[fix][wip] GlmMoeDsa: try implement DSA

src/transformers/models/glm_moe_dsa/configuration_glm_moe_dsa.py

@@ -35,90 +35,91 @@ class GlmMoeDsaConfig(PreTrainedConfig):
 
     Args:
         vocab_size (`int`, *optional*, defaults to 154880):
-            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Glm4MoeLiteModel`]
+            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GlmMoeDsaModel`].
         hidden_size (`int`, *optional*, defaults to 6144):
             Dimension of the hidden representations.
         intermediate_size (`int`, *optional*, defaults to 12288):
-            Dimension of the MLP representations.
+            Dimension of the dense MLP representations.
         moe_intermediate_size (`int`, *optional*, defaults to 2048):
-            Dimension of the MoE representations.
+            Dimension of the MoE expert representations.
         num_hidden_layers (`int`, *optional*, defaults to 78):
             Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 64):
             Number of attention heads for each attention layer in the Transformer decoder.
         num_key_value_heads (`int`, *optional*, defaults to 64):
-            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
-            `num_attention_heads`.
+            Number of key-value heads for Grouped Query Attention. If equal to `num_attention_heads`, uses MHA.
         n_shared_experts (`int`, *optional*, defaults to 1):
-            Number of shared experts.
+            Number of shared experts in MoE layers.
         n_routed_experts (`int`, *optional*, defaults to 256):
-            Number of routed experts.
+            Number of routed experts in MoE layers.
         routed_scaling_factor (`float`, *optional*, defaults to 2.5):
-            Scaling factor or routed experts.
+            Scaling factor for routed experts.
         kv_lora_rank (`int`, *optional*, defaults to 512):
-            Rank of the LoRA matrices for key and value projections.
+            Rank of the LoRA matrices for key and value projections (MLA).
         q_lora_rank (`int`, *optional*, defaults to 2048):
-            Rank of the LoRA matrices for query projections.
+            Rank of the LoRA matrices for query projections (MLA).
         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 256):
-            Dimension of the value heads.
         qk_nope_head_dim (`int`, *optional*, defaults to 192):
             Dimension of the query/key heads that don't use rotary position embeddings.
+        v_head_dim (`int`, *optional*, defaults to 256):
+            Dimension of the value heads.
         n_group (`int`, *optional*, defaults to 1):
             Number of groups for routed experts.
         topk_group (`int`, *optional*, defaults to 1):
-            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+            Number of selected groups for each token.
         num_experts_per_tok (`int`, *optional*, defaults to 8):
-            Number of selected experts, None means dense model.
+            Number of experts selected per token.
         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.
+            The non-linear activation function in the decoder.
         max_position_embeddings (`int`, *optional*, defaults to 202752):
             The maximum sequence length that this model might ever be used with.
         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`.
+            Whether or not the model should return the last key/values attentions.
         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
+            Whether to tie weight embeddings.
         rope_parameters (`RopeParameters`, *optional*):
-            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
-            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
-            with longer `max_position_embeddings`.
+            Configuration parameters for the RoPE embeddings, including `rope_theta` and optional scaling parameters.
         rope_interleave (`bool`, *optional*, defaults to `True`):
             Whether to interleave the rotary position embeddings.
         mlp_layer_types (`list`, *optional*):
-            MLP (Moe vs Dense) pattern for each layer.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            MLP type pattern for each layer (`"dense"` or `"sparse"`). Defaults to 3 dense + rest sparse.
+        attention_bias (`bool`, *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.
         index_topk (`int`, *optional*, defaults to 2048):
-            Number of top tokens selected by the indexer for retrieval/attention in each step.
+            Number of top tokens selected by the indexer for sparse attention.
+        index_head_dim (`int`, *optional*, defaults to 128):
+            Head dimension for the indexer projections (DSA).
+        index_n_heads (`int | None`, *optional*, defaults to 32):
+            Number of heads for the indexer projections (DSA).
+        indexer_rope_interleave (`bool`, *optional*, defaults to `True`):
+            Whether the indexer uses interleaved rotary position embeddings.
+
 
     ```python
-    >>> from transformers import Glm4MoeLiteModel, Glm4MoeLiteConfig
+    >>> from transformers import GlmMoeDsaConfig, GlmMoeDsaModel
 
-    >>> # Initializing a GLM-MOE-DSA style configuration
+    >>> # Initializing a GLM-MoE-DSA configuration
     >>> configuration = GlmMoeDsaConfig()
 
+    >>> # Initializing a model from the configuration
+    >>> model = GlmMoeDsaModel(configuration)
+
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
@@ -158,73 +159,85 @@ def __init__(
         kv_lora_rank: int | None = 512,
         q_lora_rank: int | None = 2048,
         qk_rope_head_dim: int | None = 64,
-        v_head_dim: int | None = 256,
         qk_nope_head_dim: int | None = 192,
+        v_head_dim: int | None = 256,
         n_group: int | None = 1,
         topk_group: int | None = 1,
         num_experts_per_tok: int | None = 8,
         norm_topk_prob: bool | None = True,
         hidden_act: str | None = "silu",
         max_position_embeddings: int | None = 202752,
         initializer_range: float | None = 0.02,
-        rms_norm_eps: int | None = 1e-5,
+        rms_norm_eps: float | None = 1e-5,
         use_cache: bool | None = True,
         pad_token_id: int | None = None,
         bos_token_id: int | None = 0,
         eos_token_id: int | None = 1,
         tie_word_embeddings: bool | None = False,
         rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
-        rope_interleave: bool | None = True,
         mlp_layer_types=None,
         attention_bias: bool | None = False,
         attention_dropout: float | None = 0.0,
         index_topk: int | None = 2048,
+        index_head_dim: int | None = 128,
+        index_n_heads: int | None = 32,
         **kwargs,
     ):
+        # Model dimensions
         self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
         self.hidden_size = hidden_size
         self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
         self.num_hidden_layers = num_hidden_layers
+        self.max_position_embeddings = max_position_embeddings
 
-        # Default to MoE from the fourth layer and on
-        if mlp_layer_types is None:
-            mlp_layer_types = ["dense"] * min(3, self.num_hidden_layers) + ["sparse"] * (self.num_hidden_layers - 3)
-        layer_type_validation(mlp_layer_types, self.num_hidden_layers, attention=False)
-        self.mlp_layer_types = mlp_layer_types
-
-        self.moe_intermediate_size = moe_intermediate_size
+        # Attention dimensions (MLA)
         self.num_attention_heads = num_attention_heads
-        self.n_shared_experts = n_shared_experts
-        self.n_routed_experts = n_routed_experts
-        self.routed_scaling_factor = routed_scaling_factor
+        self.num_key_value_heads = num_key_value_heads
         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.v_head_dim = v_head_dim
         self.head_dim = qk_rope_head_dim
+
+        # MoE parameters
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.routed_scaling_factor = routed_scaling_factor
         self.n_group = n_group
         self.topk_group = topk_group
         self.num_experts_per_tok = num_experts_per_tok
         self.norm_topk_prob = norm_topk_prob
-        self.rope_interleave = rope_interleave
-        self.num_key_value_heads = num_key_value_heads
+
+        # MLP layer types: first 3 dense, rest sparse
+        self.mlp_layer_types = mlp_layer_types
+        if self.mlp_layer_types is None:
+            self.mlp_layer_types = ["dense"] * min(3, num_hidden_layers) + ["sparse"] * (num_hidden_layers - 3)
+        layer_type_validation(self.mlp_layer_types, self.num_hidden_layers, attention=False)
+
+        # Indexer (DSA) parameters
+        self.index_topk = index_topk
+        self.index_head_dim = index_head_dim
+        self.index_n_heads = index_n_heads
+
+        # General config
         self.hidden_act = hidden_act
         self.initializer_range = initializer_range
-        self.index_topk = index_topk
         self.rms_norm_eps = rms_norm_eps
         self.use_cache = use_cache
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout
         self.rope_parameters = rope_parameters
-        self.pad_token_id = pad_token_id
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.tie_word_embeddings = tie_word_embeddings
 
-        super().__init__(**kwargs)
+        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__ = ["GlmMoeDsaConfig"]