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# coding=utf-8
# Copyright 2024 AstraMind and the HuggingFace Inc. team. All rights reserved.
""" Quasar model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
QUASAR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"AstraMindAI/AstraQuasar-4B": "https://huggingface.co./AstraMindAI/AstraQuasar-4B/resolve/main/config.json",
}
#from microsoft/phi-2, Phi -> Quasar
class QuasarConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`QuasarModel`]. It is used to instantiate an Quasar
model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 51200):
Vocabulary size of the Quasar model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`QuasarModel`].
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
resid_pdrop (`float`, *optional*, defaults to 0.0):
Dropout probability for mlp outputs.
embd_pdrop (`int`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio after computing the attention scores.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Quasar-1 and Quasar-1.5 supports up to 2048
tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
is an experimental feature, subject to breaking API changes in future versions.
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
Percentage of the query and keys which will have rotary embedding.
qk_layernorm (`bool`, *optional*, defaults to `False`):
Whether or not to normalize the Queries and Keys after projecting the hidden states.
bos_token_id (`int`, *optional*, defaults to 1):
Denotes beginning of sequences token id.
eos_token_id (`int`, *optional*, defaults to 2):
Denotes end of sequences token id.
duplicate_trick (`bool`, *optional*, defaults to `True`):
Whether to use the trick of self layers calling
duplicate_grad (`bool`, *optional*, defaults to `True`):
Whether or not to do a double grad step during training. Thi is not compatible with Gradient Checkpointing
remove_ff_bias (`bool`, *optional*, defaults to `True`):
Whether or not to remove feed forward bias
gated_activation (`bool`, *optional*, defaults to `False`):
Whether or not to use a GeluGLU Activation
simple_norm (`bool`, *optional*, defaults to `False`):
Whether or not to use a simpler version of RMS Layer Norm
sliding_window ('int', *optional* defaults to 2048):
If specified it enables a sliding context window to extend the moel context from 2048 to 32K
Example:
```python
>>> from transformers import AutoModel, AutoConfig
>>> # Initializing a Quasar style configuration
>>> configuration = AutoConfig.from_pretrained("AstraMindAI/AstraQuasar-4B")
>>> # Initializing a model from the configuration
>>> model = QuasarModel(configuration, trust_remote_code=True)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "quasar"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=51200,
hidden_size=2560,
intermediate_size=8192,
num_hidden_layers=24,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="gelu_new",
max_position_embeddings=32768,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
partial_rotary_factor=0.5,
qk_layernorm=False,
bos_token_id=1,
eos_token_id=2,
sliding_window=2048,
simple_norm=False,
remove_ff_bias=True,
gated_activation=False,
duplicate_trick=True,
duplicate_grad=True,
layer_ranges=[[0, 16],[8, 21],[12, 25],[16, 29],[25, 32]],
**kwargs,
):
self.sliding_window = sliding_window
self.simple_norm = simple_norm
self.remove_ff_bias = remove_ff_bias
self.gated_activation = gated_activation
self.duplicate_trick = duplicate_trick
self.duplicate_grad = duplicate_grad
self.layer_ranges = layer_ranges if layer_ranges is not None else []
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.partial_rotary_factor = partial_rotary_factor
self.qk_layernorm = qk_layernorm
self._rope_scaling_validation()
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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