191 lines
8.7 KiB
Python
191 lines
8.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copied from
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# https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/blob/main/configuration_exaone.py
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# Copyright 2021 The LG AI Research EXAONE Lab. 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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"""Exaone model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: dict[str, str] = {}
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class ExaoneConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:
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`~transformers.ExaoneModel`. It is used to instantiate a GPT Lingvo model
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according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar
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configuration to that of the Exaone
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Configuration objects inherit from {class}`~transformers.PretrainedConfig`
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and can be used to control the model outputs. Read the documentation from :
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class:`~transformers.PretrainedConfig` for more information.
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Args:
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vocab_size ({obj}`int`, `optional`, defaults to 50257):
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Vocabulary size of the GPT Lingvo model. Defines the number of
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different tokens that can be represented by the {obj}`inputs_ids`
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passed when calling {class}`~transformers.ExaoneModel`. Vocabulary
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size of the model.
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Defines the different tokens that can be represented by the
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`inputs_ids` passed to the forward method of :class:
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`~transformers.EXAONEModel`.
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hidden_size ({obj}`int`, `optional`, defaults to 2048):
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Dimensionality of the encoder layers and the pooler layer.
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num_layers ({obj}`int`, `optional`, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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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
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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
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implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi
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Head Attention (MHA), if `num_key_value_heads=1 the model will use
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Multi Query Attention (MQA) 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 by meanpooling
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all the original heads within that group. For more details checkout
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[this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not
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specified, will default to `num_attention_heads`.
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rotary_pct (`float`, *optional*, defaults to 0.25):
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percentage of hidden dimensions to allocate to rotary embeddings
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intermediate_size ({obj}`int`, `optional`, defaults to 8192):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in
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the Transformer encoder.
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activation_function ({obj}`str` or {obj}`function`, `optional`,
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defaults to {obj}`"gelu_new"`):
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The non-linear activation function (function or string) in the
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encoder and pooler. If string, {obj}`"gelu"`, {obj}`"relu"`,
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{obj}`"selu"` and {obj}`"gelu_new"` are supported.
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embed_dropout ({obj}`float`, `optional`, defaults to 0.0):
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The dropout probabilitiy for all fully connected layers in the
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embeddings, encoder, and pooler.
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attention_dropout ({obj}`float`, `optional`, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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max_position_embeddings ({obj}`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
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(e.g., 512 or 1024 or 2048).
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type_vocab_size ({obj}`int`, `optional`, defaults to 2):
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The vocabulary size of the {obj}`token_type_ids` passed when calling
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{class}`~transformers.EXAONEModel`.
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initializer_range ({obj}`float`, `optional`, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_epsilon ({obj}`float`, `optional`, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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use_cache ({obj}`bool`, `optional`, defaults to {obj}`True`):
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Whether or not the model should return the last key/values
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attentions (not used by all models).
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Only relevant if ``config.is_decoder=True``.
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gradient_checkpointing ({obj}`bool`, `optional`,
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defaults to {obj}`False`):
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If True, use gradient checkpointing to save memory at the expense
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of slower backward pass.
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Example::
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>>> from transformers import ExoneModel, ExaoneConfig
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>>> # Initializing a EXAONE configuration
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>>> configuration = ExaoneConfig()
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>>> # Initializing a model from configuration
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>>> model = ExoneModel(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 = "exaone"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"num_hidden_layers": "num_layers"}
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def __init__(
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self,
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vocab_size=102400,
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max_position_embeddings=2048,
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hidden_size=2048,
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num_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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intermediate_size=None,
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activation_function="silu",
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rotary_pct=0.25,
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resid_dropout=0.0,
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embed_dropout=0.0,
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attention_dropout=0.0,
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layer_norm_epsilon=1e-6,
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initializer_range=0.02,
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use_cache=True,
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bos_token_id=0,
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eos_token_id=2,
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tie_word_embeddings=True,
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**kwargs,
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):
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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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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.num_layers = num_layers
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_layers
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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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if intermediate_size:
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self.intermediate_size = intermediate_size
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else:
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self.intermediate_size = hidden_size * 4
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self.activation_function = activation_function
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self.resid_dropout = resid_dropout
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self.embed_dropout = embed_dropout
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self.attention_dropout = attention_dropout
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.rotary_pct = rotary_pct
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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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self.use_logit_cap = kwargs.pop("use_logit_cap", False)
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self.ln_no_scale = kwargs.pop("ln_no_scale", False)
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self.use_gated = kwargs.pop("use_gated", False)
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self.use_emb_norm = kwargs.pop("use_emb_norm", False)
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self.use_rotary_pos = kwargs.pop("use_rotary_pos", False)
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self.rotary_type = kwargs.pop("rotary_type", None)
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self.scaling_factor = kwargs.pop("scaling_factor", 1)
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self.use_absolute_pos = kwargs.pop("use_absolute_pos", True)
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self.use_extra_logit = kwargs.pop("use_extra_logit", True)
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self.rotary_expand_length = kwargs.pop("rotary_expand_length", None)
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self.rotary_base = kwargs.pop("rotary_base", 10000.0)
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self.use_qkv_fuse = kwargs.pop("use_qkv_fuse", False)
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self.rescale_before_lm_head = kwargs.pop("rescale_before_lm_head",
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(rotary_pct == 0.25))
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if self.use_rotary_pos:
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self.use_absolute_pos = False
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