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iomgaa 2025-09-01 21:15:37 +08:00
parent 05e93cbc48
commit 17b66131b6
7 changed files with 319 additions and 249 deletions
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1
.python-version Normal file
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@ -0,0 +1 @@
3.13

62
.vscode/launch.json vendored
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@ -6,37 +6,37 @@
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"program": "${workspaceFolder}/train_pretrain_accelerate.py", "program": "${workspaceFolder}/train_pretrain_accelerate.py",
"args": [ // "args": [
"--out_dir", "out", // "--out_dir", "out",
"--epochs", "3", // "--epochs", "3",
"--embedding_epoch", "2", // "--embedding_epoch", "2",
"--batch_size", "128", // "--batch_size", "128",
"--learning_rate", "8e-5", // "--learning_rate", "8e-5",
"--dtype", "bfloat16", // "--dtype", "bfloat16",
"--use_swanlab", // "--use_swanlab",
"--swanlab_project", "MiniMind-Pretrain", // "--swanlab_project", "MiniMind-Pretrain",
"--num_workers", "1", // "--num_workers", "1",
"--accumulation_steps", "16", // "--accumulation_steps", "16",
"--grad_clip", "0.5", // "--grad_clip", "0.5",
"--warmup_iters", "0", // "--warmup_iters", "0",
"--log_interval", "1", // "--log_interval", "1",
"--save_interval", "10000", // "--save_interval", "10000",
"--dim", "512", // "--dim", "512",
"--n_layers", "8", // "--n_layers", "8",
"--max_seq_len", "512", // "--max_seq_len", "512",
"--data_path", "./dataset/stable/merged_pretrain.jsonl", // "--data_path", "./dataset/stable/merged_pretrain.jsonl",
"--profile", // "--profile",
"--profile_interval", "10", // "--profile_interval", "10",
"--use_flash_attn", // "--use_flash_attn",
"--knowledge_num", "1048576", // "--knowledge_num", "1048576",
"--knowledge_length", "32", // "--knowledge_length", "32",
"--database_init_path", "./dataset/stable/sentence_trex_data.json", // "--database_init_path", "./dataset/stable/sentence_trex_data.json",
"--fast_clustering", // "--fast_clustering",
"--cluster_cache_path", "./cache/cluster_tokens_single.pt", // "--cluster_cache_path", "./cache/cluster_tokens_single.pt",
"--memory_monitor_interval", "10", // "--memory_monitor_interval", "10",
"--model_type", "model", // "--model_type", "model",
"--model_size", "538" // "--model_size", "538"
], // ],
"env": { "env": {
"CUDA_VISIBLE_DEVICES": "0", "CUDA_VISIBLE_DEVICES": "0",
"NCCL_DEBUG": "INFO", "NCCL_DEBUG": "INFO",

6
main.py Normal file
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@ -0,0 +1,6 @@
def main():
print("Hello from minimind!")
if __name__ == "__main__":
main()

8
model/model_memory.py
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@ -337,6 +337,8 @@ class MiniMindBlock(nn.Module):
self.memory_gate = MemoryGate(config) self.memory_gate = MemoryGate(config)
self.gated_memory_fusion = GatedMemoryFusion(config) self.gated_memory_fusion = GatedMemoryFusion(config)
self.attentionpool = nn.Linear(16, 1)
def forward(self, x, pos_cis, memory_bank, tok_embeddings, collect_ema_stats=False): def forward(self, x, pos_cis, memory_bank, tok_embeddings, collect_ema_stats=False):
""" """
Args: Args:
@ -369,11 +371,13 @@ class MiniMindBlock(nn.Module):
# 解码token_ids为特征向量并立即压缩避免显存爆炸 # 解码token_ids为特征向量并立即压缩避免显存爆炸
selected_embeddings = tok_embeddings(selected_token_ids) # [batch * seq_len * num_selected, knowledge_length, dim] selected_embeddings = tok_embeddings(selected_token_ids) # [batch * seq_len * num_selected, knowledge_length, dim]
knowledge_length = selected_token_ids.size(-1)
# 立即压缩：knowledge_length * dim -> knowledge_dim 避免显存爆炸 # 立即压缩：knowledge_length * dim -> knowledge_dim 避免显存爆炸
# 使用平均池化压缩knowledge_length维度 # 使用平均池化压缩knowledge_length维度
pooled_memory = selected_embeddings.mean(dim=1) # [batch * seq_len * num_selected, dim] # pooled_memory = selected_embeddings.mean(dim=1) # [batch * seq_len * num_selected, dim]
attn_weights = self.attention(selected_embeddings)
attn_weights = torch.softmax(attn_weights, dim=1)
pooled_memory = torch.sum(selected_embeddings * attn_weights, dim=1)
selected_memory = pooled_memory.view(bsz, seq_len, num_selected, self.dim) # [batch, seq_len, num_selected, dim] selected_memory = pooled_memory.view(bsz, seq_len, num_selected, self.dim) # [batch, seq_len, num_selected, dim]

1
pyproject.toml
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@ -134,7 +134,6 @@ dependencies = [
"scikit-learn==1.5.1", "scikit-learn==1.5.1",
"scipy==1.15.2", "scipy==1.15.2",
"sentence-transformers==2.3.1", "sentence-transformers==2.3.1",
"sentencepiece==0.2.0",
"sentry-sdk==2.26.1", "sentry-sdk==2.26.1",
"setproctitle==1.3.5", "setproctitle==1.3.5",
"simhash==2.1.2", "simhash==2.1.2",

35
train_pretrain_accelerate.py
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@ -776,7 +776,7 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
sentence, sentence,
add_special_tokens=True, add_special_tokens=True,
truncation=True, truncation=True,
max_length=knowledge_length, max_length=len(sentence),
padding=False, padding=False,
return_tensors="pt" return_tensors="pt"
)['input_ids'].squeeze().tolist() )['input_ids'].squeeze().tolist()
@ -1007,9 +1007,21 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
balance_loss = 0 balance_loss = 0
if hasattr(res, 'aux_loss') and res.aux_loss is not None: if hasattr(res, 'aux_loss') and res.aux_loss is not None:
balance_loss = res.aux_loss balance_loss = res.aux_loss
# 获取余弦相似度统计信息（如果模型支持）
cosine_stats = {}
if hasattr(res, 'cosine_stats') and res.cosine_stats is not None:
cosine_stats = res.cosine_stats
# 计算平均余弦相似度
avg_cosine_similarity = 0.0
if cosine_stats:
# 计算所有层的平均余弦相似度
cosine_similarities = [v for k, v in cosine_stats.items() if k.endswith('_avg_cosine_similarity')]
if cosine_similarities:
avg_cosine_similarity = np.mean(cosine_similarities)
# 计算总损失 # 计算总损失
total_loss = ce_loss + args.balance_loss_coef * balance_loss total_loss = ce_loss + args.balance_loss_coef * balance_loss + 10 * (1-avg_cosine_similarity)
loss = total_loss / args.accumulation_steps loss = total_loss / args.accumulation_steps
# 计时前向传播结束 (只在主进程进行) # 计时前向传播结束 (只在主进程进行)
@ -1207,10 +1219,6 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
if hasattr(res, 'layer_stats') and res.layer_stats is not None: if hasattr(res, 'layer_stats') and res.layer_stats is not None:
layer_stats = res.layer_stats layer_stats = res.layer_stats
# 获取余弦相似度统计信息（如果模型支持）
cosine_stats = {}
if hasattr(res, 'cosine_stats') and res.cosine_stats is not None:
cosine_stats = res.cosine_stats
# 构建日志字典 # 构建日志字典
log_dict = { log_dict = {
@ -1233,13 +1241,6 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
# 添加记忆库更新统计 # 添加记忆库更新统计
log_dict.update(memory_update_stats) log_dict.update(memory_update_stats)
# 计算平均余弦相似度
avg_cosine_similarity = 0.0
if cosine_stats:
# 计算所有层的平均余弦相似度
cosine_similarities = [v for k, v in cosine_stats.items() if k.endswith('_avg_cosine_similarity')]
if cosine_similarities:
avg_cosine_similarity = np.mean(cosine_similarities)
# 添加层级统计信息（选择性添加关键指标） # 添加层级统计信息（选择性添加关键指标）
if layer_stats: if layer_stats:
@ -1320,7 +1321,7 @@ def main():
parser.add_argument("--out_dir", type=str, default="out") parser.add_argument("--out_dir", type=str, default="out")
parser.add_argument("--epochs", type=int, default=4) parser.add_argument("--epochs", type=int, default=4)
parser.add_argument("--embedding_epoch", type=int, default=2, help="embedding训练的epoch数") parser.add_argument("--embedding_epoch", type=int, default=2, help="embedding训练的epoch数")
parser.add_argument("--batch_size", type=int, default=60) parser.add_argument("--batch_size", type=int, default=20)
parser.add_argument("--learning_rate", type=float, default=2e-4) parser.add_argument("--learning_rate", type=float, default=2e-4)
parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--dtype", type=str, default="bfloat16")
parser.add_argument("--use_swanlab", default=True, action="store_true") # 替换wandb参数 parser.add_argument("--use_swanlab", default=True, action="store_true") # 替换wandb参数
@ -1337,7 +1338,7 @@ def main():
parser.add_argument('--max_seq_len', default=512, type=int) parser.add_argument('--max_seq_len', default=512, type=int)
parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument('--use_moe', default=False, type=bool)
parser.add_argument('--disable_db', action='store_true', help="禁用数据库功能，使用固定值1e-4替代") parser.add_argument('--disable_db', action='store_true', help="禁用数据库功能，使用固定值1e-4替代")
parser.add_argument("--data_path", type=str, default="/home/pci/ycz/Code/Minimind/dataset/stable/merged_pretrain.jsonl") parser.add_argument("--data_path", type=str, default="/home/iomgaa/Code/Minimind/dataset/stable/merged_pretrain.jsonl")
parser.add_argument("--pretrained_embedding_path", type=str, default=None, help="Path to pretrained token embedding weights (.pth file)") parser.add_argument("--pretrained_embedding_path", type=str, default=None, help="Path to pretrained token embedding weights (.pth file)")
parser.add_argument("--profile", action="store_true", default=True, help="启用性能分析") parser.add_argument("--profile", action="store_true", default=True, help="启用性能分析")
parser.add_argument("--profile_interval", type=int, default=10, help="性能分析打印间隔（步数）") parser.add_argument("--profile_interval", type=int, default=10, help="性能分析打印间隔（步数）")
@ -1345,9 +1346,9 @@ def main():
parser.add_argument("--knowledge_num", type=int, default=960400,help="知识库的数据数目") parser.add_argument("--knowledge_num", type=int, default=960400,help="知识库的数据数目")
parser.add_argument("--knowledge_length", type=int, default=8,help="知识库的句子长度") parser.add_argument("--knowledge_length", type=int, default=8,help="知识库的句子长度")
parser.add_argument("--knowledge_dim", type=int, default=128,help="知识库的向量维度") parser.add_argument("--knowledge_dim", type=int, default=128,help="知识库的向量维度")
parser.add_argument("--database_init_path", type=str, default="/home/pci/ycz/Code/Minimind/dataset/stable/sentence_trex_data.json", help="数据库初始化路径") parser.add_argument("--database_init_path", type=str, default="/home/iomgaa/Code/Minimind/dataset/stable/sentence_trex_data.json", help="数据库初始化路径")
parser.add_argument("--fast_clustering", action="store_true", default=True, help="使用快速近似聚类算法（适用于大数据集）") parser.add_argument("--fast_clustering", action="store_true", default=True, help="使用快速近似聚类算法（适用于大数据集）")
parser.add_argument("--cluster_cache_path", type=str, default="/home/pci/ycz/Code/Minimind/cache/cluster_tokens_single.pt", help="聚类结果缓存文件路径") parser.add_argument("--cluster_cache_path", type=str, default="/home/iomgaa/Code/Minimind/cache/cluster_tokens_single.pt", help="聚类结果缓存文件路径")
parser.add_argument("--recompute_clusters", action="store_true", default=False, help="强制重新计算聚类，忽略缓存文件") parser.add_argument("--recompute_clusters", action="store_true", default=False, help="强制重新计算聚类，忽略缓存文件")
parser.add_argument("--memory_monitor", action="store_true", default=False, help="启用内存监控") parser.add_argument("--memory_monitor", action="store_true", default=False, help="启用内存监控")
parser.add_argument("--memory_monitor_interval", type=int, default=10, help="内存监控间隔（步数）") parser.add_argument("--memory_monitor_interval", type=int, default=10, help="内存监控间隔（步数）")

455
uv.lock generated
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@ -7,8 +7,8 @@ resolution-markers = [
"python_full_version >= '3.13' and sys_platform != 'linux'", "python_full_version >= '3.13' and sys_platform != 'linux'",
"python_full_version == '3.12.*' and sys_platform != 'linux'", "python_full_version == '3.12.*' and sys_platform != 'linux'",
"python_full_version == '3.11.*' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform == 'linux'",
"python_full_version == '3.11.*' and sys_platform != 'linux'",
"python_full_version < '3.11' and sys_platform == 'linux'", "python_full_version < '3.11' and sys_platform == 'linux'",
"python_full_version == '3.11.*' and sys_platform != 'linux'",
"python_full_version < '3.11' and sys_platform != 'linux'", "python_full_version < '3.11' and sys_platform != 'linux'",
] ]
@ -236,21 +236,21 @@ wheels = [
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{ name = "typing-extensions" }, { name = "typing-extensions" },
] ]
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