from transformers.configuration_utils import PretrainedConfig class SarvamMoEConfig(PretrainedConfig): model_type = "sarvam_moe" def __init__( self, vocab_size=262144, hidden_size=4096, intermediate_size=8192, num_hidden_layers=19, num_attention_heads=16, num_key_value_heads=4, hidden_act="silu", use_qkv_bias=False, use_bias=False, rms_norm_eps=1e-06, tie_word_embeddings=False, embedding_dropout=0.0, attention_dropout=0.0, output_dropout=0.0, initializer_range=0.006, max_position_embeddings=4096, rope_theta=10000.0, use_cache=True, max_window_layers=19, rope_scaling=None, pad_token_id=0, eos_token_id=1, num_experts=128, num_shared_experts=1, num_experts_per_tok=6, n_group=1, topk_group=1, moe_intermediate_size=1024, first_k_dense_replace=1, head_dim=256, output_router_logits=False, use_qk_norm=True, moe_router_enable_expert_bias=True, routed_scaling_factor=2.5, attn_implementation: str = "eager", **kwargs, ): self.num_hidden_layers = num_hidden_layers self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.use_qkv_bias = use_qkv_bias self.use_bias = use_bias self.rms_norm_eps = rms_norm_eps self.embedding_dropout = embedding_dropout self.attention_dropout = attention_dropout self.output_dropout = output_dropout self.initializer_range = initializer_range self.max_position_embeddings = max_position_embeddings self.rope_theta = rope_theta self.use_cache = use_cache self.max_window_layers = max_window_layers self.head_dim = head_dim or hidden_size // num_attention_heads self.rope_scaling = rope_scaling self.use_qk_norm = use_qk_norm self.moe_router_enable_expert_bias = moe_router_enable_expert_bias self.routed_scaling_factor = routed_scaling_factor self.num_experts = num_experts self.num_shared_experts = num_shared_experts self.num_experts_per_tok = num_experts_per_tok self.n_group = n_group self.topk_group = topk_group self.moe_intermediate_size = moe_intermediate_size self.first_k_dense_replace = first_k_dense_replace self.output_router_logits = output_router_logits self.attn_implementation = attn_implementation self._attn_implementation = attn_implementation self.base_model_tp_plan = { "layers.*.attention.query_key_value": "colwise", "layers.*.attention.dense": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", "layers.*.mlp.experts.*.gate_proj": "colwise", "layers.*.mlp.experts.*.up_proj": "colwise", "layers.*.mlp.experts.*.down_proj": "rowwise", "layers.*.mlp.shared_experts.gate_proj": "colwise", "layers.*.mlp.shared_experts.up_proj": "colwise", "layers.*.mlp.shared_experts.down_proj": "rowwise", } self.base_model_pp_plan = { "word_embeddings": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, )