| 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, | |
| ) | |