Menyesuaikan Gemma menggunakan JAX dan Flax

Lihat di ai.google.dev Menjalankan di Google Colab Terbuka di Vertex AI Lihat sumber di GitHub

Ringkasan

Gemma adalah kumpulan model bahasa besar terbuka yang ringan dan canggih, berdasarkan riset dan teknologi Google DeepMind Gemini. Tutorial ini menunjukkan cara menyempurnakan model Gemma 2B Instructions untuk tugas terjemahan bahasa Inggris-Prancis menggunakan library gemma Google DeepMind, JAX (library komputasi numerik berperforma tinggi), Flax (library jaringan neural berbasis JAX), Chex (library utilitas untuk menulis kode JAX yang andal), Optax (pemprosesan gradien dan set data Texty yang andal). Meskipun Flax tidak digunakan langsung di notebook ini, Flax digunakan untuk membuat Gemma.

Library gemma ditulis dengan JAX, Flax, Orbax (library berbasis JAX untuk utilitas pelatihan seperti checkpointing), dan SentencePiece (library tokenizer/detokenizer).

Penyiapan

1. Menyiapkan akses Kaggle untuk Gemma

Untuk menyelesaikan tutorial ini, Anda harus mengikuti petunjuk penyiapan terlebih dahulu di Penyiapan Gemma, yang menunjukkan cara melakukan hal berikut:

  • Dapatkan akses ke Gemma di kaggle.com.
  • Pilih runtime Colab dengan resource yang memadai untuk menjalankan model Gemma.
  • Membuat dan mengkonfigurasi nama pengguna dan kunci API Kaggle.

Setelah menyelesaikan penyiapan Gemma, lanjutkan ke bagian berikutnya yang berisi cara menetapkan variabel lingkungan untuk lingkungan Colab Anda.

2. Menetapkan variabel lingkungan

Tetapkan variabel lingkungan untuk KAGGLE_USERNAME dan KAGGLE_KEY. Saat diminta dengan pesan "Berikan akses?", setujui untuk memberikan akses rahasia.

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. Menginstal library gemma

Akselerasi hardware Colab gratis saat ini tidak cukup untuk menjalankan notebook ini. Jika Anda menggunakan Colab Pay As You Go atau Colab Pro, klik Edit > Setelan notebook > Pilih A100 GPU > Save untuk mengaktifkan akselerasi hardware.

Selanjutnya, Anda perlu menginstal library gemma Google DeepMind dari github.com/google-deepmind/gemma. Jika Anda mendapatkan pesan error tentang "resolver dependensi pip", Anda biasanya dapat mengabaikannya.

pip install -q git+https://github.com/google-deepmind/gemma.git

4. Mengimpor library

Notebook ini menggunakan Flax (untuk jaringan neural), JAX inti, SentencePiece (untuk tokenisasi), Chex (library utilitas untuk menulis kode JAX yang andal), dan Set Data TensorFlow.

import os
import enum
import re
import string

import chex
import jax
import jax.numpy as jnp
import optax

import tensorflow as tf
import tensorflow_datasets as tfds

from gemma import params as params_lib
from gemma import sampler as sampler_lib
from gemma import transformer as transformer_lib
import sentencepiece as spm

Memuat model Gemma

Muat model Gemma dengan kagglehub.model_download, yang menggunakan tiga argumen:

  • handle: Tuas model dari Kaggle
  • path: (String opsional) Jalur lokal
  • force_download: (Boolean opsional) Memaksa mendownload ulang model
GEMMA_VARIANT = '2b-it' # @param ['2b', '2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma/flax/{GEMMA_VARIANT}')

Downloading from https://www.kaggle.com/api/v1/models/google/gemma/flax/2b-it/2/download...
100%|██████████| 3.67G/3.67G [00:26<00:00, 147MB/s]
Extracting model files...

print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2

Periksa lokasi bobot model dan tokenizer, lalu tetapkan variabel jalur. Direktori tokenizer akan berada di direktori utama tempat Anda mendownload model, sedangkan bobot model akan berada di sub-direktori. Contoh:

  • File tokenizer.model akan berada di /LOCAL/PATH/TO/gemma/flax/2b-it/2).
  • Checkpoint model akan berada di /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it).
CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2/2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma/flax/2b-it/2/tokenizer.model

Memuat dan menyiapkan set data MTNT dan tokenizer Gemma

Anda akan menggunakan set data MTNT (Machine Translation of Noisy Text), yang tersedia dari Set Data TensorFlow.

Unduh bagian {i>dataset<i} bahasa Inggris-ke-Prancis dari {i>dataset<i} MTNT, dan kemudian ambil sampel dua contoh. Setiap sampel dalam set data berisi dua entri: src: kalimat bahasa Inggris asli; dan dst: terjemahan bahasa Prancis yang sesuai.

ds = tfds.load("mtnt/en-fr", split="train")

ds = ds.take(2)
ds = ds.as_numpy_iterator()

for idx, example in enumerate(ds):
  print(f'Example {idx}:')
  for key, val in example.items():
    print(f'{key}: {val}')
  print()

Downloading and preparing dataset 35.08 MiB (download: 35.08 MiB, generated: 11.33 MiB, total: 46.41 MiB) to /root/tensorflow_datasets/mtnt/en-fr/1.0.0...
Dl Completed...: 0 url [00:00, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Extraction completed...: 0 file [00:00, ? file/s]
Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]
Generating train examples...:   0%|          | 0/35692 [00:00<?, ? examples/s]
Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-train.tfrecord*...:   0%|          …
Generating test examples...:   0%|          | 0/1020 [00:00<?, ? examples/s]
Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-test.tfrecord*...:   0%|          |…
Generating valid examples...:   0%|          | 0/811 [00:00<?, ? examples/s]
Shuffling /root/tensorflow_datasets/mtnt/en-fr/1.0.0.incomplete6YJMND/mtnt-valid.tfrecord*...:   0%|          …
Dataset mtnt downloaded and prepared to /root/tensorflow_datasets/mtnt/en-fr/1.0.0. Subsequent calls will reuse this data.
Example 0:
dst: b'Le groupe de " toutes les \xc3\xa9toiles potentielles de la conf\xc3\xa9rence de l\'Est mais qui ne s\'en sortent pas dans le groupe de l\'Ouest ".'
src: b'The group of \xe2\x80\x9ceastern conference potential all stars but not making it in the West\xe2\x80\x9d group.'

Example 1:
dst: b"Kameron est-elle un peu aigrie de son manque de temps \xc3\xa0 l'\xc3\xa9cran ?"
src: b'Is Kameron a Little Salty About Her Lack of Air Time?'

Muat tokenizer Gemma yang dibuat menggunakan sentencepiece.SentencePieceProcessor:

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True

SesuaikanSentencePieceProcessor untuk tugas terjemahan bahasa Inggris ke bahasa Prancis. Karena Anda akan melakukan penyempurnaan pada bagian bahasa Inggris dari model Gemma, Anda perlu membuat beberapa penyesuaian, seperti:

  • Awalan input: Menambahkan awalan umum ke setiap input menandakan tugas terjemahan. Misalnya, Anda dapat menggunakan perintah dengan awalan seperti Translate this into French: [INPUT_SENTENCE].

  • Akhiran awal terjemahan: Menambahkan akhiran di akhir setiap perintah akan menginstruksikan model Gemma kapan tepatnya proses terjemahan dimulai. Baris baru akan melakukan tugasnya.

  • Token model bahasa: Model Gemma mengharapkan token "awal urutan" di awal setiap urutan, jadi menambahkan token "akhir urutan" di akhir setiap contoh pelatihan seharusnya sudah memadai.

    Buat wrapper kustom di sekitar SentencePieceProcessor sebagai berikut:

class GemmaTokenizer:

  def __init__(self,
               spm_processor: spm.SentencePieceProcessor):
    self._spm_processor = spm_processor

  @property
  def pad_id(self) -> int:
    """Fast access to the pad ID."""
    return self._spm_processor.pad_id()

  def tokenize(self,
               example: str | bytes,
               prefix: str = '',
               suffix: str = '',
               add_eos: bool = True) -> jax.Array:
    """
    The tokenization function.

    Args:
      example: Input string to tokenize.
      prefix:  Prefix to add to the input string.
      suffix:  Suffix to add to the input string.
      add_eos: If True, add an "end of sentence" token at the end of the output
               sequence.
    Returns:
      Tokens corresponding to the input string.
    """
    int_list = [self._spm_processor.bos_id()]
    int_list.extend(self._spm_processor.EncodeAsIds(prefix + example + suffix))
    if add_eos:
      int_list.append(self._spm_processor.eos_id())

    return jnp.array(int_list, dtype=jnp.int32)

  def tokenize_tf_op(self,
                     str_tensor: tf.Tensor,
                     prefix: str = '',
                     suffix: str = '',
                     add_eos: bool = True) -> tf.Tensor:
    """A TensorFlow operator for the tokenize function."""
    encoded = tf.numpy_function(
        self.tokenize,
        [str_tensor, prefix, suffix, add_eos],
        tf.int32)
    encoded.set_shape([None])
    return encoded

  def to_string(self, tokens: jax.Array) -> str:
    """Convert an array of tokens to a string."""
    return self._spm_processor.EncodeIds(tokens.tolist())

Cobalah dengan membuat instance GemmaTokenizer kustom baru Anda, lalu menerapkannya pada sampel kecil set data MTNT:

tokenizer = GemmaTokenizer(vocab)

def tokenize_source(tokenizer, example: tf.Tensor):
  return tokenizer.tokenize_tf_op(example,
                                  prefix='Translate this into French:\n',
                                  suffix='\n',
                                  add_eos=False)
def tokenize_destination(tokenizer, example: tf.Tensor):
  return tokenizer.tokenize_tf_op(example,
                                  add_eos=True)

ds = tfds.load("mtnt/en-fr",split="train")
ds = ds.take(2)
ds = ds.map(lambda x: {'src': tokenize_source(tokenizer, x['src']),
                       'dst': tokenize_destination(tokenizer, x['dst'])})
ds = ds.as_numpy_iterator()

for idx, example in enumerate(ds):
  print(f'Example {idx}:')
  for key, val in example.items():
    print(f'{key}: {val}')
  print()

Example 0:
src: [     2  49688    736   1280   6987 235292    108    651   2778    576
   1080 104745  11982   5736    832   8995    901    780   3547    665
    575    573   4589 235369   2778 235265    108]
dst: [     2   2025  29653    581    664  16298   1437  55563  41435   7840
    581    683 111452    581    533 235303   9776   4108   2459    679
    485 235303    479   6728    579   1806   2499    709  29653    581
    533 235303 101323  16054      1]

Example 1:
src: [     2  49688    736   1280   6987 235292    108   2437  87150    477
    476  11709 230461   8045   3636  40268    576   4252   4897 235336
    108]
dst: [     2 213606    477   1455 235290   3510    748   8268 191017   2809
    581   2032  69972    581  11495   1305    533 235303  65978   1654
      1]

Buat loader data untuk seluruh set data MTNT:

@chex.dataclass(frozen=True)
class TrainingInput:
  # Input tokens provided to the model.
  input_tokens: jax.Array

  # A mask that determines which tokens contribute to the target loss
  # calculation.
  target_mask: jax.Array

class DatasetSplit(enum.Enum):
  TRAIN = 'train'
  VALIDATION = 'valid'

class MTNTDatasetBuilder:
  """The dataset builder for the MTNT dataset."""

  N_ITEMS = {DatasetSplit.TRAIN: 35_692,
             DatasetSplit.VALIDATION: 811}

  BUFFER_SIZE_SHUFFLE = 10_000
  TRANSLATION_PREFIX = 'Translate this into French:\n'
  TRANSLATION_SUFFIX = '\n'

  def __init__(self,
               tokenizer : GemmaTokenizer,
               max_seq_len: int):
    """Constructor.

    Args:
      tokenizer: Gemma tokenizer to use.
      max_seq_len: size of each sequence in a given batch.
    """
    self._tokenizer = tokenizer
    self._base_data = {
        DatasetSplit.TRAIN: tfds.load("mtnt/en-fr",split="train"),
        DatasetSplit.VALIDATION: tfds.load("mtnt/en-fr",split="valid"),
    }
    self._max_seq_len = max_seq_len

  def _tokenize_source(self, example: tf.Tensor):
    """Tokenization function for the source."""
    return self._tokenizer.tokenize_tf_op(example,
                                          prefix=self.TRANSLATION_PREFIX,
                                          suffix=self.TRANSLATION_SUFFIX,
                                          add_eos=False)

  def _tokenize_destination(self, example: tf.Tensor):
    """Tokenization function for the French translation."""
    return self._tokenizer.tokenize_tf_op(example,
                                          add_eos=True)

  def _pad_up_to_max_len(self,
                         input_tensor: tf.Tensor,
                         pad_value: int | bool,
                         ) -> tf.Tensor:
    """Pad the given tensor up to sequence length of a batch."""
    seq_len = tf.shape(input_tensor)[0]
    to_pad = tf.maximum(self._max_seq_len - seq_len, 0)
    return tf.pad(input_tensor,
                  [[0, to_pad]],
                  mode='CONSTANT',
                  constant_values=pad_value,
                  )

  def _to_training_input(self,
                         src_tokens: jax.Array,
                         dst_tokens: jax.Array,
                         ) -> TrainingInput:
    """Build a training input from a tuple of source and destination tokens."""

    # The input sequence fed to the model is simply the concatenation of the
    # source and the destination.
    tokens = tf.concat([src_tokens, dst_tokens], axis=0)

    # To prevent the model from updating based on the source (input)
    # tokens, add a target mask to each input.
    q_mask = tf.zeros_like(src_tokens, dtype=tf.bool)
    a_mask = tf.ones_like(dst_tokens, dtype=tf.bool)
    mask = tf.concat([q_mask, a_mask], axis=0)

    # If the output tokens sequence is smaller than the target sequence size,
    # then pad it with pad tokens.
    tokens = self._pad_up_to_max_len(tokens, self._tokenizer.pad_id)

    # Don't want to perform the backward pass on the pad tokens.
    mask = self._pad_up_to_max_len(mask, False)

    return TrainingInput(input_tokens=tokens, target_mask=mask)


  def get_train_dataset(self, batch_size: int, num_epochs: int):
    """Build the training dataset."""

    # Tokenize each sample.
    ds = self._base_data[DatasetSplit.TRAIN].map(lambda x : (self._tokenize_source(x['src']),
                                                             self._tokenize_destination(x['dst'])))

    # Convert the samples to training inputs.
    ds = ds.map(lambda x, y: self._to_training_input(x, y))

    # Remove the samples that are too long.
    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)

    # Shuffle the dataset.
    ds = ds.shuffle(buffer_size=self.BUFFER_SIZE_SHUFFLE)

    # Repeat if necessary.
    ds = ds.repeat(num_epochs)

    # Build batches.
    ds = ds.batch(batch_size, drop_remainder=True)
    return ds

  def get_validation_dataset(self, batch_size: int):
    """Build the validation dataset."""

    # Same steps as in `get_train_dataset`, but without shuffling and no repetition.
    ds = self._base_data[DatasetSplit.VALIDATION].map(lambda x : (self._tokenize_source(x['src']),
                                                                  self._tokenize_destination(x['dst'])))
    ds = ds.map(lambda x, y: self._to_training_input(x, y))
    ds = ds.filter(lambda x: tf.shape(x.input_tokens)[0] <= self._max_seq_len)
    ds = ds.batch(batch_size, drop_remainder=True)
    return ds

Coba MTNTDatasetBuilder dengan membuat instance GemmaTokenizer kustom lagi, lalu menerapkannya pada set data MTNT, dan mengambil sampel dari dua contoh:

tokenizer = GemmaTokenizer(vocab)

dataset_builder = MTNTDatasetBuilder(tokenizer, max_seq_len=20)
ds = dataset_builder.get_train_dataset(3, 1)
ds = ds.take(2)
ds = ds.as_numpy_iterator()

for idx, example in enumerate(ds):
  print(f'Example {idx}:')
  for key, val in example.items():
    print(f'{key}: {val}')
  print()

WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>
WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>
WARNING:tensorflow:Mapping types may not work well with tf.nest. Prefer using MutableMapping for <class '__main__.TrainingInput'>
Example 0:
input_tokens: [[     2  49688    736   1280   6987 235292    108  10924    665  12302
  235341    108      2   4397  63011   1437  38696   1241      1      0]
 [     2  49688    736   1280   6987 235292    108  13835   1517 235265
     108      2  69875    540  19713 235265      1      0      0      0]
 [     2  49688    736   1280   6987 235292    108   6956   1586 235297
  235265    108      2  78368   1586 235297 235265      1      0      0]]
target_mask: [[False False False False False False False False False False False False
   True  True  True  True  True  True  True False]
 [False False False False False False False False False False False  True
   True  True  True  True  True False False False]
 [False False False False False False False False False False False False
   True  True  True  True  True  True False False]]

Example 1:
input_tokens: [[     2  49688    736   1280   6987 235292    108  18874 235341    108
       2 115905   6425   1241      1      0      0      0      0      0]
 [     2  49688    736   1280   6987 235292    108   7574   3356 235341
     108      2   7997  20707   1241      1      0      0      0      0]
 [     2  49688    736   1280   6987 235292    108   8703    665 235265
     108      2 235338 235303  90006  20133 235265      1      0      0]]
target_mask: [[False False False False False False False False False False  True  True
   True  True  True False False False False False]
 [False False False False False False False False False False False  True
   True  True  True  True False False False False]
 [False False False False False False False False False False False  True
   True  True  True  True  True  True False False]]

Mengonfigurasi model

Sebelum mulai meningkatkan model Gemma, Anda perlu mengonfigurasinya.

Pertama, muat dan format checkpoint model Gemma dengan metode gemma.params.load_and_format_params:

params = params_lib.load_and_format_params(CKPT_PATH)

Untuk otomatis memuat konfigurasi yang benar dari checkpoint model Gemma, gunakan gemma.transformer.TransformerConfig. Argumen cache_size adalah jumlah langkah waktu dalam cache Transformer Gemma. Setelah itu, buat instance model Gemma sebagai model_2b dengan gemma.transformer.Transformer (yang mewarisi dari flax.linen.Module).

config_2b = transformer_lib.TransformerConfig.from_params(
    params,
    cache_size=30
)

model_2b = transformer_lib.Transformer(config=config_2b)

Menyesuaikan model

Di bagian ini, Anda akan:

  • Gunakan class gemma.transformer.Transformer untuk membuat penerusan maju dan fungsi kerugian.
  • Membangun vektor posisi dan mask atensi untuk token
  • Bangun fungsi langkah pelatihan dengan Flax.
  • Buat langkah validasi tanpa proses mundur.
  • Membuat loop pelatihan.
  • Sempurnakan model Gemma.

Tentukan penerusan maju dan fungsi kerugian menggunakan class gemma.transformer.Transformer. Transformer Gemma mewarisi dari flax.linen.Module, dan menawarkan dua metode penting:

  • init: Melakukan inisialisasi parameter model.

  • apply: Menjalankan fungsi __call__ model menggunakan sekumpulan parameter tertentu.

    Karena Anda menggunakan bobot Gemma terlatih, Anda tidak perlu menggunakan fungsi init.

def forward_and_loss_fn(params,
                        *,
                        model: transformer_lib.Transformer,
                        input_tokens: jax.Array,            # Shape [B, L]
                        input_mask: jax.Array,              # Shape [B, L]
                        positions: jax.Array,               # Shape [B, L]
                        attention_mask: jax.Array,          # [B, L, L]
                        ) -> jax.Array:
  """The forward pass and the loss function.

  Args:
    params: Model's input parameters.
    model: The Gemma transformer model to call.
    input_tokens: Input tokens sequence, shape [B, L].
    input_mask: Tokens to ignore when computing the loss, shape [B, L].
    positions: Relative position of each token, shape [B, L].
    attention_mask: Input attention mask, shape [B, L].

  Returns:
    The softmax cross-entropy loss for the next-token prediction task.
  """

  # The forward pass on the input data.
  # No attention cache is needed here.
  logits, _ = model.apply(
        params,
        input_tokens,
        positions,
        None,              # Attention cache is None.
        attention_mask,
    )

  # Exclude the last step as it does not appear in the targets.
  logits = logits[0, :-1]

  # Similarly, the first token cannot be predicted.
  target_tokens = input_tokens[0, 1:]
  target_mask = input_mask[0, 1:]

  # Convert the target labels to one-hot encoded vectors.
  one_hot = jax.nn.one_hot(target_tokens, logits.shape[-1])

  # Don't update on unwanted tokens.
  one_hot = one_hot * target_mask.astype(one_hot.dtype)[...,None]

  # Define the normalization factor.
  norm_factor = 1 / (jnp.sum(target_mask) + 1e-8)

  # Return the negative log likelihood (NLL) loss.
  return -jnp.sum(jax.nn.log_softmax(logits) * one_hot) * norm_factor

Class gemma.transformer.Transformer memerlukan vektor attention_mask dan positions di samping setiap input. Anda dapat membuatnya dengan membuat fungsi kustom yang menggunakan Transformer.build_positions_from_mask dan Transformer.make_causal_attn_mask:

def get_attention_mask_and_positions(example: jax.Array,
                                     pad_id : int,
                                     )-> tuple[jax.Array, jax.Array]:
  """Builds the position and attention mask vectors from the given tokens."""
  pad_mask = example != pad_id
  current_token_position = transformer_lib.build_positions_from_mask(pad_mask)
  attention_mask = transformer_lib.make_causal_attn_mask(pad_mask)
  return current_token_position, attention_mask

Bangun fungsi train_step yang melakukan penerusan mundur dan mengupdate parameter model sebagaimana mestinya, dengan kondisi:

def train_step(model: transformer_lib.Transformer,
               params,
               optimizer: optax.GradientTransformation,
               opt_state: optax.OptState,
               pad_id: int,
               example: TrainingInput):
  """Train step.

  Args:
    model: The Gemma transformer model.
    params: The model's input parameters.
    optimizer: The Optax optimizer to use.
    opt_state: The input optimizer's state.
    pad_id: ID of the pad token.
    example: Input batch.

  Returns:
    The training loss, the updated parameters, and the updated optimizer state.
  """

  # Build the position and attention mask vectors.
  positions, attention_mask = get_attention_mask_and_positions(example.input_tokens, pad_id)

  # The forward and backward passes.
  train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(params,
                                                             model=model,
                                                             input_tokens=example.input_tokens,
                                                             input_mask=example.target_mask,
                                                             positions=positions,
                                                             attention_mask=attention_mask)
  # Update the parameters.
  updates, opt_state = optimizer.update(grads, opt_state)
  params = optax.apply_updates(params, updates)

  return train_loss, params, opt_state

Bangun fungsi validation_step tanpa penerusan mundur:

def validation_step(model: transformer_lib.Transformer,
                    params,
                    pad_id: int,
                    example: TrainingInput,
                    ):
  positions, attention_mask = get_attention_mask_and_positions(example.input_tokens, pad_id)
  val_loss = forward_and_loss_fn(params,
                                 model=model,
                                 input_tokens=example.input_tokens,
                                 input_mask=example.target_mask,
                                 positions=positions,
                                 attention_mask=attention_mask)
  return val_loss

Tentukan loop pelatihan menggunakan optax.sgd untuk pengoptimal SGD:

@chex.dataclass(frozen=True)
class TrainingConfig:
  learning_rate: float
  num_epochs: int
  eval_every_n: int
  batch_size: int
  max_steps: int | None = None

def train_loop(
    model: transformer_lib.Transformer,
    params,
    dataset_builder: MTNTDatasetBuilder,
    training_cfg: TrainingConfig):

  # Apply `jax.jit` on the training step, making the whole loop much more efficient.
  compiled_train_step = jax.jit(train_step, static_argnames=['model', 'optimizer'])

  # Apply `jax.jit` on the validation step.
  compiled_validation_step = jax.jit(validation_step, static_argnames=['model'])

  # To save memory, use the SGD optimizer instead of the usual Adam optimizer.
  # Note that for this specific example, SGD is more than enough.
  optimizer = optax.sgd(training_cfg.learning_rate)
  opt_state = optimizer.init(params)

  # Build the training dataset.
  train_ds = dataset_builder.get_train_dataset(batch_size=training_cfg.batch_size,
                                               num_epochs=training_cfg.num_epochs)
  train_ds = train_ds.as_numpy_iterator()

  # Build the validation dataset, with a limited number of samples for this demo.
  validation_ds = dataset_builder.get_validation_dataset(batch_size=training_cfg.batch_size)
  validation_ds = validation_ds.take(50)

  n_steps = 0
  avg_loss=0

  # A first round of the validation loss.
  n_steps_eval = 0
  eval_loss = 0
  val_iterator = validation_ds.as_numpy_iterator()
  for val_example in val_iterator:
    eval_loss += compiled_validation_step(model,
                                          params,
                                          dataset_builder._tokenizer.pad_id,
                                          val_example)
    n_steps_eval += 1
  print(f"Start, validation loss: {eval_loss/n_steps_eval}")

  for train_example in train_ds:
    train_loss, params, opt_state = compiled_train_step(model=model,
                                                        params=params,
                                                        optimizer=optimizer,
                                                        opt_state=opt_state,
                                                        pad_id=dataset_builder._tokenizer.pad_id,
                                                        example=train_example)
    n_steps += 1
    avg_loss += train_loss
    if n_steps % training_cfg.eval_every_n == 0:
      eval_loss = 0

      n_steps_eval = 0
      val_iterator = validation_ds.as_numpy_iterator()
      for val_example in val_iterator:
        eval_loss += compiled_validation_step(model,
                                              params,
                                              dataset_builder._tokenizer.pad_id,
                                              val_example)
        n_steps_eval +=1
      avg_loss /= training_cfg.eval_every_n
      eval_loss /= n_steps_eval
      print(f"STEP {n_steps} training loss: {avg_loss} - eval loss: {eval_loss}")
      avg_loss=0
    if training_cfg.max_steps is not None and n_steps > training_cfg.max_steps:
      break
  return params

Mulailah menyempurnakan model Gemma dalam jumlah langkah terbatas (SEQ_SIZE) untuk memastikan model ini sesuai dalam memori:

SEQ_SIZE = 25
tokenizer = GemmaTokenizer(vocab)
dataset_builder= MTNTDatasetBuilder(tokenizer, SEQ_SIZE)
training_cfg = TrainingConfig(learning_rate=1e-4,
                              num_epochs=1,
                              eval_every_n=20,
                              batch_size=1,
                              max_steps=100)

params = train_loop(model=model_2b,
                    params={'params': params['transformer']},
                    dataset_builder=dataset_builder,
                    training_cfg=training_cfg)

Start, validation loss: 10.647212982177734
STEP 20 training loss: 3.3015992641448975 - eval loss: 2.686880111694336
STEP 40 training loss: 5.375057220458984 - eval loss: 2.6751961708068848
STEP 60 training loss: 2.6599338054656982 - eval loss: 2.663877010345459
STEP 80 training loss: 4.822389125823975 - eval loss: 2.3333375453948975
STEP 100 training loss: 2.0131142139434814 - eval loss: 2.360811948776245

Kerugian pelatihan dan kerugian validasi seharusnya turun dengan jumlah setiap langkah.

Membuat sampler dengan gemma.sampler.Sampler. Smart Bidding menggunakan checkpoint model Gemma dan tokenizer.

sampler = sampler_lib.Sampler(
    transformer=model_2b,
    vocab=vocab,
    params=params['params'],
)

Gunakan sampler untuk memeriksa apakah model Anda dapat melakukan terjemahan. Argumen total_generation_steps di gemma.sampler.Sampler adalah jumlah langkah yang dilakukan saat membuat respons. Untuk memastikan input cocok dengan format pelatihan, gunakan awalan Translate this into French:\n dengan karakter baris baru di bagian akhir. Hal ini menandakan model untuk memulai terjemahan.

sampler(
    ["Translate this into French:\nHello, my name is Morgane.\n"],
    total_generation_steps=100,
    ).text

["C'est Bonjour, mon nom est Morgane.C'est Bonjour, mon nom est Morgane."]

Pelajari lebih lanjut