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llm_test.py
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llm_test.py
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import argparse
import os
import sqlite3
import time
from contextlib import contextmanager, nullcontext
from typing import Any
import vllm.envs as envs
from simple_term_menu import TerminalMenu
from vllm import LLM, SamplingParams
from vllm.inputs.data import TokensPrompt
class LlmKwargs(dict):
def __init__(self, args: argparse.Namespace) -> None:
self.kwargs = {
'model': args.model,
'kv_cache_dtype': args.kv_cache_dtype,
'tensor_parallel_size': args.tensor_parallel_size,
'dtype': args.dtype,
'quantization': args.quantization,
'enforce_eager': args.enforce_eager,
}
self.prompt = args.prompt if args.input_len == -1 else [
0
] * args.input_len
self.batch_size = args.batch_size
self.sampling_params = SamplingParams(temperature=args.temperature,
top_p=1,
max_tokens=args.max_tokens,
ignore_eos=args.ignore_eos)
self.prompt = "There is a round table in the middle of the"
def __setitem__(self, key: str, value: str) -> None:
self.kwargs[key] = value
def __str__(self) -> str:
res = "===================\n"
for key, value in self.kwargs.items():
res += f"{key}: {value}\n"
res += f"Sampling params: {self.sampling_params}\n"
res += "\n Misc: \n"
res += f"Prompt: {self.prompt}\n"
res += f"Batch size: {self.batch_size}\n"
return res
def select_model(llm_kwargs: LlmKwargs):
# Create a list of all the subfolders of a folder
import os
folder = "/models"
folders = [f.path for f in os.scandir(folder) if f.is_dir()]
subfolders = []
for subfolder in folders:
subfolders.extend(
[f.path for f in os.scandir(subfolder) if f.is_dir()])
folders.extend(subfolders)
folder_idx = menu(folders)
llm_kwargs["model"] = folders[folder_idx]
def select_prompt(llm_kwargs: LlmKwargs):
llm_kwargs.prompt = input("Enter a prompt: ")
def select_batch_size(llm_kwargs: LlmKwargs):
llm_kwargs.batch_size = int(input("Enter a batch size: "))
def select_max_tokens(llm_kwargs: LlmKwargs):
llm_kwargs.sampling_params.max_tokens = int(input("Enter max tokens: "))
def select_input_len(llm_kwargs: LlmKwargs):
llm_kwargs.prompt = [0] * int(input("Enter input length: "))
def select_temperature(llm_kwargs: LlmKwargs):
llm_kwargs.sampling_params.temperature = float(
input("Enter temperature: "))
def select_ignore_eos(llm_kwargs: LlmKwargs):
llm_kwargs.sampling_params.ignore_eos = [False, True][menu([False, True])]
values = {
"model": select_model,
"kv_cache_dtype": ["auto", "fp8"],
"tensor_parallel_size": [1, 2, 4, 8],
"dtype": ["auto", "float16", "bfloat16"],
"quantization": ["None", "fp8", "compressed-tensors", "fbgemm-fp8"],
"enforce_eager": [True, False],
"prompt": select_prompt,
"batch_size": select_batch_size,
"max_tokens": select_max_tokens,
"input_len": select_input_len,
"temperature": select_temperature,
"ignore_eos": select_ignore_eos,
"Done": None
}
def menu(items):
terminal_menu = TerminalMenu([str(x) for x in items])
menu_entry_index = terminal_menu.show()
if menu_entry_index is None:
print("Aborted")
exit(1)
return menu_entry_index
def interactive(llm_kwargs: LlmKwargs):
while True:
selected = menu(list(values.keys()))
key = list(values.keys())[selected]
value = values[list(values.keys())[selected]]
if value is None:
return
if callable(value):
value(llm_kwargs)
elif isinstance(value, list):
new_value = type(value[0])(value[menu(value)])
if new_value == 'None':
new_value = None
llm_kwargs[key] = new_value
print(llm_kwargs)
def recreate_trace(args: argparse.Namespace):
from rocpd.schema import RocpdSchema
if envs.VLLM_RPD_PROFILER_DIR is None:
envs.VLLM_RPD_PROFILER_DIR = os.path.join(os.path.curdir, "trace.rpd")
try:
os.remove(envs.VLLM_RPD_PROFILER_DIR)
except FileNotFoundError:
pass
schema = RocpdSchema()
connection = sqlite3.connect(envs.VLLM_RPD_PROFILER_DIR)
schema.writeSchema(connection)
connection.commit()
def main(args: argparse.Namespace):
@contextmanager
def rpd_profiler_context():
from rpdTracerControl import rpdTracerControl as rpd
llm.start_profile()
yield
llm.stop_profile()
rpd.top_totals()
llm_args = LlmKwargs(args)
print(llm_args)
if args.interactive:
interactive(llm_args)
batch_size = llm_args.batch_size
if args.rpd:
recreate_trace(args)
llm = LLM(**llm_args.kwargs)
start_time = time.perf_counter()
with rpd_profiler_context() if args.rpd else nullcontext():
outs = llm.generate([TokensPrompt(prompt_token_ids=llm_args.prompt)] *
llm_args.batch_size if isinstance(
llm_args.prompt, list) else [llm_args.prompt] *
batch_size,
sampling_params=llm_args.sampling_params)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
out_lengths = [len(x.token_ids) for out in outs for x in out.outputs]
num_tokens = sum(out_lengths)
print(
f"{num_tokens} tokens. {num_tokens / batch_size} on average. {num_tokens / elapsed_time:.2f} tokens/s. {elapsed_time} seconds"
)
for out in outs:
print("===========")
print(out.outputs[0].text)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='LLM Test much')
parser.add_argument('-m',
'--model',
type=str,
help='Model to use',
default='/models/llama-2-7b-chat-hf')
parser.add_argument('-i',
'--interactive',
action='store_true',
help='Interactive mode')
parser.add_argument('--kv_cache_dtype',
type=str,
help='KV Cache Data Type',
choices=values["kv_cache_dtype"],
default='auto')
parser.add_argument('-tp',
'--tensor-parallel-size',
type=int,
default=1,
choices=values["tensor_parallel_size"])
parser.add_argument('--dtype',
type=str,
default='auto',
choices=values["dtype"])
parser.add_argument('--quantization',
type=str,
default=None,
choices=values["quantization"])
parser.add_argument('--prompt',
type=str,
default="There is a round table in the middle of the")
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--max-tokens', type=int, default=256)
parser.add_argument('--enforce-eager', action='store_true')
parser.add_argument('--rpd', action='store_true')
parser.add_argument('--input-len', type=int, default=-1)
parser.add_argument('--temperature', type=float, default=0)
parser.add_argument('--ignore-eos', action='store_true')
args = parser.parse_args()
main(args)