# Copyright 2023 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.
import typing as tp
from jax.sharding import PartitionSpec
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,
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,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
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
# 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.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.
"""
return (
("model/embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),
(
"attn/(q_proj|k_proj|v_proj)/kernel",
PartitionSpec(("fsdp", "sp"), "tp"),
),
("attn/o_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("linear/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("linear_1/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("linear_v/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("gate/kernel", PartitionSpec(("fsdp", "sp"))),
("post_attn_norm/kernel", PartitionSpec(None)),
("pre_attn_norm/kernel", PartitionSpec(None)),
("pre_moe_norm/kernel", PartitionSpec(None)),
("post_moe_norm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] def attach_custom_arguments(
self,
tie_word_embeddings: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
bits: tp.Optional[int] = None,
**kwargs,
):
"""The attach_custom_arguments function adds the following arguments to the Transformer class:
Args:
self: Refer to the current object
tie_word_embeddings: bool: Tie the word embeddings to the
decoder
gradient_checkpointing: str: Control the amount of memory
used by jax
bits: tp.Optional[int]: Determine the number of bits used in
the quantization
"""
self.tie_word_embeddings = tie_word_embeddings
self.gradient_checkpointing = gradient_checkpointing
self.bits = bits
[docs] @staticmethod
def get_weight_decay_exclusions():
return tuple()
[docs] @staticmethod
def rng_keys():
return "params", "dropout"
@property
def granted_freq_max_position_embedding(self) -> int:
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)