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Multi turn benchmark progress bar for synthetic conversation generation (#28394)

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Signed-off-by: Ido Segev <idos@pliops.com>
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Ido Segev 2025-11-11 13:06:04 +02:00 committed by GitHub
parent 9973e6e04a
commit d381eb967f
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2 changed files with 17 additions and 4 deletions
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18
benchmarks/multi_turn/bench_dataset.py
View File

@ -11,6 +11,7 @@ from bench_utils import (
Color, Color,
logger, logger,
) )
from tqdm import tqdm
from transformers import AutoTokenizer # type: ignore from transformers import AutoTokenizer # type: ignore
# Conversation ID is a string (e.g: "UzTK34D") # Conversation ID is a string (e.g: "UzTK34D")
@ -417,6 +418,10 @@ def generate_conversations(
data = file.read() data = file.read()
tokens_in_file = tokenizer.encode(data, add_special_tokens=False) tokens_in_file = tokenizer.encode(data, add_special_tokens=False)
list_of_tokens.extend(tokens_in_file) list_of_tokens.extend(tokens_in_file)
logger.info(
f"Loaded {len(tokens_in_file)} tokens from file {filename}, "
f"total tokens so far: {len(list_of_tokens)}"
)
conversations: ConversationsMap = {} conversations: ConversationsMap = {}
conv_id = 0 conv_id = 0
@ -449,18 +454,25 @@ def generate_conversations(
) )
base_offset += common_prefix_tokens base_offset += common_prefix_tokens
for conv_id in range(args.num_conversations): for conv_id in tqdm(
range(args.num_conversations),
total=args.num_conversations,
desc="Generating conversations",
unit="conv",
):
# Generate a single conversation # Generate a single conversation
messages: MessagesList = [] messages: MessagesList = []
nturns = turn_count[conv_id] nturns = turn_count[conv_id]
# User prompt token count per turn (with lower limit) # User prompt token count per turn (with lower limit)
input_token_count: np.ndarray = args.input_num_tokens.sample(nturns) input_token_count: np.ndarray = args.input_num_tokens.sample(nturns).astype(int)
input_token_count = np.maximum(input_token_count, base_prompt_token_count) input_token_count = np.maximum(input_token_count, base_prompt_token_count)
# Assistant answer token count per turn (with lower limit) # Assistant answer token count per turn (with lower limit)
output_token_count: np.ndarray = args.output_num_tokens.sample(nturns) output_token_count: np.ndarray = args.output_num_tokens.sample(nturns).astype(
int
)
output_token_count = np.maximum(output_token_count, 1) output_token_count = np.maximum(output_token_count, 1)
user_turn = True user_turn = True

3
benchmarks/multi_turn/requirements.txt
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@ -2,4 +2,5 @@ numpy>=1.24
pandas>=2.0.0 pandas>=2.0.0
aiohttp>=3.10 aiohttp>=3.10
transformers>=4.46 transformers>=4.46
xlsxwriter>=3.2.1 xlsxwriter>=3.2.1
tqdm>=4.66