vrr/kvcache-ai-ktransformers
2
0
Fork 0
mirror of https://github.com/kvcache-ai/ktransformers.git synced 2025-09-11 07:44:35 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

update rope calculation; update modeling.py; update gate for moe

Browse source
This commit is contained in:
Azure 2025-02-01 07:32:21 +00:00
parent 5a50b34627
commit f873558a89
11 changed files with 402 additions and 412 deletions
Download patch file Download diff file Expand all files Collapse all files

235
ktransformers/models/configuration_deepseek_v3.py Normal file
View file

@ -0,0 +1,235 @@
# coding=utf-8
# Copyright 2025 bzantium and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on the DeepSeekV3 implementations from the DeepSeek AI team. (https://huggingface.co/deepseek-ai/DeepSeek-V3)
# 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
#
# http://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.
"""DeepSeekV3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class DeepseekV3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DeepSeek-V3.
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 129280):
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DeepseekV3Model`]
hidden_size (`int`, *optional*, defaults to 7168):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18432):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 2048):
Dimension of the MoE representations.
num_hidden_layers (`int`, *optional*, defaults to 61):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 128):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 128):
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`.
n_shared_experts (`int`, *optional*, defaults to 1):
Number of shared experts.
n_routed_experts (`int`, *optional*, defaults to 256):
Number of routed experts.
routed_scaling_factor (`float`, *optional*, defaults to 2.5):
Scaling factor or routed experts.
kv_lora_rank (`int`, *optional*, defaults to 512):
Rank of the LoRA matrices for key and value projections.
q_lora_rank (`int`, *optional*, defaults to 1536):
Rank of the LoRA matrices for query projections.
qk_rope_head_dim (`int`, *optional*, defaults to 64):
Dimension of the query/key heads that use rotary position embeddings.
v_head_dim (`int`, *optional*, defaults to 128):
Dimension of the value heads.
qk_nope_head_dim (`int`, *optional*, defaults to 128):
Dimension of the query/key heads that don't use rotary position embeddings.
n_group (`int`, *optional*, defaults to 8):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to 4):
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
num_experts_per_tok (`int`, *optional*, defaults to 8):
Number of selected experts, None means dense model.
first_k_dense_replace (`int`, *optional*, defaults to 3):
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
\--k dense layers--/
norm_topk_prob (`bool`, *optional*, defaults to `True`):
Whether to normalize the weights of the routed experts.
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
Auxiliary loss weight coefficient.
Whether to compute the auxiliary loss for each individual sample.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
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-06):
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*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
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 a 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.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import DeepseekV3Model, DeepseekV3Config
>>> # Initializing a Deepseek-V3 style configuration
>>> configuration = DeepseekV3Config()
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "deepseek_v3"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `DeepseekV3Model`
base_model_tp_plan = {
"layers.*.gate_proj": "colwise",
"layers.*.up_proj": "colwise",
"layers.*.down_proj": "rowwise",
}
def __init__(
self,
vocab_size=129280,
hidden_size=7168,
intermediate_size=18432,
moe_intermediate_size=2048,
num_hidden_layers=61,
num_attention_heads=128,
num_key_value_heads=128,
n_shared_experts=1,
n_routed_experts=256,
routed_scaling_factor=2.5,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
n_group=8,
topk_group=4,
num_experts_per_tok=8,
first_k_dense_replace=3,
norm_topk_prob=True,
aux_loss_alpha=0.001,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**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.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.q_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.head_dim = qk_rope_head_dim
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.aux_loss_alpha = aux_loss_alpha
# 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
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
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,
)
__all__ = ["DeepseekV3Config"]