mirror of
https://github.com/SakanaAI/doc-to-lora.git
synced 2026-07-23 17:01:04 +02:00
q gen can take ds_names + self_gen summ/struct/cot prob + ctx qa v3 data
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parent
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9 changed files with 406 additions and 209 deletions
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@ -175,7 +175,7 @@ run uv run data/generate_fw_edu_qa_v2.py --shard_pattern "000_00000" --n_qa_pair
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run uv run data/generate_fw_edu_qa_v2_repeat.py --shard_pattern "min_0_to_2000/000*level_0*" --n_qa_pairs=5 --vllm_model=google/gemma-3-12b-it;
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run uv run data/generate_fw_edu_qa_v2_repeat.py --shard_pattern "min_0_to_2000/000*level_1*" --n_qa_pairs=5 --vllm_model=google/gemma-3-12b-it;
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run uv run data/generate_fw_edu_qa_v2_repeat.py --shard_pattern "min_0_to_2000/000*level_2*" --n_qa_pairs=5 --vllm_model=google/gemma-3-12b-it
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run uv run data/generate_fw_edu_qa_v3.py --n_qa_pairs 3 --shard_pattern '*' --debug --question_weight 1 --use_case_weight 1 --creative_weight 1 --generic_weight 1
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run uv run python data/generate_fw_edu_qa_v3.py --shard_pattern '*' --question_weight 3 --use_case_weight 1 --creative_weight 1 --generic_weight 1
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```
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2. Self-generated response QA data (depends on step 0 and 1)
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@ -16,8 +16,11 @@ from q_generation_prompts import ( # get_structuring_seed_prompt,; get_summariz
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)
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from vllm import LLM, SamplingParams
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from ctx_to_lora.data.processing import load_and_process_dataset
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STOP_STRINGS = {
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"google/gemma-3-12b-it": ["<eos>", "<end_of_turn>"],
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"google/gemma-3-4b-it": ["<eos>", "<end_of_turn>"],
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}
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@ -112,15 +115,28 @@ if __name__ == "__main__":
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parser.add_argument(
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"--vllm_model",
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type=str,
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default=os.environ.get("vllm_model", "google/gemma-3-12b-it"),
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default=os.environ.get("vllm_model", "google/gemma-3-4b-it"),
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help="VLLM model to use for generation",
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)
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parser.add_argument(
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"--shard_pattern",
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type=str,
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required=True,
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default=None,
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help="Pattern to match shard files (e.g., '000_0000*')",
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)
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parser.add_argument(
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"--ds_names", # list of names
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type=str,
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nargs="+",
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default=None,
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help="dataset names",
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)
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parser.add_argument(
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"--split",
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type=str,
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default=None,
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help="Dataset split to use (e.g., 'train', 'validation')",
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)
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# parser.add_argument(
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# "--n_qa_pairs",
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# type=int,
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@ -176,6 +192,9 @@ if __name__ == "__main__":
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)
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args = parser.parse_args()
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assert bool(args.shard_pattern) ^ bool(args.ds_names)
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if args.ds_names:
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assert bool(args.split)
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weights = [
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args.question_weight,
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@ -203,17 +222,28 @@ if __name__ == "__main__":
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shard_pattern = args.shard_pattern
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# n_qa_pairs = args.n_qa_pairs
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paths = glob(
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f"./data/raw_datasets/fineweb_edu/sample/100BT/{shard_pattern}.parquet"
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)
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split = "train[:100]" if args.debug else "train"
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for path in paths:
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ds = load_dataset(
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"parquet",
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data_files=path,
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split=split,
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if args.shard_pattern is not None:
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paths = glob(
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f"./data/raw_datasets/fineweb_edu/sample/100BT/{shard_pattern}.parquet"
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)
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elif args.ds_names:
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paths = args.ds_names
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else:
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raise ValueError("Either shard_pattern or ds_names must be provided.")
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split = "train[:100]" if args.debug else args.split or "train"
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for path in paths:
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if args.shard_pattern is not None:
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ds = load_dataset(
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"parquet",
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data_files=path,
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split=split,
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)
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elif args.ds_names:
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ds = load_and_process_dataset(path, args.split, num_proc=16)
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ds = ds.rename_column("context", "text")
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print(f"Loaded {len(ds)} samples from {path}")
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ds = ds.filter(
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length_filter,
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fn_kwargs={"min_len": args.min_length, "max_len": args.max_length},
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@ -6,7 +6,7 @@ from tqdm import tqdm
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if __name__ == "__main__":
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ds = load_dataset("google-research-datasets/natural_questions")
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ds = ds.shuffle(seed=42)
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for split in ["validation"]:
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for split in ["train", "validation"]:
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out = []
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for i, sample in enumerate(tqdm(ds[split])):
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ctx = " ".join(
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@ -11,9 +11,11 @@ from vllm import LLM, SamplingParams
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from ctx_to_lora.data.definitions import (
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CLOSED_QA_INTX_TEMPLATES,
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COT_TEMPLATES,
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RAW_DATA_DIR,
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SELF_GEN_DATA_DIR,
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SELF_GEN_SYSTEM_MSG,
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STRUCTURING_PROMPTS,
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SUMMARIZATION_PROMPTS,
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)
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from ctx_to_lora.data.processing import (
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filter_none,
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@ -31,48 +33,27 @@ STOP_STRINGS = {
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# TODO: !!!!
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# see https://huggingface.co/datasets/YuxinJiang/LTE_train_data/viewer/default/train?row=97&views%5B%5D=train&sql_row=2
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# based on https://arxiv.org/pdf/2402.11905
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SYSTEM_TEMPLATE = (
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"### SYSTEM INSTRUCTION ###\n"
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"You are an honest and helpful assistant.\n"
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# "You must use the information provided in the context for responding to the question.\n"
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# "If the context does not contain enough information to answer the question, you must say so.\n"
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# "If that is the case, you can answer based on your knowledge, but you must clearly state that you are doing so.\n"
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"**DO NOT** hallucinate or make up information.\n"
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"### END OF SYSTEM INSTRUCTION ###"
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SELF_GEN_SYSTEM_MSG = (
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# "### SYSTEM INSTRUCTION ###\n"
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# "You are an honest and helpful assistant.\n"
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# "**DO NOT** hallucinate or make up information.\n"
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# "### END OF SYSTEM INSTRUCTION ###"
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"You are an honest and helpful assistant."
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)
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CTX_Q_SEP = "\n\n---\n\n# User\n"
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SELF_QA_INTX = (
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# "---\n\n"
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"# System Instruction\n"
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"- The information provided is up-to-date information.\n"
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"- The information provided is up-to-date information and/or the user instruction.\n"
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"- When the provided information is not relevant to the question, ***ignore*** it and answer the question based on your knowledge.\n"
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"- If the provided information is related to the question, incorporate it in your response."
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+ CTX_Q_SEP
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"- If the provided information is related to the question, incorporate it in your response.\n"
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"- If the provided information is an instruction, follow the instruction carefully.\n"
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"\n---\n\n"
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"# User Input\n"
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)
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PROMPT_TEMPLATE = "{context}" + CTX_Q_SEP + "{question}"
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PRE_CTX = "# Provided Information\n"
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PRE_CTX = ""
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PROMPT_TEMPLATE_QA = "# Information\n{context}\n\n---\n\n" + SELF_QA_INTX + "{question}"
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"""
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---
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# System Instruction
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- The information provided is up-to-date information.
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- When the provided information is not relevant to the question, ***ignore*** it and answer the question based on your knowledge.
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- If the provided information is related to the question, incorporate it in your response.
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---
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# User
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What team is Mbappe playing for?
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"""
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PROMPT_TEMPLATE = PRE_CTX + "{context}\n\n---\n\n" + SELF_QA_INTX + "{question}"
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MODEL_CTX_LEN = {
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"google/gemma-2-27b-it": 8192,
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@ -104,15 +85,27 @@ def truncate_middle_if_too_long(
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return input_ids
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def get_prompt(context: str, q: str, use_qa_template: bool) -> str:
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template = PROMPT_TEMPLATE_QA if use_qa_template else PROMPT_TEMPLATE
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return template.format(context=context, question=q)
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def get_prompt(context: str, q: str) -> str:
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return PROMPT_TEMPLATE.format(context=context, question=q)
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def add_closed_qa_prompt(q: str, closed_qa_prob: float = 0.1) -> str:
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if random.random() <= closed_qa_prob:
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q = random.choice(CLOSED_QA_INTX_TEMPLATES).format(input=q)
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return q
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def augment_prompt(sample, closed_qa_prob, cot_prob):
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z = random.random()
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should_add_closed_qa = ("type" not in sample) or (sample["type"] == "question")
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if should_add_closed_qa and (z < closed_qa_prob):
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sample["prompts"] = add_closed_qa_prompt(sample["prompts"])
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elif closed_qa_prob <= z < (closed_qa_prob + cot_prob):
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sample["prompts"] = add_cot_prompt(sample["prompts"])
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return sample
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def add_cot_prompt(prompts: list[str]) -> str:
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return [random.choice(COT_TEMPLATES).format(input=q) for q in prompts]
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def add_closed_qa_prompt(prompts: list[str]) -> str:
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return [random.choice(CLOSED_QA_INTX_TEMPLATES).format(input=q) for q in prompts]
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def load_config(config_path: str) -> dict:
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@ -122,12 +115,47 @@ def load_config(config_path: str) -> dict:
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return config
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# def check_should_skip(txt: str, vllm_model: str) -> bool:
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# """Check if the response should be skipped based on stop strings."""
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# for stop in STOP_STRINGS[vllm_model]:
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# if stop in txt[-len(stop) :]:
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# return (txt.split(stop)[0], False) # Found a valid stop string
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# return (txt, True) # No valid stop string found, skip this response
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def add_paraphrasing_prompt(samples, summ_prob: float, struct_prob: float):
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"""Augment batch with summarization / structuring prompts given separate probabilities.
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The original samples are extended (in-place) with newly created paraphrased variants.
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"""
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if summ_prob == 0 and struct_prob == 0:
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return samples
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n_samples = len(samples["ctx_ids"]) if "ctx_ids" in samples else 0
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if n_samples == 0:
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return samples
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n_summ = int(n_samples * summ_prob)
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n_struct = int(n_samples * struct_prob)
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all_indices = list(range(n_samples))
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random.shuffle(all_indices)
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summ_indices = all_indices[:n_summ]
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struct_indices = all_indices[n_summ : n_summ + n_struct]
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def _add(kind_indices, prompts_source, prompt_type):
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out = dict()
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if not kind_indices:
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return out
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for k in samples:
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if k == "prompts":
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sampled_prompts = random.choices(prompts_source, k=len(kind_indices))
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out[k] = [[p] for p in sampled_prompts]
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elif k == "type":
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out[k] = [prompt_type] * len(kind_indices)
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else:
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out[k] = [samples[k][i] for i in kind_indices]
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return out
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summ_samples = _add(summ_indices, SUMMARIZATION_PROMPTS, "summarization")
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struct_samples = _add(struct_indices, STRUCTURING_PROMPTS, "structuring")
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# Extend original samples
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for k in samples:
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if summ_indices:
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samples[k] += summ_samples[k]
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if struct_indices:
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samples[k] += struct_samples[k]
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return samples
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def get_dataset_configs(
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@ -178,11 +206,7 @@ def get_dataset_configs(
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def create_messages(
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ctxs: list[str],
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questions: list[list[str]],
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vllm_model: str,
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system_template: str,
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use_qa_template: bool,
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ctxs: list[str], questions: list[list[str]], vllm_model: str, system_template: str
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) -> list[list[dict]]:
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"""Create chat messages for the model."""
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# if "gemma" in vllm_model:
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@ -191,9 +215,7 @@ def create_messages(
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[
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{
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"role": "user",
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"content": system_template
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+ "\n\n\n"
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+ get_prompt(ctx, q, use_qa_template),
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"content": (system_template + "\n\n\n" + get_prompt(ctx, q)).strip(),
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}
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]
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for ctx, q_list in zip(ctxs, questions)
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@ -218,42 +240,63 @@ def self_generate(
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system_template: str,
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parquet_file: str | None = None,
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do_truncate: bool = False,
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use_qa_template: bool = True,
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max_new_tokens: int = 1024,
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) -> None:
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"""Process a single dataset and generate QA pairs."""
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shard_name = ""
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# Check for conflicts between CLI args and dataset name
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# Conflict checks for ds_name-derived overrides
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if ds_name is not None:
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# temperature & closed_qa already handled later; add new ones
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if "_temp_" in ds_name and args.temp != 0.0:
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raise ValueError(
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f"Multiple sources of truth for temperature: CLI arg --temp={args.temp} "
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f"and dataset name contains temp specification. Please use only one."
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f"Multiple sources of truth for temperature: CLI arg --temp={args.temp} and dataset name contains temp specification."
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)
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if "_closed_qa_prob_" in ds_name and args.closed_qa_prob != 0.0:
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raise ValueError(
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f"Multiple sources of truth for closed_qa_prob: CLI arg --closed_qa_prob={args.closed_qa_prob} "
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f"and dataset name contains closed_qa_prob specification. Please use only one."
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f"Multiple sources of truth for closed_qa_prob: CLI arg --closed_qa_prob={args.closed_qa_prob} and dataset name contains closed_qa_prob specification."
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)
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if "_summ_prob_" in ds_name and args.summ_prob != 0.0:
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raise ValueError(
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f"Multiple sources of truth for summ_prob: CLI arg --summ_prob={args.summ_prob} and dataset name contains summ_prob specification."
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)
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if "_struct_prob_" in ds_name and args.struct_prob != 0.0:
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raise ValueError(
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f"Multiple sources of truth for struct_prob: CLI arg --struct_prob={args.struct_prob} and dataset name contains struct_prob specification."
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)
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# Use CLI args as default, override with dataset name if present
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# Base values from args
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temp = args.temp
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closed_qa_prob = args.closed_qa_prob
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summ_prob = args.summ_prob
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struct_prob = args.struct_prob
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cot_prob = args.cot_prob
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# Overrides from ds_name pattern if present
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if ds_name is not None:
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if "_temp_" in ds_name:
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m = re.search(r"_temp_([\d.]+)", ds_name)
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if m:
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temp = float(m.group(1))
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if "_closed_qa_prob_" in ds_name:
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m = re.search(r"_closed_qa_prob_([\d.]+)", ds_name)
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if m:
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closed_qa_prob = float(m.group(1))
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if "_summ_prob_" in ds_name:
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m = re.search(r"_summ_prob_([\d.]+)", ds_name)
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if m:
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summ_prob = float(m.group(1))
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if "_struct_prob_" in ds_name:
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m = re.search(r"_struct_prob_([\d.]+)", ds_name)
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if m:
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struct_prob = float(m.group(1))
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if ds_name is not None and "_temp_" in ds_name:
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temp_match = re.search(r"_temp_([\d.]+)", ds_name)
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if temp_match:
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temp = float(temp_match.group(1))
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if ds_name is not None and "_closed_qa_prob_" in ds_name:
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prob_match = re.search(r"_closed_qa_prob_([\d.]+)", ds_name)
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if prob_match:
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closed_qa_prob = float(prob_match.group(1))
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print(f"Processing dataset: {ds_name}, split: {split}")
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print(f"Using temperature: {temp}")
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print(f"Using closed QA prompt probability: {closed_qa_prob}")
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print(f"Use QA template: {use_qa_template}")
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print(f"Using chain-of-thought prompt probability: {cot_prob}")
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print(f"Using summarization augmentation probability: {summ_prob}")
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print(f"Using structuring augmentation probability: {struct_prob}")
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if parquet_file:
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print(f"Loading dataset from parquet file: {parquet_file}")
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@ -272,18 +315,19 @@ def self_generate(
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print(f"Loading dataset: {ds_name} with split: {split}")
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kwargs = dict(ds_name=ds_name, split=split)
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ds = load_and_process_dataset(**kwargs, num_proc=8)
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ds = load_and_process_dataset(**kwargs, num_proc=8, remove_cols=False)
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print(f"Loaded dataset: {ds_name} with split: {split}")
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if args.debug:
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ds = ds.take(10)
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ds = ds.take(50)
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ds = ds.filter(filter_none, batched=False, num_proc=8)
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print(f"0. {len(ds)=}")
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tk = get_tokenizer(args.vllm_model, train=True)
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ctx_q_sep_tokens = tk(CTX_Q_SEP, add_special_tokens=False)["input_ids"]
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n_qa_sep_tokens = len(ctx_q_sep_tokens)
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self_qa_intx_tokens = tk(SELF_QA_INTX, add_special_tokens=False)["input_ids"]
|
||||
n_self_qa_intx_tokens = len(self_qa_intx_tokens)
|
||||
pre_ctx_tokens = tk(PRE_CTX, add_special_tokens=False)["input_ids"]
|
||||
n_pre_ctx_tokens = len(pre_ctx_tokens)
|
||||
sys_tokens = tk(system_template.split("\n")[0], add_special_tokens=False)[
|
||||
|
|
@ -299,15 +343,46 @@ def self_generate(
|
|||
keep_in_memory=True,
|
||||
)
|
||||
|
||||
ds = ds.map(
|
||||
augment_prompt,
|
||||
fn_kwargs={"closed_qa_prob": closed_qa_prob, "cot_prob": cot_prob},
|
||||
keep_in_memory=True,
|
||||
num_proc=16,
|
||||
)
|
||||
|
||||
ds = ds.map(
|
||||
add_paraphrasing_prompt,
|
||||
batched=True,
|
||||
fn_kwargs={
|
||||
"summ_prob": summ_prob,
|
||||
"struct_prob": struct_prob,
|
||||
},
|
||||
batch_size=50_000,
|
||||
keep_in_memory=True,
|
||||
)
|
||||
|
||||
ctxs = [sample["context"] for sample in ds]
|
||||
questions = [
|
||||
[add_closed_qa_prompt(q, closed_qa_prob) for q in sample["prompts"] if q]
|
||||
for sample in ds
|
||||
]
|
||||
|
||||
questions = ds["prompts"]
|
||||
# for sample in ds:
|
||||
# if ("type" not in sample) or (
|
||||
# "type" in sample and sample["type"] == "question"
|
||||
# ):
|
||||
# # for generated qa, apply to only "question" type
|
||||
# questions.append(
|
||||
# [
|
||||
# add_closed_qa_prompt(q, closed_qa_prob)
|
||||
# for q in sample["prompts"]
|
||||
# if q
|
||||
# ]
|
||||
# )
|
||||
# else:
|
||||
# questions.append([q for q in sample["prompts"] if q])
|
||||
|
||||
print(f"Loaded {len(ctxs)} contexts and {len(questions)} questions")
|
||||
|
||||
k = 16
|
||||
fpath = f"{SELF_GEN_DATA_DIR}/{args.vllm_model}_temp_{temp}_closed_qa_prob_{closed_qa_prob}/{ds_name}/{split}/ds{shard_name}"
|
||||
fpath = f"{SELF_GEN_DATA_DIR}/{args.vllm_model}_temp_{temp}_closed_qa_prob_{closed_qa_prob}_cot_prob_{cot_prob}_summ_prob_{summ_prob}_struct_prob_{struct_prob}/{ds_name}/{split}/ds{shard_name}"
|
||||
|
||||
chunk_size = 1_000
|
||||
for chunk_idx, start in enumerate(range(0, len(ctxs), chunk_size)):
|
||||
|
|
@ -316,11 +391,7 @@ def self_generate(
|
|||
chunk_ctxs = ctxs[start : start + chunk_size]
|
||||
chunk_questions = questions[start : start + chunk_size]
|
||||
chunk_messages = create_messages(
|
||||
chunk_ctxs,
|
||||
chunk_questions,
|
||||
args.vllm_model,
|
||||
SELF_GEN_SYSTEM_MSG,
|
||||
use_qa_template,
|
||||
chunk_ctxs, chunk_questions, args.vllm_model, SELF_GEN_SYSTEM_MSG
|
||||
)
|
||||
|
||||
if do_truncate:
|
||||
|
|
@ -334,7 +405,7 @@ def self_generate(
|
|||
truncate_middle_if_too_long(
|
||||
ids,
|
||||
max_length=MODEL_CTX_LEN[args.vllm_model],
|
||||
max_new_tokens=max_new_tokens,
|
||||
max_new_tokens=1024,
|
||||
)
|
||||
for ids in tokenized_contents["input_ids"]
|
||||
]
|
||||
|
|
@ -354,8 +425,8 @@ def self_generate(
|
|||
llm,
|
||||
temp,
|
||||
tk,
|
||||
ctx_q_sep_tokens,
|
||||
n_qa_sep_tokens,
|
||||
self_qa_intx_tokens,
|
||||
n_self_qa_intx_tokens,
|
||||
sys_tokens,
|
||||
n_sys_tokens,
|
||||
chunk_ctxs,
|
||||
|
|
@ -363,7 +434,6 @@ def self_generate(
|
|||
chunk_questions,
|
||||
chunk_messages,
|
||||
k,
|
||||
max_new_tokens,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -373,8 +443,8 @@ def execute_qa_generation(
|
|||
llm,
|
||||
temp,
|
||||
tk,
|
||||
ctx_q_sep_tokens,
|
||||
n_qa_sep_tokens,
|
||||
self_qa_intx_tokens,
|
||||
n_self_qa_intx_tokens,
|
||||
sys_tokens,
|
||||
n_sys_tokens,
|
||||
ctxs,
|
||||
|
|
@ -382,12 +452,11 @@ def execute_qa_generation(
|
|||
questions,
|
||||
messages,
|
||||
k,
|
||||
max_tokens,
|
||||
):
|
||||
completions = llm.chat(
|
||||
messages,
|
||||
sampling_params=SamplingParams(
|
||||
max_tokens=max_tokens,
|
||||
max_tokens=1024,
|
||||
logprobs=k,
|
||||
temperature=temp,
|
||||
seed=42,
|
||||
|
|
@ -395,8 +464,6 @@ def execute_qa_generation(
|
|||
skip_special_tokens=False,
|
||||
include_stop_str_in_output=True,
|
||||
),
|
||||
chat_template=tk.chat_template,
|
||||
chat_template_content_format="string",
|
||||
)
|
||||
|
||||
self_gen_data = {
|
||||
|
|
@ -459,13 +526,13 @@ def execute_qa_generation(
|
|||
|
||||
q_start = None
|
||||
for ii in range(
|
||||
len(prompt_ids) - n_qa_sep_tokens,
|
||||
len(prompt_ids) - n_self_qa_intx_tokens,
|
||||
-1,
|
||||
-1,
|
||||
):
|
||||
if prompt_ids[ii : ii + n_qa_sep_tokens] == ctx_q_sep_tokens:
|
||||
if prompt_ids[ii : ii + n_self_qa_intx_tokens] == self_qa_intx_tokens:
|
||||
# found the start of the user input
|
||||
q_start = ii + n_qa_sep_tokens
|
||||
q_start = ii + n_self_qa_intx_tokens
|
||||
break
|
||||
|
||||
# bos + question + eos + start model turn + response + eos
|
||||
|
|
@ -523,7 +590,7 @@ def execute_qa_generation(
|
|||
|
||||
if args.debug:
|
||||
for sample in samples:
|
||||
print(f"context={tk.decode(sample['ctx_ids'])}")
|
||||
# print(f"context={tk.decode(sample['ctx_ids'])}")
|
||||
print(f"QA={[tk.decode(ids) for ids in sample['input_ids']]}")
|
||||
|
||||
for input_ids, (start, end) in zip(
|
||||
|
|
@ -532,12 +599,6 @@ def execute_qa_generation(
|
|||
print(f"start={start}, end={end}")
|
||||
print(f"response={tk.decode(input_ids[start:end])}")
|
||||
|
||||
for input_ids, (start, end) in zip(
|
||||
sample["input_ids"], sample["prompt_start_end"]
|
||||
):
|
||||
print(f"start={start}, end={end}")
|
||||
print(f"prompt={tk.decode(input_ids[start:end])}")
|
||||
|
||||
print(f"logprobs_vals={[x.shape for x in sample['logprobs_vals']]}")
|
||||
print(f"logprobs_indices={[x.shape for x in sample['logprobs_indices']]}")
|
||||
for indices in sample["logprobs_indices"]:
|
||||
|
|
@ -614,22 +675,29 @@ def parse_args() -> argparse.Namespace:
|
|||
default=0.0,
|
||||
help="Probability of using closed QA prompt template (default: 0.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cot_prob",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Probability of using chain-of-thought prompt template (default: 0.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--summ_prob",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Probability of adding a summarization variant (default: 0.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--struct_prob",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Probability of adding a structuring variant (default: 0.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--do_truncate",
|
||||
action="store_true",
|
||||
help="Truncate contexts to fit model context length",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_new_tokens",
|
||||
type=int,
|
||||
default=1024,
|
||||
help="Maximum number of new tokens to generate (default: 1024)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--remove_qa_template",
|
||||
action="store_true",
|
||||
help="Remove the QA template from the prompt (default: False)",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
|
|
@ -670,15 +738,7 @@ if __name__ == "__main__":
|
|||
for ds_name, split in dataset_configs:
|
||||
print(f"Processing dataset: {ds_name}, split: {split}")
|
||||
self_generate(
|
||||
ds_name,
|
||||
split,
|
||||
args,
|
||||
llm,
|
||||
system_template=SYSTEM_TEMPLATE,
|
||||
parquet_file=None,
|
||||
do_truncate=args.do_truncate,
|
||||
use_qa_template=not args.remove_qa_template,
|
||||
max_new_tokens=args.max_new_tokens,
|
||||
ds_name, split, args, llm, SELF_GEN_SYSTEM_MSG, args.do_truncate
|
||||
)
|
||||
else:
|
||||
assert args.glob_pattern, (
|
||||
|
|
@ -693,8 +753,6 @@ if __name__ == "__main__":
|
|||
split=args.split,
|
||||
args=args,
|
||||
llm=llm,
|
||||
system_template=SYSTEM_TEMPLATE,
|
||||
system_template=SELF_GEN_SYSTEM_MSG,
|
||||
do_truncate=args.do_truncate,
|
||||
use_qa_template=not args.remove_qa_template,
|
||||
max_new_tokens=args.max_new_tokens,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ dependencies = [
|
|||
"pandas",
|
||||
"plotly",
|
||||
"rouge-score",
|
||||
"vllm==0.8.5",
|
||||
"vllm==0.8.4",
|
||||
"huggingface-hub[hf-transfer]>=0.32.0",
|
||||
"opt-einsum>=3.4.0",
|
||||
"kagglehub[hf-datasets]>=0.3.12",
|
||||
|
|
@ -37,6 +37,7 @@ dependencies = [
|
|||
"bitsandbytes>=0.46.1",
|
||||
"google-cloud-storage>=3.2.0",
|
||||
"wonderwords>=2.2.0",
|
||||
"tokenizers==0.21.0",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
|
|
|
|||
|
|
@ -21,32 +21,72 @@ SELF_GEN_SYSTEM_MSG = (
|
|||
)
|
||||
|
||||
|
||||
REPEAT_PROMPTS = [
|
||||
"Repeat the information.",
|
||||
"Repeat the information above.",
|
||||
"Repeat the above information.",
|
||||
# "Repeat exactly what was written.",
|
||||
"Duplicate the information.",
|
||||
"Echo the information. Do not output other words.",
|
||||
"Repeat the information provided.",
|
||||
"Copy the information. Reply with just the information.",
|
||||
"Replicate the given information precisely.",
|
||||
"Repeat the entire provided information above.",
|
||||
"Duplicate exactly the provided information.",
|
||||
"Repeat the information that appears above.",
|
||||
# "Reproduce exactly what appears above.",
|
||||
# "Repeat the article.",
|
||||
"Repeat verbatim the given information.",
|
||||
"Output the same information as provided.",
|
||||
"Output an exact copy of the information above.",
|
||||
"Provide a replica of the information.",
|
||||
"Output an identical copy of the information.",
|
||||
"Repeat the information without any changes.",
|
||||
"Repeat the provided information.",
|
||||
"Repeat the provided information above.",
|
||||
"Repeat precisely what is written in the information above.",
|
||||
SUMMARIZATION_PROMPTS = [
|
||||
"Summarize the article."
|
||||
"Provide a concise summary capturing the main idea and key detail.",
|
||||
"Write a one-paragraph executive summary of the passage. Avoid fluff.",
|
||||
"Create a bullet list of the core points from the text. No intro line.",
|
||||
"Summarize the content for a general audience with no domain knowledge.",
|
||||
"Produce a headline that best represents the passage.",
|
||||
"Give a headline and a subheadline separated by a newline summarizing the article.",
|
||||
"Write a tweet-length summary (max 240 characters, no hashtags).",
|
||||
"Condense the passage into a plain-language abstract.",
|
||||
"Explain the passage as if speaking to a 10-year-old.",
|
||||
"Summarize the information presented.",
|
||||
"Extract and list the three most critical facts. No extra wording.",
|
||||
"Provide a neutral summary (no opinion words) in one paragraph.",
|
||||
"Summarize emphasizing quantitative data only. Ignore narrative.",
|
||||
"Provide a facts-only summary: list raw data points, no interpretation.",
|
||||
"Summarize by extracting all key figures and associated units only.",
|
||||
"Provide a brief TL;DR starting with 'TL;DR:'.",
|
||||
"Summarize in exactly 25 words. If needed, rephrase to fit.",
|
||||
"Produce a question-and-answer style summary with 3 Q&A pairs capturing essence.",
|
||||
"Provide a summary that only uses the 600 most common English words.",
|
||||
"Create a concise abstract in academic tone.",
|
||||
"Write a marketing-style summary appealing to a broad audience.",
|
||||
"Summarize in the form of a newspaper lead (who, what, when, where, why).",
|
||||
"Write a summary that preserves all numbers and proper nouns verbatim.",
|
||||
"Produce a satirical-style summary (remain respectful).",
|
||||
"Summarize with a formal tone using no contractions.",
|
||||
"Summarize with an informal conversational tone",
|
||||
"Summarize as if reporting to a technical expert (dense, precise).",
|
||||
"Summarize as if explaining to a non-technical stakeholder (clear, simplified).",
|
||||
"Summarize including exactly one direct quote (short) from the text if present.",
|
||||
"Summarize into a structured JSON with keys: topic, key_points (array), tone. Do not add commentary.",
|
||||
"Write a poetic-style 2-line summary (no rhyme required).",
|
||||
"Summarize in exactly 3 bullet points.",
|
||||
"Summarize in question form: pose 2-3 questions that capture essence.",
|
||||
"Summarize as a press release opening paragraph.",
|
||||
"Summarize focusing on benefits to end users.",
|
||||
"Summarize avoiding any jargon; replace technical terms with common words.",
|
||||
"Produce a summary in exactly 2 haiku-style lines (not strict syllables).",
|
||||
"Summarize as if drafting the opening line of an encyclopedia entry.",
|
||||
]
|
||||
|
||||
COT_TEMPLATES = [
|
||||
"{input} Think out loud before giving an answer.",
|
||||
"{input} Think step by step to ensure a correct final answer.",
|
||||
"{input} List any assumptions, reason through the steps, then provide the final answer.",
|
||||
"{input} Explain your reasoning clearly first.",
|
||||
"{input} Reason carefully: identify what is known, what is needed, derive intermediate conclusions, then answer.",
|
||||
"{input} Enumerate relevant facts, derive implications, eliminate alternatives, then state the answer.",
|
||||
"{input} Think through possible interpretations, choose the most plausible, reason it out, then answer.",
|
||||
"{input} Consider edge cases or exceptions while reasoning step by step before finalizing your answer.",
|
||||
"{input} List data given, infer what is missing, derive needed values logically, then answer.",
|
||||
"{input} Use logical deduction step by step; avoid jumping to the conclusion. Conclude with the answer.",
|
||||
"{input} Reason transparently: gather, analyze, synthesize, conclude. Final answer after the reasoning.",
|
||||
]
|
||||
|
||||
STRUCTURING_PROMPTS = [
|
||||
f"Can you structure the information in the {format} format? Be sure to include precise information like any dates, times, names, and numerical values."
|
||||
for format in ["JSON", "YAML", "TOML", "bullet"]
|
||||
]
|
||||
|
||||
STRUCTURING_PROMPTS += [
|
||||
"Can you structure the information in plain text that is easy to read? Be sure to include precise information like any dates, times, names, and numerical values."
|
||||
]
|
||||
|
||||
|
||||
# for chunking
|
||||
CTX_AFFIXES = {
|
||||
"google/gemma-3-1b-it": {
|
||||
|
|
@ -120,9 +160,9 @@ DS_KWARGS = {
|
|||
),
|
||||
),
|
||||
"hotpot_qa": dict(
|
||||
train=dict(path="hotpotqa/hotpot_qa", name="fullwiki", split="train[900:]"),
|
||||
train=dict(path="hotpotqa/hotpot_qa", name="fullwiki", split="train"),
|
||||
validation=dict(
|
||||
path="hotpotqa/hotpot_qa", name="fullwiki", split="train[:900]"
|
||||
path="hotpotqa/hotpot_qa", name="fullwiki", split="validation[:900]"
|
||||
),
|
||||
test=dict(path="hotpotqa/hotpot_qa", name="fullwiki", split="validation"),
|
||||
),
|
||||
|
|
@ -133,16 +173,15 @@ DS_KWARGS = {
|
|||
),
|
||||
),
|
||||
"pwc": dict(
|
||||
train=dict(path="sggetao/PwC", split="train[900:]"),
|
||||
validation=dict(path="sggetao/PwC", split="train[:900]"),
|
||||
train=dict(path="sggetao/PwC", split="train"),
|
||||
validation=dict(path="sggetao/PwC", split="test[:900]"),
|
||||
test=dict(path="sggetao/PwC", split="test"),
|
||||
),
|
||||
"pwc_compact": dict(
|
||||
train=dict(
|
||||
path="parquet",
|
||||
data_files="data/raw_datasets/pwc_compact/train/ds.parquet",
|
||||
# correspond to skipping to first 900 samples in the original dataset
|
||||
split="train[60:]",
|
||||
split="train",
|
||||
),
|
||||
),
|
||||
"pwc_compact_tiny": dict(
|
||||
|
|
@ -158,8 +197,8 @@ DS_KWARGS = {
|
|||
validation=dict(path="sggetao/PwC", split="train[:900]"),
|
||||
),
|
||||
"squad": dict(
|
||||
train=dict(path="data/raw_datasets/squad", split="train[900:]"),
|
||||
validation=dict(path="data/raw_datasets/squad", split="train[:900]"),
|
||||
train=dict(path="data/raw_datasets/squad", split="train"),
|
||||
validation=dict(path="data/raw_datasets/squad", split="validation[:1000]"),
|
||||
test=dict(path="data/raw_datasets/squad", split="validation"),
|
||||
# train=dict(path="rajpurkar/squad", split="train[900:]"),
|
||||
# validation=dict(path="rajpurkar/squad", split="train[:900]"),
|
||||
|
|
@ -565,6 +604,11 @@ DS_KWARGS = {
|
|||
),
|
||||
# for negative training and evaluating
|
||||
"negative_nq": dict(
|
||||
train=dict(
|
||||
path="json",
|
||||
data_files="data/raw_datasets/negative_natural_questions/train.jsonl",
|
||||
split="train",
|
||||
),
|
||||
validation=dict(
|
||||
path="json",
|
||||
data_files="data/raw_datasets/negative_natural_questions/validation.jsonl",
|
||||
|
|
@ -607,6 +651,44 @@ DS_KWARGS = {
|
|||
train=dict(path="HuggingFaceTB/smoltalk", name="systemchats-30k", split="train")
|
||||
),
|
||||
"ctx-q-gsm": dict(train=dict(path="letta-ai/stateful-gsm-symbolic", split="train")),
|
||||
"automathtext_web": dict(
|
||||
dict(
|
||||
train=dict(
|
||||
path="math-ai/AutoMathText", name="web-0.50-to-1.00", split="train"
|
||||
)
|
||||
)
|
||||
),
|
||||
"automathtext_code_python": dict(
|
||||
dict(
|
||||
train=dict(
|
||||
path="math-ai/AutoMathText",
|
||||
name="code-python-0.50-to-1.00",
|
||||
split="train",
|
||||
)
|
||||
)
|
||||
),
|
||||
"automathtext_code_notebook": dict(
|
||||
dict(
|
||||
train=dict(
|
||||
path="math-ai/AutoMathText",
|
||||
name="code-jupyter-notebook-0.50-to-1.00",
|
||||
split="train",
|
||||
)
|
||||
)
|
||||
),
|
||||
"automathtext_arxiv": dict(
|
||||
dict(
|
||||
train=dict(
|
||||
path="math-ai/AutoMathText", name="arxiv-0.50-to-1.00", split="train"
|
||||
)
|
||||
)
|
||||
),
|
||||
"codeparrot": dict(
|
||||
dict(train=dict(path="transformersbook/codeparrot", split="train"))
|
||||
),
|
||||
"programming_books": dict(
|
||||
dict(train=dict(path="open-phi/programming_books_llama", split="train"))
|
||||
),
|
||||
}
|
||||
|
||||
# add ctx_numbers
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ def closed_qa_prompting(prompt: str):
|
|||
|
||||
|
||||
def chat_to_str(messages: list[dict[str, str]]):
|
||||
return "\n\n".join(
|
||||
return "Below is the chat history from the current user.\n\n" + "\n\n".join(
|
||||
[
|
||||
"Message from: {role}\n{content}".format(
|
||||
**{**m, "role": m["role"].capitalize()}
|
||||
|
|
@ -97,11 +97,9 @@ def get_preprocessing_fn(
|
|||
elif ds_name == "negative_nq":
|
||||
|
||||
def f(sample):
|
||||
q = sample["prompt"]
|
||||
prompt = closed_qa_prompting(q) if not is_eval else q
|
||||
return {
|
||||
"context": sample["context"],
|
||||
"prompt": prompt,
|
||||
"prompt": sample["prompt"],
|
||||
"response": sample["answer"],
|
||||
}
|
||||
|
||||
|
|
@ -199,6 +197,7 @@ def get_preprocessing_fn(
|
|||
}
|
||||
|
||||
elif ds_name == "booksum":
|
||||
# TODO: sample from SUMM_PROMPTS once improved data gen branch is merged
|
||||
prompt_templates = [
|
||||
"Summarization the provided text.",
|
||||
"# Summary",
|
||||
|
|
@ -299,6 +298,33 @@ def get_preprocessing_fn(
|
|||
response = sample["answer"]
|
||||
return {"context": ctx, "prompt": q, "response": response}
|
||||
|
||||
elif ds_name.startswith("automathtext"):
|
||||
|
||||
def f(sample):
|
||||
return {
|
||||
"context": sample["text"],
|
||||
"prompt": "PLACEHOLDER. PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
"response": "PLACEHOLDER PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
}
|
||||
|
||||
elif "codeparrot" == ds_name:
|
||||
|
||||
def f(sample):
|
||||
return {
|
||||
"context": sample["content"],
|
||||
"prompt": "PLACEHOLDER. PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
"response": "PLACEHOLDER. PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
}
|
||||
|
||||
elif "programming_books" == ds_name:
|
||||
|
||||
def f(sample):
|
||||
return {
|
||||
"context": sample["markdown"],
|
||||
"prompt": "PLACEHOLDER. PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
"response": "PLACEHOLDER. PLEASE GENERATE QUESTIONS FOR THIS DATASET.",
|
||||
}
|
||||
|
||||
if is_eval and (ds_name in EVAL_INTX_TEMPLATES):
|
||||
prompt_template = EVAL_INTX_TEMPLATES[ds_name]
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,6 @@ from ctx_to_lora.data.definitions import (
|
|||
DS_KWARGS,
|
||||
IGNORE_INDEX,
|
||||
RAW_DATA_DIR,
|
||||
REPEAT_PROMPTS,
|
||||
SELF_GEN_DATA_DIR,
|
||||
TRANSFORMED_DATA_DIR,
|
||||
)
|
||||
|
|
@ -93,10 +92,6 @@ def load_answers(ds_name, split):
|
|||
return ds
|
||||
|
||||
|
||||
def get_repeat_prompt():
|
||||
return random.choice(REPEAT_PROMPTS)
|
||||
|
||||
|
||||
def get_ds_kwargs(ds_name: str, split: str) -> dict[str, Any]:
|
||||
# custom logic for slicing iterable datasets
|
||||
take, skip = None, None
|
||||
|
|
@ -169,6 +164,7 @@ def load_and_process_dataset(
|
|||
ds_name: str,
|
||||
split: str,
|
||||
num_proc: int,
|
||||
remove_cols: bool = True,
|
||||
):
|
||||
logger.info(f"Loading dataset {ds_name} with split {split}...")
|
||||
try:
|
||||
|
|
@ -184,9 +180,11 @@ def load_and_process_dataset(
|
|||
raise ValueError(
|
||||
f"Failed to load dataset {ds_name} with split {split}. Error: {e}"
|
||||
)
|
||||
cols_to_remove = [
|
||||
col for col in ds.column_names if col not in COLS_TO_KEEP_PREPROCESSING
|
||||
]
|
||||
cols_to_remove = None
|
||||
if remove_cols:
|
||||
cols_to_remove = [
|
||||
col for col in ds.column_names if col not in COLS_TO_KEEP_PREPROCESSING
|
||||
]
|
||||
is_eval = split != "train"
|
||||
ds = ds.map(
|
||||
get_preprocessing_fn(ds_name, is_eval),
|
||||
|
|
|
|||
42
uv.lock
generated
42
uv.lock
generated
|
|
@ -719,6 +719,7 @@ dependencies = [
|
|||
{ name = "setuptools" },
|
||||
{ name = "tensorboard" },
|
||||
{ name = "tensorboardx" },
|
||||
{ name = "tokenizers" },
|
||||
{ name = "torchmetrics" },
|
||||
{ name = "transformers" },
|
||||
{ name = "vllm" },
|
||||
|
|
@ -754,9 +755,10 @@ requires-dist = [
|
|||
{ name = "setuptools" },
|
||||
{ name = "tensorboard" },
|
||||
{ name = "tensorboardx" },
|
||||
{ name = "tokenizers", specifier = "==0.21.0" },
|
||||
{ name = "torchmetrics" },
|
||||
{ name = "transformers", specifier = "==4.51.3" },
|
||||
{ name = "vllm", specifier = "==0.8.5" },
|
||||
{ name = "vllm", specifier = "==0.8.4" },
|
||||
{ name = "wandb" },
|
||||
{ name = "wonderwords", specifier = ">=2.2.0" },
|
||||
]
|
||||
|
|
@ -5064,27 +5066,27 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.21.1"
|
||||
version = "0.21.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "huggingface-hub" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/92/76/5ac0c97f1117b91b7eb7323dcd61af80d72f790b4df71249a7850c195f30/tokenizers-0.21.1.tar.gz", hash = "sha256:a1bb04dc5b448985f86ecd4b05407f5a8d97cb2c0532199b2a302a604a0165ab", size = 343256, upload-time = "2025-03-13T10:51:18.189Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/20/41/c2be10975ca37f6ec40d7abd7e98a5213bb04f284b869c1a24e6504fd94d/tokenizers-0.21.0.tar.gz", hash = "sha256:ee0894bf311b75b0c03079f33859ae4b2334d675d4e93f5a4132e1eae2834fe4", size = 343021, upload-time = "2024-11-27T13:11:23.89Z" }
|
||||
wheels = [
|
||||
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|
||||
[[package]]
|
||||
|
|
@ -5502,7 +5504,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "vllm"
|
||||
version = "0.8.5"
|
||||
version = "0.8.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
|
|
@ -5561,9 +5563,9 @@ dependencies = [
|
|||
{ name = "xformers", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||
{ name = "xgrammar", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5c/37/7846c244e6637c15f6d5ed6d3f572eedd02ed8ab80aec8c3eb228f35b23c/vllm-0.8.5.tar.gz", hash = "sha256:c7e04d1046304397b4580334038b558fe491af155fdea508224f140172cf9a82", size = 7340498, upload-time = "2025-04-28T23:59:48.417Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e6/d6/9d412cdaa92c3ab6250cef51217d37395b2aa372c6c14f90b1668adbbf63/vllm-0.8.4.tar.gz", hash = "sha256:522b13dd16c6c773dec0cb4c42ea591623d03ef94d16db8128ece2600017e6ac", size = 6667631, upload-time = "2025-04-15T00:30:10.147Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/26/7e/08d7a75c47792fd07d02b2be6fe6adbb29dfcbd31e94784844d7f730d0bc/vllm-0.8.5-cp38-abi3-manylinux1_x86_64.whl", hash = "sha256:74bfe92953bee1269c1e1c27827bc156777751cdd6a3457ee8e27dd8ebf1e247", size = 326421025, upload-time = "2025-04-28T23:59:40.401Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8e/cb/03dc1299e0456ff3d58a11f63682ef29aaf5b1bd7f21bfe0690d7ce6fc40/vllm-0.8.4-cp38-abi3-manylinux1_x86_64.whl", hash = "sha256:e346749ee8df48cdcd935d00a7fc123a1e17d9904b064401e74fc6ad73b8104a", size = 294098962, upload-time = "2025-04-15T00:30:02.293Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue