530 lines
23 KiB
Python
530 lines
23 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/mistral3/modular_mistral3.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_mistral3.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 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 dataclasses import dataclass
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from typing import Optional, Union
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import torch
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
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from ...modeling_utils import PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling
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from ..auto import AutoModel
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from .configuration_mistral3 import Mistral3Config
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@use_kernel_forward_from_hub("RMSNorm")
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class Mistral3RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Mistral3RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class Mistral3PatchMerger(nn.Module):
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"""
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Learned merging of spatial_merge_size ** 2 patches
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"""
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def __init__(self, config: Mistral3Config):
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super().__init__()
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self.config = config
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hidden_size = config.vision_config.hidden_size
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self.spatial_merge_size = config.spatial_merge_size
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self.patch_size = self.config.vision_config.patch_size
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self.merging_layer = nn.Linear(hidden_size * self.spatial_merge_size**2, hidden_size, bias=False)
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def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor) -> torch.Tensor:
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image_sizes = [
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(image_size[0] // self.patch_size, image_size[1] // self.patch_size) for image_size in image_sizes
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]
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tokens_per_image = [h * w for h, w in image_sizes]
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d = image_features.shape[-1]
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permuted_tensor = []
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for image_index, image_tokens in enumerate(image_features.split(tokens_per_image)):
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# Reshape image_tokens into a 2D grid
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h, w = image_sizes[image_index]
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image_grid = image_tokens.view(h, w, d).permute(2, 0, 1).unsqueeze(0)
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grid = torch.nn.functional.unfold(
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image_grid, kernel_size=self.spatial_merge_size, stride=self.spatial_merge_size
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)
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grid = grid.view(d * self.spatial_merge_size**2, -1).t()
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permuted_tensor.append(grid)
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image_features = torch.cat(permuted_tensor, dim=0)
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image_features = self.merging_layer(image_features)
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return image_features
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class Mistral3MultiModalProjector(nn.Module):
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def __init__(self, config: Mistral3Config):
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super().__init__()
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self.norm = Mistral3RMSNorm(config.vision_config.hidden_size, eps=config.text_config.rms_norm_eps)
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self.patch_merger = Mistral3PatchMerger(config)
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# We have hidden_size * the number of vision feature layers
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num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
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self.linear_1 = nn.Linear(
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config.vision_config.hidden_size * num_feature_layers,
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config.text_config.hidden_size,
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bias=config.multimodal_projector_bias,
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)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(
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config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
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)
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def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor):
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image_features = self.norm(image_features)
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image_features = self.patch_merger(image_features, image_sizes)
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hidden_states = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Mistral3 causal language model (or autoregressive) outputs.
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"""
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)
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class Mistral3CausalLMOutputWithPast(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.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 and after projecting the 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[list[torch.FloatTensor]] = 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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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Mistral3 outputs, with hidden states and attentions.
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"""
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)
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class Mistral3ModelOutputWithPast(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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@auto_docstring
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class Mistral3PreTrainedModel(PreTrainedModel):
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config: Mistral3Config
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base_model_prefix = ""
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supports_gradient_checkpointing = True
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_skip_keys_device_placement = "past_key_values"
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_supports_flash_attn = True
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_supports_sdpa = True
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_can_compile_fullgraph = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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@auto_docstring(
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custom_intro="""
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The Mistral3 model which consists of a vision backbone and a language model, without a language modeling head.
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"""
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)
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class Mistral3Model(Mistral3PreTrainedModel):
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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def __init__(self, config: Mistral3Config):
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super().__init__(config)
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self.vision_tower = AutoModel.from_config(config.vision_config)
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self.multi_modal_projector = Mistral3MultiModalProjector(config)
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self.language_model = AutoModel.from_config(config.text_config)
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self.post_init()
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def get_input_embeddings(self):
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return self.language_model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def set_decoder(self, decoder):
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self.language_model = decoder
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def get_decoder(self):
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return self.language_model
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def get_image_features(
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self,
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pixel_values: torch.FloatTensor,
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image_sizes: torch.Tensor,
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vision_feature_layer: Optional[Union[int, list[int]]] = None,
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**kwargs,
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):
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"""
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Obtains image last hidden states from the vision tower and apply multimodal projection.
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Args:
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pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
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The tensors corresponding to the input images.
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vision_feature_layer (`Union[int, list[int]]`, *optional*):
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The index of the layer to select the vision feature. If multiple indices are provided,
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the vision feature of the corresponding indices will be concatenated to form the
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vision features.
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image_sizes (`torch.Tensor`, *optional*):
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Tensor containing the image sizes as returned by the processor.
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Returns:
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image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
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"""
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vision_feature_layer = (
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vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
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)
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kwargs = {k: v for k, v in kwargs.items() if v is not None}
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# this is not memory efficient at all (output_hidden_states=True) will save all the hidden states.
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image_outputs = self.vision_tower(pixel_values, image_sizes=image_sizes, output_hidden_states=True, **kwargs)
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# If we have one vision feature layer, return the corresponding hidden states,
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# otherwise, select the hidden states of each feature layer and concatenate them
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if isinstance(vision_feature_layer, int):
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selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
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else:
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hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
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selected_image_feature = torch.cat(hs_pool, dim=-1)
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image_features = self.multi_modal_projector(selected_image_feature.squeeze(0), image_sizes)
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downsample_ratio = self.vision_tower.patch_size * self.config.spatial_merge_size
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split_sizes = [(height // downsample_ratio) * (width // downsample_ratio) for height, width in image_sizes]
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image_features = torch.split(image_features.squeeze(0), split_sizes)
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return image_features
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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pixel_values: torch.FloatTensor = None,
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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_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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vision_feature_layer: Optional[Union[int, list[int]]] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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image_sizes: torch.Tensor = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Union[tuple, Mistral3ModelOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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vision_feature_layer = (
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vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
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)
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings()(input_ids)
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if pixel_values is not None:
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image_features = self.get_image_features(
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pixel_values=pixel_values,
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vision_feature_layer=vision_feature_layer,
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image_sizes=image_sizes,
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)
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image_features = torch.cat(image_features, dim=0)
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if input_ids is None:
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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)
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special_image_mask = special_image_mask.all(-1)
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else:
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special_image_mask = input_ids == self.config.image_token_id
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n_image_tokens = (special_image_mask).sum()
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special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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n_image_features = image_features.shape[0] * image_features.shape[1]
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raise ValueError(
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f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
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)
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image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
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inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
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outputs = self.language_model(
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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cache_position=cache_position,
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**kwargs,
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)
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return Mistral3ModelOutputWithPast(
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last_hidden_state=outputs.last_hidden_state,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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image_hidden_states=image_features if pixel_values is not None else None,
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)
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@auto_docstring(
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custom_intro="""
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The MISTRAL3 model which consists of a vision backbone and a language model.
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"""
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)
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class Mistral3ForConditionalGeneration(Mistral3PreTrainedModel, GenerationMixin):
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_checkpoint_conversion_mapping = {
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"^language_model.model": "model.language_model",
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"^vision_tower": "model.vision_tower",
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"^multi_modal_projector": "model.multi_modal_projector",
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"^language_model.lm_head": "lm_head",
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}
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(self, config: Mistral3Config):
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super().__init__(config)
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self.model = Mistral3Model(config)
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self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
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self.post_init()
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def get_input_embeddings(self):
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return self.model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.model.set_input_embeddings(value)
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def get_output_embeddings(self) -> nn.Module:
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return self.lm_head
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def set_decoder(self, decoder):
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self.model.set_decoder(decoder)
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def get_decoder(self):
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return self.model.get_decoder
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def get_image_features(
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self,
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pixel_values: torch.FloatTensor,
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image_sizes: torch.Tensor,
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vision_feature_layer: Optional[Union[int, list[int]]] = None,
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**kwargs,
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):
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return self.model.get_image_features(
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pixel_values=pixel_values,
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image_sizes=image_sizes,
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vision_feature_layer=vision_feature_layer,
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**kwargs,
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)
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# Make modules available throught conditional class for BC
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@property
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def language_model(self):
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return self.model.language_model
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@property
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def vision_tower(self):
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return self.model.vision_tower
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@property
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def multi_modal_projector(self):
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return self.model.multi_modal_projector
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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pixel_values: torch.FloatTensor = None,
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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_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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logits_to_keep: Union[int, torch.Tensor] = 0,
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image_sizes: Optional[torch.Tensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> Union[tuple, Mistral3CausalLMOutputWithPast]:
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r"""
|
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
|
|
|
|
>>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
|
|
>>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
|
|
|
|
>>> prompt = "<s>[INST][IMG]What is the image?[/INST]"
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|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"What is the image?The image depicts two cats lying on a pink blanket."
|
|
```"""
|
|
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,
|
|
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,
|
|
return_dict=True,
|
|
cache_position=cache_position,
|
|
image_sizes=image_sizes,
|
|
**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:
|
|
loss = self.loss_function(
|
|
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
|
)
|
|
|
|
return Mistral3CausalLMOutputWithPast(
|
|
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,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
cache_position=None,
|
|
logits_to_keep=None,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
|
|
if cache_position[0] == 0:
|
|
# 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
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = ["Mistral3Model", "Mistral3PreTrainedModel", "Mistral3ForConditionalGeneration"]
|