--- DeepSpeed Flops Profiler --- Profile Summary at step 10: Notations: data parallel size (dp_size), model parallel size(mp_size), number of parameters (params), number of multiply-accumulate operations(MACs), number of floating-point operations (flops), floating-point operations per second ...
import torch.nn as nn model = nn.Linear(5, 5) input = torch.randn(16, 5) params = {name: p for name, p in model.named_parameters()} tangents = {name: torch.rand_like(p) for name, p in params.items()} with fwAD.dual_level(): for name, p in params.items(): delattr(mo...
开门见山的说,PyTorch 在进行深度学习训练的时候,有 4 大部分的显存开销,分别是模型参数(parameters),模型参数的梯度(gradients),优化器状态(optimizer states)以及中间激活值(intermediate activations)或者叫中间结果(intermediate results)。 而通过 Checkpoint 技术,我们可以通过一种取巧的方式,使用 PyTorch 提供的 “n...
Tensor myadd_cpu(const Tensor& self_, const Tensor& other_) { TORCH_CHECK(self_.sizes() == other_.sizes()); TORCH_INTERNAL_ASSERT(self_.device().type() == DeviceType::CPU); TORCH_INTERNAL_ASSERT(other_.device().type() == DeviceType::CPU); Tensor self = self_.contiguous(); Ten...
().mean * 1e6 # Lets define the hyper-parameters of our input batch_size = 32 max_sequence_len = 1024 num_heads = 32 embed_dimension = 32 dtype = torch.float16 query = torch.rand(batch_size, num_heads, max_sequence_len, embed_dimension, device=device, dtype=dtype) key = torch....
device parameters have been replaced with npu in the function below: torch.logspace, torch.randint torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch....
(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) init_start_event.record() for epoch in range(1, args.epochs + 1): train(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=sampler1) test(model, rank, world_size, ...
unused_parameters=False, check_reduction=False)将给定的module进行分布式封装， 其将输入在batch...
# ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) # pytorch的官网建议使用DistributedDataParallel来代替DataParallel, ...
# Define the loss function and optimizercriterion = nn.CrossEntropyLoss()optimizer = optim.AdamW(model.parameters(), lr=5e-6) # Training loopnum_epochs = 25 # Number of epochs to train for for epoch in tqdm(range(num_epochs)): # loop...