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_BASE_: "base_model_bert_l12_h192.yaml"
SHARED_TARGETS:
# -
# NAME: 'ImageNet1k'
# SHARED_TARGETS_CFG:
# FILE_PATH: 'open_source_dataset/imagenet_class_name_CLIP_with_endoftext.pkl'
# DISTRIBUTED: False
-
NAME: 'Vocab_Word'
SHARED_TARGETS_CFG:
FILE_PATH: 'open_source_dataset/vocabulary_CLIP_with_endoftext.pkl'
DISTRIBUTED: True
# -
# NAME: 'Kinetics400'
# SHARED_TARGETS_CFG:
# FILE_PATH: 'open_source_dataset/k400_class_name_CLIP_with_endoftext.pkl'
# DISTRIBUTED: False
TASKS:
# -
# NAME: imagenet
# DATASETS:
# TRAIN: 'ImageNetDataset'
# VAL: 'ImageNetDataset'
# TASK_TYPE: 'image_classification'
# DATASET_NAME: 'ImageNet1k'
# TARGET_SET: ['ImageNet1k']
# DATALOADER:
# TRAIN_BATCH_SIZE: 720
# # TEST_BATCH_SIZE: 2
# NUM_WORKERS: 4
# FEATS_FOLDER: 'cluster2:s3://imagenet'
# ANNO_FOLDER: 'open_source_dataset/imagenet/meta'
# SAMPLING_WEIGHT: 2.5
# CLASS_NAME_FILE: 'open_source_dataset/imagenet_class_name.pkl'
# MIXUP: 0.8
# CUTMIX: 1.0
# MIXUP_PROB: 1.0
# MIXUP_SWITCH_PROB: 0.5
# MIXUP_MODE: 'batch'
# MIXUP_LABEL_SMOOTHING: 0.1
# MODEL:
# MAX_SEQ_LEN: -1
# LABELS_NUM: 1000
# TEMP_NAME: logit_scale_img_cls
# LOSSES:
# NAMES: ['SoftTargetCrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 1.0
# REDUCTION: 'mean'
# # LOSS_FP32: True
# INFERENCE:
# NAME: 'ImageNetEvaler'
# ID_KEY: 'image_id'
# VALUE: 'cls_logits'
# VAL_ANNFILE: 'open_source_dataset/imagenet/meta/val.txt'
# TEST_ANNFILE: ''
# GENERATION_MODE: False
# -
# NAME: K400_retrieve
# DATASETS:
# TRAIN: 'VideoDataSet'
# VAL: 'VideoDataSet'
# TASK_TYPE: 'video_classification'
# DATASET_NAME: 'K400'
# TARGET_SET: ['Kinetics400']
# DATALOADER:
# TRAIN_BATCH_SIZE: 12 # 256
# TEST_BATCH_SIZE: 4 # debug
# NUM_WORKERS: 4 # debug 4
# FEATS_FOLDER: 'open_source_dataset/K400_official'
# ANNO_FOLDER: 'open_source_dataset/K400_official'
# S3_PATH: 's3://K400/'
# FRAMES_PER_CLIP: 8
# STRIDE: 32
# FILE_EXTENSION: ''
# ANNO_FILE: 'annotation.json'
# TIMESFORMER_AUG: True
# SAMPLING_WEIGHT: 1.0
# MODEL:
# MAX_SEQ_LEN: -1
# TEMP_NAME: logit_scale_video_cls
# LOSSES:
# NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
# LABELSMOOTHING: 0.1
# LOSS_WEIGHT: 1.0
# INFERENCE:
# NAME: 'MiTEvaler'
# ID_KEY: 'video_name'
# VALUE: 'label'
# VAL_ANNFILE: 'open_source_dataset/K400_official/annotation.json'
# TEST_ANNFILE: ''
# GENERATION_MODE: False
# NUM_VIEWS: 1
# -
# NAME: bookswiki_pretrain
# DATASETS:
# TRAIN: 'GeneralCorpusDataset'
# TASK_TYPE: 'text_mlm'
# DATASET_NAME: 'BooksWiki'
# TARGET_SET: ['Vocab_Word']
# VERSION: 'v2'
# DATALOADER:
# TRAIN_BATCH_SIZE: 512
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 2
# ANNO_FOLDER: 'open_source_dataset/text_corpus' # 'open_source_dataset/bert_pretrain_data/bookswiki'
# # ANNO_FOLDER: 'open_source_dataset/bert_pretrain_data/bookswiki'
# SEQ_PER_SAMPLE: 1
# SAMPLER: NodeDistributed
# CACHE_MODE: True
# SEQ_PER_SAMPLE: 128
# MIN_SEQ_PER_SAMPLE: 128
# APPEND_EOS: True
# ONE_STREAM: False
# SAMPLING_WEIGHT: 3.5
# RANDOM_MASK: True
# MODEL:
# MAX_SEQ_LEN: 128
# TEMP_NAME: logit_scale_text_mlm
# LOSSES:
# NAMES: ['CrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 0.33333
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# GENERATION_MODE: False
# -
# NAME: mscoco_retrieve
# DATASETS:
# TRAIN: 'ImageTextPairDataset'
# TEST: 'ImageTextPairDataset'
# TASK_TYPE: 'image_retrieval'
# DATASET_NAME: 'MSCOCO'
# DATALOADER:
# TRAIN_BATCH_SIZE: 100
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 1
# FEATS_FOLDER: 'open_source_dataset/mscoco_dataset/coco_origin'
# ANNO_FOLDER: 'open_source_dataset/mscoco_dataset/new_annotations'
# S3_PATH: 's3://coco/'
# SEQ_PER_SAMPLE: 1
# CACHE_MODE: True
# CIRCULAR_CACHE_MODE: False
# ZIP_MODE: False
# CACHE_ORIGIN_IMAGE: False
# RANDOM_CAPTION: False
# AS_NUMPY_AS_POSSIBLE: False
# SAMPLING_WEIGHT: 1.0
# TRANSFORM: 'clip_transforms'
# MODEL:
# MAX_SEQ_LEN: 50
# TEMP_NAME: logit_scale_retrieve
# LOSSES:
# NAMES: ['LabelSmoothingCrossEntropy', 'Accuracy']
# LABELSMOOTHING: 0.1
# LOSS_WEIGHT: 1.0
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# ID_KEY: 'image_id'
# VALUE: 'caption'
# NAME: 'RetrievalEvaler'
# VAL_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_val_set0_2014.jsonline'
# TEST_ANNFILE: 'open_source_dataset/flickr30k/all_data_final_test_set0_2014.jsonline'
# GENERATION_MODE: False
########## Image Captioning ###########
# -
# NAME: cc12m_caption
# DATASETS:
# TRAIN: 'ImageTextPairDataset'
# TASK_TYPE: 'image_caption'
# DATASET_NAME: 'CC12M'
# TARGET_SET: ['Vocab_Word']
# DATALOADER:
# TRAIN_BATCH_SIZE: 300
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 2
# S3_ANNO_FOLDER: 's3://cc12m/'
# ANNO_FOLDER: 'open_source_dataset/c12m/'
# ANNO_FILENAME: 'train_available.json'
# FEATS_FOLDER: 'open_source_dataset/c12m/'
# S3_PATH: 's3://cc12m/'
# SEQ_PER_SAMPLE: 1
# SAMPLER: NodeDistributed
# CACHE_MODE: True
# CIRCULAR_CACHE_MODE: False
# ZIP_MODE: False
# CACHE_ORIGIN_IMAGE: False
# RANDOM_CAPTION: False
# AS_NUMPY_AS_POSSIBLE: False
# SAMPLING_WEIGHT: 1.6889
# TRANSFORM: 'clip_transforms'
# MODEL:
# MAX_SEQ_LEN: 50
# TEMP_NAME: logit_scale_caption
# LOSSES:
# NAMES: ['CrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 0.33333
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# GENERATION_MODE: False
# -
# NAME: cc3m_caption
# DATASETS:
# TRAIN: 'ImageTextPairDataset'
# TASK_TYPE: 'image_caption'
# DATASET_NAME: 'CC3M'
# TARGET_SET: ['Vocab_Word']
# DATALOADER:
# TRAIN_BATCH_SIZE: 300
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 2
# ANNO_FOLDER: 's3://cc3m/'
# ANNO_FILENAME: 'train_spacy.json'
# FEATS_FOLDER: 'open_source_dataset/cc3m/'
# S3_PATH: 's3://cc3m/'
# SEQ_PER_SAMPLE: 1
# SAMPLER: NodeDistributed
# CACHE_MODE: True
# CIRCULAR_CACHE_MODE: False
# ZIP_MODE: False
# CACHE_ORIGIN_IMAGE: False
# RANDOM_CAPTION: False
# AS_NUMPY_AS_POSSIBLE: False
# SAMPLING_WEIGHT: 0.8780
# TRANSFORM: 'clip_transforms'
# MODEL:
# MAX_SEQ_LEN: 50
# TEMP_NAME: logit_scale_caption
# LOSSES:
# NAMES: ['CrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 0.33333
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# GENERATION_MODE: False
# -
# NAME: vg_caption
# DATASETS:
# TRAIN: 'ImageTextPairDataset'
# TASK_TYPE: 'image_caption'
# DATASET_NAME: 'VG'
# TARGET_SET: ['Vocab_Word']
# DATALOADER:
# TRAIN_BATCH_SIZE: 300
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 2
# FEATS_FOLDER: 'open_source_dataset/visual_genome/images'
# ANNO_FOLDER: 'open_source_dataset/visual_genome/annotations'
# S3_PATH: 's3://visual_genome/images'
# ANNO_FILENAME: 'vg_captions_128filter.json'
# SEQ_PER_SAMPLE: 1
# CACHE_MODE: True
# CIRCULAR_CACHE_MODE: False
# ZIP_MODE: False
# CACHE_ORIGIN_IMAGE: False
# RANDOM_CAPTION: False
# AS_NUMPY_AS_POSSIBLE: False
# SAMPLING_WEIGHT: 0.5895
# TRANSFORM: 'clip_transforms'
# MODEL:
# MAX_SEQ_LEN: 30
# TEMP_NAME: logit_scale_caption
# LOSSES:
# NAMES: ['CrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 0.33333
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# GENERATION_MODE: True
-
NAME: mscoco_caption
DATASETS:
TRAIN: 'ImageTextPairDataset'
# VAL: 'ImageTextPairDataset'
TEST: 'ImageTextPairDataset'
TASK_TYPE: 'image_caption'
DATASET_NAME: 'MSCOCO'
TARGET_SET: ['Vocab_Word']
DATALOADER:
TRAIN_BATCH_SIZE: 32
TEST_BATCH_SIZE: 2
NUM_WORKERS: 4
FEATS_FOLDER: 'open_source_dataset/mscoco_dataset/coco_origin'
ANNO_FOLDER: 'open_source_dataset/mscoco_dataset/new_annotations'
S3_PATH: 's3://coco/'
SEQ_PER_SAMPLE: 1
CACHE_MODE: True
CIRCULAR_CACHE_MODE: False
ZIP_MODE: False
CACHE_ORIGIN_IMAGE: False
RANDOM_CAPTION: False
AS_NUMPY_AS_POSSIBLE: False
SAMPLING_WEIGHT: 0.3817
TRANSFORM: 'clip_transforms'
RANDOM_MASK: True
MODEL:
MAX_SEQ_LEN: 50
EVAL_MAX_SEQ_LEN: 21
TEMP_NAME: logit_scale_caption
LOSSES:
NAMES: ['CrossEntropy', 'Accuracy']
LOSS_WEIGHT: 0.33333
REDUCTION: 'mean'
DECODE_STRATEGY:
NAME: 'CaptionBeamSearcherV3'
BEAM_SIZE: 2
# LEN_PENALTY: 2.0
INFERENCE:
NAME: 'COCOEvaler'
VOCAB: 'CLIP'
ID_KEY: 'image_id'
VALUE: 'caption'
VAL_ANNFILE: 'open_source_dataset/mscoco_dataset/new_annotations/captions_val5k.json'
TEST_ANNFILE: 'open_source_dataset/mscoco_dataset/new_annotations/captions_test5k.json'
GENERATION_MODE: True
# -
# NAME: sbu_caption
# DATASETS:
# TRAIN: 'ImageTextPairDataset'
# TASK_TYPE: 'image_caption'
# DATASET_NAME: 'SBU'
# TARGET_SET: ['Vocab_Word']
# DATALOADER:
# TRAIN_BATCH_SIZE: 300
# TEST_BATCH_SIZE: 32
# NUM_WORKERS: 1
# S3_ANNO_FOLDER: 's3://SBU/annotations'
# ANNO_FOLDER: 'open_source_dataset/sbucaption/annotations'
# ANNO_FILENAME: 'subcaption.json'
# FEATS_FOLDER: 'open_source_dataset/sbucaption/'
# S3_PATH: 's3://SBU/images'
# SEQ_PER_SAMPLE: 1
# SAMPLER: NodeDistributed
# CACHE_MODE: True
# CIRCULAR_CACHE_MODE: False
# ZIP_MODE: False
# CACHE_ORIGIN_IMAGE: False
# RANDOM_CAPTION: False
# AS_NUMPY_AS_POSSIBLE: False
# SAMPLING_WEIGHT: 0.4618
# TRANSFORM: 'clip_transforms'
# MODEL:
# MAX_SEQ_LEN: 50
# TEMP_NAME: logit_scale_caption
# LOSSES:
# NAMES: ['CrossEntropy', 'Accuracy']
# LOSS_WEIGHT: 0.33333
# REDUCTION: 'mean'
# INFERENCE:
# VOCAB: 'CLIP'
# GENERATION_MODE: False
ENGINE:
NAME: 'UnifiedTrainer'
MODEL:
META_ARCHITECTURE: 'MultiTaskTransformerEncoder'
ENCODER: 'UnifiedBertEncoder'
IN_TUNING: True # use IN1k instead of 22k
SHARE_LAYERNORM: True
BERT:
NORMALIZE_DECISION: "BERTPre"
DROP_PATH_PROB: 0.0
DROP_PATH_PROB_FIXED: True
MODEL_EMA: False
MODEL_EMA_DECAY: 0.9999
MAEParamsInit: True
POSEMBEDFIX: True
IMG_INPUT_SIZE: 224
PATCH_SIZE: 16
LAYER_SCALE: True
LAYER_SCALE_INIT: 1e-3
LAYER_SCALE_FP32: True
GATE_FP32: False
TAG_TRANSFORM_FP32: False
DATALOADER:
USE_WEIGHTED_SAMPLER: True
UNIFIED_DATASET: True
NUM_WORKERS: 32
STRATEGY: 'turn'
PADDING_TO_MAX: False # True for debugging or token moe with distributed moe
####################################### Optimizer #######################################
SOLVER:
NAME: 'Adam'
TORCH_OPTIMIZER: True
PARAMS_SEPERATE: True
# PARAMS_GROUP: True
# EPOCH: 1
MAX_ITER: 150000
CHECKPOINT_PERIOD: 5000
EVAL_PERIOD: 500000
BASE_LR: 0.001
BIAS_LR_FACTOR: 1.0
WEIGHT_DECAY: 0.05
WEIGHT_DECAY_NORM: 0.0
WEIGHT_DECAY_BIAS: 0.0
WEIGHT_DECAY_EMBEDDING: 0.0
MOMENTUM: 0.9
DAMPENING: 0.0
NESTEROV: 0.0
BETAS: [0.9, 0.95]
EPS: 1e-6
GRAD_CLIP: 0.1
GRAD_CLIP_TYPE: 'norm'
ACCUM_ITER: 0
AMP_FP16: True
APEX_FP16: False # dangerous
WRITE_PERIOD: 50
MIN_LOSS_SCLE: 2048.0
# BF16: False # True
# ZEROSTAGE: 2
LOSS_SCALE_WINDOW: 200
FORCE_SOFTMAX_FP16: True
FORCE_LN_FP16: True
FORCE_NORM_FP16: True
# FORCE_TEMP_FP16: True
FORCE_EMBED_FP16: True
####################################### lr scheduler #######################################
LR_SCHEDULER:
NAME: 'WarmupCosine'
WARMUP: 5000
MIN_LR: 0.000001
####################################### evaluation #######################################
INFERENCE:
VOCAB: 'CLIP'
ITER_BASED: True
find_unused_parameters: true
# ENCODERS:
# -
# NAME: VisualEncoder
# TYPE: VisualEncoder
# DROP_PATH_PROB: 0.0
# HIDDEN_SIZE: 192
# HIDDEN_DROPOUT_PROB: 0.
# HIDDEN_ACT: "gelu"
# NUM_ATTENTION_HEADS: 3
# INTERMEDIATE_SIZE: 768
# INTERMEDIATE_DROP: 0.
# FFN_DROPOUT_PROB: 0.
# ATTENTION_PROBS_DROPOUT_PROB: 0.
# NUM_HIDDEN_LAYERS: 6
# NUM_GENERATION_LAYERS: 0
# DROP_PATH_PROB_FIXED: True
# -
# NAME: TextEncoder
# TYPE: TextEncoder
# DROP_PATH_PROB: 0.0
# HIDDEN_SIZE: 192
# HIDDEN_DROPOUT_PROB: 0.
# HIDDEN_ACT: "gelu"
# NUM_ATTENTION_HEADS: 3
# INTERMEDIATE_SIZE: 768
# INTERMEDIATE_DROP: 0.
# FFN_DROPOUT_PROB: 0.
# ATTENTION_PROBS_DROPOUT_PROB: 0.
# NUM_HIDDEN_LAYERS: 6
# NUM_GENERATION_LAYERS: 0
# DROP_PATH_PROB_FIXED: True
MOE:
MOE: True
MOE_TYPE: 'attribute'
TAG_Transform: True
ATTRIBUTE_LENGTH: 8
EP_WORLD_SIZE: 1 # tag moe only
NUM_EXPERTS: 8
TOP_K: 2
CAPACITY_FACTOR: 3.0
EVAL_MIN_CAPACITY: 4.0
MIN_CAPACITY: 4
NOISY_GATE_POLICY: 'vmoe'
MOE_PARAM_GROUP: True
MOE_EXPERT_TYPE: 'FFN,SA'
SA_LINEAR_OUT_MOE: True
MOE_EXPERT_LOCATION: 'all' # 'odd'
# MOE_LAYER_START_IDX: 3
# MOE_LAYER_END_IDX: 21
# MOE_LAYER_START_IDX: 18
# MOE_LAYER_END_IDX: 12
BATCH_PRIO: True
USE_TUTEL: True
FFN_SHARE_GATE_DECISION: True |