2658 lines
127 KiB
Python
2658 lines
127 KiB
Python
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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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import copy
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import math
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from collections.abc import Callable, Sequence
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from typing import Any, Optional, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, SlidingWindowLayer
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import BaseModelOutputWithPast
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from ...modeling_rope_utils import rope_config_validation
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
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from ..auto import AutoModel
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from ..gemma2.configuration_gemma2 import Gemma2Config
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from ..gemma2.modeling_gemma2 import (
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Gemma2MLP,
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Gemma2PreTrainedModel,
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Gemma2RotaryEmbedding,
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eager_attention_forward,
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rotate_half,
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)
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from ..gemma3.modeling_gemma3 import (
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Gemma3Attention,
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Gemma3DecoderLayer,
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Gemma3ForCausalLM,
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Gemma3RMSNorm,
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Gemma3TextModel,
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Gemma3TextScaledWordEmbedding,
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)
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from ..paligemma.modeling_paligemma import (
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PaliGemmaCausalLMOutputWithPast,
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PaliGemmaForConditionalGeneration,
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PaliGemmaModel,
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PaligemmaModelOutputWithPast,
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)
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from ..timm_wrapper.configuration_timm_wrapper import TimmWrapperConfig
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logger = logging.get_logger(__name__)
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class Gemma3nTextConfig(Gemma2Config, 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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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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PretrainedConfig.__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
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self.laurel_rank = laurel_rank
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|
|
||
|
|
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(TimmWrapperConfig):
|
||
|
|
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.architecture = architecture
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.do_pooling = do_pooling
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.vocab_offset = vocab_offset
|
||
|
|
self.rms_norm_eps = rms_norm_eps
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
>>> from transformers import Gemma3nForConditionalGeneration, Gemma3nConfig, Gemma3nTextConfig
|
||
|
|
|
||
|
|
>>> # Initializing a MobileNet vision config, which is loaded from TIMM
|
||
|
|
>>> vision_config = Gemma3nVisionConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a Gemma3n Audio config
|
||
|
|
>>> audio_config = Gemma3nAudioConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a Gemma3n Text config
|
||
|
|
>>> text_config = Gemma3nTextConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a Gemma3n gemma-3-4b style configuration
|
||
|
|
>>> configuration = Gemma3nConfig(text_config, vision_config, audio_config)
|
||
|
|
|
||
|
|
>>> # Initializing a model from the gemma-3-4b style configuration
|
||
|
|
>>> model = Gemma3nTextConfig(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "gemma3n"
|
||
|
|
sub_configs = {
|
||
|
|
"text_config": Gemma3nTextConfig,
|
||
|
|
"vision_config": Gemma3nVisionConfig,
|
||
|
|
"audio_config": Gemma3nAudioConfig,
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
text_config: Optional[Union[Gemma3nTextConfig, dict[str, Any]]] = None,
|
||
|
|
vision_config: Optional[Union[Gemma3nVisionConfig, dict[str, Any]]] = None,
|
||
|
|
audio_config: Optional[Union[Gemma3nAudioConfig, dict[str, Any]]] = None,
|
||
|
|
audio_soft_tokens_per_image: int = 188,
|
||
|
|
vision_soft_tokens_per_image: int = 256,
|
||
|
|
boi_token_id: int = 255_999,
|
||
|
|
eoi_token_id: int = 262_144,
|
||
|
|
image_token_id: int = 262_145,
|
||
|
|
boa_token_id: int = 256_000,
|
||
|
|
eoa_token_id: int = 262_272,
|
||
|
|
audio_token_id: int = 262_273,
|
||
|
|
initializer_range: float = 0.02,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nModelOutputWithPast(PaligemmaModelOutputWithPast):
|
||
|
|
r"""
|
||
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
||
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
|
||
|
|
|
||
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
||
|
|
`past_key_values` input) to speed up sequential decoding.
|
||
|
|
image_hidden_states (`torch.FloatTensor`, *optional*):
|
||
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
||
|
|
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
|
||
|
|
audio_hidden_states (`torch.FloatTensor`, *optional*):
|
||
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
||
|
|
audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
|
||
|
|
"""
|
||
|
|
|
||
|
|
audio_hidden_states: Optional[torch.FloatTensor] = None
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nCausalLMOutputWithPast(PaliGemmaCausalLMOutputWithPast):
|
||
|
|
r"""
|
||
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
||
|
|
Language modeling loss (for next-token prediction).
|
||
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`):
|
||
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
||
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
||
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
|
||
|
|
|
||
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
||
|
|
`past_key_values` input) to speed up sequential decoding.
|
||
|
|
image_hidden_states (`torch.FloatTensor`, *optional*):
|
||
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
||
|
|
image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
|
||
|
|
audio_hidden_states (`torch.FloatTensor`, *optional*):
|
||
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
||
|
|
audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
|
||
|
|
"""
|
||
|
|
|
||
|
|
audio_hidden_states: Optional[torch.FloatTensor] = None
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nRMSNorm(Gemma3RMSNorm):
|
||
|
|
def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True):
|
||
|
|
super().__init__(dim, eps=eps)
|
||
|
|
del self.weight
|
||
|
|
self.with_scale = with_scale
|
||
|
|
|
||
|
|
if self.with_scale:
|
||
|
|
self.weight = nn.Parameter(torch.ones(dim))
|
||
|
|
else:
|
||
|
|
self.register_buffer("weight", torch.tensor(1.0), persistent=False)
|
||
|
|
|
||
|
|
def _norm(self, x):
|
||
|
|
return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
||
|
|
|
||
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
# Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16)
|
||
|
|
# See https://github.com/huggingface/transformers/pull/29402
|
||
|
|
output = self._norm(x.float()) * self.weight.float()
|
||
|
|
return output.type_as(x)
|
||
|
|
|
||
|
|
|
||
|
|
# ==== Audio Encoder ====
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioRelativePositionEmbedding(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.num_heads = self.config.conf_num_attention_heads
|
||
|
|
self.channels = self.config.hidden_size
|
||
|
|
self.head_dim = self.channels // self.num_heads
|
||
|
|
self.max_backward = max(0, self.config.conf_attention_context_left - 1)
|
||
|
|
self.max_forward = self.config.conf_attention_context_right
|
||
|
|
|
||
|
|
self.pos_proj = nn.Linear(self.channels, self.num_heads * self.head_dim, bias=False)
|
||
|
|
|
||
|
|
min_timescale = 1.0
|
||
|
|
max_timescale = 1.0e4
|
||
|
|
num_timescales = self.channels // 2
|
||
|
|
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(num_timescales - 1, 1)
|
||
|
|
inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
|
||
|
|
self.register_buffer(
|
||
|
|
"inv_timescales",
|
||
|
|
inv_timescales.float().unsqueeze(0).unsqueeze(0),
|
||
|
|
persistent=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _get_timing_signal_1d_pos(self, position: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
|
||
|
|
position = position.float().unsqueeze(-1)
|
||
|
|
scaled_time = position * self.inv_timescales.to(device=position.device, dtype=torch.float32)
|
||
|
|
timing_signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=-1)
|
||
|
|
return timing_signal.type(dtype)
|
||
|
|
|
||
|
|
def _relative_shift(
|
||
|
|
self,
|
||
|
|
term_bd_before_shift: torch.Tensor,
|
||
|
|
batch_size: int,
|
||
|
|
num_heads: int,
|
||
|
|
num_query_blocks: int,
|
||
|
|
query_block_size: int,
|
||
|
|
key_context_size: int,
|
||
|
|
max_span_plus_1: int,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
"""Performs the relative shift.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
term_bd_before_shift: Tensor of shape [B, N, U, W, F_span]. batch_size
|
||
|
|
(B), num_heads (N), num_query_blocks (U), query_block_size (W),
|
||
|
|
key_context_size (C = W+L+R), max_span_plus_1 (F_span = L+R+1).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Tensor of shape [B, N, U, W, C].
|
||
|
|
"""
|
||
|
|
# term_bd_before_shift shape: [B, N, U, W, F_span]
|
||
|
|
# Target shape after shift: [B, N, U, W, C]
|
||
|
|
|
||
|
|
# Padding amount for the last dimension (F_span) to become (C + 1)
|
||
|
|
# C = key_context_size
|
||
|
|
# F_span = max_span_plus_1
|
||
|
|
pad_amount_last_dim = (key_context_size + 1) - max_span_plus_1
|
||
|
|
|
||
|
|
# PyTorch F.pad expects (pad_left, pad_right, pad_top, pad_bottom ...)
|
||
|
|
# We only pad the last dimension on the right.
|
||
|
|
padding_tuple = (0, pad_amount_last_dim)
|
||
|
|
|
||
|
|
term_bd_padded = nn.functional.pad(term_bd_before_shift, padding_tuple)
|
||
|
|
# Shape after pad: [B, N, U, W, C+1]
|
||
|
|
|
||
|
|
# Reshape for slicing (emulating JAX's behavior)
|
||
|
|
# [B, N, U, W * (C+1)]
|
||
|
|
term_bd_reshaped = term_bd_padded.reshape(
|
||
|
|
(
|
||
|
|
batch_size,
|
||
|
|
num_heads,
|
||
|
|
num_query_blocks,
|
||
|
|
query_block_size * (key_context_size + 1),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
# Slice to effective [B, N, U, W * C]
|
||
|
|
term_bd_sliced = term_bd_reshaped[:, :, :, : query_block_size * key_context_size]
|
||
|
|
|
||
|
|
# Reshape back to [B, N, U, W, C]
|
||
|
|
term_bd_shifted = term_bd_sliced.reshape(
|
||
|
|
(
|
||
|
|
batch_size,
|
||
|
|
num_heads,
|
||
|
|
num_query_blocks,
|
||
|
|
query_block_size,
|
||
|
|
key_context_size,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return term_bd_shifted
|
||
|
|
|
||
|
|
def forward(self, queries: torch.Tensor, keys: torch.Tensor) -> torch.Tensor:
|
||
|
|
# queries: [B, U, W, N, H] (batch, num_query_blocks, query_block_size, num_heads, head_dim)
|
||
|
|
# keys: [B, U, C, N, H] (batch, num_query_blocks, key_context_size, num_heads, head_dim)
|
||
|
|
# C = W + L + R (key_context_size)
|
||
|
|
# F_span = L + R + 1 (max_span + 1)
|
||
|
|
|
||
|
|
batch_size, num_query_blocks, query_block_size, num_heads, head_dim = queries.shape
|
||
|
|
_, _, key_context_size, _, _ = keys.shape
|
||
|
|
|
||
|
|
# Relative positions for sinusoidal embeddings: [L, L-1, ..., -R]
|
||
|
|
# Length is L+R+1 = self.max_span + 1
|
||
|
|
pos_indices = torch.arange(self.max_backward, -self.max_forward - 1, -1, device=queries.device).unsqueeze(
|
||
|
|
0
|
||
|
|
) # Shape [1, F_span]
|
||
|
|
|
||
|
|
max_span_plus_1 = pos_indices.shape[1] # F_span
|
||
|
|
|
||
|
|
sin_emb_timing_signal = self._get_timing_signal_1d_pos(
|
||
|
|
pos_indices, dtype=queries.dtype
|
||
|
|
) # Shape [1, F_span, self.channels]
|
||
|
|
|
||
|
|
# Project sinusoidal embeddings: [1, F_span, self.channels] -> [1, F_span, N*H]
|
||
|
|
projected_sin_emb = self.pos_proj(sin_emb_timing_signal)
|
||
|
|
# Reshape to [1, F_span, N, H] then squeeze to [F_span, N, H]
|
||
|
|
sin_emb = projected_sin_emb.reshape(1, max_span_plus_1, self.num_heads, self.head_dim).squeeze(
|
||
|
|
0
|
||
|
|
) # Shape [F, N, H]
|
||
|
|
|
||
|
|
# term_ac: Query-Key content interaction
|
||
|
|
# queries: [B, U, W, N, H] -> permute to [B, N, U, W, H] for matmul
|
||
|
|
# keys: [B, U, C, N, H] -> permute to [B, N, U, H, C] for matmul
|
||
|
|
queries_p = queries.permute(0, 3, 1, 2, 4) # [B, N, U, W, H]
|
||
|
|
keys_p_t = keys.permute(0, 3, 1, 4, 2) # [B, N, U, H, C]
|
||
|
|
term_ac = torch.matmul(queries_p, keys_p_t) # [B, N, U, W, C]
|
||
|
|
|
||
|
|
# term_bd: Query-Position interaction
|
||
|
|
# Original einsum: term_bd_unshifed = torch.einsum('buwnh,fnh->bnuwf', queries, sin_emb)
|
||
|
|
# queries shape: [B, U, W, N, H]
|
||
|
|
# sin_emb shape: [F, N, H]
|
||
|
|
# Target output shape: [B, N, U, W, F]
|
||
|
|
|
||
|
|
# Permute queries to [B, N, U, W, H] for easier broadcasting with sin_emb
|
||
|
|
q_permuted = queries.permute(0, 3, 1, 2, 4)
|
||
|
|
|
||
|
|
# Permute sin_emb to [N, H, F] to prepare for matmul
|
||
|
|
# sin_emb original is [F, N, H]
|
||
|
|
s_permuted = sin_emb.permute(1, 2, 0) # Shape: [N, H, F]
|
||
|
|
|
||
|
|
# Reshape queries for matmul: [B, N, U*W, H]
|
||
|
|
q_reshaped = q_permuted.reshape(batch_size, num_heads, num_query_blocks * query_block_size, head_dim)
|
||
|
|
|
||
|
|
# Perform matmul: [B, N, U*W, H] @ [N, H, F]
|
||
|
|
# s_permuted ([N, H, F]) will be broadcast to [B, N, H, F]
|
||
|
|
# Result: [B, N, U*W, F]
|
||
|
|
term_bd_unshifed_matmul = torch.matmul(q_reshaped, s_permuted)
|
||
|
|
|
||
|
|
# Reshape to target [B, N, U, W, F]
|
||
|
|
term_bd_unshifed = term_bd_unshifed_matmul.reshape(
|
||
|
|
batch_size,
|
||
|
|
num_heads,
|
||
|
|
num_query_blocks,
|
||
|
|
query_block_size,
|
||
|
|
max_span_plus_1,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Apply relative shift to term_bd_unshifed
|
||
|
|
term_bd_shifted = self._relative_shift(
|
||
|
|
term_bd_unshifed,
|
||
|
|
batch_size,
|
||
|
|
num_heads,
|
||
|
|
num_query_blocks,
|
||
|
|
query_block_size,
|
||
|
|
key_context_size,
|
||
|
|
max_span_plus_1,
|
||
|
|
) # Shape [B, N, U, W, C]
|
||
|
|
|
||
|
|
return term_ac + term_bd_shifted
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioAttention(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.num_heads = self.config.conf_num_attention_heads
|
||
|
|
self.hidden_size = self.config.hidden_size
|
||
|
|
self.head_dim = self.hidden_size // self.num_heads
|
||
|
|
|
||
|
|
self.chunk_size = self.config.conf_attention_chunk_size
|
||
|
|
self.max_future_horizon = self.config.conf_attention_context_right
|
||
|
|
self.max_past_horizon = max(0, self.config.conf_attention_context_left - 1)
|
||
|
|
self.attention_logits_soft_cap = self.config.conf_attention_logit_cap
|
||
|
|
self.context_size = self.chunk_size + self.max_past_horizon + self.max_future_horizon
|
||
|
|
|
||
|
|
self.relative_position_embedding = Gemma3nAudioRelativePositionEmbedding(config)
|
||
|
|
self.per_dim_scale = nn.Parameter(torch.zeros((self.head_dim,)))
|
||
|
|
|
||
|
|
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
||
|
|
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
||
|
|
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
||
|
|
|
||
|
|
q_scale = self.head_dim**-0.5
|
||
|
|
r_softplus_0 = 1.0 / torch.nn.functional.softplus(torch.tensor(0.0))
|
||
|
|
self.register_buffer("q_scale", (q_scale * r_softplus_0).clone().detach(), persistent=False)
|
||
|
|
|
||
|
|
lower_causal_mask = torch.tril(
|
||
|
|
torch.ones((self.context_size, self.chunk_size), dtype=torch.bool),
|
||
|
|
diagonal=0,
|
||
|
|
).T
|
||
|
|
upper_causal_mask = torch.tril(
|
||
|
|
torch.ones((self.chunk_size, self.context_size), dtype=torch.bool),
|
||
|
|
diagonal=self.max_past_horizon + self.max_future_horizon,
|
||
|
|
)
|
||
|
|
local_causal_valid_mask = torch.ones((self.chunk_size, self.context_size), dtype=torch.bool)
|
||
|
|
local_causal_valid_mask = local_causal_valid_mask * lower_causal_mask * upper_causal_mask
|
||
|
|
self.register_buffer("local_causal_valid_mask", local_causal_valid_mask, persistent=False)
|
||
|
|
|
||
|
|
self.register_buffer(
|
||
|
|
"softcap",
|
||
|
|
torch.tensor(self.attention_logits_soft_cap).float(),
|
||
|
|
persistent=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _pad_dim1(self, x: torch.Tensor, pad_left: int, pad_right: int) -> torch.Tensor:
|
||
|
|
batch, _, *tail_shape = x.shape
|
||
|
|
left = x.new_zeros((batch, pad_left, *tail_shape))
|
||
|
|
right = x.new_zeros((batch, pad_right, *tail_shape))
|
||
|
|
x = torch.cat([left, x, right], dim=1)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def _convert_to_block(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Turns a sequence to non overlapping blocks.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hidden_states: a tensor of [batch, time, ...].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tensor of [batch, num_blocks, block_size, ...], with necessary
|
||
|
|
paddings,
|
||
|
|
where output[:, i, ...] are x[:, i*block_size:(i+1)*block_size, ...].
|
||
|
|
"""
|
||
|
|
shape = hidden_states.shape
|
||
|
|
b, t = shape[:2]
|
||
|
|
num_blocks = (t + self.chunk_size - 1) // self.chunk_size
|
||
|
|
|
||
|
|
if (padding_len := num_blocks * self.chunk_size - t) > 0:
|
||
|
|
hidden_states = self._pad_dim1(hidden_states, 0, padding_len)
|
||
|
|
|
||
|
|
permute_dims = (b, num_blocks, self.chunk_size) + shape[2:]
|
||
|
|
hidden_states = hidden_states.reshape(permute_dims).contiguous()
|
||
|
|
return hidden_states
|
||
|
|
|
||
|
|
def _extract_block_context(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Extracts temporal context for every block.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hidden_states: a tensor of [batch, time, ...].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tensor of [batch, num_blocks, context_size, ...], with necessary
|
||
|
|
paddings,
|
||
|
|
where context_size = block_size + left_context + right_context,
|
||
|
|
and output[:, i, ...] are x[:, start-left_context:end+right_context,
|
||
|
|
...],
|
||
|
|
start = i * block_size, end = (i + 1) * block_size.
|
||
|
|
"""
|
||
|
|
pad_left = self.max_past_horizon
|
||
|
|
# The JAX equivalent padding for signal.frame with pad_mode='valid' is
|
||
|
|
# (left_context, right_context + block_size - 1) on the time dimension.
|
||
|
|
# PyTorch's _pad_dim1 applies padding symmetrically if only one value is given,
|
||
|
|
# or (pad_dim_start, pad_dim_end) if two are given.
|
||
|
|
# Our _pad_dim1(x, pad_left, pad_right) pads dim -2 (time for [B,T,N,H])
|
||
|
|
# or dim 1 (time for [B,T]).
|
||
|
|
# The current pad_right calculation matches the JAX effective padding.
|
||
|
|
pad_right = self.max_future_horizon + self.chunk_size - 1
|
||
|
|
hidden_states = self._pad_dim1(hidden_states, pad_left, pad_right)
|
||
|
|
|
||
|
|
frame_len = self.context_size
|
||
|
|
frame_step = self.chunk_size
|
||
|
|
|
||
|
|
# Directly use unfold without the subframe_factor logic
|
||
|
|
# x.unfold(dimension, size, step)
|
||
|
|
# dimension=1 (time dimension, assuming x is [B, T_padded, ...])
|
||
|
|
# size=frame_len (context_size)
|
||
|
|
# step=frame_step (chunk_size)
|
||
|
|
x_unfolded = hidden_states.unfold(dimension=1, size=frame_len, step=frame_step)
|
||
|
|
|
||
|
|
# If x was [B, T_padded], x_unfolded is [B, num_blocks, frame_len]
|
||
|
|
# If x was [B, T_padded, N, H], x_unfolded is [B, num_blocks, N, H, frame_len]
|
||
|
|
# We want to match JAX's typical output for such operations which might be
|
||
|
|
# [B, num_blocks, frame_len, N, H] if N, H are present.
|
||
|
|
# The relative_position_embedding expects keys as [B, U, C, N, H].
|
||
|
|
# If x_unfolded is [B, U, N, H, C(frame_len)], we need to move C.
|
||
|
|
if hidden_states.ndim > 2 and x_unfolded.ndim > 3: # Check if inner dimensions (like N, H) exist
|
||
|
|
# Current shape after unfold for [B, T_pad, N, H] is [B, U, N, H, C]
|
||
|
|
# Target shape for keys in RPE: [B, U, C, N, H]
|
||
|
|
x_unfolded = torch.movedim(x_unfolded, source=-1, destination=2)
|
||
|
|
|
||
|
|
return x_unfolded.contiguous()
|
||
|
|
|
||
|
|
def forward(self, hidden_states: torch.Tensor, mask: torch.BoolTensor) -> torch.Tensor:
|
||
|
|
# sl.Dense uses jax.numpy.einsum("...a,abcd->...bcd") and jax.numpy.select()
|
||
|
|
qkv_shape = (*hidden_states.shape[:-1], self.num_heads, self.head_dim)
|
||
|
|
query_states = self.q_proj(hidden_states).reshape(qkv_shape).contiguous()
|
||
|
|
key_states = self.k_proj(hidden_states).reshape(qkv_shape).contiguous()
|
||
|
|
value_states = self.v_proj(hidden_states).reshape(qkv_shape).contiguous()
|
||
|
|
|
||
|
|
per_dim_scale_sp = torch.nn.functional.softplus(self.per_dim_scale)
|
||
|
|
|
||
|
|
broadcast_shape = (1, 1, 1, self.head_dim)
|
||
|
|
per_dim_scale_sp_broadcast = per_dim_scale_sp.view(broadcast_shape)
|
||
|
|
query_states = query_states * self.q_scale * per_dim_scale_sp_broadcast
|
||
|
|
|
||
|
|
batch_size, q_time = query_states.shape[:2]
|
||
|
|
|
||
|
|
query_blocks = self._convert_to_block(query_states)
|
||
|
|
key_blocks = self._extract_block_context(key_states)
|
||
|
|
value_blocks = self._extract_block_context(value_states)
|
||
|
|
num_query_blocks = query_blocks.shape[1]
|
||
|
|
|
||
|
|
# 1. Create a mask indicating originally valid positions.
|
||
|
|
original_valid_mask = ~mask # True for valid, False for padded
|
||
|
|
|
||
|
|
# 2. Extract blocks from this validity mask.
|
||
|
|
extracted_valid_mask_blocks = self._extract_block_context(original_valid_mask)
|
||
|
|
|
||
|
|
# If subframe_factor was used in _extract_block_context for a [B, T] input mask,
|
||
|
|
# the shape might be [B, U, C/SF, SF]. Reshape to [B, U, C].
|
||
|
|
# batch_size and num_query_blocks are known from query_blocks.
|
||
|
|
# self.context_size is C.
|
||
|
|
if (
|
||
|
|
extracted_valid_mask_blocks.ndim == 4
|
||
|
|
and extracted_valid_mask_blocks.shape[2] * extracted_valid_mask_blocks.shape[3] == self.context_size
|
||
|
|
):
|
||
|
|
extracted_valid_mask_blocks = extracted_valid_mask_blocks.reshape(
|
||
|
|
batch_size, num_query_blocks, self.context_size
|
||
|
|
)
|
||
|
|
# After potential reshape, ensure it's [B, U, C] if it was from a [B,T] mask.
|
||
|
|
# This assertion might be too strict if _extract_block_context handles higher-rank inputs differently,
|
||
|
|
# but for the mask case, this should hold.
|
||
|
|
if extracted_valid_mask_blocks.shape != (
|
||
|
|
batch_size,
|
||
|
|
num_query_blocks,
|
||
|
|
self.context_size,
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
"Shape of extracted_valid_mask_blocks"
|
||
|
|
f" {extracted_valid_mask_blocks.shape} is not ({batch_size},"
|
||
|
|
f" {num_query_blocks}, {self.context_size}) after potential reshape."
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. Expand dimensions for broadcasting with logits and causal mask.
|
||
|
|
# Target shape for broadcasting with logits [B,N,U,W,C]
|
||
|
|
# extracted_valid_mask_blocks to [B, 1, U, 1, C]
|
||
|
|
condition_from_input_validity = extracted_valid_mask_blocks.unsqueeze(1).unsqueeze(-2)
|
||
|
|
|
||
|
|
# self.local_causal_valid_mask is [W, C], True where allowed by local window.
|
||
|
|
# Expand to [1, 1, 1, W, C]
|
||
|
|
condition_from_causality = self.local_causal_valid_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0)
|
||
|
|
|
||
|
|
# 4. Combine the two conditions.
|
||
|
|
# final_condition will be True where a key is *both* originally valid *and* causally accessible.
|
||
|
|
# Broadcasts to [B, 1, U, W, C]
|
||
|
|
final_condition_for_where = torch.logical_and(
|
||
|
|
condition_from_input_validity,
|
||
|
|
condition_from_causality.to(condition_from_input_validity.device), # Ensure same device
|
||
|
|
)
|
||
|
|
|
||
|
|
# Embed queries and keys
|
||
|
|
logits = self.relative_position_embedding(query_blocks, key_blocks)
|
||
|
|
|
||
|
|
# Apply attention logit softcap
|
||
|
|
# Ensure softcap is on the same device as logits
|
||
|
|
softcap_val = self.softcap.to(logits.device)
|
||
|
|
logits = logits / softcap_val
|
||
|
|
logits = torch.tanh(logits)
|
||
|
|
logits = logits * softcap_val
|
||
|
|
|
||
|
|
# Apply the combined mask.
|
||
|
|
# final_condition_for_where will broadcast with logits [B,N,U,W,C]
|
||
|
|
logits = torch.where(final_condition_for_where, logits, torch.finfo(logits.dtype).min)
|
||
|
|
probabilities = torch.nn.functional.softmax(logits, dim=-1, dtype=torch.float32).to(dtype=value_blocks.dtype)
|
||
|
|
|
||
|
|
# context_vectors is adapted from jax.numpy.einsum("BNuwc,BucNH->BuwNH", ...)
|
||
|
|
b_dim, n_dim, u_dim, w_dim, c_dim = probabilities.shape
|
||
|
|
h_dim = value_blocks.shape[-1]
|
||
|
|
prob_bun = probabilities.permute(0, 2, 1, 3, 4).reshape(-1, w_dim, c_dim)
|
||
|
|
v_bun = value_blocks.permute(0, 1, 3, 2, 4).reshape(-1, c_dim, h_dim)
|
||
|
|
result_bmm = torch.bmm(prob_bun, v_bun)
|
||
|
|
context_vectors = result_bmm.reshape(b_dim, u_dim, n_dim, w_dim, h_dim).permute(0, 1, 3, 2, 4)
|
||
|
|
context_vectors = context_vectors.reshape(
|
||
|
|
(
|
||
|
|
batch_size,
|
||
|
|
num_query_blocks * self.chunk_size,
|
||
|
|
self.num_heads,
|
||
|
|
self.head_dim,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
context_vectors = context_vectors[:, :q_time]
|
||
|
|
|
||
|
|
return context_vectors
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioCumulativeGroupNorm(nn.Module):
|
||
|
|
"""Applies Group Normalization cumulatively over the time dimension.
|
||
|
|
|
||
|
|
This layer normalizes the input by calculating the mean and variance
|
||
|
|
cumulatively over the time dimension (dim 1). The statistics are computed
|
||
|
|
over all feature dimensions (specified by `feature_dims` and `num_channels`)
|
||
|
|
for elements marked as valid by the optional `mask`.
|
||
|
|
|
||
|
|
If a `mask` is provided (True for valid, False for invalid/padded),
|
||
|
|
invalid time steps do not contribute to the statistics calculation, and
|
||
|
|
their corresponding output values are zeroed out.
|
||
|
|
|
||
|
|
Scale and bias, if enabled, are applied per-channel (last dimension).
|
||
|
|
This behavior is similar to JAX's `GroupNormalization` with `num_groups=1`
|
||
|
|
and `cumulative=True`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_channels: int, # Number of channels (size of the last dimension)
|
||
|
|
feature_dims: Sequence[int], # Sizes of non-channel feature dimensions, e.g., (H, W) for input [B,T,H,W,C]
|
||
|
|
eps: float = 1e-3,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.num_channels = num_channels
|
||
|
|
self.feature_dims = tuple(feature_dims)
|
||
|
|
self.eps = eps
|
||
|
|
|
||
|
|
# Scale parameter depends only on the channel dimension
|
||
|
|
self.weight = nn.Parameter(torch.ones(num_channels))
|
||
|
|
|
||
|
|
# Axes for normalization: all dimensions except Batch (0) and Time (1).
|
||
|
|
# For input [B, T, *feature_dims, C], these are dims from 2 onwards.
|
||
|
|
self.reduction_axes = tuple(range(2, 2 + len(self.feature_dims) + 1))
|
||
|
|
|
||
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Applies cumulative group norm, optionally using a mask.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hidden_states: Input tensor, shape [B, T, *feature_dims, C].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Normalized tensor with the same shape as x.
|
||
|
|
"""
|
||
|
|
expected_input_suffix = self.feature_dims + (self.num_channels,)
|
||
|
|
if hidden_states.shape[2:] != expected_input_suffix:
|
||
|
|
raise ValueError(
|
||
|
|
f"Input tensor shape suffix {hidden_states.shape[2:]} does not match expected"
|
||
|
|
f" suffix (feature_dims + num_channels) {expected_input_suffix}"
|
||
|
|
)
|
||
|
|
|
||
|
|
input_dtype = hidden_states.dtype
|
||
|
|
# Calculations are performed in float32 for numerical stability.
|
||
|
|
calc_dtype = torch.float32
|
||
|
|
x_calc = hidden_states.to(calc_dtype)
|
||
|
|
|
||
|
|
# Prepare a broadcastable mask (`mask_calc`).
|
||
|
|
# If no mask is provided, treat all elements as valid
|
||
|
|
# (mask_calc is all ones).
|
||
|
|
# Otherwise, expand the [B, T] mask to [B, T, 1, ..., 1] for broadcasting.
|
||
|
|
mask_calc = torch.ones_like(x_calc, dtype=calc_dtype)
|
||
|
|
|
||
|
|
# Cumulative Statistics Calculation
|
||
|
|
# 1. Sum of values over reduction axes at each time step.
|
||
|
|
sum_values_at_t = torch.sum(x_calc, dim=self.reduction_axes, keepdim=True)
|
||
|
|
# 2. Cumulative sum of values over time.
|
||
|
|
cum_sum_values = torch.cumsum(sum_values_at_t, dim=1)
|
||
|
|
|
||
|
|
# 3. Count of valid elements in the normalization group at each time step.
|
||
|
|
# (A "group" here consists of all features at a given Batch, Time).
|
||
|
|
elements_in_group_at_t = torch.sum(mask_calc, dim=self.reduction_axes, keepdim=True)
|
||
|
|
# 4. Cumulative count of valid elements over time.
|
||
|
|
cum_count_elements = torch.cumsum(elements_in_group_at_t, dim=1)
|
||
|
|
# Avoid division by zero if all preceding elements were masked.
|
||
|
|
safe_cum_count_elements = torch.clamp(cum_count_elements, min=1.0)
|
||
|
|
|
||
|
|
# 5. Cumulative mean.
|
||
|
|
cum_mean = cum_sum_values / safe_cum_count_elements
|
||
|
|
|
||
|
|
# 6. Sum of squared differences from the cumulative mean.
|
||
|
|
# Only sum for valid elements: (x_calc - cum_mean)^2 * mask_calc.
|
||
|
|
# Using x_calc here for the difference, as cum_mean already accounts for masking.
|
||
|
|
squared_diff_from_mean = (x_calc - cum_mean).pow(2)
|
||
|
|
sum_sq_diff_at_t = torch.sum(squared_diff_from_mean, dim=self.reduction_axes, keepdim=True)
|
||
|
|
|
||
|
|
# 7. Cumulative sum of squared differences over time.
|
||
|
|
cum_sum_sq_diff = torch.cumsum(sum_sq_diff_at_t, dim=1)
|
||
|
|
|
||
|
|
# 8. Cumulative variance.
|
||
|
|
cum_variance = cum_sum_sq_diff / safe_cum_count_elements
|
||
|
|
|
||
|
|
# Normalize the input using the calculated cumulative statistics:
|
||
|
|
# (x - E[x]) / sqrt(Var[x] + eps)
|
||
|
|
normalized_x = (x_calc - cum_mean) * torch.rsqrt(cum_variance + self.eps)
|
||
|
|
|
||
|
|
# Apply affine transformation (scale and bias) if enabled.
|
||
|
|
# Scale and bias are applied per-channel (last dimension).
|
||
|
|
scale = self.weight.to(calc_dtype)
|
||
|
|
# Reshape for broadcasting: [C] -> [1, ..., 1, C]
|
||
|
|
scale_view_shape = [1] * (hidden_states.dim() - 1) + [self.num_channels]
|
||
|
|
normalized_x = normalized_x * scale.view(scale_view_shape)
|
||
|
|
|
||
|
|
# Zero out outputs for time steps that were originally masked (where mask_calc is 0).
|
||
|
|
# This ensures padded/invalid positions in the input result in zero output.
|
||
|
|
final_output = normalized_x * mask_calc
|
||
|
|
|
||
|
|
return final_output.to(input_dtype)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioSSCPConvBlock(nn.Module):
|
||
|
|
"""A single convolution block for the SubSampleConvProjection.
|
||
|
|
|
||
|
|
This block consists of a 2D convolution, followed by CumulativeGroupNorm,
|
||
|
|
and a ReLU activation. It handles manual padding for the convolution.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
config: Gemma3nAudioConfig,
|
||
|
|
idx: int,
|
||
|
|
input_freq_dim: int, # Changed from input_spatial_dim
|
||
|
|
manual_padding: tuple[int, int, int, int] = (0, 0, 0, 0),
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.manual_padding = manual_padding
|
||
|
|
|
||
|
|
# in_channels is 1 for the first block, or C_out from previous block's conv
|
||
|
|
in_channels = 1 if idx == 0 else self.config.sscp_conv_channel_size[idx - 1]
|
||
|
|
out_channels = self.config.sscp_conv_channel_size[idx]
|
||
|
|
kernel_h, kernel_w = self.config.sscp_conv_kernel_size[idx]
|
||
|
|
stride_h, stride_w = self.config.sscp_conv_stride_size[idx]
|
||
|
|
|
||
|
|
self.conv = nn.Conv2d(
|
||
|
|
in_channels=in_channels,
|
||
|
|
out_channels=out_channels,
|
||
|
|
kernel_size=(
|
||
|
|
kernel_h,
|
||
|
|
kernel_w,
|
||
|
|
), # Kernel (kH, kW) operates on (Time, Freq_dim)
|
||
|
|
stride=(stride_h, stride_w),
|
||
|
|
padding=(0, 0), # Manual padding is used
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Calculate output frequency dimension (f_out_conv) after this convolution.
|
||
|
|
# input_freq_dim is the unpadded width (feature dimension).
|
||
|
|
# self.manual_padding is (pad_F_left, pad_F_right, pad_T_top, pad_T_bottom)
|
||
|
|
f_in_padded = input_freq_dim + self.manual_padding[0] + self.manual_padding[1]
|
||
|
|
f_out_conv = (f_in_padded - kernel_w) // stride_w + 1
|
||
|
|
|
||
|
|
self.norm = Gemma3nAudioCumulativeGroupNorm(
|
||
|
|
num_channels=out_channels, # Channels of the conv output
|
||
|
|
feature_dims=(f_out_conv,), # The frequency dimension size after conv
|
||
|
|
eps=self.config.sscp_conv_group_norm_eps,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.activation = nn.ReLU()
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
|
||
|
|
# Input audio_encodings is [B, C_in, T_in, F_in] (e.g., C_in=1)
|
||
|
|
# manual_padding is (pad_F_left, pad_F_right, pad_T_top, pad_T_bottom)
|
||
|
|
# F.pad applies to last two dims: F_in then T_in
|
||
|
|
audio_encodings_padded = F.pad(audio_encodings, self.manual_padding, mode="constant", value=0.0)
|
||
|
|
# Expected padded shape for F_in, k_w=3, pad_F=(1,1) -> F_padded = F_in+2
|
||
|
|
# Expected padded shape for T_in, k_h=3, pad_T=(0,2) -> T_padded = T_in+2
|
||
|
|
audio_encodings_conv = self.conv(audio_encodings_padded)
|
||
|
|
# Expected conv output shape: [B, C_out, T_out, F_out]
|
||
|
|
# Input to norm is [B, T_out, F_out, C_out]
|
||
|
|
x_for_norm = audio_encodings_conv.permute(0, 2, 3, 1).contiguous()
|
||
|
|
x_normed = self.norm(x_for_norm)
|
||
|
|
# Output of norm is [B, T_out, F_out, C_out], permute back to [B, C_out, T_out, F_out]
|
||
|
|
audio_encodings_normed = x_normed.permute(0, 3, 1, 2).contiguous()
|
||
|
|
return self.activation(audio_encodings_normed)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioSubSampleConvProjection(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
current_f_for_block_input = config.input_feat_size # Start with original feature dim
|
||
|
|
calculated_block_padding = []
|
||
|
|
calculated_f_out_dims = [] # Tracking frequency dimension output sizes
|
||
|
|
|
||
|
|
for i in range(2): # Assuming 2 conv layers as per sscp_conv_... arrays
|
||
|
|
kernel_h, kernel_w = config.sscp_conv_kernel_size[i]
|
||
|
|
stride_h, stride_w = config.sscp_conv_stride_size[i]
|
||
|
|
|
||
|
|
# Padding for Time (Height for Conv2d) - REVERSE_CAUSAL like
|
||
|
|
# JAX 'reverse_causal' padding is (0, kernel_size - 1)
|
||
|
|
pad_t_top = 0
|
||
|
|
pad_t_bottom = kernel_h - 1
|
||
|
|
|
||
|
|
# Frequency Padding (Width for Conv2d)
|
||
|
|
# Based on JAX effective padding (1,1) for F_in=10, K_w=3, S_w=2
|
||
|
|
# and the successful test configuration.
|
||
|
|
# If kernel/stride/input_freq for frequency changes, this might need re-evaluation
|
||
|
|
# to match generic JAX 'SAME' behavior if it differs.
|
||
|
|
pad_f_left = 1
|
||
|
|
pad_f_right = 1
|
||
|
|
|
||
|
|
manual_padding_tuple = (
|
||
|
|
pad_f_left,
|
||
|
|
pad_f_right,
|
||
|
|
pad_t_top,
|
||
|
|
pad_t_bottom,
|
||
|
|
)
|
||
|
|
calculated_block_padding.append(manual_padding_tuple)
|
||
|
|
|
||
|
|
# Calculate output frequency dimension after this convolution
|
||
|
|
# This uses the actual padding applied and kernel/stride.
|
||
|
|
f_in_padded = current_f_for_block_input + pad_f_left + pad_f_right
|
||
|
|
f_out_after_conv = (f_in_padded - kernel_w) // stride_w + 1 # Assuming dilation_w = 1
|
||
|
|
calculated_f_out_dims.append(f_out_after_conv)
|
||
|
|
current_f_for_block_input = f_out_after_conv
|
||
|
|
|
||
|
|
self.conv_0 = Gemma3nAudioSSCPConvBlock(
|
||
|
|
idx=0,
|
||
|
|
input_freq_dim=config.input_feat_size, # Pass original feature dim
|
||
|
|
config=config,
|
||
|
|
manual_padding=calculated_block_padding[0],
|
||
|
|
)
|
||
|
|
self.conv_1 = Gemma3nAudioSSCPConvBlock(
|
||
|
|
idx=1,
|
||
|
|
input_freq_dim=calculated_f_out_dims[0], # Output freq dim from conv_0
|
||
|
|
config=config,
|
||
|
|
manual_padding=calculated_block_padding[1],
|
||
|
|
)
|
||
|
|
final_c_out = config.sscp_conv_channel_size[-1]
|
||
|
|
final_f_out = calculated_f_out_dims[-1] # Final frequency dimension
|
||
|
|
self.input_proj_in_features = final_c_out * final_f_out
|
||
|
|
self.input_proj_linear = nn.Linear(self.input_proj_in_features, self.config.hidden_size, bias=False)
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
|
||
|
|
# audio_encodings is [B, T, F_in]
|
||
|
|
# Reshape to [B, 1, T, F_in] (Batch, Channels=1, Height=Time, Width=F_in)
|
||
|
|
audio_encodings_reshaped = audio_encodings.unsqueeze(1)
|
||
|
|
x = self.conv_0(audio_encodings_reshaped)
|
||
|
|
x = self.conv_1(x)
|
||
|
|
# x from conv_1 is [B, C_out_1, T_out_1, F_out_1]
|
||
|
|
b, c_out, t_out, f_out = x.shape
|
||
|
|
# Permute to [B, T_out_1, F_out_1, C_out_1] then flatten F_out_1 and C_out_1
|
||
|
|
x_permuted = x.permute(0, 2, 3, 1).contiguous()
|
||
|
|
output_flattened = x_permuted.view(b, t_out, f_out * c_out)
|
||
|
|
output = self.input_proj_linear(output_flattened)
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioConformerAttention(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.post_in_features = self.config.hidden_size
|
||
|
|
self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
|
||
|
|
self.pre_attn_norm = Gemma3nRMSNorm(self.config.hidden_size)
|
||
|
|
self.attn = Gemma3nAudioAttention(config)
|
||
|
|
self.post = nn.Linear(self.post_in_features, self.config.hidden_size, bias=False)
|
||
|
|
self.post_norm = Gemma3nRMSNorm(self.config.hidden_size)
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
|
||
|
|
audio_encodings_input_to_attn = audio_encodings
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
audio_encodings_norm = self.pre_attn_norm(audio_encodings)
|
||
|
|
# Output of self.attn is [B, T, NumHeads, HeadDim]
|
||
|
|
audio_encodings_attn_out = self.attn(audio_encodings_norm, audio_mel_mask)
|
||
|
|
|
||
|
|
# Reshape from [B, T, NumHeads, HeadDim] to [B, T, NumHeads * HeadDim]
|
||
|
|
# NumHeads * HeadDim = hidden_size
|
||
|
|
b, t, num_heads, head_dim = audio_encodings_attn_out.shape
|
||
|
|
audio_encodings_reshaped = audio_encodings_attn_out.reshape(b, t, num_heads * head_dim)
|
||
|
|
|
||
|
|
audio_encodings = self.post(audio_encodings_reshaped)
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
return audio_encodings_input_to_attn + self.post_norm(audio_encodings)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioConformerFeedForward(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
|
||
|
|
|
||
|
|
self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
|
||
|
|
self.ffw_layer_1 = nn.Linear(self.config.hidden_size, self.config.hidden_size * 4, bias=False)
|
||
|
|
self.ffw_layer_2 = nn.Linear(self.config.hidden_size * 4, self.config.hidden_size, bias=False)
|
||
|
|
self.post_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
|
||
|
|
self.post_layer_scale = torch.tensor(self.config.conf_residual_weight)
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
|
||
|
|
residual = audio_encodings
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
audio_encodings = self.pre_layer_norm(audio_encodings)
|
||
|
|
audio_encodings: torch.Tensor = self.ffw_layer_1(audio_encodings)
|
||
|
|
audio_encodings = nn.functional.silu(audio_encodings)
|
||
|
|
audio_encodings: torch.Tensor = self.ffw_layer_2(audio_encodings)
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
audio_encodings = self.post_layer_norm(audio_encodings)
|
||
|
|
return residual + (audio_encodings * self.post_layer_scale)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioConformerLightConv1d(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
|
||
|
|
self.linear_start = nn.Linear(self.config.hidden_size, self.config.hidden_size * 2, bias=False)
|
||
|
|
self.depthwise_conv1d = nn.Conv1d(
|
||
|
|
in_channels=self.config.hidden_size,
|
||
|
|
out_channels=self.config.hidden_size,
|
||
|
|
kernel_size=self.config.conf_conv_kernel_size,
|
||
|
|
stride=1,
|
||
|
|
padding=0, # Manual causal padding
|
||
|
|
groups=self.config.hidden_size, # Depthwise
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
|
||
|
|
self.conv_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
|
||
|
|
self.linear_end = nn.Linear(self.config.hidden_size, self.config.hidden_size, bias=False)
|
||
|
|
|
||
|
|
self.causal_padding = self.config.conf_conv_kernel_size - 1
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor) -> torch.Tensor:
|
||
|
|
audio_encodings_residual = audio_encodings # Save for residual connection
|
||
|
|
|
||
|
|
audio_encodings = self.pre_layer_norm(audio_encodings)
|
||
|
|
audio_encodings = self.linear_start(audio_encodings)
|
||
|
|
audio_encodings = torch.nn.functional.glu(audio_encodings, dim=-1)
|
||
|
|
# Permute for Conv1d: [B, T, D] -> [B, D, T]
|
||
|
|
audio_encodings_permuted = audio_encodings.permute(0, 2, 1)
|
||
|
|
# Apply manual causal padding
|
||
|
|
audio_encodings_permuted_padded = F.pad(audio_encodings_permuted, (self.causal_padding, 0))
|
||
|
|
audio_encodings = self.depthwise_conv1d(audio_encodings_permuted_padded)
|
||
|
|
# Permute back: [B, D, T_out] -> [B, T_out, D]
|
||
|
|
audio_encodings = audio_encodings.permute(0, 2, 1)
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
audio_encodings = self.conv_norm(audio_encodings)
|
||
|
|
audio_encodings = nn.functional.silu(audio_encodings)
|
||
|
|
audio_encodings = self.linear_end(audio_encodings)
|
||
|
|
output = audio_encodings + audio_encodings_residual
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioConformerBlock(nn.Module):
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.ffw_layer_start = Gemma3nAudioConformerFeedForward(self.config)
|
||
|
|
self.attention = Gemma3nAudioConformerAttention(self.config)
|
||
|
|
self.lconv1d = Gemma3nAudioConformerLightConv1d(self.config)
|
||
|
|
self.ffw_layer_end = Gemma3nAudioConformerFeedForward(self.config)
|
||
|
|
self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
|
||
|
|
self.norm = Gemma3nRMSNorm(self.config.hidden_size)
|
||
|
|
|
||
|
|
def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
|
||
|
|
audio_encodings = self.ffw_layer_start(audio_encodings)
|
||
|
|
audio_encodings = self.attention(audio_encodings, audio_mel_mask)
|
||
|
|
validity_mask_for_lconv = ~audio_mel_mask # True for valid
|
||
|
|
audio_encodings_for_lconv_input = audio_encodings * validity_mask_for_lconv.unsqueeze(-1).to(
|
||
|
|
audio_encodings.dtype
|
||
|
|
)
|
||
|
|
audio_encodings = self.lconv1d(audio_encodings_for_lconv_input)
|
||
|
|
|
||
|
|
audio_encodings = self.ffw_layer_end(audio_encodings)
|
||
|
|
audio_encodings = torch.clamp(audio_encodings, -self.gradient_clipping, self.gradient_clipping)
|
||
|
|
output = self.norm(audio_encodings)
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nAudioEncoder(PreTrainedModel):
|
||
|
|
"""An audio encoder based on the [Universal Speech Model](https://arxiv.org/abs/2303.01037) architecture."""
|
||
|
|
|
||
|
|
config: Gemma3nAudioConfig
|
||
|
|
|
||
|
|
main_input_name = "audio_mel"
|
||
|
|
|
||
|
|
def __init__(self, config: Gemma3nAudioConfig):
|
||
|
|
super().__init__(config)
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.subsample_conv_projection = Gemma3nAudioSubSampleConvProjection(config)
|
||
|
|
self.conformer = nn.ModuleList(
|
||
|
|
[Gemma3nAudioConformerBlock(config) for _ in range(config.conf_num_hidden_layers)]
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self, audio_mel: torch.Tensor, audio_mel_mask: torch.BoolTensor
|
||
|
|
) -> tuple[torch.Tensor, torch.BoolTensor]:
|
||
|
|
"""Encodes a batch of MELs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
audio_mel: a torch.Tensor of shape [batch, num_frames, num_channels,
|
||
|
|
mel_bins].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
audio_encodings: a torch.Tensor of shape
|
||
|
|
`[batch_size, self.config.audio_soft_tokens_per_image,
|
||
|
|
self.config.audio_config.hidden_size]`
|
||
|
|
audio_mel_mask: a torch.BoolTensor of shape [batch, num_frames].
|
||
|
|
"""
|
||
|
|
audio_encodings = self.subsample_conv_projection(audio_mel) # audio_encodings: [B, T_sub, D]
|
||
|
|
|
||
|
|
# Subsample the input audio_mel_mask to match the time dimension of audio_encodings (T_sub)
|
||
|
|
t_sub = audio_encodings.shape[1]
|
||
|
|
|
||
|
|
time_stride_product = 1
|
||
|
|
for stride_pair_idx in range(len(self.config.sscp_conv_stride_size)):
|
||
|
|
time_stride_product *= self.config.sscp_conv_stride_size[stride_pair_idx][0]
|
||
|
|
|
||
|
|
# Create indices for gathering from the original mask.
|
||
|
|
# These indices map to original time steps corresponding to the start of each
|
||
|
|
# receptive field in the subsampled output.
|
||
|
|
indices = torch.arange(t_sub, device=audio_mel_mask.device) * time_stride_product
|
||
|
|
indices = torch.clamp(indices, max=audio_mel_mask.shape[1] - 1) # Ensure indices are valid
|
||
|
|
|
||
|
|
# Expand indices for batch compatibility if B > 1 and indices is 1D.
|
||
|
|
if audio_mel_mask.ndim > 1 and indices.ndim == 1:
|
||
|
|
indices = indices.unsqueeze(0).expand(audio_mel_mask.shape[0], -1) # [B, T_sub]
|
||
|
|
elif (
|
||
|
|
audio_mel_mask.ndim == indices.ndim
|
||
|
|
and audio_mel_mask.shape[0] == 1
|
||
|
|
and indices.shape[0] != 1
|
||
|
|
and t_sub == indices.shape[0]
|
||
|
|
):
|
||
|
|
# Handle case where B=1 but indices became [T_sub] instead of [1, T_sub]
|
||
|
|
indices = indices.unsqueeze(0)
|
||
|
|
|
||
|
|
current_mask = torch.gather(audio_mel_mask, 1, indices) # [B, T_sub]
|
||
|
|
|
||
|
|
for block in self.conformer:
|
||
|
|
audio_encodings = block(audio_encodings, current_mask) # Pass the processed mask
|
||
|
|
|
||
|
|
if self.config.conf_reduction_factor > 1:
|
||
|
|
audio_encodings = audio_encodings[:, :: self.config.conf_reduction_factor]
|
||
|
|
# Reduce the mask as well
|
||
|
|
current_mask = current_mask[:, :: self.config.conf_reduction_factor]
|
||
|
|
|
||
|
|
audio_encodings = audio_encodings.masked_fill(current_mask.unsqueeze(-1), 0.0)
|
||
|
|
return audio_encodings, current_mask
|
||
|
|
|
||
|
|
|
||
|
|
# ==== Language Model ====
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextScaledWordEmbedding(Gemma3TextScaledWordEmbedding):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextLaurelBlock(nn.Module):
|
||
|
|
"""Learned Augmented Residual Layer"""
|
||
|
|
|
||
|
|
def __init__(self, config: Gemma3nTextConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
|
||
|
|
self.linear_left = nn.Linear(self.config.hidden_size, self.config.laurel_rank, bias=False)
|
||
|
|
self.linear_right = nn.Linear(self.config.laurel_rank, self.config.hidden_size, bias=False)
|
||
|
|
self.post_laurel_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
|
||
|
|
|
||
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
laurel_hidden_states: torch.Tensor = self.linear_left(hidden_states)
|
||
|
|
laurel_hidden_states: torch.Tensor = self.linear_right(laurel_hidden_states)
|
||
|
|
normed_laurel_hidden_states = self.post_laurel_norm(laurel_hidden_states)
|
||
|
|
return hidden_states + normed_laurel_hidden_states
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextMLP(Gemma2MLP):
|
||
|
|
def __init__(self, config: Gemma3nTextConfig, layer_idx: int = 0):
|
||
|
|
super().__init__(config)
|
||
|
|
self.intermediate_size = config.intermediate_size[layer_idx]
|
||
|
|
self.activation_sparsity = config.activation_sparsity_pattern[layer_idx]
|
||
|
|
|
||
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
gate_proj = self.gate_proj(hidden_states)
|
||
|
|
if self.activation_sparsity > 0.0:
|
||
|
|
gate_proj = self._gaussian_topk(gate_proj)
|
||
|
|
activations = self.act_fn(gate_proj)
|
||
|
|
up_proj = self.up_proj(hidden_states)
|
||
|
|
down_proj = self.down_proj(activations * up_proj)
|
||
|
|
return down_proj
|
||
|
|
|
||
|
|
def _gaussian_topk(self, inputs: torch.Tensor) -> torch.Tensor:
|
||
|
|
target_sparsity_tensor = torch.tensor(self.activation_sparsity, dtype=torch.float32, device=inputs.device)
|
||
|
|
# normal_dist and std_multiplier are adapted from jax.scipy.stats.norm.ppf().
|
||
|
|
#
|
||
|
|
# References:
|
||
|
|
# * https://docs.jax.dev/en/latest/_autosummary/jax.scipy.stats.norm.ppf.html
|
||
|
|
# * https://pytorch.org/docs/stable/distributions.html#torch.distributions.normal.Normal
|
||
|
|
# * https://pytorch.org/docs/stable/distributions.html#torch.distributions.transformed_distribution.TransformedDistribution.icdf
|
||
|
|
normal_dist = torch.distributions.normal.Normal(0, 1)
|
||
|
|
std_multiplier: torch.Tensor = normal_dist.icdf(target_sparsity_tensor)
|
||
|
|
std_multiplier = std_multiplier.type(inputs.dtype)
|
||
|
|
inputs_mean = torch.mean(inputs, dim=-1, keepdim=True)
|
||
|
|
inputs_std = torch.std(inputs, dim=-1, keepdim=True, unbiased=False)
|
||
|
|
cutoff_x = inputs_mean + inputs_std * std_multiplier
|
||
|
|
return nn.functional.relu(inputs - cutoff_x)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextAltUp(nn.Module):
|
||
|
|
"""Alternating Updates (AltUp)
|
||
|
|
|
||
|
|
The AltUp module wraps transformer layers. The `predict` step modifies the
|
||
|
|
input to the transformer layer, and the `correct` step propagates the output
|
||
|
|
of the transformer layer to the sparsely updated dimensions.
|
||
|
|
|
||
|
|
See more in the research paper:
|
||
|
|
|
||
|
|
https://proceedings.neurips.cc/paper_files/paper/2023/file/f2059277ac6ce66e7e5543001afa8bb5-Paper-Conference.pdf
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, config: Gemma3nTextConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.correct_output_scale = nn.Parameter(torch.zeros(self.config.hidden_size))
|
||
|
|
self.correction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs, bias=False)
|
||
|
|
self.prediction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs**2, bias=False)
|
||
|
|
self.modality_router = nn.Linear(self.config.hidden_size, self.config.altup_num_inputs, bias=False)
|
||
|
|
self.router_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
|
||
|
|
self.register_buffer("router_input_scale", torch.tensor(self.config.hidden_size**-1.0), persistent=False)
|
||
|
|
|
||
|
|
def compute_router_modalities(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
router_inputs = self.router_norm(x) * self.router_input_scale
|
||
|
|
routed = self.modality_router(router_inputs)
|
||
|
|
return torch.tanh(routed.float()).type_as(x)
|
||
|
|
|
||
|
|
def predict(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Predicts the output of a layer using a trainable map.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hidden_states: A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` derived by
|
||
|
|
stacking the input embeddings and preprocessing the last `num_altup_inputs - 1` matrices.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` containing the predictions.
|
||
|
|
"""
|
||
|
|
modalities = self.compute_router_modalities(hidden_states[self.config.altup_active_idx])
|
||
|
|
|
||
|
|
if self.training and self.config.altup_coef_clip is not None:
|
||
|
|
self.prediction_coefs.weight.data.clamp_(-self.config.altup_coef_clip, self.config.altup_coef_clip)
|
||
|
|
|
||
|
|
# Project and then transpose all 2D matrices contained so that mulmat gives the correct result
|
||
|
|
all_coefs: torch.Tensor = (
|
||
|
|
self.prediction_coefs(modalities)
|
||
|
|
.reshape(*modalities.shape[:-1], self.config.altup_num_inputs, self.config.altup_num_inputs)
|
||
|
|
.permute(0, 1, 3, 2)
|
||
|
|
)
|
||
|
|
|
||
|
|
# permute hidden_states to [batch_size, num_tokens, hidden_size, altup_num_inputs]
|
||
|
|
predictions = torch.matmul(hidden_states.permute(1, 2, 3, 0), all_coefs)
|
||
|
|
predictions = predictions.permute(3, 0, 1, 2) # undo the permute
|
||
|
|
predictions += hidden_states # add the original input
|
||
|
|
return predictions.contiguous().type_as(hidden_states)
|
||
|
|
|
||
|
|
def correct(self, predictions: torch.Tensor, activated: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Corrects the predictions relative to the
|
||
|
|
|
||
|
|
Args:
|
||
|
|
predictions: A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` derived by
|
||
|
|
stacking the input embeddings and preprocessing the last `num_altup_inputs - 1` matrices.
|
||
|
|
activated: A 3D tensor of shape `[batch_size, num_tokens, hidden_size]` containing the activated inputs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A 4D tensor of shape `[num_altup_inputs, batch_size, num_tokens, hidden_size]` correcting the original
|
||
|
|
predictions relative to the activated input embeddings.
|
||
|
|
"""
|
||
|
|
modalities = self.compute_router_modalities(activated)
|
||
|
|
innovation = activated - predictions[self.config.altup_active_idx] # (batch, num_tokens, hidden_size)
|
||
|
|
innovation = innovation.repeat(self.config.altup_num_inputs, 1, 1, 1) # Repeat on dim0 to match predictions
|
||
|
|
|
||
|
|
if self.config.altup_coef_clip is not None:
|
||
|
|
self.correction_coefs.weight.data.clamp_(-self.config.altup_coef_clip, self.config.altup_coef_clip)
|
||
|
|
|
||
|
|
# all_coefs adapted from jax.numpy.einsum("...p,pi->...i", ...)
|
||
|
|
# Permute to (altup_num_inputs, batch_size, num_tokens) as the last dim is a scalar applied to each altup input
|
||
|
|
# and expand on dim1 for broadcastability
|
||
|
|
all_coefs: torch.Tensor = self.correction_coefs(modalities) + 1.0
|
||
|
|
all_coefs = all_coefs.permute(2, 0, 1).unsqueeze(-1)
|
||
|
|
|
||
|
|
corrected = torch.mul(innovation, all_coefs)
|
||
|
|
corrected += predictions # add the original input
|
||
|
|
return corrected.contiguous().type_as(activated)
|
||
|
|
|
||
|
|
def forward(self, corrected: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
|
||
|
|
(which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
|
||
|
|
`scale_corrected_output`
|
||
|
|
"""
|
||
|
|
return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
|
||
|
|
|
||
|
|
def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
|
||
|
|
return self.forward(corrected)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextRotaryEmbedding(Gemma2RotaryEmbedding):
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def apply_rotary_pos_emb(
|
||
|
|
x: torch.Tensor,
|
||
|
|
cos: torch.Tensor,
|
||
|
|
sin: torch.Tensor,
|
||
|
|
position_ids: Optional[torch.Tensor] = None,
|
||
|
|
unsqueeze_dim: int = 1,
|
||
|
|
):
|
||
|
|
"""Applies Rotary Position Embedding to the query and key tensors.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x (`torch.Tensor`): The tensor to embed.
|
||
|
|
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||
|
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||
|
|
position_ids (`torch.Tensor`, *optional*):
|
||
|
|
Deprecated and unused.
|
||
|
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||
|
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||
|
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||
|
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||
|
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||
|
|
Returns:
|
||
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||
|
|
"""
|
||
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
||
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
||
|
|
return (x * cos) + (rotate_half(x) * sin)
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextAttention(Gemma3Attention):
|
||
|
|
def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
|
||
|
|
super().__init__()
|
||
|
|
del self.attn_logit_softcapping
|
||
|
|
del self.scaling
|
||
|
|
self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
|
||
|
|
|
||
|
|
first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
|
||
|
|
self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
|
||
|
|
# Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
|
||
|
|
layer_type = config.layer_types[layer_idx]
|
||
|
|
self.kv_shared_layer_index = (
|
||
|
|
first_kv_shared_layer_idx - 1 - config.layer_types[first_kv_shared_layer_idx - 1 :: -1].index(layer_type)
|
||
|
|
if self.is_kv_shared_layer
|
||
|
|
else None
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
position_embeddings: torch.Tensor,
|
||
|
|
attention_mask: Optional[torch.Tensor],
|
||
|
|
past_key_value: Optional[Cache] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
||
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
||
|
|
input_shape = hidden_states.shape[:-1]
|
||
|
|
hidden_shape = (*input_shape, -1, self.config.head_dim)
|
||
|
|
|
||
|
|
cos, sin = position_embeddings
|
||
|
|
|
||
|
|
query_states = self.q_proj(hidden_states).view(hidden_shape)
|
||
|
|
query_states = self.q_norm(query_states)
|
||
|
|
query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)
|
||
|
|
query_states = query_states.transpose(1, 2)
|
||
|
|
|
||
|
|
if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
|
||
|
|
# In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
|
||
|
|
layer = past_key_value.layers[self.kv_shared_layer_index]
|
||
|
|
# Device of past layer may be different from current one
|
||
|
|
indices = cache_position.to(layer.keys.device)
|
||
|
|
# Sliding window cache layers might have smaller size (for full layers, we never go beyond)
|
||
|
|
if isinstance(layer, SlidingWindowLayer):
|
||
|
|
if cache_position.shape[0] > layer.get_max_cache_shape():
|
||
|
|
indices = slice(0, layer.get_max_cache_shape())
|
||
|
|
else:
|
||
|
|
indices = indices.clamp(min=0, max=layer.get_max_cache_shape() - 1)
|
||
|
|
|
||
|
|
# Device of past layer may be different from current one
|
||
|
|
key_states = layer.keys[:, :, indices].to(query_states.device)
|
||
|
|
value_states = layer.values[:, :, indices].to(query_states.device)
|
||
|
|
else:
|
||
|
|
key_states = self.k_proj(hidden_states).view(hidden_shape)
|
||
|
|
key_states = self.k_norm(key_states)
|
||
|
|
key_states = apply_rotary_pos_emb(key_states, cos, sin, unsqueeze_dim=2)
|
||
|
|
key_states = key_states.transpose(1, 2)
|
||
|
|
|
||
|
|
value_states = self.v_proj(hidden_states).view(hidden_shape)
|
||
|
|
value_states = self.v_norm(value_states)
|
||
|
|
value_states = value_states.transpose(1, 2)
|
||
|
|
|
||
|
|
if past_key_value is not None:
|
||
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||
|
|
cache_kwargs = {
|
||
|
|
"sin": sin,
|
||
|
|
"cos": cos,
|
||
|
|
"cache_position": cache_position,
|
||
|
|
"sliding_window": self.sliding_window,
|
||
|
|
}
|
||
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
|
|
||
|
|
attention_interface: Callable = eager_attention_forward
|
||
|
|
if self.config._attn_implementation != "eager":
|
||
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||
|
|
|
||
|
|
attn_output, attn_weights = attention_interface(
|
||
|
|
self,
|
||
|
|
query_states,
|
||
|
|
key_states,
|
||
|
|
value_states,
|
||
|
|
attention_mask,
|
||
|
|
dropout=self.attention_dropout if self.training else 0.0,
|
||
|
|
scaling=1.0,
|
||
|
|
sliding_window=self.sliding_window,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||
|
|
attn_output = self.o_proj(attn_output)
|
||
|
|
return attn_output, attn_weights
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nTextDecoderLayer(Gemma3DecoderLayer):
|
||
|
|
def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
|
||
|
|
super().__init__(config, layer_idx)
|
||
|
|
self.mlp = Gemma3nTextMLP(config, layer_idx=layer_idx)
|
||
|
|
|
||
|
|
self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
|
||
|
|
self.act_fn = ACT2FN[config.hidden_activation]
|
||
|
|
|
||
|
|
self.altup = Gemma3nTextAltUp(config)
|
||
|
|
self.laurel = Gemma3nTextLaurelBlock(config)
|
||
|
|
self.self_attn = Gemma3nTextAttention(config, layer_idx)
|
||
|
|
self.per_layer_input_gate = nn.Linear(self.hidden_size, self.hidden_size_per_layer_input, bias=False)
|
||
|
|
self.per_layer_projection = nn.Linear(self.hidden_size_per_layer_input, self.hidden_size, bias=False)
|
||
|
|
self.post_per_layer_input_norm = Gemma3nRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
position_embeddings_global: torch.Tensor,
|
||
|
|
position_embeddings_local: torch.Tensor,
|
||
|
|
per_layer_input: torch.Tensor,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_value: Optional[Cache] = None,
|
||
|
|
output_attentions: Optional[bool] = False,
|
||
|
|
use_cache: Optional[bool] = False,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
**kwargs,
|
||
|
|
) -> tuple[torch.Tensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
|
|
predictions = self.altup.predict(hidden_states)
|
||
|
|
active_prediction = predictions[self.config.altup_active_idx]
|
||
|
|
|
||
|
|
active_prediction_normed = self.input_layernorm(active_prediction)
|
||
|
|
laurel_output = self.laurel(active_prediction_normed)
|
||
|
|
|
||
|
|
# apply global RoPE to non-sliding layer only
|
||
|
|
if self.self_attn.is_sliding:
|
||
|
|
position_embeddings = position_embeddings_local
|
||
|
|
else:
|
||
|
|
position_embeddings = position_embeddings_global
|
||
|
|
|
||
|
|
attn, self_attn_weights = self.self_attn(
|
||
|
|
hidden_states=active_prediction_normed,
|
||
|
|
position_embeddings=position_embeddings,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_value=past_key_value,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
use_cache=use_cache,
|
||
|
|
cache_position=cache_position,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
attn = self.post_attention_layernorm(attn)
|
||
|
|
|
||
|
|
attn_gated = active_prediction + attn
|
||
|
|
attn_laurel = (attn_gated + laurel_output) / math.sqrt(2)
|
||
|
|
|
||
|
|
attn_norm = self.pre_feedforward_layernorm(attn_laurel)
|
||
|
|
attn_ffw = self.mlp(attn_norm)
|
||
|
|
attn_ffw_norm = self.post_feedforward_layernorm(attn_ffw)
|
||
|
|
attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
|
||
|
|
corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
|
||
|
|
|
||
|
|
first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
|
||
|
|
if self.config.altup_correct_scale:
|
||
|
|
first_prediction = self.altup.scale_corrected_output(first_prediction)
|
||
|
|
|
||
|
|
# per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
|
||
|
|
first_prediction = self.per_layer_input_gate(first_prediction)
|
||
|
|
first_prediction = self.act_fn(first_prediction)
|
||
|
|
first_prediction = torch.multiply(first_prediction, per_layer_input)
|
||
|
|
|
||
|
|
# per_layer_projection adapted from jax.numpy.einsum("btp,pd->btd", ...)
|
||
|
|
first_prediction = self.per_layer_projection(first_prediction)
|
||
|
|
first_prediction = self.post_per_layer_input_norm(first_prediction)
|
||
|
|
corrected_predictions[1:] += first_prediction
|
||
|
|
|
||
|
|
outputs = (corrected_predictions,)
|
||
|
|
|
||
|
|
if output_attentions:
|
||
|
|
outputs += (self_attn_weights,)
|
||
|
|
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nPreTrainedModel(Gemma2PreTrainedModel):
|
||
|
|
config: Gemma3nConfig
|
||
|
|
base_model_prefix = ""
|
||
|
|
_no_split_modules = ["Gemma3nTextDecoderLayer"]
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
Gemma2PreTrainedModel._init_weights(module)
|
||
|
|
if isinstance(module, Gemma3nAudioCumulativeGroupNorm):
|
||
|
|
module.weight.data.fill_(1.0)
|
||
|
|
elif isinstance(module, Gemma3nAudioAttention):
|
||
|
|
module.per_dim_scale.data.zero_()
|
||
|
|
elif isinstance(module, Gemma3nTextAltUp):
|
||
|
|
module.correct_output_scale.data.zero_()
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring(custom_intro="The base Gemma 3n language model without a language modeling head.")
|
||
|
|
class Gemma3nTextModel(Gemma3TextModel):
|
||
|
|
config: Gemma3nTextConfig
|
||
|
|
|
||
|
|
def __init__(self, config: Gemma3nTextConfig):
|
||
|
|
super().__init__(config)
|
||
|
|
|
||
|
|
self.hidden_size = config.hidden_size
|
||
|
|
self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
|
||
|
|
|
||
|
|
self.embed_tokens_per_layer = Gemma3nTextScaledWordEmbedding(
|
||
|
|
config.vocab_size_per_layer_input,
|
||
|
|
config.num_hidden_layers * config.hidden_size_per_layer_input,
|
||
|
|
self.padding_idx,
|
||
|
|
embed_scale=config.hidden_size_per_layer_input**0.5,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.per_layer_model_projection = nn.Linear(
|
||
|
|
self.hidden_size,
|
||
|
|
config.num_hidden_layers * config.hidden_size_per_layer_input,
|
||
|
|
bias=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.per_layer_projection_norm = Gemma3nRMSNorm(config.hidden_size_per_layer_input, eps=config.rms_norm_eps)
|
||
|
|
self.layers = nn.ModuleList(
|
||
|
|
[Gemma3nTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.norm = Gemma3nRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
|
|
||
|
|
self.altup_projections = nn.ModuleList(
|
||
|
|
[nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.altup_unembed_projections = nn.ModuleList(
|
||
|
|
[nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.register_buffer("per_layer_projection_scale", torch.tensor(self.hidden_size**-0.5), persistent=False)
|
||
|
|
self.register_buffer("per_layer_input_scale", torch.rsqrt(torch.tensor(2.0)), persistent=False)
|
||
|
|
self.rotary_emb = Gemma3nTextRotaryEmbedding(config=config)
|
||
|
|
|
||
|
|
# TODO (raushan): Fix this after RoPE refactor. For now we hack it by
|
||
|
|
# reassigning thetas when we want to create a local RoPE layer. Config
|
||
|
|
# defaults should hold values for global RoPE.
|
||
|
|
config = copy.deepcopy(config)
|
||
|
|
config.rope_theta = config.rope_local_base_freq
|
||
|
|
config.rope_scaling = {"rope_type": "default"}
|
||
|
|
self.rotary_emb_local = Gemma3nTextRotaryEmbedding(config=config)
|
||
|
|
|
||
|
|
def get_per_layer_inputs(self, input_ids: torch.LongTensor) -> torch.Tensor:
|
||
|
|
return self.embed_tokens_per_layer(input_ids).reshape(
|
||
|
|
*input_ids.shape,
|
||
|
|
self.config.num_hidden_layers,
|
||
|
|
self.hidden_size_per_layer_input,
|
||
|
|
)
|
||
|
|
|
||
|
|
def project_per_layer_inputs(
|
||
|
|
self,
|
||
|
|
inputs_embeds: torch.Tensor,
|
||
|
|
per_layer_inputs: Optional[torch.Tensor] = None,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
|
||
|
|
per_layer_projection *= self.per_layer_projection_scale.to(
|
||
|
|
dtype=inputs_embeds.dtype, device=per_layer_projection.device
|
||
|
|
)
|
||
|
|
per_layer_projection = per_layer_projection.reshape(
|
||
|
|
*inputs_embeds.shape[:-1],
|
||
|
|
self.config.num_hidden_layers,
|
||
|
|
self.hidden_size_per_layer_input,
|
||
|
|
)
|
||
|
|
per_layer_projection = self.per_layer_projection_norm(per_layer_projection)
|
||
|
|
|
||
|
|
if per_layer_inputs is None:
|
||
|
|
return per_layer_projection
|
||
|
|
|
||
|
|
if per_layer_projection.shape != per_layer_inputs.shape:
|
||
|
|
# per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
|
||
|
|
per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
|
||
|
|
|
||
|
|
return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
|
||
|
|
dtype=inputs_embeds.dtype, device=per_layer_projection.device
|
||
|
|
)
|
||
|
|
|
||
|
|
@can_return_tuple
|
||
|
|
@auto_docstring
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
|
per_layer_inputs: Optional[torch.Tensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_values: Optional[Cache] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
**kwargs: Unpack[TransformersKwargs],
|
||
|
|
) -> BaseModelOutputWithPast:
|
||
|
|
r"""
|
||
|
|
per_layer_inputs (torch.Tensor, *optional*, defaults to None):
|
||
|
|
Pre-computed per-layer embeddings. If None, they are derived from input_ids if provided.
|
||
|
|
"""
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
|
|
||
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
if self.gradient_checkpointing and self.training and use_cache:
|
||
|
|
logger.warning_once(
|
||
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||
|
|
)
|
||
|
|
use_cache = False
|
||
|
|
|
||
|
|
if input_ids is not None:
|
||
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
||
|
|
per_layer_inputs = self.get_per_layer_inputs(input_ids)
|
||
|
|
|
||
|
|
per_layer_inputs = self.project_per_layer_inputs(inputs_embeds, per_layer_inputs)
|
||
|
|
|
||
|
|
if use_cache and past_key_values is None and not self.training:
|
||
|
|
past_key_values = DynamicCache()
|
||
|
|
|
||
|
|
if cache_position is None:
|
||
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||
|
|
cache_position = torch.arange(
|
||
|
|
past_seen_tokens,
|
||
|
|
past_seen_tokens + inputs_embeds.shape[1],
|
||
|
|
device=inputs_embeds.device,
|
||
|
|
)
|
||
|
|
|
||
|
|
if position_ids is None:
|
||
|
|
position_ids = cache_position.unsqueeze(0)
|
||
|
|
|
||
|
|
# It may already have been prepared by e.g. `generate`
|
||
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
||
|
|
# Prepare mask arguments
|
||
|
|
mask_kwargs = {
|
||
|
|
"config": self.config,
|
||
|
|
"input_embeds": inputs_embeds,
|
||
|
|
"attention_mask": attention_mask,
|
||
|
|
"cache_position": cache_position,
|
||
|
|
"past_key_values": past_key_values,
|
||
|
|
"position_ids": position_ids,
|
||
|
|
}
|
||
|
|
# Create the masks
|
||
|
|
causal_mask_mapping = {
|
||
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
||
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
||
|
|
}
|
||
|
|
|
||
|
|
# embed positions
|
||
|
|
hidden_states_0 = inputs_embeds
|
||
|
|
|
||
|
|
# Initialize RoPE embeddings
|
||
|
|
position_embeddings_global = self.rotary_emb(hidden_states_0, position_ids)
|
||
|
|
position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
|
||
|
|
|
||
|
|
# Expand hidden_states to support per-layer inputs
|
||
|
|
target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
|
||
|
|
epsilon_tensor = torch.tensor(1e-5)
|
||
|
|
|
||
|
|
temp_hidden_states = [hidden_states_0]
|
||
|
|
for i in range(1, self.config.altup_num_inputs):
|
||
|
|
# altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
|
||
|
|
altup_proj = self.altup_projections[i - 1](hidden_states_0)
|
||
|
|
current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
|
||
|
|
new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
|
||
|
|
new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
|
||
|
|
current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
|
||
|
|
temp_hidden_states.append(current_hidden_state)
|
||
|
|
|
||
|
|
hidden_states = torch.stack(temp_hidden_states, dim=0) # [num_altup_inputs, batch, seq_len, hidden_size]
|
||
|
|
|
||
|
|
# decoder layers
|
||
|
|
all_hidden_states = () if output_hidden_states else None
|
||
|
|
all_self_attns = () if output_attentions else None
|
||
|
|
|
||
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||
|
|
if output_hidden_states:
|
||
|
|
all_hidden_states += (hidden_states,)
|
||
|
|
|
||
|
|
causal_mask = causal_mask_mapping[decoder_layer.attention_type]
|
||
|
|
per_layer_input = per_layer_inputs[:, :, decoder_layer.layer_idx, :]
|
||
|
|
|
||
|
|
layer_outputs = decoder_layer(
|
||
|
|
hidden_states,
|
||
|
|
position_embeddings_global,
|
||
|
|
position_embeddings_local,
|
||
|
|
per_layer_input,
|
||
|
|
attention_mask=causal_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_value=past_key_values,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
use_cache=use_cache,
|
||
|
|
cache_position=cache_position,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = layer_outputs[0]
|
||
|
|
|
||
|
|
if output_attentions:
|
||
|
|
all_self_attns += (layer_outputs[1],)
|
||
|
|
|
||
|
|
# add hidden states from the last decoder layer (but before reprojecting to stay consistent with layer output)
|
||
|
|
if output_hidden_states:
|
||
|
|
all_hidden_states += (hidden_states,)
|
||
|
|
|
||
|
|
# Per-layer inputs to single output
|
||
|
|
target_magnitude = torch.mean(hidden_states[0] ** 2, dim=-1, keepdim=True) ** 0.5
|
||
|
|
temp_hidden_states = [hidden_states[0]]
|
||
|
|
for i in range(1, self.config.altup_num_inputs):
|
||
|
|
# altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
|
||
|
|
altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
|
||
|
|
current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
|
||
|
|
new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
|
||
|
|
new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
|
||
|
|
current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
|
||
|
|
temp_hidden_states.append(current_hidden_state)
|
||
|
|
|
||
|
|
hidden_states = torch.stack(temp_hidden_states)
|
||
|
|
hidden_states = torch.mean(hidden_states, dim=0)
|
||
|
|
hidden_states = self.norm(hidden_states)
|
||
|
|
|
||
|
|
return BaseModelOutputWithPast(
|
||
|
|
last_hidden_state=hidden_states,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
hidden_states=all_hidden_states,
|
||
|
|
attentions=all_self_attns,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring(custom_intro="The base Gemma 3n language model with a language modeling head.")
|
||
|
|
class Gemma3nForCausalLM(Gemma3ForCausalLM):
|
||
|
|
_checkpoint_conversion_mapping = {"model.language_model": "model"}
|
||
|
|
base_model_prefix = "model"
|
||
|
|
|
||
|
|
|
||
|
|
class Gemma3nMultimodalEmbedder(nn.Module):
|
||
|
|
"""Embeds token ids or soft tokens for multimodal content into language model space."""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
multimodal_config: Union[Gemma3nAudioConfig, Gemma3nVisionConfig],
|
||
|
|
text_config: Gemma3nTextConfig,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.multimodal_hidden_size = multimodal_config.hidden_size
|
||
|
|
self.eps = multimodal_config.rms_norm_eps
|
||
|
|
self.vocab_offset = multimodal_config.vocab_offset
|
||
|
|
self.vocab_size = multimodal_config.vocab_size
|
||
|
|
self.text_hidden_size = text_config.hidden_size
|
||
|
|
|
||
|
|
self.embedding = nn.Embedding(self.vocab_size, self.multimodal_hidden_size)
|
||
|
|
self.hard_embedding_norm = Gemma3nRMSNorm(self.multimodal_hidden_size, eps=self.eps)
|
||
|
|
self.soft_embedding_norm = Gemma3nRMSNorm(self.multimodal_hidden_size, eps=self.eps)
|
||
|
|
self.embedding_projection = nn.Linear(self.multimodal_hidden_size, self.text_hidden_size, bias=False)
|
||
|
|
self.embedding_post_projection_norm = Gemma3nRMSNorm(self.text_hidden_size, eps=self.eps, with_scale=False)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
"""Embeds token ids or soft tokens for multimodal content into language model space.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_ids: A torch.LongTensor containing the token ids to embed. Values should be in the range
|
||
|
|
`[vocab_offset, vocab_offset + vocab_size)`.
|
||
|
|
inputs_embeds: A torch.Tensor containing the soft tokens to embed.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A torch.Tensor of embeddings with shape `[batch_size, seq_len, self.config.text_config.hidden_size]`.
|
||
|
|
"""
|
||
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
if inputs_embeds is not None:
|
||
|
|
emb_norm = self.soft_embedding_norm(inputs_embeds)
|
||
|
|
else:
|
||
|
|
hard_emb = self.embedding(input_ids - self.vocab_offset)
|
||
|
|
emb_norm = self.hard_embedding_norm(hard_emb)
|
||
|
|
|
||
|
|
emb_norm_proj = self.embedding_projection(emb_norm)
|
||
|
|
return self.embedding_post_projection_norm(emb_norm_proj)
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring(
|
||
|
|
custom_intro="""
|
||
|
|
The base Gemma 3n model comprising a vision backbone, an audio backbone, and a language model without a
|
||
|
|
language modeling head.
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
class Gemma3nModel(PaliGemmaModel):
|
||
|
|
_checkpoint_conversion_mapping = {}
|
||
|
|
|
||
|
|
def __init__(self, config: Gemma3nConfig):
|
||
|
|
super().__init__()
|
||
|
|
del self.multi_modal_projector # Replaced by Gemma3nVisionEmbedder
|
||
|
|
self.vocab_size_per_layer_input = config.text_config.vocab_size_per_layer_input
|
||
|
|
self.audio_tower = AutoModel.from_config(config.audio_config)
|
||
|
|
self.embed_vision = Gemma3nMultimodalEmbedder(config.vision_config, config.text_config)
|
||
|
|
self.embed_audio = Gemma3nMultimodalEmbedder(config.audio_config, config.text_config)
|
||
|
|
|
||
|
|
def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
Projects the last hidden state from the vision model into language model space.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
|
||
|
|
The tensors corresponding to the input images.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
|
||
|
|
"""
|
||
|
|
vision_outputs = self.vision_tower(
|
||
|
|
pixel_values=pixel_values, do_pooling=False, return_dict=True
|
||
|
|
).last_hidden_state
|
||
|
|
# Convert from (batch, channels, height, width) to (batch, height * width, channels) where:
|
||
|
|
# height == width and height * width == Gemma3nConfig.vision_soft_tokens_per_image.
|
||
|
|
vision_outputs = vision_outputs.reshape(
|
||
|
|
vision_outputs.shape[0],
|
||
|
|
self.config.vision_config.hidden_size,
|
||
|
|
self.config.vision_soft_tokens_per_image,
|
||
|
|
).permute(0, 2, 1)
|
||
|
|
# Normalize and embed the soft tokens into language model space.
|
||
|
|
vision_outputs *= self.config.vision_config.hidden_size**0.5
|
||
|
|
return self.embed_vision(inputs_embeds=vision_outputs)
|
||
|
|
|
||
|
|
@can_return_tuple
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None, # text inputs
|
||
|
|
pixel_values: Optional[torch.FloatTensor] = None, # vision inputs
|
||
|
|
input_features: Optional[torch.FloatTensor] = None, # audio inputs
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
input_features_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
|
||
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
labels: Optional[torch.LongTensor] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
**lm_kwargs,
|
||
|
|
) -> Gemma3nCausalLMOutputWithPast:
|
||
|
|
r"""
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
|
|
config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import requests
|
||
|
|
>>> from transformers import AutoProcessor, Gemma3nForConditionalGeneration
|
||
|
|
|
||
|
|
>>> model = Gemma3nForConditionalGeneration.from_pretrained("google/gemma3n2-3b-mix-224")
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("google/gemma3n2-3b-mix-224")
|
||
|
|
|
||
|
|
>>> prompt = "Where is the cat standing?"
|
||
|
|
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
|
||
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||
|
|
|
||
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> # Generate
|
||
|
|
>>> generate_ids = model.generate(**inputs,)
|
||
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
|
|
"Where is the cat standing?\nsnow"
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
|
||
|
|
if input_ids is not None:
|
||
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
||
|
|
|
||
|
|
# Prepare per-layer inputs from inputs_ids
|
||
|
|
per_layer_inputs_mask = torch.logical_and(input_ids >= 0, input_ids < self.vocab_size_per_layer_input)
|
||
|
|
per_layer_inputs_tokens = torch.where(per_layer_inputs_mask, input_ids, torch.zeros_like(input_ids))
|
||
|
|
per_layer_inputs = self.language_model.get_per_layer_inputs(per_layer_inputs_tokens)
|
||
|
|
|
||
|
|
# Handle vision tokens (>= embed_vision.vocab_offset and < embed_audio.vocab_offset)
|
||
|
|
vision_mask = torch.logical_and(
|
||
|
|
input_ids >= self.embed_vision.vocab_offset, input_ids < self.embed_audio.vocab_offset
|
||
|
|
)
|
||
|
|
dummy_vision_token_id = self.embed_vision.vocab_offset + self.embed_vision.vocab_size - 1
|
||
|
|
vision_input_ids = torch.where(vision_mask, input_ids, dummy_vision_token_id).to(inputs_embeds.device)
|
||
|
|
vision_embeds = self.embed_vision(input_ids=vision_input_ids)
|
||
|
|
expanded_vision_mask = vision_mask.unsqueeze(-1).expand_as(inputs_embeds)
|
||
|
|
inputs_embeds = torch.where(expanded_vision_mask, vision_embeds, inputs_embeds)
|
||
|
|
|
||
|
|
# Handle audio tokens (>= embed_audio.vocab_offset)
|
||
|
|
audio_mask = input_ids >= self.embed_audio.vocab_offset
|
||
|
|
dummy_audio_token_id = self.embed_audio.vocab_offset + self.embed_audio.vocab_size - 1
|
||
|
|
audio_input_ids = torch.where(audio_mask, input_ids, dummy_audio_token_id).to(inputs_embeds.device)
|
||
|
|
audio_embeds = self.embed_audio(input_ids=audio_input_ids)
|
||
|
|
expanded_audio_mask = audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
|
||
|
|
inputs_embeds = torch.where(expanded_audio_mask, audio_embeds, inputs_embeds)
|
||
|
|
else:
|
||
|
|
per_layer_inputs = None
|
||
|
|
|
||
|
|
# Merge text and images
|
||
|
|
if pixel_values is not None:
|
||
|
|
image_features = self.get_image_features(pixel_values)
|
||
|
|
|
||
|
|
if input_ids is None:
|
||
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
||
|
|
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
|
||
|
|
special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
|
||
|
|
|
||
|
|
if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
|
||
|
|
image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
|
||
|
|
raise ValueError(
|
||
|
|
f"Number of images does not match number of special image tokens in the input text. "
|
||
|
|
f"Got {image_tokens_in_text} image tokens in the text and "
|
||
|
|
f"{image_features.shape[0] * image_features.shape[1]} tokens from image embeddings."
|
||
|
|
)
|
||
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
||
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
||
|
|
|
||
|
|
# Merge text and audio
|
||
|
|
if input_features is not None and input_features_mask is not None:
|
||
|
|
audio_features, audio_mask = self.get_audio_features(input_features, ~input_features_mask)
|
||
|
|
|
||
|
|
# The Gemma3nProcessor expects all audio will be 30s in length and inserts 188 audio soft tokens into the
|
||
|
|
# text to account for this. However, the audio preprocessing and encoder do not gurarantee they will
|
||
|
|
# produce 188 soft tokens; they will produce at most that many tokens, but they may produce fewer tokens
|
||
|
|
# depending on the length of the longest audio input in the batch. When we encounter this situation, we pad
|
||
|
|
# the audio feature out to 188 soft tokens with the emebedding of the last token in the embed_audio vocab.
|
||
|
|
audio_padding_toks = torch.tensor([[self.vocab_size - 1]], dtype=torch.long, device=audio_features.device)
|
||
|
|
audio_padding_embs = self.embed_audio(input_ids=audio_padding_toks)
|
||
|
|
audio_features = torch.where(audio_mask.unsqueeze(-1), audio_padding_embs, audio_features)
|
||
|
|
|
||
|
|
audio_batch_size, audio_seq_len, audio_embed_dim = audio_features.shape
|
||
|
|
extra_padding_tokens = self.config.audio_soft_tokens_per_image - audio_seq_len
|
||
|
|
extra_padding_features = audio_padding_embs.expand(audio_batch_size, extra_padding_tokens, audio_embed_dim)
|
||
|
|
|
||
|
|
audio_features = torch.cat((audio_features, extra_padding_features), dim=1)
|
||
|
|
|
||
|
|
if input_ids is None:
|
||
|
|
special_audio_mask = inputs_embeds == self.embed_audio(
|
||
|
|
input_ids=torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
special_audio_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
|
||
|
|
special_audio_mask = special_audio_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
|
||
|
|
|
||
|
|
if not is_torchdynamo_compiling() and inputs_embeds[special_audio_mask].numel() != audio_features.numel():
|
||
|
|
audio_tokens_in_text = (special_audio_mask).sum(dim=1).sum(dim=0)[0]
|
||
|
|
raise ValueError(
|
||
|
|
f"Number of audio input features does not match number of special audio tokens in the input text. "
|
||
|
|
f"Got {audio_tokens_in_text} audio tokens in the text and "
|
||
|
|
f"{audio_features.shape[0] * audio_features.shape[1]} tokens from audio embeddings."
|
||
|
|
)
|
||
|
|
audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
||
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_features)
|
||
|
|
|
||
|
|
outputs = self.language_model(
|
||
|
|
input_ids=None,
|
||
|
|
per_layer_inputs=per_layer_inputs,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
use_cache=use_cache,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=True,
|
||
|
|
cache_position=cache_position,
|
||
|
|
**lm_kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
return Gemma3nModelOutputWithPast(
|
||
|
|
last_hidden_state=outputs.last_hidden_state,
|
||
|
|
past_key_values=outputs.past_key_values if use_cache else None,
|
||
|
|
hidden_states=outputs.hidden_states,
|
||
|
|
attentions=outputs.attentions,
|
||
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
||
|
|
audio_hidden_states=audio_features if input_features is not None else None,
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_audio_features(
|
||
|
|
self, input_features: torch.Tensor, input_features_mask: torch.Tensor
|
||
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
"""
|
||
|
|
Projects the last hidden state from the audio encoder into language model space.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_features (`torch.FloatTensor]` of shape `(num_images, seq_length, num_features)`):
|
||
|
|
The tensors corresponding to the input audio.
|
||
|
|
input_features_mask (`torch.FloatTensor]` of shape `(num_images, seq_length)`):
|
||
|
|
The attention mask for the input audio.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
audio_features (`torch.Tensor`): Audio feature tensor of shape `(num_images, audio_length, embed_dim)`).
|
||
|
|
"""
|
||
|
|
audio_outputs, audio_mask = self.audio_tower(input_features, input_features_mask)
|
||
|
|
return self.embed_audio(inputs_embeds=audio_outputs), audio_mask
|
||
|
|
|
||
|
|
def _update_causal_mask(self, **super_kwargs):
|
||
|
|
raise AttributeError("We don't want to inherit it")
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring(
|
||
|
|
custom_intro="""
|
||
|
|
The base Gemma 3n model comprising a vision backbone, an audio backbone, a language model, and a language modeling
|
||
|
|
head.
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
class Gemma3nForConditionalGeneration(PaliGemmaForConditionalGeneration):
|
||
|
|
_checkpoint_conversion_mapping = {}
|
||
|
|
base_model_prefix = "model"
|
||
|
|
|
||
|
|
@property
|
||
|
|
def audio_tower(self):
|
||
|
|
return self.model.audio_tower
|
||
|
|
|
||
|
|
@property
|
||
|
|
def multi_modal_projector(self):
|
||
|
|
raise AttributeError("Use embed_vision instead of multi_modal_projector.")
|
||
|
|
|
||
|
|
@can_return_tuple
|
||
|
|
@auto_docstring
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None, # text inputs
|
||
|
|
pixel_values: Optional[torch.FloatTensor] = None, # vision inputs
|
||
|
|
input_features: Optional[torch.FloatTensor] = None, # audio inputs
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
input_features_mask: Optional[torch.Tensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
|
||
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
labels: Optional[torch.LongTensor] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
|
|
**lm_kwargs,
|
||
|
|
) -> Gemma3nCausalLMOutputWithPast:
|
||
|
|
r"""
|
||
|
|
input_features_mask (torch.Tensor, *optional*, defaults to None):
|
||
|
|
The attention mask for the input audio.
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
|
|
config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are
|
||
|
|
ignored (masked), the loss is only computed for the tokens with labels in
|
||
|
|
`[0, ..., config.text_config.vocab_size]`.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import requests
|
||
|
|
>>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
|
||
|
|
|
||
|
|
>>> model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma-3-4b-it")
|
||
|
|
>>> processor = AutoProcessor.from_pretrained("google/gemma-3-4b-it")
|
||
|
|
|
||
|
|
>>> messages = [
|
||
|
|
... {
|
||
|
|
... "role": "system",
|
||
|
|
... "content": [
|
||
|
|
... {"type": "text", "text": "You are a helpful assistant."}
|
||
|
|
... ]
|
||
|
|
... },
|
||
|
|
... {
|
||
|
|
... "role": "user", "content": [
|
||
|
|
... {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
|
||
|
|
... {"type": "text", "text": "Where is the cat standing?"},
|
||
|
|
... ]
|
||
|
|
... },
|
||
|
|
... ]
|
||
|
|
|
||
|
|
>>> inputs = processor.apply_chat_template(
|
||
|
|
... messages,
|
||
|
|
... tokenizer=True,
|
||
|
|
... return_dict=True,
|
||
|
|
... return_tensors="pt",
|
||
|
|
... add_generation_prompt=True
|
||
|
|
... )
|
||
|
|
>>> # Generate
|
||
|
|
>>> generate_ids = model.generate(**inputs)
|
||
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
|
|
"user\nYou are a helpful assistant.\n\n\n\n\n\nWhere is the cat standing?\nmodel\nBased on the image, the cat is standing in a snowy area, likely outdoors. It appears to"
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
output_hidden_states = (
|
||
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
|
)
|
||
|
|
|
||
|
|
outputs = self.model(
|
||
|
|
input_ids=input_ids,
|
||
|
|
pixel_values=pixel_values,
|
||
|
|
input_features=input_features,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
input_features_mask=input_features_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
token_type_ids=token_type_ids,
|
||
|
|
cache_position=cache_position,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
labels=labels,
|
||
|
|
use_cache=use_cache,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=True,
|
||
|
|
**lm_kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = outputs.last_hidden_state
|
||
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||
|
|
if (final_logit_softcapping := self.config.get_text_config().final_logit_softcapping) is not None:
|
||
|
|
logits = logits / final_logit_softcapping
|
||
|
|
logits = torch.tanh(logits)
|
||
|
|
logits = logits * final_logit_softcapping
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if labels is not None:
|
||
|
|
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
||
|
|
logits = logits.float()
|
||
|
|
shift_logits = logits[..., :-1, :]
|
||
|
|
shift_labels = labels[..., 1:]
|
||
|
|
if attention_mask is not None:
|
||
|
|
# we use the input attention mask to shift the logits and labels, because it is 2D.
|
||
|
|
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
|
||
|
|
shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
|
||
|
|
shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
|
||
|
|
shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
|
||
|
|
else:
|
||
|
|
shift_logits = shift_logits.contiguous()
|
||
|
|
shift_labels = shift_labels.contiguous()
|
||
|
|
# Flatten the tokens
|
||
|
|
loss_fct = nn.CrossEntropyLoss()
|
||
|
|
|
||
|
|
flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
|
||
|
|
flat_labels = shift_labels.view(-1).to(shift_logits.device)
|
||
|
|
loss = loss_fct(flat_logits, flat_labels)
|
||
|
|
|
||
|
|
return Gemma3nCausalLMOutputWithPast(
|
||
|
|
loss=loss,
|
||
|
|
logits=logits,
|
||
|
|
past_key_values=outputs.past_key_values,
|
||
|
|
hidden_states=outputs.hidden_states,
|
||
|
|
attentions=outputs.attentions,
|
||
|
|
image_hidden_states=outputs.image_hidden_states,
|
||
|
|
audio_hidden_states=outputs.audio_hidden_states,
|
||
|
|
)
|
||
|
|
|
||
|
|
def prepare_inputs_for_generation(
|
||
|
|
self,
|
||
|
|
input_ids,
|
||
|
|
past_key_values=None,
|
||
|
|
inputs_embeds=None,
|
||
|
|
cache_position=None,
|
||
|
|
position_ids=None,
|
||
|
|
pixel_values=None,
|
||
|
|
input_features=None,
|
||
|
|
attention_mask=None,
|
||
|
|
input_features_mask=None,
|
||
|
|
token_type_ids=None,
|
||
|
|
use_cache=True,
|
||
|
|
logits_to_keep=None,
|
||
|
|
labels=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# Overwritten -- custom `position_ids` and `pixel_values` handling
|
||
|
|
model_inputs = super().prepare_inputs_for_generation(
|
||
|
|
input_ids,
|
||
|
|
past_key_values=past_key_values,
|
||
|
|
inputs_embeds=inputs_embeds,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
position_ids=position_ids,
|
||
|
|
cache_position=cache_position,
|
||
|
|
use_cache=use_cache,
|
||
|
|
logits_to_keep=logits_to_keep,
|
||
|
|
token_type_ids=token_type_ids,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
# If we're in cached decoding stage, multimodal inputs should be None because input ids do not contain special
|
||
|
|
# tokens anymore. Otherwise multimodal inputs should be passed to model.
|
||
|
|
# NOTE: use_cache=False always needs pixel_values, input_features, and input_features_mask
|
||
|
|
if cache_position[0] == 0:
|
||
|
|
model_inputs["pixel_values"] = pixel_values
|
||
|
|
model_inputs["input_features"] = input_features
|
||
|
|
model_inputs["input_features_mask"] = input_features_mask
|
||
|
|
|
||
|
|
return model_inputs
|
||
|
|
|
||
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(self, **super_kwargs):
|
||
|
|
raise AttributeError("Do not inherit _prepare_4d_causal_attention_mask_with_cache_position from PaliGemma")
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"Gemma3nAudioConfig",
|
||
|
|
"Gemma3nAudioEncoder",
|
||
|
|
"Gemma3nConfig",
|
||
|
|
"Gemma3nForCausalLM",
|
||
|
|
"Gemma3nForConditionalGeneration",
|
||
|
|
"Gemma3nModel",
|
||
|
|
"Gemma3nPreTrainedModel", # noqa: F822
|
||
|
|
"Gemma3nTextConfig",
|
||
|
|
"Gemma3nTextModel",
|
||
|
|
"Gemma3nVisionConfig",
|
||
|
|
]
|