wehub-resource-sync
214 lines
9.2 KiB
Python
214 lines
9.2 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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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"""Qwen2 model configuration"""
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from ..configuration_utils import PretrainedConfig
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__all__ = [
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"QWEN2_PRETRAINED_INIT_CONFIGURATION",
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"Qwen2Config",
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"QWEN2_PRETRAINED_RESOURCE_FILES_MAP",
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]
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QWEN2_PRETRAINED_INIT_CONFIGURATION = {
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# Hypothetical model weights (tiny-random-llama & micro-random-llama) for test only
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"__internal_testing__/micro-random-llama": {
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"architectures": ["LlamaForCausalLM"],
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"hidden_size": 64,
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"initializer_range": 0.02,
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"intermediate_size": 1000,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 8,
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"num_hidden_layers": 1,
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"rms_norm_eps": 1e-06,
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"vocab_size": 32000,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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},
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"__internal_testing__/tiny-random-llama": {
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"architectures": ["LlamaForCausalLM"],
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 8,
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"num_hidden_layers": 2,
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"rms_norm_eps": 1e-06,
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"vocab_size": 32000,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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},
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}
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# Hypothetical model weights (tiny-random-llama) for test only
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QWEN2_PRETRAINED_RESOURCE_FILES_MAP = {
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"model_state": {
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"__internal_testing__/micro-random-llama": "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/micro-random-llama/model_state.pdparams",
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"__internal_testing__/tiny-random-llama": "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-llama/model_state.pdparams",
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},
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}
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class Qwen2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
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Qwen2 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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Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
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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 Qwen2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Qwen2Model`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 22016):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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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 32):
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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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```python
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>>> from transformers import Qwen2Model, Qwen2Config
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>>> # Initializing a Qwen2 style configuration
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>>> configuration = Qwen2Config()
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>>> # Initializing a model from the Qwen2-7B style configuration
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>>> model = Qwen2Model(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"
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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=4096,
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intermediate_size=22016,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=32768,
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# seq_length=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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pad_token_id=151643,
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bos_token_id=151643,
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eos_token_id=151643,
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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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use_flash_attention_for_generation=False,
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alibi=False,
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use_last_token_for_generation=False,
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attention_bias=True,
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attention_dropout=0.0,
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rope_scaling_factor=1.0,
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rope_scaling_type=None,
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dpo_config=None,
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use_fused_head_and_loss_fn=False,
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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.seq_length = seq_length
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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
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self.max_window_layers = max_window_layers
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_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.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_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.use_cache = use_cache
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self.rope_scaling_factor = rope_scaling_factor
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self.rope_scaling_type = rope_scaling_type
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.dpo_config = dpo_config
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self.use_flash_attention_for_generation = use_flash_attention_for_generation
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self.alibi = alibi
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self.use_fused_head_and_loss_fn = use_fused_head_and_loss_fn
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self.use_last_token_for_generation = use_last_token_for_generation
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_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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