# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
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# Licensed under the Apache License, Version 2.0 (the "License");
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# https://www.apache.org/licenses/LICENSE-2.0
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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
[docs]@register_config("grok-1")
class Grok1Config(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 32000):
Vocabulary size of the Grok-1 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 32768):
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 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
Number of key and value heads for each attention layer in the Transformer encoder.
attn_output_multiplier (`float`, *optional*, defaults to 1.0):
The multiplier value applied to the attention output.
max_attn_value (`float`, *optional*, defaults to 1.0):
The maximum value of the attention weights.
max_position_embeddings (`int`, *optional*, defaults to 4096):
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).
embedding_multiplier_scale (`float`, *optional*, defaults to 1.0):
The scale factor for the embedding layer.
output_multiplier_scale (`float`, *optional*, defaults to 1.0):
The scale factor for the output layer.
rms_norm_eps (`float`, *optional*, defaults to 1e-5):
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*):
The index of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 1):
The index of the beginning of sequence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 2):
The index of the end 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.
num_experts_per_tok (`int`, *optional*, defaults to 2):
The number of experts per token.
num_experts (`int`, *optional*, defaults to 8):
The number of experts.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether to output router logits.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The router auxiliary loss coefficient.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
bits (`int`, *optional*):
The number of bits to quantize the model to.
"""
model_type: str = "grok-1"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=32768,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
attn_output_multiplier=1.0,
max_attn_value=1.0,
max_position_embeddings=4096,
embedding_multiplier_scale: float = 1.0,
output_multiplier_scale: float = 1.0,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
num_experts_per_tok=2,
num_experts=8,
output_router_logits=False,
router_aux_loss_coef=0.001,
rope_theta: float = 10000,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: int | None = None,
**kwargs,
):
"""Initializes a Grok1Config object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 32000.
hidden_size (int, optional): Hidden size. Defaults to 4096.
intermediate_size (int, optional): Intermediate size of the feed-forward network. Defaults to 32768.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 32.
num_attention_heads (int, optional): Number of attention heads. Defaults to 32.
num_key_value_heads (int, optional): Number of key/value heads (for GQA). Defaults to 32.
attn_output_multiplier (float, optional): Multiplier for attention output. Defaults to 1.0.
max_attn_value (float, optional): Maximum attention value. Defaults to 1.0.
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 4096.
embedding_multiplier_scale (float, optional): Scale factor for embeddings. Defaults to 1.0.
output_multiplier_scale (float, optional): Scale factor for the output layer. Defaults to 1.0.
rms_norm_eps (float, optional): Epsilon for RMS normalization. Defaults to 1e-5.
use_cache (bool, optional): Whether to use KV cache. Defaults to True.
pad_token_id (int, optional): Padding token ID. Defaults to None.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 1.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 2.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to True.
num_experts_per_tok (int, optional): Number of experts to route per token. Defaults to 2.
num_experts (int, optional): Total number of experts. Defaults to 8.
output_router_logits (bool, optional): Whether to output router logits. Defaults to False.
router_aux_loss_coef (float, optional): Coefficient for router auxiliary loss. Defaults to 0.001.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
**kwargs: Additional keyword arguments.
"""
self.vocab_size = vocab_size
self.attn_output_multiplier = attn_output_multiplier
self.max_attn_value = max_attn_value
self.max_position_embeddings = max_position_embeddings
self.embedding_multiplier_scale = embedding_multiplier_scale
self.output_multiplier_scale = output_multiplier_scale
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.initializer_range = initializer_range
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.gradient_checkpointing = gradient_checkpointing
self.bits = bits
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
(r"embed_tokens/embedding", pmag.resolve(ColumnWise)),
(r"attn/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"attn/o_proj/kernel", pmag.resolve(RowWise)),
(r"attn/.*proj/bias", pmag.resolve(Replicated)),
(r"gate/kernel", pmag.resolve(Replicated if self.use_expert_tensor_mode else ColumnWise)),
(r"gate/bias", pmag.resolve(Replicated)),
(r"experts/(linear|linear_v)/kernel", pmag.resolve(ColumnWise)),
(r"experts/linear_1/kernel", pmag.resolve(RowWise)),
(r"experts/.*linear.*/bias", pmag.resolve(Replicated)),
(
r".*(pre_attn_norm|post_attn_norm|pre_moe_norm|post_moe_norm|norm)/kernel",
pmag.resolve(Replicated),
),
(r"lm_head/kernel", pmag.resolve(ColumnWise)),
(r"lm_head/bias", pmag.resolve(Replicated)),
(r".*bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)