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Multi turn benchmark progress bar for synthetic conversation generation (#28394)
Signed-off-by: Ido Segev <idos@pliops.com>
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@ -11,6 +11,7 @@ from bench_utils import (
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Color,
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logger,
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)
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from tqdm import tqdm
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from transformers import AutoTokenizer # type: ignore
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# Conversation ID is a string (e.g: "UzTK34D")
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@ -417,6 +418,10 @@ def generate_conversations(
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data = file.read()
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tokens_in_file = tokenizer.encode(data, add_special_tokens=False)
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list_of_tokens.extend(tokens_in_file)
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logger.info(
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f"Loaded {len(tokens_in_file)} tokens from file {filename}, "
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f"total tokens so far: {len(list_of_tokens)}"
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)
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conversations: ConversationsMap = {}
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conv_id = 0
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@ -449,18 +454,25 @@ def generate_conversations(
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)
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base_offset += common_prefix_tokens
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for conv_id in range(args.num_conversations):
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for conv_id in tqdm(
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range(args.num_conversations),
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total=args.num_conversations,
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desc="Generating conversations",
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unit="conv",
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):
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# Generate a single conversation
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messages: MessagesList = []
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nturns = turn_count[conv_id]
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# User prompt token count per turn (with lower limit)
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input_token_count: np.ndarray = args.input_num_tokens.sample(nturns)
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input_token_count: np.ndarray = args.input_num_tokens.sample(nturns).astype(int)
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input_token_count = np.maximum(input_token_count, base_prompt_token_count)
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# Assistant answer token count per turn (with lower limit)
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output_token_count: np.ndarray = args.output_num_tokens.sample(nturns)
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output_token_count: np.ndarray = args.output_num_tokens.sample(nturns).astype(
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int
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)
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output_token_count = np.maximum(output_token_count, 1)
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user_turn = True
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@ -2,4 +2,5 @@ numpy>=1.24
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pandas>=2.0.0
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aiohttp>=3.10
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transformers>=4.46
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xlsxwriter>=3.2.1
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xlsxwriter>=3.2.1
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tqdm>=4.66
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