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fix: Bedrock provider returning None due to inheritance order

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Bedrock provider was returning None for both streaming and non-streaming
inference, causing 'NoneType' object has no attribute 'choices' errors.

Primary fix: Reorder inheritance to put mixin classes before protocol class
in BedrockInferenceAdapter so actual implementations are called.

Additional AWS Bedrock API compatibility fixes:
- Fix non-streaming: use res["body"].read() instead of next(res["body"])
- Fix streaming: add proper event structure checks and safe access
- Disable repetition_penalty (not supported by Bedrock Llama models)

Fixes #3621
This commit is contained in:
skamenan7 2025-09-30 16:42:29 -04:00
parent 606f4cf281
commit 88e60c1bf6
1 changed files with 15 additions and 12 deletions
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19
llama_stack/providers/remote/inference/bedrock/bedrock.py
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@ -87,9 +87,9 @@ def _to_inference_profile_id(model_id: str, region: str = None) -> str:
class BedrockInferenceAdapter( class BedrockInferenceAdapter(
ModelRegistryHelper, ModelRegistryHelper,
Inference,
OpenAIChatCompletionToLlamaStackMixin, OpenAIChatCompletionToLlamaStackMixin,
OpenAICompletionToLlamaStackMixin, OpenAICompletionToLlamaStackMixin,
Inference,
): ):
def __init__(self, config: BedrockConfig) -> None: def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
@ -155,7 +155,7 @@ class BedrockInferenceAdapter(
async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse: async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
params = await self._get_params_for_chat_completion(request) params = await self._get_params_for_chat_completion(request)
res = self.client.invoke_model(**params) res = self.client.invoke_model(**params)
chunk = next(res["body"]) chunk = res["body"].read()
result = json.loads(chunk.decode("utf-8")) result = json.loads(chunk.decode("utf-8"))
choice = OpenAICompatCompletionChoice( choice = OpenAICompatCompletionChoice(
@ -172,11 +172,13 @@ class BedrockInferenceAdapter(
event_stream = res["body"] event_stream = res["body"]
async def _generate_and_convert_to_openai_compat(): async def _generate_and_convert_to_openai_compat():
for chunk in event_stream: for event in event_stream:
chunk = chunk["chunk"]["bytes"] if "chunk" in event:
result = json.loads(chunk.decode("utf-8")) chunk_data = event["chunk"]["bytes"]
result = json.loads(chunk_data.decode("utf-8"))
if "generation" in result:
choice = OpenAICompatCompletionChoice( choice = OpenAICompatCompletionChoice(
finish_reason=result["stop_reason"], finish_reason=result.get("stop_reason"),
text=result["generation"], text=result["generation"],
) )
yield OpenAICompatCompletionResponse(choices=[choice]) yield OpenAICompatCompletionResponse(choices=[choice])
@ -193,8 +195,9 @@ class BedrockInferenceAdapter(
if sampling_params.max_tokens: if sampling_params.max_tokens:
options["max_gen_len"] = sampling_params.max_tokens options["max_gen_len"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0: # Note: repetition_penalty is not supported by AWS Bedrock Llama models
options["repetition_penalty"] = sampling_params.repetition_penalty # if sampling_params.repetition_penalty > 0:
# options["repetition_penalty"] = sampling_params.repetition_penalty
prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model)) prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))