Sync from v0.13
This commit is contained in:
@@ -1,13 +1,21 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import io
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import json
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import os
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import sys
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import time
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import traceback
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from dataclasses import dataclass, field
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from typing import List, Optional
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import aiohttp
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import huggingface_hub.constants
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from tqdm.asyncio import tqdm
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from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
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# NOTE(simon): do not import vLLM here so the benchmark script
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# can run without vLLM installed.
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AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
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@@ -19,8 +27,13 @@ class RequestFuncInput:
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prompt_len: int
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output_len: int
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model: str
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best_of: int = 1
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use_beam_search: bool = False
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model_name: str | None = None
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logprobs: int | None = None
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extra_body: dict | None = None
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multi_modal_content: dict | list[dict] | None = None
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ignore_eos: bool = False
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language: str | None = None
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request_id: str | None = None
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@dataclass
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@@ -28,49 +41,65 @@ class RequestFuncOutput:
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generated_text: str = ""
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success: bool = False
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latency: float = 0.0
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output_tokens: int = 0
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ttft: float = 0.0 # Time to first token
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itl: List[float] = field(
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default_factory=list) # List of inter-token latencies
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itl: list[float] = field(default_factory=list) # list of inter-token latencies
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tpot: float = 0.0 # avg next-token latencies
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prompt_len: int = 0
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error: str = ""
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async def async_request_tgi(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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api_url = request_func_input.api_url
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assert api_url.endswith("generate_stream")
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async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
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assert not request_func_input.use_beam_search
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async with aiohttp.ClientSession(
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trust_env=True, timeout=AIOHTTP_TIMEOUT
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) as session:
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params = {
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"best_of": request_func_input.best_of,
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"max_new_tokens": request_func_input.output_len,
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"do_sample": True,
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"temperature": 0.01, # TGI does not accept 0.0 temperature.
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"top_p": 0.99, # TGI does not accept 1.0 top_p.
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"truncate": request_func_input.prompt_len,
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"ignore_eos_token": request_func_input.ignore_eos,
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}
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payload = {
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"inputs": request_func_input.prompt,
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"parameters": params,
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}
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headers = None
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if request_func_input.request_id:
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headers = {"x-request-id": request_func_input.request_id}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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if request_func_input.ignore_eos:
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output.output_tokens = request_func_input.output_len
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else:
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output.output_tokens = None
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ttft = 0.0
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload) as response:
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async with session.post(
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url=api_url, json=payload, headers=headers
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) as response:
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if response.status == 200:
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk_bytes = chunk_bytes.decode("utf-8")
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chunk = remove_prefix(chunk_bytes.decode("utf-8"),
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"data:")
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# NOTE: Sometimes TGI returns a ping response without
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# any data, we should skip it.
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if chunk_bytes.startswith(":"):
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continue
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chunk = chunk_bytes.removeprefix("data:")
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data = json.loads(chunk)
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timestamp = time.perf_counter()
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@@ -81,14 +110,16 @@ async def async_request_tgi(
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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output.itl.append(timestamp - most_recent_timestamp)
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most_recent_timestamp = timestamp
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output.latency = most_recent_timestamp - st
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output.success = True
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output.generated_text = data["generated_text"]
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else:
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output.error = response.reason or ""
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output.success = False
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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@@ -101,14 +132,14 @@ async def async_request_tgi(
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async def async_request_trt_llm(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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api_url = request_func_input.api_url
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assert api_url.endswith("generate_stream")
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async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
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assert not request_func_input.use_beam_search
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assert request_func_input.best_of == 1
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async with aiohttp.ClientSession(
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trust_env=True, timeout=AIOHTTP_TIMEOUT
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) as session:
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payload = {
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"accumulate_tokens": True,
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"text_input": request_func_input.prompt,
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@@ -117,6 +148,11 @@ async def async_request_trt_llm(
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"max_tokens": request_func_input.output_len,
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"stream": True,
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}
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if request_func_input.ignore_eos:
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payload["min_length"] = request_func_input.output_len
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headers = None
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if request_func_input.request_id:
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headers = {"x-request-id": request_func_input.request_id}
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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@@ -124,28 +160,28 @@ async def async_request_trt_llm(
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload) as response:
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async with session.post(
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url=api_url, json=payload, headers=headers
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) as response:
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if response.status == 200:
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = remove_prefix(chunk_bytes.decode("utf-8"),
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"data:")
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chunk = chunk_bytes.decode("utf-8").removeprefix("data:")
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data = json.loads(chunk)
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output.generated_text += data["text_output"]
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timestamp = time.perf_counter()
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# First token
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if ttft == 0.0:
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ttft = time.perf_counter() - st
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ttft = timestamp - st
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output.ttft = ttft
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# Decoding phase
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else:
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output.itl.append(timestamp -
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most_recent_timestamp)
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output.itl.append(timestamp - most_recent_timestamp)
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most_recent_timestamp = timestamp
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@@ -167,18 +203,27 @@ async def async_request_trt_llm(
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async def async_request_deepspeed_mii(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
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assert request_func_input.best_of == 1
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assert not request_func_input.use_beam_search
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api_url = request_func_input.api_url
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assert api_url.endswith(("completions", "profile")), (
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"OpenAI Completions API URL must end with 'completions' or 'profile'."
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)
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async with aiohttp.ClientSession(
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trust_env=True, timeout=AIOHTTP_TIMEOUT
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) as session:
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payload = {
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"model": request_func_input.model,
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"prompt": request_func_input.prompt,
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"max_tokens": request_func_input.output_len,
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"temperature": 0.01, # deepspeed-mii does not accept 0.0 temp.
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"top_p": 1.0,
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}
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headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
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if request_func_input.request_id:
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headers["x-request-id"] = request_func_input.request_id
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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@@ -189,12 +234,22 @@ async def async_request_deepspeed_mii(
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st = time.perf_counter()
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try:
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async with session.post(url=request_func_input.api_url,
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json=payload) as response:
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async with session.post(
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url=api_url, json=payload, headers=headers
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) as response:
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if response.status == 200:
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parsed_resp = await response.json()
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output.latency = time.perf_counter() - st
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output.generated_text = parsed_resp["text"][0]
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if "choices" in parsed_resp:
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output.generated_text = parsed_resp["choices"][0]["text"]
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elif "text" in parsed_resp:
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output.generated_text = parsed_resp["text"][0]
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else:
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output.error = (
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"Unexpected response format: "
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"neither 'choices' nor 'text' found"
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)
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output.success = False
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output.success = True
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else:
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output.error = response.reason or ""
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@@ -211,152 +266,91 @@ async def async_request_deepspeed_mii(
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async def async_request_openai_completions(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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api_url = request_func_input.api_url
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assert api_url.endswith(
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"v1/completions"
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), "OpenAI Completions API URL must end with 'v1/completions'."
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assert api_url.endswith(("completions", "profile")), (
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"OpenAI Completions API URL must end with 'completions' or 'profile'."
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)
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async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
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assert not request_func_input.use_beam_search
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async with aiohttp.ClientSession(
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trust_env=True, timeout=AIOHTTP_TIMEOUT
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) as session:
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payload = {
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"model": request_func_input.model,
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"model": request_func_input.model_name
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if request_func_input.model_name
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else request_func_input.model,
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"prompt": request_func_input.prompt,
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"temperature": 0.0,
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"best_of": request_func_input.best_of,
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"repetition_penalty": 1.0,
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"max_tokens": request_func_input.output_len,
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"logprobs": request_func_input.logprobs,
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"stream": True,
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"stream_options": {
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"include_usage": True,
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},
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}
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headers = {
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"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
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}
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if request_func_input.ignore_eos:
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payload["ignore_eos"] = request_func_input.ignore_eos
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if request_func_input.extra_body:
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payload.update(request_func_input.extra_body)
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headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
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if request_func_input.request_id:
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headers["x-request-id"] = request_func_input.request_id
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output = RequestFuncOutput()
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output.prompt_len = request_func_input.prompt_len
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generated_text = ""
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ttft = 0.0
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st = time.perf_counter()
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most_recent_timestamp = st
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try:
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async with session.post(url=api_url, json=payload,
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headers=headers) as response:
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async with session.post(
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url=api_url, json=payload, headers=headers
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) as response:
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if response.status == 200:
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first_chunk_received = False
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async for chunk_bytes in response.content:
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chunk_bytes = chunk_bytes.strip()
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if not chunk_bytes:
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continue
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chunk = remove_prefix(chunk_bytes.decode("utf-8"),
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"data: ")
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if chunk == "[DONE]":
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latency = time.perf_counter() - st
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else:
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chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
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if chunk != "[DONE]":
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data = json.loads(chunk)
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if data["choices"][0]["text"]:
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# NOTE: Some completion API might have a last
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# usage summary response without a token so we
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# want to check a token was generated
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if choices := data.get("choices"):
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# Note that text could be empty here
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# e.g. for special tokens
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text = choices[0].get("text")
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timestamp = time.perf_counter()
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# First token
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if ttft == 0.0:
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ttft = time.perf_counter() - st
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output.ttft = ttft
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# Decoding phase
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# NOTE: Some completion API might have a last
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# usage summary response without a token so we
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# do not want to include as inter-token-latency
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elif data.get("usage", None) is None:
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output.itl.append(timestamp -
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most_recent_timestamp)
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most_recent_timestamp = timestamp
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generated_text += data["choices"][0]["text"]
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output.generated_text = generated_text
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output.success = True
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output.latency = latency
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except Exception:
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output.success = False
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exc_info = sys.exc_info()
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output.error = "".join(traceback.format_exception(*exc_info))
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if pbar:
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pbar.update(1)
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return output
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async def async_request_openai_chat_completions(
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request_func_input: RequestFuncInput,
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pbar: Optional[tqdm] = None,
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) -> RequestFuncOutput:
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api_url = request_func_input.api_url
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assert api_url.endswith(
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"v1/chat/completions"
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), "OpenAI Chat Completions API URL must end with 'v1/chat/completions'."
|
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async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
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assert not request_func_input.use_beam_search
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payload = {
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"model": request_func_input.model,
|
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"messages": [
|
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{
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"role": "user",
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"content": request_func_input.prompt,
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||||
},
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||||
],
|
||||
"temperature": 0.0,
|
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"max_tokens": request_func_input.output_len,
|
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"stream": True,
|
||||
}
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headers = {
|
||||
"Content-Type": "application/json",
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||||
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
|
||||
}
|
||||
|
||||
output = RequestFuncOutput()
|
||||
output.prompt_len = request_func_input.prompt_len
|
||||
|
||||
generated_text = ""
|
||||
ttft = 0.0
|
||||
st = time.perf_counter()
|
||||
most_recent_timestamp = st
|
||||
try:
|
||||
async with session.post(url=api_url, json=payload,
|
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headers=headers) as response:
|
||||
if response.status == 200:
|
||||
async for chunk_bytes in response.content:
|
||||
chunk_bytes = chunk_bytes.strip()
|
||||
if not chunk_bytes:
|
||||
continue
|
||||
|
||||
chunk = remove_prefix(chunk_bytes.decode("utf-8"),
|
||||
"data: ")
|
||||
if chunk == "[DONE]":
|
||||
latency = time.perf_counter() - st
|
||||
else:
|
||||
timestamp = time.perf_counter()
|
||||
data = json.loads(chunk)
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||||
|
||||
delta = data["choices"][0]["delta"]
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||||
if delta.get("content", None):
|
||||
# First token
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||||
if ttft == 0.0:
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||||
if not first_chunk_received:
|
||||
first_chunk_received = True
|
||||
ttft = time.perf_counter() - st
|
||||
output.ttft = ttft
|
||||
|
||||
# Decoding phase
|
||||
else:
|
||||
output.itl.append(timestamp -
|
||||
most_recent_timestamp)
|
||||
|
||||
generated_text += delta["content"]
|
||||
|
||||
most_recent_timestamp = timestamp
|
||||
output.itl.append(timestamp - most_recent_timestamp)
|
||||
|
||||
most_recent_timestamp = timestamp
|
||||
generated_text += text or ""
|
||||
if usage := data.get("usage"):
|
||||
output.output_tokens = usage.get("completion_tokens")
|
||||
if first_chunk_received:
|
||||
output.success = True
|
||||
else:
|
||||
output.success = False
|
||||
output.error = (
|
||||
"Never received a valid chunk to calculate TTFT."
|
||||
"This response will be marked as failed!"
|
||||
)
|
||||
output.generated_text = generated_text
|
||||
output.success = True
|
||||
output.latency = latency
|
||||
output.latency = most_recent_timestamp - st
|
||||
else:
|
||||
output.error = response.reason or ""
|
||||
output.success = False
|
||||
@@ -370,12 +364,276 @@ async def async_request_openai_chat_completions(
|
||||
return output
|
||||
|
||||
|
||||
# Since vllm must support Python 3.8, we can't use str.removeprefix(prefix)
|
||||
# introduced in Python 3.9
|
||||
def remove_prefix(text: str, prefix: str) -> str:
|
||||
if text.startswith(prefix):
|
||||
return text[len(prefix):]
|
||||
return text
|
||||
async def async_request_openai_chat_completions(
|
||||
request_func_input: RequestFuncInput,
|
||||
pbar: tqdm | None = None,
|
||||
) -> RequestFuncOutput:
|
||||
api_url = request_func_input.api_url
|
||||
assert api_url.endswith(("chat/completions", "profile")), (
|
||||
"OpenAI Chat Completions API URL must end with 'chat/completions'."
|
||||
)
|
||||
|
||||
async with aiohttp.ClientSession(
|
||||
trust_env=True, timeout=AIOHTTP_TIMEOUT
|
||||
) as session:
|
||||
content = [{"type": "text", "text": request_func_input.prompt}]
|
||||
if request_func_input.multi_modal_content:
|
||||
mm_content = request_func_input.multi_modal_content
|
||||
if isinstance(mm_content, list):
|
||||
content.extend(mm_content)
|
||||
elif isinstance(mm_content, dict):
|
||||
content.append(mm_content)
|
||||
else:
|
||||
raise TypeError(
|
||||
"multi_modal_content must be a dict or list[dict] for openai-chat"
|
||||
)
|
||||
payload = {
|
||||
"model": request_func_input.model_name
|
||||
if request_func_input.model_name
|
||||
else request_func_input.model,
|
||||
"messages": [
|
||||
{"role": "user", "content": content},
|
||||
],
|
||||
"temperature": 0.0,
|
||||
"max_completion_tokens": request_func_input.output_len,
|
||||
"stream": True,
|
||||
"stream_options": {
|
||||
"include_usage": True,
|
||||
},
|
||||
}
|
||||
if request_func_input.ignore_eos:
|
||||
payload["ignore_eos"] = request_func_input.ignore_eos
|
||||
if request_func_input.extra_body:
|
||||
payload.update(request_func_input.extra_body)
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
|
||||
}
|
||||
if request_func_input.request_id:
|
||||
headers["x-request-id"] = request_func_input.request_id
|
||||
|
||||
output = RequestFuncOutput()
|
||||
output.prompt_len = request_func_input.prompt_len
|
||||
|
||||
generated_text = ""
|
||||
ttft = 0.0
|
||||
st = time.perf_counter()
|
||||
most_recent_timestamp = st
|
||||
try:
|
||||
async with session.post(
|
||||
url=api_url, json=payload, headers=headers
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
async for chunk_bytes in response.content:
|
||||
chunk_bytes = chunk_bytes.strip()
|
||||
if not chunk_bytes:
|
||||
continue
|
||||
chunk_bytes = chunk_bytes.decode("utf-8")
|
||||
# NOTE: SSE comments (often used as pings) start with a colon.
|
||||
# These are not JSON data payload and should be skipped.
|
||||
if chunk_bytes.startswith(":"):
|
||||
continue
|
||||
|
||||
chunk = chunk_bytes.removeprefix("data: ")
|
||||
|
||||
if chunk != "[DONE]":
|
||||
timestamp = time.perf_counter()
|
||||
data = json.loads(chunk)
|
||||
|
||||
if choices := data.get("choices"):
|
||||
content = choices[0]["delta"].get("content")
|
||||
# First token
|
||||
if ttft == 0.0:
|
||||
ttft = timestamp - st
|
||||
output.ttft = ttft
|
||||
|
||||
# Decoding phase
|
||||
else:
|
||||
output.itl.append(timestamp - most_recent_timestamp)
|
||||
|
||||
generated_text += content or ""
|
||||
elif usage := data.get("usage"):
|
||||
output.output_tokens = usage.get("completion_tokens")
|
||||
|
||||
most_recent_timestamp = timestamp
|
||||
|
||||
output.generated_text = generated_text
|
||||
output.success = True
|
||||
output.latency = most_recent_timestamp - st
|
||||
else:
|
||||
output.error = response.reason or ""
|
||||
output.success = False
|
||||
except Exception:
|
||||
output.success = False
|
||||
exc_info = sys.exc_info()
|
||||
output.error = "".join(traceback.format_exception(*exc_info))
|
||||
|
||||
if pbar:
|
||||
pbar.update(1)
|
||||
return output
|
||||
|
||||
|
||||
async def async_request_openai_audio(
|
||||
request_func_input: RequestFuncInput,
|
||||
pbar: tqdm | None = None,
|
||||
) -> RequestFuncOutput:
|
||||
# Lazy import without PlaceholderModule to avoid vllm dep.
|
||||
import soundfile
|
||||
|
||||
api_url = request_func_input.api_url
|
||||
assert api_url.endswith(("transcriptions", "translations")), (
|
||||
"OpenAI Chat Completions API URL must end with 'transcriptions' "
|
||||
)
|
||||
"or `translations`."
|
||||
|
||||
async with aiohttp.ClientSession(
|
||||
trust_env=True, timeout=AIOHTTP_TIMEOUT
|
||||
) as session:
|
||||
content = [{"type": "text", "text": request_func_input.prompt}]
|
||||
payload = {
|
||||
"model": request_func_input.model_name
|
||||
if request_func_input.model_name
|
||||
else request_func_input.model,
|
||||
"temperature": 0.0,
|
||||
"max_completion_tokens": request_func_input.output_len,
|
||||
"stream": True,
|
||||
"language": "en",
|
||||
# Flattened due to multipart/form-data
|
||||
"stream_include_usage": True,
|
||||
"stream_continuous_usage_stats": True,
|
||||
}
|
||||
if request_func_input.extra_body:
|
||||
payload.update(request_func_input.extra_body)
|
||||
headers = {
|
||||
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
|
||||
}
|
||||
if request_func_input.request_id:
|
||||
headers["x-request-id"] = request_func_input.request_id
|
||||
|
||||
# Send audio file
|
||||
def to_bytes(y, sr):
|
||||
buffer = io.BytesIO()
|
||||
soundfile.write(buffer, y, sr, format="WAV")
|
||||
buffer.seek(0)
|
||||
return buffer
|
||||
|
||||
mm_audio = request_func_input.multi_modal_content
|
||||
if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
|
||||
raise TypeError("multi_modal_content must be a dict containing 'audio'")
|
||||
with to_bytes(*mm_audio["audio"]) as f:
|
||||
form = aiohttp.FormData()
|
||||
form.add_field("file", f, content_type="audio/wav")
|
||||
for key, value in payload.items():
|
||||
form.add_field(key, str(value))
|
||||
|
||||
output = RequestFuncOutput()
|
||||
output.prompt_len = request_func_input.prompt_len
|
||||
|
||||
generated_text = ""
|
||||
ttft = 0.0
|
||||
st = time.perf_counter()
|
||||
most_recent_timestamp = st
|
||||
try:
|
||||
async with session.post(
|
||||
url=api_url, data=form, headers=headers
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
async for chunk_bytes in response.content:
|
||||
chunk_bytes = chunk_bytes.strip()
|
||||
if not chunk_bytes:
|
||||
continue
|
||||
|
||||
chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
|
||||
if chunk != "[DONE]":
|
||||
timestamp = time.perf_counter()
|
||||
data = json.loads(chunk)
|
||||
|
||||
if choices := data.get("choices"):
|
||||
content = choices[0]["delta"].get("content")
|
||||
# First token
|
||||
if ttft == 0.0:
|
||||
ttft = timestamp - st
|
||||
output.ttft = ttft
|
||||
|
||||
# Decoding phase
|
||||
else:
|
||||
output.itl.append(
|
||||
timestamp - most_recent_timestamp
|
||||
)
|
||||
|
||||
generated_text += content or ""
|
||||
elif usage := data.get("usage"):
|
||||
output.output_tokens = usage.get(
|
||||
"completion_tokens"
|
||||
)
|
||||
|
||||
most_recent_timestamp = timestamp
|
||||
|
||||
output.generated_text = generated_text
|
||||
output.success = True
|
||||
output.latency = most_recent_timestamp - st
|
||||
else:
|
||||
output.error = response.reason or ""
|
||||
output.success = False
|
||||
except Exception:
|
||||
output.success = False
|
||||
exc_info = sys.exc_info()
|
||||
output.error = "".join(traceback.format_exception(*exc_info))
|
||||
|
||||
if pbar:
|
||||
pbar.update(1)
|
||||
return output
|
||||
|
||||
|
||||
def get_model(pretrained_model_name_or_path: str) -> str:
|
||||
if os.getenv("VLLM_USE_MODELSCOPE", "False").lower() == "true":
|
||||
from modelscope import snapshot_download
|
||||
|
||||
from vllm.model_executor.model_loader.weight_utils import get_lock
|
||||
|
||||
# Use file lock to prevent multiple processes from
|
||||
# downloading the same model weights at the same time.
|
||||
with get_lock(pretrained_model_name_or_path):
|
||||
model_path = snapshot_download(
|
||||
model_id=pretrained_model_name_or_path,
|
||||
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
|
||||
ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"],
|
||||
)
|
||||
|
||||
return model_path
|
||||
return pretrained_model_name_or_path
|
||||
|
||||
|
||||
def get_tokenizer(
|
||||
pretrained_model_name_or_path: str,
|
||||
tokenizer_mode: str = "auto",
|
||||
trust_remote_code: bool = False,
|
||||
**kwargs,
|
||||
) -> PreTrainedTokenizer | PreTrainedTokenizerFast:
|
||||
if pretrained_model_name_or_path is not None and not os.path.exists(
|
||||
pretrained_model_name_or_path
|
||||
):
|
||||
pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
|
||||
if tokenizer_mode == "slow":
|
||||
if kwargs.get("use_fast", False):
|
||||
raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
|
||||
kwargs["use_fast"] = False
|
||||
if tokenizer_mode == "mistral":
|
||||
try:
|
||||
from vllm.tokenizers.mistral import MistralTokenizer
|
||||
except ImportError as e:
|
||||
raise ImportError(
|
||||
"MistralTokenizer requires vllm package.\n"
|
||||
"Please install it with `pip install vllm` "
|
||||
"to use mistral tokenizer mode."
|
||||
) from e
|
||||
return MistralTokenizer.from_pretrained(str(pretrained_model_name_or_path))
|
||||
else:
|
||||
return AutoTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
trust_remote_code=trust_remote_code,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
ASYNC_REQUEST_FUNCS = {
|
||||
@@ -385,5 +643,15 @@ ASYNC_REQUEST_FUNCS = {
|
||||
"deepspeed-mii": async_request_deepspeed_mii,
|
||||
"openai": async_request_openai_completions,
|
||||
"openai-chat": async_request_openai_chat_completions,
|
||||
"openai-audio": async_request_openai_audio,
|
||||
"tensorrt-llm": async_request_trt_llm,
|
||||
"scalellm": async_request_openai_completions,
|
||||
"sglang": async_request_openai_completions,
|
||||
"llama.cpp": async_request_openai_completions,
|
||||
}
|
||||
|
||||
OPENAI_COMPATIBLE_BACKENDS = [
|
||||
k
|
||||
for k, v in ASYNC_REQUEST_FUNCS.items()
|
||||
if v in (async_request_openai_completions, async_request_openai_chat_completions)
|
||||
]
|
||||
|
||||
Reference in New Issue
Block a user