# 加载编译后的模型loaded_model=torch.jit.load("compiled_model.pt")# 创建新的输入数据input_data=torch.randn(1,1,28,28)# 将输入数据放入指定设备(例如GPU,如果可用)device=torch.device("cuda"iftorch.cuda.is_available()else"cpu")loaded_model.to(device)input_data=input_data.to(device)# 进行推...
model(x) self.assertEqual(cnt.frame_count, 1) def test_overwrite_call_impl(self): torch._dynamo.reset() model = ToyModel() self.assertTrue(model._compiled_call_impl is None) model.compile() self.assertTrue(model._compiled_call_impl is not None) def test_save(self): torch._dynamo.re...
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、...
model.eval() with torch.no_grad(): for data in test_dataloader: imgs, targets = data model_load = torch.load("model_save/model_62500_GPU.pth", map_location=torch.device("cpu")) model.load_state_dict(model_load) outputs = model(imgs) accuracy = (outputs.argmax(1) == targets).s...
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...
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=...
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...
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(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...
answer_logits=model_traced(*example_inputs) And finally save the resulting TorchScript output on local disk model_traced.save('./compiled-model-bs-{batch_size}.pt') As shown in the preceding code, you can usecompiler_argsandoptimizationsto opti...