GemmaBackbone model

• 원본 링크 : https://keras.io/api/keras_nlp/models/gemma/gemma_backbone/
• 최종 확인 : 2024-11-26

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GemmaBackbone class

keras_nlp.models.GemmaBackbone(
    vocabulary_size,
    num_layers,
    num_query_heads,
    num_key_value_heads,
    hidden_dim,
    intermediate_dim,
    head_dim,
    query_head_dim_normalize=True,
    use_post_ffw_norm=False,
    use_post_attention_norm=False,
    attention_logit_soft_cap=None,
    final_logit_soft_cap=None,
    use_sliding_window_attention=False,
    sliding_window_size=4096,
    layer_norm_epsilon=1e-06,
    dropout=0,
    dtype=None,
    **kwargs
)

Gemma core network with hyperparameters.

This backbone implements the base Transformer network for the Gemma model. It includes the embedding lookups and transformer layers. This backbone will output the final hidden states for each token, not generative predictions over the vocabulary space. For a higher-level object for text generation, see keras_hub.models.GemmaCausalLM.

The default constructor gives a fully customizable, randomly initialized Gemma model with any number of layers, heads, and embedding dimensions. To load preset architectures and weights, use the from_preset constructor.

Arguments

• vocabulary_size: int. The size of the token vocabulary.
• num_layers: int. The number of transformer layers.
• num_query_heads: int. The number of heads for the query projections in the attention layer.
• num_key_value_heads: int. The number of heads for the key and value projections in the attention layer.
• hidden_dim: int. The size of the transformer hidden state at the end of each transformer layer.
• intermediate_dim: int. The output dimension of the first Dense layer in a two-layer feedforward network for each transformer.
• head_dim: int. The size of each attention head.
• layer_norm_epsilon: float. The epsilon value user for every layer norm in the transformer model.
• dropout: float. Dropout probability for the Transformer encoder.
• query_head_dim_normalize: boolean. If True normalize the query before attention with head_dim. If False, normalize the query with hidden_dim / num_query_heads. Defaults to True.
• use_post_ffw_norm: boolean. Whether to normalize after the feedforward block. Defaults to False.
• use_post_attention_norm: boolean. Whether to normalize after the attention block. Defaults to False.
• attention_logit_soft_cap: None or int. Soft cap for the attention logits. Defaults to None.
• final_logit_soft_cap: None or int. Soft cap for the final logits. Defaults to None. use_sliding_window_attention boolean. Whether to use sliding local window attention. Defaults to False.
• sliding_window_size: int. Size of the sliding local window. Defaults to 4096.
• dtype: string or keras.mixed_precision.DTypePolicy. The dtype to use for the models computations and weights. Note that some computations, such as softmax and layer normalization will always be done a float32 precision regardless of dtype.

Example

input_data = {
    "token_ids": np.ones(shape=(1, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}
# Pretrained Gemma decoder.
model = keras_hub.models.GemmaBackbone.from_preset("gemma_2b_en")
model(input_data)
# Randomly initialized Gemma decoder with custom config.
model = keras_hub.models.GemmaBackbone(
    vocabulary_size=50257,
    num_layers=12,
    num_query_heads=12,
    num_key_value_heads=1,
    hidden_dim=768,
    intermediate_dim=3072,
    head_dim=64,
)
model(input_data)

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from_preset method

GemmaBackbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Backbone from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)
# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)

token_embedding property

keras_nlp.models.GemmaBackbone.token_embedding

A keras.layers.Embedding instance for embedding token ids.

This layer embeds integer token ids to the hidden dim of the model.

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enable_lora method

GemmaBackbone.enable_lora(rank)

Enable Lora on the backbone.

Calling this method will freeze all weights on the backbone, while enabling Lora on the query & value EinsumDense layers of the attention layers.

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get_layout_map method

GemmaBackbone.get_layout_map(
    device_mesh, model_parallel_dim_name="model", data_parallel_dim_name="batch"
)

Get a keras.distribution.LayoutMap for model parallel distribution.

The returned LayoutMap contains the sharding spec for the gemma backbone weights, so that you can use it to distribute weights across the accelerators.

Example

# Feel free to change the mesh shape to balance data and model parallelism
mesh = keras.distribution.DeviceMesh(
    shape=(1, 8), axis_names=('batch', 'model'),
    devices=keras.distribution.list_devices())
layout_map = GemmaBackbone.get_layout_map(
    mesh, model_parallel_dim_name="model")
distribution = keras.distribution.ModelParallel(
    layout_map=layout_map, batch_dim_name='batch')
with distribution.scope():
   gemma_model = keras_hub.models.GemmaCausalLM.from_preset()

To see how the layout map was applied, load the model then run (for one decoder block):

embedding_layer = gemma_model.backbone.get_layer("token_embedding")
decoder_block_1 = gemma_model.backbone.get_layer('decoder_block_1')
for variable in embedding_layer.weights + decoder_block_1.weights:
    print(f'{variable.path:<58}  {str(variable.shape):<16}  {str(variable.value.sharding.spec)}')

Arguments

• device_mesh: The keras.distribution.DeviceMesh instance for distribution.
• model_parallel_dim_name: The axis name of the device mesh, where the weights should be partition on.
• data_parallel_dim_name: The axis name of the device mesh, where the data should be partition on.

Return: keras.distribution.LayoutMap that contains the sharding spec for all the model weights.