# 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,
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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("deepseek_v2")
class DeepseekV2Config(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 102400):
Vocabulary size of the DeepseekV2 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 11008):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
moe_intermediate_size (`int`, *optional*, defaults to 1407):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the MoE layer.
num_hidden_layers (`int`, *optional*, defaults to 30):
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.
n_shared_experts (`int`, *optional*):
Number of shared experts.
n_routed_experts (`int`, *optional*):
Number of routed experts.
ep_size (`int`, *optional*, defaults to 1):
Expert parallel size.
routed_scaling_factor (`float`, *optional*, defaults to 1.0):
Routed scaling factor.
kv_lora_rank (`int`, *optional*, defaults to 512):
KV LoRA rank.
q_lora_rank (`int`, *optional*, defaults to 1536):
Q LoRA rank.
qk_rope_head_dim (`int`, *optional*, defaults to 64):
QK rope head dimension.
v_head_dim (`int`, *optional*, defaults to 128):
V head dimension.
qk_nope_head_dim (`int`, *optional*, defaults to 128):
QK nope head dimension.
topk_method (`str`, *optional*, defaults to `"gready"`):
Top-k method.
n_group (`int`, *optional*):
Number of groups.
topk_group (`int`, *optional*):
Top-k group.
num_experts_per_tok (`int`, *optional*):
Number of experts per token.
moe_layer_freq (`int`, *optional*, defaults to 1):
MoE layer frequency.
first_k_dense_replace (`int`, *optional*, defaults to 0):
First k dense replace.
norm_topk_prob (`bool`, *optional*, defaults to `False`):
Whether to normalize top-k probabilities.
scoring_func (`str`, *optional*, defaults to `"softmax"`):
Scoring function.
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
Auxiliary loss alpha.
seq_aux (`bool`, *optional*, defaults to `True`):
Whether to use sequence auxiliary loss.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) to use in the encoder and pooler. If string,
`"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported.
max_position_embeddings (`int`, *optional*, defaults to 2048):
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*):
The index of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 100000):
The index of the beginning of sequence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 100001):
The index of the end of sequence token in the vocabulary.
pretraining_tp (`int`, *optional*, defaults to 1):
Pretraining TP.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use attention bias.
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.
use_scan_mlp (`bool`, *optional*, defaults to `False`):
Whether to use scan for MLP.
scan_mlp_chunk_size (`int`, *optional*, defaults to 1024):
The chunk size for scan MLP.
bits (`int`, *optional*):
The number of bits to quantize the model to.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The rope scaling configuration.
"""
model_type: str = "deepseek_v2"
def __init__(
self,
vocab_size=102400,
hidden_size=4096,
intermediate_size=11008,
moe_intermediate_size=1407,
num_hidden_layers=30,
num_attention_heads=32,
num_key_value_heads=32,
n_shared_experts=None,
n_routed_experts=None,
ep_size=1,
routed_scaling_factor=1.0,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
topk_method="gready",
n_group=None,
topk_group=None,
num_experts_per_tok=None,
moe_layer_freq=1,
first_k_dense_replace=0,
norm_topk_prob=False,
scoring_func="softmax",
aux_loss_alpha=0.001,
seq_aux=True,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=100000,
eos_token_id=100001,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
attention_bias=False,
attention_dropout=0.0,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
use_scan_mlp: bool = False,
scan_mlp_chunk_size: int = 1024,
bits: tp.Optional[int] = None,
rope_scaling: tp.Dict[str, tp.Union[str, float]] = None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
self.aux_loss_alpha = aux_loss_alpha
self.seq_aux = seq_aux
# 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.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.gradient_checkpointing = gradient_checkpointing
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,
use_scan_mlp=use_scan_mlp,
scan_mlp_chunk_size=scan_mlp_chunk_size,
bits=bits,
**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", ("sp", "fsdp"))),
("self_attn/q_a_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/q_b_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/kv_a_proj_with_mqa/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/kv_b_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/o_proj/kernel", PartitionSpec("tp", ("sp", "fsdp"))),
("self_attn/q_a_layernorm/kernel", PartitionSpec(None)),
("self_attn/kv_a_layernorm/kernel", PartitionSpec(None)),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] @staticmethod
def get_weight_decay_exclusions():
return tuple()
[docs] @staticmethod
def rng_keys():
return "params", "dropout", "fcm"
@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,
)