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614 lines
31 KiB
614 lines
31 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/t5gemma2/modular_t5gemma2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_t5gemma2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any
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from ...configuration_utils import PreTrainedConfig, layer_type_validation
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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from ..siglip import SiglipVisionConfig
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logger = logging.get_logger(__name__)
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class T5Gemma2TextConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5Gemma2TextModel`]. It is used to instantiate the encoder's
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text model portion of the T5Gemma2 Model according to the specified arguments, defining the model architecture. Instantiating
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a configuration with the defaults will yield a similar configuration to that of the T5Gemma2Text-7B.
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e.g. [google/t5gemma2_text-7b](https://huggingface.co/google/t5gemma2_text-7b)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 262208):
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Vocabulary size of the T5Gemma2Text model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`T5Gemma2TextModel`]
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hidden_size (`int`, *optional*, defaults to 2304):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 9216):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 26):
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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 4):
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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, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`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 `"gelu_pytorch_tanh"`
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if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
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max_position_embeddings (`int`, *optional*, defaults to 131072):
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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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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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query_pre_attn_scalar (`float`, *optional*, defaults to 256):
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Scaling factor used on the attention scores
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sliding_window (`int`, *optional*, defaults to 4096):
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In T5Gemma2Text, every other layer uses sliding window attention. This is the size of the sliding window.
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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final_logit_softcapping (`float`, *optional*):
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Scaling factor when applying tanh softcapping on the logits.
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attn_logit_softcapping (`float`, *optional*):
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Scaling factor when applying tanh softcapping on the attention scores.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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"""
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model_type = "t5gemma2_text"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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default_theta = {"global": 1_000_000.0, "local": 10_000.0}
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def __init__(
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self,
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vocab_size: int | None = 262_208,
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hidden_size: int | None = 2304,
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intermediate_size: int | None = 9216,
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num_hidden_layers: int | None = 26,
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num_attention_heads: int | None = 8,
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num_key_value_heads: int | None = 4,
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head_dim: int | None = 256,
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hidden_activation: str | None = "gelu_pytorch_tanh",
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max_position_embeddings: int | None = 131_072,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-6,
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use_cache: bool | None = True,
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pad_token_id: int | None = 0,
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eos_token_id: int | None = 1,
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bos_token_id: int | None = 2,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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query_pre_attn_scalar: int | None = 256,
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sliding_window: int | None = 4096,
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layer_types: list[str] | None = None,
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final_logit_softcapping: float | None = None,
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attn_logit_softcapping: float | None = None,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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**kwargs,
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):
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.vocab_size = vocab_size
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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.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.query_pre_attn_scalar = query_pre_attn_scalar
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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.attn_logit_softcapping = attn_logit_softcapping
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self.layer_types = layer_types
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# BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
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self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6)
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types, self.num_hidden_layers)
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
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rope_scaling = kwargs.pop("rope_scaling", None)
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# Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters`
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# as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format
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default_rope_params = {
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"sliding_attention": {"rope_type": "default"},
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"full_attention": {"rope_type": "default"},
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}
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self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params
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if rope_scaling is not None:
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self.rope_parameters["full_attention"].update(rope_scaling)
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# Set default values if not present
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if self.rope_parameters.get("full_attention") is None:
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self.rope_parameters["full_attention"] = {"rope_type": "default"}
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self.rope_parameters["full_attention"].setdefault(
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"rope_theta", kwargs.pop("rope_theta", self.default_theta["global"])
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)
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if self.rope_parameters.get("sliding_attention") is None:
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self.rope_parameters["sliding_attention"] = {"rope_type": "default"}
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self.rope_parameters["sliding_attention"].setdefault(
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"rope_theta", kwargs.pop("rope_local_base_freq", self.default_theta["local"])
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)
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# Standardize and validate the correctness of rotary position embeddings parameters
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self.standardize_rope_params()
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self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
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return kwargs
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class T5Gemma2EncoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5Gemma2EncoderForConditionalGeneration`]. It is used to instantiate an
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T5Gemma2EncoderForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the PaliGemma-2B.
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e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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text_config (`Union[T5Gemma2EncoderTextConfig, dict]`, *optional*):
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The config object of the text backbone.
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vision_config (`Union[AutoConfig, dict]`, *optional*):
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Custom vision config or dict.
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mm_tokens_per_image (`int`, *optional*, defaults to 256):
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The number of tokens per image embedding.
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boi_token_index (`int`, *optional*, defaults to 255999):
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The begin-of-image token index to wrap the image prompt.
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eoi_token_index (`int`, *optional*, defaults to 256000):
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The end-of-image token index to wrap the image prompt.
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image_token_index (`int`, *optional*, defaults to 262144):
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The image token index to encode the image prompt.
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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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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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Example:
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```python
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>>> from transformers import T5Gemma2EncoderForConditionalGeneration, T5Gemma2EncoderConfig, SiglipVisionConfig, T5Gemma2EncoderTextConfig
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>>> # Initializing a Siglip-like vision config
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>>> vision_config = SiglipVisionConfig()
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>>> # Initializing a T5Gemma2Encoder Text config
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>>> text_config = T5Gemma2EncoderTextConfig()
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>>> # Initializing a T5Gemma2Encoder gemma-3-4b style configuration
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>>> configuration = T5Gemma2EncoderConfig(vision_config, text_config)
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>>> # Initializing a model from the gemma-3-4b style configuration
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>>> model = T5Gemma2EncoderTextConfig(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "t5gemma2_encoder"
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attribute_map = {
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"image_token_id": "image_token_index",
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"boi_token_id": "boi_token_index",
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"eoi_token_id": "eoi_token_index",
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}
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sub_configs = {
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"text_config": T5Gemma2TextConfig,
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"vision_config": SiglipVisionConfig,
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}
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def __init__(
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self,
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text_config: T5Gemma2TextConfig | dict[str, Any] | None = None,
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vision_config: SiglipVisionConfig | dict[str, Any] | None = None,
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mm_tokens_per_image: int | None = 256,
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boi_token_index: int | None = 255_999,
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eoi_token_index: int | None = 256_000,
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image_token_index: int | None = 262_144,
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initializer_range: float | None = 0.02,
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tie_word_embeddings: bool | None = True,
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**kwargs,
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):
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if text_config is None:
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text_config = T5Gemma2TextConfig()
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logger.info("text_config is None, using default T5Gemma2EncoderTextConfig text config.")
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elif isinstance(text_config, dict):
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text_config = T5Gemma2TextConfig(**text_config)
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if isinstance(vision_config, dict):
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vision_config = SiglipVisionConfig(**vision_config)
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elif vision_config is None:
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vision_config = SiglipVisionConfig()
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logger.info("vision_config is None, using default SiglipVisionConfig vision config.")
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self.text_config = text_config
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self.vision_config = vision_config
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self.mm_tokens_per_image = mm_tokens_per_image
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self.boi_token_index = boi_token_index
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self.eoi_token_index = eoi_token_index
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self.image_token_index = image_token_index
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self.initializer_range = initializer_range
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**kwargs)
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class T5Gemma2DecoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5Gemma2DecoderModel`]. It is used to instantiate the decoder
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text model portion of the T5Gemma2 Model according to the specified arguments, defining the model architecture. Instantiating
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a configuration with the defaults will yield a similar configuration to that of the T5Gemma2Decoder-7B.
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e.g. [google/t5gemma2_text-7b](https://huggingface.co/google/t5gemma2_text-7b)
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 262208):
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Vocabulary size of the T5Gemma2Decoder model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`T5Gemma2DecoderModel`]
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hidden_size (`int`, *optional*, defaults to 2304):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 9216):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 26):
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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 4):
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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
|
|
`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
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|
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
|
|
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 `"gelu_pytorch_tanh"`
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|
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
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|
max_position_embeddings (`int`, *optional*, defaults to 131072):
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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.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
|
The epsilon used by the rms normalization layers.
|
|
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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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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|
query_pre_attn_scalar (`float`, *optional*, defaults to 256):
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Scaling factor used on the attention scores
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|
sliding_window (`int`, *optional*, defaults to 4096):
|
|
In T5Gemma2Decoder, every other layer uses sliding window attention. This is the size of the sliding window.
|
|
layer_types (`list`, *optional*):
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|
Attention pattern for each layer.
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|
final_logit_softcapping (`float`, *optional*):
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|
Scaling factor when applying tanh softcapping on the logits.
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|
attn_logit_softcapping (`float`, *optional*):
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Scaling factor when applying tanh softcapping on the attention scores.
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|
rope_parameters (`RopeParameters`, *optional*):
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|
Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
|
|
a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
|
|
with longer `max_position_embeddings`.
|
|
"""
|
|
|
|
model_type = "t5gemma2_decoder"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
base_model_tp_plan = {
|
|
"layers.*.self_attn.q_proj": "colwise",
|
|
"layers.*.self_attn.k_proj": "colwise",
|
|
"layers.*.self_attn.v_proj": "colwise",
|
|
"layers.*.self_attn.o_proj": "rowwise",
|
|
"layers.*.mlp.gate_proj": "colwise",
|
|
"layers.*.mlp.up_proj": "colwise",
|
|
"layers.*.mlp.down_proj": "rowwise",
|
|
}
|
|
base_model_pp_plan = {
|
|
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
|
|
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
|
|
"norm": (["hidden_states"], ["hidden_states"]),
|
|
}
|
|
default_theta = {"global": 1_000_000.0, "local": 10_000.0}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size: int | None = 262_208,
|
|
hidden_size: int | None = 2304,
|
|
intermediate_size: int | None = 9216,
|
|
num_hidden_layers: int | None = 26,
|
|
num_attention_heads: int | None = 8,
|
|
num_key_value_heads: int | None = 4,
|
|
head_dim: int | None = 256,
|
|
hidden_activation: str | None = "gelu_pytorch_tanh",
|
|
max_position_embeddings: int | None = 131_072,
|
|
initializer_range: float | None = 0.02,
|
|
rms_norm_eps: int | None = 1e-6,
|
|
use_cache: bool | None = True,
|
|
pad_token_id: int | None = 0,
|
|
eos_token_id: int | None = 1,
|
|
bos_token_id: int | None = 2,
|
|
attention_bias: bool | None = False,
|
|
attention_dropout: float | None = 0.0,
|
|
query_pre_attn_scalar: int | None = 256,
|
|
sliding_window: int | None = 4096,
|
|
layer_types: list[str] | None = None,
|
|
final_logit_softcapping: float | None = None,
|
|
attn_logit_softcapping: float | None = None,
|
|
rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
|
|
**kwargs,
|
|
):
|
|
self.pad_token_id = pad_token_id
|
|
self.bos_token_id = bos_token_id
|
|
self.eos_token_id = eos_token_id
|
|
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.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
|
|
|
|
# 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, self.num_hidden_layers)
|
|
|
|
self.rope_parameters = rope_parameters
|
|
super().__init__(**kwargs)
|
|
|
|
def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
|
|
rope_scaling = kwargs.pop("rope_scaling", None)
|
|
|
|
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters`
|
|
# as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format
|
|
default_rope_params = {
|
|
"sliding_attention": {"rope_type": "default"},
|
|
"full_attention": {"rope_type": "default"},
|
|
}
|
|
self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params
|
|
if rope_scaling is not None:
|
|
self.rope_parameters["full_attention"].update(rope_scaling)
|
|
|
|
# Set default values if not present
|
|
if self.rope_parameters.get("full_attention") is None:
|
|
self.rope_parameters["full_attention"] = {"rope_type": "default"}
|
|
self.rope_parameters["full_attention"].setdefault(
|
|
"rope_theta", kwargs.pop("rope_theta", self.default_theta["global"])
|
|
)
|
|
if self.rope_parameters.get("sliding_attention") is None:
|
|
self.rope_parameters["sliding_attention"] = {"rope_type": "default"}
|
|
self.rope_parameters["sliding_attention"].setdefault(
|
|
"rope_theta", kwargs.pop("rope_local_base_freq", self.default_theta["local"])
|
|
)
|
|
|
|
# Standardize and validate the correctness of rotary position embeddings parameters
|
|
self.standardize_rope_params()
|
|
self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
|
|
return kwargs
|
|
|
|
|
|
class T5Gemma2Config(PreTrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`T5Gemma2Model`]. It is used to instantiate an T5Gemma2
|
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
|
defaults will yield a similar configuration to a hypothetical balanced Gemma3 encoder-decoder model.
|
|
e.g. [google/t5gemma-2-270m-270m](https://huggingface.co/google/t5gemma-2-270m-270m)
|
|
Configuration objects inherit from [PreTrainedConfig] and can be used to control the model outputs. Read the
|
|
documentation from [PreTrainedConfig] for more information.
|
|
|
|
Args:
|
|
encoder (`Union[T5Gemma2EncoderConfig, dict]`, optional, *optional*):
|
|
Configuration for the encoder.
|
|
decoder (`Union[T5Gemma2DecoderConfig, dict]`, optional, *optional*):
|
|
Configuration for the decoder.
|
|
is_encoder_decoder (bool, optional, *optional*, defaults to `True`):
|
|
Whether the model is used as an encoder/decoder or not.
|
|
dropout_rate (`float`, *optional*, defaults to 0.0):
|
|
The ratio for all dropout layers (following T5).
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for attention.
|
|
classifier_dropout_rate (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for classifier (following T5).
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
image_token_index (`int`, *optional*, defaults to 256001):
|
|
The image token index to encode the image prompt. Defaults to 256001, which is right after the eoi_token_index.
|
|
Note this is different from Gemma 3.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
|
|
Whether to tie weight embeddings
|
|
|
|
```python
|
|
>>> from transformers import T5Gemma2Config, T5Gemma2Model
|
|
>>> t5gemma2_config = T5Gemma2Config.from_pretrained("google/t5gemma-270m-270m")
|
|
>>> model = T5Gemma2Model(t5gemma2_config)
|
|
```
|
|
"""
|
|
|
|
model_type = "t5gemma2"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
sub_configs = {
|
|
"encoder": T5Gemma2EncoderConfig,
|
|
"decoder": T5Gemma2DecoderConfig,
|
|
}
|
|
|
|
attribute_map = {
|
|
"image_token_id": "image_token_index",
|
|
"eoi_token_id": "eoi_token_index",
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
encoder: T5Gemma2EncoderConfig | dict[str, Any] | None = None,
|
|
decoder: T5Gemma2DecoderConfig | dict[str, Any] | None = None,
|
|
is_encoder_decoder: bool = True,
|
|
dropout_rate: float = 0.0,
|
|
attention_dropout: float = 0.0,
|
|
classifier_dropout_rate: float = 0.0,
|
|
initializer_range: float = 0.02,
|
|
image_token_index: int = 256_001,
|
|
tie_word_embeddings: bool | None = True,
|
|
**kwargs,
|
|
):
|
|
if isinstance(encoder, dict):
|
|
encoder = T5Gemma2EncoderConfig(**encoder)
|
|
elif encoder is None:
|
|
encoder = T5Gemma2EncoderConfig()
|
|
logger.info("encoder is None, using default T5Gemma2EncoderConfig encoder config.")
|
|
else:
|
|
if not isinstance(encoder, T5Gemma2EncoderConfig):
|
|
raise ValueError(f"{type(encoder)} is not supported.")
|
|
|
|
if isinstance(decoder, dict):
|
|
decoder = T5Gemma2DecoderConfig(**decoder)
|
|
elif decoder is None:
|
|
decoder = T5Gemma2DecoderConfig()
|
|
logger.info("decoder is None, using default T5Gemma2DecoderConfig decoder config.")
|
|
else:
|
|
if not isinstance(decoder, T5Gemma2DecoderConfig):
|
|
raise ValueError(f"{type(decoder)} is not supported.")
|
|
|
|
if encoder.text_config.hidden_size != decoder.hidden_size:
|
|
raise ValueError(
|
|
"Imbalanced encoder-decoder is not supported in T5Gemma2: "
|
|
f"encoder ({encoder.text_config.hidden_size}) vs decoder ({decoder.hidden_size})."
|
|
)
|
|
|
|
if not is_encoder_decoder:
|
|
raise ValueError("T5Gemma2Model only support encoder-decoder modeling.")
|
|
|
|
if encoder.text_config.vocab_size != decoder.vocab_size:
|
|
raise ValueError(
|
|
"Imbalanced encoder-decoder vocabulary size is not supported in T5Gemma2: "
|
|
f"encoder ({encoder.text_config.vocab_size}) vs decoder ({decoder.vocab_size})."
|
|
)
|
|
|
|
# Encoder.
|
|
encoder.text_config.dropout_rate = dropout_rate
|
|
encoder.text_config.attention_dropout = attention_dropout
|
|
encoder.vision_config.attention_dropout = attention_dropout
|
|
encoder.image_token_index = image_token_index
|
|
self.encoder = encoder
|
|
|
|
# Decoder.
|
|
decoder.dropout_rate = dropout_rate
|
|
decoder.attention_dropout = attention_dropout
|
|
self.decoder = decoder
|
|
|
|
for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id", "vocab_size"]:
|
|
if special_token_key not in kwargs:
|
|
kwargs[special_token_key] = getattr(decoder, special_token_key)
|
|
|
|
self.classifier_dropout_rate = classifier_dropout_rate
|
|
self.initializer_range = initializer_range
|
|
self.eoi_token_index = encoder.eoi_token_index
|
|
self.image_token_index = image_token_index
|
|
self.tie_word_embeddings = tie_word_embeddings
|
|
|
|
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
|
|
|
|
|
|
__all__ = ["T5Gemma2Config", "T5Gemma2TextConfig", "T5Gemma2EncoderConfig", "T5Gemma2DecoderConfig"]
|