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修正了key分解、负载均衡等错误

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iomgaa 2025-06-06 11:25:59 +08:00
parent 64e92473c3
commit 000e17a93f
5 changed files with 363 additions and 454 deletions
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102
.vscode/launch.json vendored Normal file
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@ -0,0 +1,102 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "Debug Train Pretrain Accelerate",
"type": "python",
"request": "launch",
"program": "${workspaceFolder}/train_pretrain_accelerate.py",
"console": "integratedTerminal",
"python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"cwd": "${workspaceFolder}",
"env": {
"PYTHONPATH": "${workspaceFolder}",
"CUDA_VISIBLE_DEVICES": "0"
},
"justMyCode": false,
"stopOnEntry": false,
"redirectOutput": true
},
{
"name": "Debug Train Pretrain Accelerate (Multi-GPU)",
"type": "python",
"request": "launch",
"program": "${workspaceFolder}/train_pretrain_accelerate.py",
"console": "integratedTerminal",
"python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"args": [
"--hidden_size", "512",
"--max_seq_len", "512",
"--n_layers", "8",
"--batch_size", "8",
"--epochs", "1"
],
"cwd": "${workspaceFolder}",
"env": {
"PYTHONPATH": "${workspaceFolder}",
"CUDA_VISIBLE_DEVICES": "0,1"
},
"justMyCode": false,
"stopOnEntry": false,
"redirectOutput": true
},
{
"name": "Debug Train Pretrain Accelerate (Small Test)",
"type": "python",
"request": "launch",
"program": "${workspaceFolder}/train_pretrain_accelerate.py",
"console": "integratedTerminal",
"python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"args": [
"--hidden_size", "512",
"--max_seq_len", "512",
"--n_layers", "8",
"--batch_size", "2",
"--epochs", "1",
"--log_interval", "10",
"--save_interval", "100",
"--accumulation_steps", "4"
],
"cwd": "${workspaceFolder}",
"env": {
"PYTHONPATH": "${workspaceFolder}",
"CUDA_VISIBLE_DEVICES": "0",
"WANDB_MODE": "offline"
},
"justMyCode": false,
"stopOnEntry": false,
"redirectOutput": true
},
{
"name": "Debug ExtractDB Comparison",
"type": "python",
"request": "launch",
"program": "${workspaceFolder}/train_pretrain_accelerate.py",
"console": "integratedTerminal",
"python": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"args": [
"--hidden_size", "512",
"--max_seq_len", "256",
"--n_layers", "4",
"--batch_size", "2",
"--epochs", "1",
"--log_interval", "10",
"--save_interval", "200",
"--accumulation_steps", "2",
"--comparison_mode",
"--knowledge_num", "256",
"--knowledge_length", "64",
"--comparison_mode"
],
"cwd": "${workspaceFolder}",
"env": {
"PYTHONPATH": "${workspaceFolder}",
"CUDA_VISIBLE_DEVICES": "0",
"WANDB_MODE": "offline"
},
"justMyCode": false,
"stopOnEntry": false,
"redirectOutput": true
}
]
}

18
.vscode/settings.json vendored Normal file
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@ -0,0 +1,18 @@
{
"python.pythonPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"python.defaultInterpreterPath": "/home/iomgaa/miniconda3/envs/accelerate/bin/python",
"python.terminal.activateEnvironment": true,
"python.terminal.activateEnvInCurrentTerminal": true,
"python.linting.enabled": true,
"python.linting.pylintEnabled": false,
"python.linting.flake8Enabled": true,
"python.formatting.provider": "black",
"python.analysis.autoImportCompletions": true,
"python.analysis.typeCheckingMode": "off",
"files.exclude": {
"**/__pycache__": true,
"**/*.pyc": true,
"**/.git": false,
"**/wandb": false
}
}

2
model/LMConfig.py
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@ -39,6 +39,7 @@ class LMConfig(PretrainedConfig):
####################################################
knowledge_num: int = 64*64,
knowledge_length: int = 8,
knowledge_dim: int = 128,
**kwargs,
):
self.dim = dim
@ -72,4 +73,5 @@ class LMConfig(PretrainedConfig):
####################################################
self.knowledge_num = knowledge_num
self.knowledge_length = knowledge_length
self.knowledge_dim = knowledge_dim
super().__init__(**kwargs)

665
model/model.py
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@ -11,14 +11,9 @@ import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from torch import nn, einsum
from einops import rearrange, repeat
def exists(val):
return val is not None
# RMSNorm 类定义了一个用于归一化输入张量的模块。
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
@ -31,7 +26,7 @@ class RMSNorm(torch.nn.Module):
def forward(self, x):
return self.weight * self._norm(x.float()).type_as(x)
# precompute_pos_cis 函数用于预计算位置编码（复数版本）。
def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device) # type: ignore
@ -39,7 +34,7 @@ def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return pos_cis
# apply_rotary_emb 函数用于应用旋转位置编码（复数版本）。
def apply_rotary_emb(xq, xk, pos_cis):
def unite_shape(pos_cis, x):
ndim = x.ndim
@ -55,200 +50,166 @@ def apply_rotary_emb(xq, xk, pos_cis):
xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
# precompute_pos_cis_real 函数用于预计算位置编码（实数版本）。
def precompute_pos_cis_real(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
"""使用实数张量实现位置编码，避免使用复数张量
这个函数与precompute_pos_cis完全等价，但使用实数张量而非复数张量。
原始函数生成形状为[seq_len, dim//2]的复数张量，其中实部全为1，虚部为旋转角度。
这个函数生成形状为[seq_len, dim]的实数张量，其中偶数索引是cos(角度)，奇数索引是sin(角度)。
"""
# 确保dim是偶数
if dim % 2 != 0:
raise ValueError(f"维度必须是偶数，但得到了 {dim}")
# 复制原始函数的频率计算逻辑
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device)
freqs = torch.outer(t, freqs).float()
# 计算cos和sin值
# 在复数版本中，pos_cis = torch.polar(torch.ones_like(freqs), freqs)
# 等价于 cos(freqs) + i*sin(freqs)
cos = torch.cos(freqs)
sin = torch.sin(freqs)
# 创建实数张量，交错排列cos和sin
pos_emb = torch.zeros((end, dim), device=freqs.device)
pos_emb[:, 0::2] = cos # 偶数索引放cos
pos_emb[:, 1::2] = sin # 奇数索引放sin
return pos_emb
# apply_rotary_emb_real 函数用于应用旋转位置编码（实数版本）。
def apply_rotary_emb_real(xq, xk, pos_emb):
"""使用实数张量实现旋转位置编码，避免使用复数张量
这个函数与apply_rotary_emb完全等价，但使用实数张量而非复数张量。
原始函数将输入张量转换为复数形式，与位置编码相乘，然后再转回实数形式。
这个函数直接使用实数运算实现相同的旋转操作。
"""
# 获取形状信息
bsz, seq_len, n_heads, head_dim = xq.shape
# 确保pos_emb形状正确
assert pos_emb.shape[0] >= seq_len, f"位置编码长度 {pos_emb.shape[0]} 小于序列长度 {seq_len}"
assert pos_emb.shape[1] == head_dim, f"位置编码维度 {pos_emb.shape[1]} 与头维度 {head_dim} 不匹配"
# 截取需要的位置编码长度
pos_emb = pos_emb[:seq_len]
# 将pos_emb调整为广播形状 [1, seq_len, 1, head_dim]
pos_emb = pos_emb.unsqueeze(0).unsqueeze(2)
# 将head_dim分成两半
half_head_dim = head_dim // 2
# 提取cos和sin值（偶数索引是cos，奇数索引是sin）
cos = pos_emb[..., 0::2]
sin = pos_emb[..., 1::2]
# 将xq和xk重新排列，以便进行旋转操作
# 原始复数版本中，xq和xk被重塑为复数张量，其中实部和虚部交错排列
# 在实数版本中，我们需要将偶数索引和奇数索引分开处理
# 分离偶数和奇数索引
xq_even = xq[..., 0::2] # 偶数索引，对应复数的实部
xq_odd = xq[..., 1::2] # 奇数索引，对应复数的虚部
xk_even = xk[..., 0::2]
xk_odd = xk[..., 1::2]
# 应用旋转（等价于复数乘法）
# (a + bi)(cos + sin*i) = (a*cos - b*sin) + (a*sin + b*cos)i
# 其中a是偶数索引，b是奇数索引
xq_out_even = xq_even * cos - xq_odd * sin # 新的偶数索引（实部）
xq_out_odd = xq_even * sin + xq_odd * cos # 新的奇数索引（虚部）
xk_out_even = xk_even * cos - xk_odd * sin
xk_out_odd = xk_even * sin + xk_odd * cos
# 重新组合偶数和奇数索引
xq_out = torch.zeros_like(xq)
xk_out = torch.zeros_like(xk)
xq_out[..., 0::2] = xq_out_even
xq_out[..., 1::2] = xq_out_odd
xk_out[..., 0::2] = xk_out_even
xk_out[..., 1::2] = xk_out_odd
return xq_out.type_as(xq), xk_out.type_as(xk)
# repeat_kv 函数用于重复键值对。
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
bs, slen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, :, None, :]
.expand(bs, slen, n_kv_heads, n_rep, head_dim)
.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
)
class Attention(nn.Module):
def __init__(self, args: LMConfig):
class KnowledgeDataset(nn.Module):
def __init__(self, params, tok_embeddings, is_train=True):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
assert args.n_heads % self.n_kv_heads == 0
self.n_local_heads = args.n_heads
self.n_local_kv_heads = self.n_kv_heads
self.n_rep = self.n_local_heads // self.n_local_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
self.attn_dropout = nn.Dropout(args.dropout)
self.resid_dropout = nn.Dropout(args.dropout)
self.dropout = args.dropout
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
mask = torch.triu(mask, diagonal=1)
self.register_buffer("mask", mask, persistent=False)
self.is_train = is_train
self.params = params
self.tok_embeddings = tok_embeddings
def forward(self,
x: torch.Tensor,
pos_cis: torch.Tensor,
db_value=None):
bsz, seq_len, _ = x.shape #bsz: 批量大小, seq_len: 序列长度, _: 隐藏维度
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) #将输入张量x分别通过线性层wq, wk, wv进行变换，得到查询、键和值。
xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) #将变换后的张量xq重塑为形状为(bsz, seq_len, n_local_heads, head_dim)的形状。
xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xk重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xv重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
# 应用旋转位置编码（使用实数版本）
xq, xk = apply_rotary_emb_real(xq, xk, pos_cis)
# kv_cache实现 REMOVED
# if past_key_value is not None:
# xk = torch.cat([past_key_value[0], xk], dim=1)
# xv = torch.cat([past_key_value[1], xv], dim=1)
# past_kv = (xk, xv) if use_cache else None
# 重复键值对
xq, xk, xv = (
xq.transpose(1, 2),
repeat_kv(xk, self.n_rep).transpose(1, 2),
repeat_kv(xv, self.n_rep).transpose(1, 2)
# 嵌入参数
self.knowledge_dim = params.knowledge_dim
self.key_dim = self.knowledge_dim // 2
self.to_queries = nn.Sequential(
nn.Linear(params.dim, self.knowledge_dim, bias=False),
)
# 如果提供了db_value，根据头的数量调整它的形状并与xv合并
if db_value is not None:
# 确保db_value的形状与xv兼容，假设db_value形状为[B, N, H, D]
if db_value.ndim == 4: # [B, N, H, D]
db_value = db_value.transpose(1, 2) # -> [B, H, N, D]
## 数据库参数
self.knowledge_num = params.knowledge_num
self.knowledge_length = params.knowledge_length
self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True)
self.product_key_topk = min(16, self.knowledge_num)
# 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))
# 检查是否需要调整D维度
if db_value.shape[-1] != xv.shape[-1]:
# 如果db_value的维度与xv不同，可以添加一个投影层
# 或者在这里使用简单的调整方法
# 这里我们简单地通过均值池化或重复来调整维度
if db_value.shape[-1] > xv.shape[-1]:
# 降维
factor = db_value.shape[-1] // xv.shape[-1]
db_value = db_value.view(bsz, self.n_local_heads, seq_len, factor, xv.shape[-1])
db_value = db_value.mean(dim=3)
else:
# 升维
factor = xv.shape[-1] // db_value.shape[-1]
db_value = db_value.unsqueeze(-1).repeat(1, 1, 1, 1, factor)
db_value = db_value.view(bsz, self.n_local_heads, seq_len, xv.shape[-1])
# 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
self.register_buffer('knowledge_dataset',
torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long)
)
# 将db_value与xv相加或融合
# 这里我们简单地将它们相加，但你也可以使用其他融合方法
xv = xv + db_value
# 计算step数目，用于动态调整权重
self.step_counter = 0
# 使用Flash Attention
if self.flash and seq_len != 1:
dropout_p = self.dropout if self.training else 0.0
output = F.scaled_dot_product_attention(
xq, xk, xv,
attn_mask=None,
dropout_p=dropout_p,
is_causal=True
)
else:
scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
scores += self.mask[:, :, :seq_len, :seq_len]
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
scores = self.attn_dropout(scores)
output = scores @ xv
output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
output = self.resid_dropout(self.wo(output))
return output
def intelligent_selection(self, query, all_scores, all_indices):
"""智能分层选择策略"""
if self.is_train == False:
return all_scores, all_indices
batch_size = all_scores.size(0)
device = all_scores.device
dtype = all_scores.dtype
# 对每个batch进行分层选择
enhanced_scores = all_scores.clone()
query_features = query.mean(dim=1) # [batch_size, dim]
# 预先计算所有候选条目的嵌入（批量优化）
all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0)
unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True)
# 批量计算唯一候选条目的嵌入
candidate_tokens = self.knowledge_dataset[unique_indices]
flat_tokens = candidate_tokens.view(-1)
flat_embeddings = self.tok_embeddings(flat_tokens)
#获取flat_tokens对应的index
pre_update_indices = unique_indices.view(-1)
pre_update_embeddings = flat_embeddings.view(
len(unique_indices), self.knowledge_length, -1
)
unique_candidate_features = flat_embeddings.view(
len(unique_indices), self.knowledge_length, -1
).mean(dim=1) # [num_unique_candidates, dim]
# 归一化候选特征（优化相似度计算）
normalized_candidates = F.normalize(unique_candidate_features, dim=-1)
normalized_queries = F.normalize(query_features, dim=-1)
# 收集所有batch的best_tokens
batch_best_tokens = []
batch_best_tokens_embeddings = []
for batch_idx in range(batch_size):
indices = all_indices[batch_idx]
# 获取当前batch候选条目对应的特征索引
start_idx = batch_idx * len(indices)
end_idx = start_idx + len(indices)
batch_inverse_indices = inverse_indices[start_idx:end_idx]
# 使用预计算的归一化特征进行优化相似度计算
batch_candidate_features = normalized_candidates[batch_inverse_indices]
query_feature = normalized_queries[batch_idx]
# 使用矩阵乘法计算余弦相似度
similarity_scores = torch.mv(batch_candidate_features, query_feature)
# 找到最大相似度分数的索引
max_similarity_idx = torch.argmax(similarity_scores)
# 获取最大相似度对应的候选条目索引
best_candidate_idx = indices[max_similarity_idx]
# 获取对应的tokens
best_tokens = self.knowledge_dataset[best_candidate_idx]
best_tokens_embeddings = self.tok_embeddings(best_tokens)
# 将当前batch的best_tokens添加到列表中
batch_best_tokens.append(best_tokens)
batch_best_tokens_embeddings.append(best_tokens_embeddings)
# 将所有batch的best_tokens堆叠成一个张量
# [batch_size, knowledge_length]
all_best_tokens = torch.stack(batch_best_tokens, dim=0)
all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0)
# 获取
# 使用重新计算的embeddings更新self.keys
if self.is_train:
self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
# 更新被修改过的key
with torch.no_grad():
self.has_update_keys[pre_update_indices] = 1
return all_best_tokens, all_best_tokens_embeddings
def _update_keys_with_embeddings(self, pre_update_indices, pre_update_embeddings):
# 使用pre_update_embeddings更新self.keys
with torch.no_grad():
pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [337, 512]
pre_update_embeddings = self.to_queries(pre_update_embeddings)
self.keys[pre_update_indices] = pre_update_embeddings
def search_index(self,x):
batch_size, seq_len, dim = x.shape
# collapse sequence dimension by averaging
x_flat = x.mean(dim=1) # [batch_size, dim]
queries = self.to_queries(x_flat) # [batch_size, 2*dim_key]
# queries = queries.reshape(batch_size, 2, self.key_dim)
# queries = queries.permute(1, 0, 2)
# 2. 计算queries与keys的相似度
sim = torch.einsum('b d, k d -> b k', queries, self.keys)
# 3. 在两个子空间分别做top-k
scores_and_indices = sim.topk(self.product_key_topk, dim=-1)
scores, indices = scores_and_indices[0], scores_and_indices[1]
# 5. 应用智能分层选择策略
best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
# 6. 更新1%的keys
if self.is_train:
# 获取未更新过的keys的索引
not_updated_indices = torch.where(self.has_update_keys == 0)[0]
# 如果有未更新的keys，随机选择num_update_keys个进行更新
if len(not_updated_indices) > 0:
num_update_keys = int(self.knowledge_num * 0.01)
perm = torch.randperm(len(not_updated_indices))[:num_update_keys]
pre_update_indices = not_updated_indices[perm]
pre_update_tokens = self.knowledge_dataset[pre_update_indices]
pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1))
pre_update_embeddings = pre_update_embeddings.view(num_update_keys, self.knowledge_length, -1)
self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
return best_tokens, best_tokens_embeddings
class CrossAttention(nn.Module):
def __init__(
@ -295,6 +256,58 @@ class CrossAttention(nn.Module):
return context
class Attention(nn.Module):
def __init__(self, args: LMConfig):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
assert args.n_heads % self.n_kv_heads == 0
self.n_local_heads = args.n_heads
self.n_local_kv_heads = self.n_kv_heads
self.n_rep = self.n_local_heads // self.n_local_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
self.attn_dropout = nn.Dropout(args.dropout)
self.resid_dropout = nn.Dropout(args.dropout)
self.dropout = args.dropout
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
mask = torch.triu(mask, diagonal=1)
self.register_buffer("mask", mask, persistent=False)
def forward(self,
x: torch.Tensor,
pos_cis: torch.Tensor):
bsz, seq_len, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
xq, xk = apply_rotary_emb(xq, xk, pos_cis)
if self.flash and seq_len != 1:
dropout_p = self.dropout if self.training else 0.0
output = F.scaled_dot_product_attention(
xq, xk, xv,
attn_mask=None,
dropout_p=dropout_p,
is_causal=True
)
else:
scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
scores += self.mask[:, :, :seq_len, :seq_len]
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
scores = self.attn_dropout(scores)
output = scores @ xv
output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
output = self.resid_dropout(self.wo(output))
return output
class FeedForward(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
@ -427,169 +440,30 @@ class MOEFeedForward(nn.Module):
class MiniMindBlock(nn.Module):
def __init__(self, layer_id: int, config: LMConfig):
def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset):
super().__init__()
self.n_heads = config.n_heads
self.dim = config.dim
self.head_dim = config.dim // config.n_heads
self.attention = Attention(config)
self.cross_att = CrossAttention(config)
self.self_attention = Attention(config)
self.cross_attention = CrossAttention(config)
self.knowledge_dataset = knowledge_dataset
self.layer_id = layer_id
self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
# 假设num_experts是已定义的总专家数量的平方根
# 查询生成的参数
# 创建查询生成模块
# if weight_down_embed is not None:
# self.to_queries = nn.Sequential(
# nn.Linear(config.dim, self.dim_key * 2, bias=False),
# # nn.Unflatten(2, (2, self.n_heads, self.dim_key)) # 替代Rearrange
# )
# # 超参数
# self.product_key_topk = min(16, self.num_keys) # 确保不超过num_keys
# self.num_experts_per_head_topk = 1 # 最终每个头选取的专家数
def forward(self, x, db_value, pos_cis):
# import pdb;pdb.set_trace()
# db_value = None
# # 如果有weight_down_embed，使用Product Key机制
# if self.weight_down_embed is not None:
# # 1. 生成queries
# batch_size, seq_len, dim = x.shape
# # collapse sequence dimension by averaging
# x_flat = x.mean(dim=1) # [batch_size, dim]
# queries = self.to_queries(x_flat) # [batch_size, 2*dim_key]
# queries = queries.reshape(batch_size, 2, self.dim_key) # [batch_size, 2, dim_key]
# queries = queries.permute(1, 0, 2) # [2, batch_size, dim_key]
# # 2. 计算queries与keys的相似度
# sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
# # 3. 在两个子空间分别做top-k
# scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
# scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
# indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
# # 4. 组合两个子空间的分数和索引
# all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
# all_scores = all_scores.view(*all_scores.shape[:-2], -1)
# all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
# all_indices = all_indices.view(*all_indices.shape[:-2], -1)
# # 5. 最终top-k选择
# scores, pk_indices = all_scores.topk(self.num_experts_per_head_topk, dim=-1)
# indices = all_indices.gather(-1, pk_indices)
# # 6. 从embedding中获取专家值
# # 从embedding中获取值
# flat_indices = indices.view(-1) # 将索引展平为一维张量
# db_values = self.weight_down_embed(flat_indices)
# # 重塑回原始形状
# db_value = db_values.view(batch_size, -1, dim)
# 注意力计算
h_attn = self.attention(
def forward(self, x, pos_cis):
h_attn = self.self_attention(
self.attention_norm(x),
pos_cis,
db_value=db_value
pos_cis
)
h_attn = self.cross_att(h_attn, db_value)
# 残差连接
db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
h_attn = self.cross_attention(h_attn, db_embeddings)
h = x + h_attn
# 前馈神经网络
out = h + self.feed_forward(self.ffn_norm(h))
return out
class ExtractDB(nn.Module):
def __init__(self,params):
# 修改专家数量和知识维度，确保能开方
super().__init__()
self.batch_size = None
self.dim = params.dim
self.dim_key = self.dim // 2
self.knowledge_num = params.knowledge_num # 100专家，确保是完全平方数
# 将knowledge_dim设置为与head_dim相同，以便在attention中直接使用
self.head_dim = params.dim // params.n_heads
self.knowledge_length = params.knowledge_length
# 使用register_buffer代替nn.Parameter，避免梯度问题
# self.register_buffer('weight_down_embed', torch.randn(self.knowledge_num, self.knowledge_length) * 0.02)
self.register_buffer('weight_down_embed',torch.randint(low=0,high=6400, size=(self.knowledge_num, self.knowledge_length),dtype=torch.long))
self.num_keys = int(math.sqrt(self.knowledge_num)) if self.knowledge_num > 0 else 0
self.product_key_topk = min(16, self.num_keys)
self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.dim_key) * 0.02)
self.num_experts_per_head_topk = 1
self.to_queries = nn.Sequential(
nn.Linear(params.dim, self.dim_key * 2, bias=False),
)
def q_to_k(self,x):
# 1. 生成queries
self.batch_size, seq_len, dim = x.shape
# collapse sequence dimension by averaging
x_flat = x.mean(dim=1) # [batch_size, dim]
queries = self.to_queries(x_flat) # [batch_size, 2*dim_key]
queries = queries.reshape(self.batch_size, 2, self.dim_key) # [batch_size, 2, dim_key]
queries = queries.permute(1, 0, 2) # [2, batch_size, dim_key]
# 2. 计算queries与keys的相似度
sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
# 3. 在两个子空间分别做top-k
scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
# 4. 组合两个子空间的分数和索引
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
all_scores = all_scores.view(*all_scores.shape[:-2], -1)
all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
all_indices = all_indices.view(*all_indices.shape[:-2], -1)
# 5. 最终top-k选择
scores, pk_indices = all_scores.topk(self.num_experts_per_head_topk, dim=-1)
indices = all_indices.gather(-1, pk_indices)
flat_indices = indices.view(-1)
return flat_indices
def get_data(self, index):
# 直接从GPU获取embedding
db_values = self.weight_down_embed[index]#变成token了所以是1,后续再过emb
# db_value = db_values.view(self.batch_size,-1)
return db_values
@torch.no_grad()
def updata_value(self, k, v):#要加一个从向量返回index的过程
# 直接更新buffer上的值 (不需要梯度)
v_reshaped = v.view(v.size(0), -1)
# 确保数据类型匹配
v_reshaped = v_reshaped.to(dtype=self.weight_down_embed.dtype)
self.weight_down_embed[k] = v_reshaped
return out
class MiniMindLM(PreTrainedModel):
@ -601,46 +475,15 @@ class MiniMindLM(PreTrainedModel):
self.vocab_size, self.n_layers = params.vocab_size, params.n_layers
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.dropout = nn.Dropout(params.dropout)
# 移除旧的weight_down_embed声明
self.extract_db = ExtractDB(self.params)
# 将self.weight_down_embed传递给每个MiniMindBlock
self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)])
self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) for l in range(self.n_layers)])
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
self.database_output = nn.Linear(params.dim, params.knowledge_length, bias=False)
self.tok_embeddings.weight = self.output.weight
self.database_output.weight = self.output.weight
# Calculate input dimension
input_dim = (self.params.max_seq_len-1)*self.params.n_layers
# Use a bottleneck architecture to reduce parameters
bottleneck_dim = 256 # Significantly smaller bottleneck dimension
# Factorized shared downsampling using two smaller convolutions
self.shared_downsample = nn.Sequential(
# First reduce input dimension to bottleneck
nn.Conv1d(input_dim, bottleneck_dim, kernel_size=1, padding='same'),
nn.ReLU(), # Non-linearity to improve representation capacity
# Then expand to target dimension
nn.Conv1d(bottleneck_dim, 128*8, kernel_size=1, padding='same')
)
# Specific layers for v path
self.downsample_v_specific = nn.Sequential(
nn.Conv1d(128*8, 128, kernel_size=1, padding='same'),
nn.Conv1d(128, self.params.knowledge_length, kernel_size=1, padding='same')
)
# Specific layers for q path
self.downsample_q_specific = nn.Sequential(
nn.Conv1d(128*8, 512, kernel_size=1, padding='same')
)
# 使用实数版本的位置编码，避免复数张量可能导致的段错误
self.register_buffer("pos_cis_real",
precompute_pos_cis_real(dim=params.dim // params.n_heads, theta=params.rope_theta),
self.register_buffer("pos_cis",
precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
persistent=False)
self.params = params
self.OUT = CausalLMOutputWithPast()
def forward(self,
input_ids: Optional[torch.Tensor] = None,
@ -648,63 +491,12 @@ class MiniMindLM(PreTrainedModel):
**args):
start_pos = args.get('start_pos', 0)
h = self.dropout(self.tok_embeddings(input_ids))
pos_cis_real = self.pos_cis_real[start_pos:start_pos + input_ids.size(1)]
h_list = []
pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
for l, layer in enumerate(self.layers):
# 禁用数据库模式，使用固定值替代数据库查询
if self.params.disable_db:
# 创建一个形状为[batch_size, n_layers, dim]的tensor，所有元素值为1e-4
batch_size = h.size(0)
db_value = torch.full((batch_size, self.n_layers, self.params.dim), 1e-4,
dtype=h.dtype, device=h.device)
else:
# 正常模式，使用数据库查询
# import pdb;pdb.set_trace()
index = self.extract_db.q_to_k(h)
token_idx = self.extract_db.get_data(index) #这里是index
db_value =self.tok_embeddings(token_idx)
h = layer(
h, db_value, pos_cis_real
h, pos_cis
)
h_list.append(h.unsqueeze(0))
h_tensor = torch.cat(h_list, dim=0).permute(1, 0, 2, 3)
# 只在非禁用数据库模式下执行数据库更新逻辑
if not self.params.disable_db:
# 使用detach()分离计算图，避免多次反向传播
h_tensor_detached = h_tensor.detach()
h_tensor_detached = h_tensor_detached.reshape(h_tensor_detached.shape[0], -1, self.params.dim)
# 数据库更新逻辑与主计算图分离
with torch.no_grad():
# Compute shared downsampling layer once
shared_features = self.shared_downsample(h_tensor_detached)
# Get features from v path - now we output embedding-dimension vectors
z_v_features = self.downsample_v_specific(shared_features)
batch_z, seq_len, dim_z = z_v_features.shape
# Reshape to batch_size * knowledge_length, dim
z_v_flat = z_v_features.reshape(-1, dim_z)
# Direct token prediction - like the main language model head
token_logits = self.database_output(z_v_flat) # [batch_z * seq_len, vocab_size]
# Get token indices directly from logits
token_indices_flat = torch.argmax(token_logits, dim=-1)
token_indices = token_indices_flat.reshape(batch_z, -1)
# Process query path as before
z_q = self.downsample_q_specific(shared_features)
z_k = self.extract_db.q_to_k(z_q)
# self.extract_db.updata_value(z_k, token_indices)
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.output(self.norm(h)[:, slice_indices, :])
aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
@ -717,12 +509,6 @@ class MiniMindLM(PreTrainedModel):
output.aux_loss = aux_loss
# 尝试添加其他属性（如果支持的话）
# try:
# output.hidden_states = h
# except:
# pass
return output
@torch.inference_mode()
@ -755,13 +541,14 @@ class MiniMindLM(PreTrainedModel):
return res
def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
start, first_seq = input_ids.shape[1], True
start, first_seq, past_kvs = input_ids.shape[1], True, None
while input_ids.shape[1] < max_new_tokens - 1:
if first_seq:
out, first_seq = self(input_ids, **args), False
else:
out = self(input_ids[:, -1:], start_pos=input_ids.shape[1] - 1, **args)
logits = out.logits[:, -1, :]
out = self(input_ids[:, -1:],
start_pos=input_ids.shape[1] - 1, **args)
logits, past_kvs = out.logits[:, -1, :], out.past_key_values
logits[:, list(set(input_ids.tolist()[0]))] /= rp
logits /= (temperature + 1e-9)
if top_p is not None and top_p < 1.0:
@ -777,4 +564,4 @@ class MiniMindLM(PreTrainedModel):
input_ids = torch.cat((input_ids, input_ids_next), dim=1)
yield input_ids[:, start:]
if input_ids_next.item() == eos_token_id:
break
break

30
train_pretrain_accelerate.py
View File

@ -74,8 +74,8 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
nn.init.ones_(module.weight)
# 初始化位置编码相关参数
if hasattr(model.extract_db, 'keys'):
nn.init.normal_(model.extract_db.keys, mean=0.0, std=0.02)
if hasattr(model.knowledge_dataset, 'keys'):
nn.init.normal_(model.knowledge_dataset.keys, mean=0.0, std=0.02)
Logger("Default model initialization completed")
@ -434,11 +434,11 @@ def init_model(lm_config, pretrained_embedding_path=None, database_init_path=Non
Logger(f"Failed to save cluster results: {e}")
# 3. 初始化模型的weight_down_embed
if hasattr(model, 'extract_db') and hasattr(model.extract_db, 'weight_down_embed'):
model.extract_db.weight_down_embed.data.copy_(clustered_tensor)
Logger("Successfully initialized model.extract_db.weight_down_embed with clustered data")
if hasattr(model, 'knowledge_dataset') and hasattr(model.knowledge_dataset, 'knowledge_dataset'):
model.knowledge_dataset.knowledge_dataset.data.copy_(clustered_tensor)
Logger("Successfully initialized model.knowledge_dataset.knowledge_dataset with clustered data")
else:
Logger("Warning: Could not find model.extract_db.weight_down_embed to initialize")
Logger("Warning: Could not find model.knowledge_dataset.knowledge_dataset to initialize")
# 存储为全局变量作为备选
globals()['clustered_database'] = clustered_tensor
@ -659,21 +659,21 @@ def train_epoch(epoch, accelerator, model, train_loader, optimizer, scheduler, a
def main():
parser = argparse.ArgumentParser(description="MiniMind Pretraining with Accelerate")
parser.add_argument("--out_dir", type=str, default="out")
parser.add_argument("--epochs", type=int, default=3)
parser.add_argument("--batch_size", type=int, default=24)
parser.add_argument("--epochs", type=int, default=4)
parser.add_argument("--batch_size", type=int, default=48)
parser.add_argument("--learning_rate", type=float, default=2e-4)
parser.add_argument("--dtype", type=str, default="bfloat16")
parser.add_argument("--use_wandb", default=True, action="store_true")
parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain")
parser.add_argument("--num_workers", type=int, default=48)
parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument("--accumulation_steps", type=int, default=32)
parser.add_argument("--grad_clip", type=float, default=1.0)
parser.add_argument("--warmup_iters", type=int, default=0)
parser.add_argument("--log_interval", type=int, default=100)
parser.add_argument("--save_interval", type=int, default=10000)
parser.add_argument('--dim', default=1024, type=int)
parser.add_argument('--n_layers', default=32, type=int)
parser.add_argument('--max_seq_len', default=1024, type=int)
parser.add_argument('--dim', default=512, type=int)
parser.add_argument('--n_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=512, type=int)
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("--data_path", type=str, default="./dataset/pretrain_hq.jsonl")
@ -681,11 +681,11 @@ def main():
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("--use_flash_attn", action="store_true", default=True, help="启用FlashAttention")
parser.add_argument("--knowledge_num", type=int, default=65536,help="知识库的数据数目")
parser.add_argument("--knowledge_length", type=int, default=64,help="知识库的句子长度")
parser.add_argument("--knowledge_num", type=int, default=4096,help="知识库的数据数目")
parser.add_argument("--knowledge_length", type=int, default=16,help="知识库的句子长度")
parser.add_argument("--database_init_path", type=str, default="./dataset/database_init.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("--cluster_cache_path", type=str, default="./cache/cluster_tokens.pt", help="聚类结果缓存文件路径")
parser.add_argument("--cluster_cache_path", type=str, default="./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")
args = parser.parse_args()