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test, recording
# What does this PR do? ## Test Plan # What does this PR do? ## Test Plan
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228 changed files with 86861 additions and 64604 deletions
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@ -173,5 +173,6 @@ class MetaReferenceInferenceImpl(
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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raise NotImplementedError("OpenAI chat completion not supported by meta-reference inference provider")
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@ -96,6 +96,7 @@ class SentenceTransformersInferenceImpl(
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prompt_logprobs: int | None = None,
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# for fill-in-the-middle type completion
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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raise NotImplementedError("OpenAI completion not supported by sentence transformers provider")
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@ -124,5 +125,6 @@ class SentenceTransformersInferenceImpl(
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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raise NotImplementedError("OpenAI chat completion not supported by sentence transformers provider")
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@ -158,6 +158,7 @@ class BedrockInferenceAdapter(
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prompt_logprobs: int | None = None,
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# for fill-in-the-middle type completion
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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raise NotImplementedError("OpenAI completion not supported by the Bedrock provider")
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@ -186,5 +187,6 @@ class BedrockInferenceAdapter(
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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raise NotImplementedError("OpenAI chat completion not supported by the Bedrock provider")
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@ -63,5 +63,6 @@ class DatabricksInferenceAdapter(OpenAIMixin):
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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raise NotImplementedError()
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@ -54,6 +54,7 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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raise NotImplementedError()
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@ -100,6 +100,7 @@ class PassthroughInferenceAdapter(Inference):
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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client = self._get_client()
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model_obj = await self.model_store.get_model(model)
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@ -124,6 +125,7 @@ class PassthroughInferenceAdapter(Inference):
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user=user,
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guided_choice=guided_choice,
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prompt_logprobs=prompt_logprobs,
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**kwargs,
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)
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return await client.inference.openai_completion(**params)
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@ -153,6 +155,7 @@ class PassthroughInferenceAdapter(Inference):
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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client = self._get_client()
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model_obj = await self.model_store.get_model(model)
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@ -181,6 +184,7 @@ class PassthroughInferenceAdapter(Inference):
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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**kwargs,
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)
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return await client.inference.openai_chat_completion(**params)
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@ -57,6 +57,7 @@ class RunpodInferenceAdapter(OpenAIMixin):
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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):
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"""Override to add RunPod-specific stream_options requirement."""
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if stream and not stream_options:
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@ -86,4 +87,5 @@ class RunpodInferenceAdapter(OpenAIMixin):
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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**kwargs,
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)
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@ -102,6 +102,7 @@ class VLLMInferenceAdapter(OpenAIMixin):
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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max_tokens = max_tokens or self.config.max_tokens
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@ -136,4 +137,5 @@ class VLLMInferenceAdapter(OpenAIMixin):
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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**kwargs,
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)
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@ -247,6 +247,7 @@ class LiteLLMOpenAIMixin(
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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model_obj = await self.model_store.get_model(model)
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params = await prepare_openai_completion_params(
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@ -271,6 +272,7 @@ class LiteLLMOpenAIMixin(
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prompt_logprobs=prompt_logprobs,
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api_key=self.get_api_key(),
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api_base=self.api_base,
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**kwargs,
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)
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return await litellm.atext_completion(**params)
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@ -299,6 +301,7 @@ class LiteLLMOpenAIMixin(
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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# Add usage tracking for streaming when telemetry is active
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from llama_stack.providers.utils.telemetry.tracing import get_current_span
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@ -335,6 +338,7 @@ class LiteLLMOpenAIMixin(
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user=user,
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api_key=self.get_api_key(),
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api_base=self.api_base,
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**kwargs,
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)
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return await litellm.acompletion(**params)
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@ -247,6 +247,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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**kwargs: Any,
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) -> OpenAICompletion:
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"""
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Direct OpenAI completion API call.
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@ -261,6 +262,9 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
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if guided_choice:
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extra_body["guided_choice"] = guided_choice
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# Merge any additional kwargs into extra_body
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extra_body.update(kwargs)
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# TODO: fix openai_completion to return type compatible with OpenAI's API response
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resp = await self.client.completions.create(
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**await prepare_openai_completion_params(
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@ -313,6 +317,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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**kwargs: Any,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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"""
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Direct OpenAI chat completion API call.
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@ -361,7 +366,10 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
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user=user,
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)
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resp = await self.client.chat.completions.create(**params)
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# Pass any additional provider-specific parameters as extra_body
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extra_body = kwargs if kwargs else {}
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resp = await self.client.chat.completions.create(**params, extra_body=extra_body)
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return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return]
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