120 lines
5.7 KiB
Python
120 lines
5.7 KiB
Python
# coding=utf-8
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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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from transformers 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 BaichuanM1Config(PretrainedConfig):
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r"""
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Configuration objects inherit from [`PretrainedConfig`] and control the behavior of model outputs. For more details,
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refer to the documentation of [`PretrainedConfig`].
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Args:
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vocab_size (`int`, *optional*, defaults to 133120):
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The size of the vocabulary used by the model.
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hidden_size (`int`, *optional*, defaults to 4096):
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The dimensionality of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 22016):
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The dimensionality of the intermediate (MLP) representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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The number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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The 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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The number of key-value heads used to implement Grouped Query Attention (GQA).
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- If `num_key_value_heads == num_attention_heads`, the model uses Multi-Head Attention (MHA).
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- If `num_key_value_heads == 1`, the model uses Multi-Query Attention (MQA).
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- Otherwise, the model uses Grouped Query Attention (GQA).
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When converting a multi-head checkpoint to a GQA checkpoint, each group's key and value heads are constructed
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by mean-pooling the original heads within that group. For more details, refer to [this paper](https://arxiv.org/pdf/2305.13245.pdf).
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If not specified, this defaults to `32`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (either a string or a callable function) used in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length the model can handle.
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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 value used by the RMS normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether the model should return the last key/value attentions. This is only relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie the model's input and output word embeddings.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the Rotary Position Embeddings (RoPE).
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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to enable sliding window attention.
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sliding_window (`int`, *optional*, defaults to 4096):
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The size of the sliding window for sliding window attention (SWA). If not specified, it defaults to `2048`.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio applied to the attention probabilities.
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"""
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model_type = "baichuan"
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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=133120,
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hidden_size=5120,
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intermediate_size=17408,
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num_hidden_layers=40,
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num_attention_heads=40,
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num_key_value_heads=2,
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num_swa_attention_heads: int = 20,
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num_swa_key_value_heads=8,
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sliding_window_layers: list = None,
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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=100000.0,
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sliding_window=2048,
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attention_dropout=0.0,
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conv_window = 2,
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**kwargs,
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):
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self.sliding_window_layers = sliding_window_layers
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self.num_swa_key_value_heads = num_swa_key_value_heads
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self.num_swa_attention_heads = num_swa_attention_heads
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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.sliding_window = sliding_window
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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_dropout = attention_dropout
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self.conv_window = conv_window
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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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)
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