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| # coding=utf-8 | |
| # Copyright 2024 The Qwen team, Alibaba Group 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. | |
| """Qwen2MoE model configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| import torch | |
| logger = logging.get_logger(__name__) | |
| class Qwen2Config(PretrainedConfig): | |
| def __init__( | |
| self, | |
| vocab_size=151936, | |
| hidden_size=4096, | |
| intermediate_size=22016, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=32, | |
| hidden_act="silu", | |
| max_position_embeddings=32768, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| tie_word_embeddings=False, | |
| rope_theta=10000.0, | |
| use_sliding_window=False, | |
| sliding_window=4096, | |
| max_window_layers=28, | |
| attention_dropout=0.0, | |
| **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 | |
| self.use_sliding_window = use_sliding_window | |
| self.sliding_window = sliding_window | |
| self.max_window_layers = max_window_layers | |
| # for backward compatibility | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_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.rope_theta = rope_theta | |
| self.attention_dropout = attention_dropout | |
| super().__init__( | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| class Qwen2MoeConfig(PretrainedConfig): | |
| model_type = "qwen2_moe" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| vocab_size=151936, | |
| hidden_size=2048, | |
| intermediate_size=5632, | |
| num_hidden_layers=24, | |
| num_attention_heads=16, | |
| num_key_value_heads=16, | |
| hidden_act="silu", | |
| max_position_embeddings=32768, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| tie_word_embeddings=False, | |
| rope_theta=10000.0, | |
| use_sliding_window=False, | |
| sliding_window=4096, | |
| max_window_layers=28, | |
| attention_dropout=0.0, | |
| decoder_sparse_step=1, | |
| moe_intermediate_size=1408, | |
| shared_expert_intermediate_size=5632, | |
| num_experts_per_tok=4, | |
| num_experts=60, | |
| norm_topk_prob=False, | |
| output_router_logits=False, | |
| router_aux_loss_coef=0.001, | |
| mlp_only_layers=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 | |
| self.use_sliding_window = use_sliding_window | |
| self.sliding_window = sliding_window | |
| self.max_window_layers = max_window_layers | |
| 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.rope_theta = rope_theta | |
| self.attention_dropout = attention_dropout | |
| # MoE arguments | |
| self.decoder_sparse_step = decoder_sparse_step | |
| self.moe_intermediate_size = moe_intermediate_size | |
| self.shared_expert_intermediate_size = shared_expert_intermediate_size | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.num_experts = num_experts | |
| self.norm_topk_prob = norm_topk_prob | |
| self.output_router_logits = output_router_logits | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers | |
| super().__init__( | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| class UpcyclingQwen2MoeConfig(Qwen2Config): | |
| model_type="upcycling-qwen2-moe" | |
| #upcycling form Qwen2-1_5B | |
| def __init__( | |
| self, | |
| decoder_sparse_step=1, | |
| num_experts_per_tok=2, | |
| num_experts=7, | |
| norm_topk_prob=False, | |
| output_router_logits=False, | |
| router_aux_loss_coef=0.000, | |
| mlp_only_layers=None,#MoE only last 2 layers | |
| share_flag=False, | |
| attn_init_change=False, | |
| language_gate=True, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| # MoE arguments | |
| self.decoder_sparse_step = decoder_sparse_step | |
| self.moe_intermediate_size = self.intermediate_size | |
| self.shared_expert_intermediate_size = self.intermediate_size | |
| self.norm_topk_prob = norm_topk_prob | |
| self.output_router_logits = output_router_logits | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| # self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers | |
| self.mlp_only_layers=torch.arange(self.num_hidden_layers).tolist()[:-2] | |
| self.share_flag=share_flag | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.num_experts = num_experts | |
| self.attn_init_change=attn_init_change | |
| self.language_gate=language_gate | |