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1643 lines
70 KiB
1643 lines
70 KiB
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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from collections.abc import Callable
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from typing import Any, Optional
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import torch
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import torch.nn as nn
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from ... import initialization as init
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from ...cache_utils import DynamicCache, EncoderDecoderCache, StaticCache
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from ...configuration_utils import PreTrainedConfig, layer_type_validation
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from ...generation import GenerationConfig, GenerationMixin, GenerationMode
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from ...masking_utils import create_bidirectional_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPooling,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, RopeParameters
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import (
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TransformersKwargs,
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auto_docstring,
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can_return_tuple,
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logging,
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torch_compilable_check,
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)
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from ...utils.generic import OutputRecorder, check_model_inputs
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from ..auto import AutoModel
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from ..gemma3.configuration_gemma3 import Gemma3Config, Gemma3TextConfig
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from ..gemma3.modeling_gemma3 import (
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Gemma3Attention,
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Gemma3MLP,
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Gemma3MultiModalProjector,
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Gemma3PreTrainedModel,
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Gemma3RMSNorm,
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Gemma3RotaryEmbedding,
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Gemma3TextScaledWordEmbedding,
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apply_rotary_pos_emb,
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create_causal_mask,
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create_sliding_window_causal_mask,
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eager_attention_forward,
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)
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from ..siglip import SiglipVisionConfig
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from ..t5gemma.modeling_t5gemma import (
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T5GemmaClassificationHead,
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T5GemmaEncoderLayer,
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T5GemmaLMHead,
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bidirectional_mask_function,
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)
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logger = logging.get_logger(__name__)
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class T5Gemma2TextConfig(Gemma3TextConfig, 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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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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PreTrainedConfig.__init__(**kwargs)
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class T5Gemma2EncoderConfig(Gemma3Config):
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model_type = "t5gemma2_encoder"
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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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class T5Gemma2DecoderConfig(Gemma3TextConfig, 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
|
|
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 T5Gemma2Decoder, 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
|
|
with longer `max_position_embeddings`.
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|
"""
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model_type = "t5gemma2_decoder"
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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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|
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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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PreTrainedConfig.__init__(**kwargs)
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class T5Gemma2Config(PreTrainedConfig):
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r"""
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|
This is the configuration class to store the configuration of a [`T5Gemma2Model`]. It is used to instantiate an T5Gemma2
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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.
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|
e.g. [google/t5gemma-2-270m-270m](https://huggingface.co/google/t5gemma-2-270m-270m)
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|
Configuration objects inherit from [PreTrainedConfig] and can be used to control the model outputs. Read the
|
|
documentation from [PreTrainedConfig] for more information.
|
|
|
|
Args:
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encoder (`Union[T5Gemma2EncoderConfig, dict]`, optional, *optional*):
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Configuration for the encoder.
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decoder (`Union[T5Gemma2DecoderConfig, 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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for attention.
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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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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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image_token_index (`int`, *optional*, defaults to 256001):
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The image token index to encode the image prompt. Defaults to 256001, which is right after the eoi_token_index.
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Note this is different from Gemma 3.
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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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```python
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>>> from transformers import T5Gemma2Config, T5Gemma2Model
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>>> t5gemma2_config = T5Gemma2Config.from_pretrained("google/t5gemma-270m-270m")
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>>> model = T5Gemma2Model(t5gemma2_config)
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```
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"""
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model_type = "t5gemma2"
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keys_to_ignore_at_inference = ["past_key_values"]
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sub_configs = {
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"encoder": T5Gemma2EncoderConfig,
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"decoder": T5Gemma2DecoderConfig,
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}
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attribute_map = {
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"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)
|
|
|
|
|
|
class T5Gemma2RMSNorm(Gemma3RMSNorm):
|
|
pass
|
|
|
|
|
|
class T5Gemma2MLP(Gemma3MLP):
|
|
def __init__(self, config: T5Gemma2TextConfig):
|
|
super().__init__(config)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
def forward(self, x):
|
|
hidden_states = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
|
|
hidden_states = self.dropout(hidden_states)
|
|
down_proj = self.down_proj(hidden_states)
|
|
return down_proj
|
|
|
|
|
|
class T5Gemma2RotaryEmbedding(Gemma3RotaryEmbedding):
|
|
def __init__(self, config: T5Gemma2TextConfig, device=None):
|
|
super().__init__(config, device)
|
|
|
|
@staticmethod
|
|
def compute_default_rope_parameters(
|
|
config: T5Gemma2TextConfig | None = None,
|
|
device: Optional["torch.device"] = None,
|
|
seq_len: int | None = None,
|
|
layer_type: str | None = None,
|
|
) -> tuple["torch.Tensor", float]:
|
|
return super().compute_default_rope_parameters(config, device, seq_len, layer_type)
|
|
|
|
|
|
class T5Gemma2SelfAttention(Gemma3Attention):
|
|
def __init__(self, config: T5Gemma2TextConfig, layer_idx: int):
|
|
super().__init__(config, layer_idx)
|
|
self.is_causal = False # Only used by the encoder
|
|
|
|
|
|
class T5Gemma2MergedAttention(Gemma3Attention):
|
|
"""Merged self-attention and cross-attention for decoder."""
|
|
|
|
def __init__(self, config: T5Gemma2TextConfig, layer_idx: int):
|
|
super().__init__(config, layer_idx)
|
|
self.is_causal = False # Fused causal and encoder mask
|
|
|
|
def forward(
|
|
self,
|
|
# decoder self-attention inputs
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
merged_attention_mask: torch.Tensor | None,
|
|
# cross-attention inputs
|
|
encoder_hidden_states: torch.Tensor,
|
|
# cache inputs
|
|
past_key_values: EncoderDecoderCache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
# others
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
|
|
# attention shapes.
|
|
input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.head_dim)
|
|
cross_input_shape = encoder_hidden_states.shape[:-1]
|
|
cross_hidden_shape = (*cross_input_shape, -1, self.head_dim)
|
|
|
|
# self-attention.
|
|
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_values is not None:
|
|
# self-attention.
|
|
# 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}
|
|
self_attention_cache = past_key_values.self_attention_cache
|
|
key_states, value_states = self_attention_cache.update(
|
|
key_states, value_states, self.layer_idx, cache_kwargs
|
|
)
|
|
|
|
# cross-attention.
|
|
is_updated = past_key_values.is_updated.get(self.layer_idx)
|
|
cross_attention_cache = past_key_values.cross_attention_cache
|
|
|
|
if past_key_values is None or not is_updated:
|
|
cross_key_states = self.k_proj(encoder_hidden_states).view(cross_hidden_shape).transpose(1, 2)
|
|
cross_value_states = self.v_proj(encoder_hidden_states).view(cross_hidden_shape).transpose(1, 2)
|
|
|
|
cross_key_states = self.k_norm(cross_key_states)
|
|
|
|
if past_key_values is not None:
|
|
cross_key_states, cross_value_states = cross_attention_cache.update(
|
|
cross_key_states, cross_value_states, self.layer_idx
|
|
)
|
|
past_key_values.is_updated[self.layer_idx] = True
|
|
else:
|
|
cross_key_states = cross_attention_cache.layers[self.layer_idx].keys
|
|
cross_value_states = cross_attention_cache.layers[self.layer_idx].values
|
|
|
|
# merged attention.
|
|
query_states = query_states
|
|
cross_key_size = cross_input_shape[1]
|
|
key_states = torch.cat([key_states, cross_key_states], dim=2)
|
|
value_states = torch.cat([value_states, cross_value_states], dim=2)
|
|
|
|
attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
|
|
self.config._attn_implementation, eager_attention_forward
|
|
)
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
merged_attention_mask,
|
|
dropout=self.attention_dropout if self.training else 0.0,
|
|
scaling=self.scaling,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
# decompose merged attention weights into self & cross attention weights
|
|
if attn_weights is not None:
|
|
self_attn_weights = attn_weights[..., :-cross_key_size]
|
|
cross_attn_weights = attn_weights[..., -cross_key_size:]
|
|
else:
|
|
self_attn_weights, cross_attn_weights = None, None
|
|
return attn_output, self_attn_weights, cross_attn_weights
|
|
|
|
|
|
def sliding_window_mask_function(sliding_window: int, is_causal=True) -> Callable:
|
|
"""
|
|
This creates uni/bidirectional attention mask with sliding window.
|
|
"""
|
|
|
|
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
|
|
if is_causal:
|
|
left_window_size, right_window_size = sliding_window, 0
|
|
else:
|
|
left_window_size, right_window_size = ((sliding_window + 1) // 2, (sliding_window) // 2 + 1)
|
|
|
|
dist = q_idx - kv_idx
|
|
left_mask = (dist >= 0) & (dist < left_window_size)
|
|
right_mask = (dist < 0) & (-dist < right_window_size)
|
|
return left_mask | right_mask
|
|
|
|
return inner_mask
|
|
|
|
|
|
class T5Gemma2EncoderLayer(T5GemmaEncoderLayer):
|
|
pass
|
|
|
|
|
|
class T5Gemma2DecoderLayer(T5GemmaEncoderLayer):
|
|
"""Decoder sub-layer: merged attention instead of vanilla self-attention."""
|
|
|
|
def __init__(self, config, layer_idx: int):
|
|
super().__init__(config, layer_idx)
|
|
|
|
# replace vanilla self-attention with merged attention to support joint cross-attention.
|
|
self.self_attn = T5Gemma2MergedAttention(
|
|
config=config,
|
|
layer_idx=layer_idx,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
merged_attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: EncoderDecoderCache | None = None,
|
|
use_cache: bool | None = False,
|
|
cache_position: torch.LongTensor | None = None,
|
|
encoder_hidden_states: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> torch.FloatTensor:
|
|
residual = hidden_states
|
|
hidden_states = self.pre_self_attn_layernorm(hidden_states)
|
|
|
|
hidden_states, _, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
merged_attention_mask=merged_attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
**kwargs,
|
|
)
|
|
hidden_states = self.post_self_attn_layernorm(hidden_states)
|
|
hidden_states = residual + self.dropout(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 + self.dropout(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class T5Gemma2LMHead(T5GemmaLMHead):
|
|
pass
|
|
|
|
|
|
class T5Gemma2ClassificationHead(T5GemmaClassificationHead):
|
|
pass
|
|
|
|
|
|
class T5Gemma2MultiModalProjector(Gemma3MultiModalProjector):
|
|
def __init__(self, config: T5Gemma2EncoderConfig):
|
|
super().__init__(config)
|
|
|
|
|
|
class T5Gemma2TextScaledWordEmbedding(Gemma3TextScaledWordEmbedding):
|
|
"""T5Gemma2 Embedding: override to add eoi token embedding separately."""
|
|
|
|
def __init__(
|
|
self,
|
|
num_embeddings: int,
|
|
embedding_dim: int,
|
|
padding_idx: int,
|
|
embed_scale: float = 1.0,
|
|
eoi_token_index: int = 256_000,
|
|
):
|
|
super().__init__(num_embeddings, embedding_dim, padding_idx, embed_scale)
|
|
self.eoi_token_index = eoi_token_index
|
|
self.eoi_embedding = nn.Parameter(torch.zeros(self.embedding_dim))
|
|
|
|
def forward(self, input_ids: torch.Tensor):
|
|
input_embeddings = super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
|
|
input_embeddings[input_ids == self.eoi_token_index] = self.eoi_embedding.to(input_embeddings.dtype)
|
|
return input_embeddings
|
|
|
|
|
|
@auto_docstring
|
|
class T5Gemma2PreTrainedModel(Gemma3PreTrainedModel):
|
|
config: T5Gemma2Config
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
|
|
# Mask creation is incompatible
|
|
# FA due to non-default creation / SWA
|
|
_supports_flash_attn = False
|
|
# Flex due to custom masks not compatible to be merged after creation
|
|
_supports_flex_attn = False
|
|
|
|
_no_split_modules = [
|
|
"T5Gemma2EncoderLayer",
|
|
"T5Gemma2DecoderLayer",
|
|
"SiglipVisionEmbeddings",
|
|
"SiglipEncoderLayer",
|
|
"SiglipMultiheadAttentionPoolingHead",
|
|
]
|
|
_can_record_outputs = {
|
|
"hidden_states": [T5Gemma2EncoderLayer, T5Gemma2DecoderLayer],
|
|
"attentions": [
|
|
OutputRecorder(T5Gemma2SelfAttention, index=1, layer_name="self_attn"),
|
|
OutputRecorder(T5Gemma2MergedAttention, index=1, layer_name="self_attn"),
|
|
OutputRecorder(T5Gemma2MergedAttention, index=2, layer_name="cross_attn"),
|
|
],
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
PreTrainedModel._init_weights(self, module)
|
|
if isinstance(module, T5Gemma2MultiModalProjector):
|
|
init.zeros_(module.mm_input_projection_weight)
|
|
elif isinstance(module, T5Gemma2TextScaledWordEmbedding):
|
|
init.zeros_(module.eoi_embedding)
|
|
init.constant_(module.embed_scale, module.scalar_embed_scale)
|
|
elif isinstance(module, T5Gemma2ClassificationHead):
|
|
scale = module.out_proj.weight.shape[0] ** -0.5
|
|
init.normal_(module.out_proj.weight, mean=0.0, std=self.config.initializer_range * scale)
|
|
if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
|
|
init.zeros_(module.out_proj.bias)
|
|
# We initialize with 0s to be 1 centered as the RMSNorm here does (1 + weight)
|
|
elif "RMSNorm" in module.__class__.__name__:
|
|
init.zeros_(module.weight)
|
|
elif isinstance(module, T5Gemma2RotaryEmbedding):
|
|
for layer_type in module.layer_types:
|
|
rope_init_fn = module.compute_default_rope_parameters
|
|
if module.rope_type[layer_type] != "default":
|
|
rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type[layer_type]]
|
|
curr_inv_freq, _ = rope_init_fn(module.config, layer_type=layer_type)
|
|
init.copy_(getattr(module, f"{layer_type}_inv_freq"), curr_inv_freq)
|
|
init.copy_(getattr(module, f"{layer_type}_original_inv_freq"), curr_inv_freq)
|
|
|
|
def prepare_decoder_input_ids_from_labels(self, input_ids):
|
|
"""
|
|
Shifts input_ids to the right, prepends the decoder_start_token_id, and handles
|
|
pad_token_id replacement for labels that were -100.
|
|
This is a common preparation step for decoder inputs in sequence-to-sequence models.
|
|
"""
|
|
decoder_config = self.config.decoder
|
|
decoder_start_token_id = decoder_config.bos_token_id
|
|
pad_token_id = decoder_config.pad_token_id
|
|
|
|
if decoder_start_token_id is None:
|
|
raise ValueError("self.model.config.decoder.bos_token_id has to be defined. ")
|
|
|
|
# shift inputs to the right
|
|
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
|
|
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
|
|
shifted_input_ids[..., 0] = decoder_start_token_id
|
|
|
|
if pad_token_id is None:
|
|
raise ValueError("self.model.config.decoder.pad_token_id has to be defined.")
|
|
|
|
# Is this T5 specific?
|
|
# replace possible -100 values in labels by `pad_token_id`
|
|
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
|
|
|
|
return shifted_input_ids
|
|
|
|
|
|
class T5Gemma2TextEncoder(T5Gemma2PreTrainedModel):
|
|
config: T5Gemma2TextConfig
|
|
_can_record_outputs = {
|
|
"attentions": T5Gemma2SelfAttention,
|
|
"hidden_states": T5Gemma2EncoderLayer,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
config: T5Gemma2TextConfig,
|
|
eoi_token_index: int = 256_000,
|
|
):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = T5Gemma2TextScaledWordEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
self.padding_idx,
|
|
embed_scale=config.hidden_size**0.5,
|
|
eoi_token_index=eoi_token_index,
|
|
)
|
|
self.norm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.gradient_checkpointing = False
|
|
|
|
self.layers = nn.ModuleList(
|
|
[T5Gemma2EncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
self.rotary_emb = T5Gemma2RotaryEmbedding(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
# Unused for processor compatibility kept in signature.
|
|
token_type_ids: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutput:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
# As we want to pass `past_key_values=None` explicitly everywhere, we need to pop them from kwargs if present
|
|
kwargs.pop("past_key_values", None)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if position_ids is None:
|
|
position_ids = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)
|
|
|
|
if not isinstance(self_attn_mask_mapping := attention_mask, dict):
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
self_attn_mask_mapping = {
|
|
"full_attention": create_bidirectional_mask(**mask_kwargs),
|
|
"sliding_attention": create_bidirectional_mask(
|
|
**mask_kwargs,
|
|
and_mask_function=sliding_window_mask_function(self.config.sliding_window, is_causal=False),
|
|
),
|
|
}
|
|
|
|
# input layer
|
|
hidden_states = inputs_embeds
|
|
|
|
# global and local position embeddings
|
|
position_embeddings = {}
|
|
for layer_type in self.config.layer_types:
|
|
position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)
|
|
|
|
# dropout
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
for layer_module in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
position_embeddings[layer_module.attention_type],
|
|
self_attn_mask_mapping[layer_module.attention_type],
|
|
position_ids,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
)
|
|
|
|
|
|
class T5Gemma2Encoder(T5Gemma2PreTrainedModel):
|
|
config: T5Gemma2EncoderConfig
|
|
|
|
def __init__(
|
|
self,
|
|
config: T5Gemma2EncoderConfig,
|
|
eoi_token_index: int = 256_000,
|
|
):
|
|
super().__init__(config)
|
|
|
|
self.text_model = T5Gemma2TextEncoder._from_config(config.text_config, eoi_token_index=eoi_token_index)
|
|
self.vision_tower = AutoModel.from_config(config=config.vision_config)
|
|
self.multi_modal_projector = T5Gemma2MultiModalProjector(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.text_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
return self.text_model.set_input_embeddings(new_embeddings)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def get_image_features(
|
|
self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> tuple | BaseModelOutputWithPooling:
|
|
# pixel_values: (batch_size, channels, height, width)
|
|
# image_features: Image feature tensor of shape (num_images, image_length, embed_dim).
|
|
vision_outputs = self.vision_tower(pixel_values=pixel_values, return_dict=True, **kwargs)
|
|
last_hidden_state = vision_outputs.last_hidden_state
|
|
image_features = self.multi_modal_projector(last_hidden_state)
|
|
vision_outputs.pooler_output = image_features
|
|
|
|
return vision_outputs
|
|
|
|
def get_image_placeholder_mask(
|
|
self,
|
|
input_ids: torch.LongTensor | None,
|
|
inputs_embeds: torch.FloatTensor | None,
|
|
image_features: torch.FloatTensor,
|
|
):
|
|
"""
|
|
Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
|
|
equal to the length of multimodal features. If the lengths are different, an error is raised.
|
|
"""
|
|
image_token_id = self.config.image_token_id
|
|
if input_ids is None:
|
|
if inputs_embeds is None:
|
|
raise ValueError("Either `input_ids` or `inputs_embeds` has to be provided.")
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == image_token_id
|
|
|
|
n_image_tokens = special_image_mask.sum()
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
n_image_features = image_features.shape[0] * image_features.shape[1]
|
|
torch_compilable_check(
|
|
inputs_embeds[special_image_mask].numel() == image_features.numel(),
|
|
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}",
|
|
)
|
|
return special_image_mask
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
# Unused for processor compatibility kept in signature.
|
|
token_type_ids: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutput:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.text_model.embed_tokens(input_ids)
|
|
|
|
if pixel_values is not None:
|
|
image_features = self.get_image_features(pixel_values, return_dict=True).pooler_output
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
|
|
image_mask = self.get_image_placeholder_mask(
|
|
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
|
|
)
|
|
|
|
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_features)
|
|
|
|
hidden_states = self.text_model(
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
**kwargs,
|
|
)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
)
|
|
|
|
|
|
class T5Gemma2Decoder(T5Gemma2PreTrainedModel):
|
|
config: T5Gemma2DecoderConfig
|
|
_can_record_outputs = {
|
|
"attentions": OutputRecorder(T5Gemma2MergedAttention, index=1),
|
|
"cross_attentions": OutputRecorder(T5Gemma2MergedAttention, index=2),
|
|
"hidden_states": T5Gemma2DecoderLayer,
|
|
}
|
|
|
|
def __init__(self, config: T5Gemma2DecoderConfig, eoi_token_index: int = 256_000):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = T5Gemma2TextScaledWordEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
config.pad_token_id,
|
|
embed_scale=config.hidden_size**0.5,
|
|
eoi_token_index=eoi_token_index,
|
|
)
|
|
self.norm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.gradient_checkpointing = False
|
|
|
|
self.layers = nn.ModuleList(
|
|
[T5Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
self.rotary_emb = T5Gemma2RotaryEmbedding(config)
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: EncoderDecoderCache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
encoder_hidden_states: torch.Tensor | None = None,
|
|
encoder_attention_mask: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPastAndCrossAttentions:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
if encoder_hidden_states is None:
|
|
raise ValueError("`encoder_hidden_states` must be given in decoder")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if not self.training and use_cache and past_key_values is None:
|
|
past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), 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)
|
|
|
|
if not isinstance(self_attn_mask_mapping := attention_mask, dict):
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values.self_attention_cache if past_key_values is not None else None,
|
|
"position_ids": position_ids,
|
|
}
|
|
# this masking function did nothing to masking but forces `allow_is_causal_skip` to be False
|
|
# as we always need a mask during decoding for merged attention.
|
|
mask_kwargs["and_mask_function"] = lambda *args: torch.tensor(True, dtype=torch.bool)
|
|
self_attn_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
if not isinstance(cross_attn_mask_mapping := encoder_attention_mask, dict):
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": encoder_hidden_states,
|
|
"attention_mask": encoder_attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": None,
|
|
"position_ids": None,
|
|
}
|
|
cross_attn_mask_mapping = {
|
|
"full_attention": create_causal_mask(
|
|
**mask_kwargs,
|
|
or_mask_function=bidirectional_mask_function(encoder_attention_mask),
|
|
),
|
|
}
|
|
|
|
merged_attn_mask_mapping = {
|
|
"full_attention": torch.cat(
|
|
[self_attn_mask_mapping["full_attention"], cross_attn_mask_mapping["full_attention"]], dim=-1
|
|
),
|
|
"sliding_attention": torch.cat(
|
|
[self_attn_mask_mapping["sliding_attention"], cross_attn_mask_mapping["full_attention"]], dim=-1
|
|
),
|
|
}
|
|
|
|
# input layer
|
|
hidden_states = inputs_embeds
|
|
|
|
# global and local position embeddings
|
|
position_embeddings = {}
|
|
for layer_type in self.config.layer_types:
|
|
position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)
|
|
|
|
# dropout
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
for layer_module in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
position_embeddings[layer_module.attention_type],
|
|
merged_attn_mask_mapping[layer_module.attention_type],
|
|
position_ids,
|
|
past_key_values,
|
|
use_cache,
|
|
cache_position,
|
|
encoder_hidden_states,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class T5Gemma2Model(T5Gemma2PreTrainedModel):
|
|
_tied_weights_keys = {
|
|
"decoder.embed_tokens.weight": "encoder.text_model.embed_tokens.weight",
|
|
"decoder.embed_tokens.eoi_embedding": "encoder.text_model.embed_tokens.eoi_embedding",
|
|
}
|
|
|
|
def __init__(self, config: T5Gemma2Config):
|
|
super().__init__(config)
|
|
|
|
# setup encoder and decoder
|
|
self.encoder = T5Gemma2Encoder(config.encoder, config.eoi_token_index)
|
|
self.decoder = T5Gemma2Decoder(config.decoder, config.eoi_token_index)
|
|
|
|
self.post_init()
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def get_input_embeddings(self):
|
|
return self.encoder.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
return self.encoder.set_input_embeddings(new_embeddings)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
# encoder inputs
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
# decoder inputs
|
|
decoder_input_ids: torch.LongTensor | None = None,
|
|
decoder_attention_mask: torch.BoolTensor | None = None,
|
|
decoder_position_ids: torch.LongTensor | None = None,
|
|
# others (mainly inference or cache related)
|
|
encoder_outputs: BaseModelOutput | None = None,
|
|
past_key_values: EncoderDecoderCache | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
decoder_inputs_embeds: torch.Tensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Seq2SeqModelOutput:
|
|
r"""
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
|
|
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
|
"""
|
|
# encoder
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
pixel_values=pixel_values,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
encoder_hidden_states = encoder_outputs.last_hidden_state
|
|
|
|
# decoder
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
position_ids=decoder_position_ids,
|
|
inputs_embeds=decoder_inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=attention_mask,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
return Seq2SeqModelOutput(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class T5Gemma2ForConditionalGeneration(T5Gemma2PreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {
|
|
"lm_head.out_proj.weight": "model.encoder.text_model.embed_tokens.weight",
|
|
}
|
|
_tp_plan = {"lm_head.out_proj": "colwise_gather_output"}
|
|
_pp_plan = {"lm_head.out_proj": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config: T5Gemma2Config):
|
|
super().__init__(config)
|
|
|
|
self.model = T5Gemma2Model(config)
|
|
self.vocab_size = config.decoder.vocab_size
|
|
self.lm_head = T5Gemma2LMHead(config.decoder.hidden_size, self.vocab_size)
|
|
self.loss_type = "ForMaskedLM"
|
|
|
|
self.post_init()
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.out_proj = new_embeddings
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.out_proj
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
def get_encoder(self):
|
|
return self.model.get_encoder()
|
|
|
|
def get_decoder(self):
|
|
return self.model.get_decoder()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def get_image_features(
|
|
self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> tuple | BaseModelOutputWithPooling:
|
|
return self.get_encoder().get_image_features(pixel_values, **kwargs)
|
|
|
|
@property
|
|
def vision_tower(self):
|
|
return self.get_encoder().vision_tower
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
# encoder inputs
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
# decoder inputs
|
|
decoder_input_ids: torch.LongTensor | None = None,
|
|
decoder_attention_mask: torch.BoolTensor | None = None,
|
|
decoder_position_ids: torch.LongTensor | None = None,
|
|
# others (mainly inference or cache related)
|
|
encoder_outputs: BaseModelOutput | None = None,
|
|
past_key_values: EncoderDecoderCache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
decoder_inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput:
|
|
r"""
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
|
|
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
|
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.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.vocab_size]`.
|
|
"""
|
|
|
|
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
|
|
# get decoder inputs from shifting lm labels to the right
|
|
decoder_input_ids = self.prepare_decoder_input_ids_from_labels(labels)
|
|
|
|
decoder_outputs: Seq2SeqModelOutput = self.model(
|
|
input_ids=input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
decoder_position_ids=decoder_position_ids,
|
|
encoder_outputs=encoder_outputs,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
decoder_inputs_embeds=decoder_inputs_embeds,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = decoder_outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
decoder_config = self.config.decoder
|
|
if decoder_config.final_logit_softcapping is not None:
|
|
logits = logits / decoder_config.final_logit_softcapping
|
|
logits = torch.tanh(logits)
|
|
logits = logits * decoder_config.final_logit_softcapping
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Input has right-shifted so we directly perform masked lm loss
|
|
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.decoder_hidden_states,
|
|
decoder_attentions=decoder_outputs.decoder_attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=decoder_outputs.encoder_last_hidden_state,
|
|
encoder_hidden_states=decoder_outputs.encoder_hidden_states,
|
|
encoder_attentions=decoder_outputs.encoder_attentions,
|
|
)
|
|
|
|
def _prepare_cache_for_generation(
|
|
self,
|
|
generation_config: GenerationConfig,
|
|
model_kwargs: dict,
|
|
generation_mode: GenerationMode,
|
|
batch_size: int,
|
|
max_cache_length: int,
|
|
) -> bool:
|
|
"""Override cache preparation to support T5Gemma2-specific EncoderDecoder Cache."""
|
|
|
|
# Build cache and past_key_values structure first and then override as needed.
|
|
super()._prepare_cache_for_generation(
|
|
generation_config,
|
|
model_kwargs,
|
|
generation_mode,
|
|
batch_size,
|
|
max_cache_length,
|
|
)
|
|
|
|
# If use_cache is False, do not prepare the cache.
|
|
if generation_config.use_cache is False:
|
|
return
|
|
|
|
cache_implementation = generation_config.cache_implementation
|
|
if cache_implementation is None:
|
|
offload_cache = False
|
|
else:
|
|
offload_cache = "offloaded" in generation_config.cache_implementation
|
|
|
|
# Main change: use full cache for cross-attention.
|
|
cross_attn_config = copy.deepcopy(self.config.get_text_config(decoder=True))
|
|
|
|
# cross-attention does not use sliding window
|
|
del cross_attn_config.sliding_window
|
|
del cross_attn_config.layer_types
|
|
|
|
cross_attn_cache_kwargs = {
|
|
"config": cross_attn_config,
|
|
"offloading": offload_cache,
|
|
}
|
|
|
|
past_key_values = model_kwargs.get("past_key_values")
|
|
if past_key_values is not None:
|
|
if not isinstance(past_key_values, EncoderDecoderCache):
|
|
raise ValueError(
|
|
"The `past_key_values` in `model_kwargs` must be of type `EncoderDecoderCache` for T5Gemma2 model."
|
|
)
|
|
|
|
# Cache already established, no need to re-initialize.
|
|
if len(past_key_values.is_updated) > 0 and past_key_values.is_updated.get(0):
|
|
return
|
|
|
|
cross_attn_cls = type(past_key_values.cross_attention_cache)
|
|
if cross_attn_cls == StaticCache:
|
|
cross_attn_cache_kwargs["max_cache_len"] = model_kwargs["encoder_outputs"][0].shape[1]
|
|
# Update cross-attention cache only (switch from sliding_window to full).
|
|
past_key_values.cross_attention_cache = cross_attn_cls(**cross_attn_cache_kwargs)
|
|
else:
|
|
# Initialize new cache.
|
|
model_kwargs["past_key_values"] = EncoderDecoderCache(
|
|
DynamicCache(
|
|
**{
|
|
"config": self.config.get_text_config(decoder=True),
|
|
"offloading": offload_cache,
|
|
}
|
|
), # self-attention cache
|
|
DynamicCache(), # cross-attention cache
|
|
)
|
|
|
|
if hasattr(self, "_cache") and self._cache is not None:
|
|
if not isinstance(self._cache, EncoderDecoderCache):
|
|
raise ValueError("The internal cache must be of type `EncoderDecoderCache` for T5Gemma2 model.")
|
|
|
|
self._cache = model_kwargs["past_key_values"]
|
|
|
|
|
|
@auto_docstring
|
|
class T5Gemma2ForSequenceClassification(T5Gemma2PreTrainedModel):
|
|
def __init__(self, config: T5Gemma2Config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.hidden_size = config.decoder.hidden_size
|
|
|
|
self.model = T5Gemma2Model(config)
|
|
|
|
classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
|
|
self.score = T5Gemma2ClassificationHead(self.hidden_size, self.num_labels, classifier_dropout)
|
|
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 = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
decoder_input_ids: torch.LongTensor | None = None,
|
|
decoder_attention_mask: torch.Tensor | None = None,
|
|
decoder_position_ids: torch.LongTensor | None = None,
|
|
encoder_outputs: BaseModelOutput | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
decoder_inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> SequenceClassifierOutput:
|
|
r"""
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
|
|
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
|
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).
|
|
"""
|
|
if inputs_embeds is not None or decoder_inputs_embeds is not None:
|
|
raise NotImplementedError(
|
|
f"Passing input embeddings is currently not supported for {self.__class__.__name__}."
|
|
)
|
|
|
|
if input_ids is None:
|
|
raise ValueError("You have to specify input_ids")
|
|
|
|
if decoder_input_ids is None:
|
|
decoder_input_ids = self.prepare_decoder_input_ids_from_labels(input_ids)
|
|
|
|
outputs: Seq2SeqModelOutput = self.model(
|
|
input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
decoder_position_ids=decoder_position_ids,
|
|
encoder_outputs=encoder_outputs,
|
|
inputs_embeds=inputs_embeds,
|
|
decoder_inputs_embeds=decoder_inputs_embeds,
|
|
use_cache=False,
|
|
**kwargs,
|
|
)
|
|
|
|
last_hidden_state = outputs.last_hidden_state
|
|
hidden_states = outputs.decoder_hidden_states
|
|
attentions = outputs.decoder_attentions
|
|
|
|
logits = self.score(last_hidden_state)
|
|
|
|
batch_size = input_ids.shape[0]
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (decoder_input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(decoder_input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
last_non_pad_token = torch.clamp(last_non_pad_token, max=decoder_input_ids.shape[-1] - 1)
|
|
|
|
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 SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
hidden_states=hidden_states,
|
|
attentions=attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class T5Gemma2ForTokenClassification(T5Gemma2PreTrainedModel):
|
|
def __init__(self, config: T5Gemma2Config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.hidden_size = config.decoder.hidden_size
|
|
|
|
self.model = T5Gemma2Model(config)
|
|
|
|
classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
|
|
self.score = T5Gemma2ClassificationHead(self.hidden_size, self.num_labels, classifier_dropout)
|
|
|
|
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 = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
decoder_input_ids: torch.LongTensor | None = None,
|
|
decoder_attention_mask: torch.Tensor | None = None,
|
|
decoder_position_ids: torch.LongTensor | None = None,
|
|
encoder_outputs: BaseModelOutput | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
decoder_inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> TokenClassifierOutput:
|
|
r"""
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
|
|
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
|
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).
|
|
"""
|
|
if inputs_embeds is not None or decoder_inputs_embeds is not None:
|
|
raise NotImplementedError(
|
|
f"Passing input embeddings is currently not supported for {self.__class__.__name__}."
|
|
)
|
|
|
|
if input_ids is None:
|
|
raise ValueError("You have to specify input_ids")
|
|
|
|
if decoder_input_ids is None:
|
|
decoder_input_ids = self.prepare_decoder_input_ids_from_labels(input_ids)
|
|
|
|
outputs: Seq2SeqModelOutput = self.model(
|
|
input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
decoder_input_ids=decoder_input_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
decoder_position_ids=decoder_position_ids,
|
|
encoder_outputs=encoder_outputs,
|
|
inputs_embeds=inputs_embeds,
|
|
decoder_inputs_embeds=decoder_inputs_embeds,
|
|
use_cache=False,
|
|
**kwargs,
|
|
)
|
|
last_hidden_state = outputs.last_hidden_state
|
|
hidden_states = outputs.decoder_hidden_states
|
|
attentions = outputs.decoder_attentions
|
|
|
|
logits = self.score(last_hidden_state)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.config)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=hidden_states,
|
|
attentions=attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"T5Gemma2Config",
|
|
"T5Gemma2TextConfig",
|
|
"T5Gemma2EncoderConfig",
|
|
"T5Gemma2DecoderConfig",
|
|
"T5Gemma2ForConditionalGeneration",
|
|
"T5Gemma2Model",
|
|
"T5Gemma2Encoder",
|
|
"T5Gemma2PreTrainedModel",
|
|
"T5Gemma2ForSequenceClassification",
|
|
"T5Gemma2ForTokenClassification",
|
|
]
|