mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-16 17:59:29 +00:00
Merge branch 'main' into use-secret-str
This commit is contained in:
Matthew Farrellee
commit
39854f4562
14 changed files with 196 additions and 473 deletions
|
|
@ -4,19 +4,12 @@
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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 llama_stack.apis.inference import Inference
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from .config import WatsonXConfig
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async def get_adapter_impl(config: WatsonXConfig, _deps) -> Inference:
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# import dynamically so `llama stack build` does not fail due to missing dependencies
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async def get_adapter_impl(config: WatsonXConfig, _deps):
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# import dynamically so the import is used only when it is needed
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from .watsonx import WatsonXInferenceAdapter
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if not isinstance(config, WatsonXConfig):
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raise RuntimeError(f"Unexpected config type: {type(config)}")
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adapter = WatsonXInferenceAdapter(config)
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return adapter
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__all__ = ["get_adapter_impl", "WatsonXConfig"]
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@ -7,16 +7,18 @@
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import os
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from typing import Any
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
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from llama_stack.schema_utils import json_schema_type
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class WatsonXProviderDataValidator(BaseModel):
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url: str
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api_key: str
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project_id: str
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model_config = ConfigDict(
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from_attributes=True,
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extra="forbid",
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)
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watsonx_api_key: str | None
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@json_schema_type
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@ -26,8 +28,8 @@ class WatsonXConfig(RemoteInferenceProviderConfig):
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description="A base url for accessing the watsonx.ai",
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)
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project_id: str | None = Field(
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default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"),
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description="The Project ID key",
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default=None,
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description="The watsonx.ai project ID",
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)
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timeout: int = Field(
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default=60,
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|
|
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|
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@ -1,47 +0,0 @@
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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 llama_stack.models.llama.sku_types import CoreModelId
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from llama_stack.providers.utils.inference.model_registry import build_hf_repo_model_entry
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MODEL_ENTRIES = [
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build_hf_repo_model_entry(
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"meta-llama/llama-3-3-70b-instruct",
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CoreModelId.llama3_3_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-2-13b-chat",
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CoreModelId.llama2_13b.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-3-1-70b-instruct",
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CoreModelId.llama3_1_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-3-1-8b-instruct",
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CoreModelId.llama3_1_8b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-3-2-11b-vision-instruct",
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CoreModelId.llama3_2_11b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
|
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"meta-llama/llama-3-2-1b-instruct",
|
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CoreModelId.llama3_2_1b_instruct.value,
|
||||
),
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build_hf_repo_model_entry(
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"meta-llama/llama-3-2-3b-instruct",
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||||
CoreModelId.llama3_2_3b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-3-2-90b-vision-instruct",
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CoreModelId.llama3_2_90b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/llama-guard-3-11b-vision",
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CoreModelId.llama_guard_3_11b_vision.value,
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),
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]
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|
|
@ -4,240 +4,120 @@
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# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
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from collections.abc import AsyncGenerator, AsyncIterator
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from typing import Any
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from ibm_watsonx_ai.foundation_models import Model
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from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
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from openai import AsyncOpenAI
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import requests
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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CompletionRequest,
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GreedySamplingStrategy,
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Inference,
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OpenAIChatCompletion,
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OpenAIChatCompletionChunk,
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OpenAICompletion,
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OpenAIEmbeddingsResponse,
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OpenAIMessageParam,
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OpenAIResponseFormatParam,
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TopKSamplingStrategy,
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TopPSamplingStrategy,
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)
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from llama_stack.log import get_logger
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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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prepare_openai_completion_params,
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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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logger = get_logger(name=__name__, category="inference::watsonx")
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from llama_stack.apis.inference import ChatCompletionRequest
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from llama_stack.apis.models import Model
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from llama_stack.apis.models.models import ModelType
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from llama_stack.providers.remote.inference.watsonx.config import WatsonXConfig
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from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
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# Note on structured output
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# WatsonX returns responses with a json embedded into a string.
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# Examples:
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class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
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_model_cache: dict[str, Model] = {}
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# ChatCompletionResponse(completion_message=CompletionMessage(content='```json\n{\n
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# "first_name": "Michael",\n "last_name": "Jordan",\n'...)
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# Not even a valid JSON, but we can still extract the JSON from the content
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def __init__(self, config: WatsonXConfig):
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LiteLLMOpenAIMixin.__init__(
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self,
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litellm_provider_name="watsonx",
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api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
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provider_data_api_key_field="watsonx_api_key",
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)
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self.available_models = None
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self.config = config
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# CompletionResponse(content=' \nThe best answer is $\\boxed{\\{"name": "Michael Jordan",
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# "year_born": "1963", "year_retired": "2003"\\}}$')
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# Find the start of the boxed content
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def get_base_url(self) -> str:
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return self.config.url
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async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
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# Get base parameters from parent
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params = await super()._get_params(request)
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|
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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=MODEL_ENTRIES)
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|
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logger.info(f"Initializing watsonx InferenceAdapter({config.url})...")
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self._config = config
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self._openai_client: AsyncOpenAI | None = None
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self._project_id = self._config.project_id
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||||
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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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|
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def _get_openai_client(self) -> AsyncOpenAI:
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if not self._openai_client:
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self._openai_client = AsyncOpenAI(
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||||
base_url=f"{self._config.url}/openai/v1",
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||||
api_key=self._config.api_key,
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)
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return self._openai_client
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|
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async def _get_params(self, request: 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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|
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if isinstance(request.sampling_params.strategy, TopPSamplingStrategy):
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input_dict["params"][GenParams.TOP_P] = request.sampling_params.strategy.top_p
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input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.strategy.temperature
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if isinstance(request.sampling_params.strategy, TopKSamplingStrategy):
|
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input_dict["params"][GenParams.TOP_K] = request.sampling_params.strategy.top_k
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if isinstance(request.sampling_params.strategy, GreedySamplingStrategy):
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input_dict["params"][GenParams.TEMPERATURE] = 0.0
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input_dict["params"][GenParams.STOP_SEQUENCES] = ["<|endoftext|>"]
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|
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params = {
|
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**input_dict,
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||||
}
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# Add watsonx.ai specific parameters
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params["project_id"] = self.config.project_id
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params["time_limit"] = self.config.timeout
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return params
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|
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async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
# Copied from OpenAIMixin
|
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async def check_model_availability(self, model: str) -> bool:
|
||||
"""
|
||||
Check if a specific model is available from the provider's /v1/models.
|
||||
|
||||
async def openai_completion(
|
||||
self,
|
||||
model: str,
|
||||
prompt: str | list[str] | list[int] | list[list[int]],
|
||||
best_of: int | None = None,
|
||||
echo: bool | None = None,
|
||||
frequency_penalty: float | None = None,
|
||||
logit_bias: dict[str, float] | None = None,
|
||||
logprobs: bool | None = None,
|
||||
max_tokens: int | None = None,
|
||||
n: int | None = None,
|
||||
presence_penalty: float | None = None,
|
||||
seed: int | None = None,
|
||||
stop: str | list[str] | None = None,
|
||||
stream: bool | None = None,
|
||||
stream_options: dict[str, Any] | None = None,
|
||||
temperature: float | None = None,
|
||||
top_p: float | None = None,
|
||||
user: str | None = None,
|
||||
guided_choice: list[str] | None = None,
|
||||
prompt_logprobs: int | None = None,
|
||||
suffix: str | None = 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
|
||||
:param model: The model identifier to check.
|
||||
:return: True if the model is available dynamically, False otherwise.
|
||||
"""
|
||||
if not self._model_cache:
|
||||
await self.list_models()
|
||||
return model in self._model_cache
|
||||
|
||||
async def openai_chat_completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: list[OpenAIMessageParam],
|
||||
frequency_penalty: float | None = None,
|
||||
function_call: str | dict[str, Any] | None = None,
|
||||
functions: list[dict[str, Any]] | None = None,
|
||||
logit_bias: dict[str, float] | None = None,
|
||||
logprobs: bool | None = None,
|
||||
max_completion_tokens: int | None = None,
|
||||
max_tokens: int | None = None,
|
||||
n: int | None = None,
|
||||
parallel_tool_calls: bool | None = None,
|
||||
presence_penalty: float | None = None,
|
||||
response_format: OpenAIResponseFormatParam | None = None,
|
||||
seed: int | None = None,
|
||||
stop: str | list[str] | None = None,
|
||||
stream: bool | None = None,
|
||||
stream_options: dict[str, Any] | None = None,
|
||||
temperature: float | None = None,
|
||||
tool_choice: str | dict[str, Any] | None = None,
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
top_logprobs: int | None = None,
|
||||
top_p: float | None = None,
|
||||
user: str | None = None,
|
||||
) -> 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 list_models(self) -> list[Model] | None:
|
||||
self._model_cache = {}
|
||||
models = []
|
||||
for model_spec in self._get_model_specs():
|
||||
functions = [f["id"] for f in model_spec.get("functions", [])]
|
||||
# Format: {"embedding_dimension": 1536, "context_length": 8192}
|
||||
|
||||
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)
|
||||
# Example of an embedding model:
|
||||
# {'model_id': 'ibm/granite-embedding-278m-multilingual',
|
||||
# 'label': 'granite-embedding-278m-multilingual',
|
||||
# 'model_limits': {'max_sequence_length': 512, 'embedding_dimension': 768},
|
||||
# ...
|
||||
provider_resource_id = f"{self.__provider_id__}/{model_spec['model_id']}"
|
||||
if "embedding" in functions:
|
||||
embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
|
||||
context_length = model_spec["model_limits"]["max_sequence_length"]
|
||||
embedding_metadata = {
|
||||
"embedding_dimension": embedding_dimension,
|
||||
"context_length": context_length,
|
||||
}
|
||||
model = Model(
|
||||
identifier=model_spec["model_id"],
|
||||
provider_resource_id=provider_resource_id,
|
||||
provider_id=self.__provider_id__,
|
||||
metadata=embedding_metadata,
|
||||
model_type=ModelType.embedding,
|
||||
)
|
||||
self._model_cache[provider_resource_id] = model
|
||||
models.append(model)
|
||||
if "text_chat" in functions:
|
||||
model = Model(
|
||||
identifier=model_spec["model_id"],
|
||||
provider_resource_id=provider_resource_id,
|
||||
provider_id=self.__provider_id__,
|
||||
metadata={},
|
||||
model_type=ModelType.llm,
|
||||
)
|
||||
# In theory, I guess it is possible that a model could be both an embedding model and a text chat model.
|
||||
# In that case, the cache will record the generator Model object, and the list which we return will have
|
||||
# both the generator Model object and the text chat Model object. That's fine because the cache is
|
||||
# only used for check_model_availability() anyway.
|
||||
self._model_cache[provider_resource_id] = model
|
||||
models.append(model)
|
||||
return models
|
||||
|
||||
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
|
||||
# LiteLLM provides methods to list models for many providers, but not for watsonx.ai.
|
||||
# So we need to implement our own method to list models by calling the watsonx.ai API.
|
||||
def _get_model_specs(self) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Retrieves foundation model specifications from the watsonx.ai API.
|
||||
"""
|
||||
url = f"{self.config.url}/ml/v1/foundation_model_specs?version=2023-10-25"
|
||||
headers = {
|
||||
# Note that there is no authorization header. Listing models does not require authentication.
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
response = requests.get(url, headers=headers)
|
||||
|
||||
# --- Process the Response ---
|
||||
# Raise an exception for bad status codes (4xx or 5xx)
|
||||
response.raise_for_status()
|
||||
|
||||
# If the request is successful, parse and return the JSON response.
|
||||
# The response should contain a list of model specifications
|
||||
response_data = response.json()
|
||||
if "resources" not in response_data:
|
||||
raise ValueError("Resources not found in response")
|
||||
return response_data["resources"]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue