image-match/python/py/Lib/site-packages/transformers/models/ministral/modular_ministral.py

# Copyright 2025 Mistral AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import torch
from huggingface_hub.dataclasses import strict
from torch import nn

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PreTrainedConfig
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from ..mistral.configuration_mistral import MistralConfig
from ..qwen2.modeling_qwen2 import (
    Qwen2Attention,
    Qwen2DecoderLayer,
    Qwen2ForCausalLM,
    Qwen2ForQuestionAnswering,
    Qwen2ForSequenceClassification,
    Qwen2ForTokenClassification,
    Qwen2MLP,
    Qwen2Model,
    Qwen2PreTrainedModel,
    Qwen2RMSNorm,
    Qwen2RotaryEmbedding,
)


@auto_docstring(checkpoint="mistralai/Ministral-8B-Instruct-2410")
@strict
class MinistralConfig(MistralConfig):
    r"""
    Example:

    ```python
    >>> from transformers import MinistralModel, MinistralConfig

    >>> # Initializing a Ministral 8B style configuration
    >>> configuration = MinistralConfig()

    >>> # Initializing a model from the Ministral 8B style configuration
    >>> model = MinistralModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "ministral"

    layer_types: list[str] | None = None

    def __post_init__(self, **kwargs):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if self.sliding_window is not None else "full_attention"
            ] * self.num_hidden_layers

        PreTrainedConfig.__post_init__(self, **kwargs)


class MinistralMLP(Qwen2MLP):
    pass


class MinistralAttention(Qwen2Attention):
    def __init__(self, config, layer_idx: int):
        super().__init__(config, layer_idx)
        # Match Mistral: q/k/v do not have bias
        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)


class MinistralRMSNorm(Qwen2RMSNorm):
    pass


class MinistralDecoderLayer(Qwen2DecoderLayer):
    pass


class MinistralPreTrainedModel(Qwen2PreTrainedModel):
    pass


class MinistralRotaryEmbedding(Qwen2RotaryEmbedding):
    pass


class MinistralModel(Qwen2Model):
    def __init__(self, config: MinistralConfig):
        super().__init__(config)
        del self.has_sliding_layers

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | 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 position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        # 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,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


class MinistralForCausalLM(Qwen2ForCausalLM):
    pass


class MinistralForSequenceClassification(Qwen2ForSequenceClassification):
    pass


class MinistralForTokenClassification(Qwen2ForTokenClassification):
    pass


class MinistralForQuestionAnswering(Qwen2ForQuestionAnswering):
    pass


__all__ = [
    "MinistralConfig",
    "MinistralPreTrainedModel",
    "MinistralModel",
    "MinistralForCausalLM",
    "MinistralForSequenceClassification",
    "MinistralForTokenClassification",
    "MinistralForQuestionAnswering",
]