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feat(providers): Groq now uses LiteLLM openai-compat (#1303)

Browse source 
Groq has never supported raw completions anyhow. So this makes it easier
to switch it to LiteLLM. All our test suite passes.

I also updated all the openai-compat providers so they work with api
keys passed from headers. `provider_data`

## Test Plan

```bash
LLAMA_STACK_CONFIG=groq \
   pytest -s -v tests/client-sdk/inference/test_text_inference.py \
   --inference-model=groq/llama-3.3-70b-versatile --vision-inference-model=""
```

Also tested (openai, anthropic, gemini) providers. No regressions.
This commit is contained in:
Ashwin Bharambe 2025-02-27 13:16:50 -08:00 • committed by GitHub
parent 564f0e5f93
commit 928a39d17b
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GPG key ID: B5690EEEBB952194
23 changed files with 165 additions and 1004 deletions
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11
llama_stack/providers/remote/inference/anthropic/anthropic.py
View file

@ -12,11 +12,16 @@ from .models import MODEL_ENTRIES
class AnthropicInferenceAdapter(LiteLLMOpenAIMixin):
def __init__(self, config: AnthropicConfig) -> None:
LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
api_key_from_config=config.api_key,
provider_data_api_key_field="anthropic_api_key",
)
self.config = config
async def initialize(self) -> None:
pass
await super().initialize()
async def shutdown(self) -> None:
pass
await super().shutdown()

7
llama_stack/providers/remote/inference/anthropic/config.py
View file

@ -11,6 +11,13 @@ from pydantic import BaseModel, Field
from llama_stack.schema_utils import json_schema_type
class AnthropicProviderDataValidator(BaseModel):
anthropic_api_key: Optional[str] = Field(
default=None,
description="API key for Anthropic models",
)
@json_schema_type
class AnthropicConfig(BaseModel):
api_key: Optional[str] = Field(

7
llama_stack/providers/remote/inference/gemini/config.py
View file

@ -11,6 +11,13 @@ from pydantic import BaseModel, Field
from llama_stack.schema_utils import json_schema_type
class GeminiProviderDataValidator(BaseModel):
gemini_api_key: Optional[str] = Field(
default=None,
description="API key for Gemini models",
)
@json_schema_type
class GeminiConfig(BaseModel):
api_key: Optional[str] = Field(

11
llama_stack/providers/remote/inference/gemini/gemini.py
View file

@ -12,11 +12,16 @@ from .models import MODEL_ENTRIES
class GeminiInferenceAdapter(LiteLLMOpenAIMixin):
def __init__(self, config: GeminiConfig) -> None:
LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
api_key_from_config=config.api_key,
provider_data_api_key_field="gemini_api_key",
)
self.config = config
async def initialize(self) -> None:
pass
await super().initialize()
async def shutdown(self) -> None:
pass
await super().shutdown()

9
llama_stack/providers/remote/inference/groq/__init__.py
View file

@ -4,23 +4,14 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pydantic import BaseModel
from llama_stack.apis.inference import Inference
from .config import GroqConfig
class GroqProviderDataValidator(BaseModel):
groq_api_key: str
async def get_adapter_impl(config: GroqConfig, _deps) -> Inference:
# import dynamically so the import is used only when it is needed
from .groq import GroqInferenceAdapter
if not isinstance(config, GroqConfig):
raise RuntimeError(f"Unexpected config type: {type(config)}")
adapter = GroqInferenceAdapter(config)
return adapter

11
llama_stack/providers/remote/inference/groq/config.py
View file

@ -11,6 +11,13 @@ from pydantic import BaseModel, Field
from llama_stack.schema_utils import json_schema_type
class GroqProviderDataValidator(BaseModel):
groq_api_key: Optional[str] = Field(
default=None,
description="API key for Groq models",
)
@json_schema_type
class GroqConfig(BaseModel):
api_key: Optional[str] = Field(
@ -25,8 +32,8 @@ class GroqConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> Dict[str, Any]:
return {
"url": "https://api.groq.com",
"api_key": "${env.GROQ_API_KEY}",
"api_key": api_key,
}

130
llama_stack/providers/remote/inference/groq/groq.py
View file

@ -4,130 +4,26 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import warnings
from typing import AsyncIterator, List, Optional, Union
import groq
from groq import Groq
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
InterleavedContent,
InterleavedContentItem,
LogProbConfig,
Message,
ResponseFormat,
TextTruncation,
ToolChoice,
ToolConfig,
)
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.models.llama.datatypes import SamplingParams, ToolDefinition, ToolPromptFormat
from llama_stack.providers.remote.inference.groq.config import GroqConfig
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
from .groq_utils import (
convert_chat_completion_request,
convert_chat_completion_response,
convert_chat_completion_response_stream,
)
from .models import _MODEL_ENTRIES
from .models import MODEL_ENTRIES
class GroqInferenceAdapter(Inference, ModelRegistryHelper, NeedsRequestProviderData):
class GroqInferenceAdapter(LiteLLMOpenAIMixin):
_config: GroqConfig
def __init__(self, config: GroqConfig):
ModelRegistryHelper.__init__(self, model_entries=_MODEL_ENTRIES)
self._config = config
def completion(
self,
model_id: str,
content: InterleavedContent,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]:
# Groq doesn't support non-chat completion as of time of writing
raise NotImplementedError()
async def chat_completion(
self,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
tool_config: Optional[ToolConfig] = None,
) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
model_id = self.get_provider_model_id(model_id)
if model_id == "llama-3.2-3b-preview":
warnings.warn(
"Groq only contains a preview version for llama-3.2-3b-instruct. "
"Preview models aren't recommended for production use. "
"They can be discontinued on short notice."
"More details: https://console.groq.com/docs/models"
)
request = convert_chat_completion_request(
request=ChatCompletionRequest(
model=model_id,
messages=messages,
sampling_params=sampling_params,
response_format=response_format,
tools=tools,
stream=stream,
logprobs=logprobs,
tool_config=tool_config,
)
LiteLLMOpenAIMixin.__init__(
self,
model_entries=MODEL_ENTRIES,
api_key_from_config=config.api_key,
provider_data_api_key_field="groq_api_key",
)
self.config = config
try:
response = self._get_client().chat.completions.create(**request)
except groq.BadRequestError as e:
if e.body.get("error", {}).get("code") == "tool_use_failed":
# For smaller models, Groq may fail to call a tool even when the request is well formed
raise ValueError("Groq failed to call a tool", e.body.get("error", {})) from e
else:
raise e
async def initialize(self):
await super().initialize()
if stream:
return convert_chat_completion_response_stream(response)
else:
return convert_chat_completion_response(response)
async def embeddings(
self,
model_id: str,
contents: List[str] | List[InterleavedContentItem],
text_truncation: Optional[TextTruncation] = TextTruncation.none,
output_dimension: Optional[int] = None,
task_type: Optional[EmbeddingTaskType] = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
def _get_client(self) -> Groq:
if self._config.api_key is not None:
return Groq(api_key=self._config.api_key)
else:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.groq_api_key:
raise ValueError(
'Pass Groq API Key in the header X-LlamaStack-Provider-Data as { "groq_api_key": "<your api key>" }'
)
return Groq(api_key=provider_data.groq_api_key)
async def shutdown(self):
await super().shutdown()

245
llama_stack/providers/remote/inference/groq/groq_utils.py
View file

@ -1,245 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import warnings
from typing import AsyncGenerator, Literal
from groq import Stream
from groq.types.chat.chat_completion import ChatCompletion
from groq.types.chat.chat_completion_assistant_message_param import (
ChatCompletionAssistantMessageParam,
)
from groq.types.chat.chat_completion_chunk import ChatCompletionChunk
from groq.types.chat.chat_completion_message_param import ChatCompletionMessageParam
from groq.types.chat.chat_completion_system_message_param import (
ChatCompletionSystemMessageParam,
)
from groq.types.chat.chat_completion_tool_param import ChatCompletionToolParam
from groq.types.chat.chat_completion_user_message_param import (
ChatCompletionUserMessageParam,
)
from groq.types.chat.completion_create_params import CompletionCreateParams
from groq.types.shared.function_definition import FunctionDefinition
from llama_stack.apis.common.content_types import (
TextDelta,
ToolCallDelta,
ToolCallParseStatus,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseEvent,
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
CompletionMessage,
Message,
StopReason,
ToolCall,
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.models.llama.datatypes import ToolParamDefinition
from llama_stack.providers.utils.inference.openai_compat import (
UnparseableToolCall,
convert_tool_call,
get_sampling_strategy_options,
)
def convert_chat_completion_request(
request: ChatCompletionRequest,
) -> CompletionCreateParams:
"""
Convert a ChatCompletionRequest to a Groq API-compatible dictionary.
Warns client if request contains unsupported features.
"""
if request.logprobs:
# Groq doesn't support logprobs at the time of writing
warnings.warn("logprobs are not supported yet")
if request.response_format:
# Groq's JSON mode is beta at the time of writing
warnings.warn("response_format is not supported yet")
if request.sampling_params.repetition_penalty != 1.0:
# groq supports frequency_penalty, but frequency_penalty and sampling_params.repetition_penalty
# seem to have different semantics
# frequency_penalty defaults to 0 is a float between -2.0 and 2.0
# repetition_penalty defaults to 1 and is often set somewhere between 1.0 and 2.0
# so we exclude it for now
warnings.warn("repetition_penalty is not supported")
if request.tool_config.tool_prompt_format != ToolPromptFormat.json:
warnings.warn("tool_prompt_format is not used by Groq. Ignoring.")
sampling_options = get_sampling_strategy_options(request.sampling_params)
return CompletionCreateParams(
model=request.model,
messages=[_convert_message(message) for message in request.messages],
logprobs=None,
frequency_penalty=None,
stream=request.stream,
max_tokens=request.sampling_params.max_tokens or None,
temperature=sampling_options.get("temperature", 1.0),
top_p=sampling_options.get("top_p", 1.0),
tools=[_convert_groq_tool_definition(tool) for tool in request.tools or []],
tool_choice=(request.tool_config.tool_choice.value if request.tool_config.tool_choice else None),
)
def _convert_message(message: Message) -> ChatCompletionMessageParam:
if message.role == "system":
return ChatCompletionSystemMessageParam(role="system", content=message.content)
elif message.role == "user":
return ChatCompletionUserMessageParam(role="user", content=message.content)
elif message.role == "assistant":
return ChatCompletionAssistantMessageParam(role="assistant", content=message.content)
else:
raise ValueError(f"Invalid message role: {message.role}")
def _convert_groq_tool_definition(tool_definition: ToolDefinition) -> dict:
# Groq requires a description for function tools
if tool_definition.description is None:
raise AssertionError("tool_definition.description is required")
tool_parameters = tool_definition.parameters or {}
return ChatCompletionToolParam(
type="function",
function=FunctionDefinition(
name=tool_definition.tool_name,
description=tool_definition.description,
parameters={key: _convert_groq_tool_parameter(param) for key, param in tool_parameters.items()},
),
)
def _convert_groq_tool_parameter(tool_parameter: ToolParamDefinition) -> dict:
param = {
"type": tool_parameter.param_type,
}
if tool_parameter.description is not None:
param["description"] = tool_parameter.description
if tool_parameter.required is not None:
param["required"] = tool_parameter.required
if tool_parameter.default is not None:
param["default"] = tool_parameter.default
return param
def convert_chat_completion_response(
response: ChatCompletion,
) -> ChatCompletionResponse:
# groq only supports n=1 at time of writing, so there is only one choice
choice = response.choices[0]
if choice.finish_reason == "tool_calls":
tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]
if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
# If we couldn't parse a tool call, jsonify the tool calls and return them
return ChatCompletionResponse(
completion_message=CompletionMessage(
stop_reason=StopReason.end_of_message,
content=json.dumps(tool_calls, default=lambda x: x.model_dump()),
),
logprobs=None,
)
else:
# Otherwise, return tool calls as normal
return ChatCompletionResponse(
completion_message=CompletionMessage(
tool_calls=tool_calls,
stop_reason=StopReason.end_of_message,
# Content is not optional
content="",
),
logprobs=None,
)
else:
return ChatCompletionResponse(
completion_message=CompletionMessage(
content=choice.message.content,
stop_reason=_map_finish_reason_to_stop_reason(choice.finish_reason),
),
)
def _map_finish_reason_to_stop_reason(
finish_reason: Literal["stop", "length", "tool_calls"],
) -> StopReason:
"""
Convert a Groq chat completion finish_reason to a StopReason.
finish_reason: Literal["stop", "length", "tool_calls"]
- stop -> model hit a natural stop point or a provided stop sequence
- length -> maximum number of tokens specified in the request was reached
- tool_calls -> model called a tool
"""
if finish_reason == "stop":
return StopReason.end_of_turn
elif finish_reason == "length":
return StopReason.out_of_tokens
elif finish_reason == "tool_calls":
return StopReason.end_of_message
else:
raise ValueError(f"Invalid finish reason: {finish_reason}")
async def convert_chat_completion_response_stream(
stream: Stream[ChatCompletionChunk],
) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
event_type = ChatCompletionResponseEventType.start
for chunk in stream:
choice = chunk.choices[0]
if choice.finish_reason:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta=TextDelta(text=choice.delta.content or ""),
logprobs=None,
stop_reason=_map_finish_reason_to_stop_reason(choice.finish_reason),
)
)
elif choice.delta.tool_calls:
# We assume there is only one tool call per chunk, but emit a warning in case we're wrong
if len(choice.delta.tool_calls) > 1:
warnings.warn("Groq returned multiple tool calls in one chunk. Using the first one, ignoring the rest.")
# We assume Groq produces fully formed tool calls for each chunk
tool_call = convert_tool_call(choice.delta.tool_calls[0])
if isinstance(tool_call, ToolCall):
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=ToolCallDelta(
tool_call=tool_call,
parse_status=ToolCallParseStatus.succeeded,
),
)
)
else:
# Otherwise it's an UnparseableToolCall - return the raw tool call
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=ToolCallDelta(
tool_call=tool_call.model_dump_json(),
parse_status=ToolCallParseStatus.failed,
),
)
)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=TextDelta(text=choice.delta.content or ""),
logprobs=None,
)
)
event_type = ChatCompletionResponseEventType.progress

12
llama_stack/providers/remote/inference/groq/models.py
View file

@ -7,21 +7,21 @@
from llama_stack.models.llama.sku_list import CoreModelId
from llama_stack.providers.utils.inference.model_registry import build_model_entry
_MODEL_ENTRIES = [
MODEL_ENTRIES = [
build_model_entry(
"llama3-8b-8192",
"groq/llama3-8b-8192",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_entry(
"llama-3.1-8b-instant",
"groq/llama-3.1-8b-instant",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_entry(
"llama3-70b-8192",
"groq/llama3-70b-8192",
CoreModelId.llama3_70b_instruct.value,
),
build_model_entry(
"llama-3.3-70b-versatile",
"groq/llama-3.3-70b-versatile",
CoreModelId.llama3_3_70b_instruct.value,
),
# Groq only contains a preview version for llama-3.2-3b
@ -29,7 +29,7 @@ _MODEL_ENTRIES = [
# to pass the test fixture
# TODO(aidand): Replace this with a stable model once Groq supports it
build_model_entry(
"llama-3.2-3b-preview",
"groq/llama-3.2-3b-preview",
CoreModelId.llama3_2_3b_instruct.value,
),
]

7
llama_stack/providers/remote/inference/openai/config.py
View file

@ -11,6 +11,13 @@ from pydantic import BaseModel, Field
from llama_stack.schema_utils import json_schema_type
class OpenAIProviderDataValidator(BaseModel):
openai_api_key: Optional[str] = Field(
default=None,
description="API key for OpenAI models",
)
@json_schema_type
class OpenAIConfig(BaseModel):
api_key: Optional[str] = Field(

11
llama_stack/providers/remote/inference/openai/openai.py
View file

@ -12,11 +12,16 @@ from .models import MODEL_ENTRIES
class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
def __init__(self, config: OpenAIConfig) -> None:
LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
api_key_from_config=config.api_key,
provider_data_api_key_field="openai_api_key",
)
self.config = config
async def initialize(self) -> None:
pass
await super().initialize()
async def shutdown(self) -> None:
pass
await super().shutdown()