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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.
"""Qwen2 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class DolphinConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DolphinModel`]. It is used to instantiate a
Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DolphinModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```"""
# The `model_type` attribute in the `DolphinConfig` class is a string variable that specifies the
# type of model configuration. In this case, it is set to "qwen2", indicating that the
# configuration is specifically designed for a Qwen2 model. This attribute helps identify the type
# of model configuration being used and can be useful for distinguishing between different model
# configurations or types within a codebase.
model_type = "dolphin"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=152064, # Updated to match the checkpoint
hidden_size=3584, # Updated to match the checkpoint
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,
encoder_config=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
# 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
self.encoder_config = encoder_config
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
encoder_config_dict = {
"_name_or_path": "Qwen/Qwen2-0.5B",
"add_cross_attention": False,
"architectures": ["Qwen2ForCausalLM"],
"attention_dropout": 0.0,
"bad_words_ids": None,
"begin_suppress_tokens": None,
"bos_token_id": 151643,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": None,
"decoder_start_token_id": None,
"diversity_penalty": 0.0,
"do_sample": False,
"early_stopping": False,
"encoder_config": None,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": 151643,
"exponential_decay_length_penalty": None,
"finetuning_task": None,
"forced_bos_token_id": None,
"forced_eos_token_id": None,
"hidden_act": "silu",
"hidden_size": 896,
"id2label": {"0": "LABEL_0", "1": "LABEL_1"},
"initializer_range": 0.02,
"intermediate_size": 4864,
"is_decoder": False,
"is_encoder_decoder": False,
"label2id": {"LABEL_0": 0, "LABEL_1": 1},
"length_penalty": 1.0,
"max_length": 20,
"max_position_embeddings": 131072,
"max_window_layers": 24,
"min_length": 0,
"model_type": "qwen2",
"no_repeat_ngram_size": 0,
"num_attention_heads": 14,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"num_return_sequences": 1,
"output_attentions": False,
"output_hidden_states": False,
"output_scores": False,
"pad_token_id": None,
"prefix": None,
"problem_type": None,
"pruned_heads": {},
"remove_invalid_values": False,
"repetition_penalty": 1.0,
"return_dict": True,
"return_dict_in_generate": False,
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sep_token_id": None,
"sliding_window": 131072,
"suppress_tokens": None,
"task_specific_params": None,
"temperature": 1.0,
"tf_legacy_loss": False,
"tie_encoder_decoder": False,
"tie_word_embeddings": True,
"tokenizer_class": None,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": "bfloat16",
"torchscript": False,
"typical_p": 1.0,
"use_bfloat16": False,
"use_cache": True,
"use_sliding_window": False,
"vocab_size": 151936,
"attn_implementation": None,
}
if __name__ == "__main__":
config = DolphinConfig(encoder_config=encoder_config_dict)
config.save_pretrained("dolphin-config") |