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