forked from phoenix/litellm-mirror
Merge pull request #3895 from giritatavarty-8451/litellm_triton_chatcompletion_support
Added support for Triton chat completion using trtlllm generate endpo…
This commit is contained in:
Ishaan Jaff
GitHub
commit
e8c1e87ac9
2 changed files with 165 additions and 4 deletions
|
|
@ -4,13 +4,13 @@ from enum import Enum
|
|||
import requests, copy # type: ignore
|
||||
import time
|
||||
from typing import Callable, Optional, List
|
||||
from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
|
||||
from litellm.utils import ModelResponse, Choices,Usage, map_finish_reason, CustomStreamWrapper, Message
|
||||
import litellm
|
||||
from .prompt_templates.factory import prompt_factory, custom_prompt
|
||||
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
|
||||
from .base import BaseLLM
|
||||
import httpx # type: ignore
|
||||
|
||||
import requests
|
||||
|
||||
class TritonError(Exception):
|
||||
def __init__(self, status_code, message):
|
||||
|
|
@ -30,6 +30,49 @@ class TritonChatCompletion(BaseLLM):
|
|||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def acompletion(
|
||||
self,
|
||||
data: dict,
|
||||
model_response: ModelResponse,
|
||||
api_base: str,
|
||||
logging_obj=None,
|
||||
api_key: Optional[str] = None,
|
||||
):
|
||||
|
||||
async_handler = httpx.AsyncHTTPHandler(
|
||||
timeout=httpx.Timeout(timeout=600.0, connect=5.0)
|
||||
)
|
||||
|
||||
if api_base.endswith("generate") : ### This is a trtllm model
|
||||
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.post(url=api_base, json=data)
|
||||
|
||||
|
||||
|
||||
if response.status_code != 200:
|
||||
raise TritonError(status_code=response.status_code, message=response.text)
|
||||
|
||||
_text_response = response.text
|
||||
|
||||
|
||||
if logging_obj:
|
||||
logging_obj.post_call(original_response=_text_response)
|
||||
|
||||
_json_response = response.json()
|
||||
|
||||
_output_text = _json_response["outputs"][0]["data"][0]
|
||||
# decode the byte string
|
||||
_output_text = _output_text.encode("latin-1").decode("unicode_escape").encode(
|
||||
"latin-1"
|
||||
).decode("utf-8")
|
||||
|
||||
model_response.model = _json_response.get("model_name", "None")
|
||||
model_response.choices[0].message.content = _output_text
|
||||
|
||||
return model_response
|
||||
|
||||
|
||||
async def aembedding(
|
||||
self,
|
||||
data: dict,
|
||||
|
|
@ -55,7 +98,7 @@ class TritonChatCompletion(BaseLLM):
|
|||
_json_response = response.json()
|
||||
|
||||
_outputs = _json_response["outputs"]
|
||||
_output_data = _outputs[0]["data"]
|
||||
_output_data = [ output["data"] for output in _outputs ]
|
||||
_embedding_output = {
|
||||
"object": "embedding",
|
||||
"index": 0,
|
||||
|
|
@ -84,7 +127,7 @@ class TritonChatCompletion(BaseLLM):
|
|||
"inputs": [
|
||||
{
|
||||
"name": "input_text",
|
||||
"shape": [1],
|
||||
"shape": [len(input)], #size of the input data
|
||||
"datatype": "BYTES",
|
||||
"data": input,
|
||||
}
|
||||
|
|
@ -117,3 +160,101 @@ class TritonChatCompletion(BaseLLM):
|
|||
raise Exception(
|
||||
"Only async embedding supported for triton, please use litellm.aembedding() for now"
|
||||
)
|
||||
## Using Sync completion for now - Async completion not supported yet.
|
||||
def completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: list,
|
||||
timeout: float,
|
||||
api_base: str,
|
||||
model_response: ModelResponse,
|
||||
api_key: Optional[str] = None,
|
||||
logging_obj=None,
|
||||
optional_params=None,
|
||||
client=None,
|
||||
stream=False,
|
||||
):
|
||||
# check if model is llama
|
||||
data_for_triton = {}
|
||||
type_of_model = "" ""
|
||||
if api_base.endswith("generate") : ### This is a trtllm model
|
||||
# this is a llama model
|
||||
text_input = messages[0]["content"]
|
||||
data_for_triton = {
|
||||
"text_input":f"{text_input}",
|
||||
"parameters": {
|
||||
"max_tokens": optional_params.get("max_tokens", 20),
|
||||
"bad_words":[""],
|
||||
"stop_words":[""]
|
||||
}}
|
||||
for k,v in optional_params.items():
|
||||
data_for_triton["parameters"][k] = v
|
||||
type_of_model = "trtllm"
|
||||
|
||||
elif api_base.endswith("infer"): ### This is a infer model with a custom model on triton
|
||||
# this is a custom model
|
||||
text_input = messages[0]["content"]
|
||||
data_for_triton = {
|
||||
"inputs": [{"name": "text_input","shape": [1],"datatype": "BYTES","data": [text_input] }]
|
||||
}
|
||||
|
||||
for k,v in optional_params.items():
|
||||
if not (k=="stream" or k=="max_retries"): ## skip these as they are added by litellm
|
||||
datatype = "INT32" if type(v) == int else "BYTES"
|
||||
datatype = "FP32" if type(v) == float else datatype
|
||||
data_for_triton['inputs'].append({"name": k,"shape": [1],"datatype": datatype,"data": [v]})
|
||||
|
||||
# check for max_tokens which is required
|
||||
if "max_tokens" not in optional_params:
|
||||
data_for_triton['inputs'].append({"name": "max_tokens","shape": [1],"datatype": "INT32","data": [20]})
|
||||
|
||||
type_of_model = "infer"
|
||||
else: ## Unknown model type passthrough
|
||||
data_for_triton = {
|
||||
messages[0]["content"]
|
||||
}
|
||||
|
||||
if logging_obj:
|
||||
logging_obj.pre_call(
|
||||
input=messages,
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"complete_input_dict": optional_params,
|
||||
"api_base": api_base,
|
||||
"http_client": client,
|
||||
},
|
||||
)
|
||||
handler = requests.Session()
|
||||
handler.timeout = (600.0, 5.0)
|
||||
|
||||
response = handler.post(url=api_base, json=data_for_triton)
|
||||
|
||||
|
||||
if logging_obj:
|
||||
logging_obj.post_call(original_response=response)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise TritonError(status_code=response.status_code, message=response.text)
|
||||
_json_response=response.json()
|
||||
|
||||
model_response.model = _json_response.get("model_name", "None")
|
||||
if type_of_model == "trtllm":
|
||||
# The actual response is part of the text_output key in the response
|
||||
model_response['choices'] = [ Choices(index=0, message= Message(content=_json_response['text_output']))]
|
||||
elif type_of_model == "infer":
|
||||
# The actual response is part of the outputs key in the response
|
||||
model_response['choices'] = [ Choices(index=0, message= Message(content=_json_response['outputs'][0]['data']))]
|
||||
else:
|
||||
## just passthrough the response
|
||||
model_response['choices'] = [ Choices(index=0, message= Message(content=_json_response['outputs']))]
|
||||
|
||||
"""
|
||||
response = self.acompletion(
|
||||
data=data_for_triton,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
api_base=api_base,
|
||||
api_key=api_key,
|
||||
)
|
||||
"""
|
||||
return model_response
|
||||
|
|
@ -2254,6 +2254,26 @@ def completion(
|
|||
return generator
|
||||
|
||||
response = generator
|
||||
|
||||
elif custom_llm_provider == "triton":
|
||||
api_base = (
|
||||
litellm.api_base or api_base
|
||||
)
|
||||
model_response = triton_chat_completions.completion(
|
||||
api_base=api_base,
|
||||
timeout=timeout,
|
||||
model=model,
|
||||
messages=messages,
|
||||
model_response=model_response,
|
||||
optional_params=optional_params,
|
||||
logging_obj=logging,
|
||||
)
|
||||
|
||||
## RESPONSE OBJECT
|
||||
response = model_response
|
||||
return response
|
||||
|
||||
|
||||
elif custom_llm_provider == "cloudflare":
|
||||
api_key = (
|
||||
api_key
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue