# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
from easydel.infra.utils import AttnMaskDetail, AttnMaskType
[docs]@register_config("xerxes")
class XerxesConfig(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 256128):
Vocabulary size of the xerxes model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
hidden_size (`int`, *optional*, defaults to 4096):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 16384):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 16):
Number of key and value heads for each attention layer in the Transformer encoder.
head_dim (`int`, *optional*, defaults to 256):
Dimensionality of the attention head.
max_position_embeddings (`int`, *optional*, defaults to 6144):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 2048 or 4096).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
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`.
pad_token_id (`int`, *optional*, defaults to 0):
The index of the padding token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 1):
The index of the end of sequence token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 2):
The index of the beginning of sequence token in the vocabulary.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie the weights of the input embeddings and the output embeddings.
rope_theta (`float`, *optional*, defaults to 10000.0):
The theta value to use for rotary position embeddings.
softmax_scale (`float`, *optional*, defaults to `14.9666295471`):
softmax scale for attention module.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
bits (`int`, *optional*):
The number of bits to quantize the model to.
scan_layers (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation of the layers.
"""
model_type: str = "xerxes"
def __init__(
self,
vocab_size=256128,
hidden_size=4096,
intermediate_size=16384,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
head_dim=144,
max_position_embeddings=16384,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
swish_run=False, # shown to better based on xerxes2-3b run.
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
num_local_experts: int = 4,
xe_moe: bool = True,
xe_kvnorm: bool = False,
xe_mlpnorm: bool = False,
num_experts_per_tok: int = 2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
layer_types: list[str] | None = None,
window_pattern: int | None = None,
sliding_window: int | None = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: int | None = None,
scan_layers: bool = False,
**kwargs,
):
self.gradient_checkpointing = gradient_checkpointing
self.bits = bits
self.scan_layers = scan_layers
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.num_local_experts = num_local_experts
self.num_experts_per_tok = num_experts_per_tok
self.swish_run = swish_run
self.xe_moe = xe_moe
self.xe_kvnorm = xe_kvnorm
self.xe_mlpnorm = xe_mlpnorm
self.window_pattern = window_pattern
self.sliding_window = sliding_window
self.rope_scaling = rope_scaling
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = ["full_attention" for _ in range(self.num_hidden_layers)]
for layer_idx in range(self.num_hidden_layers):
sliding_window = None
if not self.xe_kvnorm:
sliding_window = 4096 if bool((layer_idx % 2) == 0) else None
if self.window_pattern is not None:
sliding_window = self.sliding_window if bool((layer_idx + 1) % self.window_pattern) else None
if sliding_window is not None:
self.layer_types[layer_idx] = "sliding_attention"
else:
self.layer_types[layer_idx] = "full_attention"
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
bits=bits,
**kwargs,
)
self.cache_implementation = "hybrid"
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the Xerxes model (without MoE).
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
(r"embed_tokens/embedding", pmag.resolve(ColumnWise)),
(r"self_attn/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/o_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/(gate_proj|up_proj)/kernel", pmag.resolve(ColumnWise)),
(r"mlp/down_proj/kernel", pmag.resolve(RowWise)),
(r"mlp/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/gate/kernel", pmag.resolve(Replicated if self.use_expert_tensor_mode else ColumnWise)),
(r"mlp/gate/bias", pmag.resolve(Replicated)),
(r"mlp/experts/(gate_proj|up_proj)/kernel", pmag.resolve(ColumnWise)),
(r"mlp/experts/down_proj/kernel", pmag.resolve(RowWise)),
(r"mlp/experts/.*bias", pmag.resolve(Replicated)),
(
r".*/(input_layernorm|post_attention_layernorm|pre_feedforward_layernorm|post_feedforward_layernorm|norm)/kernel",
pmag.resolve(Replicated),
),
(r"lm_head/kernel", pmag.resolve(ColumnWise)),
(r"score/kernel", pmag.resolve(RowWise)),
(r".*bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)
[docs] def get_mask_details(self) -> dict[int, AttnMaskDetail]:
"""Retrieve attention mask details for each layer in the model.
This method generates a dictionary mapping layer indices to their corresponding attention mask details.
If a sliding window is defined, each layer is assigned a sliding window attention mask with the specified size.
Returns:
dict[int, AttnMaskDetail]: A dictionary where keys are layer indices (int) and values are AttnMaskDetail
objects specifying the attention mask type and size for each layer.
Notes:
- If `self.sliding_window` is None, an empty dictionary is returned.
- The method iterates over `self.num_hidden_layers` to assign mask details for each layer.
- The attention mask type is set to `AttnMaskType.SLIDING` when a sliding window is defined.
"""
mapping = {}
if self.layer_types is not None:
for layer_idx in range(self.num_hidden_layers):
mapping[layer_idx] = AttnMaskDetail(
mask_type=AttnMaskType.from_hf(self.layer_types[layer_idx]),
size=self.sliding_window,
)
return mapping