-
-
Notifications
You must be signed in to change notification settings - Fork 4.9k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Minor] Revert change in offline inference example (#10545)
Signed-off-by: Woosuk Kwon <[email protected]>
- Loading branch information
1 parent
cf656f5
commit 46fe9b4
Showing
2 changed files
with
100 additions
and
78 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,80 +1,22 @@ | ||
from dataclasses import asdict | ||
|
||
from vllm import LLM, SamplingParams | ||
from vllm.engine.arg_utils import EngineArgs | ||
from vllm.utils import FlexibleArgumentParser | ||
|
||
|
||
def get_prompts(num_prompts: int): | ||
# The default sample prompts. | ||
prompts = [ | ||
"Hello, my name is", | ||
"The president of the United States is", | ||
"The capital of France is", | ||
"The future of AI is", | ||
] | ||
|
||
if num_prompts != len(prompts): | ||
prompts = (prompts * ((num_prompts // len(prompts)) + 1))[:num_prompts] | ||
|
||
return prompts | ||
|
||
|
||
def main(args): | ||
# Create prompts | ||
prompts = get_prompts(args.num_prompts) | ||
|
||
# Create a sampling params object. | ||
sampling_params = SamplingParams(n=args.n, | ||
temperature=args.temperature, | ||
top_p=args.top_p, | ||
top_k=args.top_k, | ||
max_tokens=args.max_tokens) | ||
|
||
# Create an LLM. | ||
# The default model is 'facebook/opt-125m' | ||
engine_args = EngineArgs.from_cli_args(args) | ||
llm = LLM(**asdict(engine_args)) | ||
|
||
# Generate texts from the prompts. | ||
# The output is a list of RequestOutput objects | ||
# that contain the prompt, generated text, and other information. | ||
outputs = llm.generate(prompts, sampling_params) | ||
# Print the outputs. | ||
for output in outputs: | ||
prompt = output.prompt | ||
generated_text = output.outputs[0].text | ||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") | ||
|
||
|
||
if __name__ == '__main__': | ||
parser = FlexibleArgumentParser() | ||
parser = EngineArgs.add_cli_args(parser) | ||
group = parser.add_argument_group("SamplingParams options") | ||
group.add_argument("--num-prompts", | ||
type=int, | ||
default=4, | ||
help="Number of prompts used for inference") | ||
group.add_argument("--max-tokens", | ||
type=int, | ||
default=16, | ||
help="Generated output length for sampling") | ||
group.add_argument('--n', | ||
type=int, | ||
default=1, | ||
help='Number of generated sequences per prompt') | ||
group.add_argument('--temperature', | ||
type=float, | ||
default=0.8, | ||
help='Temperature for text generation') | ||
group.add_argument('--top-p', | ||
type=float, | ||
default=0.95, | ||
help='top_p for text generation') | ||
group.add_argument('--top-k', | ||
type=int, | ||
default=-1, | ||
help='top_k for text generation') | ||
|
||
args = parser.parse_args() | ||
main(args) | ||
# Sample prompts. | ||
prompts = [ | ||
"Hello, my name is", | ||
"The president of the United States is", | ||
"The capital of France is", | ||
"The future of AI is", | ||
] | ||
# Create a sampling params object. | ||
sampling_params = SamplingParams(temperature=0.8, top_p=0.95) | ||
|
||
# Create an LLM. | ||
llm = LLM(model="facebook/opt-125m") | ||
# Generate texts from the prompts. The output is a list of RequestOutput objects | ||
# that contain the prompt, generated text, and other information. | ||
outputs = llm.generate(prompts, sampling_params) | ||
# Print the outputs. | ||
for output in outputs: | ||
prompt = output.prompt | ||
generated_text = output.outputs[0].text | ||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,80 @@ | ||
from dataclasses import asdict | ||
|
||
from vllm import LLM, SamplingParams | ||
from vllm.engine.arg_utils import EngineArgs | ||
from vllm.utils import FlexibleArgumentParser | ||
|
||
|
||
def get_prompts(num_prompts: int): | ||
# The default sample prompts. | ||
prompts = [ | ||
"Hello, my name is", | ||
"The president of the United States is", | ||
"The capital of France is", | ||
"The future of AI is", | ||
] | ||
|
||
if num_prompts != len(prompts): | ||
prompts = (prompts * ((num_prompts // len(prompts)) + 1))[:num_prompts] | ||
|
||
return prompts | ||
|
||
|
||
def main(args): | ||
# Create prompts | ||
prompts = get_prompts(args.num_prompts) | ||
|
||
# Create a sampling params object. | ||
sampling_params = SamplingParams(n=args.n, | ||
temperature=args.temperature, | ||
top_p=args.top_p, | ||
top_k=args.top_k, | ||
max_tokens=args.max_tokens) | ||
|
||
# Create an LLM. | ||
# The default model is 'facebook/opt-125m' | ||
engine_args = EngineArgs.from_cli_args(args) | ||
llm = LLM(**asdict(engine_args)) | ||
|
||
# Generate texts from the prompts. | ||
# The output is a list of RequestOutput objects | ||
# that contain the prompt, generated text, and other information. | ||
outputs = llm.generate(prompts, sampling_params) | ||
# Print the outputs. | ||
for output in outputs: | ||
prompt = output.prompt | ||
generated_text = output.outputs[0].text | ||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") | ||
|
||
|
||
if __name__ == '__main__': | ||
parser = FlexibleArgumentParser() | ||
parser = EngineArgs.add_cli_args(parser) | ||
group = parser.add_argument_group("SamplingParams options") | ||
group.add_argument("--num-prompts", | ||
type=int, | ||
default=4, | ||
help="Number of prompts used for inference") | ||
group.add_argument("--max-tokens", | ||
type=int, | ||
default=16, | ||
help="Generated output length for sampling") | ||
group.add_argument('--n', | ||
type=int, | ||
default=1, | ||
help='Number of generated sequences per prompt') | ||
group.add_argument('--temperature', | ||
type=float, | ||
default=0.8, | ||
help='Temperature for text generation') | ||
group.add_argument('--top-p', | ||
type=float, | ||
default=0.95, | ||
help='top_p for text generation') | ||
group.add_argument('--top-k', | ||
type=int, | ||
default=-1, | ||
help='top_k for text generation') | ||
|
||
args = parser.parse_args() | ||
main(args) |