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204 lines
8.7 KiB
204 lines
8.7 KiB
# Copyright 2025 Arcee AI 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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"""PyTorch Arcee model."""
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from transformers.utils import auto_docstring, logging
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from ...modeling_rope_utils import RopeParameters
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from ..llama.configuration_llama import LlamaConfig
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from ..llama.modeling_llama import (
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LlamaForCausalLM,
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LlamaForQuestionAnswering,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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)
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from ..nemotron.modeling_nemotron import NemotronMLP
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logger = logging.get_logger(__name__)
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class ArceeConfig(LlamaConfig):
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r"""
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This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee
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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 AFM-4.5B-Base.
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Pre-trained weights are available at
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[arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B)
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and were used to build the examples below.
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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 Arcee model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ArceeModel`]
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hidden_size (`int`, *optional*, defaults to 2560):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 18432):
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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 checkout [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 `"relu2"`):
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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 4096):
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The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 tokens.
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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-05):
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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 128000):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 128001):
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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_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`, *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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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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head_dim (`int`, *optional*):
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The attention head dimension. If None, it will default to hidden_size // num_attention_heads
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```python
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>>> from transformers import ArceeModel, ArceeConfig
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>>> # Initializing an Arcee AFM-4.5B-Base style configuration
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>>> configuration = ArceeConfig()
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>>> # Initializing a model from the AFM-4.5B-Base style configuration
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>>> model = ArceeModel(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 = "arcee"
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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.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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def __init__(
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self,
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vocab_size: int | None = 32000,
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hidden_size: int | None = 2560,
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intermediate_size: int | None = 18432,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = None,
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hidden_act: str | None = "relu2",
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max_position_embeddings: int | None = 4096,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 128000,
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eos_token_id: int | None = 128001,
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tie_word_embeddings: bool | None = False,
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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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mlp_bias: bool | None = False,
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head_dim: int | None = None,
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**kwargs,
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):
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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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initializer_range=initializer_range,
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rms_norm_eps=rms_norm_eps,
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use_cache=use_cache,
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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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rope_parameters=rope_parameters,
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attention_bias=attention_bias,
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attention_dropout=attention_dropout,
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mlp_bias=mlp_bias,
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head_dim=head_dim,
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**kwargs,
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)
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del self.pretraining_tp
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class ArceeMLP(NemotronMLP):
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pass
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@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
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class ArceeForCausalLM(LlamaForCausalLM):
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pass
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@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
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class ArceeForSequenceClassification(LlamaForSequenceClassification):
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pass
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@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
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class ArceeForQuestionAnswering(LlamaForQuestionAnswering):
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pass
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@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
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class ArceeForTokenClassification(LlamaForTokenClassification):
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pass
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__all__ = [
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"ArceeConfig",
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"ArceeForCausalLM",
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"ArceeForQuestionAnswering",
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"ArceeForSequenceClassification",
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"ArceeForTokenClassification",
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"ArceeModel", # noqa: F822
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"ArceePreTrainedModel", # noqa: F822
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]
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