still
wandb: 429 encountered (Filestream rate limit exceeded, retrying in 4.7 seconds.), retrying request
wandb: 429 encountered (Filestream rate limit exceeded, retrying in 2.0 seconds.), retrying request
wandb: 429 encountered (Filestream rate limit exceeded, retrying in 4.1 seconds.), retrying request
My runs aren’t even logging, they only log at the end a single number. Why is wandb complaining?
This is the code:
def experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights():
"""
Get divs using pt ft, pt (rand, rand ft?)
- div c4
- div wt = wt-103
Then with unioned datasets
- div c4+wt, uniform [0.5, 0.5]
- # div c4+wt, data set size proportions (using GBs)
- div c4+wt, respect doremi
- div c4+wt, respect the pile
- div c4+wt, respect gpt3 weights
then repeat all with pt (no ft)
"""
import random
from diversity.data_mixtures import get_uniform_data_mixture_for_c4_wt103, get_doremi_based_data_mixture_for_c4_wt103, get_llama_v1_based_data_mixture_for_c4_wt103
probabilities = []
data_mixture_name = None
streaming = True
data_files = [None]
seed = 0
# -- Setup wandb
import wandb
# - Dryrun
# mode = 'dryrun'; num_batches = 3
mode = 'dryrun'; num_batches = 3; seed = random.randint(0, 2**32 - 1)
# - Online (real experiment)
mode='online'; num_batches = 600; seed = random.randint(0, 2**32 - 1)
# - c4 wt single
# path, name = 'c4', 'en'
# path, name = "wikitext", 'wikitext-103-v1'
# path, name = 'Skylion007/openwebtext', None
# - c4 wt mix
# path, name, data_files = ['c4', 'wikitext'], ['en', 'wikitext-103-v1'], [None, None]
# probabilities, data_mixture_name = get_uniform_data_mixture_for_c4_wt103()
# probabilities, data_mixture_name = get_doremi_based_data_mixture_for_c4_wt103()
# probabilities, data_mixture_name = get_llama_v1_based_data_mixture_for_c4_wt103()
# probabilities, data_mixture_name = [0.75, 0.25], '[0.75, 0.25]'
# probabilities, data_mixture_name = [0.25, 0.75], '[0.25, 0.75]'
# - pile, pile cc single
# path, name = 'EleutherAI/pile', 'all'
# path, name = 'conceptofmind/pile_cc', 'sep_ds'
# - 5 subsets of pile using hf data set viewer (parquet))
from diversity.pile_subset_urls import urls_hacker_news, urls_nih_exporter, urls_pubmed, urls_uspto
# path, name, data_files = 'conceptofmind/pile_cc', 'sep_ds', [None]
# path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news
# path, name, data_files = 'parquet', 'nih_exporter', urls_nih_exporter
# path, name, data_files = 'parquet', 'pubmed', urls_pubmed
path, name, data_files = 'parquet', 'uspto', urls_uspto
# - 5 subsets of the pile interleaved
# from diversity.pile_subset_urls import urls_hacker_news, urls_nih_exporter, urls_pubmed, urls_uspto
# from diversity.data_mixtures import get_uniform_data_mixture_5subsets_of_pile, get_doremi_data_mixture_5subsets_of_pile, get_llama_v1_data_mixtures_5subsets_of_pile
# path, name, data_files = ['conceptofmind/pile_cc'] + ['parquet'] * 4, ['sep_ds'] + ['hacker_news', 'nih_exporter', 'pubmed', 'uspto'], [None] + [urls_hacker_news, urls_nih_exporter, urls_pubmed, urls_uspto]
# probabilities, data_mixture_name = get_uniform_data_mixture_5subsets_of_pile()
# probabilities, data_mixture_name = get_llama_v1_data_mixtures_5subsets_of_pile(name)
# probabilities, data_mixture_name = get_doremi_data_mixture_5subsets_of_pile(name)
# - not changing
batch_size = 512
today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')
run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'
print(f'\n---> {run_name=}\n')
# - Init wandb
debug: bool = mode == 'dryrun'
run = wandb.init(mode=mode, project="beyond-scale", name=run_name, save_code=True)
wandb.config.update({"num_batches": num_batches, "path": path, "name": name, "today": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files, 'seed': seed})
# run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891
print(f'{debug=}')
print(f'{wandb.config=}')
# -- Get probe network
from datasets import load_dataset
from datasets.iterable_dataset import IterableDataset
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
probe_network = GPT2LMHeadModel.from_pretrained("gpt2")
device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu")
probe_network = probe_network.to(device)
# -- Get data set
def my_load_dataset(path, name, data_files=data_files):
print(f'{path=} {name=} {streaming=} {data_files=}')
if path == 'json' or path == 'bin' or path == 'csv':
print(f'{data_files_prefix+name=}')
return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split="train").with_format("torch")
elif path == 'parquet':
print(f'{data_files=}')
return load_dataset(path, data_files=data_files, streaming=streaming, split="train").with_format("torch")
else:
return load_dataset(path, name, streaming=streaming, split="train").with_format("torch")
# - get data set for real now
if isinstance(path, str):
dataset = my_load_dataset(path, name, data_files)
else:
# -Interleaving datasets
print('- Interleaving datasets')
datasets = [my_load_dataset(path, name, data_files).with_format("torch") for path, name, data_files in zip(path, name, data_files)]
# datasets = [my_load_dataset(path, name).with_format("torch") for path, name in zip(path, name)]
if any('parquet' == p for p in path) or path == 'parquest': # idk why I need to do this, I checked very carefully and deleted all columns so interleaved data set matched but when doing this with c4 & wikitext it fails but with the parquet it works https://discuss.huggingface.co/t/why-does-deleting-the-columns-before-giving-it-to-interleave-work-but-sometimes-it-does-not-work/50879
dataset_descriptions = [dataset.description for dataset in datasets] # print description if available
print(f'{dataset_descriptions=}')
# - make sure all datasets have the same columns to avoid interleave to complain
all_columns = [col for dataset in datasets for col in dataset.column_names]
print(f'{all_columns=}')
columns_to_remove = [col for dataset in datasets for col in dataset.column_names if col != 'text']
columns_to_remove = list(set(columns_to_remove)) # remove duplicates
print(f'{columns_to_remove=}')
datasets = [dataset.remove_columns(columns_to_remove) for dataset in datasets]
# - interleave
print(f'{probabilities=}')
dataset_descriptions = [dataset.description for dataset in datasets] # print description if available
print(f'{dataset_descriptions=}')
dataset = interleave_datasets(datasets, probabilities)
# dataset = dataset.remove_columns(columns_to_remove)
print(f'{dataset=}')
print(f'{dataset.column_names=}')
print(f'{dataset=}')
print(f'{type(dataset)=}')
# datasets.iterable_dataset.IterableDataset
# datasets.arrow_dataset.Dataset
# dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode
raw_text_batch = dataset.take(batch_size) if streaming else dataset.select(range(batch_size))
print(f'{raw_text_batch=}')
print(f'{next(iter(raw_text_batch))=}')
column_names = next(iter(raw_text_batch)).keys()
print(f'{column_names=}')
# - Prepare functions to tokenize batch
def preprocess(examples):
return tokenizer(examples["text"], padding="max_length", max_length=128, truncation=True, return_tensors="pt")
remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader
def map(batch):
return batch.map(preprocess, batched=True, remove_columns=remove_columns)
tokenized_batch = map(raw_text_batch)
print(f'{next(iter(tokenized_batch))=}')
# -- Compute diversity coefficient
print(f'-- Compute diversity coefficient')
print(f'{seed=}, {streaming=}')
# - Debug run
# results: dict = get_diversity_coefficient(dataset, map, probe_network, num_batches=3, seed=seed, debug=True, shuffle=False) # (quick debug) hardcoded for debugging
# results: dict = get_diversity_coefficient(dataset, map, probe_network, num_batches=3, seed=seed, debug=True, shuffle=True) # (slow debug) hardcoded for debugging
# results: dict = get_diversity_coefficient(dataset, map, probe_network, num_batches=3, seed=seed, debug=False, shuffle=False) # (real) hardcoded for debugging
# - Real run
# assert not debug, f'Err: {debug=} for real run'
results: dict = get_diversity_coefficient(dataset, map, probe_network, num_batches=num_batches, seed=seed, debug=debug, shuffle=False)
# results: dict = get_diversity_coefficient(dataset, map, probe_network, num_batches=num_batches, seed=seed, debug=debug, shuffle=True)
# - Log results
div_coeff, div_coeff_ci = results['div_coeff'], results['div_coeff_ci']
print(f'{div_coeff=} {div_coeff_ci=}')
wandb.log({'div_coeff': div_coeff, 'div_coeff_ci': div_coeff_ci})
# -- Save results or not
save_results = True
if save_results:
output_dir = Path(f'~/data/div_coeff/{today}').expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
np.save(output_dir / f'distance_matrix{today}.npy', results['distance_matrix'])
np.save(output_dir / f'results{today}.npy', results)
# Save results as a pretty-printed JSON
results = {key: str(value) for key, value in results.items()}
with open(output_dir / f'results{today}.json', 'w') as f:
json.dump(results, f, indent=4)
# - wandb save
base_path = str(output_dir.parent)
wandb.save(str(output_dir / f'distance_matrix{today}.npy'), base_path=base_path)
wandb.save(str(output_dir / f'results{today}.npy'), base_path=base_path)
wandb.save(str(output_dir / f'results{today}.json'), base_path=base_path)
wandb.save(__file__)
Those are the oly times I call wandb @_scott
especially since It’s blocking training which hasn’t even been started - so I’ve no idea why its reaching arbitrary ratelimits