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#           This file was automatically generated from src/transformers/models/minicpm3/modular_minicpm3.py.
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# Copyright 2026 The OpenBMB Team 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 math

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="openbmb/MiniCPM3-4B")
@strict
class MiniCPM3Config(PreTrainedConfig):
    r"""
    kv_lora_rank (`int`, *optional*, defaults to 256):
        Rank of the low-rank KV projection in multi-head latent attention.
    q_lora_rank (`int`, *optional*, defaults to 768):
        Rank of the low-rank query projection in multi-head latent attention. If `None`, the query projection
        is a single dense projection rather than a low-rank one.
    qk_nope_head_dim (`int`, *optional*, defaults to 64):
        Dimension of the non-RoPE part of each query/key head.
    qk_rope_head_dim (`int`, *optional*, defaults to 32):
        Dimension of the RoPE part of each query/key head.
    v_head_dim (`int`, *optional*):
        Dimension of each value head. If `None`, defaults to `hidden_size // num_attention_heads`.
    scale_emb (`int` or `float`, *optional*, defaults to 12):
        Multiplier applied to input embeddings.
    scale_depth (`int` or `float`, *optional*, defaults to 1.4):
        Multiplier for residual connections; the effective scaling is `scale_depth / sqrt(num_hidden_layers)`.
        If `None`, defaults to `sqrt(num_hidden_layers)` (no-op scaling).
    dim_model_base (`int`, *optional*, defaults to 256):
        Base model dimension used to scale logits before the language model head. If `None`,
        defaults to `hidden_size` (no-op scaling).

    Example:

    ```python
    >>> from transformers import MiniCPM3Model, MiniCPM3Config
    >>> # Initializing a MiniCPM3 style configuration
    >>> configuration = MiniCPM3Config()
    >>> # Initializing a model from the configuration
    >>> model = MiniCPM3Model(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "minicpm3"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.q_b_proj": "colwise",
        "layers.*.self_attn.kv_a_proj_with_mqa": "mla_kv_a_proj",
        "layers.*.self_attn.kv_b_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    # Only fields whose defaults differ from `LlamaConfig` are redeclared here; the rest are inherited.
    # Defaults match the `openbmb/MiniCPM3-4B` checkpoint.
    vocab_size: int = 73448
    hidden_size: int = 2560
    intermediate_size: int = 6400
    num_hidden_layers: int = 62
    num_attention_heads: int = 40
    num_key_value_heads: int | None = 40
    hidden_act: str = "silu"
    max_position_embeddings: int = 32768
    initializer_range: float = 0.1
    rms_norm_eps: float = 1e-5
    use_cache: bool = True
    pad_token_id: int | None = None
    bos_token_id: int | None = 1
    eos_token_id: int | list[int] | None = 2
    pretraining_tp: int | None = 1
    tie_word_embeddings: bool = True
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: int | float | None = 0.0
    mlp_bias: bool = False
    head_dim: int | None = None
    kv_lora_rank: int = 256
    q_lora_rank: int | None = 768
    qk_nope_head_dim: int = 64
    qk_rope_head_dim: int = 32
    v_head_dim: int | None = None
    scale_emb: int | float = 12
    scale_depth: int | float | None = 1.4
    dim_model_base: int | None = 256

    def __post_init__(self, **kwargs):
        # In MLA the per-head dim used by RoPE is the rotary part, not `hidden_size / num_attention_heads`.
        self.head_dim = self.qk_rope_head_dim
        # When explicitly set to `None`, these collapse to no-op scalings (e.g. for a randomly
        # initialised tiny model); the class defaults otherwise match `openbmb/MiniCPM3-4B`.
        if self.v_head_dim is None:
            self.v_head_dim = self.hidden_size // self.num_attention_heads
        if self.scale_depth is None:
            self.scale_depth = math.sqrt(self.num_hidden_layers)
        if self.dim_model_base is None:
            self.dim_model_base = self.hidden_size
        if self.head_dim is None:
            self.head_dim = self.hidden_size // self.num_attention_heads
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        super().__post_init__(**kwargs)

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})."
            )

    @property
    def logits_scaling(self) -> float:
        # Hidden states are divided by this factor before the LM head (`1` when `dim_model_base == hidden_size`).
        return self.hidden_size / self.dim_model_base


__all__ = ["MiniCPM3Config"]
