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176 lines
8.3 KiB
176 lines
8.3 KiB
4 days ago
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# Copyright 2024 Cohere 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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"""Cohere model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class CohereConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
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model according to the specified arguments, defining the model architecture.
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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. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model.
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Args:
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vocab_size (`int`, *optional*, defaults to 256000):
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Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`CohereModel`]
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hidden_size (`int`, *optional*, defaults to 8192):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 22528):
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Dimension of the MLP representations.
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logit_scale (`float`, *optional*, defaults to 0.0625):
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The scaling factor for the output logits.
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num_hidden_layers (`int`, *optional*, defaults to 40):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 64):
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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 8192):
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization.
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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*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 5):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 255001):
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End of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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attention_bias (`bool`, defaults to `False`, *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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use_qk_norm (`bool`, *optional*, defaults to `False`):
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Whether to use query-key normalization in the attention
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```python
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>>> from transformers import CohereModel, CohereConfig
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>>> # Initializing a Cohere model configuration
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>>> configuration = CohereConfig()
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>>> # Initializing a model from the Cohere configuration
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>>> model = CohereModel(configuration) # doctest: +SKIP
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>>> # Accessing the model configuration
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>>> configuration = model.config # doctest: +SKIP
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```"""
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model_type = "cohere"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 500000.0
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size: int | None = 256000,
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hidden_size: int | None = 8192,
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intermediate_size: int | None = 22528,
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logit_scale: float | None = 0.0625,
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num_hidden_layers: int | None = 40,
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num_attention_heads: int | None = 64,
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num_key_value_heads: int | None = None,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 8192,
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initializer_range: float | None = 0.02,
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layer_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = 0,
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bos_token_id: int | None = 5,
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eos_token_id: int | None = 255001,
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tie_word_embeddings: bool | None = True,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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use_qk_norm: bool | None = 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.hidden_size = hidden_size
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self.logit_scale = logit_scale
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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.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.use_qk_norm = use_qk_norm
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self.rope_parameters = rope_parameters
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self.tie_word_embeddings = tie_word_embeddings
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(**kwargs)
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__all__ = ["CohereConfig"]
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