681 lines
36 KiB
Python
681 lines
36 KiB
Python
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/gemma3n/modular_gemma3n.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_gemma3n.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Sequence
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from typing import Any, Optional, Union
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...modeling_rope_utils import rope_config_validation
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from ...utils import is_timm_available, logging, requires_backends
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if is_timm_available():
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from timm.data import ImageNetInfo, infer_imagenet_subset
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logger = logging.get_logger(__name__)
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class Gemma3nTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Gemma3nTextModel`]. It is used to instantiate an
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Gemma3nTextModel model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B, e.g.
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[google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).
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Configuration objects that inherit from [`Gemma3nTextConfig`] and can be used to control the model outputs. Read
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the documentation from [`Gemma3nTextConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 262400):
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Vocabulary size of the Gemma3nText model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`Gemma3nTextModel`]
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vocab_size_per_layer_input (`int`, *optional*, defaults to 262144):
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Vocabulary size of the per-layer text embeddings that augment the standard embeddings.
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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hidden_size_per_layer_input (`int`, *optional*, defaults to 256):
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Dimension of the hidden representations for per-layer emebeddings.
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intermediate_size (`int` or `Sequence[int]`, *optional*, defaults to 16384):
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Dimension of the MLP representations. MatFormer configurations may wish to provide a sequence of integers
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to account for vairable intermediate_size values across layers. In such cases,
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`len(intermediate_size) == num_hidden_layers`.
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num_hidden_layers (`int`, *optional*, defaults to 35):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 2):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout this
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[paper](https://arxiv.org/pdf/2305.13245.pdf). If not specified, will default to `num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 256):
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The attention head dimension.
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hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the decoder. Will default to
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`"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"`
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activation function.
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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 1):
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End of stream token id.
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bos_token_id (`int`, *optional*, defaults to 2):
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Beginning of stream token id.
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rope_theta (`float`, *optional*, defaults to 1000000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings used in gloabl attention.
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NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we
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recommend you to update this value accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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rope_local_base_freq (float, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings for local attention.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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sliding_window (`int`, *optional*, defaults to 512):
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This is the size of the sliding window used by local attention layers.
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layer_types (`Optional`, *optional*):
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A sequence of strings defining the attention type for that layer as either "sliding_attention" or
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"full_attention". If not provided, `layer_types` will de inferred from `num_hidden_layers` using a pattern
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of four "sliding_attention" layers followed one "full_attention". The last layer in the model should always
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be a "full_attention" layer.
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final_logit_softcapping (`float`, *optional*, defaults to 30.0):
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Scaling factor when applying tanh softcapping on the logits.
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altup_active_idx (`int`, *optional*, defaults to 0):
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The index of the prediction from which AltUp will compute additional predictions or correct
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altup_coef_clip (`float`, *optional*, defaults to 120.0):
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The maximum amplitude of an AltUp prediction or correction coeficient weight.
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altup_correct_scale (`bool`, *optional*, defaults to `True`):
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If True, apply the `AltUp.correct_output_scale` to the corrected prediction at `altup_active_idx`.
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altup_num_inputs (`int`, *optional*, defaults to 4):
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The number of predictions that AltUp should be make given the input sequence.
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num_kv_shared_layers (`int`, *optional*, defaults to 15):
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The number of layer that share KV cache values. During the forward pass, the last `num_kv_shared_layers`
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layers in the model "share" the KV values in that each local and global layer in this range uses the KV
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cache values computed for the last local or global layer, respectively, before entering this range. The
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value should be `num_kv_shared_layers` should be a scalar of `sliding_window_pattern`.
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laurel_rank (int, *optional*, defaults to 64):
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The intermediate size for the linear projections in the Learned Augmented Residual Layer.
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activation_sparsity_pattern (Sequence[float], *optional*, defaults to `(0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)`):
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The sparsity factor used to extract the top-k activations for a given layer. The provided Sequence must
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explicitly provide a sparsity value for each layer in the model.
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```python
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>>> from transformers import Gemma3nTextModel, Gemma3nTextConfig
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>>> # Initializing a Gemma3nText gemma3n_text-E4B style configuration
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>>> configuration = Gemma3nTextConfig()
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>>> # Initializing a model from the gemma3n_text-E4B style configuration
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>>> model = Gemma3nTextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "gemma3n_text"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size: int = 262_400,
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vocab_size_per_layer_input: int = 262_144,
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hidden_size: int = 2048,
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hidden_size_per_layer_input: int = 256,
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intermediate_size: Union[int, Sequence[int]] = 16_384,
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num_hidden_layers: int = 35,
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num_attention_heads: int = 8,
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num_key_value_heads: int = 2,
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head_dim: int = 256,
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hidden_activation: str = "gelu_pytorch_tanh",
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max_position_embeddings: int = 32_768,
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initializer_range: float = 0.02,
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rms_norm_eps: float = 1e-6,
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use_cache: bool = True,
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pad_token_id: int = 0,
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eos_token_id: int = 1,
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bos_token_id: int = 2,
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rope_theta: float = 1_000_000.0,
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rope_scaling: Optional[dict[str, Any]] = None,
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rope_local_base_freq: float = 10_000.0,
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attention_bias: bool = False,
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attention_dropout: float = 0.0,
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sliding_window: int = 512,
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layer_types: Optional[Sequence[str]] = None,
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final_logit_softcapping: float = 30.0,
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altup_active_idx: int = 0,
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altup_coef_clip: float = 120.0,
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altup_correct_scale: bool = True,
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altup_num_inputs: int = 4,
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num_kv_shared_layers: int = 15,
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laurel_rank: int = 64,
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activation_sparsity_pattern: Optional[Union[float, Sequence[float]]] = (0.95,) * 10 + (0.0,) * 25,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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if isinstance(intermediate_size, Sequence) and (intsize_len := len(intermediate_size)) != num_hidden_layers:
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raise ValueError(
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"intermediate_size must have an explicit intermediate size for every layer or one for all layers. "
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f"Expected {num_hidden_layers} values but got {intsize_len}."
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)
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elif not isinstance(intermediate_size, Sequence):
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intermediate_size = [intermediate_size] * num_hidden_layers
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self.vocab_size = vocab_size
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self.vocab_size_per_layer_input = vocab_size_per_layer_input
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.head_dim = head_dim
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self.num_key_value_heads = num_key_value_heads
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.hidden_activation = hidden_activation
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self.sliding_window = sliding_window
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self.final_logit_softcapping = final_logit_softcapping
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self.layer_types = layer_types
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self.rope_local_base_freq = rope_local_base_freq
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self.rope_scaling = rope_scaling
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rope_config_validation(self)
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if layer_types is None:
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self.layer_types = [
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"full_attention" if (i + 1) % 5 == 0 else "sliding_attention" for i in range(self.num_hidden_layers)
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]
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else:
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self.layer_types = layer_types
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layer_type_validation(self.layer_types)
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self.hidden_size_per_layer_input = hidden_size_per_layer_input
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self.num_kv_shared_layers = num_kv_shared_layers
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self.altup_active_idx = altup_active_idx
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self.altup_coef_clip = altup_coef_clip
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self.altup_correct_scale = altup_correct_scale
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||
|
|
self.altup_num_inputs = altup_num_inputs
|
||
|
|
|
||
|
|
self.laurel_rank = laurel_rank
|
||
|
|
|
||
|
|
if activation_sparsity_pattern is None:
|
||
|
|
activation_sparsity_pattern = [0.0] * num_hidden_layers
|
||
|
|
|
||
|
|
if (len_asp := len(activation_sparsity_pattern)) != num_hidden_layers:
|
||
|
|
raise ValueError(
|
||
|
|
"activation_sparsity_pattern must have an explicit activation sparsity value for every layer."
|
||
|
|
f"Expected {num_hidden_layers} values but got {len_asp}."
|
||
|
|
)
|
||
|
|
self.activation_sparsity_pattern = activation_sparsity_pattern
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`Gemma3nAudioEncoder`]. It is used to instantiate
|
||
|
|
an `Gemma3nAudioEncoder` model according to the specified arguments, defining the model architecture. Instantiating
|
||
|
|
a configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B, e.g.,
|
||
|
|
[google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).
|
||
|
|
|
||
|
|
Configuration objects that inherit from [`Gemma3nAudioConfig`] and can be used to control the model outputs. Read
|
||
|
|
the documentation from [`Gemma3nAudioConfig`] for more information.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
vocab_size (`int`, *optional*, defaults to 128):
|
||
|
|
Vocabulary size of the additional hard-token embeddings for audio model. These augment the embeddings
|
||
|
|
included in the `Gemma3nTextModel` to provide, e.g., the end of audio and audio soft token placeholder
|
||
|
|
tokens when converting `input_ids` to embeddings in the `Gemma3nForConditionalGeneration` model.
|
||
|
|
vocab_offset (`int`, *optional*, defaults to 262272):
|
||
|
|
Offset between the tokenizer vocab index for the token ids embedded by `Gemma3nMultimodalEmbedder` and the
|
||
|
|
0-indexed `Gemma3nMultimodalEmbedder.embedding` table.
|
||
|
|
input_feat_size (`int`, *optional*, defaults to 128):
|
||
|
|
The number of channels in each mel-spectrogram frame.
|
||
|
|
hidden_size (`int`, *optional*, defaults to 1536):
|
||
|
|
Dimension of the hidden representations.
|
||
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||
|
|
The epsilon used by the rms normalization layers.
|
||
|
|
gradient_clipping (`float`, *optional*, defaults to 10000000000.0):
|
||
|
|
Clipping value used to stablize extremely large gradient values.
|
||
|
|
conf_attention_chunk_size (`int`, *optional*, defaults to 12):
|
||
|
|
The sub-sequence size for local attention processing inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_attention_context_left (`int`, *optional*, defaults to 13):
|
||
|
|
The left context size of the local attention inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_attention_context_right (`int`, *optional*, defaults to 0):
|
||
|
|
The right context size of the local attention inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_attention_logit_cap (`float`, *optional*, defaults to 50.0):
|
||
|
|
Logit cap applied during local attention inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_num_attention_heads (`int`, *optional*, defaults to 8):
|
||
|
|
The number of attention heads in local attention inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_num_hidden_layers (`int`, *optional*, defaults to 12):
|
||
|
|
The number of layers that use local attention inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_conv_kernel_size (`int`, *optional*, defaults to 5):
|
||
|
|
Convolution kernel size for the conformer block inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_reduction_factor (`int`, *optional*, defaults to 4):
|
||
|
|
Reduction factor used in the conformer block inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
conf_residual_weight (`float`, *optional*, defaults to 0.5):
|
||
|
|
Residual connection weight inside the Conformer ("conf") section of the
|
||
|
|
Universal Speech Model.
|
||
|
|
sscp_conv_channel_size (`tuple(int, int)`, *optional*, defaults to `(128, 32)`):
|
||
|
|
The channel sizes for the first and second convolutional layers in the Sub-sample Convolution Projection
|
||
|
|
("sscp") section of the Universal Speech Model.
|
||
|
|
sscp_conv_group_norm_eps (`float`, *optional*, defaults to 0.001):
|
||
|
|
Epsilon used in group normalization in the subsample convolution projection in the Sub-sample Convolution
|
||
|
|
Projection ("sscp") section of the Universal Speech Model.
|
||
|
|
sscp_conv_kernel_size (`tuple(tuple(int, int), tuple(int, int))`, *optional*, defaults to `((3, 3), (3, 3))`):
|
||
|
|
Kernel sizes of the two convolutional layers in the subsample convolution projection in the Sub-sample
|
||
|
|
Convolution Projection ("sscp") section of the Universal Speech Model. The kernel sizes are specified as a
|
||
|
|
tuple of height and width for each layer, where the height corresponds to the time dimension and the width
|
||
|
|
corresponds to the frequency dimension.
|
||
|
|
sscp_conv_stride_size (`tuple(tuple(int, int), tuple(int, int))`, *optional*, defaults to `((2, 2), (2, 2))`):
|
||
|
|
Stride sizes of the two convolutional layers in the subsample convolution projection in the Sub-sample
|
||
|
|
Convolution Projection ("sscp") section of the Universal Speech Model. The stride sizes are specified as a
|
||
|
|
tuple of height and width for each layer, where the height corresponds to the time dimension and the width
|
||
|
|
corresponds to the frequency dimension.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import Gemma3nAudioConfig, Gemma3nAudioEncoder
|
||
|
|
|
||
|
|
>>> # Initializing a Gemma3nAudioEncoder gemma3n_audio-E4B-style configuration
|
||
|
|
>>> configuration = Gemma3nAudioConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a model from the gemma3n_audio-E4B style configuration
|
||
|
|
>>> model = Gemma3nAudioEncoder(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
model_type = "gemma3n_audio"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size: int = 128,
|
||
|
|
vocab_offset: int = 262_144 + 128, # text vocab size + vision vocab size
|
||
|
|
input_feat_size: int = 128,
|
||
|
|
hidden_size: int = 1536,
|
||
|
|
rms_norm_eps: float = 1e-6,
|
||
|
|
gradient_clipping: float = 10_000_000_000.0,
|
||
|
|
conf_attention_chunk_size: int = 12,
|
||
|
|
conf_attention_context_left: int = 13,
|
||
|
|
conf_attention_context_right: int = 0,
|
||
|
|
conf_attention_logit_cap: float = 50.0,
|
||
|
|
conf_num_attention_heads: int = 8,
|
||
|
|
conf_num_hidden_layers: int = 12,
|
||
|
|
conf_conv_kernel_size: int = 5,
|
||
|
|
conf_reduction_factor: int = 4,
|
||
|
|
conf_residual_weight: float = 0.5,
|
||
|
|
sscp_conv_channel_size: tuple[int, int] = (128, 32),
|
||
|
|
sscp_conv_group_norm_eps: float = 1e-3,
|
||
|
|
sscp_conv_kernel_size: tuple[tuple[int, int], tuple[int, int]] = (
|
||
|
|
(3, 3),
|
||
|
|
(3, 3),
|
||
|
|
),
|
||
|
|
sscp_conv_stride_size: tuple[tuple[int, int], tuple[int, int]] = (
|
||
|
|
(2, 2),
|
||
|
|
(2, 2),
|
||
|
|
),
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
self.input_feat_size = input_feat_size
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.rms_norm_eps = rms_norm_eps
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.vocab_offset = vocab_offset
|
||
|
|
self.gradient_clipping = gradient_clipping
|
||
|
|
self.conf_attention_chunk_size = conf_attention_chunk_size
|
||
|
|
self.conf_attention_context_left = conf_attention_context_left
|
||
|
|
self.conf_attention_context_right = conf_attention_context_right
|
||
|
|
self.conf_attention_logit_cap = conf_attention_logit_cap
|
||
|
|
self.conf_num_attention_heads = conf_num_attention_heads
|
||
|
|
self.conf_num_hidden_layers = conf_num_hidden_layers
|
||
|
|
self.conf_conv_kernel_size = conf_conv_kernel_size
|
||
|
|
self.conf_reduction_factor = conf_reduction_factor
|
||
|
|
self.conf_residual_weight = conf_residual_weight
|
||
|
|
self.sscp_conv_channel_size = sscp_conv_channel_size
|
||
|
|
self.sscp_conv_group_norm_eps = sscp_conv_group_norm_eps
|
||
|
|
self.sscp_conv_kernel_size = sscp_conv_kernel_size
|
||
|
|
self.sscp_conv_stride_size = sscp_conv_stride_size
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nVisionConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration for a timm backbone [`TimmWrapper`]. It is used to
|
||
|
|
instantiate an timm model model according to the specified arguments, defining the model architecture.
|
||
|
|
Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma 3n E4B
|
||
|
|
vision tower, e.g. [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B).
|
||
|
|
|
||
|
|
Configuration objects inherit from [`Gemma3nVisionConfig`] and can be used to control the model outputs. Read the
|
||
|
|
documentation from [`Gemma3nVisionConfig`] for more information.
|
||
|
|
|
||
|
|
Config loads imagenet label descriptions and stores them in `id2label` attribute, `label2id` attribute for default
|
||
|
|
imagenet models is set to `None` due to occlusions in the label descriptions.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||
|
|
do_pooling (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether to do pooling for the last_hidden_state in `TimmWrapper` or not.
|
||
|
|
architecture (`str`, *optional*, defaults to `"mobilenetv5_300m_enc"`):
|
||
|
|
Determines vision architecture for TimmWrapper.
|
||
|
|
hidden_size (`int`, *optional*, defaults to 2048):
|
||
|
|
Dimension of the hidden representations.
|
||
|
|
vocab_size (`int`, *optional*, defaults to 128):
|
||
|
|
Vocabulary size of the additional hard-token embeddings for vision model.
|
||
|
|
vocab_offset (`int`, *optional*, defaults to 262144):
|
||
|
|
Offset between the tokenizer vocab index for the token ids embedded by `Gemma3nMultimodalEmbedder` and the
|
||
|
|
0-indexed `Gemma3nMultimodalEmbedder.embedding` table.
|
||
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||
|
|
The epsilon used by the rms normalization layers.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
>>> from transformers import Gemma3nVisionConfig, TimmWrapper
|
||
|
|
|
||
|
|
>>> # Initializing a TimmWrapper gemma3n_vision-E4B-style configuration
|
||
|
|
>>> configuration = Gemma3nVisionConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a gemma3n_vision-E4B-style TimmWrapper from the configuration
|
||
|
|
>>> model = TimmWrapper(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
model_type = "gemma3n_vision"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
initializer_range: float = 0.02,
|
||
|
|
do_pooling: bool = False,
|
||
|
|
architecture: str = "mobilenetv5_300m_enc",
|
||
|
|
hidden_size: int = 2048,
|
||
|
|
vocab_size: int = 128,
|
||
|
|
vocab_offset: int = 262_144,
|
||
|
|
rms_norm_eps: float = 1e-06,
|
||
|
|
model_args: Optional[dict] = None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.do_pooling = do_pooling
|
||
|
|
self.model_args = model_args # named "model_args" for BC with timm
|
||
|
|
self.architecture = architecture
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.vocab_offset = vocab_offset
|
||
|
|
self.rms_norm_eps = rms_norm_eps
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_dict(cls, config_dict: dict[str, Any], **kwargs):
|
||
|
|
label_names = config_dict.get("label_names", None)
|
||
|
|
is_custom_model = "num_labels" in kwargs or "id2label" in kwargs
|
||
|
|
|
||
|
|
# if no labels added to config, use imagenet labeller in timm
|
||
|
|
if label_names is None and not is_custom_model:
|
||
|
|
requires_backends(cls, ["timm"])
|
||
|
|
imagenet_subset = infer_imagenet_subset(config_dict)
|
||
|
|
if imagenet_subset:
|
||
|
|
dataset_info = ImageNetInfo(imagenet_subset)
|
||
|
|
synsets = dataset_info.label_names()
|
||
|
|
label_descriptions = dataset_info.label_descriptions(as_dict=True)
|
||
|
|
label_names = [label_descriptions[synset] for synset in synsets]
|
||
|
|
|
||
|
|
if label_names is not None and not is_custom_model:
|
||
|
|
kwargs["id2label"] = dict(enumerate(label_names))
|
||
|
|
|
||
|
|
# if all label names are unique, create label2id mapping as well
|
||
|
|
if len(set(label_names)) == len(label_names):
|
||
|
|
kwargs["label2id"] = {name: i for i, name in enumerate(label_names)}
|
||
|
|
else:
|
||
|
|
kwargs["label2id"] = None
|
||
|
|
|
||
|
|
# timm config stores the `num_classes` attribute in both the root of config and in the "pretrained_cfg" dict.
|
||
|
|
# We are removing these attributes in order to have the native `transformers` num_labels attribute in config
|
||
|
|
# and to avoid duplicate attributes
|
||
|
|
num_labels_in_kwargs = kwargs.pop("num_labels", None)
|
||
|
|
num_labels_in_dict = config_dict.pop("num_classes", None)
|
||
|
|
|
||
|
|
# passed num_labels has priority over num_classes in config_dict
|
||
|
|
kwargs["num_labels"] = num_labels_in_kwargs or num_labels_in_dict
|
||
|
|
|
||
|
|
# pop num_classes from "pretrained_cfg",
|
||
|
|
# it is not necessary to have it, only root one is used in timm
|
||
|
|
if "pretrained_cfg" in config_dict and "num_classes" in config_dict["pretrained_cfg"]:
|
||
|
|
config_dict["pretrained_cfg"].pop("num_classes", None)
|
||
|
|
|
||
|
|
return super().from_dict(config_dict, **kwargs)
|
||
|
|
|
||
|
|
def to_dict(self) -> dict[str, Any]:
|
||
|
|
output = super().to_dict()
|
||
|
|
output["num_classes"] = self.num_labels
|
||
|
|
output["label_names"] = list(self.id2label.values())
|
||
|
|
output.pop("id2label", None)
|
||
|
|
output.pop("label2id", None)
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`Gemma3nForConditionalGeneration`]. It is used to
|
||
|
|
instantiate a Gemma3nForConditionalGeneration according to the specified arguments, defining the model
|
||
|
|
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
|
||
|
|
Gemma3n-E4B.
|
||
|
|
|
||
|
|
e.g. [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B)
|
||
|
|
|
||
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||
|
|
documentation from [`PretrainedConfig`] for more information.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text_config (`Union[Gemma3nTextConfig, dict]`, *optional*):
|
||
|
|
The config object of the text backbone.
|
||
|
|
vision_config (`Union[AutoConfig, dict]`, *optional*):
|
||
|
|
Custom vision config or dict.
|
||
|
|
audio_config (`Union[AutoConfig, dict]`, *optional*):
|
||
|
|
Custom audio config or dict.
|
||
|
|
audio_soft_tokens_per_image (`int`, *optional*, defaults to 188):
|
||
|
|
The number of soft tokens per audio clip.
|
||
|
|
vision_soft_tokens_per_image (`int`, *optional*, defaults to 256):
|
||
|
|
The number of soft tokens per image.
|
||
|
|
boi_token_id (`int`, *optional*, defaults to 255999):
|
||
|
|
The begin-of-image token index to wrap the image prompt.
|
||
|
|
eoi_token_id (`int`, *optional*, defaults to 262144):
|
||
|
|
The end-of-image token index to wrap the image prompt.
|
||
|
|
image_token_id (`int`, *optional*, defaults to 262145):
|
||
|
|
The image token index to encode the image prompt.
|
||
|
|
boa_token_id (`int`, *optional*, defaults to 256000):
|
||
|
|
The begin-of-audio token index to wrap the audio prompt.
|
||
|
|
eoa_token_id (`int`, *optional*, defaults to 262272):
|
||
|
|
The end-of-audio token index to wrap the audio prompt.
|
||
|
|
audio_token_id (`int`, *optional*, defaults to 262273):
|
||
|
|
The audio token index to encode the audio prompt.
|
||
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||
|
|
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
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>>> from transformers import Gemma3nForConditionalGeneration, Gemma3nConfig, Gemma3nTextConfig
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>>> # Initializing a MobileNet vision config, which is loaded from TIMM
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>>> vision_config = Gemma3nVisionConfig()
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>>> # Initializing a Gemma3n Audio config
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>>> audio_config = Gemma3nAudioConfig()
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>>> # Initializing a Gemma3n Text config
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>>> text_config = Gemma3nTextConfig()
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>>> # Initializing a Gemma3n gemma-3-4b style configuration
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>>> configuration = Gemma3nConfig(text_config, vision_config, audio_config)
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>>> # Initializing a model from the gemma-3-4b style configuration
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>>> model = Gemma3nTextConfig(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "gemma3n"
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sub_configs = {
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"text_config": Gemma3nTextConfig,
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"vision_config": Gemma3nVisionConfig,
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"audio_config": Gemma3nAudioConfig,
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}
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def __init__(
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self,
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text_config: Optional[Union[Gemma3nTextConfig, dict[str, Any]]] = None,
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vision_config: Optional[Union[Gemma3nVisionConfig, dict[str, Any]]] = None,
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audio_config: Optional[Union[Gemma3nAudioConfig, dict[str, Any]]] = None,
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audio_soft_tokens_per_image: int = 188,
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vision_soft_tokens_per_image: int = 256,
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boi_token_id: int = 255_999,
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eoi_token_id: int = 262_144,
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image_token_id: int = 262_145,
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|
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boa_token_id: int = 256_000,
|
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|
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eoa_token_id: int = 262_272,
|
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|
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audio_token_id: int = 262_273,
|
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|
|
initializer_range: float = 0.02,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
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|
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|
|
if isinstance(text_config, dict):
|
||
|
|
text_config = Gemma3nTextConfig(**text_config)
|
||
|
|
elif text_config is None:
|
||
|
|
text_config = Gemma3nTextConfig()
|
||
|
|
logger.info("text_config is None. Using default Gemma3nTextConfig.")
|
||
|
|
|
||
|
|
if isinstance(vision_config, dict):
|
||
|
|
vision_config = Gemma3nVisionConfig(**vision_config)
|
||
|
|
elif vision_config is None:
|
||
|
|
vision_config = Gemma3nVisionConfig()
|
||
|
|
logger.info("vision_config is None. Using default Gemma3nVisionConfig.")
|
||
|
|
|
||
|
|
if isinstance(audio_config, dict):
|
||
|
|
audio_config = Gemma3nAudioConfig(**audio_config)
|
||
|
|
elif audio_config is None:
|
||
|
|
audio_config = Gemma3nAudioConfig()
|
||
|
|
logger.info("audio_config is None. Using default Gemma3nAudioConfig.")
|
||
|
|
|
||
|
|
self.text_config = text_config
|
||
|
|
self.vision_config = vision_config
|
||
|
|
self.audio_config = audio_config
|
||
|
|
|
||
|
|
self.audio_soft_tokens_per_image = audio_soft_tokens_per_image
|
||
|
|
self.vision_soft_tokens_per_image = vision_soft_tokens_per_image
|
||
|
|
self.boi_token_id = boi_token_id
|
||
|
|
self.eoi_token_id = eoi_token_id
|
||
|
|
self.image_token_id = image_token_id
|
||
|
|
self.boa_token_id = boa_token_id
|
||
|
|
self.eoa_token_id = eoa_token_id
|
||
|
|
self.audio_token_id = audio_token_id
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ["Gemma3nAudioConfig", "Gemma3nConfig", "Gemma3nTextConfig", "Gemma3nVisionConfig"]
|