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| # coding=utf-8 | |
| # Copyright 2026 The XHToken team and the HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from transformers import PretrainedConfig | |
| class Spark2_5Config(PretrainedConfig): | |
| model_type = "spark2_5" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| base_model_tp_plan = { | |
| "layers.*.self_attn.q_k_v_proj": "colwise", | |
| "layers.*.self_attn.g_proj": "colwise", | |
| "layers.*.self_attn.out_proj": "rowwise", | |
| "layers.*.mlp.gate_proj": "colwise", | |
| "layers.*.mlp.up_proj": "colwise", | |
| "layers.*.mlp.down_proj": "rowwise", | |
| } | |
| base_model_pp_plan = { | |
| "embedding": (["input_ids"], ["inputs_embeds"]), | |
| "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), | |
| "norm": (["hidden_states"], ["hidden_states"]), | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=32000, | |
| hidden_size=4096, | |
| intermediate_size=11008, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=None, | |
| hidden_act="gelu", | |
| max_position_embeddings=2048, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| pad_token_id=None, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| tie_word_embeddings=False, | |
| rope_parameters=None, | |
| attention_bias=False, | |
| attention_dropout=0.0, | |
| mlp_bias=False, | |
| head_dim=None, | |
| headwise_attn_output_gate=False, | |
| gate_attn_act_mode="sigmoid", | |
| sliding_window=None, | |
| layer_types=None, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_heads | |
| if num_attention_heads % num_key_value_heads != 0: | |
| raise ValueError( | |
| f"num_attention_heads ({num_attention_heads}) must be divisible by num_key_value_heads ({num_key_value_heads})" | |
| ) | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.attention_bias = attention_bias | |
| self.attention_dropout = attention_dropout | |
| self.mlp_bias = mlp_bias | |
| self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads | |
| self.headwise_attn_output_gate = headwise_attn_output_gate | |
| self.gate_attn_act_mode = gate_attn_act_mode | |
| self.sliding_window = sliding_window | |
| self.rope_parameters = rope_parameters | |
| if layer_types is None: | |
| layer_types = ["full_attention"] * num_hidden_layers | |
| if len(layer_types) != num_hidden_layers: | |
| raise ValueError( | |
| f"layer_types length ({len(layer_types)}) must match num_hidden_layers ({num_hidden_layers})" | |
| ) | |
| self.layer_types = layer_types | |
| 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, | |
| ) | |
| def get_rope_theta(self, layer_type): | |
| params = self.rope_parameters.get(layer_type, {}) | |
| return params.get("rope_theta", 10000) | |
| def get_partial_rotary_factor(self, layer_type): | |
| params = self.rope_parameters.get(layer_type, {}) | |
| return params.get("partial_rotary_factor", 1.0) | |
| __all__ = ["Spark2_5Config"] | |
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