因此,推荐在加载模型时将device_map设置为device_map='balanced_low_0',然后将数据加载在cuda:0上:input = input.to(cuda:0),这样,我们显式地告诉pytorch,在分配模型时cuda:0分配最少的模型片段,而加载数据时把数据都加载在cuda:0上,从而数据和模型就分离开来,不会同时出现在一张卡上挤爆其显存!
- device_map:Huggingface - 模型并行 on ToyModel - 模型并行:on ResNet - 不需要引入额外的 torch api 支持; 在huggingface中device_map可以支持模型并行的实现 参考链接: https://d2l.ai/chapter_computational-performance/parameterserver.htmlhttps://www.cs.cmu.edu/~muli/file/ps.pdf...
将map_location函数中的参数设置torch.load()为cuda:device_id。这会将模型加载到给定的GPU设备。 调用model.to(torch.device('cuda'))将模型的参数张量转换为CUDA张量,无论在cpu上训练还是gpu上训练,保存的模型参数都是参数张量不是cuda张量,因此,cpu设备上不需要使用torch.to(torch.device("cpu"))。 二、实例...
RuntimeError: Function MatmulBackward0 returned an invalid gradient at index 0 - expected device npu:7 but got npu:0 EI0009: Transport init error. Reason: [Create][DestLink]Create Dest error! creakLink para:rank[0]-localUserrank[0]-localIpAddr[172.17.0.2], dst_rank[1]-remoteUserrank[1...
deviceMap_);// Record the future in the context.sharedContext->addOutstandingRpc(jitFuture);// 'recv' function sends the gradients over the wire using RPC, it doesn't// need to return anything for any downstream autograd function.returnvariable_list(); ...
def forward(self, x):device = x.devicehalf_dim = self.dim // 2emb = math.log(self.theta) / (half_dim - 1)emb = torch.exp(torch.arange(half_dim, device=device) * -emb)emb = x[:, None] * emb[None, :]emb = torch.cat((emb.sin(), e...
class ImageDatasetMap(Dataset): def __init__(self, bucket_name: str, image_list: List[str], y, transform=None): self.bucket_name = bucket_name self.X = image_list self.y = y self.transform = transform def __len__(self):return len(self.y) def __getitem__...
std::unordered_map<Function*, ExecInfo> exec_info; int owner; GraphTask(bool keep_graph, bool grad_mode): has_error(false), \ outstanding_tasks(0), keep_graph(keep_graph), grad_mode(grad_mode), owner(NO_DEVICE) {} }; 在Engine的execute函数执...
deviceMap_); // Record the future in the context. sharedContext->addOutstandingRpc(jitFuture); // 'recv' function sends the gradients over the wire using RPC, it doesn't // need to return anything for any downstream autograd function. ...
对于风格损失我们还需要引入Gram矩阵来帮助我们表示图像的风格特征,我们读入图像卷积层的输出形状为C × H × W ,C是卷积核的通道数,每个卷积核学习图像不同特征,每个卷积核输出H × W 代表这张图像的一个feature map,读入RGB图像的三色通道相当于三个feature map,我们用Gram矩阵来计算feature map间的相似性,得到...