proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N out = torch.bmm...-1).permute(0, 2, 1) key = key.view(b, c, -1) value = value.view(b, c, -1).permute(0, 2, 1) att = torch.bmm...(query, key) if self.use_scale: att = att.div(c**0.5) att = self.softmax(att) x = torch.bmm(att,..., c * h * w) p = p.view(b, 1, c * h * w) g = g.view(b, c * h * w, 1) att = torch.bmm...(p, g) if self.use_scale: att = att.div((c*h*w)**0.5) x = torch.bmm(att
(self, queries, keys, values, valid_lens=None): d = queries.shape[-1] self.scores = torch.bmm...math.sqrt(d) self.attention_weights = masked_softmax(self.scores, valid_lens) return torch.bmm...=None): print(queries) d = queries.shape[-1] print(d) self.scores = torch.bmm...self.scores) self.attention_weights = masked_softmax(self.scores, valid_lens) return torch.bmm...self.scores) self.attention_weights = masked_softmax(self.scores, valid_lens) return torch.bmm
同理,由于 torch.bmm 函数不支持广播,相对应的输入的两个张量必须为 3D。...import torch input = torch.randn(10, 3, 4) other = torch.randn(10, 4, 2) result = torch.bmm(input,
h = self.h(x).view(m_batchsize, -1, width * height) # B * C * (W * H) attention = torch.bmm...) # B * (W * H) * (W * H) attention = self.softmax(attention) self_attetion = torch.bmm
proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H) energy = torch.bmm...proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N out = torch.bmm...然后我们用torch.bmm()来做矩阵的乘法:(N,Channel//8)和(Channel//8,N)两个矩阵相乘,得到一个(N,N)的矩阵。
x.view(*size[:2],-1) f,g,h = self.query(x),self.key(x),self.value(x) beta = F.softmax(torch.bmm...(f.transpose(1,2), g), dim=1) o = self.gamma * torch.bmm(h, beta) + x return o.view
(N) g1 = self.g(x).view(m_batchsize, -1, width * height) # B X C x (*W*H) energy = torch.bmm...(N) X (N) h1 = self.h(x).view(m_batchsize, -1, width * height) # B X C X N out = torch.bmm
break a = random.randint(-10,10,size=(8,8)) 然而,让我们思考一个问题, 4.5 本节源码 3 83 apple 57345 uni4E00 torch.bmm
triton在TFLOPS这个指标层面是能够超过cublas的实现,但是后面我通过nsight system对每个kernel的具体执行时间进行了profiling,发现在torch.matmul或者torch.bmm...None, :] < N) tl.store(C_ptr, c, mask=c_mask) 然后写一个简单的单元测试,确保通过triton写出来的kernel能够和torch.matmul/torch.bmm...dtype=torch.float16) b = torch.randn((4, 512, 512), device='cuda', dtype=torch.float16) torch_output = torch.bmm...16x4096x4096, 16x4096x4096) 通过nsight system + nvtx就可以看到每个kernel的具体实现情况: img 添加图片注释,不超过 140 字(可选) 使用torch.bmm
input): matrix3x3 = self.input_transform(input) # batch matrix multiplication xb = torch.bmm...xb = F.relu(self.bn1(self.conv1(xb))) matrix64x64 = self.feature_transform(xb) xb = torch.bmm...if outputs.is_cuda: id3x3 = id3x3.cuda() id64x64 = id64x64.cuda() diff3x3 = id3x3 - torch.bmm...(m3x3, m3x3.transpose(1, 2)) diff64x64 = id64x64 - torch.bmm(m64x64, m64x64.transpose(1, 2)) return
input_embedding = input_embedding.unsqueeze(2) # [batch_size, embed_size, 1] pos_dot = torch.bmm...2), 1] pos_dot = pos_dot.squeeze(2) # [batch_size, (window * 2)] neg_dot = torch.bmm...tensor的第一个维度必须相同,后面两个维度必须满足矩阵乘法的要求 batch1 = torch.randn(10, 3, 4) batch2 = torch.randn(10, 4, 5) res = torch.bmm
运算符 @ 用于进行两个矩阵的点乘运算 torch.mm 用于进行两个矩阵点乘运算, 要求输入的矩阵为2维 torch.bmm 用于批量进行矩阵点乘运算, 要求输入的矩阵为3维 torch.matmul...# 第三个维度: 多少列 data1 = torch.randn(3, 4, 5) data2 = torch.randn(3, 5, 8) data = torch.bmm...张量的阿达玛积运算 mul 和运算符 * 的用法 点积运算: 运算符 @ 用于进行两个矩阵的点乘运算 torch.mm 用于进行两个矩阵点乘运算, 要求输入的矩阵为2维 torch.bmm
torch.bmm(torch.ones((2,1,3), dtype = torch.float), torch.ones((2,3,2), dtype = torch.float)) tensor(...-1] # set transpose_b=True to swap the last two dimensions of key scores = torch.bmm...masked_softmax(scores, valid_length)) print("attention_weight\n",attention_weights) return torch.bmm...(-1) attention_weights = self.dropout(masked_softmax(scores, valid_length)) return torch.bmm
proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H) energy = torch.bmm...proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N out = torch.bmm...步骤二: energy = torch.bmm(proj_query,proj_key) 这一步是将batch_size中的每一对proj_query和proj_key分别进行矩阵相乘,输出为B×(W...out = torch.bmm(proj_value,attention.permute(0,2,1) ) out = out.view(m_batchsize,C,width,height) 在对proj_value
n_pts = input.size()[2] matrix3x3 = self.input_transform(input) input_transform_output = torch.bmm...input_transform_output) matrix64x64 = self.feature_transform(x) feature_transform_output = torch.bmm...id3x3.cuda() id64x64 = id64x64.cuda() # Calculate matrix differences diff3x3 = id3x3 - torch.bmm...(m3x3, m3x3.transpose(1, 2)) diff64x64 = id64x64 - torch.bmm(m64x64, m64x64.transpose(1, 2))
self.key(x).view(n_batch, C, -1) v = self.value(x).view(n_batch, C, -1) content_content = torch.bmm...energy = content_content + content_position attention = self.softmax(energy) out = torch.bmm
encoder_outputs, hidden): seq_len = encoder_outputs.size(1) # 计算注意力权重 attn_weights = torch.bmm...2)) attn_weights = torch.softmax(attn_weights, dim=2) # 加权求和得到上下文向量 context = torch.bmm
self.embedding # 特征进行cross interaction for i in range(self.cross_num): emb_tmp = torch.bmm
V = self.v(x) # V: batch_size * seq_len * dim_v atten = nn.Softmax(dim=-1)(torch.bmm..._norm_fact # Q * K.T() # batch_size * seq_len * seq_len output = torch.bmm(atten,V)
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