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1309 lines
56 KiB
1309 lines
56 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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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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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import Optional
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import torch
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import torch.nn as nn
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from ... import initialization as init
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from ...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 ...integrations import use_kernel_func_from_hub, use_kernelized_func
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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_layers import GenericForSequenceClassification, GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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BaseModelOutputWithPooling,
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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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)
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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 ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
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from ...utils.generic import check_model_inputs, maybe_autocast
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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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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: torch.FloatTensor | None = 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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It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
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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: torch.FloatTensor | None = None
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logits: torch.FloatTensor | None = None
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past_key_values: Cache | None = None
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hidden_states: tuple[torch.FloatTensor] | None = None
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attentions: tuple[torch.FloatTensor] | None = None
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image_hidden_states: torch.FloatTensor | None = 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.scalar_embed_scale = embed_scale
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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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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: Gemma3TextConfig, device=None, layer_type=None):
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super().__init__()
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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.layer_types = list(set(config.layer_types))
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self.rope_type = {}
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for layer_type in self.layer_types:
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rope_params = self.config.rope_parameters[layer_type]
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if rope_params is None:
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continue
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self.rope_type[layer_type] = rope_params["rope_type"]
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rope_init_fn: Callable = self.compute_default_rope_parameters
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if self.rope_type[layer_type] != "default":
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rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type[layer_type]]
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curr_inv_freq, curr_attention_scaling = rope_init_fn(self.config, device, layer_type=layer_type)
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self.register_buffer(f"{layer_type}_inv_freq", curr_inv_freq, persistent=False)
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self.register_buffer(f"{layer_type}_original_inv_freq", curr_inv_freq.clone(), persistent=False)
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setattr(self, f"{layer_type}_attention_scaling", curr_attention_scaling)
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@staticmethod
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def compute_default_rope_parameters(
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config: Gemma3TextConfig | None = None,
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device: Optional["torch.device"] = None,
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seq_len: int | None = None,
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layer_type: str | None = None,
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) -> tuple["torch.Tensor", float]:
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"""
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Computes the inverse frequencies according to the original RoPE implementation
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Args:
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config ([`~transformers.PreTrainedConfig`]):
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The model configuration.
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device (`torch.device`):
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The device to use for initialization of the inverse frequencies.
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seq_len (`int`, *optional*):
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The current sequence length. Unused for this type of RoPE.
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layer_type (`str`, *optional*):
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The current layer type if the model has different RoPE parameters per type.
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Should not be used unless `config.layer_types is not None`
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Returns:
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Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
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post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
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"""
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# For backward compatibility standardize the `rope_parameters_dict` if it uses old format
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base = config.rope_parameters[layer_type]["rope_theta"]
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dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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attention_factor = 1.0 # Unused in this type of RoPE
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# Compute the inverse frequencies
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
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)
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return inv_freq, attention_factor
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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, layer_type=None):
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inv_freq = getattr(self, f"{layer_type}_inv_freq")
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attention_scaling = getattr(self, f"{layer_type}_attention_scaling")
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inv_freq_expanded = 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 maybe_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() * attention_scaling
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sin = emb.sin() * 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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@use_kernel_func_from_hub("rotary_pos_emb")
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def apply_rotary_pos_emb(q, k, cos, sin, 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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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: torch.Tensor | None,
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dropout: float = 0.0,
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scaling: float | None = None,
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softcap: float | None = 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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@use_kernelized_func(apply_rotary_pos_emb)
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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.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
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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 = not self.config.use_bidirectional_attention
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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.layer_type == "sliding_attention" else None
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self.is_sliding = self.layer_type == "sliding_attention"
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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 = None,
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attention_mask: torch.Tensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
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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_values 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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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
|
|
self.hidden_size = config.hidden_size
|
|
self.layer_idx = layer_idx
|
|
self.attention_type = config.layer_types[layer_idx]
|
|
self.self_attn = Gemma3Attention(config=config, layer_idx=layer_idx)
|
|
self.mlp = Gemma3MLP(config)
|
|
self.input_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.pre_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: torch.Tensor = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_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=past_key_values,
|
|
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
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3PreTrainedModel(PreTrainedModel):
|
|
config: Gemma3Config
|
|
base_model_prefix = "model"
|
|
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,
|
|
}
|
|
input_modalities = ("image", "text")
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, Gemma3MultiModalProjector):
|
|
init.zeros_(module.mm_input_projection_weight)
|
|
# 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, Gemma3TextScaledWordEmbedding):
|
|
init.constant_(module.embed_scale, module.scalar_embed_scale)
|
|
elif isinstance(module, Gemma3RotaryEmbedding):
|
|
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 _bidirectional_window_overlay(sliding_window: int) -> Callable[[int, int, int, int], bool]:
|
|
"""
|
|
Enables a bidirectional mask within the sliding window.
|
|
"""
|
|
|
|
def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
|
|
"""A token can attend to any other token if their absolute distance is within
|
|
the (exclusive) sliding window size (distance < sliding_window)."""
|
|
return abs(q_idx - kv_idx) < sliding_window
|
|
|
|
return inner_mask
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3TextModel(Gemma3PreTrainedModel):
|
|
config: Gemma3TextConfig
|
|
input_modalities = ("text",)
|
|
|
|
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)
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
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.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache(config=self.config)
|
|
|
|
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,
|
|
}
|
|
sliding_mask_kwargs = mask_kwargs.copy()
|
|
|
|
if self.config.use_bidirectional_attention:
|
|
mask_kwargs["or_mask_function"] = lambda *args: torch.tensor(True, dtype=torch.bool)
|
|
sliding_mask_kwargs["or_mask_function"] = _bidirectional_window_overlay(self.config.sliding_window)
|
|
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
|
|
}
|
|
|
|
# embed positions
|
|
hidden_states = inputs_embeds
|
|
position_embeddings = {}
|
|
for layer_type in self.config.layer_types:
|
|
position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_embeddings=position_embeddings[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Gemma3ForCausalLM(Gemma3PreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
|
|
_tp_plan = {"lm_head": "colwise_gather_output"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
config: Gemma3TextConfig
|
|
|
|
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()
|
|
|
|
@can_return_tuple
|
|
@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: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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?"
|
|
```"""
|
|
# 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,
|
|
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, _, 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)
|
|
|
|
|
|
def token_type_ids_mask_function(
|
|
token_type_ids: torch.Tensor | None,
|
|
image_group_ids: torch.Tensor | None,
|
|
) -> Callable | None:
|
|
"""
|
|
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_q_idx = torch.where(q_idx < token_type_ids.shape[1], q_idx, 0)
|
|
safe_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
|
|
|
|
token_type_ids_at_q_idx = token_type_ids[batch_idx, safe_q_idx]
|
|
token_type_ids_at_q_idx = torch.where(q_idx < token_type_ids.shape[1], token_type_ids_at_q_idx, 0)
|
|
|
|
token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_kv_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_q_idx = image_group_ids[batch_idx, safe_q_idx]
|
|
image_group_ids_at_q_idx = torch.where(q_idx < image_group_ids.shape[1], image_group_ids_at_q_idx, -1)
|
|
|
|
image_group_ids_at_kv_idx = image_group_ids[batch_idx, safe_kv_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_at_q_idx == 1) & (token_type_ids_at_kv_idx == 1)
|
|
same_image_block = image_group_ids_at_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
|
|
|
|
|
|
def create_causal_mask_mapping(
|
|
config: PreTrainedConfig,
|
|
input_embeds: torch.Tensor,
|
|
attention_mask: torch.Tensor | None,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache | None,
|
|
position_ids: torch.Tensor | None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
is_training: bool = False,
|
|
is_first_iteration: bool | None = None,
|
|
**kwargs,
|
|
) -> dict:
|
|
"""
|
|
Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
|
|
for all kinds of forward passes. Gemma3 uses a bidirectional mask for images.
|
|
|
|
Uses `pixel_values` as an optional input to disambiguate edge cases.
|
|
"""
|
|
if is_training and token_type_ids is None:
|
|
raise ValueError("`token_type_ids` is required as a model input when training")
|
|
|
|
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,
|
|
}
|
|
# NOTE: this `may_have_image_input` logic is not flawless, it fails when we're using a cache eagerly initialized
|
|
# (e.g. compiled prefill) AND `pixel_values` are not provided (i.e. the image data is provided through other
|
|
# means). Determining prefill in that case requires checking data values, which is not compile-compatible.
|
|
is_first_iteration = (
|
|
is_first_iteration
|
|
if is_first_iteration is not None
|
|
else (past_key_values is None or not past_key_values.is_initialized or pixel_values is not None)
|
|
)
|
|
if token_type_ids is not None and is_first_iteration:
|
|
# We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
|
|
# undo the causal masking)
|
|
|
|
# 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)
|
|
is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
|
|
new_image_start = is_image & ~is_previous_image
|
|
image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
|
|
image_group_ids = torch.where(is_image, image_group_ids, -1)
|
|
mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
|
|
token_type_ids.to(cache_position.device), image_group_ids
|
|
)
|
|
|
|
return create_masks_for_generate(**mask_kwargs)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Base Gemma3 model which consists of a vision backbone and a language model without 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.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)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring(custom_intro="Projects the last hidden state from the vision model into language model space.")
|
|
def get_image_features(
|
|
self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> tuple | BaseModelOutputWithPooling:
|
|
vision_outputs = self.vision_tower(pixel_values=pixel_values, return_dict=True, **kwargs)
|
|
last_hidden_state = vision_outputs.last_hidden_state
|
|
vision_outputs.pooler_output = self.multi_modal_projector(last_hidden_state)
|
|
|
|
return vision_outputs
|
|
|
|
def get_placeholder_mask(
|
|
self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, 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.
|
|
"""
|
|
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
|
|
|
|
n_image_tokens = special_image_mask.sum()
|
|
n_image_features = image_features.shape[0] * image_features.shape[1]
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
torch_compilable_check(
|
|
inputs_embeds[special_image_mask].numel() == image_features.numel(),
|
|
f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
|
|
)
|
|
return special_image_mask
|
|
|
|
@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,
|
|
past_key_values: Cache | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
**lm_kwargs: Unpack[TransformersKwargs],
|
|
) -> 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 httpx
|
|
>>> from io import BytesIO
|
|
>>> 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"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> 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")
|
|
|
|
# Replace image id with 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, return_dict=True).pooler_output
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
special_image_mask = self.get_placeholder_mask(
|
|
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
|
|
)
|
|
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):
|
|
causal_mask_mapping = create_causal_mask_mapping(
|
|
self.config,
|
|
inputs_embeds,
|
|
attention_mask,
|
|
cache_position,
|
|
past_key_values,
|
|
position_ids,
|
|
token_type_ids,
|
|
pixel_values,
|
|
is_training=self.training,
|
|
)
|
|
|
|
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,
|
|
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,
|
|
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": "model.language_model.embed_tokens.weight"}
|
|
# we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
|
|
# Fix: https://github.com/huggingface/transformers/issues/40564
|
|
accepts_loss_kwargs = False
|
|
|
|
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)
|
|
|
|
@auto_docstring
|
|
def get_image_features(self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]):
|
|
return self.model.get_image_features(pixel_values, **kwargs)
|
|
|
|
@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,
|
|
past_key_values: Cache | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**lm_kwargs: Unpack[TransformersKwargs],
|
|
) -> 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 httpx
|
|
>>> from io import BytesIO
|
|
>>> 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"
|
|
```
|
|
"""
|
|
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,
|
|
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)
|
|
|
|
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,
|
|
is_first_iteration=False,
|
|
**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,
|
|
is_first_iteration=is_first_iteration,
|
|
**kwargs,
|
|
)
|
|
|
|
# Pixel values are used only in the first iteration if available
|
|
# In subsquent iterations, they are already merged with text and cached
|
|
# NOTE: first iteration doesn't have to be prefill, it can be the first
|
|
# iteration with a question and cached system prompt (continue generate from cache). NOTE: use_cache=False needs pixel_values always
|
|
if is_first_iteration or not use_cache:
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
@staticmethod
|
|
def create_masks_for_generate(
|
|
config: PreTrainedConfig,
|
|
input_embeds: torch.Tensor,
|
|
attention_mask: torch.Tensor | None,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache | None,
|
|
position_ids: torch.Tensor | None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
is_first_iteration: bool | None = False,
|
|
**kwargs,
|
|
) -> dict:
|
|
# Uses the overwritten `create_masks_for_generate` with `token_type_ids` masking
|
|
return create_causal_mask_mapping(
|
|
config,
|
|
input_embeds,
|
|
attention_mask,
|
|
cache_position,
|
|
past_key_values,
|
|
position_ids,
|
|
token_type_ids,
|
|
is_first_iteration=is_first_iteration,
|
|
**{k: v for k, v in kwargs.items() if k != "pixel_values"},
|
|
)
|
|
|
|
|
|
class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
|
|
_checkpoint_conversion_mapping = {
|
|
"^language_model.model": "model.language_model",
|
|
"^vision_tower": "model.vision_tower",
|
|
"^multi_modal_projector": "model.multi_modal_projector",
|
|
}
|
|
|
|
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 = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
token_type_ids: torch.LongTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = 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,
|
|
)
|
|
|
|
|
|
class Gemma3TextForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
|
|
"""
|
|
Gemma3TextForSequenceClassification is a text-only sequence classification model that works with Gemma3TextConfig.
|
|
It uses the generic sequence classification implementation for efficiency and consistency.
|
|
"""
|
|
|
|
config: Gemma3TextConfig
|
|
input_modalities = ("text",)
|
|
|
|
|
|
__all__ = [
|
|
"Gemma3PreTrainedModel",
|
|
"Gemma3TextModel",
|
|
"Gemma3ForCausalLM",
|
|
"Gemma3ForConditionalGeneration",
|
|
"Gemma3Model",
|
|
"Gemma3ForSequenceClassification",
|
|
"Gemma3TextForSequenceClassification",
|
|
]
|