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make TGI work well
This commit is contained in:
Hardik Shah
parent
e58c7f6c37
commit
021dd0d35d
9 changed files with 617 additions and 326 deletions
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@ -8,7 +8,7 @@
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import logging
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from typing import AsyncGenerator, List, Optional
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from huggingface_hub import AsyncInferenceClient, HfApi
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from huggingface_hub import AsyncInferenceClient, HfApi, InferenceClient
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from llama_stack.apis.common.content_types import (
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InterleavedContent,
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@ -21,6 +21,7 @@ from llama_stack.apis.inference import (
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EmbeddingsResponse,
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EmbeddingTaskType,
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Inference,
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JsonSchemaResponseFormat,
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LogProbConfig,
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Message,
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ResponseFormat,
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@ -33,16 +34,23 @@ from llama_stack.apis.inference import (
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ToolPromptFormat,
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)
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from llama_stack.apis.models import Model
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from llama_stack.log import get_logger
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from llama_stack.models.llama.sku_list import all_registered_models
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from llama_stack.providers.datatypes import ModelsProtocolPrivate
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from llama_stack.providers.utils.inference.model_registry import (
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ModelRegistryHelper,
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build_hf_repo_model_entry,
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ModelRegistryHelper,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_chat_completion_request_to_openai_params,
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convert_completion_request_to_openai_params,
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convert_message_to_openai_dict_new,
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convert_openai_chat_completion_choice,
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convert_openai_chat_completion_stream,
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convert_tooldef_to_openai_tool,
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get_sampling_options,
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OpenAICompatCompletionChoice,
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OpenAICompatCompletionResponse,
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get_sampling_options,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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process_completion_response,
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@ -55,7 +63,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
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from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
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log = logging.getLogger(__name__)
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logger = get_logger(name=__name__, category="inference")
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def build_hf_repo_model_entries():
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@ -77,7 +85,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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def __init__(self) -> None:
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self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
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self.huggingface_repo_to_llama_model_id = {
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model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo
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model.huggingface_repo: model.descriptor()
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for model in all_registered_models()
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if model.huggingface_repo
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}
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async def shutdown(self) -> None:
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@ -103,6 +113,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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if response_format:
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raise ValueError(f"TGI does not support Response Format for completions.")
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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@ -153,13 +166,17 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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return options
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async def _get_params_for_completion(self, request: CompletionRequest) -> dict:
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prompt, input_tokens = await completion_request_to_prompt_model_input_info(request)
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prompt, input_tokens = await completion_request_to_prompt_model_input_info(
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request
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)
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return dict(
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prompt=prompt,
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stream=request.stream,
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details=True,
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max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens),
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max_new_tokens=self._get_max_new_tokens(
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request.sampling_params, input_tokens
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),
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**self._build_options(request.sampling_params, request.response_format),
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)
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@ -168,14 +185,16 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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params = await self._get_params_for_completion(request)
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async def _generate_and_convert_to_openai_compat():
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s = await self.client.text_generation(**params)
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async for chunk in s:
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s = self.client.text_generation(**params)
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for chunk in s:
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token_result = chunk.token
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finish_reason = None
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if chunk.details:
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finish_reason = chunk.details.finish_reason
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choice = OpenAICompatCompletionChoice(text=token_result.text, finish_reason=finish_reason)
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choice = OpenAICompatCompletionChoice(
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text=token_result.text, finish_reason=finish_reason
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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@ -186,7 +205,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params_for_completion(request)
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r = await self.client.text_generation(**params)
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r = self.client.text_generation(**params)
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choice = OpenAICompatCompletionChoice(
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finish_reason=r.details.finish_reason,
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@ -215,6 +234,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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from rich.pretty import pprint
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pprint(messages)
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request = ChatCompletionRequest(
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model=model.provider_resource_id,
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messages=messages,
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@ -226,53 +248,22 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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tool_config=tool_config,
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)
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params = await convert_chat_completion_request_to_openai_params(request)
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import json
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# print(json.dumps(params, indent=2))
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pprint(params)
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response = self.client.chat.completions.create(**params)
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if stream:
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return self._stream_chat_completion(request)
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return convert_openai_chat_completion_stream(
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response, enable_incremental_tool_calls=True
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)
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else:
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return await self._nonstream_chat_completion(request)
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async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
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params = await self._get_params(request)
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r = await self.client.text_generation(**params)
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choice = OpenAICompatCompletionChoice(
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finish_reason=r.details.finish_reason,
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text="".join(t.text for t in r.details.tokens),
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)
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response = OpenAICompatCompletionResponse(
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choices=[choice],
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)
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return process_chat_completion_response(response, request)
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async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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async def _generate_and_convert_to_openai_compat():
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s = await self.client.text_generation(**params)
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async for chunk in s:
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token_result = chunk.token
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choice = OpenAICompatCompletionChoice(text=token_result.text)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_chat_completion_stream_response(stream, request):
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yield chunk
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async def _get_params(self, request: ChatCompletionRequest) -> dict:
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prompt, input_tokens = await chat_completion_request_to_model_input_info(
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request, self.register_helper.get_llama_model(request.model)
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)
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return dict(
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prompt=prompt,
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stream=request.stream,
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details=True,
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max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens),
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**self._build_options(request.sampling_params, request.response_format),
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)
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return convert_openai_chat_completion_choice(response.choices[0])
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async def embeddings(
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self,
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@ -287,18 +278,21 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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class TGIAdapter(_HfAdapter):
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async def initialize(self, config: TGIImplConfig) -> None:
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log.info(f"Initializing TGI client with url={config.url}")
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self.client = AsyncInferenceClient(
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model=config.url,
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)
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endpoint_info = await self.client.get_endpoint_info()
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logger.info(f"Initializing TGI client with url={config.url}")
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# unfortunately, the TGI async client does not work well with proxies
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# so using sync client for now instead
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self.client = InferenceClient(model=f"{config.url}")
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endpoint_info = self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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class InferenceAPIAdapter(_HfAdapter):
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async def initialize(self, config: InferenceAPIImplConfig) -> None:
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self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
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self.client = AsyncInferenceClient(
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model=config.huggingface_repo, token=config.api_token.get_secret_value()
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)
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endpoint_info = await self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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@ -316,4 +310,6 @@ class InferenceEndpointAdapter(_HfAdapter):
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# Initialize the adapter
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self.client = endpoint.async_client
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self.model_id = endpoint.repository
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self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])
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self.max_tokens = int(
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endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
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)
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