Delete configuration_upcycling_qwen2_moe.py

configuration_upcycling_qwen2_moe.py

@@ -1,180 +0,0 @@
-# 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
-
-        
-