这里我们使用TorchScript将模型编译成可在不同设备(如CPU、GPU、甚至是移动设备)上运行的格式: # 将模型设置为评估模式model.eval()# 将模型转换为TorchScript格式scripted_model=torch.jit.script(model)# 保存编译后的模型scripted_model.save("compiled_model.pt") 1. 2. 3
torch.save/load torch.compiled models (pytorch#97565)Opening this so I can discuss with @albanD I built a proof of concept of an in place API for an nn.Module that allows us to save and load a torch.compiled model with no issues https://github.com/msaroufim/mlsys-experiments/blob/ma...
self).__init__()self.model=nn.Sequential(# input:3@32x32# 6@28x28nn.Conv2d(in_channels=3,out_channels=6,kernel_size=5,padding=0,stride=1),nn.ReLU(inplace=True),# 6@14x14nn.MaxPool2d(kernel_size=2,stride=2,padding=0),# 16@10x10nn.Conv2d(in_channels=6,out_channels=16,ker...
model=models.resnet18().cuda()optimizer=torch.optim.SGD(model.parameters(),lr=0.01)compiled_model=torch.compile(model)# 关键一行 x=torch.randn(16,3,224,224).cuda()optimizer.zero_grad()out=compiled_model(x)out.sum().backward()optimizer.step() PyTorch 团队在 163 个开源模型(包括图像分类、...
state_dict(), "model_save/model_{}_GPU.pth".format(total_train_step)) print("the model of {} training step was saved! ".format(total_train_step)) writer.close() 方式(way)2: 1.network structure model.to(device=torch.device("cuda")) 2.loss function cross_entropy_loss.to(device=...
batch_size = 32 max_sequence_len = 256 x = torch.rand(batch_size, max_sequence_len, embed_dimension, device=device, dtype=dtype) print( f"The non compiled module runs in {benchmark_torch_function_in_microseconds(model, x):.3f} microseconds") compiled_model = torch.compile(model) # Le...
compiled_model = torch.compile(model) 1. compiled_model保存对模型的引用,并将前向函数编译为更优化的版本。编译模型时,我们给几个knobs来调整它 def torch.compile(model: Callable, *, mode: Optional[str] = "default", #默认模式是尝试高效编译的预设,而不会花费太长时间进行编译或使用额外的内存。其他...
is using non compiled model self.model=create_model("resnet18",num_classes=10) will not having this error How to reproduce the bug # Full Code:importtorchimportlightningasLimporttorchmetricsimporttorch.nnasnnimporttorchvisionimporttorchvision.transformsastransformsfromtimmimportcreate_modelclassCIFAR10Data...
compiled_model(x)print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))# For even more insights, you can export the trace and use ``chrome://tracing`` to view the results## .. code-block:: python## prof.export_chrome_trace("compiled_causal_attention_trace.json...
xm.save(model.state_dict(), path_to_save) After you have completed adapting your training script, proceed to Run PyTorch Training Jobs with SageMaker Training Compiler. For distributed training In addition to the changes listed in the previous For single GPU training section, add the following ...