big_vision/configs/proj/paligemma/transfers/widgetcap.py

# Copyright 2024 Big Vision Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# pylint: disable=line-too-long
r"""PaliGemma transfer to widgetcap (bbox drawn in the picture).
"""

import big_vision.configs.common as bvcc
from big_vision.configs.proj.paligemma.transfers.common import combine_and_keep_train, combine_and_keep_eval, TOKENIZER


def training_data(res, *, final_split, text_len=32):
  """Creates training data config.

  See (internal link)
  You can add more arguments beside `res`, but give them good defaults.

  Args:
    res: The requested image resolution (eg 224).
    final_split: Train on all train+dev data.
    text_len: The max text length.

  Returns:
    The ConfigDict for the input section.
  """
  c = bvcc.parse_arg('')  # Just make a configdict without extra import.
  c.data = dict(
      name='widgetcap',
      split='train+dev' if final_split else 'train',
  )
  c.pp = '|'.join([
      'decode',
      f'resize({res}, antialias=True)',
      'draw_bbox',
      'value_range(-1, 1)',
      'strfmt("caption en", outkey="prefix")',
      'choice_no_replacement(inkey="texts", outkey="suffix")',
      combine_and_keep_train(text_len),
  ])
  return c


def add_eval(c, res, text_len=32, **kw):
  """Captioning evaluator with cider/bleu-4/meteor/rouge/spice metrics."""
  # Input eval pp without ground truth text and random crop.
  pp_eval = '|'.join([
      'copy("texts", "captions")',  # GT for evaluator.
      'decode',
      f'resize({res}, antialias=True)',
      'draw_bbox',
      'value_range(-1, 1)',
      'strfmt("caption en", outkey="prefix")',
      combine_and_keep_eval(text_len, keep=('image/id', 'captions')),
  ])

  for name, split in [
      ('val', 'dev'),
      ('eval', 'test'),
  ]:
    c.evals[f'widgetcap/{name}'] = dict(
        type='proj.paligemma.transfers.coco_caption',
        pred='decode', pred_kw={'max_decode_len': text_len},
        data=dict(
            name='widgetcap',
            split=split,
        ),
        log_percent=0.1, tokenizer=TOKENIZER, pp_fn=pp_eval)
    c.evals[f'widgetcap/{name}'].update(kw)


def add_eval_pplx(c, res, text_len=32):
  """Perplexity evaluator to test runs before implementing the real deal."""
  c_train = training_data(res, final_split=True, text_len=text_len)  # Use mostly same settings as training.
  for name, split in [
      ('minitrain', 'train[:5%]'),  # To gauge memorization.
      ('minival', 'dev'),  # To tune hparams.
      ('eval', 'test'),  # To compute final publishable scores.
  ]:
    c.evals[f'widgetcap/{name}/pplx'] = dict(
        type='proj.paligemma.perplexity', pred='logits',
        key='text', shift_labels=True,
        log_percent=0.05,  # Eval ~20x per run; it's cheap.
        data={**c_train.data, 'split': split},
        pp_fn=c_train.pp,
    )


def sweep_best(add, arg=None):
  """Train with best hyper-params."""
  c = bvcc.parse_arg(arg, final_split=False)
  # Based on sweeps (internal link) (widgetcap/val/cider).
  # NOTE: dropout always on, see get_config.
  add(lr=3e-6, wd=3e-7, total_epochs=4, **bvcc.arg(res=224, **c))
  add(lr=3e-6, wd=3e-7, total_epochs=4, **bvcc.arg(res=448, **c))
  # Not better: add(lr=3e-6, wd=3e-7, total_epochs=4, **bvcc.arg(res=896, **c))


sweep = sweep_best  # Choose which sweep to run.


def get_config(arg=None):
  """Config for training."""
  c = bvcc.parse_arg(arg, mode='xm', res=224, final_split=False)

  c.input = training_data(c.res, final_split=c.final_split)

  # Instead of epochs, you can also use `total_examples` or `total_steps`.
  c.total_epochs = 4
  c.input.batch_size = 64
  c.optax_name = 'scale_by_adam'
  c.optax = dict(b2=0.999)
  c.lr = 3e-6
  c.wd = 3e-7
  c.grad_clip_norm = 1.0
  c.label_smoothing = 0.1
  c.schedule = dict(decay_type='cosine', warmup_percent=0.05)

  # Add evaluators.
  c.evals = {}
  add_eval(c, c.res, batch_size=1024)
  add_eval_pplx(c, c.res)

  # Model section.
  c.model_name = 'proj.paligemma.paligemma'
  c.model = {}
  c.model.img = dict(variant='So400m/14', pool_type='none', scan=True)
  c.model.llm = dict(vocab_size=256_000 + 1024 + 128, dropout=0.1)
  c.model_init = f'pt_{c.res}'

  # FSDP strategy.
  c.mesh = [('data', -1)]
  c.sharding_strategy = [('.*', 'fsdp(axis="data")')]
  c.sharding_rules = [('act_batch', ('data',))]

  # These probably do not need any change/tuning
  c.input.shuffle_buffer_size = 50_000
  c.log_training_steps = 50
  c.ckpt_steps = 1_000
  c.pp_modules = [
      'ops_general',
      'ops_image',
      'ops_text',
      'proj.paligemma.ops',
      'proj.paligemma.widgetcap',
  ]

  # Update configs for quicker local runs and avoid swapping.
  if c.mode in ('runlocal', 'mock'):
    c.input.shuffle_buffer_size = None
    for ev in c.evals.values():
      ev.data.split = ev.data.split.split('[')[0] + '[:16]'

  if c.mode == 'runlocal':
    c.log_training_steps = 1
    c.input.batch_size = 2

  c.seed = 0
  return c


def metrics(arg=None):  # pylint: disable=unused-argument
  # This function defines the default flatboard. If you want, it can be a lot
  # fancier too, but the simplest way is a list of metric names.
  m = ['training_loss']
  for split in ('eval', 'minival', 'minitrain'):
    m.append(f'widgetcap/{split}/pplx/avg')
  for split in ('val', 'eval'):
    m.append(f'widgetcap/{split}/cider')
  return m