114 lines
5.3 KiB
Python
Executable File
114 lines
5.3 KiB
Python
Executable File
# Copyright 2023 Stability 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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""" StableLM Epoch model configuration"""
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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 StableLMEpochConfig(PretrainedConfig):
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r"""
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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 50_304):
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Vocabulary size of the StableLM model. Defines the number of different tokens that
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can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
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intermediate_size (`int`, *optional*, defaults to 6912):
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Dimension of the MLP representations.
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hidden_size (`int`, *optional*, defaults to 2560):
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Dimension of the decoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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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*):
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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
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`num_attention_heads`.
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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).
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rope_pct (`float`, *optional*, defaults to 1.0):
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Percentage of hidden dimensions to allocate to rotary embeddings.
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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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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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initializer_range (`float`, *optional*, defaults to 1e-5):
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The standard deviation of the truncated_normal_initializer for initializing
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all weight matrices.
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norm_eps (`float`, *optional*, defaults to 1e-8):
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The epsilon used by the 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
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(not used by all models). Only relevant if `config.is_decoder=True`.
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use_qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not the model should use bias for qkv layers.
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tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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"""
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model_type = "stablelm_epoch"
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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=50_304,
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intermediate_size=6912,
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hidden_size=2560,
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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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rope_pct=0.25,
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rope_theta=10_000,
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max_position_embeddings=4096,
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initializer_range=0.02,
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norm_eps=1.0e-5,
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use_cache=True,
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use_qkv_bias=True,
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bos_token_id=0,
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eos_token_id=2,
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tie_word_embeddings=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.intermediate_size = intermediate_size
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self.hidden_size = hidden_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.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.rope_pct = rope_pct
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self.rope_theta = rope_theta
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self.initializer_range = initializer_range
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.use_qkv_bias = use_qkv_bias
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(
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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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