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configuration_longcat_flash.py

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+
+"""LongcatFlash model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+LONGCAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class LongcatFlashConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LongcatFlashModel`]. It is used to instantiate an LongcatFlash
+    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 LongcatFlash.
+    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 131072):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LongcatFlashModel`]
+        hidden_size (`int`, *optional*, defaults to 7168):
+            Dimension of the hidden representations.
+        ffn_hidden_size (`int`, *optional*, defaults to 18432):
+            Dimension of the MLP representations.
+        expert_ffn_hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the MoE representations.
+        num_layers (`int`, *optional*, defaults to 61):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 128):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 128):
+            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 checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        n_routed_experts (`int`, *optional*, defaults to 256):
+            Number of routed experts.
+        routed_scaling_factor (`float`, *optional*, defaults to 2.5):
+            Scaling factor or routed experts.
+        kv_lora_rank (`int`, *optional*, defaults to 512):
+            Rank of the LoRA matrices for key and value projections.
+        q_lora_rank (`int`, *optional*, defaults to 1536):
+            Rank of the LoRA matrices for query projections.
+        qk_rope_head_dim (`int`, *optional*, defaults to 64):
+            Dimension of the query/key heads that use rotary position embeddings.
+        v_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the value heads.
+        qk_nope_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of the query/key heads that don't use rotary position embeddings.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the weights of the routed experts.
+        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):
+            The maximum sequence length that this model might ever be used with.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            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*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 1):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        attention_method (`str`, *optional*, defaults to `"MLA"`):
+            The attention method to use.
+        initializer_range (`float`, *optional*, defaults to 0.006):
+            The initializer range for the model.
+        router_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the router.
+        zero_expert_num (`int`, *optional*, defaults to `None`):
+            The number of zero experts to use.
+        zero_expert_type (`str`, *optional*, defaults to `None`):
+            The type of zero expert to use.
+
+    ```python
+    >>> from transformers import LongcatFlashModel, LongcatFlashConfig
+
+    >>> # Initializing a LongcatFlash style configuration
+    >>> configuration = LongcatFlashConfig()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "longcat_flash"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.*.gate_proj": "local_colwise",
+        "layers.*.mlp.experts.*.up_proj": "local_colwise",
+        "layers.*.mlp.experts.*.down_proj": "local_rowwise",
+        "layers.*.mlps.*.gate_proj": "local_colwise",
+        "layers.*.mlps.*.up_proj": "local_colwise",
+        "layers.*.mlps.*.down_proj": "local_rowwise",
+    }
+    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=131072,
+        hidden_size=7168,
+        ffn_hidden_size=18432,
+        expert_ffn_hidden_size=2048,
+        num_layers=61,
+        num_attention_heads=128,
+        num_key_value_heads=None,
+        n_routed_experts=256,
+        routed_scaling_factor=1,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        mla_scale_q_lora=True,
+        mla_scale_kv_lora=True,
+        moe_topk=8,
+        norm_topk_prob=False,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        attention_method='MLA',
+        initializer_range=0.006,
+        router_bias=False,
+        zero_expert_num=None,
+        zero_expert_type=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.expert_ffn_hidden_size = expert_ffn_hidden_size
+        self.num_layers = num_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_routed_experts = n_routed_experts
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.moe_topk = moe_topk
+        self.norm_topk_prob = norm_topk_prob
+        self.mla_scale_q_lora = mla_scale_q_lora
+        self.mla_scale_kv_lora = mla_scale_kv_lora
+        self.attention_method = attention_method
+        self.initializer_range = initializer_range
+        self.router_bias = router_bias
+        self.zero_expert_num = zero_expert_num
+        self.zero_expert_type = zero_expert_type
+
+        if self.attention_method == "MLA":
+            self.head_dim = qk_rope_head_dim
+        else:
+            ValueError('attention_method should be one of ["MLA"]')
+
+
+        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.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        rope_config_validation(self)
+
+        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,
+        )
+
+    @property
+    def num_hidden_layers(self):
+        return self.num_layers
+
+
+__all__ = ["LongcatFlashConfig"]