189 lines
6.4 KiB
Python
189 lines
6.4 KiB
Python
# coding=utf-8
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# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""BailingHybrid model configuration"""
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import enum
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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from sglang.srt.configs.mamba_utils import Mamba2CacheParams, Mamba2StateShape
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logger = logging.get_logger(__name__)
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class HybridLayerType(enum.Enum):
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full_attention = "attention"
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linear_attention = "linear_attention"
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class BailingHybridConfig(PretrainedConfig):
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model_type = "bailing_hybrid"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=157184,
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hidden_size=2048,
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intermediate_size=5120,
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num_hidden_layers=20,
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num_attention_heads=16,
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num_key_value_heads=4,
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hidden_act="silu",
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use_qkv_bias=False, # bailing only
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use_bias=False, # bailing only
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rms_norm_eps=1e-06,
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tie_word_embeddings=False, # PretrainedConfig key, here change default value.
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embedding_dropout=0.0,
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attention_dropout=0.0,
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output_dropout=0.0,
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initializer_range=0.02,
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max_position_embeddings=32768,
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rope_theta=600000.0,
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use_cache=True,
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max_window_layers=20,
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rope_scaling=None,
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pad_token_id=156892,
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eos_token_id=156892,
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num_experts=256,
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num_shared_experts=1,
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num_experts_per_tok=8,
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n_group=8,
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topk_group=4,
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moe_intermediate_size=512,
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first_k_dense_replace=1,
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head_dim=128,
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output_router_logits=False,
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use_qk_norm=True,
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num_nextn_predict_layers=0,
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mtp_loss_scaling_factor=0,
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moe_router_enable_expert_bias=True,
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routed_scaling_factor=1.0,
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layer_group_size=1,
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group_norm_size=1,
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linear_silu=False,
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kv_lora_rank=512,
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q_lora_rank=None,
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qk_rope_head_dim=64,
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v_head_dim=128,
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qk_nope_head_dim=128,
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rope_interleave=True,
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**kwargs,
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):
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self.num_hidden_layers = num_hidden_layers
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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_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.use_qkv_bias = use_qkv_bias
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self.use_bias = use_bias
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self.rms_norm_eps = rms_norm_eps
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self.embedding_dropout = embedding_dropout
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self.attention_dropout = attention_dropout
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self.output_dropout = output_dropout
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self.num_nextn_predict_layers = num_nextn_predict_layers
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self.mtp_loss_scaling_factor = mtp_loss_scaling_factor
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self.initializer_range = initializer_range
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.use_cache = use_cache
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self.max_window_layers = max_window_layers
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self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
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self.rope_scaling = rope_scaling
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self.use_qk_norm = use_qk_norm
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self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
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self.routed_scaling_factor = routed_scaling_factor
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# MoE configs
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self.num_experts = num_experts
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self.num_shared_experts = num_shared_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.n_group = n_group
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self.topk_group = topk_group
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self.moe_intermediate_size = moe_intermediate_size
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self.first_k_dense_replace = first_k_dense_replace
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self.output_router_logits = output_router_logits
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# Linear configs
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self.layer_group_size = layer_group_size
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self.group_norm_size = group_norm_size
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self.linear_silu = linear_silu
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self.num_linear_key_value_heads = num_attention_heads
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# mla
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self.kv_lora_rank = kv_lora_rank
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self.q_lora_rank = q_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.rope_interleave = rope_interleave
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self.for_nextn_model = False
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super().__init__(
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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@property
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def layers_block_type(self):
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if self.for_nextn_model:
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return [HybridLayerType.full_attention.value]
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layer_type_list = []
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for l in range(self.num_hidden_layers):
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if (l + 1) % self.layer_group_size == 0:
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layer_type_list.append(HybridLayerType.full_attention.value)
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else:
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layer_type_list.append(HybridLayerType.linear_attention.value)
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return layer_type_list
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@property
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def linear_layer_ids(self):
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return [
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i
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for i, type_value in enumerate(self.layers_block_type)
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if type_value == HybridLayerType.linear_attention.value
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]
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@property
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def full_attention_layer_ids(self):
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return [
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i
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for i, type_value in enumerate(self.layers_block_type)
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if type_value == HybridLayerType.full_attention.value
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]
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@property
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def mamba2_cache_params(self) -> Mamba2CacheParams:
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from sglang.srt.layers.dp_attention import get_attention_tp_size
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shape = Mamba2StateShape.create(
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tp_world_size=get_attention_tp_size(),
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intermediate_size=0,
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n_groups=0,
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num_heads=self.num_linear_key_value_heads,
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head_dim=self.head_dim,
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state_size=self.head_dim,
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conv_kernel=1,
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)
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return Mamba2CacheParams(shape=shape, layers=self.linear_layer_ids)
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