1184 lines
52 KiB
Python
1184 lines
52 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import copy
|
|
import warnings
|
|
from collections.abc import Callable
|
|
from typing import Any, Optional, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.checkpoint
|
|
|
|
from ...cache_utils import Cache, DynamicCache
|
|
from ...configuration_utils import PretrainedConfig, layer_type_validation
|
|
from ...masking_utils import create_causal_mask, create_masks_for_generate, create_sliding_window_causal_mask
|
|
from ...modeling_flash_attention_utils import FlashAttentionKwargs
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
|
|
from ...modeling_rope_utils import rope_config_validation
|
|
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
|
|
from ...processing_utils import Unpack
|
|
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
|
|
from ...utils.deprecation import deprecate_kwarg
|
|
from ..gemma2.configuration_gemma2 import Gemma2Config
|
|
from ..gemma2.modeling_gemma2 import (
|
|
Gemma2Attention,
|
|
Gemma2ForCausalLM,
|
|
Gemma2MLP,
|
|
Gemma2Model,
|
|
Gemma2PreTrainedModel,
|
|
Gemma2RMSNorm,
|
|
Gemma2RotaryEmbedding,
|
|
apply_rotary_pos_emb,
|
|
eager_attention_forward,
|
|
)
|
|
from ..paligemma.modeling_paligemma import (
|
|
PaligemmaCausalLMOutputWithPast,
|
|
PaliGemmaForConditionalGeneration,
|
|
PaliGemmaModel,
|
|
PaligemmaModelOutputWithPast,
|
|
)
|
|
from ..siglip import SiglipVisionConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class Gemma3TextConfig(Gemma2Config, PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`Gemma3TextModel`]. It is used to instantiate an Gemma3Text
|
|
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 Gemma3Text-7B.
|
|
e.g. [google/gemma3_text-7b](https://huggingface.co/google/gemma3_text-7b)
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
Args:
|
|
vocab_size (`int`, *optional*, defaults to 262208):
|
|
Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`Gemma3TextModel`]
|
|
hidden_size (`int`, *optional*, defaults to 2304):
|
|
Dimension of the hidden representations.
|
|
intermediate_size (`int`, *optional*, defaults to 9216):
|
|
Dimension of the MLP representations.
|
|
num_hidden_layers (`int`, *optional*, defaults to 26):
|
|
Number of hidden layers in the Transformer decoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 8):
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 4):
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
|
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
|
by meanpooling all the original heads within that group. For more details, check out [this
|
|
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
|
|
`num_attention_heads`.
|
|
head_dim (`int`, *optional*, defaults to 256):
|
|
The attention head dimension.
|
|
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
|
|
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
|
|
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
|
|
max_position_embeddings (`int`, *optional*, defaults to 131072):
|
|
The maximum sequence length that this model might ever be used with.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
|
The epsilon used by the rms normalization layers.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
pad_token_id (`int`, *optional*, defaults to 0):
|
|
Padding token id.
|
|
eos_token_id (`int`, *optional*, defaults to 1):
|
|
End of stream token id.
|
|
bos_token_id (`int`, *optional*, defaults to 2):
|
|
Beginning of stream token id.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
|
|
Whether to tie weight embeddings
|
|
rope_theta (`float`, *optional*, defaults to 1000000.0):
|
|
The base period of the RoPE embeddings.
|
|
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
|
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
query_pre_attn_scalar (`float`, *optional*, defaults to 256):
|
|
Scaling factor used on the attention scores
|
|
sliding_window (`int`, *optional*, defaults to 4096):
|
|
In Gemma3Text, every other layer uses sliding window attention. This is the size of the sliding window.
|
|
layer_types (`list`, *optional*):
|
|
Attention pattern for each layer.
|
|
final_logit_softcapping (`float`, *optional*):
|
|
Scaling factor when applying tanh softcapping on the logits.
|
|
attn_logit_softcapping (`float`, *optional*):
|
|
Scaling factor when applying tanh softcapping on the attention scores.
|
|
rope_scaling (`Dict`, *optional*):
|
|
Dictionary containing the scaling configuration for the RoPE embeddings used in global attention. NOTE: if you apply new rope type
|
|
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
|
accordingly.
|
|
Expected contents:
|
|
`rope_type` (`str`):
|
|
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
|
'llama3'], with 'default' being the original RoPE implementation.
|
|
`factor` (`float`, *optional*):
|
|
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
|
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
|
original maximum pre-trained length.
|
|
`original_max_position_embeddings` (`int`, *optional*):
|
|
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
|
pretraining.
|
|
`attention_factor` (`float`, *optional*):
|
|
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
|
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
|
`factor` field to infer the suggested value.
|
|
`beta_fast` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 32.
|
|
`beta_slow` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 1.
|
|
`short_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`long_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`low_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
|
`high_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
|
rope_local_base_freq (float, *optional*, defaults to 10000.0):
|
|
The base period of the RoPE embeddings for local attention.
|
|
|
|
```python
|
|
>>> from transformers import Gemma3TextModel, Gemma3TextConfig
|
|
>>> # Initializing a Gemma3Text gemma3_text-7b style configuration
|
|
>>> configuration = Gemma3TextConfig()
|
|
>>> # Initializing a model from the gemma3_text-7b style configuration
|
|
>>> model = Gemma3TextModel(configuration)
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```
|
|
"""
|
|
|
|
model_type = "gemma3_text"
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=262_208,
|
|
hidden_size=2304,
|
|
intermediate_size=9216,
|
|
num_hidden_layers=26,
|
|
num_attention_heads=8,
|
|
num_key_value_heads=4,
|
|
head_dim=256,
|
|
hidden_activation="gelu_pytorch_tanh",
|
|
max_position_embeddings=131_072,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-6,
|
|
use_cache=True,
|
|
pad_token_id=0,
|
|
eos_token_id=1,
|
|
bos_token_id=2,
|
|
tie_word_embeddings=True,
|
|
rope_theta=1_000_000.0,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
query_pre_attn_scalar=256,
|
|
sliding_window=4096,
|
|
layer_types=None,
|
|
final_logit_softcapping=None,
|
|
attn_logit_softcapping=None,
|
|
rope_scaling=None,
|
|
rope_local_base_freq=10_000.0,
|
|
**kwargs,
|
|
):
|
|
PretrainedConfig.__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=tie_word_embeddings,
|
|
**kwargs,
|
|
)
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.head_dim = head_dim
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
self.hidden_activation = hidden_activation
|
|
self.query_pre_attn_scalar = query_pre_attn_scalar
|
|
self.sliding_window = sliding_window
|
|
self.final_logit_softcapping = final_logit_softcapping
|
|
self.attn_logit_softcapping = attn_logit_softcapping
|
|
self.layer_types = layer_types
|
|
|
|
self.rope_local_base_freq = rope_local_base_freq
|
|
self.rope_scaling = rope_scaling
|
|
rope_config_validation(self)
|
|
|
|
# BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
|
|
self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6)
|
|
|
|
if self.layer_types is None:
|
|
self.layer_types = [
|
|
"sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention"
|
|
for i in range(self.num_hidden_layers)
|
|
]
|
|
layer_type_validation(self.layer_types)
|
|
|
|
@property
|
|
def sliding_window_pattern(self):
|
|
warnings.warn(
|
|
"The `sliding_window_pattern` attribute is deprecated and will be removed in v4.55.0.",
|
|
FutureWarning,
|
|
)
|
|
return self._sliding_window_pattern
|
|
|
|
@sliding_window_pattern.setter
|
|
def sliding_window_pattern(self, value):
|
|
self._sliding_window_pattern = value
|
|
|
|
|
|
class Gemma3Config(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`Gemma3ForConditionalGeneration`]. It is used to instantiate an
|
|
Gemma3ForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration
|
|
with the defaults will yield a similar configuration to that of the PaliGemma-2B.
|
|
|
|
e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b)
|
|
|
|
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[Gemma3TextConfig, dict]`, *optional*):
|
|
The config object of the text backbone.
|
|
vision_config (`Union[AutoConfig, dict]`, *optional*):
|
|
Custom vision config or dict.
|
|
mm_tokens_per_image (`int`, *optional*, defaults to 256):
|
|
The number of tokens per image embedding.
|
|
boi_token_index (`int`, *optional*, defaults to 255999):
|
|
The begin-of-image token index to wrap the image prompt.
|
|
eoi_token_index (`int`, *optional*, defaults to 256000):
|
|
The end-of-image token index to wrap the image prompt.
|
|
image_token_index (`int`, *optional*, defaults to 262144):
|
|
The image token index to encode the image 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 Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig
|
|
|
|
>>> # Initializing a Siglip-like vision config
|
|
>>> vision_config = SiglipVisionConfig()
|
|
|
|
>>> # Initializing a Gemma3 Text config
|
|
>>> text_config = Gemma3TextConfig()
|
|
|
|
>>> # Initializing a Gemma3 gemma-3-4b style configuration
|
|
>>> configuration = Gemma3Config(vision_config, text_config)
|
|
|
|
>>> # Initializing a model from the gemma-3-4b style configuration
|
|
>>> model = Gemma3TextConfig(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "gemma3"
|
|
attribute_map = {
|
|
"image_token_id": "image_token_index",
|
|
"boi_token_id": "boi_token_index",
|
|
"eoi_token_id": "eoi_token_index",
|
|
}
|
|
sub_configs = {
|
|
"text_config": Gemma3TextConfig,
|
|
"vision_config": SiglipVisionConfig,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
text_config: Optional[Union[Gemma3TextConfig, dict[str, Any]]] = None,
|
|
vision_config: Optional[Union[SiglipVisionConfig, dict[str, Any]]] = None,
|
|
mm_tokens_per_image: int = 256,
|
|
boi_token_index: int = 255_999,
|
|
eoi_token_index: int = 256_000,
|
|
image_token_index: int = 262_144,
|
|
initializer_range: float = 0.02,
|
|
**kwargs,
|
|
):
|
|
if text_config is None:
|
|
text_config = Gemma3TextConfig()
|
|
logger.info("text_config is None, using default Gemma3TextConfig text config.")
|
|
elif isinstance(text_config, dict):
|
|
text_config = Gemma3TextConfig(**text_config)
|
|
|
|
if isinstance(vision_config, dict):
|
|
vision_config = SiglipVisionConfig(**vision_config)
|
|
elif vision_config is None:
|
|
vision_config = SiglipVisionConfig()
|
|
logger.info("vision_config is None, using default SiglipVisionConfig vision config.")
|
|
|
|
self.text_config = text_config
|
|
self.vision_config = vision_config
|
|
self.mm_tokens_per_image = mm_tokens_per_image
|
|
self.boi_token_index = boi_token_index
|
|
self.eoi_token_index = eoi_token_index
|
|
self.image_token_index = image_token_index
|
|
self.initializer_range = initializer_range
|
|
|
|
super().__init__(**kwargs)
|
|
|
|
|
|
class Gemma3ModelOutputWithPast(PaligemmaModelOutputWithPast):
|
|
pass
|
|
|
|
|
|
class Gemma3CausalLMOutputWithPast(PaligemmaCausalLMOutputWithPast):
|
|
pass
|
|
|
|
|
|
class Gemma3TextScaledWordEmbedding(nn.Embedding):
|
|
"""
|
|
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
|
|
"""
|
|
|
|
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
|
|
super().__init__(num_embeddings, embedding_dim, padding_idx)
|
|
self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
|
|
|
|
def forward(self, input_ids: torch.Tensor):
|
|
return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
|
|
|
|
|
|
class Gemma3MLP(Gemma2MLP):
|
|
def __init__(self, config: Gemma3TextConfig):
|
|
super().__init__(config)
|
|
|
|
|
|
class Gemma3RMSNorm(Gemma2RMSNorm):
|
|
def __init__(self, dim: int, eps: float = 1e-6):
|
|
super().__init__()
|
|
|
|
|
|
class Gemma3RotaryEmbedding(Gemma2RotaryEmbedding):
|
|
def __init__(self, config: Gemma3TextConfig, device=None):
|
|
super().__init__(config)
|
|
|
|
|
|
# Weird way to inherit but otherwise the sliding window gets defined first and can't access `is_sliding`
|
|
class Gemma3Attention(Gemma2Attention):
|
|
def __init__(self, config: Gemma3TextConfig, layer_idx: int):
|
|
self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
|
|
|
|
super().__init__()
|
|
self.sliding_window = config.sliding_window if self.is_sliding else None
|
|
|
|
self.q_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
|
|
self.k_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
|
|
|
|
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.head_dim)
|
|
|
|
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
query_states = self.q_norm(query_states)
|
|
key_states = self.k_norm(key_states)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
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}
|
|
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=self.scaling,
|
|
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 Gemma3DecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: Gemma3TextConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.layer_idx = layer_idx
|
|
self.attention_type = config.layer_types[layer_idx]
|
|
self.self_attn = Gemma3Attention(config=config, layer_idx=layer_idx)
|
|
self.mlp = Gemma3MLP(config)
|
|
self.input_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.pre_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
@deprecate_kwarg("last_cache_position", version="4.53.0")
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings_global: torch.Tensor,
|
|
position_embeddings_local: 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.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# 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
|
|
|
|
hidden_states, self_attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
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,
|
|
)
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.pre_feedforward_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = self.post_feedforward_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
GEMMA3_START_DOCSTRING = None
|
|
|
|
|
|
class Gemma3PreTrainedModel(Gemma2PreTrainedModel):
|
|
base_model_prefix = ""
|
|
_no_split_modules = [
|
|
"Gemma3DecoderLayer",
|
|
"SiglipVisionEmbeddings",
|
|
"SiglipEncoderLayer",
|
|
"SiglipMultiheadAttentionPoolingHead",
|
|
]
|
|
|
|
def _init_weights(self, module):
|
|
Gemma2PreTrainedModel._init_weights(module)
|
|
if isinstance(module, Gemma3MultiModalProjector):
|
|
module.mm_input_projection_weight.data.zero_()
|
|
|
|
|
|
class Gemma3TextModel(Gemma2Model):
|
|
config: Gemma3TextConfig
|
|
|
|
def __init__(self, config: Gemma3TextConfig):
|
|
super().__init__(config)
|
|
|
|
# Gemma3 downcasts the below to bfloat16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
|
|
self.embed_tokens = Gemma3TextScaledWordEmbedding(
|
|
config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
|
|
)
|
|
|
|
# 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 = Gemma3RotaryEmbedding(config=config)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = 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:
|
|
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 inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
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 = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings_global = self.rotary_emb(hidden_states, position_ids)
|
|
position_embeddings_local = self.rotary_emb_local(hidden_states, position_ids)
|
|
|
|
# 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,)
|
|
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
position_embeddings_global=position_embeddings_global,
|
|
position_embeddings_local=position_embeddings_local,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
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],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
class Gemma3ForCausalLM(Gemma2ForCausalLM):
|
|
config: Gemma3TextConfig
|
|
base_model_prefix = "language_model"
|
|
|
|
def __init__(self, config: Gemma3TextConfig):
|
|
super().__init__(config)
|
|
self.model = Gemma3TextModel(config)
|
|
|
|
|
|
class Gemma3MultiModalProjector(nn.Module):
|
|
def __init__(self, config: Gemma3Config):
|
|
super().__init__()
|
|
|
|
self.mm_input_projection_weight = nn.Parameter(
|
|
torch.zeros(config.vision_config.hidden_size, config.text_config.hidden_size)
|
|
)
|
|
|
|
self.mm_soft_emb_norm = Gemma3RMSNorm(
|
|
config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps
|
|
)
|
|
|
|
self.patches_per_image = int(config.vision_config.image_size // config.vision_config.patch_size)
|
|
self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
|
|
self.kernel_size = self.patches_per_image // self.tokens_per_side
|
|
self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)
|
|
|
|
def forward(self, vision_outputs: torch.Tensor):
|
|
batch_size, _, seq_length = vision_outputs.shape
|
|
|
|
reshaped_vision_outputs = vision_outputs.transpose(1, 2)
|
|
reshaped_vision_outputs = reshaped_vision_outputs.reshape(
|
|
batch_size, seq_length, self.patches_per_image, self.patches_per_image
|
|
)
|
|
reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
|
|
|
|
pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
|
|
pooled_vision_outputs = pooled_vision_outputs.flatten(2)
|
|
pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
|
|
|
|
normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
|
|
|
|
projected_vision_outputs = torch.matmul(normed_vision_outputs, self.mm_input_projection_weight)
|
|
return projected_vision_outputs.type_as(vision_outputs)
|
|
|
|
|
|
def token_type_ids_mask_function(
|
|
token_type_ids: Optional[torch.Tensor],
|
|
image_group_ids: Optional[torch.Tensor],
|
|
tokens_per_image: int,
|
|
) -> Optional[Callable]:
|
|
"""
|
|
This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
|
|
not start and end indices.
|
|
"""
|
|
# Do not return an additional mask in this case
|
|
if token_type_ids is None:
|
|
return None
|
|
|
|
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
|
|
# If it's 1 for both query and key/value, we are in an image block
|
|
# NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
|
|
# Since vmap doesn't support `if statement` we workaround it with `torch.where`
|
|
safe_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
|
|
token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_idx]
|
|
token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)
|
|
|
|
image_group_ids_at_kv_idx = image_group_ids[batch_idx, safe_idx]
|
|
image_group_ids_at_kv_idx = torch.where(kv_idx < image_group_ids.shape[1], image_group_ids_at_kv_idx, -1)
|
|
|
|
is_image_block = (token_type_ids[batch_idx, q_idx] == 1) & (token_type_ids_at_kv_idx == 1)
|
|
same_image_block = image_group_ids[batch_idx, q_idx] == image_group_ids_at_kv_idx
|
|
|
|
# This is bidirectional attention whenever we are dealing with image tokens
|
|
return is_image_block & same_image_block
|
|
|
|
return inner_mask
|
|
|
|
|
|
class Gemma3Model(PaliGemmaModel):
|
|
# we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
|
|
accepts_loss_kwargs = False
|
|
|
|
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).last_hidden_state
|
|
image_features = self.multi_modal_projector(vision_outputs)
|
|
return image_features
|
|
|
|
def _update_causal_mask(self, **super_kwargs):
|
|
raise AttributeError("We don't want to inherit it")
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
attention_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,
|
|
return_dict: Optional[bool] = None,
|
|
**lm_kwargs,
|
|
) -> Union[tuple, Gemma3ModelOutputWithPast]:
|
|
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
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# Replace image id woth PAD if the image token if OOV, to avoid index-errors
|
|
if input_ids is not None and self.config.image_token_id >= self.vocab_size:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
llm_input_ids = input_ids.clone()
|
|
llm_input_ids[special_image_mask] = 0
|
|
else:
|
|
llm_input_ids = input_ids
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(llm_input_ids)
|
|
|
|
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
|
|
)
|
|
|
|
# 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)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).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 but {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)
|
|
|
|
# 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.get_text_config(),
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
if token_type_ids is not None and inputs_embeds.shape[1] != 1:
|
|
# We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
|
|
|
|
# First find where a new image block starts: 1 if image and previous not image
|
|
# The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
|
|
is_image = (token_type_ids == 1).to(cache_position.device)
|
|
new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
|
|
image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
|
|
image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
|
|
mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
|
|
token_type_ids.to(cache_position.device), image_group_ids, self.config.mm_tokens_per_image
|
|
)
|
|
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
outputs = self.language_model(
|
|
attention_mask=causal_mask_mapping,
|
|
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 Gemma3ModelOutputWithPast(
|
|
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,
|
|
)
|
|
|
|
|
|
class Gemma3ForConditionalGeneration(PaliGemmaForConditionalGeneration):
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
attention_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,
|
|
return_dict: Optional[bool] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**lm_kwargs,
|
|
) -> Union[tuple, Gemma3CausalLMOutputWithPast]:
|
|
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, 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,
|
|
... tokenize=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
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
pixel_values=pixel_values,
|
|
token_type_ids=token_type_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
labels=labels,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
**lm_kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
# 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, :])
|
|
|
|
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)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return Gemma3CausalLMOutputWithPast(
|
|
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,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
pixel_values=None,
|
|
attention_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, pixel values should be None because input ids do not contain special image token anymore
|
|
# Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
|
|
if cache_position[0] == 0:
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(self, **super_kwargs):
|
|
raise AttributeError("We don't want to inherit it")
|
|
|
|
@staticmethod
|
|
def create_masks_for_generate(
|
|
config: PretrainedConfig,
|
|
input_embeds: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Optional[Cache],
|
|
position_ids: Optional[torch.Tensor],
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> dict:
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"config": config.get_text_config(),
|
|
"input_embeds": input_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
# Add the token type ids mask for generate as well
|
|
if token_type_ids is not None and input_embeds.shape[1] != 1:
|
|
# We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
|
|
|
|
# First find where a new image block starts: 1 if image and previous not image
|
|
# The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
|
|
is_image = (token_type_ids == 1).to(cache_position.device)
|
|
new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
|
|
image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
|
|
image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
|
|
mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
|
|
token_type_ids.to(cache_position.device), image_group_ids, config.mm_tokens_per_image
|
|
)
|
|
|
|
return create_masks_for_generate(**mask_kwargs)
|
|
|
|
|
|
class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = Gemma3Model(config)
|
|
self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: Optional[torch.FloatTensor] = 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,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> SequenceClassifierOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
|
|
transformer_outputs = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
pixel_values=pixel_values,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
token_type_ids=token_type_ids,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
hidden_states = transformer_outputs.last_hidden_state
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
if self.config.text_config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.text_config.pad_token_id is None:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"Gemma3Config",
|
|
"Gemma3TextConfig",
|
|
"Gemma3PreTrainedModel", # noqa: F822
|
|
"Gemma3TextModel",
|
|
"Gemma3ForCausalLM",
|
|
"Gemma3ForConditionalGeneration",
|
|
"Gemma3Model",
|
|
"Gemma3ForSequenceClassification",
|
|
]
|