From 3ec9eddd5b8def15d09bf65a92f5016424bd5673 Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Tue, 10 Oct 2023 10:20:23 -0500 Subject: [PATCH 01/47] fix import error by moving resumable pipeline to function (#376) Signed-off-by: Xue, Chendi --- RecDP/pyrecdp/primitives/llmutils/filter.py | 4 +++- RecDP/pyrecdp/primitives/llmutils/profanity_filter.py | 2 +- RecDP/pyrecdp/primitives/llmutils/text_fixer.py | 2 +- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/RecDP/pyrecdp/primitives/llmutils/filter.py b/RecDP/pyrecdp/primitives/llmutils/filter.py index be2e10873..5e89a4ddf 100644 --- a/RecDP/pyrecdp/primitives/llmutils/filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/filter.py @@ -1,6 +1,5 @@ import argparse -from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.core.utils import Timer from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter @@ -14,6 +13,7 @@ def filter_by_blocklist_spark(spark_df): def filter_by_blocklist(data_dir, data_file_type, out_dir): from pyrecdp.primitives.operations import URLFilter + from pyrecdp.LLM import ResumableTextPipeline if data_file_type == 'jsonl': reader = JsonlReader(data_dir) @@ -41,6 +41,7 @@ def filter_by_bad_words_spark(spark_df, language="en"): def filter_by_bad_words(data_dir, data_file_type, out_dir, language="en"): from pyrecdp.primitives.operations import BadwordsFilter + from pyrecdp.LLM import ResumableTextPipeline if data_file_type == 'jsonl': reader = JsonlReader(data_dir) @@ -68,6 +69,7 @@ def filter_by_length_spark(spark_df, minimum_length=100, maximum_length=-1): def filter_by_length(data_dir, data_file_type, out_dir, minimum_length=100, maximum_length=-1): from pyrecdp.primitives.operations import LengthFilter + from pyrecdp.LLM import ResumableTextPipeline if data_file_type == 'jsonl': reader = JsonlReader(data_dir) diff --git a/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py b/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py index f670fe6aa..bb4de85b7 100644 --- a/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py @@ -1,6 +1,5 @@ import argparse -from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.core.utils import Timer from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter @@ -14,6 +13,7 @@ def profanity_filter_spark(spark_df): def profanity_filter(data_dir, data_file_type, out_dir): from pyrecdp.primitives.operations import ProfanityFilter + from pyrecdp.LLM import ResumableTextPipeline if data_file_type == 'jsonl': reader = JsonlReader(data_dir) diff --git a/RecDP/pyrecdp/primitives/llmutils/text_fixer.py b/RecDP/pyrecdp/primitives/llmutils/text_fixer.py index e3c531f1d..c0c527ba1 100644 --- a/RecDP/pyrecdp/primitives/llmutils/text_fixer.py +++ b/RecDP/pyrecdp/primitives/llmutils/text_fixer.py @@ -1,6 +1,5 @@ import argparse -from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.core.utils import Timer from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter from pyrecdp.primitives.operations import TextFix @@ -13,6 +12,7 @@ def text_fixer_spark(spark_df, text_type='html'): def text_fixer(data_dir, data_file_type, out_dir, text_type='html'): + from pyrecdp.LLM import ResumableTextPipeline if data_file_type == 'jsonl': reader = JsonlReader(data_dir) From 308fa7a723243f4b9895c642d2214bca20cb3a81 Mon Sep 17 00:00:00 2001 From: ChaoJun Zhang Date: Wed, 11 Oct 2023 00:38:27 +0800 Subject: [PATCH 02/47] [v1.2][ISSUE-306] PII Remove support remove entity types specified by the user. (#371) * pii remove operator support remove types * Update text_pii_remove.py by adding entity_types to settings --------- Co-authored-by: Chendi.Xue --- .../pii/detect/name_password_detection.py | 12 +- .../primitives/llmutils/pii/detect/utils.py | 13 ++ .../primitives/llmutils/pii/pii_detection.py | 21 ++- .../pyrecdp/primitives/llmutils/pii_remove.py | 9 +- .../primitives/operations/text_pii_remove.py | 61 +++++--- RecDP/tests/test_llmutils.py | 133 ++++++++++-------- 6 files changed, 161 insertions(+), 88 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py diff --git a/RecDP/pyrecdp/primitives/llmutils/pii/detect/name_password_detection.py b/RecDP/pyrecdp/primitives/llmutils/pii/detect/name_password_detection.py index 2ac25cdeb..18904a995 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii/detect/name_password_detection.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii/detect/name_password_detection.py @@ -1,21 +1,25 @@ from transformers import Pipeline +from .utils import PIIEntityType -def detect_name_password(content: str, pipeline: Pipeline): +def detect_name_password(content: str, pipeline: Pipeline, entity_types=None): """Detects name and password in a string using bigcode/starpii model Args: - pipe: a transformer model + entity_types: detection types + pipeline: a transformer model content (str): A string containing the text to be analyzed. - sentences ([str]): A string array Returns: A list of dicts containing the tag type, the matched string, and the start and end indices of the match. """ + if entity_types is None: + entity_types = [PIIEntityType.NAME, PIIEntityType.PASSWORD] matches = [] try: for entity in pipeline(content): entity_group = entity["entity_group"] - if "NAME" == entity_group or "PASSWORD" == entity_group: + if ("NAME" == entity_group and PIIEntityType.NAME in entity_types) or \ + ("PASSWORD" == entity_group and PIIEntityType.PASSWORD in entity_types): matches.append( { "tag": entity_group, diff --git a/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py b/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py new file mode 100644 index 000000000..55a618c26 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py @@ -0,0 +1,13 @@ +from enum import Enum, auto + + +class PIIEntityType(Enum): + IP_ADDRESS = auto() + NAME = auto() + EMAIL = auto() + PHONE_NUMBER = auto() + PASSWORD = auto() + + @classmethod + def all(cls): + return [member for name, member in PIIEntityType.__members__.items()] diff --git a/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py b/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py index 1a8d325dc..797e225c4 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py @@ -2,18 +2,29 @@ from .detect.emails_detection import detect_email from .detect.phones_detection import detect_phones from .detect.name_password_detection import detect_name_password +from .detect.utils import PIIEntityType from transformers import Pipeline +from typing import List -def scan_pii_text(text:str, pipeline: Pipeline): + +def scan_pii_text(text: str, pipeline: Pipeline, entity_types: List[PIIEntityType] = None): result = [] # use a regex to detect ip addresses - result = result + detect_ip(text) + + if entity_types is None: + entity_types = PIIEntityType.all() + + if PIIEntityType.IP_ADDRESS in entity_types: + result = result + detect_ip(text) # use a regex to detect emails - result = result + detect_email(text) + if PIIEntityType.EMAIL in entity_types: + result = result + detect_email(text) # for phone number use phonenumbers tool - result = result + detect_phones(text) + if PIIEntityType.PHONE_NUMBER in entity_types: + result = result + detect_phones(text) # for phone number use phonenumbers tool - result = result + detect_name_password(text, pipeline) + if PIIEntityType.NAME in entity_types or PIIEntityType.PASSWORD in entity_types: + result = result + detect_name_password(text, pipeline, entity_types) return result diff --git a/RecDP/pyrecdp/primitives/llmutils/pii_remove.py b/RecDP/pyrecdp/primitives/llmutils/pii_remove.py index 168d1657e..c3c0250d1 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii_remove.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii_remove.py @@ -2,11 +2,13 @@ import logging from pyspark.sql.dataframe import DataFrame -def pii_remove(dataset: DataFrame, model_root_path=None, text_column="text", new_text_column="text", show_secret_column=True, - secret_column="__SECRETS__"): + +def pii_remove(dataset: DataFrame, model_root_path=None, text_column="text", show_secret_column=True, inplace=True, + entity_types=None): from pyrecdp.primitives.operations import PIIRemoval spark_df = dataset - op = PIIRemoval(text_key=text_column, inplace=True, model_root_path=model_root_path, debug_mode=show_secret_column) + op = PIIRemoval(text_key=text_column, inplace=inplace, model_root_path=model_root_path, + debug_mode=show_secret_column, entity_types=entity_types) ret = op.process_spark(spark_df.sparkSession, spark_df) return ret @@ -73,6 +75,7 @@ def getArgs(): # add an option of evaluating the pipeline on the PII benchmark we built return parser.parse_args() + if __name__ == "__main__": logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) diff --git a/RecDP/pyrecdp/primitives/operations/text_pii_remove.py b/RecDP/pyrecdp/primitives/operations/text_pii_remove.py index 83929cefc..b8c235001 100644 --- a/RecDP/pyrecdp/primitives/operations/text_pii_remove.py +++ b/RecDP/pyrecdp/primitives/operations/text_pii_remove.py @@ -2,24 +2,39 @@ from ray.data import Dataset from pyspark.sql import DataFrame import os -from transformers import pipeline +from typing import List from pyrecdp.primitives.llmutils.pii.pii_detection import scan_pii_text from pyrecdp.primitives.llmutils.pii.pii_redaction import redact_pii_text, random_replacements +from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType -def prepare_func_pii_removal(model_root_path = "", debug_mode = True): - replacements = random_replacements() + +def prepare_pipeline(model_root_path=None, entity_types: List[PIIEntityType] = None): _model_key = "bigcode/starpii" model_key = _model_key if model_root_path is None else os.path.join(model_root_path, _model_key) - pipeline_inst = pipeline(model = model_key, task='token-classification', grouped_entities=True) + if entity_types is None: + entity_types = PIIEntityType.all() + + is_name_password_detect = PIIEntityType.NAME in entity_types or PIIEntityType.PASSWORD in entity_types + + if is_name_password_detect: + from transformers import pipeline + return pipeline(model=model_key, task='token-classification', grouped_entities=True) + else: + return None + + +def prepare_func_pii_removal(model_root_path=None, debug_mode=True, entity_types: List[PIIEntityType] = None): + replacements = random_replacements() + pipeline_inst = prepare_pipeline(model_root_path, entity_types) def process_debug(sample): - secrets = scan_pii_text(sample, pipeline_inst) + secrets = scan_pii_text(sample, pipeline_inst, entity_types) text, is_modified = redact_pii_text(sample, secrets, replacements) return text, is_modified, str(secrets) - + def process(sample): - secrets = scan_pii_text(sample, pipeline_inst) + secrets = scan_pii_text(sample, pipeline_inst, entity_types) text, _ = redact_pii_text(sample, secrets, replacements) return text @@ -30,8 +45,10 @@ def process(sample): class PIIRemoval(BaseLLMOperation): - def __init__(self, text_key = 'text', inplace = True, model_root_path = "", debug_mode = False): - settings = {'text_key': text_key, 'inplace': inplace, 'model_root_path': model_root_path, 'debug_mode': debug_mode} + def __init__(self, text_key='text', inplace=True, model_root_path="", debug_mode=False, + entity_types: List[PIIEntityType] = None): + settings = {'text_key': text_key, 'inplace': inplace, 'model_root_path': model_root_path, + 'debug_mode': debug_mode, 'entity_types': entity_types} super().__init__(settings) self.text_key = text_key self.inplace = inplace @@ -40,22 +57,26 @@ def __init__(self, text_key = 'text', inplace = True, model_root_path = "", debu self.support_spark = True self.support_ray = True self.debug_mode = debug_mode - + self.entity_types = entity_types + def process_rayds(self, ds: Dataset) -> Dataset: if self.inplace: new_name = self.text_key else: new_name = 'pii_clean_text' if self.actual_func is None: - self.actual_func = prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode) + self.actual_func = prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode, + entity_types=self.entity_types) if self.debug_mode: def process_row(sample: dict, text_key, new_name, actual_func, *actual_func_args): - sample[new_name], sample['is_modified_by_pii'], sample['secrets'] = actual_func(sample[text_key], *actual_func_args) + sample[new_name], sample['is_modified_by_pii'], sample['secrets'] = actual_func(sample[text_key], + *actual_func_args) return sample + return ds.map(lambda x: process_row(x, self.text_key, new_name, self.actual_func)) else: return ds.map(lambda x: self.process_row(x, self.text_key, new_name, self.actual_func)) - + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.functions as F from pyspark.sql import types as T @@ -63,7 +84,7 @@ def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: new_name = self.text_key else: new_name = 'pii_clean_text' - + if self.debug_mode: schema = T.StructType([ T.StructField(new_name, T.StringType(), False), @@ -71,10 +92,14 @@ def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: T.StructField('secrets', T.StringType(), False) ]) existing_cols = [col_name for col_name in spark_df.columns if col_name != new_name] - pii_remove_udf = F.udf(prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode), schema) - return spark_df.withColumn('pii_ret', pii_remove_udf(F.col(self.text_key))).select(*existing_cols, "pii_ret.*") + pii_remove_udf = F.udf(prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode, + entity_types=self.entity_types), schema) + return spark_df.withColumn('pii_ret', pii_remove_udf(F.col(self.text_key))).select(*existing_cols, + "pii_ret.*") else: - pii_remove_udf = F.udf(prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode)) + pii_remove_udf = F.udf(prepare_func_pii_removal(self.model_root_path, debug_mode=self.debug_mode, + entity_types=self.entity_types)) return spark_df.withColumn(new_name, pii_remove_udf(F.col(self.text_key))) -LLMOPERATORS.register(PIIRemoval) \ No newline at end of file + +LLMOPERATORS.register(PIIRemoval) diff --git a/RecDP/tests/test_llmutils.py b/RecDP/tests/test_llmutils.py index 874f74fe5..10e106224 100644 --- a/RecDP/tests/test_llmutils.py +++ b/RecDP/tests/test_llmutils.py @@ -16,29 +16,42 @@ cur_dir = str(Path(__file__).parent.resolve()) + class SparkContext: - def __init__(self, dataset_path = None): + def __init__(self, dataset_path=None): self.dataset_path = dataset_path self.rdp = SparkDataProcessor() - def __enter__(self): + def __enter__(self): self.spark = self.rdp.spark if self.dataset_path is not None: reader = JsonlReader(self.dataset_path) self.ds = reader.process_spark(self.spark) return self - + def __exit__(self, exc_type, exc_value, exc_traceback): pass - + def show(self, ds): pd = ds.toPandas() display(pd) - + + +def pii_remove_help(input_file: str, entity_types=None): + from pyrecdp.primitives.llmutils import pii_remove + from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE + with SparkContext(input_file) as ctx: + spark_df = ctx.ds + model_root_path = os.path.join(RECDP_MODELS_CACHE, "huggingface") + output_dataset = pii_remove(dataset=spark_df, text_column="text", model_root_path=model_root_path, + show_secret_column=True, inplace=True, entity_types=entity_types) + ctx.show(output_dataset) + + class Test_LLMUtils(unittest.TestCase): def setUp(self): print(f"\n******\nTesting Method Name: {self._testMethodName}\n******") - + def test_quality_classifier(self): from pyrecdp.primitives.llmutils import quality_classifier file_path = os.path.join(cur_dir, "data/llm_data/arxiv_sample_100.jsonl") @@ -54,37 +67,51 @@ def test_quality_classifier_spark(self): spark_df = spark.read.json(data_file) quality_classifier_df = quality_classifier_spark(spark_df) quality_classifier_df.show() - + def test_diversity_analysis(self): from pyrecdp.primitives.llmutils import diversity data_dir = "tests/data/llm_data/" output_path = "tests/data/diversity_out" in_type = "jsonl" diversity(data_dir, in_type, output_path) - -# ***** This test is to provide an example for EasyData ***** # + + # ***** This test is to provide an example for EasyData ***** # def test_near_dedup_spark(self): - from pyrecdp.primitives.llmutils import near_dedup_spk + from pyrecdp.primitives.llmutils import near_dedup_spk with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: spark_df = ctx.ds ret_df = near_dedup_spk(spark_df) ctx.show(ret_df) - + def test_global_dedup_spark(self): - from pyrecdp.primitives.llmutils import global_dedup_spk + from pyrecdp.primitives.llmutils import global_dedup_spk with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: spark_df = ctx.ds ret_df = global_dedup_spk(spark_df) ctx.show(ret_df) - + def test_pii_remove_spark(self): - from pyrecdp.primitives.llmutils import pii_remove - from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE - with SparkContext("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl") as ctx: - spark_df = ctx.ds - model_root_path = os.path.join(RECDP_MODELS_CACHE, "huggingface") - output_dataset = pii_remove(dataset=spark_df,text_column="text", model_root_path=model_root_path, show_secret_column=True) - ctx.show(output_dataset) + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl") + + def test_pii_remove_email_spark(self): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl", entity_types=[PIIEntityType.EMAIL]) + + def test_pii_remove_phone_spark(self): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl", entity_types=[PIIEntityType.PHONE_NUMBER]) + + def test_pii_remove_name_spark(self): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl", entity_types=[PIIEntityType.NAME]) + + def test_pii_remove_password_spark(self): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl", entity_types=[PIIEntityType.PASSWORD]) + + def test_pii_remove_ip_spark(self): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType + pii_remove_help("tests/data/llm_data/tiny_c4_sample_for_pii.jsonl", entity_types=[PIIEntityType.IP_ADDRESS]) def test_language_identify_spark(self): from pyrecdp.primitives.llmutils import language_identify_spark @@ -100,29 +127,30 @@ def test_sentence_split_spark(self): import pandas as pd with SparkContext() as ctx: samples = [( - 'Smithfield employs 3,700 people at its plant in Sioux Falls, ' - 'South Dakota. The plant slaughters 19,500 pigs a day — 5 ' - 'percent of U.S. pork.', - 'Smithfield employs 3,700 people at its plant in Sioux Falls, ' - 'South Dakota.\nThe plant slaughters 19,500 pigs a day — 5 ' - 'percent of U.S. pork.')] + 'Smithfield employs 3,700 people at its plant in Sioux Falls, ' + 'South Dakota. The plant slaughters 19,500 pigs a day — 5 ' + 'percent of U.S. pork.', + 'Smithfield employs 3,700 people at its plant in Sioux Falls, ' + 'South Dakota.\nThe plant slaughters 19,500 pigs a day — 5 ' + 'percent of U.S. pork.')] spark_df = ctx.spark.createDataFrame(pd.DataFrame(samples, columns=["text", "target"])) ret_df = sentence_split(spark_df) ctx.show(ret_df) for _, row in ret_df.toPandas().iterrows(): self.assertEqual(row["text"], row["target"]) - -################################################################ -# This test is used to make sure our codes in llm-ray is still working - #from pyrecdp.primitives.llmutils import shrink_document_MP, text_to_jsonl_MP, global_hash_mp, global_dedup + ################################################################ + + # This test is used to make sure our codes in llm-ray is still working + # from pyrecdp.primitives.llmutils import shrink_document_MP, text_to_jsonl_MP, global_hash_mp, global_dedup def test_llm_ray_near_dedup(self): from pyrecdp.core.utils import Timer import shutil, argparse, pickle from pyrecdp.primitives.llmutils.utils import read_json - from pyrecdp.primitives.llmutils.near_dedup import minHashLSH_prepare, generate_connected_components, generate_duplicates_dict + from pyrecdp.primitives.llmutils.near_dedup import minHashLSH_prepare, generate_connected_components, \ + generate_duplicates_dict from pyrecdp.primitives.llmutils.shrink_jsonl import shrink_document_MP - + data_dir = "tests/data/llm_data" data_files = [os.path.join(data_dir, i) for i in os.listdir(data_dir)] dup_dir = "tests/data/PILE_dup_out" @@ -142,8 +170,7 @@ def test_llm_ray_near_dedup(self): if os.path.exists(dup_dir): shutil.rmtree(dup_dir, ignore_errors=True) results = pipeline.saveAsTextFile(dup_dir) - - + with Timer(f"generate_connected_components all"): dup_connected_args = argparse.Namespace() dup_connected_args.input_dir = dup_dir @@ -153,7 +180,7 @@ def test_llm_ray_near_dedup(self): generate_connected_components.generate_connected_components_mp( dup_connected_args ) - + with Timer(f"generate_duplicates_dict all"): dup_docs = os.path.join(dup_dir, "duplicates.pickle") dup_dict_args = argparse.Namespace() @@ -162,7 +189,7 @@ def test_llm_ray_near_dedup(self): ) dup_dict_args.out_file = dup_docs generate_duplicates_dict.generate_duplicates(dup_dict_args) - + dup_dict = pickle.load(open(os.path.join(dup_dir, "duplicates.pickle"), 'rb')) dup_sum = 0 for _, v in dup_dict.items(): @@ -176,8 +203,7 @@ def test_llm_ray_near_dedup(self): print(f"Completed!!") print(f" total processed {total_length} documents") print(f" total detected {dup_sum} duplicated documents") - print(f" duplicate ratio is {dup_sum/total_length}") - + print(f" duplicate ratio is {dup_sum / total_length}") def test_llm_ray_convert_jsonl(self): from pyrecdp.primitives.llmutils.text_to_jsonl import text_to_jsonl_MP @@ -185,7 +211,6 @@ def test_llm_ray_convert_jsonl(self): out_dir = "tests/data/pmc_jsonl" text_to_jsonl_MP(data_dir, out_dir, 2) - def test_llm_ray_global_hash_jsonl(self): import pandas as pd from pyrecdp.primitives.llmutils.global_hash import global_hash_mp @@ -198,36 +223,35 @@ def test_llm_ray_global_hash_jsonl(self): data_dir = "tests/data/PILE" with Timer("execute global_hash_mp"): global_hash_mp(source, data_dir, in_type, n_parallel, out_dir, is_norm) - + from pyrecdp.primitives.llmutils.global_dedup import get_hash_indexing out_dir = "tests/data/global_hash_index" data_dir = "tests/data/global_hash_out/" with Timer("execute get_hash_indexing"): get_hash_indexing(data_dir, out_dir) - + from pyrecdp.primitives.llmutils.global_dedup import combine_hash_indexing out_dir = "tests/data/combined_hash_index/" data_dir_dir = ["tests/data/global_hash_index/"] with Timer("execute combine_hash_indexing"): combine_hash_indexing(data_dir_dir, out_dir) - + from pyrecdp.primitives.llmutils.global_dedup import get_duplication_list data_dir = "tests/data/global_hash_index" out_dir = "tests/data/duplications_index" with Timer("execute get_duplication_list"): get_duplication_list(data_dir, out_dir) - + from pyrecdp.primitives.llmutils import index_based_reduction in_dir = "tests/data/global_hash_out" dup_dir = "tests/data/duplications_index" out_dir = "tests/data/global_dedup/deduplicated" with Timer("execute index_based_reduction"): index_based_reduction(in_dir, dup_dir, out_dir) - + pdf = pd.read_parquet(out_dir) display(pdf) - - + def test_llm_ray_global_hash_parquet(self): import pandas as pd from pyrecdp.primitives.llmutils.global_hash import global_hash_mp @@ -240,36 +264,35 @@ def test_llm_ray_global_hash_parquet(self): data_dir = "tests/data/PILE" with Timer("execute global_hash_mp"): global_hash_mp(source, data_dir, in_type, n_parallel, out_dir, is_norm) - + from pyrecdp.primitives.llmutils.global_dedup import get_hash_indexing out_dir = "tests/data/global_hash_index" data_dir = "tests/data/global_hash_out/" with Timer("execute get_hash_indexing"): get_hash_indexing(data_dir, out_dir) - + from pyrecdp.primitives.llmutils.global_dedup import combine_hash_indexing out_dir = "tests/data/combined_hash_index/" data_dir_dir = ["tests/data/global_hash_index/"] with Timer("execute combine_hash_indexing"): combine_hash_indexing(data_dir_dir, out_dir) - + from pyrecdp.primitives.llmutils.global_dedup import get_duplication_list data_dir = "tests/data/global_hash_index" out_dir = "tests/data/duplications_index" with Timer("execute get_duplication_list"): get_duplication_list(data_dir, out_dir) - + from pyrecdp.primitives.llmutils import index_based_reduction in_dir = "tests/data/global_hash_out" dup_dir = "tests/data/duplications_index" out_dir = "tests/data/global_dedup/deduplicated" with Timer("execute index_based_reduction"): index_based_reduction(in_dir, dup_dir, out_dir) - + pdf = pd.read_parquet(out_dir) display(pdf) - def test_llm_ray_global_dedup(self): from pyrecdp.primitives.llmutils.global_dedup import global_dedup import pandas as pd @@ -279,9 +302,3 @@ def test_llm_ray_global_dedup(self): global_dedup(data_dir, out_dir, "PILE", in_type) pdf = pd.read_parquet(out_dir + 'deduplicated') display(pdf) - - - - - - \ No newline at end of file From 6d98c205ebbb234577e5872c117e2638365e4884 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Wed, 11 Oct 2023 02:20:18 +0800 Subject: [PATCH 03/47] [v1.2][ISSUE-306] Solve circular reference issue in filter and some arguments bug. (#373) * Solve circular reference issue in filter and some arguments bug. * Added filtered data statistics --------- Co-authored-by: Chendi.Xue --- RecDP/pyrecdp/primitives/llmutils/filter.py | 17 +++++++------ .../primitives/llmutils/profanity_filter.py | 2 +- .../pyrecdp/primitives/llmutils/text_fixer.py | 6 ++--- .../pyrecdp/primitives/operations/__init__.py | 2 +- .../primitives/operations/text_filter.py | 25 +++++++++++++++---- 5 files changed, 35 insertions(+), 17 deletions(-) diff --git a/RecDP/pyrecdp/primitives/llmutils/filter.py b/RecDP/pyrecdp/primitives/llmutils/filter.py index 5e89a4ddf..d3fd795d8 100644 --- a/RecDP/pyrecdp/primitives/llmutils/filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/filter.py @@ -11,7 +11,8 @@ def filter_by_blocklist_spark(spark_df): return ret -def filter_by_blocklist(data_dir, data_file_type, out_dir): +def filter_by_blocklist(data_dir, out_dir, data_file_type="jsonl"): + from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import URLFilter from pyrecdp.LLM import ResumableTextPipeline @@ -39,7 +40,8 @@ def filter_by_bad_words_spark(spark_df, language="en"): return ret -def filter_by_bad_words(data_dir, data_file_type, out_dir, language="en"): +def filter_by_bad_words(data_dir, out_dir, data_file_type="jsonl", language="en"): + from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import BadwordsFilter from pyrecdp.LLM import ResumableTextPipeline @@ -67,7 +69,8 @@ def filter_by_length_spark(spark_df, minimum_length=100, maximum_length=-1): return ret -def filter_by_length(data_dir, data_file_type, out_dir, minimum_length=100, maximum_length=-1): +def filter_by_length(data_dir, out_dir, data_file_type="jsonl", minimum_length=100, maximum_length=-1): + from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import LengthFilter from pyrecdp.LLM import ResumableTextPipeline @@ -93,7 +96,7 @@ def filter_by_length(data_dir, data_file_type, out_dir, minimum_length=100, maxi parser.add_argument("--data_dir", dest="data_dir", type=str) parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") parser.add_argument("--output_dir", dest="output_dir", type=str, default="") - parser.add_argument("--filter_type", dest="support length,bad_words,url_blocklist", type=str, default="length") + parser.add_argument("--filter_type", dest="filter_type", type=str, default="length") args = parser.parse_args() data_dir = args.data_dir data_file_type = args.data_file_type @@ -101,13 +104,13 @@ def filter_by_length(data_dir, data_file_type, out_dir, minimum_length=100, maxi filter_type = args.filter_type if "length" == filter_type: with Timer(f"Processing {filter_type} filter for {data_dir}"): - filter_by_length(data_dir, data_file_type, output_dir) + filter_by_length(data_dir, output_dir, data_file_type) elif "bad_words" == filter_type: with Timer(f"Processing {filter_type} filter for {data_dir}"): - filter_by_bad_words(data_dir, data_file_type, output_dir) + filter_by_bad_words(data_dir, output_dir, data_file_type) elif "url_blocklist" == filter_type: with Timer(f"Processing {filter_type} filter for {data_dir}"): - filter_by_blocklist(data_dir, data_file_type, output_dir) + filter_by_blocklist(data_dir, output_dir, data_file_type) else: raise NotImplementedError(f"{filter_type} is not supported in RecDP LLM Filter yet.") diff --git a/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py b/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py index bb4de85b7..177709ea9 100644 --- a/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/profanity_filter.py @@ -11,7 +11,7 @@ def profanity_filter_spark(spark_df): return ret -def profanity_filter(data_dir, data_file_type, out_dir): +def profanity_filter(data_dir, out_dir, data_file_type="jsonl"): from pyrecdp.primitives.operations import ProfanityFilter from pyrecdp.LLM import ResumableTextPipeline diff --git a/RecDP/pyrecdp/primitives/llmutils/text_fixer.py b/RecDP/pyrecdp/primitives/llmutils/text_fixer.py index c0c527ba1..4cf9a949e 100644 --- a/RecDP/pyrecdp/primitives/llmutils/text_fixer.py +++ b/RecDP/pyrecdp/primitives/llmutils/text_fixer.py @@ -1,7 +1,6 @@ import argparse from pyrecdp.core.utils import Timer -from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter from pyrecdp.primitives.operations import TextFix @@ -13,6 +12,7 @@ def text_fixer_spark(spark_df, text_type='html'): def text_fixer(data_dir, data_file_type, out_dir, text_type='html'): from pyrecdp.LLM import ResumableTextPipeline + from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter if data_file_type == 'jsonl': reader = JsonlReader(data_dir) @@ -36,7 +36,7 @@ def text_fixer(data_dir, data_file_type, out_dir, text_type='html'): parser.add_argument("--data_dir", dest="data_dir", type=str) parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") parser.add_argument("--output_dir", dest="output_dir", type=str, default="") - parser.add_argument("--text_type", dest="text_type,support:html,latex,codes", type=str, default="html") + parser.add_argument("--text_type", dest="text_type", type=str, default="html") args = parser.parse_args() data_dir = args.data_dir data_file_type = args.data_file_type @@ -44,4 +44,4 @@ def text_fixer(data_dir, data_file_type, out_dir, text_type='html'): text_type = args.text_type with Timer(f"Processing text fixer for {data_dir}"): - text_fixer(data_dir, data_file_type, output_dir, text_type) \ No newline at end of file + text_fixer(data_dir, data_file_type, output_dir, text_type) diff --git a/RecDP/pyrecdp/primitives/operations/__init__.py b/RecDP/pyrecdp/primitives/operations/__init__.py index 382691a07..18a5fa5d9 100644 --- a/RecDP/pyrecdp/primitives/operations/__init__.py +++ b/RecDP/pyrecdp/primitives/operations/__init__.py @@ -13,4 +13,4 @@ from .text_pii_remove import PIIRemoval from .text_deduplication import FuzzyDeduplicate, GlobalDeduplicate from .text_qualityscorer import TextQualityScorer -from .text_diversityindicate import TextDiversityIndicate \ No newline at end of file +from .text_diversityindicate import TextDiversityIndicate diff --git a/RecDP/pyrecdp/primitives/operations/text_filter.py b/RecDP/pyrecdp/primitives/operations/text_filter.py index 057d1d440..0ce439c3b 100644 --- a/RecDP/pyrecdp/primitives/operations/text_filter.py +++ b/RecDP/pyrecdp/primitives/operations/text_filter.py @@ -2,7 +2,6 @@ from ray.data import Dataset from pyspark.sql import DataFrame import os -from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE import pyspark.sql.functions as F @@ -63,7 +62,11 @@ def process_rayds(self, ds: Dataset) -> Dataset: self.check_length = prepare_func_filter_by_length(self.minimum_length, self.maximum_length) if self.inplace: # remove unwanted text row inplace - return ds.filter(lambda x: self.check_length(x[self.text_key])) + original_num = ds.count() + filtered_ds = ds.filter(lambda x: self.check_length(x[self.text_key])) + filtered_num = filtered_ds.count() + print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") + return filtered_ds else: raise NotImplementedError("We only support inplace modification for LengthFilter.") @@ -93,7 +96,11 @@ def process_rayds(self, ds: Dataset) -> Dataset: self.check_badwords = prepare_func_filter_by_badwords(self.language) if self.inplace: # remove unwanted text row inplace - return ds.filter(lambda x: self.check_badwords(x[self.text_key])) + original_num = ds.count() + filtered_ds = ds.filter(lambda x: self.check_badwords(x[self.text_key])) + filtered_num = filtered_ds.count() + print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") + return filtered_ds else: raise NotImplementedError("We only support inplace modification for BadwordsFilter.") @@ -121,7 +128,11 @@ def process_rayds(self, ds: Dataset) -> Dataset: self.check_profanity = prepare_func_filter_by_profanity() if self.inplace: # remove unwanted text row inplace - return ds.filter(lambda x: self.check_profanity(x[self.text_key])) + original_num = ds.count() + filtered_ds = ds.filter(lambda x: self.check_profanity(x[self.text_key])) + filtered_num = filtered_ds.count() + print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") + return filtered_ds else: raise NotImplementedError("We only support inplace modification for ProfanityFilter.") @@ -209,7 +220,11 @@ def process_rayds(self, ds: Dataset) -> Dataset: blacklist = load_blacklist_set() if self.inplace: # remove unwanted text row inplace - return ds.filter(lambda x: get_url_from_meta(x) not in blacklist) + original_num = ds.count() + filtered_ds = ds.filter(lambda x: get_url_from_meta(x) not in blacklist) + filtered_num = filtered_ds.count() + print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") + return filtered_ds else: raise NotImplementedError("We only support inplace modification for URLFilter.") From 905f7788ba191e27705577982607d5a74d22ad42 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Wed, 11 Oct 2023 02:24:39 +0800 Subject: [PATCH 04/47] [v1.2][ISSUE-306] Support diversity analysis on ray. (#372) * Support diversity analysis on ray. * Deep copy the model to fix model read-only issue. --------- Co-authored-by: Chendi.Xue --- RecDP/pyrecdp/primitives/llmutils/__init__.py | 2 +- .../primitives/llmutils/diversity_analysis.py | 228 +++--------------- .../operations/text_diversityindicate.py | 86 ++++--- RecDP/tests/test_llmutils.py | 9 +- 4 files changed, 95 insertions(+), 230 deletions(-) diff --git a/RecDP/pyrecdp/primitives/llmutils/__init__.py b/RecDP/pyrecdp/primitives/llmutils/__init__.py index f030caf9b..a0c5fcb07 100644 --- a/RecDP/pyrecdp/primitives/llmutils/__init__.py +++ b/RecDP/pyrecdp/primitives/llmutils/__init__.py @@ -19,4 +19,4 @@ from .quality_classifier import quality_classifier, quality_classifier_spark from .text_fixer import text_fixer from .sentence_split import sentence_split -from .diversity_analysis import DiversityAnalysis, diversity \ No newline at end of file +from .diversity_analysis import diversity_indicate diff --git a/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py b/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py index 5351e74e8..8fbf2a08a 100644 --- a/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py +++ b/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py @@ -1,197 +1,47 @@ -# This tool is referred from alibaba data juicer project and used for -# analyzing the verb-noun structure of the SFT dataset and plots its diversity in sunburst format. +import argparse -import os - -import pandas -import pandas as pd -import spacy -from pyrecdp.core.model_utils import MODEL_ZOO, prepare_model -from pyrecdp.primitives.spark_data_processor.data_processor import DataProcessor as SparkDataProcessor from pyrecdp.core.utils import Timer -from pyrecdp.primitives.llmutils.utils import get_target_file_list, read_parquet, read_json -from pyspark.sql import functions as F -from pyspark.sql.types import StructType, StructField, StringType -from pyspark.sql.functions import udf - - -# Modify from self_instruct, please refer to -# https://github.com/yizhongw/self-instruct/blob/main/self_instruct/instruction_visualize.ipynb -def find_root_verb_and_its_dobj(tree_root): - """ - Find the verb and its object closest to the root. - - :param tree_root: the root of lexical tree - :return: valid verb and its object. - """ - # first check if the current node and its children satisfy the condition - if tree_root.pos_ == 'VERB': - for child in tree_root.children: - if child.dep_ == 'dobj' and child.pos_ == 'NOUN': - return tree_root.lemma_ if len( - tree_root.lemma_) else tree_root.text, child.lemma_ if len( - child.lemma_) else child.text - return tree_root.lemma_ if len( - tree_root.lemma_) else tree_root.text, None - # if not, check its children - for child in tree_root.children: - return find_root_verb_and_its_dobj(child) - # if no children satisfy the condition, return None - return None, None - - -# Modify from self_instruct, please refer to -# https://github.com/yizhongw/self-instruct/blob/main/self_instruct/instruction_visualize.ipynb -def find_root_verb_and_its_dobj_in_string(nlp, s, first_sent=True): - """ - Find the verb and its object closest to the root of lexical tree of input - string. - - :param nlp: the diversity model to analyse the diversity strings - :param s: the string to be analysed - :param first_sent: whether to analyse the first sentence in the - input string only. If it's true, return the analysis result of - the first sentence no matter it's valid or not. If it's false, - return the first valid result over all sentences - :return: valid verb and its object of this string - """ - doc = nlp(s) - for sent in doc.sents: - verb, noun = find_root_verb_and_its_dobj(sent.root) - if first_sent or (verb is not None and noun is not None): - return verb, noun - return None, None - - -def get_diversity(dataset, top_k_verbs=20, top_k_nouns=4, **kwargs): - """ - Given the lexical tree analysis result, return the diversity results. - - :param dataset: lexical tree analysis result - :param top_k_verbs: only keep the top_k_verbs largest verb groups - :param top_k_nouns: only keep the top_k_nouns largest noun groups - for each verb group - :param kwargs: extra args - :return: the diversity results - """ - phrases = pd.DataFrame(dataset).dropna() - - top_verbs = phrases.groupby(['verb' - ]).size().nlargest(top_k_verbs).reset_index() - - df = phrases[phrases['verb'].isin(top_verbs['verb'].tolist())] - df = df.groupby(['verb', 'noun']).size().reset_index().rename(columns={ - 0: 'count' - }).sort_values(by=['count'], ascending=False) - - df = df.groupby('verb').apply(lambda x: x.sort_values( - 'count', ascending=False).head(top_k_nouns)).reset_index(drop=True) - return df +from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter -class DiversityAnalysis: - """Apply diversity analysis for each sample and get an overall analysis - result.""" +def diversity_indicate_spark(spark_df): + from pyrecdp.primitives.operations import TextDiversityIndicate + op = TextDiversityIndicate() + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret - def __init__(self, dataset, output_path, lang_or_model='en'): - """Initialization method :param dataset: the dataset to be analysed - :param output_path: path to store the analysis results :param - lang_or_model: the diversity model or a specific language used to load - the diversity model.""" - self.dataset = dataset - self.output_path = output_path - if not os.path.exists(self.output_path): - os.makedirs(self.output_path) - self.lang_or_model = lang_or_model +def diversity_indicate(data_dir, data_file_type, out_dir, language="en"): + from pyrecdp.primitives.operations import TextDiversityIndicate + from pyrecdp.LLM import ResumableTextPipeline - def compute(self, lang_or_model=None): - """ - Apply lexical tree analysis on each sample. - - :param lang_or_model: the diversity model or a specific language - used to load the diversity model - :param column_name: the name of column to be analysed - :return: the analysis result. - """ - # load diversity model - lang_or_model = lang_or_model if lang_or_model else self.lang_or_model - if isinstance(lang_or_model, str): - diversity_model = MODEL_ZOO.get( - prepare_model(lang_or_model, 'spacy')) - else: - diversity_model = lang_or_model - - assert isinstance(diversity_model, spacy.Language) - - schema = StructType([ - StructField("verb", StringType(), True), - StructField("noun", StringType(), True) - ]) - - def find_verb_noun(sample): - try: - verb, noun = find_root_verb_and_its_dobj_in_string( - diversity_model, sample) - except Exception as e: - print(str(e)) - verb, noun = None, None - return verb, noun - - operator = udf(find_verb_noun, schema) - dataset = self.dataset.withColumn('diversity', operator(F.col("text"))) - dataset = dataset.select("filename_docid", "text", "meta", "diversity.*") - return dataset.toPandas() - - def analyse(self, - lang_or_model=None, - column_name='text', - postproc_func=get_diversity, - **postproc_kwarg): - """ - Apply diversity analysis on the whole dataset. - - :param lang_or_model: the diversity model or a specific language - used to load the diversity model - :param column_name: the name of column to be analysed - :param postproc_func: function to analyse diversity. In default, - it's function get_diversity - :param postproc_kwarg: arguments of the postproc_func - :return: - """ - # get the lexical tree analysis result - raw_df = self.compute(lang_or_model=lang_or_model) - # get the result of diversity analysis - df = postproc_func(raw_df, **postproc_kwarg) - - # export to result report file - df.to_csv(os.path.join(self.output_path, 'diversity.csv')) - df.to_markdown(os.path.join(self.output_path, 'diversity.md')) - return df - - -def diversity(data_dir, in_type, output_path, language="en", enable_ray=False): - if enable_ray: - rdp = SparkDataProcessor(spark_mode='ray') + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) else: - rdp = SparkDataProcessor() - spark = rdp.spark - try: - with Timer(f"Load data from {in_type} file"): - - data_files = get_target_file_list(data_dir, in_type) - data_files = [os.path.join(data_dir, f) for f in data_files] - if in_type == 'parquet': - spark_df = read_parquet(data_files, spark) - elif in_type == 'jsonl': - spark_df = read_json(data_files, spark) - total_data_num = spark_df.count() - with Timer("Analyzing data"): - diversity_analysis = DiversityAnalysis( - spark_df, output_path) - - diversity_analysis.analyse(lang_or_model=language) - except Exception as e: - spark.stop() - print("Failed", e) - + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + TextDiversityIndicate(language=language), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--data_dir", dest="data_dir", type=str) + parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") + parser.add_argument("--output_dir", dest="output_dir", type=str, default="") + parser.add_argument("--language", dest="language", type=str, default="en") + args = parser.parse_args() + data_dir = args.data_dir + data_file_type = args.data_file_type + output_dir = args.output_dir + language = args.language + with Timer(f"Processing diversity analysis for {data_dir}"): + diversity_indicate(data_dir, data_file_type, output_dir, language) diff --git a/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py b/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py index 29c734615..1e8265909 100644 --- a/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py +++ b/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py @@ -7,9 +7,6 @@ import pandas as pd import spacy from pyrecdp.core.model_utils import MODEL_ZOO, prepare_model -from pyrecdp.primitives.spark_data_processor.data_processor import DataProcessor as SparkDataProcessor -from pyrecdp.core.utils import Timer -from pyrecdp.primitives.llmutils.utils import get_target_file_list, read_parquet, read_json from pyspark.sql import functions as F from pyspark.sql.types import StructType, StructField, StringType from pyspark.sql.functions import udf @@ -43,7 +40,6 @@ def find_root_verb_and_its_dobj(tree_root): # if no children satisfy the condition, return None return None, None - # Modify from self_instruct, please refer to # https://github.com/yizhongw/self-instruct/blob/main/self_instruct/instruction_visualize.ipynb def find_root_verb_and_its_dobj_in_string(nlp, s, first_sent=True): @@ -71,13 +67,14 @@ class DiversityAnalysis: """Apply diversity analysis for each sample and get an overall analysis result.""" - def __init__(self, dataset, lang_or_model='en'): + def __init__(self, dataset, text_key="text", lang_or_model='en'): """Initialization method :param dataset: the dataset to be analysed :param output_path: path to store the analysis results :param lang_or_model: the diversity model or a specific language used to load the diversity model.""" self.dataset = dataset + self.text_key = text_key self.lang_or_model = lang_or_model def compute(self, lang_or_model=None): @@ -89,6 +86,7 @@ def compute(self, lang_or_model=None): :param column_name: the name of column to be analysed :return: the analysis result. """ + # load diversity model lang_or_model = lang_or_model if lang_or_model else self.lang_or_model if isinstance(lang_or_model, str): @@ -99,23 +97,37 @@ def compute(self, lang_or_model=None): assert isinstance(diversity_model, spacy.Language) - schema = StructType([ - StructField("verb", StringType(), True), - StructField("noun", StringType(), True) - ]) - - def find_verb_noun(sample): - try: - verb, noun = find_root_verb_and_its_dobj_in_string( - diversity_model, sample) - except Exception as e: - print(str(e)) - verb, noun = None, None - return verb, noun - existing_columns = self.dataset.columns - operator = udf(find_verb_noun, schema) - dataset = self.dataset.withColumn('diversity', operator(F.col("text"))) - dataset = dataset.select(*existing_columns, "diversity.*") + if isinstance(self.dataset, DataFrame): + schema = StructType([ + StructField("verb", StringType(), True), + StructField("noun", StringType(), True) + ]) + + def find_verb_noun_spark(sample): + try: + verb, noun = find_root_verb_and_its_dobj_in_string( + diversity_model, sample) + except Exception as e: + print(str(e)) + verb, noun = None, None + return verb, noun + + existing_columns = self.dataset.columns + operator = udf(find_verb_noun_spark, schema) + dataset = self.dataset.withColumn('diversity', operator(F.col(self.text_key))) + dataset = dataset.select("diversity.*").toPandas() + else: + import copy + + def find_verb_noun_ray(sample): + try: + nlp = copy.deepcopy(diversity_model) + verb, noun = find_root_verb_and_its_dobj_in_string(nlp, sample) + except Exception as e: + print(str(e)) + verb, noun = None, None + return {'verb': verb, 'noun': noun} + dataset = self.dataset.map(lambda x: find_verb_noun_ray(x[self.text_key])).to_pandas() return dataset def get_diversity(self, dataset, top_k_verbs=20, top_k_nouns=4, **kwargs): @@ -132,7 +144,7 @@ def get_diversity(self, dataset, top_k_verbs=20, top_k_nouns=4, **kwargs): phrases = pd.DataFrame(dataset).dropna() top_verbs = phrases.groupby(['verb' - ]).size().nlargest(top_k_verbs).reset_index() + ]).size().nlargest(top_k_verbs).reset_index() df = phrases[phrases['verb'].isin(top_verbs['verb'].tolist())] df = df.groupby(['verb', 'noun']).size().reset_index().rename(columns={ @@ -159,14 +171,14 @@ def analyse(self, :return: """ # get the lexical tree analysis result - raw_df = self.compute(lang_or_model=lang_or_model) - pdf = raw_df.toPandas() + pdf = self.compute(lang_or_model=lang_or_model) # get the result of diversity analysis df = self.get_diversity(pdf, **postproc_kwarg) return df + class TextDiversityIndicate(BaseLLMOperation): - def __init__(self, text_key = 'text', language = 'en', out_dir = ''): + def __init__(self, text_key='text', language='en', out_dir=''): settings = {'text_key': text_key, 'language': language, 'out_dir': out_dir} super().__init__(settings) self.text_key = text_key @@ -174,19 +186,21 @@ def __init__(self, text_key = 'text', language = 'en', out_dir = ''): self.output_path = out_dir self.inplace = False self.support_spark = True - self.support_ray = False - + self.support_ray = True + def process_rayds(self, ds: Dataset) -> Dataset: - raise NotImplementedError("Not implemented yet") - return ds.map(lambda x: self.process_row(x, self.text_key, new_name, text_bytesize)) - + diversity_analysis = DiversityAnalysis(ds, text_key=self.text_key, lang_or_model=self.language) + analyse_df = diversity_analysis.analyse(lang_or_model=self.language) + analyse_df.to_csv(os.path.join(self.output_path, 'diversity.csv')) + analyse_df.to_markdown(os.path.join(self.output_path, 'diversity.md')) + return ds + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - import pyspark.sql.functions as F - from pyspark.sql import types as T - diversity_analysis = DiversityAnalysis(spark_df) + diversity_analysis = DiversityAnalysis(spark_df, text_key=self.text_key, lang_or_model=self.language) analyse_df = diversity_analysis.analyse(lang_or_model=self.language) analyse_df.to_csv(os.path.join(self.output_path, 'diversity.csv')) analyse_df.to_markdown(os.path.join(self.output_path, 'diversity.md')) return spark_df - -LLMOPERATORS.register(TextDiversityIndicate) \ No newline at end of file + + +LLMOPERATORS.register(TextDiversityIndicate) diff --git a/RecDP/tests/test_llmutils.py b/RecDP/tests/test_llmutils.py index 10e106224..17bf44128 100644 --- a/RecDP/tests/test_llmutils.py +++ b/RecDP/tests/test_llmutils.py @@ -69,13 +69,14 @@ def test_quality_classifier_spark(self): quality_classifier_df.show() def test_diversity_analysis(self): - from pyrecdp.primitives.llmutils import diversity + from pyrecdp.primitives.llmutils import diversity_indicate data_dir = "tests/data/llm_data/" output_path = "tests/data/diversity_out" in_type = "jsonl" - diversity(data_dir, in_type, output_path) + diversity_indicate(data_dir, in_type, output_path) + +# ***** This test is to provide an example for EasyData ***** # - # ***** This test is to provide an example for EasyData ***** # def test_near_dedup_spark(self): from pyrecdp.primitives.llmutils import near_dedup_spk with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: @@ -301,4 +302,4 @@ def test_llm_ray_global_dedup(self): data_dir = "tests/data/PILE" global_dedup(data_dir, out_dir, "PILE", in_type) pdf = pd.read_parquet(out_dir + 'deduplicated') - display(pdf) + display(pdf) \ No newline at end of file From 90b5bc728de1369aafd6e469b5f7bef0f1434e05 Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Tue, 10 Oct 2023 17:52:33 -0500 Subject: [PATCH 05/47] [v1.2][ISSUE-306]Readme update (#378) * update README Signed-off-by: Xue, Chendi * update README to align with latest Signed-off-by: Xue, Chendi * update readme Signed-off-by: Xue, Chendi --------- Signed-off-by: Xue, Chendi --- RecDP/README.md | 23 +- .../notebooks/llmutils/bytesize.ipynb | 529 ++++++++++++++++++ .../examples/notebooks/llmutils/reader.ipynb | 180 ++++++ .../llmutils/resumable_pipeline.ipynb | 180 ++++++ .../examples/notebooks/llmutils/writer.ipynb | 302 ++++++++++ RecDP/pyrecdp/LLM/README.md | 114 ++-- RecDP/pyrecdp/autofe/README.md | 14 +- 7 files changed, 1259 insertions(+), 83 deletions(-) create mode 100644 RecDP/examples/notebooks/llmutils/bytesize.ipynb create mode 100644 RecDP/examples/notebooks/llmutils/reader.ipynb create mode 100644 RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb create mode 100644 RecDP/examples/notebooks/llmutils/writer.ipynb diff --git a/RecDP/README.md b/RecDP/README.md index 7b7cb6921..c1c8eab72 100644 --- a/RecDP/README.md +++ b/RecDP/README.md @@ -37,37 +37,30 @@ transformed_train_df = pipeline.fit_transform() Low Code to build your own pipeline ``` -from pyrecdp.LLM import TextPipeline -pipeline = TextPipeline("usecase/finetune_pipeline.yaml") +from pyrecdp.LLM import ResumableTextPipeline +pipeline = ResumableTextPipeline("usecase/finetune_pipeline.yaml") ret = pipeline.execute() ``` or ``` from pyrecdp.primitives.operations import * -from pyrecdp.LLM import TextPipeline +from pyrecdp.LLM import ResumableTextPipeline -pipeline = TextPipeline() +pipeline = ResumableTextPipeline() ops = [ - JsonlReader(input_dir = "in_path"), + JsonlReader("data/"), URLFilter(), LengthFilter(), ProfanityFilter(), TextFix(), LanguageIdentify(), - Classify(), - FuzzyDeduplicate(), - DocumentSplit(), - GlobalDeduplicate(), PIIRemoval(), - ParquetWriter(out_dir = "out_path"), + PerfileParquetWriter("ResumableTextPipeline_output") ] -ret = pipeline.add_operations(ops).execute(dataset) +pipeline.add_operations(ops) +pipeline.execute() ``` -* Pre-Training LLM Quality Control - -WIP... - ## LICENSE * Apache 2.0 diff --git a/RecDP/examples/notebooks/llmutils/bytesize.ipynb b/RecDP/examples/notebooks/llmutils/bytesize.ipynb new file mode 100644 index 000000000..20feffaf6 --- /dev/null +++ b/RecDP/examples/notebooks/llmutils/bytesize.ipynb @@ -0,0 +1,529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "NUdnJWOJLGvF", + "metadata": { + "id": "NUdnJWOJLGvF" + }, + "source": [ + "# RecDP LLM - TextBytesize\n", + "\n", + "TextBytesize is a tool to get total byte size of text in the data.\n", + "\n", + "### We support two types of input and output:\n", + "\n", + "example 1:\n", + "* Expect Input format: a folder of *.jsonl.\n", + "* Expect Output format: a folder of *.jsonl after reduction." + ] + }, + { + "cell_type": "markdown", + "id": "C1FFwhr3MwA1", + "metadata": { + "id": "C1FFwhr3MwA1" + }, + "source": [ + "# Get started" + ] + }, + { + "cell_type": "markdown", + "id": "tMaSNIKIMz3T", + "metadata": { + "id": "tMaSNIKIMz3T" + }, + "source": [ + "## Install pyrecdp and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ro4yOk5IMvRY", + "metadata": { + "id": "ro4yOk5IMvRY" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "id": "pnHK2VTvM5rg", + "metadata": { + "id": "pnHK2VTvM5rg" + }, + "source": [ + "## 2. prepare your own data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" + }, + "outputs": [], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "file_names = ['NIH_sample.jsonl']\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "!wget -P /content/test_data {\" \".join(file_list)}" + ] + }, + { + "cell_type": "markdown", + "id": "J-125gFtNyOg", + "metadata": { + "id": "J-125gFtNyOg" + }, + "source": [ + "## 3. get bytesize" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "vkGRn9uJN4B0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkGRn9uJN4B0", + "outputId": "8aa9d021-99f5-4de8-932e-2fccb933f70e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NIH_sample.jsonl\n" + ] + } + ], + "source": [ + "! ls /content/test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "SMK9KcV4N7al", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SMK9KcV4N7al", + "outputId": "7e93835c-a7d0-4612-e9aa-7c6f452d5bef" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "Load data with RowID started ...\n", + "Load data with RowID took 2.592839109000124 sec\n", + "num_bands is 9, ranges is 13\n", + "generate minHashLsh started ...\n", + "generate minHashLsh took 63.16276241699961 sec\n", + "generate_connected_components all started ...\n", + "Started graph building\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loop on file: 100%|██████████| 2/2 [00:00<00:00, 5312.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of the set of duplicates: 13 0.01398324966430664\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 71275.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of connected components: 7 0.02257537841796875\n", + "Graph generated duplicates list!!! 0.022832632064819336\n", + "generate_connected_components all took 0.02385874599985982 sec\n", + "generate_duplicates_dict all started ...\n", + "Processing duplicates!!!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 7/7 [00:00<00:00, 46163.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of duplicate documents that will be removed: 12\n", + "generate_duplicates_dict all took 0.009320153000317077 sec\n", + "Completed!!\n", + " total processed 10000 documents\n", + " total detected 12 duplicated documents\n", + " duplicate ratio is 0.0012\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Detected duplications are:\n" + ] + }, + { + "data": { + "text/plain": [ + "[['NIH_sample.jsonl@1769', 'NIH_sample.jsonl@1764', 'NIH_sample.jsonl@1765'],\n", + " ['NIH_sample.jsonl@245',\n", + " 'NIH_sample.jsonl@243',\n", + " 'NIH_sample.jsonl@246',\n", + " 'NIH_sample.jsonl@248',\n", + " 'NIH_sample.jsonl@244',\n", + " 'NIH_sample.jsonl@247'],\n", + " ['NIH_sample.jsonl@1191', 'NIH_sample.jsonl@1190'],\n", + " ['NIH_sample.jsonl@7746', 'NIH_sample.jsonl@7745'],\n", + " ['NIH_sample.jsonl@9026', 'NIH_sample.jsonl@8561'],\n", + " ['NIH_sample.jsonl@8200', 'NIH_sample.jsonl@7354'],\n", + " ['NIH_sample.jsonl@3037', 'NIH_sample.jsonl@3024']]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_files = [\"/content/test_data/NIH_sample.jsonl\"]\n", + "dup_dir = \"/content/fuzzy_dedup\"\n", + "\n", + "ngram_size = 13 # num_words to do compare\n", + "num_perm = 256 # num_permutation to hold this whole document.\n", + "# ranges and bands will impact the probabilities of false positive and false negative.\n", + "ranges = 13\n", + "bands = 9\n", + "\n", + "from pyrecdp.primitives.llmutils import near_dedup\n", + "import pandas as pd\n", + "\n", + "near_dedup(data_files, dup_dir, ngram_size, num_perm, bands, ranges)\n", + "\n", + "## Validate codes\n", + "import pickle\n", + "print(\"Detected duplications are:\")\n", + "connects, num_pair, index_list = pickle.load(open(f\"{dup_dir}/connected_components.pickle\", 'rb'))\n", + "connected_component_reverse = [[index_list[j] for j in i] for i in connects]\n", + "connected_component_reverse" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "81NiajWFTy2n", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "81NiajWFTy2n", + "outputId": "d8efdf8d-6561-411a-b190-acf8d289b331" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resetting to 1 for number of processes\n", + "parallelize with 1 processes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 12.83it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Reduction is completed, checkout the new jsonl filesize\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NIH_sample.jsonl\n", + "9988\n" + ] + } + ], + "source": [ + "# apply duplication list to original data to remove duplication\n", + "\n", + "from pyrecdp.primitives.llmutils import shrink_document_MP\n", + "import os\n", + "\n", + "data_dir = \"/content/test_data/\"\n", + "dup_dir = \"/content/fuzzy_dedup\"\n", + "dup_dict = os.path.join(dup_dir, \"duplicates.pickle\")\n", + "out_dir = os.path.join(dup_dir, \"output\")\n", + "\n", + "shrink_document_MP(data_dir, dup_dict, out_dir)\n", + "\n", + "# validate\n", + "print(\"\\nReduction is completed, checkout the new jsonl filesize\")\n", + "! ls \"/content/fuzzy_dedup/output\"\n", + "! cat /content/fuzzy_dedup/output/* | wc -l" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "jFOflHdsQ2XI", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jFOflHdsQ2XI", + "outputId": "88c1b3aa-8999-4563-cf4a-a65f5efbf0fc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First duplication is ['NIH_sample.jsonl@1769', 'NIH_sample.jsonl@1764', 'NIH_sample.jsonl@1765']\n", + "You'll see the similar content in above documents\n", + "NIH_sample.jsonl@1769\n", + "{\"meta\": {\"APPLICATION_ID\": 2044519}, \"text\": \"The overall aim of study is to test the \\\"matching hypothesis\\\" that alcohol treatment effectiveness can be increased by assigning clients with certain characteristics to particular treatments. The present application proposes to continue work initiated and conducted over the past five years. The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching; and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments, Twelve Step, Cognitive Behavioral, or Motivational Enhancement, have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects, clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. The Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs, reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n", + "NIH_sample.jsonl@1764\n", + "{\"meta\": {\"APPLICATION_ID\": 2044494}, \"text\": \"The overall aim of this study is to test the \\\"matching hypothesis\\\" that alcohol treatment effectiveness can be increased by assigning clients with certain characteristics to particular treatments. The present application proposes to continue work initiated and conducted over the past five years. The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching- and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments (Twelve Step, Cognitive Behavioral, or Motivational Enhancement) have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. Finally, it should be noted that the Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n", + "NIH_sample.jsonl@1765\n", + "{\"meta\": {\"APPLICATION_ID\": 2044499}, \"text\": \"The overall aim of study is to test the \\\"matching hypothesis\\\" that alcohol treatment effectiveness can be increased by assigning clients with certain characteristics to particular treatments. The present application proposes to continue work initiated and conducted over the past five years. The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching; and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments, Twelve Step, Cognitive Behavioral, or Motivational Enhancement, have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. The Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs, reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n" + ] + } + ], + "source": [ + "# Visual compare based on detection\n", + "print(f\"First duplication is {connected_component_reverse[0]}\")\n", + "print(\"You'll see the similar content in above documents\")\n", + "\n", + "for f_id in connected_component_reverse[0]:\n", + " print(f_id)\n", + " f_name, rid = f_id.split(\"@\")\n", + " ! sed -n {rid}p {f_name}\n" + ] + }, + { + "cell_type": "markdown", + "id": "okSSgtKDTnuE", + "metadata": { + "id": "okSSgtKDTnuE" + }, + "source": [ + "## 3. fuzzy deduplicate (unified detection and reduction)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "HQ_Ax_wxTnC9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HQ_Ax_wxTnC9", + "outputId": "98031a36-2956-434d-a576-b86bd782ea5c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "input is \n", + "+--------------------+--------------------+\n", + "| text| meta|\n", + "+--------------------+--------------------+\n", + "|The National Dome...|{\"APPLICATION_ID\"...|\n", + "|The Office of Pla...|{\"APPLICATION_ID\"...|\n", + "|Improving outcome...|{\"APPLICATION_ID\"...|\n", + "|This project is i...|{\"APPLICATION_ID\"...|\n", + "|The CCDF Policies...|{\"APPLICATION_ID\"...|\n", + "|The overall purpo...|{\"APPLICATION_ID\"...|\n", + "|This contract wil...|{\"APPLICATION_ID\"...|\n", + "|The purpose of th...|{\"APPLICATION_ID\"...|\n", + "|The purpose of th...|{\"APPLICATION_ID\"...|\n", + "|Intimate partner ...|{\"APPLICATION_ID\"...|\n", + "|ACF's Office of R...|{\"APPLICATION_ID\"...|\n", + "|The Temporary Ass...|{\"APPLICATION_ID\"...|\n", + "|Investing in Qual...|{\"APPLICATION_ID\"...|\n", + "|Current developme...|{\"APPLICATION_ID\"...|\n", + "|The proposed diss...|{\"APPLICATION_ID\"...|\n", + "|As the US populat...|{\"APPLICATION_ID\"...|\n", + "|Through employing...|{\"APPLICATION_ID\"...|\n", + "|The proposed mixe...|{\"APPLICATION_ID\"...|\n", + "|To better serve D...|{\"APPLICATION_ID\"...|\n", + "|A long standing r...|{\"APPLICATION_ID\"...|\n", + "+--------------------+--------------------+\n", + "only showing top 20 rows\n", + "\n", + "Total num_rows of input is 10000\n", + "num_bands is 9, ranges is 13\n", + "generate minHashLsh started ...\n", + "generate minHashLsh took 64.54865999799995 sec\n", + "generate_connected_components => duplicates started ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:00<00:00, 69283.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generate_connected_components => duplicates took 0.8337461669998447 sec\n", + "deduplicate input data started ...\n", + "deduplicate input data took 0.9185702700001457 sec\n", + "Completed!!\n", + " total processed 10000 documents\n", + " total detected 12 duplicated documents, exact deduplicated counts is 12\n", + " duplicate ratio is 0.0012\n", + "output is\n", + "+--------------+--------------------+--------------------+\n", + "|filename_docid| text| meta|\n", + "+--------------+--------------------+--------------------+\n", + "| global_id@0|The National Dome...|{\"APPLICATION_ID\"...|\n", + "| global_id@1|The Office of Pla...|{\"APPLICATION_ID\"...|\n", + "| global_id@2|Improving outcome...|{\"APPLICATION_ID\"...|\n", + "| global_id@3|This project is i...|{\"APPLICATION_ID\"...|\n", + "| global_id@4|The CCDF Policies...|{\"APPLICATION_ID\"...|\n", + "| global_id@5|The overall purpo...|{\"APPLICATION_ID\"...|\n", + "| global_id@6|This contract wil...|{\"APPLICATION_ID\"...|\n", + "| global_id@7|The purpose of th...|{\"APPLICATION_ID\"...|\n", + "| global_id@8|The purpose of th...|{\"APPLICATION_ID\"...|\n", + "| global_id@9|Intimate partner ...|{\"APPLICATION_ID\"...|\n", + "| global_id@10|ACF's Office of R...|{\"APPLICATION_ID\"...|\n", + "| global_id@11|The Temporary Ass...|{\"APPLICATION_ID\"...|\n", + "| global_id@12|Investing in Qual...|{\"APPLICATION_ID\"...|\n", + "| global_id@13|Current developme...|{\"APPLICATION_ID\"...|\n", + "| global_id@14|The proposed diss...|{\"APPLICATION_ID\"...|\n", + "| global_id@15|As the US populat...|{\"APPLICATION_ID\"...|\n", + "| global_id@16|Through employing...|{\"APPLICATION_ID\"...|\n", + "| global_id@17|The proposed mixe...|{\"APPLICATION_ID\"...|\n", + "| global_id@18|To better serve D...|{\"APPLICATION_ID\"...|\n", + "| global_id@19|A long standing r...|{\"APPLICATION_ID\"...|\n", + "+--------------+--------------------+--------------------+\n", + "only showing top 20 rows\n", + "\n", + "Total num_rows of output is 9988\n" + ] + } + ], + "source": [ + "from pyrecdp.core import SparkDataProcessor\n", + "from pyspark.sql.types import StructType, StructField, StringType\n", + "import pyspark.sql.functions as F\n", + "from pyrecdp.primitives.llmutils import near_dedup_spk\n", + "\n", + "data_files = [\"/content/test_data/NIH_sample.jsonl\"]\n", + "dup_dir = \"/content/fuzzy_dedup_spark\"\n", + "\n", + "ngram_size = 13\n", + "num_perm = 256\n", + "bands = 9\n", + "ranges = 13\n", + "rdp = SparkDataProcessor()\n", + "spark = rdp.spark\n", + "schema = StructType([\n", + " StructField(\"text\", StringType(), True),\n", + " StructField(\"meta\", StringType(), True)\n", + "])\n", + "spark_df = spark.read.text(data_files)\n", + "spark_df = spark_df.withColumn('jsonData', F.from_json(F.col('value'), schema)).select(\"jsonData.*\")\n", + "print(\"input is \")\n", + "spark_df.show()\n", + "print(f\"Total num_rows of input is {spark_df.count()}\")\n", + "\n", + "ret_df = near_dedup_spk(spark_df, ngram_size, num_perm, bands, ranges)\n", + "\n", + "print(\"output is\")\n", + "ret_df.show()\n", + "print(f\"Total num_rows of output is {ret_df.count()}\")\n", + "del rdp" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RecDP/examples/notebooks/llmutils/reader.ipynb b/RecDP/examples/notebooks/llmutils/reader.ipynb new file mode 100644 index 000000000..80d84f95d --- /dev/null +++ b/RecDP/examples/notebooks/llmutils/reader.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "BT-BjcaHj3eh" + }, + "source": [ + "## Install pyrecdp from github" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E3ffXVymdeKg" + }, + "outputs": [], + "source": [ + "! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KVAcXKenj_sZ" + }, + "source": [ + "## Install jdk for pyspark running" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Z1dPdx3dwf1" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMqBJ9eckIs6" + }, + "source": [ + "### Prepare test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2SFjDgfLd4qE" + }, + "outputs": [], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/github_sample_50.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/pii_test.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JjTnnzw_kRVV" + }, + "source": [ + "## Import filter functions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "Swy-H3HQepjA" + }, + "outputs": [], + "source": [ + "from pyrecdp.primitives.llmutils.text_fixer import text_fixer\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XXGify_rkXHZ" + }, + "source": [ + "## Specify input data path, output path,input data type and text types" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "UvIWsnV-fYmv" + }, + "outputs": [], + "source": [ + "data_dir = \"/content/test_data\"\n", + "out_dir = \"/content/output\"\n", + "in_type = \"jsonl\"\n", + "text_types = [\"html\", 'latex', \"codes\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nj0LuIsFkhRr" + }, + "source": [ + "## Process the 'text' column and remove unnecessary control characters, HTML elements and comments\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vftAYZMwfllh", + "outputId": "21041b9e-3643-427c-ca71-0eca0bf622b9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "Load data from jsonl file started ...\n", + "Load data from jsonl file took 0.7552096419999543 sec\n", + "Processing data started ...\n", + "Processing data took 0.12260321699977794 sec\n", + "Save data started ...\n", + "Save data took 11.986804642999687 sec\n", + "Completed!!\n", + " Loaded and processed total 602 documents\n" + ] + } + ], + "source": [ + "text_fixer(data_dir, in_type, out_dir,text_types)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb b/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb new file mode 100644 index 000000000..80d84f95d --- /dev/null +++ b/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "BT-BjcaHj3eh" + }, + "source": [ + "## Install pyrecdp from github" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E3ffXVymdeKg" + }, + "outputs": [], + "source": [ + "! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KVAcXKenj_sZ" + }, + "source": [ + "## Install jdk for pyspark running" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Z1dPdx3dwf1" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMqBJ9eckIs6" + }, + "source": [ + "### Prepare test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2SFjDgfLd4qE" + }, + "outputs": [], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/github_sample_50.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/pii_test.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JjTnnzw_kRVV" + }, + "source": [ + "## Import filter functions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "Swy-H3HQepjA" + }, + "outputs": [], + "source": [ + "from pyrecdp.primitives.llmutils.text_fixer import text_fixer\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XXGify_rkXHZ" + }, + "source": [ + "## Specify input data path, output path,input data type and text types" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "UvIWsnV-fYmv" + }, + "outputs": [], + "source": [ + "data_dir = \"/content/test_data\"\n", + "out_dir = \"/content/output\"\n", + "in_type = \"jsonl\"\n", + "text_types = [\"html\", 'latex', \"codes\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nj0LuIsFkhRr" + }, + "source": [ + "## Process the 'text' column and remove unnecessary control characters, HTML elements and comments\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vftAYZMwfllh", + "outputId": "21041b9e-3643-427c-ca71-0eca0bf622b9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "Load data from jsonl file started ...\n", + "Load data from jsonl file took 0.7552096419999543 sec\n", + "Processing data started ...\n", + "Processing data took 0.12260321699977794 sec\n", + "Save data started ...\n", + "Save data took 11.986804642999687 sec\n", + "Completed!!\n", + " Loaded and processed total 602 documents\n" + ] + } + ], + "source": [ + "text_fixer(data_dir, in_type, out_dir,text_types)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RecDP/examples/notebooks/llmutils/writer.ipynb b/RecDP/examples/notebooks/llmutils/writer.ipynb new file mode 100644 index 000000000..a8947fa8a --- /dev/null +++ b/RecDP/examples/notebooks/llmutils/writer.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Install pyrecdp from github" + ], + "metadata": { + "id": "IJ8RaakCnsyW" + }, + "id": "IJ8RaakCnsyW" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" + }, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Install jdk for pyspark running" + ], + "metadata": { + "id": "TWv2WvL6nzTA" + }, + "id": "TWv2WvL6nzTA" + }, + { + "cell_type": "code", + "source": [ + "!DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" + ], + "metadata": { + "id": "vk2j9kKbSrLT" + }, + "id": "vk2j9kKbSrLT", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Prepare test data" + ], + "metadata": { + "id": "ljHxOJL8n-5E" + }, + "id": "ljHxOJL8n-5E" + }, + { + "cell_type": "code", + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl" + ], + "metadata": { + "id": "bWDi0vtLoHwm" + }, + "id": "bWDi0vtLoHwm", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Import classifier function" + ], + "metadata": { + "id": "XusjrHT4tOdj" + }, + "id": "XusjrHT4tOdj" + }, + { + "cell_type": "code", + "source": [ + "from pyrecdp.primitives.llmutils import classify_spark, language_identify_spark\n", + "from pyrecdp.primitives.llmutils.utils import get_target_file_list\n", + "from huggingface_hub import hf_hub_download\n", + "from pyrecdp.core import SparkDataProcessor" + ], + "metadata": { + "id": "SkccgiE9oNyL" + }, + "id": "SkccgiE9oNyL", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Specify variables" + ], + "metadata": { + "id": "w0kSy1Fotwcl" + }, + "id": "w0kSy1Fotwcl" + }, + { + "cell_type": "code", + "source": [ + "fasttext_model_dir = hf_hub_download(repo_id=\"facebook/fasttext-language-identification\", filename=\"model.bin\")\n", + "data_dir = \"/content/test_data/\"\n", + "data_file = '/content/test_data/arxiv_sample_100.jsonl'\n", + "classify_save_path = \"/content/test_data/output/classify_spark\"" + ], + "metadata": { + "id": "xM8XygxBXKfh" + }, + "id": "xM8XygxBXKfh", + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Load data" + ], + "metadata": { + "id": "TVbiJxG3uNX5" + }, + "id": "TVbiJxG3uNX5" + }, + { + "cell_type": "code", + "source": [ + "rdp = SparkDataProcessor()\n", + "spark=rdp.spark\n", + "spark_df = spark.read.json(data_file)\n", + "lid_df = language_identify_spark(spark_df, fasttext_model_dir)\n", + "print(\"input is\")\n", + "lid_df.show()" + ], + "metadata": { + "id": "ex3QUs1tuVb0", + "outputId": "d4d104a8-4b47-4996-c8fc-996e73e554bd", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "ex3QUs1tuVb0", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "/root/.cache/huggingface/hub/models--facebook--fasttext-language-identification/snapshots/3af127d4124fc58b75666f3594bb5143b9757e78/model.bin\n", + "input is\n", + "+--------------------+--------------------+--------+\n", + "| meta| text|language|\n", + "+--------------------+--------------------+--------+\n", + "|{2203.15369, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{math/9807097, en...|\\section{Introduc...|eng_Latn|\n", + "|{2008.06948, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{cond-mat/9807071...|\\section{Introduc...|eng_Latn|\n", + "|{2210.10650, en, ...|\\section{\\label{s...|eng_Latn|\n", + "|{astro-ph/9807119...|\\section{Introduc...|eng_Latn|\n", + "|{2111.03152, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{1606.04992, en, ...|\\n\\n\\section{Intr...|eng_Latn|\n", + "|{1608.03404, en, ...|\\section{introduc...|eng_Latn|\n", + "|{1904.10101, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{cond-mat/9807275...|\\section{Introduc...|eng_Latn|\n", + "|{2109.05334, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{1512.06966, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{2112.04926, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{2202.01000, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{2209.13421, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{1103.5603, en, 2...|\\section{Introduc...|eng_Latn|\n", + "|{1001.3679, en, 2...|\\section{Introduc...|eng_Latn|\n", + "|{1702.08222, en, ...|\\section{Introduc...|eng_Latn|\n", + "|{2201.05495, en, ...|\\section{Introduc...|eng_Latn|\n", + "+--------------------+--------------------+--------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Split the data into sub buckets according to lang column" + ], + "metadata": { + "id": "QEMpJ79yuf8W" + }, + "id": "QEMpJ79yuf8W" + }, + { + "cell_type": "code", + "source": [ + "classify_spark(lid_df, \"language\", classify_save_path, \"file://\")" + ], + "metadata": { + "id": "iQlDQ8SrkuN9", + "outputId": "9dbebae7-89b6-4f1b-cf52-8d91588bb72e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "iQlDQ8SrkuN9", + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spilt data started ...\n", + "Spilt data took 28.158131373999822 sec\n", + "Completed!!\n", + " total classify the spark dataframe by language for 100 documents\n", + " All the classified data are saving under the folder: file:///content/test_data/output/classify_spark\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DataFrame[meta: struct, text: string, language: string]" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!ls -al $classify_save_path" + ], + "metadata": { + "id": "Yl9V2MAVvk29", + "outputId": "7d99fd6e-6506-49f5-b2a4-52105d960964", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "Yl9V2MAVvk29", + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "total 16\n", + "drwxr-xr-x 3 root root 4096 Oct 7 10:30 .\n", + "drwxr-xr-x 3 root root 4096 Oct 7 10:29 ..\n", + "drwxr-xr-x 2 root root 4096 Oct 7 10:29 'language=eng_Latn'\n", + "-rw-r--r-- 1 root root 0 Oct 7 10:30 _SUCCESS\n", + "-rw-r--r-- 1 root root 8 Oct 7 10:30 ._SUCCESS.crc\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "wKsYSPSnpU8x" + }, + "id": "wKsYSPSnpU8x", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "colab": { + "provenance": [], + "toc_visible": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/RecDP/pyrecdp/LLM/README.md b/RecDP/pyrecdp/LLM/README.md index 0caf2e162..b26219f0d 100644 --- a/RecDP/pyrecdp/LLM/README.md +++ b/RecDP/pyrecdp/LLM/README.md @@ -9,25 +9,27 @@ RecDP LLM is a set of python components that enables quick and easy establish of | Type | notebook | Description | supports | Verified dataset & size | | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ---------------------------------------------------- | ------------------------------------- | -| [ Convert ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/convert.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/convert.ipynb) | Convert individual document, jsonl, csv to parquet | text, jsonl, csv | RedPajama - 2 TB | -| [ Filter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/filter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/filter.ipynb) | Filter out document based on condition | profanity_check, black-list, url_based, length_based | RedPajama - 2 TB | -| [ Language Identify ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/language_identify.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/language_identify.ipynb) | Inentify major language type of document | en, zh, fr, de, .. total 25 langs | RedPajama - 2 TB | -| [ Classify ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/convert.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/classify.ipynb) | Classify data into sub buckets | meta fields, language | RefinedWeb - 1.7 TB | -| [ Fuzzy Deduplicator ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/classify.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/fuzzy_deduplication.ipynb) | Detect and reduce duplication based on document context | minHashLSH, minHashLSH-shortdoc | PILE - 200 GB | -| [ Decontamination ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/decontaminate.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/decontamination.ipynb) | Detect and reduce duplication based on exact same content | sha256-hash | RefinedWeb - 1.7 TB, RedPajama - 2 TB | -| [ PII Removal ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/pii_remove.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/pii_removal.ipynb) | Detect and replace personal infomation in document | email, phone, ip, username, password | RefinedWeb - 1.7 TB | -| [ Text Normalization ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/text_normalization.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_normalization.ipynb) | Fix and clean texts | ftfy, punctuation_normalization | RedPajama - 2 TB , RedPajama - 2 TB | -| [ Text Fixer ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/text_fixer.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_fixer.ipynb) | Clean repeated format in html, latex, codes | html, latex, codes | RefinedWeb - 1.7 TB | -| [ Document splitter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/sentence_split.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/document_split.ipynb) | Split Document into multiple sub documents | chapter_based, length_based | RefinedWeb - 1.7 TB | +| [ Filter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_filter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/filter.ipynb) | Filter out document based on condition | profanity-based, black-list, url_based, length_based | RedPajama - 2 TB | +| [ Text Bytesize ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_bytesize.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/bytesize.ipynb) | Get text bytes size | | RedPajama - 2 TB | +| [ Text Fixer ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_fixer.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_fixer.ipynb) | Clean repeated format in html, latex, codes | html, latex, codes | RefinedWeb - 1.7 TB | +| [ Language Identify ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_language_identify.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/language_identify.ipynb) | Inentify major language type of document | en, zh, fr, de, .. total 25 langs | RedPajama - 2 TB | +| [ Fuzzy Deduplicator ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_deduplication.py#L99) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/fuzzy_deduplication.ipynb) | Detect and reduce duplication based on document context | minHashLSH | PILE - 200 GB | +| [ Global Decuplicator ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_deduplication.py#L194) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/decontamination.ipynb) | Detect and reduce duplication based on exact same content | sha256-hash | RefinedWeb - 1.7 TB, RedPajama - 2 TB | +| [ Document splitter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_split.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/document_split.ipynb) | Split Document into multiple sub documents | chapter_based, length_based | RefinedWeb - 1.7 TB | +| [ PII Removal ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_pii_remove.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/pii_removal.ipynb) | Detect and replace personal infomation in document | email, phone, ip, username, password | RefinedWeb - 1.7 TB | +| [ Reader ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_reader.py#L16) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/reader.ipynb) | Read data from directory | jsonl, parquet, | RefinedWeb - 1.7 TB | +| [ Converter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_converter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/convert.ipynb) | Read and convert unstructed data to unified format | html, document, image, pdf, ... | RefinedWeb - 1.7 TB | +| [ Writer ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_writer.py#L7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/writer.ipynb) | write data to directory | jsonl, parquet | RefinedWeb - 1.7 TB | +| [ ClassifyWriter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_writer.py#L47) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/classify.ipynb) | Classify and write data into sub buckets | meta fields, language | RefinedWeb - 1.7 TB | +| [ Prompt Enhancement ](#) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/prompt_enhancement.ipynb) | creates high-complexity instructions from existing instruct-tuned LLM models | PromptSource, self-instruct, evol-instruct(wizardLM) | alpaca | -## LLM data quality enhancement module +## LLM Data Quality Analysis -| Type | notebook | Description | supports | -| :------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------- | :--------------------------------------------------- | -| [ Prompt Enhancement ](#) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/prompt_enhancement.ipynb) | creates high-complexity instructions from existing instruct-tuned LLM models | PromptSource, self-instruct, evol-instruct(wizardLM) | -| [ Text Quality Assessment ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/quality_classifier.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_quality_assessment.ipynb) | Assess text quality with a logistic regression classifier | GPT-3 quality scorer, | -| [ Data Diversity Control ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/data_diversity_control.ipynb) | Control data's diversity and coverage | rouge-l similarity | -| [ Toxicity and Bias Control ](#) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb) | Calculates toxicity scores for text objects and filters according to the threshold | TBD | +| Diversity | GPT-3 Scoring | Toxicity | +| :-------- | :---------- | :------------| +| Visualize the diversity distribution of data | Leverage GPT-3 to scoring | Visualize Toxicity probability | +| ![diversity](/RecDP/resources/diversity_analysis.png) | ![quality](/RecDP/resources/quality_scoring.png) | ![toxicity](/RecDP/resources/toxicity_analysis.png)| +| [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_quality_assessment.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/data_diversity_control.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb) | # Getting Start @@ -39,70 +41,60 @@ pip install pyrecdp --pre ``` ## * run with pipeline +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb) ``` from pyrecdp.primitives.operations import * from pyrecdp.LLM import TextPipeline from IPython.display import display -pipeline = TextPipeline() +from pyrecdp.primitives.operations import * +from pyrecdp.LLM import ResumableTextPipeline + +pipeline = ResumableTextPipeline() ops = [ - JsonlReader(input_dir = "in_path"), + JsonlReader("data/"), URLFilter(), LengthFilter(), ProfanityFilter(), TextFix(), LanguageIdentify(), - Classify(), - FuzzyDeduplicate(), - DocumentSplit(), - GlobalDeduplicate(), PIIRemoval(), - ParquetWriter(out_dir = "out_path"), + PerfileParquetWriter("ResumableTextPipeline_output") ] -ret = pipeline.add_operations(ops).execute(dataset) - -pd = ret.preview_as_pandas() -display(pd) - +pipeline.add_operations(ops) +pipeline.execute() pipeline.export_to_yaml("custom_llm_data_pipeline.yaml") ``` - -## * run with individual component - * check colab links of individual component above - * cmdline mode provided to process your data from data_dir - * python function mode provided to easily integrate to your codes - * spark dataframe mode provided to easily integrate to your spark env - - -## LLM Data Quality Analysis - -| Diversity | GPT-3 Scoring | Toxicity | -| :-------- | :---------- | :------------| -| Visualize the diversity distribution of data | Leverage GPT-3 to scoring | Visualize Toxicity probability | -| ![diversity](/RecDP/resources/diversity_analysis.png) | ![quality](/RecDP/resources/quality_scoring.png) | ![toxicity](/RecDP/resources/toxicity_analysis.png)| -| [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_quality_assessment.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/data_diversity_control.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb) | - - - -## Use Cases - -### * Foundation Model data preparation +or ``` -from pyrecdp.LLM import TextPipeline -pipeline = TextPipeline("usecase/itl_foundation_pipeline.yaml") +from pyrecdp.LLM import ResumableTextPipeline +pipeline = TextPipeline("custom_llm_data_pipeline.yaml") ret = pipeline.execute() -pd = ret.preview_as_pandas() -display(pd) ``` -### * FineTune Model Data Preparation -``` -from pyrecdp.LLM import TextPipeline -pipeline = TextPipeline("usecase/itl_finetune_pipeline.yaml") -ret = pipeline.execute() -pd = ret.preview_as_pandas() -display(pd) +## * run with individual component + * cmdline mode ``` +python pyrecdp/primitives/llmutils/language_identify.py \ + --data_dir tests/llm_data \ + --language_identify_output_dir output \ + --fasttext_model_dir ./cache/RecDP/models/lib.bin +``` + * operation-based API - ray mode +``` +from pyrecdp.primitives.operations import LengthFilter + +dataset = … # Ray Dataset +op = LengthFilter() +op.process_rayds(dataset) +``` + * operation-based API - spark mode +``` +from pyrecdp.primitives.operations import LengthFilter +sparkdf = … # Spark Dataframe +op = LengthFilter() +op.process_spark(sparkdf) +``` \ No newline at end of file diff --git a/RecDP/pyrecdp/autofe/README.md b/RecDP/pyrecdp/autofe/README.md index ad4813db2..72f7161ee 100644 --- a/RecDP/pyrecdp/autofe/README.md +++ b/RecDP/pyrecdp/autofe/README.md @@ -14,10 +14,10 @@ Steps explained: | Workflow Name | Description | | --- | --- | -| [NYC taxi fare](applications/nyc_taxi_fare/interactive_notebook.ipynb) | Fare prediction based on NYC taxi dataset | -| [Amazon Product Review](applications/amazon_product_review/interactive_notebook.ipynb) | Product recommandation based on reviews from Amazon | -| [IBM Card Transaction Fraud Detect](applications/fraud_detect/interactive_notebook.ipynb) | Recognize fraudulent credit card transactions | -| [Twitter Recsys](applications/twitter_recsys/interactive_notebook.ipynb) | Real-world task of tweet engagement prediction | -| [Outbrain](applications/outbrain_ctr/interactive_notebook.ipynb) | Click prediction for recommendation system | -| [Covid19 TabUtils](applications/covid19_tabutils/interactive_notebook.ipynb) | integration example with Tabular Utils | -| [PredictiveAssetsMaintenance](applications/predictive_assets_maintenance/interactive_notebook.ipynb) | integration example with predictive assets maintenance use case | +| [NYC taxi fare](https://colab.research.google.com/github/intel/auto-feature-engineering/blob/main/applications/nyc_taxi_fare/interactive_notebook.ipynb) | Fare prediction based on NYC taxi dataset | +| [Amazon Product Review](https://colab.research.google.com/github/intel/auto-feature-engineering/blob/main/applications/amazon_product_review/interactive_notebook.ipynb) | Product recommandation based on reviews from Amazon | +| [IBM Card Transaction Fraud Detect](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/fraud_detection/recdp_aucpr.ipynb) | Recognize fraudulent credit card transactions | +| [Recsys 2023](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/recsys2023/recdp.ipynb) | Real-world task of tweet engagement prediction | +| [Outbrain](https://colab.research.google.com/github/intel/auto-feature-engineering/blob/main/applications/outbrain_ctr/interactive_notebook.ipynb) | Click prediction for recommendation system | +| [Covid19 TabUtils](https://colab.research.google.com/github/intel/auto-feature-engineering/blob/main/applications/covid19_tabutils/interactive_notebook.ipynb) | integration example with Tabular Utils | +| [PredictiveAssetsMaintenance](https://colab.research.google.com/github/intel/auto-feature-engineering/blob/main/applications/predictive_assets_maintenance/interactive_notebook.ipynb) | integration example with predictive assets maintenance use case | From a91a25ba1ae37eef91c59886356556b74f4f0390 Mon Sep 17 00:00:00 2001 From: Yu-Zhou Date: Wed, 11 Oct 2023 23:41:02 +0800 Subject: [PATCH 06/47] [v1.2][ISSUE-374] deltatuner denas-ssf multiple model validation and model merge (#375) * change save path * simplify test code * rename path * add * add * add * add * add * bug fix * add * add * add * add * add * restore * add * add * add * add * Dtuner models (#7) * bug fix * add * add * update readme * delete * update test scripts * update * support direct eval after merging model * update * update * ssf load previous config * automatic fill deltaargs * update config * copy code to merged dir * add test * update readme for merge model * bug fix * check the code file existence * update readme * update model name list * refine test scripts * bug fix * bug fix * allow tokenizer to be None * bug fix * bug fix * fix import * update path * move merge testing scripts --- e2eAIOK/deltatuner/README.md | 10 +- e2eAIOK/deltatuner/deltatuner/deltatuner.py | 4 +- .../deltatuner/deltatuner/deltatuner_model.py | 16 +- .../deltatuner/tuner/deltatuner_ssf.py | 45 +++++ e2eAIOK/deltatuner/deltatuner/utils/config.py | 6 +- example/instruction_tuning_pipeline/README.md | 4 + .../finetune_clm.py | 50 ++++-- tests/deltatuner/cicd/run_denas_lora.sh | 85 ++++------ tests/deltatuner/cicd/run_denas_ssf.sh | 46 ++--- tests/deltatuner/cicd/run_lora.sh | 85 ++++------ tests/deltatuner/cicd/run_ssf.sh | 48 ++---- tests/deltatuner/cicd/run_ssf_model.sh | 47 ++++++ tests/deltatuner/finetune/run_denas_lora.sh | 92 +++++----- tests/deltatuner/finetune/run_denas_ssf.sh | 47 ++++++ tests/deltatuner/finetune/run_lora.sh | 93 +++++------ tests/deltatuner/finetune/run_ssf.sh | 47 ++++++ .../deltatuner/finetune/ssf/llama2-7b-lora.sh | 24 --- .../finetune/ssf/llama2-7b-ssf-allmodules.sh | 135 --------------- .../finetune/ssf/llama2-7b-ssf-denas.sh | 157 ------------------ tests/deltatuner/finetune/ssf/mpt-7b-full.sh | 23 --- tests/deltatuner/finetune/ssf/mpt-7b-lora.sh | 24 --- .../ssf/mpt-7b-ssf-allmodules-denas.sh | 120 ------------- .../finetune/ssf/mpt-7b-ssf-allmodules.sh | 98 ----------- tests/deltatuner/finetune/ssf/mpt-7b-ssf.sh | 51 ------ .../{finetune/ssf => merge_model}/readme.md | 22 +-- .../ssf-merge-test.sh} | 40 ++--- 26 files changed, 461 insertions(+), 958 deletions(-) create mode 100644 tests/deltatuner/cicd/run_ssf_model.sh create mode 100644 tests/deltatuner/finetune/run_denas_ssf.sh create mode 100644 tests/deltatuner/finetune/run_ssf.sh delete mode 100755 tests/deltatuner/finetune/ssf/llama2-7b-lora.sh delete mode 100755 tests/deltatuner/finetune/ssf/llama2-7b-ssf-allmodules.sh delete mode 100755 tests/deltatuner/finetune/ssf/llama2-7b-ssf-denas.sh delete mode 100755 tests/deltatuner/finetune/ssf/mpt-7b-full.sh delete mode 100755 tests/deltatuner/finetune/ssf/mpt-7b-lora.sh delete mode 100755 tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules-denas.sh delete mode 100755 tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules.sh delete mode 100755 tests/deltatuner/finetune/ssf/mpt-7b-ssf.sh rename tests/deltatuner/{finetune/ssf => merge_model}/readme.md (81%) rename tests/deltatuner/{finetune/ssf/llama2-7b-ssf.sh => merge_model/ssf-merge-test.sh} (51%) diff --git a/e2eAIOK/deltatuner/README.md b/e2eAIOK/deltatuner/README.md index b6d6d5b40..3c503e1b8 100644 --- a/e2eAIOK/deltatuner/README.md +++ b/e2eAIOK/deltatuner/README.md @@ -84,10 +84,10 @@ Please refer to [example page](https://github.com/intel/e2eAIOK/tree/main/exampl | Model | LoRA | SSF | |--------------| ---- | ---- | | GPT-2 | ✅ | | -| GPT-J | ✅ | | -| Bloom | ✅ | | -| OPT | ✅ | | -| GPT-Neo | ✅ | | -| Falcon | ✅ | | +| GPT-J | ✅ | ✅ | +| Bloom | ✅ | ✅ | +| OPT | ✅ | ✅ | +| GPT-Neo | ✅ | ✅ | +| Falcon | ✅ | ✅ | | LLaMA | ✅ | ✅ | | MPT | ✅ | ✅ | diff --git a/e2eAIOK/deltatuner/deltatuner/deltatuner.py b/e2eAIOK/deltatuner/deltatuner/deltatuner.py index f6a3adbf3..2c172d98b 100644 --- a/e2eAIOK/deltatuner/deltatuner/deltatuner.py +++ b/e2eAIOK/deltatuner/deltatuner/deltatuner.py @@ -39,9 +39,9 @@ def optimize(model, tokenizer, algo: str="auto", adapter_name: str="default", de if isinstance(peft_config, LoraConfig) or isinstance(peft_config, SSFConfig): if peft_config.task_type not in MODEL_TYPE_TO_DELTATUNER_MODEL_MAPPING: - model = DeltaTunerModel(model, tokenizer, peft_config, adapter_name, deltatuning_args) + model = DeltaTunerModel(model, peft_config, adapter_name, deltatuning_args, tokenizer) else: - model = MODEL_TYPE_TO_DELTATUNER_MODEL_MAPPING[peft_config.task_type](model, tokenizer, peft_config, adapter_name, deltatuning_args) + model = MODEL_TYPE_TO_DELTATUNER_MODEL_MAPPING[peft_config.task_type](model, peft_config, adapter_name, deltatuning_args, tokenizer) return model else: raise NotImplementedError("Current algorithm {} is not supported in deltatuner. ".format(algo)) \ No newline at end of file diff --git a/e2eAIOK/deltatuner/deltatuner/deltatuner_model.py b/e2eAIOK/deltatuner/deltatuner/deltatuner_model.py index 8d6baefaa..917c55f18 100644 --- a/e2eAIOK/deltatuner/deltatuner/deltatuner_model.py +++ b/e2eAIOK/deltatuner/deltatuner/deltatuner_model.py @@ -52,7 +52,7 @@ def setup_seed(seed): torch.manual_seed(seed) class DeltaTunerModel(PeftModel, torch.nn.Module): - def __init__(self, model, tokenizer: AutoTokenizer, peft_config: PeftConfig, adapter_name: str = "default", denas_config: DeltaTunerArguments = None): + def __init__(self, model, peft_config: PeftConfig, adapter_name: str = "default", denas_config: DeltaTunerArguments = None, tokenizer: AutoTokenizer = None): torch.nn.Module.__init__(self) self.base_model = model self.tokenizer = tokenizer @@ -104,7 +104,8 @@ def _init_denas_params_(self): self.denas_config.model_id = self.base_model.config._name_or_path self.denas_config.tokenizer = self.tokenizer self.denas_config.max_param_limits = sum(param.numel() for param in self.base_model.parameters() if param.requires_grad) / 10.**6 if self.denas_config.max_param_limits is None else self.denas_config.max_param_limits - self.denas_config.budget_latency_max = network_latency(self.base_model, self.tokenizer, batch_size=self.denas_config.batch_size) if self.denas_config.budget_latency_max is not None else self.denas_config.budget_latency_max + if self.tokenizer: + self.denas_config.budget_latency_max = network_latency(self.base_model, self.tokenizer, batch_size=self.denas_config.batch_size) if self.denas_config.budget_latency_max is not None else self.denas_config.budget_latency_max def search(self, denas_config, super_net, search_space): setup_seed(denas_config.random_seed) @@ -219,6 +220,13 @@ def from_pretrained( from .mapping import DELTATUNER_TYPE_TO_CONFIG_MAPPING, MODEL_TYPE_TO_DELTATUNER_MODEL_MAPPING denas_config = kwargs.pop("denas_config", None) + best_structure_file = os.path.join(model_id, "best_model_structure.txt") + if os.path.isfile(best_structure_file): + denas_config.denas = True + denas_config.best_model_structure = best_structure_file + else: + denas_config.denas = False + # load the config if config is None: peft_type_name = PeftConfig._get_peft_type( @@ -376,8 +384,8 @@ def load_adapter(self, model_id: str, adapter_name: str, is_trainable: bool = Fa return load_result class DelatunerModelForCausalLM(DeltaTunerModel): - def __init__(self, model: PeftModel, tokenizer: AutoTokenizer, peft_config: PeftConfig, adapter_name: str = "default", denas_config: DeltaTunerArguments = None): - super().__init__(model, tokenizer, peft_config, adapter_name, denas_config) + def __init__(self, model: PeftModel, peft_config: PeftConfig, adapter_name: str = "default", denas_config: DeltaTunerArguments = None, tokenizer: AutoTokenizer = None): + super().__init__(model, peft_config, adapter_name, denas_config, tokenizer) self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation def forward( diff --git a/e2eAIOK/deltatuner/deltatuner/tuner/deltatuner_ssf.py b/e2eAIOK/deltatuner/deltatuner/tuner/deltatuner_ssf.py index 48cff341b..2a8716695 100644 --- a/e2eAIOK/deltatuner/deltatuner/tuner/deltatuner_ssf.py +++ b/e2eAIOK/deltatuner/deltatuner/tuner/deltatuner_ssf.py @@ -1,3 +1,4 @@ +import os import math import re import warnings @@ -80,6 +81,50 @@ class SSFConfig(PeftConfig): def __post_init__(self): self.peft_type = DeltaTunerType.SSF + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: str, subfolder: Optional[str] = None, **kwargs): + r""" + This method loads the configuration of your adapter model from a directory. + + Args: + pretrained_model_name_or_path (`str`): + The directory or the Hub repository id where the configuration is saved. + kwargs (additional keyword arguments, *optional*): + Additional keyword arguments passed along to the child class initialization. + """ + from peft.utils import CONFIG_NAME + + path = ( + os.path.join(pretrained_model_name_or_path, subfolder) + if subfolder is not None + else pretrained_model_name_or_path + ) + + hf_hub_download_kwargs, class_kwargs, _ = cls._split_kwargs(kwargs) + + if os.path.isfile(os.path.join(path, CONFIG_NAME)): + config_file = os.path.join(path, CONFIG_NAME) + else: + try: + config_file = hf_hub_download( + pretrained_model_name_or_path, CONFIG_NAME, subfolder=subfolder, **hf_hub_download_kwargs + ) + except Exception: + raise ValueError(f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'") + + loaded_attributes = cls.from_json_file(config_file) + + config_cls = cls + + config = config_cls(**class_kwargs) + + for key, value in loaded_attributes.items(): + if hasattr(config, key): + setattr(config, key, value) + + return config + + class DeltaSSFSearchSpace: @classmethod def generate_search_space(cls, model, denas_config): diff --git a/e2eAIOK/deltatuner/deltatuner/utils/config.py b/e2eAIOK/deltatuner/deltatuner/utils/config.py index 1b3fea166..64d34c058 100644 --- a/e2eAIOK/deltatuner/deltatuner/utils/config.py +++ b/e2eAIOK/deltatuner/deltatuner/utils/config.py @@ -1,11 +1,13 @@ import enum from dataclasses import field from typing import List, Optional, Tuple, Union +from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING class DeltaTunerType(str, enum.Enum): SSF = "SSF" -TRANSFORMERS_MODELS_TO_SSF_TARGET_MODULES_MAPPING = { +TRANSFORMERS_MODELS_TO_SSF_TARGET_MODULES_MAPPING = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING +TRANSFORMERS_MODELS_TO_SSF_TARGET_MODULES_MAPPING.update({ "llama": ["q_proj", "v_proj"], "mpt": ["Wqkv","out_proj","up_proj","down_proj"] -} \ No newline at end of file +}) \ No newline at end of file diff --git a/example/instruction_tuning_pipeline/README.md b/example/instruction_tuning_pipeline/README.md index 59b047b25..6c2779a0e 100644 --- a/example/instruction_tuning_pipeline/README.md +++ b/example/instruction_tuning_pipeline/README.md @@ -125,6 +125,10 @@ python example/instruction_tuning_pipeline/finetune_clm.py \ --bf16 True 2>&1 | tee log/mpt-lora-run-1epoch.log ``` +- If you are using 4th Xeon or later (SPR etc.), please specify the `--bf16 --no_cuda` args; +- If you are using 3th Xeon or before (ICX etc.): please specify the `--no_cuda` args; +- If you are using GPU server: please specify the `--fp16` args. + ## Evaluate the model For model evaluation, we follow the same method in [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), which evaluate 4 key benchmarks in the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). diff --git a/example/instruction_tuning_pipeline/finetune_clm.py b/example/instruction_tuning_pipeline/finetune_clm.py index 6da81f8fc..6740ad098 100755 --- a/example/instruction_tuning_pipeline/finetune_clm.py +++ b/example/instruction_tuning_pipeline/finetune_clm.py @@ -20,6 +20,8 @@ import datasets import logging import os +import json +import errno import sys import transformers from transformers.modeling_utils import unwrap_model @@ -292,6 +294,10 @@ class FinetuneArguments: default=False, metadata={"help": "save merged model"}, ) + merge_model_code_dir: Optional[str] = field( + default="", + metadata={"help": "the code path of base model with enable bias on target modules for ssf algo"}, + ) PROMPT_DICT = { "prompt_with_input": ( @@ -700,12 +706,7 @@ def concatenate_data(dataset, max_seq_length): if finetune_args.delta: if finetune_args.resume_peft != "": - if finetune_args.delta == "ssf": - peft_config = SSFConfig(target_modules=deltatuner_args.ssf_target_modules, - bias="none", - task_type="CAUSAL_LM", - ) - model = DeltaTunerModel.from_pretrained(model, finetune_args.resume_peft, config=peft_config, denas_config=deltatuner_args) + model = DeltaTunerModel.from_pretrained(model, finetune_args.resume_peft, denas_config=deltatuner_args) else: model = deltatuner.optimize(model, tokenizer, algo=finetune_args.delta, deltatuning_args=deltatuner_args) logger.info("***deltatuner optimized model parameter***") @@ -757,6 +758,37 @@ def concatenate_data(dataset, max_seq_length): training_args.output_dir, state_dict=unwrapped_model.state_dict() ) + if finetune_args.save_merged_model: + if isinstance(model, PeftModel): + model = model.merge_and_unload() + saved_dir = os.path.join(training_args.output_dir, "merged_model") + os.makedirs(saved_dir, exist_ok=True) + print(f"copy base model config to {saved_dir}") + os.system(f"cp {model_args.model_name_or_path}/* {saved_dir}") + print(f"remove unnecessary file from {model_args.model_name_or_path}") + os.system(f"rm {saved_dir}/*.bin* {saved_dir}/*.safetensors*") + print(f"Save merged model to {saved_dir}") + torch.save(model.state_dict(), os.path.join(saved_dir, "pytorch_model.bin")) + if finetune_args.delta == 'ssf': + if os.path.exists(finetune_args.merge_model_code_dir): + print(f"copy merged model code to {saved_dir}") + os.system(f"cp {finetune_args.merge_model_code_dir}/* {saved_dir}") + else: + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), finetune_args.merge_model_code_dir) + pre_train_config_file = os.path.join(saved_dir, "config.json") + with open(pre_train_config_file, "r") as file: + config_json = json.load(file) + adapter_config_file = os.path.join(finetune_args.resume_peft, "adapter_config.json") + with open(adapter_config_file, "r") as file: + adapter_config_json = json.load(file) + config_json['target_modules'] = adapter_config_json['target_modules'] + + best_structure_file = os.path.join(finetune_args.resume_peft, "best_model_structure.txt") + if os.path.isfile(best_structure_file): + with open(best_structure_file, "r") as file: + best_structure_json = json.loads(file.readline().strip()) + config_json['best_model_structure'] = best_structure_json + # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") @@ -771,12 +803,6 @@ def concatenate_data(dataset, max_seq_length): trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) - if finetune_args.save_merged_model: - if isinstance(model, PeftModel): - merged_model = model.merge_and_unload() - os.makedirs(os.path.join(training_args.output_dir,"merged_model"),exist_ok=True) - torch.save(merged_model.state_dict(),os.path.join(training_args.output_dir,"merged_model","pytorch_model.bin")) - print(f"Save merged model to {training_args.output_dir}") def profile_model(model, train_dataset, data_collator, args): from transformers import get_scheduler diff --git a/tests/deltatuner/cicd/run_denas_lora.sh b/tests/deltatuner/cicd/run_denas_lora.sh index 6b165109c..35226069d 100644 --- a/tests/deltatuner/cicd/run_denas_lora.sh +++ b/tests/deltatuner/cicd/run_denas_lora.sh @@ -7,56 +7,41 @@ pip uninstall wandb -y cd /home/vmagent/app/e2eaiok -mkdir -p log models - DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" -# fine-tune mpt-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path $DATA_PATH"/mpt-7b" \ - --train_file $DATA_PATH"/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/mpt_denas-lora_model \ - --peft lora \ - --delta lora \ - --debugs --max_epochs 1 --population_num 1 --crossover_num 1 --mutation_num 1 --select_num 1 \ - --trust_remote_code True \ - --no_cuda \ - 2>&1 | tee log/mpt-denas-lora-run-1epoch.log +mkdir -p $LOG_PATH $MODEL_SAVE_PATH -# fine-tune llama2-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path $DATA_PATH"/Llama-2-7b-hf" \ - --train_file $DATA_PATH"/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/llama2_denas-lora_model \ - --peft lora \ - --delta lora \ - --debugs --max_epochs 1 --population_num 1 --crossover_num 1 --mutation_num 1 --select_num 1 \ - --trust_remote_code True \ - --no_cuda \ - 2>&1 | tee log/llama2-denas-lora-run-1epoch.log +# fine-tune with denas-lora +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_denas-lora" + log_save_path=$LOG_PATH"/"${model_name}"_denas-lora-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file $DATA_PATH"/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --do_train \ + --do_eval \ + --validation_split_percentage 30 \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --output_dir $model_save_path \ + --peft lora \ + --delta lora \ + --denas True \ + --debugs --max_epochs 1 --population_num 1 --crossover_num 1 --mutation_num 1 --select_num 1 \ + --trust_remote_code True \ + --no_cuda \ + 2>&1 | tee $log_save_path +done diff --git a/tests/deltatuner/cicd/run_denas_ssf.sh b/tests/deltatuner/cicd/run_denas_ssf.sh index 07c4aef89..e62794cc2 100644 --- a/tests/deltatuner/cicd/run_denas_ssf.sh +++ b/tests/deltatuner/cicd/run_denas_ssf.sh @@ -6,38 +6,21 @@ pip uninstall wandb -y cd /home/vmagent/app/e2eaiok -mkdir -p log - DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" -#run mpt with ssf and denas, bf16 -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16" \ - --debugs --max_epochs 1 --population_num 1 --crossover_num 1 --mutation_num 1 --select_num 1 \ - --delta ssf \ - --denas True \ - 2>&1 | tee "log/mpt-7b-ssf-allmodules-denas-bf16-run-1epoch.log" +mkdir -p $LOG_PATH $MODEL_SAVE_PATH -#run llama with ssf and denas, bf16 -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ +# fine-tune with denas-ssf +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_denas-ssf" + log_save_path=$LOG_PATH"/"${model_name}"_denas-ssf-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ --train_file "$DATA_PATH/alpaca_data.json" \ --dataset_concatenation \ --per_device_train_batch_size 8 \ @@ -54,8 +37,9 @@ python example/instruction_tuning_pipeline/finetune_clm.py \ --save_strategy epoch \ --trust_remote_code True \ --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ + --output_dir $model_save_path \ --debugs --max_epochs 1 --population_num 1 --crossover_num 1 --mutation_num 1 --select_num 1 \ --delta ssf \ --denas True \ - 2>&1 | tee log/llama2-7b-ssf-denas-bf16-1epoch.log \ No newline at end of file + 2>&1 | tee $log_save_path +done diff --git a/tests/deltatuner/cicd/run_lora.sh b/tests/deltatuner/cicd/run_lora.sh index 8db7a6f86..923463ecb 100644 --- a/tests/deltatuner/cicd/run_lora.sh +++ b/tests/deltatuner/cicd/run_lora.sh @@ -7,56 +7,41 @@ pip uninstall wandb -y cd /home/vmagent/app/e2eaiok -mkdir -p log models - DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" + +mkdir -p $LOG_PATH $MODEL_SAVE_PATH -# fine-tune mpt-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path $DATA_PATH"/mpt-7b" \ - --train_file $DATA_PATH"/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/mpt_lora_model \ - --peft lora \ - --denas False \ - --debugs \ - --trust_remote_code True \ - --no_cuda \ - 2>&1 | tee log/mpt-lora-run-1epoch.log -# fine-tune llama2-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path $DATA_PATH"/Llama-2-7b-hf" \ - --train_file $DATA_PATH"/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/llama2_lora_model \ - --peft lora \ - --denas False \ - --debugs \ - --trust_remote_code True \ - --no_cuda \ - 2>&1 | tee log/llama2-lora-run-1epoch.log +# fine-tune with lora +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"-lora" + log_save_path=$LOG_PATH"/"${model_name}"-lora-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file $DATA_PATH"/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --do_train \ + --do_eval \ + --validation_split_percentage 30 \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --output_dir $model_save_path \ + --peft lora \ + --denas False \ + --debugs \ + --trust_remote_code True \ + --no_cuda \ + 2>&1 | tee $log_save_path +done diff --git a/tests/deltatuner/cicd/run_ssf.sh b/tests/deltatuner/cicd/run_ssf.sh index 4bb1800e9..f1c3cbc0d 100644 --- a/tests/deltatuner/cicd/run_ssf.sh +++ b/tests/deltatuner/cicd/run_ssf.sh @@ -6,38 +6,21 @@ pip uninstall wandb -y cd /home/vmagent/app/e2eaiok -mkdir -p log - DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" -#run mpt with ssf, bf16 -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-bf16" \ - --delta ssf \ - --denas False \ - --debugs \ - 2>&1 | tee log/mpt-ssf-run-allmodules-bf16-1epoch.log - -#run llama2 with ssf, bf16 -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ +mkdir -p $LOG_PATH $MODEL_SAVE_PATH + +# fine-tune with ssf +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_ssf" + log_save_path=$LOG_PATH"/"${model_name}"_ssf-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ --train_file "$DATA_PATH/alpaca_data.json" \ --dataset_concatenation \ --per_device_train_batch_size 8 \ @@ -54,8 +37,9 @@ python example/instruction_tuning_pipeline/finetune_clm.py \ --save_strategy epoch \ --trust_remote_code True \ --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-bf16" \ + --output_dir $model_save_path \ --delta ssf \ --denas False \ --debugs \ - 2>&1 | tee log/llama2-7b-ssf-bf16-1epoch.log \ No newline at end of file + 2>&1 | tee $log_save_path +done diff --git a/tests/deltatuner/cicd/run_ssf_model.sh b/tests/deltatuner/cicd/run_ssf_model.sh new file mode 100644 index 000000000..a4e954abd --- /dev/null +++ b/tests/deltatuner/cicd/run_ssf_model.sh @@ -0,0 +1,47 @@ +set -x + +cd /home/vmagent/app/e2eaiok/e2eAIOK/deltatuner +pip install -e . + +cd /home/vmagent/app/e2eaiok + +DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" + +mkdir -p $LOG_PATH $MODEL_SAVE_PATH + +# fine-tune with ssf +# mosaicml/mpt-7b gpt2 EleutherAI/gpt-j-6b bigscience/bloom-560m facebook/opt-125m EleutherAI/gpt-neo-125m tiiuae/falcon-7b +model_name_list="EleutherAI/gpt-j-6b bigscience/bloom-560m facebook/opt-125m EleutherAI/gpt-neo-125m tiiuae/falcon-7b" +for model_name in $model_name_list +do + model_name_or_path=${model_name} + short_model_name=`echo $model_name | cut -d/ -f2` + model_save_path=${MODEL_SAVE_PATH}"/"${short_model_name}"_ssf" + log_save_path=$LOG_PATH"/"${short_model_name}"_ssf-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file "$DATA_PATH/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --validation_split_percentage 30 \ + --do_train \ + --do_eval \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --trust_remote_code True \ + --output_dir $model_save_path \ + --delta ssf \ + --denas False \ + --debugs \ + --no_cuda \ + 2>&1 | tee $log_save_path + # rm -rf ~/.cache/huggingface/hub +done diff --git a/tests/deltatuner/finetune/run_denas_lora.sh b/tests/deltatuner/finetune/run_denas_lora.sh index 801f4e6da..0904bb613 100644 --- a/tests/deltatuner/finetune/run_denas_lora.sh +++ b/tests/deltatuner/finetune/run_denas_lora.sh @@ -1,56 +1,48 @@ #!/bin/bash set -x -mkdir -p log models -# fine-tune mpt-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "/home/vmagent/app/dataset/mpt-7b" \ - --train_file "/home/vmagent/app/dataset/stanford_alpaca/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/mpt_denas-lora_model \ - --peft lora \ - --delta lora \ - --trust_remote_code True \ - --no_cuda \ - --bf16 True 2>&1 | tee log/mpt-denas-lora-run-1epoch.log +DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" -# fine-tune llama2-7b with denas-lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "/home/vmagent/app/dataset/Llama-2-7b-hf" \ - --train_file "/home/vmagent/app/dataset/stanford_alpaca/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/llama2_denas-lora_model \ - --peft lora \ - --delta lora \ - --trust_remote_code True \ - --no_cuda \ - --bf16 True 2>&1 | tee log/llama2-denas-lora-run-1epoch.log +mkdir -p $LOG_PATH $MODEL_SAVE_PATH +# fine-tune with denas-lora +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_denas-lora" + log_save_path=$LOG_PATH"/"${model_name}"_denas-lora-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file $DATA_PATH"/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --do_train \ + --do_eval \ + --validation_split_percentage 30 \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --output_dir $model_save_path \ + --peft lora \ + --delta lora \ + --denas True \ + --trust_remote_code True \ + --no_cuda \ + 2>&1 | tee $log_save_path +done -# used for assign target modules to lora -# --lora_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ -# --lora_target_modules Wqkv out_proj up_proj down_proj \ \ No newline at end of file +########################## advanced options ########################## +# note 0: used for assign target modules + # llama: --lora_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ + # mpt: --lora_target_modules Wqkv out_proj up_proj down_proj \ +# note 1: used for 4th Xeon or later (SPR etc.): --bf16 --no_cuda +# note 2: used for 3th Xeon or before (ICX etc.): --no_cuda +# note 3: used for GPU server: --fp16 \ No newline at end of file diff --git a/tests/deltatuner/finetune/run_denas_ssf.sh b/tests/deltatuner/finetune/run_denas_ssf.sh new file mode 100644 index 000000000..e1b68ab04 --- /dev/null +++ b/tests/deltatuner/finetune/run_denas_ssf.sh @@ -0,0 +1,47 @@ +set -x + +DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" + +mkdir -p $LOG_PATH $MODEL_SAVE_PATH + +# fine-tune with denas-ssf +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_denas-ssf" + log_save_path=$LOG_PATH"/"${model_name}"_denas-ssf-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file "$DATA_PATH/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --validation_split_percentage 30 \ + --do_train \ + --do_eval \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --trust_remote_code True \ + --no_cuda \ + --output_dir $model_save_path \ + --delta ssf \ + --denas True \ + 2>&1 | tee $log_save_path +done + + +########################## advanced options ########################## +# note 0: used for assign target modules + # llama: --ssf_target_module q_proj v_proj k_proj o_proj up_proj down_proj \ + # mpt: --ssf_target_module Wqkv out_proj up_proj down_proj \ +# note 1: used for 4th Xeon or later (SPR etc.): --bf16 --no_cuda +# note 2: used for 3th Xeon or before (ICX etc.): --no_cuda +# note 3: used for GPU server: --fp16 \ No newline at end of file diff --git a/tests/deltatuner/finetune/run_lora.sh b/tests/deltatuner/finetune/run_lora.sh index ba4b331ae..91a8bf5b4 100644 --- a/tests/deltatuner/finetune/run_lora.sh +++ b/tests/deltatuner/finetune/run_lora.sh @@ -1,56 +1,49 @@ #!/bin/bash set -x -mkdir -p log models -# fine-tune mpt-7b with lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "/home/vmagent/app/dataset/mpt-7b" \ - --train_file "/home/vmagent/app/dataset/stanford_alpaca/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/mpt_lora_model \ - --peft lora \ - --denas False \ - --trust_remote_code True \ - --no_cuda \ - --bf16 True 2>&1 | tee log/mpt-lora-run-1epoch.log +DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" -# fine-tune llama2-7b with lora -python example/instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "/home/vmagent/app/dataset/Llama-2-7b-hf" \ - --train_file "/home/vmagent/app/dataset/stanford_alpaca/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --do_train \ - --do_eval \ - --validation_split_percentage 30 \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --output_dir models/llama2_lora_model \ - --peft lora \ - --denas False \ - --trust_remote_code True \ - --no_cuda \ - --bf16 True 2>&1 | tee log/llama2-lora-run-1epoch.log +mkdir -p $LOG_PATH $MODEL_SAVE_PATH -# used for assign target modules to lora -# --lora_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ -# --lora_target_modules Wqkv out_proj up_proj down_proj \ \ No newline at end of file +# fine-tune with lora +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"-lora" + log_save_path=$LOG_PATH"/"${model_name}"-lora-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file $DATA_PATH"/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --do_train \ + --do_eval \ + --validation_split_percentage 30 \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --output_dir $model_save_path \ + --peft lora \ + --denas False \ + --trust_remote_code True \ + --no_cuda \ + 2>&1 | tee $log_save_path +done + + +########################## advanced options ########################## +# note 0: used for assign target modules + # llama: --lora_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ + # mpt: --lora_target_modules Wqkv out_proj up_proj down_proj \ +# note 1: used for 4th Xeon or later (SPR etc.): --bf16 --no_cuda +# note 2: used for 3th Xeon or before (ICX etc.): --no_cuda +# note 3: used for GPU server: --fp16 diff --git a/tests/deltatuner/finetune/run_ssf.sh b/tests/deltatuner/finetune/run_ssf.sh new file mode 100644 index 000000000..c9732c0ff --- /dev/null +++ b/tests/deltatuner/finetune/run_ssf.sh @@ -0,0 +1,47 @@ +set -x + +DATA_PATH="/home/vmagent/app/data" +LOG_PATH=$DATA_PATH"/dtuner_test/log" +MODEL_SAVE_PATH=$DATA_PATH"/dtuner_test/models" + +mkdir -p $LOG_PATH $MODEL_SAVE_PATH + +# fine-tune with ssf +model_name_list="mpt-7b Llama-2-7b-hf" +for model_name in $model_name_list +do + model_name_or_path=${DATA_PATH}"/"${model_name} + model_save_path=${MODEL_SAVE_PATH}"/"${model_name}"_ssf" + log_save_path=$LOG_PATH"/"${model_name}"_ssf-1epoch.log" + python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $model_name_or_path \ + --train_file "$DATA_PATH/alpaca_data.json" \ + --dataset_concatenation \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 8 \ + --gradient_accumulation_steps 1 \ + --validation_split_percentage 30 \ + --do_train \ + --do_eval \ + --learning_rate 1e-4 \ + --num_train_epochs 1 \ + --logging_steps 100 \ + --save_total_limit 1 \ + --log_level info \ + --save_strategy epoch \ + --trust_remote_code True \ + --no_cuda \ + --output_dir $model_save_path \ + --delta ssf \ + --denas False \ + 2>&1 | tee $log_save_path +done + + +########################## advanced options ########################## +# note 0: used for assign target modules + # llama: --ssf_target_module q_proj v_proj k_proj o_proj up_proj down_proj \ + # mpt: --ssf_target_module Wqkv out_proj up_proj down_proj \ +# note 1: used for 4th Xeon or later (SPR etc.): --bf16 --no_cuda +# note 2: used for 3th Xeon or before (ICX etc.): --no_cuda +# note 3: used for GPU server: --fp16 \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/llama2-7b-lora.sh b/tests/deltatuner/finetune/ssf/llama2-7b-lora.sh deleted file mode 100755 index 662074530..000000000 --- a/tests/deltatuner/finetune/ssf/llama2-7b-lora.sh +++ /dev/null @@ -1,24 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-lora" \ - --peft lora \ - --denas False \ - | tee llama2-7b-lora-1epoch.log diff --git a/tests/deltatuner/finetune/ssf/llama2-7b-ssf-allmodules.sh b/tests/deltatuner/finetune/ssf/llama2-7b-ssf-allmodules.sh deleted file mode 100755 index 7077fda90..000000000 --- a/tests/deltatuner/finetune/ssf/llama2-7b-ssf-allmodules.sh +++ /dev/null @@ -1,135 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules" \ - --ssf_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ - --delta ssf \ - --denas False \ - | tee llama2-7b-ssf-allmodules-1epoch.log - -#bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules-bf16" \ - --ssf_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ - --delta ssf \ - --bf16 True \ - --denas False \ - | tee llama2-7b-ssf-allmodules-bf16-1epoch.log - -#merge -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules-merge" \ - --ssf_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ - --delta ssf \ - --resume_peft "$DATA_PATH/llama2-7b-ssf-allmodules" \ - --save_merged_model True - -#merge, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules-bf16-merge" \ - --ssf_target_modules q_proj v_proj k_proj o_proj up_proj down_proj \ - --delta ssf \ - --resume_peft "$DATA_PATH/llama2-7b-ssf-allmodules-bf16" \ - --save_merged_model True \ - --bf16 True - -#evaluate merged model -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/llama2-7b-ssf-allmodules-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules-merge/eval_merged" \ - --debugs - -#evaluate merged model, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/llama2-7b-ssf-allmodules-bf16-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-allmodules-bf16-merge/eval_merged" \ - --bf16 True \ - --debugs \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/llama2-7b-ssf-denas.sh b/tests/deltatuner/finetune/ssf/llama2-7b-ssf-denas.sh deleted file mode 100755 index aa6fb3704..000000000 --- a/tests/deltatuner/finetune/ssf/llama2-7b-ssf-denas.sh +++ /dev/null @@ -1,157 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas" \ - --delta ssf \ - --denas True \ - | tee llama2-7b-ssf-denas-1epoch.log - -#bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ - --delta ssf \ - --denas True \ - --bf16 True \ - | tee llama2-7b-ssf-denas-bf16-1epoch.log - -#evaluate model, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16/eval" \ - --delta ssf \ - --bf16 True \ - --resume_peft "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ - --denas "$DATA_PATH/llama2-7b-ssf-denas-bf16/best_model_structure.txt" \ - --debugs - -#merge -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-merge" \ - --delta ssf \ - --resume_peft "$DATA_PATH/llama2-7b-ssf-denas" \ - --save_merged_model True \ - --denas "$DATA_PATH/llama2-7b-ssf-denas/best_model_structure.txt" \ - --debugs - -#merge, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16-merge" \ - --delta ssf \ - --bf16 True \ - --resume_peft "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ - --save_merged_model True \ - --denas "$DATA_PATH/llama2-7b-ssf-denas-bf16/best_model_structure.txt" \ - --debugs - -#evaluate merged model -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/llama2-7b-ssf-denas-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-merge/eval_merge" \ - --debugs - -#evaluate merged model, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/llama2-7b-ssf-denas-bf16-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16-merge/eval_merge" \ - --bf16 True \ - --debugs \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/mpt-7b-full.sh b/tests/deltatuner/finetune/ssf/mpt-7b-full.sh deleted file mode 100755 index 27211fc0d..000000000 --- a/tests/deltatuner/finetune/ssf/mpt-7b-full.sh +++ /dev/null @@ -1,23 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-full" \ - | tee mpt-lora-run-1epoch.log diff --git a/tests/deltatuner/finetune/ssf/mpt-7b-lora.sh b/tests/deltatuner/finetune/ssf/mpt-7b-lora.sh deleted file mode 100755 index 8b9e695f3..000000000 --- a/tests/deltatuner/finetune/ssf/mpt-7b-lora.sh +++ /dev/null @@ -1,24 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-lora" \ - --peft lora \ - | tee mpt-lora-run-1epoch.log diff --git a/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules-denas.sh b/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules-denas.sh deleted file mode 100755 index 251152050..000000000 --- a/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules-denas.sh +++ /dev/null @@ -1,120 +0,0 @@ -DATA_PATH="/home/data" - -#bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16" \ - --delta ssf \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - --bf16 True \ - --denas True \ - | tee "mpt-7b-ssf-allmodules-denas-bf16-run-1epoch.log" - -#merge -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge" \ - --delta ssf \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - --resume_peft "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16" \ - --save_merged_model True \ - --denas "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16/best_model_structure.txt" \ - --debugs - -#merge,bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge" \ - --delta ssf \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - --bf16 True \ - --resume_peft "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16" \ - --save_merged_model True \ - --denas "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16/best_model_structure.txt" \ - --debugs - -#evaluate merged model -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge/eval_merge" \ - --debugs - -#evaluate merged model, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-denas-bf16-merge/eval_merge" \ - --bf16 True \ - --debugs \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules.sh b/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules.sh deleted file mode 100755 index ff9f34385..000000000 --- a/tests/deltatuner/finetune/ssf/mpt-7b-ssf-allmodules.sh +++ /dev/null @@ -1,98 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules" \ - --delta ssf \ - --denas False \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - | tee mpt-ssf-run-allmodules-1epoch.log - -# bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-bf16" \ - --delta ssf \ - --denas False \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - --bf16 True \ - | tee mpt-ssf-run-allmodules-bf16-1epoch.log - -# merge, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-bf16-merge" \ - --delta ssf \ - --ssf_target_modules Wqkv out_proj up_proj down_proj \ - --bf16 True \ - --resume_peft "$DATA_PATH/mpt-7b-ssf-allmodules-bf16" \ - --save_merged_model True - -#evaluate merged model, bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b-ssf-allmodules-bf16-merge/merged_model" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_eval \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-allmodules-bf16-merge/eval_merged" \ - --bf16 True \ - --debugs diff --git a/tests/deltatuner/finetune/ssf/mpt-7b-ssf.sh b/tests/deltatuner/finetune/ssf/mpt-7b-ssf.sh deleted file mode 100755 index f4d301b45..000000000 --- a/tests/deltatuner/finetune/ssf/mpt-7b-ssf.sh +++ /dev/null @@ -1,51 +0,0 @@ -DATA_PATH="/home/data" - -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf" \ - --denas False \ - --delta ssf \ - | tee mpt-ssf-run-1epoch.log - -# bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/mpt-7b" \ - --train_file "$DATA_PATH/alpaca_data.json" \ - --dataset_concatenation \ - --per_device_train_batch_size 8 \ - --per_device_eval_batch_size 8 \ - --gradient_accumulation_steps 1 \ - --validation_split_percentage 30 \ - --do_train \ - --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ - --save_total_limit 1 \ - --log_level info \ - --save_strategy epoch \ - --trust_remote_code True \ - --tokenizer_name "$DATA_PATH/gpt-neox-20b" \ - --no_cuda \ - --output_dir "$DATA_PATH/mpt-7b-ssf-bf16" \ - --delta ssf \ - --bf16 True \ - --denas False \ - | tee mpt-ssf-run-1epoch.log \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/readme.md b/tests/deltatuner/merge_model/readme.md similarity index 81% rename from tests/deltatuner/finetune/ssf/readme.md rename to tests/deltatuner/merge_model/readme.md index 25bcbd9c4..517c8a45e 100644 --- a/tests/deltatuner/finetune/ssf/readme.md +++ b/tests/deltatuner/merge_model/readme.md @@ -1,6 +1,5 @@ -# Run Deltatuner with ssf +## Run Deltatuner with Merged model -## Llama2-7b ### 1. Finetune the model If use Denas, can use command: @@ -28,7 +27,6 @@ python instruction_tuning_pipeline/finetune_clm.py \ --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ --delta ssf \ --denas True \ - --bf16 True \ | tee llama2-7b-ssf-denas-bf16-1epoch.log ``` @@ -40,13 +38,10 @@ python instruction_tuning_pipeline/finetune_clm.py \ --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ --train_file "$DATA_PATH/alpaca_data.json" \ --dataset_concatenation \ - --per_device_train_batch_size 8 \ --per_device_eval_batch_size 8 \ --gradient_accumulation_steps 1 \ --validation_split_percentage 30 \ --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ --logging_steps 100 \ --save_total_limit 1 \ --log_level info \ @@ -55,22 +50,18 @@ python instruction_tuning_pipeline/finetune_clm.py \ --no_cuda \ --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16-merge" \ --delta ssf \ - --bf16 True \ --resume_peft "$DATA_PATH/llama2-7b-ssf-denas-bf16" \ --save_merged_model True \ - --denas "$DATA_PATH/llama2-7b-ssf-denas-bf16/best_model_structure.txt" \ + --merge_model_code_dir "instruction_tuning_pipeline/models/llama2-ssf" \ --debugs ``` -### 3. Evaluate merged model As ssf will enable bias, while the default Llama2 disable all the bias, to enable the full parameters of the adapter, we need to change the model definition. -First copy the updated model codes with the merged weights. -```shell -cp instruction_tuning_pipeline/models/llama2-ssf/* $DATA_PATH/llama2-7b-ssf-denas-bf16-merge/merged_model -``` +First, specify the `merge_model_code_dir` args, it will copy the updated model codes with the merged weights. -Then update the "best_model_structure" and "target_modules" setting in config.json. if not enable denas and not change "target_modules" default settings, can skip correpsonding setting. +Then it will automatically update the "best_model_structure" and "target_modules" setting in `config.json`. if "denas" or "target_modules" are not enabled/changed, it will skip the corresponding setting. +The changed `config.json` looks like this: ```shell ... "best_model_structure": {"num_hidden_layers": [1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1]}, # change to your best structure, skip to keep default @@ -78,6 +69,8 @@ Then update the "best_model_structure" and "target_modules" setting in config.js ... ``` +### 3. Evaluate merged model + Finally we can directly evalute the merged model. ```shell python instruction_tuning_pipeline/finetune_clm.py \ @@ -94,5 +87,4 @@ python instruction_tuning_pipeline/finetune_clm.py \ --trust_remote_code True \ --no_cuda \ --output_dir "$DATA_PATH/llama2-7b-ssf-denas-bf16-merge/eval_merge" \ - --bf16 True ``` \ No newline at end of file diff --git a/tests/deltatuner/finetune/ssf/llama2-7b-ssf.sh b/tests/deltatuner/merge_model/ssf-merge-test.sh similarity index 51% rename from tests/deltatuner/finetune/ssf/llama2-7b-ssf.sh rename to tests/deltatuner/merge_model/ssf-merge-test.sh index 39299cf36..f1bfbcefe 100755 --- a/tests/deltatuner/finetune/ssf/llama2-7b-ssf.sh +++ b/tests/deltatuner/merge_model/ssf-merge-test.sh @@ -1,49 +1,43 @@ -DATA_PATH="/home/data" +DATA_PATH="/home/vmagent/app/data" -python instruction_tuning_pipeline/finetune_clm.py \ + +# merge model +python example/instruction_tuning_pipeline/finetune_clm.py \ --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ --train_file "$DATA_PATH/alpaca_data.json" \ --dataset_concatenation \ - --per_device_train_batch_size 8 \ --per_device_eval_batch_size 8 \ --gradient_accumulation_steps 1 \ --validation_split_percentage 30 \ - --do_train \ --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ --save_total_limit 1 \ --log_level info \ --save_strategy epoch \ --trust_remote_code True \ --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf" \ + --output_dir "$DATA_PATH/dtuner_test/models/llama2-7b-ssf-denas-bf16-merge" \ --delta ssf \ - --denas False \ - | tee llama2-7b-ssf-1epoch.log + --resume_peft "$DATA_PATH/dtuner_test/models/llama2-7b-ssf-denas-bf16" \ + --save_merged_model True \ + --merge_model_code_dir $DATA_PATH"/dtuner_test/ssf_models_code/llama-7b-ssf" \ + --bf16 True \ + --debugs -#bf16 -python instruction_tuning_pipeline/finetune_clm.py \ - --model_name_or_path "$DATA_PATH/Llama-2-7b-hf" \ + +# evaluate merged model +python example/instruction_tuning_pipeline/finetune_clm.py \ + --model_name_or_path $DATA_PATH"/dtuner_test/models/llama2-7b-ssf-denas-bf16-merge/merged_model" \ --train_file "$DATA_PATH/alpaca_data.json" \ --dataset_concatenation \ - --per_device_train_batch_size 8 \ --per_device_eval_batch_size 8 \ --gradient_accumulation_steps 1 \ --validation_split_percentage 30 \ - --do_train \ --do_eval \ - --learning_rate 1e-4 \ - --num_train_epochs 1 \ - --logging_steps 100 \ --save_total_limit 1 \ --log_level info \ - --save_strategy epoch \ --trust_remote_code True \ --no_cuda \ - --output_dir "$DATA_PATH/llama2-7b-ssf-bf16" \ - --delta ssf \ --bf16 True \ - --denas False \ - | tee llama2-7b-ssf-bf16-1epoch.log \ No newline at end of file + --output_dir $DATA_PATH"/dtuner_test/models/llama2-7b-ssf-denas-bf16-merge/eval_merge" \ + --debugs + From 392d460b65e87ddb01a5ac5557e33b62da6f4133 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Thu, 12 Oct 2023 02:23:54 +0800 Subject: [PATCH 07/47] Support logger and statistics in operation. (#379) --- RecDP/pyrecdp/LLM/TextPipeline.py | 82 +++++------ RecDP/pyrecdp/primitives/operations/base.py | 130 +++++++++++++----- .../primitives/operations/logging_utils.py | 13 ++ .../primitives/operations/text_filter.py | 22 ++- RecDP/setup.py | 3 +- 5 files changed, 162 insertions(+), 88 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/operations/logging_utils.py diff --git a/RecDP/pyrecdp/LLM/TextPipeline.py b/RecDP/pyrecdp/LLM/TextPipeline.py index e71823d08..353d40f28 100644 --- a/RecDP/pyrecdp/LLM/TextPipeline.py +++ b/RecDP/pyrecdp/LLM/TextPipeline.py @@ -15,28 +15,27 @@ import time import os import psutil - -logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.ERROR, datefmt='%I:%M:%S') -logger = logging.getLogger(__name__) +from pyrecdp.primitives.operations.logging_utils import logger total_mem = int(psutil.virtual_memory().total * 0.6) total_cores = psutil.cpu_count(logical=False) + class TextPipeline(BasePipeline): def __init__(self, pipeline_file=None): super().__init__() if pipeline_file != None: self.import_from_yaml(pipeline_file) else: - #add a data set input place holder + # add a data set input place holder op = DatasetReader() self.add_operation(op) - + def __del__(self): if hasattr(self, 'engine_name') and self.engine_name == 'ray': if ray.is_initialized(): ray.shutdown() - + def check_platform(self, executable_sequence): is_spark = True is_ray = True @@ -54,18 +53,19 @@ def check_platform(self, executable_sequence): elif is_spark: return 'spark' else: - print(f"We can't identify an uniform engine for this pipeline. \n Operations work on Ray are {ray_list}. \n Operations work on Spark are {spark_list}") + print( + f"We can't identify an uniform engine for this pipeline. \n Operations work on Ray are {ray_list}. \n Operations work on Spark are {spark_list}") return 'mixed' - - def execute(self, ds = None): + + def execute(self, ds=None): # prepare pipeline if not hasattr(self, 'executable_pipeline') or not hasattr(self, 'executable_sequence'): self.executable_pipeline, self.executable_sequence = self.create_executable_pipeline() executable_pipeline = self.executable_pipeline executable_sequence = self.executable_sequence - + engine_name = self.check_platform(executable_sequence) - + if engine_name == 'ray': print("init ray") if not ray.is_initialized(): @@ -103,18 +103,18 @@ def execute(self, ds = None): if isinstance(ds, DataFrame): ds = ds.cache() total_len = ds.count() - + self.engine_name = engine_name - + # fetch result return ds - - def add_operation(self, config): + + def add_operation(self, config): # get current max operator id max_idx = self.pipeline.get_max_idx() cur_idx = max_idx + 1 find_children_skip = False - + if not isinstance(config, dict): op = config if max_idx == -1: @@ -122,7 +122,7 @@ def add_operation(self, config): else: pipeline_chain = self.to_chain() leaf_child = [pipeline_chain[-1]] - + config = { "children": leaf_child, "inline_function": op, @@ -130,24 +130,24 @@ def add_operation(self, config): find_children_skip = True children = config["children"] inline_function = config["inline_function"] - + if not isinstance(children, list) and children is not None: children = [children] - + # ====== Start to add it to pipeline ====== # if isinstance(inline_function, types.FunctionType): config = { "func_name": inline_function, } self.pipeline[cur_idx] = Operation( - cur_idx, children, output = None, op = "ray_python", config = config) + cur_idx, children, output=None, op="ray_python", config=config) elif isinstance(inline_function, BaseOperation): op_name = inline_function.op.op - #config = vars(inline_function) + # config = vars(inline_function) config = inline_function.op.config self.pipeline[cur_idx] = Operation( - cur_idx, children, output = None, op = op_name, config = config) - + cur_idx, children, output=None, op=op_name, config=config) + # we need to find nexts if find_children_skip: return self.pipeline @@ -166,21 +166,22 @@ def add_operation(self, config): for k, v in found.items(): self.pipeline[idx].children[k] = v return self.pipeline - + def add_operations(self, config_list): for op in config_list: self.add_operation(op) return self.pipeline - + def profile(self): # TODO: print analysis and log for each component. pass - + + class ResumableTextPipeline(TextPipeline): # Provide a pipeline for large dir. We will handle files one by one and resume when pipeline broken. def __init__(self, pipeline_file=None): super().__init__(pipeline_file) - + def execute(self): # Fix pipeline output_dir = "" @@ -194,8 +195,10 @@ def execute(self): output_dir = op.config['output_dir'] if output_dir == "": output_dir = f"ResumableTextPipeline_output_{time.strftime('%Y%m%d%H%M%S')}" - self.add_operation(PerfileParquetWriter(output_dir = output_dir)) - + self.add_operation(PerfileParquetWriter(output_dir=output_dir)) + + logger.add(os.path.join(output_dir, "pipeline.log")) + # prepare output dir and record system os.makedirs(output_dir, exist_ok=True) status_log_path = os.path.join(output_dir, 'status.log') @@ -208,15 +211,15 @@ def execute(self): done_files = [] self.export(os.path.join(output_dir, "pipeline.json")) self.plot() - + # prepare pipeline if not hasattr(self, 'executable_pipeline') or not hasattr(self, 'executable_sequence'): self.executable_pipeline, self.executable_sequence = self.create_executable_pipeline() executable_pipeline = self.executable_pipeline executable_sequence = self.executable_sequence - + engine_name = self.check_platform(executable_sequence) - + if engine_name != 'ray': raise NotImplementedError("ResumableTextPipeline only support operations with ray mode") self.engine_name = engine_name @@ -236,14 +239,14 @@ def execute(self): sub_pipelines = op.cache elif len(sub_pipelines) > 0: op_chain.append(op) - + for ds_reader, source_id in (pbar := tqdm(sub_pipelines, total=len(sub_pipelines))): # check if we should skip if source_id in done_files: print(f"skip {source_id}, it was processed in last round") del ds_reader continue - + # If not skip, then pbar.set_description(f"ResumableTextPipeline, current on {source_id}") start = time.time() @@ -259,10 +262,11 @@ def execute(self): status_tracker.flush() done_files.append(status_tracker) del ds_reader - - print(f"Completed!\nResumableTextPipeline will not return dataset, please check {output_dir} for verification.") - + for op in op_chain: + logger.info(f"{op.__class__.__name__}: {op.summarize()}") + + logger.info( + f"Completed! ResumableTextPipeline will not return dataset, please check {output_dir} for verification.") + # fetch result return None - - \ No newline at end of file diff --git a/RecDP/pyrecdp/primitives/operations/base.py b/RecDP/pyrecdp/primitives/operations/base.py index 2d7a6bbc7..67bd9738b 100644 --- a/RecDP/pyrecdp/primitives/operations/base.py +++ b/RecDP/pyrecdp/primitives/operations/base.py @@ -1,5 +1,10 @@ import json +from dataclasses import dataclass +from functools import wraps + import pandas as pd +from ray.data import Dataset + from .dataframe import * from pyspark.sql import DataFrame as SparkDataFrame from pyspark import RDD as SparkRDD @@ -7,27 +12,28 @@ from IPython.display import display from pyrecdp.core.registry import Registry + class Operation: def __init__(self, idx, children, output, op, config): self.idx = idx - self.children = children #input operation - self.output = output #output schema - self.op = op #func name - self.config = config #operation config + self.children = children # input operation + self.output = output # output schema + self.op = op # func name + self.config = config # operation config def __repr__(self): return repr(self.dump()) - + def dump(self): dump_dict = { - #'idx': self.idx, + # 'idx': self.idx, 'children': self.children, - #'output': self.output, + # 'output': self.output, 'op': self.op, 'config': dump_fix(self.config) } return dump_dict - + def instantiate(self): from .data import DataFrameOperation, DataLoader from .merge import MergeOperation @@ -40,7 +46,8 @@ def instantiate(self): from .type import TypeInferOperation from .tuple import TupleOperation from .custom import CustomOperation - from .encode import OnehotEncodeOperation, ListOnehotEncodeOperation, TargetEncodeOperation, CountEncodeOperation + from .encode import OnehotEncodeOperation, ListOnehotEncodeOperation, TargetEncodeOperation, \ + CountEncodeOperation from pyrecdp.primitives.estimators.lightgbm import LightGBM operations_ = { @@ -73,13 +80,16 @@ def instantiate(self): return FeaturetoolsOperation(self) except: raise NotImplementedError(f"operation {self.op} is not implemented.") - + @staticmethod def load(idx, dump_dict): obj = Operation(idx, dump_dict['children'], None, dump_dict['op'], dump_dict['config']) return obj + BASEOPERATORS = Registry('BaseOperation') + + class BaseOperation: def __init__(self, op_base): # option1: for get_function_pd use @@ -92,22 +102,22 @@ def __init__(self, op_base): self.support_spark_rdd = False self.support_spark_ray = False self.fast_without_dpp = False - + def __repr__(self) -> str: return self.op.op - + def describe(self) -> str: return str(self.op.dump()) - - def execute_pd(self, pipeline, trans_type = 'fit_transform'): + + def execute_pd(self, pipeline, trans_type='fit_transform'): _proc = self.get_function_pd(trans_type) if not self.op.children or len(self.op.children) == 0: pass else: child_output = pipeline[self.op.children[0]].cache self.cache = _proc(child_output) - - def execute_spark(self, pipeline, rdp, trans_type = 'fit_transform'): + + def execute_spark(self, pipeline, rdp, trans_type='fit_transform'): _convert = None if not self.op.children or len(self.op.children) == 0: pass @@ -144,37 +154,44 @@ def execute_spark(self, pipeline, rdp, trans_type = 'fit_transform'): _proc = self.get_function_pd(trans_type) else: raise ValueError(f"child cache is not recognized {child_output}") - + if _convert: child_output = _convert(child_output) pipeline[self.op.children[0]].cache = child_output self.cache = _proc(child_output) - #print(self.cache.take(1)) - - def get_function_spark_rdd(self, rdp, trans_type = 'fit_transform'): + # print(self.cache.take(1)) + + def get_function_spark_rdd(self, rdp, trans_type='fit_transform'): actual_func = self.get_function_pd(trans_type) + def transform(iter, *args): for x in iter: yield actual_func(x, *args) + def base_spark_feature_generator(rdd): return rdd.mapPartitions(transform) + return base_spark_feature_generator + LLMOPERATORS = Registry('BaseLLMOperation') + + class BaseLLMOperation(BaseOperation): - def __init__(self, args_dict = {}): + def __init__(self, args_dict={}): self.op = Operation(-1, None, [], f'{self.__class__.__name__}', args_dict) self.cache = None self.support_spark = False self.support_ray = True - + self.statistics = OperationStatistics(0, 0, 0, 0) + @classmethod def instantiate(cls, op_obj, config): ins = cls(**config) ins.op = op_obj return ins - - def execute_ray(self, pipeline, child_ds = None): + + def execute_ray(self, pipeline, child_ds=None): child_output = [] if child_ds is not None: self.cache = self.process_rayds(child_ds) @@ -184,7 +201,7 @@ def execute_ray(self, pipeline, child_ds = None): child_output.append(pipeline[op].cache) self.cache = self.process_rayds(*child_output) return self.cache - + def execute_spark(self, pipeline, rdp): child_output = [] children = self.op.children if self.op.children is not None else [] @@ -192,32 +209,75 @@ def execute_spark(self, pipeline, rdp): child_output.append(pipeline[op].cache) self.cache = self.process_spark(rdp.spark, *child_output) return self.cache - - def process_rayds(self, ds = None): + + def process_rayds(self, ds=None): return self.cache - - def process_spark(self, spark, df = None): + + def process_spark(self, spark, df=None): return self.cache - + def process_row(self, sample: dict, text_key, new_name, actual_func, *actual_func_args) -> dict: sample[new_name] = actual_func(sample[text_key], *actual_func_args) return sample - + def process_batch(self, sample: dict) -> dict: raise NotImplementedError(f"{self.__class__.__name__} does not support process_batch yet") - + + def get_modified_rows(self): + self.statistics.total_changed = self.statistics.total_in - self.statistics.total_out + + def summarize(self) -> str: + self.get_modified_rows() + return ( + f"A total of {self.statistics.total_in} rows of data were processed, using {self.statistics.used_time} seconds, " + f"with {self.statistics.total_changed} rows modified or removed, {self.statistics.total_out} rows of data remaining.") + + class DummyOperation(BaseOperation): def __init__(self, op_base): super().__init__(op_base) self.support_spark_dataframe = True self.support_spark_rdd = True - def get_function_pd(self, trans_type = 'fit_transform'): + def get_function_pd(self, trans_type='fit_transform'): def dummy_op(df): return df + return dummy_op - def get_function_spark(self, rdp, trans_type = 'fit_transform'): + def get_function_spark(self, rdp, trans_type='fit_transform'): def dummy_op(df): return df - return dummy_op \ No newline at end of file + + return dummy_op + + +@dataclass +class OperationStatistics: + total_in: int + total_out: int + total_changed: int + used_time: float + + +def statistics_decorator(func): + def wrapper(self, *args, **kwargs): + if isinstance(args[0], Dataset): + ds = args[0] + self.statistics.total_in += ds.count() + start = time.time() + result = func(self, *args, **kwargs) + self.statistics.total_out += result.count() + elapse = time.time() - start + self.statistics.used_time += elapse + else: + df = args[1] + self.statistics.total_in += df.count() + start = time.time() + result = func(self, *args, **kwargs) + self.statistics.total_out += result.count() + elapse = time.time() - start + self.statistics.used_time += elapse + return result + + return wrapper diff --git a/RecDP/pyrecdp/primitives/operations/logging_utils.py b/RecDP/pyrecdp/primitives/operations/logging_utils.py new file mode 100644 index 000000000..b3f43d273 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/logging_utils.py @@ -0,0 +1,13 @@ +import logging +import sys +from loguru import logger + +level = "INFO" +logging.root.setLevel(level) + +# configure loguru +default_log_path = "/tmp/recdp/log/llmutils.log" +logger.configure(handlers=[ + {"sink": sys.stdout}, + {"sink": default_log_path, "rotation": "10 MB"}, +]) diff --git a/RecDP/pyrecdp/primitives/operations/text_filter.py b/RecDP/pyrecdp/primitives/operations/text_filter.py index 0ce439c3b..13b6e3254 100644 --- a/RecDP/pyrecdp/primitives/operations/text_filter.py +++ b/RecDP/pyrecdp/primitives/operations/text_filter.py @@ -1,4 +1,4 @@ -from .base import BaseLLMOperation, LLMOPERATORS +from .base import BaseLLMOperation, LLMOPERATORS, statistics_decorator from ray.data import Dataset from pyspark.sql import DataFrame import os @@ -57,19 +57,18 @@ def __init__(self, text_key='text', minimum_length=100, maximum_length=-1): self.support_ray = True self.support_spark = True + @statistics_decorator def process_rayds(self, ds: Dataset) -> Dataset: if self.check_length is None: self.check_length = prepare_func_filter_by_length(self.minimum_length, self.maximum_length) if self.inplace: # remove unwanted text row inplace - original_num = ds.count() filtered_ds = ds.filter(lambda x: self.check_length(x[self.text_key])) - filtered_num = filtered_ds.count() - print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") return filtered_ds else: raise NotImplementedError("We only support inplace modification for LengthFilter.") + @statistics_decorator def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.types as T check_length_udf = F.udf(prepare_func_filter_by_length(self.minimum_length, self.maximum_length), @@ -91,19 +90,18 @@ def __init__(self, text_key='text', language='en'): self.support_ray = True self.support_spark = True + @statistics_decorator def process_rayds(self, ds: Dataset) -> Dataset: if self.check_badwords is None: self.check_badwords = prepare_func_filter_by_badwords(self.language) if self.inplace: # remove unwanted text row inplace - original_num = ds.count() filtered_ds = ds.filter(lambda x: self.check_badwords(x[self.text_key])) - filtered_num = filtered_ds.count() - print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") return filtered_ds else: raise NotImplementedError("We only support inplace modification for BadwordsFilter.") + @statistics_decorator def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.types as T check_badwords_udf = F.udf(prepare_func_filter_by_badwords(self.language), T.BooleanType()) @@ -123,19 +121,18 @@ def __init__(self, text_key='text', inplace=True): self.support_ray = True self.support_spark = True + @statistics_decorator def process_rayds(self, ds: Dataset) -> Dataset: if self.check_profanity is None: self.check_profanity = prepare_func_filter_by_profanity() if self.inplace: # remove unwanted text row inplace - original_num = ds.count() filtered_ds = ds.filter(lambda x: self.check_profanity(x[self.text_key])) - filtered_num = filtered_ds.count() - print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") return filtered_ds else: raise NotImplementedError("We only support inplace modification for ProfanityFilter.") + @statistics_decorator def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.types as T check_profanity_udf = F.udf(prepare_func_filter_by_profanity(), T.BooleanType()) @@ -216,18 +213,17 @@ def __init__(self, text_key='text'): self.support_spark = True self.support_ray = True + @statistics_decorator def process_rayds(self, ds: Dataset) -> Dataset: blacklist = load_blacklist_set() if self.inplace: # remove unwanted text row inplace - original_num = ds.count() filtered_ds = ds.filter(lambda x: get_url_from_meta(x) not in blacklist) - filtered_num = filtered_ds.count() - print(f"Processed a total of {original_num} rows of data, filtered out {original_num - filtered_num} rows of data") return filtered_ds else: raise NotImplementedError("We only support inplace modification for URLFilter.") + @statistics_decorator def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: get_url_from_meta_udf = F.udf(get_url_from_meta) get_domain_udf = F.udf(get_domain) diff --git a/RecDP/setup.py b/RecDP/setup.py index 90957746e..da2c6f4fa 100644 --- a/RecDP/setup.py +++ b/RecDP/setup.py @@ -78,5 +78,6 @@ "spacy", "torch", "Faker", - "ray" + "ray", + "loguru", ]) From d6eb7c9f017ae0b0868af8e9e27ca66282b6fb6b Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Wed, 11 Oct 2023 15:52:55 -0500 Subject: [PATCH 08/47] [v1.2][ISSUE-306]Notebook update using pipeline (#380) * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory * Created using Colaboratory --- .../notebooks/llmutils/bytesize.ipynb | 2686 ++++++++-- .../notebooks/llmutils/classify.ipynb | 1326 ++++- .../examples/notebooks/llmutils/convert.ipynb | 521 +- .../notebooks/llmutils/decontamination.ipynb | 1379 +++-- .../llmutils/fuzzy_deduplication.ipynb | 748 +-- .../examples/notebooks/llmutils/reader.ipynb | 4256 ++++++++++++++- .../llmutils/resumable_pipeline.ipynb | 4700 ++++++++++++++++- 7 files changed, 13493 insertions(+), 2123 deletions(-) diff --git a/RecDP/examples/notebooks/llmutils/bytesize.ipynb b/RecDP/examples/notebooks/llmutils/bytesize.ipynb index 20feffaf6..936cb3659 100644 --- a/RecDP/examples/notebooks/llmutils/bytesize.ipynb +++ b/RecDP/examples/notebooks/llmutils/bytesize.ipynb @@ -1,529 +1,2233 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "NUdnJWOJLGvF", - "metadata": { - "id": "NUdnJWOJLGvF" - }, - "source": [ - "# RecDP LLM - TextBytesize\n", - "\n", - "TextBytesize is a tool to get total byte size of text in the data.\n", - "\n", - "### We support two types of input and output:\n", - "\n", - "example 1:\n", - "* Expect Input format: a folder of *.jsonl.\n", - "* Expect Output format: a folder of *.jsonl after reduction." - ] - }, - { - "cell_type": "markdown", - "id": "C1FFwhr3MwA1", - "metadata": { - "id": "C1FFwhr3MwA1" - }, - "source": [ - "# Get started" - ] - }, - { - "cell_type": "markdown", - "id": "tMaSNIKIMz3T", - "metadata": { - "id": "tMaSNIKIMz3T" - }, - "source": [ - "## Install pyrecdp and dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ro4yOk5IMvRY", - "metadata": { - "id": "ro4yOk5IMvRY" - }, - "outputs": [], - "source": [ - "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", - "! pip install pyrecdp --pre\n", - "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" - ] - }, - { - "cell_type": "markdown", - "id": "pnHK2VTvM5rg", - "metadata": { - "id": "pnHK2VTvM5rg" - }, - "source": [ - "## 2. prepare your own data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", - "metadata": { - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" - }, - "outputs": [], - "source": [ - "%mkdir -p /content/test_data\n", - "%cd /content/test_data\n", - "file_names = ['NIH_sample.jsonl']\n", - "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", - "!wget -P /content/test_data {\" \".join(file_list)}" - ] - }, - { - "cell_type": "markdown", - "id": "J-125gFtNyOg", - "metadata": { - "id": "J-125gFtNyOg" - }, - "source": [ - "## 3. get bytesize" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "vkGRn9uJN4B0", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vkGRn9uJN4B0", - "outputId": "8aa9d021-99f5-4de8-932e-2fccb933f70e" - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "NIH_sample.jsonl\n" - ] - } - ], - "source": [ - "! ls /content/test_data" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "SMK9KcV4N7al", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SMK9KcV4N7al", - "outputId": "7e93835c-a7d0-4612-e9aa-7c6f452d5bef" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Will assign 1 cores and 10386 M memory for spark\n", - "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", - "Load data with RowID started ...\n", - "Load data with RowID took 2.592839109000124 sec\n", - "num_bands is 9, ranges is 13\n", - "generate minHashLsh started ...\n", - "generate minHashLsh took 63.16276241699961 sec\n", - "generate_connected_components all started ...\n", - "Started graph building\n" - ] + "cell_type": "markdown", + "id": "NUdnJWOJLGvF", + "metadata": { + "id": "NUdnJWOJLGvF" + }, + "source": [ + "# RecDP LLM - TextBytesize\n", + "\n", + "TextBytesize is a tool to get total byte size of text in the data.\n", + "\n", + "### We support two types of input and output:\n", + "\n", + "example 1:\n", + "* Expect Input format: a folder of *.jsonl.\n", + "* Expect Output format: a folder of *.jsonl after reduction." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "loop on file: 100%|██████████| 2/2 [00:00<00:00, 5312.61it/s]\n" - ] + "cell_type": "markdown", + "id": "C1FFwhr3MwA1", + "metadata": { + "id": "C1FFwhr3MwA1" + }, + "source": [ + "# Get started" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "length of the set of duplicates: 13 0.01398324966430664\n" - ] + "cell_type": "markdown", + "id": "tMaSNIKIMz3T", + "metadata": { + "id": "tMaSNIKIMz3T" + }, + "source": [ + "## Install pyrecdp and dependencies" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 13/13 [00:00<00:00, 71275.75it/s]\n" - ] + "cell_type": "code", + "execution_count": null, + "id": "ro4yOk5IMvRY", + "metadata": { + "id": "ro4yOk5IMvRY" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of connected components: 7 0.02257537841796875\n", - "Graph generated duplicates list!!! 0.022832632064819336\n", - "generate_connected_components all took 0.02385874599985982 sec\n", - "generate_duplicates_dict all started ...\n", - "Processing duplicates!!!\n" - ] + "cell_type": "markdown", + "id": "pnHK2VTvM5rg", + "metadata": { + "id": "pnHK2VTvM5rg" + }, + "source": [ + "## 2. prepare your own data" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7/7 [00:00<00:00, 46163.72it/s]" - ] + "cell_type": "code", + "execution_count": 2, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/test_data\n", + "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 21626664 (21M) [text/plain]\n", + "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", + "\n", + "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", + "\n", + "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", + "\n" + ] + } + ], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "file_names = ['NIH_sample.jsonl']\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "!wget -P /content/test_data {\" \".join(file_list)}" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of duplicate documents that will be removed: 12\n", - "generate_duplicates_dict all took 0.009320153000317077 sec\n", - "Completed!!\n", - " total processed 10000 documents\n", - " total detected 12 duplicated documents\n", - " duplicate ratio is 0.0012\n" - ] + "cell_type": "markdown", + "id": "J-125gFtNyOg", + "metadata": { + "id": "J-125gFtNyOg" + }, + "source": [ + "## 3. get bytesize" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "code", + "execution_count": 3, + "id": "vkGRn9uJN4B0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkGRn9uJN4B0", + "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NIH_sample.jsonl\n" + ] + } + ], + "source": [ + "! ls /content/test_data" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Detected duplications are:\n" - ] + "cell_type": "markdown", + "source": [ + "### 3.1 PIPELINE based API" + ], + "metadata": { + "id": "HZIPEzB477Bm" + }, + "id": "HZIPEzB477Bm" }, { - "data": { - "text/plain": [ - "[['NIH_sample.jsonl@1769', 'NIH_sample.jsonl@1764', 'NIH_sample.jsonl@1765'],\n", - " ['NIH_sample.jsonl@245',\n", - " 'NIH_sample.jsonl@243',\n", - " 'NIH_sample.jsonl@246',\n", - " 'NIH_sample.jsonl@248',\n", - " 'NIH_sample.jsonl@244',\n", - " 'NIH_sample.jsonl@247'],\n", - " ['NIH_sample.jsonl@1191', 'NIH_sample.jsonl@1190'],\n", - " ['NIH_sample.jsonl@7746', 'NIH_sample.jsonl@7745'],\n", - " ['NIH_sample.jsonl@9026', 'NIH_sample.jsonl@8561'],\n", - " ['NIH_sample.jsonl@8200', 'NIH_sample.jsonl@7354'],\n", - " ['NIH_sample.jsonl@3037', 'NIH_sample.jsonl@3024']]" + "cell_type": "code", + "execution_count": 5, + "id": "SMK9KcV4N7al", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704, + "referenced_widgets": [ + "2f6ad7bbc0964d7696c7dec5d0e66f44", + "60dd954b33414adfabf772f49ea2f3a2", + "93697baecfd442fa8d8db709cfc82eee", + "77548643af8d430db70bfb0af3707f91", + "92f4fcc6115c4f809c15b942ca0d11d4", + "90a32f5a48a74578bad33421c0e1599c", + "ec2613a4e1c04b2789a4ca8a257bda53", + "f955ca8d9b9e451791a5aa53bb984a71", + "0d52219723f944eb8b34e5aee50298be", + "58951323a0804ad8ad9d68dd4292f35f", + "c9d153b598854d0686849b48f84aa01e" + ] + }, + "id": "SMK9KcV4N7al", + "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:34,510\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:11:34,568\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 17.940069602999984 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " meta \\\n", + "0 {'APPLICATION_ID': 100065} \n", + "1 {'APPLICATION_ID': 100066} \n", + "2 {'APPLICATION_ID': 100067} \n", + "3 {'APPLICATION_ID': 100068} \n", + "4 {'APPLICATION_ID': 100069} \n", + "... ... \n", + "9995 {'APPLICATION_ID': 2120612} \n", + "9996 {'APPLICATION_ID': 2120613} \n", + "9997 {'APPLICATION_ID': 2120616} \n", + "9998 {'APPLICATION_ID': 2120620} \n", + "9999 {'APPLICATION_ID': 2120624} \n", + "\n", + " text bytesize \n", + "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", + "1 The Office of Planning, Research and Evaluatio... 3181 \n", + "2 Improving outcomes for low-income fathers and ... 1777 \n", + "3 This project is implementing 36-month follow-u... 1760 \n", + "4 The CCDF Policies Database is a source of info... 2157 \n", + "... ... ... \n", + "9995 Project: Research and produce a videotape that... 1241 \n", + "9996 While relapse prevention has been studied and ... 1281 \n", + "9997 The proposed study on recruitment, adherence a... 2867 \n", + "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", + "9999 The overall goal of this study is to develop a... 1463 \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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\n" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/\"),\n", + " TextBytesize(text_key = 'text'),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "ret = pipeline.execute()\n", + "ret.to_pandas()" ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_files = [\"/content/test_data/NIH_sample.jsonl\"]\n", - "dup_dir = \"/content/fuzzy_dedup\"\n", - "\n", - "ngram_size = 13 # num_words to do compare\n", - "num_perm = 256 # num_permutation to hold this whole document.\n", - "# ranges and bands will impact the probabilities of false positive and false negative.\n", - "ranges = 13\n", - "bands = 9\n", - "\n", - "from pyrecdp.primitives.llmutils import near_dedup\n", - "import pandas as pd\n", - "\n", - "near_dedup(data_files, dup_dir, ngram_size, num_perm, bands, ranges)\n", - "\n", - "## Validate codes\n", - "import pickle\n", - "print(\"Detected duplications are:\")\n", - "connects, num_pair, index_list = pickle.load(open(f\"{dup_dir}/connected_components.pickle\", 'rb'))\n", - "connected_component_reverse = [[index_list[j] for j in i] for i in connects]\n", - "connected_component_reverse" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "81NiajWFTy2n", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "81NiajWFTy2n", - "outputId": "d8efdf8d-6561-411a-b190-acf8d289b331" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "resetting to 1 for number of processes\n", - "parallelize with 1 processes\n" - ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 12.83it/s]" - ] + "cell_type": "markdown", + "source": [ + "### 3.2 Operation-based API" + ], + "metadata": { + "id": "WJJXs5Co8EsB" + }, + "id": "WJJXs5Co8EsB" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Reduction is completed, checkout the new jsonl filesize\n" - ] + "cell_type": "markdown", + "source": [ + "#### prepare Ray and Spark context" + ], + "metadata": { + "id": "K49OFih-8aUc" + }, + "id": "K49OFih-8aUc" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "code", + "source": [ + "import psutil\n", + "import ray\n", + "from pyrecdp.core import SparkDataProcessor\n", + "from pyspark.sql import DataFrame\n", + "from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE\n", + "\n", + "total_mem = int(psutil.virtual_memory().total * 0.5)\n", + "total_cores = psutil.cpu_count(logical=False)\n", + "\n", + "class RayContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + "\n", + " def __enter__(self):\n", + " if not ray.is_initialized():\n", + " try:\n", + " ray.init(object_store_memory=total_mem, num_cpus=total_cores)\n", + " except:\n", + " ray.init()\n", + "\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_rayds(None)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " if ray.is_initialized():\n", + " ray.shutdown()\n", + "\n", + " def show(self, ds):\n", + " pd = ds.to_pandas()\n", + " display(pd)\n", + "\n", + "class SparkContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + " self.rdp = SparkDataProcessor()\n", + "\n", + " def __enter__(self):\n", + " self.spark = self.rdp.spark\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_spark(self.spark)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " pass\n", + "\n", + " def show(self, ds):\n", + " pd = ds.toPandas()\n", + " display(pd)" + ], + "metadata": { + "id": "-F2f_JCo8I0R" + }, + "id": "-F2f_JCo8I0R", + "execution_count": 6, + "outputs": [] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "NIH_sample.jsonl\n", - "9988\n" - ] - } - ], - "source": [ - "# apply duplication list to original data to remove duplication\n", - "\n", - "from pyrecdp.primitives.llmutils import shrink_document_MP\n", - "import os\n", - "\n", - "data_dir = \"/content/test_data/\"\n", - "dup_dir = \"/content/fuzzy_dedup\"\n", - "dup_dict = os.path.join(dup_dir, \"duplicates.pickle\")\n", - "out_dir = os.path.join(dup_dir, \"output\")\n", - "\n", - "shrink_document_MP(data_dir, dup_dict, out_dir)\n", - "\n", - "# validate\n", - "print(\"\\nReduction is completed, checkout the new jsonl filesize\")\n", - "! ls \"/content/fuzzy_dedup/output\"\n", - "! cat /content/fuzzy_dedup/output/* | wc -l" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "jFOflHdsQ2XI", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "source": [ + "# Ray mode\n", + "\n", + "from pyrecdp.primitives.operations import *\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "\n", + "op = TextBytesize()\n", + "with RayContext(\"/content/test_data/\") as ctx:\n", + " ctx.show(op.process_rayds(ctx.ds))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513, + "referenced_widgets": [ + "41fbe06aba494f75ba11aeaf3fd6fa94", + "2784ff8eb12c471eac0c335cc3169c5c", + "de524508eb0947749b3f13c6710f1dbb", + "dbe163714118476e98ec10efc2e6f1e1", + "1dc281fa098a40f6a07af047f0edd1a9", + "789f99f9724442d59f05be15b3231888", + "bc776eb0348c49348497687be7316166", + "05302bd682724596bf8a61dbca5ae2e4", + "81f6b801a20c4f5c9ab21fc0dbccbd5d", + "c96dd696d0a84e8485b705750be517d7", + "61d3c42680814b0a95ccfac427d5c553" + ] + }, + "id": "zjFGFuWm8uLW", + "outputId": "750ead08-4002-4102-a6c7-c90f1694a0d7" + }, + "id": "zjFGFuWm8uLW", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:16:49,727\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:16:49,743\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:16:49,748\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:16:49,752\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00\n", + "
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0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...1460
1{'APPLICATION_ID': 100066}The Office of Planning, Research and Evaluatio...3181
2{'APPLICATION_ID': 100067}Improving outcomes for low-income fathers and ...1777
3{'APPLICATION_ID': 100068}This project is implementing 36-month follow-u...1760
4{'APPLICATION_ID': 100069}The CCDF Policies Database is a source of info...2157
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9995{'APPLICATION_ID': 2120612}Project: Research and produce a videotape that...1241
9996{'APPLICATION_ID': 2120613}While relapse prevention has been studied and ...1281
9997{'APPLICATION_ID': 2120616}The proposed study on recruitment, adherence a...2867
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The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching; and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments, Twelve Step, Cognitive Behavioral, or Motivational Enhancement, have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects, clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. The Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs, reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n", - "NIH_sample.jsonl@1764\n", - "{\"meta\": {\"APPLICATION_ID\": 2044494}, \"text\": \"The overall aim of this study is to test the \\\"matching hypothesis\\\" that alcohol treatment effectiveness can be increased by assigning clients with certain characteristics to particular treatments. The present application proposes to continue work initiated and conducted over the past five years. The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching- and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments (Twelve Step, Cognitive Behavioral, or Motivational Enhancement) have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. Finally, it should be noted that the Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n", - "NIH_sample.jsonl@1765\n", - "{\"meta\": {\"APPLICATION_ID\": 2044499}, \"text\": \"The overall aim of study is to test the \\\"matching hypothesis\\\" that alcohol treatment effectiveness can be increased by assigning clients with certain characteristics to particular treatments. The present application proposes to continue work initiated and conducted over the past five years. The specific aims of study are: to test primary and secondary a priori matching hypotheses over the course of 15 months of follow-up; to conduct psychometric and other analyses of patient, treatment process, and outcome variables to test these matching hypotheses; to examine alternative analytic strategies and variables for testing matching; and to determine the extent to which matching effects persist over a three year period following treatment completion. Data sets collected from the Project MATCH outpatient (N=954) and aftercare (N=774) randomized clinical trial studies will be analyzed to achieve these aims. Clients randomly assigned to one of three treatments, Twelve Step, Cognitive Behavioral, or Motivational Enhancement, have been assessed prior to treatment on baseline and matching variables and followed, at 3 month intervals, for 15 months from treatment assignment to measure their alcohol consumption and other dimensions of outcome. To test longer term matching effects clients in the outpatient arm of the study will be contacted and assessed at 39 months after treatment initiation. In year 08 these data will be analyzed to address this question. It is anticipated that study results will impact on alcohol treatment research and delivery for the next decade. The Project MATCH Steering Committee, following discussion with NIAAA Staff, has chosen to submit a generic proposal across each of the CRUs, reflecting the common protocol and cooperative process that has guided the project across its first five years of operation. However, each CRU has provided site-specific information in the budget, budget justifications, listing of key personnel, consultant/consortium agreements, and human subjects sections of their respective proposals.\"}\n" - ] + "cell_type": "code", + "execution_count": 9, + "id": "81NiajWFTy2n", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + }, + "id": "81NiajWFTy2n", + "outputId": "db70e3fb-8033-4c08-b58b-b24c4e87aa42" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " text \\\n", + "0 The National Domestic Violence Hotline (NDVH) ... \n", + "1 The Office of Planning, Research and Evaluatio... \n", + "2 Improving outcomes for low-income fathers and ... \n", + "3 This project is implementing 36-month follow-u... \n", + "4 The CCDF Policies Database is a source of info... \n", + "... ... \n", + "9995 Project: Research and produce a videotape that... \n", + "9996 While relapse prevention has been studied and ... \n", + "9997 The proposed study on recruitment, adherence a... \n", + "9998 Recent studies suggest that HIV epidemics are ... \n", + "9999 The overall goal of this study is to develop a... \n", + "\n", + " meta bytesize \n", + "0 {\"APPLICATION_ID\":100065} 1460 \n", + "1 {\"APPLICATION_ID\":100066} 3181 \n", + "2 {\"APPLICATION_ID\":100067} 1777 \n", + "3 {\"APPLICATION_ID\":100068} 1760 \n", + "4 {\"APPLICATION_ID\":100069} 2157 \n", + "... ... ... \n", + "9995 {\"APPLICATION_ID\":2120612} 1241 \n", + "9996 {\"APPLICATION_ID\":2120613} 1281 \n", + "9997 {\"APPLICATION_ID\":2120616} 2867 \n", + "9998 {\"APPLICATION_ID\":2120620} 2424 \n", + "9999 {\"APPLICATION_ID\":2120624} 1463 \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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0The National Domestic Violence Hotline (NDVH) ...{\"APPLICATION_ID\":100065}1460
1The Office of Planning, Research and Evaluatio...{\"APPLICATION_ID\":100066}3181
2Improving outcomes for low-income fathers and ...{\"APPLICATION_ID\":100067}1777
3This project is implementing 36-month follow-u...{\"APPLICATION_ID\":100068}1760
4The CCDF Policies Database is a source of info...{\"APPLICATION_ID\":100069}2157
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9995Project: Research and produce a videotape that...{\"APPLICATION_ID\":2120612}1241
9996While relapse prevention has been studied and ...{\"APPLICATION_ID\":2120613}1281
9997The proposed study on recruitment, adherence a...{\"APPLICATION_ID\":2120616}2867
9998Recent studies suggest that HIV epidemics are ...{\"APPLICATION_ID\":2120620}2424
9999The overall goal of this study is to develop a...{\"APPLICATION_ID\":2120624}1463
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\n" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Spark mode\n", + "\n", + "from pyrecdp.primitives.operations import *\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "\n", + "op = TextBytesize()\n", + "with SparkContext(\"/content/test_data/\") as ctx:\n", + " ctx.show(op.process_spark(ctx.spark, ctx.ds))" + ] } - ], - "source": [ - "# Visual compare based on detection\n", - "print(f\"First duplication is {connected_component_reverse[0]}\")\n", - "print(\"You'll see the similar content in above documents\")\n", - "\n", - "for f_id in connected_component_reverse[0]:\n", - " print(f_id)\n", - " f_name, rid = f_id.split(\"@\")\n", - " ! sed -n {rid}p {f_name}\n" - ] - }, - { - "cell_type": "markdown", - "id": "okSSgtKDTnuE", - "metadata": { - "id": "okSSgtKDTnuE" - }, - "source": [ - "## 3. fuzzy deduplicate (unified detection and reduction)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "HQ_Ax_wxTnC9", - "metadata": { + ], + "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "provenance": [] }, - "id": "HQ_Ax_wxTnC9", - "outputId": "98031a36-2956-434d-a576-b86bd782ea5c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Will assign 1 cores and 10386 M memory for spark\n", - "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", - "input is \n", - "+--------------------+--------------------+\n", - "| text| meta|\n", - "+--------------------+--------------------+\n", - "|The National Dome...|{\"APPLICATION_ID\"...|\n", - "|The Office of Pla...|{\"APPLICATION_ID\"...|\n", - "|Improving outcome...|{\"APPLICATION_ID\"...|\n", - "|This project is i...|{\"APPLICATION_ID\"...|\n", - "|The CCDF Policies...|{\"APPLICATION_ID\"...|\n", - "|The overall purpo...|{\"APPLICATION_ID\"...|\n", - "|This contract wil...|{\"APPLICATION_ID\"...|\n", - "|The purpose of th...|{\"APPLICATION_ID\"...|\n", - "|The purpose of th...|{\"APPLICATION_ID\"...|\n", - "|Intimate partner ...|{\"APPLICATION_ID\"...|\n", - "|ACF's Office of R...|{\"APPLICATION_ID\"...|\n", - "|The Temporary Ass...|{\"APPLICATION_ID\"...|\n", - "|Investing in Qual...|{\"APPLICATION_ID\"...|\n", - "|Current developme...|{\"APPLICATION_ID\"...|\n", - "|The proposed diss...|{\"APPLICATION_ID\"...|\n", - "|As the US populat...|{\"APPLICATION_ID\"...|\n", - "|Through employing...|{\"APPLICATION_ID\"...|\n", - "|The proposed mixe...|{\"APPLICATION_ID\"...|\n", - "|To better serve D...|{\"APPLICATION_ID\"...|\n", - "|A long standing r...|{\"APPLICATION_ID\"...|\n", - "+--------------------+--------------------+\n", - "only showing top 20 rows\n", - "\n", - "Total num_rows of input is 10000\n", - "num_bands is 9, ranges is 13\n", - "generate minHashLsh started ...\n", - "generate minHashLsh took 64.54865999799995 sec\n", - "generate_connected_components => duplicates started ...\n" - ] + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 13/13 [00:00<00:00, 69283.29it/s]\n" - ] + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "generate_connected_components => duplicates took 0.8337461669998447 sec\n", - "deduplicate input data started ...\n", - "deduplicate input data took 0.9185702700001457 sec\n", - "Completed!!\n", - " total processed 10000 documents\n", - " total detected 12 duplicated documents, exact deduplicated counts is 12\n", - " duplicate ratio is 0.0012\n", - "output is\n", - "+--------------+--------------------+--------------------+\n", - "|filename_docid| text| meta|\n", - "+--------------+--------------------+--------------------+\n", - "| global_id@0|The National Dome...|{\"APPLICATION_ID\"...|\n", - "| global_id@1|The Office of Pla...|{\"APPLICATION_ID\"...|\n", - "| global_id@2|Improving outcome...|{\"APPLICATION_ID\"...|\n", - "| global_id@3|This project is i...|{\"APPLICATION_ID\"...|\n", - "| global_id@4|The CCDF Policies...|{\"APPLICATION_ID\"...|\n", - "| global_id@5|The overall purpo...|{\"APPLICATION_ID\"...|\n", - "| global_id@6|This contract wil...|{\"APPLICATION_ID\"...|\n", - "| global_id@7|The purpose of th...|{\"APPLICATION_ID\"...|\n", - "| global_id@8|The purpose of th...|{\"APPLICATION_ID\"...|\n", - "| global_id@9|Intimate partner ...|{\"APPLICATION_ID\"...|\n", - "| global_id@10|ACF's Office of R...|{\"APPLICATION_ID\"...|\n", - "| global_id@11|The Temporary Ass...|{\"APPLICATION_ID\"...|\n", - "| global_id@12|Investing in Qual...|{\"APPLICATION_ID\"...|\n", - "| global_id@13|Current developme...|{\"APPLICATION_ID\"...|\n", - "| 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"@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } } - ], - "source": [ - "from pyrecdp.core import SparkDataProcessor\n", - "from pyspark.sql.types import StructType, StructField, StringType\n", - "import pyspark.sql.functions as F\n", - "from pyrecdp.primitives.llmutils import near_dedup_spk\n", - "\n", - "data_files = [\"/content/test_data/NIH_sample.jsonl\"]\n", - "dup_dir = \"/content/fuzzy_dedup_spark\"\n", - "\n", - "ngram_size = 13\n", - "num_perm = 256\n", - "bands = 9\n", - "ranges = 13\n", - "rdp = SparkDataProcessor()\n", - "spark = rdp.spark\n", - "schema = StructType([\n", - " StructField(\"text\", StringType(), True),\n", - " StructField(\"meta\", StringType(), True)\n", - "])\n", - "spark_df = spark.read.text(data_files)\n", - "spark_df = spark_df.withColumn('jsonData', F.from_json(F.col('value'), schema)).select(\"jsonData.*\")\n", - "print(\"input is \")\n", - "spark_df.show()\n", - "print(f\"Total num_rows of input is {spark_df.count()}\")\n", - "\n", - "ret_df = near_dedup_spk(spark_df, ngram_size, num_perm, bands, ranges)\n", - "\n", - "print(\"output is\")\n", - "ret_df.show()\n", - "print(f\"Total num_rows of output is {ret_df.count()}\")\n", - "del rdp" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/RecDP/examples/notebooks/llmutils/classify.ipynb b/RecDP/examples/notebooks/llmutils/classify.ipynb index a8947fa8a..89ed8bf84 100644 --- a/RecDP/examples/notebooks/llmutils/classify.ipynb +++ b/RecDP/examples/notebooks/llmutils/classify.ipynb @@ -3,275 +3,1323 @@ { "cell_type": "markdown", "source": [ - "# Install pyrecdp from github" + "# RecDP LLM - ClassifyWriter\n", + "\n", + "We provide multiple Writer methods for different purpose\n", + "* ClassifyJsonlWriter\n", + "* ClassifyParquetWriter" ], "metadata": { - "id": "IJ8RaakCnsyW" + "id": "IVNSf_HhXNpO" }, - "id": "IJ8RaakCnsyW" + "id": "IVNSf_HhXNpO" }, { - "cell_type": "code", - "execution_count": null, - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "cell_type": "markdown", + "source": [ + "# Get Started" + ], "metadata": { - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" + "id": "q_NEHGhhXbw_" }, - "outputs": [], - "source": [ - "!pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" - ] + "id": "q_NEHGhhXbw_" }, { "cell_type": "markdown", "source": [ - "# Install jdk for pyspark running" + "## 1. Install pyrecdp and dependencies" ], "metadata": { - "id": "TWv2WvL6nzTA" + "id": "rg9GUZ4-XfDg" }, - "id": "TWv2WvL6nzTA" + "id": "rg9GUZ4-XfDg" }, { "cell_type": "code", "source": [ - "!DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" ], "metadata": { - "id": "vk2j9kKbSrLT" + "id": "Wjweg3dbXhI-" }, - "id": "vk2j9kKbSrLT", + "id": "Wjweg3dbXhI-", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ - "# Prepare test data" + "## 2. Prepare test data" ], "metadata": { - "id": "ljHxOJL8n-5E" + "id": "w1JiRsdHXkCu" }, - "id": "ljHxOJL8n-5E" + "id": "w1JiRsdHXkCu" }, { "cell_type": "code", "source": [ "%mkdir -p /content/test_data\n", "%cd /content/test_data\n", - "!wget https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl" + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" ], "metadata": { - "id": "bWDi0vtLoHwm" + "id": "cGVSGiYlXlqY" }, - "id": "bWDi0vtLoHwm", + "id": "cGVSGiYlXlqY", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ - "# Import classifier function" + "## 3. Classify Data based on language" ], "metadata": { - "id": "XusjrHT4tOdj" + "id": "jL9eVqZPXlFF" }, - "id": "XusjrHT4tOdj" + "id": "jL9eVqZPXlFF" + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 Write output as parquet" + ], + "metadata": { + "id": "OLBNRL68YMyT" + }, + "id": "OLBNRL68YMyT" }, { "cell_type": "code", "source": [ - "from pyrecdp.primitives.llmutils import classify_spark, language_identify_spark\n", - "from pyrecdp.primitives.llmutils.utils import get_target_file_list\n", - "from huggingface_hub import hf_hub_download\n", - "from pyrecdp.core import SparkDataProcessor" + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "# Below is just a quick example of using some of the operation,\n", + "# full operation list please refer to RecDP LLM readme.\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/tiny_c4_sample.jsonl\"),\n", + " LanguageIdentify(),\n", + " ClassifyParquetWriter('output/tiny_c4_sample_parquet', 'language'),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "result = pipeline.execute()\n", + "del pipeline" ], "metadata": { - "id": "SkccgiE9oNyL" + "id": "wKsYSPSnpU8x", + "outputId": "653dee67-fb28-4c93-fdbb-cbd6bec693b9", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "id": "SkccgiE9oNyL", - "execution_count": 6, - "outputs": [] + "id": "wKsYSPSnpU8x", + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init spark\n", + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "execute with spark started ...\n", + "\n", + "execute with spark took 22.19131917599998 sec\n" + ] + } + ] }, { - "cell_type": "markdown", + "cell_type": "code", "source": [ - "# Specify variables" + "## View Result\n", + "! ls output/tiny_c4_sample_parquet" ], "metadata": { - "id": "w0kSy1Fotwcl" + "id": "2UgpZyVpZFkk", + "outputId": "40ffb9bd-f098-4f54-d79e-1343b12010d6", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "id": "w0kSy1Fotwcl" + "id": "2UgpZyVpZFkk", + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "'language=eng_Latn' 'language=kor_Hang' 'language=yue_Hant' _SUCCESS\n" + ] + } + ] }, { "cell_type": "code", "source": [ - "fasttext_model_dir = hf_hub_download(repo_id=\"facebook/fasttext-language-identification\", filename=\"model.bin\")\n", - "data_dir = \"/content/test_data/\"\n", - "data_file = '/content/test_data/arxiv_sample_100.jsonl'\n", - "classify_save_path = \"/content/test_data/output/classify_spark\"" + "import pandas as pd\n", + "pd.read_parquet(\"output/tiny_c4_sample_parquet/language=eng_Latn\")" ], "metadata": { - "id": "xM8XygxBXKfh" + "id": "UkScD2OzZvd0", + "outputId": "7826f001-6065-45b2-e154-ae136b7c9e09", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } }, - "id": "xM8XygxBXKfh", - "execution_count": 5, - "outputs": [] + "id": "UkScD2OzZvd0", + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " text \\\n", + "0 lorazepam nombre comercial mexico From an inte... \n", + "1 It is possible to love someone who does not lo... \n", + "2 Canon PIXMA TS9520 All-in-One Print / Scan / C... \n", + "3 For those who plan on buying an iPad this Satu... \n", + "4 After tipping 25 tokens in a day, you'll be ab... \n", + ".. ... \n", + "416 Sunrise is an equal opportunity employer. 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/root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", + "Successfully built pyrecdp alt-profanity-check fasttext pyspark wget\n", + "Installing collected packages: wget, sentencepiece, selectolax, phonenumbers, xxhash, scipy, safetensors, pyspark, pybind11, loguru, jsonlines, jedi, ftfy, dill, scikit-learn, networkit, multiprocess, huggingface-hub, fasttext, Faker, datasketch, woodwork, tokenizers, lightgbm, alt-profanity-check, transformers, ray, pandas-flavor, featuretools, datasets, category-encoders, pyrecdp\n", + " Attempting uninstall: scipy\n", + " Found existing installation: scipy 1.11.3\n", + " Uninstalling scipy-1.11.3:\n", + " Successfully uninstalled scipy-1.11.3\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.2.2\n", + " Uninstalling scikit-learn-1.2.2:\n", + " Successfully uninstalled scikit-learn-1.2.2\n", + " Attempting uninstall: lightgbm\n", + " Found existing installation: lightgbm 4.0.0\n", + " Uninstalling lightgbm-4.0.0:\n", + " Successfully uninstalled lightgbm-4.0.0\n", + "Successfully installed Faker-19.10.0 alt-profanity-check-1.3.0 category-encoders-2.6.2 datasets-2.14.5 datasketch-1.5.9 dill-0.3.7 fasttext-0.9.2 featuretools-1.27.0 ftfy-6.1.1 huggingface-hub-0.16.4 jedi-0.19.1 jsonlines-3.1.0 lightgbm-3.3.5 loguru-0.7.2 multiprocess-0.70.15 networkit-10.1 pandas-flavor-0.6.0 phonenumbers-8.13.22 pybind11-2.11.1 pyrecdp-1.6.1b2023101201 pyspark-3.4.0 ray-2.7.1 safetensors-0.4.0 scikit-learn-1.3.0 scipy-1.10.1 selectolax-0.3.16 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.0 wget-3.2 woodwork-0.26.0 xxhash-3.4.1\n" + ] + } + ], "source": [ "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", "! pip install pyrecdp --pre\n", @@ -71,7 +481,7 @@ "%mkdir -p /content/test_data\n", "%cd /content/test_data\n", "file_names = ['PMC100320.md', 'PMC100321.md', 'PMC100322.md', 'PMC100323.md', 'PMC100324.md', 'PMC100325.md', 'PMC100326.md', 'PMC100327.md']\n", - "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/pmc/{i}\" for i in file_names]\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/pmc/{i}\" for i in file_names]\n", "!wget -P /content/test_data {\" \".join(file_list)}" ], "metadata": { @@ -84,7 +494,7 @@ { "cell_type": "markdown", "source": [ - "## 3. convert data" + "## 3. convert dat - from seperate text files to a unified parquet" ], "metadata": { "id": "dhrC92TbIriM" @@ -101,7 +511,7 @@ "base_uri": "https://localhost:8080/" }, "id": "2MyAkmk2InU6", - "outputId": "96e6079f-29dc-4b80-cd14-61c37671d98a" + "outputId": "12eb62c5-06ec-43bc-a42c-0fde52ef5a26" }, "id": "2MyAkmk2InU6", "execution_count": 6, @@ -110,8 +520,8 @@ "output_type": "stream", "name": "stdout", "text": [ - "PMC100320.md\tPMC100321.md PMC100323.md PMC100325.md PMC100327.md\n", - "PMC100320.md.1\tPMC100322.md PMC100324.md PMC100326.md\n" + "PMC100320.md PMC100322.md PMC100324.md PMC100326.md\n", + "PMC100321.md PMC100323.md PMC100325.md PMC100327.md\n" ] } ] @@ -133,13 +543,13 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1921 + "height": 335 }, "id": "g6udosvfI9oo", - "outputId": "7bedd21d-e911-41e4-b3b0-c65f077eb6aa" + "outputId": "716dd4c5-9d4c-482d-8c8b-a255cbb6a7c8" }, "id": "g6udosvfI9oo", - "execution_count": 8, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -152,7 +562,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "convert.py: 100%|██████████| 1/1 [00:05<00:00, 5.36s/it]\n" + "convert.py: 100%|██████████| 1/1 [00:13<00:00, 13.49s/it]\n" ] }, { @@ -160,30 +570,28 @@ "data": { "text/plain": [ " text \\\n", - "0 Background\\n==========\\n\\nHealth information s... \n", - "1 Background\\n==========\\n\\nCongenital fibrosis ... \n", - "2 Background\\n==========\\n\\nMost current methods... \n", - "3 Background\\n==========\\n\\nNerve growth factor ... \n", - "4 Background\\n==========\\n\\nIn 1990 the National... \n", - "5 Background\\n==========\\n\\nCongenital fibrosis ... \n", - "6 Background\\n==========\\n\\nIdentification of sp... \n", - "7 Background\\n==========\\n\\nThe manner in which ... \n", - "8 Background\\n==========\\n\\nOver the last 50 yea... \n", + "0 Background\\n==========\\n\\nIdentification of sp... \n", + "1 Background\\n==========\\n\\nHealth information s... \n", + "2 Background\\n==========\\n\\nOver the last 50 yea... \n", + "3 Background\\n==========\\n\\nThe manner in which ... \n", + "4 Background\\n==========\\n\\nMost current methods... \n", + "5 Background\\n==========\\n\\nNerve growth factor ... \n", + "6 Background\\n==========\\n\\nCongenital fibrosis ... \n", + "7 Background\\n==========\\n\\nIn 1990 the National... \n", "\n", - " meta \n", - "0 {'filename': 'PMC100325.md'} \n", - "1 {'filename': 'PMC100320.md'} \n", - "2 {'filename': 'PMC100327.md'} \n", - "3 {'filename': 'PMC100322.md'} \n", - "4 {'filename': 'PMC100324.md'} \n", - "5 {'filename': 'PMC100320.md.1'} \n", - "6 {'filename': 'PMC100321.md'} \n", - "7 {'filename': 'PMC100326.md'} \n", - "8 {'filename': 'PMC100323.md'} " + " meta \n", + "0 {'filename': 'PMC100321.md'} \n", + "1 {'filename': 'PMC100325.md'} \n", + "2 {'filename': 'PMC100323.md'} \n", + "3 {'filename': 'PMC100326.md'} \n", + "4 {'filename': 'PMC100327.md'} \n", + "5 {'filename': 'PMC100322.md'} \n", + "6 {'filename': 'PMC100320.md'} \n", + "7 {'filename': 'PMC100324.md'} " ], "text/html": [ "\n", - "
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1NIH_sample.jsonl@24ab814d1699f93fb9d7500f7216065c3d870526aae0637...The Office of Planning, Research and Evaluatio...{'APPLICATION_ID': 100066}PILE-NIH_NIH_sample.jsonl3181
2NIH_sample.jsonl@31ec7d83045ce5002e2692acdb30a204292c02da7fd65f5...Improving outcomes for low-income fathers and ...{'APPLICATION_ID': 100067}PILE-NIH_NIH_sample.jsonl1777
3NIH_sample.jsonl@420deec0fa0d5f4995357049d3110ef1fe661579b2ea4a4...This project is implementing 36-month follow-u...{'APPLICATION_ID': 100068}PILE-NIH_NIH_sample.jsonl1760
4NIH_sample.jsonl@56581b71fc72f4c94bb743a029478291cba4cadf576155e...The CCDF Policies Database is a source of info...{'APPLICATION_ID': 100069}PILE-NIH_NIH_sample.jsonl2157
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9993NIH_sample.jsonl@99961fa5db880d2a122674fdebc4ecd39b056e2d96d3957f60...Project: Research and produce a videotape that...{'APPLICATION_ID': 2120612}PILE-NIH_NIH_sample.jsonl1241
9994NIH_sample.jsonl@999779247d33552267e1cb0a2a5af570d28e8f55c555b342d4...While relapse prevention has been studied and ...{'APPLICATION_ID': 2120613}PILE-NIH_NIH_sample.jsonl1281
9995NIH_sample.jsonl@9998fae920171a632df483a495172cc4085da8ec1232c30334...The proposed study on recruitment, adherence a...{'APPLICATION_ID': 2120616}PILE-NIH_NIH_sample.jsonl2867
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\n" + ] + }, + "metadata": {}, + "execution_count": 9 } ] } diff --git a/RecDP/examples/notebooks/llmutils/reader.ipynb b/RecDP/examples/notebooks/llmutils/reader.ipynb index 80d84f95d..c3d05b702 100644 --- a/RecDP/examples/notebooks/llmutils/reader.ipynb +++ b/RecDP/examples/notebooks/llmutils/reader.ipynb @@ -1,180 +1,4086 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "BT-BjcaHj3eh" - }, - "source": [ - "## Install pyrecdp from github" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "E3ffXVymdeKg" - }, - "outputs": [], - "source": [ - "! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KVAcXKenj_sZ" - }, - "source": [ - "## Install jdk for pyspark running" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4Z1dPdx3dwf1" - }, - "outputs": [], - "source": [ - "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bMqBJ9eckIs6" - }, - "source": [ - "### Prepare test data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2SFjDgfLd4qE" - }, - "outputs": [], - "source": [ - "%mkdir -p /content/test_data\n", - "%cd /content/test_data\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/github_sample_50.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/pii_test.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JjTnnzw_kRVV" - }, - "source": [ - "## Import filter functions" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "Swy-H3HQepjA" - }, - "outputs": [], - "source": [ - "from pyrecdp.primitives.llmutils.text_fixer import text_fixer\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XXGify_rkXHZ" - }, - "source": [ - "## Specify input data path, output path,input data type and text types" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "UvIWsnV-fYmv" - }, - "outputs": [], - "source": [ - "data_dir = \"/content/test_data\"\n", - "out_dir = \"/content/output\"\n", - "in_type = \"jsonl\"\n", - "text_types = [\"html\", 'latex', \"codes\"]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nj0LuIsFkhRr" - }, - "source": [ - "## Process the 'text' column and remove unnecessary control characters, HTML elements and comments\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# RecDP LLM - Reader\n", + "\n", + "We provide multiple Reader methods for different purpose\n", + "* JsonlReader\n", + "* ParquetReader\n", + "* SourcedJsonlReader\n", + "* SourcedParquetReader" + ], + "metadata": { + "id": "dYQq3DgCQWtu" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Get Started" + ], + "metadata": { + "id": "6o5wKI-0Q3_K" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BT-BjcaHj3eh" + }, + "source": [ + "## 1. Install pyrecdp and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E3ffXVymdeKg" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMqBJ9eckIs6" + }, + "source": [ + "## 2. Prepare test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2SFjDgfLd4qE" + }, + "outputs": [], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.parquet" + ] }, - "id": "vftAYZMwfllh", - "outputId": "21041b9e-3643-427c-ca71-0eca0bf622b9" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Will assign 1 cores and 10386 M memory for spark\n", - "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", - "Load data from jsonl file started ...\n", - "Load data from jsonl file took 0.7552096419999543 sec\n", - "Processing data started ...\n", - "Processing data took 0.12260321699977794 sec\n", - "Save data started ...\n", - "Save data took 11.986804642999687 sec\n", - "Completed!!\n", - " Loaded and processed total 602 documents\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "JjTnnzw_kRVV" + }, + "source": [ + "## 3. Reader" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 JsonlReader" + ], + "metadata": { + "id": "M3LlsCNWRwiw" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Swy-H3HQepjA", + "outputId": "ed5801e5-07d5-44bc-c18f-e8c071afe919", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617, + "referenced_widgets": [ + "9989f80e386c4d2089fde9d111d4380a", + "5046e836749e430283fc5d83e0adefea", + "76fe681245484067bc9c33cf697688f6", + "b1d932b232a54d218d1f951179ec0b58", + "4e1d0496e9904f989b64b98036980a1c", + "1a26aabdfcd649378b06ba30a5dc5ce7", + "94a407209e3c4b2b9698cc30e7dc4dd0", + "c29a53da225e44aa95e9eef92618c445", + "4324eeeee08e4c5ab7af580fb36552ee", + "b55707a4cd094c2fae1b6eec1b3c9bfe", + "c3a8181a5b2f4893b9de4a365cb1c8ab" + ] + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 20:05:58,658\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133116928 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 20:05:59,792\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 20:06:02,237\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 20:06:02,278\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)]\n", + "2023-10-11 20:06:02,288\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 20:06:02,292\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00\n", + "
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metatext
0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...
1{'APPLICATION_ID': 100066}The Office of Planning, Research and Evaluatio...
2{'APPLICATION_ID': 100067}Improving outcomes for low-income fathers and ...
3{'APPLICATION_ID': 100068}This project is implementing 36-month follow-u...
4{'APPLICATION_ID': 100069}The CCDF Policies Database is a source of info...
.........
9995{'APPLICATION_ID': 2120612}Project: Research and produce a videotape that...
9996{'APPLICATION_ID': 2120613}While relapse prevention has been studied and ...
9997{'APPLICATION_ID': 2120616}The proposed study on recruitment, adherence a...
9998{'APPLICATION_ID': 2120620}Recent studies suggest that HIV epidemics are ...
9999{'APPLICATION_ID': 2120624}The overall goal of this study is to develop a...
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\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "# Below is just a quick example of using some of the operation,\n", + "# full operation list please refer to RecDP LLM readme.\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/NIH_sample.jsonl\"),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "result = pipeline.execute().to_pandas()\n", + "del pipeline\n", + "result" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.2 ParquetReader" + ], + "metadata": { + "id": "17unVQqtR0zu" + } + }, + { + "cell_type": "code", + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "# Below is just a quick example of using some of the operation,\n", + "# full operation list please refer to RecDP LLM readme.\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " ParquetReader(\"/content/test_data/NIH_sample.parquet\"),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "result = pipeline.execute().to_pandas()\n", + "del pipeline\n", + "result" + ], + "metadata": { + "id": "GymNEmEpR0BU", + "outputId": "c1c60679-5474-4832-e05d-5c6ac8825645", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669, + "referenced_widgets": [ + "bec2ffdb72404bdb9ac2c189e3e03c47", + "8e57b5bf918d4c6e811ef7d369218f9d", + "753415f88b884138ae6dcddba8a6f2b0", + "8d8bed98eadb4a539d7ed07f67bbb1b2", + "538a19cc1c58459b9209b0f0f05e6862", + "13ae6831f1b149e6b361c490a8bd03ab", + "198b47dc2d514a07bb5358a8f68c96b7", + "07c185bc54e548fba1c515ce872e3534", + "45f3e35ed0224c30ab3d9722bab8bee4", + "5a48a01f5fc045a28169c0ae92cad28e", + "4c724965347e405ba857cac12f17c8f4", + "9a67a6dbddd14c12b4ae255acc4f4d8c", + "62fabe87e79d44e39d3152b632b09379", + "9b83c29b39ff46ab8f1883ff13270129", + "0f07ea2978b64887846725b03952dbd4", + "26fd3e4edfc443a1b767f19c4e44399b", + "2cccad9169f54e53a203b2616faa3ff7", + "e41a8ab25b3146d2b0c98e6b7b46332b", + "8f5d07d1f284462c8e26599f192cc719", + "654f84c0eb874a28ab65d4fe271b5082", + "4038c2837cee4fca8760b9ff558d8983", + "2006233f573f46a4b758ff2aa9e5c2e3" + ] + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 20:06:10,708\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133116928 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 20:06:11,882\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:259: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m pq_ds.pieces, **prefetch_remote_args\n", + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:269: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m self._pq_pieces = [_SerializedPiece(p) for p in pq_ds.pieces]\n", + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:270: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=8691)\u001b[0m self._pq_paths = [p.path for p in pq_ds.pieces]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[2m\u001b[36m(pid=8691) \u001b[0mParquet Files Sample 0: 0%| | 0/1 [00:00\n", + "
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textmeta
0The National Domestic Violence Hotline (NDVH) ...{'APPLICATION_ID': 100065}
1The Office of Planning, Research and Evaluatio...{'APPLICATION_ID': 100066}
2Improving outcomes for low-income fathers and ...{'APPLICATION_ID': 100067}
3This project is implementing 36-month follow-u...{'APPLICATION_ID': 100068}
4The CCDF Policies Database is a source of info...{'APPLICATION_ID': 100069}
.........
9995Project: Research and produce a videotape that...{'APPLICATION_ID': 2120612}
9996While relapse prevention has been studied and ...{'APPLICATION_ID': 2120613}
9997The proposed study on recruitment, adherence a...{'APPLICATION_ID': 2120616}
9998Recent studies suggest that HIV epidemics are ...{'APPLICATION_ID': 2120620}
9999The overall goal of this study is to develop a...{'APPLICATION_ID': 2120624}
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10000 rows × 2 columns

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\n", + "
\n", + " \n" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.3 SourcedJsonlReader" + ], + "metadata": { + "id": "8vF5ByysR_hC" + } + }, + { + "cell_type": "code", + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "# Below is just a quick example of using some of the operation,\n", + "# full operation list please refer to RecDP LLM readme.\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " SourcedJsonlReader(\"/content/test_data/NIH_sample.jsonl\"),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "result = pipeline.execute().to_pandas()\n", + "del pipeline\n", + "result" + ], + "metadata": { + "id": "A_afRiPdR-4h", + "outputId": "0454e352-6031-4775-a161-9dd0f85098a5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704, + "referenced_widgets": [ + "95967a3523084969a0d70f0d0e7f6287", + "17f63609fa9c4afb9ea4fff15537e6d6", + "178bbcde4a6340fa8b1f90e9451ae0c3", + "8e4db74eb909472ebe6ac1685feff87d", + "ef32c460594942a5a8e71c28511bbe25", + "b14f8de8c5924512a30d2404cacf7403", + "5d566297dda446e7bcdd36548982a0ee", + "74d8d6e9217b4ae2974fa941e16d0c5d", + "d1db87dc789f468082fd194f4e98c5c2", + "75a2857f6a9b44c9b373bd6060ee839a", + "e48cf024f8e3425c985886dcfa748bab" + ] + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 20:08:28,554\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 20:08:28,776\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 20:08:33,099\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 20:08:33,140\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map()]\n", + "2023-10-11 20:08:33,146\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 20:08:33,151\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00) pid=9510)\u001b[0m 2023-10-11 20:08:42.951151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-11 20:08:49,300\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 17.068184013999826 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " meta \\\n", + "0 {'APPLICATION_ID': 100065} \n", + "1 {'APPLICATION_ID': 100066} \n", + "2 {'APPLICATION_ID': 100067} \n", + "3 {'APPLICATION_ID': 100068} \n", + "4 {'APPLICATION_ID': 100069} \n", + "... ... \n", + "9995 {'APPLICATION_ID': 2120612} \n", + "9996 {'APPLICATION_ID': 2120613} \n", + "9997 {'APPLICATION_ID': 2120616} \n", + "9998 {'APPLICATION_ID': 2120620} \n", + "9999 {'APPLICATION_ID': 2120624} \n", + "\n", + " text source_id \n", + "0 The National Domestic Violence Hotline (NDVH) ... 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0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...NIH_sample.jsonl
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This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 20:09:00,233\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:259: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m pq_ds.pieces, **prefetch_remote_args\n", + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:269: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m self._pq_pieces = [_SerializedPiece(p) for p in pq_ds.pieces]\n", + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m /usr/local/lib/python3.10/dist-packages/ray/data/datasource/parquet_datasource.py:270: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Use the '.fragments' attribute instead\n", + "\u001b[2m\u001b[36m(_get_reader pid=10028)\u001b[0m self._pq_paths = [p.path for p in pq_ds.pieces]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[2m\u001b[36m(pid=10028) \u001b[0mParquet Files Sample 0: 0%| | 0/1 [00:00 TaskPoolMapOperator[ReadParquet->SplitBlocks(40)] -> TaskPoolMapOperator[Map()]\n", + "2023-10-11 20:09:05,764\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 20:09:05,766\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/1600 [00:00) pid=10028)\u001b[0m 2023-10-11 20:09:19.738467: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 21.284709955000153 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " text \\\n", + "0 The National Domestic Violence Hotline (NDVH) ... \n", + "1 The Office of Planning, Research and Evaluatio... \n", + "2 Improving outcomes for low-income fathers and ... \n", + "3 This project is implementing 36-month follow-u... \n", + "4 The CCDF Policies Database is a source of info... \n", + "... ... \n", + "9995 Project: Research and produce a videotape that... \n", + "9996 While relapse prevention has been studied and ... \n", + "9997 The proposed study on recruitment, adherence a... \n", + "9998 Recent studies suggest that HIV epidemics are ... \n", + "9999 The overall goal of this study is to develop a... \n", + "\n", + " meta source_id \n", + "0 {'APPLICATION_ID': 100065} NIH_sample.parquet \n", + "1 {'APPLICATION_ID': 100066} NIH_sample.parquet \n", + "2 {'APPLICATION_ID': 100067} NIH_sample.parquet \n", + "3 {'APPLICATION_ID': 100068} NIH_sample.parquet \n", + "4 {'APPLICATION_ID': 100069} NIH_sample.parquet \n", + "... ... ... \n", + "9995 {'APPLICATION_ID': 2120612} NIH_sample.parquet \n", + "9996 {'APPLICATION_ID': 2120613} NIH_sample.parquet \n", + "9997 {'APPLICATION_ID': 2120616} NIH_sample.parquet \n", + "9998 {'APPLICATION_ID': 2120620} NIH_sample.parquet \n", + "9999 {'APPLICATION_ID': 2120624} NIH_sample.parquet \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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2Improving outcomes for low-income fathers and ...{'APPLICATION_ID': 100067}NIH_sample.parquet
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+ } + } } - ], - "source": [ - "text_fixer(data_dir, in_type, out_dir,text_types)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb b/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb index 80d84f95d..354dfca34 100644 --- a/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb +++ b/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb @@ -1,180 +1,4530 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "BT-BjcaHj3eh" - }, - "source": [ - "## Install pyrecdp from github" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "E3ffXVymdeKg" - }, - "outputs": [], - "source": [ - "! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KVAcXKenj_sZ" - }, - "source": [ - "## Install jdk for pyspark running" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4Z1dPdx3dwf1" - }, - "outputs": [], - "source": [ - "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bMqBJ9eckIs6" - }, - "source": [ - "### Prepare test data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2SFjDgfLd4qE" - }, - "outputs": [], - "source": [ - "%mkdir -p /content/test_data\n", - "%cd /content/test_data\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/github_sample_50.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/pii_test.jsonl\n", - "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JjTnnzw_kRVV" - }, - "source": [ - "## Import filter functions" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "Swy-H3HQepjA" - }, - "outputs": [], - "source": [ - "from pyrecdp.primitives.llmutils.text_fixer import text_fixer\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XXGify_rkXHZ" - }, - "source": [ - "## Specify input data path, output path,input data type and text types" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "UvIWsnV-fYmv" - }, - "outputs": [], - "source": [ - "data_dir = \"/content/test_data\"\n", - "out_dir = \"/content/output\"\n", - "in_type = \"jsonl\"\n", - "text_types = [\"html\", 'latex', \"codes\"]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nj0LuIsFkhRr" - }, - "source": [ - "## Process the 'text' column and remove unnecessary control characters, HTML elements and comments\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# RecDP LLM - ResumableTextPipeline\n", + "\n", + "ResumableTextPipeline is designed for processing large dataset with failover support.\n", + "\n", + "User can link all the todo operations one by one and provide to pipeline. We will use a track log to capture the progress.\n", + "\n", + "If interruption happens in the middle of process, re-lauch the pipeline will resume from where left." + ], + "metadata": { + "id": "nDhyz4A3JF-4" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Get started" + ], + "metadata": { + "id": "ygSsNjXDKCqx" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BT-BjcaHj3eh" + }, + "source": [ + "## 1. Install pyrecdp and dependencies" + ] }, - "id": "vftAYZMwfllh", - "outputId": "21041b9e-3643-427c-ca71-0eca0bf622b9" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Will assign 1 cores and 10386 M memory for spark\n", - "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", - "Load data from jsonl file started ...\n", - "Load data from jsonl file took 0.7552096419999543 sec\n", - "Processing data started ...\n", - "Processing data took 0.12260321699977794 sec\n", - "Save data started ...\n", - "Save data took 11.986804642999687 sec\n", - "Completed!!\n", - " Loaded and processed total 602 documents\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E3ffXVymdeKg" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMqBJ9eckIs6" + }, + "source": [ + "## 2. Prepare test data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2SFjDgfLd4qE" + }, + "outputs": [], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl\n", + "!wget -P /content/test_data https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JjTnnzw_kRVV" + }, + "source": [ + "## 3. ResumableTextPipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "Swy-H3HQepjA", + "outputId": "87d5cdef-210a-4c96-8fb0-c38654abb41c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "ae3ac3305bbb4ef9bef2753ce8c80ea8", + "31a23eee85054cf68defd51eedc0d923", + "e35281c9c108444f91e3d2e99956c9c4", + "cb8a105d2d284cc3ad6c11ed9e0ac80a", + "e87438b63b454b6eb01c0e08eca279fa", + "f624e5cb29d04673bf562e6d117627b4", + "1ec6125904114da8b567079e7367fed3", + "691057d7d91c4c25a67c7125367affb5", + "fe997b4ba3664f488a729a98cfd5af40", + "01ec888ca5494abb808e1ff1a6b95b42", + "1064623521ff483aa68c97204c20686d", + "0fa70a2f2c404ae1a02b6e95a5eeeb94", + "666b5908fba046049a22a1c4034d0bee", + "e23f4cc64e674a53ab06331e47bf93af", + "a9c7a686804a4ee6921c531ac1aa5e61", + "389470a5ae154d8aad4167cbab7eca78", + "275b3eeeabcb4584ae4e60dc4b96d33a", + "a00d2947fd3245d3a5cec6fdb01080d2", + "c1f5351c03264b2ea3051c3fb653c59e", + "80f34a0fdb1f45dc9e6bf3d73fb9ae1f", + "c3326f3d36694b27bd38e8439848b4c4", + "c9bfd02da6414593938ce4d579e3ac4b", + "edb8c9187ce745f7a2dc83d455539fb4", + "d2a52fc97ede472ca03f019fa36e19ed", + "8c7b4eb9f5954db1b5a70cca9a98df26", + "0a524ae60f5a4fc8ab52be77170b1a6a", + "5254e81922a443d28b0f94cdca67b949", + "8bec093113ab442a81c2b189c0e842d1", + "73037f8ee21d4860a066b5e4c65401c5", + "08c52b09a8624c21a4f8816f45e4c783", + "a6c5cba7ccfd4a64acff223546df2bb4", + "2706c366900e4d0cbe7f48b0a64b9f94", + "14439e584bda44b69a1e966a7a40db05", + "94273a24a1c142a5a313acfc4d16374f", + "b68d1e2d2d654ccc960e0ae46f1bc0bc", + "900ab18e01594486a8539155c77ed120", + "dd061b7c6c29416990d1e16991dbfc69", + "f299cb222ede4c00b4f4868302a5ab1f", + "faaece59156943efa36304036cba6ded", + "dbaed49746634fa6a75c42567dcc12ff", + "d659e960c6b940fc991634b2d6e32c9a", + "2d057967f9104759a1fe126859a52148", + "cfe65ab4c32a41a09b8c2697f919249c", + "4d801d07fd1841e7bac890e46af6e8f7", + "34ea5ca01a5e4993a83b1e289e5e5be8", + "578539174be8486aa9a6f03064f3babc", + "bf01405b26be4630b8352e638e1a27d9", + "e3f800c0d292412eae99c0a77072034b", + "ee7fb0f1962f4fd28b2ae71fb731ba5c", + "59b4b05e28754a2f99b5cbf4050ab407", + "15885936a1cd4fa3815ab0cd633c3aff", + "334cedb034804e5bb229833c5ab72208", + "876ebf393fab4009ac06119de4f9ba9f", + "1bb6610f891240db8852ab2875c4de68", + "e0801f8d48704e22a98146e4bdb4d6f5", + "640c84dbb0114187abd1f3ab66f3f110", + "0b8e994fa907486eb91281c2d6fd075e", + "7ad5846e59884e8f9cdf9d6ebb0ebec9", + "a2a458c292ca4499b3498b1ce7e6a6f8", + "f06ccfdb213d40bba42d1fc919d1ab9a", + "25989f97fa124f1da1929982ea4d081b", + "548be59310e4413696ffb48bbf57225c", + "92de0b523d6e4bf5bd1bd96fc95c73e4", + "f92ffdb6309d4c6a979595fb516386df", + "d42f4011aa644090810a62467aeda5f0", + "589087f298484943a1b3ada5723d90ad", + "3344220e93cd4752ab8743f99fa2465f", + "7de96f0a8d3f4227b763dad8e465179b", + "35a9b88ffc2a4f17a5816b9e72ebacde", + "d1449e09f7044d1bae5531d6c696a78c", + "dbdd40c5a8854fb19b18fd07e6ec8ab8", + "00b98577de18459da340ec93a48704af", + "ad1c192b6d434e03b674976daafa93ed", + "fa6e2318d19d48bc9d16be031425f64e", + "33e5e1df09f543d7b6a3b7565f155187", + "ba3427245939424d8ecd04fe8322cf47", + "fc89e9814e2c40d5b32512dc83cb3549", + "4a7e258c7edc43968785d2f25336ec63", + "1ddd6cf2ca8e4cc5abe6febedfafe95b", + "c2a3b04b6c67481681f641d36f340b59", + "ae41f0fb8a014440a7950bd057c829fb", + "6c67ebc8ece34226af33e9c5cb9e554f", + "f93898579b554030aa61e620d35d8204", + "d629a9f72dad44cfb27ce741f4785488", + "46376820119f430aa647cbc1bf253a47", + "ae4bd1ce767742698d4f593e260c251b", + "08669172df55499fa14b3c825afa6951", + "0d3e9467f77d4c7aafffaa1fad4c7e86", + "1512d99e5f5c4c59b4aa69eac84d55e1", + "47b618795e694bc3a116ea649b691ddf", + "a83989f9c7844ef487194fedfdb7605d", + "bef8a3954fc1456bb9118321a941ed18", + "108d4af4018e4046ad46f83fb8f5a5b7", + "f260dd3f20bc4bd18f339f0f8f5c1465", + "b9768e09d22344778c34197bb3b7fee5", + "81fb1fd186e54da1ba09a8941d026288", + "8243867fce014a74b079936b1eaadf6d", + "d3ac30e5245c46c5b6e3b6059899b59c", + "cfd60973d8554ffd90512c264e122fdd" + ] + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray with total mem of 8167956480\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 19:35:25,199\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 19:35:26,357\tINFO worker.py:1642 -- Started a local Ray instance.\n", + "2023-10-11 19:35:30,545\tINFO read_api.py:406 -- To satisfy the requested parallelism of 2, each read task output is split into 2 smaller blocks.\n", + "2023-10-11 19:35:30,607\tINFO read_api.py:406 -- To satisfy the requested parallelism of 5, each read task output is split into 5 smaller blocks.\n", + "ResumableTextPipeline, current on tiny_c4_sample.jsonl: 0%| | 0/2 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(2)] -> TaskPoolMapOperator[Map()]\n", + "2023-10-11 19:35:30,654\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:35:30,660\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/4 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(2)] -> TaskPoolMapOperator[Map()->Filter()]\n", + "2023-10-11 19:35:32,651\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:35:32,652\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/4 [00:00)->Filter() pid=1731)\u001b[0m 2023-10-11 19:35:40.103464: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Processed a total of 449 rows of data, filtered out 8 rows of data\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 19:35:44,114\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(2)] -> TaskPoolMapOperator[Map()->Filter()->Filter()]\n", + "2023-10-11 19:35:44,116\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:35:44,120\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/4 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(2)] -> TaskPoolMapOperator[Map()->Filter()->Filter()->Map()->Write]\n", + "2023-10-11 19:36:13,799\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:36:13,803\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/4 [00:00)->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "ResumableTextPipeline, current on arxiv_sample_100.jsonl: 50%|█████ | 1/2 [00:50<00:50, 50.65s/it]2023-10-11 19:36:21,291\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(5)] -> TaskPoolMapOperator[Map()]\n", + "2023-10-11 19:36:21,293\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:36:21,296\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(5)] -> TaskPoolMapOperator[Map()->Filter()]\n", + "2023-10-11 19:36:21,814\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:36:21,817\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00)->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "2023-10-11 19:36:38,064\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(5)] -> TaskPoolMapOperator[Map()->Filter()->Filter()]\n", + "2023-10-11 19:36:38,066\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:36:38,070\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Processed a total of 100 rows of data, filtered out 0 rows of data\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00)->Filter()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter() pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "2023-10-11 19:36:45,890\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(5)] -> TaskPoolMapOperator[Map()->Filter()->Filter()->Map()->Write]\n", + "2023-10-11 19:36:45,892\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 19:36:45,897\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Processed a total of 100 rows of data, filtered out 0 rows of data\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00)->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "\u001b[2m\u001b[36m(Map()->Filter()->Filter()->Map()->Write pid=1731)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", + "ResumableTextPipeline, current on arxiv_sample_100.jsonl: 100%|██████████| 2/2 [01:36<00:00, 48.47s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Completed!\n", + "ResumableTextPipeline will not return dataset, please check ResumableTextPipeline_output for verification.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "# Below is just a quick example of using some of the operation,\n", + "# full operation list please refer to RecDP LLM readme.\n", + "\n", + "pipeline = ResumableTextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data\"),\n", + " LengthFilter(),\n", + " ProfanityFilter(),\n", + " LanguageIdentify(),\n", + " PerfileParquetWriter(\"ResumableTextPipeline_output\")\n", + "]\n", + "pipeline.add_operations(ops)\n", + "pipeline.execute()" + ] + }, + { + "cell_type": "code", + "source": [ + "pipeline.plot()" + ], + "metadata": { + "id": "nXNuIgNnKkIE", + "outputId": "7859fc9c-51ae-46d6-b260-b5aba85ead74", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 568 + } + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\n0\n\n0:DatasetReader\n\n[]\n\n\n\n1\n\n1:PerfileSourcedJsonlReader\n\n['input_dir: /content/test_data']\n\n\n\n0->1\n\n\n\n\n\n2\n\n2:LengthFilter\n\n['text_key: text', 'minimum_length: 100', 'maximum_length: -1']\n\n\n\n1->2\n\n\n\n\n\n3\n\n3:ProfanityFilter\n\n['text_key: text']\n\n\n\n2->3\n\n\n\n\n\n4\n\n4:LanguageIdentify\n\n['text_key: text', 'inplace: False', 'fasttext_model_dir: ']\n\n\n\n3->4\n\n\n\n\n\n5\n\n5:PerfileParquetWriter\n\n['output_dir: ResumableTextPipeline_output']\n\n\n\n4->5\n\n\n\n\n\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 4. View processed data" + ], + "metadata": { + "id": "YtHaUIcpPyeR" + } + }, + { + "cell_type": "code", + "source": [ + "! cat ResumableTextPipeline_output/status.log" + ], + "metadata": { + "id": "UfTm1M-fLKi_", + "outputId": "988d79db-4748-4981-efdf-e366ca6e91bd", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tiny_c4_sample.jsonl, 50.65073895454407 secs\n", + "arxiv_sample_100.jsonl, 46.28073239326477 secs\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "! ls ResumableTextPipeline_output/" + ], + "metadata": { + "id": "Kaau8EZBPsTe", + "outputId": "003752d8-cd9b-4913-8d4e-2a72b6e8995e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "arxiv_sample_100.jsonl\tpipeline.json status.log tiny_c4_sample.jsonl\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "pd.read_parquet(\"ResumableTextPipeline_output/arxiv_sample_100.jsonl\")" + ], + "metadata": { + "id": "Kuz_Oh3EP4gI", + "outputId": "8486400e-8aac-47b2-e7dc-57d9147e1c20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " text \\\n", + "0 \\section{Introduction}\\n\\label{sec:intro}\\n\\n\\... \n", + "1 \\section{Introduction}\\n\\nOne of the fundament... \n", + "2 \\section{Introduction}\\n\\nContinuous Engineeri... \n", + "3 \\section{Introduction}\\nWhen granular material... \n", + "4 \\section{\\label{sec:intro}Introduction}\\n \\nDe... \n", + ".. ... \n", + "95 \\section{Introduction\\label{sec-introduction}}... \n", + "96 \\section{Introduction}\\n\\nThroughout this pape... \n", + "97 \\section{Introduction}\\n\\nIn what follows, $k\\... \n", + "98 \\chapter{Comments on the Measurement of Vermee... \n", + "99 \\section{Introduction}\\n\\n\\nThe Cluster Variat... \n", + "\n", + " meta source_id \\\n", + "0 {'timestamp': '2022-03-30T02:27:00', 'yymm': '... arxiv_sample_100.jsonl \n", + "1 {'timestamp': '1998-07-19T14:33:52', 'yymm': '... arxiv_sample_100.jsonl \n", + "2 {'timestamp': '2020-08-18T02:18:33', 'yymm': '... arxiv_sample_100.jsonl \n", + "3 {'timestamp': '2002-03-19T12:47:20', 'yymm': '... arxiv_sample_100.jsonl \n", + "4 {'timestamp': '2022-10-20T02:16:28', 'yymm': '... arxiv_sample_100.jsonl \n", + ".. ... ... \n", + "95 {'timestamp': '2023-01-09T02:13:57', 'yymm': '... arxiv_sample_100.jsonl \n", + "96 {'timestamp': '2022-02-15T02:07:14', 'yymm': '... arxiv_sample_100.jsonl \n", + "97 {'timestamp': '2019-07-16T02:03:16', 'yymm': '... arxiv_sample_100.jsonl \n", + "98 {'timestamp': '2021-03-17T01:10:13', 'yymm': '... arxiv_sample_100.jsonl \n", + "99 {'timestamp': '2005-08-09T11:19:35', 'yymm': '... arxiv_sample_100.jsonl \n", + "\n", + " language \n", + "0 eng_Latn \n", + "1 eng_Latn \n", + "2 eng_Latn \n", + "3 eng_Latn \n", + "4 eng_Latn \n", + ".. ... \n", + "95 eng_Latn \n", + "96 eng_Latn \n", + "97 eng_Latn \n", + "98 eng_Latn \n", + "99 eng_Latn \n", + "\n", + "[100 rows x 4 columns]" + ], + "text/html": [ + "\n", + "
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"provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 049f2d2cc85460d6b7b22d41f9dce69b0a3135f3 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Thu, 12 Oct 2023 23:24:48 +0800 Subject: [PATCH 09/47] Refactor filters operation support users can build their own filter easily. (#383) . --- RecDP/pyrecdp/primitives/llmutils/filter.py | 3 - .../pyrecdp/primitives/operations/__init__.py | 2 +- .../primitives/operations/filter/__init__.py | 5 + .../operations/filter/badwords_filter.py | 38 +++ .../primitives/operations/filter/base.py | 36 +++ .../operations/filter/length_filter.py | 21 ++ .../operations/filter/profanity_filter.py | 13 + .../operations/filter/url_filter.py | 101 ++++++++ .../primitives/operations/text_filter.py | 243 ------------------ 9 files changed, 215 insertions(+), 247 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/operations/filter/__init__.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/base.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/length_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/profanity_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/url_filter.py delete mode 100644 RecDP/pyrecdp/primitives/operations/text_filter.py diff --git a/RecDP/pyrecdp/primitives/llmutils/filter.py b/RecDP/pyrecdp/primitives/llmutils/filter.py index d3fd795d8..bb8fc5b7a 100644 --- a/RecDP/pyrecdp/primitives/llmutils/filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/filter.py @@ -12,7 +12,6 @@ def filter_by_blocklist_spark(spark_df): def filter_by_blocklist(data_dir, out_dir, data_file_type="jsonl"): - from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import URLFilter from pyrecdp.LLM import ResumableTextPipeline @@ -41,7 +40,6 @@ def filter_by_bad_words_spark(spark_df, language="en"): def filter_by_bad_words(data_dir, out_dir, data_file_type="jsonl", language="en"): - from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import BadwordsFilter from pyrecdp.LLM import ResumableTextPipeline @@ -70,7 +68,6 @@ def filter_by_length_spark(spark_df, minimum_length=100, maximum_length=-1): def filter_by_length(data_dir, out_dir, data_file_type="jsonl", minimum_length=100, maximum_length=-1): - from pyrecdp.LLM import ResumableTextPipeline from pyrecdp.primitives.operations import LengthFilter from pyrecdp.LLM import ResumableTextPipeline diff --git a/RecDP/pyrecdp/primitives/operations/__init__.py b/RecDP/pyrecdp/primitives/operations/__init__.py index 18a5fa5d9..55600f917 100644 --- a/RecDP/pyrecdp/primitives/operations/__init__.py +++ b/RecDP/pyrecdp/primitives/operations/__init__.py @@ -6,7 +6,7 @@ from .text_writer import PerfileParquetWriter, ParquetWriter, JsonlWriter, ClassifyJsonlWriter, ClassifyParquetWriter from .text_normalize import TextNormalize from .text_bytesize import TextBytesize -from .text_filter import LengthFilter, BadwordsFilter, ProfanityFilter, URLFilter +from .filter import * from .text_fixer import TextFix from .text_language_identify import LanguageIdentify from .text_split import DocumentSplit diff --git a/RecDP/pyrecdp/primitives/operations/filter/__init__.py b/RecDP/pyrecdp/primitives/operations/filter/__init__.py new file mode 100644 index 000000000..1c2819406 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/__init__.py @@ -0,0 +1,5 @@ +from .base import BaseFilter +from .profanity_filter import ProfanityFilter +from .badwords_filter import BadwordsFilter +from .length_filter import LengthFilter +from .url_filter import URLFilter diff --git a/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py b/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py new file mode 100644 index 000000000..027261ce4 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py @@ -0,0 +1,38 @@ +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter + +import os + + +def prepare_func_filter_by_badwords(language="en"): + from pyrecdp.primitives.llmutils.utils import get_llmutils_home + import re + llmutils_path = get_llmutils_home() + bad_words_lists_path = os.path.join(llmutils_path, "bad_words_lists", language) + with open(bad_words_lists_path, "r") as f: + lines = f.readlines() + bad_words_list = [s.replace('\n', '') for s in lines] + bad_words_pattern = " | ".join(bad_words_list) + + def check_badwords(text): + found = re.search(bad_words_pattern, text) + if found: + return False + else: + return True + + return check_badwords + + +class BadwordsFilter(BaseFilter): + def __init__(self, language='en'): + settings = {'language': language} + super().__init__(args_dict=settings) + self.language = language + + def compute(self, text) -> bool: + func = prepare_func_filter_by_badwords(self.language) + func(text) + + +LLMOPERATORS.register(BadwordsFilter) diff --git a/RecDP/pyrecdp/primitives/operations/filter/base.py b/RecDP/pyrecdp/primitives/operations/filter/base.py new file mode 100644 index 000000000..686d3b7c6 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/base.py @@ -0,0 +1,36 @@ +from pyspark.sql import DataFrame +from ray.data import Dataset + +from pyrecdp.primitives.operations.base import BaseLLMOperation, statistics_decorator +import pyspark.sql.functions as F + + +class BaseFilter(BaseLLMOperation): + def __init__(self, args_dict={}): + super().__init__(args_dict=args_dict) + self.text_key = 'text' + self.inplace = True + self.support_ray = True + self.support_spark = True + + @statistics_decorator + def process_rayds(self, ds: Dataset) -> Dataset: + if self.inplace: + # remove unwanted text row inplace + filtered_ds = ds.filter(lambda x: self.compute(x[self.text_key])) + return filtered_ds + else: + raise NotImplementedError(f"We only support inplace modification for {self.__class__.__name__}.") + + @statistics_decorator + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: + if self.inplace: + + import pyspark.sql.types as T + compute_udf = F.udf(self.compute, T.BooleanType()) + return spark_df.filter(compute_udf(F.col(self.text_key))) + else: + raise NotImplementedError(f"We only support inplace modification for {self.__class__.__name__}.") + + def compute(self, *args, **kwargs) -> bool: + return True \ No newline at end of file diff --git a/RecDP/pyrecdp/primitives/operations/filter/length_filter.py b/RecDP/pyrecdp/primitives/operations/filter/length_filter.py new file mode 100644 index 000000000..265067718 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/length_filter.py @@ -0,0 +1,21 @@ +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter + + +class LengthFilter(BaseFilter): + def __init__(self, minimum_length=100, maximum_length=-1): + settings = {'minimum_length': minimum_length, "maximum_length": maximum_length} + super().__init__(args_dict=settings) + self.minimum_length = minimum_length + self.maximum_length = maximum_length + + def compute(self, text) -> bool: + if len(text) < self.minimum_length or (self.maximum_length != -1 and len(text) > self.maximum_length): + return False + else: + return True + + +LLMOPERATORS.register(LengthFilter) + + diff --git a/RecDP/pyrecdp/primitives/operations/filter/profanity_filter.py b/RecDP/pyrecdp/primitives/operations/filter/profanity_filter.py new file mode 100644 index 000000000..9e31e3d2f --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/profanity_filter.py @@ -0,0 +1,13 @@ +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter +from profanity_check import predict + + +class ProfanityFilter(BaseFilter): + def compute(self, text) -> bool: + scores = predict([text]) + ret = not bool(scores[0]) + return ret + + +LLMOPERATORS.register(ProfanityFilter) diff --git a/RecDP/pyrecdp/primitives/operations/filter/url_filter.py b/RecDP/pyrecdp/primitives/operations/filter/url_filter.py new file mode 100644 index 000000000..5592c40c5 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/url_filter.py @@ -0,0 +1,101 @@ +from functools import lru_cache + +from pyrecdp.primitives.operations.filter.base import BaseFilter +from pyrecdp.primitives.operations.base import LLMOPERATORS, statistics_decorator +from pyspark.sql import DataFrame +import os +import pyspark.sql.functions as F + +BLACKLIST_URL = "https://dsi.ut-capitole.fr/blacklists/download/blacklists.tar.gz" +BLACKLIST_CATEGORIES = ["adult", "phishing", "dating", "gambling", "filehosting", "ddos", "agressif", "chat", + "mixed_adult","arjel"] + + +def prepare_blacklist(): + from pyrecdp.datasets.download import download + BLACKLIST_URL = "https://dsi.ut-capitole.fr/blacklists/download/blacklists.tar.gz" + + blacklist_tar_path = "blacklists" + downloader = download(name=blacklist_tar_path, url=BLACKLIST_URL, unzip=True) + return downloader.saved_path + +@lru_cache +def load_blacklist_spark_df(spark): + saved_path = prepare_blacklist() + from pyspark.sql.types import StructType, StructField, StringType + data_schema = StructType([ + StructField('domain', StringType()), + ]) + blacklist_df: DataFrame = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema=data_schema) + for category in BLACKLIST_CATEGORIES: + domain_file = os.path.join(saved_path, category, "domains") + df = spark.read.text(domain_file) + df = df.withColumnRenamed("value", "domain") + blacklist_df = blacklist_df.union(df) + return blacklist_df + +@lru_cache +def load_blacklist_set(): + saved_path = prepare_blacklist() + print("*********************_______________________________________") + blacklist = [] + for category in BLACKLIST_CATEGORIES: + domain_file = os.path.join(saved_path, category, "domains") + with open(domain_file, "r") as f: + lines = f.readlines() + blacklist.extend(lines) + return set(blacklist) + + +def get_url_from_meta(x): + import json + try: + meta_obj = json.loads(x) + if 'url' in meta_obj: + return meta_obj['url'] + else: + return "Not Provided" + except: + return "Not Provided" + + +def get_domain(x): + from urllib.parse import urlparse + domain = urlparse(x).hostname + if not domain: + domain = "Not Provided" + return domain + + +class URLFilter(BaseFilter): + def __init__(self): + super().__init__() + self.text_key = "meta" + self.blacklist = load_blacklist_set() + + @statistics_decorator + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: + get_url_from_meta_udf = F.udf(get_url_from_meta) + get_domain_udf = F.udf(get_domain) + if self.inplace: + # remove unwanted text row inplace + blacklist_df = load_blacklist_spark_df(spark) + source_df = spark_df + with_domain_df = source_df.withColumn('domain', get_domain_udf(get_url_from_meta_udf('meta'))) + left_anti_df = with_domain_df.join(blacklist_df, on='domain', how='left_anti') + return left_anti_df + else: + raise NotImplementedError("We only support inplace modification for URLFilter.") + + def compute(self, sample) -> bool: + url = get_url_from_meta(sample) + domain = get_domain(url) + if domain in self.blacklist: + return False + else: + return True + + +LLMOPERATORS.register(URLFilter) + + diff --git a/RecDP/pyrecdp/primitives/operations/text_filter.py b/RecDP/pyrecdp/primitives/operations/text_filter.py deleted file mode 100644 index 13b6e3254..000000000 --- a/RecDP/pyrecdp/primitives/operations/text_filter.py +++ /dev/null @@ -1,243 +0,0 @@ -from .base import BaseLLMOperation, LLMOPERATORS, statistics_decorator -from ray.data import Dataset -from pyspark.sql import DataFrame -import os -import pyspark.sql.functions as F - - -def prepare_func_filter_by_length(minimum_length=100, maximum_length=10000): - def check_length(text): - if len(text) < minimum_length or (maximum_length != -1 and len(text) > maximum_length): - return False - else: - return True - - return check_length - - -def prepare_func_filter_by_badwords(language): - from pyrecdp.primitives.llmutils.utils import get_llmutils_home - import re - llmutils_path = get_llmutils_home() - bad_words_lists_path = os.path.join(llmutils_path, "bad_words_lists", language) - with open(bad_words_lists_path, "r") as f: - lines = f.readlines() - bad_words_list = [s.replace('\n', '') for s in lines] - bad_words_pattern = " | ".join(bad_words_list) - - def check_badwords(text): - found = re.search(bad_words_pattern, text) - if found: - return False - else: - return True - - return check_badwords - - -def prepare_func_filter_by_profanity(): - from profanity_check import predict - def check_profanity(text): - scores = predict([text]) - ret = not bool(scores[0]) - return ret - - return check_profanity - - -class LengthFilter(BaseLLMOperation): - def __init__(self, text_key='text', minimum_length=100, maximum_length=-1): - settings = {'text_key': text_key, 'minimum_length': minimum_length, 'maximum_length': maximum_length} - super().__init__(settings) - self.text_key = text_key - self.inplace = True - self.minimum_length = minimum_length - self.maximum_length = maximum_length - self.check_length = None - self.support_ray = True - self.support_spark = True - - @statistics_decorator - def process_rayds(self, ds: Dataset) -> Dataset: - if self.check_length is None: - self.check_length = prepare_func_filter_by_length(self.minimum_length, self.maximum_length) - if self.inplace: - # remove unwanted text row inplace - filtered_ds = ds.filter(lambda x: self.check_length(x[self.text_key])) - return filtered_ds - else: - raise NotImplementedError("We only support inplace modification for LengthFilter.") - - @statistics_decorator - def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - import pyspark.sql.types as T - check_length_udf = F.udf(prepare_func_filter_by_length(self.minimum_length, self.maximum_length), - T.BooleanType()) - return spark_df.filter(check_length_udf(F.col(self.text_key))) - - -LLMOPERATORS.register(LengthFilter) - - -class BadwordsFilter(BaseLLMOperation): - def __init__(self, text_key='text', language='en'): - settings = {'text_key': text_key, 'language': language} - super().__init__(settings) - self.text_key = text_key - self.inplace = True - self.language = language - self.check_badwords = None - self.support_ray = True - self.support_spark = True - - @statistics_decorator - def process_rayds(self, ds: Dataset) -> Dataset: - if self.check_badwords is None: - self.check_badwords = prepare_func_filter_by_badwords(self.language) - if self.inplace: - # remove unwanted text row inplace - filtered_ds = ds.filter(lambda x: self.check_badwords(x[self.text_key])) - return filtered_ds - else: - raise NotImplementedError("We only support inplace modification for BadwordsFilter.") - - @statistics_decorator - def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - import pyspark.sql.types as T - check_badwords_udf = F.udf(prepare_func_filter_by_badwords(self.language), T.BooleanType()) - return spark_df.filter(check_badwords_udf(F.col(self.text_key))) - - -LLMOPERATORS.register(BadwordsFilter) - - -class ProfanityFilter(BaseLLMOperation): - def __init__(self, text_key='text', inplace=True): - settings = {'text_key': text_key} - super().__init__(settings) - self.text_key = text_key - self.inplace = inplace - self.check_profanity = None - self.support_ray = True - self.support_spark = True - - @statistics_decorator - def process_rayds(self, ds: Dataset) -> Dataset: - if self.check_profanity is None: - self.check_profanity = prepare_func_filter_by_profanity() - if self.inplace: - # remove unwanted text row inplace - filtered_ds = ds.filter(lambda x: self.check_profanity(x[self.text_key])) - return filtered_ds - else: - raise NotImplementedError("We only support inplace modification for ProfanityFilter.") - - @statistics_decorator - def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - import pyspark.sql.types as T - check_profanity_udf = F.udf(prepare_func_filter_by_profanity(), T.BooleanType()) - return spark_df.filter(check_profanity_udf(F.col(self.text_key))) - - -LLMOPERATORS.register(ProfanityFilter) - - -BLACKLIST_URL = "https://dsi.ut-capitole.fr/blacklists/download/blacklists.tar.gz" -BLACKLIST_STORE_PATH = "/tmp" -BLACKLIST_CATEGORIES = ["adult", "phishing", "dating", "gambling", "filehosting", "ddos", "agressif", "chat", - "mixed_adult", - "arjel"] - - -def prepare_blacklist(): - from pyrecdp.datasets.download import download - BLACKLIST_URL = "https://dsi.ut-capitole.fr/blacklists/download/blacklists.tar.gz" - - blacklist_tar_path = "blacklists" - downloader = download(name=blacklist_tar_path, url=BLACKLIST_URL, unzip=True) - return downloader.saved_path - - -def load_blacklist_spark_df(spark): - saved_path = prepare_blacklist() - from pyspark.sql.types import StructType, StructField, StringType - data_schema = StructType([ - StructField('domain', StringType()), - ]) - blacklist_df: DataFrame = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema=data_schema) - for category in BLACKLIST_CATEGORIES: - domain_file = os.path.join(saved_path, category, "domains") - df = spark.read.text(domain_file) - df = df.withColumnRenamed("value", "domain") - blacklist_df = blacklist_df.union(df) - return blacklist_df - - -def load_blacklist_set(): - saved_path = prepare_blacklist() - blacklist = [] - for category in BLACKLIST_CATEGORIES: - domain_file = os.path.join(saved_path, category, "domains") - with open(domain_file, "r") as f: - lines = f.readlines() - blacklist.extend(lines) - return set(blacklist) - - -def get_url_from_meta(x): - import json - try: - meta_obj = json.loads(x) - if 'url' in meta_obj: - return meta_obj['url'] - else: - return "Not Provided" - except: - return "Not Provided" - - -def get_domain(x): - from urllib.parse import urlparse - domain = urlparse(x).hostname - if not domain: - domain = "Not Provided" - return domain - - -class URLFilter(BaseLLMOperation): - def __init__(self, text_key='text'): - settings = {'text_key': text_key} - super().__init__(settings) - self.text_key = text_key - self.inplace = True - self.support_spark = True - self.support_ray = True - - @statistics_decorator - def process_rayds(self, ds: Dataset) -> Dataset: - blacklist = load_blacklist_set() - if self.inplace: - # remove unwanted text row inplace - filtered_ds = ds.filter(lambda x: get_url_from_meta(x) not in blacklist) - return filtered_ds - else: - raise NotImplementedError("We only support inplace modification for URLFilter.") - - @statistics_decorator - def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - get_url_from_meta_udf = F.udf(get_url_from_meta) - get_domain_udf = F.udf(get_domain) - - if self.inplace: - # remove unwanted text row inplace - blacklist_df = load_blacklist_spark_df(spark) - source_df = spark_df - with_domain_df = source_df.withColumn('domain', get_domain_udf(get_url_from_meta_udf('meta'))) - left_anti_df = with_domain_df.join(blacklist_df, on='domain', how='left_anti') - return left_anti_df - - else: - raise NotImplementedError("We only support inplace modification for URLFilter.") - - -LLMOPERATORS.register(URLFilter) From 9f8794f747cd33ce9b1ec2aaeb93995395bb16e2 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Thu, 12 Oct 2023 23:28:14 +0800 Subject: [PATCH 10/47] Add statistics_flag for user to choose in TextPipeline. (#382) --- RecDP/pyrecdp/LLM/TextPipeline.py | 12 ++++++-- RecDP/pyrecdp/primitives/operations/base.py | 32 ++++++++++++--------- 2 files changed, 28 insertions(+), 16 deletions(-) diff --git a/RecDP/pyrecdp/LLM/TextPipeline.py b/RecDP/pyrecdp/LLM/TextPipeline.py index 353d40f28..62e28c8bc 100644 --- a/RecDP/pyrecdp/LLM/TextPipeline.py +++ b/RecDP/pyrecdp/LLM/TextPipeline.py @@ -181,6 +181,12 @@ class ResumableTextPipeline(TextPipeline): # Provide a pipeline for large dir. We will handle files one by one and resume when pipeline broken. def __init__(self, pipeline_file=None): super().__init__(pipeline_file) + # Enabling this option will result in a decrease in execution speed + self.statistics_flag = False + + def enable_statistics(self): + logger.warning("Enabling this option will result in a decrease in execution speed") + self.statistics_flag = True def execute(self): # Fix pipeline @@ -251,6 +257,7 @@ def execute(self): pbar.set_description(f"ResumableTextPipeline, current on {source_id}") start = time.time() for idx, op in enumerate(op_chain): + op.statistics_flag = self.statistics_flag if idx == 0: op.execute_ray(executable_pipeline, ds_reader) elif isinstance(op, PerfileParquetWriter): @@ -262,8 +269,9 @@ def execute(self): status_tracker.flush() done_files.append(status_tracker) del ds_reader - for op in op_chain: - logger.info(f"{op.__class__.__name__}: {op.summarize()}") + if self.statistics_flag: + for op in op_chain: + logger.info(f"{op.__class__.__name__}: {op.summarize()}") logger.info( f"Completed! ResumableTextPipeline will not return dataset, please check {output_dir} for verification.") diff --git a/RecDP/pyrecdp/primitives/operations/base.py b/RecDP/pyrecdp/primitives/operations/base.py index 67bd9738b..d9379428c 100644 --- a/RecDP/pyrecdp/primitives/operations/base.py +++ b/RecDP/pyrecdp/primitives/operations/base.py @@ -184,6 +184,7 @@ def __init__(self, args_dict={}): self.support_spark = False self.support_ray = True self.statistics = OperationStatistics(0, 0, 0, 0) + self.statistics_flag = False @classmethod def instantiate(cls, op_obj, config): @@ -262,22 +263,25 @@ class OperationStatistics: def statistics_decorator(func): def wrapper(self, *args, **kwargs): - if isinstance(args[0], Dataset): - ds = args[0] - self.statistics.total_in += ds.count() - start = time.time() - result = func(self, *args, **kwargs) - self.statistics.total_out += result.count() - elapse = time.time() - start - self.statistics.used_time += elapse + if self.statistics_flag: + if isinstance(args[0], Dataset): + ds = args[0] + self.statistics.total_in += ds.count() + start = time.time() + result = func(self, *args, **kwargs) + self.statistics.total_out += result.count() + elapse = time.time() - start + self.statistics.used_time += elapse + else: + df = args[1] + self.statistics.total_in += df.count() + start = time.time() + result = func(self, *args, **kwargs) + self.statistics.total_out += result.count() + elapse = time.time() - start + self.statistics.used_time += elapse else: - df = args[1] - self.statistics.total_in += df.count() - start = time.time() result = func(self, *args, **kwargs) - self.statistics.total_out += result.count() - elapse = time.time() - start - self.statistics.used_time += elapse return result return wrapper From 5c9418d7067e783fbe5d46eaa352f43f097ea637 Mon Sep 17 00:00:00 2001 From: haojinIntel Date: Fri, 13 Oct 2023 00:39:20 +0800 Subject: [PATCH 11/47] [v1.2][ISSUE-306]Support threshold filter for language identify operator. (#381) * Add threshold for language identify * Update text_language_identify.py --------- Co-authored-by: Chendi.Xue --- .../operations/text_language_identify.py | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/RecDP/pyrecdp/primitives/operations/text_language_identify.py b/RecDP/pyrecdp/primitives/operations/text_language_identify.py index c2e1da67a..0ee00f9f9 100644 --- a/RecDP/pyrecdp/primitives/operations/text_language_identify.py +++ b/RecDP/pyrecdp/primitives/operations/text_language_identify.py @@ -172,17 +172,17 @@ def predict(model, text: str, k: int = 1): labels = [l.replace("__label__", "") for l in labels] return labels, scores -def construct_classifier(fasttext_model_dir, language_identify_field, language_identify_output_field): +def construct_classifier(fasttext_model_dir, language_identify_field, language_identify_output_field, threshold): print(fasttext_model_dir) if os.path.isfile(fasttext_model_dir): model_path = fasttext_model_dir else: model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin") model = Path(model_path) - return Classifier(model, language_identify_field, language_identify_output_field) + return Classifier(model, language_identify_field, language_identify_output_field, threshold=threshold) -def prepare_func_language_id(fasttext_model_dir, language_identify_field, language_identify_output_field): - classifier = construct_classifier(fasttext_model_dir, language_identify_field, language_identify_output_field) +def prepare_func_language_id(fasttext_model_dir, language_identify_field, language_identify_output_field, threshold): + classifier = construct_classifier(fasttext_model_dir, language_identify_field, language_identify_output_field, threshold) def generate_lang_label(content): # 0. apply normalization to content content = {classifier.field: content} @@ -191,12 +191,13 @@ def generate_lang_label(content): return generate_lang_label class LanguageIdentify(BaseLLMOperation): - def __init__(self, text_key = 'text', inplace = False, fasttext_model_dir = ""): - settings = {'text_key': text_key, 'inplace': inplace, 'fasttext_model_dir': fasttext_model_dir} + def __init__(self, text_key = 'text', inplace = False, fasttext_model_dir = "", threshold = 0): + settings = {'text_key': text_key, 'inplace': inplace, 'fasttext_model_dir': fasttext_model_dir, 'threshold': threshold} super().__init__(settings) self.text_key = text_key self.inplace = False self.fasttext_model_dir = fasttext_model_dir + self.threshold = threshold self.actual_func = None self.support_spark = True self.support_ray = True @@ -207,14 +208,16 @@ def process_rayds(self, ds: Dataset) -> Dataset: else: new_name = 'language' if self.actual_func is None: - self.actual_func = prepare_func_language_id(fasttext_model_dir = self.fasttext_model_dir, language_identify_field = self.text_key, language_identify_output_field = new_name) + self.actual_func = prepare_func_language_id(fasttext_model_dir = self.fasttext_model_dir, + language_identify_field = self.text_key, language_identify_output_field = new_name, threshold = self.threshold) return ds.map(lambda x: self.process_row(x, self.text_key, new_name, self.actual_func)) def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.functions as F from pyspark.sql import types as T new_name = "language" - language_id_udf = F.udf(prepare_func_language_id(fasttext_model_dir = self.fasttext_model_dir, language_identify_field = self.text_key, language_identify_output_field = new_name)) + language_id_udf = F.udf(prepare_func_language_id(fasttext_model_dir = self.fasttext_model_dir, + language_identify_field = self.text_key, language_identify_output_field = new_name, threshold = self.threshold)) return spark_df.withColumn(new_name, language_id_udf(F.col(self.text_key))) -LLMOPERATORS.register(LanguageIdentify) \ No newline at end of file +LLMOPERATORS.register(LanguageIdentify) From 99b75eb59e014267e86721c8c96d58222fedc8a9 Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Thu, 12 Oct 2023 13:29:11 -0500 Subject: [PATCH 12/47] [v1.2][ISSUE-306]update pipeline to support import with json and use mode to init pipeline (#377) * update UT Signed-off-by: Xue, Chendi * update codes Signed-off-by: Xue, Chendi * update json to delete lid Signed-off-by: Xue, Chendi --------- Signed-off-by: Xue, Chendi --- .../unittest_llmutils_operations.yml | 30 +++++++++++ RecDP/pyrecdp/LLM/TextPipeline.py | 4 +- RecDP/pyrecdp/core/pipeline.py | 7 ++- .../bashrun_unittest_llmutils_operations.sh | 23 +++++++++ .../cicd/bashrun_unittest_llmutilspipeline.sh | 2 +- RecDP/tests/data/import_test_pipeline.json | 42 +++++++++++++++ RecDP/tests/test_llmutils_operations.py | 51 +------------------ RecDP/tests/test_llmutils_pipelines.py | 42 +++++++++++++++ 8 files changed, 147 insertions(+), 54 deletions(-) create mode 100644 .github/workflows/unittest_llmutils_operations.yml create mode 100644 RecDP/tests/cicd/bashrun_unittest_llmutils_operations.sh create mode 100644 RecDP/tests/data/import_test_pipeline.json create mode 100644 RecDP/tests/test_llmutils_pipelines.py diff --git a/.github/workflows/unittest_llmutils_operations.yml b/.github/workflows/unittest_llmutils_operations.yml new file mode 100644 index 000000000..bce07bdc9 --- /dev/null +++ b/.github/workflows/unittest_llmutils_operations.yml @@ -0,0 +1,30 @@ +name: Unittest LLM Operations +on: + pull_request: + branches: + - main + paths: + - 'RecDP/pyrecdp/LLM/**' + - 'RecDP/pyrecdp/primitives/operations/**' + - 'RecDP/tests/cicd/bashrun_unittest_llmutilspipeline.sh' + - 'RecDP/tests/test_llmutils_operations.py' + +jobs: + unittest: + name: Unittest LLM + runs-on: self-hosted + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Build Docker + run: | + cd RecDP && cd Dockerfile && docker build -t pyrecdp-test-env . -f DockerfileUbuntu --build-arg https_proxy=${https_proxy} && cd .. && yes | docker container prune && yes | docker image prune + + - name: RUN Unittests + run: | + cd RecDP && docker run --rm --name unittest-pyrecdp-autofe-pandas --shm-size=300g --privileged --network host --device=/dev/dri -e RECDP_MODELS_CACHE=/home/vmagent/models -v `pwd`:/home/vmagent/app/ -v `pwd`/../../models:/home/vmagent/models/ -w /home/vmagent/app/ pyrecdp-test-env /bin/bash -c "sh tests/cicd/bashrun_unittest_llmutils_operations.sh" + + - name: Clean env + run: | + docker ps --filter name=unittest-pyrecdp-autofe-pandas; while read line; do docker stop unittest-pyrecdp-autofe-pandas;done diff --git a/RecDP/pyrecdp/LLM/TextPipeline.py b/RecDP/pyrecdp/LLM/TextPipeline.py index 62e28c8bc..ecaf3dc9a 100644 --- a/RecDP/pyrecdp/LLM/TextPipeline.py +++ b/RecDP/pyrecdp/LLM/TextPipeline.py @@ -25,7 +25,9 @@ class TextPipeline(BasePipeline): def __init__(self, pipeline_file=None): super().__init__() if pipeline_file != None: - self.import_from_yaml(pipeline_file) + self.import_from_json(pipeline_file) if pipeline_file.endswith( + '.json') else self.import_from_yaml(pipeline_file) + self.plot() else: # add a data set input place holder op = DatasetReader() diff --git a/RecDP/pyrecdp/core/pipeline.py b/RecDP/pyrecdp/core/pipeline.py index 98ccaf7fb..c79b03932 100644 --- a/RecDP/pyrecdp/core/pipeline.py +++ b/RecDP/pyrecdp/core/pipeline.py @@ -95,7 +95,7 @@ def delete_operation(self, id): self.transformed_end_idx = children[0] del self.pipeline[cur_idx] - def plot(self): + def plot(self, filename=None): f = graphviz.Digraph(format='svg') edges = [] nodes = [] @@ -133,7 +133,10 @@ def add_break(input): for edge in edges: f.edge(*edge) try: - f.render(filename='pipeline', view = False) + if filename is None: + f.render(filename='pipeline_plot', view=False) + else: + f.render(filename=filename, view=False) except: pass return f diff --git a/RecDP/tests/cicd/bashrun_unittest_llmutils_operations.sh b/RecDP/tests/cicd/bashrun_unittest_llmutils_operations.sh new file mode 100644 index 000000000..43363dda5 --- /dev/null +++ b/RecDP/tests/cicd/bashrun_unittest_llmutils_operations.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +failed_tests="" +echo "Setup pyrecdp latest package" +python setup.py sdist && pip install dist/pyrecdp-*.*.*.tar.gz + +python -m unittest tests.test_llmutils_operations.Test_LLMUtils_Operations + +# echo "test_llmutils_pipeline.Test_LLMUtils_Pipeline.test_TextGlobalDeduplicate" +# python -m unittest tests.test_llmutils_pipeline.Test_LLMUtils_Pipeline.test_TextGlobalDeduplicate +# if [ $? != 0 ]; then +# failed_tests=${failed_tests}"tests.test_llmutils_pipeline.Test_LLMUtils_Pipeline.test_TextGlobalDeduplicate\n" +# fi + + + +# if [ -z ${failed_tests} ]; then +# echo "All tests are passed" +# else +# echo "*** Failed Tests are: ***" +# echo -e ${failed_tests} +# exit 1 +# fi diff --git a/RecDP/tests/cicd/bashrun_unittest_llmutilspipeline.sh b/RecDP/tests/cicd/bashrun_unittest_llmutilspipeline.sh index 0d61828ab..5c718bc89 100644 --- a/RecDP/tests/cicd/bashrun_unittest_llmutilspipeline.sh +++ b/RecDP/tests/cicd/bashrun_unittest_llmutilspipeline.sh @@ -4,7 +4,7 @@ failed_tests="" echo "Setup pyrecdp latest package" python setup.py sdist && pip install dist/pyrecdp-*.*.*.tar.gz -python -m unittest tests.test_llmutils_operations +python -m unittest tests.test_llmutils_pipelines.Test_LLMUtils_Pipeline # echo "test_llmutils_pipeline.Test_LLMUtils_Pipeline.test_TextGlobalDeduplicate" # python -m unittest tests.test_llmutils_pipeline.Test_LLMUtils_Pipeline.test_TextGlobalDeduplicate diff --git a/RecDP/tests/data/import_test_pipeline.json b/RecDP/tests/data/import_test_pipeline.json new file mode 100644 index 000000000..3e78a38ad --- /dev/null +++ b/RecDP/tests/data/import_test_pipeline.json @@ -0,0 +1,42 @@ +{ + "0": { + "children": null, + "op": "DatasetReader", + "config": {} + }, + "1": { + "children": [ + 0 + ], + "op": "PerfileSourcedJsonlReader", + "config": { + "input_dir": "tests/data/llm_data/" + } + }, + "2": { + "children": [ + 1 + ], + "op": "LengthFilter", + "config": { + "minimum_length": 100, + "maximum_length": -1 + } + }, + "3": { + "children": [ + 2 + ], + "op": "ProfanityFilter", + "config": {} + }, + "4": { + "children": [ + 3 + ], + "op": "PerfileParquetWriter", + "config": { + "output_dir": "ResumableTextPipeline_output_import" + } + } +} \ No newline at end of file diff --git a/RecDP/tests/test_llmutils_operations.py b/RecDP/tests/test_llmutils_operations.py index 99599bf42..bf090b093 100644 --- a/RecDP/tests/test_llmutils_operations.py +++ b/RecDP/tests/test_llmutils_operations.py @@ -210,53 +210,4 @@ def test_qualityscore_spark(self): def test_diversityindicate_spark(self): op = TextDiversityIndicate() with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: - ctx.show(op.process_spark(ctx.spark, ctx.ds)) - -class RDS: - def __init__(self, ds): - self.ds_engine = 'spark' if isinstance(ds, DataFrame) else 'ray' - self.ds = ds - def to_pandas(self): - print(self.ds_engine) - if self.ds_engine == 'ray': - return self.ds.to_pandas() - elif self.ds_engine == 'spark': - return self.ds.toPandas() - else: - pass - -class Test_LLMUtils_Pipeline(unittest.TestCase): - - def setUp(self) -> None: - print(f"\n******\nTesting Method Name: {self._testMethodName}\n******") - - def test_TextSourceId(self): - pipeline = TextPipeline() - ops = [ - SourcedJsonlReader("tests/data/llm_data/", source_prefix="") - ] - pipeline.add_operations(ops) - ret = pipeline.execute() - pd = RDS(ret).to_pandas() - display(pd) - - def test_TextPIIRemoval_resumable(self): - pipeline = ResumableTextPipeline() - ops = [ - JsonlReader("tests/data/llm_data/"), - PIIRemoval(model_root_path = os.path.join(RECDP_MODELS_CACHE, "huggingface")) - ] - pipeline.add_operations(ops) - pipeline.execute() - - def test_TextPipeline_resumable(self): - pipeline = ResumableTextPipeline() - ops = [ - JsonlReader("tests/data/llm_data/"), - LengthFilter(), - ProfanityFilter(), - LanguageIdentify(fasttext_model_dir = os.path.join(RECDP_MODELS_CACHE, "lid.bin")), - PerfileParquetWriter("ResumableTextPipeline_output_20231004205724") - ] - pipeline.add_operations(ops) - pipeline.execute() \ No newline at end of file + ctx.show(op.process_spark(ctx.spark, ctx.ds)) \ No newline at end of file diff --git a/RecDP/tests/test_llmutils_pipelines.py b/RecDP/tests/test_llmutils_pipelines.py new file mode 100644 index 000000000..e9cc7487f --- /dev/null +++ b/RecDP/tests/test_llmutils_pipelines.py @@ -0,0 +1,42 @@ +import unittest +import sys +import pandas as pd +from pathlib import Path +import os +from IPython.display import display + +pathlib = str(Path(__file__).parent.parent.resolve()) +print(pathlib) +try: + import pyrecdp +except: + print("Not detect system installed pyrecdp, using local one") + sys.path.append(pathlib) +from pyrecdp.primitives.operations import * +from pyrecdp.LLM import TextPipeline, ResumableTextPipeline +from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE +from pyrecdp.core import SparkDataProcessor + + +class Test_LLMUtils_Pipeline(unittest.TestCase): + + def setUp(self) -> None: + print(f"\n******\nTesting Method Name: {self._testMethodName}\n******") + + def test_ResumableTextPipeline(self): + pipeline = ResumableTextPipeline() + ops = [ + JsonlReader("tests/data/llm_data/"), + LengthFilter(), + ProfanityFilter(), + LanguageIdentify(fasttext_model_dir = os.path.join(RECDP_MODELS_CACHE, "lid.bin")), + PerfileParquetWriter("ResumableTextPipeline_output_20231004205724") + ] + pipeline.add_operations(ops) + pipeline.execute() + del pipeline + + def test_ResumableTextPipeline_import(self): + pipeline = ResumableTextPipeline(pipeline_file = 'tests/data/import_test_pipeline.json') + pipeline.execute() + del pipeline From 3ce73379645a6c03196d63192402414e790d739a Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Thu, 12 Oct 2023 17:05:19 -0500 Subject: [PATCH 13/47] [v1.2][ISSUE-306]Customer op (#384) * support customer map and filter function meanwhile, support spark in ResumableTextPipeline Signed-off-by: Xue, Chendi * update README Signed-off-by: Xue, Chendi --------- Signed-off-by: Xue, Chendi --- .../notebooks/llmutils/custom_filter.ipynb | 2233 +++++++++++++++++ .../notebooks/llmutils/custom_map.ipynb | 2233 +++++++++++++++++ .../llmutils/repetition_remove.ipynb | 2233 +++++++++++++++++ RecDP/pyrecdp/LLM/README.md | 13 +- RecDP/pyrecdp/LLM/TextPipeline.py | 135 +- RecDP/pyrecdp/core/pipeline.py | 4 +- .../pyrecdp/primitives/operations/__init__.py | 1 + RecDP/pyrecdp/primitives/operations/base.py | 13 +- .../operations/filter/badwords_filter.py | 2 +- .../primitives/operations/filter/base.py | 1 - .../primitives/operations/text_custom.py | 58 + .../operations/text_qualityscorer.py | 1 - .../primitives/operations/text_reader.py | 37 +- .../primitives/operations/text_writer.py | 57 +- RecDP/tests/data/import_test_pipeline.json | 2 +- RecDP/tests/test_llmutils_operations.py | 32 + RecDP/tests/test_llmutils_pipelines.py | 63 +- 17 files changed, 7050 insertions(+), 68 deletions(-) create mode 100644 RecDP/examples/notebooks/llmutils/custom_filter.ipynb create mode 100644 RecDP/examples/notebooks/llmutils/custom_map.ipynb create mode 100644 RecDP/examples/notebooks/llmutils/repetition_remove.ipynb create mode 100644 RecDP/pyrecdp/primitives/operations/text_custom.py diff --git a/RecDP/examples/notebooks/llmutils/custom_filter.ipynb b/RecDP/examples/notebooks/llmutils/custom_filter.ipynb new file mode 100644 index 000000000..936cb3659 --- /dev/null +++ b/RecDP/examples/notebooks/llmutils/custom_filter.ipynb @@ -0,0 +1,2233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "NUdnJWOJLGvF", + "metadata": { + "id": "NUdnJWOJLGvF" + }, + "source": [ + "# RecDP LLM - TextBytesize\n", + "\n", + "TextBytesize is a tool to get total byte size of text in the data.\n", + "\n", + "### We support two types of input and output:\n", + "\n", + "example 1:\n", + "* Expect Input format: a folder of *.jsonl.\n", + "* Expect Output format: a folder of *.jsonl after reduction." + ] + }, + { + "cell_type": "markdown", + "id": "C1FFwhr3MwA1", + "metadata": { + "id": "C1FFwhr3MwA1" + }, + "source": [ + "# Get started" + ] + }, + { + "cell_type": "markdown", + "id": "tMaSNIKIMz3T", + "metadata": { + "id": "tMaSNIKIMz3T" + }, + "source": [ + "## Install pyrecdp and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ro4yOk5IMvRY", + "metadata": { + "id": "ro4yOk5IMvRY" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "id": "pnHK2VTvM5rg", + "metadata": { + "id": "pnHK2VTvM5rg" + }, + "source": [ + "## 2. prepare your own data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/test_data\n", + "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 21626664 (21M) [text/plain]\n", + "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", + "\n", + "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", + "\n", + "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", + "\n" + ] + } + ], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "file_names = ['NIH_sample.jsonl']\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "!wget -P /content/test_data {\" \".join(file_list)}" + ] + }, + { + "cell_type": "markdown", + "id": "J-125gFtNyOg", + "metadata": { + "id": "J-125gFtNyOg" + }, + "source": [ + "## 3. get bytesize" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "vkGRn9uJN4B0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkGRn9uJN4B0", + "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NIH_sample.jsonl\n" + ] + } + ], + "source": [ + "! ls /content/test_data" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 PIPELINE based API" + ], + "metadata": { + "id": "HZIPEzB477Bm" + }, + "id": "HZIPEzB477Bm" + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "SMK9KcV4N7al", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704, + "referenced_widgets": [ + "2f6ad7bbc0964d7696c7dec5d0e66f44", + "60dd954b33414adfabf772f49ea2f3a2", + "93697baecfd442fa8d8db709cfc82eee", + "77548643af8d430db70bfb0af3707f91", + "92f4fcc6115c4f809c15b942ca0d11d4", + "90a32f5a48a74578bad33421c0e1599c", + "ec2613a4e1c04b2789a4ca8a257bda53", + "f955ca8d9b9e451791a5aa53bb984a71", + "0d52219723f944eb8b34e5aee50298be", + "58951323a0804ad8ad9d68dd4292f35f", + "c9d153b598854d0686849b48f84aa01e" + ] + }, + "id": "SMK9KcV4N7al", + "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:34,510\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:11:34,568\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 17.940069602999984 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " meta \\\n", + "0 {'APPLICATION_ID': 100065} \n", + "1 {'APPLICATION_ID': 100066} \n", + "2 {'APPLICATION_ID': 100067} \n", + "3 {'APPLICATION_ID': 100068} \n", + "4 {'APPLICATION_ID': 100069} \n", + "... ... \n", + "9995 {'APPLICATION_ID': 2120612} \n", + "9996 {'APPLICATION_ID': 2120613} \n", + "9997 {'APPLICATION_ID': 2120616} \n", + "9998 {'APPLICATION_ID': 2120620} \n", + "9999 {'APPLICATION_ID': 2120624} \n", + "\n", + " text bytesize \n", + "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", + "1 The Office of Planning, Research and Evaluatio... 3181 \n", + "2 Improving outcomes for low-income fathers and ... 1777 \n", + "3 This project is implementing 36-month follow-u... 1760 \n", + "4 The CCDF Policies Database is a source of info... 2157 \n", + "... ... ... \n", + "9995 Project: Research and produce a videotape that... 1241 \n", + "9996 While relapse prevention has been studied and ... 1281 \n", + "9997 The proposed study on recruitment, adherence a... 2867 \n", + "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", + "9999 The overall goal of this study is to develop a... 1463 \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...1460
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\n" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/\"),\n", + " TextBytesize(text_key = 'text'),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "ret = pipeline.execute()\n", + "ret.to_pandas()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.2 Operation-based API" + ], + "metadata": { + "id": "WJJXs5Co8EsB" + }, + "id": "WJJXs5Co8EsB" + }, + { + "cell_type": "markdown", + "source": [ + "#### prepare Ray and Spark context" + ], + "metadata": { + "id": "K49OFih-8aUc" + }, + "id": "K49OFih-8aUc" + }, + { + "cell_type": "code", + "source": [ + "import psutil\n", + "import ray\n", + "from pyrecdp.core import SparkDataProcessor\n", + "from pyspark.sql import DataFrame\n", + "from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE\n", + "\n", + "total_mem = int(psutil.virtual_memory().total * 0.5)\n", + "total_cores = psutil.cpu_count(logical=False)\n", + "\n", + "class RayContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + "\n", + " def __enter__(self):\n", + " if not ray.is_initialized():\n", + " try:\n", + " ray.init(object_store_memory=total_mem, num_cpus=total_cores)\n", + " except:\n", + " ray.init()\n", + "\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_rayds(None)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " if ray.is_initialized():\n", + " ray.shutdown()\n", + "\n", + " def show(self, ds):\n", + " pd = ds.to_pandas()\n", + " display(pd)\n", + "\n", + "class SparkContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + " self.rdp = SparkDataProcessor()\n", + "\n", + " def __enter__(self):\n", + " self.spark = self.rdp.spark\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_spark(self.spark)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " pass\n", + "\n", + " def show(self, ds):\n", + " pd = ds.toPandas()\n", + " display(pd)" + ], + "metadata": { + "id": "-F2f_JCo8I0R" + }, + "id": "-F2f_JCo8I0R", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Ray mode\n", + "\n", + "from pyrecdp.primitives.operations import *\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "\n", + "op = TextBytesize()\n", + "with RayContext(\"/content/test_data/\") as ctx:\n", + " ctx.show(op.process_rayds(ctx.ds))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513, + "referenced_widgets": [ + "41fbe06aba494f75ba11aeaf3fd6fa94", + "2784ff8eb12c471eac0c335cc3169c5c", + "de524508eb0947749b3f13c6710f1dbb", + "dbe163714118476e98ec10efc2e6f1e1", + "1dc281fa098a40f6a07af047f0edd1a9", + "789f99f9724442d59f05be15b3231888", + "bc776eb0348c49348497687be7316166", + "05302bd682724596bf8a61dbca5ae2e4", + "81f6b801a20c4f5c9ab21fc0dbccbd5d", + "c96dd696d0a84e8485b705750be517d7", + "61d3c42680814b0a95ccfac427d5c553" + ] + }, + "id": "zjFGFuWm8uLW", + "outputId": "750ead08-4002-4102-a6c7-c90f1694a0d7" + }, + "id": "zjFGFuWm8uLW", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:16:49,727\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:16:49,743\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:16:49,748\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:16:49,752\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00\n", + "
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3{'APPLICATION_ID': 100068}This project is implementing 36-month follow-u...1760
4{'APPLICATION_ID': 100069}The CCDF Policies Database is a source of info...2157
............
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3This project is implementing 36-month follow-u...{\"APPLICATION_ID\":100068}1760
4The CCDF Policies Database is a source of info...{\"APPLICATION_ID\":100069}2157
............
9995Project: Research and produce a videotape that...{\"APPLICATION_ID\":2120612}1241
9996While relapse prevention has been studied and ...{\"APPLICATION_ID\":2120613}1281
9997The proposed study on recruitment, adherence a...{\"APPLICATION_ID\":2120616}2867
9998Recent studies suggest that HIV epidemics are ...{\"APPLICATION_ID\":2120620}2424
9999The overall goal of this study is to develop a...{\"APPLICATION_ID\":2120624}1463
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input and output:\n", + "\n", + "example 1:\n", + "* Expect Input format: a folder of *.jsonl.\n", + "* Expect Output format: a folder of *.jsonl after reduction." + ] + }, + { + "cell_type": "markdown", + "id": "C1FFwhr3MwA1", + "metadata": { + "id": "C1FFwhr3MwA1" + }, + "source": [ + "# Get started" + ] + }, + { + "cell_type": "markdown", + "id": "tMaSNIKIMz3T", + "metadata": { + "id": "tMaSNIKIMz3T" + }, + "source": [ + "## Install pyrecdp and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ro4yOk5IMvRY", + "metadata": { + "id": "ro4yOk5IMvRY" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "id": "pnHK2VTvM5rg", + "metadata": { + "id": "pnHK2VTvM5rg" + }, + "source": [ + "## 2. prepare your own data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/test_data\n", + "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 21626664 (21M) [text/plain]\n", + "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", + "\n", + "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", + "\n", + "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", + "\n" + ] + } + ], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "file_names = ['NIH_sample.jsonl']\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "!wget -P /content/test_data {\" \".join(file_list)}" + ] + }, + { + "cell_type": "markdown", + "id": "J-125gFtNyOg", + "metadata": { + "id": "J-125gFtNyOg" + }, + "source": [ + "## 3. get bytesize" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "vkGRn9uJN4B0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkGRn9uJN4B0", + "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NIH_sample.jsonl\n" + ] + } + ], + "source": [ + "! ls /content/test_data" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 PIPELINE based API" + ], + "metadata": { + "id": "HZIPEzB477Bm" + }, + "id": "HZIPEzB477Bm" + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "SMK9KcV4N7al", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704, + "referenced_widgets": [ + "2f6ad7bbc0964d7696c7dec5d0e66f44", + "60dd954b33414adfabf772f49ea2f3a2", + "93697baecfd442fa8d8db709cfc82eee", + "77548643af8d430db70bfb0af3707f91", + "92f4fcc6115c4f809c15b942ca0d11d4", + "90a32f5a48a74578bad33421c0e1599c", + "ec2613a4e1c04b2789a4ca8a257bda53", + "f955ca8d9b9e451791a5aa53bb984a71", + "0d52219723f944eb8b34e5aee50298be", + "58951323a0804ad8ad9d68dd4292f35f", + "c9d153b598854d0686849b48f84aa01e" + ] + }, + "id": "SMK9KcV4N7al", + "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:34,510\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:11:34,568\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 17.940069602999984 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " meta \\\n", + "0 {'APPLICATION_ID': 100065} \n", + "1 {'APPLICATION_ID': 100066} \n", + "2 {'APPLICATION_ID': 100067} \n", + "3 {'APPLICATION_ID': 100068} \n", + "4 {'APPLICATION_ID': 100069} \n", + "... ... \n", + "9995 {'APPLICATION_ID': 2120612} \n", + "9996 {'APPLICATION_ID': 2120613} \n", + "9997 {'APPLICATION_ID': 2120616} \n", + "9998 {'APPLICATION_ID': 2120620} \n", + "9999 {'APPLICATION_ID': 2120624} \n", + "\n", + " text bytesize \n", + "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", + "1 The Office of Planning, Research and Evaluatio... 3181 \n", + "2 Improving outcomes for low-income fathers and ... 1777 \n", + "3 This project is implementing 36-month follow-u... 1760 \n", + "4 The CCDF Policies Database is a source of info... 2157 \n", + "... ... ... \n", + "9995 Project: Research and produce a videotape that... 1241 \n", + "9996 While relapse prevention has been studied and ... 1281 \n", + "9997 The proposed study on recruitment, adherence a... 2867 \n", + "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", + "9999 The overall goal of this study is to develop a... 1463 \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...1460
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\n" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/\"),\n", + " TextBytesize(text_key = 'text'),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "ret = pipeline.execute()\n", + "ret.to_pandas()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.2 Operation-based API" + ], + "metadata": { + "id": "WJJXs5Co8EsB" + }, + "id": "WJJXs5Co8EsB" + }, + { + "cell_type": "markdown", + "source": [ + "#### prepare Ray and Spark context" + ], + "metadata": { + "id": "K49OFih-8aUc" + }, + "id": "K49OFih-8aUc" + }, + { + "cell_type": "code", + "source": [ + "import psutil\n", + "import ray\n", + "from pyrecdp.core import SparkDataProcessor\n", + "from pyspark.sql import DataFrame\n", + "from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE\n", + "\n", + "total_mem = int(psutil.virtual_memory().total * 0.5)\n", + "total_cores = psutil.cpu_count(logical=False)\n", + "\n", + "class RayContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + "\n", + " def __enter__(self):\n", + " if not ray.is_initialized():\n", + " try:\n", + " ray.init(object_store_memory=total_mem, num_cpus=total_cores)\n", + " except:\n", + " ray.init()\n", + "\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_rayds(None)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " if ray.is_initialized():\n", + " ray.shutdown()\n", + "\n", + " def show(self, ds):\n", + " pd = ds.to_pandas()\n", + " display(pd)\n", + "\n", + "class SparkContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + " self.rdp = SparkDataProcessor()\n", + "\n", + " def __enter__(self):\n", + " self.spark = self.rdp.spark\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_spark(self.spark)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " pass\n", + "\n", + " def show(self, ds):\n", + " pd = ds.toPandas()\n", + " display(pd)" + ], + "metadata": { + "id": "-F2f_JCo8I0R" + }, + "id": "-F2f_JCo8I0R", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Ray mode\n", + "\n", + "from pyrecdp.primitives.operations import *\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "\n", + "op = TextBytesize()\n", + "with RayContext(\"/content/test_data/\") as ctx:\n", + " ctx.show(op.process_rayds(ctx.ds))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513, + "referenced_widgets": [ + "41fbe06aba494f75ba11aeaf3fd6fa94", + "2784ff8eb12c471eac0c335cc3169c5c", + "de524508eb0947749b3f13c6710f1dbb", + "dbe163714118476e98ec10efc2e6f1e1", + "1dc281fa098a40f6a07af047f0edd1a9", + "789f99f9724442d59f05be15b3231888", + "bc776eb0348c49348497687be7316166", + "05302bd682724596bf8a61dbca5ae2e4", + "81f6b801a20c4f5c9ab21fc0dbccbd5d", + "c96dd696d0a84e8485b705750be517d7", + "61d3c42680814b0a95ccfac427d5c553" + ] + }, + "id": "zjFGFuWm8uLW", + "outputId": "750ead08-4002-4102-a6c7-c90f1694a0d7" + }, + "id": "zjFGFuWm8uLW", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:16:49,727\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:16:49,743\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:16:49,748\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:16:49,752\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00\n", + "
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9996While relapse prevention has been studied and ...{\"APPLICATION_ID\":2120613}1281
9997The proposed study on recruitment, adherence a...{\"APPLICATION_ID\":2120616}2867
9998Recent studies suggest that HIV epidemics are ...{\"APPLICATION_ID\":2120620}2424
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support two types of input and output:\n", + "\n", + "example 1:\n", + "* Expect Input format: a folder of *.jsonl.\n", + "* Expect Output format: a folder of *.jsonl after reduction." + ] + }, + { + "cell_type": "markdown", + "id": "C1FFwhr3MwA1", + "metadata": { + "id": "C1FFwhr3MwA1" + }, + "source": [ + "# Get started" + ] + }, + { + "cell_type": "markdown", + "id": "tMaSNIKIMz3T", + "metadata": { + "id": "tMaSNIKIMz3T" + }, + "source": [ + "## Install pyrecdp and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ro4yOk5IMvRY", + "metadata": { + "id": "ro4yOk5IMvRY" + }, + "outputs": [], + "source": [ + "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", + "! pip install pyrecdp --pre\n", + "# ! pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "id": "pnHK2VTvM5rg", + "metadata": { + "id": "pnHK2VTvM5rg" + }, + "source": [ + "## 2. prepare your own data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/test_data\n", + "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 21626664 (21M) [text/plain]\n", + "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", + "\n", + "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", + "\n", + "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", + "\n" + ] + } + ], + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "file_names = ['NIH_sample.jsonl']\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "!wget -P /content/test_data {\" \".join(file_list)}" + ] + }, + { + "cell_type": "markdown", + "id": "J-125gFtNyOg", + "metadata": { + "id": "J-125gFtNyOg" + }, + "source": [ + "## 3. get bytesize" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "vkGRn9uJN4B0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vkGRn9uJN4B0", + "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NIH_sample.jsonl\n" + ] + } + ], + "source": [ + "! ls /content/test_data" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 PIPELINE based API" + ], + "metadata": { + "id": "HZIPEzB477Bm" + }, + "id": "HZIPEzB477Bm" + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "SMK9KcV4N7al", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704, + "referenced_widgets": [ + "2f6ad7bbc0964d7696c7dec5d0e66f44", + "60dd954b33414adfabf772f49ea2f3a2", + "93697baecfd442fa8d8db709cfc82eee", + "77548643af8d430db70bfb0af3707f91", + "92f4fcc6115c4f809c15b942ca0d11d4", + "90a32f5a48a74578bad33421c0e1599c", + "ec2613a4e1c04b2789a4ca8a257bda53", + "f955ca8d9b9e451791a5aa53bb984a71", + "0d52219723f944eb8b34e5aee50298be", + "58951323a0804ad8ad9d68dd4292f35f", + "c9d153b598854d0686849b48f84aa01e" + ] + }, + "id": "SMK9KcV4N7al", + "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "init ray\n", + "init ray with total mem of 8167961395, total core of 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", + "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray started ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:11:34,510\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:11:34,568\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 17.940069602999984 sec\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " meta \\\n", + "0 {'APPLICATION_ID': 100065} \n", + "1 {'APPLICATION_ID': 100066} \n", + "2 {'APPLICATION_ID': 100067} \n", + "3 {'APPLICATION_ID': 100068} \n", + "4 {'APPLICATION_ID': 100069} \n", + "... ... \n", + "9995 {'APPLICATION_ID': 2120612} \n", + "9996 {'APPLICATION_ID': 2120613} \n", + "9997 {'APPLICATION_ID': 2120616} \n", + "9998 {'APPLICATION_ID': 2120620} \n", + "9999 {'APPLICATION_ID': 2120624} \n", + "\n", + " text bytesize \n", + "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", + "1 The Office of Planning, Research and Evaluatio... 3181 \n", + "2 Improving outcomes for low-income fathers and ... 1777 \n", + "3 This project is implementing 36-month follow-u... 1760 \n", + "4 The CCDF Policies Database is a source of info... 2157 \n", + "... ... ... \n", + "9995 Project: Research and produce a videotape that... 1241 \n", + "9996 While relapse prevention has been studied and ... 1281 \n", + "9997 The proposed study on recruitment, adherence a... 2867 \n", + "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", + "9999 The overall goal of this study is to develop a... 1463 \n", + "\n", + "[10000 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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0{'APPLICATION_ID': 100065}The National Domestic Violence Hotline (NDVH) ...1460
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\n" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = TextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data/\"),\n", + " TextBytesize(text_key = 'text'),\n", + "]\n", + "pipeline.add_operations(ops)\n", + "ret = pipeline.execute()\n", + "ret.to_pandas()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.2 Operation-based API" + ], + "metadata": { + "id": "WJJXs5Co8EsB" + }, + "id": "WJJXs5Co8EsB" + }, + { + "cell_type": "markdown", + "source": [ + "#### prepare Ray and Spark context" + ], + "metadata": { + "id": "K49OFih-8aUc" + }, + "id": "K49OFih-8aUc" + }, + { + "cell_type": "code", + "source": [ + "import psutil\n", + "import ray\n", + "from pyrecdp.core import SparkDataProcessor\n", + "from pyspark.sql import DataFrame\n", + "from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE\n", + "\n", + "total_mem = int(psutil.virtual_memory().total * 0.5)\n", + "total_cores = psutil.cpu_count(logical=False)\n", + "\n", + "class RayContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + "\n", + " def __enter__(self):\n", + " if not ray.is_initialized():\n", + " try:\n", + " ray.init(object_store_memory=total_mem, num_cpus=total_cores)\n", + " except:\n", + " ray.init()\n", + "\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_rayds(None)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " if ray.is_initialized():\n", + " ray.shutdown()\n", + "\n", + " def show(self, ds):\n", + " pd = ds.to_pandas()\n", + " display(pd)\n", + "\n", + "class SparkContext:\n", + " def __init__(self, dataset_path):\n", + " self.dataset_path = dataset_path\n", + " self.rdp = SparkDataProcessor()\n", + "\n", + " def __enter__(self):\n", + " self.spark = self.rdp.spark\n", + " reader = JsonlReader(self.dataset_path)\n", + " self.ds = reader.process_spark(self.spark)\n", + " return self\n", + "\n", + " def __exit__(self, exc_type, exc_value, exc_traceback):\n", + " pass\n", + "\n", + " def show(self, ds):\n", + " pd = ds.toPandas()\n", + " display(pd)" + ], + "metadata": { + "id": "-F2f_JCo8I0R" + }, + "id": "-F2f_JCo8I0R", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Ray mode\n", + "\n", + "from pyrecdp.primitives.operations import *\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "\n", + "op = TextBytesize()\n", + "with RayContext(\"/content/test_data/\") as ctx:\n", + " ctx.show(op.process_rayds(ctx.ds))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513, + "referenced_widgets": [ + "41fbe06aba494f75ba11aeaf3fd6fa94", + "2784ff8eb12c471eac0c335cc3169c5c", + "de524508eb0947749b3f13c6710f1dbb", + "dbe163714118476e98ec10efc2e6f1e1", + "1dc281fa098a40f6a07af047f0edd1a9", + "789f99f9724442d59f05be15b3231888", + "bc776eb0348c49348497687be7316166", + "05302bd682724596bf8a61dbca5ae2e4", + "81f6b801a20c4f5c9ab21fc0dbccbd5d", + "c96dd696d0a84e8485b705750be517d7", + "61d3c42680814b0a95ccfac427d5c553" + ] + }, + "id": "zjFGFuWm8uLW", + "outputId": "750ead08-4002-4102-a6c7-c90f1694a0d7" + }, + "id": "zjFGFuWm8uLW", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023-10-11 18:16:49,727\tINFO read_api.py:406 -- To satisfy the requested parallelism of 20, each read task output is split into 20 smaller blocks.\n", + "2023-10-11 18:16:49,743\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-11 18:16:49,748\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-11 18:16:49,752\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/400 [00:00\n", + "
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3{'APPLICATION_ID': 100068}This project is implementing 36-month follow-u...1760
4{'APPLICATION_ID': 100069}The CCDF Policies Database is a source of info...2157
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9995Project: Research and produce a videotape that...{\"APPLICATION_ID\":2120612}1241
9996While relapse prevention has been studied and ...{\"APPLICATION_ID\":2120613}1281
9997The proposed study on recruitment, adherence a...{\"APPLICATION_ID\":2120616}2867
9998Recent studies suggest that HIV epidemics are ...{\"APPLICATION_ID\":2120620}2424
9999The overall goal of this study is to develop a...{\"APPLICATION_ID\":2120624}1463
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"model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/RecDP/pyrecdp/LLM/README.md b/RecDP/pyrecdp/LLM/README.md index b26219f0d..bf1f22dc4 100644 --- a/RecDP/pyrecdp/LLM/README.md +++ b/RecDP/pyrecdp/LLM/README.md @@ -9,14 +9,17 @@ RecDP LLM is a set of python components that enables quick and easy establish of | Type | notebook | Description | supports | Verified dataset & size | | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ---------------------------------------------------- | ------------------------------------- | -| [ Filter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_filter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/filter.ipynb) | Filter out document based on condition | profanity-based, black-list, url_based, length_based | RedPajama - 2 TB | +| [ Filter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/filter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/filter.ipynb) | Filter out document based on condition | profanity-based, black-list, url_based, length_based | RedPajama - 2 TB | | [ Text Bytesize ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_bytesize.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/bytesize.ipynb) | Get text bytes size | | RedPajama - 2 TB | | [ Text Fixer ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_fixer.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_fixer.ipynb) | Clean repeated format in html, latex, codes | html, latex, codes | RefinedWeb - 1.7 TB | | [ Language Identify ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_language_identify.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/language_identify.ipynb) | Inentify major language type of document | en, zh, fr, de, .. total 25 langs | RedPajama - 2 TB | | [ Fuzzy Deduplicator ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_deduplication.py#L99) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/fuzzy_deduplication.ipynb) | Detect and reduce duplication based on document context | minHashLSH | PILE - 200 GB | | [ Global Decuplicator ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_deduplication.py#L194) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/decontamination.ipynb) | Detect and reduce duplication based on exact same content | sha256-hash | RefinedWeb - 1.7 TB, RedPajama - 2 TB | +| [ Repetition Removal ](#) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/repetition_remove.ipynb) | Detect and reduce repetition context in same document | | RefinedWeb - 1.7 TB, RedPajama - 2 TB | | [ Document splitter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_split.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/document_split.ipynb) | Split Document into multiple sub documents | chapter_based, length_based | RefinedWeb - 1.7 TB | | [ PII Removal ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_pii_remove.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/pii_removal.ipynb) | Detect and replace personal infomation in document | email, phone, ip, username, password | RefinedWeb - 1.7 TB | +| [ User Defined Transform ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_custom.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/custom_map.ipynb) | Easy way to plugin user defined map function | parallel with ray or spark | RefinedWeb - 1.7 TB | +| [ User Defined Filter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_custom.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/custom_filter.ipynb) | Easy way to plugin user defined filter function | parallel with ray or spark | RefinedWeb - 1.7 TB | | [ Reader ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_reader.py#L16) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/reader.ipynb) | Read data from directory | jsonl, parquet, | RefinedWeb - 1.7 TB | | [ Converter ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_converter.py) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/convert.ipynb) | Read and convert unstructed data to unified format | html, document, image, pdf, ... | RefinedWeb - 1.7 TB | | [ Writer ](https://github.com/intel/e2eAIOK/blob/main/RecDP/pyrecdp/primitives/operations/text_writer.py#L7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/writer.ipynb) | write data to directory | jsonl, parquet | RefinedWeb - 1.7 TB | @@ -29,7 +32,7 @@ RecDP LLM is a set of python components that enables quick and easy establish of | :-------- | :---------- | :------------| | Visualize the diversity distribution of data | Leverage GPT-3 to scoring | Visualize Toxicity probability | | ![diversity](/RecDP/resources/diversity_analysis.png) | ![quality](/RecDP/resources/quality_scoring.png) | ![toxicity](/RecDP/resources/toxicity_analysis.png)| -| [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_quality_assessment.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/data_diversity_control.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb) | +| [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/data_diversity_control.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/text_quality_assessment.ipynb) | [learn more](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb) | # Getting Start @@ -43,10 +46,6 @@ pip install pyrecdp --pre ## * run with pipeline [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/e2eAIOK/blob/main/RecDP/examples/notebooks/llmutils/resumable_pipeline.ipynb) ``` -from pyrecdp.primitives.operations import * -from pyrecdp.LLM import TextPipeline -from IPython.display import display - from pyrecdp.primitives.operations import * from pyrecdp.LLM import ResumableTextPipeline @@ -68,7 +67,7 @@ pipeline.export_to_yaml("custom_llm_data_pipeline.yaml") or ``` from pyrecdp.LLM import ResumableTextPipeline -pipeline = TextPipeline("custom_llm_data_pipeline.yaml") +pipeline = ResumableTextPipeline("custom_llm_data_pipeline.yaml") ret = pipeline.execute() ``` diff --git a/RecDP/pyrecdp/LLM/TextPipeline.py b/RecDP/pyrecdp/LLM/TextPipeline.py index ecaf3dc9a..d38bbbc29 100644 --- a/RecDP/pyrecdp/LLM/TextPipeline.py +++ b/RecDP/pyrecdp/LLM/TextPipeline.py @@ -2,7 +2,7 @@ from pyrecdp.core.pipeline import BasePipeline from pyrecdp.primitives.operations import Operation, BaseOperation from pyrecdp.primitives.operations.text_reader import DatasetReader, PerfileReader -from pyrecdp.primitives.operations.text_writer import PerfileParquetWriter +from pyrecdp.primitives.operations.text_writer import PerfileParquetWriter, PerfileJsonlWriter import logging from pyrecdp.core.utils import Timer, deepcopy from IPython.display import display @@ -111,7 +111,7 @@ def execute(self, ds=None): # fetch result return ds - def add_operation(self, config): + def add_operation(self, config, function_type = 'map', text_key = 'text'): # get current max operator id max_idx = self.pipeline.get_max_idx() cur_idx = max_idx + 1 @@ -139,10 +139,17 @@ def add_operation(self, config): # ====== Start to add it to pipeline ====== # if isinstance(inline_function, types.FunctionType): config = { - "func_name": inline_function, + "func": inline_function, + "text_key": text_key } - self.pipeline[cur_idx] = Operation( - cur_idx, children, output=None, op="ray_python", config=config) + if function_type == 'map': + self.pipeline[cur_idx] = Operation( + cur_idx, children, output=None, op="TextCustomerMap", config=config) + elif function_type == 'filter': + self.pipeline[cur_idx] = Operation( + cur_idx, children, output=None, op="TextCustomerFilter", config=config) + else: + raise NotImplementedError(f"{function_type} is not supported as customer function yet") elif isinstance(inline_function, BaseOperation): op_name = inline_function.op.op # config = vars(inline_function) @@ -201,6 +208,9 @@ def execute(self): if op.op in ['ParquetWriter', 'PerfileParquetWriter']: op.op = 'PerfileParquetWriter' output_dir = op.config['output_dir'] + if op.op in ['JsonlWriter', 'PerfileJsonlWriter']: + op.op = 'PerfileJsonlWriter' + output_dir = op.config['output_dir'] if output_dir == "": output_dir = f"ResumableTextPipeline_output_{time.strftime('%Y%m%d%H%M%S')}" self.add_operation(PerfileParquetWriter(output_dir=output_dir)) @@ -227,50 +237,87 @@ def execute(self): executable_sequence = self.executable_sequence engine_name = self.check_platform(executable_sequence) - - if engine_name != 'ray': - raise NotImplementedError("ResumableTextPipeline only support operations with ray mode") self.engine_name = engine_name - if not ray.is_initialized(): - print(f"init ray with total mem of {total_mem}") - try: - ray.init(object_store_memory=total_mem, num_cpus=total_cores) - except: - ray.init() - # explode one pipeline to multiple sub pipeline (per file) sub_pipelines = [] op_chain = [] - for op in executable_sequence: - if isinstance(op, PerfileReader): - op.execute_ray(executable_pipeline) - sub_pipelines = op.cache - elif len(sub_pipelines) > 0: - op_chain.append(op) - - for ds_reader, source_id in (pbar := tqdm(sub_pipelines, total=len(sub_pipelines))): - # check if we should skip - if source_id in done_files: - print(f"skip {source_id}, it was processed in last round") - del ds_reader - continue - - # If not skip, then - pbar.set_description(f"ResumableTextPipeline, current on {source_id}") - start = time.time() - for idx, op in enumerate(op_chain): - op.statistics_flag = self.statistics_flag - if idx == 0: - op.execute_ray(executable_pipeline, ds_reader) - elif isinstance(op, PerfileParquetWriter): - op.execute_ray(executable_pipeline, source_id) - else: + + if engine_name == 'ray': + if not ray.is_initialized(): + print(f"init ray with total mem of {total_mem}") + try: + ray.init(object_store_memory=total_mem, num_cpus=total_cores) + except: + ray.init() + + for op in executable_sequence: + if isinstance(op, PerfileReader): op.execute_ray(executable_pipeline) - elapse = time.time() - start - status_tracker.write(f"{source_id}, {elapse} secs\n") - status_tracker.flush() - done_files.append(status_tracker) - del ds_reader + sub_pipelines = op.cache + elif len(sub_pipelines) > 0: + op_chain.append(op) + + for ds_reader, source_id in (pbar := tqdm(sub_pipelines, total=len(sub_pipelines))): + # check if we should skip + if source_id in done_files: + print(f"skip {source_id}, it was processed in last round") + del ds_reader + continue + + # If not skip, then + pbar.set_description(f"ResumableTextPipeline, current on {source_id}") + start = time.time() + for idx, op in enumerate(op_chain): + op.statistics_flag = self.statistics_flag + if idx == 0: + op.execute_ray(executable_pipeline, ds_reader) + elif isinstance(op, PerfileParquetWriter) or isinstance(op, PerfileJsonlWriter): + op.execute_ray(executable_pipeline, source_id) + else: + op.execute_ray(executable_pipeline) + elapse = time.time() - start + status_tracker.write(f"{source_id}, {elapse} secs\n") + status_tracker.flush() + done_files.append(status_tracker) + del ds_reader + elif engine_name == 'spark': + if not hasattr(self, 'rdp') or self.rdp is None: + self.rdp = SparkDataProcessor() + + for op in executable_sequence: + if isinstance(op, PerfileReader): + op.execute_spark(executable_pipeline, rdp = self.rdp) + sub_pipelines = op.cache + elif len(sub_pipelines) > 0: + op_chain.append(op) + + # execute + for ds_reader, source_id in (pbar := tqdm(sub_pipelines, total=len(sub_pipelines))): + # check if we should skip + if source_id in done_files: + print(f"skip {source_id}, it was processed in last round") + del ds_reader + continue + + # If not skip, then + pbar.set_description(f"ResumableTextPipeline, current on {source_id}") + start = time.time() + for idx, op in enumerate(op_chain): + op.statistics_flag = self.statistics_flag + if idx == 0: + op.execute_spark(executable_pipeline, rdp = self.rdp, child_ds = ds_reader) + elif isinstance(op, PerfileParquetWriter) or isinstance(op, PerfileJsonlWriter): + op.execute_spark(executable_pipeline, source_id = source_id) + else: + op.execute_spark(executable_pipeline, rdp = self.rdp) + elapse = time.time() - start + status_tracker.write(f"{source_id}, {elapse} secs\n") + status_tracker.flush() + done_files.append(status_tracker) + del ds_reader + else: + raise NotImplementedError(f"ResumableTextPipeline is not support {engine_name} yet") + if self.statistics_flag: for op in op_chain: logger.info(f"{op.__class__.__name__}: {op.summarize()}") diff --git a/RecDP/pyrecdp/core/pipeline.py b/RecDP/pyrecdp/core/pipeline.py index c79b03932..770a0e819 100644 --- a/RecDP/pyrecdp/core/pipeline.py +++ b/RecDP/pyrecdp/core/pipeline.py @@ -19,7 +19,9 @@ def __repr__(self): return repr(self.pipeline) def export(self, file_path = None): - json_object = self.pipeline.json_dump() + import copy + pipeline_deepcopy = copy.deepcopy(self.pipeline) + json_object = pipeline_deepcopy.json_dump() if file_path: # Writing to sample.json with open(file_path, "w") as outfile: diff --git a/RecDP/pyrecdp/primitives/operations/__init__.py b/RecDP/pyrecdp/primitives/operations/__init__.py index 55600f917..e3a91df65 100644 --- a/RecDP/pyrecdp/primitives/operations/__init__.py +++ b/RecDP/pyrecdp/primitives/operations/__init__.py @@ -14,3 +14,4 @@ from .text_deduplication import FuzzyDeduplicate, GlobalDeduplicate from .text_qualityscorer import TextQualityScorer from .text_diversityindicate import TextDiversityIndicate +from .text_custom import TextCustomerMap, TextCustomerFilter diff --git a/RecDP/pyrecdp/primitives/operations/base.py b/RecDP/pyrecdp/primitives/operations/base.py index d9379428c..6ba41fbf5 100644 --- a/RecDP/pyrecdp/primitives/operations/base.py +++ b/RecDP/pyrecdp/primitives/operations/base.py @@ -203,12 +203,15 @@ def execute_ray(self, pipeline, child_ds=None): self.cache = self.process_rayds(*child_output) return self.cache - def execute_spark(self, pipeline, rdp): + def execute_spark(self, pipeline, rdp, child_ds=None): child_output = [] - children = self.op.children if self.op.children is not None else [] - for op in children: - child_output.append(pipeline[op].cache) - self.cache = self.process_spark(rdp.spark, *child_output) + if child_ds is not None: + self.cache = self.process_spark(rdp.spark, child_ds) + else: + children = self.op.children if self.op.children is not None else [] + for op in children: + child_output.append(pipeline[op].cache) + self.cache = self.process_spark(rdp.spark, *child_output) return self.cache def process_rayds(self, ds=None): diff --git a/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py b/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py index 027261ce4..c3b358edc 100644 --- a/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py +++ b/RecDP/pyrecdp/primitives/operations/filter/badwords_filter.py @@ -32,7 +32,7 @@ def __init__(self, language='en'): def compute(self, text) -> bool: func = prepare_func_filter_by_badwords(self.language) - func(text) + return func(text) LLMOPERATORS.register(BadwordsFilter) diff --git a/RecDP/pyrecdp/primitives/operations/filter/base.py b/RecDP/pyrecdp/primitives/operations/filter/base.py index 686d3b7c6..9ecdd0d24 100644 --- a/RecDP/pyrecdp/primitives/operations/filter/base.py +++ b/RecDP/pyrecdp/primitives/operations/filter/base.py @@ -25,7 +25,6 @@ def process_rayds(self, ds: Dataset) -> Dataset: @statistics_decorator def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: if self.inplace: - import pyspark.sql.types as T compute_udf = F.udf(self.compute, T.BooleanType()) return spark_df.filter(compute_udf(F.col(self.text_key))) diff --git a/RecDP/pyrecdp/primitives/operations/text_custom.py b/RecDP/pyrecdp/primitives/operations/text_custom.py new file mode 100644 index 000000000..c1a6fe92b --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/text_custom.py @@ -0,0 +1,58 @@ +from .base import BaseLLMOperation, LLMOPERATORS, statistics_decorator +from ray.data import Dataset +from pyspark.sql import DataFrame +from .filter import BaseFilter + +def text_bytesize(s): + return len(s.encode('utf-8')) + +class TextCustomerMap(BaseLLMOperation): + def __init__(self, func, text_key = 'text'): + settings = {'func': func, 'text_key': text_key} + super().__init__(settings) + self.support_spark = True + self.support_ray = True + self.func = func + self.text_key = text_key + self.new_key = f"{func.__name__}_text" + + def process_rayds(self, ds: Dataset) -> Dataset: + return ds.map(lambda x: self.process_row(x, self.text_key, self.new_key, self.func)) + + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: + import pyspark.sql.functions as F + from pyspark.sql import types as T + custom_udf = F.udf(self.func) + return spark_df.withColumn(self.new_key, custom_udf(F.col(self.text_key))) + +LLMOPERATORS.register(TextCustomerMap) + +class TextCustomerFilter(BaseFilter): + def __init__(self, func, text_key = 'text'): + settings = {'func': func, 'text_key': text_key} + super().__init__(settings) + self.support_spark = True + self.support_ray = True + self.func = func + self.text_key = text_key + + @statistics_decorator + def process_rayds(self, ds: Dataset) -> Dataset: + if self.inplace: + # remove unwanted text row inplace + filtered_ds = ds.filter(lambda x: self.func(x[self.text_key])) + return filtered_ds + else: + raise NotImplementedError(f"We only support inplace modification for {self.__class__.__name__}.") + + @statistics_decorator + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: + import pyspark.sql.functions as F + from pyspark.sql import types as T + if self.inplace: + compute_udf = F.udf(self.func, T.BooleanType()) + return spark_df.filter(compute_udf(F.col(self.text_key))) + else: + raise NotImplementedError(f"We only support inplace modification for {self.__class__.__name__}.") + +LLMOPERATORS.register(TextCustomerFilter) \ No newline at end of file diff --git a/RecDP/pyrecdp/primitives/operations/text_qualityscorer.py b/RecDP/pyrecdp/primitives/operations/text_qualityscorer.py index 71c42bbd7..b1ce85a74 100644 --- a/RecDP/pyrecdp/primitives/operations/text_qualityscorer.py +++ b/RecDP/pyrecdp/primitives/operations/text_qualityscorer.py @@ -290,7 +290,6 @@ def __init__(self, text_key = 'text', model='gpt3'): def process_rayds(self, ds: Dataset) -> Dataset: raise NotImplementedError("Not implemented yet") - return ds.map(lambda x: self.process_row(x, self.text_key, new_name, text_bytesize)) def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: import pyspark.sql.functions as F diff --git a/RecDP/pyrecdp/primitives/operations/text_reader.py b/RecDP/pyrecdp/primitives/operations/text_reader.py index 678c237a6..8b1b3aab4 100644 --- a/RecDP/pyrecdp/primitives/operations/text_reader.py +++ b/RecDP/pyrecdp/primitives/operations/text_reader.py @@ -160,9 +160,10 @@ class PerfileReader: class PerfileSourcedJsonlReader(SourcedReader, PerfileReader): def __init__(self, input_dir = "", source_prefix = ""): super().__init__(input_dir = input_dir, source_prefix = source_prefix) - self.support_spark = False + self.support_spark = True + self.support_ray = True - def process_rayds(self, ds) -> Dataset: + def process_rayds(self, ds): import ray.data as rd files_with_subtask, input_dir = self.get_files_with_subtask("jsonl") @@ -182,12 +183,32 @@ def convert_json(s, source_str): ds = rd.read_text(file_path).map(lambda x: convert_json(x, source_id)) self.cache.append((ds, source_id)) return self.cache + + def process_spark(self, spark, spark_df: DataFrame = None): + files_with_subtask, input_dir = self.get_files_with_subtask("jsonl") + to_read_list = [(sub_task, os.path.join(input_dir, f)) for sub_task, file_list in files_with_subtask.items() for f in file_list] + self.cache = [] + from pyspark.sql.types import StructType, StructField, StringType + import pyspark.sql.functions as F + schema = StructType([ + StructField("text",StringType(),True), + StructField("meta",StringType(),True) + ]) + + for sub_task, file_path in to_read_list: + source_id = os.path.join(self.source_prefix, sub_task, os.path.basename(file_path)) + df = spark.read.text(file_path) + df = df.withColumn('jsonData', F.from_json(F.col('value'), schema)).select("jsonData.*") + df = df.withColumn('source_id', F.lit(source_id)) + self.cache.append((df, source_id)) + return self.cache LLMOPERATORS.register(PerfileSourcedJsonlReader) class PerfileSourcedParquetReader(SourcedReader, PerfileReader): def __init__(self, input_dir = "", source_prefix = ""): super().__init__(input_dir = input_dir, source_prefix = source_prefix) - self.support_spark = False + self.support_spark = True + self.support_ray = True def process_rayds(self, ds) -> Dataset: import ray.data as rd @@ -203,4 +224,14 @@ def add_source(s, source_str): self.cache.append((ds, source_id)) return self.cache + def process_spark(self, spark, spark_df: DataFrame = None): + files_with_subtask, input_dir = self.get_files_with_subtask("jsonl") + to_read_list = [(sub_task, os.path.join(input_dir, f)) for sub_task, file_list in files_with_subtask.items() for f in file_list] + self.cache = [] + for sub_task, file_path in to_read_list: + source_id = os.path.join(self.source_prefix, sub_task, os.path.basename(file_path)) + df = spark.read.parquet(file_path) + self.cache.append((df, source_id)) + return self.cache + LLMOPERATORS.register(PerfileSourcedParquetReader) diff --git a/RecDP/pyrecdp/primitives/operations/text_writer.py b/RecDP/pyrecdp/primitives/operations/text_writer.py index 8c15fcf4b..88b7d357d 100644 --- a/RecDP/pyrecdp/primitives/operations/text_writer.py +++ b/RecDP/pyrecdp/primitives/operations/text_writer.py @@ -79,7 +79,45 @@ def __init__(self, output_dir): settings = {'output_dir': output_dir} super().__init__(settings) self.support_ray = True - self.support_spark = False + self.support_spark = True + self.output_dir = output_dir + + def execute_ray(self, pipeline, source_id): + child_output = [] + children = self.op.children if self.op.children is not None else [] + for op in children: + child_output.append(pipeline[op].cache) + self.cache = self.process_rayds(source_id, *child_output) + return self.cache + + def process_rayds(self, source_id, ds: Dataset) -> Dataset: + to_save = os.path.join(self.output_dir, source_id) + if os.path.exists(to_save) and os.path.isdir(to_save): + shutil.rmtree(to_save) + ds.write_parquet(to_save) + return ds + + def execute_spark(self, pipeline, source_id): + child_output = [] + children = self.op.children if self.op.children is not None else [] + for op in children: + child_output.append(pipeline[op].cache) + self.cache = self.process_spark(source_id, *child_output) + return self.cache + + def process_spark(self, source_id, spark_df: DataFrame = None) -> DataFrame: + to_save = os.path.join(self.output_dir, source_id) + spark_df.write.mode("overwrite").parquet(to_save) + return spark_df + +LLMOPERATORS.register(PerfileParquetWriter) + +class PerfileJsonlWriter(BaseLLMOperation): + def __init__(self, output_dir): + settings = {'output_dir': output_dir} + super().__init__(settings) + self.support_ray = True + self.support_spark = True self.output_dir = output_dir def execute_ray(self, pipeline, source_id): @@ -94,7 +132,20 @@ def process_rayds(self, source_id, ds: Dataset) -> Dataset: to_save = os.path.join(self.output_dir, source_id) if os.path.exists(to_save) and os.path.isdir(to_save): shutil.rmtree(to_save) - ds.write_parquet(os.path.join(self.output_dir, source_id)) + ds.write_json(to_save) return ds + + def execute_spark(self, pipeline, source_id): + child_output = [] + children = self.op.children if self.op.children is not None else [] + for op in children: + child_output.append(pipeline[op].cache) + self.cache = self.process_spark(source_id, *child_output) + return self.cache + + def process_spark(self, source_id, spark_df: DataFrame = None) -> DataFrame: + to_save = os.path.join(self.output_dir, source_id) + spark_df.write.mode("overwrite").json(to_save) + return spark_df -LLMOPERATORS.register(PerfileParquetWriter) \ No newline at end of file +LLMOPERATORS.register(PerfileJsonlWriter) \ No newline at end of file diff --git a/RecDP/tests/data/import_test_pipeline.json b/RecDP/tests/data/import_test_pipeline.json index 3e78a38ad..7b0ac004c 100644 --- a/RecDP/tests/data/import_test_pipeline.json +++ b/RecDP/tests/data/import_test_pipeline.json @@ -36,7 +36,7 @@ ], "op": "PerfileParquetWriter", "config": { - "output_dir": "ResumableTextPipeline_output_import" + "output_dir": "ResumableTextPipeline_output" } } } \ No newline at end of file diff --git a/RecDP/tests/test_llmutils_operations.py b/RecDP/tests/test_llmutils_operations.py index bf090b093..1ce8bba4c 100644 --- a/RecDP/tests/test_llmutils_operations.py +++ b/RecDP/tests/test_llmutils_operations.py @@ -137,6 +137,22 @@ def test_sentence_split_ray(self): op = DocumentSplit() with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: ctx.show(op.process_rayds(ctx.ds)) + + def test_customermap_ray(self): + def proc(text): + return f'processed_{text}' + + op = TextCustomerMap(func=proc, text_key='text') + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + + def test_customerfilter_ray(self): + def cond(text): + return len(text) < 200 + + op = TextCustomerFilter(func=cond, text_key='text') + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) ### ====== Spark ====== ### @@ -209,5 +225,21 @@ def test_qualityscore_spark(self): def test_diversityindicate_spark(self): op = TextDiversityIndicate() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_customermap_spark(self): + def proc(text): + return f'processed_{text}' + + op = TextCustomerMap(func=proc, text_key='text') + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_customerfilter_spark(self): + def cond(text): + return len(text) < 200 + + op = TextCustomerFilter(func=cond, text_key='text') with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: ctx.show(op.process_spark(ctx.spark, ctx.ds)) \ No newline at end of file diff --git a/RecDP/tests/test_llmutils_pipelines.py b/RecDP/tests/test_llmutils_pipelines.py index e9cc7487f..c44a400db 100644 --- a/RecDP/tests/test_llmutils_pipelines.py +++ b/RecDP/tests/test_llmutils_pipelines.py @@ -22,6 +22,21 @@ class Test_LLMUtils_Pipeline(unittest.TestCase): def setUp(self) -> None: print(f"\n******\nTesting Method Name: {self._testMethodName}\n******") + + def tearDown(self) -> None: + print("Test completed, view results and delete output") + import pandas as pd + import os + import shutil + output_path = "ResumableTextPipeline_output" + try: + dir_name = [i for i in os.listdir("ResumableTextPipeline_output") if i.endswith('jsonl')][0] + print(dir_name) + display(pd.read_parquet(os.path.join("ResumableTextPipeline_output", dir_name))) + shutil.rmtree("ResumableTextPipeline_output") + except Exception as e: + print(e) + return super().tearDown() def test_ResumableTextPipeline(self): pipeline = ResumableTextPipeline() @@ -30,7 +45,7 @@ def test_ResumableTextPipeline(self): LengthFilter(), ProfanityFilter(), LanguageIdentify(fasttext_model_dir = os.path.join(RECDP_MODELS_CACHE, "lid.bin")), - PerfileParquetWriter("ResumableTextPipeline_output_20231004205724") + PerfileParquetWriter("ResumableTextPipeline_output") ] pipeline.add_operations(ops) pipeline.execute() @@ -40,3 +55,49 @@ def test_ResumableTextPipeline_import(self): pipeline = ResumableTextPipeline(pipeline_file = 'tests/data/import_test_pipeline.json') pipeline.execute() del pipeline + + def test_ResumableTextPipeline_customerfilter_op(self): + def cond(text): + return text > 0.9 + + pipeline = ResumableTextPipeline() + ops = [ + JsonlReader("tests/data/llm_data/"), + TextQualityScorer(), + TextCustomerFilter(cond, text_key='doc_score'), + PerfileParquetWriter("ResumableTextPipeline_output") + ] + pipeline.add_operations(ops) + pipeline.plot() + pipeline.execute() + del pipeline + + + + def test_ResumableTextPipeline_customermap_op(self): + def classify(text): + return 1 if text > 0.8 else 0 + + pipeline = ResumableTextPipeline() + ops = [ + JsonlReader("tests/data/llm_data/"), + TextQualityScorer(), + TextCustomerMap(classify, text_key='doc_score'), + PerfileParquetWriter("ResumableTextPipeline_output") + ] + pipeline.add_operations(ops) + pipeline.plot() + pipeline.execute() + del pipeline + + def test_ResumableTextPipeline_customer_function(self): + def proc(text): + return f'processed_{text}' + + pipeline = ResumableTextPipeline() + pipeline.add_operation(JsonlReader("tests/data/llm_data/")) + pipeline.add_operation(proc, text_key='text') + pipeline.add_operation(PerfileParquetWriter("ResumableTextPipeline_output")) + pipeline.plot() + pipeline.execute() + del pipeline \ No newline at end of file From cc08fc14af5462b30d5101497e492a4ea6a33780 Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Thu, 12 Oct 2023 19:49:15 -0500 Subject: [PATCH 14/47] [v1.2][ISSUE-306]provide example notebook for customer function (#385) * Created using Colaboratory * Created using Colaboratory --- .../notebooks/llmutils/custom_filter.ipynb | 2339 +++++------------ .../notebooks/llmutils/custom_map.ipynb | 1958 ++------------ 2 files changed, 871 insertions(+), 3426 deletions(-) diff --git a/RecDP/examples/notebooks/llmutils/custom_filter.ipynb b/RecDP/examples/notebooks/llmutils/custom_filter.ipynb index 936cb3659..24141c1d5 100644 --- a/RecDP/examples/notebooks/llmutils/custom_filter.ipynb +++ b/RecDP/examples/notebooks/llmutils/custom_filter.ipynb @@ -40,12 +40,422 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ro4yOk5IMvRY", "metadata": { - "id": "ro4yOk5IMvRY" + "id": "ro4yOk5IMvRY", + "outputId": "7e7a46cf-d1af-487e-fed4-be652442a594", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Reading package lists... 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/root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", + "Successfully built pyrecdp alt-profanity-check fasttext pyspark wget\n", + "Installing collected packages: wget, sentencepiece, selectolax, phonenumbers, xxhash, scipy, safetensors, pyspark, pybind11, loguru, jsonlines, jedi, ftfy, dill, scikit-learn, networkit, multiprocess, huggingface-hub, fasttext, Faker, datasketch, woodwork, tokenizers, lightgbm, alt-profanity-check, transformers, ray, pandas-flavor, featuretools, datasets, category-encoders, pyrecdp\n", + " Attempting uninstall: scipy\n", + " Found existing installation: scipy 1.11.3\n", + " Uninstalling scipy-1.11.3:\n", + " Successfully uninstalled scipy-1.11.3\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.2.2\n", + " Uninstalling scikit-learn-1.2.2:\n", + " Successfully uninstalled scikit-learn-1.2.2\n", + " Attempting uninstall: lightgbm\n", + " Found existing installation: lightgbm 4.0.0\n", + " Uninstalling lightgbm-4.0.0:\n", + " Successfully uninstalled lightgbm-4.0.0\n", + "Successfully installed Faker-19.10.0 alt-profanity-check-1.3.0 category-encoders-2.6.2 datasets-2.14.5 datasketch-1.5.9 dill-0.3.7 fasttext-0.9.2 featuretools-1.27.0 ftfy-6.1.1 huggingface-hub-0.16.4 jedi-0.19.1 jsonlines-3.1.0 lightgbm-3.3.5 loguru-0.7.2 multiprocess-0.70.15 networkit-10.1 pandas-flavor-0.6.0 phonenumbers-8.13.22 pybind11-2.11.1 pyrecdp-1.6.1b2023101306 pyspark-3.4.0 ray-2.7.1 safetensors-0.4.0 scikit-learn-1.3.0 scipy-1.10.1 selectolax-0.3.16 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.0 wget-3.2 woodwork-0.26.0 xxhash-3.4.1\n" + ] + } + ], "source": [ "! DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre\n", "! pip install pyrecdp --pre\n", @@ -71,7 +481,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + "outputId": "ea5e6451-43bb-4dd2-9efc-b44b7141d8b4" }, "outputs": [ { @@ -79,16 +489,16 @@ "name": "stdout", "text": [ "/content/test_data\n", - "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "--2023-10-12 23:32:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", - "Length: 21626664 (21M) [text/plain]\n", - "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", + "Length: 1062126 (1.0M) [text/plain]\n", + "Saving to: ‘/content/test_data/tiny_c4_sample.jsonl’\n", "\n", - "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", + "tiny_c4_sample.json 100%[===================>] 1.01M 4.96MB/s in 0.2s \n", "\n", - "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", + "2023-10-12 23:32:38 (4.96 MB/s) - ‘/content/test_data/tiny_c4_sample.jsonl’ saved [1062126/1062126]\n", "\n" ] } @@ -97,7 +507,7 @@ "%mkdir -p /content/test_data\n", "%cd /content/test_data\n", "file_names = ['NIH_sample.jsonl']\n", - "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl\" for i in file_names]\n", "!wget -P /content/test_data {\" \".join(file_list)}" ] }, @@ -108,7 +518,7 @@ "id": "J-125gFtNyOg" }, "source": [ - "## 3. get bytesize" + "## 3. User Defined Filter" ] }, { @@ -120,14 +530,14 @@ "base_uri": "https://localhost:8080/" }, "id": "vkGRn9uJN4B0", - "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + "outputId": "2f745f19-2195-4d7f-db2a-9ce837835178" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "NIH_sample.jsonl\n" + "tiny_c4_sample.jsonl\n" ] } ], @@ -145,6 +555,20 @@ }, "id": "HZIPEzB477Bm" }, + { + "cell_type": "code", + "source": [ + "# Define your filter condition\n", + "def cond(text):\n", + " return text > 0.9" + ], + "metadata": { + "id": "EZuBCm3XIm_U" + }, + "id": "EZuBCm3XIm_U", + "execution_count": 4, + "outputs": [] + }, { "cell_type": "code", "execution_count": 5, @@ -152,23 +576,10 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 704, - "referenced_widgets": [ - "2f6ad7bbc0964d7696c7dec5d0e66f44", - "60dd954b33414adfabf772f49ea2f3a2", - "93697baecfd442fa8d8db709cfc82eee", - "77548643af8d430db70bfb0af3707f91", - "92f4fcc6115c4f809c15b942ca0d11d4", - "90a32f5a48a74578bad33421c0e1599c", - "ec2613a4e1c04b2789a4ca8a257bda53", - "f955ca8d9b9e451791a5aa53bb984a71", - "0d52219723f944eb8b34e5aee50298be", - "58951323a0804ad8ad9d68dd4292f35f", - "c9d153b598854d0686849b48f84aa01e" - ] + "height": 260 }, "id": "SMK9KcV4N7al", - "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + "outputId": "95ca03d9-003c-4b03-d640-46ab5e503c2e" }, "outputs": [ { @@ -245,99 +656,161 @@ "output_type": "stream", "name": "stdout", "text": [ - "init ray\n", - "init ray with total mem of 8167961395, total core of 1\n" + "\u001b[32m2023-10-12 23:33:02.075\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mpyrecdp.LLM.TextPipeline\u001b[0m:\u001b[36menable_statistics\u001b[0m:\u001b[36m197\u001b[0m - \u001b[33m\u001b[1mEnabling this option will result in a decrease in execution speed\u001b[0m\n", + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ - "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", - "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + "ResumableTextPipeline, current on tiny_c4_sample.jsonl: 0%| | 0/1 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", - "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "ResumableTextPipeline, current on tiny_c4_sample.jsonl: 100%|██████████| 1/1 [00:21<00:00, 21.74s/it]" ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + "\n" ] - }, + } + ], + "source": [ + "# plugin into pipeline\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = ResumableTextPipeline()\n", + "pipeline.enable_statistics()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data\"),\n", + " TextQualityScorer(),\n", + " TextCustomerFilter(cond, text_key='doc_score'),\n", + " PerfileParquetWriter(\"ResumableTextPipeline_output\")\n", + "]\n", + "pipeline.add_operations(ops)\n", + "pipeline.execute()\n", + "del pipeline" + ] + }, + { + "cell_type": "code", + "source": [ + "# View output\n", + "! ls ResumableTextPipeline_output" + ], + "metadata": { + "id": "MumDgk0TL4jH", + "outputId": "b3fd0240-9c4b-4ece-e352-e9a0aed5b14d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "MumDgk0TL4jH", + "execution_count": 6, + "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "execute with ray took 17.940069602999984 sec\n" + "pipeline.json pipeline.log status.log tiny_c4_sample.jsonl\n" ] - }, + } + ] + }, + { + "cell_type": "code", + "source": [ + "# After Filter\n", + "import pandas as pd\n", + "pd.read_parquet(\"ResumableTextPipeline_output/tiny_c4_sample.jsonl\")" + ], + "metadata": { + "id": "pRljCRLlL3D9", + "outputId": "d6d12af1-1a2d-4d6e-c427-18deab7cd13a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "id": "pRljCRLlL3D9", + "execution_count": 7, + "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - " meta \\\n", - "0 {'APPLICATION_ID': 100065} \n", - "1 {'APPLICATION_ID': 100066} \n", - "2 {'APPLICATION_ID': 100067} \n", - "3 {'APPLICATION_ID': 100068} \n", - "4 {'APPLICATION_ID': 100069} \n", - "... ... \n", - "9995 {'APPLICATION_ID': 2120612} \n", - "9996 {'APPLICATION_ID': 2120613} \n", - "9997 {'APPLICATION_ID': 2120616} \n", - "9998 {'APPLICATION_ID': 2120620} \n", - "9999 {'APPLICATION_ID': 2120624} \n", - "\n", - " text bytesize \n", - "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", - "1 The Office of Planning, Research and Evaluatio... 3181 \n", - "2 Improving outcomes for low-income fathers and ... 1777 \n", - "3 This project is implementing 36-month follow-u... 1760 \n", - "4 The CCDF Policies Database is a source of info... 2157 \n", - "... ... ... \n", - "9995 Project: Research and produce a videotape that... 1241 \n", - "9996 While relapse prevention has been studied and ... 1281 \n", - "9997 The proposed study on recruitment, adherence a... 2867 \n", - "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", - "9999 The overall goal of this study is to develop a... 1463 \n", - "\n", - "[10000 rows x 3 columns]" + " text \\\n", + "0 It is possible to love someone who does not lo... \n", + "1 Canon PIXMA TS9520 All-in-One Print / Scan / C... \n", + "2 For those who plan on buying an iPad this Satu... \n", + "3 After tipping 25 tokens in a day, you'll be ab... \n", + "4 When cute redhead Lola Fae gets caught flickin... \n", + ".. ... \n", + "305 This dark haired angel really loves to play wi... \n", + "306 Who were the first two guys in the scene. 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"# RecDP LLM - TextBytesize\n", + "# RecDP LLM - User Defined Map Function\n", "\n", - "TextBytesize is a tool to get total byte size of text in the data.\n", - "\n", - "### We support two types of input and output:\n", - "\n", - "example 1:\n", - "* Expect Input format: a folder of *.jsonl.\n", - "* Expect Output format: a folder of *.jsonl after reduction." + "Support to plugin user defined function to pipeline and run with ray or spark" ] }, { @@ -64,40 +58,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", "metadata": { - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d326b334-d005-42e1-ee4e-619d9c0b388b" + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/test_data\n", - "--2023-10-11 18:10:37-- https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/NIH_sample.jsonl\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 21626664 (21M) [text/plain]\n", - "Saving to: ‘/content/test_data/NIH_sample.jsonl’\n", - "\n", - "NIH_sample.jsonl 100%[===================>] 20.62M 114MB/s in 0.2s \n", - "\n", - "2023-10-11 18:10:38 (114 MB/s) - ‘/content/test_data/NIH_sample.jsonl’ saved [21626664/21626664]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "%mkdir -p /content/test_data\n", "%cd /content/test_data\n", "file_names = ['NIH_sample.jsonl']\n", - "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/PILE/{i}\" for i in file_names]\n", + "file_list = [f\"https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/tiny_c4_sample.jsonl\" for i in file_names]\n", "!wget -P /content/test_data {\" \".join(file_list)}" ] }, @@ -108,7 +79,7 @@ "id": "J-125gFtNyOg" }, "source": [ - "## 3. get bytesize" + "## 3. User Defined Function" ] }, { @@ -120,14 +91,14 @@ "base_uri": "https://localhost:8080/" }, "id": "vkGRn9uJN4B0", - "outputId": "fb931ee0-a81c-414e-aa6c-60b3db1ab4cd" + "outputId": "98ebbaf0-d853-4dd0-b5a7-eafd1717e40e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "NIH_sample.jsonl\n" + "tiny_c4_sample.jsonl\n" ] } ], @@ -147,29 +118,46 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "SMK9KcV4N7al", "metadata": { + "id": "SMK9KcV4N7al" + }, + "outputs": [], + "source": [ + "# Define your own function\n", + "def classify(text):\n", + " return 1 if text > 0.8 else 0" + ] + }, + { + "cell_type": "code", + "source": [ + "# plugin into pipeline\n", + "from pyrecdp.LLM import TextPipeline, ResumableTextPipeline\n", + "from pyrecdp.primitives.operations import *\n", + "\n", + "pipeline = ResumableTextPipeline()\n", + "ops = [\n", + " JsonlReader(\"/content/test_data\"),\n", + " TextQualityScorer(),\n", + " TextCustomerMap(classify, text_key='doc_score'),\n", + " PerfileParquetWriter(\"ResumableTextPipeline_output\")\n", + "]\n", + "pipeline.add_operations(ops)\n", + "pipeline.execute()\n", + "del pipeline" + ], + "metadata": { + "id": "KeVkLyrRBJ0d", + "outputId": "64e52513-5729-45c4-adda-4daff6b5e59f", "colab": { "base_uri": "https://localhost:8080/", - "height": 704, - "referenced_widgets": [ - "2f6ad7bbc0964d7696c7dec5d0e66f44", - "60dd954b33414adfabf772f49ea2f3a2", - "93697baecfd442fa8d8db709cfc82eee", - "77548643af8d430db70bfb0af3707f91", - "92f4fcc6115c4f809c15b942ca0d11d4", - "90a32f5a48a74578bad33421c0e1599c", - "ec2613a4e1c04b2789a4ca8a257bda53", - "f955ca8d9b9e451791a5aa53bb984a71", - "0d52219723f944eb8b34e5aee50298be", - "58951323a0804ad8ad9d68dd4292f35f", - "c9d153b598854d0686849b48f84aa01e" - ] - }, - "id": "SMK9KcV4N7al", - "outputId": "17b2f0cc-92f7-4ff5-bdb0-7d3fb338c305" + "height": 191 + } }, + "id": "KeVkLyrRBJ0d", + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -245,99 +233,139 @@ "output_type": "stream", "name": "stdout", "text": [ - "init ray\n", - "init ray with total mem of 8167961395, total core of 1\n" + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ - "2023-10-11 18:11:28,068\tWARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 6133121024 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=8.37gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n", - "2023-10-11 18:11:29,354\tINFO worker.py:1642 -- Started a local Ray instance.\n" + "ResumableTextPipeline, current on tiny_c4_sample.jsonl: 0%| | 0/1 [00:00 TaskPoolMapOperator[ReadText->SplitBlocks(20)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", - "2023-10-11 18:11:34,576\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-10-11 18:11:34,582\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "ResumableTextPipeline, current on tiny_c4_sample.jsonl: 100%|██████████| 1/1 [00:24<00:00, 24.62s/it]" ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "Running 0: 0%| | 0/400 [00:00Map() pid=1685)\u001b[0m 2023-10-11 18:11:46.345224: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "2023-10-11 18:11:50,723\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n" + "\n" ] - }, + } + ] + }, + { + "cell_type": "code", + "source": [ + "# View output\n", + "! ls ResumableTextPipeline_output" + ], + "metadata": { + "id": "BA5uiJPlBdzt", + "outputId": "c59bcacd-5994-4f13-b47a-d9a04017b9b3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "BA5uiJPlBdzt", + "execution_count": 7, + "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "execute with ray took 17.940069602999984 sec\n" + "pipeline.json pipeline.log status.log tiny_c4_sample.jsonl\n" ] - }, + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "pd.read_parquet(\"ResumableTextPipeline_output/tiny_c4_sample.jsonl\")" + ], + "metadata": { + "id": "8LKIxfkIBaIs", + "outputId": "8a834079-7baa-4523-d9aa-1b986e761fc5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "id": "8LKIxfkIBaIs", + "execution_count": 9, + "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - " meta \\\n", - "0 {'APPLICATION_ID': 100065} \n", - "1 {'APPLICATION_ID': 100066} \n", - "2 {'APPLICATION_ID': 100067} \n", - "3 {'APPLICATION_ID': 100068} \n", - "4 {'APPLICATION_ID': 100069} \n", - "... ... \n", - "9995 {'APPLICATION_ID': 2120612} \n", - "9996 {'APPLICATION_ID': 2120613} \n", - "9997 {'APPLICATION_ID': 2120616} \n", - "9998 {'APPLICATION_ID': 2120620} \n", - "9999 {'APPLICATION_ID': 2120624} \n", - "\n", - " text bytesize \n", - "0 The National Domestic Violence Hotline (NDVH) ... 1460 \n", - "1 The Office of Planning, Research and Evaluatio... 3181 \n", - "2 Improving outcomes for low-income fathers and ... 1777 \n", - "3 This project is implementing 36-month follow-u... 1760 \n", - "4 The CCDF Policies Database is a source of info... 2157 \n", - "... ... ... \n", - "9995 Project: Research and produce a videotape that... 1241 \n", - "9996 While relapse prevention has been studied and ... 1281 \n", - "9997 The proposed study on recruitment, adherence a... 2867 \n", - "9998 Recent studies suggest that HIV epidemics are ... 2424 \n", - "9999 The overall goal of this study is to develop a... 1463 \n", - "\n", - "[10000 rows x 3 columns]" + " text \\\n", + "0 lorazepam nombre comercial mexico From an inte... \n", + "1 It is possible to love someone who does not lo... \n", + "2 Canon PIXMA TS9520 All-in-One Print / Scan / C... \n", + "3 For those who plan on buying an iPad this Satu... \n", + "4 After tipping 25 tokens in a day, you'll be ab... \n", + ".. ... \n", + "444 Sunrise is an equal opportunity employer. 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"grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "61d3c42680814b0a95ccfac427d5c553": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } } }, "nbformat": 4, From 3e3e65b93ea8aada9c13ef3fdb24f4bccc6dc95f Mon Sep 17 00:00:00 2001 From: ChaoJun Zhang Date: Sat, 14 Oct 2023 01:24:11 +0800 Subject: [PATCH 15/47] [v1.2][ISSUE-306] Turn off ner detection to improve performance (#386) * performance improve for pii remove via disable NER by default * Update text_pii_remove.py fix settings in PIIRemoval --------- Co-authored-by: Chendi.Xue --- .../primitives/llmutils/pii/detect/utils.py | 19 ++- .../primitives/llmutils/pii/pii_detection.py | 5 +- .../pyrecdp/primitives/llmutils/pii_remove.py | 115 +++++------------- .../primitives/operations/text_pii_remove.py | 33 ++--- 4 files changed, 66 insertions(+), 106 deletions(-) diff --git a/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py b/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py index 55a618c26..2143a96e0 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii/detect/utils.py @@ -9,5 +9,20 @@ class PIIEntityType(Enum): PASSWORD = auto() @classmethod - def all(cls): - return [member for name, member in PIIEntityType.__members__.items()] + def default(cls): + return [PIIEntityType.IP_ADDRESS,PIIEntityType.EMAIL,PIIEntityType.PHONE_NUMBER] + + @classmethod + def parse(cls, entity): + if "name" == entity: + return PIIEntityType.NAME + elif "password" == entity: + return PIIEntityType.PASSWORD + elif "email" == entity: + return PIIEntityType.EMAIL + elif "phone_number" == entity: + return PIIEntityType.PHONE_NUMBER + elif "ip" == entity: + return PIIEntityType.PHONE_NUMBER + else: + raise NotImplementedError(f" entity type {entity} is not supported!") diff --git a/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py b/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py index 797e225c4..4fa46e646 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii/pii_detection.py @@ -3,8 +3,8 @@ from .detect.phones_detection import detect_phones from .detect.name_password_detection import detect_name_password from .detect.utils import PIIEntityType -from transformers import Pipeline +from transformers import Pipeline from typing import List @@ -13,7 +13,7 @@ def scan_pii_text(text: str, pipeline: Pipeline, entity_types: List[PIIEntityTyp # use a regex to detect ip addresses if entity_types is None: - entity_types = PIIEntityType.all() + entity_types = PIIEntityType.default() if PIIEntityType.IP_ADDRESS in entity_types: result = result + detect_ip(text) @@ -24,7 +24,6 @@ def scan_pii_text(text: str, pipeline: Pipeline, entity_types: List[PIIEntityTyp if PIIEntityType.PHONE_NUMBER in entity_types: result = result + detect_phones(text) - # for phone number use phonenumbers tool if PIIEntityType.NAME in entity_types or PIIEntityType.PASSWORD in entity_types: result = result + detect_name_password(text, pipeline, entity_types) return result diff --git a/RecDP/pyrecdp/primitives/llmutils/pii_remove.py b/RecDP/pyrecdp/primitives/llmutils/pii_remove.py index c3c0250d1..be80beb58 100644 --- a/RecDP/pyrecdp/primitives/llmutils/pii_remove.py +++ b/RecDP/pyrecdp/primitives/llmutils/pii_remove.py @@ -1,5 +1,4 @@ import argparse -import logging from pyspark.sql.dataframe import DataFrame @@ -13,92 +12,38 @@ def pii_remove(dataset: DataFrame, model_root_path=None, text_column="text", sho return ret -def getArgs(): - parser = argparse.ArgumentParser(description="PII detection and redaction") - parser.add_argument( - "--input_format", - default="json", - type=str, - help="HF repo name/path of the dataset or file format if loading dataset from local", - ) - parser.add_argument( - "--input_path", - default="/root/arxiv_sample.jsonl", - type=str, - help="Data files to use.", - ) - parser.add_argument( - "--text_column", - default="text", - type=str, - help="Text column to use, if will be renamed to content", - ) +def main(config): + from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType - parser.add_argument( - "--output_format", - default="json", - type=str, - choices=["parquet", "json"], - help="The export format to save the processed output, default is arrow", - ) - - parser.add_argument( - "--output_path", - default="tmp", - type=str, - help="Path to save the processed output on disk", - ) - parser.add_argument( - "--spark_mode", - default="local", - type=str, - choices=["local", "yarn", "standalone", "ray"], - help="The spark mode to use", - ) - parser.add_argument( - "--spark_master", - type=str, - help="The network address of the machine that running the Spark master process", - ) - parser.add_argument( - "--num_instances", - default=4, - type=int, - help="Number of CPUs to use per worker", - ) - parser.add_argument( - "--keep_secret_column", - default=True, - type=bool, - help="Whether to add secret column in output file, this is useful for debug", - ) - # add an option of evaluating the pipeline on the PII benchmark we built - return parser.parse_args() - - -if __name__ == "__main__": - logger = logging.getLogger(__name__) - logger.setLevel(logging.INFO) - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - handlers=[ - logging.FileHandler("pii.log"), - logging.StreamHandler() - ] - ) - - args = getArgs() - logger.info(f"** The job is running with the following arguments: **\n{args}\n **** ") + print(f"entity types: {args.entity_types}") + if config.entity_types is None or len(config.entity_types) == 0: + pii_entity_types = PIIEntityType.default() + else: + pii_entity_types = [PIIEntityType.parse(entity) for entity in config.entity_types] from pyrecdp.core import SparkDataProcessor - - sparkDP = SparkDataProcessor(spark_mode=args.spark_mode, spark_master=args.spark_master, - num_instances=args.num_instances) + sparkDP = SparkDataProcessor() spark = sparkDP.spark - input_dataset = spark.read.load(path=args.input_path, format=args.input_format) - output_dataset = pii_remove(input_dataset, text_column=args.text_column) - output_dataset.write.save(path=args.output_path, format=args.output_format, mode="overwrite") + input_dataset = spark.read.load(path=config.in_dir, format=config.input_format) + output_dataset = pii_remove(input_dataset, text_column=config.text_column, + model_root_path=config.model_root_path, inplace=True, + entity_types=pii_entity_types, show_secret_column=config.debug, ) + output_dataset.write.save(path=config.out_dir, format=config.output_format, mode="overwrite") - logger.info(f" ===== Dataset saved successfully =====") + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--in_dir", dest="in_dir", type=str) + parser.add_argument("-o", "--out_dir", dest="out_dir", type=str) + parser.add_argument("-fi", "--input_format", dest="input_format", type=str, choices=["parquet", "json"], default="json") + parser.add_argument("-fo", "--output_format", dest="output_format", type=str, choices=["parquet", "json"], default="parquet") + parser.add_argument("-e", "--entity_types", dest="entity_types", nargs="*", default=[], type=str) + parser.add_argument("-k", "--text_column", dest="text_column", type=str, default="text") + parser.add_argument("-inplace", dest="inplace", type=bool, default=True) + parser.add_argument("-d", "--debug", dest="debug", type=bool, default=False) + parser.add_argument("-m", "--model_root_path", dest="model_root_path", type=str, default=None) + args = parser.parse_args() + + from pyrecdp.core.utils import Timer + with Timer(f"PII Remove for {args.in_dir}"): + main(args) diff --git a/RecDP/pyrecdp/primitives/operations/text_pii_remove.py b/RecDP/pyrecdp/primitives/operations/text_pii_remove.py index b8c235001..4eb8141e5 100644 --- a/RecDP/pyrecdp/primitives/operations/text_pii_remove.py +++ b/RecDP/pyrecdp/primitives/operations/text_pii_remove.py @@ -9,32 +9,33 @@ from pyrecdp.primitives.llmutils.pii.detect.utils import PIIEntityType -def prepare_pipeline(model_root_path=None, entity_types: List[PIIEntityType] = None): - _model_key = "bigcode/starpii" - model_key = _model_key if model_root_path is None else os.path.join(model_root_path, _model_key) - if entity_types is None: - entity_types = PIIEntityType.all() - - is_name_password_detect = PIIEntityType.NAME in entity_types or PIIEntityType.PASSWORD in entity_types +def prepare_pipeline(model_root_path=None): + from transformers import pipeline + from transformers import AutoTokenizer - if is_name_password_detect: - from transformers import pipeline - return pipeline(model=model_key, task='token-classification', grouped_entities=True) - else: - return None + _model_key = "bigcode/starpii" + _model_key = _model_key if model_root_path is None else os.path.join(model_root_path, _model_key) + tokenizer = AutoTokenizer.from_pretrained(_model_key, model_max_length=512) + return pipeline(model=_model_key, task='token-classification', tokenizer=tokenizer, grouped_entities=True) def prepare_func_pii_removal(model_root_path=None, debug_mode=True, entity_types: List[PIIEntityType] = None): replacements = random_replacements() - pipeline_inst = prepare_pipeline(model_root_path, entity_types) + + has_name_detection = entity_types is not None and PIIEntityType.NAME in entity_types + has_password_detection = entity_types is not None and PIIEntityType.PASSWORD in entity_types + if has_name_detection or has_password_detection: + pipe_inst = prepare_pipeline(model_root_path) + else: + pipe_inst = None def process_debug(sample): - secrets = scan_pii_text(sample, pipeline_inst, entity_types) + secrets = scan_pii_text(sample, pipe_inst, entity_types) text, is_modified = redact_pii_text(sample, secrets, replacements) return text, is_modified, str(secrets) def process(sample): - secrets = scan_pii_text(sample, pipeline_inst, entity_types) + secrets = scan_pii_text(sample, pipe_inst, entity_types) text, _ = redact_pii_text(sample, secrets, replacements) return text @@ -57,7 +58,7 @@ def __init__(self, text_key='text', inplace=True, model_root_path="", debug_mode self.support_spark = True self.support_ray = True self.debug_mode = debug_mode - self.entity_types = entity_types + self.entity_types = PIIEntityType.default() if entity_types is None else entity_types def process_rayds(self, ds: Dataset) -> Dataset: if self.inplace: From 4f0c9c007b3d315bd5e60d4a40f92d93a65a5648 Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Tue, 17 Oct 2023 23:38:18 +0800 Subject: [PATCH 16/47] [v1.2][ISSUE-306]Support more filters. (#387) * Support more filters. . * Skip token_num_filter UT because of huggingface download issue. . * Optimize TokenNumFilter . --- RecDP/pyrecdp/core/utils.py | 124 +++++++++++- RecDP/pyrecdp/primitives/llmutils/filter.py | 189 +++++++++++++++++- .../primitives/operations/filter/__init__.py | 6 + .../operations/filter/alphanumeric_filter.py | 25 +++ .../filter/average_line_length_filter.py | 25 +++ .../primitives/operations/filter/constant.py | 20 ++ .../filter/maximum_line_length_filter.py | 26 +++ .../filter/special_characters_filter.py | 27 +++ .../operations/filter/token_num_filter.py | 34 ++++ .../operations/filter/word_num_filter.py | 35 ++++ RecDP/setup.py | 1 + RecDP/tests/test_llmutils_operations.py | 74 +++++++ 12 files changed, 584 insertions(+), 2 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/operations/filter/alphanumeric_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/average_line_length_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/constant.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/maximum_line_length_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/special_characters_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/token_num_filter.py create mode 100644 RecDP/pyrecdp/primitives/operations/filter/word_num_filter.py diff --git a/RecDP/pyrecdp/core/utils.py b/RecDP/pyrecdp/core/utils.py index db24cb0af..33b6ba674 100644 --- a/RecDP/pyrecdp/core/utils.py +++ b/RecDP/pyrecdp/core/utils.py @@ -1,3 +1,5 @@ +import re + import numpy as np import pandas as pd import copy @@ -234,4 +236,124 @@ def update_linklist(linklist, key, value): if key not in linklist: linklist[key] = [] linklist[key].append(value) - return linklist \ No newline at end of file + return linklist + + +def split_on_whitespace(document, new_line=False, tab=False): + """ + This method also removes concatenated spaces. + + :param document: document to be splited + :param new_line: whether to split document with '\\\\n' + :param tag: whether to split document with '\\\\t' + :return: word list obtained after splitting document + """ + sep = [' '] + new_line * ['\n'] + tab * ['\t'] + sep = '|'.join(sep) + split_document = re.split(sep, document) + split_document = [word for word in split_document if word] + return split_document + + +def get_words_from_document(document, + token_func=None, + new_line=True, + tab=True,): + """ + Get words from a document. Useful to compute ratios, like the + stopwords ratio. + + :param document: document that need to split words + :param token_func: function of tokenizer, if specified, the function + will be used for split document into different tokens. + :param new_line: whether to use `\\\\n' to split words + :param tab: whether to use '\\\\t' to split words + :return: word list obtained from document + """ + if token_func: + words = token_func(document) + else: + words = split_on_whitespace(document, new_line, tab) + return words + + +def strip(document, strip_characters): + """ + Way faster than document.strip(strip_characters) since strip_characters is + now a set instead of a str, and it contains a lot of elements (all the + emojis). + + :param document: document to be processed + :param strip_characters: characters uesd for stripping document + :return: stripped document + """ + if not document: + return document + beg_ind = 0 + end_ind = len(document) + for i in range(len(document)): + if document[i] in strip_characters: + beg_ind += 1 + else: + break + for i in range(1, len(document) + 1): + if document[-i] in strip_characters: + end_ind -= 1 + else: + break + document_stripped = document[beg_ind:end_ind] + return document_stripped + + +def words_augmentation(words, group_size, join_char): + """ + Augment words, especially for Chinese (without a space between words) and + Vietnamese (with a space between syllables). + + :param word: word list to be augmented + :param group_size: the size of word groups that need to be merged + :param join_char: characters to be added between word group + :return: word list after augment + """ + augmentation = [ + join_char.join(words[i:i + group_size]) + for i in range(len(words) - group_size + 1) + ] + return augmentation + + +def words_refinement(words, + lower_case=False, + strip_chars=None, + use_words_aug=False, + words_aug_group_sizes=[2], + words_aug_join_char=''): + """ + Refine split words. Non reversible since the document is split on + multiple characters, words are stripped of special characters and + characters are converted to lower case. + + :param words: the word list to be augmented + :param lower_case: whether to convert word to lowercase + :param strip_chars: chars that need to be stripped in words + :param use_words_aug: whether to use word augmentation + :param words_aug_group_sizes: the size of word groups that need to + be merged + :param words_aug_join_char: characters to be added between word + group + :return: refined words or word list + """ + + if lower_case: + words = [word.lower() for word in words] + if strip_chars: + words = [strip(word, strip_chars) for word in words] + words = [word for word in words if word] + if use_words_aug: + augmentation = [ + words_augmentation(words, group_size, words_aug_join_char) + for group_size in words_aug_group_sizes + ] + augmentation = [word for augm in augmentation for word in augm] + words = words + augmentation + return words diff --git a/RecDP/pyrecdp/primitives/llmutils/filter.py b/RecDP/pyrecdp/primitives/llmutils/filter.py index bb8fc5b7a..03ab16d47 100644 --- a/RecDP/pyrecdp/primitives/llmutils/filter.py +++ b/RecDP/pyrecdp/primitives/llmutils/filter.py @@ -1,4 +1,6 @@ import argparse +import os +import sys from pyrecdp.core.utils import Timer from pyrecdp.primitives.operations import JsonlReader, ParquetReader, PerfileParquetWriter @@ -87,13 +89,180 @@ def filter_by_length(data_dir, out_dir, data_file_type="jsonl", minimum_length=1 pipeline.add_operations(ops) pipeline.execute() +def filter_by_alphanumeric_spark(spark_df, min_ratio=0.25, max_ratio=sys.maxsize): + from pyrecdp.primitives.operations import AlphanumericFilter + op = AlphanumericFilter(min_ratio=min_ratio, max_ratio=max_ratio) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_alphanumeric(data_dir, out_dir, data_file_type="jsonl", min_ratio=0.25, max_ratio=sys.maxsize): + from pyrecdp.primitives.operations import AlphanumericFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + AlphanumericFilter(min_ratio=min_ratio, max_ratio=max_ratio), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +def filter_by_average_line_length_spark(spark_df, min_len=10, max_len=sys.maxsize): + from pyrecdp.primitives.operations import AverageLineLengthFilter + op = AverageLineLengthFilter(min_len=min_len, max_len=max_len) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_average_line_length(data_dir, out_dir, data_file_type="jsonl", min_len=10, max_len=sys.maxsize): + from pyrecdp.primitives.operations import AverageLineLengthFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + AverageLineLengthFilter(min_len=min_len, max_len=max_len), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +def filter_by_maximum_line_length_spark(spark_df, min_len=10, max_len=sys.maxsize): + from pyrecdp.primitives.operations import MaximumLineLengthFilter + op = MaximumLineLengthFilter(min_len=min_len, max_len=max_len) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_maximum_line_length(data_dir, out_dir, data_file_type="jsonl", min_len=10, max_len=sys.maxsize): + from pyrecdp.primitives.operations import MaximumLineLengthFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + MaximumLineLengthFilter(min_len=min_len, max_len=max_len), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +def filter_by_special_characters_spark(spark_df, min_ratio=0.0, max_ratio=0.25): + from pyrecdp.primitives.operations import SpecialCharactersFilter + op = SpecialCharactersFilter(min_ratio=min_ratio, max_ratio=max_ratio) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_special_characters(data_dir, out_dir, data_file_type="jsonl", min_ratio=0.0, max_ratio=0.25): + from pyrecdp.primitives.operations import SpecialCharactersFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + SpecialCharactersFilter(min_ratio=min_ratio, max_ratio=max_ratio), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +def filter_by_token_num_spark(spark_df, min_num=10, max_num=sys.maxsize): + from pyrecdp.primitives.operations import TokenNumFilter + op = TokenNumFilter(min_num=min_num, max_num=max_num) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_token_num(data_dir, out_dir, data_file_type="jsonl", min_num=10, max_num=sys.maxsize): + from pyrecdp.primitives.operations import TokenNumFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + TokenNumFilter(min_num=min_num, max_num=max_num), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +def filter_by_word_num_spark(spark_df, min_num=10, max_num=sys.maxsize, language='en'): + from pyrecdp.primitives.operations import WordNumFilter + op = WordNumFilter(min_num=min_num, max_num=max_num, language=language) + ret = op.process_spark(spark_df.sparkSession, spark_df) + return ret + + +def filter_by_word_num(data_dir, out_dir, data_file_type="jsonl", min_num=10, max_num=sys.maxsize, language='en'): + from pyrecdp.primitives.operations import WordNumFilter + from pyrecdp.LLM import ResumableTextPipeline + + if data_file_type == 'jsonl': + reader = JsonlReader(data_dir) + elif data_file_type == 'parquet': + reader = ParquetReader(data_dir) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + + pipeline = ResumableTextPipeline() + ops = [ + reader, + WordNumFilter(min_num=min_num, max_num=max_num, language=language), + PerfileParquetWriter(out_dir) + ] + pipeline.add_operations(ops) + pipeline.execute() + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--data_dir", dest="data_dir", type=str) parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") parser.add_argument("--output_dir", dest="output_dir", type=str, default="") - parser.add_argument("--filter_type", dest="filter_type", type=str, default="length") + parser.add_argument("--filter_type", dest="filter_type", type=str, default="word_num") args = parser.parse_args() data_dir = args.data_dir data_file_type = args.data_file_type @@ -108,6 +277,24 @@ def filter_by_length(data_dir, out_dir, data_file_type="jsonl", minimum_length=1 elif "url_blocklist" == filter_type: with Timer(f"Processing {filter_type} filter for {data_dir}"): filter_by_blocklist(data_dir, output_dir, data_file_type) + elif "alphanumeric" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_alphanumeric(data_dir, output_dir, data_file_type) + elif "average_line_length" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_average_line_length(data_dir, output_dir, data_file_type) + elif "maximum_line_length" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_maximum_line_length(data_dir, output_dir, data_file_type) + elif "special_characters" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_special_characters(data_dir, output_dir, data_file_type) + elif "token_num" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_token_num(data_dir, output_dir, data_file_type) + elif "word_num" == filter_type: + with Timer(f"Processing {filter_type} filter for {data_dir}"): + filter_by_word_num(data_dir, output_dir, data_file_type) else: raise NotImplementedError(f"{filter_type} is not supported in RecDP LLM Filter yet.") diff --git a/RecDP/pyrecdp/primitives/operations/filter/__init__.py b/RecDP/pyrecdp/primitives/operations/filter/__init__.py index 1c2819406..60980d473 100644 --- a/RecDP/pyrecdp/primitives/operations/filter/__init__.py +++ b/RecDP/pyrecdp/primitives/operations/filter/__init__.py @@ -3,3 +3,9 @@ from .badwords_filter import BadwordsFilter from .length_filter import LengthFilter from .url_filter import URLFilter +from .alphanumeric_filter import AlphanumericFilter +from .average_line_length_filter import AverageLineLengthFilter +from .maximum_line_length_filter import MaximumLineLengthFilter +from .special_characters_filter import SpecialCharactersFilter +from .token_num_filter import TokenNumFilter +from .word_num_filter import WordNumFilter \ No newline at end of file diff --git a/RecDP/pyrecdp/primitives/operations/filter/alphanumeric_filter.py b/RecDP/pyrecdp/primitives/operations/filter/alphanumeric_filter.py new file mode 100644 index 000000000..920bc5cdd --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/alphanumeric_filter.py @@ -0,0 +1,25 @@ +import sys + +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter + + +# This filter is referred from alibaba data juicer project +class AlphanumericFilter(BaseFilter): + def __init__(self, min_ratio=0.25, max_ratio=sys.maxsize): + settings = {'min_ratio': min_ratio, "max_ratio": max_ratio} + super().__init__(args_dict=settings) + self.min_ratio = min_ratio + self.max_ratio = max_ratio + + def compute(self, text) -> bool: + alnum_count = sum(map(lambda char: 1 if char.isalnum() else 0, text)) + alnum_ratio = (alnum_count / len(text)) if len(text) != 0 else 0.0 + if self.min_ratio <= alnum_ratio <= self.max_ratio: + return True + else: + return False + +LLMOPERATORS.register(AlphanumericFilter) + + diff --git a/RecDP/pyrecdp/primitives/operations/filter/average_line_length_filter.py b/RecDP/pyrecdp/primitives/operations/filter/average_line_length_filter.py new file mode 100644 index 000000000..7d42a68dd --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/average_line_length_filter.py @@ -0,0 +1,25 @@ +import sys + +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter + + +# This filter is referred from alibaba data juicer project +class AverageLineLengthFilter(BaseFilter): + def __init__(self, min_len=10, max_len=sys.maxsize): + settings = {'min_len': min_len, "max_len": max_len} + super().__init__(args_dict=settings) + self.min_len = min_len + self.max_len = max_len + + def compute(self, text) -> bool: + lines = text.splitlines() + avg_line_length = len(text) / len(lines) if len(lines) != 0 else 0.0 + if self.min_len <= avg_line_length <= self.max_len: + return True + else: + return False + +LLMOPERATORS.register(AverageLineLengthFilter) + + diff --git a/RecDP/pyrecdp/primitives/operations/filter/constant.py b/RecDP/pyrecdp/primitives/operations/filter/constant.py new file mode 100644 index 000000000..93ce5ba3e --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/constant.py @@ -0,0 +1,20 @@ +import string +import emoji + + +# special characters +MAIN_SPECIAL_CHARACTERS = string.punctuation + string.digits \ + + string.whitespace +OTHER_SPECIAL_CHARACTERS = ( + "’ “— ™ – •‘œ　   ˜ ‚ƒ„’“”–ー一▬…✦�­£​•€«»°·═" + "×士＾˘⇓↓↑←→（）§″′´¿−±∈¢ø‚„½¼¾¹²³―⁃，ˌ¸‹›ʺˈʻ¦‐⠀‰……‑≤≥‖" + "◆●■►▼▲▴∆▻¡★☆✱ːº。¯˜¥ɪ≈†上ン：∼⁄・♡✓⊕․．⋅÷１‟；،、¨ाাी्े◦˚" + "゜ʼ≖ʼ¤ッツシ℃√！【】‿∞➤～πه۩☛₨➩☻๑٪♥ıॽ《‘©﴿٬？▷Г♫∟™ª₪®「—❖" + "」﴾》" +) +EMOJI = list(emoji.EMOJI_DATA.keys()) +SPECIAL_CHARACTERS = set(MAIN_SPECIAL_CHARACTERS + OTHER_SPECIAL_CHARACTERS) +SPECIAL_CHARACTERS.update(EMOJI) + + +HF_TOKENIZER = 'EleutherAI/pythia-6.9b-deduped' \ No newline at end of file diff --git a/RecDP/pyrecdp/primitives/operations/filter/maximum_line_length_filter.py b/RecDP/pyrecdp/primitives/operations/filter/maximum_line_length_filter.py new file mode 100644 index 000000000..0d7c41a99 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/maximum_line_length_filter.py @@ -0,0 +1,26 @@ +import sys + +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter + + +# This filter is referred from alibaba data juicer project +class MaximumLineLengthFilter(BaseFilter): + def __init__(self, min_len=10, max_len=sys.maxsize): + settings = {'min_len': min_len, "max_len": max_len} + super().__init__(args_dict=settings) + self.min_len = min_len + self.max_len = max_len + + def compute(self, text) -> bool: + lines = text.splitlines() + line_lengths = list(map(len, lines)) + max_line_length = max(line_lengths) if line_lengths else 0.0 + if self.min_len <= max_line_length <= self.max_len: + return True + else: + return False + +LLMOPERATORS.register(MaximumLineLengthFilter) + + diff --git a/RecDP/pyrecdp/primitives/operations/filter/special_characters_filter.py b/RecDP/pyrecdp/primitives/operations/filter/special_characters_filter.py new file mode 100644 index 000000000..2840945d4 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/special_characters_filter.py @@ -0,0 +1,27 @@ +import sys + +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter +from pyrecdp.primitives.operations.filter.constant import SPECIAL_CHARACTERS + + +# This filter is referred from alibaba data juicer project +class SpecialCharactersFilter(BaseFilter): + def __init__(self, min_ratio=0.0, max_ratio=0.25): + settings = {'min_ratio': min_ratio, "max_ratio": max_ratio} + super().__init__(args_dict=settings) + self.min_ratio = max_ratio + self.max_ratio = max_ratio + + def compute(self, text) -> bool: + special_char_ratio = ( + len([c + for c in text if c in SPECIAL_CHARACTERS]) / + len(text)) if len(text) != 0 else 0.0 + if self.min_ratio <= special_char_ratio <= self.max_ratio: + return True + else: + return False + + +LLMOPERATORS.register(SpecialCharactersFilter) diff --git a/RecDP/pyrecdp/primitives/operations/filter/token_num_filter.py b/RecDP/pyrecdp/primitives/operations/filter/token_num_filter.py new file mode 100644 index 000000000..dbcb310c4 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/token_num_filter.py @@ -0,0 +1,34 @@ +import sys + +from pyrecdp.core.model_utils import get_model, prepare_model +from pyrecdp.core.utils import get_words_from_document +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter +from pyrecdp.primitives.operations.filter.constant import HF_TOKENIZER + + +# This filter is referred from alibaba data juicer project +class TokenNumFilter(BaseFilter): + def __init__(self, min_num=10, max_num=sys.maxsize): + settings = {'min_num': min_num, "max_num": max_num} + super().__init__(args_dict=settings) + self.min_num = min_num + self.max_num = max_num + self.model_key = prepare_model(model_type='huggingface', + model_key=HF_TOKENIZER) + self.tokenizer = get_model(self.model_key, model_type='huggingface') + + def compute(self, text) -> bool: + + tokens = get_words_from_document( + text, + token_func=self.tokenizer.tokenize if self.tokenizer else None + ) + num_token = len(tokens) + if self.min_num <= num_token <= self.max_num: + return True + else: + return False + + +LLMOPERATORS.register(TokenNumFilter) diff --git a/RecDP/pyrecdp/primitives/operations/filter/word_num_filter.py b/RecDP/pyrecdp/primitives/operations/filter/word_num_filter.py new file mode 100644 index 000000000..4aa004d08 --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/filter/word_num_filter.py @@ -0,0 +1,35 @@ +import sys + +from pyrecdp.core.model_utils import get_model +from pyrecdp.core.utils import get_words_from_document, words_refinement +from pyrecdp.primitives.operations.base import LLMOPERATORS +from pyrecdp.primitives.operations.filter.base import BaseFilter +from pyrecdp.primitives.operations.filter.constant import SPECIAL_CHARACTERS + + +# This filter is referred from alibaba data juicer project +class WordNumFilter(BaseFilter): + def __init__(self, min_num=10, max_num=sys.maxsize, language='en'): + settings = {'min_num': min_num, "max_num": max_num, "language": language} + super().__init__(args_dict=settings) + self.min_num = min_num + self.max_num = max_num + self.language = language + self.model_key = None + + def compute(self, text) -> bool: + + tokenizer = get_model(self.model_key, lang=self.language, + model_type='sentencepiece') + words = get_words_from_document( + text, token_func=tokenizer.encode_as_pieces if tokenizer else None) + + words = words_refinement(words, strip_chars=SPECIAL_CHARACTERS) + num_words = len(words) + if self.min_num <= num_words <= self.max_num: + return True + else: + return False + + +LLMOPERATORS.register(WordNumFilter) diff --git a/RecDP/setup.py b/RecDP/setup.py index da2c6f4fa..c1c8f3626 100644 --- a/RecDP/setup.py +++ b/RecDP/setup.py @@ -80,4 +80,5 @@ "Faker", "ray", "loguru", + "emoji==2.2.0", ]) diff --git a/RecDP/tests/test_llmutils_operations.py b/RecDP/tests/test_llmutils_operations.py index 1ce8bba4c..ed0359ac9 100644 --- a/RecDP/tests/test_llmutils_operations.py +++ b/RecDP/tests/test_llmutils_operations.py @@ -113,6 +113,50 @@ def test_filter_by_url_ray(self): with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: ctx.show(op.process_rayds(ctx.ds)) + def test_filter_by_alphanumeric_ray(self): + pass + # Ray version not supported yet + op = AlphanumericFilter() + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + + + def test_filter_by_average_line_length_ray(self): + pass + # Ray version not supported yet + op = AverageLineLengthFilter() + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + + def test_filter_by_maximum_line_length_ray(self): + pass + # Ray version not supported yet + op = MaximumLineLengthFilter() + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + + def test_filter_by_special_characters_ray(self): + pass + # Ray version not supported yet + op = SpecialCharactersFilter() + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + + + # def test_filter_by_token_num_ray(self): + # pass + # # Ray version not supported yet + # op = TokenNumFilter() + # with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + # ctx.show(op.process_rayds(ctx.ds)) + + def test_filter_by_word_num_ray(self): + pass + # Ray version not supported yet + op = WordNumFilter() + with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_rayds(ctx.ds)) + def test_text_fixer_ray(self): op = TextFix() with RayContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: @@ -191,6 +235,36 @@ def test_filter_by_url_spark(self): with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: ctx.show(op.process_spark(ctx.spark, ctx.ds)) + def test_filter_by_alphanumeric_spark(self): + op = AlphanumericFilter() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_filter_by_average_line_length_spark(self): + op = AverageLineLengthFilter() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_filter_by_maximum_line_length_spark(self): + op = MaximumLineLengthFilter() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_filter_by_special_characters_spark(self): + op = SpecialCharactersFilter() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + # def test_filter_by_token_num_spark(self): + # op = TokenNumFilter() + # with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + # ctx.show(op.process_spark(ctx.spark, ctx.ds)) + + def test_filter_by_word_num_spark(self): + op = WordNumFilter() + with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: + ctx.show(op.process_spark(ctx.spark, ctx.ds)) + def test_text_fixer_spark(self): op = TextFix() with SparkContext("tests/data/llm_data/tiny_c4_sample.jsonl") as ctx: From 922d2cb9ee0c63ad337c6c74fdfba66ba58c2aac Mon Sep 17 00:00:00 2001 From: Yao Qing Date: Tue, 17 Oct 2023 23:46:42 +0800 Subject: [PATCH 17/47] Enhanced the ability of diversity analysis to process full text. (#389) . --- .../primitives/llmutils/diversity_analysis.py | 8 ++- .../operations/text_diversityindicate.py | 59 ++++++++++++------- 2 files changed, 43 insertions(+), 24 deletions(-) diff --git a/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py b/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py index 8fbf2a08a..08f9e33bd 100644 --- a/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py +++ b/RecDP/pyrecdp/primitives/llmutils/diversity_analysis.py @@ -11,7 +11,7 @@ def diversity_indicate_spark(spark_df): return ret -def diversity_indicate(data_dir, data_file_type, out_dir, language="en"): +def diversity_indicate(data_dir, data_file_type, out_dir, language="en", first_sent=True): from pyrecdp.primitives.operations import TextDiversityIndicate from pyrecdp.LLM import ResumableTextPipeline @@ -25,7 +25,7 @@ def diversity_indicate(data_dir, data_file_type, out_dir, language="en"): pipeline = ResumableTextPipeline() ops = [ reader, - TextDiversityIndicate(language=language), + TextDiversityIndicate(language=language, out_dir=out_dir, first_sent=first_sent), PerfileParquetWriter(out_dir) ] pipeline.add_operations(ops) @@ -38,10 +38,12 @@ def diversity_indicate(data_dir, data_file_type, out_dir, language="en"): parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") parser.add_argument("--output_dir", dest="output_dir", type=str, default="") parser.add_argument("--language", dest="language", type=str, default="en") + parser.add_argument("--first_sent", dest="first_sent", type=bool, default=False) args = parser.parse_args() data_dir = args.data_dir data_file_type = args.data_file_type output_dir = args.output_dir language = args.language + first_sent = args.first_sent with Timer(f"Processing diversity analysis for {data_dir}"): - diversity_indicate(data_dir, data_file_type, output_dir, language) + diversity_indicate(data_dir, data_file_type, output_dir, language=language, first_sent=first_sent) diff --git a/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py b/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py index 1e8265909..38e8b28cb 100644 --- a/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py +++ b/RecDP/pyrecdp/primitives/operations/text_diversityindicate.py @@ -8,7 +8,7 @@ import spacy from pyrecdp.core.model_utils import MODEL_ZOO, prepare_model from pyspark.sql import functions as F -from pyspark.sql.types import StructType, StructField, StringType +from pyspark.sql.types import StructType, StructField, StringType, ArrayType from pyspark.sql.functions import udf from .base import BaseLLMOperation, LLMOPERATORS @@ -40,6 +40,7 @@ def find_root_verb_and_its_dobj(tree_root): # if no children satisfy the condition, return None return None, None + # Modify from self_instruct, please refer to # https://github.com/yizhongw/self-instruct/blob/main/self_instruct/instruction_visualize.ipynb def find_root_verb_and_its_dobj_in_string(nlp, s, first_sent=True): @@ -56,18 +57,21 @@ def find_root_verb_and_its_dobj_in_string(nlp, s, first_sent=True): :return: valid verb and its object of this string """ doc = nlp(s) + verb_noun_pairs = [] for sent in doc.sents: verb, noun = find_root_verb_and_its_dobj(sent.root) - if first_sent or (verb is not None and noun is not None): - return verb, noun - return None, None + if first_sent: + return [(verb, noun)] + else: + verb_noun_pairs.append((verb, noun)) + return verb_noun_pairs class DiversityAnalysis: """Apply diversity analysis for each sample and get an overall analysis result.""" - def __init__(self, dataset, text_key="text", lang_or_model='en'): + def __init__(self, dataset, text_key="text", lang_or_model='en', first_sent=True): """Initialization method :param dataset: the dataset to be analysed :param output_path: path to store the analysis results :param lang_or_model: the diversity model or a specific language used to load @@ -76,6 +80,7 @@ def __init__(self, dataset, text_key="text", lang_or_model='en'): self.dataset = dataset self.text_key = text_key self.lang_or_model = lang_or_model + self.first_sent = first_sent def compute(self, lang_or_model=None): """ @@ -97,25 +102,33 @@ def compute(self, lang_or_model=None): assert isinstance(diversity_model, spacy.Language) + first_sent_flag = self.first_sent + if isinstance(self.dataset, DataFrame): - schema = StructType([ - StructField("verb", StringType(), True), - StructField("noun", StringType(), True) - ]) + schema = ArrayType( + StructType([ + StructField("verb", StringType(), True), + StructField("noun", StringType(), True), + ])) def find_verb_noun_spark(sample): + verb_noun_pairs = [] try: - verb, noun = find_root_verb_and_its_dobj_in_string( - diversity_model, sample) + verb_noun_pairs = find_root_verb_and_its_dobj_in_string( + diversity_model, sample, first_sent=first_sent_flag) except Exception as e: print(str(e)) - verb, noun = None, None - return verb, noun + return verb_noun_pairs + - existing_columns = self.dataset.columns operator = udf(find_verb_noun_spark, schema) - dataset = self.dataset.withColumn('diversity', operator(F.col(self.text_key))) - dataset = dataset.select("diversity.*").toPandas() + spark_df = self.dataset.withColumn('diversity', operator(F.col(self.text_key))) + pd_df = spark_df.select("diversity").toPandas() + df_explode = pd_df.explode('diversity').reset_index(drop=True) + df_explode = df_explode.dropna() + dataset = df_explode.apply(lambda x: [x['diversity']['verb'], x['diversity']['noun']], axis=1, + result_type='expand') + dataset.columns = ['verb', 'noun'] else: import copy @@ -127,6 +140,7 @@ def find_verb_noun_ray(sample): print(str(e)) verb, noun = None, None return {'verb': verb, 'noun': noun} + dataset = self.dataset.map(lambda x: find_verb_noun_ray(x[self.text_key])).to_pandas() return dataset @@ -178,25 +192,28 @@ def analyse(self, class TextDiversityIndicate(BaseLLMOperation): - def __init__(self, text_key='text', language='en', out_dir=''): - settings = {'text_key': text_key, 'language': language, 'out_dir': out_dir} + def __init__(self, text_key='text', language='en', out_dir='', first_sent=True): + settings = {'text_key': text_key, 'language': language, 'out_dir': out_dir, 'first_sent': first_sent} super().__init__(settings) self.text_key = text_key self.language = language self.output_path = out_dir self.inplace = False self.support_spark = True - self.support_ray = True + self.support_ray = False + self.first_sent = first_sent def process_rayds(self, ds: Dataset) -> Dataset: - diversity_analysis = DiversityAnalysis(ds, text_key=self.text_key, lang_or_model=self.language) + diversity_analysis = DiversityAnalysis(ds, text_key=self.text_key, lang_or_model=self.language, + first_sent=self.first_sent) analyse_df = diversity_analysis.analyse(lang_or_model=self.language) analyse_df.to_csv(os.path.join(self.output_path, 'diversity.csv')) analyse_df.to_markdown(os.path.join(self.output_path, 'diversity.md')) return ds def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: - diversity_analysis = DiversityAnalysis(spark_df, text_key=self.text_key, lang_or_model=self.language) + diversity_analysis = DiversityAnalysis(spark_df, text_key=self.text_key, lang_or_model=self.language, + first_sent=self.first_sent) analyse_df = diversity_analysis.analyse(lang_or_model=self.language) analyse_df.to_csv(os.path.join(self.output_path, 'diversity.csv')) analyse_df.to_markdown(os.path.join(self.output_path, 'diversity.md')) From cc820e67c40d375ff84d576ed66c2b9b2201602a Mon Sep 17 00:00:00 2001 From: haojinIntel Date: Wed, 18 Oct 2023 00:34:32 +0800 Subject: [PATCH 18/47] [v1.2][ISSUE-306] Support toxicity score (#388) * Support toxicity score * Add ut for toxicity score * Use detoxify lib * Change the default model type for detoxify * Add notebook for toxicity score --------- Co-authored-by: Chendi.Xue --- .../llmutils/toxicity_bias_control.ipynb | 1959 ++++++++++++++++- RecDP/pyrecdp/primitives/llmutils/__init__.py | 1 + .../primitives/llmutils/toxicity_score.py | 60 + .../pyrecdp/primitives/operations/__init__.py | 1 + .../primitives/operations/text_toxicity.py | 49 + RecDP/setup.py | 5 +- RecDP/tests/test_llmutils.py | 22 + 7 files changed, 2064 insertions(+), 33 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/llmutils/toxicity_score.py create mode 100644 RecDP/pyrecdp/primitives/operations/text_toxicity.py diff --git a/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb b/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb index bc181c9b5..0ac111f2c 100644 --- a/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb +++ b/RecDP/examples/notebooks/llmutils/toxicity_bias_control.ipynb @@ -1,33 +1,1930 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Install pyrecdp from github" + ], + "metadata": { + "id": "gCJta9Hf0Ujr" + }, + "id": "gCJta9Hf0Ujr" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a", + "metadata": { + "id": "736fb211-dbe6-4ca9-a1b1-db2cff2d287a" + }, + "outputs": [], + "source": [ + "!pip install 'git+https://github.com/intel/e2eAIOK.git#egg=pyrecdp&subdirectory=RecDP'" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Install jdk for pyspark running" + ], + "metadata": { + "id": "yqkf9Lp_0ewm" + }, + "id": "yqkf9Lp_0ewm" + }, + { + "cell_type": "code", + "source": [ + "!DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre" + ], + "metadata": { + "id": "mG9Sdne_0eeu" + }, + "id": "mG9Sdne_0eeu", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Prepare test data" + ], + "metadata": { + "id": "Ql-cPdL20mju" + }, + "id": "Ql-cPdL20mju" + }, + { + "cell_type": "code", + "source": [ + "%mkdir -p /content/test_data\n", + "%cd /content/test_data\n", + "!wget https://raw.githubusercontent.com/intel/e2eAIOK/main/RecDP/tests/data/llm_data/arxiv_sample_100.jsonl" + ], + "metadata": { + "id": "cvNCnxwH0l9d" + }, + "id": "cvNCnxwH0l9d", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Import toxicity score function" + ], + "metadata": { + "id": "Qnb7CQId0rg1" + }, + "id": "Qnb7CQId0rg1" + }, + { + "cell_type": "code", + "source": [ + "from pyrecdp.primitives.llmutils import toxicity_score_spark, toxicity_score\n", + "from pyrecdp.core import SparkDataProcessor" + ], + "metadata": { + "id": "lfMoRQIE0yel", + "outputId": "99df35e5-746f-453f-f2c6-66e05a4e188e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 92 + } + }, + "id": "lfMoRQIE0yel", + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "JAVA_HOME is not set, use default value of /usr/lib/jvm/java-8-openjdk-amd64/\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\t\t\t\n", + "\t\t" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Specify variables" + ], + "metadata": { + "id": "Um_HZnOa1AKj" + }, + "id": "Um_HZnOa1AKj" + }, + { + "cell_type": "code", + "source": [ + "data_file = '/content/test_data/arxiv_sample_100.jsonl'\n", + "save_path = '/content/test_data/output/toxicity_score'" + ], + "metadata": { + "id": "QhEX1nqN08d-" + }, + "id": "QhEX1nqN08d-", + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Load data" + ], + "metadata": { + "id": "ZxgNBBVi1HNe" + }, + "id": "ZxgNBBVi1HNe" + }, + { + "cell_type": "code", + "source": [ + "rdp = SparkDataProcessor()\n", + "spark = rdp.spark\n", + "spark_df = spark.read.json(data_file)\n", + "spark_df.show()" + ], + "metadata": { + "id": "02pLvz7d0UJm", + "outputId": "b66e2547-a58e-4b78-8810-93def84f7d63", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "02pLvz7d0UJm", + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Will assign 1 cores and 10386 M memory for spark\n", + "per core memory size is 10.143 GB and shuffle_disk maximum capacity is 8589934592.000 GB\n", + "+--------------------+--------------------+\n", + "| meta| text|\n", + "+--------------------+--------------------+\n", + "|{2203.15369, en, ...|\\section{Introduc...|\n", + "|{math/9807097, en...|\\section{Introduc...|\n", + "|{2008.06948, en, ...|\\section{Introduc...|\n", + "|{cond-mat/9807071...|\\section{Introduc...|\n", + "|{2210.10650, en, ...|\\section{\\label{s...|\n", + "|{astro-ph/9807119...|\\section{Introduc...|\n", + "|{2111.03152, en, ...|\\section{Introduc...|\n", + "|{1606.04992, en, ...|\\n\\n\\section{Intr...|\n", + "|{1608.03404, en, ...|\\section{introduc...|\n", + "|{1904.10101, en, ...|\\section{Introduc...|\n", + "|{cond-mat/9807275...|\\section{Introduc...|\n", + "|{2109.05334, en, ...|\\section{Introduc...|\n", + "|{1512.06966, en, ...|\\section{Introduc...|\n", + "|{2112.04926, en, ...|\\section{Introduc...|\n", + "|{2202.01000, en, ...|\\section{Introduc...|\n", + "|{2209.13421, en, ...|\\section{Introduc...|\n", + "|{1103.5603, en, 2...|\\section{Introduc...|\n", + "|{1001.3679, en, 2...|\\section{Introduc...|\n", + "|{1702.08222, en, ...|\\section{Introduc...|\n", + "|{2201.05495, en, ...|\\section{Introduc...|\n", + "+--------------------+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Process the 'text' column and generate toxicity score which will determine whether the row kept or not(spark dataframe interface)" + ], + "metadata": { + "id": "LFQM-OWI1Lkb" + }, + "id": "LFQM-OWI1Lkb" + }, + { + "cell_type": "code", + "source": [ + "toxicity_score_spark_df = toxicity_score_spark(spark_df)\n", + "toxicity_score_spark_df.show()" + ], + "metadata": { + "id": "W6055vAX1TgX", + "outputId": "57939c8d-8df2-445c-ce28-38053de8b651", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 592, + "referenced_widgets": [ + "e09f242bcc804099b93a5cddf36154a2", + "f3d97fb15127406dad0db89907348bdd", + "6891f6c9cfb34d39bfcff8de98a8cd38", + "a00afeaf8ff04c0884bf98b3069f646e", + "8265e565849543a4a442bc98c1cb0332", + "517b9994fdd64400af1274dda172e340", + "b78e75185ae34070a4847137697f06ab", + "0441bc5d0c6a48478d9710bf7f978f87", + "2f11156a37124b94bab10a2636447179", + "7eae0419355d4b98bd115b76566abc74", + "e60d4ef9173a4ae19c5773cba968d087", + "f1ce6b0e84be48caa8e54c2503fbb071", + "f4af0d845d94424383f84f78a1ae787b", + "b3c176f25bc84be88daaa2fbe21c4863", + "cb7855349b264e3abd8b325a34810f5e", + "8343ed13bd81457680821e81e0f4bc1d", + "a2d0b7f0c13746d797c79f39c5867288", + "6c622f1123084690a2a31172af5aceab", + 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preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-17 13:41:13,153\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00Map()->Write pid=3321)\u001b[0m 2023-10-17 13:41:28.868788: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-10-17 13:44:18,638\tWARNING plan.py:567 -- Warning: The Ray cluster currently does not have any available CPUs. The Dataset job will hang unless more CPUs are freed up. A common reason is that cluster resources are used by Actors or Tune trials; see the following link for more details: https://docs.ray.io/en/master/data/dataset-internals.html#datasets-and-tune\n", + "2023-10-17 13:44:18,661\tINFO streaming_executor.py:93 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadText->SplitBlocks(5)] -> TaskPoolMapOperator[Map(convert_json)->Map()]\n", + "2023-10-17 13:44:18,664\tINFO streaming_executor.py:94 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=False, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-10-17 13:44:18,666\tINFO streaming_executor.py:96 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Running 0: 0%| | 0/25 [00:00Map() pid=4454)\u001b[0m 2023-10-17 13:45:30.356916: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "execute with ray took 403.94510068599993 sec\n", + "b63fe36529b64d6c8bcc397fdc69cb5f_000000_000000.parquet\n", + "b63fe36529b64d6c8bcc397fdc69cb5f_000001_000000.parquet\n", + "b63fe36529b64d6c8bcc397fdc69cb5f_000002_000000.parquet\n", + "b63fe36529b64d6c8bcc397fdc69cb5f_000003_000000.parquet\n", + "b63fe36529b64d6c8bcc397fdc69cb5f_000004_000000.parquet\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Tl_ZMnKA1gwy" + }, + "id": "Tl_ZMnKA1gwy", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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diversity_indicate +from .toxicity_score import toxicity_score, toxicity_score_spark diff --git a/RecDP/pyrecdp/primitives/llmutils/toxicity_score.py b/RecDP/pyrecdp/primitives/llmutils/toxicity_score.py new file mode 100644 index 000000000..a8badab02 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/toxicity_score.py @@ -0,0 +1,60 @@ +import argparse +from pyrecdp.core.utils import Timer + + +def toxicity_score_spark(spark_df, text_key='text', threshold=0, model_type="multilingual", huggingface_config_path=None): + from pyrecdp.primitives.operations import TextToxicity + op = TextToxicity(text_key=text_key, threshold=threshold, model_type=model_type, huggingface_config_path=huggingface_config_path) + toxicity_df = op.process_spark(spark_df.sparkSession, spark_df) + return toxicity_df + + +def toxicity_score(dataset_path, + result_path, + data_file_type, + text_key='text', + threshold=0, + model_type="multilingual", + huggingface_config_path=None): + + from pyrecdp.LLM import TextPipeline + from pyrecdp.primitives.operations import JsonlReader, ParquetReader, TextToxicity, ParquetWriter + + pipeline = TextPipeline() + if data_file_type == 'jsonl': + reader = JsonlReader(dataset_path) + elif data_file_type == 'parquet': + reader = ParquetReader(dataset_path) + else: + raise NotImplementedError(f"{data_file_type} is not supported in RecDP LLM ResumableTextPipeline yet.") + ops = [ + reader, + TextToxicity(text_key=text_key, threshold=threshold, model_type=model_type, huggingface_config_path=huggingface_config_path), + ParquetWriter(result_path) + ] + pipeline.add_operations(ops) + pipeline.execute() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--dataset_path", dest="dataset_path", type=str) + parser.add_argument("--result_path", dest="result_path", type=str) + parser.add_argument("--threshold", dest="threshold", type=float, default=0) + parser.add_argument("--text_key", dest="text_key", type=str, default="text") + parser.add_argument("--data_file_type", dest="data_file_type", type=str, default="jsonl") + parser.add_argument("--model_type", dest="model_type", type=str, default="multilingual") + parser.add_argument("--huggingface_config_path", dest="huggingface_config_path", type=str, default=None) + args = parser.parse_args() + + dataset_path = args.dataset_path + result_path = args.result_path + threshold = args.threshold + text_key = args.text_key + data_file_type = args.data_file_type + model_type = args.model_type + huggingface_config_path = args.huggingface_config_path + + with Timer(f"Generate toxicity score for {dataset_path}"): + toxicity_score(dataset_path, result_path, data_file_type, text_key=text_key, + threshold=threshold, model_type=model_type, huggingface_config_path=huggingface_config_path) diff --git a/RecDP/pyrecdp/primitives/operations/__init__.py b/RecDP/pyrecdp/primitives/operations/__init__.py index e3a91df65..290449134 100644 --- a/RecDP/pyrecdp/primitives/operations/__init__.py +++ b/RecDP/pyrecdp/primitives/operations/__init__.py @@ -15,3 +15,4 @@ from .text_qualityscorer import TextQualityScorer from .text_diversityindicate import TextDiversityIndicate from .text_custom import TextCustomerMap, TextCustomerFilter +from .text_toxicity import TextToxicity diff --git a/RecDP/pyrecdp/primitives/operations/text_toxicity.py b/RecDP/pyrecdp/primitives/operations/text_toxicity.py new file mode 100644 index 000000000..09dbe0e7d --- /dev/null +++ b/RecDP/pyrecdp/primitives/operations/text_toxicity.py @@ -0,0 +1,49 @@ +import os + +from .base import BaseLLMOperation, LLMOPERATORS +from ray.data import Dataset +from pyspark.sql import DataFrame +from detoxify import Detoxify + +def prepare_func_text_toxicity(model_type="multilingual", threshold=0, huggingface_config_path=None): + model = Detoxify(model_type, huggingface_config_path=huggingface_config_path) + + def generate_toxicity_label(content): + result = model.predict(content) + if result["toxicity"] < threshold: + return None + else: + return float(result["toxicity"]) + + return generate_toxicity_label + + +class TextToxicity(BaseLLMOperation): + def __init__(self, text_key='text', threshold=0, model_type="multilingual", huggingface_config_path=None): + settings = {'text_key': text_key, 'threshold': threshold, 'model_type': model_type, 'huggingface_config_path': huggingface_config_path} + super().__init__(settings) + self.support_spark = True + self.support_ray = True + self.actual_func = None + self.text_key = text_key + self.new_key = f"{text_key}_toxicity" + self.threshold = threshold + self.model_type = model_type + self.huggingface_config_path = huggingface_config_path + + def process_rayds(self, ds: Dataset) -> Dataset: + if self.actual_func is None: + self.actual_func = prepare_func_text_toxicity(model_type=self.model_type, + threshold=self.threshold, huggingface_config_path=self.huggingface_config_path) + return ds.map(lambda x: self.process_row(x, self.text_key, self.new_key, self.actual_func)) + + def process_spark(self, spark, spark_df: DataFrame) -> DataFrame: + import pyspark.sql.functions as F + if self.actual_func is None: + self.actual_func = F.udf(prepare_func_text_toxicity(model_type=self.model_type, + threshold=self.threshold, huggingface_config_path=self.huggingface_config_path)) + + return spark_df.withColumn(self.new_key, self.actual_func(F.col(self.text_key))) + + +LLMOPERATORS.register(TextToxicity) diff --git a/RecDP/setup.py b/RecDP/setup.py index c1c8f3626..4c4a000cb 100644 --- a/RecDP/setup.py +++ b/RecDP/setup.py @@ -73,12 +73,13 @@ "huggingface-hub==0.16.4", "loguru==0.7.2", "tabulate==0.9.0", - "sentencepiece==0.1.99", + "sentencepiece", "selectolax", "spacy", "torch", "Faker", "ray", "loguru", - "emoji==2.2.0", + "detoxify", + "emoji==2.2.0" ]) diff --git a/RecDP/tests/test_llmutils.py b/RecDP/tests/test_llmutils.py index 17bf44128..545a0c520 100644 --- a/RecDP/tests/test_llmutils.py +++ b/RecDP/tests/test_llmutils.py @@ -68,6 +68,28 @@ def test_quality_classifier_spark(self): quality_classifier_df = quality_classifier_spark(spark_df) quality_classifier_df.show() + def test_toxicity_score(self): + from pyrecdp.primitives.llmutils import toxicity_score + from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE + huggingface_config_path = os.path.join(RECDP_MODELS_CACHE, "models--xlm-roberta-base") + file_path = os.path.join(cur_dir, "data/llm_data/tiny_c4_sample_for_pii.jsonl") + save_path = os.path.join(cur_dir, "data/output/toxicity_score") + toxicity_score(file_path, save_path, "jsonl", text_key='text', + threshold=0, model_type="multilingual", huggingface_config_path=huggingface_config_path) + + def test_toxicity_score_spark(self): + from pyrecdp.primitives.llmutils import toxicity_score_spark + from pyrecdp.core import SparkDataProcessor + from pyrecdp.core.cache_utils import RECDP_MODELS_CACHE + huggingface_config_path = os.path.join(RECDP_MODELS_CACHE, "models--xlm-roberta-base") + data_file = f'file://{os.path.join(cur_dir, "data/llm_data/tiny_c4_sample_for_pii.jsonl")}' + rdp = SparkDataProcessor() + spark = rdp.spark + spark_df = spark.read.json(data_file) + toxicity_score_df = toxicity_score_spark(spark_df, text_key='text', + threshold=0, model_type="multilingual", huggingface_config_path=huggingface_config_path) + toxicity_score_df.show() + def test_diversity_analysis(self): from pyrecdp.primitives.llmutils import diversity_indicate data_dir = "tests/data/llm_data/" From f6ee233a4bd7d1d4f10256cd68aa7575948c696b Mon Sep 17 00:00:00 2001 From: ChaoJun Zhang Date: Wed, 18 Oct 2023 23:06:32 +0800 Subject: [PATCH 19/47] [v1.2][ISSUE-306] Support extract text from pdf, image and docx (#390) * support extract text from pdf, image and docx * support extract text from pdf, image and docx * support extract text from pdf, image and docx --- RecDP/pyrecdp/primitives/document/__init__.py | 0 .../primitives/llmutils/document/__init__.py | 0 .../primitives/llmutils/document/extractor.py | 157 ++++++++++++++++++ .../primitives/llmutils/document/reader.py | 154 +++++++++++++++++ .../primitives/llmutils/document/schema.py | 19 +++ .../primitives/llmutils/document/writer.py | 19 +++ .../primitives/llmutils/document_extractor.py | 69 ++++++++ RecDP/setup.py | 6 +- .../llm_data/document/english-and-korean.png | Bin 0 -> 305401 bytes .../data/llm_data/document/handbook-872p.docx | Bin 0 -> 482883 bytes .../document/layout-parser-paper-10p.jpg | Bin 0 -> 5191434 bytes .../llm_data/document/layout-parser-paper.pdf | Bin 0 -> 4686220 bytes RecDP/tests/test_llmutils.py | 28 +++- 13 files changed, 450 insertions(+), 2 deletions(-) create mode 100644 RecDP/pyrecdp/primitives/document/__init__.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document/__init__.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document/extractor.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document/reader.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document/schema.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document/writer.py create mode 100644 RecDP/pyrecdp/primitives/llmutils/document_extractor.py create mode 100644 RecDP/tests/data/llm_data/document/english-and-korean.png create mode 100644 RecDP/tests/data/llm_data/document/handbook-872p.docx create mode 100644 RecDP/tests/data/llm_data/document/layout-parser-paper-10p.jpg create mode 100644 RecDP/tests/data/llm_data/document/layout-parser-paper.pdf diff --git a/RecDP/pyrecdp/primitives/document/__init__.py b/RecDP/pyrecdp/primitives/document/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/RecDP/pyrecdp/primitives/llmutils/document/__init__.py b/RecDP/pyrecdp/primitives/llmutils/document/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/RecDP/pyrecdp/primitives/llmutils/document/extractor.py b/RecDP/pyrecdp/primitives/llmutils/document/extractor.py new file mode 100644 index 000000000..a16686d48 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/document/extractor.py @@ -0,0 +1,157 @@ +import logging +import os +from typing import Optional, Type + +from .reader import * +from .writer import DocumentWriter + +DEFAULT_SUPPORTED_SUFFIX = [ + ".pdf", + ".jpg", + ".png", + ".jpeg", + ".doc", + ".docx", + ".ppt", + ".pptx", + ".xls", + ".xlsx", +] + +CUSTOMIZE_SUPPORTED_SUFFIX: Dict[str, Type[DocumentReader]] = { + ".pdf": PDFReader, + ".docx": DocxReader, + ".doc": DocxReader, + ".jpg": ImageReader, + ".jpeg": ImageReader, + ".png": ImageReader, +} + +logger = logging.getLogger(__name__) + + +class DocumentExtractor: + def __init__( + self, + output_file: str, + input_dir: Optional[str] = None, + glob: str = "**/[!.]*", + input_files: Optional[List] = None, + exclude: Optional[List] = None, + exclude_hidden: bool = True, + silent_errors: bool = False, + recursive: bool = False, + encoding: str = "utf-8", + required_exts: Optional[List[str]] = None, + ) -> None: + if not input_dir and not input_files: + raise ValueError("Must provide either `input_dir` or `input_files`.") + if not output_file: + raise ValueError("Must provide either `output_file` or `writer`.") + self.glob = glob + self.encoding = encoding + self.silent_errors = silent_errors + self.exclude = exclude + self.recursive = recursive + self.exclude_hidden = exclude_hidden + self.required_exts = required_exts + self.file_extractor = {} + if input_files: + self.input_files = [] + for path in input_files: + if not os.path.isfile(path): + raise ValueError(f"File {path} does not exist.") + input_file = Path(path) + self.input_files.append(input_file) + elif input_dir: + if not os.path.isdir(input_dir): + raise ValueError(f"Directory {input_dir} does not exist.") + self.input_dir = Path(input_dir) + self.exclude = exclude + self.input_files = self._add_files(self.input_dir) + + self.supported_suffix = DEFAULT_SUPPORTED_SUFFIX + self.output_file = output_file + + def _add_files(self, input_dir: Path) -> List[Path]: + """Add files.""" + all_files = set() + rejected_files = set() + + if self.exclude is not None: + for excluded_pattern in self.exclude: + if self.recursive: + # Recursive glob + for file in input_dir.rglob(excluded_pattern): + rejected_files.add(Path(file)) + else: + # Non-recursive glob + for file in input_dir.glob(excluded_pattern): + rejected_files.add(Path(file)) + + p = Path(input_dir) + file_refs = list(p.rglob(self.glob) if self.recursive else p.glob(self.glob)) + + for ref in file_refs: + # Manually check if file is hidden or directory instead of + # in glob for backwards compatibility. + is_dir = ref.is_dir() + skip_because_hidden = self.exclude_hidden and ref.name.startswith(".") + skip_because_bad_ext = ( + self.required_exts is not None and ref.suffix not in self.required_exts + ) + skip_because_excluded = ref in rejected_files + + if ( + is_dir + or skip_because_hidden + or skip_because_bad_ext + or skip_because_excluded + ): + continue + else: + all_files.add(ref) + + new_input_files = sorted(all_files) + + if len(new_input_files) == 0: + raise ValueError(f"No files found in {input_dir}.") + + # print total number of files added + logger.debug( + f"> [DocumentExtractor] Total files added: {len(new_input_files)}" + ) + + return new_input_files + + def execute(self): + from tqdm import tqdm + pbar = tqdm(total=len(self.input_files)) + with DocumentWriter(self.output_file) as writer: + for input_file in self.input_files: + self.writeDocument(input_file, pbar, writer) + + def writeDocument(self, input_file, pbar, writer: DocumentWriter): + try: + file_suffix = input_file.suffix.lower() + if file_suffix in self.supported_suffix: + if file_suffix in CUSTOMIZE_SUPPORTED_SUFFIX: + if file_suffix not in self.file_extractor: + self.file_extractor[file_suffix] = CUSTOMIZE_SUPPORTED_SUFFIX[file_suffix]() + reader = self.file_extractor[file_suffix] + docs = reader.load_data(input_file) + else: + reader = UnstructuredReader(input_file) + docs = reader.load_data() + for doc in docs: + writer.write(doc) + else: + logger.info(f"Skip loading file {input_file!s}: file suffix {file_suffix} is not supported") + + except Exception as e: + if self.silent_errors: + logger.warning(f"Error loading file {input_file!s}: {e}") + else: + raise e + finally: + pbar.update(1) diff --git a/RecDP/pyrecdp/primitives/llmutils/document/reader.py b/RecDP/pyrecdp/primitives/llmutils/document/reader.py new file mode 100644 index 000000000..f4c9ea320 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/document/reader.py @@ -0,0 +1,154 @@ +from abc import abstractmethod, ABC +from pathlib import Path +from typing import Any, List, Dict, Union + +from .schema import Document + + +class DocumentReader(ABC): + """Utilities for loading data from a directory.""" + + @abstractmethod + def load_data(self, *args: Any, **load_kwargs: Any) -> List[Document]: + """Load data from the input directory.""" + + +class PDFReader(DocumentReader): + """PDF parser.""" + + def load_data(self, file: Path, password: str = None) -> List[Document]: + """Parse file.""" + + import pypdf + + with open(file, "rb") as fp: + # Create a PDF object + pdf = pypdf.PdfReader(file, password=password) + + # Get the number of pages in the PDF document + num_pages = len(pdf.pages) + + # Iterate over every page + docs = [] + for page in range(num_pages): + # Extract the text from the page + page_text = pdf.pages[page].extract_text() + page_label = pdf.page_labels[page] + metadata = {"page_label": page_label, "source": file.name} + docs.append(Document(text=page_text, metadata=metadata)) + return docs + + +class DocxReader(DocumentReader): + """Docx parser.""" + + def load_data(self, file: Path) -> List[Document]: + """Parse file.""" + import docx + doc = docx.Document(file) + + # read in each paragraph in file + return [Document(text=p.text, metadata={"source": file.name}) for p in doc.paragraphs] + + +class ImageReader(DocumentReader): + """Image parser. + + Extract text from images using pytesseract. + + """ + + def __init__( + self, + keep_image: bool = False, + ): + self._keep_image = keep_image + + def load_data(self, file: Path) -> List[Document]: + """Parse file.""" + from PIL import Image + from pytesseract import pytesseract + # load document image + image = Image.open(file) + if image.mode != "RGB": + image = image.convert("RGB") + + # Parse image into text + text_str = pytesseract.image_to_string(image) + + return [ + Document(text=text_str, metadata={"source": str(file)}) + ] + + +class UnstructuredReader(DocumentReader): + """Loader that uses `Unstructured`.""" + + def __init__( + self, + file_path: Union[str, List[str]], + mode: str = "elements", + **unstructured_kwargs: Any, + ): + _valid_modes = {"single", "elements", "paged"} + if mode not in _valid_modes: + raise ValueError( + f"Got {mode} for `mode`, but should be one of `{_valid_modes}`" + ) + self.mode = mode + self.file_path = file_path + self.unstructured_kwargs = unstructured_kwargs + + def _get_elements(self) -> List: + from unstructured.partition.auto import partition + return partition(filename=self.file_path, **self.unstructured_kwargs) + + def _get_metadata(self) -> dict: + return {"source": self.file_path} + + def load_data(self) -> List[Document]: + """Load file.""" + elements = self._get_elements() + if self.mode == "elements": + docs: List[Document] = list() + for element in elements: + metadata = self._get_metadata() + # NOTE(MthwRobinson) - the attribute check is for backward compatibility + # with unstructured<0.4.9. The metadata attributed was added in 0.4.9. + if hasattr(element, "metadata"): + metadata.update(element.metadata.to_dict()) + if hasattr(element, "category"): + metadata["category"] = element.category + docs.append(Document(text=str(element), metadata=metadata)) + elif self.mode == "paged": + text_dict: Dict[int, str] = {} + meta_dict: Dict[int, Dict] = {} + + for idx, element in enumerate(elements): + metadata = self._get_metadata() + if hasattr(element, "metadata"): + metadata.update(element.metadata.to_dict()) + page_number = metadata.get("page_number", 1) + + # Check if this page_number already exists in docs_dict + if page_number not in text_dict: + # If not, create new entry with initial text and metadata + text_dict[page_number] = str(element) + "\n\n" + meta_dict[page_number] = metadata + else: + # If exists, append to text and update the metadata + text_dict[page_number] += str(element) + "\n\n" + meta_dict[page_number].update(metadata) + + # Convert the dict to a list of Document objects + docs = [ + Document(text=text_dict[key], metadata=meta_dict[key]) + for key in text_dict.keys() + ] + elif self.mode == "single": + metadata = self._get_metadata() + text = "\n\n".join([str(el) for el in elements]) + docs = [Document(text=text, metadata=metadata)] + else: + raise ValueError(f"mode of {self.mode} not supported.") + return docs diff --git a/RecDP/pyrecdp/primitives/llmutils/document/schema.py b/RecDP/pyrecdp/primitives/llmutils/document/schema.py new file mode 100644 index 000000000..1e0a73313 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/document/schema.py @@ -0,0 +1,19 @@ +from dataclasses import dataclass, asdict + + +@dataclass +class Document: + """Class for storing a piece of text and associated metadata.""" + text: str + """String text.""" + metadata: dict + """Arbitrary metadata about the page content (e.g., source, relationships to other + documents, etc.). + """ + + @property + def __dict__(self): + return asdict(self) + + def json(self): + return str(self.__dict__) diff --git a/RecDP/pyrecdp/primitives/llmutils/document/writer.py b/RecDP/pyrecdp/primitives/llmutils/document/writer.py new file mode 100644 index 000000000..b86a011b3 --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/document/writer.py @@ -0,0 +1,19 @@ +import os + +from .schema import Document + + +class DocumentWriter: + def __init__(self, file: str): + self.file = file + + def __enter__(self): + self.writer = open(self.file, 'wb') + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.writer.close() + + def write(self, doc: Document): + if doc.text.strip() != "": + self.writer.write(str.encode(doc.json() + os.linesep)) diff --git a/RecDP/pyrecdp/primitives/llmutils/document_extractor.py b/RecDP/pyrecdp/primitives/llmutils/document_extractor.py new file mode 100644 index 000000000..2774d5d0b --- /dev/null +++ b/RecDP/pyrecdp/primitives/llmutils/document_extractor.py @@ -0,0 +1,69 @@ +from typing import Optional + +from pyrecdp.primitives.llmutils.document.extractor import DocumentExtractor +from pyrecdp.primitives.llmutils.document.reader import * +from pyrecdp.primitives.llmutils.document.writer import DocumentWriter + + +def pdf_to_text(in_file: str, out_file: str, password: str = None): + reader = PDFReader() + with DocumentWriter(out_file) as writer: + for doc in reader.load_data(Path(in_file), password): + writer.write(doc) + + +def docx_to_text(in_file: str, out_file: str): + reader = DocxReader() + with DocumentWriter(out_file) as writer: + for doc in reader.load_data(Path(in_file)): + writer.write(doc) + + +def image_to_text(in_file: str, out_file: str): + reader = ImageReader() + with DocumentWriter(out_file) as writer: + for doc in reader.load_data(Path(in_file)): + writer.write(doc) + + +def document_to_text(output_file: str, + input_dir: Optional[str] = None, + glob: str = "**/*.*", + input_files: Optional[List[str]] = None, + silent_errors: bool = False, + recursive: bool = False, + required_exts: Optional[List[str]] = None): + from pyrecdp.core.utils import Timer + with Timer(f"Document converter with args [input_dir= {input_dir}, glob={glob}, input_files={input_files}]"): + converter = DocumentExtractor( + output_file=output_file, + input_dir=input_dir, + glob=glob, + input_files=input_files, + silent_errors=silent_errors, + recursive=recursive, + required_exts=required_exts) + converter.execute() + + +if __name__ == '__main__': + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("-d", "--input_dir", dest="input_dir", type=str) + parser.add_argument("-g", "--glob", dest="glob", type=str, default="**/*.pdf") + parser.add_argument("-i", "--input_files", dest="input_files", type=str, nargs="*", default=None) + parser.add_argument("-o", "--output_file", dest="output_file", type=str) + parser.add_argument("-e", "--silent_errors", dest="silent_errors", default=False, type=bool) + parser.add_argument("-r", "--recursive", dest="recursive", type=bool, default=False) + parser.add_argument("-t", "--required_exts", dest="required_exts", type=str, nargs="*", default=None) + args = parser.parse_args() + + document_to_text( + output_file=args.output_file, + input_dir=args.input_dir, + glob=args.glob, + input_files=args.input_files, + silent_errors=args.silent_errors, + recursive=args.recursive, + required_exts=args.required_exts) diff --git a/RecDP/setup.py b/RecDP/setup.py index 4c4a000cb..672af4aca 100644 --- a/RecDP/setup.py +++ b/RecDP/setup.py @@ -81,5 +81,9 @@ "ray", "loguru", "detoxify", - "emoji==2.2.0" + "emoji==2.2.0", + "pypdf", + "python-docx", + "pdfminer", + "unstructured[ppt,pptx,xlsx]", ]) diff --git a/RecDP/tests/data/llm_data/document/english-and-korean.png b/RecDP/tests/data/llm_data/document/english-and-korean.png new file mode 100644 index 0000000000000000000000000000000000000000..4a54d93ff41d739c34b436a215931fa34086bea9 GIT binary patch literal 305401 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