Original: An In-depth Study of Stochastic Backpropagation
Revised: An In-depth Study of Stochastic Backpropagation

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AffiliationAddress 
Revised: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP)when training deep neural networks for standard image classification and objectdetection tasks.
Revised: In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP)when training deep neural networks for standard image classification and objectdetection tasks.

Original: During backward propagation, SBP calculates the gradients byonly using a subset of the feature maps to save memory and the computation cost.
Revised: During backward propagation, SBP calculates the gradients byonly using a subset of the feature maps to save memory and the computation cost.

Original: We interpret SBP as an efficient way to implement stochastic gradient decent byperforming backpropagation dropout, which results in considerable memory savingand training process speed up with a minimal impact to the overall model accuracy.
Revised: We interpret SBP as an efficient way to implement stochastic gradient decent byperforming backpropagation dropout, which leads to considerable memory savingand training process speedup, with a minimal impact on the overall model accuracy.

Original: We offer  good practice to applying SBP in training deep image classificationmodels, which can be adopted in learning a wide range of deep neural networks.
Revised: We offer a good practice to apply SBP in training deep image classificationmodels, which can be adopted in learning a wide range of deep neural networks.

Original: Experiments on image classification and object detection show that SBP can saveup to 40% of GPU memory with less than 1% accuracy degradation.
Revised: Experiments on image classification and object detection show that SBP can saveup to 40% of GPU memory with less than 1% accuracy degradation

Original:  Introduction
Revised:  Introduction

Original: A common practice to improve accuracy when training deep networks is to increase the inputresolution [20], model depth [8], and width [23].
Revised: A common practice to improve accuracy when training deep networks is to increase the inputresolution [20], model depth [8], and width [23].

Original: This can significantly increases the GPU memoryusage, making training on devices with limited resources difficult.
Revised: This can significantly increase the GPU memoryusage, making training on devices with limited resources difficult.

Original: For example, when training anobject detector using ConvNeXt-Base [14] as  backbone with batch size 2,it requires over 17 GBmemory which will not  fit on most modern GPUs.
Revised: For example, when training anobject detector using ConvNeXt-Base [14] as the backbone with batch size 2, over 17 GB of memoryis required, which will not be able to fit on most modern GPUs.

Original: Many prior works have focused onthe problem of designing memory efficient training method,especially for large model training on high resolution images or videos.
Revised: Many prior studies have focused on designing memory-efficient training method, especially forlarge model training on high resolution images or videos.

Original: Mixed-precision training [17]uses lower precision to represent the network weights and activations for certain layers of thenetwork.
Revised: Mixed-precision training [17] uses lowerprecision to represent the network weights and activations for certain layers of the network.

Original: Gradient checkpointing [3] restores intermediate feature maps by recomputing nodes inthe computation graph during the backward pass, which has the extra cost effectively performingthe forward computation twice.
Revised: Gradientcheckpointing [3] restores intermediate feature maps by recomputing nodes in the computationgraph during the backward pass, which requires extra computations.

Original: Gradient accumulation splits a mini-batch into smaller chunks tocalculate gradients iteratively, which slows downthe training process.
Revised: Gradient accumulation splits amini-batch into smaller chunks to calculate gradients iteratively, which slows down training process.

Original: A recently developed method, Stochastic Backpropagation (SBP) [4], provides a new angle onmemory efficient training.
Revised: A recently developed method, Stochastic Backpropagation (SBP) [4], offers a new angle on memoryefficient training.

Original: It performs partial execution of gradients during backpropagation onselected frames when training video models [1, 19].
Revised: It performs partial execution of gradients during backpropagation on selectedframes when training video models [1, 19].

Original: It attains considerable memory saving by onlycaching a subset of frame feature maps without significant accuracy loss.
Revised: It achieves considerable memory saving by only cachinga subset of frame feature maps without significant accuracy loss.

Original: However,  [4] only applies SBP to video based models of two architectures and does not thoroughly explore the design space ofthe method.
Revised: However, the prior work [4] onlyapplies SBP to video-based models of two architectures and does not thoroughly explore the designspace of the method.

Original: The explanation provided in the paper for the effectiveness of SBP is simple but narrow –it attributes the validity of the approximation to the high redundancy of video.
Revised: The explanation provided in the paper for the effectiveness of SBP is simple butnarrow – it attributes the validity of the approximation to the high redundancy of video.

Original: This leaves many questions around SBP unanswered.
Revised: This leaves many questions around SBP unanswered.

Original: Here we ask the questions: (1) Is SBP’seffectiveness limited to operating on the redundancy of video frames or can it effectively leveragespatial redundancy?
Revised: Here we ask the questions: (1) Is SBP’seffectiveness limited to operating on the redundancy of video frames or can it effectively leveragespatial redundancy?

Original: (2) If so can, SBP be generalized to image based tasks, with more limitedredundancy?
Revised: (2) If so, can SBP be generalized to image-based tasks, with more limitedredundancy?

Original: (3) Is SBP generalizable to different networks (e.g., MLP, CNN, Transformer) and operators?
Revised: (3) Is SBP generalizable to different networks (e.g., MLP, CNN, Transformer) andoperators?

Original:  What are the key design choices when implementing SBP?
Revised: (4) What are the key design choices when implementing SBP?

Original: To answer these questions, we first formalize the SBP process in a general form and analyze theeffect  SBPhas on the calculation of gradients.
Revised: To answer these questions, we first formalize the SBP process in a general form and analyze theeffect of SBP on the calculation of gradients.

Original: With this formulation we show how to generalize the
Revised: With this formulation, we show how to generalize the

Original: SBP idea to image models and present an in-depth study of the design choices available when using
Revised: SBP idea to image models and present an in-depth study of the design choices available when using

Original: SBP.
Revised: SBP.

Original: During backward propagation, SBP calculatesthe gradients from a subset of spatial featuremaps to save memory and computation cost.
Revised: During backward propagation, SBP calculates gradients from a subset of spatial feature mapsto save memory and computation cost.

Original: We observe that the calculated gradients by SBP arehighly correlated with the standard SGD gradients, indicating SBP is a reasonable approximation.
Revised: We observe that the calculated gradients by SBP are highlycorrelated with the standard SGD gradients, indicating SBP is a reasonable approximation.

Original: Wealso provide a new, straightforward implementation of the idea that requires only a few lines of code.
Revised: We alsoprovide a new, straightforward implementation of the idea that requires only a few lines of code.

Original: We explore several important design strategies which have a noticeable impact on the performance.
Revised: Weexplore several important design strategies which have a noticeable impact on the performance.

Original: We validate the generalizability of SBP on two common tasks (image classificaton and detection)with two popular network architectures: ViT [7] and ConvNeXt [14].
Revised: Wevalidate the generalizability of SBP on two common tasks (image classificaton and detection) withtwo popular network architectures: ViT [7] and ConvNeXt [14].

Original: We show  SBP can effectivelytrain image tasks with less than 1% loss of accuracy in a memory efficient fashion.  
Revised: We show that SBP can effectivelytrain image tasks with less than 1% loss of accuracy in a memory-efficient fashion.  Related Work

Original: Understanding Stochastic Back-Propagation (SBP)
Revised: Since deep neural networks are highly extended in both depth [8] and breadth [23] in recent years,designing memory efficient training methods has attracted a fair amount of attention. By saving andrecomputing nodes in the computation graph during backpropagation, Gradient Checkpoint [3] cansave a large amount of memory (up to about 50%). Besides, gradient accumulation splits the largesample batch into several mini batches and runs them iteratively without updating model parameters. This saves the GPU memory proportionally without affecting the accuracy, because accumulatingthe gradients of these sub-iteration and then updating the model parameters is identical to directlyoptimizing the model global batch size.

Revised: However, these two methods slow down the training process.

Revised: Multigrid [22] proposes to use different mini-batch shapes to speedup the model training.

Revised: Sparse

Revised: Network [6] particularly targets the recognition tasks but can only save the memory theoretically.

Revised: Sideways [15] and its follow-up [16] reduce the memory cost by overwriting activations whenevernew ones become available, but they are limited to causal models.

Revised: SBP [4] reduces a large portion of

Revised: GPU memory by only backpropagating gradients from incomplete execution.

Original: In this section, we first formalize the process of Stochastic Back-Propagation (SBP) (sec. 2.1) andshow how to compute the gradient for two common operations (sec. 2.2).
Revised: In this section, we first formalize the process of Stochastic Backpropagation (SBP) (sec. 3.1) andshow how to compute the gradient for two common operations (sec. 3.2).

Original: With these formulationsestablished, we analyze how to perform the chain rule of gradient calculations for SBP (sec. 2.3).
Revised: With these formulationsestablished, we analyze how to perform the chain rule of gradient calculations for SBP (sec. 3.3).

Original: Finally, we present a simple implementation of SBP in practice with only a few lines of code (sec. 2.4).
Revised: Finally, we present a simple implementation of SBP in practice with only a few lines of code (sec. 3.4).

Original: 2.1 Formulation of SBP
Revised: 3.1 Formulation of SBP

Original: In this section, we present a general fomulation of SBP [4].
Revised: In this section, we present a general formulation of SBP [4].

Original: We denote a loss function L (Θ) tolearn a model on a dataset D
Revised: We denote a loss function L (Θ) tolearn a model on a dataset D

Original: = { s i } Ni =1 , where Θ is the collection (vector, matrix, or tensor) of allparameters of the model, N is the total number of training samples, s i ∈ R T × H × W × C is an inputdata sample, where H and W are spatial resolution height and width, C is the input  channel size,57 T = 1 represents an image and T > 1 represents a video.
Revised: = { s i } Ni =1 , where Θ is the collection (vector, matrix, or tensor) of allparameters of the model, N is the total number of training samples, s i ∈ R T × H × W × C is an inputdata sample, where H and W are spatial resolution height and width respectively, C is the input 70 channel size, T = 1 represents an image and T > 1 represents a video.

Original: In each training iteration, the model with SBP processes a mini-batch of data samples S ∈ R B × T × H × W × C ⊆ D to compute the loss in theforward pass, where B is the batch size.
Revised: In each training iteration,the model with SBP processes a mini-batch of data samples S ∈ R B × T × H × W × C ⊆ D to computethe loss in the forward pass, where B is the batch size.

Original: During the backward pass, differing from the traditionalback-propagation that relies on the full feature maps X i ∈ R B × T i × H
Revised: During the backward pass, different fromthe traditional backpropagation that relies on the full feature maps X i ∈ R B × T i × H

Original: i × W i ×
Revised: i × W i ×

Original: C i ( i is the index of thelayers) an incomplete execution for back-propagation (i.e., SBP) only utilizes a subset of the featuremaps X subi ∈ R T subi × H subi × W subi×
Revised: C i ( i is theindex of the layers), an incomplete execution for backpropagation (i.e., SBP) only utilizes a subset ofthe feature maps X subi [Equation] subi

Original:  C i to approximate the gradient calculation but keep all the fullforward paths.
Revised: [Equation] H subi × W subi × C i to approximate the gradient calculation.

Original: That is, it updates the model weights as
Revised: That is, itupdates the model weights as

Original: [Equation]
Revised: [Equation]

Original: where η is the learning rate.
Revised: where η is the learning rate.

Original: Compared to the traditional back-propagation that uses the fullfeature maps, SBP saves memory by only caching a subset of feature maps.
Revised: SBP saves memory by only caching a subset of feature maps in thebackward pass, but it keeps all the full forward paths.

Original: Note that the methodin [4] is a special case of (1) as itonly performsbackward calculation on  a subset of video frames X T subi i ∈ R T subi × H
Revised: Note that the method in [4] is a special case of(1) as it performs on the frame level, i.e., using a subset of video frames X T subi i ∈ R T subi × H

Original: i × W i × C i .
Revised: i × W i × C i .

Original: 2.2 Backward Gradient Calculation
Revised: 3.2 Backward Gradient Calculation

Original: Next, we derivethe equations to compute the gradients during the backwards phase of SBP.
Revised: Next, we derive equations to compute the gradient during the backwards phase of SBP.

Original: Wedenote that at layer i of the model, an operator f 
Revised: We denotethat at layer i of the model, an operator f i with learnable weights

Original: i with learnable weights W f i to processes the featuremaps X i ∈ R B × T i × H
Revised: W f i to processes the feature maps X i ∈ R B × T i × H

Original: i × W i × C ini , where C ini is the input channel size.
Revised: i × W i × C ini , where C ini refers to the input channel size.

Original: 2.2.1 Calculation of Linear Layers
Revised: 3.2.1 Calculation of Linear Layers

Original: We start with exploring f i as a point-wise convolutional layer (i.e., PW-Conv).
Revised: We start with exploring f i as a point-wise convolutional layer (i.e., PW-Conv).

Original: PW-Conv is widely 74 used in the state-of-the-art computer vision models such as ViT [7], ConvNeXt [14], and Video
Revised: PW-Conv is widely 86 used in the state-of-the-art computer vision models such as ViT [7], ConvNeXt [14], and Video

Original: Swin [13].
Revised: Swin [13].

Original: It is equivalent to the linear layers in Transformer MLP blocks [14], and is also known as 76 channel-wise fully connection layer.
Revised: It is equivalent to the linear layers in Transformer MLP blocks [14], and is also known as 88 channel-wise fully connected layer.

Original: It can be either implemented through the linear layer, or the 1 × 1convolution.
Revised: It can be either implemented through the linear layer, or the 1 × 1convolution.

Original: It is mainly used to enrich the channel information with weights W f i ∈ R C ini × C outi , andthe analysis on PW-Conv can be easily extended to other layers.
Revised: It is mainly used to enrich the channel information with weights W f i ∈ R C ini × C outi , andthe analysis on PW-Conv can be easily extended to other layers.

Original: The forward pass of this layer is (the 79 bias term is omitted):
Revised: The forward pass of this layer is (the 91 bias term is omitted):

Original: [Equation]
Revised: [Equation]

Original:  where X i +1 ∈ R B × T
Revised: 2) where X i +1 ∈ R B × T

Original: i × H
Revised: i × H

Original: i
Revised: i

Original: × W i × C outi .
Revised: × W i × C outi .

Original: During the traditional backward pass with full backpropagation, the gradients of both activations and weights are calculated by:
Revised: During the traditional backward pass with full backpropagation, the gradients of both activations and weights are calculated by:

Original: [Equation]
Revised: [Equation]

Original: It requires to store the weights W f i ∈ R C ini × C outi and the full feature map X i ∈ R B × T
Revised: It requires storing the weights W f i ∈ R C ini × C outi and the full feature maps X i ∈ R B × T

Original: i × H
Revised: i × H

Original: i
Revised: i

Original: × W i × C inito compute the gradients.
Revised: × W i × C inito compute the gradients.

Original: In many cases, B × T i × H
Revised: In many cases, B × T i × H

Original: i × W i
Revised: i × W i

Original: ≫ C outi , i.e., caching of feature mapsdominants the memory usage.
Revised: ≫ C outi , i.e., caching of feature mapsdominates the memory usage.

Original: In order to effectively save GPU memory during training, SBP onlycaches a subset of the feature map for back-propagation.
Revised: In order to effectively save GPU memory during training, SBP onlycaches a subset of the feature map for backpropagation.

Original: In SBP, we split the full feature map X i into two subsets X keepi and X dropi , to denote thecorresponding indices of the sampled feature maps to be kept and dropped for the backwardgradients calculation.
Revised: In the backward pass of SBP case, we split the full feature maps X i into two subsets X keepi and X dropi , which denote the corresponding indices of the sampled feature maps that are kept anddropped, respectively.

Original: An example of X keepi can be X i [: , even index on T, : , : , :] for video, or X i [: , : , even index on H, odd index on W, :] for image (e.g., Fig.
Revised: An example of X keepi can be X i [: , even index on T, : , : , :] for video, or X i [: , :, even index on H, odd index on W, :] for image (e.g., Fig.

Original: 3 left), and X dropi is the complementary part of the feature map.
Revised: 3 left), and X dropi is the complementarypart of the feature maps.

Original: We have X keepi ∩ X dropi = ∅ and X keepi ∪ X dropi =
Revised: We have X keepi ∩ X dropi = ∅ and X keepi ∪ X dropi =

Original: X i .
Revised: X i .

Original: We denotekeep-ratio r as the number of gradients kept indices j ∈ Z keepi over the number of all indices.
Revised: We denotekeep-ratio r as the number of gradients kept indices j ∈ Z keepi over the number of all indices.

Original: One advantage of PW-Conv operator to simplify the SBP problem is, the nodes in  spatial or temporal93 dimensions are calculated independently through the forward and backward passes. Hence, with
Revised: One advantage of simplifying the SBP problem by using PW-Conv operator is that the nodes in 105 spatial or temporal dimensions can be calculated independently through the forward and backwardpasses.

Original: X keepi and X dropi we can update the forward pass separately on the kept and dropped indices:X [Equation] +
Revised: Therefore, we can update the forward pass separately on the kept and dropped indices:

Original: [Equation]

Original: =

Original: [ X keepi +

Original: [Equation]

Original: , X dropi +

Original: [Equation]

Original: ] =

Original: [ X keepi , X dropi

Original: [Equation]

Original: i , i

Original: [Equation]
Revised: [Equation]

Original: e

Original: [Equation]

Original: , X

Original: [Equation]

Original: [Equation]

Original: (4) as well as the backward:
Revised: as well as the backward:

Original: [Equation]
Revised: [Equation]

Original: SBP drops all the gradients calculation with superscripts drop and keeps all the gradients calculationwith superscripts keep , thus it saves memory and computation on the dependencies of droppedindices.
Revised: SBP drops all the gradients calculation with superscripts drop and keeps all the gradients calculationwith superscripts keep , thus it saves memory and computation on the dependencies of droppedindices.

Original: In other words, the memory usage and the backward computation cost is proportional tothe keep-ratio of the original case.
Revised: In other words, the memory usage and the backward computation cost is proportional tothe keep-ratio of the original case.

Original: Mathematically, it sets dX dropi +1 = 0 (thus dX dropi = 0 ) and usesdW keepf i =
Revised: Mathematically, it sets dX dropi +1 = 0 (thus dX dropi = 0 ) and usesdW keepf i =

Original: X keepiT dX keepi +1 as a stochastic approximation of the original weight gradients dW f i toupdate the model weights.
Revised: X keepiT dX keepi +1 as a stochastic approximation of the original weight gradients dW f i toupdate the model weights.

Original: 2.2.2 Calculation of Convolutional Layers
Revised: 3.2.2 Calculation of Convolutional Layers

Original: We then derive the calculation of a general convolutional layer  as f i .
Revised: We then derive the calculation of a general convolutional layer (ConvLayer) as f i .

Original: SBP of convolutional layer is 104 more involved as unlike the PW-Conv, the  nodes  in spatial or temporal dimensions are no longerindependent in forward and backward calculation.
Revised: Unlike the PW- 116 Conv, nodes of general ConvLayer in spatial or temporal dimensions are no longer independent inforward and backward calculations.

Original: Assume that we apply a convolutional kernel106 k × k with stride s .
Revised: Assume that we apply a convolutional kernel k × k with stride s .

Original: The regular back-propagation of the convolution layer is calculated as follows:
Revised: The regular back-propagation of the ConvLayer is calculated as follows:

Original: [Equation]
Revised: [Equation]

Original: 6) where ∗ is the convolution operation, zeropad is to pad the edge of the matrix with zeros, and W ′ f irepresents the 180 ◦ rotation of W f i .
Revised: 6) where ∗ is the convolution operation, zeropad is to pad the edge of the matrix with zeros, and W ′ f irepresents the 180 ◦ rotation of W f i .

Original: Due to the convolution operator in the gradient calculation, given a  location j on the input featuremap as x ji ∈ X i , all the elements from dX i +1 inside the region of the kernel size k would contributeto the gradient calculation of element x ji .
Revised: In the gradient calculation of ConvLayer at a given location j on the input feature map as x ji ∈ X i ,all the elements from dX i +1 inside the region of the kernel size k will contribute to the gradientcalculation of element x ji .

Original: Therefore, in SBP of convolutional layers, when we setdX dropi +1 = 0 for certain areas, in general, two things would happen: (a) even if j ∈ Z drop [-anddx j i +
-] = 0 , dx j i would not be 0 due to the contribution of the neighbors of j on dX i +1 ; (b) dx j iwould be an approximate gradient unless all dx pi +1 with p in the neighbor areas of j in terms of thekernel size k are kept and there is no chain rule effects on dx pi +1 .
Revised: Therefore, in SBP of ConvLayer, when we set dX dropi +1 = 0 for certainareas, in general, two things will happen: (a) even if j ∈ Z drop [-anddx j i +
-]and dx ji +1 = 0 , dx ji will not be 0 dueto the contribution of the neighbors of j on dX i +1 ; (b) dx ji will be an approximated gradient unlessall dx pi +1 with p in the neighbor areas of j in terms of the kernel size k are kept and there is no chainrule effect on dx pi +1 .

Original: However, there is a special scenario,i.e., when s ≥ k ,in which the neighbor effects disappear due to the fact that dX i +1 needs to be zerointerweaved in gradient calculation.
Revised: However, there is a special scenario: when s ≥ k , the neighbor effects disappeardue to the fact that dX i +1 needs to be zero interweaved in gradient calculation.

Original: In such a scenario, nodes in spatial or temporal dimensions aregetting more independent and the scenario isgetting similar to that of PW-Conv.
Revised: As a result, nodes inspatial or temporal dimensions are getting more independent, which is similar to that of PW-Conv.

Original: 2.2.3 Discussion
Revised: 3.2.3 Discussion

Original: From the derivation above, we see that the SBP approximation behaves differently depending on theoperator.
Revised: From the derivation above, we see that the SBP approximation performs differently when the operatorchanges.

Original: For layers such as linear layer or PW-Conv, the gradients (i.e., dx i ) are either 0 or exact 1 ,while for convolutional layers with kernel size larger than 1, most of the gradients on the activationsare neither 0 nor exact.
Revised: For layers such as linear layer or PW-Conv, the gradients (i.e., dx i ) are either 0 or exact 1 . While for convolutional layers with kernel size larger than 1, most of the gradients on the activationsare neither 0 nor exact.

Original: Due to space limitation, we only derived SBP on two most popular operators.
Revised: Due to space limitation, we only derived SBP on two most popular operators.

Original: For derivation of other operators please refer to the supplemental material.
Revised: For derivation of other operators, please refer to the supplemental material.

Original: 2.3 Chain Rule Effect
Revised: 3.3 Chain Rule Effect

Original: We have derived the calculation of SBP on a single layer in  previous section.
Revised: We have derived the calculation of SBP on a single layer in the previous section.

Original: In this section, wederive the calculation of SBP on a stack of multiple layers.
Revised: In this section, wederive the calculation of SBP on a stack of multiple layers.

Original: For simplicity, we take two layers as anexample, which can be easily generalized to more layers.
Revised: For simplicity, we take two layers as anexample, which can be easily generalized to more layers.

Original: 2.3.1 Two PW-Conv Layers
Revised: 3.3.1 Two PW-Conv Layers

Original: We first consider a two PW-Conv layer scenario.
Revised: We first consider a two PW-Conv layer scenario.

Original: We assume a layer before f i , i.e., f i − 1 , is another 131 PW-Conv layer with weights W 
Revised: We assume a layer before f i , i.e., f i − 1 , is another 142 PW-Conv layer with weights W f

Original: [Equation]
Revised: [Equation]

Original: The forward pass of this layer is (the bias term is also omitted):
Revised: The forward pass of this layer is (the bias term is also omitted):

Original: [Equation]
Revised: [Equation]

Original: Then we can calculate the gradients of weights and activations in layer
Revised: Then we can calculate the gradients of weights and activations in layer

Original: f
Revised: f

Original: i − 1 similar to Eq.
Revised: i − 1 similar to Eq.

Original: [Equation]
Revised: [Equation]

Original: We first consider the case that there is no additional gradient dropout at layer f
Revised: We first consider the case when there is no additional gradient dropout at layer f

Original: i − 1 .
Revised: i − 1 .

Original: Since dX i +1has gradients at kept indices (i.e., dX keepi +1 ) and 0 at dropped indices (i.e., dX dropi +1 ), Eq.
Revised: Since dX i +1has gradients at kept indices (i.e., dX keepi +1 ) and 0 at dropped indices (i.e., dX dropi +1 ), Eq.

Original: (8) can beupdated to:
Revised: (8) can beupdated to:

Original: [Equation]
Revised: [Equation]

Original: From the equation we can see that if there is no additional gradient dropout applied on layer f i − 1 ,through the chain rule, the keep and drop set would transfer from X i +1 to X i − 1 identically.
Revised: From the equation, we can see that if there is no additional gradient dropout applied on layer f i − 1 ,through the chain rule, the keep and drop set will transfer from X i +1 to X i − 1 identically.

Original: If we apply gradient dropout on layer f i − 1 as well, i.e., on layer
Revised: If we apply gradient dropout on layer f i − 1 as well, i.e., on layer

Original: f i − 1  we split dX i into dX keepi anddX dropi .
Revised: f i − 1 , we split dX i into dX keepi anddX dropi .

Original: Assume  the keep / drop indices set at layer i + 1 is Z keepi +

Revised: Assume that the keep / drop indices set at layer i + 1 is Z keepi +

Original: and Z dropi +1 , and at layer i isZ keepiand
Revised: and Z dropi +1 , and at layer i is

Original: Z  dropi , then through Eq.
Revised: Z keepi and Z dropi , respectively. Then through Eq.

Original: (8) and Eq.
Revised: (8) and Eq.

Original: (9), the keep / drop indices set on layer i − 1 would be:
Revised: (9), the keep / drop indices set on layeri − 1 will be:

Original: [Equation]
Revised: [Equation]

Original: Eq.
Revised: Eq.

Original: (9) becomes:
Revised: (9) becomes:

Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: where keep ′
Revised: where keep ′

Original: =
Revised: =

Original: Z keepi − 1 and drop ′ =
Revised: Z keepi − 1 and drop ′ =

Original: Z dropi − 1 .
Revised: Z dropi − 1 .

Revised: # f: an arbitrary operation # grad_keep_idx: sampled indices where gradients are kept # grad_drop_idx: sampled indices where gradients are dropped def sbp_f(f, inputs, grad_keep_idx, grad_drop_idx): # initiate outputs outputs = torch.zeros(output_shape, device=inputs.device)

Revised: # forward with gradient calculation, gradients will be calculated with torch.autograd with torch.enable_grad(): outputs[grad_keep_idx] = f(inputs[grad_keep_idx]) # forward without gradient calculation with torch.no_grad(): outputs[grad_drop_idx] = f(inputs[grad_drop_idx]) return outputs

Original: 2.3.2 PW-Conv + Conv Layers
Revised: 3.3.2 PW-Conv + ConvLayers

Original: Now let’s look at the chain-rule effect of the case when f i − 1 is a convolutional layer with kernel size 146 k > 1.
Revised: Now let’s look at the chain-rule effect of the case when f i − 1 is a convolutional layer with kernel size 158 k > 1.

Original: Assume that we apply a convolutional kernel k × k with stride s .
Revised: Assume that we apply a convolutional kernel k × k with stride s .

Original: Using the chain rule we canupdate the Eq.
Revised: Using the chain rule, we canupdate the Eq.

Original: (6) as follows:
Revised: (6) as follows:

Original: [Equation]
Revised: [Equation]

Original: # f: an arbitrary operation # grad_keep_idx: sampled indices where gradients are kept # grad_drop_idx: sampled indices where gradients are dropped def sbp_f(f, inputs, grad_keep_idx, grad_drop_idx): # initiate outputs outputs = torch.zeros(output_shape, device=inputs.device)

Original: # forward with gradient calculation, gradients will be calculated with torch.autograd with torch.enable_grad(): outputs[grad_keep_idx] = f(inputs[grad_keep_idx]) # forward without gradient calculation with torch.no_grad(): outputs[grad_drop_idx] = f(inputs[grad_drop_idx]) return outputs

Original: We can see from the equation that each dx i − 1 receives the backward gradients from the outputs ofits neighbors, which might be dropped out and thus the gradient at dx i − 1 are no longerthe exact.
Revised: We can see from the equation that each dx i − 1 receives the backward gradients from the outputs ofits neighbors, which might be dropped out, thus the gradient at dx i − 1 is no longer exact.

Original: From the Eq.
Revised: From the Eq.

Original: The dropout at layer i makes the matrix dX i sparse (only indices at X keepi +1 are nonzero).
Revised: The dropout at layer i makes the matrix dX i sparse (only indices at X keepi +1 arenon-zero).

Original: If we conduct dropout at the current layer f i − 1 (i.e., we have dX keep i and dX drop i ), the matrixdX i will become even more sparse unless the dropout is happening on the same location.
Revised: If we conduct dropout at the current layer f i − 1 (i.e., we have dX keepi and dX dropi ), thematrix dX i will become even more sparse unless the dropout is happening on the same location.

Original: Stackinglayers further and further in this way, the chain rule effect may lead towards a very sparse outputderivative matrix (e.g., dX i ), in which the nodes are getting more spatial or temporal independent ingradient calculation like what is  in the PW-Conv operator.
Revised: Ifwe stack layers further and further in this way, the chain rule effect may lead to a very sparse outputderivative matrix (e.g., dX i ), in which the nodes are getting more spatial or temporal independentlyin gradient calculation like what is happening in the PW-Conv operator.

Original: However, stacking convolutional layers (due to the space limitation, we skip the math derivation)would not have such  trend, as the gradient dropout in convolutional layer only creates approximategradient but not many zeros, due to the neighbor effects in calculation.
Revised: However, stacking ConvLayers (due to the space limitation, we skip the math derivation) will nothave such a trend, as the gradient dropout in ConvLayer only creates approximated gradient but notmany zeros, because of the neighbor effects of gradient calculation.

Original: 2.3.3 Discussion
Revised: 3.3.3 Discussion

Original: Overall, in terms of the chain rule effect, we can see from Eq.
Revised: Overall, in terms of the chain rule effect, we can see from Eq.

Original: (11) that stacking PW-Conv layers,especially in an interweaving way may lead to a sparse keep set.
Revised: (11) that stacking PW-Conv layers,especially in an interweaving way may lead to a sparse keep set.

Original: However, if we always drop gradientat the fix position then this won’t happen.
Revised: However, if we always drop gradientat the same positions across layers, this won’t happen.

Original: Stacking PW-Conv + Conv layers (in Eq.
Revised: Stacking PW-Conv + ConvLayers (in Eq.

Original: (12)) also tendsto make the keep set sparse, while stacking Conv layers doesn’t have such effect, though it stacks thenon-exact gradient at almost every position and tends to make the gradients gradually move awayfrom the original ones.
Revised: (12))also tends to make the keep set sparse. While stacking ConvLayers doesn’t have such effect, thoughit stacks the non-exact gradient at almost every position and tends to make the gradients graduallymove away from the original ones.

Original: 2.4 Efficient Implementation
Revised: 3.4 Efficient Implementation

Original: With a deeper understanding of how SBP works, we provide a simple and efficient implementation ofthe SBP technique in Alg.
Revised: With a deeper understanding of how SBP works, we provide a simple and efficient implementation ofthe SBP technique in Alg.

Original: We point out that this is more efficient than the prior[4] work  as there is(1) no need to do the re-forward in the backward so it is computationally more efficient, and (2) noneed to manually cache the intermediate feature maps and fill zero gradients in the backward, whichis much simpler.
Revised: We point out that this is more efficient than the prior work [4] as there is(1) no need to do the re-forward in the backward, thus it is computationally more efficient, and (2) noneed to manually cache the intermediate feature maps and fill zero gradients in the backward, whichis much simpler.

Original: We emphasize that it is very simple and easy to implement, it only needs 3 lines ofcode to do the forward, and the backward (no need to implement manually) can be seamlessly takencare of by the ease of “autograd engine” in the deep learning frameworks.
Revised: We emphasize that it is very simple and easy to implement, it only needs 3 linesof code to do the forward, and the backward (no need to implement manually) can be seamlesslyhandled by the ease of “autograd engine” in the deep learning frameworks.

Original: It supports point-wiseconvolutional layers, the dot-product attention  and multilayer perception (MLP) in popular networksincluding ViT [7] and its variants[7, 12, 13], ConvNeXt [14] and hybrid architectures [5].
Revised: It supports point-wiseconvolutional layers, the dot-product attention layers, and multilayer perception (MLP) in popularnetworks including ViT [7] and its variants

Original:  Design Strategies
Revised:  Design Strategies

Original: In this section, we investigate the effectiveness of different design strategies of SBP on image tasks.
Revised: In this section, we investigate the effectiveness of different design strategies of SBP on image tasks.

Original: We use ViT-Tiny [7] model to evaluate the top-1 accuracy on ImageNet [18] validation dataset.
Revised: We use ViT-Tiny [7] model to evaluate the top-1 accuracy on ImageNet [18] validation dataset.

Original: The
Revised: The

Original:  ViT-Tiny is a smaller version of the ViT [7] variants, it uses patch size 16 with 12 transformer layers,the embedding dimension is 192 and number of heads is 3, i.e., each head has dimension 64.
Revised: [Figure] [Figure] ViT-Tiny is a smaller version of the ViT [7] variants, it uses patch size 16 with 12 transformer blocks(more than 80 layers), the embedding dimension is 192 and number of heads is 3, i.e., each head hasdimension 64.

Original: Theexperiments are trained for 100 epochs on the ImageNet training dataset.
Revised: The experiments are trained for 100 epochs on the ImageNet training dataset.

Original: The conclusions remain forlarger models and higher training epochs.
Revised: Theconclusions remain for larger models and higher training epochs.

Original: How many layers should be applied with SBP?
Revised: How many layers should be applied with SBP?

Original: To answer this question, we progressively apply
Revised: To answer this question, we progressively apply

Original: SBP to transformer layers from the last layers to early layers and present their results in Fig.
Revised: SBP to transformer layers from the last layers to early layers and present their results in Fig.

Original: 1 Left.
Revised: 1 Left.

Original: As shown, we observe a sweet spot when applying SBP to 8 out of 12  layers, which saves 33% of GPU memory with an accuracy drop within 1.5%.
Revised: As shown, we observe a sweet spot when applying SBP to 8 out of 12 block layers, which saves 33%of GPU memory with an accuracy drop within 1.5%.

Original: Although applying SBP on all the transformer [Figure] [Figure] layers can save the most GPU memory, it leads to a large accuracy drop.
Revised: Although applying SBP on all the transformerlayers can save the most GPU memory, it causes a large accuracy drop.

Original: Therefore, unless statedotherwise, we apply SBP on 2/3 of all layers of ViT models for the rest of this work.
Revised: Therefore, unless statedotherwise, we apply SBP on 2/3 of all layers of ViT models for the rest of this work.

Original: How much gradients should be kept in SBP?
Revised: How much gradients should be kept in SBP?

Original: In this section, we discuss the trade-off between thegradients keep-ratio and accuracy in SBP.
Revised: In this section, we discuss the trade-off between thegradients keep-ratio and accuracy in SBP.

Original: As a reminder, keep-ratio r is the number of gradientskept indices over the number of all indices, and it represents the percentage of spatial feature mapvalues to calculate the gradients.
Revised: As a reminder, keep-ratio r is the number of gradients keptindices over the number of all indices, and it represents the percentage of spatial feature map valuesused to calculate the gradients.

Original: In general, the higher the keep-ratio, the more gradient informationis preserved, and the higher the accuracy, but the lessis memory  saved.
Revised: In general, the higher the keep-ratio is, the more gradient informationis preserved, and the higher the accuracy is, but the less memory is saved.

Original: A natural question  is what isa good keep-ratio to balance the accuracy drop and memory saving.
Revised: A natural question arises:what is a good keep-ratio to balance the accuracy drop and memory saving?

Original: In Fig.
Revised: In Fig.

Original: 1 Middle, we show the model’s accuracy with respect to the keep-ratio of gradients.
Revised: 1 Middle, we show the trade-off between model accuracy and the keep-ratio of gradients.

Original: In thiscase, we apply the same keep-ratio to all the SBP layers at the same gradients kept indices and wecall it uniform keep-ratios.
Revised: Inthis case, we apply the same keep-ratio to all the SBP layers at the same gradients kept indices and wecall it uniform keep-ratios method.

Original: We observe that setting  keep-ratio to be 0.5 is a golden number, andhaving keep-ratio to be 0.25 is too aggressive, which results in a dramatic accuracy drop (3.04%)of the model.
Revised: We observe that setting the keep-ratio to be 0.5 is a golden rule,and having a keep-ratio of 0.25 is too aggressive, which results in a dramatic accuracy drop (3.04%)of the model.

Original: We also explore progressive keep-ratios, which we linearly increase / decrease thekeep-ratios from early to late layers while keeping their average keep-ratio to be 0.5.
Revised: We also explore different keep-ratio methods by linearly increasing or decreasing thekeep-ratios from early to late layers while keeping their average keep-ratio to be 0.5.

Original: In detail, thekeep-ratios are [0 . 25 , 0 .
Revised: Specifically, thekeep-ratios are [0 . 25 , 0 .

Original: 75]  or its inverse on 8 layers.
Revised: 75] (increasing) or its inverse (decreasing)on 8 transformer blocks.

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 1 Right, both models perform significantly worse.
Revised: 1 Right, both models perform significantly worse.

Original: We simulate the gradient calculation on a 4 layer network (Conv1 - Conv2 - Linear3 - Linear4 - Loss).
Revised: We simulate the gradient calculation on ViT-Tiny with 2000 data batches randomly sampled from the ImageNet training dataset.

Original: We apply different keep-ratio methods on the first 3 layers, and calculate the correlation (measuredby cosine similarity) of thegradients weights of applying SBP to the original exact gradients withoutapplying SBP.
Revised: We apply different keep-ratio methods on the model with the same fixedweights, and calculate the correlation (measured by cosine similarity) between the weights gradientof applying SBP to the original exact gradient without applying SBP.

Original: From Fig.
Revised: From Fig.

Original: 2, we observe that the weights gradients have a stronger correlation byusing uniform keep-ratios compared to increasing or  decreasing keep-ratios.
Revised: 2, we observe that theweights gradient has a stronger correlation by using uniform keep-ratios compared to increasing or [Figure] decreasing keep-ratios.

Original: We believe uniformkeep-ratios can keep the consistency of gradient dropping locations between different layers andcan preserve gradient information at these spatial locations.
Revised: We believe uniform keep-ratios can keep the consistency of gradient droppinglocations between different layers and can preserve gradient information at these spatial locations.

Original: However, non-uniform(progressiveincreasing or decreasing) keep-ratios will have different dropping locations between different layers.
Revised: However, non-uniform keep-ratios will have different dropping locations between different layers.

Original: As an overall result, it may loss more gradient information on more spatial locations and produce aless accurate estimation of the original gradients and may have a lower accuracy performance.
Revised: As an overall result, it may loss more gradient information on more spatial locations and produce aless accurate estimation of the original gradients and may have a lower accuracy performance.

Original: How to sample the gradient keep mask?
Revised: How to sample the gradient keep mask?

Original: SBP calculates the gradients with only using a subset offeature maps, a key question is how to sample the subset of feature maps to mask the kept gradients.
Revised: SBP calculates the gradients by only using a subset offeature maps. A key question is how to sample the subset of feature maps to mask the kept gradients.

Original: If we fix the spatial locations of the gradient kept indexes, then the backward only propagates thevalid gradients (non-zeros) on these fixed locations, which force the model only learns from fixedpartial spatial information and therefore severely hurts the model performance.
Revised: If we fix the spatial locations of the gradient kept indices for every training step, the backward onlypropagates the valid gradients (non-zeros) on these fixed locations, which forces the model to onlylearn from fixed partial spatial information and therefore severely hurts the model performance.

Original: A better strategy is to[Figure] randomly alternate the gradient kept indexes in each training step, such that it can statistically visitevery spatial location.
Revised: Abetter strategy is to randomly sample the gradient kept indices in each training step, so that it canstatistically visit every spatial location with equal importance.

Original: We examine two mask sampling strategies: grid-wise mask sampling and random mask sampling, see
Revised: We examine two mask sampling strategies: grid-wise mask sampling and random mask sampling.

Original:  Fig.
Revised: See Fig.

Original: 3 left as a particular example of keep-ratio 0.5.
Revised: 3 left as a particular example of keep-ratio 0.5.

Original: We report the accuracy in Fig.
Revised: We report the accuracy in Fig.

Original: 4 left.
Revised: 4 left.

Original: Overall,grid-wise mask sampling achieves 0 .
Revised: Overall,grid-wise mask sampling achieves 0 .

Original: 5% ∼ 1 .
Revised: 5% ∼ 1 .

Original: 7% higher accuracy over random mask sampling.
Revised: 7% higher accuracy over random mask sampling.

Original: In
Revised: In

Original: Fig.
Revised: Fig.

Original: 3, we also observe that grid-wise sampling has a stronger correlation for weights gradients.
Revised: 3, A stronger correlation is also observed by using the grid-wise sampling.

Original: Webelieve this is because grid-wise sampling is a more structured sampling method and it provides amore accurate estimation of gradients calculation.
Revised: One explanationis that grid-wise sampling is a more structured sampling method and it provides a more accurateestimation of gradients calculation.

Original: Unless stated otherwise, we use grid-wise samplingin this paper.
Revised: Unless stated otherwise, we use grid-wise sampling in this paper.

Original: How to apply SBP on MHSA?
Revised: How to apply SBP on MHSA?

Original: In general,  transformer block consists of two sub-blocks: Multi-Head Self-Attention (MHSA) and Multi-layer perception (MLP).
Revised: In general, the transformer block consists of two sub-blocks: MultiHead Self-Attention (MHSA) and Multi-layer perception (MLP).

Original: Theoretically, the memory usage ofapplying SBP on MLP (two linear layers) is proportional to the keep-ratio r .
Revised: Theoretically, the memory usage ofapplying SBP on MLP (two linear layers) is proportional to the keep-ratio r .

Original: However, the memorysaving of applying SBP on MHSA depends on the sampling method on  query, key, value (QKV) andthe ratio of head dimension d over the number of tokens n .
Revised: However, the memorysaving of applying SBP on MHSA depends on the sampling method on the query, key, value (QKV),and the ratio of head dimension d over the number of tokens n .

Original: From [4],the activation maps neededfor gradients calculation in MHSA includes the inputs and outputs ( 2 hdn ), QKV vectors (each withhdn ), the attention weight maps ( 2 hnn before and after the softmax), thus requires the memory of3 hdn + 2 hnn in total, where h is the number of heads.
Revised: From [4], activation maps neededfor gradients calculation in MHSA include the inputs and outputs ( 2 hdn ), QKV vectors (each withhdn ), the attention weight maps ( 2 hnn before and after the softmax), thus requiring the memory of3 hdn + 2 hnn in total, where h is the number of heads.

Original: Dropping gradients on query only (Drop
Revised: Dropping gradients on query only (Drop

Original: Query) or on all QKV (Drop QKV) enjoys the following memory usage ratio:
Revised: Query) or on all QKV (Drop QKV) enjoys the following memory usage ratio:

Original: Query only:
Revised: Query only:

Original: [Equation]
Revised: [Equation]

Original: In video transformers, the number of tokens n are much larger than the head dimension d (i.e., invideo Swin Transformer, n = 392 and d = 32 , dn = 0 . 082 ) and thus the attention weight maps 2 hnncomprise most of the memory.
Revised: In video transformers, the number of tokens n is much larger than the head dimension d (i.e., invideo Swin Transformer, n = 392 and d = 32 , dn = 0 . 082 ) and thus the attention weight maps 2 hnn [Figure] comprise most of the memory.

Original: Therefore, dropping gradients on query only in  video model is goodenough to save memory such that its memory is linearly proportional to the keep ratio r [4].
Revised: Therefore, dropping gradients on query only in the video model isgood enough to save memory so that its memory is linearly proportional to the keep ratio r [4].

Original: However, in image task models such as ViT with a typical input size 224 and patch size 16, n =14 × 14 = 196 and d = 64 , dn = 0 . 327 .
Revised: However, in image task models such as ViT with a typical input size 224 and patch size 16, n =14 × 14 = 196 and d = 64 , dn = 0 . 327 .

Original: Then with keep-ratio r = 0 . 5 (or r = 0 . 25 ), droppinggradients on query alone uses 0 .
Revised: With keep-ratio r = 0 . 5 (or r = 0 . 25 ), dropping gradientson query alone uses 0 .

Original: 61 × (or 0 .
Revised: 61 × (or 0 .

Original: 42 × ) memory while dropping all QKV reduces its memoryusage ratio to 0 .
Revised: 42 × ) memory while dropping all QKV reduces its memory usageratio to 0 .

Original: 46 × (or 0 . 22 × ).
Revised: 46 × (or 0 . 22 × ).

Original: From Fig.
Revised: From Fig.

Original: 4, dropping  on all QKV also gains better accuracy.
Revised: 4, dropping gradients on all QKV also gains better accuracy.

Original: Wealso report the performance of dropping gradients along the attention heads but  did not observeaccuracy improvement.
Revised: Wealso report the performance of dropping gradients along the attention heads, but we did not observeaccuracy improvement.

Original: Hence, we apply SBP on all QKV for image-based transformers.
Revised: Hence, we apply SBP on all QKV for image-based transformers.

Original: We plot the cosine similarity of the weights gradients of applying SBP and the original exact gradientsin Fig.
Revised: We plot the cosine similarity of the weights gradients of applying SBP and the original exact gradientsin Fig.

Original: One interesting observation is that Drop QKV has a slightly better correlation to Drop Query [Figure] [Table] and a much higher correlation to Drop Head.
Revised: One interesting observation is that Drop QKV has a slightly better correlation to Drop Queryand a much higher correlation to Drop Head.

Original: We believe an accurate estimation of the gradientsmight be an important factor to the model performance trained with SBP, which provides an insightfor further investigation.
Revised: We believe an accurate estimation of the gradientsmight be an important factor to the model performance trained with SBP, which provides insight forfurther investigation.

Original:  Generalizability of SBP
Revised:  Generalizability of SBP

Original: We evaluate the generalizability of our proposed SBP on two computer vision benchmarks: imageclassification on ImageNet [18] and object detection on COCO [11].
Revised: We evaluate the generalizability of our proposed SBP on two computer vision benchmarks: imageclassification on ImageNet [18] and object detection on COCO [11].

Original: In order to do a fair comparison ofthe same network, we keep the training hyper-parameters of optimizer, augmentation, regularization,batch size and learning rate the same for a given model, and only adopt different stochastic depthaugmentation [10] for different model sizes.
Revised: In order to do a fair comparison ofthe same network, we keep the training hyper-parameters of optimizer, augmentation, regularization,batch size and learning rate the same for a given model, and only adopt different stochastic depthaugmentation [10] for different model sizes.

Original: The Mixed Precision Training [17] method is enabledfor faster training.
Revised: The Mixed Precision Training [17] method is enabledfor faster training.

Original: All experiments are conducted on 8 × Tesla 16GB V100.
Revised: All experiments are conducted on 8 × Tesla 16GB V100.

Original: 4.1 Classification on ImageNet
Revised: 5.1 Classification on ImageNet

Original: We report the memory and accuracy trade-off in Tab. 1 for two state-of-the-art networks: transformerbased ViT [7] and convolutional-based ConvNeXt [14].
Revised: We report the memory and accuracy trade-off in Tab. 1 for two state-of-the-art networks: transformerbased ViT [7] and convolutional-based ConvNeXt [14].

Original: We train 300 epochs for both these twonetworks.
Revised: We train 300 epochs for both these twonetworks.

Original: We use DeiT’s [21] training recipe for ViT and use the official one of ConvNeXt.
Revised: We use DeiT’s [21] training recipe for ViT and use the official one of ConvNeXt.

Original: For ViT models, we apply SBP on the last 8 transformer blocks including MHSA and MLP layerswith the keep-ratio of 0.5, reducing GPU memory usage by approximately 30%.
Revised: For ViT models, we apply SBP on the last 8 transformer blocks including MHSA and MLP layerswith the keep-ratio of 0.5, reducing GPU memory usage by approximately 30%.

Original: Note that thememory saving ratio of the network is not proportional the the drop-ratio ( 1 − keep-ratio) because weonly apply SBP on 23 of all the layers.
Revised: Note that thememory saving ratio of the network is not proportional the the drop-ratio ( 1 − keep-ratio) because weonly apply SBP on 23 of all the layers.

Original: With keep ratio of 0.5, the SBP technique can still effectivelylearn with an accuracy drop of only 0.04% and 0.43% for ViT-Tiny and ViT-Base, respectively.
Revised: With keep ratio of 0.5, the SBP technique can still effectivelylearn with an accuracy drop of only 0.04% and 0.43% for ViT-Tiny and ViT-Base, respectively.

Original: For ConvNeXt models, it builds the basic block with a depth-wise convolutional (DW-Conv) layerwith kernel size 7 × 7 followed by two point-wise convolutional (PW-Conv) layers 2 and the DW-Convlayer only consumes a small portion of GPU memory (less than 10%).
Revised: For ConvNeXt models, it builds the basic block with a depth-wise convolutional (DW-Conv) layerwith kernel size 7 × 7 followed by two point-wise convolutional (PW-Conv) layers 2 and the DW-Convlayer only consumes a small portion of GPU memory (less than 10%).

Original: It has multi-scale stages withearly stages having higher redundancy and occupy most of the GPU memory.
Revised: It has multi-scale stages whereearly stages has higher redundancy and occupies most of the GPU memory.

Original: Therefore, we apply
Revised: Therefore, we apply

Original: SBP on the PW-Conv and down-sampling layers in the first three stages, and do not apply it on thelast stage.
Revised: SBP on the PW-Conv and down-sampling layers in the first three stages, and do not apply it on thelast stage.

Original: With  keep ratio of 0.5, ConvNeXt-Tiny and ConvNeXt-Base can efficiently learn themodel with a GPU memory saving of above 40% and an accuracy drop within 0.5%. 
Revised: With a keep ratio of 0.5, ConvNeXt-Tiny and ConvNeXt-Base can efficiently learn themodel with a GPU memory saving of above 40% and an accuracy drop within 0.5%. [Table]

Original: [Table]
Revised: [Table]

Original: 4.2 Object Detection and Segmentation on COCO
Revised: 5.2 Object Detection and Segmentation on COCO

Original: We fine-tune Mask R-CNN [9] and Cascade Mask R-CNN
Revised: We fine-tune Mask R-CNN [9] and Cascade Mask R-CNN

Original: [2] on the COCO dataset [11] with
Revised: [2] on the COCO dataset [11] with

Original: ConvNeXt-Tiny and ConvNeXt-Base backbones pretrained on ImageNet-1K, respectively.
Revised: ConvNeXt-Tiny and ConvNeXt-Base backbones pretrained on ImageNet-1K, respectively.

Original: Followingthe same hyper-parameter settings of [14], we apply SBP on the ConvNeXt backbones to savetraining memory.
Revised: Followingthe same hyper-parameter settings of [14], we apply SBP on the ConvNeXt backbones to savetraining memory.

Original: Results in Tab.
Revised: Results in Tab.

Original: 2 show that SBP with keep-ratio of 0.5 can still learn the detectiontask reasonably well with 0.2% and 0.7% loss on the box and mask average precision (AP) for
Revised: 2 show that SBP with keep-ratio of 0.5 can still learn the detectiontask reasonably well with 0.2% and 0.7% loss on the box and mask average precision (AP) for

Original: ConvNeXt-Base and ConvNeXt-Tiny backbones, respectively.
Revised: ConvNeXt-Base and ConvNeXt-Tiny backbones, respectively.

Original: However, it only consumes about0.7 × of the GPU memory.
Revised: However, it only consumes about0.7 × of the GPU memory.

Original: Note that training detection models on high resolution images (up to 800 × 1333) is memory intensiveeven for a very small batch size, e.g., with backbone of ConvNeXt-Base and batch size of 2, trainingrequires 17.4GB of GPU memory which may lead to out of memory error for common devicessuch as Tesla 16GB V100.
Revised: Note that training detection models on high resolution images (up to 800 × 1333) is memory intensiveeven for a very small batch size, e.g., with backbone of ConvNeXt-Base and batch size of 2, thetraining requires 17.4GB of GPU memory which may lead to out of memory error for commondevices such as Tesla 16GB V100.

Original: While with the help of SBP, the model canbe still effectively trained on GPUs with 12.5GB memory, which is much more feasible for many research groups.
Revised: While with the help of SBP, the model can still be effectivelytrained on GPUs with 12.5GB memory, which is much more feasible for many research groups.

Original: Here we apply SBP only in the backbone, we believe it has a potential to apply SBP on more other layers in thedetection model to achieve more promising efficiency results.  Related Work
Revised: Herewe apply SBP only in the backbone, we believe it has a potential to apply SBP on more other layersin the detection model to achieve more promising results in terms of efficiency.

Original: Since deep neural networks are highly extended in both depth [8] and breadth [23] in recent years,designing memory efficient training methods has attracted a fair amount of attention.

Original: By savingand recomputing nodes in the computation graph during backpropagation, Gradient Checkpoint [3]can save a large amount of memory (up to about 50%).

Original: On the other hand, gradient accumulationsplits the large sample batch into several mini batches and runs them iteratively without updatingthe model parameters.

Original: This saves the GPU memory proportionally without affecting the accuracybecause accumulating the gradients of these sub-iteration and then updating the model parameters isidentical to directly optimizing the model global batch size.

Original: However, these two methods are slowdown the training process.

Original: Multigrid [22] proposes to use different mini-batch shapes to speedupthe model training.

Original: Sparse Network [6] particularly targets on the recognition tasks but can onlysave the memory theoretically.

Original: Sideways [15] and its follow-up [16] reduce the memory cost byoverwriting activations whenever new ones become available but can only be applied to causalmodels.

Original: Stochastic Backpropagation (SBP) [4] reduces a large portion of GPU memory by onlybackpropagating gradients from incomplete execution.

Original:  Discussion
Revised:  Discussion

Original: In this work, we present a comprehensive study of the Stochastic Back-Propagation (SBP) mechanismwith a generalized implementation.
Revised: In this work, we present a comprehensive study of the Stochastic Back-Propagation (SBP) mechanismwith a generalized implementation.

Original: We analyze the effect of different design strategies to optimizethe trade-off between accuracy and memory.
Revised: We analyze the effect of different design strategies to optimizethe trade-off between accuracy and memory.

Original: We show that our approach can reduce 40% of the GPUmemory when training image classification models under various deep learning backbones.
Revised: We show that our approach can reduce 40% of the GPUmemory when training image classification models under various deep learning backbones.

Original: Limitations:There are several limitations of our current work.
Revised: Limitations:

Original: In general, SBP is a memory efficienttraining method, but as byproduct, it still causes slightly loss of accuracy.
Revised: In general, SBP is a memory efficient training method, but as byproduct, it stillcauses slight loss of accuracy.

Original: In our current results, SBP brings only a small amount of training speedup( ∼ 1 . 1 × ), how to further speed up the trainingprocess is part of our future work.
Revised: In our current results, SBP produces only a small amount of trainingspeedup( ∼ 1 . 1 × ). How to further speed up the training process is part of our future work.

Original: Societal impact: Our work studies the stochastic backpropagation mechanism, which can be usedfor training general deep learning models.
Revised: Societal impact: Our work studies the stochastic backpropagation mechanism, which can be usedfor training general deep learning models.

Original: Misuse can potentially cause societal harm.
Revised: Misuse can potentially cause societal harm.

Original: However, webelieve our work is a general approach and is consist with the common practice in machine learning.
Revised: However, webelieve our work is a general approach and is consist with the common practice in machine learning.

Original: Checklist
Revised: Checklist

Original: For all authors...
Revised: For all authors...

Original: (a) Do the main claims made
Revised: (a) Do the main claims made

Original: in
Revised: in

Original: the abstract and introduction accurately reflectthe
Revised: the abstract and introduction accurately reflect

Original:  paper’scontributions and scope
Revised: the paper’scontributions and scope

Original: Yes]
Revised: [Yes]

Original: (b) Did you describe the limitations of your work?
Revised: (b) Did you describe the limitations of your work?

Original: [Yes] See Section 6.
Revised: [Yes] See Section 6.

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] See
Revised: [Yes] See

Original: Section 6.
Revised: Section 6.

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms to
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms to

Original: them?
Revised: them?

Original: [Yes]
Revised: [Yes]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

Original: (a) Did you state the full set of assumptions of all theoretical results?
Revised: (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you include complete proofs of all theoretical results?
Revised: (b) Did you include complete proofs of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main experi-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: mental results (either in the supplemental material or as a URL)?
Revised: mental results (either in the supplemental material or as a URL)?

Original: [Yes] We will add
Revised: [Yes] We will add

Original: instructions in the supplemental material.
Revised: instructions in the supplemental material.

Original: (b) Did you specify all the training details (e.g., data splits, hyperparameters,
Revised: (b) Did you specify all the training details (e.g., data splits, hyperparameters,

Original: how they
Revised: how they

Original: were chosen)?
Revised: were chosen)?

Original: [Yes] See Section 4’s first paragraph and Section 4.1’s first paragraph.
Revised: [Yes] See Section 5’s first paragraph and Section 5.1’s first paragraph.

Original: (c) Did you report error bars (e.g., with respect to the random seed after running exper-
Revised: (c) Did you report error bars (e.g., with respect to the random seed after running exper-

Original: iments multiple times)?
Revised: iments multiple times)?

Original: [No] We will include these error bars in the supplemental
Revised: [No] We will include these error bars in the supplemental

Original: material.
Revised: material.

Original: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?
Revised: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?

Original: [Yes] See Section 4’s first paragraph.
Revised: [Yes] See Section 5’s first paragraph.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?
Revised: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] We cited the all
Revised: [Yes] We cited the all

Original: datasets we used, such as ImageNet [18] ad COCO [11].
Revised: datasets we used, such as ImageNet [18] ad COCO [11].

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [No] ImageNet is under BSD 3-ClauseLicense and COCO is under Creative Commons Attribution 4.0 License.
Revised: [No] ImageNet is under BSD 3-ClauseLicense and COCO is under Creative Commons Attribution 4.0 License.

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [No]
Revised: [No]

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re
Revised: (d) Did you discuss whether and how consent was obtained from people whose data you’re

Original: using/curating?
Revised: using/curating?

Original: [No]
Revised: [No]

Original: (e) Did you discuss whether the data you are using/curating contains personally identi-
Revised: (e) Did you discuss whether the data you are using/curating contains personally identi-

Original: fiable information or offensive content?
Revised: fiable information or offensive content?

Original: [No] We do not have sufficient resource to
Revised: [No] We do not have sufficient resource to

Original: exhaustively screen offensive content from the datasets we used.
Revised: exhaustively screen offensive content from the datasets we used.

Original: We expect, becausethe datasets only contain images of people with daily activities and common objects,
Revised: We expect, because the datasets only contain images of people with daily activities and common objects,

Original: they are unlikely to contain offensive content.
Revised: they are unlikely to contain offensive content.

Original: They are both very widely used.
Revised: They are both very widely used.

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if

Original: applicable?
Revised: applicable?

Original: [N/A]
Revised: [N/A]

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Original: Board (IRB) approvals, if applicable?
Revised: Board (IRB) approvals, if applicable?

Original: [N/A]
Revised: [N/A]

Original: (c) Did you include the estimated hourly wage paid to participants and the total amountspent on participant compensation?
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amountspent on participant compensation?

Original: [N/A]
Revised: [N/A]