""" - Used in RPv2 exploration, including: - Plot certain quality signals - Get doc/char counts (e.g. after filtering) - Store minhashes of filtered documents for further dedup """ import gzip import orjson import numpy as np import pandas as pd import os import matplotlib.pyplot as plt from tqdm import tqdm import copy import multiprocessing import random import pathlib import seaborn as sns from rules.rules import gopher_rules_pass import pyarrow ROOT_PATH = "/home1/BharatGPT_Data/RedPajamaV2" DATA_ROOT_PATH = "/home1/BharatGPT_Data/RedPajamaV2/data" PLOTS_ROOT_PATH = "/home1/BharatGPT_Data/RedPajamaV2/plots" SNAPSHOT = "2023-14" LANGUAGE = "en" PARTITION_KEY = "head" SIGNALS_DIR = os.path.join(DATA_ROOT_PATH, "quality_signals", SNAPSHOT) DUPLICATES_DIR = os.path.join(DATA_ROOT_PATH, "duplicates", SNAPSHOT) MINHASH_DIR = os.path.join(DATA_ROOT_PATH, "minhash", SNAPSHOT) NUM_CORES = 60 SEED = 2024 DO_PLOT = False COUNT_ONLY = True STORE_SIGNALS = False OUTPUT_FILES = False # constants for random 100 NUM_SHARDS_PROCESSED = 100 if DO_PLOT: PLOTS_DIR = os.path.join(PLOTS_ROOT_PATH, PARTITION_KEY, f"random_{NUM_SHARDS_PROCESSED}_gopher", "quality_rep_log") # makes dir if not present NUM_DOCS_HEAD = 2533743 NUM_DOCS_MIDDLE = 3722022 NUM_CHARS_HEAD = 12932890455 NUM_CHARS_MIDDLE = 17883781733 NUM_SHARDS_PROCESSED = 5000 if OUTPUT_FILES: OUT_DIR = os.path.join(DATA_ROOT_PATH, f"minhash_filtered", SNAPSHOT) # makes dir if not present random.seed(SEED) np.random.seed(SEED) assert sorted(os.listdir(SIGNALS_DIR)) == sorted(os.listdir(DUPLICATES_DIR)) assert (not DO_PLOT) or STORE_SIGNALS # DO_PLOT implies STORE_SIGNALS if COUNT_ONLY: assert DO_PLOT == False, "Plotting requires storing signal values" all_shards_counts = { "doc_count": 0, "char_count": 0 } elif STORE_SIGNALS: all_shards_signals = { "ccnet_perplexity": [], # https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2 # mentions that data is raw (no deduplicated) # but based on doc length it looks line-deduped # so we use ccnet_length and not ccnet_original_length "ccnet_length": [], "rps_doc_stop_word_fraction": [], "rps_doc_lorem_ipsum": [] } all_shards_signals_empty = copy.deepcopy(all_shards_signals) # non-parallelized # for shard in tqdm(sorted(os.listdir(SIGNALS_DIR))[:10]): # signals_path = os.path.join(SIGNALS_DIR, shard, f"{LANGUAGE}_{PARTITION}.signals.json.gz") # duplicates_path = os.path.join(DUPLICATES_DIR, shard, f"{LANGUAGE}_{PARTITION}.duplicates.parquet") # shard_dups = pd.read_parquet(duplicates_path) # shard_dups_set = set(shard_dups["doc_id"].tolist()) # with gzip.open(signals_path, 'r') as signals_file: # for line in signals_file: # signals_dict = json.loads(line) # if signals_dict["id"] not in shard_dups_set: # # print(json.dumps(signals_dict, indent=4)) # for k in list(all_shards_signals.keys()): # assert len(signals_dict["quality_signals"][k]) == 1 # assert len(signals_dict["quality_signals"][k][0]) == 3 # all_shards_signals[k].append(signals_dict["quality_signals"][k][0][-1]) partitions_dict = { "head": ["head"], "middle": ["middle"], "head_middle": ["head", "middle"], } for partition in partitions_dict[PARTITION_KEY]: def process_shard(shard): # check if done already if OUTPUT_FILES: out_path_shard = os.path.join(OUT_DIR, shard) out_file_path = os.path.join(out_path_shard, f"{LANGUAGE}_{partition}_filtered.minhash.parquet") if os.path.exists(out_file_path): raise Exception("ERROR: output file already present") return [] minhash_path = os.path.join(MINHASH_DIR, shard, f"{LANGUAGE}_{partition}.minhash.parquet") try: df = pd.read_parquet(minhash_path) except pyarrow.lib.ArrowInvalid as __e: # occurs with empty file print(f"ERROR with shard {shard}: empty minhash file") return [] signals_path = os.path.join(SIGNALS_DIR, shard, f"{LANGUAGE}_{partition}.signals.json.gz") try: duplicates_path = os.path.join(DUPLICATES_DIR, shard, f"{LANGUAGE}_{partition}.duplicates.parquet") shard_dups = pd.read_parquet(duplicates_path, columns=["doc_id"]) shard_dups_set = set(shard_dups["doc_id"].tolist()) except pyarrow.lib.ArrowInvalid as __e: # occurs with empty file shard_dups_set = set() results = None if COUNT_ONLY: results = { "doc_count": 0, "char_count": 0 } elif STORE_SIGNALS: results = copy.deepcopy(all_shards_signals_empty) elif OUTPUT_FILES: results = [] # filtered indices idx = -1 with gzip.open(signals_path, 'r') as signals_file: for line in signals_file: idx += 1 signals_dict = orjson.loads(line) if signals_dict["id"] not in shard_dups_set and gopher_rules_pass(signals_dict): """ Note about exact duplicates: https://github.com/togethercomputer/RedPajama-Data/issues/84#issuecomment-1840299911 One copy remains with this method """ if COUNT_ONLY: results["doc_count"] += 1 results["char_count"] += signals_dict["quality_signals"]["ccnet_length"][0][2] elif STORE_SIGNALS: for k in list(results.keys()): assert len(signals_dict["quality_signals"][k]) == 1 assert len(signals_dict["quality_signals"][k][0]) == 3 results[k].append(signals_dict["quality_signals"][k][0][2]) elif OUTPUT_FILES: results.append(idx) if OUTPUT_FILES: df = df.iloc[results, :] pathlib.Path(out_path_shard).mkdir(parents=True, exist_ok=True) df.to_parquet(os.path.join(out_path_shard, f"{LANGUAGE}_{partition}_filtered.minhash.parquet")) return results with multiprocessing.Pool(NUM_CORES) as pool: shards_list = os.listdir(SIGNALS_DIR) all_results = list(tqdm(pool.imap(process_shard, random.sample(sorted(shards_list), k=NUM_SHARDS_PROCESSED)), total=NUM_SHARDS_PROCESSED)) for results in all_results: if COUNT_ONLY: for k in list(all_shards_counts.keys()): all_shards_counts[k] += results[k] elif STORE_SIGNALS: for k in list(all_shards_signals.keys()): all_shards_signals[k].extend(results[k]) # print(json.dumps(all_shards_signals)) if COUNT_ONLY: print(all_shards_counts) if DO_PLOT: pathlib.Path(PLOTS_DIR).mkdir(parents=True, exist_ok=True) for k, v in all_shards_signals.items(): # plt.hist(v, bins=100) # linear # log with plt # hist, bins = np.histogram(v, bins=100) # logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins)) # plt.hist(v, weights=np.ones(len(v))/(NUM_DOCS_HEAD+NUM_DOCS_MIDDLE), bins=logbins) # log # plt.xscale("log") # unweighted sns.histplot(x=v, bins=100, log_scale=True) # weights: docs percentage of head_middle # sns.histplot(x=v, weights=np.ones(len(v))*100/(NUM_DOCS_HEAD+NUM_DOCS_MIDDLE), bins=100, log_scale=True) # weight by char percentage # sns.histplot(x=v, weights=np.array(all_shards_signals["ccnet_length"])*100/(NUM_CHARS_HEAD+NUM_CHARS_MIDDLE), bins=100, log_scale=True) plt.savefig(os.path.join(PLOTS_DIR, f"{k}.png")) plt.close() # print(len(all_shards_signals["ccnet_perplexity"])) # print(sum(all_shards_signals["ccnet_length"])) # print(type(all_shards_signals["ccnet_length"][0]))