ydshieh
commited on
Commit
•
5cb8b84
1
Parent(s):
790ecb1
clean repo
Browse files- .gitattributes +2 -0
- train.json → coco_data/train.json +0 -0
- val.json → coco_data/val.json +0 -0
- outputs-wit/.gitattributes +0 -1
- outputs-wit/ckpt_7/.gitattributes +0 -2
- outputs-wit/ckpt_7/config.json +0 -3
- outputs-wit/ckpt_7/flax_model.msgpack +0 -3
- outputs-wit/events.out.tfevents.1626423408.t1v-n-cab111a8-w-0.820839.3.v2 +0 -3
- outputs-wit/summary.txt +0 -21
- run_summarization_coco.py → run_image_caption.py +0 -0
- run_summarization.py +0 -832
- test_wit_dataset_script.py +0 -23
- wit_data_dir/dev/dev.tsv +0 -3
- wit_data_dir/test/test.tsv +0 -3
- wit_data_dir/train/train.tsv +0 -3
- wit_dataset_script.py +0 -145
.gitattributes
CHANGED
@@ -21,3 +21,5 @@ wit_data_dir/test/test.tsv filter=lfs diff=lfs merge=lfs -text
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train.json filter=lfs diff=lfs merge=lfs -text
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val.json filter=lfs diff=lfs merge=lfs -text
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outputs/ckpt_5/flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
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train.json filter=lfs diff=lfs merge=lfs -text
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val.json filter=lfs diff=lfs merge=lfs -text
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outputs/ckpt_5/flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
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C:/Users/33611/Desktop/hub/vit-gpt2/coco_data/train.json filter=lfs diff=lfs merge=lfs -text
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C:/Users/33611/Desktop/hub/vit-gpt2/coco_data/val.json filter=lfs diff=lfs merge=lfs -text
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train.json → coco_data/train.json
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val.json → coco_data/val.json
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outputs-wit/.gitattributes
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events.out.tfevents.1626423408.t1v-n-cab111a8-w-0.820839.3.v2 filter=lfs diff=lfs merge=lfs -text
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outputs-wit/ckpt_7/.gitattributes
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flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
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outputs-wit/ckpt_7/config.json
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outputs-wit/summary.txt
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Epoch... (1/20 | Loss: 2.377821683883667, Learning Rate: 9.501103704678826e-06)
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Epoch... (1/20 | Eval Loss: 2.2912707328796387 | Eval rouge1: 17.1532 | Eval rouge2: 2.1991 | Eval rougeL: 12.1665 | Eval rougeLsum: 13.7971 | Eval gen_len: 58.3398 |)
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Predict Loss: 2.3000617027282715 | Predict rouge1: 17.3228 | Predict rouge2: 2.1974 | Predict rougeL: 12.2257 | Predict rougeLsum: 13.863 | Predict gen_len: 58.0887 |)
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Epoch... (2/20 | Loss: 2.318763017654419, Learning Rate: 9.001103535410948e-06)
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Epoch... (2/20 | Eval Loss: 2.2495603561401367 | Eval rouge1: 13.6938 | Eval rouge2: 0.963 | Eval rougeL: 10.0782 | Eval rougeLsum: 10.8405 | Eval gen_len: 58.3382 |)
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Predict Loss: 2.2592482566833496 | Predict rouge1: 13.7749 | Predict rouge2: 0.9371 | Predict rougeL: 10.0138 | Predict rougeLsum: 10.8695 | Predict gen_len: 58.1836 |)
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Epoch... (3/20 | Loss: 2.3419060707092285, Learning Rate: 8.501104275637772e-06)
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Epoch... (3/20 | Eval Loss: 2.22269344329834 | Eval rouge1: 12.0579 | Eval rouge2: 0.7251 | Eval rougeL: 9.092 | Eval rougeLsum: 9.3802 | Eval gen_len: 60.7578 |)
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Predict Loss: 2.233069896697998 | Predict rouge1: 12.5721 | Predict rouge2: 0.8881 | Predict rougeL: 9.4823 | Predict rougeLsum: 9.7638 | Predict gen_len: 60.5006 |)
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Epoch... (4/20 | Loss: 2.2800769805908203, Learning Rate: 8.001104106369894e-06)
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Epoch... (4/20 | Eval Loss: 2.2039794921875 | Eval rouge1: 14.2541 | Eval rouge2: 0.7585 | Eval rougeL: 10.3604 | Eval rougeLsum: 11.1679 | Eval gen_len: 60.3655 |)
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Predict Loss: 2.214798927307129 | Predict rouge1: 14.4009 | Predict rouge2: 0.8344 | Predict rougeL: 10.3895 | Predict rougeLsum: 11.2357 | Predict gen_len: 60.2483 |)
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Epoch... (5/20 | Loss: 2.220062494277954, Learning Rate: 7.501103482354665e-06)
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Epoch... (5/20 | Eval Loss: 2.1913952827453613 | Eval rouge1: 14.1698 | Eval rouge2: 0.8184 | Eval rougeL: 10.2918 | Eval rougeLsum: 11.245 | Eval gen_len: 60.1311 |)
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Predict Loss: 2.202223300933838 | Predict rouge1: 14.4567 | Predict rouge2: 0.9169 | Predict rougeL: 10.5117 | Predict rougeLsum: 11.3823 | Predict gen_len: 59.875 |)
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Epoch... (6/20 | Loss: 2.105752944946289, Learning Rate: 7.001103767834138e-06)
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Epoch... (6/20 | Eval Loss: 2.1800718307495117 | Eval rouge1: 14.6613 | Eval rouge2: 0.924 | Eval rougeL: 10.5021 | Eval rougeLsum: 11.672 | Eval gen_len: 61.7065 |)
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Predict Loss: 2.1911959648132324 | Predict rouge1: 14.972 | Predict rouge2: 0.9993 | Predict rougeL: 10.7166 | Predict rougeLsum: 11.843 | Predict gen_len: 61.8092 |)
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Epoch... (7/20 | Loss: 2.1191587448120117, Learning Rate: 6.50110359856626e-06)
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Epoch... (7/20 | Eval Loss: 2.1725244522094727 | Eval rouge1: 12.9676 | Eval rouge2: 1.1282 | Eval rougeL: 9.5649 | Eval rougeLsum: 10.702 | Eval gen_len: 59.4275 |)
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Predict Loss: 2.1837007999420166 | Predict rouge1: 13.161 | Predict rouge2: 1.1852 | Predict rougeL: 9.7344 | Predict rougeLsum: 10.9045 | Predict gen_len: 59.3945 |)
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run_summarization_coco.py → run_image_caption.py
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run_summarization.py
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 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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"""
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Fine-tuning the library models for summarization.
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"""
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# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
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import sys, os
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current_path = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_path)
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import logging
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import os
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import sys
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import time
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from dataclasses import dataclass, field
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from functools import partial
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from pathlib import Path
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from typing import Callable, Optional
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import datasets
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import nltk # Here to have a nice missing dependency error message early on
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import numpy as np
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from datasets import Dataset, load_dataset, load_metric
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from tqdm import tqdm
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import jax
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import jax.numpy as jnp
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import optax
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import transformers
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from filelock import FileLock
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from flax import jax_utils, traverse_util
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from flax.jax_utils import unreplicate
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoTokenizer,
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FlaxAutoModelForSeq2SeqLM,
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HfArgumentParser,
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TrainingArguments,
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is_tensorboard_available,
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)
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from transformers.file_utils import is_offline_mode
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from transformers import ViTFeatureExtractor, GPT2Tokenizer, GPT2Config
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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
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logger = logging.getLogger(__name__)
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try:
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nltk.data.find("tokenizers/punkt")
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except (LookupError, OSError):
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if is_offline_mode():
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raise LookupError(
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"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
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)
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with FileLock(".lock") as lock:
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nltk.download("punkt", quiet=True)
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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dtype: Optional[str] = field(
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default="float32",
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metadata={
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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text_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
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)
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summary_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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max_source_length: Optional[int] = field(
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default=1024,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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max_target_length: Optional[int] = field(
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default=128,
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metadata={
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"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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val_max_target_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
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"This argument is also used to override the `max_length` param of `model.generate`, which is used "
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"during evaluation."
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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},
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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source_prefix: Optional[str] = field(
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default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
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)
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predict_with_generate: bool = field(
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default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
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)
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num_beams: Optional[int] = field(
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default=None,
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metadata={
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"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
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"which is used during evaluation."
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if self.val_max_target_length is None:
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self.val_max_target_length = self.max_target_length
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summarization_name_mapping = {
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"amazon_reviews_multi": ("review_body", "review_title"),
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"big_patent": ("description", "abstract"),
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"cnn_dailymail": ("article", "highlights"),
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"orange_sum": ("text", "summary"),
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"pn_summary": ("article", "summary"),
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"psc": ("extract_text", "summary_text"),
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"samsum": ("dialogue", "summary"),
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"thaisum": ("body", "summary"),
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"xglue": ("news_body", "news_title"),
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"xsum": ("document", "summary"),
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"wiki_summary": ("article", "highlights"),
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}
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class TrainState(train_state.TrainState):
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dropout_rng: jnp.ndarray
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def replicate(self):
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return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
244 |
-
|
245 |
-
|
246 |
-
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
247 |
-
"""
|
248 |
-
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
249 |
-
Shuffle batches if `shuffle` is `True`.
|
250 |
-
"""
|
251 |
-
steps_per_epoch = len(dataset) // batch_size
|
252 |
-
|
253 |
-
if shuffle:
|
254 |
-
batch_idx = jax.random.permutation(rng, len(dataset))
|
255 |
-
else:
|
256 |
-
batch_idx = jnp.arange(len(dataset))
|
257 |
-
|
258 |
-
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
259 |
-
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
260 |
-
|
261 |
-
for idx in batch_idx:
|
262 |
-
batch = dataset[idx]
|
263 |
-
batch = {k: jnp.array(v) for k, v in batch.items()}
|
264 |
-
|
265 |
-
batch = shard(batch)
|
266 |
-
|
267 |
-
yield batch
|
268 |
-
|
269 |
-
|
270 |
-
def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
|
271 |
-
summary_writer.scalar("train_time", train_time, step)
|
272 |
-
|
273 |
-
train_metrics = get_metrics(train_metrics)
|
274 |
-
for key, vals in train_metrics.items():
|
275 |
-
tag = f"train_{key}"
|
276 |
-
for i, val in enumerate(vals):
|
277 |
-
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
278 |
-
|
279 |
-
for metric_name, value in eval_metrics.items():
|
280 |
-
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
281 |
-
|
282 |
-
|
283 |
-
def create_learning_rate_fn(
|
284 |
-
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
|
285 |
-
) -> Callable[[int], jnp.array]:
|
286 |
-
"""Returns a linear warmup, linear_decay learning rate function."""
|
287 |
-
steps_per_epoch = train_ds_size // train_batch_size
|
288 |
-
num_train_steps = steps_per_epoch * num_train_epochs
|
289 |
-
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
290 |
-
decay_fn = optax.linear_schedule(
|
291 |
-
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
292 |
-
)
|
293 |
-
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
294 |
-
return schedule_fn
|
295 |
-
|
296 |
-
|
297 |
-
def main():
|
298 |
-
# See all possible arguments in src/transformers/training_args.py
|
299 |
-
# or by passing the --help flag to this script.
|
300 |
-
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
301 |
-
|
302 |
-
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
303 |
-
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
304 |
-
# If we pass only one argument to the script and it's the path to a json file,
|
305 |
-
# let's parse it to get our arguments.
|
306 |
-
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
307 |
-
else:
|
308 |
-
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
309 |
-
|
310 |
-
if (
|
311 |
-
os.path.exists(training_args.output_dir)
|
312 |
-
and os.listdir(training_args.output_dir)
|
313 |
-
and training_args.do_train
|
314 |
-
and not training_args.overwrite_output_dir
|
315 |
-
):
|
316 |
-
raise ValueError(
|
317 |
-
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
318 |
-
"Use --overwrite_output_dir to overcome."
|
319 |
-
)
|
320 |
-
|
321 |
-
# Make one log on every process with the configuration for debugging.
|
322 |
-
logging.basicConfig(
|
323 |
-
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
324 |
-
datefmt="%m/%d/%Y %H:%M:%S",
|
325 |
-
level=logging.INFO,
|
326 |
-
)
|
327 |
-
# Setup logging, we only want one process per machine to log things on the screen.
|
328 |
-
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
329 |
-
if jax.process_index() == 0:
|
330 |
-
datasets.utils.logging.set_verbosity_warning()
|
331 |
-
transformers.utils.logging.set_verbosity_info()
|
332 |
-
else:
|
333 |
-
datasets.utils.logging.set_verbosity_error()
|
334 |
-
transformers.utils.logging.set_verbosity_error()
|
335 |
-
|
336 |
-
# Set the verbosity to info of the Transformers logger (on main process only):
|
337 |
-
logger.info(f"Training/evaluation parameters {training_args}")
|
338 |
-
|
339 |
-
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
|
340 |
-
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
341 |
-
# (the dataset will be downloaded automatically from the datasets Hub).
|
342 |
-
#
|
343 |
-
# For CSV/JSON files this script will use the first column for the full texts and the second column for the
|
344 |
-
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
|
345 |
-
#
|
346 |
-
if data_args.dataset_name is not None:
|
347 |
-
# Downloading and loading a dataset from the hub.
|
348 |
-
dataset = load_dataset(
|
349 |
-
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir='./wit_data_dir/'
|
350 |
-
)
|
351 |
-
else:
|
352 |
-
data_files = {}
|
353 |
-
if data_args.train_file is not None:
|
354 |
-
data_files["train"] = data_args.train_file
|
355 |
-
extension = data_args.train_file.split(".")[-1]
|
356 |
-
if data_args.validation_file is not None:
|
357 |
-
data_files["validation"] = data_args.validation_file
|
358 |
-
extension = data_args.validation_file.split(".")[-1]
|
359 |
-
if data_args.test_file is not None:
|
360 |
-
data_files["test"] = data_args.test_file
|
361 |
-
extension = data_args.test_file.split(".")[-1]
|
362 |
-
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
363 |
-
|
364 |
-
vit_name_path = 'google/vit-base-patch16-224-in21k'
|
365 |
-
gpt2_name_path = 'asi/gpt-fr-cased-small'
|
366 |
-
|
367 |
-
gpt2_config = GPT2Config.from_pretrained(gpt2_name_path)
|
368 |
-
gpt2_config.add_cross_attention = True
|
369 |
-
|
370 |
-
|
371 |
-
vit_gpt2_name_path = ''
|
372 |
-
|
373 |
-
feature_extractor = ViTFeatureExtractor.from_pretrained(vit_name_path)
|
374 |
-
|
375 |
-
tokenizer = GPT2Tokenizer.from_pretrained(gpt2_name_path)
|
376 |
-
|
377 |
-
if not vit_gpt2_name_path:
|
378 |
-
assert vit_name_path
|
379 |
-
assert gpt2_name_path
|
380 |
-
vit_gpt2_model = FlaxViTGPT2LMForConditionalGeneration.from_vit_gpt2_pretrained(
|
381 |
-
vit_name_path, gpt2_name_path
|
382 |
-
)
|
383 |
-
else:
|
384 |
-
vit_gpt2_model = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(
|
385 |
-
vit_gpt2_name_path
|
386 |
-
)
|
387 |
-
|
388 |
-
model = vit_gpt2_model
|
389 |
-
model.config.is_encoder_decoder = True
|
390 |
-
model.config.decoder_start_token_id = gpt2_config.bos_token_id
|
391 |
-
model.config.bos_token_id = gpt2_config.bos_token_id
|
392 |
-
model.config.eos_token_id = gpt2_config.eos_token_id
|
393 |
-
model.config.pad_token_id = gpt2_config.pad_token_id
|
394 |
-
|
395 |
-
# Preprocessing the datasets.
|
396 |
-
# We need to tokenize inputs and targets.
|
397 |
-
if training_args.do_train:
|
398 |
-
column_names = dataset["train"].column_names
|
399 |
-
elif training_args.do_eval:
|
400 |
-
column_names = dataset["validation"].column_names
|
401 |
-
elif training_args.do_predict:
|
402 |
-
column_names = dataset["test"].column_names
|
403 |
-
else:
|
404 |
-
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
|
405 |
-
return
|
406 |
-
|
407 |
-
image_file_column = 'image_file'
|
408 |
-
caption_column = 'caption'
|
409 |
-
pixels_file_column = 'pixels_file'
|
410 |
-
|
411 |
-
# Temporarily set max_target_length for training.
|
412 |
-
max_target_length = data_args.max_target_length
|
413 |
-
|
414 |
-
# In Flax, for seq2seq models we need to pass `decoder_input_ids`
|
415 |
-
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
|
416 |
-
# for that dynamically import the `shift_tokens_right` function from the model file
|
417 |
-
model_module = __import__(vit_gpt2_model.__module__, fromlist=["shift_tokens_right"])
|
418 |
-
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
|
419 |
-
|
420 |
-
# Setting padding="max_length" as we need fixed length inputs for jitted functions
|
421 |
-
def preprocess_function(examples):
|
422 |
-
|
423 |
-
pixels_file = examples[pixels_file_column]
|
424 |
-
if not pixels_file:
|
425 |
-
assert examples[image_file_column]
|
426 |
-
_pixel_values = []
|
427 |
-
for y in examples[image_file_column]:
|
428 |
-
with Image.open(y) as image:
|
429 |
-
encoder_inputs = feature_extractor(images=image, return_tensors="np")
|
430 |
-
x = encoder_inputs.pixel_values
|
431 |
-
_pixel_values.append(x)
|
432 |
-
pixel_values = np.concatenate(_pixel_values)
|
433 |
-
else:
|
434 |
-
pixel_values = np.concatenate([np.load(x) for x in pixels_file])
|
435 |
-
|
436 |
-
targets = examples[caption_column]
|
437 |
-
|
438 |
-
# Add eos_token!!
|
439 |
-
targets = [x + ' ' + tokenizer.eos_token for x in targets]
|
440 |
-
|
441 |
-
model_inputs = {}
|
442 |
-
model_inputs['pixel_values'] = pixel_values
|
443 |
-
|
444 |
-
# Setup the tokenizer for targets
|
445 |
-
with tokenizer.as_target_tokenizer():
|
446 |
-
labels = tokenizer(
|
447 |
-
targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
|
448 |
-
)
|
449 |
-
|
450 |
-
model_inputs["labels"] = labels["input_ids"]
|
451 |
-
|
452 |
-
#print(labels["input_ids"])
|
453 |
-
#print(gpt2_config.pad_token_id)
|
454 |
-
#rint(gpt2_config.bos_token_id)
|
455 |
-
|
456 |
-
decoder_input_ids = shift_tokens_right_fn(
|
457 |
-
jnp.array(labels["input_ids"]), gpt2_config.pad_token_id, gpt2_config.bos_token_id
|
458 |
-
)
|
459 |
-
model_inputs["input_ids"] = np.asarray(decoder_input_ids)
|
460 |
-
|
461 |
-
# We need decoder_attention_mask so we can ignore pad tokens from loss
|
462 |
-
model_inputs["attention_mask"] = labels["attention_mask"]
|
463 |
-
|
464 |
-
return model_inputs
|
465 |
-
|
466 |
-
if training_args.do_train:
|
467 |
-
if "train" not in dataset:
|
468 |
-
raise ValueError("--do_train requires a train dataset")
|
469 |
-
train_dataset = dataset["train"]
|
470 |
-
if data_args.max_train_samples is not None:
|
471 |
-
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
472 |
-
|
473 |
-
train_dataset = train_dataset.map(
|
474 |
-
preprocess_function,
|
475 |
-
batched=True,
|
476 |
-
num_proc=data_args.preprocessing_num_workers,
|
477 |
-
remove_columns=column_names,
|
478 |
-
load_from_cache_file=not data_args.overwrite_cache,
|
479 |
-
desc="Running tokenizer on train dataset",
|
480 |
-
)
|
481 |
-
|
482 |
-
if training_args.do_eval:
|
483 |
-
max_target_length = data_args.val_max_target_length
|
484 |
-
if "validation" not in dataset:
|
485 |
-
raise ValueError("--do_eval requires a validation dataset")
|
486 |
-
eval_dataset = dataset["validation"]
|
487 |
-
if data_args.max_eval_samples is not None:
|
488 |
-
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
489 |
-
eval_dataset = eval_dataset.map(
|
490 |
-
preprocess_function,
|
491 |
-
batched=True,
|
492 |
-
num_proc=data_args.preprocessing_num_workers,
|
493 |
-
remove_columns=column_names,
|
494 |
-
load_from_cache_file=not data_args.overwrite_cache,
|
495 |
-
desc="Running tokenizer on validation dataset",
|
496 |
-
)
|
497 |
-
|
498 |
-
if training_args.do_predict:
|
499 |
-
max_target_length = data_args.val_max_target_length
|
500 |
-
if "test" not in dataset:
|
501 |
-
raise ValueError("--do_predict requires a test dataset")
|
502 |
-
predict_dataset = dataset["test"]
|
503 |
-
if data_args.max_predict_samples is not None:
|
504 |
-
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
|
505 |
-
predict_dataset = predict_dataset.map(
|
506 |
-
preprocess_function,
|
507 |
-
batched=True,
|
508 |
-
num_proc=data_args.preprocessing_num_workers,
|
509 |
-
remove_columns=column_names,
|
510 |
-
load_from_cache_file=not data_args.overwrite_cache,
|
511 |
-
desc="Running tokenizer on prediction dataset",
|
512 |
-
)
|
513 |
-
|
514 |
-
# Metric
|
515 |
-
metric = load_metric("rouge")
|
516 |
-
|
517 |
-
def postprocess_text(preds, labels):
|
518 |
-
preds = [pred.strip() for pred in preds]
|
519 |
-
labels = [label.strip() for label in labels]
|
520 |
-
|
521 |
-
# rougeLSum expects newline after each sentence
|
522 |
-
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
|
523 |
-
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
|
524 |
-
|
525 |
-
return preds, labels
|
526 |
-
|
527 |
-
def compute_metrics(preds, labels):
|
528 |
-
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
529 |
-
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
530 |
-
|
531 |
-
# Some simple post-processing
|
532 |
-
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
533 |
-
|
534 |
-
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
535 |
-
# Extract a few results from ROUGE
|
536 |
-
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
|
537 |
-
|
538 |
-
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
|
539 |
-
result["gen_len"] = np.mean(prediction_lens)
|
540 |
-
result = {k: round(v, 4) for k, v in result.items()}
|
541 |
-
return result
|
542 |
-
|
543 |
-
# Enable tensorboard only on the master node
|
544 |
-
has_tensorboard = is_tensorboard_available()
|
545 |
-
if has_tensorboard and jax.process_index() == 0:
|
546 |
-
try:
|
547 |
-
from flax.metrics.tensorboard import SummaryWriter
|
548 |
-
|
549 |
-
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
550 |
-
except ImportError as ie:
|
551 |
-
has_tensorboard = False
|
552 |
-
logger.warning(
|
553 |
-
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
554 |
-
)
|
555 |
-
else:
|
556 |
-
logger.warning(
|
557 |
-
"Unable to display metrics through TensorBoard because the package is not installed: "
|
558 |
-
"Please run pip install tensorboard to enable."
|
559 |
-
)
|
560 |
-
|
561 |
-
# Initialize our training
|
562 |
-
rng = jax.random.PRNGKey(training_args.seed)
|
563 |
-
rng, dropout_rng = jax.random.split(rng)
|
564 |
-
|
565 |
-
# Store some constant
|
566 |
-
num_epochs = int(training_args.num_train_epochs)
|
567 |
-
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
568 |
-
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
569 |
-
steps_per_epoch = len(train_dataset) // train_batch_size
|
570 |
-
total_train_steps = steps_per_epoch * num_epochs
|
571 |
-
|
572 |
-
# Create learning rate schedule
|
573 |
-
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
574 |
-
len(train_dataset),
|
575 |
-
train_batch_size,
|
576 |
-
training_args.num_train_epochs,
|
577 |
-
training_args.warmup_steps,
|
578 |
-
training_args.learning_rate,
|
579 |
-
)
|
580 |
-
|
581 |
-
# We use Optax's "masking" functionality to not apply weight decay
|
582 |
-
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
583 |
-
# mask boolean with the same structure as the parameters.
|
584 |
-
# The mask is True for parameters that should be decayed.
|
585 |
-
# Note that this mask is specifically adapted for FlaxBart.
|
586 |
-
# For FlaxT5, one should correct the layer norm parameter naming
|
587 |
-
# accordingly - see `run_t5_mlm_flax.py` e.g.
|
588 |
-
def decay_mask_fn(params):
|
589 |
-
flat_params = traverse_util.flatten_dict(params)
|
590 |
-
layer_norm_params = [
|
591 |
-
(name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
|
592 |
-
]
|
593 |
-
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
|
594 |
-
return traverse_util.unflatten_dict(flat_mask)
|
595 |
-
|
596 |
-
# create adam optimizer
|
597 |
-
adamw = optax.adamw(
|
598 |
-
learning_rate=linear_decay_lr_schedule_fn,
|
599 |
-
b1=training_args.adam_beta1,
|
600 |
-
b2=training_args.adam_beta2,
|
601 |
-
eps=training_args.adam_epsilon,
|
602 |
-
weight_decay=training_args.weight_decay,
|
603 |
-
mask=decay_mask_fn,
|
604 |
-
)
|
605 |
-
|
606 |
-
# Setup train state
|
607 |
-
state = TrainState.create(apply_fn=vit_gpt2_model.__call__, params=vit_gpt2_model.params, tx=adamw, dropout_rng=dropout_rng)
|
608 |
-
|
609 |
-
# label smoothed cross entropy
|
610 |
-
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
|
611 |
-
"""
|
612 |
-
The label smoothing implementation is adapted from Flax's official example:
|
613 |
-
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
|
614 |
-
"""
|
615 |
-
vocab_size = logits.shape[-1]
|
616 |
-
confidence = 1.0 - label_smoothing_factor
|
617 |
-
low_confidence = (1.0 - confidence) / (vocab_size - 1)
|
618 |
-
normalizing_constant = -(
|
619 |
-
confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
|
620 |
-
)
|
621 |
-
soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
|
622 |
-
|
623 |
-
loss = optax.softmax_cross_entropy(logits, soft_labels)
|
624 |
-
loss = loss - normalizing_constant
|
625 |
-
|
626 |
-
# ignore padded tokens from loss
|
627 |
-
loss = loss * padding_mask
|
628 |
-
loss = loss.sum() / padding_mask.sum()
|
629 |
-
return loss
|
630 |
-
|
631 |
-
# Define gradient update step fn
|
632 |
-
def train_step(state, batch, label_smoothing_factor=0.0):
|
633 |
-
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
634 |
-
|
635 |
-
def compute_loss(params):
|
636 |
-
labels = batch.pop("labels")
|
637 |
-
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
638 |
-
loss = loss_fn(logits, labels, batch["attention_mask"], label_smoothing_factor)
|
639 |
-
return loss
|
640 |
-
|
641 |
-
grad_fn = jax.value_and_grad(compute_loss)
|
642 |
-
loss, grad = grad_fn(state.params)
|
643 |
-
grad = jax.lax.pmean(grad, "batch")
|
644 |
-
|
645 |
-
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
646 |
-
|
647 |
-
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
648 |
-
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
649 |
-
|
650 |
-
return new_state, metrics
|
651 |
-
|
652 |
-
# Define eval fn
|
653 |
-
def eval_step(params, batch, label_smoothing_factor=0.0):
|
654 |
-
labels = batch.pop("labels")
|
655 |
-
logits = model(**batch, params=params, train=False)[0]
|
656 |
-
loss = loss_fn(logits, labels, batch["attention_mask"], label_smoothing_factor)
|
657 |
-
|
658 |
-
# summarize metrics
|
659 |
-
metrics = {"loss": loss}
|
660 |
-
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
661 |
-
return metrics
|
662 |
-
|
663 |
-
# Define generation function
|
664 |
-
max_length = (
|
665 |
-
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
|
666 |
-
)
|
667 |
-
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
|
668 |
-
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
|
669 |
-
|
670 |
-
def generate_step(params, batch):
|
671 |
-
model.params = params
|
672 |
-
# output_ids = model.generate(batch["pixel_values"], **gen_kwargs)
|
673 |
-
|
674 |
-
#encoder_outputs = model.encode(pixel_values=batch['pixel_values'])
|
675 |
-
#output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], encoder_outputs=encoder_outputs, **gen_kwargs)
|
676 |
-
|
677 |
-
# encoder_outputs = model.encode(pixel_values=batch['pixel_values'], params=params, train=False)
|
678 |
-
output_ids = model.generate(batch['pixel_values'], **gen_kwargs)
|
679 |
-
|
680 |
-
|
681 |
-
return output_ids.sequences
|
682 |
-
|
683 |
-
# Create parallel version of the train and eval step
|
684 |
-
p_train_step = jax.pmap(
|
685 |
-
partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
|
686 |
-
)
|
687 |
-
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
|
688 |
-
p_generate_step = jax.pmap(generate_step, "batch")
|
689 |
-
|
690 |
-
# Replicate the train state on each device
|
691 |
-
state = state.replicate()
|
692 |
-
|
693 |
-
logger.info("***** Running training *****")
|
694 |
-
logger.info(f" Num examples = {len(train_dataset)}")
|
695 |
-
logger.info(f" Num Epochs = {num_epochs}")
|
696 |
-
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
697 |
-
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
698 |
-
logger.info(f" Total optimization steps = {total_train_steps}")
|
699 |
-
|
700 |
-
train_time = 0
|
701 |
-
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
702 |
-
for epoch in epochs:
|
703 |
-
# ======================== Training ================================
|
704 |
-
train_start = time.time()
|
705 |
-
|
706 |
-
# Create sampling rng
|
707 |
-
rng, input_rng = jax.random.split(rng)
|
708 |
-
train_metrics = []
|
709 |
-
|
710 |
-
# Generate an epoch by shuffling sampling indices from the train dataset
|
711 |
-
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
712 |
-
steps_per_epoch = len(train_dataset) // train_batch_size
|
713 |
-
# train
|
714 |
-
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
715 |
-
batch = next(train_loader)
|
716 |
-
state, train_metric = p_train_step(state, batch)
|
717 |
-
train_metrics.append(train_metric)
|
718 |
-
|
719 |
-
train_time += time.time() - train_start
|
720 |
-
|
721 |
-
train_metric = unreplicate(train_metric)
|
722 |
-
|
723 |
-
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
|
724 |
-
epochs.write(desc)
|
725 |
-
epochs.desc = desc
|
726 |
-
logger.info(desc)
|
727 |
-
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
728 |
-
fp.write(desc + '\n')
|
729 |
-
|
730 |
-
|
731 |
-
# ======================== Evaluating ==============================
|
732 |
-
eval_metrics = []
|
733 |
-
eval_preds = []
|
734 |
-
eval_labels = []
|
735 |
-
|
736 |
-
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
737 |
-
eval_steps = len(eval_dataset) // eval_batch_size
|
738 |
-
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
739 |
-
# Model forward
|
740 |
-
batch = next(eval_loader)
|
741 |
-
labels = batch["labels"]
|
742 |
-
|
743 |
-
metrics = p_eval_step(state.params, batch)
|
744 |
-
eval_metrics.append(metrics)
|
745 |
-
|
746 |
-
# generation
|
747 |
-
if data_args.predict_with_generate:
|
748 |
-
generated_ids = p_generate_step(state.params, batch)
|
749 |
-
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
750 |
-
eval_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
|
751 |
-
|
752 |
-
# normalize eval metrics
|
753 |
-
eval_metrics = get_metrics(eval_metrics)
|
754 |
-
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
755 |
-
|
756 |
-
# compute ROUGE metrics
|
757 |
-
rouge_desc = ""
|
758 |
-
if data_args.predict_with_generate:
|
759 |
-
rouge_metrics = compute_metrics(eval_preds, eval_labels)
|
760 |
-
eval_metrics.update(rouge_metrics)
|
761 |
-
rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
|
762 |
-
|
763 |
-
# Print metrics and update progress bar
|
764 |
-
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
|
765 |
-
epochs.write(desc)
|
766 |
-
epochs.desc = desc
|
767 |
-
logger.info(desc)
|
768 |
-
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
769 |
-
fp.write(desc + '\n')
|
770 |
-
|
771 |
-
|
772 |
-
# Save metrics
|
773 |
-
if has_tensorboard and jax.process_index() == 0:
|
774 |
-
cur_step = epoch * (len(train_dataset) // train_batch_size)
|
775 |
-
write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
|
776 |
-
|
777 |
-
# ======================== Prediction loop ==============================
|
778 |
-
if training_args.do_predict:
|
779 |
-
logger.info("*** Predict ***")
|
780 |
-
|
781 |
-
pred_metrics = []
|
782 |
-
pred_generations = []
|
783 |
-
pred_labels = []
|
784 |
-
|
785 |
-
pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size)
|
786 |
-
pred_steps = len(predict_dataset) // eval_batch_size
|
787 |
-
for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
|
788 |
-
# Model forward
|
789 |
-
batch = next(pred_loader)
|
790 |
-
labels = batch["labels"]
|
791 |
-
|
792 |
-
metrics = p_eval_step(state.params, batch)
|
793 |
-
pred_metrics.append(metrics)
|
794 |
-
|
795 |
-
# generation
|
796 |
-
if data_args.predict_with_generate:
|
797 |
-
generated_ids = p_generate_step(state.params, batch)
|
798 |
-
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
799 |
-
pred_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
|
800 |
-
|
801 |
-
# normalize prediction metrics
|
802 |
-
pred_metrics = get_metrics(pred_metrics)
|
803 |
-
pred_metrics = jax.tree_map(jnp.mean, pred_metrics)
|
804 |
-
|
805 |
-
# compute ROUGE metrics
|
806 |
-
rouge_desc = ""
|
807 |
-
if data_args.predict_with_generate:
|
808 |
-
rouge_metrics = compute_metrics(pred_generations, pred_labels)
|
809 |
-
pred_metrics.update(rouge_metrics)
|
810 |
-
rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
|
811 |
-
|
812 |
-
# Print metrics
|
813 |
-
desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
|
814 |
-
epochs.write(desc)
|
815 |
-
epochs.desc = desc
|
816 |
-
logger.info(desc)
|
817 |
-
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
818 |
-
fp.write(desc + '\n')
|
819 |
-
|
820 |
-
|
821 |
-
# save checkpoint after each epoch and push checkpoint to the hub
|
822 |
-
if jax.process_index() == 0:
|
823 |
-
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
|
824 |
-
model.save_pretrained(
|
825 |
-
os.path.join(training_args.output_dir, f'ckpt_{epoch+1}'),
|
826 |
-
params=params,
|
827 |
-
push_to_hub=training_args.push_to_hub,
|
828 |
-
commit_message=f"Saving weights and logs of epoch {epoch+1}",
|
829 |
-
)
|
830 |
-
|
831 |
-
if __name__ == "__main__":
|
832 |
-
main()
|
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|
test_wit_dataset_script.py
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
import csv
|
2 |
-
import json
|
3 |
-
import os
|
4 |
-
|
5 |
-
import datasets
|
6 |
-
import pandas as pd
|
7 |
-
import numpy as np
|
8 |
-
|
9 |
-
ds = datasets.load_dataset('./wit_dataset_script.py', data_dir='./wit_data_dir/')
|
10 |
-
test_ds = ds['test']
|
11 |
-
|
12 |
-
|
13 |
-
def transform(example):
|
14 |
-
|
15 |
-
example['pixel_values'] = np.load(example['pixels_file'])
|
16 |
-
return example
|
17 |
-
|
18 |
-
|
19 |
-
test_ds = test_ds.map(transform)
|
20 |
-
|
21 |
-
for x in test_ds:
|
22 |
-
print(x)
|
23 |
-
break
|
|
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|
wit_data_dir/dev/dev.tsv
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:ef1ecdcd132885a8f29c8707fad649431c6ff3d9bbd295d56b8520e7046c0eb7
|
3 |
-
size 1418232
|
|
|
|
|
|
wit_data_dir/test/test.tsv
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:f0517292749005808b1d1d75343c76b8b16c3ed74fde030f7af8b611ad7b4d5d
|
3 |
-
size 1406997
|
|
|
|
|
|
wit_data_dir/train/train.tsv
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:267de5cc6965e95f44795e78019ad9d0dfc648bee83a9fdb9cf9a92e8e4ac9d3
|
3 |
-
size 45417661
|
|
|
|
|
|
wit_dataset_script.py
DELETED
@@ -1,145 +0,0 @@
|
|
1 |
-
import csv
|
2 |
-
import json
|
3 |
-
import os
|
4 |
-
|
5 |
-
import datasets
|
6 |
-
import pandas as pd
|
7 |
-
import numpy as np
|
8 |
-
|
9 |
-
|
10 |
-
# TODO: Add BibTeX citation
|
11 |
-
# Find for instance the citation on arxiv or on the dataset repo/website
|
12 |
-
_CITATION = """\
|
13 |
-
@InProceedings{huggingface:dataset,
|
14 |
-
title = {A great new dataset},
|
15 |
-
author={huggingface, Inc.
|
16 |
-
},
|
17 |
-
year={2020}
|
18 |
-
}
|
19 |
-
"""
|
20 |
-
|
21 |
-
# TODO: Add description of the dataset here
|
22 |
-
# You can copy an official description
|
23 |
-
_DESCRIPTION = """\
|
24 |
-
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
|
25 |
-
"""
|
26 |
-
|
27 |
-
# TODO: Add a link to an official homepage for the dataset here
|
28 |
-
_HOMEPAGE = ""
|
29 |
-
|
30 |
-
# TODO: Add the licence for the dataset here if you can find it
|
31 |
-
_LICENSE = ""
|
32 |
-
|
33 |
-
# TODO: Add link to the official dataset URLs here
|
34 |
-
# The HuggingFace dataset library don't host the datasets but only point to the original files
|
35 |
-
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
36 |
-
_URLs = {
|
37 |
-
}
|
38 |
-
|
39 |
-
|
40 |
-
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
|
41 |
-
class WITDataset(datasets.GeneratorBasedBuilder):
|
42 |
-
"""TODO: Short description of my dataset."""
|
43 |
-
|
44 |
-
VERSION = datasets.Version("1.1.0")
|
45 |
-
|
46 |
-
DEFAULT_CONFIG_NAME = "en"
|
47 |
-
|
48 |
-
def _info(self):
|
49 |
-
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
|
50 |
-
|
51 |
-
features = datasets.Features(
|
52 |
-
{
|
53 |
-
"id": datasets.Value("int64"),
|
54 |
-
"lang": datasets.Value("string"),
|
55 |
-
"caption": datasets.Value("string"),
|
56 |
-
"context": datasets.Value("string"),
|
57 |
-
"image_url": datasets.Value("string"),
|
58 |
-
"page_url": datasets.Value("string"),
|
59 |
-
"image_file": datasets.Value("string"),
|
60 |
-
"pixels_file": datasets.Value("string")
|
61 |
-
# These are the features of your dataset like images, labels ...
|
62 |
-
}
|
63 |
-
)
|
64 |
-
|
65 |
-
return datasets.DatasetInfo(
|
66 |
-
# This is the description that will appear on the datasets page.
|
67 |
-
description=_DESCRIPTION,
|
68 |
-
# This defines the different columns of the dataset and their types
|
69 |
-
features=features, # Here we define them above because they are different between the two configurations
|
70 |
-
# If there's a common (input, target) tuple from the features,
|
71 |
-
# specify them here. They'll be used if as_supervised=True in
|
72 |
-
# builder.as_dataset.
|
73 |
-
supervised_keys=None,
|
74 |
-
# Homepage of the dataset for documentation
|
75 |
-
homepage=_HOMEPAGE,
|
76 |
-
# License for the dataset if available
|
77 |
-
license=_LICENSE,
|
78 |
-
# Citation for the dataset
|
79 |
-
citation=_CITATION,
|
80 |
-
)
|
81 |
-
|
82 |
-
def _split_generators(self, dl_manager):
|
83 |
-
"""Returns SplitGenerators."""
|
84 |
-
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
|
85 |
-
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
|
86 |
-
|
87 |
-
data_dir = self.config.data_dir
|
88 |
-
|
89 |
-
return [
|
90 |
-
datasets.SplitGenerator(
|
91 |
-
name=datasets.Split.TRAIN,
|
92 |
-
# These kwargs will be passed to _generate_examples
|
93 |
-
gen_kwargs={
|
94 |
-
"data_dir": os.path.join(data_dir, "train"),
|
95 |
-
"split": "train",
|
96 |
-
},
|
97 |
-
),
|
98 |
-
datasets.SplitGenerator(
|
99 |
-
name=datasets.Split.TEST,
|
100 |
-
# These kwargs will be passed to _generate_examples
|
101 |
-
gen_kwargs={
|
102 |
-
"data_dir": os.path.join(data_dir, "test"),
|
103 |
-
"split": "test"
|
104 |
-
},
|
105 |
-
),
|
106 |
-
datasets.SplitGenerator(
|
107 |
-
name=datasets.Split.VALIDATION,
|
108 |
-
# These kwargs will be passed to _generate_examples
|
109 |
-
gen_kwargs={
|
110 |
-
"data_dir": os.path.join(data_dir, "dev"),
|
111 |
-
"split": "dev",
|
112 |
-
},
|
113 |
-
),
|
114 |
-
]
|
115 |
-
|
116 |
-
def _generate_examples(
|
117 |
-
self, data_dir, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
118 |
-
):
|
119 |
-
""" Yields examples as (key, example) tuples. """
|
120 |
-
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
121 |
-
# The `key` is here for legacy reason (tfds) and is not important in itself.
|
122 |
-
|
123 |
-
df = pd.read_csv(os.path.join(data_dir, f'{split}.tsv'), sep='\t')
|
124 |
-
|
125 |
-
for id_, row in df.iterrows():
|
126 |
-
|
127 |
-
_id = row[0]
|
128 |
-
|
129 |
-
# null caption and context
|
130 |
-
if type(row[4]) != str or type(row[5]) != str:
|
131 |
-
continue
|
132 |
-
|
133 |
-
image_file = os.path.join(data_dir, 'images', f'{_id}.jpg')
|
134 |
-
pixels_file = os.path.join(data_dir, 'numpy', f'{_id}.npy')
|
135 |
-
|
136 |
-
yield id_, {
|
137 |
-
"id": row[0],
|
138 |
-
"lang": row[1],
|
139 |
-
"caption": row[4],
|
140 |
-
"context": row[5],
|
141 |
-
"image_url": row[2],
|
142 |
-
"page_url": row[3],
|
143 |
-
"image_file": image_file,
|
144 |
-
"pixels_file": pixels_file
|
145 |
-
}
|
|
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