self.beta=nn.Parameter(torch.zeros(num_features))defforward(self,X):layer_mean=X.mean(dim=(-1),keepdim=True)layer_var=X.var(dim=(-1),keepdim=True,unbiased=False)X_norm=(X-layer_mean)/torch.sqrt(layer_var+self.eps)returnself.gamma*X_norm+self.beta# 示例使用bn=BatchNorm(num_featur...
norm_layer=norm_layer, layer=8, self_attention=self_attention, blur=blur, outermost=True) self.embedder = nn.Embedding(embedding_num, embedding_dim) def _prepare_style(self, style_or_label): if style_or_label is not None and 'LongTensor' in style_or_label.type(): return self.embedder(...
# 假设输入是一个词向量,大小为 [batch_size, seq_len, hidden_dim] batch_size, seq_len, hidden_dim = 2, 3, 4 # 2 个句子,每句 3 个词,每词 4 维 x = torch.rand(batch_size, seq_len, hidden_dim) # 定义 LayerNorm layer_norm = nn.LayerNorm(hidden_dim) # 按照 hidden_dim 做归...
self.beta = nn.Parameter(torch.zeros(normalized_shape))defforward(self, x): rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.epsilon) x = x / rmsreturnself.gamma * x + self.beta# 创建RMSNorm层rms_norm = RMSNorm(normalized_shape=64)# 输入数据x = torch.randn(16...
layer_norm = torch.nn.LayerNorm(dim, elementwise_affine =False) print("y: ", layer_norm(embedding)) eps: float =0.00001 mean = torch.mean(embedding[:, :, :], dim=(-1), keepdim=True) var = torch.square(embedding[:, :,...
self.norm1 = norm_layer(dim) ^^^ File "F:\ComfyUI_dapaopao\python_dapao\Lib\site-packages\apex\normalization\fused_layer_norm.py", line 776, in __init__ fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda") ^^^ File "importlib...
#2.自己手动来对最后一个维度进行计算,如果和上面的结果一样,说明layernorm的计算和我们的计算是一样的eps: float = 0.00001#计算了embedding张量在最后一个维度上的均值,并将结果存储在变量mean中。#dim=(-1):这个参数指定了要在哪个维度上计算均值。在这里,-1表示最后一个维度,所以这个函数会计算embedding张量...
var = ((X - mean) ** 2).mean(dim=0) # (num_features,) else: # 使用二维卷积层的情况,计算通道维上(axis=1)的均值和方差。 mean = X.mean(dim=(0, 2, 3), keepdim=True) # (1,num_features,1,1) 保持X的形状,以便后面可以做广播运算 ...
mean = torch.mean(x, dim=[1], keepdim=True) # 这里注意是torch.mean而不是torch.sum # 所以通过torch.var函数是不可以的 var = torch.mean((x - mean) ** 2, dim=[1], keepdim=True)+ 1e-5 print("my LayerNorm=", var,(x - mean) / torch.sqrt(var)) ...
constint numx=blockDim.x*blockDim.y;constint thrx=threadIdx.x+threadIdx.y*blockDim.x;// 如果提供了gamma和beta参数,或者我们只是在做RMS计算,我们会用一种特别的方式来计算输出值。if(gamma!=NULL&&(beta!=NULL||rms_only)