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vllm-v0.6.2/vllm/transformers_utils/configs/solar.py
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vllm-v0.6.2/vllm/transformers_utils/configs/solar.py
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Solar 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 SolarConfig(PretrainedConfig):
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r"""
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This is the configuration class to store
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the configuration of a [`SolarModel`].
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It is used to instantiate an LLaMA model
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according to the specified arguments,
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defining the model architecture.
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Instantiating a configuration with the
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defaults will yield a similar
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configuration to that of the LLaMA-7B.
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Configuration objects inherit from [`PretrainedConfig`]
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and can be used to control the model outputs.
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Read the documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the LLaMA model.
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Defines the number of different tokens
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that can be represented by the `inputs_ids`
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passed when calling [`SolarModel`]
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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 11008):
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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 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
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in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that
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should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`,
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the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model
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will use Multi Query Attention (MQA)
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otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint,
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each group key and value head should be constructed
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by meanpooling all the original heads within that group.
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For more details checkout [this paper]
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(https://arxiv.org/pdf/2305.13245.pdf).
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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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in the decoder.
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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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Solar 1 supports up to 2048 tokens,
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Solar 2 up to 4096, CodeSolar up to 16384.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of
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the truncated_normal_initializer for initializing
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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
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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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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank
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used during pretraining.
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Please refer to [this
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document](https://huggingface.co/docs/
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transformers/main/
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perf_train_gpu_many#tensor-parallelism)
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to understand more about it. This value is
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necessary to ensure exact reproducibility
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of the pretraining results.
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Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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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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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for
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the RoPE embeddings.
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Currently supports two scaling
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strategies: linear and dynamic.
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Their scaling factor must be a float greater than 1.
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The expected format is
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`{"type": strategy name, "factor": scaling factor}`.
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When using this flag, don't update
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`max_position_embeddings` to the expected new maximum.
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See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/
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dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking
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API changes in future versions.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value
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and output projection layers during self-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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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj
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layers in the MLP layers.
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sliding_window (`int`, *optional*, defaults to 2047):
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Sliding window attention window size. If not specified,
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will default to `2047`.
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```python
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>>> from transformers import SolarModel, SolarConfig
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>>> # Initializing a Solar-pro style configuration
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>>> configuration = SolarConfig()
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>>> # Initializing a model from the Solar-pro style configuration
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>>> model = SolarModel(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 = "solar"
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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=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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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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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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sliding_window=2047,
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bskcn_1=None,
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bskcn_2=None,
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bskcn_3=None,
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bskcn_4=None,
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bskcn_tv=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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# 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.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.sliding_window = sliding_window
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self.bskcn_1 = bskcn_1 if bskcn_1 is not None else [12, 20, 32, 44]
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self.bskcn_2 = bskcn_2 if bskcn_2 is not None else [20, 32]
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self.bskcn_3 = bskcn_3 if bskcn_3 is not None else [16, 24, 36, 48]
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self.bskcn_4 = bskcn_4 if bskcn_4 is not None else [28, 40]
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self.bskcn_tv = bskcn_tv if bskcn_tv is not None else [0.9, 0.8]
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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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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if (not isinstance(self.rope_scaling, dict)
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or len(self.rope_scaling) != 2):
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields,"
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" `type` and `factor`, "
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f"got {self.rope_scaling}")
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in [
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"linear",
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"dynamic",
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]:
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raise ValueError(f"`rope_scaling`'s type field must be one of "
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f"['linear', 'dynamic'], got {rope_scaling_type}")
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if (rope_scaling_factor is None
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or not isinstance(rope_scaling_factor, float)
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or rope_scaling_factor <= 1.0):
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raise ValueError(
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f"`rope_scaling`'s factor field must be a float > 1,"
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f" got {rope_scaling_factor}")
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