模型状态内存(Model State Memory): 深度学习模型的状态可归为:优化器状态、梯度和参数这三个基本过程。 激活内存(Activation Memory):在优化了模型状态内存之后,人们发现激活函数也会导致瓶颈。激活函数计算位于前向传播之中,用于支持后向传播。 碎片内存(Fragmented Memory):深度学习模型的低效有时是由于
模型状态内存(Model State Memory): 深度学习模型的状态可归为:优化器状态、梯度和参数这三个基本过程。 激活内存(Activation Memory):在优化了模型状态内存之后,人们发现激活函数也会导致瓶颈。激活函数计算位于前向传播之中,用于支持后向传播。 碎片内存(Fragmented Memory):深度学习模型的低效有时是由于内存碎片所导...
在目标6中Meta意识到 Peak Memory Optimization (也就是我们编译优化常说的activation liveness) 是LLM优先项(priority). 目前在Megatron- LM 中经过dynamo优化的模型要比不优化的快20%左右,是一个性能大杀器。 Pytorch- Distributed Vision-OKR: 从早期的 DDP开始 ,在Google提出 Device Mesh概念,以及NV提出的<手...
device: npu dtype: bf16enable_activation_checkpointing: true epochs: 10 …… INFO:torchtune.utils._logging:Model is initialized with precision torch.bfloat16. INFO:torchtune.utils._logging:Memory stats after model init: NPU peak memory allocation: 1.55 GiB NPU peak memory reserved: 1.61 GiB N...
模型状态内存(Model State Memory): 深度学习模型的状态可归为:优化器状态、梯度和参数这三个基本过程。 激活内存(Activation Memory):在优化了模型状态内存之后,人们发现激活函数也会导致瓶颈。激活函数计算位于前向传播之中,用于支持后向传播。 碎片内存(Fragmented Memory):深度学习模型的低效有时是由于内存碎片所导...
Activation checkpointing avoids saving intermediate tensors in order to save memory. It does so by recomputing the forward pass on demand to obtain the intermediate values required for gradient computation during backward. For pipelining, we are splitting up the backward computation intostage_backward...
enable_activation_checkpointing: True custom_sharded_layers: ['tok_embeddings', 'output'] fsdp_cpu_offload: True compile: False # set it to True for better memory and performance compile=False # pytorch compile, set to true for perf/memory improvement# set it to True for better memory...
(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1 if i % 2 is 0 else 2, batch_norm=i is not 0, activation='LeakyReLu')) in_channels = out_channels self.conv_blocks = nn.Sequential(*conv_blocks) # 固定输出大小 self.adaptive_pool = nn.Adaptive...
把reducer的autograd_hook函数添加进去每个grad_accumulator_之中,变量index是hook的参数。这个 hook 挂在 autograd graph 之上,在 backward 时负责梯度同步。grad_accumulator 执行完后,autograd_hook 就会运行。 gradAccToVariableMap_ 存了grad_accumulator & index 的对应关系(函数指针和参数张量的对应关系),这样以后在...
It is really important for you to commit to memory and practice these bits of tensor jargon:rankis the number of axes or dimensions in a tensor;shapeis the size of each axis of a tensor. Alexis Says Watch out because the term “dimension” is sometimes used in twoways. Consider that we...