"truncate_input_tokens"
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]
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if request.sampling_params.additional_params.get("return_options"):
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input_dict["params"][GenParams.RETURN_OPTIONS] = request.sampling_params.additional_params[
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"return_options"
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]
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params = {
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**input_dict,
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}
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return params
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async def embeddings(
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self,
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model_id: str,
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contents: List[str] | List[InterleavedContentItem],
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text_truncation: Optional[TextTruncation] = TextTruncation.none,
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output_dimension: Optional[int] = None,
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task_type: Optional[EmbeddingTaskType] = None,
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) -> EmbeddingsResponse:
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pass
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