196 lines
9.3 KiB
Python
196 lines
9.3 KiB
Python
# coding=utf-8
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# Copyright 2024 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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"""GraniteMoe model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...modeling_rope_utils import rope_config_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class GraniteMoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the GraniteMoe-3B.
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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 32000):
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Vocabulary size of the GraniteMoe model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`GraniteMoeModel`]
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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 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 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, check out [this
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paper](https://huggingface.co/papers/2305.13245). 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) 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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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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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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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 the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. 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/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking 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 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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embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
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logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
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residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
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attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
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num_local_experts (`int`, *optional*, defaults to 8): total number of experts
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num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss.
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxiliary loss coefficient
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```python
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>>> from transformers import GraniteMoeModel, GraniteMoeConfig
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>>> # Initializing a GraniteMoe granitemoe-3b style configuration
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>>> configuration = GraniteMoeConfig()
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>>> # Initializing a model from the granitemoe-7b style configuration
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>>> model = GraniteMoeModel(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 = "granitemoe"
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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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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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embedding_multiplier=1.0,
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logits_scaling=1.0,
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residual_multiplier=1.0,
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attention_multiplier=1.0,
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num_local_experts=8,
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num_experts_per_tok=2,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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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.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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# this model has rope embedding type, hardcoded for BC
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self.position_embedding_type = "rope"
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.embedding_multiplier = embedding_multiplier
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self.logits_scaling = logits_scaling
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self.residual_multiplier = residual_multiplier
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self.attention_multiplier = attention_multiplier
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self.num_local_experts = num_local_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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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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rope_config_validation(self)
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__all__ = ["GraniteMoeConfig"]
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