wehub-resource-sync
121 lines
4.9 KiB
Python
121 lines
4.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers.configuration_utils import PretrainedConfig
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class LagunaConfig(PretrainedConfig):
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model_type = "laguna"
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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.g_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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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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def __init__(
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self,
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vocab_size: int = 100352,
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hidden_size: int = 2048,
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intermediate_size: int = 8192,
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num_hidden_layers: int = 40,
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num_attention_heads: int = 48,
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num_key_value_heads: int = 8,
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head_dim: int = 128,
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qkv_bias: bool = False,
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attention_bias: bool = False,
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gating: bool | str = True,
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hidden_act: str = "silu",
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max_position_embeddings: int = 131072,
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initializer_range: float = 0.02,
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rms_norm_eps: float = 1e-6,
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use_cache: bool = True,
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tie_word_embeddings: bool = False,
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rope_theta: float = 500000.0,
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rope_scaling: dict | None = None,
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rope_parameters: dict | None = None,
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partial_rotary_factor: float = 1.0,
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attention_dropout: float = 0.0,
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sliding_window: int | None = None,
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layer_types: list[str] | None = None,
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swa_attention_sink_enabled: bool = False,
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swa_rope_parameters: dict | None = None,
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num_attention_heads_per_layer: list[int] | None = None,
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num_experts: int = 256,
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num_experts_per_tok: int = 8,
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moe_intermediate_size: int = 512,
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shared_expert_intermediate_size: int = 512,
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norm_topk_prob: bool = True,
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decoder_sparse_step: int = 1,
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mlp_only_layers: list[int] | None = None,
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router_aux_loss_coef: float = 0.001,
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output_router_logits: bool = False,
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moe_routed_scaling_factor: float = 1.0,
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moe_apply_router_weight_on_input: bool = False,
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**kwargs,
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):
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if mlp_only_layers is None:
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mlp_only_layers = [0]
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# Accept either v4-style (rope_theta + rope_scaling) or v5-style
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# (rope_parameters). Translate v5 → v4 so downstream code has one path.
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if rope_parameters is not None:
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rp = dict(rope_parameters)
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rope_theta = float(rp.pop("rope_theta", rope_theta))
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rt = rp.pop("rope_type", None)
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if rt is not None and rt != "default":
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rope_scaling = {"rope_type": rt, **rp}
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elif rp and rope_scaling is None:
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rope_scaling = {"rope_type": "default", **rp}
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self.vocab_size = vocab_size
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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.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.qkv_bias = qkv_bias
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self.attention_bias = attention_bias
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self.gating = gating
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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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.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.attention_dropout = attention_dropout
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self.sliding_window = sliding_window
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self.layer_types = layer_types
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self.swa_attention_sink_enabled = swa_attention_sink_enabled
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self.swa_rope_parameters = swa_rope_parameters
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self.num_attention_heads_per_layer = num_attention_heads_per_layer
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self.num_experts = num_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.moe_intermediate_size = moe_intermediate_size
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self.shared_expert_intermediate_size = shared_expert_intermediate_size
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self.norm_topk_prob = norm_topk_prob
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self.decoder_sparse_step = decoder_sparse_step
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self.mlp_only_layers = mlp_only_layers
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self.router_aux_loss_coef = router_aux_loss_coef
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self.output_router_logits = output_router_logits
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self.moe_routed_scaling_factor = moe_routed_scaling_factor
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self.moe_apply_router_weight_on_input = moe_apply_router_weight_on_input
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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__all__ = ["LagunaConfig"]
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