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Upload create_dataset.py with huggingface_hub

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  1. create_dataset.py +150 -0
create_dataset.py ADDED
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+ import asyncio
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+ from collections import defaultdict
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+ from dataclasses import dataclass
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+ import json
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+ import random
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+ import pandas as pd
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+ from inference_swarm import InferenceSwarm, InferenceSwarmConfig
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+ from huggingface_hub import AsyncInferenceClient
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+ from transformers import AutoTokenizer, HfArgumentParser
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+ from tqdm.asyncio import tqdm_asyncio
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+ from datasets import load_dataset, Dataset
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+ import time
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+ from huggingface_hub import HfApi
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+
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+ api = HfApi()
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+
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+
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+ @dataclass
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+ class Args:
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+ max_samples: int = 128
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+ """The maximum umber of samples to generate (use -1 for all))"""
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+ max_new_tokens: int = 1500
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+ """Max new tokens"""
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+ temperature: float = 1.0
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+ """Generation temperature"""
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+ constitution_path: str = "examples/hh/constitution.json"
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+ """Path to the constitution"""
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+ repo_id: str = "cai-conversation-dev"
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+ """The repo id to push to"""
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+ timestamp: bool = True
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+ """Whether to add a timestamp to the repo_id"""
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+ push_to_hub: bool = False
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+ """Whether to push to hub"""
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+
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+
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+ parser = HfArgumentParser((Args, InferenceSwarmConfig))
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+ args, isc = parser.parse_args_into_dataclasses()
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+ if args.timestamp:
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+ args.repo_id += str(int(time.time()))
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+ tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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+ tokenizer.add_special_tokens({"sep_token": "", "cls_token": "", "mask_token": "", "pad_token": "[PAD]"})
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+ with open(args.constitution_path) as f:
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+ data = json.load(f)
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+ constitutions = data["constitutions"]
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+ system_chat = data["system_chat"]
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+ system_chat = [item for sublist in system_chat for item in sublist]
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+ ds = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")
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+ for key in ds:
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+ max_samples = len(ds[key]) if args.max_samples == -1 else args.max_samples
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+ ds[key] = ds[key].select(range(max_samples))
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+
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+
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+ def extract(example):
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+ # Extract the "Human:" prompts
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+ example = example["chosen"]
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+ split_text = example.split("\n\n")
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+ for segment in split_text:
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+ if "Human:" in segment:
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+ return {"prompt": segment.split(": ")[1]}
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+
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+
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+ ds = ds.map(extract)
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+ ds.remove_columns(["chosen", "rejected"])
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+ rate_limit = 500 * isc.instances
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+ semaphore = asyncio.Semaphore(rate_limit)
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+ with InferenceSwarm(isc) as inference_swarm:
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+ client = AsyncInferenceClient(model=inference_swarm.endpoint)
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+ STOP_SEQ = ["User:", "###", "<|endoftext|>"]
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+
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+ async def process_text(split, i, task):
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+ chat = system_chat.copy()
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+ constitution = random.choice(constitutions)
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+ token_length = 0
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+ row = {}
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+ for prompt, prompt_key, response_key in [
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+ (task, "init_prompt", "init_response"),
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+ (constitution["critic"], "critic_prompt", "critic_response"),
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+ (constitution["revision"], "revision_prompt", "revision_response"),
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+ ]:
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+ async with semaphore:
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+ prompt_dict = {"role": "user", "content": prompt}
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+ chat.append(prompt_dict)
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+ completion = await client.text_generation(
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+ prompt=tokenizer.apply_chat_template(chat, tokenize=False),
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+ max_new_tokens=args.max_new_tokens,
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+ stop_sequences=STOP_SEQ,
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+ temperature=args.temperature,
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+ )
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+ for stop_seq in STOP_SEQ:
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+ if completion.endswith(stop_seq):
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+ completion = completion[: -len(stop_seq)].rstrip()
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+ response_dict = {"role": "assistant", "content": completion}
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+ chat.append(response_dict)
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+ token_length += len(tokenizer.encode(completion))
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+ row[prompt_key] = prompt
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+ row[response_key] = completion
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+ return split, i, token_length, row
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+
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+ async def main():
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+ start_time = time.time()
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+ tasks = [process_text(split, idx, row["prompt"]) for split in ds for idx, row in enumerate(ds[split])]
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+ print(f"WARNING: the first generation can hang like this for up to 1 hour because it will finish the first two turns of conversation of the entire dataset")
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+ results = await tqdm_asyncio.gather(*tasks)
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+ end_time = time.time()
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+
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+ total_duration = end_time - start_time
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+ total_tokens = sum(result[2] for result in results)
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+ overall_tokens_per_second = total_tokens / total_duration if total_duration > 0 else 0
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+ print(f"Overall Tokens per Second: {overall_tokens_per_second}")
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+ all_ds = defaultdict(lambda: defaultdict(list))
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+ for result in results:
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+ [all_ds[result[0]][key].append(value) for key, value in result[3].items()]
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+
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+ def process(example):
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+ return {
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+ "prompt": example["init_prompt"].strip(),
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+ "messages": [
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+ {"role": "user", "content": example["init_prompt"].strip()},
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+ {"role": "assistant", "content": example["revision_response"].strip()},
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+ ],
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+ "chosen": [
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+ {"role": "user", "content": example["init_prompt"].strip()},
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+ {"role": "assistant", "content": example["revision_response"].strip()},
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+ ],
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+ "rejected": [
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+ {"role": "user", "content": example["init_prompt"].strip()},
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+ {"role": "assistant", "content": example["init_response"].strip()},
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+ ],
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+ }
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+
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+ for split in all_ds:
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+ df = pd.DataFrame(all_ds[split])
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+ print("=" * 10 + split + "=" * 10)
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+ print(df)
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+ post_ds = Dataset.from_dict(all_ds[split])
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+ post_ds = post_ds.map(process)
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+ if args.push_to_hub:
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+ post_ds.select(range(len(post_ds) // 2)).push_to_hub(args.repo_id, split=f"{split}_sft")
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+ post_ds.select(range(len(post_ds) // 2, len(post_ds))).push_to_hub(args.repo_id, split=f"{split}_prefs")
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+ if "/" not in args.repo_id: # find the current user
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+ repo_id = f"{api.whoami()['name']}/{args.repo_id}"
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+ for file, name in zip([__file__, args.constitution_path], ["create_dataset.py", "constitution.json"]):
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+ api.upload_file(
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+ path_or_fileobj=file,
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+ path_in_repo=name,
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+ repo_id=repo_id,
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+ repo_type="dataset",
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+ )
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+
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+ asyncio.run(main())