1301 lines
56 KiB
Python
1301 lines
56 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/gemma3/modular_gemma3.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_gemma3.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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import torch.nn as nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import PretrainedConfig
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from ...generation import GenerationMixin
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from ...masking_utils import create_causal_mask, create_masks_for_generate, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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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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ModelOutput,
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TransformersKwargs,
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auto_docstring,
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can_return_tuple,
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is_torchdynamo_compiling,
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logging,
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)
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from ...utils.deprecation import deprecate_kwarg
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from ...utils.generic import check_model_inputs
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from ..auto import AutoModel
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from .configuration_gemma3 import Gemma3Config, Gemma3TextConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Gemma3 outputs, with hidden states and attentions.
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"""
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)
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class Gemma3ModelOutputWithPast(BaseModelOutputWithPast):
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r"""
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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image_hidden_states: Optional[torch.FloatTensor] = None
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Gemma3 causal language model (or autoregressive) outputs.
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"""
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)
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class Gemma3CausalLMOutputWithPast(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss (for next-token prediction).
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: Optional[torch.FloatTensor] = None
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past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None
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hidden_states: Optional[tuple[torch.FloatTensor]] = None
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attentions: Optional[tuple[torch.FloatTensor]] = None
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image_hidden_states: Optional[torch.FloatTensor] = None
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class Gemma3TextScaledWordEmbedding(nn.Embedding):
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"""
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This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
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"""
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def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
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super().__init__(num_embeddings, embedding_dim, padding_idx)
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self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
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def forward(self, input_ids: torch.Tensor):
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return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
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class Gemma3MLP(nn.Module):
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def __init__(self, config: Gemma3TextConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_activation]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Gemma3RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.zeros(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float())
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# Llama does x.to(float16) * w whilst Gemma3 is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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output = output * (1.0 + self.weight.float())
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return output.type_as(x)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.eps}"
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class Gemma3RotaryEmbedding(nn.Module):
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def __init__(self, config: Gemma3TextConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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dropout: float = 0.0,
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scaling: Optional[float] = None,
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softcap: Optional[float] = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if scaling is None:
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scaling = module.head_dim**-0.5
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if softcap is not None:
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attn_weights = attn_weights / softcap
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attn_weights = torch.tanh(attn_weights)
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attn_weights = attn_weights * softcap
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Gemma3Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Gemma3TextConfig, layer_idx: int):
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super().__init__()
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self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = config.query_pre_attn_scalar**-0.5
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self.attention_dropout = self.config.attention_dropout
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self.is_causal = True
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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self.attn_logit_softcapping = self.config.attn_logit_softcapping
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self.sliding_window = config.sliding_window if self.is_sliding else None
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self.q_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
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self.k_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=self.attention_dropout if self.training else 0.0,
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scaling=self.scaling,
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sliding_window=self.sliding_window,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Gemma3DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Gemma3TextConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.layer_idx = layer_idx
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self.attention_type = config.layer_types[layer_idx]
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self.self_attn = Gemma3Attention(config=config, layer_idx=layer_idx)
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self.mlp = Gemma3MLP(config)
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self.input_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
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self.pre_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
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self.post_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
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@deprecate_kwarg("last_cache_position", version="4.53.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings_global: torch.Tensor,
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position_embeddings_local: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# apply global RoPE to non-sliding layer only
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if self.self_attn.is_sliding:
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position_embeddings = position_embeddings_local
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else:
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position_embeddings = position_embeddings_global
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.pre_feedforward_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = self.post_feedforward_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3PreTrainedModel(PreTrainedModel):
|
|
config: Gemma3Config
|
|
base_model_prefix = ""
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = [
|
|
"Gemma3DecoderLayer",
|
|
"SiglipVisionEmbeddings",
|
|
"SiglipEncoderLayer",
|
|
"SiglipMultiheadAttentionPoolingHead",
|
|
]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": Gemma3DecoderLayer,
|
|
"attentions": Gemma3Attention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, Gemma3MultiModalProjector):
|
|
module.mm_input_projection_weight.data.zero_()
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3TextModel(Gemma3PreTrainedModel):
|
|
config: Gemma3TextConfig
|
|
|
|
def __init__(self, config: Gemma3TextConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
# Gemma3 downcasts the below to bfloat16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
|
|
self.embed_tokens = Gemma3TextScaledWordEmbedding(
|
|
config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[Gemma3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = Gemma3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = Gemma3RotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
|
|
# TODO: raushan fix this after RoPE refactor. For now we hack it by reassigning thetas
|
|
# when we want to create a local RoPE layer. Config defaults should hold values for global RoPE
|
|
config = copy.deepcopy(config)
|
|
config.rope_theta = config.rope_local_base_freq
|
|
config.rope_scaling = {"rope_type": "default"}
|
|
self.rotary_emb_local = Gemma3RotaryEmbedding(config=config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training and use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
|
)
|
|
use_cache = False
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None and not self.training:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens,
|
|
past_seen_tokens + inputs_embeds.shape[1],
|
|
device=inputs_embeds.device,
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
# It may already have been prepared by e.g. `generate`
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
# embed positions
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings_global = self.rotary_emb(hidden_states, position_ids)
|
|
position_embeddings_local = self.rotary_emb_local(hidden_states, position_ids)
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
position_embeddings_global=position_embeddings_global,
|
|
position_embeddings_local=position_embeddings_local,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3ForCausalLM(Gemma3PreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
config: Gemma3TextConfig
|
|
base_model_prefix = "language_model"
|
|
|
|
def __init__(self, config: Gemma3TextConfig):
|
|
super().__init__(config)
|
|
self.model = Gemma3TextModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs,
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, Gemma3ForCausalLM
|
|
|
|
>>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
|
|
|
|
>>> prompt = "What is your favorite condiment?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"What is your favorite condiment?"
|
|
```"""
|
|
|
|
if self.training and self.config._attn_implementation != "eager":
|
|
logger.warning_once(
|
|
"It is strongly recommended to train Gemma3 models with the `eager` attention implementation "
|
|
f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = 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, :])
|
|
if self.config.final_logit_softcapping is not None:
|
|
logits = logits / self.config.final_logit_softcapping
|
|
logits = torch.tanh(logits)
|
|
logits = logits * self.config.final_logit_softcapping
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class Gemma3MultiModalProjector(nn.Module):
|
|
def __init__(self, config: Gemma3Config):
|
|
super().__init__()
|
|
|
|
self.mm_input_projection_weight = nn.Parameter(
|
|
torch.zeros(config.vision_config.hidden_size, config.text_config.hidden_size)
|
|
)
|
|
|
|
self.mm_soft_emb_norm = Gemma3RMSNorm(
|
|
config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps
|
|
)
|
|
|
|
self.patches_per_image = int(config.vision_config.image_size // config.vision_config.patch_size)
|
|
self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
|
|
self.kernel_size = self.patches_per_image // self.tokens_per_side
|
|
self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)
|
|
|
|
def forward(self, vision_outputs: torch.Tensor):
|
|
batch_size, _, seq_length = vision_outputs.shape
|
|
|
|
reshaped_vision_outputs = vision_outputs.transpose(1, 2)
|
|
reshaped_vision_outputs = reshaped_vision_outputs.reshape(
|
|
batch_size, seq_length, self.patches_per_image, self.patches_per_image
|
|
)
|
|
reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
|
|
|
|
pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
|
|
pooled_vision_outputs = pooled_vision_outputs.flatten(2)
|
|
pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
|
|
|
|
normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
|
|
|
|
projected_vision_outputs = torch.matmul(normed_vision_outputs, self.mm_input_projection_weight)
|
|
return projected_vision_outputs.type_as(vision_outputs)
|
|
|
|
|
|
def token_type_ids_mask_function(
|
|
token_type_ids: Optional[torch.Tensor],
|
|
image_group_ids: Optional[torch.Tensor],
|
|
tokens_per_image: int,
|
|
) -> Optional[Callable]:
|
|
"""
|
|
This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
|
|
not start and end indices.
|
|
"""
|
|
# Do not return an additional mask in this case
|
|
if token_type_ids is None:
|
|
return None
|
|
|
|
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
|
|
# If it's 1 for both query and key/value, we are in an image block
|
|
# NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
|
|
# Since vmap doesn't support `if statement` we workaround it with `torch.where`
|
|
safe_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
|
|
token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_idx]
|
|
token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)
|
|
|
|
image_group_ids_at_kv_idx = image_group_ids[batch_idx, safe_idx]
|
|
image_group_ids_at_kv_idx = torch.where(kv_idx < image_group_ids.shape[1], image_group_ids_at_kv_idx, -1)
|
|
|
|
is_image_block = (token_type_ids[batch_idx, q_idx] == 1) & (token_type_ids_at_kv_idx == 1)
|
|
same_image_block = image_group_ids[batch_idx, q_idx] == image_group_ids_at_kv_idx
|
|
|
|
# This is bidirectional attention whenever we are dealing with image tokens
|
|
return is_image_block & same_image_block
|
|
|
|
return inner_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Base Gemma3 model which consists of a vision backbone and a language model withou language modeling head.,
|
|
"""
|
|
)
|
|
class Gemma3Model(Gemma3PreTrainedModel):
|
|
_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
|
|
# we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
|
|
accepts_loss_kwargs = False
|
|
|
|
def __init__(self, config: Gemma3Config):
|
|
super().__init__(config)
|
|
self.vision_tower = AutoModel.from_config(config=config.vision_config)
|
|
self.multi_modal_projector = Gemma3MultiModalProjector(config)
|
|
self.vocab_size = config.text_config.vocab_size
|
|
|
|
language_model = AutoModel.from_config(config=config.text_config)
|
|
self.language_model = language_model
|
|
|
|
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.language_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.language_model.set_input_embeddings(value)
|
|
|
|
def set_decoder(self, decoder):
|
|
self.language_model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.language_model
|
|
|
|
def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Projects the last hidden state from the vision model into language model space.
|
|
|
|
Args:
|
|
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
|
|
The tensors corresponding to the input images.
|
|
Returns:
|
|
image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
|
|
"""
|
|
vision_outputs = self.vision_tower(pixel_values=pixel_values).last_hidden_state
|
|
image_features = self.multi_modal_projector(vision_outputs)
|
|
return image_features
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**lm_kwargs,
|
|
) -> Union[tuple, Gemma3ModelOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
|
|
|
|
>>> model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma32-3b-mix-224")
|
|
>>> processor = AutoProcessor.from_pretrained("google/gemma32-3b-mix-224")
|
|
|
|
>>> prompt = "Where is the cat standing?"
|
|
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(**inputs,)
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Where is the cat standing?\nsnow"
|
|
```"""
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# Replace image id woth PAD if the image token if OOV, to avoid index-errors
|
|
if input_ids is not None and self.config.image_token_id >= self.vocab_size:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
llm_input_ids = input_ids.clone()
|
|
llm_input_ids[special_image_mask] = 0
|
|
else:
|
|
llm_input_ids = input_ids
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(llm_input_ids)
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
# Merge text and images
|
|
if pixel_values is not None:
|
|
image_features = self.get_image_features(pixel_values)
|
|
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
|
|
if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
|
|
image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
|
|
raise ValueError(
|
|
f"Number of images does not match number of special image tokens in the input text. "
|
|
f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
|
|
"tokens from image embeddings."
|
|
)
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
|
|
|
# It may already have been prepared by e.g. `generate`
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"config": self.config.get_text_config(),
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
if token_type_ids is not None and inputs_embeds.shape[1] != 1:
|
|
# We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
|
|
|
|
# First find where a new image block starts: 1 if image and previous not image
|
|
# The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
|
|
is_image = (token_type_ids == 1).to(cache_position.device)
|
|
new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
|
|
image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
|
|
image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
|
|
mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
|
|
token_type_ids.to(cache_position.device), image_group_ids, self.config.mm_tokens_per_image
|
|
)
|
|
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
outputs = self.language_model(
|
|
attention_mask=causal_mask_mapping,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
cache_position=cache_position,
|
|
**lm_kwargs,
|
|
)
|
|
|
|
return Gemma3ModelOutputWithPast(
|
|
last_hidden_state=outputs.last_hidden_state,
|
|
past_key_values=outputs.past_key_values if use_cache else None,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Base Gemma3 model which consists of a vision backbone and a language model without language modeling head.,
|
|
"""
|
|
)
|
|
class Gemma3ForConditionalGeneration(Gemma3PreTrainedModel, GenerationMixin):
|
|
_checkpoint_conversion_mapping = {
|
|
"^language_model.model": "model.language_model",
|
|
"^vision_tower": "model.vision_tower",
|
|
"^multi_modal_projector": "model.multi_modal_projector",
|
|
"^language_model.lm_head": "lm_head",
|
|
}
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config: Gemma3Config):
|
|
super().__init__(config)
|
|
self.model = Gemma3Model(config)
|
|
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
|
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)
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model.set_decoder(decoder)
|
|
|
|
def get_decoder(self):
|
|
return self.model.get_decoder()
|
|
|
|
def get_image_features(self, pixel_values):
|
|
return self.model.get_image_features(pixel_values)
|
|
|
|
# Make modules available throught conditional class for BC
|
|
@property
|
|
def language_model(self):
|
|
return self.model.language_model
|
|
|
|
@property
|
|
def vision_tower(self):
|
|
return self.model.vision_tower
|
|
|
|
@property
|
|
def multi_modal_projector(self):
|
|
return self.model.multi_modal_projector
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**lm_kwargs,
|
|
) -> Union[tuple, Gemma3CausalLMOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
|
|
|
|
>>> model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma-3-4b-it")
|
|
>>> processor = AutoProcessor.from_pretrained("google/gemma-3-4b-it")
|
|
|
|
>>> messages = [
|
|
... {
|
|
... "role": "system",
|
|
... "content": [
|
|
... {"type": "text", "text": "You are a helpful assistant."}
|
|
... ]
|
|
... },
|
|
... {
|
|
... "role": "user", "content": [
|
|
... {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
|
|
... {"type": "text", "text": "Where is the cat standing?"},
|
|
... ]
|
|
... },
|
|
... ]
|
|
|
|
>>> inputs = processor.apply_chat_template(
|
|
... messages,
|
|
... tokenize=True,
|
|
... return_dict=True,
|
|
... return_tensors="pt",
|
|
... add_generation_prompt=True
|
|
... )
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(**inputs)
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"user\nYou are a helpful assistant.\n\n\n\n\n\nWhere is the cat standing?\nmodel\nBased on the image, the cat is standing in a snowy area, likely outdoors. It appears to"
|
|
```
|
|
"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
pixel_values=pixel_values,
|
|
token_type_ids=token_type_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
labels=labels,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
**lm_kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
|
logits = logits.float()
|
|
shift_logits = logits[..., :-1, :]
|
|
shift_labels = labels[..., 1:]
|
|
if attention_mask is not None:
|
|
# we use the input attention mask to shift the logits and labels, because it is 2D.
|
|
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
|
|
shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
|
|
shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
|
|
shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
|
|
else:
|
|
shift_logits = shift_logits.contiguous()
|
|
shift_labels = shift_labels.contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
|
|
flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
|
|
flat_labels = shift_labels.view(-1).to(shift_logits.device)
|
|
loss = loss_fct(flat_logits, flat_labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return Gemma3CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=outputs.image_hidden_states,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
use_cache=True,
|
|
logits_to_keep=None,
|
|
labels=None,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- custom `position_ids` and `pixel_values` handling
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
cache_position=cache_position,
|
|
use_cache=use_cache,
|
|
logits_to_keep=logits_to_keep,
|
|
token_type_ids=token_type_ids,
|
|
**kwargs,
|
|
)
|
|
|
|
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
|
|
# Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
|
|
if cache_position[0] == 0:
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
@staticmethod
|
|
def create_masks_for_generate(
|
|
config: PretrainedConfig,
|
|
input_embeds: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Optional[Cache],
|
|
position_ids: Optional[torch.Tensor],
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> dict:
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"config": config.get_text_config(),
|
|
"input_embeds": input_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
# Add the token type ids mask for generate as well
|
|
if token_type_ids is not None and input_embeds.shape[1] != 1:
|
|
# We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
|
|
|
|
# First find where a new image block starts: 1 if image and previous not image
|
|
# The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
|
|
is_image = (token_type_ids == 1).to(cache_position.device)
|
|
new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
|
|
image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
|
|
image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
|
|
mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
|
|
token_type_ids.to(cache_position.device), image_group_ids, config.mm_tokens_per_image
|
|
)
|
|
|
|
return create_masks_for_generate(**mask_kwargs)
|
|
|
|
|
|
class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = Gemma3Model(config)
|
|
self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> SequenceClassifierOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
|
|
transformer_outputs = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
pixel_values=pixel_values,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
token_type_ids=token_type_ids,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
hidden_states = transformer_outputs.last_hidden_state
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
if self.config.text_config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.text_config.pad_token_id is None:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"Gemma3PreTrainedModel",
|
|
"Gemma3TextModel",
|
|
"Gemma3ForCausalLM",
|
|
"Gemma3ForConditionalGeneration",
|
|
"Gemma3Model",
|
|
"Gemma3ForSequenceClassification",
|
|
]
|