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177 lines
No EOL
8.7 KiB
Python
177 lines
No EOL
8.7 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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"""Qwen2MoE model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class Qwen2MoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen2MoeModel`]. It is used to instantiate a
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Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of
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Qwen1.5-MoE-A2.7B" [Qwen/Qwen1.5-MoE-A2.7B"](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B").
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 151936):
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Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Qwen2MoeModel`]
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 5632):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 16):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to use sliding window attention.
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sliding_window (`int`, *optional*, defaults to 4096):
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Sliding window attention (SWA) window size. If not specified, will default to `4096`.
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max_window_layers (`int`, *optional*, defaults to 28):
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The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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decoder_sparse_step (`int`, *optional*, defaults to 1):
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The frequency of the MoE layer.
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moe_intermediate_size (`int`, *optional*, defaults to 1408):
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Intermediate size of the routed expert.
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shared_expert_intermediate_size (`int`, *optional*, defaults to 5632):
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Intermediate size of the shared expert.
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num_experts_per_tok (`int`, *optional*, defaults to 4):
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Number of selected experts.
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num_experts (`int`, *optional*, defaults to 60):
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Number of routed experts.
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norm_topk_prob (`bool`, *optional*, defaults to `False`):
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Whether to normalize the topk probabilities.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabeling this will also
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allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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mlp_only_layers (`List[int]`, *optional*, defaults to `[]`):
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Indicate which layers use Qwen2MoeMLP rather than Qwen2MoeSparseMoeBlock
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The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
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If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
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```python
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>>> from transformers import Qwen2MoeModel, Qwen2MoeConfig
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>>> # Initializing a Qwen2MoE style configuration
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>>> configuration = Qwen2MoeConfig()
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>>> # Initializing a model from the Qwen1.5-MoE-A2.7B" style configuration
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>>> model = Qwen2MoeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "qwen2_moe"
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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=151936,
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hidden_size=2048,
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intermediate_size=5632,
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num_hidden_layers=24,
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num_attention_heads=16,
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num_key_value_heads=16,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=28,
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attention_dropout=0.0,
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decoder_sparse_step=1,
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moe_intermediate_size=1408,
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shared_expert_intermediate_size=5632,
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num_experts_per_tok=4,
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num_experts=60,
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norm_topk_prob=False,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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mlp_only_layers=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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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.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window if use_sliding_window else None
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self.max_window_layers = max_window_layers
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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.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.attention_dropout = attention_dropout
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# MoE arguments
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self.decoder_sparse_step = decoder_sparse_step
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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.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.norm_topk_prob = norm_topk_prob
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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) |