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configuration_minimax_m2.py

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-#           This file was automatically generated from src/transformers/models/minimax_m2/modular_minimax_m2.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_minimax_m2.py file directly. One of our CI enforces this.
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-# coding=utf-8
-# Copyright 2025 the HuggingFace 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.
-
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class MiniMaxM2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MiniMaxM2Model`]. It is used to instantiate an
-    MiniMaxM2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the MiniMaxM2-7B-v0.1 or MiniMaxM2-7B-Instruct-v0.1.
-
-    [minimax_m2ai/MiniMaxM2-8x7B](https://huggingface.co/minimax_m2ai/MiniMaxM2-8x7B)
-    [minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1](https://huggingface.co/minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1)
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the MiniMaxM2 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MiniMaxM2Model`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 14336):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 8):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
-        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
-            The maximum sequence length that this model might ever be used with. MiniMaxM2's sliding window attention
-            allows sequence of up to 4096*32 tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*):
-            The id of the padding token.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            The id of the "end-of-sequence" token.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 1000000.0):
-            The base period of the RoPE embeddings.
-        sliding_window (`int`, *optional*):
-            Sliding window attention window size. If not specified, will default to `4096`.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        num_experts_per_tok (`int`, *optional*, defaults to 2):
-            The number of experts to route per-token, can be also interpreted as the `top-k` routing
-            parameter
-        num_local_experts (`int`, *optional*, defaults to 8):
-            Number of experts per Sparse MLP layer.
-        output_router_logits (`bool`, *optional*, defaults to `False`):
-            Whether or not the router logits should be returned by the model. Enabling this will also
-            allow the model to output the auxiliary loss. See [here]() for more details
-        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
-            The aux loss factor for the total loss.
-        router_jitter_noise (`float`, *optional*, defaults to 0.0):
-            Amount of noise to add to the router.
-
-    ```python
-    >>> from transformers import MiniMaxM2Model, MiniMaxM2Config
-
-    >>> # Initializing a MiniMaxM2 7B style configuration
-    >>> configuration = MiniMaxM2Config()
-
-    >>> # Initializing a model from the MiniMaxM2 7B style configuration
-    >>> model = MiniMaxM2Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "minimax_m2"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "layers.*.block_sparse_moe.gate": "colwise_rep",  # we need to replicate here to correctly route experts
-        "layers.*.block_sparse_moe.experts.*.w1": "colwise",
-        "layers.*.block_sparse_moe.experts.*.w2": "rowwise",
-        "layers.*.block_sparse_moe.experts.*.w3": "colwise",
-    }
-    base_model_pp_plan = {
-        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
-        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
-        "norm": (["hidden_states"], ["hidden_states"]),
-    }
-
-    def __init__(
-        self,
-        vocab_size=32000,
-        hidden_size=4096,
-        intermediate_size=14336,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=8,
-        head_dim=None,
-        hidden_act="silu",
-        max_position_embeddings=4096 * 32,
-        initializer_range=0.02,
-        rms_norm_eps=1e-5,
-        use_cache=True,
-        pad_token_id=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        rope_theta=1e6,
-        sliding_window=None,
-        attention_dropout=0.0,
-        num_experts_per_tok=2,
-        num_local_experts=8,
-        output_router_logits=False,
-        router_aux_loss_coef=0.001,
-        router_jitter_noise=0.0,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.sliding_window = sliding_window
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_dropout = attention_dropout
-        self.head_dim = head_dim
-
-        self.num_experts_per_tok = num_experts_per_tok
-        self.num_local_experts = num_local_experts
-        self.output_router_logits = output_router_logits
-        self.router_aux_loss_coef = router_aux_loss_coef
-        self.router_jitter_noise = router_jitter_noise
-
-        self.use_qk_norm = kwargs.pop("use_qk_norm", False)
-        self.rotary_dim = kwargs.pop("rotary_dim", self.head_dim)
-        self.partial_rotary_factor = kwargs.pop("partial_rotary_factor", 1)
-        if self.head_dim is not None:
-            self.partial_rotary_factor = self.rotary_dim / self.head_dim
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["MiniMaxM2Config"]