244 lines
10 KiB
Python
244 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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# Copyright 2023 Cerebras Systems.
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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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"""JAIS 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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class JAISConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a
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[`JAISModel`]. It is used to instantiate a JAIS model according to the
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specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used
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to control the model outputs. Read the documentation from
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[`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50257):
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Vocabulary size of the JAIS model. Defines the number of different
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tokens that can be represented by the
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`inputs_ids` passed when calling [`JAISModel`].
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n_positions (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used
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with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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n_embd (`int`, *optional*, defaults to 768):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the
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Transformer encoder.
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n_inner (`int`, *optional*, defaults to None):
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Dimensionality of the inner feed-forward layers. `None` will set
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it to 4 times n_embd
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activation_function (`str`, *optional*, defaults to `"gelu"`):
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Activation function, to be selected in the list
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`["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`.
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in
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the embeddings, encoder, and pooler.
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embd_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon to use in the layer normalization layers.
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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
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initializing all weight matrices.
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scale_attn_weights (`bool`, *optional*, defaults to `True`):
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Scale attention weights by dividing by sqrt(hidden_size)..
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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
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attentions (not used by all models).
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scale_attn_by_inverse_layer_idx (`bool`, *optional*, default `True`):
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Whether to additionally scale attention weights
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by `1 / layer_idx + 1`.
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reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
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Whether to scale keys (K) prior to computing attention
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(dot-product)
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and upcast attention dot-product/softmax to float() when training
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with mixed precision.
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position_embedding_type (`str`, *optional*, defaults to `"learned"`):
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Positional embedding can be either `"alibi"` or `"learned"`.
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mup_width_scale (`float`, *optional*, defaults to 1.0):
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muP parameter to scale learning rate and initializers. Calculated
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as (`d_model,0 / d_model`), where
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`d_model` is the model's width and `d_model,0` is the proxy
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model's width.
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mup_embeddings_scale (`float`, *optional*, defaults to 1.0):
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muP parameter to scale token and position embeddings.
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mup_output_alpha (`float`, *optional*, defaults to 1.0):
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muP parameter to scale output logits
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(`output_logits_scale = mup_output_alpha * mup_width_scale`).
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mup_scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`):
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Scale attention weights by dividing by hidden_size instead of
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sqrt(hidden_size). Need to set scale_attn_weights to `True` as
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well.
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alibi_scaling (`dict`, *optional*):
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Dictionary containing the scaling configuration for ALiBi
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embeddings. Currently only supports linear
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scaling strategy. Can specify either the scaling `factor` (must be
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a float greater than 1) for fixed scaling
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or `train_seq_len` for dynamic scaling on input samples with
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sequence length > `train_seq_len`. The expected
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formats are `{"type": strategy name, "factor": scaling factor}` or
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`{"type": strategy name,
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"train_seq_len": training sequence length}`.
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architectures (`list`, *optional*, defaults to ['JAISLMHeadModel']):
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architecture names for Jais.
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Example:
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```python
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>>> from transformers import JAISConfig, JAISModel
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>>> # Initializing a JAIS configuration
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>>> configuration = JAISConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = JAISModel(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 = "jais"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"hidden_size": "n_embd",
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"max_position_embeddings": "n_positions",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=50257,
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n_positions=1024,
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n_embd=768,
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n_layer=12,
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n_head=12,
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n_inner=None,
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activation_function="gelu_new",
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attn_pdrop=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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scale_attn_weights=True,
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use_cache=True,
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bos_token_id=50256,
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eos_token_id=50256,
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scale_attn_by_inverse_layer_idx=False,
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reorder_and_upcast_attn=False,
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position_embedding_type="learned",
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mup_width_scale=1.0,
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mup_embeddings_scale=1.0,
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mup_output_alpha=1.0,
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mup_scale_qk_dot_by_d=False,
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alibi_scaling=None,
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architectures=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_inner = n_inner
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self.activation_function = activation_function
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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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.scale_attn_weights = scale_attn_weights
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self.use_cache = use_cache
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self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
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self.reorder_and_upcast_attn = reorder_and_upcast_attn
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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.position_embedding_type = position_embedding_type
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self.mup_width_scale = mup_width_scale
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self.mup_embeddings_scale = mup_embeddings_scale
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self.mup_output_alpha = mup_output_alpha
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self.mup_scale_qk_dot_by_d = mup_scale_qk_dot_by_d
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self.alibi_scaling = alibi_scaling
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self._alibi_scaling_validation()
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if architectures is None:
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architectures = ["JAISLMHeadModel"]
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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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architectures=architectures,
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**kwargs,
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)
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def _alibi_scaling_validation(self):
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"""
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Validate the `alibi_scaling` configuration.
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"""
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if self.alibi_scaling is None:
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return
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if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2:
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raise ValueError(
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"`alibi_scaling` must be a dictionary with two fields, "
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"`type` and `factor` or `type` and `train_seq_len`, "
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f"got {self.alibi_scaling}"
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)
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alibi_scaling_type = self.alibi_scaling.get("type", None)
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alibi_scaling_factor = self.alibi_scaling.get("factor", None)
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alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
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if alibi_scaling_type is None or alibi_scaling_type != "linear":
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raise ValueError(
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f"`alibi_scaling`'s type field must be 'linear', "
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f"got {alibi_scaling_type}"
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)
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if (
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alibi_scaling_factor is not None
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and not isinstance(alibi_scaling_factor, float)
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or (alibi_scaling_factor is not None and alibi_scaling_factor <= 1.0)
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):
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raise ValueError(
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f"`alibi_scaling`'s factor field must be a float > 1.0, "
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f"got {alibi_scaling_factor}"
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)
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if (
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alibi_dynamic_scaling is not None
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and not isinstance(alibi_dynamic_scaling, int)
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or (alibi_dynamic_scaling is not None and alibi_dynamic_scaling <= 1)
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):
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raise ValueError(
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f"`alibi_scaling`'s `train_seq_len` field must be an "
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f"integer > 1, got {alibi_dynamic_scaling}"
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)
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