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1327f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | # Copyright 2025 The Scenic Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""WIT Retrieval + Captioning Pre-Training."""
import ml_collections
TRAIN_DATA_SIZE = 1_000_000_000
def get_config() -> ml_collections.ConfigDict:
"""Returns the base experiment configuration."""
config = ml_collections.ConfigDict()
config.experiment_name = 'image_caption_debug'
config.optimizer = 'adafactor'
n_device = 128
batch_size = 12 * 2 * n_device
config.optimizer_configs = ml_collections.ConfigDict()
config.optimizer_configs.momentum = None
# config.optimizer_configs.momentum = 0.9
# config.optimizer_configs.dtype_momentum = 'bfloat16'
config.optimizer_configs.weight_decay_rate = 2e-3
config.optimizer_configs.clipping_threshold = 5.0
config.optimizer_configs.skip_scale_and_bias_regularization = True
config.frozen_patterns = []
config.not_frozen_patterns = [('value_perceiver/.*', 0.3),
# ('text_encoder/.*', 0.1),
# ('img_encoder/.*', 0.1),
('shared_token_embedder/.*', 0.1),
('query_head/.*', 0.2), ('out_decoder/.*', 1),
('key_head/.*', 0.2), ('head_out/.*', 0.2),
('fusion_encoder/.*', 0.5),
('att_transform/.*', 0.3),
('dataset_gate/.*', 0.5)]
config.grad_clip_configs = ml_collections.ConfigDict()
config.grad_clip_configs.clip_method = 'clip_by_global_norm'
config.grad_clip_configs.clip_value = 1.0
config.kb_dataset_names = ['wit_table', 'cc12m_table', 'vqa_table']
config.kb_dataset_configs = [{}, {}, {}]
config.batch_size = batch_size
config.eval_batch_size = batch_size
config.rng_seed = 0
config.update_num = False
config.num_training_epochs = 1
config.data_dtype_str = 'bfloat16'
# Model
config.model_name = 'knowledge_fid'
config.model = ml_collections.ConfigDict()
config.model.image_model = 'vit'
config.model.t5_name = 't5_1_1_base'
# ['t5_1_1_small', 't5_1_1_base', 't5_1_1_large', 't5_1_1_xl', 't5_1_1_xxl']
config.model.num_fusion_layers = 6
config.model.n_compressed_tokens = 32
config.model.key_dim = 512
config.model.dropout_rate = 0.0
config.model.temperature = 0.2
config.model.retr_k = 10
config.model.retr_data_ratio = 0.2
config.model.label_smoothing = 1e-2
config.model.vit_name = 'B/16'
config.model.vit_model_path = 'JFT3b-B/16'
# [JFT3b-B/32, JFT3b-B/16, JFT3b-L/16, JFT3b-g/14, JFT3b-G/14]
config.model.t5_frozen_base = False
config.model.vit_num_frozen_layers = 1 / 2
config.model.retrieve_local = False
config.model.use_psudo_retr = True
config.model.disentangle = True
config.model.gap = True
config.model.retrieval_ratio = 1e-2
config.model.n_knowledge_source = len(config.kb_dataset_names)
config.model.qa = False
config.frozen_memory = False
config.vocab_size = 32120
config.autoregressive_decoding = ml_collections.ConfigDict()
config.autoregressive_decoding.num_decodes = 1
config.autoregressive_decoding.beam_search = False
# Dataset.
config.dataset_name = 'web_image_text_generation'
config.dataset_configs = ml_collections.ConfigDict()
# Learning rate.
config.num_train_examples = TRAIN_DATA_SIZE
steps_per_epoch = TRAIN_DATA_SIZE // config.batch_size
config.lr_configs = ml_collections.ConfigDict()
config.lr_configs.total_steps = int(config.num_training_epochs *
steps_per_epoch)
config.lr_configs.learning_rate_schedule = 'compound'
config.lr_configs.factors = 'constant * rsqrt_decay * linear_warmup'
config.lr_configs.warmup_steps = 10000
config.lr_configs.timescale = 10000
# config.lr_configs.steps_per_cycle = config.lr_configs.total_steps
config.lr_configs.base_learning_rate = 1e-4
config.lr_configs.end_learning_rate = 1e-6
# Logging.
config.log_summary_steps = 100
config.log_eval_steps = 1000
config.checkpoint_steps = 5000
config.write_summary = True
config.xprof = True # Profile using xprof
config.checkpoint = True # Do checkpointing.
config.debug_train = False # Debug mode during training.
config.debug_eval = False # Debug mode during eval.
# Initalisation configs
config.init_from = ml_collections.ConfigDict()
# Initializing from a vidcap model.
config.init_from.load_key_encoder = False
config.init_from.encoder = ml_collections.ConfigDict()
config.init_from.encoder.init_from_vit = False
config.init_from.encoder.checkpoint_path = None
return config
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