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update vllm_worker.py #3330

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dd2470c
Add exception capture to vllm worker fix #3328
dingjbAsia May 13, 2024
af2771a
Merge remote-tracking branch 'upstream/main'
dingjbAsia Jul 29, 2024
fb6ebad
black reformat
dingjbAsia Jul 29, 2024
36353eb
Merge branch 'lm-sys:main' into main
jbding Jun 16, 2025
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208 changes: 109 additions & 99 deletions fastchat/serve/vllm_worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
from vllm.sampling_params import SamplingParams
from vllm.utils import random_uuid

from fastchat.constants import ErrorCode, SERVER_ERROR_MSG
from fastchat.serve.base_model_worker import BaseModelWorker
from fastchat.serve.model_worker import (
logger,
Expand Down Expand Up @@ -66,108 +67,117 @@ def __init__(

async def generate_stream(self, params):
self.call_ct += 1

context = params.pop("prompt")
request_id = params.pop("request_id")
temperature = float(params.get("temperature", 1.0))
top_p = float(params.get("top_p", 1.0))
top_k = params.get("top_k", -1.0)
presence_penalty = float(params.get("presence_penalty", 0.0))
frequency_penalty = float(params.get("frequency_penalty", 0.0))
max_new_tokens = params.get("max_new_tokens", 256)
stop_str = params.get("stop", None)
stop_token_ids = params.get("stop_token_ids", None) or []
if self.tokenizer.eos_token_id is not None:
stop_token_ids.append(self.tokenizer.eos_token_id)
echo = params.get("echo", True)
use_beam_search = params.get("use_beam_search", False)
best_of = params.get("best_of", None)

request = params.get("request", None)

# Handle stop_str
stop = set()
if isinstance(stop_str, str) and stop_str != "":
stop.add(stop_str)
elif isinstance(stop_str, list) and stop_str != []:
stop.update(stop_str)

for tid in stop_token_ids:
if tid is not None:
s = self.tokenizer.decode(tid)
if s != "":
stop.add(s)

# make sampling params in vllm
top_p = max(top_p, 1e-5)
if temperature <= 1e-5:
top_p = 1.0

sampling_params = SamplingParams(
n=1,
temperature=temperature,
top_p=top_p,
use_beam_search=use_beam_search,
stop=list(stop),
stop_token_ids=stop_token_ids,
max_tokens=max_new_tokens,
top_k=top_k,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
best_of=best_of,
)
results_generator = engine.generate(context, sampling_params, request_id)

async for request_output in results_generator:
prompt = request_output.prompt
if echo:
text_outputs = [
prompt + output.text for output in request_output.outputs
]
else:
text_outputs = [output.text for output in request_output.outputs]
text_outputs = " ".join(text_outputs)

partial_stop = any(is_partial_stop(text_outputs, i) for i in stop)
# prevent yielding partial stop sequence
if partial_stop:
continue

aborted = False
if request and await request.is_disconnected():
await engine.abort(request_id)
request_output.finished = True
aborted = True
for output in request_output.outputs:
output.finish_reason = "abort"

prompt_tokens = len(request_output.prompt_token_ids)
completion_tokens = sum(
len(output.token_ids) for output in request_output.outputs
try:
context = params.pop("prompt")
request_id = params.pop("request_id")
temperature = float(params.get("temperature", 1.0))
top_p = float(params.get("top_p", 1.0))
top_k = params.get("top_k", -1.0)
presence_penalty = float(params.get("presence_penalty", 0.0))
frequency_penalty = float(params.get("frequency_penalty", 0.0))
max_new_tokens = params.get("max_new_tokens", 256)
stop_str = params.get("stop", None)
stop_token_ids = params.get("stop_token_ids", None) or []
if self.tokenizer.eos_token_id is not None:
stop_token_ids.append(self.tokenizer.eos_token_id)
echo = params.get("echo", True)
use_beam_search = params.get("use_beam_search", False)
best_of = params.get("best_of", None)

request = params.get("request", None)

# Handle stop_str
stop = set()
if isinstance(stop_str, str) and stop_str != "":
stop.add(stop_str)
elif isinstance(stop_str, list) and stop_str != []:
stop.update(stop_str)

for tid in stop_token_ids:
if tid is not None:
s = self.tokenizer.decode(tid)
if s != "":
stop.add(s)

# make sampling params in vllm
top_p = max(top_p, 1e-5)
if temperature <= 1e-5:
top_p = 1.0
sampling_params = SamplingParams(
n=1,
temperature=temperature,
top_p=top_p,
use_beam_search=use_beam_search,
stop=list(stop),
stop_token_ids=stop_token_ids,
max_tokens=max_new_tokens,
top_k=top_k,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
best_of=best_of,
)
results_generator = engine.generate(context, sampling_params, request_id)

async for request_output in results_generator:
prompt = request_output.prompt
if echo:
text_outputs = [
prompt + output.text for output in request_output.outputs
]
else:
text_outputs = [output.text for output in request_output.outputs]
text_outputs = " ".join(text_outputs)

partial_stop = any(is_partial_stop(text_outputs, i) for i in stop)
# prevent yielding partial stop sequence
if partial_stop:
continue

aborted = False
if request and await request.is_disconnected():
await engine.abort(request_id)
request_output.finished = True
aborted = True
for output in request_output.outputs:
output.finish_reason = "abort"

prompt_tokens = len(request_output.prompt_token_ids)
completion_tokens = sum(
len(output.token_ids) for output in request_output.outputs
)
ret = {
"text": text_outputs,
"error_code": 0,
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
},
"cumulative_logprob": [
output.cumulative_logprob for output in request_output.outputs
],
"finish_reason": (
request_output.outputs[0].finish_reason
if len(request_output.outputs) == 1
else [output.finish_reason for output in request_output.outputs]
),
}
# Emit twice here to ensure a 'finish_reason' with empty content in the OpenAI API response.
# This aligns with the behavior of model_worker.
if request_output.finished:
yield (
json.dumps({**ret, **{"finish_reason": None}}) + "\0"
).encode()
yield (json.dumps(ret) + "\0").encode()

if aborted:
break
except ValueError as e:
ret = {
"text": text_outputs,
"error_code": 0,
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
},
"cumulative_logprob": [
output.cumulative_logprob for output in request_output.outputs
],
"finish_reason": request_output.outputs[0].finish_reason
if len(request_output.outputs) == 1
else [output.finish_reason for output in request_output.outputs],
"text": f"{SERVER_ERROR_MSG}\n\n({e})",
"error_code": ErrorCode.INTERNAL_ERROR,
}
# Emit twice here to ensure a 'finish_reason' with empty content in the OpenAI API response.
# This aligns with the behavior of model_worker.
if request_output.finished:
yield (json.dumps({**ret, **{"finish_reason": None}}) + "\0").encode()
yield (json.dumps(ret) + "\0").encode()

if aborted:
break
yield json.dumps(ret).encode() + b"\0"

async def generate(self, params):
async for x in self.generate_stream(params):
Expand Down