332 lines
16 KiB
Python
332 lines
16 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/t5gemma/modular_t5gemma.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_t5gemma.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Optional, Union
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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class T5GemmaModuleConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5GemmaModuleModel`]. It is used to instantiate an T5GemmaModule
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the T5GemmaModule-7B.
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e.g. [google/t5_gemma_module-7b](https://huggingface.co/google/t5_gemma_module-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 256000):
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Vocabulary size of the T5GemmaModule model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`T5GemmaModuleModel`]
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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 8192):
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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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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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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 T5GemmaModule, 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*, defaults to 30.0):
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scaling factor when applying tanh softcapping on the logits.
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attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
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scaling factor when applying tanh softcapping on the attention scores.
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```python
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>>> from transformers import T5GemmaModuleModel, T5GemmaModuleConfig
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>>> # Initializing a T5GemmaModule t5_gemma_module-7b style configuration
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>>> configuration = T5GemmaModuleConfig()
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>>> # Initializing a model from the t5_gemma_module-7b style configuration
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>>> model = T5GemmaModuleModel(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 = "t5_gemma_module"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=256000,
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hidden_size=2304,
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intermediate_size=9216,
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num_hidden_layers=26,
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num_attention_heads=8,
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num_key_value_heads=4,
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head_dim=256,
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hidden_activation="gelu_pytorch_tanh",
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max_position_embeddings=8192,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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eos_token_id=1,
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bos_token_id=2,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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attention_bias=False,
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attention_dropout=0.0,
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query_pre_attn_scalar=256,
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sliding_window=4096,
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layer_types=None,
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final_logit_softcapping=30.0,
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attn_logit_softcapping=50.0,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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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.rope_theta = rope_theta
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.hidden_activation = hidden_activation
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self.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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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types)
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class T5GemmaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`T5GemmaModel`]. It is used to instantiate an T5Gemma
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to a hypothetical balanced Gemma2 encoder-decoder model.
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e.g. [google/t5gemma-2b-2b-prefixlm-it](https://huggingface.co/google/t5gemma-2b-2b-prefixlm-it)
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```python
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>>> from transformers import T5GemmaConfig, T5GemmaModel
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>>> t5gemma_config = T5GemmaConfig.from_pretrained("google/t5gemma-2b-2b-prefixlm-it")
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>>> model = T5GemmaModel(t5gemma_config)
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```
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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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encoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
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Configuration for the encoder.
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decoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
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Configuration for the decoder.
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is_encoder_decoder (bool, optional, *optional*, defaults to `True`):
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Whether the model is used as an encoder/decoder or not.
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dropout_rate (`float`, *optional*, defaults to 0.0):
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The ratio for all dropout layers (following T5).
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classifier_dropout_rate (`float`, *optional*, defaults to 0.0):
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The dropout ratio for classifier (following T5).
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for attention.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether tie input and output embeddings.
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vocab_size (`int`, *optional*, defaults to 256000):
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Vocabulary size of the T5Gemma model (the same as Gemma 2).
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kwargs (additional keyword arguments, optional, *optional*):
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Will be passed to the PretrainedConfig base class.
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"""
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model_type = "t5gemma"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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# encoder
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"encoder.layers.*.self_attn.q_proj": "colwise",
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"encoder.layers.*.self_attn.k_proj": "colwise",
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"encoder.layers.*.self_attn.v_proj": "colwise",
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"encoder.layers.*.self_attn.o_proj": "rowwise",
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"encoder.layers.*.mlp.gate_proj": "colwise",
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"encoder.layers.*.mlp.up_proj": "colwise",
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"encoder.layers.*.mlp.down_proj": "rowwise",
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# decoder
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"decoder.layers.*.self_attn.q_proj": "colwise",
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"decoder.layers.*.self_attn.k_proj": "colwise",
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"decoder.layers.*.self_attn.v_proj": "colwise",
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"decoder.layers.*.self_attn.o_proj": "rowwise",
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"decoder.layers.*.cross_attn.q_proj": "colwise",
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"decoder.layers.*.cross_attn.k_proj": "colwise",
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"decoder.layers.*.cross_attn.v_proj": "colwise",
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"decoder.layers.*.cross_attn.o_proj": "rowwise",
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"decoder.layers.*.mlp.gate_proj": "colwise",
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"decoder.layers.*.mlp.up_proj": "colwise",
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"decoder.layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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# encoder
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"encoder.embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"encoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"encoder.norm": (["hidden_states"], ["hidden_states"]),
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# decoder
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"decoder.embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"decoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"decoder.norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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encoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None,
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decoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None,
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is_encoder_decoder: bool = True,
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dropout_rate: float = 0.0,
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classifier_dropout_rate: float = 0.0,
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attention_dropout: float = 0.0,
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tie_word_embeddings: bool = True,
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vocab_size: int = 256000,
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**kwargs,
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):
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if isinstance(encoder, dict):
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encoder = T5GemmaModuleConfig(**encoder)
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elif encoder is None:
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encoder = T5GemmaModuleConfig()
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else:
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assert isinstance(encoder, T5GemmaModuleConfig), f"{type(encoder)} is not supported."
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if isinstance(decoder, dict):
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decoder = T5GemmaModuleConfig(**decoder)
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elif decoder is None:
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decoder = encoder
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else:
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assert isinstance(decoder, T5GemmaModuleConfig), f"{type(decoder)} is not supported."
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encoder = T5GemmaModuleConfig(**encoder.to_dict())
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decoder = T5GemmaModuleConfig(**decoder.to_dict())
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encoder.is_decoder = False
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encoder.dropout_rate = dropout_rate
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encoder.attention_dropout = attention_dropout
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self.encoder = encoder
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decoder.is_decoder = True
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decoder.use_cache = True
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decoder.dropout_rate = dropout_rate
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decoder.attention_dropout = attention_dropout
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decoder.cross_attention_hidden_size = encoder.hidden_size
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self.decoder = decoder
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for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id"]:
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if special_token_key not in kwargs:
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kwargs[special_token_key] = getattr(decoder, special_token_key)
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super().__init__(**kwargs)
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self.is_encoder_decoder = is_encoder_decoder
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self.use_cache = kwargs.get("use_cache", decoder.use_cache)
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self.initializer_range = kwargs.get("initializer_range", decoder.initializer_range)
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self.dropout_rate = dropout_rate
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self.attention_dropout = attention_dropout
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self.classifier_dropout_rate = classifier_dropout_rate
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self.tie_word_embeddings = tie_word_embeddings
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# Used in pipeline generation.
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self.vocab_size = vocab_size
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def __setattr__(self, key, value):
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shared_attr_with_submodules = [
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"output_hidden_states",
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"output_attentions",
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"_attn_implementation",
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"dropout_rate",
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"attention_dropout",
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"vocab_size",
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]
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if key in shared_attr_with_submodules:
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setattr(self.encoder, key, value)
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setattr(self.decoder, key, value)
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super().__setattr__(key, value)
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def get_text_config(self, decoder=False):
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# Always return self, regardless of the decoder option.
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del decoder
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return self
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__all__ = ["T5GemmaConfig", "T5GemmaModuleConfig"]
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