forked from phoenix-oss/llama-stack-mirror
fix: updated watsonx inference chat apis with new repo changes (#2033)
# What does this PR do? There are new changes in repo which needs to add some additional functions to the inference which is fixed. Also need one additional params to pass some extra arguments to watsonx.ai [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) --------- Co-authored-by: Sajikumar JS <sajikumar.js@ibm.com>
This commit is contained in:
Sajikumar JS
GitHub
parent
0266b20535
commit
6cf6791de1
1 changed files with 150 additions and 32 deletions
|
|
@ -4,10 +4,11 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import AsyncGenerator, List, Optional, Union
|
||||
from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
|
||||
|
||||
from ibm_watson_machine_learning.foundation_models import Model
|
||||
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem
|
||||
from llama_stack.apis.inference import (
|
||||
|
|
@ -27,10 +28,21 @@ from llama_stack.apis.inference import (
|
|||
ToolDefinition,
|
||||
ToolPromptFormat,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import (
|
||||
GreedySamplingStrategy,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChatCompletionChunk,
|
||||
OpenAICompletion,
|
||||
OpenAIMessageParam,
|
||||
OpenAIResponseFormatParam,
|
||||
TopKSamplingStrategy,
|
||||
TopPSamplingStrategy,
|
||||
)
|
||||
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
|
||||
from llama_stack.providers.utils.inference.openai_compat import (
|
||||
OpenAICompatCompletionChoice,
|
||||
OpenAICompatCompletionResponse,
|
||||
prepare_openai_completion_params,
|
||||
process_chat_completion_response,
|
||||
process_chat_completion_stream_response,
|
||||
process_completion_response,
|
||||
|
|
@ -95,6 +107,14 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
|
||||
return Model(model_id=model_id, credentials=credentials, project_id=project_id)
|
||||
|
||||
def _get_openai_client(self) -> AsyncOpenAI:
|
||||
if not self._openai_client:
|
||||
self._openai_client = AsyncOpenAI(
|
||||
base_url=f"{self._config.url}/openai/v1",
|
||||
api_key=self._config.api_key,
|
||||
)
|
||||
return self._openai_client
|
||||
|
||||
async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
|
||||
params = await self._get_params(request)
|
||||
r = self._get_client(request.model).generate(**params)
|
||||
|
|
@ -213,36 +233,16 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
input_dict["params"][GenParams.MAX_NEW_TOKENS] = request.sampling_params.max_tokens
|
||||
if request.sampling_params.repetition_penalty:
|
||||
input_dict["params"][GenParams.REPETITION_PENALTY] = request.sampling_params.repetition_penalty
|
||||
if request.sampling_params.additional_params.get("top_p"):
|
||||
input_dict["params"][GenParams.TOP_P] = request.sampling_params.additional_params["top_p"]
|
||||
if request.sampling_params.additional_params.get("top_k"):
|
||||
input_dict["params"][GenParams.TOP_K] = request.sampling_params.additional_params["top_k"]
|
||||
if request.sampling_params.additional_params.get("temperature"):
|
||||
input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.additional_params["temperature"]
|
||||
if request.sampling_params.additional_params.get("length_penalty"):
|
||||
input_dict["params"][GenParams.LENGTH_PENALTY] = request.sampling_params.additional_params[
|
||||
"length_penalty"
|
||||
]
|
||||
if request.sampling_params.additional_params.get("random_seed"):
|
||||
input_dict["params"][GenParams.RANDOM_SEED] = request.sampling_params.additional_params["random_seed"]
|
||||
if request.sampling_params.additional_params.get("min_new_tokens"):
|
||||
input_dict["params"][GenParams.MIN_NEW_TOKENS] = request.sampling_params.additional_params[
|
||||
"min_new_tokens"
|
||||
]
|
||||
if request.sampling_params.additional_params.get("stop_sequences"):
|
||||
input_dict["params"][GenParams.STOP_SEQUENCES] = request.sampling_params.additional_params[
|
||||
"stop_sequences"
|
||||
]
|
||||
if request.sampling_params.additional_params.get("time_limit"):
|
||||
input_dict["params"][GenParams.TIME_LIMIT] = request.sampling_params.additional_params["time_limit"]
|
||||
if request.sampling_params.additional_params.get("truncate_input_tokens"):
|
||||
input_dict["params"][GenParams.TRUNCATE_INPUT_TOKENS] = request.sampling_params.additional_params[
|
||||
"truncate_input_tokens"
|
||||
]
|
||||
if request.sampling_params.additional_params.get("return_options"):
|
||||
input_dict["params"][GenParams.RETURN_OPTIONS] = request.sampling_params.additional_params[
|
||||
"return_options"
|
||||
]
|
||||
|
||||
if isinstance(request.sampling_params.strategy, TopPSamplingStrategy):
|
||||
input_dict["params"][GenParams.TOP_P] = request.sampling_params.strategy.top_p
|
||||
input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.strategy.temperature
|
||||
if isinstance(request.sampling_params.strategy, TopKSamplingStrategy):
|
||||
input_dict["params"][GenParams.TOP_K] = request.sampling_params.strategy.top_k
|
||||
if isinstance(request.sampling_params.strategy, GreedySamplingStrategy):
|
||||
input_dict["params"][GenParams.TEMPERATURE] = 0.0
|
||||
|
||||
input_dict["params"][GenParams.STOP_SEQUENCES] = ["<|endoftext|>"]
|
||||
|
||||
params = {
|
||||
**input_dict,
|
||||
|
|
@ -257,4 +257,122 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
output_dimension: Optional[int] = None,
|
||||
task_type: Optional[EmbeddingTaskType] = None,
|
||||
) -> EmbeddingsResponse:
|
||||
pass
|
||||
raise NotImplementedError("embedding is not supported for watsonx")
|
||||
|
||||
async def openai_completion(
|
||||
self,
|
||||
model: str,
|
||||
prompt: Union[str, List[str], List[int], List[List[int]]],
|
||||
best_of: Optional[int] = None,
|
||||
echo: Optional[bool] = None,
|
||||
frequency_penalty: Optional[float] = None,
|
||||
logit_bias: Optional[Dict[str, float]] = None,
|
||||
logprobs: Optional[bool] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
n: Optional[int] = None,
|
||||
presence_penalty: Optional[float] = None,
|
||||
seed: Optional[int] = None,
|
||||
stop: Optional[Union[str, List[str]]] = None,
|
||||
stream: Optional[bool] = None,
|
||||
stream_options: Optional[Dict[str, Any]] = None,
|
||||
temperature: Optional[float] = None,
|
||||
top_p: Optional[float] = None,
|
||||
user: Optional[str] = None,
|
||||
guided_choice: Optional[List[str]] = None,
|
||||
prompt_logprobs: Optional[int] = None,
|
||||
) -> OpenAICompletion:
|
||||
model_obj = await self.model_store.get_model(model)
|
||||
params = await prepare_openai_completion_params(
|
||||
model=model_obj.provider_resource_id,
|
||||
prompt=prompt,
|
||||
best_of=best_of,
|
||||
echo=echo,
|
||||
frequency_penalty=frequency_penalty,
|
||||
logit_bias=logit_bias,
|
||||
logprobs=logprobs,
|
||||
max_tokens=max_tokens,
|
||||
n=n,
|
||||
presence_penalty=presence_penalty,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
stream_options=stream_options,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
return await self._get_openai_client().completions.create(**params) # type: ignore
|
||||
|
||||
async def openai_chat_completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[OpenAIMessageParam],
|
||||
frequency_penalty: Optional[float] = None,
|
||||
function_call: Optional[Union[str, Dict[str, Any]]] = None,
|
||||
functions: Optional[List[Dict[str, Any]]] = None,
|
||||
logit_bias: Optional[Dict[str, float]] = None,
|
||||
logprobs: Optional[bool] = None,
|
||||
max_completion_tokens: Optional[int] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
n: Optional[int] = None,
|
||||
parallel_tool_calls: Optional[bool] = None,
|
||||
presence_penalty: Optional[float] = None,
|
||||
response_format: Optional[OpenAIResponseFormatParam] = None,
|
||||
seed: Optional[int] = None,
|
||||
stop: Optional[Union[str, List[str]]] = None,
|
||||
stream: Optional[bool] = None,
|
||||
stream_options: Optional[Dict[str, Any]] = None,
|
||||
temperature: Optional[float] = None,
|
||||
tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
|
||||
tools: Optional[List[Dict[str, Any]]] = None,
|
||||
top_logprobs: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
|
||||
model_obj = await self.model_store.get_model(model)
|
||||
params = await prepare_openai_completion_params(
|
||||
model=model_obj.provider_resource_id,
|
||||
messages=messages,
|
||||
frequency_penalty=frequency_penalty,
|
||||
function_call=function_call,
|
||||
functions=functions,
|
||||
logit_bias=logit_bias,
|
||||
logprobs=logprobs,
|
||||
max_completion_tokens=max_completion_tokens,
|
||||
max_tokens=max_tokens,
|
||||
n=n,
|
||||
parallel_tool_calls=parallel_tool_calls,
|
||||
presence_penalty=presence_penalty,
|
||||
response_format=response_format,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
stream_options=stream_options,
|
||||
temperature=temperature,
|
||||
tool_choice=tool_choice,
|
||||
tools=tools,
|
||||
top_logprobs=top_logprobs,
|
||||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
if params.get("stream", False):
|
||||
return self._stream_openai_chat_completion(params)
|
||||
return await self._get_openai_client().chat.completions.create(**params) # type: ignore
|
||||
|
||||
async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator:
|
||||
# watsonx.ai sometimes adds usage data to the stream
|
||||
include_usage = False
|
||||
if params.get("stream_options", None):
|
||||
include_usage = params["stream_options"].get("include_usage", False)
|
||||
stream = await self._get_openai_client().chat.completions.create(**params)
|
||||
|
||||
seen_finish_reason = False
|
||||
async for chunk in stream:
|
||||
# Final usage chunk with no choices that the user didn't request, so discard
|
||||
if not include_usage and seen_finish_reason and len(chunk.choices) == 0:
|
||||
break
|
||||
yield chunk
|
||||
for choice in chunk.choices:
|
||||
if choice.finish_reason:
|
||||
seen_finish_reason = True
|
||||
break
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue