mirror of
https://gitee.com/ascend/ModelLink.git
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301 lines
12 KiB
Python
301 lines
12 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import filecmp
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import io
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import json
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import re
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import os
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import unittest
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import functools
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from pathlib import Path
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from ascendspeed.testing_utils import (
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TestCasePlus,
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execute_subprocess_async,
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set_seed
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)
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from datasets import load_dataset
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set_seed(42)
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def write_jsonl(path, lines_num=1000, line_length=1024):
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def get_text_line(line_length):
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# XXX: fix to generate line_length
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return "It's a wonderful world. I'm just walking on air. Talk of heaven on earth. I've got more than my share. Haven't got a care. Happy all day through. It's a wonderful world. Loving wonderful you!"
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with io.open(path, "w", encoding="utf-8") as f:
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for i in range(lines_num):
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rec = dict(text=get_text_line(line_length))
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x = json.dumps(rec, indent=0, ensure_ascii=False)
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x = re.sub(r'\n', ' ', x, 0, re.M)
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f.write(x + "\n")
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@functools.lru_cache()
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def download_hf_dataset(dsetname):
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return load_dataset(dsetname)
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class MegDSTestPreprocessing(TestCasePlus):
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""" """
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def setUp(self):
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super().setUp()
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def test_preprocess_data(self):
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src_dir = self.src_dir
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data_dir = f"{self.data_dir}/gpt2"
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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# autogenerate "input.jsonl"
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input_path = f"{output_dir}/input.jsonl"
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write_jsonl(input_path)
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output_prefix =f"{output_dir}/test-ds"
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cmd = f"""
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python {src_dir}/tools/preprocess_data.py
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--input {input_path}
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--output-prefix {output_prefix}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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--workers 2
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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for ext in ["bin", "idx"]:
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tgt_path = f"{output_prefix}_text_document.{ext}"
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self.assertTrue(Path(tgt_path).exists(), )
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def compare_meg_data_files(self, tgt, ref):
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for ext in ["bin", "idx"]:
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tgt_path = f"{tgt}.{ext}"
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ref_path = f"{ref}.{ext}"
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self.assertTrue(Path(tgt_path).exists(), )
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self.assertTrue(filecmp.cmp(tgt_path, ref_path, shallow=False))
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def preprocess_partitioned_dataset(self, output_dir, dsetname, splitname, linelimit, numparts):
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"""Preprocess a dataset as a whole and in shards to prepare environment for merge test.
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Load specified HF dataset using given split and record limit.
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Write the dataset to a jsonl file and preprocess.
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Also split dataset into numparts contiguous shards, write each shard to its own jsonl, and preprocess each.
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Return path to the full dataset and a list of paths for each shard."""
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src_dir = self.src_dir
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data_dir = f"{self.data_dir}/gpt2"
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# preproces_data_dist requires one to have already downloaded the input HF dataset.
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# We do that by running this script before the test.
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dset = download_hf_dataset(dsetname)[splitname]
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# limit the test to use the first linelimit entries to be faster
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dset = dset.select(range(linelimit))
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# write jsonl file of full dataset
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json_ds = f"{output_dir}/ds-full.jsonl"
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dset.to_json(json_ds)
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# process full jsonl into indexed dataset file
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ds_full = f"{output_dir}/ds-full"
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cmd = f"""
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python {src_dir}/tools/preprocess_data.py
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--input {json_ds}
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--output-prefix {ds_full}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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""".split()
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ds_full += '_text_document'
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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# write each part to its own json file
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ds_parts = []
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for i in range(numparts):
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json_part = f"{output_dir}/ds-part-{i}.jsonl"
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dset.shard(numparts, i, contiguous=True).to_json(json_part)
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ds_part = f"{output_dir}/ds-part-{i}"
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ds_parts.append(ds_part + '_text_document')
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cmd = f"""
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python {src_dir}/tools/preprocess_data.py
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--input {json_part}
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--output-prefix {ds_part}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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return ds_full, ds_parts
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def test_merge_serial(self):
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"""Check that serial merge of partial dataset files produces the same file as the full dataset."""
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src_dir = self.src_dir
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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# process full dataset, and process the full dataset as 3 contiguous chunks
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ds_full, ds_parts = self.preprocess_partitioned_dataset(output_dir, 'stas/openwebtext-10k', 'train', 100, 3)
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# merge the part files into a single indexed dataset
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ds_merged = f"{output_dir}/ds-merged"
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cmd = f"""
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python {src_dir}/tools/merge_preprocessed_data.py
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--datasets {" ".join(ds_parts)}
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--output-prefix {ds_merged}
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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# the full dataset and the merged dataset should be identical
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self.compare_meg_data_files(ds_full, ds_merged)
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def test_merge_distributed(self):
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"""Check that serial merge of partial dataset files produces the same file as the full dataset."""
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src_dir = self.src_dir
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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# process full dataset, and process the full dataset as 3 contiguous chunks
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ds_full, ds_parts = self.preprocess_partitioned_dataset(output_dir, 'stas/openwebtext-10k', 'train', 100, 3)
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# merge the part files into a single indexed dataset
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ds_merged = f"{output_dir}/ds-merged"
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cmd = f"""
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python -m torch.distributed.launch --nproc_per_node 6 {src_dir}/tools/merge_preprocessed_data.py
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--merge distributed
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--datasets {" ".join(ds_parts)}
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--output-prefix {ds_merged}
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--torch-backend gloo
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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# the full dataset and the merged dataset should be identical
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self.compare_meg_data_files(ds_full, ds_merged)
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def test_process_data_microsoft(self):
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"""We want to be stable to Microsoft version."""
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src_dir = self.src_dir
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data_dir = f"{self.data_dir}/gpt2"
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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input_path = f"{self.tests_dir}/data/gpt2/openwebtext-1000.jsonl"
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output_prefix = f"{output_dir}/test-ds-meg-gpt2-openwebtext"
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cmd = f"""
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python {src_dir}/tools/preprocess_data.py
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--input {input_path}
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--output-prefix {output_prefix}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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--workers 2
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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self.compare_meg_data_files(f"{output_prefix}_text_document", f"{data_dir}/meg-gpt2-openwebtext_text_document")
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def test_process_data_dist_microsoft(self):
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"""We want to be stable to Microsoft version."""
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src_dir = self.src_dir
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data_dir = f"{self.data_dir}/gpt2"
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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output_prefix = f"{output_dir}/test-ds-meg-gpt2-openwebtext_1k"
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# preprocess_data_dist requires one to have already downloaded the input HF dataset.
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# We do that by running this script before the test.
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dsetname = 'stas/openwebtext-10k'
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download_hf_dataset(dsetname)
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cmd = f"""
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python -m torch.distributed.launch --nproc_per_node 2 {src_dir}/tools/preprocess_data_dist.py
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--input {dsetname}
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--count 1000
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--output-prefix {output_prefix}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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self.compare_meg_data_files(f"{output_prefix}_text_document", f"{data_dir}/meg-gpt2-openwebtext_text_document")
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def test_process_data_dist_serial_microsoft(self):
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"""We want to be stable to Microsoft version."""
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src_dir = self.src_dir
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data_dir = f"{self.data_dir}/gpt2"
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output_dir = self.get_auto_remove_tmp_dir() # "./xxx", after=False)
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output_prefix = f"{output_dir}/test-ds-meg-gpt2-openwebtext_1k"
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# preproces_data_dist requires one to have already downloaded the input HF dataset.
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# We do that by running this script before the test.
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dsetname = 'stas/openwebtext-10k'
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download_hf_dataset(dsetname)
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cmd = f"""
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python -m torch.distributed.launch --nproc_per_node 2 {src_dir}/tools/preprocess_data_dist.py
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--input {dsetname}
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--count 1000
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--merge serial
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--output-prefix {output_prefix}
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--dataset-impl mmap
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--tokenizer-type GPT2BPETokenizer
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--merge-file {data_dir}/gpt2-tiny-merges.txt
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--vocab {data_dir}/gpt2-tiny-vocab.json
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--append-eod
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""".split()
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# keep for quick debug
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# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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self.compare_meg_data_files(f"{output_prefix}_text_document", f"{data_dir}/meg-gpt2-openwebtext_text_document")
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