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影响 (多模态)大语言模型 参数量的主要网络模块，即 Linear、Embedding、Norm(LayerNorm or RMSNorm) 等 3 个部分，其中，多模态大模型还包括 Conv3D，手动计算参数量，与 PyTorch 直接计算保持一致。

PyTorch 源码：

def count_parameters(model):return sum(p.numel() for p in model.parameters() if p.requires_grad)

以 Qwen2-VL-7B-Instruct 、Qwen2-7B-Instruct、Llama-3.1-8B-Instruct 为例。

网络结构参数量：

• Linear：参数矩阵，或者加上bias，Linear(in_features=w, out_features=h, bias=True) 参数量是 x=w*h+h，当 bias=False, 则是 x=w*h。
• Embedding：认为是没有 bias 的 Linear。
• Norm：
  • LayerNorm 包括 2 个可训练参数 $\gamma$ 和 $\beta$，假设 hidden_size 的大小为 h，hidden_size 每一维都有两个参数，即 2*hidden_size
  • RMSNorm 每 1 维则只有 1 个可训练参数 , 即 hidden_size
• Conv3D：即 Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)，即参数量=输入维度*输出维度*卷积核， 3*1280*2*14*14=1505280
• RotaryEmbedding、Activition 和 Dropout：旋转位置编码、激活函数、Dropout 都没有可训练参数

Llama-3.1-8B-Instruct 参数量：

$$128256\ast 4096+32\ast (4096\ast 4096\ast 2+4096\ast 1024\ast 2+4096\ast 14336\ast 3+2\ast 4096)+4096+4096\ast 128256=8030261248=8B$$

即：

$$Parameters=Embedding+layers\ast (Linea{r}_{QKVO}+Linea{r}_{mlp}+RMSNorm)+RMSNorm+Linear$$

计算参数量：[Info] parameters: 8030261248

大语言模型 Llama-3.1-8B-Instruct 的网络结构：

LlamaForCausalLM((model): LlamaModel((embed_tokens): Embedding(128256, 4096)(layers): ModuleList((0-31): 32 x LlamaDecoderLayer((self_attn): LlamaSdpaAttention((q_proj): Linear(in_features=4096, out_features=4096, bias=False)(k_proj): Linear(in_features=4096, out_features=1024, bias=False)(v_proj): Linear(in_features=4096, out_features=1024, bias=False)(o_proj): Linear(in_features=4096, out_features=4096, bias=False)(rotary_emb): LlamaRotaryEmbedding())(mlp): LlamaMLP((gate_proj): Linear(in_features=4096, out_features=14336, bias=False)(up_proj): Linear(in_features=4096, out_features=14336, bias=False)(down_proj): Linear(in_features=14336, out_features=4096, bias=False)(act_fn): SiLU())(input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)(post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)))(norm): LlamaRMSNorm((4096,), eps=1e-05)(rotary_emb): LlamaRotaryEmbedding())(lm_head): Linear(in_features=4096, out_features=128256, bias=False)
)

多模态视觉大模型 Qwen2-VL-7B-Instruct 的网络结构：

Qwen2VLForConditionalGeneration((visual): Qwen2VisionTransformerPretrainedModel((patch_embed): PatchEmbed((proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False))(rotary_pos_emb): VisionRotaryEmbedding()(blocks): ModuleList((0-31): 32 x Qwen2VLVisionBlock((norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)(norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)(attn): VisionSdpaAttention((qkv): Linear(in_features=1280, out_features=3840, bias=True)(proj): Linear(in_features=1280, out_features=1280, bias=True))(mlp): VisionMlp((fc1): Linear(in_features=1280, out_features=5120, bias=True)(act): QuickGELUActivation()(fc2): Linear(in_features=5120, out_features=1280, bias=True))))(merger): PatchMerger((ln_q): LayerNorm((1280,), eps=1e-06, elementwise_affine=True)(mlp): Sequential((0): Linear(in_features=5120, out_features=5120, bias=True)(1): GELU(approximate='none')(2): Linear(in_features=5120, out_features=3584, bias=True))))(model): Qwen2VLModel((embed_tokens): Embedding(152064, 3584)(layers): ModuleList((0-27): 28 x Qwen2VLDecoderLayer((self_attn): Qwen2VLSdpaAttention((q_proj): Linear(in_features=3584, out_features=3584, bias=True)(k_proj): Linear(in_features=3584, out_features=512, bias=True)(v_proj): Linear(in_features=3584, out_features=512, bias=True)(o_proj): Linear(in_features=3584, out_features=3584, bias=False)(rotary_emb): Qwen2VLRotaryEmbedding())(mlp): Qwen2MLP((gate_proj): Linear(in_features=3584, out_features=18944, bias=False)(up_proj): Linear(in_features=3584, out_features=18944, bias=False)(down_proj): Linear(in_features=18944, out_features=3584, bias=False)(act_fn): SiLU())(input_layernorm): Qwen2RMSNorm((3584,), eps=1e-06)(post_attention_layernorm): Qwen2RMSNorm((3584,), eps=1e-06)))(norm): Qwen2RMSNorm((3584,), eps=1e-06)(rotary_emb): Qwen2VLRotaryEmbedding())(lm_head): Linear(in_features=3584, out_features=152064, bias=False)
)

总参数量：[Info] parameters: 8291375616

• 视觉模型的参数量：[Info] parameters model.visual: 675759104
• 语言模型的参数量：[Info] parameters model.model: 7070619136 + [Info] parameters model.lm_head: 544997376

即：675759104(8.15%) + 7070619136(85.28%) + 544997376(6.57%) = 8291375616 = 8B

Qwen2-VL-7B-Instruct 的 Qwen2VisionTransformerPretrainedModel 参数量：

• patch_embed 参数量： 3*1280*2*14*14=1505280
• blocks 参数量：[Info] parameters model.visual.blocks: 629678080
  • 详细计算公式：32*(1280*2*2 + (1280+1)*3840 + (1280+1)*1280 + 1280*5121 + 5120*1281)=629678080
• merger 参数量：

合并计算公式：

$$\begin{gathered} 3\ast 1280\ast 2\ast 14\ast 14+32\ast (1280\ast 2\ast 2+(1280+1)\ast 3840+(1280+1)\ast 1280+1280\ast 5121+5120\ast 1281)+1280\ast 2+5120\ast 5121+(5120+1)\ast 3584 \\ =675759104 \end{gathered}$$

Qwen2-VL-7B-Instruct 的 Qwen2VLModel 参数量：

$$\begin{gathered} 152064\ast 3584+28\ast ((3584+1)\ast 3584+(3584+1)\ast 512\ast 2+3584\ast 3584+3584\ast 18944\ast 3+2\ast 3584)+3584 \\ =7070619136 \\ 3584\ast 152064=544997376 \end{gathered}$$

因此，Qwen2-VL-7B 的数据量完全对齐。

测试：

# 预训练模型, 查看其词表大小
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessorprint(f"[Info] transformers version: {transformers.__version__}")def count_parameters(model):return sum(p.numel() for p in model.parameters() if p.requires_grad)# ------------ Qwen2-VL-7B ----------- #
model_path = "[your path]/llm/Qwen/Qwen2-VL-7B-Instruct"
print(f"[Info] model_path: {model_path}")# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)
configuration = model.config
print(f"[Info] Qwen2-VL-7B vocab_size: {configuration.vocab_size}")
print(model)
print(f"[Info] parameters: {count_parameters(model)}")
print(f"[Info] parameters model.visual: {count_parameters(model.visual)}")
print(f"[Info] parameters model.model: {count_parameters(model.model)}")
print(f"[Info] parameters model.lm_head: {count_parameters(model.lm_head)}")
print(f"[Info] parameters model.visual.patch_embed: {count_parameters(model.visual.patch_embed)}")
print(f"[Info] parameters model.visual.blocks: {count_parameters(model.visual.blocks)}")
print(f"[Info] parameters model.visual.blocks[0].norm1: {count_parameters(model.visual.blocks[0].norm1)}")
print(f"[Info] parameters model.visual.blocks[0].norm2: {count_parameters(model.visual.blocks[0].norm2)}")
print(f"[Info] parameters model.visual.blocks[0].attn: {count_parameters(model.visual.blocks[0].attn)}")
print(f"[Info] parameters model.visual.blocks[0].mlp: {count_parameters(model.visual.blocks[0].mlp)}")
# ------------ Qwen2-VL-7B ----------- ## ------------ Qwen2-7B ----------- #
model_path = "[your path]/llm/Qwen/Qwen2-7B-Instruct"
print(f"[Info] model_path: {model_path}")device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
print(f"[Info] Qwen2-7B vocab_size: {tokenizer.vocab_size}")
print(model)
print(f"[Info] parameters: {count_parameters(model)}")
# ------------ Qwen2-7B ----------- ## ------------ Llama-3.1-8B ----------- #
model_path = "[your path]/llm/Meta-Llama-3.1-8B-Instruct"
print(f"[Info] model_path: {model_path}")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16,device_map="auto",
)
print(f"[Info] Llama-3.1-8B vocab_size: {tokenizer.vocab_size}")
print(model)
print(f"[Info] parameters: {count_parameters(model)}")
# ------------ Llama-3.1-8B ----------- #

Qwen2-7B 的参数量是 7615616512，即 7070619136 + 544997376 = 7615616512

参考：

• 大模型的参数量是如何计算的
• 大模型参数量如何计算
• 如何根据模型结构，计算大模型的参数量？