# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/t5gemma2/modular_t5gemma2.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_t5gemma2.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy from collections.abc import Callable import torch import torch.nn as nn from ... import initialization as init from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache from ...generation import GenerationConfig, GenerationMixin, GenerationMode from ...integrations import use_kernel_forward_from_hub, use_kernelized_func from ...masking_utils import create_bidirectional_mask, create_causal_mask, create_sliding_window_causal_mask from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, Seq2SeqLMOutput, Seq2SeqModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check from ...utils.deprecation import deprecate_kwarg from ...utils.generic import maybe_autocast, merge_with_config_defaults from ...utils.output_capturing import OutputRecorder, capture_outputs from ..auto import AutoModel from .configuration_t5gemma2 import T5Gemma2Config, T5Gemma2DecoderConfig, T5Gemma2EncoderConfig, T5Gemma2TextConfig class T5Gemma2RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.zeros(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): output = self._norm(x.float()) # Llama does x.to(float16) * w whilst T5Gemma2 is (x * w).to(float16) # See https://github.com/huggingface/transformers/pull/29402 output = output * (1.0 + self.weight.float()) return output.type_as(x) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.eps}" class T5Gemma2MLP(nn.Module): def __init__(self, config: T5Gemma2TextConfig): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_activation] 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(nn.Module): @deprecate_kwarg("device", version="5.18") def __init__(self, config: T5Gemma2TextConfig, device=None): super().__init__() self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.layer_types = list(set(config.layer_types)) self.rope_type = {} for layer_type in self.layer_types: rope_params = self.config.rope_parameters[layer_type] if rope_params is None: continue self.rope_type[layer_type] = rope_params["rope_type"] rope_init_fn: Callable = self.compute_default_rope_parameters if self.rope_type[layer_type] != "default": rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type[layer_type]] curr_inv_freq, curr_attention_scaling = rope_init_fn(self.config, device, layer_type=layer_type) setattr(self, f"{layer_type}_inv_freq", nn.Buffer(curr_inv_freq, persistent=False)) setattr(self, f"{layer_type}_original_inv_freq", nn.Buffer(curr_inv_freq.clone(), persistent=False)) setattr(self, f"{layer_type}_attention_scaling", curr_attention_scaling) @staticmethod @deprecate_kwarg("device", version="5.18") def compute_default_rope_parameters( config: T5Gemma2TextConfig, device=None, layer_type: str | None = None, **kwargs ) -> tuple[torch.Tensor, float]: """ Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. layer_type (`str`): The current layer type if the model has different RoPE parameters per type. Should not be used unless `config.layer_types is not None` Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). """ # For backward compatibility standardize the `rope_parameters_dict` if it uses old format base = config.rope_parameters[layer_type]["rope_theta"] dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads attention_factor = 1.0 # Unused in this type of RoPE # Compute the inverse frequencies inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) return inv_freq.to(device), attention_factor @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids, layer_type): inv_freq = getattr(self, f"{layer_type}_inv_freq") attention_scaling = getattr(self, f"{layer_type}_attention_scaling") inv_freq_expanded = ( inv_freq[None, :, None].expand(position_ids.shape[0], -1, 1).to(dtype=torch.float, device=x.device) ) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" # Disable any outside autocast context if any, to really force fp32 with maybe_autocast(device_type=device_type, enabled=False): freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * attention_scaling sin = emb.sin() * attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) @use_kernel_forward_from_hub("rotary_pos_emb") def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor | None, dropout: float | int = 0.0, scaling: float | None = None, softcap: float | None = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: if scaling is None: scaling = module.head_dim**-0.5 key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if softcap is not None: attn_weights = attn_weights / softcap attn_weights = torch.tanh(attn_weights) attn_weights = attn_weights * softcap if attention_mask is not None: attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights @use_kernelized_func(apply_rotary_pos_emb) class T5Gemma2SelfAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: T5Gemma2TextConfig, layer_idx: int): super().__init__() self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = config.query_pre_attn_scalar**-0.5 self.attention_dropout = self.config.attention_dropout self.is_causal = False # Only used by the encoder self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) self.attn_logit_softcapping = self.config.attn_logit_softcapping self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None self.is_sliding = self.layer_type == "sliding_attention" self.q_norm = T5Gemma2RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps) self.k_norm = T5Gemma2RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: torch.Tensor = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) query_states = self.q_norm(query_states) key_states = self.k_norm(key_states) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values is not None: key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) 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, attention_mask, dropout=self.attention_dropout if self.training else 0.0, scaling=self.scaling, sliding_window=self.sliding_window, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights @use_kernelized_func(apply_rotary_pos_emb) class T5Gemma2MergedAttention(nn.Module): """Merged self-attention and cross-attention for decoder.""" def __init__(self, config: T5Gemma2TextConfig, layer_idx: int): super().__init__() self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = config.query_pre_attn_scalar**-0.5 self.attention_dropout = self.config.attention_dropout self.is_causal = False # Fused causal and encoder mask self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) self.attn_logit_softcapping = self.config.attn_logit_softcapping self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None self.is_sliding = self.layer_type == "sliding_attention" self.q_norm = T5Gemma2RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps) self.k_norm = T5Gemma2RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps) 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, # 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. self_attention_cache = past_key_values.self_attention_cache key_states, value_states = self_attention_cache.update(key_states, value_states, self.layer_idx) # 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 class T5Gemma2EncoderLayer(GradientCheckpointingLayer): """Encoder sub-layer.""" def __init__(self, config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.config = config self.layer_idx = layer_idx self.attention_type = config.layer_types[layer_idx] self.self_attn = T5Gemma2SelfAttention( config=config, layer_idx=layer_idx, ) self.pre_self_attn_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_self_attn_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp = T5Gemma2MLP(config) self.pre_feedforward_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_feedforward_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, **kwargs, ) -> tuple[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, attention_mask=attention_mask, position_ids=position_ids, past_key_values=None, **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 T5Gemma2DecoderLayer(GradientCheckpointingLayer): """Decoder sub-layer: merged attention instead of vanilla self-attention.""" def __init__(self, config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.config = config self.layer_idx = layer_idx self.attention_type = config.layer_types[layer_idx] # replace vanilla self-attention with merged attention to support joint cross-attention. self.self_attn = T5Gemma2MergedAttention( config=config, layer_idx=layer_idx, ) self.pre_self_attn_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_self_attn_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp = T5Gemma2MLP(config) self.pre_feedforward_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_feedforward_layernorm = T5Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.dropout = nn.Dropout(config.dropout_rate) 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, 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, 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(nn.Module): """Head for language modeling (generation) tasks.""" def __init__(self, hidden_size: int, vocab_size: int, bias: bool = False): super().__init__() self.out_proj = nn.Linear(hidden_size, vocab_size, bias=bias) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: logits = self.out_proj(hidden_states) return logits class T5Gemma2ClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, hidden_size: int, num_labels: int, classifier_dropout_rate: float = 0.0): super().__init__() self.dropout = nn.Dropout(p=classifier_dropout_rate) self.out_proj = nn.Linear(hidden_size, num_labels) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class T5Gemma2MultiModalProjector(nn.Module): def __init__(self, config: T5Gemma2EncoderConfig): super().__init__() self.mm_input_projection_weight = nn.Parameter( torch.zeros(config.vision_config.hidden_size, config.text_config.hidden_size) ) self.mm_soft_emb_norm = T5Gemma2RMSNorm( config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps ) self.patches_per_image = int(config.vision_config.image_size // config.vision_config.patch_size) self.tokens_per_side = int(config.mm_tokens_per_image**0.5) self.kernel_size = self.patches_per_image // self.tokens_per_side self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size) def forward(self, vision_outputs: torch.Tensor): batch_size, _, hidden_size = vision_outputs.shape reshaped_vision_outputs = vision_outputs.transpose(1, 2) reshaped_vision_outputs = reshaped_vision_outputs.reshape( batch_size, hidden_size, self.patches_per_image, self.patches_per_image ) reshaped_vision_outputs = reshaped_vision_outputs.contiguous() pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs) pooled_vision_outputs = pooled_vision_outputs.flatten(2) pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2) normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs) projected_vision_outputs = torch.matmul(normed_vision_outputs, self.mm_input_projection_weight) return projected_vision_outputs.type_as(vision_outputs) class T5Gemma2TextScaledWordEmbedding(nn.Embedding): """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) self.scalar_embed_scale = embed_scale self.embed_scale = nn.Buffer(torch.tensor(embed_scale), persistent=False) 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(PreTrainedModel): config: T5Gemma2Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = [ "T5Gemma2EncoderLayer", "T5Gemma2DecoderLayer", ] _skip_keys_device_placement = ["past_key_values"] # Mask creation is incompatible # FA due to non-default creation / SWA _supports_flash_attn = False _supports_sdpa = True # Flex due to custom masks not compatible to be merged after creation _supports_flex_attn = False _can_compile_fullgraph = True _supports_attention_backend = True # Recording is declared on the text encoder/decoder classes; None avoids inheriting the gemma3 dict _can_record_outputs = None input_modalities = ("image", "text") @torch.no_grad() def _init_weights(self, module): super()._init_weights(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, labels: torch.Tensor): """ 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 = labels.new_zeros(labels.shape) shifted_input_ids[..., 1:] = labels[..., :-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 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 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() @merge_with_config_defaults @capture_outputs @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, "inputs_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 set(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 i, layer_module in enumerate(self.layers[: self.config.num_hidden_layers]): hidden_states = layer_module( hidden_states, position_embeddings[self.config.layer_types[i]], self_attn_mask_mapping[self.config.layer_types[i]], 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.full((), 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).to(inputs_embeds.device) n_image_features = image_features.shape[0] * image_features.shape[1] torch_compilable_check( n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(), f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}", ) return special_image_mask @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) outputs = self.text_model( inputs_embeds=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, **kwargs, ) return outputs 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() @merge_with_config_defaults @capture_outputs @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, 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 position_ids is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens position_ids = position_ids.unsqueeze(0) if not isinstance(self_attn_mask_mapping := attention_mask, dict): # Always materialize the mask (no `is_causal` skip) as it is concatenated with the cross-attention # mask below for merged attention during decoding. mask_kwargs = { "config": self.config, "inputs_embeds": inputs_embeds, "attention_mask": attention_mask, "past_key_values": past_key_values.self_attention_cache if past_key_values is not None else None, "position_ids": position_ids, "allow_is_causal_skip": False, } 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): cross_attn_mask_mapping = { "full_attention": create_bidirectional_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, allow_is_bidirectional_skip=False, ) } 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 set(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 i, layer_module in enumerate(self.layers[: self.config.num_hidden_layers]): hidden_states = layer_module( hidden_states, position_embeddings[self.config.layer_types[i]], merged_attn_mask_mapping[self.config.layer_types[i]], position_ids, past_key_values, use_cache, 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, modality: str | None = None): 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, **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, 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, modality: str | None = None): return self.model.get_encoder(modality=modality) 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, 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, **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 cross_attn_config.sliding_window = None cross_attn_config.layer_types = ["full_attention"] * cross_attn_config.num_hidden_layers 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__ = [ "T5Gemma2ForConditionalGeneration", "T5Gemma2Model", "T5Gemma2Encoder", "T5Gemma2PreTrainedModel", "T5Gemma2ForSequenceClassification", "T5Gemma2ForTokenClassification", ]