diff --git a/2003.10258/main_diagram/main_diagram.drawio b/2003.10258/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..63f9e34f94e49142f86234d83b86fbd88603b328 --- /dev/null +++ b/2003.10258/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2003.10258/main_diagram/main_diagram.pdf b/2003.10258/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1755f25bb979c307a1fbc28d609c88ec27909a63 Binary files /dev/null and b/2003.10258/main_diagram/main_diagram.pdf differ diff --git a/2003.10258/paper_text/intro_method.md b/2003.10258/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..82f1aca33a993ff7654513a6493ce49d600e7077 --- /dev/null +++ b/2003.10258/paper_text/intro_method.md @@ -0,0 +1,195 @@ +# Introduction + +Deep neural networks have become state-of-the-art in many competitive learning challenges. The neural network acts as a flexible function approximator in an overall learning scheme. In supervised learning, the weights of the neural network are optimized by utilizing a representative set of valid input-output pairs. Whereas neural networks solve complex learning tasks [@AlexNet] in this way, concerns arise addressing the black box character [@Gilpin2019; @StopExplaining]: (1) In general, a neural network represents a complex non-linear mapping and it is difficult to show properties for this function from a mathematical point of view, verification of desired input-output relations [@verifiedLearners; @Katz2017] or inference of confidence levels in a probabilistic framework [@Gal]. (2) Furthermore, the learned abstractions and processes within the neural network are usually not interpretable or explainable to an human [@StopExplaining]. + +With our approach, we address mainly the first concern: (1) We propose a neural network which predicts provable within a sample-specific constrained output space. *ConstraintNet* encodes a certain class of constraints, a certain type of a convex polytope, in the network architecture and enables to choose a specific constraint from this class via an additional input in each forward pass independently. In this way, *ConstraintNet* allows to enforce a consistent prediction with respect to a valid output domain. We assume that the partition into valid and invalid output domains is given by an external source. This could be a human expert, a rule based model or even a second neural network. (2) Secondly, we contribute to the interpretability and explainability of neural networks: A constraint over the output is interpretable and allows to describe the decision making of *ConstraintNet* in an interpretable way, later we model output constraints for a facial landmark prediction task such that the model predicts the facial landmarks on a region which is recognized as face and locates the positions of the eyes above the nose-landmark for anatomical reasons. Therefore, the additional input encodes the output constraint and represents high level information with explainable impact on the prediction. When this input is generated by a second model, it is an intermediate variable of the total model with interpretable information. + +*ConstraintNet* addresses safety-critical applications in particular. Neural networks tend to generalize to new data with high accuracy on average. However, there remains a risk of unforseeable and unintended behavior in rare cases. Instead of monitoring the output of the neural network with a second algorithm and intervening when safety-critical behavior is detected, we suggest to constrain the output space with *ConstraintNet* to safe solutions in the first place. Imagine a neural network as motion planner. In this case, sensor detections or map data constrain the output space to only collision free trajectories. + +Apart from safety-critical applications, *ConstraintNet* can be applied to predict within a region of interest in various use cases. in medical image processing, this region could be annotated by a human expert to restrict the localization of an anatomical landmark. + +We demonstrate the modeling of constraints on several facial landmark prediction tasks. Furthermore, we illustrate the application to a follow object controller for vehicles as a safety-critical application. We have promising results on ongoing experiments and plan to publish in future. + +# Method + +**Construction approach.** We propose the approach visualized in Fig. [1](#architecture_approach){reference-type="ref" reference="architecture_approach"} to create the architecture of *ConstraintNet* for a specific class of constraints $\mathfrak{C}$. The key idea is a final layer $\phi: \mathcal{Z} \!\times\! \mathcal{S} \!\to\! \mathcal{Y}$ without learnable parameters which maps the output of the previous layers $z\!\in\!\mathcal{Z}$ on the constrained output space $\mathcal{C}(s)$ depending on the constraint parameter $s$. Given a class of constraints $\mathfrak{C}\! = +\! \{\mathcal{C}(s) \! \subset \! \mathcal{Y} : s\! \in \! \mathcal{S} \}$, we require that $\phi$ fulfills: $$\begin{equation} + \label{central_prop_phi} + \forall s\! \in \! \mathcal{S} \; \forall z \! \in \! \mathcal{Z}: \phi(z,s) \in \mathcal{C}(s). +\end{equation}$$ When $\phi$ is furthermore (piecewise) differentiable with respect to $z$ we call $\phi$ constraint guard layer for $\mathfrak{C}$. + +The constraint guard layer $\phi$ has no adjustable parameters and therefore the logic is learned by the previous layers $h_{\theta}$ with parameters $\theta$. In the ideal case, *ConstraintNet* predicts the same true output $y$ for a data point $x$ under different but valid constraints. This behavior requires that $h_{\theta}$ depends on $s$ in addition to $x$. Without this requirement, $z\!=\!h_{\theta}(\cdot)$ would have the same value for fixed $x$, and $\phi$ would project this $z$ for different but valid constraint parameters $s$ in general on different outputs. We transform $s$ into an appropriate representation $g(s)$ and consider it as an additional input of $h_{\theta}$, $h_{\theta}: +\! \mathcal{X} \! \times \! g(\mathcal{S}) \!\to\! \mathcal{Z}$. For the construction of $h_{\theta}$, we propose to start with a common neural network architecture with input domain $\mathcal{X}$ and output domain $\mathcal{Z}$. In a next step, this neural network can be extended to add an additional input for $g(s)$. We propose to concatenate $g(s)$ to the output of an intermediate layer since it is information with a higher level of abstraction. + +Finally, we construct *ConstraintNet* for the considered class of constraints $\mathfrak{C}$ by applying the layers $h_{\theta}$ and the corresponding constraint guard layer $\phi$ subsequently: + +$$\begin{equation} + f_{\theta}(x,s) = + \phi\big(h_{\theta}(x, g(s)),s\big). +\end{equation}$$ + +The required property for $\phi$ in Eq. [\[central_prop_phi\]](#central_prop_phi){reference-type="ref" reference="central_prop_phi"} implies that *ConstraintNet* predicts within the constrained output space $\mathcal{C}(s)$ according to Eq. [\[central_prop\]](#central_prop){reference-type="ref" reference="central_prop"}. Furthermore, the constraint guard layer propagates gradients and backpropagation is amenable. + +**Construction by modifying a CNN.** Fig. [2](#cnn_modified){reference-type="ref" reference="cnn_modified"} illustrates the construction of *ConstraintNet* by using a convolutional neural network (CNN) for the generation of the intermediate variable $z\!=\!h_{\theta}(x, g(s))$, where $h_{\theta}$ is a CNN. As an example, a nose landmark prediction task on face images is shown. The output constraints are triangles randomly located around the nose, convex polytopes with three vertices. Such constraints can be specified by a constraint parameter $s$ consisting of the concatenated vertex coordinates. The constraint guard layer $\phi$ for convex polytopes is modeled in the next section and requires a three dimensional intermediate variable $z\!\in\!\mathbb{R}^3$ for triangles. The previous layers $h_{\theta}$ map the image data $x \!\in\!\mathcal{X}$ on the three dimensional intermediate variable $z\!\in\!\mathbb{R}^3$. A CNN with output domain $\mathcal{Z}\!=\!\mathbb{R}^{N_z}$ can be realized by adding a dense layer with $N_z$ output neurons and linear activations. To incorporate the dependency of $h_{\theta}$ on $s$, we suggest to concatenate the output of an intermediate convolutional layer by a tensor representation $g(s)$ of $s$. Thereby, we extend the input of the next layer in a natural way. + +![Construction of *ConstraintNet* by extending a CNN. For illustration purposes, we show a nose landmark prediction on an image $x$ with an output constraint in form of a triangle, a convex polytope with three vertices $\{v^{(i)}(s)\}_{i=1}^3$. The constraint parameter $s$ specifies the chosen constraint and consists in this case of concatenated vertex coordinates. A tensor representation $g(s)$ of $s$ is concatenated to the output of an intermediate convolutional layer and extends the input of the next layer. Instead of creating the final output for the nose landmark with a 2-dimensional dense layer, a 3-dimensional intermediate representation $z$ is generated. The constraint guard layer $\phi$ applies a softmax function $\sigma$ on $z$ and weights the three vertices of the triangle with the softmax outputs. This guarantees a prediction $\hat y$ within the specified triangle. []{#cnn_modified label="cnn_modified"} ](architecture.pdf){#cnn_modified width="90%"} + +In this subsection we model the constraint guard layer for different classes of constraints. Primarily, we consider output constraints in form of convex polytopes. However, our approach is also applicable to problem-specific constraints. As an example, we construct the constraint guard layer for constraints in form of sectors of a circle. Furthermore, we model constraints for different parts of the output. + +**Convex polytopes.** We consider convex polytopes $\mathcal{P}$ in $\mathbb{R}^N$ which can be described by the convex hull of $M$ $N$-dimensional vertices $\{ v^{(i)}\}_{i=1}^M$: $$\begin{equation} + \label{convex_polytope} + \mathcal{P}\bigl(\{ v^{(i)}\}_{i=1}^M \bigr)\!=\!\bigl \{ \sum_{i} p_i + v^{(i)} : p_i \! \geq \! 0, \; \\ + \sum_{i} p_i \!=\! 1 \bigr \}. +\end{equation}$$ + +We assume that the vertices $v^{(i)}(s)$ are functions of the constraint parameter $s$ and define output constraints via $\mathcal{C}(s) \! = \!\mathcal{P}(\{ +v^{(i)}(s)\}_{i=1}^M)$. The constraint guard layer for a class of these constraints $\mathfrak{C}\!=\! +\{\mathcal{C}(s):s\! \in\!\mathcal{S} \}$ can easily be constructed with $z\!\in\!\mathbb{R}^M$: + +$$\begin{equation} + \label{convex_phi} + \phi(z,s)= \sum_{i} \sigma_i(z) + v^{(i)}(s). +\end{equation}$$ + +$\sigma_i(\cdot)$ denotes the $i$th component of the the $M$-dimensional softmax function $\sigma:\mathbb{R}^M \! \to \! \mathbb{R}^M$. The required property of $\phi$ in Eq. [\[central_prop_phi\]](#central_prop_phi){reference-type="ref" reference="central_prop_phi"} follows directly from the properties $0 +\! < \! \sigma_i(\cdot) +\! < \! 1$ and $\sum_i \sigma_i(\cdot)\!=\!1$ of the softmax function. However, some vertices $v^{(i)}$ might not be reachable exactly but upto arbitrary accuracy because $\sigma_i(\cdot) \! \neq \! 1$. Note that $\phi$ is differentiable with respect to $z$. + +**Sectors of a circle.** Consider a sector of a circle $\mathcal{O}$ with center position $(x_c, y_c)$ and radius $R$. We assume that the sector is symmetric with respect to the vertical line $x\!=\!x_c$ and covers $\Psi$ radian. Then the sector of a circle can be described by the following set of points: $$\begin{alignat} +{2} + \label{sector_circle} + \mathcal{O}(x_c, y_c, R, \Psi)\!=& \bigl \{ r \! \cdot \! (\sin \varphi, + \cos \varphi ) \! + \! (x_c, y_c) \! \in \! \mathbb{R}^2 : \nonumber \\ + & r \! \in \! [0,R], \varphi \! \in \! [-\Psi/2, +\Psi/2 ] \bigr \}. +\end{alignat}$$ + +With $s\!=\!(x_c, y_c, R, \Psi)$, the output constraints can be written as $\mathcal{C}(s)\!=\!\mathcal{O}(x_c, y_c, R, \Psi)$. It is obvious that the following constraint guard layer with an intermediate variable $z \! \in \! +\mathbb{R}^2$ fulfills Eq. [\[central_prop_phi\]](#central_prop_phi){reference-type="ref" reference="central_prop_phi"} for a class of these constraints $\mathfrak{C}\!=\! +\{\mathcal{C}(s):s\! \in\!\mathcal{S} \}$: $$\begin{alignat} +{2} \label{phi_sector_circle} + \phi(z,s) =& \; r(z_1)\!\cdot \! \bigl(\sin \varphi(z_2),\cos \varphi(z_2) + \bigr)\! + +\!(x_c,y_c), \\ +r(z_1) =& \; R \cdot \operatorname{sig}(z_1), \label{phi_sector_circle_2} + \\ +\varphi(z_2) =& \; \Psi\cdot(\operatorname{sig}(z_2)-1/2). \label{phi_sector_circle_3} +\end{alignat}$$ + +Note that we use the sigmoid function $\operatorname{sig}(t)\!=\!1/(1\!+\!\exp(-t))$ to map a real number to the interval $(0,1)$. + +**Constraints on output parts.** We consider an output $y$ with $K$ parts $y^{(k)}$ ($k \! \in \! +\{1,\dots,K\}$): $$\begin{equation} +y = (y^{(1)},\dots,y^{(K)}) \in \mathcal{Y}\!=\! \mathcal{Y}^{(1)} \times \cdots \times +\mathcal{Y}^{(K)}. +\end{equation}$$ Each output part $y^{(k)}$ should be constrained independently to an output constraint $\mathcal{C}^{(k)}(s^{(k)})$ of a part-specific class of constraints: $$\begin{equation} + \mathfrak{C}^{(k)}= \, \{\mathcal{C}^{(k)}(s^{(k)}) \! \subset \! + \mathcal{Y}^{(k)} : s^{(k) } \! \in \! + \mathcal{S}^{(k)}\}. +\end{equation}$$ This is equivalent to constrain the overall output $y$ to $\mathcal{C}(s)\! = \! \mathcal{C}^{(1)}(s^{(1)}) \! \times \! \cdots \! \times +\! \mathcal{C}^{(K)}(s^{(K)})$ with $s = (s^{(1)}, \dots, s^{(K)})$. The class of constraints for the overall output is then given by: $$\begin{equation} + \mathfrak{C}= \{\mathcal{C}(s)\! \subset \! \mathcal{Y} : s\! \in \! + \mathcal{S}^{(1)} \times \cdots \times \mathcal{S}^{(K)} \! \}. +\end{equation}$$ + +Assume that the constraint guard layers $\phi^{(k)}$ for the parts are given, for $\mathfrak{C}^{(k)}$. Then an overall constraint guard layer $\phi$, for $\mathfrak{C}$, can be constructed by concatenating the constraint guard layers of the parts: $$\begin{alignat} +{2} + \phi(z,s)=&\,\big(\phi^{(1)}(z^{(1)},s^{(1)}),\dots,\phi^{(K)}(z^{(K)}, + s^{(K)})\big), + \label{parts_phi} \\ + z=& \,(z^{(1)},\! \dots,\! z^{(K)}). +\end{alignat}$$ The validity of the property in Eq. [\[central_prop_phi\]](#central_prop_phi){reference-type="ref" reference="central_prop_phi"} for $\phi$ with respect to $\mathfrak{C}$ follows immediately from the validity of this property for $\phi^{(k)}$ with respect to $\mathfrak{C}^{(k)}$. + +In supervised learning the parameters $\theta$ of a neural network are learned from data by utilizing a set of input-output pairs $\{(x_i, y_i)\}_{i=1}^{N}$. However, *ConstraintNet* has an additional input $s\!\in\! \mathcal{S}$ which is not unique for a sample. The constraint parameter $s$ provides information in form of a region restricting the true output and therefore the constraint parameter $s_i$ for a sample $(x_i,y_i)$ could be any element of a set of valid constraint parameters $\mathcal{S}_{y_i} \! +=\!\{s\!\in\!\mathcal{S}: y_i \! \in \! \mathcal{C}(s) \}$. + +We propose to sample $s_i$ from this set $\mathcal{S}_{y_i}$ to create representative input-output pairs $(x_i, s_i, y_i)$. This sampling procedure enables *ConstraintNet* to be trained with standard supervised learning algorithms for neural networks. Note that many input-output pairs can be generated from the same data point $(x_i, y_i)$ by sampling different constraint parameters $s_i$. Therefore, *ConstraintNet* is forced to learn an invariant prediction for the same sample under different constraint parameters. + +:::: algorithm +::: algorithmic +\ + +$\theta \gets \text{random initialization}$ + +$I_{batch} \gets get\_batch\_indices(\text{batch})$ $L(\theta) \gets \frac{1}{|I_{batch}|}\sum_{i\in I_{batch}} + l(y_i, + \hat y_i)+\lambda R(\theta)$ $\theta \gets update(\theta,\nabla_{\theta} L)$ +::: +:::: + +We train *ConstraintNet* with gradient-based optimization and sample $s_i$ within the training loop as it is shown in Algorithm [\[pseudocode\]](#pseudocode){reference-type="ref" reference="pseudocode"}. The learning objective is given by: $$\begin{equation} + \label{objective} +\arg \min_{\theta} L(\theta) = \frac{1}{N}\sum_{i=1}^N l(y_i, \hat y_i)+\lambda +R(\theta), +\end{equation}$$ with $l(\cdot)$ being the sample loss, $R(\cdot)$ a regularization term and $\lambda$ a weighting factor. The sample loss term $l(y_i, \hat y_i)$ penalizes deviations of the neural network prediction $\hat y_i$ from the ground truth $y_i$. We apply *ConstraintNet* to regression problems and use mean squared error as sample loss. + +In this section, we apply *ConstraintNet* on a facial landmark prediction task and a follow object controller for vehicles. The output constraints for the facial landmark prediction task restrict the solution space to consistent outputs, whereas the constraints for the follow object controller help to prevent collisions and to avoid violations of legislation standards. We want to highlight that both applications are exemplary. The main goal is an illustrative demonstration for leveraging output constraints with *ConstraintNet* in applications. + +In our first application, we consider a facial landmark prediction for the nose $(\hat +x_n,\hat y_n)$, the left eye $(\hat x_{le}, \hat y_{le})$ and the right eye $(\hat x_{re},\hat y_{re})$ on image data. We assume that each image pictures a face. We introduce constraints to confine the landmark predictions for nose, left eye and right eye to a bounding box which might be given by a face detector. Then, we extend these constraints and enforce relative positions between landmarks such as *the eyes are above the nose*. These constraints are visualized in the top row of Fig. [3](#constr_visualized){reference-type="ref" reference="constr_visualized"}. The bottom row shows constraints for the nose landmark in form of a triangle and a sector of a circle. These constraints can be realized with the constraint guard layers in Eq. [\[convex_phi\]](#convex_phi){reference-type="ref" reference="convex_phi"} and Eq. [\[phi_sector_circle\]](#phi_sector_circle){reference-type="ref" reference="phi_sector_circle"}. However, they are of less practical relevance. + +**Modified CNN architecture.** First of all, we define the output of *ConstraintNet* according to: $$\begin{equation} +\hat y = (\hat x_n, \hat x_{le}, \hat x_{re}, \hat y_n, \hat y_{le}, \hat +y_{re}), +\end{equation}$$ and denote the $x$-cooridnates $\hat y^{(k_x)}$ with $k_x \! \in \! \{ 1,2,3 \}$ and the $y$-coordinates $\hat y^{(k_y)}$ with $k_y \! \in \! \{ 4,5,6 \}$. *ConstraintNet* can be constructed by modifying a CNN according to Fig. [2](#cnn_modified){reference-type="ref" reference="cnn_modified"} and Sec. [\[network_architecture\]](#network_architecture){reference-type="ref" reference="network_architecture"}. ResNet50 [@ResNet] is a common CNN architecture which is used for many classification and regression tasks in computer vision [@regressionStudy]. In the case of regression, the prediction is usually generated by a final dense layer with linear activations. The modifications comprise adopting the output dimension of the final dense layer with linear acitivations to match the required dimension of $z$, adding the constraint guard layer $\phi$ for the considered class of constraints $\mathfrak{C}$ and inserting a representation $g(s)$ of the constraint parameter $s$ at the stage of intermediate layers. We define $g(s)$ as tensor and identify channels $c \! \in \! \{1,\dots, dim(s) +\}$ with the components of the constraint parameter $s$, then we set all entries within a channel to a rescaled value of the corresponding constraint parameter component $s_c$: $$\begin{gather} + g_{c, w, h}(s) = \lambda_c \cdot s_c \label{constr_repr}, \\ + w \! \in \! \{1,\dots,W \},\, h \! \in \! \{1,\dots,H \}. +\end{gather}$$ $W$ and $H$ denote the width and height of the tensor and each $\lambda_c$ is a rescaling factor. We suggest to choose the factors $\lambda_c$ such that $s_c$ is rescaled to approximately the scale of the values in the output of the layer which is extended by $g(s)$. + +![ Top left: Landmark predictions for nose, left and right eye are confined to a bounding box around the face. Top right: In addition to the bounding box constraint, relations between landmarks are introduced, namely the eyes are above the nose and the left eye is in fact to the left of the right eye. Bottom: The nose landmark is constrained to a domain in form of a triangle (left) or a sector of a circle (right), respectively. ](./constraints_types.png){#constr_visualized width="0.7 \\linewidth"} + +**Bounding box constraints.** The bounding box is specified by a left boundary $l^{(x)}$, a right boundary $u^{(x)}$, a top boundary $l^{(y)}$ and a bottom boundary $u^{(y)}$. Note that the $y$-axis starts at the top of the image and points downwards. Confining the landmark predictions to a bounding box is equivalent to constrain $\hat y ^{(k_x)}$ to the interval $[l^{(x)}, u^{(x)}]$ and $\hat y ^{(k_y)}$ to the interval $[l^{(y)}, u^{(y)}]$ independently. These intervals are one dimensional convex polytopes with the interval boundaries as vertices. Thus, we can write the output constraints for the components with the definition in Eq. [\[convex_polytope\]](#convex_polytope){reference-type="ref" reference="convex_polytope"} as: $$\begin{alignat} +{2} + \label{bb_constr_1} + \mathcal{C}^{(k_x)}(s^{(k_x)})=&\, \mathcal{P}(\{ l^{(x)}, u^{(x)} \}), \\ +\label{bb_constr_2} + \mathcal{C}^{(k_y)}(s^{(k_y)})=&\, \mathcal{P}(\{l^{(y)}, u^{(y)} \}), +\end{alignat}$$ + +with $s^{(k_x)}\!=\!(l^{(x)}, +u^{(x)})$ and $s^{(k_y)}\!=\!(l^{(y)}, u^{(y)})$. The constraint guard layers of the components are given by Eq. [\[convex_phi\]](#convex_phi){reference-type="ref" reference="convex_phi"}: $$\begin{alignat} +{2} + \phi^{(k_x)}(z^{(k_x)},s^{(k_x)})&=&\,\sigma_1(z^{(k_x)})l^{(x)}+\sigma_2(z^{(k_x)})u^{(x)}, \\ + \phi^{(k_y)}(z^{(k_y)},s^{(k_y)})&=&\,\sigma_1(z^{(k_y)})l^{(y)}+\sigma_2(z^{(k_y)})u^{(y)}, +\end{alignat}$$ with $z^{(k_x)}, z^{(k_y)}\!\in\!\mathbb{R}^2$ and $\sigma$ the 2-dimensional softmax function. Finally, the overall constraint guard layer $\phi(z,s)$ can be constructed from the constraint guard layers of the components according to Eq. [\[parts_phi\]](#parts_phi){reference-type="ref" reference="parts_phi"} and requires a $12$-dimensional intermediate variable $z \!\in \!\mathbb{R}^{12}$. + +**Enforcing relations between landmarks. []{#rel_pos label="rel_pos"}** We extend the bounding box constraints to model relations between landmarks. As an example, we enforce that the left eye is in fact to the left of the right eye ($\hat x_{le} \!\le\! \hat x_{re}$) and that the eyes are above the nose ($\hat y_{le},\hat y_{re} \! \le \! \hat y_n$). These constraints can be written as three independent constraints for the output parts $\hat y^{(1)}\!=\!\hat x_{n}$, $\hat y^{(2)}\!=\!(\hat x_{le},\hat x_{re})$, $\hat y^{(3)}\!=\!(\hat y_n, \hat y_{le}, \hat y_{re})$: $$\begin{alignat} +{1} + \label{rel_constr_1} + \mathcal{C}^{(1)}(s^{(1)}) = \,& \{ \hat x_n \! \in \! \mathbb{R} : l^{(x)} \! \le \! \hat + x_n \! \le \! + u^{(x)} \}, \\ + \label{rel_constr_2} + \mathcal{C}^{(2)}(s^{(2)}) = \, & \{(\hat x_{le}, \hat x_{re}) \! \in \! \mathbb{R}^2 + : \hat x_{le} \! \le \! \hat x_{re}, \nonumber \\ + & l^{(x)}\! \le \! \hat x_{le}, \hat x_{re} \! \le \! + u^{(x)} \}, \\ + \label{rel_constr_3} + \mathcal{C}^{(3)}(s^{(3)}) = \, & \{(\hat y_n,\hat y_{le},\hat y_{re}) \! \in + \! \mathbb{R}^3 + : \hat y_{le},\hat y_{re} \! \le \! \hat y_n, \nonumber \\ + & \, + l^{(y)} \! \le \! \hat y_n, \hat y_{le}, \hat y_{re} \! \le \! + u^{(y)} \}, +\end{alignat}$$ with constraint parameters $s^{(1)}\!=\!s^{(2)}\!=\!(l^{(x)},u^{(x)})$ and $s^{(3)}\!=\! (l^{(y)},u^{(y)})$. Fig. [4](#rel_constr){reference-type="ref" reference="rel_constr"} visualizes the constraints for the output parts: $\mathcal{C}^{(1)}$ is a line segment in $1$D, $\mathcal{C}^{(2)}$ is a triangle in $2$D and $\mathcal{C}^{(3)}$ is a pyramid with $5$ vertices in $3$D. All of these are convex polytopes and therefore the constraint guard layers for the parts $\{\phi^{(k)} \}_{k=1}^3$ are given by Eq. [\[convex_phi\]](#convex_phi){reference-type="ref" reference="convex_phi"}. Note that $\phi^{(k)}$ requires an intermediate variable $z^{(k)}$ with dimension equal to the number of vertices of the corresponding polytope. Finally, the overall constraint guard layer $\phi$ is given by combining the parts according to Eq. [\[parts_phi\]](#parts_phi){reference-type="ref" reference="parts_phi"} and depends on an intermediate variable $z\!=\!(z^{(1)},z^{(2)},z^{(3)})$ with dimension $2\!+\!3\!+\!5\!=\!10$. Note that the introduced relations between the landmarks might be violated under rotations of the image and we consider them for demonstration purposes. + +![Confining landmark predictions for the nose $(\hat x_n, \hat y_n)$, the left eye $(\hat x_{le}, \hat y_{le})$ and the right eye $(\hat x_{re}, \hat y_{re})$ to a bounding box with boundaries $l^{(x)}, u^{(x)}, l^{(y)}, u^{(y)}$, and enforcing that the eyes are above the nose ($\hat + y_{le},\hat y_{re} \! \le \! \hat y_n$) and that the left eye is to the left of the right eye ($\hat x_{le} + \!\le\! \hat x_{re}$) is equivalent to constraining the output parts $\hat y^{(1)}\!=\!\hat x_{n}$ to the line segment a), $\hat y^{(2)}\!=\!(\hat x_{le},\hat x_{re})$ to the triangle in b) and $\hat y^{(3)}\!=\!(\hat y_n, \hat y_{le}, \hat y_{re})$ to the pyramid in c). ](constraints.pdf){#rel_constr width="90%"} + +**Training.** For training of *ConstraintNet*, valid constraint parameters need to be sampled ($sample(\mathcal{S}_{y_i})$ according to Algorithm [\[pseudocode\]](#pseudocode){reference-type="ref" reference="pseudocode"}. To achieve this, random bounding boxes around the face which cover the considered facial landmarks can be created. in a first step, determine the smallest rectangle (parallel to the image boundaries) which covers the landmarks *nose*, *left eye* and *right eye*. Next, sample four integers from a given range and use them to extend each of the four rectangle boundaries independently. The sampled constraint parameter is then given by the boundaries of the generated box $l^{(x)},u^{(x)},l^{(y)}, u^{(y)}$. In inference, the bounding boxes might be given by a face detector. + +![The follow object controller (FOC) in a vehicle (ego-vehicle) is only active when another vehicle (target-vehicle) is ahead. Sensors measure the velocity of the ego-vehicle $v_{ego}$ and the relative position (distance) $x_{rel}$, the relative velocity $v_{rel}$ and the relative acceleration $a_{rel}$ of the target vehicle the coordinate system of the ego-vehicle. The FOC gets at least these sensor measurements as input and attempts to keep the distance to the target vehicle $x_{rel}$ close to a velocity dependent distance $x_{rel,set}( v_{ego})$ under consideration of comfort and safety aspects. The output of the FOC is a demanded acceleration $a_{ego,dem}$.](./foc.png){#foc width="0.95 \\linewidth"} + +The adaptive cruise control (ACC) is a common driver assistance system for longitudinal control and available in many vehicles nowadays. A follow object controller (FOC) is part of the ACC and gets activated when a vehicle (target-vehicle) is ahead. This situation is visualized in Fig. [5](#foc){reference-type="ref" reference="foc"}. The output of the FOC is a demanded acceleration $a_{ego,dem}$ for the ego-vehicle with the goal to keep a velocity dependent distance $x_{rel,set}( v_{ego})$ to the vehicle ahead (target-vehicle) under consideration of comfort and safety aspects. Common inputs $x$ for the FOC are sensor measurements such as the relative position (distance) $x_{rel}$, the relative velocity $v_{rel}$ and the relative acceleration $a_{rel}$ of the target vehicle the coordinate system of the ego-vehicle and the velocity $v_{ego}$ of the ego-vehicle. + +**Modified fully connected network.** The FOC is usually modeled explicitly based on expert knowledge and classical control theory. Improving the quality of the controller leads to models with an increasing number of separately handeled cases, a higher complexity and a higher number of adjustable parameters. Finally, adjusting the model parameters gets a tedious work. This motivates the idea to implement the FOC as a neural network $a_{ego,dem}\!=\!n_{\theta}(x)$ and learn the parameters $\theta$, in a reinforcement learning setting. Implementing the FOC with a common neural network comes at the expense of loosing safety guarantees. However, with *ConstraintNet* $a_{ego,dem}\!=\!\pi_{\theta}(x,s)$ the demanded acceleration $a_{ego,dem}$ can be confined to a safe interval $[a_{min}, a_{max}]$ (convex polytope in 1D) in each forward pass independently. A *ConstraintNet* for this output constraint can be created by modifying a neural network with several fully connected layers. The output should be two dimensional such that the constraint guard layer in Eq. [\[convex_phi\]](#convex_phi){reference-type="ref" reference="convex_phi"} for a 1D-polytope can be applied. For the representation $g(s)$ of the constraint parameter $s\!=\!(a_{min}, a_{max})$ rescaled values of the upper and lower bound are appropriate and can be added to the input. $g(s)$ is not inserted at an intermediate layer due to the smaller size of the network. + +**Constraints for safety.** The output of *ConstraintNet* should be constrained to a safe interval $[a_{min}, a_{max}]$. The interval is a convex polytope in 1D: $$\begin{align} + \label{a_constr} + \mathcal{C}(s)= \mathcal{P}(\{ a_{min}, a_{max} \}), +\end{align}$$ with $s\!=\!(a_{min}, a_{max})$. The constraint guard layer is given by Eq. [\[convex_phi\]](#convex_phi){reference-type="ref" reference="convex_phi"}. The upper bound $a_{max}$ restricts the acceleration to avoid collisions. For deriving $a_{max}$, we assume that the target vehicle accelerates constantly with its current acceleration and the ego-vehilce continues its movement in the beginning with $a_{ego,dem}$. $a_{ego,dem}$ is then limited by the requirement that it must be possible to break without violating maximal jerk and deceleration bounds and without undershooting a minimal distance to the target-vehicle. Thus, $a_{max}$ is the maximal acceleration which satisfies this condition. The maximal allowed deceleration for the ACC is given by a velocity dependent bound in ISO15622 [@iso15622] and would be an appropriate choice for $a_{min}$. + +**Training and reinforcement learning.** In comparison to supervised learning, reinforcement learning allows to learn from experience, by interacting with the environment. The quality of the interaction with the environment is measured with a reward function and the interaction self is usually implemented with a simulator. The reward function can be understood as a metric for optimal behavior and the reinforcement learning algorithm learns a policy $\pi_{\theta}$ which optimizes the reward. In our case, $\pi_{\theta}(x,s)$ is the *ConstraintNet* for the FOC. Instead of sampling the constraint parameter $s$ from a set of valid constraint parameters, exactly one valid $s$ is computed corresponding to the safe interval $[a_{min}, a_{max}]$. Thereby, deep reinforcement learning algorithms for continous control problems are applicable. One promising candidate is the Twin Delayed DDPG (TD3) algorithm [@TD3]. Note that *ConstraintNet* leads to a collision free training, training episodes are not interrupted. diff --git a/2009.08061/main_diagram/main_diagram.drawio b/2009.08061/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..dd1c1341ab4efb6fec5babd55a1be6a08e3bf4eb --- /dev/null +++ b/2009.08061/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2009.08061/main_diagram/main_diagram.pdf b/2009.08061/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c7fe5a3a0f0c032c23a870d5a5d1a552f8bc2be1 Binary files /dev/null and b/2009.08061/main_diagram/main_diagram.pdf differ diff --git a/2009.08061/paper_text/intro_method.md b/2009.08061/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..349394ee22b7fffd62c4388f62504e88e9294576 --- /dev/null +++ b/2009.08061/paper_text/intro_method.md @@ -0,0 +1,102 @@ +# Introduction + +Gaussian smoothing, introduced by @cohen19 in [-@cohen19], relies on a "base classifier," which is a mapping $f: \mathbb{R}^d \rightarrow \mathcal{Y}$ where $\mathbb{R}^d$ is the input space and $\mathcal{Y}$ is a set of $k$ classes. It defines a smoothed classifier $\bar{f}$ as $$\bar{f}(x) = \underset{c \in \mathcal{Y}}{\text{argmax}} \; \mathbb{P}(f(x + \delta) = c)$$ where $\delta \sim \mathcal{N}(0, \sigma^2 I)$ is sampled from an isometric Gaussian distribution with variance $\sigma^2$. It returns the class that is most likely to be sampled by the Gaussian distribution centered at point $x$. Let $p_1$ and $p_2$ be the probabilities of sampling the top two most likely classes. Then, $\bar{f}$ is guaranteed to be constant within an $\ell_2$-ball of radius $$R = \frac{\sigma}{2} \left( \Phi^{-1}(p_1) - \Phi^{-1}(p_2) \right)$$ where $\Phi^{-1}$ is the inverse CDF of the standard Gaussian distribution [@cohen19]. For a practical certification algorithm, a lower bound $\underline{p_1}$ on $p_1$ and an upper bound $\overline{p_2} = 1 - \underline{p_1}$ on $p_2$, with probability $1-\alpha$ for a given $\alpha \in (0, 1)$, are obtained and the certified radius is given by $R = \sigma \Phi^{-1}(\underline{p_1})$. This analysis is tight for $\ell_2$ perturbations; the bound is achieved by a worst-case classifier in which all the points in the top-class are restricted to a half-space separated by a hyperplane orthogonal to the direction of the perturbation. + +In our discussion, we diverge from the standard notation described above, and assume that the base classifier $f$ maps points in $\mathbb{R}^d$ to a $k$-tuple of confidence scores. Thus, $f: \mathbb{R}^d \rightarrow (a, b)^k$ for some $a, b \in \mathbb{R}$ and $a 0$ implies that $i$ is the predicted class. + +Standard Gaussian smoothing for establishing certified class labels essentially works by averaging binary (0/1) votes from every image in a Gaussian cloud around the input image, $x$. It then establishes the worst-case class boundary given the recorded vote, and produces a certificate. The same machinery can be applied to produce a naive certificate for confidence score; rather than averaging binary votes, we simply average scores. We then produce the worst-case class distribution, in which each class lives in a separate half-space, and generate a certificate for this worst case. + +However, the naive certificate described above throws away a lot of information. When continuous-values scores are recorded, we obtain not only the average score, but also the *distribution* of scores around the input point. By using this distributional information, we can potentially create a much stronger certificate. + +To see why, consider the extreme case of a "flat" classifier function for which every sample in the Gaussian cloud around $x$ returns the same top-class prediction score of 0.55. In this case, the average score is 0.55 as well. For a function where the *distribution* of score votes is concentrated at 0.55 (or any other value great than ), the average score will always remain at 0.55 for *any* perturbation to $x$, thus yielding an infinite certified radius. However, when using the naive approach that throws away the distribution, the worst-case class boundary with average vote 0.55 is one with confidence score 1.0 everywhere in a half-space occupying 0.55 probability, and 0.0 in a half-space with 0.45 probability. This worst-case, which uses only the average vote, produces a very small certified radius, in contrast to the infinite radius we could obtain from observing the distribution of votes. + +Below, we first provide a simple bound that produces a certificate by averaging scores around the input image, and directly applying the framework from [@cohen19]. Then, we describe a more refined method that uses distributional information to obtain stronger bounds. + +# Method + +In this section, we describe a method that uses only the average confidence over the Gaussian distribution surrounding $x$, and not the distribution of values, to bound how much the expected score can change when $x$ is perturbed with an $\ell_2$ radius of $R$ units. This is a straightforward extension of @cohen19's [@cohen19] work to our framework. It shows that regardless the behaviour of the base classifier $f$, its smoothed version $\bar{f}$ changes slowly which is similar to the observation of bounded Lipschitz-ness made by @SalmanLRZZBY19 in [@SalmanLRZZBY19] (Lemma 2). The worst-case classifier in this case assumes value $a$ in one half space and $b$ in other, with a linear boundary between the two as illustrated in figure [1](#fig:naive_worst_case){reference-type="ref" reference="fig:naive_worst_case"}. The following theorem formally states the bounds, the proof of which is deferred to the appendix[^3]. + +::: {#thm:naive_bnd .theorem} +**Theorem 1**. *Let $\underline{e_i}(x)$ and $\overline{e_i}(x)$ be a lower-bound and an upper-bound respectively on the expected score $\bar{f}_i(x)$ for class $i$ and, let $\underline{p_i}(x) = \frac{\underline{e_i}(x) - a}{b - a}$ and $\overline{p_i}(x) = \frac{\overline{e_i}(x) - a}{b - a}$. Then, for a perturbation $x'$ of the input $x$, such that, $\left\lVert x' - x\right\rVert_2 \leq R$, $$\begin{equation} +\label{ineq:naive_lbd} +\bar{f}_i(x') \geq b \Phi_\sigma ( \Phi_\sigma^{-1} (\underline{p_i}(x)) - R) + a (1 - \Phi_\sigma ( \Phi_\sigma^{-1} (\underline{p_i}(x)) - R)) +\end{equation}$$ and $$\bar{f}_i(x') \leq b \Phi_\sigma ( \Phi_\sigma^{-1} (\overline{p_i}(x)) + R) + a (1 - \Phi_\sigma ( \Phi_\sigma^{-1} (\overline{p_i}(x)) + R))$$ where $\Phi_\sigma$ is the CDF of the univariate Gaussian distribution with $\sigma^2$ variance, i.e., $\mathcal{N}(0, \sigma^2)$.* +::: + +The bounds in section [3.1](#sec:naive_bnd){reference-type="ref" reference="sec:naive_bnd"} are a simple application of the Neyman-Pearson lemma to our framework. But this method discards a lot of information about how the class scores are distributed in the Gaussian around the input point. Rather than consolidating the confidence scores from the samples into an expectation, we propose a method that uses the cumulative distribution function of the confidence scores to obtain improved bounds on the expected class scores. + +Given an input $x$, we draw $m$ samples from the Gaussian distribution around $x$. We use the prediction of the base classifier $f$ on these points to generate bounds on the distribution function of the scores for the predicted class. These bounds, in turn, allow us to bound the amount by which the expected score of the class will decrease under an $\ell_2$ perturbation. Finally, we apply binary search to compute the radius for which this lower bound on the expected score remains above $c$. + +Consider the sampling of scores around an image $x$ using a Gaussian distribution. Let the probability with which the score of class $i$ is above $s$ be $$p_{i, s}(x) = \underset{\delta \sim \mathcal{N}(0, \sigma^2 I)}{\mathbb{P}}(f_i(x + \delta) \geq s).$$ For point $x$ and class $i$, consider the random variable $Z = -f_i(x + \delta)$ where $\delta \sim \mathcal{N}(0, \sigma^2 I)$. Let $F(s) = \mathbb{P}(Z \leq s)$ be the cumulative distribution function of $Z$ and $F_m(s) = \frac{1}{m} \sum_{j=1}^m \mathbf{1}\{Z_j \leq s\}$ be its empirical estimate. For a given $\alpha \in (0, 1)$, the Dvoretzky--Kiefer--Wolfowitz inequality [@dvoretzky1956] says that, with probability $1-\alpha$, the true CDF is bounded by the empirical CDF as follows: $$F_m(s) - \epsilon \leq F(s) \leq F_m(s) + \epsilon, \forall s,$$ where $\epsilon = \sqrt{\frac{\ln{2/\alpha}}{2m}}$. Thus, $p_{i, s}(x)$ is also bounded within $\pm \epsilon$ of its empirical estimate $\sum_{j=1}^m \mathbf{1}\{ f_i(x + \delta_j) \geq s\}$. + +The following theorem bounds the expected class score under an $\ell_2$ perturbation using bounds on the cumulative distribution of the scores. + +::: {#thm:exp_bnds .theorem} +**Theorem 2**. *Let, for class $i$, $a < s_1 \leq s_2 \leq \cdots \leq s_n < b$ be $n$ real numbers and let $\overline{p_{i, s_j}}(x)$ and $\underline{p_{i, s_j}}(x)$ be upper and lower bounds on $p_{i, s_j}(x)$ respectively derived using the Dvoretzky--Kiefer--Wolfowitz inequality, with probability $1-\alpha$, for a given $\alpha \in (0, 1)$. Then, for a perturbation $x'$ of the input $x$, such that, $\left\lVert x' - x\right\rVert_2 \leq R$, $$\begin{equation} +\label{ineq:cdf_lbd} + \bar{f}_i(x') \geq a + (s_1 - a) \Phi_\sigma(\Phi_\sigma^{-1}(\underline{p_{i, s_1}}(x)) - R) + \sum_{j = 2}^n (s_j - s_{j-1}) \Phi_\sigma(\Phi_\sigma^{-1}(\underline{p_{i, s_j}}(x)) - R) +\end{equation}$$ and $$\bar{f}_i(x') \leq s_1 + (b - s_n) \Phi_\sigma(\Phi_\sigma^{-1}(\overline{p_{i, s_n}}(x)) + R) + \sum_{j = 1}^{n-1} (s_{j+1} - s_j) \Phi_\sigma(\Phi_\sigma^{-1}(\overline{p_{i, s_j}}(x)) + R)$$ where $\Phi_\sigma$ is the CDF of the univariate Gaussian distribution with $\sigma^2$ variance, i.e., $\mathcal{N}(0, \sigma^2)$.* +::: + +The above bounds are tight for $\ell_2$ perturbations. The worst-case classifier for the lower bound is one in which the class score decreases from $b$ to $a$ in steps, taking values $s_n, s_{n-1}, \ldots, s_1$ at each level. Figure [3](#fig:worst_case){reference-type="ref" reference="fig:worst_case"} illustrates this case for three intermediate levels. A similar worst-case scenario can be constructed for the upper bound as well where the class score increases from $a$ to $b$ along the direction of the perturbation. Even though our theoretical results allow us to derive both upper and lower bounds for the expected scores, we restrict ourselves to the lower bound in our experimental results. We provide a proof sketch for this theorem in section [3.3](#sec:cert_rad){reference-type="ref" reference="sec:cert_rad"}. Our experimental results show that the CDF-based approach beats the naive bounds in practice by a significant margin, showing that having more information about the classifier at the input point can help achieve better guarantees. + +
+
+ +
Naive classifier
+
+
+ +
CDF-based classifier
+
+
Worst case classifier behaviour using (a) naive approach and (b) CDF-based method. As the center of the distribution moves from x to x′, the probability mass of the higher values of the score function (indicated in red) decreases and that of the lower values (indicated in blue) increases, bringing down the value of the expected score.
+
+ +**Computing the certified radius**Both the bounds in theorem [2](#thm:exp_bnds){reference-type="ref" reference="thm:exp_bnds"} monotonic in $R$. So, in order to find a certified radius, up to a precision $\tau$, such that the lower (upper) bound is above (below) a certain threshold we can apply binary search which will require at most $O(\log(1/\tau))$ evaluations of the bound. + +We present a brief proof for theorem [2](#thm:exp_bnds){reference-type="ref" reference="thm:exp_bnds"}. We use a slightly modified version of the Neyman-Pearson lemma (stated in [@cohen19]) which we prove in the appendix. + +::: {#lem:N-P .lemma} +**Lemma 3** (Neyman & Pearson, 1933). *Let $X$ and $Y$ be random variables in $\mathbb{R}^d$ with densities $\mu_X$ and $\mu_Y$. Let $h: \mathbb{R}^d \rightarrow (a, b)$ be a function. Then:* + +1. *If $S = \left\{ z \in \mathbb{R}^d \mid \frac{\mu_Y(z)}{\mu_X(z)} \leq t \right\}$ for some $t > 0$ and $\mathbb{P}(h(X) \geq s) \geq \mathbb{P}(X \in S)$, then $\mathbb{P}(h(Y) \geq s) \geq \mathbb{P}(Y \in S)$.* + +2. *If $S = \left\{ z \in \mathbb{R}^d \mid \frac{\mu_Y(z)}{\mu_X(z)} \geq t \right\}$ for some $t > 0$ and $\mathbb{P}(h(X) \geq s) \leq \mathbb{P}(X \in S)$, then $\mathbb{P}(h(Y) \geq s) \leq \mathbb{P}(Y \in S)$.* +::: + +Set $X$ to be the smoothing distribution at an input point $x$ and $Y$ to be that at $x + \epsilon$ for some perturbation vector $\epsilon$. For a class $i$, define sets $\underline{S}_{i, j} = \{ z \in \mathbb{R}^d \mid \mu_Y(z) / \mu_X(z) \leq t_{i, j} \}$ for some $t_{i, j} > 0$, such that, $\mathbb{P}(X \in \underline{S}_{i, j}) = \underline{p_{i, s_j}}(x)$. Similarly, define sets $\overline{S}_{i, j} = \{ z \in \mathbb{R}^d \mid \mu_Y(z) / \mu_X(z) \geq t'_{i, j} \}$ for some $t'_{i, j} > 0$, such that, $\mathbb{P}(X \in \overline{S}_{i, j}) = \overline{p_{i, s_j}}(x)$. Since, $\mathbb{P}(f_i(X) \geq s_j) \geq \mathbb{P}(X \in \underline{S}_{i,j})$, using lemma [3](#lem:N-P){reference-type="ref" reference="lem:N-P"} we can say that $\mathbb{P}(f_i(Y) \geq s_i) \geq \mathbb{P}(Y \in \underline{S}_{i,j})$. Therefore, $$\begin{align*} + \mathbb{E}[f_i(Y)] &\geq s_n \mathbb{P}(f_i(Y) \geq s_n) + s_{n-1} (\mathbb{P}(f_i(Y) \geq s_{n-1}) - \mathbb{P}(f_i(Y) \geq s_n))\\ + & + \cdots + s_1 (\mathbb{P}(f_i(Y) \geq s_1) - \mathbb{P}(f_i(Y) \geq s_2)) + a (1 - \mathbb{P}(f_i(Y) \geq s_1))\\ + &= a + (s_1 - a) \mathbb{P}(f_i(Y) \geq s_1) + \sum_{j = 2}^n (s_j - s_{j-1}) \mathbb{P}(f_i(Y) \geq s_j)\\ + & \geq a + (s_1 -a) \mathbb{P}(Y \in \underline{S}_{i,1}) + \sum_{j = 2}^n (s_j - s_{j-1}) \mathbb{P}(Y \in \underline{S}_{i,j}). +\end{align*}$$ Similarly, $\mathbb{P}(f_i(X) \geq s_j) \leq \mathbb{P}(X \in \overline{S}_{i, j})$ implies $\mathbb{P}(f_i(Y) \geq s_j) \leq \mathbb{P}(Y \in \overline{S}_{i, j})$ as per lemma [3](#lem:N-P){reference-type="ref" reference="lem:N-P"}. Therefore, $$\begin{align*} + \mathbb{E}[f_i(Y)] &\leq b \mathbb{P}(f_i(Y) \geq s_n) + s_n (\mathbb{P}(f_i(Y) \geq s_{n-1}) - \mathbb{P}(f_i(Y) \geq s_n))\\ + & + \cdots + s_1 (1 - \mathbb{P}(f_i(Y) \geq s_1))\\ + &= (b - s_n) \mathbb{P}(f_i(Y) \geq s_n) + \sum_{j = 1}^{n-1} (s_{j+1} - s_j) \mathbb{P}(f_i(Y) \geq s_j) + s_1\\ + & \leq s_1 + (b - s_n) \mathbb{P}(Y \in \overline{S}_{i,n}) + \sum_{j = 1}^{n-1} (s_{j+1} - s_j) \mathbb{P}(Y \in \overline{S}_{i, j}). +\end{align*}$$ + +Since, we are smoothing using an isometric Gaussian distribution with $\sigma^2$ variance, $\mu_X = \mathcal{N}(x, \sigma^2 I)$ and $\mu_Y = \mathcal{N}(x + \epsilon, \sigma^2 I)$. Then, for some $t$ and $\beta$ $$\begin{align*} + \frac{\mu_Y(z)}{\mu_Y(z)} \leq t \iff \epsilon^T z \leq \beta\\% \text{ and } + \frac{\mu_Y(z)}{\mu_Y(z)} \geq t \iff \epsilon^T z \geq \beta. +\end{align*}$$ Thus, each of the sets $\underline{S}_{i,j}$ and $\overline{S}_{i,j}$ is a half space defined by a hyper-plane orthogonal to the direction of the perturbation. This simplifies our analysis to one dimension, namely, the one along the perturbation. For each of the sets $\underline{S}_{i,j}$ and $\overline{S}_{i,j}$, we can find a point on the real number line $\Phi_\sigma^{-1}(\underline{p_{i, s_j}}(x))$ and $\Phi_\sigma^{-1}(\overline{p_{i, s_j}}(x))$ respectively such that the probability of a Gaussian sample to fall in that set is equal to the Gaussian CDF at that point. Therefore, $$\bar{f}_i(x + \epsilon) \geq a + (s_1 - a) \Phi_\sigma(\Phi_\sigma^{-1}(\underline{p_{i, s_1}}(x)) - R) + \sum_{j = 2}^n (s_j - s_{j-1}) \Phi_\sigma(\Phi_\sigma^{-1}(\underline{p_{i, s_j}}(x)) - R)$$ and $$\bar{f}_i(x + \epsilon) \leq s_1 + (b - s_n) \Phi_\sigma(\Phi_\sigma^{-1}(\overline{p_{i, s_n}}(x)) + R) + \sum_{j = 1}^{n-1} (s_{j+1} - s_j) \Phi_\sigma(\Phi_\sigma^{-1}(\overline{p_{i, s_j}}(x)) + R)$$ which completes the proof of theorem [2](#thm:exp_bnds){reference-type="ref" reference="thm:exp_bnds"}. We would like to note here that although we use the Gaussian distribution for smoothing, the modified Neyman-Pearson lemma does not make any assumptions on the shape of the distributions which allows for this proof to be adapted for other smoothing distributions as well. + +We study two notions of confidence: average prediction score of a class and the margin of average prediction score between two classes. Usually, neural networks make their predictions by outputting a prediction score for each class and then taking the argmax of the scores. Let $h: \mathbb{R}^d \rightarrow (0, 1)^k$ be a classifier mapping input points to prediction scores between 0 and 1 for each class. We assume that the scores are generated by a softmax-like layer, i.e., $0 < h_i(x) < 1, \forall i \in \{1, \ldots, k\}$ and $\sum_i h_i(x) = 1$. For $\delta \sim \mathcal{N}(0, \sigma^2 I)$, we define average prediction score for a class $i$ as $$\bar{h}_i(x) = \mathbb{E}[h_i(x + \delta)].$$ The final prediction for the smoothed classifier is made by taking an argmax over the average prediction scores of all the classes, i.e., $\text{argmax}_i \; \bar{h}_i(x)$. Thus, if for a class $j$, $\bar{h}_j(x) \geq 0.5$, then $j = \text{argmax}_i \; \bar{h}_i(x)$. + +Now, we define margin $m$ at point $x$ for a class $i$ as $$m_i(x) = h_i(x) - \max_{j \neq i} h_j(x).$$ Thus, if $i$ is the class with the highest prediction score, $m_i(x)$ is the lead it has over the second highest class (figure [\[fig:margin\]](#fig:margin){reference-type="ref" reference="fig:margin"}). And, for any other class $m_i(x)$ is the negative of the difference of the scores of that class with the highest class. We define average margin at point $x$ under smoothing distribution $\mathcal{P}$ as $$\bar{m}_i(x) = \mathbb{E}[m_i(x + \delta)].$$ + +::: wrapfigure +r0.25 ![image](figures/margin.png){width=".24\\columnwidth"} + +[]{#fig:margin label="fig:margin"} +::: + +For a pair of classes $i$ and $j$, we have, $$\begin{align*} + \bar{h}_i(x) - \bar{h}_j(x) &= \mathbb{E}[h_i(x + \delta)] - \mathbb{E}[h_j(x + \delta)]\\ + &= \mathbb{E}[h_i(x + \delta) - h_j(x + \delta)]\\ + & \geq \mathbb{E}[h_i(x + \delta) - \max_{j \neq i} h_j(x + \delta)]\\ + &= \mathbb{E}[m_i(x + \delta)] = \bar{m}_i(x)\\ + \bar{h}_i(x) &\geq \bar{h}_j(x) + \bar{m}_i(x). +\end{align*}$$ Thus, if $\bar{m}_i(x) > 0$, then class $i$ must have the highest average prediction score making it the predicted class under this notion of confidence measure. diff --git a/2010.11354/main_diagram/main_diagram.drawio b/2010.11354/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..b47343fe374f2bce3530076dc2638cbb565189ba --- /dev/null +++ b/2010.11354/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2010.11354/main_diagram/main_diagram.pdf b/2010.11354/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f83df4be103f92f97b95f41fe5cd6c84fd64c79a Binary files /dev/null and b/2010.11354/main_diagram/main_diagram.pdf differ diff --git a/2010.11354/paper_text/intro_method.md b/2010.11354/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..7e454c4a22940afcfa229aca7d8c73c7b98af6d1 --- /dev/null +++ b/2010.11354/paper_text/intro_method.md @@ -0,0 +1,174 @@ +# Introduction + +Generating sparse neural networks through pruning has recently led to a major reduction in the number of parameters, while having minimal loss in performance. Conventionally, pruning methods operate on pre-trained networks. Gen- + +*Proceedings of the* 38 th *International Conference on Machine Learning*, PMLR 139, 2021. Copyright 2021 by the author(s). + +erally, such methods use an edge scoring mechanism for eliminating the less important connections. Popular scoring mechanisms include weight magnitudes [\(Han et al.,](#page-9-0) [2015b;](#page-9-0) [Janowsky,](#page-9-0) [1989;](#page-9-0) [Park\\* et al.,](#page-10-0) [2020\)](#page-10-0), loss sensitivity with respect to units [\(Mozer & Smolensky,](#page-10-0) [1989\)](#page-10-0) and with respect to weights [\(Karnin,](#page-9-0) [1990\)](#page-9-0), Hessian [\(LeCun et al.,](#page-9-0) [1990;](#page-9-0) [Hassibi & Stork,](#page-9-0) [1993\)](#page-9-0), and first and second order Taylor expansions [\(Molchanov et al.,](#page-10-0) [2019b;a\)](#page-10-0). More recent approaches use more sophisticated variants of these scores [\(Han et al.,](#page-9-0) [2015a;](#page-9-0) [Guo et al.,](#page-9-0) [2016;](#page-9-0) [Carreira-Perpinan &](#page-9-0) ´ [Idelbayev,](#page-9-0) [2018;](#page-9-0) [Yu et al.,](#page-10-0) [2018;](#page-10-0) [Dong et al.,](#page-9-0) [2017;](#page-9-0) [Guo](#page-9-0) [et al.,](#page-9-0) [2016\)](#page-9-0). + +Further analysis of pruning has shown the existence of sparse subnetworks at initialization which, when trained, are capable of matching the performance of the fully-connected network [\(Frankle & Carbin,](#page-9-0) [2018;](#page-9-0) [Frankle et al.,](#page-9-0) [2019;](#page-9-0) [Liu](#page-10-0) [et al.,](#page-10-0) [2019;](#page-10-0) [Frankle et al.,](#page-9-0) [2020\)](#page-9-0). However, identifying such "winning ticket" networks requires expensive training and pruning cycles. More recently, SNIP [\(Lee et al.,](#page-10-0) [2019b\)](#page-10-0), [\(You et al.,](#page-10-0) [2019\)](#page-10-0) and GraSP [\(Wang et al.,](#page-10-0) [2019\)](#page-10-0) showed that it is possible to find "winning tickets" prior to training – but still having access to at least some training data to compute initial gradients. Furthermore, other work has shown that such subnetworks generalize well across datasets and tasks [\(Morcos et al.,](#page-10-0) [2019\)](#page-10-0). + +Our goal is to identify sparse subnetworks that perform almost as well as the fully connected network without *any* training data. The closest methods that tackle the same problem with our work are SynFlow [\(Tanaka et al.,](#page-10-0) [2020\)](#page-10-0) and SynFlow-L2 [\(Gebhart et al.,](#page-9-0) [2021\)](#page-9-0). The authors of [\(Tanaka](#page-10-0) [et al.,](#page-10-0) [2020\)](#page-10-0) introduced the concept of "layer collapse" in pruning – the state when all edges in a layer are eliminated while there are edges in other layers that can be pruned. They also proved that iterative pruning based on positive gradient-based scores avoids layer collapse and introduced an iterative algorithm (SynFlow) and a loss function that conserves information flow and avoids layer collapse. + +A branch of recent work focuses on the convergence and generalization properties of deep neural networks using linear approximations of the training dynamics [\(Jacot et al.,](#page-9-0) [2018;](#page-9-0) [Lee et al.,](#page-9-0) [2019a;](#page-9-0) [Arora et al.,](#page-9-0) [2019\)](#page-9-0). Under the infinite-width assumption, [\(Jacot et al.,](#page-9-0) [2018\)](#page-9-0) showed how + +1 School of Computer Science, Georgia Institute of Technology, USA. Correspondence to: Constantine Dovrolis . + +to predict at initialization output changes during training using the Neural Tangent Kernel (NTK). More recently, (Gebhart et al., 2021) decomposed the NTK into two factors: one that is only data-dependent, and another that is only architecture-dependent. This decomposition decoupled the effects of network architecture (including sparsity and selection of initial weights) from the effect of training data on convergence. The architecture-dependent factor can be thought of as the "path covariance" of the network and is referred to as *Path Kernel*. The authors of (Gebhart et al., 2021) show that the training convergence of a network can be accurately predicted using the path kernel trace. That work concluded with a pruning algorithm (SynFlow-L2) that designs sparse networks with maximum path kernel trace – aiming to optimize at least the architectural component of the network's convergence speed. + +In this work, we first show that even though SynFlow and Synflow-L2 are optimal in terms of convergence for a given network density, they result in sub-networks with "bottle-neck layers" (very small width) – leading to poor performance as compared to other data-agnostic methods that use the same number of parameters. This issue is observed even at moderate density values. This is expected given the recent results of (Golubeva et al., 2021), for instance, showing that increasing the width of sparse networks, while keeping the number of parameters constant, generally improves performance. + +We then present a method, referred to as *PHEW* (*Paths with Higher Edge Weights*), which aims to achieve the best of both worlds: high path kernel trace for fast convergence, and large network width for better generalization performance. Given an unpruned initialized network, and a target number of learnable parameters, PHEW selects a set of input-output paths that are conserved in the network and it prunes every remaining connection. The selection of the conserved paths is based strictly on their initial weight values – and not on any training data. Further, PHEW induces randomness into the path selection process using random walks biased towards higher weight-magnitudes. The network sparsification process does not require any data, and the pruned network needs to be trained only once. + +We show that selecting paths with higher edge weights forms sub-networks that have higher path kernel trace than uniform random walks – close to the trace obtained through SynFlow-L2. We also show that the use of random walks results in sub-networks having high per-layer width – similar to that of unpruned networks. Further, PHEW avoids layer-collapse by selecting and conserving input-output paths instead of individual units or connections. We compare the performance of PHEW against several pruning before-training methods and show that PHEW achieves significant improvements over SynFlow and SynFlow-L2. Additionally, + +we conduct a wide range of ablation studies to evaluate the efficacy of PHEW. + +Let $(\mathcal{X}, \mathcal{Y})$ denote the training examples, $\mathcal{L}$ the loss function, and $f(\mathcal{X}, \theta) \in \mathbb{R}^{NK}$ the network's output, where N is the number of examples and K is the output dimension. Under the gradient flow assumption, and denoting the learning rate by $\eta$ , the output of the network at time t can be approximated using the first-order Taylor expansion, + +$$f(\mathcal{X}, \boldsymbol{\theta}_{t+1}) = f(\mathcal{X}, \boldsymbol{\theta}_t) - \eta \, \boldsymbol{\Theta}_t(\mathcal{X}, \mathcal{X}) \, \nabla_f \mathcal{L}$$ + (1) + +where the matrix $\Theta_t(\mathcal{X}, \mathcal{X}) = \nabla_{\theta} f(\mathcal{X}, \theta_t) \nabla_{\theta} f(\mathcal{X}, \theta_t)^T \in \mathbb{R}^{NK \times NK}$ is the **Neural Tangent Kernel** (NTK) at time t (Jacot et al., 2018). Under the additional assumption of infinite width, the NTK has been shown to remain constant throughout training, and it allows us to exactly predict the evolution of the network's output. More recent work has shown that even networks with limited width, and any depth, closely follow the NTK dynamics (Lee et al., 2019a). For a constant NTK $\Theta_0$ , and with a mean-squared error loss, equation (1) has the closed-form solution: + +$$f(\mathcal{X}, \boldsymbol{\theta}_t) = (\mathcal{I} - e^{-\eta \boldsymbol{\Theta}_0 t}) \mathcal{Y} + e^{-\eta \boldsymbol{\Theta}_0 t} f(\mathcal{X}, \boldsymbol{\theta}_0)$$ + (2) + +Equation (2) allows us to predict the network's output given the input-output training examples, the initial weights $\theta_0$ , and the initial NTK $\Theta_0$ . Further, leveraging equation (2), it has been shown that the training convergence is faster in the directions that correspond to the larger NTK eigenvalues (Arora et al., 2019). This suggests that sparse sub-networks that preserve the larger NTK eigenvalues of the original network would converge faster and with higher sampling efficiency (Wang et al., 2019). + +More recently, an interesting decomposition of the Neural Tangent Kernel has been proposed that decouples the effects of the network architecture (and initial weights) from the data-dependent factors of the training process (Gebhart et al., 2021). We summarize this decomposition next. + +Consider a neural network $\boldsymbol{f}: \mathbb{R}^D \to \mathbb{R}^K$ with ReLU activations, parametrized by $\boldsymbol{\theta} \in \mathbb{R}^m$ . Let $\boldsymbol{\mathcal{P}}$ be the set of all input-output paths, indexed as $p=1,\ldots,P$ (we refer to a path by its index p). Let $p_i=\mathbb{I}\{\theta_i\in p\}$ represent the presence of edge-weight $\theta_i$ in path p. + +The edge-weight-product of a path is defined as the product of edge-weights present in a path, $\pi_p(\theta) = \prod_{i=1}^m \theta_i^{p_i}$ . For an input variable $\boldsymbol{x}$ , the activation status of a path is, $a_p(\boldsymbol{x}) = \prod_{\theta_i \in p} \mathbb{I}\{z_i > 0\}$ , where $z_i$ is the activation of the + +![](_page_2_Figure_1.jpeg) + +Figure 1. Comparison of the path kernel trace of sparse networks obtained using various pruning methods as well as PHEW. + +neuron connected to the previous layer through $\theta_i$ . The $k^{th}$ output of the network can be expressed as: + +$$\boldsymbol{f}^{k}(\boldsymbol{x},\boldsymbol{\theta}) = \sum_{i=1}^{D} \sum_{p \in \boldsymbol{\mathcal{P}}_{i \to k}} \pi_{p}(\boldsymbol{\theta}) a_{p}(\boldsymbol{x}) x_{i}, \quad (3)$$ + +where $\mathcal{P}_{i\to k}$ is the set of paths from input unit i to output unit k. We can now decompose the NTK using the chain rule: + +$$\Theta(\mathcal{X}, \mathcal{X}) = \nabla_{\boldsymbol{\pi}} f(\mathcal{X}) \nabla_{\boldsymbol{\theta}} \pi(\boldsymbol{\theta}) \nabla_{\boldsymbol{\theta}} \pi(\boldsymbol{\theta})^T \nabla_{\boldsymbol{\pi}} f(\mathcal{X})^T += J_{\boldsymbol{\pi}}^f(\mathcal{X}) J_{\boldsymbol{\theta}}^{\boldsymbol{\pi}} (J_{\boldsymbol{\theta}}^{\boldsymbol{\pi}})^T J_{\boldsymbol{\pi}}^f(\mathcal{X})^T += J_{\boldsymbol{\pi}}^f(\mathcal{X}) \Pi_{\boldsymbol{\theta}} J_{\boldsymbol{\pi}}^f(\mathcal{X})^T$$ +(4) + +The matrix $\Pi_{\theta}$ is referred to as the **Path Kernel** (Gebhart et al., 2021). The path kernel element for two paths p and p' is: + +$$\mathbf{\Pi}_{\boldsymbol{\theta}}(p, p') = \sum_{i=1}^{m} \left( \frac{\pi_{p}(\boldsymbol{\theta})}{\theta_{i}} \right) \left( \frac{\pi_{p'}(\boldsymbol{\theta})}{\theta_{i}} \right) p_{i} p'_{i} \qquad (5)$$ + +Note that the path kernel, $\Pi_{\theta} \in \mathbb{R}^{P \times P}$ , depends only on the network architecture and the initial weights. On the other hand, the matrix $J_{\pi}^{f}(\mathcal{X}) \in \mathbb{R}^{NK \times P}$ captures the data-dependent activations and re-weights the paths on the basis of the training data input. + +Convergence approximation: The eigenstructure of the NTK depends on how the eigenvectors of $J_{\pi}^{f}(\mathcal{X})$ map onto the eigenvectors of the path kernel $\Pi_{\theta}$ , as shown by the following result. + +**Theorem 1** (Gebhart et al., 2021): Let $\lambda_i$ be the eigenvalues of $\Theta(\mathcal{X}, \mathcal{X})$ , $v_i$ the eigenvalues of $J_{\pi}^f(\mathcal{X})$ and $w_i$ the eigenvalues of $\Pi_{\theta}$ . Then $\lambda_i \leq v_i w_i$ and $\sum_i \lambda_i \leq \sum_i v_i w_i$ . + +Given the eigenvalue decomposition of $\Theta_0$ , Theorem 1 provides an upper bound for the convergence in equation (2). $\Theta_0$ with eigenvalues $\lambda_i$ has the same eigenvectors as $e^{-\eta\Theta_0t}$ with eigenvalues $e^{-\eta\lambda_it}$ . Therefore, $\sum_i v_i w_i$ accurately captures the eigenvalues of $\Theta_0$ and it can be used to predict the convergence of the training process. Even without any + +training data, the convergence can be effectively approximated from the trace of the path kernel: + +$$Tr(\mathbf{\Pi}_{\boldsymbol{\theta}}) = \sum_{i} w_{i} = \sum_{p=1}^{P} \mathbf{\Pi}_{\boldsymbol{\theta}}(p, p) = \sum_{p=1}^{P} \sum_{i=1}^{m} \left(\frac{\pi_{p}(\boldsymbol{\theta})}{\theta_{i}}\right)^{2} p_{i}$$ +(6) + +The authors of (Gebhart et al., 2021) empirically validated the convergence predicted using the trace of the path kernel against the actual training convergence of the network. + +The previous result has an important consequence for neural network pruning. Given a fully connected neural network at initialization as well as the target density for a pruned network, maximizing the path kernel trace of the pruned network preserves the largest NTK eigenvalues of the original network. Since, the directions corresponding to the larger eigenvalues of the NTK learn faster, the sub-network obtained by maximizing the path kernel trace is also expected to converge faster and learn more efficiently. + +The path kernel framework has been applied in the design of pruning algorithms that do not require any training data (Gebhart et al., 2021; Tanaka et al., 2020). SynFlow-L2 is such an iterative pruning algorithm that removes edges (parameters) based on the following saliency function: + +$$S(\theta_i) = \theta_i \odot \frac{\partial \mathcal{R}(\boldsymbol{\theta})}{\partial \theta_i^2} = \theta_i \odot \sum_{p=1}^{P} \left(\frac{\pi_p(\boldsymbol{\theta})}{\theta_i}\right)^2 p_i \qquad (7)$$ + +The process of computing the previous saliency measure and eliminating edges with lower saliency is repeated until the required density is achieved. SynFlow-L2 maximizes the trace of the path kernel, and preserves the following data-independent loss function: + +$$\mathcal{R}(\boldsymbol{\theta}) = \mathbb{1}^T \left( \prod_{l=1}^{L+1} |\boldsymbol{\theta}^{[l]}|^2 \right) \mathbb{1} = \sum_{p=1}^P (\pi_p(\boldsymbol{\theta}))^2 \qquad (8)$$ + +where $|\boldsymbol{\theta}^{[l]}|^2$ is the matrix formed by the squares of the elements of the weight matrix at the $l^{th}$ layer and L is the number of hidden layers. + +![](_page_3_Figure_1.jpeg) + +Figure 2. Comparison of the number of remaining units at each layer. The network density is selected such that the method of Magnitude-Pruning after training is able to achieve within 5% of the unpruned network's accuracy (see text for justification). + +We can also observe empirically in Figure 1 that SynFlow-L2 achieves the highest path kernel trace compared to other state-of-the-art pruning methods. + +Another related pruning method is SynFlow-L1 (or simply "SynFlow") – proposed in (Tanaka et al., 2020). SynFlow is based on preserving the loss function $R(\boldsymbol{\theta}) = \sum_{p=1}^{P} |\pi_p(\boldsymbol{\theta})|$ , which is based on the edge-weight products along each input-output path (rather than their squares). + +In this section we analyze the resulting architecture of a sparse network that has been pruned to maximize the path kernel trace. As discussed in the previous section, SynFlow-L2 is a pruning method that has this objective. + +Consider a network with a single hidden-layer, $f: \mathbb{R}^D \to \mathbb{R}^D$ , with N hidden units and D inputs and outputs. The incoming and outgoing weights $\boldsymbol{\theta}$ of each unit are initialized by sampling from $\mathcal{N}(0,1)$ . Let the number of connections in the unpruned network be M, and let m be the target number of connections in the pruned network, so that the resulting network density is $\rho = m/M$ . + +The optimization of the path kernel trace selects the m out of M parameters that maximize: + +$$\sum_{p=1}^{P} \sum_{i=1}^{m} \left( \frac{\pi_p(\boldsymbol{\theta})}{\theta_i} \right)^2 p_i \tag{9}$$ + +In Appendix A.1, we show that this maximization results in a fully-connected network in which only $n \leq N$ of the hidden-layer units remain in the pruned network – all other units and their connections are removed. In other words, the network that maximizes the path kernel trace has the narrowest possible hidden-layer width, given a target network density. + +We also show (Appendix A.2) that this network architecture maximizes the number of input-output paths P: Given a target density $\rho$ , the maximum number of paths results when each hidden-layer has the same number of units, and the network is fully-connected. + +Intuitively, the previous results can be justified as follows, with the same weight distribution across all units of a layer, increasing the number of input-output paths P results in higher path kernel trace. To maximize P with a given number of edges m, however, forces the pruning process to only maintain the edges of the smallest possible set of units at each layer. So, the networks produced by SynFlow and SynFlow-L2 tend to have narrower layers, compared to other pruning methods that do not optimize on the basis of path kernel trace. + +To examine the previous claim empirically, and in the context of convolutional networks rather than MLPs, Figure 2 compares the number of remaining units at each layer after pruning, using the VGG19 and ResNet20 architectures. The target network density in these experiments is the lowest possible such that the method of Magnitude-Pruning (that can be performed only after training) achieves within 5% of the unpruned network's accuracy. In higher densities there is still significant redundancy, while in lower densities there is no sufficient capacity to learn the given task. For a convolutional layer, the width of a layer is the number of channels at the output of that layer. We find that both Syn-Flow and SynFlow-L2 result in pruned networks with very small width ("bottleneck layers") compared to other state-ofthe-art pruned networks of the same density. 12 Further, with SynFlow and SynFlow-L2 all layers have approximately the same number of remaining units, i.e., approximately equal width. Note that for the purposes of this analysis (Figure 2), we do not include skip connections for ResNet20 - such connections complicate the definition of "layer width" and paths, but without changing the main result of Figure 2. + +&lt;sup>1In SNIP, the widest layers get pruned more aggressively as showed in (Tanaka et al., 2020). Due to this SNIP also leads to a decrease in width, but only at the widest layers. + +&lt;sup>2GraSP and PHEW are able to preserve the same width as the unpruned network for all the layers. The curves for GraSP (green) and PHEW (blue) overlap with the curve for the unpruned network in Figure 2. + +![](_page_4_Figure_1.jpeg) + +Figure 3. The effect of increasing the layer width of SynFlow and SynFlow-L2 networks, while preserving the same set of parameters at each layer. The definition of the x-axis "Width Factor" appears in the main text. + +Several empirical studies have been conducted to understand the effect of network width and over-parametrization on learning performance [\(Neyshabur et al.,](#page-10-0) [2018;](#page-10-0) [Du et al.,](#page-9-0) [2018;](#page-9-0) [Park et al.,](#page-10-0) [2019;](#page-10-0) [Lu et al.,](#page-10-0) [2017\)](#page-10-0). However, the previous studies do not decouple the effect of increasing width from the effect of over-parametrization. Recently, [\(Gol](#page-9-0)[ubeva et al.,](#page-9-0) [2021\)](#page-9-0) examined the effect of network width under a constant number of parameters. That work conducted experiments with layer-wise random pruning. Starting with a fully-connected network, the width of each layer is increased while keeping the number of parameters the same. The experiments of [\(Golubeva et al.,](#page-9-0) [2021\)](#page-9-0) show that as the network width increases the performance also increases. Further, the distance between the Gaussian kernel formed by the sparse network and the infinitely wide kernel at initialization is indicative of the network's performance. As expected, increasing the width after a certain limit without also increasing the number of parameters will inevitably cause a drop in both test and train accuracy because of very low per-unit connectivity (especially with random pruning). + +We present similar experiments for SynFlow and SynFlow-L2 in Figure 3. For a given network density, we first obtain the layer-wise density and number of active units that result from the previous two pruning algorithms. We then gradually increase the number of active units by randomly shuffling the masks of each layer (so that the number of weights at each layer is preserved). The increase in layer width can be expressed as the fraction x = (w 0 −w)/(W −w), where W is the layer width of the unpruned network, w is the layer width in the Synflow (or Synflow-L2) pruned network, and w 0 ≥ w is the layer width that results through the shuffling method described above. The maximum value x = 1 results when w 0 = W. + +Figure 3 shows the results of these models on CIFAR-10/100 tasks using ResNet20 and VGG19. We can see that as the width increases so does the performance of the sparse network, even though the layer-wise number of edges is the same. Similar results appear in the ablation studies of [\(Fran](#page-9-0)[kle et al.,](#page-9-0) [2021\)](#page-9-0) using SynFlow. That study redistributes the + +edges of a layer, creating a uniform distribution across all units in the layer – doing so increases the performance of the network (see Appendix [C\)](#page-15-0). + +Summary: Let us summarize the observations of this section regarding the maximization of the path kernel trace – and the resulting decrease in network width. Even without any training data, pruned networks that result by maximizing the path kernel trace are expected to converge faster and learn more efficiently. As we showed however, for a given density, such methods tend to maximize the number of input-output paths, resulting in pruned networks with very narrow layers. Narrow networks, however, attain lower performance as compared to wider networks of the same layer-wise density. In the next section, we present a method that aims to achieve the best of both worlds: high path kernel trace for fast convergence, and large layer-wise width for better generalization and learning. + +# Method + +Given a weight-initialized architecture, and a target number of learnable parameters, we select a set of input-output paths that are conserved in the network – and prune every connection that does not appear in those paths. The selection of conserved paths is based strictly on their initial weights – not on any training data. The proposed method is called *"Paths with Higher Edge Weights"* (PHEW) because it has a bias in favor of higher weight connections. Further, the path selection is probabilistic, through biased random walks from input units to output units. Specifically, the next-hop of each path, from unit i to j at the next layer, is taken with a probability that is proportional to the weight magnitude of the connection from i to j. We show that conserving paths with higher edge weight product results in higher path kernel trace. The probabilistic nature of PHEW avoids the creation of "bottleneck layers" and leads to larger network width than methods with similar path kernel trace. Additionally, the procedure of selecting and conserving input-output paths completely avoids layer collapse. + +In more detail, let us initially consider a fully-connected + +MLP network with L hidden layers and $N_l$ units at each layer (we consider convolutional networks later in this section). Suppose that the weights are initialized according to Kaiming's method (He et al., 2015), i.e., they are sampled from a Normal distribution in which the variance is inversely proportional to the width of each layer: $\theta_{i,j}^l \sim \mathcal{N}(0,\sigma_l^2)$ , where $\sigma_l^2 = 2/N_l$ . + +First, let us consider two input-output paths u and b: u has been selected via a uniform random-walk in which the probability Q(j,i) that the walk moves from unit i to unit j at the next layer is the same for all j; b has been selected via the following weight-biased random-walk process: + +$$Q(j,i) = \frac{|\theta(j,i)|}{\sum_{j} |\theta(j,i)|}$$ +(10) + +where $\theta(j, i)$ is the weight of the connection from i to j. + +In Appendix A.4 we show that the biased-walk path b contributes more in the path kernel trace than path u: + +$$\mathbb{E}[\mathbf{\Pi}_{\boldsymbol{\theta}}(b,b)] = 2^{L} \times \mathbb{E}[\mathbf{\Pi}_{\boldsymbol{\theta}}(u,u)] \tag{11}$$ + +As the number of hidden layers L increases the ratio between the two terms becomes exponentially higher. On the other hand, as the layer's width increases the ratio of two values remains the same. The reason that PHEW paths result in higher path kernel trace, compared to the same number of uniformly chosen paths, is that the former tend to have higher edge weights, and thus higher $\pi_p(\theta)$ values (see Equation 6). Empirically, Figure 1 shows that PHEW achieves a path kernel trace greater than or equal to SNIP and GraSP, and close to the upper bound of SynFlow-L2. + +If the PHEW paths were chosen deterministically (say in a greedy manner, always taking the next hop with the highest weight) the path kernel trace would be slightly higher but the resulting network would have "bottlenecks" at the few units that have the highest incoming weights. PHEW avoids this problem by introducing randomness in the path selection process. Specifically, in Appendix A.3 we show that the expected number of random walks through each unit of a layer l is $W/N_l$ , where W is the required number of walks to achieve the target network density. Thus, as long as $W > N_l$ , every unit is expected to be traversed by at least one walk – and thus every unit of that layer is expected to be present in the sparsified network. + +This is very different than the behavior of SynFlow or SynFlow-L2, in which the width of several layers in the pruned network is significantly reduced. Empirically, Figure 2 confirms that PHEW achieves the larger per-layer width, compared to SynFlow and SynFlow-L2. Additionally, the per-layer width remains the same as the width of the original unpruned network. + +**Layer-Collapse:** Layer collapse is defined as a network state in which all edges of a specific layer are eliminated, + +while there are still connections in other layers (Tanaka et al., 2020). Layer collapse causes a disruption of information flow through the sparse network making the network untrainable. SynFlow and SynFlow-L2 have been shown to avoid layer collapse by iteratively computing gradient based importance scores and pruning (Tanaka et al., 2020). PHEW also avoids layer collapse due to its path-based selection and conservation process. Even a single input-output path has one connection selected at each layer, and so it is impossible for PHEW networks to undergo layer collapse. + +**Balanced, bidirectional walks:** Without any information about the task or the data, the only reasonable prior is to assume that every input unit is equally significant – and the same for every output unit. For this reason, PHEW attempts to start the same number of walks from each input. And to terminate the same number of walks at each output. + +To do so, we create paths in both directions with the same probability: forward paths from input units, and reverse paths from output units. The selection of the starting unit in each case is such that the number of walks that start (or terminate) at each input (or output) unit is approximately the same. The creation of random-walks continues until we have reached the given, target number of parameters. + +PHEW in convolutional neural networks: A convolutional layer takes as input a 3D-vector with $n_i$ channels and transforms it into another 3D-vector of $n_{i+1}$ channels. Each of the $n_{i+1}$ units in a layer produces a single 2D-channel corresponding to the $n_{i+1}$ channels. A 2D channel is produced applying convolution on the input vector with $n_i$ channels, using a 3D-filter of depth $n_i$ . Therefore each input from a unit at the previous layer has a corresponding 2D-kernel as one of the channels in the filter. So, even though MLPs have an individual weight per edge, convolutional networks have a 2D-kernel per edge. + +A random-walk can traverse an edge of a convolutional network in two ways: either traversing a single weight in the corresponding 2D kernel – or traversing the entire kernel with all its weights. Traversing a single weight from a kernel conserves that edge and produces a non-zero output channel. This creates sparse kernels and allows for the processing of multiple input channels at the same unit and with fewer parameters. On the other hand, traversing the entire 2D-kernel that corresponds to an edge means that several other kernels will be eliminated. Earlier work in pruning has shown empirically the higher performance of creating sparse kernels instead of pruning entire kernels (Blalock et al., 2020; Liu et al., 2019). Therefore, *in PHEW we choose to conserve individual parameters during a random-walk rather than conserving entire kernels*. + +![](_page_6_Figure_1.jpeg) + +Figure 4. Comparison of the Top-1 accuracy for sparse networks obtained using PHEW and other state-of-the-art dataindependent baselines. The mean is shown as a solid line while the standard deviation is shown as shaded area. + +In summary, PHEW follows a two-step process in convolutional networks: first an edge (i.e., 2D-kernel) is selected using equation [\(10\)](#page-5-0). Then a single weight is chosen from that kernel, randomly, with a probability that is proportional to the weight of the sampled parameter. We have also experimented with the approach of conserving the entire kernel, and we also present results for that case in the next section. diff --git a/2105.03801/main_diagram/main_diagram.drawio b/2105.03801/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..4941e025ae6738d351eff1c6c73370e2ac4cf216 --- /dev/null +++ b/2105.03801/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2105.03801/main_diagram/main_diagram.pdf b/2105.03801/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..42421cd4746f2718f51322049108fb47ec1a1614 Binary files /dev/null and b/2105.03801/main_diagram/main_diagram.pdf differ diff --git a/2105.03801/paper_text/intro_method.md b/2105.03801/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..7ee304989bcda630c4f18dfc1c2eaa3a17cd8927 --- /dev/null +++ b/2105.03801/paper_text/intro_method.md @@ -0,0 +1,65 @@ +# Introduction + +Transformer-based models [@vaswani2017attention] are ubiquitously state-of-art across many natural language processing (NLP) tasks, including summarization. To achieve the best results, the community has trained ever larger transformer models on larger amount of data, and/or more task-specific optimization objectives [@devlin2018bert; @raffel2020exploring; @lewis-etal-2020-bart; @brown2020language]. In long document summarization, the input sequences could be more than an order of magnitude longer than the limits of these transformer models. Although the limits can be extended, training large transformer models on long sequences is expensive and may not be possible on a standard GPU card because of the self-attention mechanism that grows quadratically with sequence length. + +To tackle the quadratic characteristic, recent works have modified self-attention mechanism and proposed variants of the transformer such that the quadratic complexity is reduced [@tay2020efficient; @kitaev2020reformer; @child2019generating; @beltagy2020longformer; @ainslie-etal-2020-etc; @zaheer2020big]. However, pre-trained weights of the modified models are not readily available. In contrast, standard models such as BERT [@devlin2018bert] or BART [@lewis-etal-2020-bart] have been trained on various target tasks, including text summarization [@liu-lapata-2019-text]. This allows practitioners to achieve good performance with less training time. Thus, we are interested in exploiting pre-trained models for long-span summarization tasks. + +We study a range of design configurations empirically and theoretically in regards to memory and compute requirements as well as their performance. We propose that long-span dependencies can be handled by two complementary methods. Firstly, inspired by modified self-attention transformers, we exploit standard transformer models by constraining attention mechanism to be local, allowing longer input spans during training. Secondly, because abstractive summarization systems perform content selection implicitly [@nallapati-etal-2016-abstractive; @lebanoff-etal-2020-cascade], to reduce memory and compute requirements an alternative method is to perform content selection explicitly before the abstractive stage. We study content selection during two phases: training time and test time. At training time, we investigate methods to select data for training fixed-span abstractive models. At test time, we extend existing model-based selection methods, and we propose a multitask content selection method that ranks sentences through extractive labelling based module [@cheng-lapata-2016-neural] and attention based module [@see-etal-2017-get]. Ultimately, we explore the combined approach, consisting of local self-attention transformer and content selection for long-document summarization. + +We conduct our experiments using a number of design configurations on the Spotify open-domain Podcast summarization dataset [@clifton-etal-2020-100000]. This dataset is challenging not only because of its long-span nature, but also because transcribed spoken utterances typically have lower information density [@li-etal-2019-keep; @manakul2020_interspeech]. Furthermore, we carry out experiments on arXiv and PubMed datasets [@cohan-etal-2018-discourse] to further demonstrate and verify the effectiveness of our approach as well as making comparisons to existing approaches. We highlight the strengths and weaknesses of our approach in different resources and tasks. The main contributions of this paper are: + +- On local self-attention, we show how to exploit a standard transformer model for long-span summarization, and we show good design considerations based on empirical results. + +- On content selection, we demonstrate the best selection method at training time, and we propose a multitask content selection (MCS) method outperforming baselines at test time. + +- Our work has set new state-of-the-art results on Spotify Podcast, arXiv and PubMed datasets in the ROUGE scores. Furthermore, with a small-scale GPU card, our approach achieves comparable or superior performance to previous state-of-the-art systems. + +
+ +
Overview of the combined architecture where we highlight different aspects of this work. N0 is the original document length, N is the input length to the generation system, and M is the summary length.
+
+ +# Method + +In Table [\[tab:combine_podcast\]](#tab:combine_podcast){reference-type="ref" reference="tab:combine_podcast"}, a performance gain is obtained in all settings by adding MCS. By comparing different configurations with MCS, it can be seen that the gain from MCS in LoBART(8k) system is the lowest. This is because the average length is 5,727, meaning that many Podcasts inputs to LoBART(8k) do not benefit from content selection. + +CUED-filt, the best single-model system in @manakul2020cued_speech, uses an attention-based content selection at both training and test time, and it is combined with fine-tuned vanilla BART. Our approach outperforms CUED-filt by improved content selection at both training time and test time as demonstrated by BART(1k)-ORC+MCS. Additionally, local self-attention allows training on longer sequences, and our LoBART(4k)-ORC+MCS system has yielded the best results. Lastly, even though LoBART(8k) requires more resource to train, it does not perform as well as LoBART(4k) due to its smaller attention window, and it also has a lower improvement when adding MCS. + +::: table* +::: + +To verify the effectiveness of our systems, we re-train BART(1k) and LoBART(4k) on arXiv and PubMed datasets. []{#section:arxiv_results label="section:arxiv_results"} Our training is different from Ext+TLM [@pilault-etal-2020-extractive] where their abstractive models are trained using inputs extracted from top two sentences in ROUGE recall for each target sentence without padding, similar to ORC$_\text{no-pad}$. Although in 1k setting, ORC$_\text{no-pad}$ yields %AgORC$_\text{no-pad}$ (defined in Section [5.1](#section:training_bart_with_cs){reference-type="ref" reference="section:training_bart_with_cs"}) of only 2.8% on arXiv (12% on PubMed), in 4k setting this is 39% on arXiv (71% on PubMed). Based on the best configurations on podcast data, we train BART(1k) and LoBART(4k) using TRC or ORC$_\text{pad-rand}$ content selection, and we train the hierarchical model on arXiv/PubMed for MCS. + +**ArXiv.** In Table [\[tab:arxiv_pubmed_result\]](#tab:arxiv_pubmed_result){reference-type="ref" reference="tab:arxiv_pubmed_result"}, both BART(1k)+MCS and LoBART(4k)+MCS outperform all existing systems. To better understand the advantages of our approach, the following systems are compared: CTRLsum versus our BART(1k) baseline; LED and BigBird versus our LoBART(4k) system. + +CTRLsum extends BART by conditioning it with extracted keywords $\mathbf{v}$ using a BERT-based model, e.g. $p(\mathbf{y}|\mathbf{X},\mathbf{v})$. Their BERT-based model uses sliding window allowing it to extract $\mathbf{v}$ in long sequences, but their BART is still limited to the first 1,024 tokens. As a result, it performs better than BART(1k), but worse than BART(1k)+MCS. + +LoBART(4k) has a similar architecture to LED(4k) without the global attention pattern for special tokens. Instead, our LoBART(4k) benefits from knowledge transferred from CNNDM and the ORC$_\text{pad-rand}$ training-time content selection, which yields a larger gain when MCS is applied, i.e. the system trained with truncated data has a smaller gain when MCS is applied. Transfer learning comparison and additional results on the impact of ORC$_\text{pad-rand}$ are provided in Appendix [10](#appendix:additional_results){reference-type="ref" reference="appendix:additional_results"}. + +Compared to BigBird, LoBART(4k) has a longer input span, e.g. 3,072 vs. 4,096. However, BigBird benefits from utilizing more recent summarization specific pre-training Pegasus [@zhang2020pegasus] which is better than our transfer learning. BigBird incorporates a global attention pattern similar to LED, and it also has a random attention pattern. Hence, LoBART without MCS performs worse. + +Ultimately, we show that adding MCS to either BART(1k) or LoBART(4k) yields a significant improvement, resulting in state-of-the-art results in both settings. Moreover, although the gain from adding MCS is comparable to the gain observed in extending LED(4k) to LED(16k), the content selection method adds less training cost. + +**PubMed.** Similarly, LoBART(4k)+MCS achieves state-of-the-art results shown in Table [\[tab:arxiv_pubmed_result\]](#tab:arxiv_pubmed_result){reference-type="ref" reference="tab:arxiv_pubmed_result"}. In contrast to the arXiv results, BART(1k)+MCS does not outperform LoBART(4k) nor BigBird, and the gain from MCS is not as high in both 1k and 4k settings. + +Local attention yields better performance on PubMed, while MCS yields better performance on arXiv. To understand this discrepancy, a fine-grained analysis is conducted. + +
+
+ +
arXiv (Len:Avg=8,584, 90th%=16,108)
+
+
+ +
PubMed (Len:Avg=3,865, 90th%=7,234)
+
+
ROUGE-1 score relative to that of BART(1k) system evaluated on different partitions by length.
+
+ +In Figure [10](#fig:ablation_len){reference-type="ref" reference="fig:ablation_len"}, we partition the test sets by input lengths, and we evaluate the performance improvement in each partition with respect to the BART(1k) baseline.[^9] The results illustrate that as the input length $N$ increases: + +- The improvement of systems *with* MCS increases and subsequently plateaus out. + +- The improvement of systems *without* MCS decreases once the input exceeds the length limit but then plateaus, suggesting that fixed-span systems without content selection perform worse once the maximum fixed-span is reached. For instance, below 4,000 input words, LoBART(4k) without MCS performs better than BART(1k)+MCS on both datasets. + +Therefore, our MCS method is more effective on arXiv compared to PubMed because the average length of PubMed documents is more than twice shorter than the average length of arXiv documents. diff --git a/2106.04559/main_diagram/main_diagram.drawio b/2106.04559/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..44b7dd2c633e81e73d1d34d167018afd47777377 --- /dev/null +++ b/2106.04559/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2106.04559/main_diagram/main_diagram.pdf b/2106.04559/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3044d5379feddd2d4239862c2b5a86cd50bf23be Binary files /dev/null and b/2106.04559/main_diagram/main_diagram.pdf differ diff --git a/2106.04559/paper_text/intro_method.md b/2106.04559/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..5e3620407a0fc3df545b6f8d791ee5fe3ed2b65e --- /dev/null +++ b/2106.04559/paper_text/intro_method.md @@ -0,0 +1,76 @@ +# Introduction + +Today a vast amount of knowledge is hidden in structured datasets, not directly accessible to nontechnical users who are not familiar with the corresponding database query language like SQL or SPARQL. Natural language database interfaces (NLDB) enable everyday users to interact with databases [\(Zelle and Mooney,](#page-7-1) [1996;](#page-7-1) [Popescu et al.,](#page-7-2) [2003;](#page-7-2) [Li and Jagadish,](#page-7-3) [2014;](#page-7-3) [Zeng et al.,](#page-7-4) [2020\)](#page-7-4). However, correctly translating natural language to executable queries is challenging, as it requires resolving all the ambiguities and subtleties of natural utterances for precise mapping. Furthermore, + +quick deployment and adoption for NLDB require zero-shot transfer to new databases without an indomain text-to-SQL parallel corpus, *i.e.* crossdatabase semantic parsing (SP), making the translation accuracy even lower. Finally, unlike in other NLP applications where partially correct results can still provide partial utility, a SQL query with a slight mistake could cause negative utility if trusted blindly or confusing to users. + +The recent Spider benchmark [\(Yu et al.,](#page-7-5) [2018a\)](#page-7-5) captures this cross-domain problem, and the stateof-the-art methods merely achieve around 70% execution accuracy at the time of this submission [2](#page-0-1) . Meanwhile, generalization to datasets collected under different protocols is even weaker [\(Suhr et al.,](#page-7-6) [2020\)](#page-7-6). Finally, users generally have no way to know if the NLDB made a mistake except in very obvious cases. The high error rate combined with the overall system's opacity makes it hard for users to trust any output from the NLDB. + +Our key observation is that our model's top-5 accuracy on Spider is 78.3%, significantly higher than the previous best single-model method at around 68%, and our own top-1 accuracy. Top-5 accuracy is the proportion of times when one of the top five hypotheses from beam-search inference is correct (in execution accuracy evaluation). For top-5 accuracy to be relevant in practice, a nontechnical user needs to be able to pick the correct hypothesis from the candidate list. To this end, we design a feedback system that can unambiguously explain the top beam-search results while presenting the differences intuitively and visually. Users can then judge which, if any, of the parses correctly reflects their intentions. The explanation system uses a hybrid of two synchronous context-free grammars, one shallow and one deep. Together, they achieve good readability for the most frequent + +∗Equal contribution + +1 System demo at [https://turing.borealisai.](https://turing.borealisai.com/) [com/](https://turing.borealisai.com/); video at [https://vimeo.com/537429187/](https://vimeo.com/537429187/9a5d41f446) [9a5d41f446](https://vimeo.com/537429187/9a5d41f446) + +2 + +query patterns while near-complete coverage overall. + +Our system, TURING, is not only interpretable, but also a highly accurate cross-domain NLDB. Our semantic parser is based on the one in [Xu et al.](#page-7-7) [\(2020\)](#page-7-7), which does not handle value prediction like many other previous state-of-the-art models on Spider. Compared to previous executable semantic parsers, we achieve significant gains with a number of techniques, but predominantly by drastically simplifying the learning problem in value prediction. The model only needs to identify the text span providing evidence for the ground-truth value. The noisy long tail text normalization step required for producing the actual value is offloaded to a deterministic search phase in post-processing. + +In summary, this work presents two steps towards a more robust NLDB: + +- 1. A state-of-the-art text-to-SQL parsing system with the best top-1 execution accuracy on the Spider development set. +- 2. A way to relax usability requirement from top-1 accuracy to top-k accuracy by explaining the different hypotheses in natural language with visual aids. + +As shown in Figure [1,](#page-2-0) TURING's interface has two main components: the database browser showing schema and selected database content, and the search panel where the users interact with the parser. Figure [1](#page-2-0) caption describes the typical user interaction using an example. + +Behind the front-end interface, TURING consists of an executable cross-domain semantic parser trained on Spider that maps user utterances to SQL query hypotheses, the SQL execution engine that runs the queries to obtain answers, and the explanation generation module that produces the explanation text and the meta-data powering explanation highlighting. The next sections will describe the semantic parsing and explanation modules. + +The backbone of TURING is a neural semantic parser which generates an executable SQL query T given a user question Q and the database schema S. We follow the state-of-the-art system [\(Xu et al.,](#page-7-7) [2020\)](#page-7-7), but extend it to generate executable SQL query instead of ignoring values in the SQL query, like many other top systems [\(Wang et al.,](#page-7-8) [2019;](#page-7-8) [Guo et al.,](#page-6-0) [2019\)](#page-6-0) on the Spider leaderboard. + +On the high-level, our SP adopts the grammarbased framework following TranX [\(Yin and Neu](#page-7-9)[big,](#page-7-9) [2018\)](#page-7-9) with an encoder-decoder neural architecture. A grammar-based transition system is designed to turn the generation process of the SQL abstract syntax tree (AST) into a sequence of tree-constructing actions to be predicted by the parser. The encoder fenc jointly encodes both the user question Q = q1 . . . q|Q| and database schema S = {s1, . . . , s|S|} consisting of tables and columns in the database. The decoder fdec is a transition-based abstract syntax decoder, which uses the encoded representation H to predict the target SQL query T. The decoder also relies on the transition system to convert the AST constructed by the predicted action sequences to the executable surface SQL query. + +To alleviate unnecessary burden on the decoder, we introduce two novel modifications to the transition system to handle the schema and value decoding. With simple, but effective value-handling, inference and regularization techniques applied on this transition system, we are able to push the execution accuracy much higher for better usability. + +Our transition system has four types of action to generate the AST, including (1) ApplyRule[r] which applies a production rule r to the latest generated node in the AST; (2) Reduce which completes the generation of the current node; (3) SelectColumn[c] which chooses a column c from the database schema S; (4) CopyToken[i] which copies a token qi from the user question Q. + +There are two key distinctions of our transition system with the previous systems. First, our transition system omits the action type SelectTable used by other transition-based SP systems [\(Wang](#page-7-8) [et al.,](#page-7-8) [2019;](#page-7-8) [Guo et al.,](#page-6-0) [2019\)](#page-6-0). This is made possible by attaching the corresponding table to each column, so that the tables in the target SQL query can be deterministically inferred from the predicted columns. Second, we simplify the value prediction by always trying to copy from the user question, instead of applying the GenToken[v] action [\(Yin](#page-7-9) [and Neubig,](#page-7-9) [2018\)](#page-7-9) which generates tokens from a large vocabulary or choose from a pre-processed picklist [\(Lin et al.,](#page-7-10) [2020\)](#page-7-10). Both of the changes constrain the output space of the decoder to ease the + +![](_page_2_Figure_0.jpeg) + +Figure 1: TURING system in action: the user selected database "Dog kennels"; the left and top panels show the database schema and table content. The user then entered "What is the average age of the dogs who have gone through any treatments?" in the search box. This question is run through the semantic parser producing multiple SQL hypotheses from beam-search, which are then explained step-by-step as shown. The differences across the hypotheses are highlighted. The tokens corresponding to table and columns are in bold. If there were more valid hypotheses, a "Show more" button would appear to reveal the additional ones. + +learning process, but the latter change unrealistically assumes that the values are always explicitly mentioned in the question. To retain the generation flexibility without putting excessive burden on the decoder, we propose a conceptually simple but effective strategy to handle the values next. + +Value prediction is a challenging, but crucial component of NLDBs, however, only limited efforts are committed to handling values properly in the current cross-domain SP literature. Value mentions + +are usually noisy, if mentioned explicitly at all, requiring commonsense or domain knowledge to be inferred. On the other hand, the number of possible values in a database can be huge, leading to sparse learning signals if the model tries to choose from the possible value candidates. + +Instead of attempting to predict the actual values directly, our SP simply learns to identify the input text spans providing evidence for the values. As mentioned earlier, we introduce the CopyToken action to copy an input span from the user question, indicating the clues for this value. The ground-truth + +CopyToken[i] actions are obtained from a tagging strategy based on heuristics and fuzzy string matching between the user question and the gold values. As a result, the decoder is able to focus on understanding the question without considering other complexities of the actual values which are difficult to learn. If the values are only implicitly mentioned in the user question, nothing is copied from the user question. We leave the identification of the actual values to a deterministic search-based inference in post-processing, after the decoding process. This yields a simpler learning task as the neural network does not need to perform domain-specific text normalization such as mapping "female" to "F" for some databases. + +Given the schema, the predicted SQL AST and the database content, the post-processing first identifies the corresponding column type (number, text, time), operation type (like, between, >, <, =, ...), and aggregation type (count, max, sum, ...). Based on these types, it infers the type and normalization required for the value. If needed, it then performs fuzzy-search in the corresponding column's values in the database. When nothing is copied, a default value is chosen based on some heuristics (e.g., when there exist only two element "Yes" and "No" in the column, the default value is "Yes"): otherwise, the most frequent element in the column is chosen. Searching the database content can also be restricted to a picklist for privacy reasons like previous works (Zeng et al., 2020; Lin et al., 2020). + +Another benefit of this simple value handling strategy is the ease to explain. The details are presented in the Sec. 4. + +Our encoder architecture follows Xu et al. (2020). The encoder, $f_{\rm enc}$ , maps the user question Q and the schema S to a joint representation $\mathcal{H}=\{\phi_1^q,\ldots,\phi_{|Q|}^q\}\cup\{\phi_1^s,\ldots,\phi_{|S|}^s\}$ . It contextualizes the question and schema jointly through both the RoBERTA-Large model similar to (Guo et al., 2019), as well as through the additional sequence of 24 relation-aware transformer (RAT) (Wang et al., 2019) layers. As mentioned in Section 3.1, tables are not predicted directly but inferred from the columns, so we augment the column representations by adding the corresponding table representations after the encoding process. + +We use a LSTM decoder $f_{\text{dec}}$ to generate the action sequence A. Formally, the + +generation process can be formulated as $\Pr(A|\mathcal{H}) = \prod_t \Pr(a_t|a_{< t},\mathcal{H})$ where $\mathcal{H}$ is the encoded representations outputted by the The LSTM state is updated encoder $f_{\rm enc}$ . following Wang et al. (2019): $m_t, h_t$ $f_{\text{LSTM}}([\boldsymbol{a}_{t-1} \| \boldsymbol{z}_{t-1} \| \boldsymbol{h}_{p_t} \| \boldsymbol{a}_{p_t} \| \boldsymbol{n}_{p_t}], \boldsymbol{m}_{t-1}, \boldsymbol{h}_{t-1}),$ where $m_t$ is the LSTM cell state, $h_t$ is the LSTM output at step t, $a_{t-1}$ is the action embedding of the previous step, $z_{t-1}$ is the context representation computed using multi-head cross-attention of $h_{t-1}$ over $\mathcal{H}$ , $p_t$ is the step corresponding to the parent AST node of the current node, and n is the node type embedding. For **ApplyRule**[r], we compute $Pr(a_t = ApplyRule[r]|a_{< t}, \mathcal{H}) =$ softmaxr $(q(\mathbf{z}_t))$ where $q(\cdot)$ is a 2-layer MLP. For **SelectColumn**[c], we use the memory augmented pointer network following Guo et al. (2019). For CopyToken[i], a pointer network is employed to copy tokens from the user question Q with a special token indicating the termination of copy. + +One of the core challenges for cross-domain SP is to generalize to unseen domains without overfitting to some specific domains during training. Empirically, we observe that applying uniform label smoothing (Szegedy et al., 2016) on the objective term for predicting **SelectColumn**[c] can effectively address the overfitting problem in the cross-domain setting. Formally, the cross-entropy for a ground-truth column $c^*$ we optimize becomes $(1-\epsilon)*\log p(c^*)+\frac{\epsilon}{K}*\sum_c\log p(c)$ , where K is the number of columns in the schema, $\epsilon$ is the weight of the label smoothing term, and $p(\cdot) \triangleq \Pr(a_t = \textbf{SelectColumn}[\cdot]|a_{< t}, \mathcal{H})$ . + +During inference, we use beam search to find the high-probability action sequences. As mentioned above, column prediction is prone to overfitting in the cross-domain setting. In addition, value prediction is dependent on the column prediction, that is, if a column is predicted incorrectly, the associated value has no chance to be predicted correctly. As a result, we introduce two hyperparameters controlling influence based on the action types in the beam, with a larger weight $\alpha>1$ for **SelectColumn** and a smaller weight $0<\beta<1$ for **CopyToken**. + +The goal of the explanation generation system is to unambiguously describe what the semantic parser understands as the user's command and allow the user to easily interpret the differences across the multiple hypotheses. Therefore, unlike a typical dialogue system setting where language generation diversity is essential, controllability and consistency are of primary importance. The generation not only needs to be 100% factually correct, but the differences in explanation also need to reflect the differences in the predicted SQLs, no more and no less. Therefore, we use a deterministic rule-based generation system instead of a neural model. + +Our explanation generator is a hybrid of two synchronous context-free grammar (SCFG) systems combined with additional heuristic post-processing steps. The two grammars trade off readability and coverage. One SCFG is shallow and simple, covering the most frequent SQL queries; the other is deep and more compositional, covering the tail of query distribution that our SP can produce for completeness. The SCFG can produce SQL and English explanation parallel. Given a SQL query, we parse it under the grammar to obtain a derivation, which we then follow to obtain the explanation text. At inference time, for a given question, if any of the SQL hypotheses cannot be parsed using the shallow SCFG, then we move onto the deep one. + +Using the deep SQL syntax trees allows almost complete coverage on the Spider domains. However, these explanations can be unnecessarily verbose as the generation process faithfully follows the re-ordered AST without 1.) compressing repeated mentions of schema elements when possible 2.) summarizing tedious details of the SQL query into higher level logical concepts. Even though these explanations are technically correct, practical explanation should allow users to spot the difference between queries easily. To this end, we design the shallow grammar similarly to the template-based explanation system in [Elgohary et al.](#page-6-1) [\(2020\)](#page-6-1), which simplifies the SQL parse trees by collapsing large subtrees into a single tree fragment. In the resulting shallow parses production rules yield non-terminal nodes corresponding to 1.) anonymized SQL templates 2.) UNION, INTERSECT, or EXCEPT operations of two templates 3.) or a template pattern followed by ORDER-BY-LIMIT clause. Our shal- + +low but wide grammar has 64 rules with those nonterminal nodes. The pre-terminal nodes are placeholders in the anonymized SQL queries such as Table name, Column name, Aggregation operator and so on. Finally, the terminal nodes are the values filling in the place holders. The advantage of this grammar is that each high-level SQL template can be associated with an English explanation template that reveals the high level logic and abstracts away from the details in the concrete queries. To further reduce the redundancy, we make assumptions to avoid unnecessarily repeating table and column names. Table. [1](#page-5-0) showcases some rules from the shallow SCFG and one example of explanation. In practice, around 75% of the examples in the Spider validation set have all beam hypotheses from our SP model parsable by the shallow grammar, with the rest handled by the deep grammar. The deep grammar has less than 50 rules. But because it is more compositional, it covers 100% of the valid SQLs that can be generated by our semantic parser. Some sample explanation by the deep grammar can be found in Table. [2.](#page-5-1) + +Finally, whenever the final value in the query differs from original text span due to post-processing, a sentence in the explanation states the change explicitly for clarity. For example, "*'Asian' in the question is matched to 'Asia' which appears in the column Continent.*" diff --git a/2106.04876/main_diagram/main_diagram.drawio b/2106.04876/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..76f378f5343539d222894a27d6b28533fb9df0da --- /dev/null +++ b/2106.04876/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2106.04876/main_diagram/main_diagram.pdf b/2106.04876/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b5a63b7309bf957667f1d52d25c9237966d2cb03 Binary files /dev/null and b/2106.04876/main_diagram/main_diagram.pdf differ diff --git a/2106.04876/paper_text/intro_method.md b/2106.04876/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..f1704f96255f731ed94f561ef2a62c7772ee0a9b --- /dev/null +++ b/2106.04876/paper_text/intro_method.md @@ -0,0 +1,259 @@ +# Introduction + +The AES algorithm [@AES] is based on the key expansion function $f$, which operates on a random $256$-bit initial key $$\begin{equation} +f : \{0,1\}^{256} \rightarrow \{0,1\}^{1920} +\end{equation}$$ + +$f$ is computed in iterations, also known as rounds. In each iteration, $128$ bits of the expansion are calculated from the previous bits. The calculation consists of both linear and non-linear operations. The non-linear ones are called the Rijndeael substitution box or S-box for short. + +The Rijndeael S-box function is described in detail in Chapter 4.2.1 of [@AES] and it is usually implemented as a look-up-table. It is composed of two transformations: (i) an affine transformation and (ii) Nyberg S-box transformation [@nyberg1991perfect]. The Nyberg S-box transformation is a mapping of an input vector to its multiplicative inverse on the Rijndael finite field: $x \rightarrow x^{-1}$ in $GF(2^8)$. This transformation is known as a perfect nonlinear transformation and satisfies certain security criteria. + +We will use the following notations: + +1. Denote the expanded key bits as $\hat{w} := (w_0, .. ,w_{n-1})$, where $n$ is the size of the expanded key, which is equal to $1920$. Denote the byte $w_i,...,w_{i+7}$ as $W_i$, and the double word $w_i,...,w_{i+31}$ as $W'_i$, so $W'_i(j) = w_{i+j}$. + +2. Let $S : \{0,1\}^8 \rightarrow \{0,1\}^8$ be a Rinjdeal S-box. We can extend the definition of $S$ to an input vector of $32$ bits, where the result is obtained by applying S on each byte separately. + +3. $c = c_1,...,c_{10}$ is a vector of fixed values that is defined in the RCON table which is given in [@AES]. This constant is used in the key expansion function $f$. + +4. $R$ is the following rotation function: $$\begin{equation} + R(w_1,...,w_7,w_8,...,w_{32}) = (w_8,...,w_{32},w_1,...,w_7) + \end{equation}$$ which is used in the key expansion function $f$. + +5. $k$ is the initial key size $256$, and $b$ is the block size $128$. + +6. $\%$ is the modulo operator and $\oplus$ is XOR operator. + +7. For each key index $i$, we denote the round number as $r(i) = \lfloor\frac{i}{b}\rfloor$, and the double word number as $d(i) = \lfloor\frac{i}{32}\rfloor$ + +The key expansion function is critical to understanding our method. Here we describe the constraints that this function inducts on the key bits. For the $i$-bit in the key, the constraints are given by: + +1. $\forall i: k \leq i < n , i \% b \leq 31, r(i) \% 2 = 0:$ $$\begin{equation} + \label{eq:Sbox_with_rotation} + w_i = w_{i - k} \oplus S(R(W'_{d(i-32)}))(i\%b) \oplus c_{\frac{r(i)}{2}} + \end{equation}$$ + +2. $\forall i: k \leq i < n , i \% b \leq 31, r(i) \% 2 = 1:$ $$\begin{equation} + \label{eq:Sbox_without_rotation} + w_i = w_{i - k} \oplus S(W'_{d(i-32)})(i\%b) + \end{equation}$$ + +3. $\forall i: k \leq i < n , i \% b > 31 :$ $$\begin{equation} + \label{eq:XOR_equation} + w_i = w_{i - k} \oplus w_{i -32} + \end{equation}$$ + +Note that each equation contains three XOR operations between variables, and in some of the equations there is a XOR with a constant value. + +A deep learning decoder for error correcting codes with a belief propagation algorithm was introduced in [@nachmani2016learning]. The decoding process uses the well-known belief propagation method and adds learnable weight to the algorithm. Specifically, they add weights to the edges in the Trellis graph. For a linear block code with $k$ information bits and $n$ output bits, the parity check matrix of the linear block code $H$ has a size of $(n-k) \times n$. + +The deep neural Belief propagation algorithm that was introduced in [@nachmani2016learning] has an input layer of $n$ bits. In the architecture that is defined in [@nachmani2016learning] there are two types of hidden layers which are interleaved: (i) variable layer for odd index layer $j$ and (ii) check layer for even index layer $j$. + +For notational convenience, we assume that the parity check matrix $H$ is regular, meaning, the sum over each row and column is fixed and denoted by $d_v$ and $d_c$ respectively. Each column of the parity check matrix $H$ is corresponding to one bit of the codeword and obtains $d_v$ variable nodes in each variable layer. Therefore, the total number of *variable processing units* in each variable layer is $E=d_v \cdot n$. Similarly, each check layer has $E=(n-k)\times d_c$ *check processing units*. + +During the decoding process, the messages propagate from the variable layer to check layers iteratively, where the input to the network is the log likelihood ratio (LLR) $\ell \in \mathbb{R}^{n}$ of each bit: $$\begin{equation} +\ell_v = \log\frac{\Pr\left(c_v=1 | y_v\right)}{\Pr\left(c_v=0 | y_v\right)}, +\end{equation}$$ where $\ell_v$ is the log likelihood ratio for each received signal $y_v$ and $c_v$ is the bit that we want to recover. + +Denote $x^j$ as the vector messages that propagate in the Trellis graph. For $j=1$ and for odd $j$, the computation in each variable node is: $$\begin{equation} +\label{eq:odd} +x^{j}_e = x^{j}_{(c,v)} = \tanh \left(\frac{1}{2}\left(l_v + \sum_{e'\in N(v)\setminus \{(c,v)\}} w_{e'}x^{j-1}_{e'}\right)\right) +\end{equation}$$ where $N(v)=\{(c,v) | H(c,v)=1\}$ is the set of all edges that connected to $v$ and each variable node indexed the edge $e=(c,v)$ in the Tanner graph. $w_e$ is a set of learnable weights. + +For even layer $j$, each check layer preforms this computation: + +$$\begin{equation} +\label{eq:even} +x^{j}_e = x^j_{(c,v)} = 2arctanh \left( \prod_{e'\in N(c) \setminus \{(c,v)\}}{x^{j-1}_{e'}}\right) +\end{equation}$$ where for each row $c$ of the parity check matrix $H$, $N(c)=\{(c,v) | H(c,v)=1\}$ is the corresponding set of edges. + +Overall, in the deep neural network that is proposed in [@nachmani2016learning] there are $L$ layers from each type (i.e. variable and check). The last layer is a marginalization layer with a sigmoid activation function which outputs $n$ bits. The $v$-th output bit is given by: + +$$\begin{equation} +o_v = \sigma \left( l_v + \sum_{e'\in N(v)}\bar{w}_{e'} x^{2L}_{e'} \right), +\label{eq:base_final} +\end{equation}$$ where $\bar{w}_{e'}$ is another set of learnable weights. Moreover, in each odd layer $j$ marginalization is performed by: $$\begin{equation} +o^{j}_v = \sigma \left( l_v + \sum_{e'\in N(v)}\bar{w}_{e'} x^{j}_{e'} \right) +\label{eq:j_final} +\end{equation}$$ + +The loss function is cross entropy on the error after each $j$ marginalization: $$\begin{equation} +\mathcal{L}=-\frac{1}{n}\sum_{h=0}^{L}\sum_{v=1}^{n} c_{v}\log(o^{2h+1}_{v})+(1-c_{v})\log(1-o^{2h+1}_{v}) +\label{eq:loss} +\end{equation}$$ where $c_{v}$ is the ground truth bit. + +
+ +
An overview of our method for neural cold boot attack. The input is the initial key of 256 bit, then the AES key expansion functions f expands it to 1920 key. The expanded key was corrupted by the cold boot model. The corrupted key is inserted into a S-box neural network Snn and neural belief propagation decoder. The neural belief propagation decoder constructs from a novel formalization of the AES key expansion function. Then the most accurate nl + nh bits are selected in insert with the corrupted key to MAX-SAT solver. The MAX-SAT solver produces the corrected AES key.
+
+ +Our architecture contains two components: (i) A variant of neural belief propagation decoder with neural S-box layers and (ii) a Partial MAX-SAT solver. The proposed model, depicted in Figure [1](#fig:arch){reference-type="ref" reference="fig:arch"}. The input to the neural belief propagation is the corrupted bits $l = l_0,..,l_{n-1}$ and it predicts an approximation for the original key $o = o_0,..,o_{n-1}$. Formally, the value of the $i$-th bit in the original key was $1$ with an approximated probability of $o_i$. The input to the Partial MAX-SAT solver is a CNF formula which part of it defined by $o' \subset o$ , where the probabilities in $o'$ correspond to bits that the network has high confidence in their values (approximately 99$\%$). The output of the Partial MAX-SAT solver is the estimation of the desired key. + +In contrast to previous cold boot attack methods, the input of our method is a floating vector over $[0,1]$ instead of binary input ${0,1}$. In this way, one can better express the decay model of the memory. In practice, this input can be measured according to the voltage, the memory sector or the amount of time that elapsed from shutting down the power to the time that the bits were read. + +The belief propagation neural network defined by a parity check matrix $H$. But, the equations in Eq. [\[eq:Sbox_with_rotation\]](#eq:Sbox_with_rotation){reference-type="ref" reference="eq:Sbox_with_rotation"},[\[eq:Sbox_without_rotation\]](#eq:Sbox_without_rotation){reference-type="ref" reference="eq:Sbox_without_rotation"} are not linear, and cannot directly express with a parity check matrix $H$. To convert these equations to linear form, one can change the variables: namely, use $W_t^{s'}:= S(W_t^{'})$ instead of $W_t^'$ on the equations, and using the fact that Eq. [\[eq:Sbox_with_rotation\]](#eq:Sbox_with_rotation){reference-type="ref" reference="eq:Sbox_with_rotation"},[\[eq:Sbox_without_rotation\]](#eq:Sbox_without_rotation){reference-type="ref" reference="eq:Sbox_without_rotation"} are linear in $W_t^{s'}$. The S-box transformation is defined only on boolean vectors. But, the belief propagation neural network uses fractions values. Therefore, to obtain a differentiable red and continuous version of the Rijndael S-box, we first train a neural network $S_{nn}$ mimic it: $$\begin{equation} + S_{nn} : x_1 , ... , x_8 \rightarrow y_1 , ... , y_{256} +\end{equation}$$ where $x_i \in [0,1]$ and $y_i \in [0,1]$. The network has three fully connected layers with $512$ ReLU activations. It is trained with a cross entropy loss function. An $argmax$ operation is preformed on the output $y$, in order to get $z_1 , ... , z_{8}$, where it achieve $100\%$ accuracy. We can extend the definition of $S_{nn}$ to an input vector of 32 bits, where the result is obtained by applying $S_{nn}$ on each byte separately. + +The S-box layer performs the transformation of the variables, which it essential to obtain a linear form. The neural S-box layer constructed from a combination of neural S-boxes, the input to it is a vector $\hat{x} \in [-1,1]^{n}$ and the output is a vector $\hat{s} \in [-1,1]^{n+32\cdot13+1}$ which obtained by concatenation of the input with an additional vector of size $13\cdot32$ (There are 13 additional rounds on the AES-256 key expansion, on each round 4 S-boxes are calculated). The additional vector calculated by applying the neural S-box on the corresponding bits of the input ($\hat{x}$). Formally, $\hat{s}$ is defined as follows: + +1. $\forall i \in [0,n-1] : \hat{x}_i = \hat{s}_i$ + +2. $\forall i \in [n,(n+13\cdot32)-2] , i \% 32 =0:$ + + $(\hat{s}_i, .., \hat{s}_{i+31})=S_{nn}(\hat{x}_{si(i)}, .., \hat{x}_{si(i)+31})$ where $si(i)=k+b\cdot((i-n)\%32)-32$ + +3. The last bit in $\hat{s}$ is $1$ (namely $\hat{s}_{n+13\cdot 32-1} = 1$) and it used as bias factor. + +So, in total each S-box layer is constructed from $4 \cdot 13$ neural S-box instances. + +In order to predict the values of the AES key from the corrupted keys, a naive way is to search the key that is close to the corrupted one over the large space of the existing key. + +However, when the decay percentage is high, the search space is extremely large, and by design, the key expansion function provides resistance against attacks in which part of the cipher key is known, see Chapter 7.5 in [@AES]. + +Instead, due to the resemblance of the AES keys and the key role of the XOR operation, we rely on network-based error correcting code methods that are suitable for block ciphers. Such codes often employ expansion functions, which are, however, linear. + +To this end, by changing the variables and approximate the S-box outputs, we convert the key expansion constraints (Eq. [\[eq:Sbox_with_rotation\]](#eq:Sbox_with_rotation){reference-type="ref" reference="eq:Sbox_with_rotation"}, [\[eq:Sbox_without_rotation\]](#eq:Sbox_without_rotation){reference-type="ref" reference="eq:Sbox_without_rotation"}, [\[eq:XOR_equation\]](#eq:XOR_equation){reference-type="ref" reference="eq:XOR_equation"}) to parity check matrix $H$ (the specific values of the matrix will be defined in the following subsection). + +The original neural belief propagation explained in the introduction. The architecture consist $L$ iterations, which each layer contains variable and check layers, and the input of each layer was the output of the previous layer (or the global input in layer $1$). But, to execute the variables transformation, we modify the architecture as follows: First we add S-box layer after each check layer. Second, we modify the layer structure: in Eq [\[eq:odd\]](#eq:odd){reference-type="ref" reference="eq:odd"} instead using $l$, the $\log$ likelihood of the signal, we used $q$, the output of the marginalization layer on the previous iteration, after going through the S-box layer. In figure [2](#fig:BPNN_with_Sboxs){reference-type="ref" reference="fig:BPNN_with_Sboxs"} we depict the architecture of the modified neural belief propagation . + +
+ +
An overview of the modified neural belief propagation decoder. The input is the corrupted key of 1920 bit. Each layer of the neural belief propagation receive two vectors: (i): the output of the previous belief propagation layer(x). (ii): The output of the values from the marginalization layer, after going through the S-box layer(q).
+
+ +[]{#subsection:Tailoring_BPNN label="subsection:Tailoring_BPNN"} + +Denote the S-box mimicking network output, given by a vector $W_i$, as $Z_i = (z_i, .. ,z_{i+7}) := S_{nn}(W_i)$. We denote the concatenation of $Z_i,Z_{i+8},Z_{i+16},Z_{i+24}$ as $\hat{Z_i}$. We can rearrange the constraints in Eq. [\[eq:Sbox_with_rotation\]](#eq:Sbox_with_rotation){reference-type="ref" reference="eq:Sbox_with_rotation"}, [\[eq:Sbox_without_rotation\]](#eq:Sbox_without_rotation){reference-type="ref" reference="eq:Sbox_without_rotation"}, [\[eq:XOR_equation\]](#eq:XOR_equation){reference-type="ref" reference="eq:XOR_equation"} as follows: + +1. $\forall i: k \leq i < n , i \% b \leq 31, r(i) \% 2 = 0:$ $$\begin{equation} + \label{eq:NN_With_rotations} + 0 = w_i \oplus w_{i - k} \oplus S_{nn}(R(W^{'}_{d(i-32)}))(i\%b) \oplus c_{\frac{r(i)}{2}} + \end{equation}$$ + +2. $\forall i: k \leq i < n , i \% b \leq 31, r(i) \% 2 = 1:$ $$\begin{equation} + \label{eq:eq_bb} + 0 = w_i \oplus w_{i - k} \oplus S_{nn}(W^{'}_{d(i-32)})(i\%b) + \end{equation}$$ + +3. $\forall i: k \leq i < n , i \% b > 31 :$ $$\begin{equation} + \label{eq:sim_xor} + 0 = w_i \oplus w_{i - k} \oplus w_{i -32} + \end{equation}$$ + +We define $x$ as the concatenate of $w$ and $\hat{Z}_i$ : $x = (w_0,..,w_{n-1},\hat{Z}_{k-32},\hat{Z}_{k-32+b},\hat{Z}_{k-32+2b}..,\hat{Z}_{n-32-b})$. By considering the XOR operation as the addition operator over $\{0,1\}^2$, assuming for simplicity that $R$ is the identity function, so $S_{nn}(R(\hat{W}_{d(i-32)})) = \hat{Z_i}$ and replacing $S_{nn}(W_{i})$ with $\hat{Z_i}$, one can transform equations  [\[eq:NN_With_rotations\]](#eq:NN_With_rotations){reference-type="ref" reference="eq:NN_With_rotations"},  [\[eq:eq_bb\]](#eq:eq_bb){reference-type="ref" reference="eq:eq_bb"},  [\[eq:sim_xor\]](#eq:sim_xor){reference-type="ref" reference="eq:sim_xor"} to a matrix form using a matrix $H'$ and a vector $u$, such that: $$\begin{equation} +\label{eq:H_with_bias} +H'x+u=0 +\end{equation}$$ where $u$ is a constant vector that consist of the RCON values $c_i$ and zeros. + +$\forall i,j : 0\leq i < n-k, 0\leq j < (n+32(\frac{n-k}{b}))$ $$\begin{equation} +\label{eq:define_H} +H'(i,j)= \begin{cases} + 1, \text{if $i \% b > 31$ , $j = i$ }\\ + 1, \text{if $i \% b > 31$ , $j = i+k$ }\\ + 1, \text{if $i \% b > 31$ , $j = i+k-32$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j = i$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j = i+k$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j=t(i+k-32)$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = i$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = i+k$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = t(i+k-32)$ }\\ + 0, \text{otherwise}\\ + \end{cases} +\end{equation}$$ Where $t(i) = n+32 r(i)+i\%b$, this function replace the $w_i$ values with their corresponding $z_i$ values. The first three cases correspond to Eq. [\[eq:sim_xor\]](#eq:sim_xor){reference-type="ref" reference="eq:sim_xor"}, The following three cases in the middle correspond to Eq. [\[eq:eq_bb\]](#eq:eq_bb){reference-type="ref" reference="eq:eq_bb"}, and the last three cases correspond to Eq. [\[eq:NN_With_rotations\]](#eq:NN_With_rotations){reference-type="ref" reference="eq:NN_With_rotations"}. $\forall i : 0\leq i 31$ , $j = i$ }\\ + 1, \text{if $i \% b > 31$ , $j = i+k$ }\\ + 1, \text{if $i \% b > 31$ , $j = i+k-32$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j = i$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j = i+k$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 1 $ , $j = t(i+k-32)$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = i$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = i+k$ }\\ + 1, \text{if $i \% b \leq 31, r(i) \% 2 = 0 $ , $j = t(i+k-32)$ }\\ + 0, \text{otherwise}\\ + \end{cases} +\end{equation}$$ + +In our experiments, in order to isolate the influence of the neural S-box component ($S_{nn}$) on the performance, we use an additional architectures. In the first architecture, we do not use $S_{nn}$, and connect $l$ to the original (without any modifications) belief propagation neural network directly. In this settings we ignore the non linear constrains, and use $H''$, a sub-matrix of $H$: $$\forall i,j : 0\leq i < n-k, 0\leq j \leq n$$ $$\begin{equation} +\label{eq:define_H''} +H''(i,j)= \begin{cases} + 1, \text{if $i \% b > 31$ and j = i }\\ + 1, \text{if $i \% b > 31$ and j = i+k }\\ + 1, \text{if $i \% b > 31$ and j = i+k-32 }\\ + 0, \text{otherwise}\\ + \end{cases} +\end{equation}$$ $$\begin{equation} +\label{eq:H''_0} +H l = 0 +\end{equation}$$ This ablation uses only linear constraints so we term it as \"LC\". The second ablation uses $H$, the full matrix, but does not contain neural S-box layers inside the neural belief propagation network (except before the first layer, which is necessary to express all the equations with linear form and can be calculated using the S-box transformation itself). This ablation used the original belief propagation neural network architecture so we term it as \"OBPNN\". + +Note that the formulation of $H$ also relevant for other variations of AES (i.e. $k$=$128$,$192$). Moreover, the same technique can be used to create deep architectures for side-channel attacks for additional ciphers, for example Serpent  [@SERPENT]. + +Based on the $H$ matrix described in Eq. [\[eq:define_H\]](#eq:define_H){reference-type="ref" reference="eq:define_H"} (or the $H''$ described in Eq. [\[eq:define_H\'\'\]](#eq:define_H''){reference-type="ref" reference="eq:define_H''"} for the ablation experiment), we construct a neural belief propagation network, as described in [3.2](#subsection:BP){reference-type="ref" reference="subsection:BP"}. The network receives an input vector $x$, that combines $l$ and add S-box layer before each iteration of the belief propagation network, and predicts the probabilities vector $o$. + +Once we obtain the initial estimate from the neural network, we use a Partial MAX SAT Solver to search for the corrected key. To run the solver, we define the followings Conjunctive Normal Form (CNF) formulas: + +1. n variables, one per bit in the key $v_1 ,..,v_n$. + +2. Converted the bit-relation in Eq. [\[eq:Sbox_with_rotation\]](#eq:Sbox_with_rotation){reference-type="ref" reference="eq:Sbox_with_rotation"},[\[eq:Sbox_without_rotation\]](#eq:Sbox_without_rotation){reference-type="ref" reference="eq:Sbox_without_rotation"},[\[eq:XOR_equation\]](#eq:XOR_equation){reference-type="ref" reference="eq:XOR_equation"} that implies by the key expansion function to a CNF formula by CNF Factorization. The result is the formula $\psi_{AES}$, that consists of $217984$ clauses and $1920$ variables. Eq. [\[eq:XOR_equation\]](#eq:XOR_equation){reference-type="ref" reference="eq:XOR_equation"} for example, which is in the following form: ($a \oplus b = c$), is replaced with the following clauses: + + $$(1)\quad \neg a \land \neg b \land \neg c \quad \quad \quad (2)\quad \neg a \land b \land c$$ $$(3)\quad a \land \neg b \land c \quad\quad\quad + (4) \quad a\land b \land \neg c$$ + + With the other equations, the result is more complicated, and each equation has been replaced by numerous clauses. We then insert these clauses into the solver as a hard formula. This formula is identical for all of the keys, and is calculated once. + +3. For each bit whose value is $1$ in the corruption key, we supply a single clause that enforces this key bit to be $1$, we denote this formula by $\psi_{memory}$. Formally : $\psi_{memory} := \wedge_{i \in [n-1], l_i = 1 } v_i$ + +4. Consider the $n_h$ bits with the highest value in the network output $o$, and the $n_l$ bits with the lowest values. These are the locations for which the network is mostly confident. Let $t_h$ be the $n_h$-th highest value in $o$, and the $t_l$ as the $n_l$-th lowest values in $o$, we take these as thresholds and define the formula: $\psi_{nn} := \Big{(}\wedge_{i \in [n-1], o_i \geq t_h}\neg v_i \Big{)}\wedge \Big{(} \wedge_{i \in [n-1], o_i \leq t_l } v_i \Big{)}$ + +We define $\psi_{AES}$ as hard formula, and $\psi_{nn}$ as soft formula. In the theoretical decay model $\psi_{memory}$ is defined as hard formula, but on the realistic decay model is defined as a soft formula. + +There is a large number of Partial MAX-SAT Solvers, which operate in a wide variety of strategies. We select the WBO Solver [@wbo_solver] with the implementation of  [@LogicNGgithub], which is based on the unsatisfiability method [@UNSAT], and other enhancements [@WBO1; @WBO2; @WBO3]. We select this solver for three main reasons: (i) We have the intuition that DPLL solvers will be suitable for this problem over randomized SAT solvers due to the large complexity of the search space (there are $2^{1920}$ vectors, and there are numerous clauses). This complexity makes us think that it is better to use a solver that scans the space in an orderly manner. We, therefore, decided to use CDCL solvers  [@CDCL1; @CDCL2; @CDCL3], the most popular variation of DPLL solvers. (ii) Since it achieved best results in different cold boot attack settings, for example [@Liaobbb]. (iii) In the early step of this research we tried to insert the complete key approximation from the neural network into a CDCL solver. Empirically, we observe that inserting the complete key approximation from the neural network into a CDCL solver does not output the correct keys. Therefore, we decided to focus on a small number of bits. We choose the bits that the neural belief propagation is relatively sure in their values, and in total, the probability that more than a few bits in the subset are errors is very small (smaller than $1 \%$). Therefore, it was natural to use the UNSAT  [@UNSAT] method which is suitable for the problem, since the number of unsatisfiability soft clauses is small with high probability. + +The input of our architecture is the vector $l_1, .. ,l_n$, which represents the corrupted key. We use the fully-connected neural network $S_{nn}$ (which imitates the Rijndael S-box, and extend its functionality to non-binary values) to calculate $\hat{Z}_k,\hat{Z}_{k+b},..,\hat{Z}_{n-k}$ for all $i \in \{k+bt-32 | 0 \leq t \leq \frac{n-k}{b}-1 \}$. In other words, the S-box layer is created by duplicating the fully-connected neural network $S_{nn}$ $\frac{n-k}{b}$ times, one instance per each calculation of $\hat{Z}_i$ . + +The combination of these variables defines the input for each layer of the neural belief propagation, it denoted by $x$: $$\begin{equation} +\label{eq:aes_input} +x = [l_1,..,l_n,\hat{Z}_k,\hat{Z}_{k+b},..,\hat{Z}_{n-k},1] +\end{equation}$$ which is of size of $n+32 \cdot (\frac{n-k}{b})+1$. Note that in layers that are not the first layer, $l$ is replaced with the output of the marginalization of the previous layer. + +The $H$ function as defined in Eq. [\[eq:define_H\]](#eq:define_H){reference-type="ref" reference="eq:define_H"}, is used as a parity check matrix for the neural belief propagation. + +The neural Belief propagation predicts the probability that each key bit was $1$. We denote these probabilities by $o = o_1,..,o_n$. Based on $l,o$, we define the following SAT instance, as described in detail in subsection [4.4](#section:SAT_SOLVER){reference-type="ref" reference="section:SAT_SOLVER"}. + +1. Define n variables, one per bit $v_1 ,..,v_n$. + +2. $\psi_{nn}$ a CNF that is induced by the neural belief propagation predictions. + +3. $\psi_{memory}$ a CNF that is induced by the corrupted key + +4. $\psi_{AES}$ a CNF that is equivalent to the key expansion constraints. + +We run WBO solver on this instance. The output of our model is the assignment that is returned from the solver. diff --git a/2106.05956/main_diagram/main_diagram.drawio b/2106.05956/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..ae51b692f1a55788b004060e048a07d321733a30 --- /dev/null +++ b/2106.05956/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2106.05956/main_diagram/main_diagram.pdf b/2106.05956/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7a26fc8e20118369032c69b6cfabc4f78f768db7 Binary files /dev/null and b/2106.05956/main_diagram/main_diagram.pdf differ diff --git a/2106.05956/paper_text/intro_method.md b/2106.05956/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..135785a25359ccf04156c7aeb9b39a7d38d5731e --- /dev/null +++ b/2106.05956/paper_text/intro_method.md @@ -0,0 +1,165 @@ +# Introduction + +Normalization techniques are often necessary to effectively train deep neural networks (DNNs) [1, 2, 3]. Arguably, the most popular of these is BatchNorm [1], whose success can be attributed to several beneficial properties that allow it to stabilize a DNN's training dynamics: for example, ability to propagate informative activation patterns in deeper layers [4, 5]; reduced dependence on initialization [6, 7, 8]; faster convergence via removal of outlier eigenvalues [9, 10]; auto-tuning of learning rates [11], equivalent to modern adaptive optimizers [12]; and smoothing of loss landscape [13, 14]. However, depending on the application scenario, BatchNorm's use can be of limited benefit or even a hindrance: for example, BatchNorm struggles when training with small batch-sizes [3, 15]; in settings with train-test distribution shifts, BatchNorm can undermine a model's accuracy [16, 17]; in meta-learning, it can lead to transductive inference [18]; and in adversarial training, it can hamper accuracy on both clean and adversarial examples by estimating incorrect statistics [19, 20]. + +To either address specific shortcomings or to replace BatchNorm in general, several recent works propose alternative normalization layers (interchangeably called normalizers in this paper). For example, Brock et al. [23] propose to match BatchNorm's forward propagation behavior in Residual + +Email: {eslubana, dickrp}@umich.edu, and hidenori.tanaka@ntt-research.com + +\*Work partially performed during an internship at Physics & Informatics Laboratories, NTT Research. + +![](_page_1_Figure_0.jpeg) + +Figure 1: Each normalization method has its own success and failure modes. We plot training curves (3 seeds) for different combinations of *normalizer* (see Table 1)*, network architecture,* and *batch-size* at largest stable initial learning rate on CIFAR-100. Learning rate is scaled linearly with batch-size [21]. Layers for which loss reaches infinity are not plotted. Test curves and several other settings are provided in the appendix. The plots show that all methods, including BatchNorm (BN), have their respective success and failure modes: e.g., LayerNorm (LN) [2] often converges slowly and Instance Normalization (IN) [22] can have unstable training with large depth or small batch-sizes. + +networks [24] by replacing it with Scaled Weight Standardization [25, 26]. Wu and He [3] design GroupNorm, a batch-independent method that groups multiple channels in a layer to perform normalization. Liu et al. [27] use an evolutionary algorithm to search for both normalizers and activation layers. Given the right training configuration, these works show their proposed normalizers often achieve similar test accuracy to BatchNorm and even outperform it on some benchmarks. This begs the question, are we ready to replace BatchNorm? To probe this question, we plot training curves for models defined using different combinations of *normalizer, network architecture, batch size,* and *learning rate* on CIFAR-100. As shown in Figure 1, clear trends begin to emerge. For example, we see LayerNorm [2] often converges at a relatively slower speed; Weight Normalization [28] cannot be trained at all for ResNets (with and without SkipInit [6]); Instance Normalization [22] results in unstable training in deeper non-residual networks, especially with small batch-sizes. Overall, evaluating hundreds of models in different settings, we see evident success/failure modes exist for all normalization techniques, including BatchNorm. + +As we noted before, prior works have established several properties to help explain such success/failure modes for the specific case of BatchNorm. However, given the pursuit of alternative normalizers in recent works, these properties need to be generalized so that one can accurately determine how normalization techniques beyond BatchNorm affect DNN training. In this work, we take a first step towards this goal by extending known properties of BatchNorm *at initialization* to several alternative normalization techniques. As we show, these properties are highly predictive of a normalizer's influence on DNN training and can help ascertain exactly when an alternative technique is capable of serving as a replacement for BatchNorm. Our contributions follow. + +- Stable Forward Propagation: In Section 3, we show activations-based normalizers are provably able to prevent exploding variance of activations in ResNets, similar to BatchNorm [5, 6]. Parametric normalizers like Weight Normalization [28] do not share this property; however, we explain why architectural modifications proposed in recent works [6, 7] can resolve this limitation. +- Informative Forward Propagation: In Section 4, we first show the ability of a normalizer to generate dissimilar activations for different inputs is a strong predictor of optimization speed. We then extend a known result for BatchNorm to demonstrate the rank of representations in the deepest layer of a Group-normalized [3] model is at least Ω(pwidth/Group Size). This helps us illustrate how use of GroupNorm can prevent high similarity of activations for different inputs if the group size is small, i.e., the number of groups is large. This suggests Instance Normalization [22] (viz., + +GroupNorm with group size equal to 1) is most likely and LayerNorm [2] (viz., GroupNorm with group size equal to layer width) is least likely to produce informative activations. + +• Stable Backward Propagation: In Section 5, we show normalization techniques that rely on individual sample and/or channel statistics (e.g., Instance Normalization [22]) suffer from an exacerbated case of gradient explosion [29], often witnessing unstable backward propagation. We show this behavior is mitigated by grouping of channels in GroupNorm, thus demonstrating a speed–stability trade-off characterized by group size. + +**Related Work:** Due to its ubiquity, past work has generally focused on understanding BatchNorm [5, 4, 6, 9, 10, 7, 13, 29, 30, 31]. A few works have studied LayerNorm [32, 33], due to its relevance in natural language processing. In contrast, we try to analyze normalization methods in deep learning in a general manner. As we show, we can identify properties in BatchNorm that readily generalize to other normalizers and are often predictive of the normalizer's impact on training. Our analysis is inspired by a rich body of work focused on understanding randomly initialized DNNs [34, 35, 36, 37, 38]. Most related to us is the contemporary work by Labatie et al. [39], who analyze the impact of different normalization layers on expressivity of activations and conclude LayerNorm leads to high similarity of activations in deeper layers. As we discuss, this result is in fact a special case of our Claim 3. + +# Method + +We first clarify the notations and operations used by the normalizers discussed in this work. Specifically, we define operators $\mu_{\{d\}}(\mathcal{T})$ and $\sigma_{\{d\}}(\mathcal{T})$ , which calculate the mean and standard deviation of a tensor ${\mathcal T}$ along the dimensions specified by set $\{d\}$ . $\|\mathcal{T}\|$ denotes the $\ell_2$ norm of $\mathcal{T}$ . RMS $_{\{d\}}(\mathcal{T})$ denotes the root mean square of $\mathcal{T}$ along dimensions specified by set $\{d\}$ . For example, for a vector $\mathbf{v} \in \mathbb{R}^n$ , we have RMS{1} $(v) = \sqrt{\sum_i v_i^2/n}$ . We assume the outputs of these operators broadcast as per requirements. $\rho(.)$ denotes the sigmoid function. We define symbols b, c, x to denote the batch, channel, and spatial dimensions. For feature maps in a CNN, x will include both the height and the width dimensions. The notation c/g denotes division of c neurons (or channels) into groups of size g. When grouping is performed, each group is normalized independently. + +**Normalization Layers:** We analyze ten normalization layers in this work. These layers were chosen to cover a broad range of ideas: e.g., activations-based layers [1, 40], parametric layers [23, 28], hand-engineered layers [3], AutoML designed layers [27], and layers [22, 2, 4] that form building blocks of recent techniques [41]. + +| Activations-Based Layers | | +|----------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------| +| $\mu_{\{d\}} = \mu_{\{d\}}(A); \sigma_{\{d\}} = \sigma_{\{d\}}(A)$ | | +| BN [1] | $\frac{\mathcal{A} - \mu_{\{b,x\}}}{\sigma_{\{b,x\}}}$ | +| LN [2] | $\frac{A - \mu_{\{c,x\}}}{\sigma_{\{c,x\}}}$ | +| IN [22] | $\frac{A - \mu_{\{x\}}}{\sigma_{\{x\}}}$ | +| GN [3] | $\frac{\mathcal{A} - \mu_{\{c/g,x\}}}{\mathcal{A} - \mu_{\{c/g,x\}}}$ | +| FRN [40] | $\frac{\sigma_{\{c/g,x\}}}{\frac{\mathcal{A}}{RMS_{\{x\}}}}$ | +| VN [4] | $\frac{A}{\sigma_{\{b,x\}}}$ | +| EvoBO [27] | $\frac{\mathcal{A}}{\max\{\sigma_{\{b,x\}},v\odot\mathcal{A}+\sigma_{\{x\}}\}}$ | +| EvoSO [27] | $\frac{\frac{\mathcal{A}\rho(v\odot\mathcal{A})}{\sigma\{c/g,x\}}}{\sigma\{c/g,x\}}$ | +| Parametric Layers | | +| $\mu_{\{d\}} = \mu_{\{d\}}(\mathcal{W}); \sigma_{\{d\}} = \sigma_{\{d\}}(\mathcal{W})$ | | +| WN [28] | $g\frac{\mathcal{W}}{ \mathcal{W} }$ | +| SWS [23] | $g \frac{W - \mu_{\{c,h,w\}}}{\sigma_{\{c,x\}}}$ | + +Table 1: Operations performed by different normalizers. A denotes layer input; W denotes incoming neuron weights to a neuron. + +1. Activations-Based Layers: BatchNorm (BN) [1], Layer-Norm (LN) [2], Instance Normalization (IN) [22], GroupNorm (GN) [3], Filter Response Normalization (FRN) [40], Variance Normalization (VN) [4], EvoNormBO [27], and EvoNoRMSO [27] fall in this category. These layers function in the activation space. Note that Variance Normalization is an ablation of BatchNorm that does not use the mean-centering operation. Typically, given activations $\mathcal{A}_L$ at layer L, these layers use an operation of the form $\mathcal{A}_{\text{norm}} = \phi\left(\frac{\gamma}{\sigma_{\{d\}}(\mathcal{A}_L)}(\mathcal{A}_L - \mu_{\{d\}}(\mathcal{A}_L)) + \beta\right)$ ). Here, $\gamma$ and $\beta$ are learned affine parameters used for controlling quantities affected by the normalization operations (such as mean, standard deviation, and RMS) and $\phi$ is a non-linearity, such as ReLU. The exact operations for these layers, minus the affine parameters, are shown in Table 1. + +2. Parametric Layers: Weight Normalization (WN) [28] and Scaled Weight Standardization (SWS) [23] fall in this category. Table 1 shows the exact operations. These layers function in the parameter space and act on a filter's weights (W) to generate normalized weights ( $W_{\text{norm}}$ ). The normalized weights $W_{\text{norm}}$ are used for processing the input: $A_{L+1} = \phi(W_{\text{norm}} * A_L)$ . + +Stable forward propagation is a necessary condition for successful DNN training [36]. In this section, we identify and demystify the role of normalization layers in preventing the problem of *exploding* or *vanishing activations* during forward propagation. These problems can result in training instability due to exploding or vanishing gradients during backward propagation [36, 38]. Building on a previous study on BatchNorm, we first show that activations-based normalizers provably avoid exponential growth of variance in ResNets1, ensuring training stability. Thereafter, we show parametric normalizers do not share this property and ensuring stable training requires explicit remedies. + +Hanin and Rolnick [38] show that for stable forward propagation in ResNets, the average variance of activations should not grow exponentially (i.e., should not explode). Interestingly, Figure 1 shows that all activations-based normalizers are able to train the standard ResNet [24] architecture stably. For BatchNorm, this behavior is provably expected. Specifically, De and Smith [6] find that to ensure variance along the batch-dimension is 1, BatchNorm rescales the $L^{\rm th}$ layer's residual path output by a factor of $\mathcal{O}\left(1/\sqrt{L}\right)$ . This causes the growth of variance in a Batch-Normalized ResNet to be linear in depth, hence avoiding exponential growth of variance in and ensuring effective training. We now show this result can be extended to other normalization techniques too. + +**Claim 1.** Similar to BatchNorm [6], GroupNorm [3] avoids exponential growth of variance in ResNets, ensuring stable training. + +Proof. We follow the same setup as De and Smith [6]. Assume the $L^{\text{th}}$ residual path $(f_L)$ is processed by a normalization layer $\mathcal{N}$ , after which it combines with the skip connection to generate the next output: $\mathbf{y}_L = \mathbf{y}_{L-1} + \mathcal{N}(f_L(\mathbf{y}_{L-1}))$ . The covariance of layer input and Residual path's output is assumed to be zero. Hence, the output's variance is: $\text{Var}(\mathbf{y}_L) = \text{Var}(\mathbf{y}_{L-1}) + \text{Var}(\mathcal{N}(f_L(\mathbf{y}_{L-1})))$ . Now, assume GroupNorm with group size G is used for normalizing the D-dimensional activation signal, i.e., $\mathcal{N} = \text{GN}(.)$ . This implies for the $g^{\text{th}}$ group, $\sigma_{g,x}(GN(f_L(\mathbf{y}_{L-1}))) = 1$ . Then, for a batch of size N, denoting the $i^{\text{th}}$ sample activations as $\mathbf{y}_L^{(i)}$ , and using $(\mathbf{y}_L^{(i)})^j$ to index the activations, we note the residual output's variance averaged along the spatial dimension is: $\langle \text{Var}(\mathcal{N}(f_L(\mathbf{y}_{L-1}))) \rangle = \frac{1}{D} \sum_{j=1}^{D} (\frac{1}{N} \sum_{i=1}^{N} (\text{GN}(f_L(\mathbf{y}_{L-1}))^j)^2) = \frac{1}{N} \sum_{i=1}^{N} (\frac{1}{D} \sum_{j=1}^{D} (\text{GN}(f_L(\mathbf{y}_{L-1})^{(i)})^j)^2) = \frac{1}{N} \sum_{i=1}^{N} \frac{1}{D} (\frac{1}{D} \sum_{j=1}^{D} (\text{GN}(f_L(\mathbf{y}_{L-1})^{(i)}))^j)^2) = 1$ . Overall, this implies $\langle \text{Var}(\mathbf{y}_L) \rangle = \langle \text{Var}(\mathbf{y}_{L-1}) \rangle + 1$ . Recursively applying this relationship for a bounded variance input, we see average variance at the $L^{\text{th}}$ layer is in $\mathcal{O}(L)$ . Thus, similar to BatchNorm, use of GroupNorm will enable stable forward propagation in ResNets by ensuring signal variance grows linearly with depth. + +![](_page_3_Figure_6.jpeg) + +Figure 2: Activations-based normalizers ensure linear and stable forward propagation, verifying Claim 1. Activation Variance (Activ. Var.) as a function of layer number in a ResNet-56 [24] processing CIFAR-100 samples. + +To understand the relevance of the above result, note that for G=1, GroupNorm is equal to Instance Normalization [22] and for G=D, GroupNorm is equal to LayerNorm [2]. Further, since the mean of the signal is assumed to be zero, the average variance along the spatial dimension is equal to the RMS $_x$ operation used by Filter Response Normalization [40]. Thus, by proving the above result for GroupNorm, we are able to show alternative activations-based normalizers listed in Table 1 also avoid the exponential growth of activation variance in ResNets. + +We show empirical demonstrations of Claim 1 in Figure 2, where the average activation variance is plotted for a ResNet-56. As can be seen, for all activations-based normalizers, the growth of variance is linear in the number of layers. At the end of a Residual module, which spatially downsamples the signal, the variance plummets. However, + +the remaining layers follow a pattern of linear growth, as expected by our result. We note our + +&lt;sup>1The case of *non-residual* networks is discussed in appendix. In brief, most normalizers help avoid exploding/vanishing activations by enforcing unit activation variance in the batch, channel, or spatial dimensions. + +![](_page_4_Figure_0.jpeg) + +Figure 3: Parametric normalizers witness exponentially growing variance, verifying Claim 2, but we can stabilize it by modifying the residual-path. We plot log activation variance as a function of layer number in a randomly initialized ResNet-56 [24], using CIFAR-100 samples, with Scaled Weight Standardization (SWS) [23] and Weight Normalization (WN) [28] for different architectures (simplified illustrations provided on top). (a) *Standard ResNet*: Both SWS and WN witness variance explosion in a standard ResNet model, as claimed in Claim 2. (b) *SkipInit*: SkipInit [6] multiplies the residual signal with a scalar $\alpha$ initialized as zero, thus preventing variance explosion in an SWS model at initialization. Meanwhile, by scaling the non-linearity after addition, a WN model continues to witness exploding variance. (c) *Non-Linearity on Residual Path*: Shifting the non-linearity to the residual path prevents variance explosion in both WN and SWS models. + +theory does not apply to EvoNorms, which are designed via AutoML. However, empirically, we see EvoNorms also avoid exponential growth of variance in ResNets. *Thus, our analysis shows, all activations-based normalizers in Table 1 share the beneficial property of stabilizing forward propagation in ResNets, similar to BatchNorm.* + +By default, parametric normalizers such as Weight Normalization [28] and Scaled Weight Standardization [23] do not preserve the variance of a signal during forward propagation, often witnessing vanishing activations. To address this limitation, properly designed output scale and bias corrections are needed. Specifically, for Weight Normalization and ReLU non-linearity, Arpit et al. [42] show the output should be modified as follows: $\mathcal{A}_{L+1} = \sqrt{\frac{2\pi}{\pi}-1}(\phi(\mathcal{W}_{norm}*\mathcal{A}_L)-\sqrt{\frac{1}{2\pi}})$ . For Scaled Weight Standardization, only output scaling is needed [23]: $\mathcal{A}_{L+1} = \phi(\sqrt{\frac{2\pi}{\pi}-1}\mathcal{W}_{norm}*\mathcal{A}_L)$ . + +In Figure 1, ResNet training curves for Weight Normalization [28] and Scaled Weight Standardization [23] were not reported as the loss diverges to infinity. As we explain in the following, this is a result of using correction factors designed to enable variance preservation in non-residual networks. + +**Claim 2.** *Unlike BatchNorm* [6], Weight Normalization [28] and Scaled Weight Standardization [23] witness unstable training due to exponential growth of variance in standard ResNets [24]. + +*Proof.* Using the correction factors above, both Weight Normalization and Scaled Weight Standardization will ensure signal variance is preserved on the residual path: $\operatorname{Var}(\mathcal{N}(f(\mathbf{y}_{L-1}))) = \operatorname{Var}(\mathbf{y}_{L-1})$ . Thus, using these methods, the output variance at layer L becomes: $\operatorname{Var}(\mathbf{y}_L) = \operatorname{Var}(\mathbf{y}_{L-1}) + \operatorname{Var}(\mathcal{N}(f(\mathbf{y}_{L-1}))) = 2\operatorname{Var}(\mathbf{y}_{L-1})$ . Recursively applying this relationship for a bounded variance input, we see signal variance at the $L^{\text{th}}$ layer is in $\mathcal{O}(2^L)$ . Thus, Weight Normalization and Scaled Weight Standardization witness exponential growth in variance. + +More generally, the above result shows if the residual path is variance preserving, ResNets will witness exploding variance with growing depth. Prior works [43, 5, 8, 6, 7, 44] have noted this result in the context of designing effective ResNet initializations. Here, we extended this result to show why Weight Normalized and Scaled Weight Standardized ResNets undergo unstable forward propagation. Empirical demonstration is provided in Figure 3a. + +In their work introducing Scaled Weight Standardization [23], Brock et al. are able to circumvent exponential growth in variance by using SkipInit [6]. Specifically, inspired by the fact that BatchNorm biases Residual paths to identity functions, De and Smith [6] propose SkipInit, which multiplies the output of the residual path by a learned scalar $\alpha$ that is initialized to zero. This suppresses the Residual path's contribution, hence avoiding exponential growth in variance (see Figure 3b). Interestingly, even + +![](_page_5_Figure_0.jpeg) + +Figure 4: **Modified residual-path allows for successful training with parametric layers.** We plot train/test accuracy (over 3 seeds) for ResNet-56 architecture on CIFAR-100 with non-linearity located on the residual path. We see parametric normalizers can train effectively if scaled non-linearities are not located after the addition operation in a ResNet. + +after using SkipInit, we find Weight Normalized ResNets witness variance explosion (see Figure 3b). To explain this behavior, we note that in the standard ResNet architecture, the non-linearity is located after the addition operation of skip connection and the residual path's signals (see Figure 3a). Thus, even if SkipInit is used to suppress the residual path, the non-linearity will still be applied to the skip connections. Since the scale correction for Weight Normalization ( $\sqrt{2\pi/\pi-1}$ ) is greater than 1, this implies the signal output is amplified at every layer to preserve signal variance; however, since convolutions are absent on the skip path, signal variance never decays. Consequently, *variance is only amplified*, causing the variance to increase exponentially in the number of layers (see Figure 3b). + +Training ResNets with Weight Normalization: The above discussion shows that for Weight Normalization, since the output has to be scaled-up to preserve signal variance, standard ResNets [24] witness exploding activations. This also hints at a solution: place the non-linearity on the Residual path. This modification (see Figure 3c) in fact results in one of the architectures proposed by He et al. in their original work on ResNets [45]. We verify the effectiveness of this modification in Figure 3c. As can be seen, the signal variance in a Weight Normalized ResNet stays essentially constant for this architecture. Furthermore, we show in Figure 4 that these models are able to match BatchNorm in performance for several training configurations. In general, our discussion here explains the exact reasons why architectures with non-linearity on residual path are better suited for parametric normalizers. Finally, we note that another ResNet architecture which boasts non-linearity on residual paths is pre-activation ResNets [45]. In their experimental setup for designing Scaled Weight Standardization [23], Brock et al. specifically focused on pre-activation ResNets [45]. This is another reason why the problem of exploding activations does not surface in their work. + +Proper magnitude of activations is a necessary, but not sufficient, condition for successful training. Here, we study another failure mode for forward propagation, *rank collapse*, where activations for different input samples become indistinguishably similar in deeper layers. This can significantly slow training as the gradient updates no longer reflect information about the input data [4]. To understand this problem's relevance, we first show why the ability to generate dissimilar activations is useful in the context of normalization methods for deep learning. Specifically, given a randomly initialized network that uses a specific normalizer, we relate its average cosine similarity of activations at the penultimate + +layer (i.e., layer before the linear classifier) with its mean training accuracy (= $\frac{\sum_{i=1}^{\text{# of epochs}} \text{Train Acc.[i]}}{\text{# of epochs}}$ , a measure of optimization speed [46]). Results for three different architectures (Non-Residual CNN with 10 layers and 20 layers as well as ResNet-56 without SkipInit) are shown in Figure 5. As can be seen, the correlation between mean training accuracy and the average cosine similarity of activations is high. In fact, for any given network architecture, one can predict which normalizer will enable the fastest convergence without even training the model. This shows normalizers which result in more dissimilar representations at initialization are likely to be more useful for training DNNs. + +We now note another interesting pattern in Figure 5: LayerNorm results in highest similarity of activations for any given architecture. To explain this, we again revisit known properties of Batch-Norm. As shown by Daneshmand et al. [4, 47], BatchNorm provably ensures activations generated by a randomly initialized network have high rank, i.e., different samples have sufficiently different activations. To derive this result, the authors consider activations for N samples at the penultimate layer, $Y \in \mathbb{R}^{\text{width} \times N}$ , and define the covariance matrix $YY^T$ , whose rank is equal to that of the + +![](_page_6_Figure_0.jpeg) + +![](_page_6_Figure_1.jpeg) + +- (a) Stable rank vs. group size. +- (b) Layer-wise Cosine Similarity. + +Figure 6: The smaller the group size, the higher the rank of the activations, verifying Claim 3 (a) We plot stable rank of activations at the penultimate layer for random Gaussian inputs. As proposed in Claim 3, we find a perfect linear fit between stable rank and values of $\sqrt{\text{Width/Group Size}}$ for different group sizes. (b) Implications of Claim 3 on CIFAR-100 sampels: by increasing group size (constant across layers), we see similarity of features at any given layer increases. This shows LayerNorm [2] cannot generate informative features, thus witnessing slow convergence (see Figure 5). + +similarity matrix $Y^TY$ . The authors then show that in a zero-mean, randomly initialized network with BatchNorm layers, the covariance matrix will have a rank at least as large as $\Omega(\sqrt{\text{width}})$ . That is, there are at least $\Omega(\sqrt{\text{width}})$ distinct directions that form the basis of the similarity matrix, hence indicating the model is capable of extracting informative activations. In the following, we propose a claim that extends this result to activations-based normalizers beyond BatchNorm. + +Claim 3. For a zero-mean, randomly initialized network with GroupNorm [3] layers, the penultimate layer activations have a rank of at least $\Omega(\sqrt{\text{width/Group Size}})$ , where width denotes the layer-width (e.g., number of channels in a CNN). + +The intuition behind the above claim is based on the proof by Daneshmand et al. [4]. In their work, the authors extend a prior result from random matrix theory which suggests multiplication of several zero-mean, randomly initialized gaussian matrices will result in a rank-one matrix [10]. The use of BatchNorm ensures that on multiplication with a randomly initialized weight matrix, the values of on-diagonal elements of the covariance matrix $YY^T$ are preserved, while the offdiagonal elements are suppressed. This leads to a lower bound of the order of $\Omega(\sqrt{\text{width}})$ on the stable rank [48] of the covariance matrix. Now, if one directly considers the similarity matrix $Y^TY$ and uses GroupNorm instead of BatchNorm, then a similar preservation and suppression of on- and offdiagonal matrix blocks should occur. Here, the block size will be equal to the Group size used for GroupNorm. This indicates the lower bound is in $\Omega(\sqrt{\text{width/Group Size}})$ . + +We provide demonstration of this claim in Figure 6a. We use a similar setup as Daneshmand et al. [4], randomly initializing a CNN with constant layer-width (64) and 30 layers. A GroupNorm layer is placed before every ReLU layer and the group size is sweeped from 1 to 64. As seen in Figure 6a, we find a perfect linear fit between the stable rank and the value of $\sqrt{^{\text{width}}/_{\text{Group Size}}}$ , validating our claim empirically as well. + +To understand the significance of Claim 3, note that the result shows if the group size is large, then use of GroupNorm cannot prevent collapse of representations (i.e., cannot result + +![](_page_6_Figure_10.jpeg) + +Figure 5: Informative forward propagation results in faster optimization. We plot mean training accuracy (= $\frac{\sum_{i=1}^{\# \text{of epochs}} \text{Train Acc.[i]}}{\# \text{of epochs}}$ ) on CIFAR-100 vs. average cosine similarity at initialization. As shown, normalizers which induce dissimilar activations converge faster. Instance Normalization was removed due to training instability (see Section 5). + +in informative activations). To demonstrate this effect, we calculate the mean cosine similarity of activations between different samples of a randomly initialized network that uses GroupNorm. We sweep the group size from layer-width to 1, thus covering the spectrum from LayerNorm (Group Size + += layer-width) to Instance Normalization (Group Size = 1). We analyze both a non-residual CNN with 20 layers and a ResNet-56. Results are shown in Figure 6b and confirm our claim that by grouping the entire layer for normalization, LayerNorm results in highly similar activations. *This explains the slow convergence behavior of LayerNorm in Figure 5*. Meanwhile, if we reduce the group size, similarity of representations decreases as well, indicating generation of informative activations. This shows use of GroupNorm with group size greater than layer-width can help prevent a collapse of features onto a single representation. Importantly, this result helps explain why GroupNorm can serve as a successful replacement for BatchNorm in similarity based self-supervised learning frameworks [49], which often witness representation collapse issues [50]. Similar to BatchNorm, GroupNorm helps discriminate between representations of different inputs, helping avoid a collapse of representations. + +Taking the results of Section 4 to the extreme should imply Instance Normalization (i.e., Group Size = 1) is the best configuration for GroupNorm, but as we noted in Figure 1, Instance Normalization witnesses unstable training. To explain this, we describe a "speed-stability" trade-off in GroupNorm in the next section by extending the property of gradient explosion in BatchNorm to alternative normalization layers. Specifically, Yang et al. [29] recently show that gradient norm in earlier layers of a randomly-initialized BatchNorm network increases exponentially with increasing model depth (see Figure 8). This shows the maximum depth of a model trainable with BatchNorm is finite. The theory leading to this result is quite involved, but a much simpler analysis can not only explain this phenomenon accurately, but also illustrate the existence of gradient explosion in alternative layers. + +**Gradient explosion in BatchNorm:** Following Luther [51], we analyze the origin of gradient explosion based on the expression of gradient backpropagated through a BatchNorm layer. We calculate the gradient of loss w.r.t. activations at layer L, denoted as $\mathbf{Y}_L \in \mathbb{R}^{d_L \times N}$ . We define two sets of intermediate variables: (i) pre-activations, generated by weight multiplication, $X_L = W_L Y_{L-1}$ and (ii) normalized pre-activations, generated by BatchNorm, $\hat{X}_L = \mathrm{BN}(X_L) = \frac{\gamma}{\sigma_{\{N\}}(X_L)}(X_L - \mu_{\{N\}}(X_L)) + \beta$ . Under these notations, the gradient backpropagated from layer L to layer L-1 is (see appendix for derivation): $\nabla_{\mathbf{Y}_{L-1}}(J) = \frac{\gamma}{\sigma_{\{N\}}(X_L)} \mathcal{W}_L^T \mathcal{P}\left[\nabla_{\hat{\mathbf{X}}_L}(J)\right]$ . Here $\mathcal{P}$ is a composition of two projection operators: $\mathcal{P}[\mathbf{Z}] = \mathcal{P}_{\mathbf{1}_N}^\perp[\mathcal{P}_{\mathrm{Ob}(\hat{\mathbf{X}}_L/\sqrt{N})}^\perp[\mathbf{Z}]]$ . The operator $\mathcal{P}_{\mathrm{Ob}(\hat{\mathbf{X}}_L/\sqrt{N})}^\perp[\mathbf{Z}] = \mathbf{Z} - \frac{1}{N}\mathrm{diag}(\mathbf{Z}\hat{\mathbf{X}}_L^T)\hat{\mathbf{X}}_L$ subtracts its input's component that is inline with the BatchNorm outputs via projection onto the Oblique manifold $\mathrm{diag}(\frac{1}{N}\hat{\mathbf{X}}_L\hat{\mathbf{X}}_L^T) = \mathrm{diag}(\mathbf{1})$ . Similarly, $\mathcal{P}_{\mathbf{1}}^\perp[\mathbf{Z}] = \mathbf{Z}(I - \frac{1}{N}\mathbf{1}_N\mathbf{1}_N^T)$ mean-centers its input along the batch dimension via projection onto $\mathbf{1}_N \in \mathbb{R}^N$ . + +Notice that at initialization, the gradient is unlikely to have a large component along specific directions such as the all-ones vector (1) or the oblique manifold defined by $\hat{\mathbf{X}}_L$ . Thus, the gradient norm will remain essentially unchanged when propagating through the projection operation ( $\mathcal{P}$ ). However, the next operation, multiplication with $\frac{\gamma}{\sigma_{\{N\}}(X_L)}$ (= $\frac{1}{\sigma_{\{N\}}(X_L)}$ at initialization) will re-scale the gradient norm according to the standard deviation of pre-activations along the batch dimension. As shown by Luther [51], for a standard, zero-mean Gaussian initialization, the pre-activations have a standard deviation equal to $\sqrt{\pi-1/\pi} < 1$ . Thus, at initialization, the division by standard deviation operation amplifies the gradient during backward propagation. For each BatchNorm layer in the model, such an amplification of the gradient will take place, hence resulting in an exponential increase in the gradient page of careful supposed in the gradient page. + +![](_page_7_Figure_5.jpeg) + +Figure 7: **Gradient norm vs. pre-activation statistics.** We see high correlation between gradient norm and inverse product of layer-wise pre-activation std. deviations. + +in the gradient norm at earlier layers. Overall, our analysis exposes an interesting tradeoff in Batch-Norm: Divison by standard deviation during forward propagation, which is important for generating dissimilar activations [4], results in gradient explosion during backward propagation, critically limiting the maximum trainable model depth! Empirically, the above analysis is quite accurate near initialization. For example, in Figure 7, we show that the correlation between the norm of the gradient at a layer ( $\|\nabla_{\mathbf{Y}_L}(J)\|$ ) and the inverse product of standard deviation of the pre-activations of layers ahead of it ( $\Pi_{l=10}^{l=1})^{l}/\sigma_{\{N\}}(\mathbf{X}_L)$ ) remains very high (0.6–0.9) over the first few hundred iterations in a 10-layer CNN trained on CIFAR-100. + +![](_page_8_Figure_0.jpeg) + +Figure 8: **Small group size increases gradient explosion, verifying Claim 4.** We use CIFAR-100 samples and plot layer-wise gradient norm for different models and batch sizes. As shown, Instance Normalization [22] undergoes highest gradient explosion, followed by BatchNorm [1], GroupNorm [3], and LayerNorm [2] in all settings. + +**Gradient Explosion in Other Normalizers:** We now extend the phenomenon of gradient explosion to other normalizers. The primary idea is that since all activation-based normalizers have a gradient expression similar to BatchNorm (i.e., projection followed by division by standard deviation), they all re-scale the gradient norm during backprop. However, the statistic used for normalization varies across normalizers, resulting in different severity of gradient explosion. + +**Claim 4.** For a given set of pre-activations, the backpropagated gradient undergoes higher average amplification through an Instance Normalization layer [22] than through a BatchNorm layer [1]. Further, GroupNorm [3] witnesses lesser gradient explosion than both these layers. + +Proof. The gradient backpropagated through the $g^{\text{th}}$ group in a GroupNorm layer with group-size G is expressed as: $\nabla_{\mathbf{Y}_{L-1}^g}(J) = \frac{\gamma}{\sigma_{\{g\}}(X_L^g)} \mathcal{W}_L^T \mathcal{P}\left[\nabla_{\hat{\mathbf{X}}_L^g}(J)\right]$ (see appendix for derivation). Here, $\mathcal{P}$ is defined as: $\mathcal{P}[\mathbf{Z}] = \mathcal{P}_1^{\perp}[\mathcal{P}_{\mathbb{S}(\hat{\mathbf{X}}_L/\sqrt{G})}^{\perp}[\mathbf{Z}]]$ , where $\mathcal{P}_{\mathbb{S}(\hat{\mathbf{X}}_L/\sqrt{G})}^{\perp}[\mathbf{Z}] = (I - \frac{1}{G}\hat{\mathbf{X}}_L^g\hat{\mathbf{X}}_L^g^T)\mathbf{Z}$ . That is, the component of gradient inline with the normalized pre-activations will be removed via projection onto the spherical manifold defined by $||\hat{\mathbf{X}}_L^g|| = \sqrt{G}$ . As can be seen, the gradient expressions for GroupNorm and BatchNorm are very similar. Hence, the discussion for gradient explosion in BatchNorm directly applies to GroupNorm as well. This implies, when Instance Normalization is used in a CNN, the gradient norm for a given channel c and the $i^{\text{th}}$ sample is amplified by the factor $\frac{1}{\sigma_{\{x\}}(\mathbf{X}_{L,i}^c)}$ (inverse of spatial standard deviation). Then, over N samples, using the arithmetic-mean $\geq$ harmonic-mean inequality, we see the average gradient amplification in Instance Normalization is greater than gradient amplification in BatchNorm: $\frac{1}{N}\sum_i \frac{1}{\sigma_{\{x\}}^2(\mathbf{X}_{L,i}^c)} \geq \frac{N}{\sum_i \sigma_{\{x\}}^2(\mathbf{X}_{L,i}^c)} = \frac{1}{\sigma_{\{N\}}^2(\mathbf{X}_L)}$ . Similarly applying arithmetic-mean $\geq$ harmonic-mean for a given sample and the $g^{\text{th}}$ group, we see average gradient amplification in Instance Normalization is greater than gradient amplification in Instance Normalization is greater than gradient amplification in GroupNorm: $\frac{1}{G}\sum_c \frac{1}{\sigma_{\{x\}}^2(\mathbf{X}_L^g,c)} \geq \frac{G}{\sum_c \sigma_{\{x\}}^2(\mathbf{X}_L^g,c)} = \frac{1}{\sigma_{\{g\}}^2(\mathbf{X}_L)}$ . Extending this last inequality by averaging over N samples, we see average gradient amplification in GroupNorm is lower than that in BatchNorm. This implies grouping of neurons in GroupNorm helps reduce gradient explosion. + +We show empirical verification of Claim 4 in Figure 8. As can be seen, the gradient norm in earlier layers follows the order Instance Normaliation $\geq$ BatchNorm $\geq$ GroupNorm $\geq$ LayerNorm, as proved in Claim 4. Further, since increasing depth implies more normalization operations, we see gradient explosion increases as depth increases. Similarly, since reducing batch-size increases gradient noise, we find gradient explosion increases with decrease in batch-size as well. + +Speed-stability trade-off in GroupNorm: Combined with Section 4, our discussion in this section helps identify a speed-stability trade-off in GroupNorm. Specifically, we find that while GroupNorm with group size equal to 1 (viz., Instance Normalization) results in more diverse features (see Claim 3), it is also more susceptible to gradient explosion and hence sees training instability for small batch-sizes/large model depth (see Figure 1). Meanwhile, when group size is equal to layerwidth (viz., LayerNorm), gradient explosion can be avoided, but the model is unable to generate informative activations and thus witnesses slower optimization. Combining these + +![](_page_8_Figure_7.jpeg) + +Figure 9: **Speed–Stability trade-off in GroupNorm** using 20-layer CNNs with batch-size 256 on CIFAR-100. We see increasing group size decreases gradient explosion (improved training stability) at the expense of high activation similarity (reduced optimization speed). + +results demonstrates that the group size in GroupNorm ensues a trade-off between high similarity of activations (influences training speed) and gradient explosion (influences training stability). To illustrate this trade-off, we can estimate training instability by fitting an exponential curve to layerwise gradient norms (measures degree of gradient explosion) and estimate training speed by calculating cosine similarity of activations at the penultimate layer at initialization (highly correlated with training speed; see Figure 6). Results are shown in Figure 9. We see increasing group size clearly trades-off the two properties related to training speed and stability, with a moderately large group size resulting in best performance. In fact, we see test accuracy is highest exactly at this point of intersection in the trade-off. This explains the success of channel grouping in GroupNorm and other successful batch-independent normalization layers like EvoNormSO [27]. Interestingly, these results also help explain why in comparison to BatchNorm, which suffers from gradient explosion and exacerbates the problem of high gradient variance in non-IID Federated learning setups [52, 53], use of GroupNorm with a properly tuned group-size helps achieve better performance [52, 54]. diff --git a/2110.06553/main_diagram/main_diagram.drawio b/2110.06553/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..0d80788703180fa77391bb10933a064f0b63c4bd --- /dev/null +++ b/2110.06553/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2110.06553/main_diagram/main_diagram.pdf b/2110.06553/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..536f6e344a47432816b18769ba6b1364081b8f2f Binary files /dev/null and b/2110.06553/main_diagram/main_diagram.pdf differ diff --git a/2110.06553/paper_text/intro_method.md b/2110.06553/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..36be727e2468bf3b5c8d9d4b91b468711b398292 --- /dev/null +++ b/2110.06553/paper_text/intro_method.md @@ -0,0 +1,73 @@ +# Method + +EEG-based emotion recognition is to classify the emotion states according to the EEG signal. As illustrated in Fig. [1](#fig: overview){reference-type="ref" reference="fig: overview"} the overview of the proposed EEG emotion transformers consists of three sub-modules which are feature preparing and re-organization, the innovative self-attention block and classification loss. The four gray blue blocks in Fig. [1](#fig: overview){reference-type="ref" reference="fig: overview"} are our contributions in this paper. + +
+ +
Overview of EEG Emotion Recognition Transformer (EeT)
+
+ +The EEG emotion classification problem is to learn a mapping function $F$ that maps the EEG input to the corresponding emotion labels: + +$$\begin{equation} +Y=F(X^{'}) +\label{DKL} +\end{equation}$$ where $X^{'}$ denotes the representation of EEG signals. $F$ denotes the mapping function i.e., neural network transformations. $Y \in \{y_1, y_2, ... , y_n\}$ denotes the emotion classification labels. In this paper, the classification cross entropy is adopted as loss function, which is defined as the formula: $$\begin{equation} +L=-\sum_{c=1}^{C}y_{c}log(y^{'}_{c}) +\label{DKL} +\end{equation}$$ where $L$ denotes the loss function of the task of EEG emotion recognition, $C$ denotes number of emotion classes, $y_{c}$ is the ground truth emotion label and $y^{'}_{c}$ is the predictors of neural networks. + +Since the EEG signal collected by a spherical EEG cap, we use a feature representation organization method to organize the signals into 4D matrices to keep as much spatial structure information as possible. We define $X=(E_{1},E_{2},...,E_{T})\in R^{C\times T}$ as an EEG sample collected in $T$ time stamps, where $C$ is the number of electrodes. $E_{t}$ denotes the EEG signal of $C$ electrodes collected at time stamp $t$. Here, we use the DE features denoted as $F_{t}=(D_{1},D_{2},...,D_{S})\in R^{C\times S}$ from $E_{t}$ as described in \[32\]. We set ${\delta[1-3Hz],\theta[4-7Hz],\alpha[8-13Hz], \beta[14-30Hz],\gamma[31-50Hz]}$ as the spectral band set $S$. To explore the interactions among spatial and temporal dimensions, we re-organize $F_{t}$ of the sample $X$ into 4D EEG representation. Specifically, the $s$th band feature $D_{s}$ from $C$ channels is transformed into a 2D map $D_{s}^{'}\in R^{V\times H}$. In other words, we reshape the 1D tensor $D_s \in R_{C}$ into 2D tensor $D_{s}^{'}\in R^{V\times H}$ $(C \leqslant (V \times H))$. When we do the same operation in each band, $F_{t}$ will be transformed into a 3D map $F_{t}^{'}=(D_{1}^{'},D_{2}^{'},...D_{S}^{'})\in R^{S\times V\times H}$. Finally we stack all the transformed 3D feature map along the temporal dimension to get the 4D EEG representation $X^{'}=(F_{1}^{'},F_{2}^{'},...,F_{T}^{'}) \in R^{T \times S\times V\times H}$. + +We divided the EEG feature of each second $D_i (i=1,2,3..S)$ into $G$ non-overlapping regions, just like the different brain regions in neuroscience. Here we regroup the $C$ EEG electrodes in the $V \times H$ matrices into region sequences, the size of each divided region is $P \times P$, so we get $G=VH/P^{2}$ regions. Each region is flatten into a vector $I(x)_{(p,t)}\in \mathbb{R}^{5P^{2}}$ with $p=1,2...,G$ representing spatial layout of EEG electrodes and $t=1,2,...T$ denoting the index over seconds. Then we linearly map each region $I(x)_{(p,t)}$ into a latent vector $z_{(p,t)}^{(0)}\in \mathbb{R}^{D}$ by means of learnable matrix $M\in\mathbb{R}^{D\times 5P^{2}}$: $$\begin{equation} +z_{(p,t)}^{(0)}={MI(x)}_{(p,t)+e_{(p,t)}^{pos}} +\label{DKL} +\end{equation}$$ + +where $e_{(p,t)}^{pos}\in\mathbb{R}^{D}$ stands for a positional embedding added to encode the spatiotemporal position of each region. The resulting sequence of embedding vectors $z_{(p,t)}^{(0)}$ stands for the input to the next layer of the self-attention block. Note that $z^i$ is output of the $ith$ layer in self-attention block. $p=1,...G$ and $t=1,...,T$ are the spatial locations and indexes over time slices respectively. + +Our Transformer consists of $L$ encoding blocks. At each block $l$, a query/key/value vector is computed for each region from the representation $z_{(p,t)}^{(l-1)}$ encoded by the preceding block: $$\begin{equation} +q_{(p,t)}^{(l,a)}=W_{Q}^{(l,a)}z_{(p,t)}^{(l-1)}\in\mathbb{R}^{D_{h}} +\end{equation}$$ $$\begin{equation} +k_{(p,t)}^{(l,a)}=W_{K}^{(l,a)}z_{(p,t)}^{(l-1)}\in\mathbb{R}^{D_{h}} +\end{equation}$$ $$\begin{equation} +v_{(p,t)}^{(l,a)}=W_{V}^{(l,a)}z_{(p,t)}^{(l-1)}\in\mathbb{R}^{D_{h}} +\end{equation}$$ $z_{(p,t)}^{(l-1)}$ need to do layer normalization before above operations. $a=1,2...,A$ is an index over multiple attention heads and $A$ denotes the total number of attention heads. The latent dimensionality for each attention head is set to $D_{h}=D/A$. + +The variants of self-attention block include spatial attention (S), time attention (T), time attention after spatial attention (S-T), and spatial attention joint time attention (S+T). The spatial attention is to learn the spatial structure information. The time attention is to learn the different contributions of different time slices. The time attention after spatial attention is the concatenation of two operations.The spatial attention joint time attention is to do two attention simultaneously. + +In the case of spatial attention, the self-attention weights $\alpha_{(p,t)}^{(a,l)} \in \mathbb{R}^{N+1}$ for query brain region $(p,t)$ are given by: $$\begin{equation} +{\alpha_{(p,t)}^{(l,a)space}}=\sigma\left ( \frac{{q_{(p,t)}^{(l,a)}}^T}{\sqrt D_{h}} \right ) \cdot \left [ k_{(0,0)}^{(l,a)}\left \{ k_{({p}', {t}')}^{(l,a)}\right \}_{\begin{matrix} +{p}'=1,...,N +\end{matrix}}\right ], +\label{spatial} +\end{equation}$$ where ${p}'$ denotes the index of the brain regions. $\sigma$ denotes the softmax activation function. The formula is to calculate the contribution of different brain regions to emotion recognition at one specific time. + +For the temporal attention, the self-attention weights $\alpha_{(p,t)}^{(l,a)} \in \mathbb{R}^{T+1}$ for query brain region $(p,t)$ are given by: $$\begin{equation} +{\alpha_{(p,t)}^{(l,a)}}=\sigma \left ( \frac{{q_{(p,t)}^{(l,a)}}^T}{\sqrt D_{h}} \right ) \cdot \left [ k_{(0,0)}^{(l,a)}\left \{ k_{({p}', {t}')}^{(l,a)}\right \}_{\begin{matrix} {t}'=1,...,T +\end{matrix}}\right ], +\label{temporal} +\end{equation}$$ where ${t}'$ denotes the index of the time slots. It is to estimate the weights of different time slots of the same EEG electrode to a specific emotion recognition. + +Spatial-Temporal (S-T) attention is to do spatial and temporal attention one-by-one. Firstly, the spatial self-attention weights are calculated as Eq. [\[spatial\]](#spatial){reference-type="ref" reference="spatial"}. Then recalibrate the output of previous layer. After that, the the temporal attention weights are learned Eq. [\[temporal\]](#temporal){reference-type="ref" reference="temporal"} from the output of spatial attention layer. + +The spatial+temporal (S+T) attention is to do spatial and temporal attention simultaneously. The self-attention weights $\alpha_{(p,t)}^{(a,l)} \in \mathbb{R}^{NT+1}$ for query brain region $(p,t)$ are given by: $$\begin{equation} +{\alpha_{(p,t)}^{(l,a)}}=\sigma \left ( \frac{{q_{(p,t)}^{(l,a)}}^T}{\sqrt D_{h}} \right ) \cdot \left [ k_{(0,0)}^{(l,a)}\left \{ k_{({p}', {t}')}^{(l,a)}\right \}_{\begin{matrix} +{p}'=1,...,N \\ {t}'=1,...,T +\end{matrix}}\right ], +\label{DKL} +\end{equation}$$ Different from S-T Attention, which regards space and time separately, the S+T attention considers contribution of the spatial and temporal dimensions simultaneously. + +The encoding $z_{(p,t)}^{(l)}$ at block $l$ is obtained by the first computing the weighted sum of value vectors using self-attention coefficients from each attention head: $$\begin{equation} + {s_{(p,t)}^{(l,a)}}= \alpha _{(p,t),(0,0)}^{(l,a)} v_{(0,0)}^{(l,a)} + \sum_{{p}'=1}^{N} \sum_{{t}'=1}^{F}\alpha _{(p,t),({p}',{t}')}^{(l,a)}v_{({p}',{t}')}^{(l,a)}, +\end{equation}$$ Then, the concatenation of these vectors from all heads is projected and passed through an multi-layer perceptron (MLP), using residual connections after each operation: $$\begin{equation} + {{z}'}_{(p,t)}^{l} = {W_{O}^{(l-1)}} \begin{bmatrix} +s_{(p,t)}^{(l,1)}\\ . +\\ . +\\ . +\\ s_{(p,t)}^{(l,A )} + +\end{bmatrix} + z_{(p,t)}^{(l-1)}, +\end{equation}$$ where $W_{O}$ is the **Value** of $z_{(p,t)}^{(l-1)}$ by concatenating $v_{(p,t)}^{(l,a)}$. $$\begin{equation} + z_{(p,t)}^{l} = MLP({{z}'}_{(p,t)}^{l}) + {{{z}'}_{(p,t)}}^{l} +\end{equation}$$ The ${{z}'}_{(p,t)}^{l}$ goes through the MLP layer to get the output of $lth$ layer. diff --git a/2112.01525/main_diagram/main_diagram.drawio b/2112.01525/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..02dd161f12ae0af047f32c8297ba32fbd1871b56 --- /dev/null +++ b/2112.01525/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2112.01525/main_diagram/main_diagram.pdf b/2112.01525/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b26b53312da1e99de60c37663cceb36184f74a9d Binary files /dev/null and b/2112.01525/main_diagram/main_diagram.pdf differ diff --git a/2112.01525/paper_text/intro_method.md b/2112.01525/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..86f9361d205a9ae8d1f0bc96e76333ad9d15734d --- /dev/null +++ b/2112.01525/paper_text/intro_method.md @@ -0,0 +1,245 @@ +# Introduction + +Symmetry is one of the most powerful tools in the deep learning repertoire. Naturally occurring symmetries lead to structured variation in natural data, and so modeling these symmetries greatly simplifies learning [1]. A key factor behind the success of Convolutional Neural Networks [2] is their ability to capture the *translational symmetry* of image data. Similarly, PointNet [3] captures *permutation symmetry* of point-clouds. These symmetries are formalized as *invariance* or *equivariance* to a group of transformations [4]. This view is taken to model a general class of *spatial symmetries*, including 2D/3D rotations [5–7]. However, this line of research has primarily focused on real-valued data. + +We explore complex-valued data which arise naturally in 1) remote sensing such as synthetic aperture radar (SAR), medical imaging such as magnetic resonance imaging (MRI), and radio frequency communications; 2) spectral representations of real-valued data such as Fourier Transform [8,9]; and + +![](_page_0_Figure_12.jpeg) + +Figure 1. An image is a function from the domain $\mathbb{R}^2$ to the codomain $\mathbb{C}^N$ . Image transformations like rotation and translation act on the domain, mapping points in $\mathbb{R}^2$ to other points, while leaving the underlying function values intact. Previous works like [5,6] aim to produce architectures invariant to domain transformations. Co-domain transformations like color distortion or complex-valued scaling, on the other hand, act on the function values only. + +3) physics and engineering applications [10]. In deep learning, complex-valued models have shown several benefits over their real-valued counterparts: larger representational capacity [11], more robust embedding [12] and associative memory [13], more efficient multi-task learning [14], and higher quality MRI image reconstruction [15]. We approach complex-valued deep learning from a symmetry perspective: Which symmetries are inherent in complex-valued data, and how do we exploit them in modeling? + +One type of symmetry inherent to complex-valued data is complex-valued scaling ambiguity [18]. For example, consider a complex-valued MRI or SAR signal $\mathbf{z}$ . Due to the nature of signal acquisition, $\mathbf{z}$ could be subject to global magnitude scaling and phase offset represented by a complex-valued scalar s, thus becoming $s \cdot \mathbf{z}$ . + +A complex-valued classifier takes input **z** and ideally should focus on discriminating among instances from differ- + +![](_page_1_Figure_0.jpeg) + +Figure 2. Our method learns invariant features with respect to complex-scaling of the input. All examples are from CIFAR 10 with our LAB encoding, undergoing multiplication by a unit complex number. ( $\mathbf{b}$ , $\mathbf{e}$ ) tSNE embedding trajectories from DCN [16] and our model. Each color represents a different example. Embeddings form tight clusters for our model, and irregular overlapping curves for DCN. ( $\mathbf{c}$ ) Visualization of our complex-valued embedding of LAB information. The $L^*$ channel is visualized as a grayscale image, and the complex-valued $a^* + ib^*$ visualized as a color image. ( $\mathbf{d}$ ) Model confidence of the correct class for a single example. Higher confidence means larger radius. DCN predictions are highly variable, while our model is robust to complex-scaling and thus constant. ( $\mathbf{f}$ ) Accuracy under complex-scaling and color jitter. Red bars represent complex-rotations sampled from different rotation ranges. Blue bars represent color jitter (as used in [17]). Our method maintains high accuracy across complex-rotations and color jitter, whereas DCN and Real-valued CNN fail. SurReal [18] is robust, but has low overall accuracy. Our method combines high accuracy with robustness. ( $\mathbf{g}$ ) Average accuracy under different rotation ranges, comparing DCN with phase normalization (dotted blue line) and without phase normalization (solid blue line) against our method. The color encoding has a complicated phase distribution, and phase normalization fails to estimate the amount of rotation, resulting in poor accuracy. Our method is thus more suitable for complicated phase distributions. + +ent classes, not on the instance-wise variation $s \cdot \mathbf{z}$ caused by complex-valued scaling. Formally, function f is called **complex-scale invariant** if $f(s \cdot \mathbf{z}) = f(\mathbf{z})$ and called **complex-scale equivariant** if $f(s \cdot \mathbf{z}) = s \cdot f(\mathbf{z})$ . + +We distinguish two types of image transformations, viewing an image as a function defined over spatial locations. Complex-valued scaling of a complex-valued image is a transformation in the co-domain of the image function, as opposed to a spatial transformation in the domain of the image (Figure 1). Formally, $I: \mathbb{R}^D \to \mathbb{C}^K$ denotes a complex-valued image of K channels in the D-dimensional space, where $\mathbb{R}$ ( $\mathbb{C}$ ) denotes the set of real (complex) numbers. Some common (D,K) are (2,1) for grayscale images, (2,3) for RGB, and (3,6+) for diffusion tensor images. + +- 1. **Domain transformation** $T: \mathbb{R}^D \to \mathbb{R}^D$ transforms the spatial coordinates of an image, resulting in a spatially warped image I(T(x)), $x \in \mathbb{R}^D$ . Translation, rotation, and scaling are examples of domain transformations. +- 2. Co-domain transformation $T': \mathbb{C}^K \to \mathbb{C}^K$ maps the pixel value to another, resulting in a color adjusted image $T'(I(x)), x \in \mathbb{R}^D$ . Complex-valued scaling and color distortions are examples of co-domain transformations. + +Complex-valued scaling thus presents not only a practical setting but also a case study for co-domain transformations. + +Existing complex-valued deep learning methods such as DCN [16] are sensitive to complex-valued scaling (Fig 2b). A pre-processing trick to remove such scaling ambiguity is to simply normalize all the pixel values by setting their + +average phase to 0 and magnitude to 1, but this process introduces artifacts when the phase distribution varies greatly with the content of the image (Figure 2g). SurReal [18] applies manifold-valued deep learning to complex-valued data, but this framework only captures the manifold aspect and not the complex algebra of complex-valued data. Thus, a more general, principled method is needed. We propose novel layer functions for complex-valued deep learning by studying how they preserve co-domain symmetry. Specifically, we study whether each layer-wise transformation achieves equivariance or invariance to complex-valued scaling. + +Our contributions: 1) We derive complex-scaling equivariant and invariant versions of common layers used in computer vision pipelines. Our model circumvents the limitations of SurReal [18] and scales to larger models and datasets. 2) Our experiments on MSTAR, CIFAR 10, CIFAR 100, and SVHN datasets demonstrate a significant gain in generalization and robustness. 3) We introduce novel complex-valued encodings of color, demonstrating the utility of using complex-valued representations for real-valued data. Complex-scaling invariance under our *LAB* encoding automatically leads to color distortion robustness without the need for color jitter augmentation. + +# Method + +In this section, we discuss details of the architectures used in our experiments. + +**CIFARnet architectures**: For CIFARnet architectures, please refer to tables 6-10. Please note that our replication of wFM [18] uses the $(\log mag, sin\theta, cos\theta)$ encoding for the manifold values, and uses the weighted average formulation. + +MSTAR architectures: For DCN, please refer to [16], + + + +| Method | Accuracy | +|-----------------------------------|--------------------| +| Division Layer
Conjugate Layer | 67.17 66.73 | +| Euclidean Distance | 67.17 | +| Manifold Distance | 68.54 | +| GTReLU (r=0) | 67.17 | +| GTReLU (r=0.1) | 68.14 | +| GTReLU (r=1) | 49.15 | + +Table 4. Ablation test results for our Type-I model on CIFAR 10 with LAB encoding. We find that Division Layer, Manifold Distance, and a GTReLU threshold of r=0.1 perform the best. + +and for the downsampling block, see Table 12. Our SurReal replication is based on Table I in [18], and our model is based on the SurReal architecture (see Table 14). We use the same real-valued ResNet as the real-valued baseline (see Table 13). In order to pass the complex-valued features into the real-valued ResNet, we convert complex features to real-valued using the $(\log mag, sin\theta, cos\theta)$ encoding, treating each as a separate real-valued channel (resulting in 15 real-valued channels from 5 complex-valued channels). + +**CDS-Large**: For the model architecture, please see Table 11. We train this model with SGD, using momentum 0.9, weight decay constant $5 \times 10^{-4}$ , using a piece-wise linear learning rate schedule starting at 0.01, increasing to 0.2 by epoch 10, then decreasing to 0.01 by epoch 100, 0.001 by 120, 0.0001 by 150, and staying constant until 200. To ensure fair comparison, use horizontal flips and random cropping augmentation as used in [16]. All models are implemented in PyTorch [53]. + +This code uses a version of the Tangent ReLU nonlinearity originally proposed by SurReal. However, the implementation we used had flipped the phase gradient mask + + + +| Code | Forward Pass | Magnitude Gradient | Phase Thresholding gradient (value) | Phase Thresholding gradient (mask) | +|----------|--------------|--------------------|-------------------------------------|---------------------------------------| +| Original | ✓ | ✓ | ✓ | Mask = 1 − (0◦ ≤
Phase ≤ 180◦
) | +| Correct | ✓ | ✓ | ✓ | Mask = 0◦ ≤
Phase ≤ 180◦ | + +Table 5. Gradient computation bug in GTReLU computed the wrong mask for phase thresholding gradients. The values were correct, but the mask was flipped. As a result, the phase gradients for phase thresholding stage were enabled for points in the lower half of the complex plane rather than the upper half of the complex plane. + +for phase thresholding. Specifically, the forward pass was correct, and the magnitude gradients were correct, but the backward pass for the phase thresholding allowed phase gradients through for θ < 0 angles rather than θ > 0 angles. The forward pass of phase thresholding in both cases is still x+, but the backward pass mask was previously 1{x < 0} rather than 1{x ≥ 0}. See Tab. [5](#page-12-0) for details. + +Result: Surprisingly, this modification increases the accuracy for MSTAR (especially for smaller dataset sizes). The true gradient mask, in contrast, has better convergence and stability (and higher accuracy for CIFAR experiments). This difference highlights the strength of each type of phase mask. For completeness, we have included results for the original ("flipped" phase gradient mask) and the true gradient mask. + +Impact on phase manipulation: GTReLU has multiple mechanisms for controlling the phase of each input. Since the learned scaling factor (i.e., the stage before thresholding) and phase-scaling (i.e., the stage after thresholding) control the phase in a learnable manner, the model can still manipulate the input phases regardless of the phase gradient mask. This explains the small effect of the bug in most settings. + +PGM mainly decides which input phases change individually (i.e., by backpropagating individual phase gradients) and which phases only undergo global phase shift/scaling (i.e., through phase-scaling and learned scaling factor). The correct PGM allows 0 ≤ θ ≤ 180 phases to change individually while the rest undergo global shift/scaling. The flipped phase gradient mask does the opposite, allowing phases clipped to 0 to get individual gradients. + +While the exact orientation in the complex plane (e.g., 0 ≤ θ ≤ 180) is meaningless due to learned scaling and CConv layers, clipped input phases are *statistically* different from non-clipped inputs since clipping concentrates the values on the positive-real line. If the original phase was noisy (e.g., in MSTAR), the flipped PGM may eliminate the impact of noisy phase inputs on the backpropagated gradient. Our current understanding is that the increased MSTAR accuracy for the "flipped phase gradient mask" may come from this increased phase robustness. In contrast, the individual phase control offered by the correct PGM may allow for better convergence and accuracy for CIFAR models. + +Table 6. SurReal CIFAR Model Architecture + + + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|-----------------|--------------|-------|---|---|------------------------------------------------| +| Complex CONV | [3, 32, 32] | 3 × 3 | 2 | 1 | [16, 16, 16] | +| G-transport | [16, 16, 16] | - | - | - | [16, 16, 16] | +| Complex CONV | [16, 16, 16] | 3 × 3 | 2 | 1 | [32, 8, 8] | +| G-transport | [32, 8, 8] | - | - | - | [32, 8, 8] | +| Complex CONV | [32, 8, 8] | 3 × 3 | 2 | 1 | [64, 4, 4] | +| G-transport | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Distance Layer | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Average Pooling | [64, 4, 4] | 4 × 4 | - | - | [64, 1, 1] | +| FC | [64] | - | - | - | [128] | +| ReLU | [128] | - | - | - | [128] | +| FC | [128] | - | - | - | [10] | + +Table 7. DCN CIFAR Model Architecture + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|-----------------|--------------|-------|---|---|------------------------------------------------| +| Complex CONV | [3, 32, 32] | 3 × 3 | 2 | 1 | [16, 16, 16] | +| CReLU | [16, 16, 16] | - | - | - | [16, 16, 16] | +| Complex CONV | [16, 16, 16] | 3 × 3 | 2 | 1 | [32, 8, 8] | +| CReLU | [32, 8, 8] | - | - | - | [32, 8, 8] | +| Complex CONV | [32, 8, 8] | 3 × 3 | 2 | 1 | [64, 4, 4] | +| CReLU | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Average Pooling | [64, 4, 4] | 4 × 4 | - | - | [64, 1, 1] | +| Complex-to-Real | [64, 1, 1] | 4 × 4 | - | - | [128] | +| FC | [128] | - | - | - | [128] | +| ReLU | [128] | - | - | - | [128] | +| FC | [128] | - | - | - | [10] | + +Table 8. Our (Type-E) CIFAR Model Architecture + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|------------------------------|--------------|-------|---|---|------------------------------------------------| +| Econv | [3, 32, 32] | 3 × 3 | 2 | 1 | [16, 16, 16] | +| Eq. GTReLU | [16, 16, 16] | - | - | - | [16, 16, 16] | +| Econv | [16, 16, 16] | 3 × 3 | 2 | 1 | [32, 8, 8] | +| Eq. GTReLU | [32, 8, 8] | - | - | - | [32, 8, 8] | +| Econv | [32, 8, 8] | 3 × 3 | 2 | 1 | [64, 4, 4] | +| Eq. GTReLU | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Average Pooling | [64, 4, 4] | 4 × 4 | - | - | [64, 1, 1] | +| Equivariant FC | [64] | - | - | - | [128] | +| Invariant Prototype Distance | [128] | - | - | - | [10] | + +Table 9. Our (Type-I) CIFAR Model Architecture + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|--------------------|--------------|-------|---|---|------------------------------------------------| +| Econv | [3, 32, 32] | 3 × 3 | 2 | 1 | [16, 16, 16] | +| Division Layer | [16, 16, 16] | 3 × 3 | - | 1 | [16, 16, 16] | +| GTReLU | [16, 16, 16] | - | - | - | [16, 16, 16] | +| Econv | [16, 16, 16] | 3 × 3 | 2 | 1 | [32, 8, 8] | +| GTReLU | [32, 8, 8] | - | - | - | [32, 8, 8] | +| Econv | [32, 8, 8] | 3 × 3 | 2 | 1 | [64, 4, 4] | +| GTReLU | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Average Pooling | [64, 4, 4] | 4 × 4 | - | - | [64, 1, 1] | +| Equivariant FC | [64] | - | - | - | [128] | +| Prototype Distance | [128] | - | - | - | [10] | + +Table 10. 2-Channel Real-Valued CIFAR Model Architecture + + + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|-----------------|--------------|-------|---|---|------------------------------------------------| +| CONV | [3, 32, 32] | 3 × 3 | 2 | 1 | [16, 16, 16] | +| ReLU | [16, 16, 16] | - | - | - | [16, 16, 16] | +| CONV | [16, 16, 16] | 3 × 3 | 2 | 1 | [32, 8, 8] | +| ReLU | [32, 8, 8] | - | - | - | [32, 8, 8] | +| CONV | [32, 8, 8] | 3 × 3 | 2 | 1 | [64, 4, 4] | +| ReLU | [64, 4, 4] | - | - | - | [64, 4, 4] | +| Average Pooling | [64, 4, 4] | 4 × 4 | - | - | [64, 1, 1] | +| FC | [64] | - | - | - | [128] | +| ReLU | [128] | - | - | - | [128] | +| FC | [128] | - | - | - | [10] | + +Table 11. Our CDS-Large Model Architecture + + + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|--------------------------------|---------------|-------|---|---|------------------------------------------------| +| Econv | [3, 32, 32] | 3 × 3 | 1 | 1 | [64, 32, 32] | +| Conjugate Layer | [64, 32, 32] | 1 × 1 | - | - | [64, 32, 32] | +| Econv (Groups=2) | [64, 32, 32] | 3 × 3 | 1 | 1 | [64, 32, 32] | +| ComplexBatchNorm | [64, 32, 32] | - | - | - | [64, 32, 32] | +| CReLU | [64, 32, 32] | - | - | - | [64, 32, 32] | +| Econv (Groups=2) | [64, 32, 32] | 3 × 3 | 1 | 1 | [128, 32, 32] | +| ComplexBatchNorm [128, 32, 32] | | - | - | - | [128, 32, 32] | +| CReLU | [128, 32, 32] | - | - | - | [128, 32, 32] | +| Eq. MaxPool | [128, 32, 32] | 2 × 2 | - | - | [128, 16, 16] | +| ResBlock(groups=2) | [128, 16, 16] | - | - | - | [128, 16, 16] | +| Econv (Groups=4) | [128, 16, 16] | 3 × 3 | 1 | 1 | [256, 16, 16] | +| ComplexBatchNorm [256, 16, 16] | | - | - | - | [256, 16, 16] | +| CReLU | [256, 16, 16] | - | - | - | [256, 16, 16] | +| Eq. MaxPool | [256, 16, 16] | 2 × 2 | - | - | [256, 8, 8] | +| Econv (Groups=2) | [256, 8, 8] | 3 × 3 | 1 | 1 | [512, 8, 8] | +| ComplexBatchNorm | [512, 8, 8] | - | - | - | [512, 8, 8] | +| CReLU | [512, 8, 8] | - | - | - | [512, 8, 8] | +| Eq. MaxPool | [512, 8, 8] | 2 × 2 | - | - | [512, 4, 4] | +| ResBlock(groups=4) | [512, 4, 4] | - | - | - | [512, 4, 4] | +| Eq. MaxPool | [512, 4, 4] | 2 × 2 | - | - | [512, 1, 1] | +| Fully Connected | [1024] | - | - | - | [10] | + +Table 12. DCN Down-sampling Block for MSTAR + + + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|------------------|---------------|-------|---|---|------------------------------------------------| +| Complex CONV | [1, 128, 128] | 3 × 3 | 2 | 1 | [12, 64, 64] | +| ComplexBatchNorm | [12, 64, 64] | - | - | - | [12, 64, 64] | +| Complex CONV | [1, 64, 64] | 3 × 3 | 2 | 1 | [12, 32, 32] | +| ComplexBatchNorm | [12, 32, 32] | - | - | - | [12, 32, 32] | + +Table 13. MSTAR Real-valued Model Architecture + + + +| Layer Type | | | | Input Shape Kernel Stride Output Shape | +|----------------|---------------|-------|---|----------------------------------------| +| CONV | [2, 100, 100] | 5 × 5 | 1 | [30, 96, 96] | +| GroupNorm+ReLU | [30, 96, 96] | - | - | [30, 96, 96] | +| ResBlock | [30, 96, 96] | - | - | [40, 96, 96] | +| MaxPool | [40, 96, 96] | 2 × 2 | 2 | [40, 48, 48] | +| CONV | [40, 48, 48] | 5 × 5 | 3 | [50, 15, 15] | +| GroupNorm+ReLU | [50, 15, 15] | - | - | [50, 15, 15] | +| ResBlock | [50, 15, 15] | - | - | [60, 15, 15] | +| CONV | [60, 15, 15] | 2 × 2 | 1 | [70, 14, 14] | +| GroupNorm+ReLU | [70, 14, 14] | - | - | [70, 14, 14] | +| AveragePool | [70, 14, 14] | - | - | [70] | +| FC | [70] | - | - | [30] | +| ReLU | [30] | - | - | [30] | +| FC | [30] | - | - | [10] | + +Table 14. Our MSTAR Model Architecture + + + +| Layer Type | | | | | Input Shape Kernel Stride Padding Output Shape | +|------------------|---------------|-------|---|---|------------------------------------------------| +| Econv (Groups=5) | [1, 100, 100] | 5 × 5 | 1 | 0 | [5, 96, 96] | +| Eq. GTReLU | [5, 96, 96] | - | - | - | [5, 96, 96] | +| Eq. MaxPool | [5, 96, 96] | 2 × 2 | 2 | - | [5, 48, 48] | +| Econv | [5, 48, 48] | 3 × 3 | 2 | 0 | [5, 23, 23] | +| Eq. GTReLU | [5, 23, 23] | - | - | - | [5, 23, 23] | +| Division Layer | [5, 23, 23] | 3 × 3 | - | - | [5, 21, 21] | +| Complex-to-Real | [5, 21, 21] | - | - | - | [15, 21, 21] | +| CONV (Groups=5) | [15, 21, 21] | 5 × 5 | 1 | - | [30, 17, 17] | +| GroupNorm+ReLU | [30, 17, 17] | - | - | - | [30, 17, 17] | +| ResBlock | [30, 17, 17] | - | - | - | [40, 17, 17] | +| MaxPool | [40, 17, 17] | 2 × 2 | 2 | - | [40, 8, 8] | +| CONV (Groups=5) | [40, 8, 8] | 5 × 5 | 3 | - | [50, 2, 2] | +| GroupNorm+ReLU | [50, 2, 2] | - | - | - | [50, 2, 2] | +| ResBlock | [50, 2, 2] | - | - | - | [60, 2, 2] | +| CONV (Groups=5) | [60, 2, 2] | 2 × 2 | 1 | - | [70, 1, 1] | +| GroupNorm+ReLU | [70, 1, 1] | - | - | - | [70, 1, 1] | +| FC | [70] | - | - | - | [30] | +| ReLU | [30] | - | - | - | [30] | +| FC | [30] | - | - | - | [10] | diff --git a/2203.03691/main_diagram/main_diagram.drawio b/2203.03691/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..cb48a8b675e2abdc11532419d96e5aa1f0000327 --- /dev/null +++ b/2203.03691/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2203.03691/main_diagram/main_diagram.pdf b/2203.03691/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c6f06e7a8263688a982b7522d52d8e6d4f1ef6c2 Binary files /dev/null and b/2203.03691/main_diagram/main_diagram.pdf differ diff --git a/2203.03691/paper_text/intro_method.md b/2203.03691/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..d6e77492fbdac0fbf8b190d9d6e92bd21a3e6916 --- /dev/null +++ b/2203.03691/paper_text/intro_method.md @@ -0,0 +1,76 @@ +# Introduction + +Attention-based architectures, such as the Transformer [@vaswani2017attention], have accelerated the progress in many natural language understanding tasks. Part of their success is a result of a parallelizable training scheme over the input length. This improves training times and allows for larger volumes of data which makes these models amenable to pretraining [@radford2018improving; @devlin2018bert]. Therefore, many current state-of-the-art models are fine-tuned extensions of large pretrained Transformers [@bommasani2021opportunities]. + +However, these models come at a significant computational cost. They require considerable resources for pretraining and fine-tuning, which induces high energy consumption [@strubell2019energy] and limits access to research [@bommasani2021opportunities]. Subsequently, @schwartz2020green argue the need for **[Green AI]{style="color: greencolor"}**. They propose a cost evaluation of a result $R$ as following: $$\begin{equation*} +Cost (R) \propto E \cdot D \cdot H, \label{eq:cost} +\end{equation*}$$ where $E$ is the computational cost measured in floating point operations (FPO) of a single example, $D$ is the dataset size, and $H$ is the number of hyperparameter configurations required during tuning. + +
+ +
The figure outlines a general model layer consisting of a token mixing component and a feature mixing component (MLP). For token mixing, MLPMixer uses an MLP with a fixed size, maximum input length N and position-specific weights. In contrast, HyperMixer generates an appropriately sized MLP based on the variable size of the input in a position-invariant way, similar to the attention mechanism. When using attention as token mixing the whole layer is equivalent to a Transformer encoder layer.
+
+ +To achieve the cost reduction for **[Green AI]{style="color: greencolor"}**  this paper proposes a simpler alternative to Transformers. We take inspiration from the computer vision community, which has recently seen a surge of research on Multi-Layer Perceptrons (MLPs). Most prominently, MLPMixer [@tolstikhin2021mlp], which is a simple architecture based on two MLPs: one for token mixing and one for feature mixing. However, the token mixing MLP learns a *fixed-size* set of *position-specific* mappings, arguably making MLPMixer's architecture too detached from the inductive biases needed for natural language understanding, in contrast to Transformers [@henderson2020unstoppable]. + +In this paper, we propose a simple variant, [*HyperMixer*](https://www.youtube.com/watch?v=7Twnmhe948A&ab_channel=Kontor.TV) (Figure [1](#fig:model-architecture){reference-type="ref" reference="fig:model-architecture"}), which creates a token mixing MLP dynamically using hypernetworks [@ha2016hypernetworks]. This variant is more appropriate, as it learns to generate a *variable-size* set of mappings in a *position-invariant* way, similar to the attention mechanism in Transformers [@vaswani2017attention]. In contrast to Transformer's quadratic complexity, Hypermixer's complexity is linear in the input length. This makes it a competitive alternative for training on longer inputs. + +::: {#tab:my_label} + Transformer MLPMixer HyperMixer + ----------------- -------------------- ------------------ ------------------ + Complexity $\mathcal{O}(N^2)$ $\mathcal{O}(N)$ $\mathcal{O}(N)$ + Pos. invariance + Variable-length + + : Properties of models under consideration. $N$ denotes the length of the input sequence. +::: + +Empirically, we demonstrate that HyperMixer works substantially better on natural language understanding tasks than the original MLPMixer and related alternatives. In comparison to Transformers, HyperMixer achieves competitive or improved results at a substantially lower cost $Cost (R) \propto E \cdot D \cdot H$: improved inference speeds (E), especially for long inputs; favorable performance in the low-resource regime (D); and efficient tuning for hyperparameters (H). We attribute HyperMixer's success to its ability to approximate an attention-like function. Further experiments on a synthetic task demonstrate that HyperMixer learns to attend to tokens in similar pattern to the attention mechanism. + +In summary, our contributions can be enumerated as follows: + +1. A novel all-MLP model, HyperMixer, with inductive biases inspired by Transformers. (Section: [2](#sec:method){reference-type="ref" reference="sec:method"}) + +2. A performance analysis of HyperMixer against competitive alternatives on the GLUE benchmark. (Section: [4.3](#sec:results:peakperformance){reference-type="ref" reference="sec:results:peakperformance"}) + +3. A comprehensive comparison of the **[Green AI]{style="color: greencolor"}**  cost of HyperMixer and Transformers. (Sections: [4.4](#sec:results:time-per-example){reference-type="ref" reference="sec:results:time-per-example"}, [4.5](#sec:results:low-resource-performance){reference-type="ref" reference="sec:results:low-resource-performance"}, [4.6](#sec:results:ease-of-tuning){reference-type="ref" reference="sec:results:ease-of-tuning"}) + +4. An ablation demonstrating that HyperMixer learns attention patterns similar to Transformers. (Section: [4.7](#sec:results:locmix-attention){reference-type="ref" reference="sec:results:locmix-attention"}) + +# Method + +In machine learning, the inductive biases of a model reflect implicit modeling assumptions which are key to facilitate learning and improve generalization on specific tasks. In NLP, well-known models with strong inductive biases include: recurrent neural networks [@elman1990finding], which assume the input to be a sequence; and recursive neural networks [@socher2013recursive], which assume a tree-structure. While both these inductive biases are reasonable, empirically, Transformers have been more successful in recent years. Furthermore, we reiterate the arguments of @henderson2020unstoppable for inductive biases in language and apply them to our model design. + +@henderson2020unstoppable attributes the Transformer's success to two concepts: *variable binding* and *systematicity*. Variable binding refers to the model's ability to represent multiple entities at once. This is arguably challenging in single-vector representations such as recurrent neural networks. However, Transformers represent each token with its own vector which accounts for variable binding as each token can be interpreted as an entity. Systematicity refers to the models ability to learn generalizable rules that reflect the structural relationship between entities [@fodor1988connectionism]. Transformers achieve systematicity through the attention mechanism which is a learnable set of functions that determines the interaction between entities. The mechanism *modulates*, for every position in the sequence, how to functionally process any other position. Moreover, these function parameters are learnable and shared across all entities. + +A general layer of MLPMixer is shown in Figure [1](#fig:model-architecture){reference-type="ref" reference="fig:model-architecture"}. Similarly to Transformers, each token is represented as a vector of features, which undergo (non-linear) transformations in multiple layers. MLPMixer employs two MLPs at each layer, one for *feature mixing* and one for *token mixing*. The feature mixing component is applied to each token vector independently, which models the interactions between features. The token mixing component is applied to each feature independently (i.e. its vector of values across tokens), which models the interactions between spatial locations or positions. This could be interpreted as a global attention mechanism which is static and position-modulated. Practically, this is achieved by transposing the dimension representing the features and the dimension representing the positions. Each vector $\textbf{x}^T_{i} \in \mathbb{R}^{N}$, representing feature $i \leq d$, of some input of fixed length $N$, is input into MLP1, which has the following form: $$\begin{equation} + \operatorname{MLP1}(\textbf{x}^T_{i}) = \textbf{W}_1 (\sigma (\textbf{W}_2^{T} \textbf{x}^T_{i})),\label{eq:mlp1} +\end{equation}$$ where $\textbf{W}_1, \textbf{W}_2 \in \mathbb{R}^{N \times d'}$, and $\sigma$ represents the $\operatorname{GELU}$ non-linearity [@hendrycks2016gaussian]. Finally, to facilitate learning, layer normalization [@ba2016layer] and skip connections [@he2016deep] are added around each MLP, respectively. + +The token mixing MLP assumes an input of fixed dimension, which is necessary as the parameters need to be shared across all examples. However, unlike images, textual input is generally of a variable dimension. Therefore, to apply MLPMixer to texts of variable length, a simplistic approach is to assume a maximum length (e.g. the maximum in the dataset). Thereafter, all inputs are padded to the maximum length and masks are applied in the token mixing MLP. This model is able to do variable binding, since each token is represented by its own vector. However, this model lacks systematicity because the rules learned to model interactions between tokens (i.e. the MLP's weights) are not shared across positions. + +HyperMixer includes systematicity into the MLPMixer architecture through the use of hypernetworks. They are used to generate the weights $\textbf{W}_1, \textbf{W}_2$ of MLP1 (Equation [\[eq:mlp1\]](#eq:mlp1){reference-type="ref" reference="eq:mlp1"}) dynamically as a function of the input. Let $\textbf{x}_{j} \in \mathbb{R}^{d}$, $j \leq N$, where $N$ is the (variable) dimension of the input, represent token $j$. We use the following parameterized functions: + +$$\begin{equation*} + \begin{aligned} + h_1, h_2 &: \mathbb{R}^{N \times d} \rightarrow \mathbb{R}^{N \times d'}, + \end{aligned} +\end{equation*}$$ to generate $\textbf{W}_1$ and $\textbf{W}_2$, respectively. + +Theoretically, $h_1$ and $h_2$ could be any function, including sophisticated networks that consider non-linear interactions between tokens, such as the attention mechanism. However, this would defeat the purpose of our model, which is simplicity. Therefore, we choose to generate the rows of the weight matrices from each token independently via another MLP. Concretely, a hypernetwork function can be defined as + +$$\begin{equation*} + h_i(\textbf{x}) = \left( +\begin{array}{c} +\operatorname{MLP^{\textbf{W}_i}}(\textbf{x}_{1} + \textbf{p}_{1}) \\ +\vdots \\ +\operatorname{MLP^{\textbf{W}_i}}(\textbf{x}_{N} + \textbf{p}_{N}) +\end{array} +\right) \in \mathbb{R}^{N \times d'}, +\end{equation*}$$ where $\operatorname{MLP^{\textbf{W}_1}}, \operatorname{MLP^{\textbf{W}_2}} : \mathbb{R}^{d} \rightarrow \mathbb{R}^{d'}$ are themselves multi-layer perceptrons with GELU non-linearity. $\textbf{p}_j \in \mathbb{R}^{d}$ is a vector that can encode additional information such as the position. In practice, we use the same absolute position embeddings as in Transformers [@vaswani2017attention]. + +Intuitively, for each token $\textbf{x}_{j}$, $h_1$ decides which information to send to the hidden layer of $\operatorname{MLP1}$, where the information from all tokens are mixed, and $h_2$ decides for each token how to extract information from the hidden layer. Note that, even though $h_1$ and $h_2$ only consider one token at once, non-linear interactions between tokens are still modeled through the hidden layer of $\operatorname{MLP1}$. + +In order to reduce the number of parameters and operations in the model, and thereby the complexity, we found it useful to tie $h_1$ and $h_2$ as by setting $\textbf{W}_2 = \textbf{W}_1$. + +In comparison to the MLPMixer defined in Section [2.2](#sec:methods:mlpmixer){reference-type="ref" reference="sec:methods:mlpmixer"}, the use of hypernetworks overcomes two challenges. Firstly, the input no longer has to be of fixed dimensionality. The hypernetwork generates a token mixing MLP of appropriate dimension as a function of the input. Secondly, the hypernetwork models the interaction between tokens with shared weights across all positions in the input. 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Thanks to the standardized interfaces, these libraries allow for immediate experimentation with the most recent research results, practically fostering the speed of further progress in the area. While their use is seamless for countless conventional use-cases of transformer models and fine-tuning to a specific end-task [\(Devlin et al.,](#page-7-0) [2019;](#page-7-0) [Radford](#page-8-2) [and Narasimhan,](#page-8-2) [2018\)](#page-8-2), divergence from this framework requires feasible, but elaborate and complex customizations, increasing the risk of logical errors and complicating the reproducibility of experiments. A characteristic group of problems + +![](_page_0_Figure_9.jpeg) + +Figure 1: Overview of AdaptOr's objective-centric training framework: Objective 1) registers its compatible head on top of the shared model, 2) performs specific input encoding, and 3) compute loss value based on its output. A Schedule implements a specific sampling curricula and AdaptOr aggregates and propagates objectives' losses and performs optimization. + +requiring significant changes to the standard pipeline are multi-step and multi-task adaptations. + +This paper introduces the AdaptOr library, which aims to simplify the more complex training processes that their training objectives can easier describe. AdaptOr challenges the conventional *modelcentric* framework, where data and task selection are constrained by the requirements of the selected language model architecture. Instead, it introduces an *objective-centric* training pipeline, with Objective as the central abstraction of the process. + +The AdaptOr framework aims to help NLP researchers and practicioners engage in projects that include any of the following: + +• Multi-objective training: when training a language model on more than one task or data set, including languages, AdaptOr can signif- + +1 + +icantly simplify the custom code base that needs to be implemented. Even if the objective is custom, the user can avoid adjustments to other parts of the training pipeline. + +- Custom data schedule: when users need to perform dynamic data sampling, AdaptOr allows them to implement a custom Schedule (see Figure [2\)](#page-3-0), leaving the data and model adjustment logic intact. This simplifies systematic experimentation and reproducibility, and minimizes the risk of errors. +- Objectives design & evaluation: AdaptOr exposes top-level declaration of training objectives, which enables easy experimentation with custom objectives. Objective-level monitoring can provide custom behavioural insights and allows for pruning less promising experiments earlier in the lengthy training process, saving computational costs. +- Robustness evaluation: The objectivecentric paradigm provides an easy robustness estimation by evaluating on out-ofdistribution samples. In the standard *sequential* adaptation scenario, objective-centric evaluation exposes characteristic flaws of adaptation, like exposure bias or catastrophic forgetting. + +This paper is structured as follows: Section [2](#page-1-0) provides an overview of recent work demonstrating the potential of multi-objective training in domain and task adaptation. Section [2.4](#page-2-0) also describes other software frameworks applicable for similar use cases. Section [3](#page-3-1) describes the design of AdaptOr, showing the users how to confidently integrate novel objectives and schedules. In Section [4,](#page-4-0) we describe and implement a set of non-trivial, yet promising domain adaptation experiments using AdaptOr and collect their results. As AdaptOr remains under active development, we close in Section [5](#page-6-0) with an outline of the upcoming features. We welcome contributions of novel objectives and schedules. + +This section provides an overview of recent work that demonstrates the potential of multi-objective training and schedules that motivated the design of AdaptOr. Our overview consists of a nonexhaustive list of applications that AdaptOr aims + +to make more accessible for practical use and in future research. + +Multi-task training has a long history in both traditional machine learning [\(Caruana,](#page-7-1) [1997\)](#page-7-1) and in deep learning [\(Crawshaw,](#page-7-2) [2020\)](#page-7-2). This section describes examples of multi-task (i.e. multi-objective) training, outlining its benefits and potential. + +Under some circumstances, multi-task training enhances distributional robustness of neural models. [Tu et al.](#page-8-3) [\(2020\)](#page-8-3) demonstrate this on adversarial data sets, exposing common heuristic biases of the language models [\(McCoy et al.,](#page-8-4) [2019\)](#page-8-4). Enhanced model generalization can also be achieved by introducing one or more latent tasks that do not directly correspond to the end task but reflect specific desired properties of the model. One of a few studies in this direction is Sharpness-Aware Minimisation of [Foret et al.](#page-7-3) [\(2021\)](#page-7-3), performing multi-objective training on image classification using cross-entropy and a novel, sharpness-aware objective, reflecting the model's monotonicity on the local neighborhood. In context of Neural Machine Translation (NMT), [Wang and Sennrich](#page-8-5) [\(2020\)](#page-8-5) incorporate Minimum Risk Training (MRT) objective [\(Ranzato](#page-8-6) [et al.,](#page-8-6) [2016\)](#page-8-6), optimising an arbitrary sequence-level measure of outputs. In composition with the traditional token-level cross-entropy objective, MRT improves distributional robustness. + +By aggregating multiple objectives, [Xie et al.](#page-8-7) [\(2019\)](#page-8-7) show that combining sentence classification objective with maximizing representation consistency to augmented samples fosters data efficiency. + +The intuition on the benefits of multi-task training presumes that by optimizing the training by multiple cost functions, the final model is less prone to the weaknesses of a specific task [\(Col](#page-7-4)[lobert et al.,](#page-7-4) [2011\)](#page-7-4), possibly reflecting on higherlevel, task-invariant properties of language [\(Bengio](#page-7-5) [et al.,](#page-7-5) [2013\)](#page-7-5). + +Exposing a model to training samples in a systematic schedule, also referred to as a *curriculum*, can lead to an improvement of the accuracy of the final model [\(Bengio et al.,](#page-7-6) [2009\)](#page-7-6). While the positive effects of more complex schedules based on sample "difficulty" with transformers remain to be explored, multiple studies show the potential of confidence-based sampling to improve accuracy + +and generalization. Biased samples can be identified, according to model's confidence [\(Pleiss et al.,](#page-8-8) [2020;](#page-8-8) [Swayamdipta et al.,](#page-8-9) [2020\)](#page-8-9) or using Bayesian methods such as the Product of Experts [\(Hinton,](#page-7-7) [2002\)](#page-7-7). Then, they can be either eliminated [\(Bras](#page-7-8) [et al.,](#page-7-8) [2020\)](#page-7-8) or downweighted [\(Utama et al.,](#page-8-10) [2020\)](#page-8-10). + +More complex scheduling methods are applied in training NMT models. [Bengio et al.](#page-6-1) [\(2015\)](#page-6-1) use decay schedule to sample from both references and the previous outputs of a NMT model, minimizing the discrepancy between training and inference. [Zhang et al.](#page-8-11) [\(2019\)](#page-8-11) successfully use the same sampling strategy in a sequence-level objective. The results of [Lu et al.](#page-7-9) [\(2020\)](#page-7-9) underline the potential of sampling in NMT training, suggesting that the accuracy of transformers on reported MT benchmarks can be outperformed by simpler RNN models by combining objectives in decay schedule. + +Despite the reported improvements, we find that custom scheduling strategies are rarely used. We attribute this to their complicated integration into the standard training process. To foster the research and applicability of scheduling methods, AdaptOr makes the implementation of custom scheduling strategies easy, comprehensible, and reproducible. + +Objective-centric frameworks are well-suited for domain adaptation techniques, where AdaptOr provides support for combining traditional end-task objectives with unsupervised adaptation or auxiliarytask objectives in a user-selected schedule. The goal of domain adaptation is to maximize performance on a specific data domain, often denoted as the *adapted* or *target domain* [\(Saunders,](#page-8-12) [2021\)](#page-8-12). + +Perhaps the most common adaptation approach using pre-trained language models is to continue pre-training on unsupervised samples of the adapted domain [\(Luong and Manning,](#page-7-10) [2015;](#page-7-10) [Lee](#page-7-11) [et al.,](#page-7-11) [2019;](#page-7-11) [Beltagy et al.,](#page-6-2) [2019\)](#page-6-2). This approach has been successfully extended in various directions. For instance, [Gururangan et al.](#page-7-12) [\(2020\)](#page-7-12) show that adapting to a shared task on different domain can enhance accuracy of the eventual application. If supervised data is sparse, other auxiliary tasks, described earlier in Section [2.1,](#page-1-1) can be used as concurrent objectives [\(Xie et al.,](#page-8-7) [2019\)](#page-8-7). + +In cases where larger volumes of data of given task is available in a different language, adaptation using cross-lingual transfer can be considered. Pretrained language models show that cross-lingual + +transfer works well with large-data unsupervised objectives [\(Lample and Conneau,](#page-7-13) [2019\)](#page-7-13), but it can also be applied for low-resource supervised objective, such as very low-resource translation [\(Neubig](#page-8-13) [and Hu,](#page-8-13) [2018\)](#page-8-13). + +If even unsupervised target-domain data is sparse, another option is to subset arbitrary unsupervised sources to automatically identify samples of adapted domain, by applying domain classifier [\(Jiang and Zhai,](#page-7-14) [2007;](#page-7-14) [Elsahar and Gallé,](#page-7-15) [2019\)](#page-7-15). If the boundary between the training and the adapted domain is known, an auxiliary objective can minimise a discrepancy of representations between the training and possibly low-resource target domain [\(Chadha and Andreopoulos,](#page-7-16) [2018\)](#page-7-16). + +Despite the possibilities, adaptation can also introduce undesired biases. In the scope of NMT, adaptation can cause problems of "catastrophic forgetting", when the model experiences performance degradation on the originally well-performing domains [\(Saunders,](#page-8-12) [2021\)](#page-8-12), or "exposure bias", when the model overfits the non-representative specifics of the target domain, such as the artifacts of data collection [\(Ranzato et al.,](#page-8-6) [2016\)](#page-8-6). Additionally, by normalizing a single type of bias, such as lexical overlap [\(McCoy et al.,](#page-8-4) [2019\)](#page-8-4), the model might degrade its accuracy on other domains [\(Utama](#page-8-10) [et al.,](#page-8-10) [2020\)](#page-8-10). Addressing multiple biases concurrently [\(Wu et al.,](#page-8-14) [2020\)](#page-8-14) can mitigate this problem. + +AdaptOr allows the knowledgeable user to construct a reproducible and robust adaptation pipeline using native multi-objective evaluation. Covering multiple domains in separate objectives, AdaptOr can expose the above pitfalls, without the need to implement complex separate evaluation routines. + +# Method + +The *Adapters* architecture [\(Houlsby et al.,](#page-7-17) [2019\)](#page-7-17), having only a small set of parameters, might be a good fit when performing adaptation of transformer with modest hardware or data. Recently, the AdapterHub library [\(Pfeiffer et al.,](#page-8-15) [2020\)](#page-8-15) makes training and sharing of Adapters convenient. Compared to AdaptOr, AdapterHub does not provide support for more complex adaptation cases, such as using multiple objectives, scheduling, or extended evaluation. However, since both libraries build upon the HuggingFace Transformers library [\(Wolf](#page-8-1) [et al.,](#page-8-1) [2020\)](#page-8-1), their close integration is feasible. + +If the robustness of models to heuristic shortcuts [\(McCoy et al.,](#page-8-4) [2019\)](#page-8-4) is the primary goal, the + +``` +1 class ParallelSchedule(Schedule): +2 def _sample_objectives(self, split: str) -> Iterator[Objective]: +3 while True: +4 for objective in self.objectives[split].values(): +5 yield objective +``` + +Figure 2: AdaptOr provides a convenient base for implementing custom sampling schedules. ParallelSchedule in the figure demonstrates an implementation of the schedule sampling the update objectives in rotation. Further, the sampling can be easily conditioned on the state of Objectives such as the recent outputs, loss, or metrics evaluations. + +Robustness Gym library [\(Goel et al.,](#page-7-18) [2021\)](#page-7-18) provides a comprehensive evaluation over an extendable set of different kinds of heuristic biases. Robustness Gym provides much deeper evaluation compared to AdaptOr Evaluators, and could be integrated as an AdaptOr Evaluator. Unlike Robustness Gym, AdaptOr enables an evaluation of robustness also on generative tasks, with specified out-of-domain data sets. + +This section describes the structure and functions of the AdaptOr framework. We introduce its primary components bottom-up. Figure [3](#page-4-1) depicts the relations of these components and compares user interaction with the traditional model-centric pipeline. + +A LangModule instance provides a management of inputs, outputs and objective-specific model components, referred to as *heads*. Once an objective with given LangModule is instantiated, an objective-compatible model is either initialised, or given by the user (see Section [3.2\)](#page-3-2) and the parameters of this model are merged with the parameters of the previously-registered objectives. + +The merge works as follows: If no previous objective was registered, then the model of the given objective is considered a base model. The models of the second- and later-registered objectives are then merged with the base model: first, pairs of PyTorch modules of the same name in the base and the new model are identified. If the dimensions and weights of these modules match, the respective module of the newly-adding model is replaced with a module of the base model. + +In the case of pre-trained transformers, the weights of heads are initialized randomly by default, resulting in a registration of a distinct head for each objective and sharing the remaining parameters. Users can control which parameters (not) to merge by explicitly setting their respective weights + +as (non-)equal. + +It is possible to use LangModule with any Py-Torch module that uses a HuggingFace tokenizer, compatible with the given neural module. Therefore, LangModule is also suitable for other models such as recurrent networks. + +Objectives are the primary component of AdaptOr's training pipeline. Most importantly, an Objective serves two functions: sample encoding and loss computation. By implementing these and choosing the type of a model's head, AdaptOr users can define and experiment with novel training objectives. If they additionally provide an explicit definition of the Objective's model (the objective\_module attribute), the new objective does not even have to comply with common model heads; shared parameters of the given objective\_module would still be merged with the given lang\_module. + +If no objective\_module is given, the Objective will request that a LangModule assigns the Objective a module of the Objective's default compatible\_head (see Section [3.1\)](#page-3-3). + +Additionally, every Objective instance performs its own logging, evaluation, and state updates, such as its convergence, based on a valuation of given val\_evaluators, or draws a progress bar, based on the state of its sample iteration. However, the training flow is guided by a Schedule (see Section [3.3\)](#page-4-2). Objectives can implement custom data sampling, but if possible, we recommended to do so in a custom Schedule instance. + +Since data encoding is also objective-specific, Objectives expose a higher-level user interface of data inputs than other frameworks: instead of encodings, users provide an Objective with a texts\_or\_path and a labels\_or\_path containing raw texts and respective labels. AdaptOr provides an implementation of standard Objectives for sequence and token classification and sequenceto-sequence tasks. When implementing a custom + +![](_page_4_Figure_0.jpeg) + +Figure 3: A comparison of interaction with a model-centric HuggingFace Trainer (left) and objective-centric AdaptOr (right): While in model-centric approach, user resolves text processing, sampling and encoding compatible with selected model of specific objective, objective-centric approach delegates these functionalities to Objective instances. Explicit definition of Objectives and Schedule on AdaptOr's user side makes otherwise complex multi-objective and custom-schedule experiments transparent and reproducible. + +Objective, note that sampling and encoding are performance bottlenecks on current high-end GPUs. + +Schedules control the training flow through the interfaces provided by HuggingFace Transformers library. Primarily, they deliver 1) a set of standard stopping strategies based on the state of the Objectives and 2) an IterableDataset instance, constructed by sampling Objectives according to a sampling strategy implemented in its \_sample\_objectives. A Schedule also ensures that outputs of distinct lang\_modules' heads are delivered to the respective Objectives for loss computation. + +This relatively complex sampling framework provides a very simple interface for custom Schedule implementations (see Section [2.2\)](#page-1-2). For instance, a pre-defined ParallelSchedule is implemented with three lines of code (see Figure [2\)](#page-3-0). + +An Adapter is customization of the HuggingFace Trainer with only minor adjustments. Specifically, Adapter redirects loss computation to a Schedule, which further distributes outputs to corresponding Objectives and extends native training logs with logs of Objectives' Evaluators. Furthermore, Adapter adjusts persistence of the models so that a model of every head can be reloaded without the use of AdaptOr, by simply using HuggingFace Transformers' AutoModelForXY.from\_pretrained. + +Based on the actively-developed HuggingFace + +Transformers library, the AdaptOr allows its users to benefit from all other native features of Hugging-Face Transformers, such as the support for the most recent models, custom logging platforms, or distributed parallel training. Furthermore, it can simplify integration with other custom libraries (see Section [2.4\)](#page-2-0). diff --git a/2203.04115/main_diagram/main_diagram.drawio b/2203.04115/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8e6f570699a23aaae9214dd7bd633db84878de84 --- /dev/null +++ b/2203.04115/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2203.04115/main_diagram/main_diagram.pdf b/2203.04115/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e4bebb4bc50438298be6d60a911889d1d78f672d Binary files /dev/null and b/2203.04115/main_diagram/main_diagram.pdf differ diff --git a/2203.04115/paper_text/intro_method.md b/2203.04115/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..4286deda05f45033a79f22d3ccddbbbc45d5f302 --- /dev/null +++ b/2203.04115/paper_text/intro_method.md @@ -0,0 +1,121 @@ +# Introduction + +Biological sequences like proteins and DNA have a broad range of applications to several impactful problems ranging from medicine to material design. For instance, design of novel anti-microbial peptides (AMPs; short sequences of amino-acids) is crucial, and identified as the first target to tackle the growing public health risks posed by increasing anti-microbial resistance [AMR; @2022amr]. This is particularly alarming according to a recent report[^1] by the World Health Organization, which predicts millions of human lives lost per year (with the potential breakdown of healthcare systems and many more indirect deaths), unless methods to efficiently control (and possibly stop) the fast-growing AMR are found. + +Considering the diverse nature of the biological targets, modes of attack, structures, as well as the evolving nature of such problems, diversity becomes a key consideration in the design of these sequences [@mullis2019diversity]. Another reason for the importance of being able to propose a *diverse* set of good candidates is that cheap screening methods (like in-silico simulations or in-vitro experiments) may not reflect well future outcomes in animals and humans, as illustrated in Figure [1](#fig:ddpipeline){reference-type="ref" reference="fig:ddpipeline"}. To maximize the chances that at least one of the candidates will work in the end, it is important for these candidates to cover as much as possible the modes of a *goodness* function that estimates future success. The design of new biological sequences involves searching over combinatorially large discrete search spaces on the order of $\mathcal{O}(10^{60})$ candidates. Machine learning methods that can exploit the combinatorial structure in these spaces (e.g., due to laws of physics and chemistry) have the potential to speed up the design process for such biological sequences [@bayes4DD; @bayesDD2; @das2021accelerated]. + +
+ +
Illustration of a typical drug discovery pipeline. In each round, a set of candidates is proposed which are evaluated under various stages of evaluation, each measuring different properties of the candidates with varying levels of precision. The design procedure is then updated using the feedback received from the evaluation phase before the next round begins. Because the early screening phases are imperfect, and the ideal “usefulness" of the candidate can be ill-defined, it is important to generate for these phases a diverse set of candidates (rather than many similar candidates who could all fail in the downstream phases).
+
+ +The development process of such biological sequences, for a particular application, involves several rounds of a candidate ideation phase (possibly starting with a random library) followed by an evaluation phase, as shown in Figure [1](#fig:ddpipeline){reference-type="ref" reference="fig:ddpipeline"}. The evaluation consists of several stages ranging from numerical simulations to expensive wet-lab experiments, possibly culminating in clinical trials. These stages filter candidates with progressively higher fidelity oracles that measure different aspects of the *usefulness* of a candidate. For example, the typical evaluation for an antibiotic drug after ideation would comprise of: (1) in-silico screening using approximate models to estimate anti-microbial activity of $\mathcal{O}(10^6)$ candidates (2) in-vitro experiments to measure single-cell effectiveness against a target bacterium species of $\mathcal{O}(10^3)$ candidates (3) trials in small mammals like mice with $\mathcal{O}(10)$ $\,$candidates (4) randomized human trials with $\mathcal{O}(1)$ candidates. These oracles are often imperfect and do not evaluate all the required properties of a candidate. + +The biological repertoire of DNA, RNA and protein sequences is extremely diverse, to support the diversity of structure, function and modes of action exploited by living organisms, where the same high-level function can be potentially executed in more than one possible manner [@mullis2019diversity]. Moreover, the ultimate success of candidate drugs also depends on satisfying multiple often conflicting desiderata, not all of which likely can be precisely estimated in-silico. This fact, combined with the overall effect of the above aggressive filtering and use of potentially imperfect oracles, needs to be addressed in the design phase through the *diversity* of the generated candidates. Diverse candidates capturing the *modes* of the imperfect oracle improve the likelihood of discovering a candidate that can satisfy all (or many) evaluation criteria, because failure in downstream stages is likely to affect nearby candidates (from the same mode of the oracle function), while different modes are likely to correspond to qualitatively different properties. + +This setup of iteratively proposing a batch of candidates and learning from the feedback provided by an oracle on that batch fits into the framework of active learning [@aggarwal2014active]. Bayesian Optimization is a common approach for such problems [@rasmussen2005gaussian; @garnett_bayesoptbook_2022]. It relies on a Bayesian surrogate model of the usefulness function of interest (e.g., the degree of binding of a candidate drug to a target protein), with an output variable $Y$ that we can think of as a reward for a candidate $X$. An acquisition function ${\cal F}$ is defined on this surrogate model and a pool of candidates will be screened to search for candidates $x$ with a high value of ${\cal F}(x)$. That acquisition function combines the expected reward function $\mu$ (e.g., $\mu(x)$ can be the probability of obtaining a successful candidate) as well as an estimator of epistemic uncertainty $\sigma(x)$ around $\mu(x)$, to favour candidates likely to bring new information to the learner. There are many possible candidate selection procedures, from random sampling to genetic algorithms evolving a population of novel candidates [@bayes4DD; @belanger2019biological; @moss2020bayesian; @swersky20amortized; @bayesDD2]. An alternative is to use Reinforcement Learning (RL) to maximize the value of a surrogate model of the oracle [@angermueller2019model]. RL methods are designed to search for a single candidate that maximizes the oracle, which can result in poor diversity and can cause candidate generation to get *stuck* in a single mode [@bengio2021flow] of the expected reward function. Additionally, as the final goal is to find *novel* designs that are different from the ones that are already known, the generative model must be able to capture the tail ends of the data distribution. + +In settings where diversity is important, another interesting way to generate candidates is to use a generative policy that can sample candidates proportionally to a reward function (for instance, the acquisition function over a surrogate model) and can be sampled i.i.d to obtain a set of candidates that covers well the modes of the reward function. A sample covering the modes approximately but naturally satisfies the ideal criterion of high scoring and diverse candidates. GFlowNets [@bengio2021flow] provide a way to learn such a stochastic policy and, unlike Markov chain Monte Carlo (MCMC) methods (which also have this ability), amortize the cost of each new i.i.d. sample (which may require a lengthy chain, with MCMC methods) into the cost of training the generative model [@Zhang2022GenerativeFN]. As such, this paper is motivated by the observation that GFlowNets are appealing in the above Bayesian optimization context, compared with existing RL and MCMC approaches in domains such as small molecule synthesis. + +In this work, we present an active learning algorithm based on a GFlowNet generator for the task of biological sequence design. In addition to this, we propose improvements to the GFlowNet training procedure to improve performance in active learning settings. We apply our proposed approach on a broad variety of biological sequence design tasks. The key contributions of this work are summarized below: + +- An active learning algorithm with GFlowNet as the generator for designing novel biological sequences. + +- Investigating the effect of off-policy updates from a static dataset to speed up training of GFlowNets. + +- Incorporating the epistemic uncertainty in the predicted expected reward to improve exploration in GFlowNets. + +- Validating the proposed algorithm on three protein and DNA design tasks. + +We consider the problem of searching over a space of discrete objects $\mathcal{X}$ to find objects $x \in \mathcal{X}$ that maximize a given usefulness measure (oracle) $f: \mathcal{X} \mapsto \mathbb{R}^{+}$. We consider the setting where this oracle can only be queried $N$ times in fixed batches of size $b$. This constitutes $N$ rounds of evaluation available to the active learning algorithm. The algorithm also has access to an initial dataset $D_0=\{(x^0_1, y^0_1), \dots, (x^0_n, y^0_n)\}$, where $y^0_i=f(x^0_i)$ from evaluations by the oracle. + +The algorithm has to propose a new batch of candidates $\mathcal{B}_i = \{x_1^i, \dots, x_b^i\}$, given the current dataset $\mathcal{D}_i$, in each round $i\in \{1, \dots, N\}$. This batch is then evaluated on the oracle to obtain the corresponding scores for the candidates $y_j^i=f(x_j^i)$. The current dataset $\mathcal{D}_i$ is then augmented with the tuples of the proposed candidates and their scores to generate the dataset for the next round, $\mathcal{D}_{i+1} = \mathcal{D}_i \cup \{(x_1^i, y_1^i), \dots, (x_b^i, y_b^i)\}$. + +This problem setup is similar to the standard black-box optimization problem [@audet2017derivative] with one difference: the initial dataset $D_0$ is available as a starting point, which is actually a common occurrence in practice, i.e., a historical dataset. This setup can also be viewed as an extension of the Offline Model Based Optimization [@trabucco2021conservative; @trabucco2021designbench] paradigm to multiple rounds instead of a single round. + +# Method + +As discussed in Section [1](#sec:intro){reference-type="ref" reference="sec:intro"}, searching for a single candidate maximizing the oracle can be problematic in the typical scenario where the available (cheap, front-line) oracle is imperfect. Instead, we are interested in looking for a diverse set of $K$ top candidates generated by the algorithm, $\mathcal{D}_{\text{Best}}= \text{TopK}(\mathcal{D}_K \setminus \mathcal{D}_0)$. We outline the key characteristics that define the set of *ideal* candidates. + +- **Performance/Usefulness Score**: The base criteria is for the set to include high scoring candidates, which can be quantified with $$\begin{equation} + \text{Mean}(\mathcal{D})=\frac{\sum_{(x_i,y_i) \in \mathcal{D}} y_i}{|\mathcal{D}|} + \end{equation}$$ + +- **Diversity**: In addition to being high scoring, we would like the candidates to capture the modes of the oracle. One way to measure this is + + $$\begin{equation} + {\small\text{Diversity}}(\mathcal{D}) \hspace*{-1mm}=\hspace*{-1mm} \frac{\sum\limits_{(x_i,y_i)\in \mathcal{D}}\sum\limits_{(x_j,y_j)\in \mathcal{D}\setminus \{(x_i,y_i)\}}\hspace*{-1mm} d(x_i, x_j)}{|\mathcal{D}|(|\mathcal{D}| - 1) } + \end{equation}$$ where $d$ is a distance measure defined over $\mathcal{X}$. + +- **Novelty**: Since we start with an initial dataset $\mathcal{D}_0$, the proposed candidates should also be different from the candidates that are already known. We measure this *novelty* in the proposed candidates as follows: $$\begin{equation} + \text{Novelty}(\mathcal{D}) = \frac{\sum_{(x_i,y_i) \in \mathcal{D}} \min_{s_j \in \mathcal{\mathcal{D}}_0} d(x_i, s_j)}{|\mathcal{D}| } + \end{equation}$$ + +All three metrics are applied on the TopK scoring candidates, i.e., for $\mathcal{D}=\mathcal{D}_{\text{Best}}$. It is important to note that either of these metrics considered *alone* can paint a misleading picture. For instance, a method can generate diverse candidates, but these candidates might be low scoring and similar to the known candidates. Thus, a method should be evaluated holistically, considering *all* the three metrics. + +GFlowNets [@bengio2021flow; @bengio2021gflownet] tackle the problem of learning a stochastic policy $\pi$ that can sequentially construct discrete objects $x\in\mathcal{X}$ with probability $\pi(x)$ using a non-negative reward function $R:\mathcal{X}\mapsto \mathbb{R}^{+}$ defined on the space $\mathcal{X}$, such that $\pi(x) \propto R(x)$. This property makes GFlowNets well-positioned to be used as a generator of diverse candidates in an active learning setting. In this section, we present our proposed active learning algorithm based on GFlowNets [@bengio2021flow]. We only present the relevant key results, and refer the reader to @bengio2021gflownet for a thorough mathematical treatment of GFlowNets. Figure [2](#fig:al_setup){reference-type="ref" reference="fig:al_setup"} provides an overview of our proposed approach and Algorithm [\[algo:multi_round\]](#algo:multi_round){reference-type="ref" reference="algo:multi_round"} describes the details of the approach. + +
+ +
GFlowNet-AL: Our proposed approach for sequence design with GFlowNets consists of three main components: (1) GFlowNet Generator πθ (green box), which generates diverse candidates with probability proportional to R(x), which is defined by the proxy, (2) Proxy (blue) which consists of a model M that can output a mean prediction μ and uncertainty estimate σ around μ, along with an acquisition function ℱ, which combines the mean and uncertainty predicted by the model, and (3) Dataset 𝒟i (yellow) which stores all the available candidates up to round i. In each round, the model M is first trained on 𝒟i. The generative policy is then trained with reward function R = ℱ(M.μ, M.σ) and data 𝒟i. A new batch of candidates ℬi is then sampled from πθ, evaluated with the Oracle 𝒪 (red) and then added to 𝒟i to obtain 𝒟i. This process repeats for N rounds of active learning.
+
+ +::: algorithm +::: + +We assume the space $\mathcal{X}$ is *compositional*, that is, object $x\in\mathcal{X}$ can be constructed using a sequence of actions taken from a set $\mathcal{A}$. After each step, we may have a partially constructed object, which we call a state $s \in \mathcal{S}$. For example, @bengio2021flow use a GFlowNet to sequentially construct a molecule by inserting an atom or a molecule fragment in a partially constructed molecule represented by a graph. In the auto-regressive case of sequence generation, the actions could just be to append a token to a partially constructed sequence. A special action indicates that the object is complete, i.e., $s = x \in {\mathcal X}$. Each transition $s{\rightarrow}s' \in \mathcal{E}$ from state $s$ to state $s'$ corresponds to an edge in a graph $G= (\mathcal{S}, \mathcal{E})$ with the set of nodes $\mathcal{S}$ and the set of edges $\mathcal{E}$. We require the graph to be directed and acyclic, meaning that actions are constructive and cannot be undone. An object $x \in \mathcal{X}$ is constructed by starting from an initial empty state $s_0$ and applying actions sequentially, and all complete trajectories must end in a special final state $s_f$. The fully constructed objects in $\mathcal{X}\subset\mathcal{S}$ are *terminating states*. The construction of an object $x$ can thus be defined as a trajectory of states $\tau = (s_0 {\rightarrow} s_1 {\rightarrow} \dots{\rightarrow} x {\rightarrow} s_f)$, and we can define $\mathcal{T}$ as the set of all trajectories. $\text{Parent}(s) = \{s': s'{\rightarrow} s \in \mathcal{E}\}$ denotes the parents for node $s$ and $\text{Child}(s) = \{s': s{\rightarrow} s' \in \mathcal{E}\}$ denotes the children of node $s$ in $G$. + +@bengio2021gflownet define a *trajectory flow* $F:\mathcal{T}\mapsto \mathbb{R}^+$. This trajectory flow $F(\tau)$ can be interpreted as the probability mass associated with trajectory $\tau$. The *edge flow* can then be defined as $F(s{\rightarrow} s') = \sum_{s{\rightarrow} s' \in \tau}F(\tau)$, and *state flow* can be defined as $F(s) = \sum_{s \in \tau}F(\tau)$. The flow associated with the final step (transition) in the trajectory $F(x{\rightarrow} s_f)$ is called the terminal flow and the objective of training a GFlowNet is to make it approximately match a given reward function $R(x)$ on every possible $x$. + +The trajectory flow $F$ is a measure over complete trajectories $\tau \in \mathcal{T}$ and it induces a corresponding probability measure $$\begin{equation} + P(\tau) = \frac{F(\tau)}{\sum_{\tau \in \mathcal{T}}F(\tau)} = \frac{F(\tau)}{Z}, +\end{equation}$$ where $Z$ denotes the total flow, and corresponds to the partition function of the the measure $F$. The forward transition probabilities $P_F$ for each step of a trajectory can then be defined as $$\begin{equation} + P_F(s|s') = \frac{F(s{\rightarrow} s')}{F(s)}. +\end{equation}$$ We can also define the probability $P_F(s)$ of visiting a terminal state $s$ as $$\begin{equation} +P_F(s) = \frac{\sum_{\tau \in \mathcal{T}: s\in\tau}F(\tau)}{Z}. +\end{equation}$$ + +A *consistent flow* satisfies the *flow consistency equation* $\forall s \in \mathcal{S}$ defined as follows: $$\begin{equation} + \sum_{s'\in \text{Parent}(s)} F(s'{\rightarrow} s) = \sum_{s'' \in \text{Child}(s)} F(s{\rightarrow} s''). +\end{equation}$$ + +It has been shown [@bengio2021flow] that for a consistent flow $F$ with the terminal flow set as the reward, i.e., $F(x{\rightarrow} s_f) = R(x)$, a policy $\pi$ defined by the forward transition probability $\pi(s'|s) = P_F(s'|s)$ samples object $x$ with probability proportional to $R(x)$ $$\begin{equation} +\pi(x) = \frac{R(x)}{Z}. +\end{equation}$$ + +GFlowNets learn to approximate an *edge flow* $F_\theta:\mathcal{E} \mapsto \mathbb{R}^+$ defined over $G$, such that the terminal flow is equal to the reward $R(x)$ and the flow is *consistent*. This is achieved by defining a loss function whose global minimum gives rise to the consistency condition. This was first formulated [@bengio2021flow] via a temporal difference-like [@sutton2018reinforcement] learning objective, called *flow-matching*: + +$$\begin{equation} + \mathcal{L}_{FM}(s; \theta) = \left(\log \frac{\sum_{s'\in \text{Parent}(s)} F_\theta(s'{\rightarrow} s)}{\sum_{s'' \in \text{Child}(s)}F_\theta(s{\rightarrow} s'')}\right)^2. + \label{eq:fm_objective} +\end{equation}$$ + +@bengio2021flow show that given trajectories $\tau_i$ sampled from an exploratory training policy $\tilde{\pi}$ with full support, an edge flow learned by minimizing Equation [\[eq:fm_objective\]](#eq:fm_objective){reference-type="ref" reference="eq:fm_objective"} is consistent. At this point, the forward transition probability defined by this flow $P_{F_\theta}(s'|s) = \frac{F_\theta(s{\rightarrow} s')}{\sum_{s'' \in \text{Child}}F_\theta(s{\rightarrow} s'')}$ would sample objects $x$ with a probability $P_F(x)$ proportionally to their reward $R(x)$. + +In practice, the trajectories for training GFlowNets are sampled from an exploratory policy that is a mixture between the GFlowNet sampler $P_{F_\theta}$ and a uniform choice of action among those allowed in each state: $$\begin{equation} +\bar{\pi}_\theta = (1-\delta)P_{F_\theta} + \delta \cdot \text{Uniform}. +\end{equation}$$ + +This uniform policy introduces exploration preventing the training from getting stuck in one or a few modes. This is analogous to $\epsilon$-greedy exploration in reinforcement learning. + +@malkin2022trajectory present an alternative objective defined over trajectories with faster credit assignment for learning GFlowNets, called *trajectory balance*, defined as follows: + +$$\begin{equation} + \mathcal{L}_{TB} (\tau;\theta) = \left(\log \frac{Z_\theta \prod_{s{\rightarrow} s' \in \tau}P_{F_\theta}(s'|s)}{R(x)}\right)^2, + \label{eq:trajbal_objective} +\end{equation}$$ where $\log Z_\theta$ is also a learnable free parameter. This objective can improve learning speed due to more efficient credit assignment, as well as robustness to long trajectories and large vocabularies. Equation [\[eq:trajbal_objective\]](#eq:trajbal_objective){reference-type="ref" reference="eq:trajbal_objective"} is the training objective we have used in this paper. + +When generating sequences in an auto-regressive fashion (appending one token at a time), as in this paper, the mapping from trajectories to states becomes *bijective*, as there is only one path to reach a particular state $s$. The directed graph $G$ then corresponds to a directed tree. Under these conditions, the flow-matching objective is equivalent to discrete-action Soft Q-Learning [@haarnoja2017reinforcement; @buesing2019approximate] with a temperature parameter $\alpha=1$, a uniform $q_{\mathbf{a}'}$, and $\gamma=1$, which obtains $\pi(x)\propto R(x)$. While the trajectory balance objective in [\[eq:trajbal_objective\]](#eq:trajbal_objective){reference-type="eqref" reference="eq:trajbal_objective"} asymptotically reaches the same solution, our results [and that of @malkin2022trajectory] suggest it does so faster. + +::: algorithm +::: + +In our active learning setting, the reward function for the GFlowNet is obtained by training a model from a dataset $\mathcal{D} = \{(x,y)\}$ of labeled sequences with input object $x$ and observed oracle reward $y$ and we would like to make sure that the GFlowNet samples correctly in the vicinity of these $x$'s (especially those for which $y$ is larger). We can observe that the flow-matching objective (Equation [\[eq:fm_objective\]](#eq:fm_objective){reference-type="ref" reference="eq:fm_objective"}) and the trajectory balance objective (Equation [\[eq:trajbal_objective\]](#eq:trajbal_objective){reference-type="ref" reference="eq:trajbal_objective"}) are *off-policy* and *offline*. This allows us to use trajectories sampled from other policies than $\pi$ during training, so long as the overall distribution of training trajectories $\tilde{\pi}$ has full support. These trajectories can be constructed from the $x$'s in a given dataset by sampling for each of them a sequence of ancestors starting from terminal state $x$ and sampling a parent according to the backward transition probability. In the auto-regressive case studied here, there is only one possible parent for each state $s$, so we immediately recover the unique trajectory leading to $x$ from $s_0$. This provides a set of offline trajectories. + +Inspired by work in RL combining on-policy and off-policy updates [@nachum2017bridging; @guo2021text], we propose incorporating trajectories from the available dataset in the training of GFlowNets. At each training step we can augment the trajectories sampled from the current forward transition policy with trajectories constructed from examples in the dataset. Let $\gamma \in [0, 1)$ denote the proportion of offline trajectories in the GFlowNet training batch. As we vary $\gamma$ from $0$ to $1$, we move from an online setting, originally presented in [@bengio2021flow], to an offline setting where we learn exclusively from the dataset. Relying exclusively on trajectories from a dataset, however, can lead to sub-optimal solutions since the dataset is unlikely to cover $\mathcal{X}$. Algorithm [\[algo:inner_loop\]](#algo:inner_loop){reference-type="ref" reference="algo:inner_loop"} describes the proposed training procedure for GFlowNets which incorporates offline trajectories. + +We hypothesize and verify experimentally in Section [5.4.1](#sec:mixingresults){reference-type="ref" reference="sec:mixingresults"}, that mixing an empirical distribution in the form of offline trajectories can provide the following potential benefits in the context of active learning: *(1) improved learning speed*: it can improve the speed of convergence since we make sure the GFlowNet approximation is good in the vicinity of the selected interesting examples from the dataset $\mathcal{D}$ *(2) lower bound on the exploration domain*: it guarantees exploration around the examples in $\mathcal{D}$. + +Another consequence of a reward function that is learned from a finite dataset $\mathcal{D} = \{(x,y)\}$ is that there will be increasing uncertainty in the model's predictions as we move away from its training $x$'s. In the context of active learning, this uncertainty can be a strong signal to guide exploration in novel parts of the space and has been traditionally used in Bayesian optimization [@angermueller2019model; @swersky20amortized; @jain2021deup]. @bengio2021gflownet hypothesize that using information about the uncertainty of the reward function can also lead to more efficient exploration in GFlowNets. We study this hypothesis, by incorporating the model uncertainty of the reward function for training GFlowNets. + +This requires two key ingredients: (a) the reward function should be a model that can provide an uncertainty estimate on its output, and (b) an acquisition function that can combine the prediction of the reward function with its uncertainty estimates to provide a scalar score. There has been significant work in developing methods that can estimate the uncertainty in neural networks, which we employ here. In our experiments, we rely on MC Dropout [@mcdropout] and ensembles [@deepensembles] to provide epistemic uncertainty estimates. As for the acquisition function, we use Upper Confidence Bound [@ucb] and Expected Improvement [@ei]. With the experiments of Section [5.4.2](#sec:uncertaintyresults){reference-type="ref" reference="sec:uncertaintyresults"}, we study the effects of these choices and observe the improvement provided by incorporating the uncertainty estimates. diff --git a/2204.03444/main_diagram/main_diagram.drawio b/2204.03444/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..2d365ed30fdfd38f0c82596834cbdc4fe81e6a4d --- /dev/null +++ b/2204.03444/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2204.03444/main_diagram/main_diagram.pdf b/2204.03444/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..29ee912be57bf21817a7f54468dadf781a4623db Binary files /dev/null and b/2204.03444/main_diagram/main_diagram.pdf differ diff --git a/2204.03444/paper_text/intro_method.md b/2204.03444/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..19bba2c198c4c6100de60c7df798ab5c052a6a58 --- /dev/null +++ b/2204.03444/paper_text/intro_method.md @@ -0,0 +1,48 @@ +# Introduction + +The task of coarsely estimating the place where a photo was taken based on a set of previously visited locations is called *Visual (Image) Geo-localization* (VG) [37, 42, 86] or *Visual Place Recognition* (VPR) [21,44] and it is addressed using image matching and retrieval methods on a database of images of known locations. We are witnessing a rapid growth of this field of research, as demonstrated by the increasing number of publications [2,10,14,22–24,28,30,36, 37,42,44,46,57,60,72,73,76–79,81,87], but this expansion + +| | Vanilla | Resize
(80%) | Data augm.
(brightness = 2) | Pred. refinement
(nearest crop) | PCA
(2048) | CRN [37] | +|-----|---------|-----------------|--------------------------------|------------------------------------|---------------|----------| +| R@1 | 63.4 | 64.3 | 68.6 | 67.0 | 56.6 | 68.8 | + +Table 1. Example of how results can be influenced by little train or test time changes to the VG pipeline. Recall@1 for a ResNet-18 with NetVLAD trained on Pitts30k and tested on Tokyo24/7. Results are thoroughly discussed in later sections. + +is accompanied by two major limitations: + +i) A focus on single metric optimization, as it is common practice to compare results solely based on the recall on chosen datasets and ignoring other factors such as execution time, hardware requirements, and scalability. All these aspects are important constraints in the design of a real-world VG system. For instance, one might gladly accept a 5% drop in accuracy if this leads to a 90% decrease of descriptors size as the resulting reduction in memory requirements enables a better scalability. Similarly, computational time and descriptor dimensionality are crucial constraints in realtime applications, given a target hardware platform. + +ii) A lack of a standardized framework to train and test VG models. It is common practice to perform direct comparisons among off-the-shelf methods that use different setups (*e.g*., data augmentation, initialization, training dataset, *etc*.) [37, 67, 85], which can hide the improvement (or lack thereof) obtained by algorithmic changes and it does not allow to pinpoint the impact of each individual component. Table 1 shows how some simple engineering choices can have big effects on the recall metric. + +Although previous benchmarks for VPR [85] and the related task of Visual Localization [58, 65] offer interesting insights, they do not address the aforementioned issues. For these reasons, we propose a new open-source benchmark that provides researchers with an all-inclusive tool to build, train, and test a wide range of commonly used VG architectures, offering the flexibility to change each component of a geo-localization pipeline. This allows to rigorously examine how each element of the system influences the final results while providing information computed on-the-fly regarding the number of parameters, FLOPs, descriptors dimensionality, *etc*. + +Using our framework, we run numerous experiments aiming to understand which components are the most suitable for a real-world application, and derive good practices depending on the target dataset and one's hardware availability. For example, we find that ResNet-50 [29] provides a good trade-off between accuracy, FLOPs and model size, and that Visual Transformers can successfully replace the CNN backbones and achieve better geo-localization performances when trained on larger datasets. Furthermore, we observed that partial negative mining and reduced resolution yield important decrease in computations without significantly compromising the performance, or even yielding gains in some cases. + +The benchmark's software and models are hosted at . + +# Method + +This section describes the VG pipeline used in our benchmark (*cf* . Fig. 1) and our experimental setup. + +The VG task is commonly tackled using an image retrieval pipeline: given a new photo (*query*) to be geolocalized, its location is estimated by matching it to a database of geo-tagged images. A VG system is thus an algorithm that first extracts descriptors for the database images (offline) and for the query photo (online), then it applies a nearest neighbors search in the descriptor space. The orange blocks in Fig. 1 show that a VG system is built through several design choices, including network architectures, negative mining methods, and engineering aspects such as image sizes and data augmentation. All of these choices impact the behavior of the system, both in terms of performance and required resources. We propose a new benchmark to systematically investigate the impact of the components of VG systems, using the modular architecture shown in Fig. 1 as a canvas to reproduce most VG methods based on CNN backbones and to develop new models based on Visual Transformers. + +This abstract model contains several components that can be modified, both during training and test time: the backbone (Sec. 4.1); feature aggregation (Sec. 4.2); mining training examples (Sec. 4.4); image resizing (Sec. 4.6); data augmentation (Sec. 4.5). We conduct a series of tests focused individually on each of these elements, to systematically show each component's influence. Due to limited space, we only summarize here the results of some experiments, while detailed results and additional experiments on pre/post-processing methods and predictions refinement, effect of pre-training and many other aspects are provided in the Appendix. + +The code of the benchmark follows the modular structure shown in Fig. 1, where each component can be modified. We further provide scripts to download and format a number of datasets, and to train and test the models making easy to perform a large number of experiments while ensuring consistency and reproducibility of results. Our codebase allows to easily reproduce the architectures used in a wide range of works [2, 26, 37, 42, 60, 63, 71, 78] and commonly used training protocols [2, 42, 78]. More details on the software are provided in Appendix B. + +We use six highly heterogeneous datasets (see Tab. 2 and maps in Appendix A), which together cover a variety of real-world scenarios: different scales, degree of inter-image variability, different camera types. For training, we use Pitts30k [2] and Mapillary Street-Level Sequences (MSLS) [78] datasets, as they provide a small and large amount of images, respectively. While Pitts30k is very homogeneous, *i.e*. all images share the same resolution, weather conditions and camera, MSLS represents a wide range of conditions from very diverse cities. Regarding MSLS, given the lack of labels for the test set, we follow [28] and report validation recalls computed on the validation set. To assess inter-dataset robustness, we also test all models on four other datasets: Tokyo 24/7 [72], Revisited San Francisco (R-SF) [13, 40, 74], Eynsham [16] and St Lucia [47]. Further details on these datasets, such as their geographical coverage, are included in Appendix A. + +In all experiments, unless otherwise specified, we use the metric of recall@N (R@N) measuring the percentage of queries for which one of the top-N retrieved images was taken within a certain distance of the query location. We mostly focus on R@1 and, following common practice in + +| | # train/val
datab./queries | # test
datab./queries | Dataset
size | Database
type | Database img. size | Queries
type | +|------------|-------------------------------|--------------------------|-----------------|------------------|--------------------|-----------------| +| Pitts30k | 20K / 15K | 10K / 6.8K | 2.0 GB | panorama | 480×640 | panorama | +| MSLS | 934K / 514K | 19K / 11K | 56 GB | front-view | $480 \times 640$ | front-view | +| Tokyo 24/7 | 0/0 | 75K / 315 | 4.0 GB | panorama | $480 \times 640$ | phone* | +| R-SF | 0/0 | 1.05M / 598 | 36 GB | panorama | $480 \times 640$ | phone* | +| Eynsham | 0/0 | 24K / 24K | 1.2 GB | panorama | 512×384 | panorama | +| St Lucia | 0/0 | 1.5K / 1.5K | 124 MB | front-view | $480 \times 640$ | front-view | + +Table 2. **Summary of the datasets:** "panorama" means images are cropped from a 360° panorama (including undistortion); "front-view" means that only one (forward facing) view is available; "phone" means photos were collected with a smartphone. "panorama" and "front-view" images were taken with car-rooftop cameras. \* Variable resolution. + +the literature [2, 9, 10, 28, 37, 42, 52, 53, 78], use 25 meters as a distance threshold, but we also investigate how results change varying thresholds and top-N (cf. Appendix D.5). For reliability, all results are averaged over three repetitions of experiments. To avoid overloading the tables, standard deviations are shown in the Appendix, where the reported experiments are a superset of the ones in this manuscript. Training is performed until recall@5 on the validation set does not improve for 3 epochs. Given the variability in datasets size (see Tab. 2), we define an epoch as a pass over 5,000 queries. We use the Adam optimizer [38] for training, as in general it leads to faster convergence and better performance than SGD. Following the widely used training protocol defined in [2], we use a batch size of 4 triplets, where each triplet is composed of an anchor (the query), a positive and 10 negatives. Following standard practice [2, 10, 28, 42, 77, 78], at train time, the positive is selected as the nearest database image in features space among those within a 10 meters radius from the query and negative images selected from those further than 25. Due to the size of each dataset, we use full database mining when training on the Pitts 30k, and partial mining when training on the MSLS (cf. 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\ No newline at end of file diff --git a/2207.08822/main_diagram/main_diagram.pdf b/2207.08822/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f9690173c5ddcc1a5dc0910e1270f45ed7e038f9 Binary files /dev/null and b/2207.08822/main_diagram/main_diagram.pdf differ diff --git a/2207.08822/paper_text/intro_method.md b/2207.08822/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..f5f989808bdf42e3181b94d564ccccf0634d4979 --- /dev/null +++ b/2207.08822/paper_text/intro_method.md @@ -0,0 +1,136 @@ +# Introduction + +Recently, deep learning models have evolved to deliver acceptable performance in many areas such as computer vision, natural language processing, and speech recognition. However, the number of parameters and computational complexity of most deep learning applications have increased significantly. This ever-increasing computational complexity makes the deployment of deep neural networks harder on edge devices. Furthermore, the energy required to train a deep learning model increases proportionately with computational complexity. Thus, the training cost of these large and complex models also increases significantly on the high-end cloud servers. Although, quantization techniques are commonly used to accelerate *inference* of deep learning models, here we target a fully integer training pipeline *(i.e. forward propagation, back-propagation and SGD)*. Unlike recent proposals in quantized back-propagation, we directly change the number representation of floating-point values. Furthermore, we show empirically and theoretically that our proposed integer training methodology is effective in training deep learning models with *integer-only arithmetic*. + +Despite the fact that accelerated training using integer back-propagation seems intriguing, there are some challenges associated with integer gradient computation. For example, (i) gradients must be scaled correctly in order to be adapted to the limited dynamic range of the integer number (e.g. `int8`: $[-128,127]$). (ii) The numerical error of the gradient must be ***unbiased*** in order to preserve the convergence trajectory of the training algorithm. A small numerical error can be accumulated through the course of training and change the convergence behaviour. (iii) The integer training must be *distribution independent*, i.e. the training method should not depend on the distribution of the gradients, weights, or training data. + +We propose a novel integer training method designed to address aforementioned challenges (i), (ii) and (iii) simultaneously. Having a linear dynamic fixed-point mapping coupled with a non-linear inverse mapping is the key to full integer training. + +To this end, we make the following contributions: + +- A hardware-friendly integer training method is proposed based on extracting the maximum floating-point exponent of the tensors as the *scale*. We propose linear fixed-point mapping of tensors, while the corresponding inverse mapping for floating-point is non-linear. Our proposed method addresses all the previously mentioned challenges: (i) it computes the scales dynamically, and moreover the scale does not need to be adjusted in the course of training; (ii) it provides an unbiased estimation of the gradients and consequently, its convergence trajectory closely follows the floating-point version; (iii) our proposed representation mapping does not depend on the distribution of the training data, weights, or gradients. + +- We study the optimality gap of our proposed integer training algorithm and show it is analogous to its floating-point counterpart in the course of training (**Theorem 1**). Our analysis of stochastic gradient descent with our proposed fixed-point gradients shows the original floating-point optimality gap is only shifted by a negligible amount (**Remark 3**). + +- Our proposed method effectively performs all operations required in modern neural networks using *integer-only arithmetic*. For instance, the computation of linear layer, convolutional layer, batch-norm and layer-norm, residual connections and also stochastic gradient descent (including gradients, momentum, weight decay, and weight update) are all performed in integer arithmetic. To the best of our knowledge, this is the first time that *back-propagation* of a batch-norm, and the computation of stochastic gradient descent (SGD) is performed in integer arithmetic with negligible loss in the accuracy for large datasets such as ImageNet. + +The rest of this paper is structured as follows. Section [2](#sec:related-works){reference-type="ref" reference="sec:related-works"} reviews some previous works in the field of quantized back-propagation and quantifies the similarities and differences with our *representation mapping* method. Section [3](#sec:methodology){reference-type="ref" reference="sec:methodology"} discusses our integer training methodology in detail. Theoretical aspects of our integer training method on SGD are studied in Section [4](#sec:sgd-analysis){reference-type="ref" reference="sec:sgd-analysis"}. Experimental results supporting our integer training methodology and convergence theory are presented in Section [5](#sec:results){reference-type="ref" reference="sec:results"}. + +# Method + +Common quantization methods, which use division and clipping techniques (refer to Appendix [7.6](#appendix:quantization_review){reference-type="ref" reference="appendix:quantization_review"}), are inefficient for back-propagation. Therefore, we decided to go one step deeper and change the number format directly. Here, we propose a hardware-friendly *number representation mapping* using the dynamic fixed-point number format where the scale is defined *per tensor*. In this approach, each tensor can be represented by its `int8` version while it is multiplied by a shared scale, as opposed to other methods that allow multiple shared scales for different partitions of the tensor (see @darvish2020pushing [Figure 4]). One shared scale per tensor makes the computations easier in the computing hardware (e.g. CPU or GPU). However, the training algorithm diverges if the representation mapping is not executed properly. To tackle this problem, we suggest two subtle changes; (i) we propose to perform fixed-point mapping of a tensors in a ***linear*** fashion while the inverse mapping is ***non-linear*** as explained in Sections [3.1](#sec:lin-quant){reference-type="ref" reference="sec:lin-quant"} and [3.2](#sec:nonlin-dequant){reference-type="ref" reference="sec:nonlin-dequant"}. This is the key to success of our algorithm since a linear fixed-point mapping allows monotonic conversion of floating-point format to fixed-point, while a non-linear inverse mapping allows preserving information. (ii) We suggest to use stochastic rounding in the back-propagation in a way that preserves the expected value of the tensors as well as their vital statistics, such as the mean. Stochastic rounding in conjunction with representation mapping is crucial to keep the integer training loss trajectory close to its floating-point counterpart. + +
+

   
+(a)(b)
+

+
(a) Linear fixed-point mapping, (b) Non-linear inverse mapping.
+
+ +Our proposed integer training method is based on manipulating the floating-point number format. This is a very simple and effective way of converting floating-point values to fixed-point values as opposed to other commonly used methods that involve division operation. Our method essentially uses *shift* and *round* operations to convert floating-point to fixed-point format, see Figure [1](#fig:quant){reference-type="ref" reference="fig:quant"}(a). + +In this method, a tensor comprising $n$ floating-point numbers $(f_1, f_2, ... , f_n)$ is converted to a fixed-point tensor. First, as shown in Figure [1](#fig:quant){reference-type="ref" reference="fig:quant"}(a), sign, exponent, and mantissa of each floating-point number are extracted using the *unpack to integer* function. For instance, $f_1$ is unpacked to $s_1,e_1,m_1$ where $(s,e,m)$ is used to denote (*sign*, *exponent*, *mantissa*). We simply find the *maximum* element of $e_1,e_2,… e_n$ i.e. $e_{\mathrm{max}}=\max(e_1,e_2,… e_n)$ to extract the shared scale of the tensor. Subsequently, the original mantissas are shifted to the right according to the difference of their exponent and $e_\mathrm{max}$, to get the individual mantissas. For instance, $m_1$ is shifted to right by $e_\mathrm{max}-e_1$ which is denoted by $m_1 >> (e_\mathrm{max}-e_1)$. By shifting mantissas to right, we intentionally push the small values to the *sub-normal* region, where the most significant bit of mantissa is not 1 in binary format i.e. $(1)_2$, see @zuras2008ieee. Pushing the mantissas to the sub-normal region is performed to align their exponents with $e_\mathrm{max}$ and this unifies the scale for fixed-point values in a tensor. In the next step, to create `int8` mantissas, the 24-bit single floating-point mantissas (i.e. 23-bit mantissa + 1 implicit hidden bit) are further rounded to 7-bit mantissas to construct signed `int8` values i.e 7-bit unsigned integer and 1 sign bit. As an example, let us assume the shifted mantissa is $(0.01011001010101010100000)_2$, then it is going to be randomly rounded to either $(0.010110)_2$ or $(0.010111)_2$ based on a probability (Refer to [7.1](#appendix:stochastic-rounding){reference-type="ref" reference="appendix:stochastic-rounding"} for details). + +Our proposed fixed-point mapping method is performed by pushing mantissa values to the sub-normal region and rounding them stochastically. Hence, the inverse mapping must be performed using a floating-point alignment module that normalizes the mantissas. A *normalized* floating-point mantissa is required to start with the most significant bit $(1)_2$. For instance, an alignment module converts $2^{127} \times (0.0101)_2$ to $2^{127-2} \times (1.0100)_2$. The alignment module is a very well-known logic circuit that is available in the commodity hardware using Leading Zero Anticipator (LZA) block [@schmookler2001lza]. Coupling a linear fixed-point mapping with a non-linear inverse mapping in this way keeps the number format close to floating-point. Moreover, combining them with stochastic rounding, results in having unbiased fixed-point variant that forces the trajectory of the integer training closely follow the floating-point counterpart. + +Figure [1](#fig:quant){reference-type="ref" reference="fig:quant"}(b) shows the inverse mapping unit. This unit receives a tensor of mantissas and a single value of shared exponent computed by an integer layer. Then a tensor of exponents is reconstructed by repeating the value of the shared exponent. The size of this tensor is equal to the mantissa tensor's size. Moreover, the mantissa tensor is rounded and its sign is extracted. Note that the rounding is used here because the output of the previous layer might have some excessive mantissa bits; this normally happens in matrix multiplication and convolution operations. Then the sign, exponent, and mantissa tensors are sent to an alignment unit that shifts the mantissa and adjust the exponent in order to normalize the floating-point number format [@zuras2008ieee]. Finally, the result is packed and carried out to the next layer. + +When the fixed-point mapping of floating-point values is completed, the fixed-point values are used to perform the *integer-only* layer computations. For instance, let us consider a linear layer which consists of a General Matrix Multiplication (GEMM) operation, but, the idea can be generalized to other types of layers. Figure [2](#fig:mixed){reference-type="ref" reference="fig:mixed"} demonstrates the layer-wise integer computations of a linear layer where the shared exponents and integer mantissas are treated separately. As shown in the Figure [2](#fig:mixed){reference-type="ref" reference="fig:mixed"}, in order to perform an integer-only GEMM operation, we need to multiply scales $(2^{e_\mathrm{max1}} \times 2^{e_\mathrm{max2}} )$. This multiplication performs an integer addition operation on the exponents $(e_\mathrm{max1}+e_\mathrm{max2})$. Furthermore, the integer mantissas are sent to an integer GEMM module to compute the output mantissa tensor. Also note that in our implementation, when the mantissa tensor is in `int8` format, multiplication is in `int16` format and accumulation is in `int32` format. + +
+

  

+
A fully integer linear layer.
+
+ +Here we provide an intuition of how our proposed representation mapping approach works. Let us denote ${A}$ as a tensor and ${\hat{A}}$ as its fixed-point version in the way that is introduced earlier. In addition, random variables $A_i$ and $\hat{A}_i$ are $i^\text{th}$ element of those tensors. Also note that $A_i$ and $\hat{A}_i$ are different *representations* of the same real number, but one is in floating-point format and the other is in dynamic fixed-point format. Thus, one can relate $A_i$ and $\hat{A}_i$ with a random error term $\delta_i$ as $\hat A_i = {A}_i + \delta_i$ . Since in our training method we used stochastic rounding [@connolly2021stochastic], $\mathbb{E}{\{\hat{A}_i\}} = A_i$, or equivalently $\mathbb{E}{\{\delta_i\}} = 0$ (see Appendix [7.1](#appendix:stochastic-rounding){reference-type="ref" reference="appendix:stochastic-rounding"}). In other words, the fixed-point value is on the average equal to the floating-point value. Note that here we consider single precision floating-point values as a surrogate of real values. + +**Linear and convolutional layers:** For these two types of layers, both forward and backward propagation computations are based on inner products. Thus, it is easy to see that our proposed fixed-point inner product $\hat{C}_{ij}= \sum_{k} \hat{A}_{ik}\hat{B}_{kj}$ is an unbiased estimator of the floating-point inner product $$\begin{equation} + \mathbb{E} \{\hat{C}_{ij}\}= \mathbb{E} \left\{\sum_k \hat{A}_{ik}\hat{B}_{kj} \right\} = \mathbb{E} \left\{\sum_k ({A}_{ik} + \delta^A_{ik}) ({B}_{kj} + \delta^B_{kj}) \right\} = \sum_k {A}_{ik}{B}_{kj} = {C}_{ij} .\ +\end{equation}$$ + +**Residual connections:** A residual connection involves element-wise addition of two tensors. Each fixed-point element $\hat{C}_{ij}= \hat{A}_{ij} + \hat{B}_{ij}$ is an unbiased estimator of its floating-point version $$\begin{equation} + \mathbb{E} \{\hat{C}_{ij}\}= \mathbb{E} \{ \hat{A}_{ij} + \hat{B}_{ij}\} = \mathbb{E} \{ ({A}_{ij} + \delta^A_{ij})+({B}_{ij} + \delta^B_{ij})\} = {A}_{ij}+{B}_{ij} = {C}_{ij} .\ +\end{equation}$$ + +**Batch-norm:** A batch-norm layer is defined as $$\begin{equation} + \hat{\omega}\frac{{\hat A} - \hat\mu }{\sqrt{\hat\sigma^2 + \epsilon}} + \hat{\beta}\text{.}\ + \label{eq:batchnorm} +\end{equation}$$ For this type of layer, it is important to compute signal statistics such as mean $\hat\mu$ and variance $\hat\sigma^2$ correctly in integer arithmetic. Thus, the fixed-point mean $\hat{\mu}$ is also an unbiased estimator of the true mean $\mu$ $$\begin{equation} + \mathbb{E} \left\{\hat{\mu} \right\}= \mathbb{E} \left\{ \frac{\sum_{i=1}^N \hat{A}_i}{N} \right\} = \frac{1}{N} \mathbb{E} \left\{ \sum_{i=1}^N ({A}_i+\delta_i) \right\}=\mu.\ +\end{equation}$$ Then the following derivation holds for fixed-point variance $\hat{\sigma}^2$ $$\begin{equation} +\begin{gathered} + \mathbb{E} \{{{\hat\sigma}^2}\}= \mathbb{E} \left\{ \frac{\sum_{i=1}^N (\hat{A}_i - \hat \mu)^2}{N} \right\} = + %\frac{1}{N} \mathbb{E} \{ \sum_{i=1}^N ({a}_i + \delta_i - \mu)^2\}=\ + %\frac{1}{N} \mathbb{E} \{ \sum_{i=1}^N ({a}_i^2 + \delta_i^2 + \mu^2 + 2a_i\delta_i -2\delta_i\mu_i - 2 a_i\mu)\} \\ + \frac{1}{N} \mathbb{E} \left\{ \sum_{i=1}^N [({A}_i - \mu)^2\ + \delta_i^2] \right\} = \sigma^2 + \sigma_\delta^2 ,\ +\end{gathered} +\end{equation}$$ where $\sigma^2$ is the true floating-point variance of the batch and $\sigma_\delta^2$ is the variance of the noise introduced by the linear mapping to fixed-point. Note that the error variance $\sigma_\delta^2$ is rather small and can be integrated to $\epsilon$ in the denominator of equation [\[eq:batchnorm\]](#eq:batchnorm){reference-type="eqref" reference="eq:batchnorm"}. + +The generic equation of weight update in the $k^\text{th}$ iteration of SGD is $$\begin{equation} + {w}_{k+1}= {w}_{k} + \alpha_k g(w_k,\xi_k),\ + \label{eq:sgd} +\end{equation}$$ where $g(w_k,\xi_k)$ is the estimated gradients of random samples of the batch generated by the seed $\xi_k$, and $\alpha_k$ is the learning rate. We make the following common assumption in the sequel. + +**Assumption 1 (Lipschitz-continuity).** The loss function $\mathcal{L}(w)$ is continuously differentiable and its gradients $\nabla \mathcal{L}(w)$ satisfies the following inequality where $L > 0$ is the Lipchitz constant $$\begin{equation} + \mathcal{L}(w) \leqslant \mathcal{L}(\bar w) + \nabla \mathcal{L}(\bar w)^\top(w-\bar w) + \frac{1}{2}L|| w- \bar w||^2_2; ~~~ ~~~ \forall ~ w,\bar w \in \mathbb{R}^d.\ +\end{equation}$$ **Assumption 2.** (i) $\mathcal{L}(w_k)$ is bounded. (ii) Estimated gradients $g(w_k,\xi_k)$ is an unbiased estimator of the true gradients of the loss function $$\nabla \mathcal{L}( w_k)^\top \mathbb{E}_{\xi_k}\{ g(w_k,\xi_k) \} = ||\nabla \mathcal{L}(w_k)||^2_2 = ||\mathbb{E}_{\xi_k}\{ g(w_k,\xi_k) \}||^2_2,$$ and (iii,a) there exist scalars $M \geqslant 0$ and $M_V \geqslant 0$ such that for all iterations of SGD $\mathbb{V}_{\xi_k} \{g(w_k,\xi_k)\} \leqslant M + M_V || \nabla \mathcal{L}( w_k)||^2_2$ . + +Note that here we define $$\mathbb{V}_{\xi_k} \{g(w_k,\xi_k)\} := \mathbb{E}_{\xi_k} \{||g(w_k,\xi_k)||^2_2\} - ||\mathbb{E}_{\xi_k} \{g(w_k,\xi_k)\} ||^2_2.$$ + +Also from *Assumption 2. (ii) and (iii,a)*, the second moment bound can be derived $$\begin{equation} + \mathbb{E}_{\xi_k} \{||g(w_k,\xi_k)||^2_2\} \leqslant M + M_G || \nabla \mathcal{L}( w_k)||^2_2 \; ~~~~~ \text{with}~~ M_G := 1+M_V. +\end{equation}$$ + +**Effect of gradient variance on convergence:** The quality of the estimated gradients $g(w_k,\xi_k)$ directly affects the convergence of the SGD. The effect of first and second moments of gradient are already studied on *real numbers* in the literature. + +**Lemma 1.** Suppose *Assumption 2* is true, then we have $$\begin{equation} + \mathbb{E}_{\xi_k}\{\mathcal{L}(w_{k+1})\} - \mathcal{L}( w_k)\leqslant -(1-\frac{1}{2}\alpha_kLM_G)\alpha_k ||\nabla \mathcal{L}( w_k)||^2_2 + \frac{1}{2} \alpha^2_k LM. \\ +\label{eq:sgd-moments} +\end{equation}$$ ***Proof:*** See @bottou2018optimization [Lemma 4.4]. + +Inequality [\[eq:sgd-moments\]](#eq:sgd-moments){reference-type="eqref" reference="eq:sgd-moments"} shows the effect of gradient variance bounds, $M$ and $M_G$, on each iterate of SGD, and shows the greater the variance, the more deterioration in the quality of SGD steps. The first term, $-(1-\frac{1}{2}\alpha_kLM_G)\alpha_k ||\nabla \mathcal{L}( w_k)||^2_2$ contributes to the decrease of the loss function while the second term, $\frac{1}{2} \alpha^2_k LM$, prevents it. Upon choosing the correct gradient estimates, the right hand side of inequality [\[eq:sgd-moments\]](#eq:sgd-moments){reference-type="eqref" reference="eq:sgd-moments"} is bounded by a deterministic quantity, and asymptotically ensures sufficient descent of the loss $\mathcal{L}(w)$. Note that the expectation $\mathbb{E}_{\xi_k}$ is taken over random samples with seed $\xi_k$. + +When performing representation mapping in the back-propagation, the quality of the gradients deteriorate. Thus, there is a need to consider the effect of fixed-point mapping variance. Then, *Assumption 2 (iii,a)* should be modified accordingly. + +**Remark 1.** Note that *Assumption 2 (i), (ii)* still hold after fixed-point mapping because of stochastic rounding, i.e. the fixed-point gradient remains an unbiased estimator of gradient (refer to Appendix [7.1](#appendix:stochastic-rounding){reference-type="ref" reference="appendix:stochastic-rounding"}). + +**Assumption 2 (iii,b).** When the gradients are in fixed-point format i.e. $\hat{g}(w_k,\xi_k)$, there exist scalars $M \geqslant 0$ , $M_V \geqslant 0$, $M^q \geqslant 0$ and $M^q_V \geqslant 0$ such that for all iterations of SGD $$\mathbb{V}_{\xi_k} \{\hat{g}(w_k,\xi_k)\} \leqslant M + M^q + (M_V+ M^q_V )|| \nabla \mathcal{L}( w_k)||^2_2 .$$ + +If $\Tilde M := M + M^q$ and $\Tilde M_V := M_V+ M^q_V$ , then *Assumption 2 (iii,b)* takes the exact form of *Assumption 2 (iii,a)* i.e. $\mathbb{V}_{\xi_k} \{g(w_k,\xi_k)\} \leqslant \Tilde M + \Tilde M_V || \nabla \mathcal{L}( w_k)||^2_2$. However, here we separated $M^q$ and $M_V^q$ to emphasize the effect of fixed-point mapping on the true gradients. + +**Remark 2.** If *Assumption 2 (iii,b)* holds true, inequality [\[eq:sgd-moments\]](#eq:sgd-moments){reference-type="eqref" reference="eq:sgd-moments"} can be transformed to its fixed-point version $$\begin{equation} +\begin{gathered} + \mathbb{E}_{\xi_k}\{\mathcal{L}(w_{k+1})\} - \mathcal{L}( w_k)\leqslant -(1-\frac{1}{2}\alpha_kL(M_G+M^q_G))\alpha_k ||\nabla \mathcal{L}(w_k)||^2_2 + \frac{1}{2} \alpha^2_k L(M+M^q) \\ + \text{with}~~ M^q_G := 1+M^q_V. +\end{gathered} +\label{eq:sgd-moments-q} +\end{equation}$$ Inequality [\[eq:sgd-moments-q\]](#eq:sgd-moments-q){reference-type="eqref" reference="eq:sgd-moments-q"} shows the effect of ***added*** representation mapping variance with bounds, $M^q$ and $M^q_G$, on each iterate of SGD. This observation shows that fixed-point mapping degrades the convergence of SGD unless its variance bounds are relatively small, or controlled by the learning rate. As an example, refer to Appendix [7.2](#appendix:lin-var-q){reference-type="ref" reference="appendix:lin-var-q"} for analytical derivations of $M^q$ and $M^q_G$ for the back-propagation of a linear layer involving a fixed-point inner product. + +**Assumption 3 (Strong convexity).** The loss function $\mathcal{L}(w)$ is differentiable and strongly convex. We recall that strong convexity for differentiable functions is equivalent to the following inequality with some constant $c > 0$ $$\begin{equation} + \mathcal{L}(w) \geqslant \mathcal{L}(\bar w) + \nabla \mathcal{L}(\bar w)^\top(w-\bar w) + \frac{1}{2}c|| w- \bar w||^2_2; ~~~ ~~~ \forall ~ w,\bar w \in \mathbb{R}^d.\ + \label{eq:convex-1} +\end{equation}$$ A strongly convex function has a unique minimum point at $w_*$ with the loss value $\mathcal{L}_* = \mathcal{L}(w_*)$. + +**Theorem 1.** Suppose *Assumptions 1, 2(i), 2(ii), 2 (iii,b), 3* are all true, then a SGD method running with fixed-point gradients i.e. $\hat g(w_k,\xi_k)$ and a fixed learning rate $0<\bar\alpha \leqslant \frac{1}{L(M_G+M_G^q)}$ satisfies the following bound for its optimality gap with the minimum loss $\mathcal{L}_*$ at the $k^\text{th}$ iteration $$\begin{align} +%\begin{gathered} + \mathbb{E}\{\mathcal{L}(w_{k}) - \mathcal{L_*} \} + \leqslant& \frac{\bar\alpha L(M+M^q)}{2c} + (1-\bar \alpha c)^{(k-1)} \left(\mathcal{L}(w_1)-\mathcal{L}_*-\frac{\bar \alpha L(M+M^q)}{2c} \right) \nonumber\\ + \xrightarrow{k\xrightarrow{}\infty} &\frac{\bar\alpha L(M+M^q)}{2c}.\ +%\end{gathered} +\label{eq:sgd-convex} +\end{align}$$ ***Proof.*** See Appendix [7.3](#appendix:proof-sgd-q){reference-type="ref" reference="appendix:proof-sgd-q"}. + +**Remark 3.** Note that when ${k\xrightarrow{}\infty}$, $\frac{\bar\alpha LM}{2c}$ is the original optimality gap (i.e. $\mathbb{E}\{\mathcal{L}(w_{k}) - \mathcal{L_*} \}$) with *real* gradients, see @bottou2018optimization [Theorem 4.6], and then, the optimality gap is also increased by $\frac{\bar\alpha LM^q}{2c}$ due to fixed-point representation. Here we argue that by keeping the variance bound $M^q$ relatively small via choosing the correct number format, we can theoretically achieve the original performance. On the other hand, optimality gap is related to $\bar\alpha$, which means smaller learning rates leads to smaller optimality gap. + +**Remark 4 (Local convexity).** Strongly convex loss is not a realistic assumption in large deep learning models. However, local convexity is a more realistic assumption i.e. $\mathcal{L}$ is convex around the minimum point ${w}_*$. Hence, inequality [\[eq:sgd-convex\]](#eq:sgd-convex){reference-type="eqref" reference="eq:sgd-convex"} still holds around the minimum point ${w}_*$ of a locally convex loss. We essentially train our ImageNet ResNet18 classification in the same way: first we train the network with a fixed learning rate until reaching a certain optimality gap; then the learning rate is reduced to a smaller fixed value in order to shrink the optimality gap. + +
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(a) Floating-point loss landscape, (b) Fixed-point int8 loss landscape c) Training loss trajectory comparison. Integer training setup: int8 linear layer, int8 convolutional layer and int8 batch-norm layer.
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+ +**Empirical evidence:** Figure [3](#fig:loss){reference-type="ref" reference="fig:loss"}(a) demonstrates the locally convex loss landscape of ResNet18 training on CIFAR10 dataset using floating-point computation. Figure [3](#fig:loss){reference-type="ref" reference="fig:loss"}(b) shows the same loss landscape in the fixed-point `int8` format. We perturbed the weights around the ${w_*}$ in $x$ and $y$ axes using Gaussian noise and evaluated the loss $\mathcal{L}$ in the $z$ axis to acquire Figures [3](#fig:loss){reference-type="ref" reference="fig:loss"}(a) and [3](#fig:loss){reference-type="ref" reference="fig:loss"}(b). Comparing these two figures pronounces our assumption of local convexity in both integer and floating-point tests. Figure [3](#fig:loss){reference-type="ref" reference="fig:loss"}(c) shows a comparison of the loss trajectory for floating-point and integer training. The fixed-point gradients are unbiased estimators of the true gradients, so the trajectory of the integer training closely follows the trajectory of its floating-point counterpart. + +**Remark 5.** We implemented an integer weight update, hence, the computations of equation [\[eq:sgd\]](#eq:sgd){reference-type="eqref" reference="eq:sgd"} is also performed in integer arithmetic. It is shown in Appendix [7.4](#appendix:wu){reference-type="ref" reference="appendix:wu"} that integer weight update with stochastic rounding is an unbiased estimator of the true weight update. diff --git a/2209.06203/main_diagram/main_diagram.drawio b/2209.06203/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..6f1b032ab7eac493856127256d5404de491c547a --- /dev/null +++ b/2209.06203/main_diagram/main_diagram.drawio @@ -0,0 +1,452 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2209.06203/main_diagram/main_diagram.pdf b/2209.06203/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1fc7876da1ea6585c5693e5224536dfc8e9117fc Binary files /dev/null and b/2209.06203/main_diagram/main_diagram.pdf differ diff --git a/2209.06203/paper_text/intro_method.md b/2209.06203/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..1615655f1e8cadf4077755cabd4dd44794c8bc59 --- /dev/null +++ b/2209.06203/paper_text/intro_method.md @@ -0,0 +1,178 @@ +# Introduction + +Causal inference increasingly makes use of machine learning methods to estimate treatment effects from observational data [e.g., @van2011targeted; @kunzel2019metalearners; @curth2021nonparametric; @kennedy2022semiparametric]. This is relevant for various fields including medicine [e.g., @bica2021real], marketing [e.g., @yang2020targeting], and policy-making [e.g., @huenermund2021causal]. Here, causal inference from observational data promises great value, especially when experiments for determining treatment effects are costly or even unethical. + +The vast majority of the machine learning methods for causal inference estimate *averaged* quantities expressed by the (conditional) mean of potential outcomes. Examples of such quantities are the average treatment effect (ATE) [e.g., @shi2019adapting; @hatt2021estimating], the conditional average treatment effect (CATE) [e.g., @shalit2017estimating; @hassanpour2019learning; @zhang2020learning], and treatment-response curves [e.g., @bica2020estimating; @nie2021vcnet]. Importantly, these estimates only describe averages *without* distributional properties. + +However, making decisions based on averaged causal quantities can be misleading and, in some applications, even dangerous [@spiegelhalter2017risk; @van2019communicating]. On the one hand, if potential outcomes have different variances or number of modes, relying on the average quantities provides incomplete information about potential outcomes, and may inadvertently lead to local -- and not global -- optima during decision-making. On the other hand, distributional knowledge is needed to account for uncertainty in potential outcomes and thus informs how likely a certain outcome is. For example, in medicine, knowing the distribution of potential outcomes is highly important [@gische2021beyond]: it gives the probability that the potential outcome lies in a desired range, and thus defines the probability of treatment success or failure.[^1] Motivated by this, we aim to estimate the ***density*** of potential outcomes. + +
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$$\begin{align*} + X := & U_X; \quad U_X \sim \text{Mixture}\big(0.5 N(0, 1) + 0.5 N(b, 1) \big) \\ + \pi(x) & = \frac{N(X; 0, 1)}{N(X; 0, 1) + N(X; b, 1)} \\ + A := & \begin{cases} + 1, & -U_A < \log \big( \pi(x) / (1 - \pi(x))\big)\\ + 0, & \text{otherwise} + \end{cases}; U_A \sim \text{Logistic}(0, 1) \\ + Y := & U_Y + \begin{cases} + X^2 -1.82 X + 2, & A = 1 \\ + 2.18 X + 1.5, & A = 0 \end{cases}; \quad U_Y \sim N(0, 1) +\end{align*}$$

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Motivating example showing the densities of observational, interventional, and counterfactual distributions of outcome Y. These are simulated via the structural causal model on the right (here: N(x; μ, σ2) are densities of the normal distribution; and b = 3 is a covariates shift, which regulates the probability of treatment assignment). Potential outcomes have different distributions but the same mean 𝔼(Y[0]) = 𝔼(Y[1]) ≈ 4.77 and the same variance var (Y[0]) = var (Y[1]) ≈ 4.06. Here, Y[a] is the potential outcome given treatment a. (a) Interventional distributions. (b) and (c) Observational and counterfactual distributions for the same outcomes. As shown here, the observational, interventional, and counterfactual distributions can be substantially different.
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+ +An example highlighting the need for estimating the ***density*** of potential outcomes is shown in Fig. [1](#fig:cond-inter-counter){reference-type="ref" reference="fig:cond-inter-counter"}. Here, we simulated outcomes according to a given structural causal model (SCM). The potential outcomes $Y[a]$ can be sampled by setting the binary treatment to a specific value in the equation for $Y$ (cf. Appendix [8](#app:background){reference-type="ref" reference="app:background"}). At the same time, by filtering for only the (un)treated population and applying the same equation with a counterfactual treatment, we obtain counterfactual outcomes $Y[a] \mid A = a'$. We observe that the potential outcomes have the same mean (i.e., $\mathbb{E}(Y[0]) = \mathbb{E}(Y[1])$) and the same variance (i.e., $\operatorname{var}(Y[0]) = \operatorname{var}(Y[1])$). Hence, the ground-truth ATE equals zero. Nevertheless, the distributions of potential outcomes (i. e., $\mathbb{P}(Y[a])$) are clearly different. Hence, in medical practice, acting upon the ATE without knowledge of the distributions of potential outcomes could have severe, negative effects. To show this, let us consider a "do nothing" treatment ($a=0$) and some medical treatment ($a=1$). Further, let us consider an outcome to be successful if some risk score $Y$ is below the threshold of five. Then, the probability of treatment success (i. e., $\mathbb{P}(Y[1] < 5.0) \approx 0.63$) is much larger than the probability of success after the "do nothing" treatment (i. e., $\mathbb{P}(Y[0] < 5.0) \approx 0.51$), highlighting the importance of treatment. + +In this paper, we aim to estimate the ***density*** of potential outcomes after intervention $a$, i. e., $\mathbb{P}(Y[a] = y)$. From this point on, we refer to this task as ***interventional density estimation*** (IDE). Estimating the density of interventions has several crucial advantages: it allows to identify multi-modalities in the distribution of potential outcomes; it allows to estimate quantiles of the distribution; and it allows to compute the probability with which a potential outcome lies in a certain range. Importantly, traditional density estimation methods are **not** applicable for IDE due to the fundamental problem of causal inference: that is, the counterfactual outcomes are typically never observed, and, hence, the sample from ground-truth interventional distribution is also inaccessible. Efficient IDE is also significantly more challenging than an efficient estimation of the averaged causal quantities. The reason is that density is a functional, infinitely-dimensional target estimand, and, hence, standard efficiency theory is **not** applicable. + +:::: table* +::: center +::: +:::: + +Existing literature offers either *semi- or non-parametric* methods for IDE.[^2] Examples are kernel density estimation [@kim2018causal] and kernel mean embeddings of distributions [@muandet2021counterfactual]. However, both methods have a crucial limitation: estimated densities could be unnormalized or even return negative values (which, by definition, is not possible). Furthermore, both methods neither scale well with the sample size nor with the dimensionality of covariates. As a remedy, @kennedy2021semiparametric introduced a theory for efficient *semi-parametric* IDE estimation, rendering fully-parametric modeling possible. However, the authors did not provide a proper, flexible instantiation of the theory: the solutions proposed in [@kennedy2021semiparametric] are either (i) non-universal (e. g., limited to the exponential family) or (ii) not proper density estimators (e. g., the truncated series estimator). + +Here, we propose a *proper fully-parametric* method. Different from semi- and non-parametric methods, our fully-parametric method has several practical advantages: it automatically provides properly normalized density estimators, it allows one to sample from the estimated density, and it generally scales well with large and high-dimensional datasets. However, to the best of our knowledge, there is no fully-parametric, deep learning method for IDE. To achieve this, we later make a non-trivial extension of the theoretical results for semi-parametric IDE estimation from [@kennedy2021semiparametric] adopted to fully-parametric IDE estimation. + +In this paper, we develop a novel, fully-parametric deep learning method: ***Interventional Normalizing Flows*** (INFs). Our INFs build upon normalizing flows (NFs) [@tabak2010density; @rezende2015variational], but which we carefully adapt for causal inference. This requires several non-trivial adaptations. Specifically, we combine two NFs: a (i) nuisance flow for estimating nuisance parameters, and a (ii) target flow for a parametric estimation of the density of potential outcomes. Here, we construct a novel, tractable optimization objective based on a one-step bias correction to allow for efficient and doubly robust estimation. In the end, we develop a two-step training procedure to train both the nuisance and the target flows. + +Overall, our **main contributions** are following:[^3] + +1. We introduce the first proper fully-parametric, deep learning method for interventional density estimation, called *Interventional Normalizing Flows*(INFs). Our INFs provide a properly normalized density estimator. + +2. We extend the results of [@kennedy2021semiparametric] and derive a tractable optimization problem with a one-step bias correction for efficient and doubly robust estimation. This allows for an effective two-step training procedure with our INFs. + +3. We demonstrate in various experiments that our INFs are highly expressive and effective. A major advantage owed to the parametric form of the target flow is that our INFs scale well to both large and high-dimensional datasets in comparison to other non- and semi-parametric methods. + +# Method + +**Notation.** Let $\mathbb{P}(Z)$ be a distribution of a random variable $Z$, and let $\mathbb{P}(Z=z)$ be its density or probability mass function. Let $\pi_a(x) = \mathbb{P}(A = a \mid X = x)$ denote the propensity score. Further, $\mathbbm{1}(\cdot)$ is the indicator function; $\mathbb{P}_n\{f(X)\} = \frac{1}{n}\sum_{i=1}^n f(X_i)$ is the sample average of a random $f(X)$; and $\mathbb{P}_b^\mathcal{B}\{f(X)\}$ is the average evaluated on a minibatch $\mathcal{B}$ of size $b$. For readability, we sometimes highlight random variables and the corresponding averaging operator in [green color]{style="color: ForestGreen"}. Furthermore, $\mathbb{P}(Y \mid X, A)$ is the conditional distribution of the outcome $Y$. + +**Problem statement.** In this work, we aim at estimating the ***interventional density*** from observational data, namely $\hat{\mathbb{P}}(Y[a] = y)$. To compare the goodness-of-fit of different estimators, we evaluate the distributional distance between the ground-truth interventional density and the estimated density. Such distributional distances include, e.g., the average log-probability and the empirical Wasserstein distance. + +We build upon the standard setting of potential outcomes framework [@rubin1974estimating], where $Y[a]$ stands for the potential outcome after intervening on treatment by setting it to $a$. That is, we consider an observational sample $\mathcal{D}$ with $d_X$-dimensional covariates $X \in \mathcal{X} \subseteq \mathbb{R}^{d_X}$, a treatment $A \in \{0, 1\}$, and a $d_Y$-dimensional continuous outcome $Y \in \mathcal{Y} \subseteq \mathbb{R}^{d_Y}$, drawn i.i.d. We consider $d_Y = 1$ if not stated explicitly. We assume the treatment to be binary, but note that our INFs also work with categorical treatments. We denote $\mathcal{D} = \{X_i, A_i, Y_i\}_{i=1}^n \sim \mathbb{P}(X, A, Y)$, where $n$ is the sample size, and $i$ is the index of an observation. For example, in critical care, the patient covariates $X$ are different risk factors (e.g., age, gender, weight, prior diseases), the treatment is whether a ventilator is applied, and the outcome is the probability of patient survival. The covariates $X$ are also called confounders if $\mathbb{P}(Y[a]) \neq \mathbb{P}(Y \mid A = a)$. + +**Identifiability.** To identify the interventional density, we make the following identifiability assumptions with respect to the data-generating mechanism of $\mathcal{D}$: (1) *Positivity:* For some $\epsilon > 0$, $\mathbb{P}(1 - \epsilon \ge \pi_a(X) \ge \epsilon) = 1$. (2) *Consistency:* If $A = a$ for some patient, then $Y = Y[a]$. (3) *Exchangeability:* $A \perp\!\!\!\!\perp Y[a] \mid X$ for all $a$. Note that these assumptions are standard in the literature [@kim2018causal; @muandet2021counterfactual; @kennedy2021semiparametric]. Under assumptions (1)--(3), the density of interventional distribution $\mathbb{P}(Y[a])$ can be expressed in terms of observational distribution with back-door adjustment, i.e., $$\begin{align} + \label{eq:backdoor} + \begin{split} + \mathbb{P}(Y[a] = y) & = \underset{X \sim \mathbb{P}(X)}{\mathbb{E}} \big( \mathbb{P}(Y = y\mid X, A=a) \big), + \end{split} +\end{align}$$ where $\mathbb{P}(Y = y \mid X, A)$ is the conditional density of the outcome. For more details on the potential outcomes framework and identifiability, we refer to Appendix [8](#app:background){reference-type="ref" reference="app:background"}. + +**Plug-in estimator.** A straightforward approach for IDE [@robins2001comment] is the following: first, one estimates the conditional outcome distribution, $\hat{\mathbb{P}}(Y \mid X, A)$ (here, any method for conditional density estimation could be used). Then, one takes a sample average over covariates $X$: $$\begin{equation} + \label{eq:plugin} + \hat{\mathbb{P}}^{\text{PI}}(Y[a] = y) = \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y = y \mid \textcolor{ForestGreen}{X}, A = a)\}. +\end{equation}$$ This estimator is an unbiased but inefficient estimator of interventional density, which is known as *semi-parametric plug-in estimator*. Semi-parametric IDE, unlike, e. g., semi-parametric ATE estimation, is highly problematic. For large sample sizes, the semi-parametric estimator requires averaging over the full sample for each evaluation point. Motivated by this, we aim to develop a proper fully-parametric estimator. + +[]{#sec:fully-parametric-ide label="sec:fully-parametric-ide"} In this section, we introduce a theory for fully-parametric estimation of interventional density. First, we provide a theoretic background, as introduced in [@kennedy2021semiparametric]. Here, we describe a projection parameter as a solution to the moment condition and then we list two estimators, i. e., *covariate-adjusted (CA) estimator* and efficient *augmented inverse propensity of treatment weighted (A-IPTW) estimator*. Second, we elaborate on the A-IPTW estimator and translate it into an optimization objective, which constitutes one of our contributions. + +We start by defining a parametric model, $\left\{g(y; \beta_a) \mid \beta_a \in \mathbb{R}^d\right\}$, where $\beta_a \in \mathbb{R}^d$ are parameters of estimator, and $g(\cdot; \beta_a)$ is a density, i. e., $\int_{y \in \mathcal{Y}} g(y; \beta_a) \mathop{}\!\mathrm{d}y = 1$. For IDE, we approximate the interventional distribution $\mathbb{P}(Y[a])$ with a distribution from our parametric model. We aim at minimizing the distributional distance (specifically KL-divergence) between $\mathbb{P}(Y[a])$ and $g(\cdot; \beta_a)$ via $$\begin{align} + \label{eq:kl-min} + \begin{split} + \hat{\beta}_a = & \mathop{\mathrm{arg\,min}}_{\beta_a}\operatorname{KL} \big(\mathbb{P}(Y[a]) \;\big\Vert\; g(\cdot; \beta_a) \big) \\ + %\argmin_{\beta_a} \int_{\mathcal{Y}} \log{\bigg(\frac{\mathbb{P}(Y[a] = y)}{g(y; \beta_a) }\bigg)} \, \mathbb{P}(Y[a] = y) \diff y \\ + = & \mathop{\mathrm{arg\,min}}_{\beta_a} \underset{Y^a \sim \mathbb{P}(Y[a])}{\mathbb{E}} \big( - \log g(Y^a; \beta_a) \big) , + \end{split} +\end{align}$$ where $\hat{\beta}_a$ are called *projection parameters* as they project the true interventional density onto a class $\{g(\cdot; \beta_a); \beta_a \in \mathbb{R}^d\}$. + +**Covariate-adjusted estimator.** Let the $d$-dimensional random variable $T(Y; \beta_a) = - \nabla_{\beta_a} \log g(Y; \beta_a)$ denote the score function. Following @kennedy2021semiparametric, the projection parameters can be equivalently expressed under mild conditions[^7] as a solution to the *moment condition* $m(\beta_a) \stackrel{!}{=} 0$, where $$\begin{align} + \label{eq:moment-cond} + \begin{split} + m(\beta_a) & = \underset{Y^a \sim \mathbb{P}(Y[a])}{\mathbb{E}} T(Y^a; \beta_a) \\ + & = \underset{X \sim \mathbb{P}(X)}{\mathbb{E}} \Big( \mathbb{E} \big( T(Y; \beta_a) \mid X, A = a \big) \Big). + \end{split} +\end{align}$$ Here, the moment condition is the expected score function of the potential outcome. Throughout the paper, we assume that the moment condition has a unique solution, and, therefore, the minimization task in Eq. [\[eq:kl-min\]](#eq:kl-min){reference-type="eqref" reference="eq:kl-min"} and the root-finding task in Eq. [\[eq:moment-cond\]](#eq:moment-cond){reference-type="eqref" reference="eq:moment-cond"} are equivalent. + +In practice, we have neither observations from the interventional distribution nor counterfactual outcomes. Therefore, we cannot use the ground-truth $\mathbb{P}(Y[a])$ but, instead, must use the plug-in estimator distribution from Eq. [\[eq:plugin\]](#eq:plugin){reference-type="eqref" reference="eq:plugin"}. Specifically, we can obtain a plug-in estimator of projection parameters, i. e., $\hat{\beta}_a^{\text{PI}}$, either by minimizing a cross-entropy loss or by solving the moment condition, both of which are equivalent: $$\begin{align} + \begin{split} + & \hat{\beta}_a^{\text{PI}} = \mathop{\mathrm{arg\,min}}_{\beta_a} \underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)\}}{\mathbb{E}} \hspace{-0.5cm} - \log g(\hat{Y}^a; \beta_a) \label{eq:projection-plugin} \\ + \Longleftrightarrow \, & \hat{m}^{\text{PI}}(\beta_a) = \underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)\}}{\mathbb{E}} \hspace{-0.5cm} T(\hat{Y}^a; \beta_a) \stackrel{!}{=} 0. + \end{split} + %\label{eq:moment-eq-plugin} +\end{align}$$ Then, we can define a *parametric covariate-adjusted* (CA) estimator as $\hat{\mathbb{P}}^{\text{CA}}(Y[a] = y) = g(y; \hat{\beta}_a^{\text{PI}})$. By choosing a sufficiently expressive class of densities for both $g$ and the conditional density estimator $\hat{\mathbb{P}}(Y \mid X, A)$ (e. g., normalizing flows), CA can be shown to consistently estimate the interventional density (see Appendix B.5 in @kennedy2021semiparametric). + +**Augmented inverse propensity of treatment weighted estimator.** In the following, we aim to develop an efficient estimator of the projection parameter $\hat{\beta}_a$ from Eq. [\[eq:kl-min\]](#eq:kl-min){reference-type="eqref" reference="eq:kl-min"} or, equivalently, the moment condition $\hat{m}(\beta_a)$ at fixed $\beta_a$ from Eq. [\[eq:moment-cond\]](#eq:moment-cond){reference-type="eqref" reference="eq:moment-cond"}. For this, we make use of semi-parametric efficiency theory [@van2003unified; @kennedy2021semiparametric]. We provide a background on efficiency theory in Appendix [8](#app:background){reference-type="ref" reference="app:background"}. + +@kennedy2022semiparametric showed that the efficient influence function $\phi_a(T, \mathbb{P})$ for the functional $\mathbb{E} ( \mathbb{E} ( T \mid X, A = a))$ equals to $$\begin{align} + \label{eq:eif} + \begin{split} + & \phi_a(T; \textcolor{BrickRed}{\mathbb{P}}) = \frac{\mathbbm{1}(A = a)}{\textcolor{BrickRed}{\pi_a(}X \textcolor{BrickRed}{)}} \Big(T - \textcolor{BrickRed}{\mathbb{E}(} T \mid X, A=a \textcolor{BrickRed}{)} \Big) \\ + & \, \, + \textcolor{BrickRed}{\mathbb{E}(} T \mid X, A=a \textcolor{BrickRed}{)} - \textcolor{BrickRed}{\underset{X \sim \mathbb{P}(X)}{\mathbb{E}} ( \mathbb{E} ( T \mid X, A = a))}. + \end{split} +\end{align}$$ Here, we use [red color]{style="color: BrickRed"} to show the nuisance parameters of $\textcolor{BrickRed}{\mathbb{P}}$ that are influencing the functional. We emphasize that the nuisance parameters (i. e., the propensity score and conditional expectations/probabilities) can be either known or estimated. + +The efficient influence function in Eq. [\[eq:eif\]](#eq:eif){reference-type="eqref" reference="eq:eif"} allows us to construct an efficient estimator of the moment condition. Following [@kennedy2021semiparametric], we transform the plug-in estimator $\hat{m}^\text{PI}(\beta_a)$ from Eq. [\[eq:projection-plugin\]](#eq:projection-plugin){reference-type="eqref" reference="eq:projection-plugin"} into an efficient estimator with the help of a *one-step bias correction*. In our case, the bias-corrected moment condition has the following form: $$\begin{equation} + \label{eq:one-step-corrected} + \hspace{-0.2cm} + \hat{m}^\text{A-IPTW}(\beta_a) = \hat{m}^\text{PI}(\beta_a) + \mathbb{P}_n\big\{\phi_a (T(Y; \beta_a); \hat{\mathbb{P}})\big\} \stackrel{!}{=} 0, +\end{equation}$$ where $\hat{\mathbb{P}} = \{\hat{\pi}_a(x), \hat{\mathbb{P}}(Y \mid X, A)\}$ are the estimated nuisance parameters of $\mathbb{P}$. We call the solution of the bias-corrected moment equation $\hat{\beta}_a^{\text{A-IPTW}}$ an *augmented inverse propensity of treatment weighted* (A-IPTW) estimator of the projection parameters. Then, estimated interventional density is $\hat{\mathbb{P}}^{\text{A-IPTW}}(Y[a] = y) = g(y; \hat{\beta}_a^{\text{A-IPTW}})$. + +Previously, @kennedy2021semiparametric proposed to directly solve the bias-corrected moment condition, i. e., a system of nonlinear equations, yet which is generally much harder to solve computationally. In contrast, we develop an optimization objective that can be directly incorporated into a loss of a deep learning density estimator. For that, we transform the bias-corrected moment condition into the following tractable optimization task (see Appendix [9](#app:aiptw-optim){reference-type="ref" reference="app:aiptw-optim"} for all details). + +We first note that the plug-in estimator of moment condition $\hat{m}^\text{PI}(\beta_a)$ can be rewritten as $$\begin{align} + & \hat{m}^{\text{PI}}(\beta_a) = \underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)\}}{\mathbb{E}} T(\hat{Y}^a; \beta_a) \\ + &= \int_\mathcal{Y} T(y; \beta_a) \, \textcolor{ForestGreen}{\mathbb{P}_n}\{ \hat{\mathbb{P}}(Y = y \mid \textcolor{ForestGreen}{X}, A = a) \} \mathop{}\!\mathrm{d}y \\ + % &= \textcolor{ForestGreen}{\mathbb{P}_n}\bigg\{ \int_\mathcal{Y} T(y; \beta_a) \, \hat{\mathbb{P}}(Y = y \mid \textcolor{ForestGreen}{X}, A = a) \diff y \bigg\} \\ + & = \textcolor{ForestGreen}{\mathbb{P}_n}\Big\{ \hat{\mathbb{E}}\big(T(Y; \beta_a) \mid \textcolor{ForestGreen}{X}, A = a \big)\Big\}, +\end{align}$$ where the last equality follows from the definition of the conditional expectation. Then, we notice that the last term of the influence function, $\textcolor{BrickRed}{\underset{X \sim \mathbb{P}(X)}{\mathbb{E}} ( \mathbb{E} ( T \mid X, A = a))}$, is, in fact, non-random and could be brought out from the sample average in Eq. [\[eq:one-step-corrected\]](#eq:one-step-corrected){reference-type="ref" reference="eq:one-step-corrected"}. Furthermore, after switching from $\mathbb{P}$ to $\hat{\mathbb{P}}$, this term exactly coincides with $\hat{m}^{\text{PI}}(\beta_a)$, so that the one-step bias-corrected equation is simplified to $$\begin{align} + & \hat{m}^\text{A-IPTW}(\beta_a) + = \underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\textcolor{BrickRed}{\hat{\mathbb{P}}(}Y \mid \textcolor{ForestGreen}{X}, A = a \textcolor{BrickRed}{)}\}}{\mathbb{E}} T(\hat{Y}^a; \beta_a) \\ + & + \textcolor{ForestGreen}{\mathbb{P}_n}\bigg\{ \frac{\mathbbm{1}(\textcolor{ForestGreen}{A} = a)}{\textcolor{BrickRed}{\hat{\pi}_a(}\textcolor{ForestGreen}{X} \textcolor{BrickRed}{)}} \Big(T(\textcolor{ForestGreen}{Y}; \beta_a) - \underset{Y \sim \textcolor{BrickRed}{\hat{\mathbb{P}}(}Y \mid \textcolor{ForestGreen}{X}, A = a \textcolor{BrickRed}{)}}{\mathbb{E}} T(Y; \beta_a) \Big) \bigg\}. +\end{align}$$ After taking the antiderivative with respect to $\beta_a$, we yield the following optimization objective $$\begin{align} + \label{eq:aiptw} + \hspace{-1.2cm} + & \scriptsize + \hat{\beta}_a^{\text{A-IPTW}} = \mathop{\mathrm{arg\,min}}_{\beta_a} \Bigg[ \underbrace{\underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)\}}{\mathbb{E}} \bigg( - \log g(\hat{Y}^a; \beta_a) \bigg) }_{\text{cross-entropy loss}} \hspace{-0.3cm} \\ + & \scriptsize + - \underbrace{\textcolor{ForestGreen}{\mathbb{P}_n} \bigg\{ \frac{\mathbbm{1}(\textcolor{ForestGreen}{A} = a)}{\hat{\pi}_a(\textcolor{ForestGreen}{X})} \Big(\log g(\textcolor{ForestGreen}{Y}; \beta_a) - \underset{Y \sim \hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)}{\mathbb{E}} \big( \log g(Y; \beta_a) \big) \Big) \bigg\}}_{\text{one-step bias correction}} \Bigg] . + \nonumber +\end{align}$$ + +Unlike the plug-in estimator ($\hat{\beta}_a^{\text{PI}}$), the A-IPTW estimator achieves efficiency and possesses a double robustness property. Here, formally speaking, we still mean efficiency with respect to the moment condition, i. e.$m(\beta_a)$. This way of defining efficiency is particularly useful when the solution to the moment condition in Eq. [\[eq:moment-cond\]](#eq:moment-cond){reference-type="eqref" reference="eq:moment-cond"} is non-unique, e. g., due to the usage of parametric deep learning models. In this case, we can informally define the so-called efficient estimation of the projection parameters with respect to the equivalence class. All the parameters $\hat{\beta}_a^{\text{A-IPTW}}$, which fall into this class, will satisfy the efficiently estimated moment condition, Eq. [\[eq:one-step-corrected\]](#eq:one-step-corrected){reference-type="eqref" reference="eq:one-step-corrected"}. + +
+
+ +
+
Overview of Interventional Normalizing Flows. Our INFs combine two normalizing flows, which we call “nuisance flow” and “target flow”. The nuisance flow estimates the nuisance parameters, i.e., the propensity score π̂a(X) and the conditional outcome distribution $\hat{\mathbb{P}}(Y \mid X, A)$. The target flow utilizes them to estimate the projection parameters β̂aA-IPTW.
+
+ +In the following, we describe our *Interventional Normalizing Flows*: a proper fully-parametric method for interventional density estimation via deep learning. First, we describe all the components of our architecture and, then, introduce an efficient estimation using one-step bias correction. + +In our INFs, we combine two normalizing flows, which we refer to as (i) *nuisance flow* and (ii) *target flow* (see Fig. [2](#fig:tar-norm-flow){reference-type="ref" reference="fig:tar-norm-flow"}). The rationale for this is based on our derivations in Section [\[sec:fully-parametric-ide\]](#sec:fully-parametric-ide){reference-type="ref" reference="sec:fully-parametric-ide"}, according to which a fully-parametric IDE requires two models: (i) one for the estimation of nuisance parameters, and (ii) one for the subsequent optimization of the learning objective with respect to projection parameters. Accordingly, both NFs in our INFs have thus different objectives: (i) the nuisance flow estimates the nuisance parameters (i.e., the propensity score and the conditional outcome distribution); and (ii) the target flow uses the estimated nuisance parameters to estimate the projection parameters. + +**(i) Nuisance flow.** The nuisance flow has three components: two fully-connected (FC) subnetworks and a conditional normalizing flow parameterized by $\theta$. The first FC subnetwork (FC$_1$) takes the covariates $X$ as input and, then, outputs a representation $R \in \mathbb{R}^{d_R}$ together with a propensity score $\hat{\pi}_a(X)$. The second FC subnetwork (FC$_2$) takes the representation $R$ and the observed treatment $A_i$ as input and, then, outputs the parameters of flow, conditioned on $X$ and $A$, i. e., $\theta(X, A)$. Together, FC$_1$ and FC$_2$ form a so-called hypernetwork [@ha2017hypernetworks] for the conditional normalizing flow, which allows us to learn the conditional outcome distribution via back-propagation. + +Let $\mathcal{L}_{\text{N}}$ be the loss of the nuisance flow. Here, we combine a conditional negative log-likelihood ($\mathcal{L}_{\text{NLL}}$) and binary cross-entropy loss for the propensity score ($\mathcal{L}_{\pi}$), i.e., $\mathcal{L}_{\text{N}}(\hat{\mathbb{P}}, \hat{\pi}_a) = \mathbb{P}_n \{\mathcal{L}_{\text{NLL}} + \alpha \mathcal{L}_{\pi}\} \text{ with } \mathcal{L}_{\text{NLL}} = - \log \hat{\mathbb{P}}(Y = Y \mid X, A); \; \mathcal{L}_{\pi} = \operatorname{BCE}(\hat{\pi}_A(X), A)$, where $\alpha > 0$ is a hyperparameter. In general, conditional normalizing flows are prone to overfitting when trained via a conditional negative log-likelihood. To address this, we later employ noise regularization [@rothfuss2019noise] in the conditional density estimation. + +**(ii) Target flow.** The target flow uses the outputs of the nuisance flow and then learns the interventional distribution. We first describe the naïve variant of the target flow without one-step bias correction (we introduce this later in Section [5.2](#sec:bias-correction){reference-type="ref" reference="sec:bias-correction"}). Different from the conditional normalizing flow in the nuisance flow, the target flow is a non-conditional normalizing flow, parameterized by $\beta_a$. Specifically, we consider two separate normalizing flows, that is, one for each potential outcome (i.e., $a = 0$ and $a = 1$, respectively).[^8] + +To fit the target flow, we must solve the moment condition from Eq. [\[eq:projection-plugin\]](#eq:projection-plugin){reference-type="eqref" reference="eq:projection-plugin"} or, equivalently, minimize a cross-entropy loss: $$\begin{align} + \label{eq:target-ce} + & \mathcal{L}_{\text{CE}}(\beta_a) = \underset{\hat{Y}^a \sim \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y \mid \textcolor{ForestGreen}{X}, A = a)\}}{\mathbb{E}} - \log g(\hat{Y}^a; \beta_a) \\ + & \scriptsize = - \int_{y \in \mathcal{Y}} \log g(y; \beta_a) \textcolor{ForestGreen}{\mathbb{P}_n}\{\hat{\mathbb{P}}(Y = y \mid \textcolor{ForestGreen}{X}, A = a)\} \mathop{}\!\mathrm{d}y, \nonumber +\end{align}$$ where the later integration is performed numerically with quadrature ($d_Y = 1$) or Monte Carlo ($d_Y > 1$) methods. + +To provide an efficient estimation for the parameters of the target flow, we augment the cross-entropy loss (Eq. [\[eq:target-ce\]](#eq:target-ce){reference-type="eqref" reference="eq:target-ce"}) with a one-step bias correction. To evaluate the bias correction term, we need to compute a conditional cross-entropy loss: $$\begin{align*} + & \mathcal{L}_{\text{CCE}}(X; \beta_a) = \underset{Y \sim \hat{\mathbb{P}}(Y \mid X, A = a)}{\mathbb{E}} \hspace{-0.4cm} - \log g(Y; \beta_a), \\ + & = - \int_{y \in \mathcal{Y}} \log g(y; \beta_a) \hat{\mathbb{P}}(Y = y \mid X, A = a) \mathop{}\!\mathrm{d}y. +\end{align*}$$ Finally, we obtain the loss of the target flow ($\mathcal{L}_{\text{T}}$), which is now suitable for our A-IPTW estimation from Eq. [\[eq:aiptw\]](#eq:aiptw){reference-type="eqref" reference="eq:aiptw"}. We thus yield $$\begin{equation} + \label{eq:tar-aiptw} + \scriptsize \mathcal{L}_{\text{T}}(\beta_a) = \mathcal{L}_{\text{CE}}(\beta_a) + \textcolor{ForestGreen}{\mathbb{P}_n} \bigg\{ \frac{\mathbbm{1}(\textcolor{ForestGreen}{A} = a)}{\hat{\pi}_a(\textcolor{ForestGreen}{X})} \Big(- \log g(\textcolor{ForestGreen}{Y}; \beta_a) - \mathcal{L}_{\text{CCE}}(\textcolor{ForestGreen}{X}; \beta_a) \Big) \bigg\}. +\end{equation}$$ + +
+ +
Results for synthetic data based on the SCM from Figure 1. Reported: mean over ten-fold train-test splits. Some runs for MDNs resulted in the log-probout = −∞ and, thus, are not shown.
+
+ +**Training.** To train both components in our INFs, we make use of a two-step training procedure. Specifically, we first fit the nuisance parameters using the nuisance flow. Then, we freeze the parameters of the nuisance flow and fit the target flow. We additionally employ the exponential moving average (EMA) of the target parameters with a smoothing hyperparameter $\gamma$ to stabilize the training for small minibatch sizes [@polyak1992acceleration]. We show the full algorithm in Appendix [10](#app:algorithm){reference-type="ref" reference="app:algorithm"} and further implementation details in Appendix [11](#app:implementation){reference-type="ref" reference="app:implementation"}. + +**Inference time.** One main advantage of our nuisance-target model is that the target flow has constant inference time (e.g., during the evaluation phase). Hence, contrary to state-of-the-art baselines, the inference of our INFs do [not]{.underline} depend on the dimensionality of covariates (or representation) and the size of the training data. This is a major advantage over semi-parametric plug-in estimators. For a detailed runtime comparison, we refer to Appendix [18](#app:runtime){reference-type="ref" reference="app:runtime"}. To this end, our method offers great scalability, such as required in medicine. diff --git a/2209.15172/main_diagram/main_diagram.drawio b/2209.15172/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..3599660450f424f3d8dbd3755f38a0915dbcc00c --- /dev/null +++ b/2209.15172/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2209.15172/paper_text/intro_method.md b/2209.15172/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..6e41ad0b0485bcbc9a98950fd8950694caab5aa6 --- /dev/null +++ b/2209.15172/paper_text/intro_method.md @@ -0,0 +1,51 @@ +# Introduction + +Text to image generation has seen major recent advances with the release of DALLE [\(Ramesh et al.,](#page-10-0) [2021\)](#page-10-0) and diffusion models such as DALLE2 [\(Ramesh et al., 2022\)](#page-10-1), CogView2 [\(Ding et al., 2022\)](#page-9-0) and Latent Diffusion [\(Rombach et al., 2022\)](#page-10-2). A natural next step is the task of generating 3D objects from text input. However, supervised methods relying on paired image-text data are less suited to text to 3D generation as large-scale paired text and 3D datasets are less available. Thus, the regime of little to no 3D data training supervision is beneficial. While this might seem daunting, recent work in text to 3D generation showed promising results without using large-scale datasets by bridging the gap using guidance from pretrained vision-language models such as CLIP [\(Radford et al., 2021\)](#page-10-3). + +At the same time, advances in differentiable neural rendering and the development of NeRF [\(Milden](#page-10-4)[hall et al., 2020\)](#page-10-4) now allow for direct optimization of a 3D representation to match input images. Combining these approaches with CLIP guidance, we can generate 3D representations from text directly without paired text–3D data, by optimizing the similarity of the text to the rendered images. Work that leverages CLIP for text to 3D generation can be grouped by the amount of 3D data required. The first set of methods train a generative model on a 3D dataset, and then optimize a mapping network from text to the latent space of the generative model using CLIP guidance and differentiable rendering. The second set of methods utilizes no 3D or text supervision and has access to only the pretrained CLIP model. We refer to this latter regime as *pure CLIP guidance*. Given the scarcity of text–3D pair datasets, we focus on this regime. + +A prominent example of the pure CLIP guidance regime is Dream Fields [\(Jain et al., 2022\)](#page-10-5) which uses Mip-NeRF [\(Barron et al., 2021\)](#page-9-1) and CLIP to guide the 3D optimization process for every new input text prompt. Unfortunately, this approach requires significant computational resources and exhibits poor quality generation with low-density artifacts when using direct voxel grid optimization (see appendix of original paper). We also find that the quality of the results in Dream Fields is largely attributable to the LiT [\(Zhai et al., 2022\)](#page-11-0) guidance model. When using the vanilla CLIP models as in our work, results are far worse. Optimizing the CLIP similarity is also prone to adversarial examples where generated images with high similarity according to CLIP have little perceived resemblance to the text description for a human [\(Liu et al., 2021\)](#page-10-6). Recent text to 3D methods use image-based augmentations as regularization to prevent these issues. However, there has been no systematic study of which of these regularizations matters and how much. In addition, there are several possible design choices for the NeRF and CLIP modules, including the use of explicit voxel grids without any neural networks vs implicit neural representations. We systematically compare these and other factors that impact generation quality, and show that it is possible to generate highly detailed 3D representations with voxel grids alone. + +Our main contributions are: 1) We conduct a systematic study of augmentations and their effect on text to 3D generation results with pure CLIP guidance; 2) We compare different CLIP backbones for guidance as well as model ensembles for finer 3D object detail; 3) We compare the regularization effects on geometry of explicit vs implicit voxel grids; and 4) We demonstrate generation of highresolution grids using CLIP guidance only. + +# Method + +**Model architecture.** For our NeRF model, we implement two variations of the voxel grid representation: $Vox_{Exp}$ , an explicit voxel grid representation, and $Vox_{Imp}$ , an implicit version that uses MLPs to predict the density and color. $Vox_{Exp}$ explicitly models the two voxel grids consisting of the density $V^{(density)} \in \mathbb{R}^{1 \times N_x \times N_y \times N_z}$ and color $V^{(rgb)} \in \mathbb{R}^{3 \times N_x \times N_y \times N_z}$ . $Vox_{Imp}$ is an implicit coordinate-based MLP voxel grid representation with positional encodings (Mildenhall et al., 2020) of the grid vertex coordinates formulated as $V^{(PE)} \in \mathbb{R}^{L \times N_x \times N_y \times N_z}$ , where L is the channel size after positional encoding of grid vertex coordinates. Separate density and color MLPs are applied on the positional encodings to obtain the density and color predictions. We base our voxel grid model implementations on DVGO. However, note that our positional encoding feature grid $V^{(PE)}$ is fixed and not learnable like the DVGO feature grid $V^{(feat)}$ for color. Our overall model illustration can be found in Fig. 2. The trainable parameters for the explicit model are the parameters of the explicit grids and the bias term in the softplus activation of the density value. For the implicit voxel grid the trainable parameters are the density and color MLPs and the bias term in the softplus function. During training we also add progressive scaling of the voxel grid resolution as in DVGO. + +Augmentations. We study the impact of combining three augmentation schemes: Background augmentation (BackAug) from Dream Fields Jain et al. (2022), Diff augment (DiffAug) Zhao et al. (2020), and perspective augmentations (PerspAug) from Text2Mesh Michel et al. (2022). BackAug consists of alpha compositing checkerboard, textures or gaussian noise backgrounds to the image. DiffAug contains several image augmentations, including color jittering, image translation, and + +cutout. Liu et al. (2021) used it for text to image generation and showed it prevents adversarial generations. PerspAug denotes random perspective transformations applied to the image. + +**Losses.** We combine the CLIP and transmittance losses introduced in Dream Fields, with losses from DVGO to reduce noise and promote smoothness. In addition, we introduce a spherical prior loss term to encourage a coherent object. For a model parameterized by $\theta$ , the CLIP loss (Eq. 2) enforces the cosine similarity between the NeRF generated image $I(\theta, p)$ for a camera pose p and the input caption $x_T$ to be high in CLIP space, The transmittance loss (Eq. 3) prevents the scene from being overcrowded by applying a loss when the average transmittance is over the threshold $\tau$ and $\text{Tr}(\theta, p)$ is the transmittance image. + + +$$\mathcal{L}_{\text{CLIP}}(\theta, \boldsymbol{p}, x_T) = -\text{Enc}_I(I(\theta, \boldsymbol{p}))^{\top} \text{Enc}_T(x_T)$$ +(2) + + +$$\mathcal{L}_{Tr} = -\min(\tau, \text{mean}(Tr(\theta, \boldsymbol{p})))$$ +(3) + +To encourage centered objects and uniform size, we introduce a spherical prior (Eq. 4) where the probability is 1 for coordinates q within a sphere of radius 1. We calculate the KL divergence between the spherical prior and the density voxel grid with the sampled point coordinates q from grid vertices (Eq. 5). This loss serves the same purpose as ray shifting in Dream Fields. However, it is not trivial to shift and scale the voxel grid directly. Therefore, we promote centering and uniform size through this loss instead. + + +$$P_{\text{sphere}}(\boldsymbol{q}) = \begin{cases} 1, & \text{if } \|\boldsymbol{q}\|_2^2 \le 1\\ 0, & \text{otherwise} \end{cases}$$ + (4) + + +$$\mathcal{L}_{\mathrm{KL}_{s}} = \sum_{q} D_{\mathrm{KL}}(P_{\mathrm{sphere}} \| \alpha^{(\mathrm{post})}(q, V^{(\mathrm{density})}))$$ + (5) + +In our model, we enable the ensembling of different CLIP models by adding a second similarity loss using a different CLIP model as shown in Eq. 6, where $\text{Enc}_{I_2}$ and $\text{Enc}_{T_2}$ are the image and text encoders for the second CLIP model respectively. + + +$$\mathcal{L}_{\text{CLIP}_2}(\theta, \boldsymbol{p}, x_T) = -\text{Enc}_{I_2}(I(\theta, \boldsymbol{p}))^{\top} \text{Enc}_{T_2}(x_T)$$ +(6) + +Following DVGO, we also add a total variation loss $\mathcal{L}_{TV}$ to reduce noise and promote smoothness and a background entropy loss $\mathcal{L}_{\sigma}$ to encourage density values be either 0 or 1. Our complete loss is shown in Eq. 7, where the $\lambda$ s are the weights for the different loss terms. + + +$$\mathcal{L}_{\text{Total}} = \mathcal{L}_{\text{CLIP}} + \lambda_{\text{Tr}} \mathcal{L}_{\text{Tr}} + \lambda_{\text{TV}} \mathcal{L}_{\text{TV}} + \lambda_{\text{KL}_s} \mathcal{L}_{\text{KL}_s} + \lambda_{\sigma} \mathcal{L}_{\sigma} + \lambda_{\text{CLIP}_2} \mathcal{L}_{\text{CLIP}_2}$$ +(7) + +We find that during optimization, it is important to schedule the $\mathcal{L}_{TV}$ and $\mathcal{L}_{KL_s}$ loss terms so that they are turned off toward the end of the optimization. Please see Appendix A.1 for more discussion and details of other hyperparameter values. diff --git a/2210.12152/main_diagram/main_diagram.drawio b/2210.12152/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..993bdf5f648f785f13ce706ac57377697401434c --- /dev/null +++ b/2210.12152/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2210.12152/paper_text/intro_method.md b/2210.12152/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..eb49d3bd18ba871ed0f8b6dc5f9dbd9fbff506e5 --- /dev/null +++ b/2210.12152/paper_text/intro_method.md @@ -0,0 +1,44 @@ +# Introduction + +Error analyses of practical NLP systems in recent history demonstrate that some of the mistakes made by state-of-the-art models would be avoided by basic human intuition (Shuster et al., 2022), and some of the most challenging tasks for models are the same ones that might be trivial to human children. With modern systems' impressive performance on tasks such as grammar correction showing that manipulating language is not the issue, LLMs seem to face a fundamental lack of common sense—an understanding of everyday phenomena and how they interact with each other and the world at large. As striking gains in subjective performance on summarization, creative text generation, and apparent language understanding continue to be called into question, the development of strong benchmarks to assess reasoning capabilities for these LLMs grows more important. + +One popular approach to measuring reasoning capability is through performance on question answering (QA) benchmark tasks where direct queries for information act as a straightforward examination of a system's "understanding." Classic QA datasets, however, are primarily concerned with retrieving factoids to answer questions of "Who", "What", "When", and "Where". These questions have been shown to be answerable (with high accuracy) by simple pattern-matching approaches (Wadhwa et al., 2018), thereby limiting their ability to measure the aforementioned reasoning capability. Looking to maintain the breadth of topics covered while increasing the difficulty of the QA task, researchers introduced multi-hop QA datasets like HotpotQA (Yang et al., 2018). While challenging, the task's extra complexity mostly leads to unnatural questions that can be addressed with iterated factoid retrieval and entity resolution, rather than a necessary understanding of how different entities interact. Noticeably absent in these prior datasets are "why" questions, which prompt for not factoids, but explanations—reasoning made explicit. + +The task of explanation uses reasoning and produces explicit, interpretable "thought" processes. Capitalizing on these properties, this paper introduces WIKIWHY, a novel dataset containing "why" + +\*Equal contribution + +![](_page_1_Figure_1.jpeg) + +Figure 1: A simple example of an entry from WIKIWHY; a cause and effect sourced from a Wikipedia passage, a "why" question and its answer about this relation, and most importantly rationale that explains why cause leads to effect. + +question-answer pairs. Each WIKIWHY entry contains a rationale explaining the QA pair's causal relation [\(Figure 1\)](#page-1-0), summing to a total of 14,238 explanation elements. In the context of recent multimodal, self-supervised approaches aiming to capture intuitions unlearnable from text alone [\(Chadha & Jain, 2021\)](#page-9-0), WIKIWHY presents an opportunity to investigate a specific kind of information absent in text: implicit commonsense assumptions. Compared to other QA datasets with rationales, WIKIWHY covers a significantly broader range of 11 topics which may prove valuable for developing the skill of applied reasoning on various specific situations. + +Our experiments in explanation generation and human evaluation demonstrate that state-of-the-art generative models struggle with producing satisfying explanations for WIKIWHY cause-effect relations. Our experiments also demonstrate how our proposed task might be used to diagnose a lack of "understanding" in certain relations. Our key contributions are thus: + +- We propose explanation within cause-effect relations as a novel problem formulation for exploring LLM reasoning ability. +- We create WIKIWHY, the first question-answering dataset focusing on reasoning within causal relations, spanning 11 topics. +- We perform experiments on state-of-the-art, generative models to investigate various settings and establish baseline results with sizable room for improvement. +- We introduce idea-level evaluation metrics for free-form text (explanation) generation and a human judgment correlation analysis, demonstrating that (1) reference similarity is strongly correlated with explanation correctness, and (2) the metrics we introduced correlate with this proxy. + +# Method + +Formally defined in [§5,](#page-5-0) we propose a generative explanation task. Previous works have made strides in assessing reasoning through multiple choice [\(Lu et al., 2022\)](#page-11-5), retrieval [\(Asai et al., 2019\)](#page-9-4), and partial generation [\(Dalvi et al., 2021\)](#page-10-1). While these works are undoubtedly crucial towards the end goal of understanding and reasoning, their task formulations have some drawbacks. Referring back to education, studies on human students have shown that multiple choice questions "obscure nuance in student thinking" [\(Hubbard et al., 2017\)](#page-10-4). Likewise, a selection decision can be correct for retriever systems but for the wrong reasons. Augmenting multi-hop factoid questions with an additional task of selecting the relevant supporting facts from the context passage, [Inoue et al.](#page-10-5) [\(2020\)](#page-10-5) emphasizes that interpretability is lost in the absence of explanation. Furthermore, text generation to combine existing ideas is arguably a different task than generating from scratch. The field of psychology defines recall (mental retrieval of information) as a distinct process from recognition (mental familiarity with the cue) [\(Mohr et al., 1989\)](#page-11-6). Neural nets' biological inspiration suggests that there might be a similar difference between cue-aided retrieval and freeform generation. In the context of NLP, we are interested in the implicit understandings and assumptions embedded in LLMs and hypothesize that an entirely generative approach is most conducive to this study. + +Explanations come in various structures, as seen in the typology defined by [Ribeiro et al.](#page-11-7) [\(2022\)](#page-11-7). Shown in [Figure 2,](#page-3-0) our work focuses on a subset of said typology. WIKIWHY includes two structures that explain cause-and-effect relations: (1) multi-hop step sequences and (2) rationale sets. While the chain structure adds intermediate conclusions between cause and effect, rationale sets contain elements that support the relation from without. The rationale set topology acts as our general, catch-all case that other structures can be condensed to. Since our data collection procedure promotes a stepwise, ordered approach, we also consider the sequential topology to respect the structure exhibited in applicable explanations. We forego the unstructured approach as even limited structure helps bring freeform generated text evaluation within reach. Finally, we opt against pursuing the most complex entailment tree organization to maintain naturalness and facilitate crowdsourcing scalability. + +The objective of WIKIWHY is to present a high-quality, challenging dataset of QA pairs with corresponding causes, effects, and explanations. We developed an extensive data collection and validation pipeline around Amazon Mechanical Turk, depicted in [Figure 5](#page-16-0) (appendix). For each stage involving crowdsourced annotations, we perform rigorous worker-level quality control to ensure the dataset's quality. The exact procedures are detailed in [subsection A.2](#page-12-5) in the appendix. + +Preprocessing. We begin with English Wikipedia's corpus of "Good Articles,"[1](#page-4-0) , whose strict criteria of verifiability and neutrality (among others) ensure that WIKIWHY does not evaluate models on misinformation or opinionated views. From these articles, we extract passages containing causal relations using causal connectives. We selected a list of causal keywords (Appendix, [§subsection A.1\)](#page-12-6) from a more extensive set of causal connectives as their presence in a passage guarantees the existence of a cause and effect relation—some excluded connectives such as "since" or "as" are highly prevalent but are not necessarily causal. The presence of a causal word pattern on its own is a very simple heuristic—in the subsequent collection steps, we hired crowdworkers to ensure the quality of each sample. + +QA Synthesis (Stage 1). Randomly sampled preprocessed Wikipedia passages containing potential causal statements were shown to qualified Amazon Mechanical Turk (MTurk) workers (see ethics statement for details), who were tasked with extracting the highlighted causal relation from the passage and re-framing it as a "why" question when possible. While automatic cause-effect relation extraction has seen recent progress [\(Yang et al., 2022\)](#page-12-2), this human intelligence task (HIT) remains vital for two reasons. First, we find that quality in cause-effect is crucial for meaningful and valid explanations in the following stage. More importantly, we depend on human annotators to add sufficient context to the text of the cause, effect, and question to disambiguate them. This enables the question and cause-effect relation to be presented to models without the context we prepared (e.g., "Why was the river diverted?" is unanswerable without additional context). This feature is key to enabling WIKIWHY to assess the information and ideas within LLMs as opposed to whatever may be present in the context. + +Explanation Synthesis (Stage 2). After verifying the quality of the examples, we prompt crowd workers to explain cause-effect pairs from stage 1. To encourage structured explanation, we supply an interface that allows sentences or ideas to be entered one at a time in separate fields. Though the input pairs should be context-independent, we provide the original passage as an aid for understanding the topic. Furthermore, we provide the link to the source article to encourage explanations leveraging topic-specific information in addition to commonsense knowledge. + +Entry Contents. In addition to the main fields of the question, answer, and explanation, each dataset entry contains the underlying relation's cause and effect, the passage the question was extracted from, the article the passage is from, and Wikipedia's topic categorization for that article. + +Topic Diversity. WIKIWHY improves upon other datasets due to its ability to examine reasoning proficiency across a broader range of concepts [\(Table 9](#page-15-0) in Appendix contains examples from the six most frequent topics). + +Rationale. The statistics for the reasoning component are shown in Appendix [Table 11.](#page-15-1) On average, each rationale contains 1.5137 elements. [Figure 4](#page-15-2) (Appendix) shows a histogram of rationale length by sentence count. WIKIWHY includes a range of rationale lengths, with more than one-third of examples (36%) containing two or more reasoning steps. + +1 [https://en.wikipedia.org/wiki/Wikipedia:Good](https://en.wikipedia.org/wiki/Wikipedia:Good_articles/all) articles/all diff --git a/2210.12714/main_diagram/main_diagram.drawio b/2210.12714/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8ffde241ca95acc49b0d1420fdcc352f03c76854 --- /dev/null +++ b/2210.12714/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2210.12714/main_diagram/main_diagram.pdf b/2210.12714/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dc728f1786dcbefe89ebf84863c6727c1f07d519 Binary files /dev/null and b/2210.12714/main_diagram/main_diagram.pdf differ diff --git a/2210.12714/paper_text/intro_method.md b/2210.12714/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..a8b1b333a87322aa4f391ea335853474f18d6255 --- /dev/null +++ b/2210.12714/paper_text/intro_method.md @@ -0,0 +1,145 @@ +# Introduction + +Knowledge Graphs (KGs) as a form of structured knowledge have drawn significant attention from academia and the industry [@DBLP:journals/tnn/JiPCMY22]. However, high-quality KGs rely almost exclusively on human-curated structured or semi-structured data. To this end, Knowledge Graph Construction (KGC) is proposed, which is the process of populating (or building from scratch) a KG with new knowledge elements (e.g., entities, relations, events). Conventionally, KGC is solved by employing task-specific discriminators for the various types of information in a pipeline manner [@DBLP:conf/tac/AngeliZCCBPPGM15; @DBLP:conf/emnlp/LuanHOH18; @DBLP:conf/emnlp/MesquitaCSMB19; @zhang2022deepke], typically including (1) entity discovery or named entity recognition [@DBLP:conf/conll/SangM03], (2) entity linking [@DBLP:conf/cikm/MilneW08], (3) relation extraction [@DBLP:journals/jmlr/ZelenkoAR03] and (4) event extraction [@DBLP:conf/emnlp/DuC20]. However, this presents limitations of error population and poor adaptability for different tasks. + +
+ +
Discrimination and generation methodologies for relation extraction. “Country-President” is the relation, and “CP” is short for “Country-President.”
+
+ +Some generative KGC methods based on the sequence-to-sequence (Seq2Seq) framework are proposed to overcome this barrier. Early work [@DBLP:conf/acl/LiuZZHZ18] has explored using the generative paradigm to solve different entity and relation extraction tasks. Powered by fast advances of generative pre-training such as T5 [@DBLP:journals/jmlr/RaffelSRLNMZLL20], and BART [@DBLP:conf/acl/LewisLGGMLSZ20], Seq2Seq paradigm has shown its great potential in unifying widespread NLP tasks. Hence, more generative KGC works [@DBLP:conf/acl/YanDJQ020; @DBLP:conf/iclr/PaoliniAKMAASXS21; @DBLP:conf/acl/0001LDXLHSW22] have been proposed, showing appealing performance in benchmark datasets. Figure [1](#fig:intro){reference-type="ref" reference="fig:intro"} illustrates an example of generative KGC for relation extraction. The target triple is preceded by the tag \, and the head entity, tail entity, and relations are also specially tagged, allowing the structural knowledge (corresponding to the output) to be obtained by inverse linearization. Despite the success of numerous generative KGC approaches, these works scattered among various tasks have not been systematically reviewed and analyzed. + +In this paper, we summarize recent progress in generative KGC (An timeline of generative KGC can be found in Appendix [8](#sec:timeline){reference-type="ref" reference="sec:timeline"}) and maintain a public repository for research convenience[^2]. We propose to organize relevant work by the generation target of models and also present the axis of the task level (Figure [3](#taxonomy_of_generation){reference-type="ref" reference="taxonomy_of_generation"}): + +- **Comprehensive review with new taxonomies**. We conduct the **first** comprehensive review of generative KGC together with new taxonomies. We review the research with different generation targets for KGC with a comprehensive comparison and summary (§[3](#tax){reference-type="ref" reference="tax"}). + +- **Theoretical insight and empirical analysis**. We provide in-depth theoretical and empirical analysis for typical generative KGC methods, illustrating the advantages and disadvantageous of different methodologies as well as remaining issues (§[4](#analysis){reference-type="ref" reference="analysis"}). + +- **Wide coverage on emerging advances and outlook on future directions**. We provide comprehensive coverage of emerging areas, including prompt-based learning. This review provides a summary of generative KGC and highlights future research directions (§[5](#future work){reference-type="ref" reference="future work"}). + +# Method + +Knowledge Graph Construction mainly aims to extract structural information from unstructured texts, such as Named Entity Recognition (NER) [@TACL2016_NER_BiLSTM], Relation Extraction (RE) [@EMNLP2015_RE_PCNN], Event Extraction (EE) [@ACL2015_EE_DMCNN], Entity Linking (EL) [@DBLP:journals/tkde/ShenWH15], and Knowledge Graph Completion [@DBLP:conf/aaai/LinLSLZ15]. + +Generally, KGC can be regarded as structure prediction tasks, where a model is trained to approximate a target function $F(x) \rightarrow y$, where $x \in \mathcal{X}$ denotes the input data and $y \in \mathcal{Y}$ denotes the output structure sequence. For instance, given a sentence, \"*Steve Jobs and Steve Wozniak co-founded Apple in 1977.*\": + +**Named Entity Recognition** aims to identify the types of entities, *e.g.*, '*Steve Job*', '*Steve Wozniak*' $\Rightarrow$ `PERSON`, '*Apple*' $\Rightarrow$ `ORG`; + +**Relation Extraction** aims to identify the relationship of the given entity pair $\langle$*Steve Job*, *Apple*$\rangle$ as `founder`; + +**Event Extraction** aims to identify the event type as `Business Start-Org` where '*co-founded*' triggers the event and (*Steve Jobs*, *Steve Wozniak*) are participants in the event as `AGENT` and `Apple` as `ORG` respectively. + +**Entity Linking** aims to link the mention *Steve Job* to `Steven Jobs (Q19837)` on Wikidata, and *Apple* to `Apple (Q312)` as well. + +**Knowledge Graph Completion** aims to complete incomplete triples $\langle$*Steve Job*, *create*, *?*$\rangle$ for blank entities `Apple`, `NeXT Inc.` and `Pixar`. + +=\[mybox,minimum height=1.5em, fill=hidden-orange!60, text width=20em, text=black,align=left,font=, inner xsep=2pt, inner ysep=4pt, \] + +
+ +
Taxonomy of Generative Knowledge Graph Construction.
+
+ +In this section, we introduce the background of discrimination and generation methodologies for KGC. The goal of the discrimination model is to predict the possible label based on the characteristics of the input sentence. As shown in Figure [1](#fig:intro){reference-type="ref" reference="fig:intro"}, given annotated sentence $x$ and a set of potentially overlapping triples $t_{j}=\{(s, r, o)\}$ in $x$, we aim to maximize the data likelihood during the training process: $$\begin{align} +p_{cls}(t|x)=\prod_{(s, r, o) \in t_{j}} p\left((s, r, o) \mid x_{j}\right) +\end{align}$$ + +Another method of discrimination is to output tags using sequential tagging for each position $i$ [@DBLP:conf/acl/ZhengWBHZX17; @DBLP:conf/aaai/DaiXLDSW19; @DBLP:conf/coling/YuZDYZC20; @DBLP:conf/coling/LiWZZYC20; @DBLP:conf/naacl/LiuFTCZHG21]. As shown in Figure [1](#fig:intro){reference-type="ref" reference="fig:intro"}, for an n-word sentence $x$, $n$ different tag sequences are annotated based on \"BIESO\" (Begin, Inside, End, Single, Outside) notation schema. The size of a set of pre-defined relations is $|R|$, and the related role orders are represented by \"1\" and \"2\". During the training model, we maximize the log-likelihood of the target tag sequence using the hidden vector $h_i$ at each position $i$: $$\begin{align} +p_{tag}(y \mid x)= \frac{\exp (h_i, y_i)}{\sum_{\mathbf{y}^{\prime} \in R} \exp \left(\exp (h_i, y_i^{\prime})\right)} +\end{align}$$ + +For the generation model, if $x$ is the input sentence and $y$ the result of linearized triplets, the target for the generation model is to autoregressively generate $y$ given $x$: $$\begin{align} +p_{gen}(y \mid x)=\prod_{i=1}^{\operatorname{len}(y)} p_{gen}\left(y_{i} \mid y_{ + +
Copy-based Sequence.
+ + +This paradigm refers to developing more robust models to copy the corresponding token (entity) directly from the input sentence during the generation process. @DBLP:conf/acl/LiuZZHZ18 designs an end-to-end model based on a copy mechanism to solve the triple overlapping problem. As shown in Figure [4](#fig:copy){reference-type="ref" reference="fig:copy"}, the model copies the head entity from the input sentence and then the tail entity. Similarly, relations are generated from target vocabulary, which is restricted to the set of special relation tokens. This paradigm avoids models generating ambiguous or hallucinative entities. In order to identify a reasonable triple extraction order, @DBLP:conf/emnlp/ZengHZLLZ19 converts the triplet generation process into a reinforcement learning process, enabling the copy mechanism to follow an efficient generative order. Since the entity copy mechanism relies on unnatural masks to distinguish between head and tail entities, @DBLP:conf/aaai/ZengZL20 maps the head and tail entities to fused feature space for entity replication by an additional nonlinear layer, which strengthens the stability of the mechanism. For document-level extraction, @DBLP:conf/emnlp/HuangTP21 proposes a TOP-k copy mechanism to alleviate the computational complexity of entity pairs. + +
+ +
Structure-linearized Sequence.
+
+ +This paradigm refers to utilizing structural knowledge and label semantics, making it prone to handling a unified output format. @DBLP:conf/acl/0001LXHTL0LC20 proposes an end-to-end event extraction model based on T5, where the output is a linearization of the extracted knowledge structure as shown in Figure [5](#fig:structure){reference-type="ref" reference="fig:structure"}. In order to avoid introducing noise, it utilizes the event schema to constrain decoding space, ensuring the output text is semantically and structurally legitimate. @DBLP:conf/acl/LouLDZC20 reformulates event detection as a Seq2Seq task and proposes a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Besides, @DBLP:journals/taslp/ZhangYDTCHHC21 [@DBLP:conf/aaai/YeZDCTHC21] introduce a contrastive learning framework with a batch dynamic attention masking mechanism to overcome the contradiction in meaning that generative architectures may produce unreliable sequences [@DBLP:journals/corr/abs-2003-08612]. Similarly, @DBLP:conf/emnlp/CabotN21 employs a simple triplet decomposition method for the relation extraction task, which is flexible and can be adapted to unified domains or longer documents. + +In the nested NER task, @DBLP:conf/acl/StrakovaSH19 proposes a flattened encoding algorithm, which outputs multiple NE tags following the BILOU scheme. The multi-label of a word is a concatenation of all intersecting tags from highest priority to lowest priority. Similarly, @DBLP:conf/acl/Zhang0TW022 eliminates the incorrect biases in the generation process according to the theory of backdoor adjustment. In EL task, @DBLP:conf/iclr/CaoI0P21 proposes Generative ENtity REtrieval (GENRE) in an autoregressive fashion conditioned on the context, which captures fine-grained interactions between context and entity name. Moreover, @DBLP:conf/acl/WangLCH0S22 [@DBLP:conf/acl/0001LDXLHSW22] extends the domain to structural heterogeneous information extraction by proposing a unified task-agnostic generation framework. + +[]{#Label-augmented label="Label-augmented"} + +
+ +
Label-augmented Sequence.
+
+ +This paradigm refers to utilizing the extra markers to indicate specific entities or relationships. As shown in Figure [6](#fig:label){reference-type="ref" reference="fig:label"}, @athiwaratkun2020augmented investigates the label-augmented paradigm for various structure prediction tasks. The output sequence copies all words in the input sentence, as it helps to reduce ambiguity. In addition, this paradigm uses square brackets or other identifiers to specify the tagging sequence for the entity of interest. The relevant labels are separated by the separator \"$|$\" within the enclosed brackets. Meanwhile, the labeled words are described with natural words so that the potential knowledge of the pre-trained model can be leveraged [@DBLP:conf/iclr/PaoliniAKMAASXS21]. Similarly, @DBLP:conf/emnlp/AthiwaratkunSKX20 naturally combines tag semantics and shares knowledge across multiple sequence labeling tasks. To retrieve entities by generating their unique names, @DBLP:conf/iclr/CaoI0P21 extends the autoregressive framework to capture the relations between context and entity name by effectively cross-encoding both. Since the length of the gold decoder targets is often longer than the corresponding input length, this paradigm is unsuitable for document-level tasks because a great portion of the gold labels will be skipped. + +[]{#Indice-based label="Indice-based"} + +This paradigm generates the indices of the words in the input text of interest directly and encodes class labels as label indices. As the output is strictly restricted, it will not generate indices that corresponding entities do not exist in the input text, except for relation labels. @DBLP:conf/aaai/NayakN20 apply the method to the relation extraction task, enabling the decoder to find all overlapping tuples with full entity names of different lengths. As shown in Figure [7](#fig:indice){reference-type="ref" reference="fig:indice"}, given the input sequence $x$, the output sequence $y$ is generated via the indices: $y = [b_{1},e_{1},t_1,\ldots,b_{i},e_{i},t_i,\ldots, b_{k},e_{k},t_k]$ where $b_i$ and $e_i$ indicates the begin and end indices of a entity tuple, $t_i$ is the index of the entity type, and $k$ is the number of entity tuples. The hidden vector is computed at decoding time by the pointer network  [@vinyals2015pointer] to get the representation of the tuple indices. Besides, @DBLP:conf/acl/YanGDGZQ20b explores the idea of generating indices for NER, which can be applied to different settings such as flat, nested, and discontinuous NER. In addition, @DBLP:conf/eacl/DuRC21 applies the method to a role-filler entity extraction task by implicitly capturing noun phrase coreference structure. + +
+ +
Indice-based Sequence.
+
+ +[]{#Blank-based label="Blank-based"} + +This paradigm refers to utilizing templates to define the appropriate order and relationship for the generated spans. @DBLP:conf/naacl/DuRC21 explores a blank-based form for event extraction tasks which includes special tokens representing event information such as event types. @DBLP:conf/naacl/LiJH21 frames document-level event argument extraction as conditional generation given a template and introduces the new document-level informative to aid the generation process. As shown in Figure [8](#fig:blank){reference-type="ref" reference="fig:blank"}, the template refers to a text describing an event type, which adds blank argument role placeholders. The output sequences are sentences where the blank placeholders are replaced by specific event arguments. Besides, @DBLP:journals/corr/abs-2108-12724 focuses on low-resource event extraction and proposes a data-efficient model called DEGREE, which utilizes label semantic information. @DBLP:conf/acl/HuangHNCP22 designs a language-agnostic template to represent the event argument structures, which facilitate the cross-lingual transfer. Instead of conventional heuristic threshold tuning, @DBLP:conf/acl/MaW0LCWS22 proposes an effective yet efficient model PAIE for extracting multiple arguments with the same role. + +
+ +
Blank-based Sequence.
+
+ +::: table* +[]{#table:entity label="table:entity"} +::: + +Recently, the literature on generative KGC has been growing rapidly. A unifying theme across many of these methods is that of end-to-end architecture or the idea that the knowledge extraction can be redefined as *text sequence to structure generation* task. Generative models can decode and control extraction targets on demand for different specific tasks, scenarios, and settings (i.e., different schema). However, due to the different forms of specific KGC tasks, there is still some disagreement in the utilization of the generation paradigms. + +As shown in Table [\[fig:sum\]](#fig:sum){reference-type="ref" reference="fig:sum"}, we make a comprehensive comparison among the paradigms mentioned above via rating based on different evaluation scopes: 1) **Semantic utilization** refers to the degree to which the model leverages the semantics of the labels. In principle, we believe that the closer the output form is to natural language, the smaller the gap between the generative model and the training task. We observe that the blank-based paradigm has a clear advantage in this scope, which uses manually constructed templates to make the output close to natural language fluency. 2) **Search space** refers to the vocabulary space searched by the decoder. Due to the application of the constraint decoding mechanism, some structure-based methods can be reduced to the same decoding space as the copy-based methods. In addition, the indice-based paradigm uses a pointer mechanism that constrains the output space to the length of the input sequence. 3) **Application scope** refers to the range of KGC tasks that can be applied. We believe that architectures with the ability to organize information more flexibly have excellent cross-task migration capabilities such as structure-based, label-based and blank-based paradigms. 4) **Template cost** refers to the cost of constructing the input and golden output text. We observe that most paradigms do not require complex template design and rely only on linear concatenation to meet the task requirement. However, the blank-based paradigm requires more labor consumption to make the template conform to the semantic fluency requirement. + +Totally in line with recent trends in NLP, a growing number of unified generation strategies require more universal architectures [@DBLP:conf/emnlp/DengTLXH21; @DBLP:conf/acl/LiTHJHXCYZW21], as they allow a remarkable degree of output flexibility. We think that future research should focus on unifying cross-task models and further improving decoding efficiency. + +::: table* +::: + +::: table* +::: + +This section provides theoretical insight into optimization and inference for generative KGC. For optimization, NLG are normally modeled by parameterized probabilistic models $p_{gen}$ over text strings $\mathbf{y}= \langle y_1, y_2, \dots \rangle$ decomposed by words $y_t$: $$\begin{align} +p_{gen}(y \mid x)=\prod_{i=1}^{\operatorname{len}(y)} p_{gen}\left(y_{i} \mid y_{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 \ No newline at end of file diff --git a/2211.00824/main_diagram/main_diagram.pdf b/2211.00824/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..78e10957ad1d5e7234c53062ab6370ae45c4234a Binary files /dev/null and b/2211.00824/main_diagram/main_diagram.pdf differ diff --git a/2211.00824/paper_text/intro_method.md b/2211.00824/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..8386b40042cdfa1b7c7057291bed6630ea42e826 --- /dev/null +++ b/2211.00824/paper_text/intro_method.md @@ -0,0 +1,202 @@ +# Introduction + +Data augmentation has emerged as an effective data pre-processing or data transformation step to mitigate overfitting , to encourage local smoothness , and to improve generalization in machine learning pipelines such as deep neural networks. + +Notably, effective data augmentation, which incorporates class-related data invariance and enriches the in-class sample, is one of the key contributing factors for representation learning with weak or self supervision . + +Given a task, we aim to generate ``good'' augmentations efficiently. As part of the machine learning model pipeline, an autonomous domain-agnostic but task-informed data augmentation mechanism is desirable. +However, a number of challenges exist. +(1) Existing augmentation operators are usually hand-crafted based on domain expert knowledge, which is not always available in some domain . +For example, widely used augmentations for natural images are not effective on medical images. +Moreover, the performance of those machine learning pipelines drastically varies with different choices of data augmentations. +(2) Existing few autonomous augmentation approaches developed lately are neither fully autonomous nor universally applicable to varying domains. +Although a few autonomous data augmentation approaches have been developed in recent years , they train policies to produce a sequence of pre-defined augmentation operations and thus are not fully automated and are limited to a few domains. +(3) Existing augmentations usually do not fully utilize the task feedback (i.e., task-agnostic) and may be sub-optimal for the targeted task. +A class of automated data augmentation methods train an extra data generative model to generate new augmentations from scratch given a real-world example . + +However, they require training a generative model, which is a non-trivial task in practice that may either relies on strong prior knowledge or a substantial increased number of training examples. + +In this paper, we first investigate the conditions required to generate domain-agnostic but task-informed data augmentations. +Consider a representation learning pipeline, we started from a probabilistic graphical model that describes the relations among the label $\rvy$, the nuisance $\nuisance$, the example $\rvx$, + +its augmentation $\rvxa$, and the latent representations $\rep$. + +We argue that a minimum-sufficient representation for the task preserves the label information but excludes other distractive information from the nuisance. +We then investigate the conditions for an augmentation $\rvxa$ that results in learning such preferred representations. +These conditions motivate an optimization objective that can be used to produce automated domain-agnostic but task-informed data augmentations for each example, without replying on pre-defined augmentation operators or specific domain knowledge. +Consequently, our proposed optimization objective addresses all aforementioned challenges. + +For practicality, we further propose a surrogate of the derived objective that can be efficiently computed from the intermediate-layer representations of the model-in-training. +The surrogate is built upon the data likelihood estimation through perceptual distance defined on the intermediate layers' representations. + +Specifically, our proposed surrogate objective maximizes the perceptual distance between $\rvx$ and $\rvxa$, under a label preserving constraint on the model prediction of $\rvxa$. +This problem can be efficiently solved by optimizing its Lagaragian relaxation. Thereby, given $\rvx$ and its label $\rvy$, the solution to our surrogate objective generates ``hard positive examples'' for $\rvx$ without loosing its label information. Once generated, $\rvxa$ is used to train the model towards producing the minimum-sufficient representation $\rep$ for the targeted task. +Our proposed method, named \oursfull (\ours), does not require any extra generative models such as Generative Adversarial Networks, unlike previous automated augmentation methods . + +We further propose a sharpness-aware criterion selecting only the most informative examples to apply our auto-augmentation on so it does not cause expensive extra computation. + +Our proposed \ours is a general and autonomous data augmentation technique +applicable to a variety of machine learning tasks, such as supervised, semi-supervised and noisy-label learning. +Moreover, we demonstrate that it can be seamlessly integrated with existing algorithms for these tasks and consistently improve their performance. + +In experiments on the three learning tasks, we equip \ours with existing methods and obtain significant improvement on both the learning efficiency and the final performance. The generated augmentations are optimized for the model-in-training in a target-task-aware manner and thus notably accelerate the slow convergence in computationally intensive tasks such as semi-supervised learning. It is worth noting that our augmentation can consistently bring improvement to tasks without domain knowledge or strong pre-defined augmentations such as medical image classification, on which previous image augmentations lead to performance degeneration. + +# Method + +Basics of Information Theory +Our analyses make frequent use of information theoretical quantities . + +Given a joint distribution $P_{\rv{X},\rv{Y}}$ and its marginal distributions $P_{\rv{X}}$, $P_{\rv{Y}}$, we define their {\em entropy} as $H(\rv{X}, \rv{Y}) = \mathrm{E}_{\rv{X},\rv{Y}}[-\log P(x, y)]$, $H(\rv{X}) = \mathrm{E}_{\rv{X}}[-\log P(x)]$, and $H(\rv{Y}) = \mathrm{E}_{\rv{Y}}[-\log P(y)]$. + +Furthermore, we define the {conditional entropy} of $\rv{X}$ given $\rv{Y}$ as $H(\rv{X}|\rv{Y}) = \mathrm{E}_{\rv{Y}}[-\log H(\rv{X}|y)] = H(\rv{X}, \rv{Y}) - H(\rv{Y})$. +Finally, we define the mutual information between $\rv{X}$ and $\rv{Y}$ as $I(\rv{X} \wedge \rv{Y}) = H(\rv{X}) - H(\rv{X}|\rv{Y}) = H(\rv{X}) + H(\rv{Y}) - H(\rv{X}, \rv{Y})$. + +Notations and Problem Setup. + +In this paper, we use bold capital letters (e.g., $\rv{X}, \rv{Y}$) to denote random variables, lowercase letters (e.g., $x, y$) to denote their realizations, and curly capital letters (e.g., $\set{X}, \set{Y}$) to denote the corresponding sample spaces. + +Since we mainly consider supervised and semi-supervised problems, we define +Let $P_{\rvx, \rvy}$ be the joint distribution of data observation $\rvx$ and label $\rv{Y}$, where $\rv{X}$ is a random vector taking values on a finite observation space $\set{X}$ (e.g., images) and $\rvy$ is a discrete random variable taking values on the label space $\set{Y}$ (e.g., classes). Our goal is to learn a classifier to predict $y \in \set{Y}$ from an observation $x \in \set{X}$. + +Task-nuisance Decomposition. + +To advance the analysis, we decouple the randomness in $\rv{X}$ into two parts, one pertaining to the label and another independent to the label. +Concretely, we define a random variable nuisance $\rv{N}$ such that {\bf1)} the nuisance $\rv{N}$ is independent to the label $\rv{Y}$, i.e., $\rv{N} \indep \rvy$; and +{\bf2)} the observation $\rv{X}$ is a deterministic function of the nuisance $\rv{N}$ and the label $\rv{Y}$, i.e., $\rv{X} = g(\rv{Y}, \rv{N})$ for some $g$. \Cref{lem:task-nuisance} demonstrates that such a random variable always exists. + +[Task-nuisance Decomposition ] + +Given a joint distribution $P_{\rvx,\rvy}$, where $\rvy$ is a discrete random variable, we can always find a random variable $\nuisance$ independent of $\rvy$ +such that $\rvx=d(\rvy,\nuisance)$, for some deterministic function $d$. + +Remarks. +We can rewrite the conditions of task-nuisance decomposition in terms of information theory. +{\bf1)} Since the nuisance $\rv{N}$ is independent to the label $\rv{Y}$, we have $I(\rv{Y} \wedge \rv{N}) = 0$; and +{\bf2)} Since the nuisance $\rv{N}$ and the label $\rv{Y}$ determines the observation $\rv{X}$, we have $H(\rv{X}|\rv{Y}, \rv{N}) = 0$. + +In real-world applications, the observation $\rv{X}$ is usually complex in a high-dimensional space $\set{X}$, making it hard to directly learn a good classifier for $\rv{Y}$. To remedy this curse of dimensionality, it is important to learn a good representation of $\rv{X}$, i.e., learn an encoder $\encfn(\cdot)$ that maps the high-dimensional observation $\rv{X}$ into a low-dimensional representation $\rv{Z}$. We illustrate the process of data generation and representation learning by a probabilistic graphical model as shown in \Cref{sfig:graph_noaug}. + +An ideal encoder should keep the important information from $\rvx$ (e.g. label-relevant information) and maximally discard the noise or nuisance of $\rvx$, such that it is much easier to learn a classifier from $\rep$ than from $\rvx$. +Based on the above intuition, we define an $\epsilon$-optimal representation of $\rvx$, which has sufficient information for classifying w.r.t. $\rvy$, while remaining little information of the nuisance. + +[$\epsilon$-Minimal Sufficient Representation ($\epsilon$-Optimal Representation)] + +For a Markov chain $\rvy \to \rvx \to \rep$, +we say thata representation $\rep$ of $\rvx$ is sufficient for $\rvy$ if $I( \rep \wedge \rvy ) = I( \rvx \wedge \rvy )$, and $\rep$ is +$\epsilon$-minimal sufficient for $\rvy$ if $\rep$ is sufficient and $I( \rep \wedge \rvx ) \leq I( \tilde{\rep} \wedge \rvx ) + \epsilon$ for all $\tilde{\rep}$ satisfying $I( \tilde{\rep} \wedge \rvy ) = I( \rvx \wedge \rvy )$. + +\textsl{Remark.} +Due to the property of mutual information, we have $0 \leq \epsilon \leq H(\rvx)$. +The lower $\epsilon$ is, the more ``minimal'' the representation is. When $\epsilon=0$, the representation is minimal sufficient, which is a desirable property as characterized by many prior works . + +\Cref{def:min_suf_eps} characterizes how good a sufficient representation is, based on how much redundant information is remained. Recall that $\rvx$ comes from a deterministic function of label $\rvy$ and nuisance $\nuisance$. The redundancy of $\rep$ can also be measured by the mutual information between $\rep$ and $\nuisance$. Achille et al. prove that if a representation $\rep$ is sufficient and is invariant to nuisance $\nuisance$, i.e., $I(\rep\wedge\nuisance)=0$, then $\rep$ is also minimal. However, since $\nuisance$ is not known, it is hard to directly encourage the representation to be invariant to $\nuisance$. + +Can we learn an $\epsilon$-minimal sufficient representation in a principled way? Inspired by the recent success of data augmentation techniques in self-supervised learning and semi-supervised learning, we find that data augmentation can implicitly encourage the representation to be invariant to the nuisance $\nuisance$. However, most augmentation methods are driven by pre-defined transformations, which do not necessarily render a minimal sufficient representation. In the next section, we will analyze the effects of data augmentation in representation learning in details. + +In this section, we investigate the role of data augmentation for learning good representations. + +We first make the following mild assumption on the underlying relationship between $\rvx$ and $\rvy$. + + There exists a deterministic function $\pi: \set{X} \to \set{Y}$, i.e., $H(\rvy|\rvx)=0$. + +Assumption requires that there exists a ``perfect classifier'' that identifies the label $y$ of the observation $x$ with no error, which is common in practice. Note that for data with ambiguity, a tie breaker can be used to map each observation to a unique label. Therefore, Assumption is realistic. + +Let $\augfn$ be a deterministic augmentation function such that $\rvx^\prime := \augfn(\rvx, \rva)$ is the augmented data, where $\rva$ is a random variable denoting the augmentation selection. +For example, if $\rvx=x$ is an image sample, $\rva=a$ is the augmentation ``rotate by 90 degree'', then $\rvx^\prime=x^\prime$ is the corresponding rotated image sample. +We learn an encoder $\encfn(\cdot)$ that maps the augmented data $\rvx\p$ to a representation $\rep\p$. +With this augmentation processes, the graphical model in \Cref{sfig:graph_noaug} is updated to \Cref{sfig:graph_aug}. + +We show in the theorem below that if the augmentation process preserves the information of $\rvy$, $\rep\p$ can be sufficient for $\rvy$. Furthermore, if the augmented data $\rvx\p$ contains no information of the original nuisance $\nuisance$, $\rep\p$ will be invariant to $\nuisance$ and thus will become a minimal sufficient representation. + + Consider label variable $\rvy$, observation variable $\rvx$ and nuisance variable $\nuisance$ satisfying Assumption . + Let $\rva$ be the augmentation variable, $\rvx^\prime$ be the augmented data, + + and $\rep^*$ be the solution to + \setlength\abovedisplayskip{-5pt} + \setlength\belowdisplayskip{-5pt} + + &\mathrm{argmax}_{\rep\p} & & I(\rep\p \wedge \rvxa) \text{ or } I(\rep\p \wedge \rvy)\\ + &\text{ subject to } & & I(\rep\p \wedge \rva) = 0. + + Then, $\rep^*$ is a $\epsilon$-minimal sufficient representation of $\rvx$ for label $\rvy$ if the following conditions hold:\\ + Condition (a): $I(\rvxa \wedge \rvy) = I(\rvx \wedge \rvy)$ ($\rvxa$ is an in-class augmentation) and \\ + Condition (b): $I(\rvxa \wedge \nuisance) \leq \epsilon$ ($\rvxa$ does not remain much information about $\nuisance$). + +Remarks. +(1) The objective of learning $\rep^*$ can be either task-independent (maximizing $I(\rep\p \wedge \rvxa)$), or task-dependent (maximizing $I(\rep\p \wedge \rvy)$). The former matches the ``InfoMax'' principle commonly used in self-supervised learning works , while the latter can be achieved by supervised training (e.g., learning a classifier of $\rep$ for $\rvy$ with cross-entropy loss).\\ + +(2) When Condition (b) holds for $\epsilon=0$, representation $\rep\p$ is optimal (minimal sufficient). + +\Cref{thm:min_suff_eps}, proved in \Cref{app:proof_main}, shows that if we have a good augmentation that maximally perturbs the label-irrelevant information while keeps the label-relevant information, +then the representation learned on the augmented data can be minimal sufficient. + +\Cref{thm:min_suff_eps} serves as a principle of constructing augmentation. Based on this principle, we propose an auto-augment algorithm in \Cref{sec:method}, and verify the algorithm in a wide range tasks in \Cref{sec:exp}. + +[5]{r}{0.3\textwidth} +\vspace{-1.em} +\includegraphics[scale=0.35]{figures/network.png} +\vspace{-2.em} +\caption{\footnotesize Network architecture.} + +In this section, we introduce our data augmentation and how to obtain the augmentation using the representation learning network $F(\cdot)$. Then we show how to plug our augmentation into the representation learning procedure of $F(\cdot)$. + +As illustrated in the previous section, an ideal data augmentation $\rvxa$ for representation learning should contain as little information about nuisance $\nuisance$ as possible while still keeping all the information about class $\rvy$. Since $\nuisance$ is not observed, we transfer the objective $\min_{\rvxa} I(\rvxa \wedge \nuisance)$ into $\min_{\rvxa} I(\rvxa \wedge \rvx)$ since $I(\rvxa \wedge \rvx)=I(\rvxa \wedge \nuisance)+I(\rvxa \wedge \rvy)$ and $I(\rvxa \wedge \rvy)$ is a constant under the constraint $I(\rvxa \wedge \rvy) = I(\rvx \wedge \rvy)$. Thus the optimization problem is: + +\min_{\rvxa} I(\rvxa \wedge \rvx) \quad +\mathrm{s.t.} I(\rvxa \wedge \rvy) = I(\rvx \wedge \rvy). + +\vspace{-0.5em} +Implementation of Mutual Information. +To solve \Cref{eq:final objective}, computing the mutual information terms $I(\rvxa \wedge \rvx)$, $I(\rvxa \wedge \rvy)$ and $I(\rvx \wedge \rvy)$ is required. +Next, we will show how to compute these terms using a neural net classifier $F(\cdot;\theta)$, parameterized by $\theta$, that consists of two components: a representation encoder $E(\cdot)$ and a predictor $M(\cdot)$. + +Specifically, $F(\cdot;\theta)=M(E(\cdot))$, where the representation encoder $E(\cdot)$ maps input $\rvx$ into representation $\rep$, and the predictor $M(\cdot)$ predicts the class of $\rep$. This is demonstrated in Figure . + +Constraint implementation. +Since $I(\rvxa \wedge \rvy)=H(\rvy)-H(\rvy|\rvxa)$ and $I(\rvx \wedge \rvy)=H(\rvy)-H(\rvy|\rvx)$, we can remove the $H(\rvy)$ term in both sides and turn the constraint into $H(\rvy|\rvx)=H(\rvy|\rvxa)$. Thus we only need to compute the conditional entropy of $\rvy$ given $\rvx$ or $\rvxa$, which can be approximated through the neural net classifier: $H(\rvy|\rvx)=\mathrm{E}_{\rvx,\rvy}\left[-{\rm log}P(y|x)\right]\approx\mathrm{E}_{\rvx,\rvy}\left[-{\rm log}(F(x;\theta)[y])\right]$, where we use softmax class probability $F(x;\theta)[y]$ to approximate the likelihood $P(y|x)$. And $H(\rvy|\rvxa)$ can be computed similarly. + +Objective implementation. +Then we show how to compute the objective $I(\rvxa \wedge \rvx)$. Since $I(\rvxa \wedge \rvx)=H(\rvx)-H(\rvx|\rvxa)$ where $H(\rvx)$ is not related to $\rvxa$ and thus can be neglected, we only need to compute $H(\rvx|\rvxa)=\mathrm{E}_{\rvx,\rvxa}\left[-{\rm log}P(x|x')\right]$. We use the Learned Perceptual Image Patch Similarity (LPIPS) between $x$ and $x'$ to compute the data likelihood $P(x|x')$ since LPIPS distance is a widely used metric to measure the data similarity in data generative model field and many previous work has shown that LPIPS distance is the best surrogate for human comparisons of similarity , compared with any other distance including $\ell_2$ and $\ell_\infty$ distance. Although such surrogate may have error, it worth noting that Theorem allows the surrogate to have $\epsilon$ error. The LPIPS distance is defined by the $\ell_2$ distance of stacked feature maps from a neural network. Here we use $F(\cdot;\theta)$ to compute the LPIPS distance. Let $F(\cdot;\theta)$ has $L$ layers and $\widehat{F_l}(\cdot;\theta)$ denotes these channel-normalized activations at the $l$-th layer of the network. Next, the activations are normalized again by layer size and flattened into a single vector $\phi(x)\triangleq(\frac{\widehat{F_1}(x;\theta)}{\sqrt{w_1 h_1}},...,\frac{\widehat{F_L}(x;\theta)}{\sqrt{w_L h_L}})$, where $w_l$ and $h_l$ are the width and height of the activations in layer $l$, respectively. The LPIPS distance between input $x$ and the augmentation $x'$ is then defined as: + +{\rm LPIPS}(x, x')\triangleq \|\phi(x)-\phi(x')\|_2. + +Constraint Relaxation for Efficiency. \quad +Now, given an input $x$, its data augmentation $x'$ can be computed by solving the following optimization problem using the neural network $F(\cdot;\theta)$ in practice: + +&\min_{x'}- \|\phi(x)-\phi(x')\|_2 +&\mathrm{ s.t.} {\rm log}F(x';\theta)[y]= {\rm log}F(x;\theta)[y]. + +The equality constraint in \Cref{eq:practice objective} is too strict to solve since it may be inefficient to search for an $x'$ that exactly satisfies ${\rm log}F(x';\theta)[y]= {\rm log}F(x;\theta)[y]$. Thus we relax the constraint with a small $\sigma$ and change the constaint into: ${\rm log}F(x';\theta)[y]\geq {\rm log}F(x;\theta)[y]-\sigma$. It's worth noting that if $\sigma$ is sufficiently small, the label is still well preserved. There is a trade-off to the value of $\sigma$, we search $\sigma$ to find a sweet spot where the problem is practical to solve and meanwhile the label is well preserved. + +There are many off-the-shelf methods that solve +\Cref{eq:practice objective}, and here we apply the Fast Lagrangian Attack Method as a demonstration. +We initialize $x'$ by $x$ plus a uniform noise. +And we find the optimal $x'$ by solving the following the Lagrangian multiplier function and gradually scheduling the value of the multiplier $\lambda$: + +\min_{x'} -\|\phi(x)-\phi(x')\|_2+\lambda \max(0, {\rm log}F(x;\theta)[y]-{\rm log}F(x';\theta)[y]-\sigma) + +The detailed procedure of the algorithm can be found in Appendix . +The algorithm has a similar form as adversarial attack in that they both find an optimal augmentation $x'$ by adding perturbations to the original image $x$. +However, the difference is that we aim to generate hard augmentation that preserves the label, while adversarial attack aims to change the class label. + +One primary advantage of \ours is that it only requires a neural net $F(\cdot;\theta)$ to produce the augmentation and $F(\cdot;\theta)$ can be the current representation learning model, so we can plug \ours into any representation learning procedure requiring no additional parameters, which is plug-and-play and parameter-free. At each step, we first fix $F(\cdot;\theta)$ to generate the augmentation $x'$ by solving \Cref{eq:lagarangian} using Algorithm . +And then we train $F(\cdot;\theta)$ by running the original representation learning algorithm using our augmentation $x'$. + +Data selection. \quad +It is not necessary to find hard positives for every sample. To save more computation, we can apply a sharpness-aware criterion, i.e., time-consistency (TCS) , to select the most informative data ($\tau\%$ data with the lowest TCS in Algorithm ) that have sharp loss landscapes, which indicate the existence of nearby hard positives, and we only apply \ours to them. It reduces the computational cost without degrading the performance because (1) the improvement brought by augmentations is limited for examples whose loss already reaches a flat minimum, while the model does not generalize well near examples with a sharp loss landscapes; and (2) the hard positives for examples with flat loss landscape are distant from the original ones and might introduce extra bias to the training. \looseness-1 + +\ours is compatible with any representation learning task minimizing a loss $L:\set{X}\times\set{Y}\times\set{W}\rightarrow\set{R_+}$, which takes in a data batch and a model to output a loss value. $\set{Y}$ here denotes the groundtruth label for labeled data and pseudo label for unlabeled data. +The pseudo-code of plugging \ours into the representation learning procedure with TCS-based data selection is provided in \Cref{alg:plug_in}. + +[!htbp] +\caption{Plug \ours into any representation learning procedure} + +[1] +\REQUIRE Loss for the targeted task $L:\set{X}\times\set{Y}\times\set{W}\rightarrow\set{R_+}$; training data $(\set{X},\set{Y})$; neural network $F(\cdot;\theta)$; class preserving margin $\epsilon$; data selection ratio $\tau$; learning rate $\eta$; +\ENSURE Model parameter $\theta$ trained with \ours +\WHILE{not converged} +\STATE Sample batch $\set{B}=\left\{(x_1, y_1),...,(x_b,y_b)\right\}\sim (\set{X},\set{Y})$; +\STATE Data selection: $\set{S}\leftarrow \tau\%$ data with the lowest TCS in $\set{B}$; +\STATE \ours: Freeze $\theta$ and solve \Cref{eq:lagarangian} using Algorithm for every sample in $\set{S}$, resulting in an augmented set $\set{A}=\left\{(x'_1,y_1),...,(x'_m,y_m)\right\}$ of size $m=|\set{S}|$; +\STATE Learning with \ours augmented data and original data: $\theta\leftarrow \theta - \eta[\nabla_\theta L(\set{B};\theta)+\nabla_\theta L(\set{A};\theta)]$; +\ENDWHILE diff --git a/2211.01365/main_diagram/main_diagram.drawio b/2211.01365/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..d5b1a42aaade2308c420189feec02421f3a8ad51 --- /dev/null +++ b/2211.01365/main_diagram/main_diagram.drawio @@ -0,0 +1,302 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2211.01365/paper_text/intro_method.md b/2211.01365/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..bdc524d2e826547b8544aadb50490fae4c39a389 --- /dev/null +++ b/2211.01365/paper_text/intro_method.md @@ -0,0 +1,80 @@ +# Introduction + +The dawn of quantum computing has ushered in a new era of technological advancement, presenting a paradigm shift in how we approach complex computational problems. Central to these endeavors are Variational Quantum Algorithms (VQAs) [@cerezo2021variational], which are indispensable for configuring and optimizing quantum computers. These algorithms serve as the backbone of significant domains, including quantum optimization [@Moll_2018] and quantum machine learning (QML)  [@biamonte2017quantum]. VQAs have also influenced advances in various other quantum learning applications [@dong2008quantum; @qml_supervised; @kerenidis2019q; @Liu_2021_qsupervised; @Huang_2022_theory; @Liu_2022; @Huang_2022_experiment; @zapata]. + +At the core of VQAs lies the challenge of effectively traversing and optimizing the high-dimensional parameter landscapes that define quantum systems. To solve this, there are two primary strategies: gradient-free [@spall1998overview] and *gradient-based* methods [@leng2022differentiable]. Gradient-free methods, while straightforward and widely used in practice, do not provide any guarantees of convergence, which often results in suboptimal solutions. On the other hand, gradient-based methods, such as those employed by the Variational Quantum Eigensolver (VQE) [@peruzzo2014variational; @tilly2022variational], *offer guarantees* for convergence. This characteristic has facilitated their application across a multitude of fields, such as high-energy physics [@PhysRevA.98.032331; @PRXQuantum.3.010324], condensed matter physics [@vqe_cmt], quantum chemistry [@vqe_chem], and important optimization problems such as max-cut problem [@farhi2014quantum; @Harrigan2021]. + +More specifically, recent developments in overparameterization theory show that gradient-based methods like gradient descent provide convergence guarantees in a variety of quantum optimization tasks [@larocca2021theory; @liu2023analytic; @you2022convergence]. New research indicates these methods surpass gradient-free techniques such as SPSA [@spall1998overview] in the context of differentiable analog quantum computers [@leng2022differentiable]. Notably, the quantum natural gradient, linked theoretically with imaginary time evolution, captures crucial geometric information, thereby enhancing quantum optimization [@Stokes2020quantumnatural]. + +Despite these advantages, the adoption of gradient-based methods in quantum systems is not without its obstacles. The computation of gradients in these hybrid quantum-classical systems is notoriously *resource-intensive* and scales linearly with the number of parameters. This computational burden presents a significant hurdle, limiting the practical application of these methods on quantum computers, and thus, the full potential of quantum optimization and machine learning. Hence, we pose the question, "*How can we accelerate gradient-based quantum optimization?*" This is crucial to harness the theoretical advantages of convergence for practical quantum optimization tasks. In this work, we answer our question affirmatively by providing order-of-magnitude speedups to VQAs. Namely, our contributions are as follows: + +- By perceiving the optimization trajectory in quantum optimization as a dynamical system, we bridge quantum natural gradient theory, overparameterization theory, and Koopman operator learning theory [@koopman_1931], renowned for accelerating dynamical systems. + +- We propose Quantum-circuit Alternating Controlled Koopman Operator Learning (QuACK), a new algorithm grounded on this theory. We scrutinize its spectral stability, convex problem convergence, complexity of speedup, and non-linear extensions using sliding window and neural network methodologies. + +- Through extensive experimentation in fields like quantum many-body physics, quantum chemistry, and quantum machine learning, we underscore QuACK's superior performance, achieving speedups over 200x, 10x, and 3x in overparameterized, smooth, non-smooth and noisy regimes respectively. + +
+
+ +
+
QuACK: Quantum-circuit Alternating Controlled Koopman Operator Learning. (a) Parameterized quantum circuits process information; loss function is evaluated via quantum measurements. Parameter updates for the quantum circuit are computed by a classical optimizer. (b) Optimization history forms a time series, the computational cost of which is proportional to the number of parameters. (c) Koopman operator learning finds an embedding of data with approximately linear dynamics from time series in (b). (d) Koopman operator predicts parameter updates with computational cost independent of the number of parameters. Loss from predicted parameters is evaluated, and optimal parameters are used as starting point for the next iteration.
+
+ +# Method + +For a quantum mechanical system with $N$ qubits, the key object that contains all the information of the system is called wave function $\psi$. It is an $l_2$-normalized complex-valued vector in the $2^N$-dimensional Hilbert space. A parameterized quantum circuit encodes a wave function as $\psi_{\boldsymbol{\theta}}$ using a set of parameters $\boldsymbol{\theta} \in \mathbb{R}^{n_{\mathrm{params}}}$ via an ansatz layer on a quantum circuit in a quantum computer, as shown in the top-left part of Figure [1](#fig:scheme_1){reference-type="ref" reference="fig:scheme_1"}. The number of parameters, $n_{\mathrm{params}}$, is typically chosen to be polynomial in the number of qubits $N$, which scales much slower than the $2^N$-scaling of the dimension of $\psi$ itself in the original Hilbert space. + +Variational quantum eigensolver (VQE) aims to solve minimal energy wave function of a Hamiltonian with parameterized quantum circuit. A Hamiltonian $\mathcal{H}$ describes interactions in a physical system, which mathematically is a Hermitian operator acting on the wave functions. The energy of a wave function is given by $\mathcal{L}(\psi) = \langle \psi | \mathcal{H} \psi \rangle$. VQE utilizes this as a loss function to find the optimal $\boldsymbol{\theta}^* = \mathop{\mathrm{arg\,min}}_{\boldsymbol{\theta}} \mathcal{L}(\boldsymbol{\theta}) = \mathop{\mathrm{arg\,min}}_{\boldsymbol{\theta}} \langle \psi_{\boldsymbol{\theta}} | \mathcal{H} \psi_{\boldsymbol{\theta}} \rangle$. Quantum machine learning follows a similar setup, aiming to minimize $\mathcal{L}(\boldsymbol{\theta})$ involved data with parameterized quantum circuits $\psi_{\boldsymbol{\theta}}$, which in QML are usually referred to as quantum neural networks. + +To minimize the loss function, one can employ a gradient-based classical optimizer such as Adam, which requires calculating the gradient $\partial \mathcal{L} / \partial \theta_i$. In the quantum case, one usually has to explicitly evaluate the loss with a perturbation in each direction $i$, for example, using the parameter-shift rule [@PhysRevA.98.032309; @PhysRevA.99.032331]: $(\mathcal{L}(\theta_i+\pi/2)-\mathcal{L}(\theta_i-\pi/2)) / 2$. This leads to a linear scaling of $n_{\mathrm{params}}$ computational cost, making quantum optimization significantly more expensive than classical backpropagation which computational complexity is independent of $n_{\mathrm{params}}$. It is worth noting that the classical computational components involved in VQE, even including training neural-network-based algorithms in the following sections, typically are much cheaper than the quantum gradient cost given the scarcity of quantum resources in practice. + +The quantum natural gradient method [@Stokes2020quantumnatural] generalizes the classical natural gradient method in classical machine learning [@amari1998natural] by extending the concept of probability to complex-valued wave functions. In the context of parameter optimization, the natural gradient for updating the parameter $\theta$ is governed by a nonlinear differential equation $\frac{d}{dt}\boldsymbol{\theta}(t) = -\eta F^{-1} \nabla_{\boldsymbol{\theta}} \mathcal{L}(\boldsymbol{\theta}(t))$, where $\eta$ denotes the scalar learning rate, and $F$ represents the quantum Fisher Information matrix defined as $F_{ij} = \langle \partial \psi_{\boldsymbol{\theta}}/ \partial \theta_i | \partial \psi_{\boldsymbol{\theta}}/ \partial \theta_j \rangle - \langle \partial \psi_{\boldsymbol{\theta}}/ \partial \theta_i | \psi_{\boldsymbol{\theta}}\rangle \langle \psi_{\boldsymbol{\theta}}| \partial \psi_{\boldsymbol{\theta}}/ \partial \theta_j \rangle.$ + +Consider a dynamical system characterized by a set of state variables ${x(t) \in \mathbb{R}^n}$, governed by a transition function $T$ such that $x(t+1)=T(x(t))$. According to the Koopman operator theory articulated by Koopman, a linear operator $\mathcal{K}$ and a function $g$ exist, satisfying $\mathcal{K} g(x(t)) = g(T(x(t))) = g(x(t+1))$, where $\mathcal{K}$ represents the Koopman operator. Generally, this operator can function in an infinite-dimensional space. However, when $\mathcal{K}$ is restricted to a finite dimensional invariant subspace with $g: \mathbb{R}^n \rightarrow \mathbb{R}^m$, the Koopman operator can be depicted as a Koopman matrix $K \in \mathbb{R}^{m \times m}$. The standard Dynamic Mode Decomposition (DMD) approach assumes $g$ to be the identity function, predicated on the notion that the underlying dynamics of $x$ are approximately linear, *i.e.*, $T$ operates as a linear function. The extended-DMD method broadens this scope, utilizing additional feature functions such as polynomial and trigonometric functions as the basis functions for $g$. Further enhancing this approach, machine learning methods for the Koopman operator leverage neural networks as universal approximators for learning $g$ [@koopman_pde_Lusch_2018]. + +:::::: wrapfigure +r0.5 + +::::: minipage +:::: algorithm +::: algorithmic +**Input:** Quantum circuit $\psi_{\boldsymbol{\theta}}$, Loss function $\mathcal{L} (\cdot)$, Iterations $n_{\mathrm{iter}}$, Koopman operator learning parameters $n_{\mathrm{sim}}$, $n_{\mathrm{DMD}}$ **Output:** Optimal parameters $\boldsymbol{\theta}^{*}$ **Initialize:** $\boldsymbol{\theta} (t_0)$ randomly *Quantum optimization or QML:* Compute gradient $\nabla_{\boldsymbol{\theta}} \mathcal{L}(\boldsymbol{\theta}(t_k))$ and update $\boldsymbol{\theta} (t_{k}) \rightarrow \boldsymbol{\theta} (t_{k+1})$ using classical gradient-based optimizer Compute $\mathcal{L} (\boldsymbol{\theta} (t_{k+1}))$ *Koopman operator learning:* Train Koopman operator $\mathcal{K}$ and predict optimization trajectory *Optimal parameters selection:* Compute $\mathcal{L} (\boldsymbol{\theta} (t_{k}))$ Determine $t_{\mathrm{opt}}$ and set $\boldsymbol{\theta} (t_{0}) \leftarrow \boldsymbol{\theta} (t_{\mathrm{opt}})$ +::: +:::: +::::: +:::::: + +Our QuACK algorithm, illustrated in Algorithm [\[alg:scheme\]](#alg:scheme){reference-type="ref" reference="alg:scheme"} and Figure [1](#fig:scheme_1){reference-type="ref" reference="fig:scheme_1"}, employs a quantum circuit $\psi_{\boldsymbol{\theta}}$ with parameters $\boldsymbol{\theta}$ for quantum optimization or QML tasks. In Panel (a) of Figure [1](#fig:scheme_1){reference-type="ref" reference="fig:scheme_1"} the loss function $\mathcal{L} (\cdot)$ is stochastically evaluated through quantum measurements, and a classical optimizer updates the parameters. In Panel (b) following $m$ gradient optimization steps, we obtain a time series of parameter updates $\boldsymbol{\theta} (t_0), \boldsymbol{\theta} (t_1), \dots, \boldsymbol{\theta} (t_m)$. Then in Panel (c) this series is utilized by the Koopman operator learning algorithm to find an embedding for approximately linear dynamics. In Panel (d) this approach predicts the parameter updates for $n_{\mathrm{DMD}}$ future gradient dynamics steps and calculates $\boldsymbol{\theta} (t_{m+1}), \boldsymbol{\theta} (t_{m+2}), \dots, \boldsymbol{\theta} (t_{m+n_{\mathrm{DMD}}})$. + +In each time step, the parameters are set directly in the quantum circuit for loss function evaluation via quantum measurements. This procedure has constant cost in terms of the number of parameters, same as the forward evaluation cost of the loss function. From the $n_{\mathrm{DMD}}$ loss function values, we identify the lowest loss and the corresponding optimal time $t_{\mathrm{opt}} = \mathop{\mathrm{arg\,min}}_{t_{m} \leq t \leq t_{m + n_{\mathrm{DMD}}}} \mathcal{L} (\boldsymbol{\theta} (t))$. This step includes the last VQE iteration $t_m$ to prevent degradation in case of inaccurate DMD predictions. To facillate the Koopman operator learning algorithm, we introduce DMD, Sliding Window DMD and neural DMD into QuACK as follows. + +Dynamic Mode Decomposition (DMD) employs a linear fit for vector dynamics $\boldsymbol{\theta} \in \mathbb{R}^n$, where $\boldsymbol{\theta} (t_{k+1}) = K \boldsymbol{\theta} (t_k)$. By concatenating $\boldsymbol{\theta}$ at $m + 1$ consecutive times, we define $\mathbf{\Theta}(t_k) \colonequals [\boldsymbol{\theta}(t_k) \ \boldsymbol{\theta}(t_{k + 1}) \cdots \boldsymbol{\theta}(t_{k + m})]$. We create data matrices $\mathbf{\Theta}(t_0)$ and $\mathbf{\Theta}(t_1)$, the latter being the one-step evolution of the former. In approximately linear dynamics, $K$ is constant for all $t_k$, and hence $\mathbf{\Theta} (t_1) \approx K \mathbf{\Theta} (t_0)$. The best fit occurs at the Frobenius loss minimum, given by $K = \mathbf{\Theta} (t_1) \mathbf{\Theta}(t_0)^+$, where $+$ is the Moore-Penrose inverse. + +When the dynamics of $\boldsymbol{\theta}$ is not linear, we can instead consider a time-delay embedding with a sliding window and concatenate the steps to form an extended data matrix [@dylewsky2022principal] $$\begin{equation} +\begin{split} +\mathbf{\Phi} (\mathbf{\Theta} (t_0)) & = +\left[\begin{matrix} +\boldsymbol{\phi}(t_{0}) & \boldsymbol{\phi}(t_{1}) & \cdots & \boldsymbol{\phi}(t_{m})\end{matrix}\right] = + \left[\begin{matrix} + \boldsymbol{\theta}(t_0) & \boldsymbol{\theta}(t_1) & \cdots & \boldsymbol{\theta}(t_{m}) \\ + \boldsymbol{\theta}(t_1) & \boldsymbol{\theta}(t_2) & \cdots & \boldsymbol{\theta}(t_{m+1}) \\ + \vdots & \vdots & \ddots & \vdots \\ + \boldsymbol{\theta}(t_d) & \boldsymbol{\theta}(t_{d+1}) & \cdots & \boldsymbol{\theta}(t_{m+d}) \end{matrix}\right] +\end{split} +. +\label{eq:delay_embed_data_matrix} +\end{equation}$$ $\mathbf{\Phi}$ is generated by a sliding window of size $d + 1$ at $m + 1$ consecutive time steps. Each column of $\mathbf{\Phi}$ is a time-delay embedding for $\mathbf{\Theta}$, and the different columns $\boldsymbol{\phi}$ in $\mathbf{\Phi}$ are embeddings at different starting times. The time-delay embedding captures some nonlinearity in the dynamics of $\boldsymbol{\theta}$, with $\mathbf{\Theta}(t_{d+1}) \approx K \mathbf{\Phi}(\mathbf{\Theta}(t_0))$, where $K \in \mathbb{R}^{n \times n (d+1)}$. The best fit is given by $$\begin{equation} +\begin{aligned} + K =& \mathop{\mathrm{arg\,min}}_{K} \| \mathbf{\Theta} (t_{d+1}) - K \mathbf{\Phi} (\mathbf{\Theta}(t_0)) \|_F + =& \mathbf{\Theta} (t_{d+1}) \mathbf{\Phi} (\mathbf{\Theta}(t_0))^+ + . +\label{eq:sw_k} +\end{aligned} +\end{equation}$$ + +During prediction, we start with $\boldsymbol{\theta} (t_{m+d+2}) = K \boldsymbol{\phi} (t_{m+1})$ and update from $\boldsymbol{\phi} (t_{m+1})$ to $\boldsymbol{\phi} (t_{m+2})$ by removing the oldest data and adding new predicted data. This iterative prediction is performed via $\boldsymbol{\theta} (t_{k + d + 1}) = K \boldsymbol{\phi} (t_{k})$. Unlike the approach of @dylewsky2022principal , we do not use an additional SVD before DMD, and our matrix $K$ is non-square. We term this method Sliding Window DMD (SW-DMD), with standard DMD being a specific case when the sliding window size is 1 ($d = 0$). + +To provide a better approximation to the nonlinear dynamics, we ask whether the hard-coded sliding window transformation $\mathbf{\Phi}$ can be a neural network. Thus, by simply reformulating $\mathbf{\Phi}$ in Eq. [\[eq:sw_k\]](#eq:sw_k){reference-type="ref" reference="eq:sw_k"} as a neural network, we formulate a natural neural objective for Koopman operator learning as $\mathop{\mathrm{arg\,min}}_{K,\alpha} \|\mathbf{\Theta}(t_{d+1})-K\mathbf{\Phi}_\alpha(\mathbf{\Theta}(t_0))\|_F,$, where $K \in \mathbb{R}^{N_{in}\times N_{out}}$ is a linear Koopman operator and $\mathbf{\Phi}_\alpha(\mathbf{\Theta}(t_0))$ is a nonlinear neural embedding by a neural network $\mathbf{\Phi}_\alpha$ with parameters $\alpha$. $\mathbf{\Phi}_\alpha \colonequals \mathrm{NN}_\alpha \circ \mathbf{\Phi}$ is a composition of the neural network architecture $\mathrm{NN}_\alpha$ and the sliding window embedding $\mathbf{\Phi}$ from the previous section. + +Drawing from the advancements in machine learning for DMD, we introduce three methods: MLP-DMD, CNN-DMD, and MLP-SW-DMD. MLP-DMD uses a straightforward MLP architecture for $\mathbf{\Phi}$, comprising two linear layers with an ELU activation and a residual connection, as shown in Appendix [9.1](#app:neural){reference-type="ref" reference="app:neural"}. Unlike MLP-DMD, CNN-DMD incorporates information between simulation steps, treating these steps as the temporal dimension and parameters as the channel dimension of a 1D CNN encoder shown in Appendix [9.1](#app:neural){reference-type="ref" reference="app:neural"}. To avoid look-ahead bias, we use causal masking of the CNN kernels, and the architecture includes two 1D-CNN layers with a bottleneck middle channel number of 1 and ELU activation to prevent overfitting. MLP-SW-DMD is similar to MLP-DMD but includes the time-delay embedding in the input, a feature we also add to CNN-DMD. As a result, MLP-DMD and CNN-DMD extend the principles of DMD, whereas MLP-SW-DMD and CNN-DMD with time-delay embedding generalize from SW-DMD. More details are available in the Appendix [9.1](#app:neural){reference-type="ref" reference="app:neural"}. diff --git a/2211.14047/main_diagram/main_diagram.drawio b/2211.14047/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..41575ace7c32241788162e4a5b6d66012529f0c0 --- /dev/null +++ b/2211.14047/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2211.14047/main_diagram/main_diagram.pdf b/2211.14047/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7ca4b3239b932e0ce4962710c62a0af222066a23 Binary files /dev/null and b/2211.14047/main_diagram/main_diagram.pdf differ diff --git a/2211.14047/paper_text/intro_method.md b/2211.14047/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..d7543ca20dfa10a4a97e317fedb60952d06a9d89 --- /dev/null +++ b/2211.14047/paper_text/intro_method.md @@ -0,0 +1,222 @@ +# Introduction + +Convolutional Neural Networks (CNN) are widely used as fundamental building blocks in the design of modern deep learning architectures, for it can extract key data features with much fewer parameters, lowering both memory requirement and computational cost. When the input data contains spatial structure, such as pictures or videos, this parsimony often does not hurt their performance. This is particularly interesting in the case of fully convolutional neural networks (FCNN) [@long2015fully], built by the composition of convolution, nonlinear activation and summing (averaging) layers, with the last layer being a permutation invariant pooling operator, see Figure [1](#fig:cnn){reference-type="ref" reference="fig:cnn"}. + +
+ +
An illustration of fully convolutional neural network.
+
+ +Consequently, a prominent feature of FCNN is that, when shifting the input data indices (e.g. picture, video, or other higher-dimensional spatial data), the output result should remain the same. This is called *shift invariance.* An example application of FCNN is image classification problems where the class label (or probability, under the softmax activation) of the image remains the same under translating the image (i.e. shifting the image pixels). A variant of FCNN applies to problems where the output data has the same size as the input data, e.g. pixel-wise segmentation of images [@badrinarayanan2017segnet]. In this case, simply stacking the fully convolutional layers is enough. We call this type of CNN equivariant fully convolutional neural network (eq-FCNN), since when shifting the input data indices, the output data indices shift by the same amount. This is called *shift equivariance*. It is believed that the success of these convolutional architectures hinges on shift invariance or equivariance, which capture intrinsic structure in spatial data. From an approximation theory viewpoint, this presents a delicate trade-off between expressiveness and invariance: layers cannot be too complex to break the invariance property, but should not be too simple that it loses approximation power. The interaction of invariance and network architectures are subjects of intense study in recent years. For example, @cohen2016steerable designed the steerable CNN to handle the motion group for robotics. Deep sets [@NIPS2017_f22e4747] are proposed to solve the permutation invariance and equivariance. Other approaches to build equivariance and shift invariance include parameter sharing [@ravanbakhsh2017equivariance] and the homogeneous space approach [@cohenwelling16; @cohen2019general]. See @bronstein2017geometric for a more recently survey. Among these architectures, the FCNN is perhaps the simplest and most widely used model. Therefore, the study of its theoretical properties is naturally a first and fundamental step for investigating other more complicated architectures. + +In this paper, we focus on the expressive power of the FCNN. Mathematically, we consider whether a function $F$ can be approximated via the FCNN (or eq-FCNN) function family in $L^p$ sense. This is also known as *universal approximation* in $L^p$. In the literature, many results on fully connected neural networks can be found, e.g. @lu2017expressive [@yarotsky2018optimal; @shen2019deep]. However, relatively few results address the approximation of shift invariant functions via fully convolutional networks. An intuitive reason is that the symmetry constraint (shift invariance) will hinder the unconditioned universal approximation. This can be also proved rigorously. In @li2022, the authors showed that if a function can be approximated by an invariant function family to arbitrary accuracy, then the function itself must be invariant. As a consequence, when we consider the approximation property of the FCNN, we should only consider shift invariant functions. This brings new difficulty for obtaining results compared to those for fully connected neural networks. For this reason, many existing results on convolutional network approximation rely on some ways of breaking shift invariance, thus applying to general function classes without symmetry constraints [@oono2019approximation]. Moreover, results on convolutional networks usually require (at least one) layers to have a large number of channels. + +In contrast, we establish universal approximation results for fully convolutional networks where shift invariance is preserved. Moreover, we show that approximation can be achieved by increasing depth at constant channel numbers, with fixed kernel size in each layer. The main result of this paper (Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}) shows that if we choose $\relu$ as the activation function and the terminal layer is chosen as a general pooling operator satisfying mild technical conditions (e.g. max, summation), then convolutional layers with at least 2 channels and kernel size at least 2 can achieve universal approximation of shift invariant functions via repeated stacking (composition). The result is sharp in the sense that neither the size of convolution kernel nor the channel number can be further reduced while preserving the universal approximation property. + +To prove the result on FCNN, we rely on the dynamical systems approach where residual neural networks are idealized as continuous-time dynamical systems. This approach was introduced in @weinan2017proposal and first used to develop stable architectures [@Haber2017] and control-based training algorithms [@li2017maximum]. This is also popularized in the machine learning literature as neural ODEs [@Chen2018]. On the approximation theory front, the dynamical systems approach was used to prove universal approximation of general model architectures through composition [@li2019deep]. The work of @li2022, extended the result to functions/networks with symmetry constraints, and as a corollary obtained a universal approximation result for residual fully convolutional networks with kernel sizes equal to the image size. The results in this paper restrict the size of kernel in a more practical way, and can handle common architectures for applications, which typically use kernel sizes ranging from $3-7$. Moreover, we also establish here the sharpness of the requirements on channel numbers and kernel sizes. + +The restriction on width and kernel size actually can provide more interesting results in the theoretical setting. This is because if we establish our approximation results using finite (and minimal) width and kernel size requirements, they can be used to obtain the universal approximation property for a variety of larger models by simply showing them to contain our minimal construction. + +In summary, the main contributions of this work are as follows: + +1. We prove the universal approximation property of both continuous and time-discretized fully convolutional neural network with residual blocks and kernel size of at least 2. This result concerns about the deep but narrow neural networks with residual blocks. We provide a sufficient condition result about the universal approximation property with respect to the shift invariance. The result does not rely on the specific choice of nonlinear activation functions, nor the choice of the last layer. + +2. Further, we prove the universal approximation property of fully convolutional neural network with ReLU activations having no less than two channels each layer, and kernel size of at least 2. + +3. Finally, we show that the channel number and kernel size requirements above are sharp, since further reducing them may lose the universal approximation property. + +4. The above three points hold true for the approximation of shift equivariant mappings via eq-FCNN. + +The paper is organized as follows: Section [2](#sec:main){reference-type="ref" reference="sec:main"} introduces the mathematical formulation and the main results of this paper. Section [3](#sec:dyn-sys){reference-type="ref" reference="sec:dyn-sys"} introduces the main analytical tool and the bridging result in this paper for the positive part of the main theorem, while Section [4](#sec:sharp-kernel-size){reference-type="ref" reference="sec:sharp-kernel-size"} and [5](#sec:sharp-channel-number){reference-type="ref" reference="sec:sharp-channel-number"} proves the sharpness part of the main theorem. The discussion and conclusion is shown in Section [6](#sec:conclusion){reference-type="ref" reference="sec:conclusion"}. In Appendix [7](#sec:eq){reference-type="ref" reference="sec:eq"} we provide the shift equivariant result, and the remaining technical proof are collected in Appendix [8](#sec:proof){reference-type="ref" reference="sec:proof"}. + +# Method + +In this section, we introduce the notation and formulation of the approximation problem, and then present our main results. We first recall the definition of convolution. Consider two rank $d$ tensors $\bm x$ and $\bm y \in \mathbb{X}$, where $\mathbb{X} := \mathbb{R}^{n_1} \times \dots \times \mathbb{R}^{n_d}$. We denote by $\bm n = [n_1, \dots, n_d]$ the data dimensions, and we define the convolution of $\bm x, \bm y$ by $\bm z = \bm x \ast \bm y$ with $$[\bm z]_{\bm i} = \sum_{\bm j} [\bm x]_{\bm j} [\bm + y]_{\bm i + \bm j - \bm 1},$$ Here, $\bm i, \bm j$ are multi-indices (beginning with $1$) and the arithmetic uses the periodic boundary condition. Taking $\bm x \in \mathbb R^{3\times 3}$ as an example, we denote $$\begin{equation} +\bm x = \begin{bmatrix} [\bm x]_{(1,1)} & [\bm x]_{(1,2)} & [\bm x]_{(1,3)} \\ [\bm x]_{(2,1)} & [\bm x]_{(2,2)} & [\bm x]_{(2,3)} \\ [\bm x]_{(3,1)} & [\bm x]_{(3,2)} & [\bm x]_{(3,3)}\end{bmatrix}, +\end{equation}$$ where $[\bm x]_{(1,4)}$ is identified with $[\bm x]_{(1,1)}$, and similarly for the other indices. + +Let us also define the translation operator $\trans_{\bm k}$ with respect to a multi-index $\bm k$ by $[\trans_{\bm k} \bm x]_{\bm i} = [\bm x]_{\bm i + \bm k}$. The key symmetry condition concerned in this paper - shift equivariance - can now be stated as the following commuting relationship: $$\trans_{\bm k} (\bm x \ast \bm y) = \bm x \ast (\trans_{\bm k} \bm y).$$ + +We now introduce the definition of the fully convolutional neural network (FCNN) architecture we subsequently study. Let $$\Fcal_{r}:=\left\{\sum_{i = 1}^{r} v_i \sigma(\bm w_i \ast \cdot + b_i \bm + 1), \bm w_i \in \mathbb X, v_i , b_i \in \mathbb R\right\},$$ be a function family representing possible forms for each convolutional layer with $r$ channels. Here, $\sigma(x) = \max(x,0)$ is the ReLU function. + +Let the final layer be a pooling operation $g: \mathbb X \to \mathbb R$ obeying the following condition : $g$ is Lipschitz, and permutation invariant with respect to all the coordinates of its input data, i.e. the value of $g$ does not depend on the order of its inputs. Examples of such a pooling operator include *summation* $g(\bm x) = \bm x \mapsto \sum_{\bm i} [\bm x]_{\bm i}$ and *max* $g(\bm x) = \bm x \mapsto \max_{\bm i} [\bm x]_{\bm i}.$ Note that this is stronger than just requiring $g$ to be shift invariant. + +In practice, the convolutional kernel used will be more restrictive, say a kernel size of $3$ or $5$. To study the effect of kernel size, we define the support for an element $\bm x \in \mathbb X$ as $\supp(\bm x) := +(j_1,j_2,\cdots,j_d)$, where $j_s$ is the minimal number such that if the multi-index $\bm i$ has $i_s > j_s$ for some $s$, then $[\bm x]_{\bm i} = 0.$ For example, the support of tensor $\bm x = \begin{bmatrix} 0 & 1 & 0 \\ 1 & 0 & 0 \\ 1 & 0 & 0 \end{bmatrix}$ is $(3,2)$. Two remarks on this definition of support are in order. First, the element $\bm x +\in \mathbb X$ with $\supp (\bm x) \le \bm j = (j_1,j_2,\cdots, j_d)$ can be identified with an element $\tilde{\bm x} \in \mathbb R^{j_1\times +j_2\times\cdots\times j_d}$. [^1] Second, a convolution kernel with size of $s$ can be regarded as a tensor $\bm w \in \mathbb X$ with support $\le \bm s = +(s,s,s,\cdots, s)$. Thus, we may define the convolutional layer family with support up to $\bm \ell$ as $$\Fcal_{r,\bm \ell} := \left\{\sum_{i = 1}^{r} v_i \sigma(\bm w_i \ast \cdot + + b_i \bm 1), \bm w_i \in \mathbb X, \supp(\bm w_i) \le \bm \ell, v_i , b_i \in + \mathbb R\right\}$$ With these notations in mind, we now introduce the following hypothesis spaces defining fully convolutional neural networks and their residual variants $$\begin{align} + \cnn_{r,\bm \ell} + &= + \{ + g \circ \bm f_m\circ \cdots \circ\bm f_1 : \bm f_1,\cdots, \bm f_m \in \Fcal_{r, \bm \ell} + , + m \geq 1 + \},\\ + \rescnn_{r,\bm \ell} + &= + \{ + g \circ (\id + \bm f_m)\circ \cdots \circ(\id + \bm f_1) : \bm f_1,\cdots, + \bm f_m \in \Fcal_{r, \bm \ell}, + m \geq 1 + \} +\end{align}$$ For any family $\mathbf{F}$ of functions $\mathbb{X}\to\mathbb{R}$, let us define $\mathbf{F} +\mathbb{R}:=\{\varphi + b, \varphi \in \mathbf{F} , b\in \mathbb R\}$. This expands the hypothesis space by adding a constant bias to the original function family $\mathbf{F}$. Observe that all functions in the families $\cnn_{\cdot,\cdot}$ and $\rescnn_{\cdot,\cdot}$ are shift invariant in the following sense. + +::: definition +**Definition 1**. *A function $\varphi : \mathbb{X} \rightarrow \R$ is called shift invariant if $\varphi(\bm x) = \varphi(\trans_{\bm k} \bm x)$ for all $\bm x \in \mathbb X, \bm k + \le \bm n.$ A function family $\mathcal X$ is called *shift invariant* if for all its member are shift invariant.* +::: + +A function family $\mathcal X$ satisfies the shift invariant universal approximation property (shift invariant UAP for short) if + +1. The function family $\mathcal X$ is shift invariant, and + +2. For any shift invariant continuous (or $L^p$) function $\psi$, tolerance $\varepsilon >0$, compact set $K \subset \mathbb X$ and $p \in [1,\infty)$, there exists $\varphi \in \mathcal X$ such that $\|\psi - \varphi\|_{L^p(K)} \le \varepsilon.$ + +Correspondingly, we define shift equivariance. A mapping $\bm \varphi : \mathbb X \to \mathbb X$ is called shift equivariant if $\bm \varphi(\trans_{\bm k}\bm x) = \trans_{\bm k}(\bm \varphi(\bm x)).$ We will prove the shift equivariant UAP in Appendix [7](#sec:eq){reference-type="ref" reference="sec:eq"}. + +The main result of this paper is as follows. [^2] + +::: {#thm:main .theorem} +**Theorem 1** (Universal Approximation Property of $\cnn$). *The following statements hold:* + +1. *The residual FCNN hypothesis space $\rescnn_{r, \bm \ell}$ possesses the shift invariant UAP for $r \ge 1$ and $\bm \ell \ge 2$. The non-residual hypothesis space $\cnn_{r, \bm \ell}$ possesses the shift invariant UAP for $r \ge 2$ and $\bm \ell \ge 2$.* + +2. *The kernel size $\bm 2$ is optimal in the following sense: for $\bm \ell$ with $\min \ell_s = 1$, then neither $\rescnn_{\infty, \bm \ell} + \mathbb + R$ nor $\cnn_{\infty, \bm \ell} + \mathbb R$ possess the shift invariant UAP.* + +3. *The channel-width requirement for non-residual fully convolutional neural network is optimal, in the sense that the function family $\cnn_{1, \bm + \infty} + \mathbb R$ does not possess the shift invariant UAP.* +::: + +Notice that due the extended hypothesis space from the added bias, the sharpness results are stronger than just implying that $\cnn_{\infty, \bm \ell}$ or $\rescnn_{\infty, \bm \ell}$ does not possess the shift invariant UAP. The reason we establish the sharpness results for $\cdot + \mathbb R$ is to ensure that the lack of approximation power does not arise from the fact that the ReLU activation function $\sigma$ has non-negative range. Note that this sign restriction does not affect the positive result, since with at least 2 channels one can produce output ranges of any sign. Although this theorem only considers the approximation of shift invariant architectures, similar result can be established for the shift equivariant architectures. We will discuss it in detail in Appendix [7](#sec:eq){reference-type="ref" reference="sec:eq"}. Furthermore, in this section we restrict the activation function $\sigma$ to be the ReLU function, but this restriction is necessary only for the non-residual case. As we will see in Appendix [8.4](#sec:proof:eqcode){reference-type="ref" reference="sec:proof:eqcode"}, for residual FCNNs we can relax our requirement on $\sigma$ to include a large variety of common activation functions. + +Theorem [1](#thm:main){reference-type="ref" reference="thm:main"} indicates the following basic trade-off in the design of deep convolutional neural network architecture: if we enlarge the depth of the neural network, then even if we choose in each layer a simple function (in this theorem, $2$ channels with each kernel in channel with size of $2$), we can still expect a high expressive power. However, the mapping adopted in each layer cannot be degenerate, otherwise it will fail to capture information of the input data. The second and third part of this Theorem tells that this degeneracy may come from either channel number or the kernel size (support of the convolutional kernel). + +We compare this theorem to existing works on the approximation theory of convolutional networks and related architectures. The existing result around the approximation capabilities of convolutional neural networks can be categorized into several classes. One either + +- takes the kernel as full-size (same size as the input) (e.g. [@li2022]) which is not often used in practice, + +- assumes a sufficiently large channel number in order to adopt some kernel learning methods (e.g. [@bietti2021approximation; @favero2021locality; @xiao2022eigenspace]) or averaging methods (e.g. [@yarotsky2018universal; @bao2019approx; @petersen2020equivalence; @jiang_approximation_2021].) + +- removes the nonlinear activation function and reduce to a linear approximation problem (e.g.  [@zhou2020universality]), then use complex fully connected terminal layer(s) to achieve approximation. + +As a consequence, few, if any, results are obtained when the kernel size is small (and the channel number is fixed). Indeed, none of the results we are aware of have considered situations where both kernel size and the width are limited. However, this is in fact the case when designing deep (residual) NNs, as the ResNet family, where the primary change is increasing depth. Our result indicates that even though each layer is relatively simple, much more complicated functions can be ultimately approximated via composition. Furthermore, our analytical techniques (especially for the residual case) does not depend on the explicit form of the activation function and the pooling operator in the last layer. + +Another highlight feature of our result is with respect to the shift invariance, which might be overlooked in some approximation result for convolutional neural network. We restrict our attention to the periodic boundary condition case, which leads to architectures that are exactly shift invariant or equivariant. This significantly confines the expression power of the hypothesis spaces. If such a symmetry is not imposed on each layer, then one can achieve universal approximation of general functions, but at the cost of breaking shift equivariance. For example, @oono2019approximation and @okumoto2021learnability drop the equivariant constraints and builds the deep convolutional neural network with zero boundary condition, achieving universal approximation property of non-symmetric functions. This is because the boundary condition will deteriorate the interior equivariance structure when the network is deep enough. Also, the shift invariance considered here is about the pixel (i.e. the input data), while some other attempts like @yang2020approximation build a wavelet-like architecture to approximate a function invariant to the spatial translation, i.e., functions satisfy that $f += f(\cdot - \bm k)$ for $\bm k \in \mathbb Z$. + +In this paper, we develop the dynamical systems approach to analyze the approximation theory of compositional architectures first introduced in [@li2019deep] without symmetry considerations, and subsequently extended to handle symmetric functions with respect to transitive subgroups of the permutation group [@li2022]. While shift symmetry is covered under this setting, the results in [@li2022] can only handle the case where the convolution filters have the same size as the input dimension. + +In contrast, the results here are established for small and constant filter (and channel) sizes. This is an important distinction, as such configurations are precisely those used in most practical applications. On the technical side, the filter size restriction requires developing new arguments to show how arbitrary point sets can be transported under a flow - a key ingredient in the proof of universal approximation through composition (See Section [8.4](#sec:proof:eqcode){reference-type="ref" reference="sec:proof:eqcode"} for a detailed discussion). Furthermore, the restriction on filter sizes also enabled us to address new questions, such as a minimal size requirements, that cannot be handled by the analysis in [@li2022]. The results and mathematical techniques for these sharpness results are new. Concretely, to provide a sharp lower bound on the filter size and channel number requirements, we develop some techniques to extract special features of functions in $\cnn_{1,\cdot} + \mathbb R$ and $\cnn_{\cdot, \bm 1} + \mathbb R$ that leads to the failure of universal approximation. Detailed constructions are found in Section [4](#sec:sharp-kernel-size){reference-type="ref" reference="sec:sharp-kernel-size"} and Section [5](#sec:sharp-channel-number){reference-type="ref" reference="sec:sharp-channel-number"}. The construction and the corresponding analysis in this part are nontrivial, and we believe that the examples are also useful in analyzing the approximation property of other architectures. + +The core technique we employ to analyze both $\cnn$ and $\rescnn$ is the dynamical systems approach: in which we idealize residual networks into continuous-time dynamical systems. In this section, we introduce the key elements of this approach. + +We first introduce the *flow map*, also called the *Poincaré mapping*, for time-homogenous dynamical systems. + +::: definition +**Definition 2** (Flow Map). *Suppose $\bm f:\mathbb X \to \mathbb X$ is Lipschitz, we define the flow map associated with $\bm f$ at time horizon $T$ as $\bm \phi(\bm f, T)(\bm x) = \bm z(T)$, where $\dot{\bm z}(t) = \bm f(\bm z (t))$ with initial data ${\bm z}(0) = \bm x$. It follows from @Arnold1973Ordinary that the mapping $\bm \phi(f,T)$ is Lipschitz for any real number $T$, and the inverse of $\bm \phi(f,T)$ is $\bm \phi(-f,T)$, hence the flow map is bi-Lipschitz.* +::: + +Based on the flow map, we define the dynamical hypothesis space for the convolutional neural network. Define the dynamical hypothesis space with convolutional kernel as $$\begin{equation} +\code_{r, \bm \ell} = \{ g \circ \bm \phi(\bm f_m, t_m)\circ \cdots \circ \bm \phi(\bm f_1, t_1) : \bm f_1,\cdots,\bm f_m \in \Fcal_{r,\bm \ell}, t_1,\cdots, t_m \in \mathbb R\}. +\end{equation}$$ The following proposition shows we can use residual blocks to approximate continuous dynamical systems. + +::: {#prop:time-disc .proposition} +**Proposition 1**. *Suppose that $\Fcal$ is a bi-Lipschitz function family. For given $$\bm \Phi = \bm \phi(\bm f_m, t_m)\circ\cdots \circ \bm \phi(\bm f_1, t_1), \quad \bm f_i \in \Fcal,$$ and compact $K \subset \mathbb X$, $\varepsilon >0$, there exists $$\widehat{\bm \Phi} = (\id + s_{m'}\bm g_{m'}) \circ \cdots \circ (\id + s_1\bm g_{1}), + \quad + \bm g_i \in \Fcal$$ for some $s_i > 0$, $i=1,\dots,m'$, such that $\|\bm \Phi - \widehat{\bm \Phi}\|_{L^p(K)} \le \varepsilon.$* +::: + +The following result shows the shift invariant UAP for the continuous hypothesis space, and its proof can be found in Appendix [7](#sec:eq){reference-type="ref" reference="sec:eq"}. + +::: {#thm:code .theorem} +**Theorem 2**. *The dynamical hypothesis space $\code_{1, \bm 2}$ satisfies the shift invariant UAP.* +::: + +The rough proof strategy is as follows. We reduce the problem to finite point transportation, i.e., we need to show that the hypothesis space can transport arbitrary but finitely many points (in different orbits under the action of the translation group) to any other set of points. This is done in the previous work of [@li2022]. A key technical difficulty here is that the kernel size is limited, thus previous known constructions of point transportation ([@li2022]) cannot achieve this. Here, we show that we can employ more composition of layers to construct auxiliary mappings to achieve this transportation property. The intuition is that finite-size kernels (satisfying some minimal requirements), when stacked many times, is as good as a full-sized kernel for domain rearrangement - a key enabler of universal approximation through composition. + +With this theorem in hand, we now prove the first part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}. + +::: proof +*Proof of the first part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}.* By the straightforward inclusion relationship, it suffices to show that the function family $\rescnn_{1,\bm 2}$ and $\cnn_{2,\bm 2}$ have UAP. For the residual version, it follows from Proposition [1](#prop:time-disc){reference-type="ref" reference="prop:time-disc"} that if $\code_{1,\bm 2}$ satisfies UAP, then so does $\rescnn_{1,\bm 2}$. In other words, a convergent time discretization inherits universal approximation properties. Thus, given Theorem [2](#thm:code){reference-type="ref" reference="thm:code"} it suffices to prove the remaining $\cnn$ case. + +We begin with a weaker result, showing that $\cnn_{3,\bm 2}$ satisfies UAP. We prove that $\rescnn_{1,\bm 2} \subset \cnn_{3,\bm 2}$. For given $\bm f = v\sigma(\bm w \ast \cdot + b \bm 1)$ with $\sigma = \relu$, we write $$\bm x + \bm f(\bm x) = \sigma(\bm x) + (-1) \sigma(-\bm x) + v\sigma(\bm w \ast \bm x + b \bm 1).$$ This relation indicates that $\rescnn_{1,\bm 2} \subset \cnn_{3,\bm 2}$, which means that $\cnn_{3,\bm 2}$ has UAP. + +However, this approach cannot handle the case $\cnn_{2,\bm 2}$, since the inclusion $\rescnn_{1,\bm 2} \subset \cnn_{2,\bm 2}$ does not hold. This leads to further modification. + +In the following, we prove that for a given $G \in \rescnn_{1,\bm 2}$, compact $K \subset \mathbb X$, there exists $H \in \cnn_{2,\bm 2}$ such that $H(\bm x) = G(\bm x)$ for all $\bm x \in K.$ Suppose that $G = g \circ \bm f_M \circ\cdots\circ\bm f_1,$ where $\bm f_i(\bm x) = \bm x + v^i \sigma(\bm w^i \ast \bm x + b^i\bm 1).$ Set $\bm \gamma_{i} = \bm f_{i}\circ\cdots \bm f_1,\quad \bm \gamma_{0} = \id$ and note that each $\bm \gamma_{i}$ is a Lipschitz mapping. We now consider a sufficiently large real number $R > 0$ such that $|\bm \gamma_i(\bm x)| \le R$ holds for all $i= 0,1,\cdots,M$ and $\bm x \in K$. This can be done since each $\gamma_{i}$ is Lipschitz, and $K$ is a compact set. Define $$\begin{equation} +\label{eq:u0}\bm u_0(\bm x) = \sigma(\bm x + R\bm 1), +\end{equation}$$ and $$\begin{equation} +\label{eq:ui}\bm u_i(\bm x) = \sigma(\bm x) + v^i\sigma(\bm w^i \ast \bm x + (b^i - (\Sigma_{\bm k}[\bm w^i]_{\bm k})R)\bm 1) \in \Fcal_{2,\bm 2} +\end{equation}$$ for $i = 1,2,\cdots,M$. Similarly, define $\bm \eta_s(\bm x) = \bm u_s\circ \cdots \circ \bm u_1 \circ \bm u_0.$ Clearly, $\eta_s \in \cnn_{2, \bm 2}$ for all $s\geq 0$. We now prove by induction that $$\begin{equation} +\label{eq:induction} +\bm\eta_i(\bm x) = \bm\gamma_i(\bm x) + R\bm 1 \text{ for }i = 0,1,\cdots,M. +\end{equation}$$ The base case ($i =0$) is obvious from the definition [\[eq:u0\]](#eq:u0){reference-type="eqref" reference="eq:u0"}, since $\bm x + R\bm 1 > \bm 0$ for all $\bm x \in K$. Suppose that [\[eq:induction\]](#eq:induction){reference-type="eqref" reference="eq:induction"} holds for $i$, then $$\begin{equation*} +\begin{split}\bm \eta_{i+1}(\bm x) = &\sigma(\bm \gamma_{i}(\bm x) + R\bm 1) + v^i\sigma(\bm w^i\ast (\bm \gamma_1(\bm x) +R\bm 1) + (b^i - (\Sigma_{\bm k}[\bm w^i]_{\bm k}) R) \bm 1)\\ + = & \bm \gamma_{i}(\bm x) + R\bm 1 + v^i\sigma(\bm w^i \ast \bm \gamma_i(\bm x) + b^i \bm 1) \\ + = & \bm \gamma_{i+1}(\bm x) + R\bm 1. +\end{split} +\end{equation*}$$ The first equation uses the definition of [\[eq:ui\]](#eq:ui){reference-type="eqref" reference="eq:ui"}, and the second equation follows from $\bm \gamma_i(\bm x) + R\bm 1 \ge 0$ and $\bm w^i \ast R\bm 1 = (\Sigma_{\bm k}[\bm w^i]_{\bm k}) R\bm 1$. This proves [\[eq:induction\]](#eq:induction){reference-type="eqref" reference="eq:induction"} by induction. Finally, we set $\bm u_{M+1}(\bm x) = \sigma(\bm x) - \sigma(R\bm 1),$ then $H(\bm x) + : = g(\bm u_{M+1}(\bm \eta_{M}(\bm x))) = g(\bm \gamma_{M}(\bm x)) = G(\bm x)$ for all $\bm x \in K$. By construction, we have $H \in \cnn_{2,\bm 2}$, therefore we have proved that the UAP holds for $\cnn_{2,\bm 2}$. ◻ +::: + +We remark that the shift equivariance of the dynamical system (and the resulting flow map) may prompt one to consider the same equation in the quotient space with respect to shift symmetry, see @cohen2016group. However, in the case of flow approximation, we found no new useful tools in the quotient space to analyze approximation, thus this abstraction is not adopted here. + +We now give a concrete examples to show that we cannot directly deduce UAP from earlier results by a quotient argument. Observe that for the non-symmetric setting, the result in [@li2019deep] requires that the control family $\mathcal F$ be (restricted) affine invariant. If we directly require this affine invariance in the quotient space, then it will be reduced to scaling invariant. However, the scaling invariant property cannot induce the UAP, and the proof of this is similar to those in Section [4](#sec:sharp-kernel-size){reference-type="ref" reference="sec:sharp-kernel-size"}. + +In this section, we prove the second part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}. Consider kernels with support $\bm \ell$ such that $\min \ell_s = 1$. Without loss of generality we can assume that $\ell_1 = 1$. + +We use the following example to illustrate the main intuition behind this sharpness result. More precisely, we show that the sum of two univariate function cannot approximate a bivariate function well. As an explicit example, we show that there exists $\varepsilon_0$ such that $$\|xy - f(x) - g(y)\|_{L^p([0,1]^2)} \ge \varepsilon_0$$ for all choice of $L^p$ functions $f$ and $g$. Suppose that for some $f,g \in L^p([0,1]^2)$, $\|xy - f(x) - g(y)\|_{L^p([0,1]^2)} = \varepsilon.$ we define $I = [0,1/2]^2$ and $\bm p_1 = [0,0], \bm p_2 = [1/2,0],\bm p_3 = [0,1/2], \bm p_4 = [1/2,1/2]$. For convenience, denote by $h(x,y) = xy$. Consider the following value $$M = \|h(\bm x + \bm p_1) + h(\bm x + \bm p_4) - h(\bm x+\bm p_2) - h(\bm x + \bm p_3)\|_{L^p(I)}.$$ Direct calculation yields that $M = 4^{-\frac{p+1}{p}}>0.$ However, for $\widehat{h}(x,y) = f(x) + g(y)$, it holds that $$\widehat h(\bm x + \bm p_1) + \widehat h(\bm x + \bm p_4) - \widehat h(\bm x+\bm p_2) - \widehat h(\bm x + \bm p_3) = 0.$$ By triangle inequality, $M \le 0 + \sum_{i = 1}^{4} \| h (\bm x + \bm p_i) - \hat{h}(\bm x+\bm p_i)\|_{L^p(K)} \le 4 \varepsilon.$ Therefore, it holds that $\varepsilon\ge \frac{M}{4} > 0$, concluding the result. + +For the general case of establishing the sharpness result, we mimic the example above. We introduce the following auxiliary space. For $\bm x \in \mathbb X$ and integer $I$, define $\bm x_{I:}$ as the tensor in $\mathbb X_1 = \mathbb R^{n_2\times\cdots \times n_d}$, such that $[\bm x_{I:}]_{(i_2,\cdots, i_d)} = [\bm x]_{(I,i_2,\cdots,i_d)}.$ Define $\Hcal$ as the mapping $\mathbb X \to \mathbb R$ such that $$\Hcal := \{g \circ \bm \varphi: \mathbb X \to \mathbb R: \exists \bm \psi :\mathbb X_1 \to \mathbb X_1, \text{ such that } [\bm \varphi(\bm x)]_{I:} = \bm \psi(\bm x_{I:}) \}.$$ We illustrate the function family $\Hcal$ in the following $\mathbb R^{3\times 3}$ example. If $F \in \Hcal$, then $F$ should have the following form: $$F = g\left( \begin{bmatrix} \bm \psi(\bm x_{(1,1)},\bm x_{(1,2)}, \bm x_{(1,3)}) \\ \bm \psi(\bm x_{(2,1)},\bm x_{(2,2)}, \bm x_{(2,3)}) \\ \bm \psi(\bm x_{(3,1)},\bm x_{(3,2)}, \bm x_{(3,3)}) \end{bmatrix} \right).$$ By the assumption on $\bm \ell$, it is straightforward to deduce that $\cnn_{r,\bm \ell}, \rescnn_{r,\bm \ell}, \code_{r,\bm \ell}$ are all in $\Hcal$. It remains to show that $\Hcal$ does not possess the shift invariant UAP. The idea follows the simple example above, by noting that in this case $x$ is now $(\bm x_{(1,1)},\bm x_{(1,2)}, \bm x_{(1,3)})$, $y$ is now $(\bm x_{(2,1)},\bm x_{(2,2)}, \bm x_{(2,3)})$, and $f(x) + g(y)$ is now some general permutation invariant function. We now carry out this proof. + +::: proof +*Proof of the second part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}.* As discussed before, it suffices to show that $\Hcal + \mathbb R$ does not satisfy UAP. Let us set $F(\bm x) = \prod_{i_1 > i_2} (\psi([\bm x]_{i_1:}) - \psi([\bm x]_{i_2:})),$ where $\psi(\bm y) = \prod_{\bm i'} [\bm y]_{\bm i'},$ and $K = [0,1]^{\bm n}$, we show that there exists a constant $\varepsilon_0 > 0$, such that for all $H \in \Hcal$, it holds $$\begin{equation} +\label{eq:Hcal} +\|F - H\|_{L^p(K)} \ge \varepsilon_0. +\end{equation}$$ Choose two subregions of $K$, $K_1 = \{ \bm x \in K, \bm x_{1:} \gg \bm x_{2,:} \gg \cdots \gg\bm x_{n_1:}\},$ and $K_2 = \{ \bm x \in K, \bm x_{2:} \gg \bm x_{1:} \gg x_{3:} \gg \cdots \gg\bm x_{n_1:}\}.$ Here, we say for $\bm z_1$ and $\bm z_2 \in \mathbb X_1$, $\bm z_1 \gg \bm z_2$ means $\min_{\bm i}[\bm z_1]_{\bm i} \ge \max_{\bm i}[\bm z_2]_{\bm i}.$ Consider the mapping $\bm \tau$, that flips first and second rows (along first index), that is, $$\begin{equation} +[\bm \tau(\bm x)]_{2:} = [\bm x]_{1:}, \quad [\bm \tau(\bm x)]_{1:} = [\bm x]_{2:}, \quad [\bm \tau(\bm x)]_{i:} = [\bm x]_{i:}, i \neq 1,2. +\end{equation}$$ Then $\bm \tau(K_1) = K_2$. By the definition of $\Hcal$, we have $(H\circ \tau)(\bm x) = H(\bm x)$ for $\bm x \in K_1$, and $H \in \Hcal + \mathbb R$. But $F\circ \tau= -F$, which implies that $$\begin{equation} + \begin{split} +2\|F\|_{L^p(K_1)} &= \|F - F\circ\bm \tau\|_{L^p(K_1)} \\ & \le \|H - H\circ\bm \tau\|_{L^p(K_1)} + \|F - H\|_{L^p(K)} + \|F\circ \bm \tau - H\circ \bm \tau\|_{L^p(K)} \\ & = 2\|F - H\|_{L^p(K)}. + \end{split} +\end{equation}$$ In the last equation, the last two terms are equal since $\bm \tau$ is measure preserving. ◻ +::: + +In this section, we show that the FCNN with only one channel per layer cannot satisfy the shift invariant UAP. The key to proving this part is the following observation. Suppose $G \in \cnn_{1,\bm \infty} + \mathbb R$, then $G$ is continuous, piecewise linear. Moreover, by direct calculation, we obtain that there exists $\bm g \in \mathbb X$, such that for a.e. $\bm x \in K$, the gradient of $G$ is $\bm 0$ or $\bm g$. The last assertion can be proved from direct calculation on the gradient of $G$. + +::::: proof +*Proof of the third part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}.* Based on the above observation, we now show that $F(\bm x) = |\bm x|$ cannot be approximated by such $G$ in the unit ball $B(\bm 0,1)$. By a change of variables we rewrite $$\begin{equation} + \int_{\bm x \in B(0,1)} |F(\bm x) - G(\bm x)|^p d \bm x + = + \int_{\bm \xi \in \partial B(0,1)}\int_0^1 |F(t\bm \xi) - G(t\bm \xi)|^p t^{|\bm n |- 1} dt dS, +\end{equation}$$ where $|\bm n| = n_1n_2\cdots n_d$. We consider the hemisphere defined by $\bm \xi \in \partial B(0, 1)$ such that $\bm \xi \cdot \bm g < 0$. On this hemisphere, $f(t) = F(t\bm \xi) = t$ is increasing while $g(t) = G(t\bm \xi)$ is decreasing in $t$. + +To proceed, we state and prove the following lemma. + +::: lemma +**Lemma 1**. *For $f : [a,b] \to \mathbb R$ that is increasing, we have $$\inf_{g \text{ decreasing in } [a,b]} \int |f - g|^p = \inf_{g \text{ constant in} [a,b]}\int |f - g|^p.$$* +::: + +::: proof +*Proof.* The $\leq$ part is obvious, so it suffices to prove the $\geq$ part. Given any decreasing $g$, set a constant $\tilde g$ such that $\tilde g(t) = g(t_0)$ if $f(t_0) = g(t_0)$ for some $t_0$, and $g = f(1)$ if there does not exist such a $t_0$. We can easily verify that $|f(t) - g(t)| \ge |f(t) - \tilde g(t)|$ for all $t\in [0,1]$. ◻ +::: + +Using this lemma, we can show that $$\begin{equation} + \begin{split} +\int_0^1|f(t) - g(t)|^pt^{|\bm n| -1} dt + \ge & + \int_{1/2}^1|f(t) - g(t)|^pdt \cdot (\frac{1}{2})^{|\bm n|-1} + \\ \ge & + (\frac{1}{2})^{|\bm n|-1}\inf_{a \in [1/2,1]} \int_{1/2}^1|f(t) - a|^pdt + \\ = & + (\frac{1}{2})^{|\bm n|-1}\inf_{a \in [1/2,1]} \frac{(1-a)^{p+1} + (a - 1/2)^{p+1}}{p+1} + \\ = & 2^{-|\bm n| + 1}\cdot 2 \cdot \frac{(1/4)^{p+1}}{p+1} =: C_p. + \end{split} +\end{equation}$$ The last line follows from the fact that the minimization problem attains its infimum at $a = \frac{3}{4}.$ Therefore, $\int_{\bm x \in B(0,1)} |F(\bm x) - G(\bm x)|^p > \frac{C_p}{2}\alpha$, where $\alpha$ is the Lebesgue measure of $\partial B(0,1)$. This implies the third part of Theorem [1](#thm:main){reference-type="ref" reference="thm:main"}. ◻ +::::: diff --git a/2212.01133/main_diagram/main_diagram.drawio b/2212.01133/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..59e1f0781798dc62f9e232cd9f5678c8213beda3 --- /dev/null +++ b/2212.01133/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2212.01133/main_diagram/main_diagram.pdf b/2212.01133/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e6656da84682f8d3b5651653589dfaf0d632471b Binary files /dev/null and b/2212.01133/main_diagram/main_diagram.pdf differ diff --git a/2212.01133/paper_text/intro_method.md b/2212.01133/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..bfc695d8208f215a19b1718f36634bcd8e46ad49 --- /dev/null +++ b/2212.01133/paper_text/intro_method.md @@ -0,0 +1,78 @@ +# Introduction + +Over the last three years, there has been a 10-fold increase in digital learners on massive open online courses (MOOCs), contributing to a popular and data-rich setting in education [@impey2021moocs; @ByTheNum39]. In enabling a completely online learning experience, MOOCs suffer from high dropout and low success rates [@aldowah2020factors]. Thus, an important task to counter these phenomena is providing personalized guidance at scale [@perez2021can]. This task requires (i) predicting student performance early enough to intervene and adjust learning pathways and (ii) interpreting which behavior contributes to failing and passing trajectories for each student. + +There exists a large body of approaches on student success prediction in MOOCs, e.g., random forests [@marras2021can; @sweeney2016next], logistic regression [@whitehill2017mooc], or neural networks [@wang2017deep; @mu21deeplearning]. Most of these methods operate post-hoc, i.e., in the context of the entire time series. Only few works have focused on predicting success early on during the course. For example, @mbo20early predicted students' pass-fail grades based on video interactions, while @mao2019one used temporal patterns to intervene early in programming tasks. Most of the work has employed hand-crafted expert-designed features, ranging from engagement-based features, such as course attendance rates [@he18utilize] or the number of online sessions [@chen2020utilizing; @lemay2020grade], to features capturing fine-grained video behavior [@DBLP:conf/edm/AkpinarRA20; @mu21deeplearning] and measuring students' learning regularity [@boroujeni2016quantify]. Recently, @marras2021can performed a meta-analysis on early success prediction features, showing that their predictive power does not often generalize across courses and raising questions about which features should be selected based on the course characteristics. Designing and extracting features for educational time series hence becomes expensive in terms of human and computational resources. Minimal literature has addressed raw time series in education. @prenkaj2021hidden used auto-encoders for risk prediction in MOOCs, but did not provide comparisons to hand-crafted baselines. + +Using raw time series in combination with neural networks has also led to black-box models. In response to this issue, there has been a strong increase in research on neural network explainability, with methods such as LIME [@lime], SHAP [@shap], and counterfactual explanations [@cem]. Only few works have however focused on explainability in the domain of education. Prior research has used LIME to provide local explanations for performance prediction models [@hasib2022lime; @vultureanu2021improving] or to build a basis for students dashboards [@scheers2021interactive]. @baranyi2020interpretable applied SHAP to interpret student dropout prediction models. However, a major shortcoming of those methods is that they do not seem to agree about what features are important in MOOCs [@swamy2022explainability]. Furthermore, interpretations are limited only to the engineered features originally during model training. On raw time series predictions, the minimal existing literature has shown attention heatmaps for temporal insights, not higher level, human-friendly actionable features for educator interventions [@ts_interp]. + +In this paper, we propose `Ripple` (Raindrop InterPretability PipeLine for Education), a novel methodology for providing interpretable early student success prediction using raw time series data. In contrast to prior work, our pipeline does not require any feature engineering, while still providing accurate predictions as well as human-friendly explanations. Our pipeline is based on the combination of a graph-based neural network approach [@zhang2021graph] for classifying raw time series of student interactions and the adaptation of concept activation vectors (TCAV) [@kim2018interpretability] for interpreting the neural network's internal state. Specifically, we use six well-defined dimensions of self-regulated learning in online courses from recent literature [@mejia2022identifying] to provide interpretability in the global and local context. To the best of our knowledge, TCAV has never been applied on time series. We evaluate our pipeline on a large educational data set including $23$ MOOCs with over $100,000$ students and millions of interactions, addressing the following research questions: + +1. Can we use raw time series as input and achieve comparable performance to hand-crafted features? + +2. Can we obtain interpretability on raw multivariate time series through learner-centric concept activation vectors? + +Our results show that graph neural networks allow us to achieve comparable or better performance with raw time series models to hand-crafted features in $18$ out of $23$ courses and beat other state-of-the-art time series baselines on $21$ out of $23$ courses. Moreover, we showcase our interpretable pipeline on a selected digital signal processing course. + +
+ +
Our Ripple time series interpretability approach from logs collection to concept vector analysis.
+
+ +# Method + +This paper targets a classification task that utilizes raw multivariate time series to predict student pass-fail labels *early* in a course. Our goal is to achieve at least comparable performance using raw time series data in comparison to hand-crafted features (e.g., @marras2021can), without compromising on interpretability. We first formalize the posed problem and then describe our methodology. + +Given a course $c$ part of the offering $\mathbb{C}$, we denote as $\mathbb S^c$ the set of students enrolled in $c$. Since each course can be run multiple times, we define a course set $\tilde{\mathbb C} = \{c_1, \ldots, c_{M^{\Tilde{\mathbb C}}}\} \subset \mathbb C$ as the set of all iterations of the same course over the years, with $M^{\tilde{\mathbb C}}$ being the total number of iterations for the course set $\tilde{\mathbb C}$. Each course $c$ includes a set of $N_c$ learning objects denoted as $\mathbb{O}^c$. Students interact with learning objects in $\mathbb{O}^c$. The interactions of a student $s \in \mathbb S^c$ are modeled as a time series $\mathbb I_s^c = \{i_1, i_2, \ldots\}$. Each interaction is represented with a tuple composed of a timestamp $t$, an action $a$, a learning object $o \in \mathbb{O}^c$, and optional metadata $m$, i.e., $i = \left(t, a, o, m \right)$. We denote as $y_s^c \in \{0,1\}$ the pass-fail label for student $s$ in course $c$. Training a classification model $\mathcal M_\theta : \mathbb I \xrightarrow{} \{0, 1\}$ is an optimization problem aimed to minimize the expectation on the following objective function: + +$$\begin{equation} +\tilde{\mathcal M_\theta} = \underset{\mathcal M_\theta}{\operatorname{argmin}} \mathop{\mathbb{E}}_{s \; \in \; \mathbb S^c} | \mathcal M_\theta(\mathbb I_s^c) - y_s^c| +\label{eq:problem-definition} +\end{equation}$$ + +To preserve transparency, we assume that the prediction $\tilde{y}_s^c = \tilde{\mathcal M_\theta}(\mathbb I_s^c)$ for student $s$ in course $c$ can be interpreted based on a set $\mathbb P$ of human-understandable educational concepts. Each concept $p \in \mathbb P$ is associated to a relative concept importance score $d^{c,p,y}$ ranging in $[0, 1]$. A value close to $0$ ($1$) means that the concept $p$ has a low (high) importance for the $y$-class model predictions based on the interactions $\mathbb I^c$. An example concept is student's *regularity* in the course. + +Following this formalization, we devised our deep learning approach consisting of the three main stages illustrated in Fig. [1](#fig:features){reference-type="ref" reference="fig:features"}: (i) data collection and preprocessing ($\mathbb I_s^c$), (ii) raw time series classification ($\tilde{\mathcal M_\theta}$), and (iii) concept-based interpretation ($\mathbb P$). We discuss each stage in more detail. + +***Collection***. We collected clickstream data involving interactions $\mathbb I$ for students $\mathbb S^c$ from MOOCs $c \in \mathbb C$, modelled as an irregular multivariate time series. We refer to our time series as irregular due to the non-uniform time interval the data was generated (e.g., a student did not interact for over a week). Multivariate refers to the learning objects involved in the actions $a \in \mathbb A$, used to model the time series $\mathbb I$. + +To model each interaction $i = \left(t, a, o, m \right)$, we considered learning objects $\mathbb{O}^c$ of type video and problems, with the video actions $\mathbb A_v \subset \mathbb A$ = {Download, Error, Load, Pause, Play, Seek, SpeedChange, Stalled} and the problem actions $\mathbb A_p \subset \mathbb A$ = {IsAssignment, IsQuiz}. An ID was assigned to each video and problem. For each problem, the number of times it was attempted by the student (Problem SubmissionNum) was also tracked. Each timestamp $t \in \mathbb N$ and action $a \in \mathbb A$, alongside the metadata $m$ of Video ID, Problem ID, and Problem SubmissionNum, were treated as separate variables in our time series. For brevity, we will refer to our irregular multivariate time series as simply raw time series. + +***Early-Dropout Filtering***. A common archetype of MOOC student is a learner who watches only a few videos or makes only a few initial interactions ($\mathbb I_s^c$). Motivations for this behavior include misaligned expectations of course material, unexpected life circumstances, or intellectual curiosity for a small subset of videos [@onah2014dropout; @goopio2021mooc]. These students can be easily predicted with fail labels ($y_s^c = 1$) by considering their (lack of) initial graded assignments using a simple logistic regression model. It does not make sense to pass this student subset to complex neural networks when they could be so concisely and accurately identified as failing without further analysis. To let our raw time series model focus on hard-to-identify students, we removed students which can be predicted as failing with $99\%$ accuracy using two weeks of assignment data, via the same model proposed in [@swamy2022meta; @swamy2022explainability][^1]. In the rest of this paper, we will refer to $\mathbb S^c$ as the set of students after this filtering. + +***Early Prediction Level Definition***. To enable our model to support instructors during the course [@borrella2021taking; @xing2019dropout; @whitehill2015beyond], we considered an *early* prediction setting. Under an early prediction level $e \in [0, 100]$ representing the percentage of the course duration at which the prediction is delivered, we considered interaction data only up to that point in time. For instance, if $e = 60\%$ and the course lasts $10$ weeks, we would consider only interactions happening in the first six course weeks. We denote the interactions of $s$ in $c$ up to the early prediction level $e$ as $\mathbb I_s^{c,e}$. + +
+ +
Raindrop time series classification approach for educational data: (1) observe an action and obtain interaction embeddings h; (2) use message passing to compute interaction embeddings for the unobserved actions; (3) learn action embeddings z from the interaction embeddings; (4) learn student embeddings s from the action embeddings; (5) course failure classification from student embeddings.
+
+ +***Motivation***. The irregularity and multivariate nature in our time series is generally hard to analyze using classical machine learning temporal models that assume fully (or regularly) observed fixed-size inputs [@ismail2019deep]. To counter these issues, a recent time series representation model (`Raindrop`) assumes that actions are dependent and leverages their hidden structure by using a directed weighted dependency graph [@zhang2021graph]. When an action $a \in \mathbb A$ is observed within an interaction $i \in \mathbb I$, this model updates $a$'s internal representation and uses the dependency graph to update those of the actions related to $a$. The intuition is that an action observed at timestamp $t$ can imply how unobserved actions would behave; updating these unobserved actions can improve the time series modeling. + +***Task Definition***. Given an irregularly sampled multivariate time series $\mathbb I^c$, where each sample $\mathbb I^c_s$ has multiple but not always observed actions and each action has a different number of observations, our model $\tilde{\mathcal M_\theta}$ first learns a function $\mathcal F : \mathbb I^c_s \xrightarrow{} \mathbb R^{*}$ that maps $\mathbb I^c_s$ to a fixed-length representation $\textbf{s}_s^c$ (student embedding) suitable for classification. Using learned $\textbf{s}_s^c$, this model then predicts the label $\tilde{y}_s^c$. The learned representation captures temporal patterns of irregular observations and considers dependencies between actions. + +***Model Learning***. To learn the representation $\textbf{s}_s^c$ for student $s$ in a course $c$, we implemented the `Raindrop` architecture described in @zhang2021graph, generating student-level embeddings using a hierarchical architecture composed of three levels aimed to model interactions, actions, and students (see Fig. [2](#fig:raindrop){reference-type="ref" reference="fig:raindrop"}). First, we built a dependency graph $\mathcal{G}_s$ for every student $s$, where nodes represent actions and directed edges indicate the relation (with a weight ranging in $[0, 1]$) between two actions. The edge weights were initialized to $1$ and optimized student-wise and time-wise via message passing, starting from the node associated to the observed action. + +When an interaction $i = \left(t, a, o, m \right)$ was fed into the model for student $s$ at time $t$, the model first embedded the interaction for the observed action (i.e., the action whose value was recorded) in an interaction embedding $\textbf{h}$ using a non-linear transformation of the input. In order to update the interaction embeddings for unobserved actions at timestamp $t$, a graph neural network was used on top of the dependency graph $\mathcal{G}_s$. Once the interactions embeddings were generated from all timestamps, temporal self attention was used to aggregate all interactions embeddings associated to a given action into a single fixed-size representation $\textbf{z}$. The student embedding $\textbf{s}_s^c$ was obtained by concatenating all action embeddings $\textbf{z}$. The final classifier is a fully-connected network that received $\textbf{s}_s^c$ and output the pass-fail label $\tilde{y}_s^c$. + +***Motivation***. Recent deep learning models, like `Raindrop`, trade transparency for accuracy. However, social, ethical and legislative requirements prominently call for model transparency, especially in education [@webb2021machine; @conati2018ai]. Identifying the possible reasons behind a predicted failure in addition to predicting it accurately is crucial for designing effective interventions. A popular approach to interpretability is the use of post-hoc explainability methods, which return importance scores in terms of the input features the model originally considered. However, these methods appear ineffective on models receiving raw time series. Because of this difficulty, there is a need to shift towards learner-centric concept explanations. With this in mind, we adopt @kim2018interpretability's human-friendly quantitative testing based on concept activation vectors (TCAV), which gives an interpretation of a neural network's internal state in terms of human-interpretable concepts not explicitly considered as an input feature by the model. Used primarily for image and occasionally text data, this technique has never been used on (educational) time series input, to the best of our knowledge. The strengths of TCAV lay in its flexibility to analyze whichever concepts an educator finds pertinent for their course setting. + +***Concept Design and Extraction***. To extract concepts for our educational scenario, we used the six learning dimensions (see Table [\[tab:profiles\]](#tab:profiles){reference-type="ref" reference="tab:profiles"}) proposed by @mejia2022identifying due to the similarity of the underlying course data, the ease in interpreting these profiles, the clear identification of actionable insights based on patterns in these dimensions, the underpinning educational theory validated in them, and their relationship with academic performance. Concerning *effort*, *control*, and *assessment*, @mejia2022identifying found differences among profiles in terms of intensity (higher / lower). The *consistency* dimension was found to capture differences in the relative intensity over the course, with the majority of students having small peaks (uniform) and only a few students working more in the first/last course weeks (first / last half). Regarding *regularity*, some students were found to regularly work on specific weekdays (higher peaks), while others did not have a clear pattern (lower peaks). For *proactivity*, most students were found to interact with the content in advance (anticipated), whereas a minority often interacted with it after the deadline (delayed). + +[]{#tab:profiles label="tab:profiles"} + +We derived our set of concepts $\mathbb P$ from @mejia2022identifying's findings, using the above patterns emerged per dimension. For each dimension, we identified two or three student patterns (e.g., the subset of students showing the highest *effort* and the subset of students showing the lowest *effort*) and devised a greedy optimization protocol to select approximately 100 students that most fit the considered pattern. We achieve this by extracting the $t = 5\%$ of top students showing the considered student pattern for each *corresponding measure* in that dimension (see Table [\[tab:profiles\]](#tab:profiles){reference-type="ref" reference="tab:profiles"}) and computing the intersection of these measure subsets. We then incrementally increased the threshold $t$ until each combined pattern subset had at least 100 students[^2]. + +::: table* +$^1$**Field.** *Bus*: Business; *CS*: Computer Science; *Eng*: Engineering; *Math*: Mathematics; *NS*: Natural Science; *SS*: Social Science.\ +$^2$**No. Students** is calculated after filtering out the early-dropout students, as detailed in the *Time Series Preprocessing* section.\ +$^3$**Passing Rate** is the (min, max) of passing rate percentage over iterations. $^4$**No. Quizzes** is the average number of quizzes. + +[]{#tab:courses label="tab:courses"} +::: + +***Concept Importance Computation***. For a given dimension, the two identified student subsets were given as an input to TCAV, which relied on them to (i) identify a hyperplane that best differentiates between the model activations produced by the subset and the activations in any model layer, and (ii) specify a CAV, i.e., the direction orthogonal to this hyperplane. Using the CAV directional derivative, we identified the importance score of each concept for the predictions our model returned. Formally, let $y$ and $\mathbb S_y$ represent the pass-fail label and the set of students with that label respectively, and let $\mathcal D^{p, l}$ be the directional CAV derivative function for concept $p \in \mathbb P$ at the model layer $l \in \mathbb L$. The TCAV importance score for $p$ is the fraction of $y$-class students whose activation vector was on average positively impacted by $p$: + +$$\operatorname{d}^{c, p, y}= \frac{1}{|\mathbb L|} \sum_{l \; \in \; \mathbb L} \frac{\left|\left\{s \in \mathbb S_y: \mathcal D^{p, l}(s)>0\right\}\right|}{\left|\mathbb S_y\right|}$$ + +TCAV importance scores range between $[0,1]$. Higher values indicate that concept $p$ has a high importance for the prediction of class $y$. The sensitivity of concepts to predictions can be specified for a population of students (global interpretation) or for individual students (local interpretation). diff --git a/2212.08914/main_diagram/main_diagram.drawio b/2212.08914/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..7e963c14da7694f216d07efa4bedd02e320965ff --- /dev/null +++ b/2212.08914/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2212.08914/paper_text/intro_method.md b/2212.08914/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..cfc967f368523d67a112b9b857a91fe44e975297 --- /dev/null +++ b/2212.08914/paper_text/intro_method.md @@ -0,0 +1,59 @@ +# Introduction + +
+ +
Comparison of streaming performances on the ASAP benchmark, where the model rank changes under different computational resources. Note that our baseline BEVDepth-Sv (built upon) consistently improves the streaming performance on different platforms.
+
+ +Vision-centric perception in autonomous driving has drawn extensive attention recently, as it can obtain richer semantic information from images with a desirable budget, compared to LiDAR-based perception. Notably, the past years have witnessed the blooming of vision-centric perception in various autonomous driving tasks (, 3D detection [@li2022bevformer; @huang2021bevdet; @huangjj2021BEVDet4D; @liu2022petr; @liu2022petrv2; @li2022bevdepth; @li2022bevstereo; @en2022m2bev; @Jin2022time; @Zeng2022sts], semantic map construction [@li2022hdmapnet; @peng2022bevsegformer; @WeixiangYang2021ProjectingYV; @zou2022hft; @pon; @vpn], motion forecasting [@AnthonyHu2021FIERYFI; @akan2022stretchbev], and depth estimation [@wang2022crafting; @monov2; @manydepth; @wei2022surround; @packnet; @FelixWimbauer2020MonoRecSD]). + +Despite the growing research interest in vision-centric approaches, the high latency of these methods still prevents the practical deployment. Specifically, in the fundamental task of autonomous-driving perception (, 3D detection), the inference time of most camera-based 3D detectors [@li2022bevformer; @huangjj2021BEVDet4D; @liu2022petrv2; @li2022bevdepth; @li2022bevstereo; @en2022m2bev; @zhang2022beverse] is longer than 300ms (on the powerful NVIDIA RTX3090), which is $\sim$``{=html}6$\times$ longer (see Tab. [\[tab:stream_cmp\]](#tab:stream_cmp){reference-type="ref" reference="tab:stream_cmp"}) than the LiDAR-based counterparts [@yin2021center; @YanYan2018SECONDSE; @AlexHLang2018PointPillarsFE]. To enable practical vision-centric perception in autonomous driving, a quantitative metric is in an urgent need to balance the accuracy and latency. However, previous autonomous-driving benchmarks [@HolgerCaesar2019nuScenesAM; @PeiSun2020ScalabilityIP; @Geiger2012CVPR; @MariusCordts2016TheCD; @bdd100k; @XinyuHuang2020TheAO; @Ben2021argo; @2021Urban; @GabrielJBrostow2008SegmentationAR; @GerhardNeuhold2017TheMV; @JakobGeyer2020A2D2AA] mainly focus on the **offline** performance metrics (, Average Precision (AP), Truth Positive (TP)), and the model latency has not been particularly studied. Although [@meng2020sap; @WeiHan2020StreamingOD] leverage the streaming perception paradigm [@meng2020sap] to measure the accuracy-latency trade-off, these benchmarks are designed for 2D detection or LiDAR-based 3D detection. + +To address the aforementioned problem, this paper proposes the **A**utonomous-driving **S**tre**A**ming **P**erception (ASAP) benchmark. To the best of our knowledge, this is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. The ASAP benchmark is instantiated on the camera-based 3D detection, which is the core task of vision-centric perception in autonomous driving. To enable the streaming evaluation of 3D detectors, an annotation-extending pipeline is devised to increase the annotation frame rate of the nuScenes dataset [@HolgerCaesar2019nuScenesAM] from 2Hz to 12Hz. The extended dataset, termed nuScenes-H (High-frame-rate annotation), is utilized to evaluate 3D detectors with 12Hz streaming inputs. Referring to the practical deployment, we delve into the problem of ASAP under different computational resources. Specifically, the **S**treaming **P**erception **U**nder const**R**ained-computation (SPUR) evaluation protocol is constructed: (1) To compare the model performance on varying platforms, multiple GPUs with different computation performances are assigned for the streaming evaluation. (2) To analyze the performance fluctuation caused by the sharing of computational resources [@duato2009efficient; @yeh2020kubeshare; @zhang2022beverse; @en2022m2bev], the streaming evaluation is performed while the GPU is simultaneously processing other perception tasks. As depicted in Fig. [1](#fig:key1){reference-type="ref" reference="fig:key1"}, the streaming performances of different methods drop steadily as the computation power is increasingly constrained. Besides, the model rank alters under various hardware constraints, suggesting that the offline performance cannot serve as the deterministic criterion for different approaches. Therefore, it is necessary to introduce our streaming paradigm to vision-centric driving perception. Based on the ASAP benchmark, we further establish simple baselines for camera-based streaming 3D detection, and experiment results show that forecasting the future state of the object can compensate for the delay in inference time. Notably, the proposed BEVDepth-Sv improves the streaming performance (mAP-S) by $\sim$``{=html}2%, $\sim$``{=html}3%, and $\sim$``{=html}16% on three GPUs (RTX3090, RTX2070S, GTX1060). + +The main contributions are summarized as follows: (1) We propose the ASAP benchmark to quantitatively evaluate the accuracy-latency trade-off of camera-based perception methods, which takes a step towards the practical vision-centric perception in autonomous driving. (2) An annotation-extending pipeline is proposed to annotate the 12Hz raw images of the popular nuScenes dataset, which facilitates the streaming evaluation on camera-based 3D detection. (3) Simple baselines are established in the ASAP benchmark, which alleviates the influence of inference delay and consistently improves the streaming performances across different hardware. (4) The SPUR evaluation protocol is constructed to facilitate the evaluation of practical deployment, where we investigate the streaming performance of the proposed baselines and seven modern camera-based 3D detectors under various computational constraints. + +# Method + +In this section, the concept of ASAP is first introduced. Then we analyze the difficulty to evaluate streaming algorithms on the original nuScenes dataset [@HolgerCaesar2019nuScenesAM], and introduce the high frame-rate nuScenes-H dataset. Subsequently, the SPUR evaluation protocol is presented to assess the streaming performance under different computational resources. Finally, we propose simple baselines to alleviate the inference time delay in streaming detection. + +Referring to the streaming paradigm [@meng2020sap], the ASAP benchmark conducts the evaluation in an online manner, and the key insight is to perform the evaluation at *every* input timestamp even if the processing of the current sample is not complete. Specifically, given streaming inputs $\{X_i\}_{i=1}^T$, where $X_i$ is the surround-view images at timestamp $t_i$ and $T$ is the total number of input timestamps. The perception algorithms are required to make an online response to the input instance, and the entire online predictions are $\{\hat{Y}_j\}_{j=1}^M$, where $\hat{Y}_j$ is the prediction at timestamp $t_j$, and $M$ represents the total number of predictions. Notably, the prediction timestamps are not synchronized with the input timestamps, and the model inference speed is typically slower than the input frame rate (, $M +


+  

+
Illustration of the proposed ASAP baselines.
+ + +**Velocity-based updating baseline.** Object velocity estimation is an essential task in the original nuScenes benchmark, and various 3D detectors [@huangjj2021BEVDet4D; @li2022bevformer; @li2022bevdepth; @Zeng2022sts; @Jin2022time; @liu2022petrv2; @li2022bevstereo] have been investigated to produce accurate velocity estimations. We empirically find that simply using the *constant velocity motion model* can benefit streaming 3D detection: $$\begin{equation} + Tr(t_{i+1}) = Tr(t_i) + (t_{i+1}-t_i)V(t_i), +\end{equation}$$ where $Tr(\cdot)$ and $V(\cdot)$ represent the predicted object translation and velocity, and $t_i$, $t_{i+1}$ denote the previous input timestamp and the current evaluation timestamp. Such a velocity-based updating strategy is straightforward, but the velocity estimations are independent in each frame, neglecting that the predicted velocity should be consistent and change smoothly. To alleviate the problem, we associate predictions in consecutive frames and refine the predictions using the first-order Kalman filter [@REKalman1960ANA]. Specifically, IoU-based greedy matching is applied to associate 3D bounding boxes across frames. And state representation in the Kalman filter is $\{x,y,z,\dot{x},\dot{y}\}$, where $(x,y,z)$ denotes the center location of the bounding box, and $\dot{x},\dot{y}$ is the estimated velocity in the BEV plane. The updated state representations are leveraged to refine per-frame predictions. For objects that do not have correspondence in sequential frames, we simply use the *constant velocity motion model* to update the locations. Notably, the velocity-based updating strategy can be applied to any modern 3D detector (, in the experiment, BEVDepth-Sv is built upon BEVDepth [@li2022bevdepth]). Besides, the updating pipeline is lightweight ($\sim$``{=html}10ms on CPU), which has a negligible impact on streaming delays. + +**Learning-based forecasting baseline.** The above baseline exploits velocity as the intermediate surrogate to predict future states. From another perspective, the streaming 3D detector should be inherently predictive of the future. Therefore, we craft a simple framework for directly forecasting the future locations of objects. Specifically, a 3D-future detector is built upon BEVDepth [@li2022bevdepth] (termed BEVDepth-Sf), where the algorithm leverages history frames as input and forecasts the detection results of the **next frame**. The model architecture and training strategy are similar to those of [@li2022bevdepth], except that the loss is calculated using annotations from the subsequent frame. For samples that do not have annotations for future frames, we discard them in the training phase. + +In this section, the experiment setup is first given. Subsequently, we delve into the computation-constrained assessment, including streaming evaluation with platforms altering and resource sharing. Finally, we analyze the association between streaming performance and input size/backbone selection. diff --git a/2302.11344/main_diagram/main_diagram.drawio b/2302.11344/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e35757aae221aba592da19b807df8c1a85395a2c --- /dev/null +++ b/2302.11344/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/2302.11344/main_diagram/main_diagram.pdf b/2302.11344/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..60562eb8c567047a30c21b7cf135b08057067c7e Binary files /dev/null and b/2302.11344/main_diagram/main_diagram.pdf differ diff --git a/2302.11344/paper_text/intro_method.md b/2302.11344/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..9fec30ea6e2f2e41341ea5ec1efb58462d363557 --- /dev/null +++ b/2302.11344/paper_text/intro_method.md @@ -0,0 +1,144 @@ +# Introduction + +The human brain has evolved to engage with and learn from an ever-changing and noisy environment, enabling humans to excel at lifelong learning. This requires it to be robust to varying degrees of distribution shifts and noise to acquire, consolidate, and transfer knowledge under uncertainty. DNNs, on the other hand, are inherently designed for batch learning from a static data distribution and therefore exhibit catastrophic forgetting [@mccloskey1989catastrophic] of previous tasks when learning tasks sequentially from a continuous stream of data. The significant gap between the lifelong learning capabilities of humans and DNNs suggests that the brain relies on fundamentally different error-based learning mechanisms. + +Among the different approaches to enabling continual learning (CL) in DNNs [@parisi2019continual], methods inspired by replay of past activations in the brain have shown promise in reducing forgetting in challenging and more realistic scenarios [@hayes2021replay; @farquhar2018towards; @van2019three]. They, however, struggle to approximate the joint distribution of tasks with a small buffer, and the model may undergo a drastic drift in representations when there is a considerable distribution shift, leading to forgetting. In particular, when a new set of classes is introduced, the new samples are poorly dispersed in the representation space, and initial model updates significantly perturb the representations of previously learned classes [@caccia2021new]. This is even more pronounced in the lower buffer regime, where it is increasingly challenging for the model to recover from the initial disruption. Therefore, it is critical for a CL agent to mitigate the abrupt drift in representations and gradually adapt to the new task. + +To this end, we look deeper into the dynamics of error-based learning in the brain. Evidence suggests that different characteristics of the error, including its size, affect how the learning process occurs in the brain [@criscimagna2010size]. In particular, sensitivity to errors decreases as a function of their magnitude, causing the brain to learn more from small errors compared to large errors [@marko2012sensitivity; @castro2014environmental]. This sensitivity is modulated through a principled mechanism that takes into account the history of past errors [@herzfeld2014memory] which suggests that the brain maintains an additional memory of errors. The robustness of the brain to high degrees of distribution shifts and its proficiency in learning under uncertainty and noise may be attributed to the principled modulation of error sensitivity and the consequent learning from low errors. + +
+ +
ESMER employs a principled mechanism for modulating the error sensitivity in a dual-memory rehearsal-based system. It includes a stable model which accumulates the structural knowledge in the working model and an episodic memory. Additionally, a memory of errors is maintained which informs the contribution of each sample in the incoming batch towards learning such that the working model learns more from low errors. The stable model is utilized to retain the relational structure of the learned classes. Finally, we employ error sensitive reservoir sampling which uses the error memory to prioritize the representation of low-loss samples in the buffer.
+
+ +DNNs, on the other hand, lack any mechanism to modulate the error sensitivity and learning scales linearly with the error size. This is particularly troublesome for CL, where abrupt distribution shifts initially cause a considerable spike in errors. These significantly larger errors associated with the samples from unobserved classes dominate the gradient updates and cause disruption of previously learned representations, especially in the absence of sufficient memory samples. To this end, we propose an *Error Sensitivity Modulation based Experience Replay (ESMER)* method that employs a principled mechanism to modulate sensitivity to errors based on its consistency with memory of past errors (Figure [1](#fig:esmer){reference-type="ref" reference="fig:esmer"}). Concretely, our method maintains a memory of errors along the training trajectory and utilizes it to adjust the contribution of each incoming sample to learning based on how far they are from the mean statistics of error memory. This allows the model to learn more from small consistent errors compared to large sudden errors, thus gradually adapting to the new task and mitigating the abrupt drift in representations at the task boundary. To keep the error memory stable, task boundary information is utilized to prevent sudden changes. Additionally, we propose an *Error-Sensitive Reservoir Sampling* approach for maintaining the buffer, which utilizes the error memory to pre-select low-loss samples from the current batch as candidates for being represented in the buffer. It ensures that only the incoming samples that have been well learned are added to the buffer that are better suited to retain information and do not cause representation drift when replaying them. The proposed sampling approach also ensures higher-quality representative samples in memory by filtering out outliers and noisy labels, which can degrade performance. + +Another salient component of the learning machinery of the brain is the efficient use of multiple memory systems that operate on different timescales [@hassabis2017neuroscience; @kumaran2016learning]. Furthermore, replay of previous neural activation patterns is considered to facilitate memory formation and consolidation [@walker2004sleep]. These components may play a role in facilitating lifelong learning in the brain and addressing the challenges of distribution shifts. Therefore, ESMER also employs experience replay in a dual-memory system. In addition to episodic memory, we maintain a semantic memory, which gradually aggregates the knowledge encoded in the weights of the working model and builds consolidated representations, which can generalize across the tasks. The two memories interact to enforce consistency in the functional space and encourage the model to adapt its representations and decision boundary to the new classes while preserving the relational structure of previous classes. The gradual aggregation of knowledge in semantic memory and the enforced relational consistency further aid in mitigating the abrupt change in representations. + +We extensively evaluate our method against state-of-the-art rehearsal-based approaches under various CL settings, which simulate different complexities in the real world including distribution shift, recurring classes, data imbalance, and varying degrees of noisy labels. ESMER provides considerable performance improvement in all scenarios, especially with a lower memory budget, demonstrating the versatility of our approach. On the challenging Seq-CIFAR10 with 200 buffer size and 50% label noise, ESMER provides more than 116% gain compared to the baseline ER method. Furthermore, we show that it mitigates abrupt representation drift, is more robust to label noise, and reduces the task recency bias, leading to uniform and stable performance across tasks. + +# Method + +We first motivate our study with an overview of the issue of abrupt representation drift in DNNs in the CL setting, followed by inspiration from the learning dynamics in the brain. Finally, we present the details of the different components of our method. + +The sequential learning of tasks in CL potentially exposes the model to varying degrees of distribution shifts in the input and output space. In particular, the introduction of previously unobserved classes at the task boundary may lead to an abrupt representation drift, leading to forgetting. [@caccia2021new] show that the samples of the new classes are poorly dispersed and lie near and within the representation space of the previous classes. Therefore, the initial parameter updates cause a significant perturbation of the representations of the previous classes. This is exacerbated by the inherent imbalance between the samples of the new classes in the current task and the stored samples of previous classes in memory, especially in the lower buffer size regime. [@caccia2021new] further show that while the model is able to recover somewhat from the disruptive parameter updates at the task transition with larger buffer sizes, the model fails to recover when the buffer size is small. Moreover, learning scales linearly with the size of the error in standard training. Hence, the considerably higher cross-entropy loss from the unobserved classes dominates learning and biases the gradients towards the new classes. These insights suggest that there is a need for a different learning paradigm tailored for CL that is more robust to abrupt distribution shifts. + +The human brain, on the other hand, has evolved to be robust to distribution shifts and to continually learn and interact with a dynamic environment. This is enabled by a complex set of mechanisms and interactions of multiple memory systems. In particular, we probe the dynamics of error-based learning in the brain to draw some insights. Unlike DNNs where learning scales linearly with error size, evidence suggests that the brain modulates its sensitivity to error as a function of error magnitude, so it learns more from small consistent errors compared to large errors [@marko2012sensitivity; @castro2014environmental; @smith2004modulation]. To enable such a mechanism, the brain appears to maintain an additional memory of errors that informs error sensitivity modulation [@herzfeld2014memory]. + +Another salient element of the learning machinery of the brain is the efficient use of multiple memory systems that operate on different timescales [@hassabis2017neuroscience; @kumaran2016learning]. Moreover, replay of previous neural activation patterns is considered to facilitate memory formation and consolidation [@walker2004sleep; @mcclelland1995there]. These elements could facilitate in enabling lifelong learning in the brain and addressing the challenges of distribution shifts. + +We hypothesize that error sensitivity modulation and the interactions of multiple memory systems that operate on different timescales may be crucial for a continual learning agent. To this end, our method aims to incorporate a principled mechanism to modulate error sensitivity based on the history of errors in a dual memory experience replay mechanism. It involves training a working model $\theta_w$ on a sequence of tasks, while maintaining two additional memories: an instance-based fixed-size episodic memory $\mathcal{M}$, which stores input samples from previous tasks, and a parametric stable model $\theta_s$, which gradually aggregates knowledge and builds structured representations. + +The CL setting considered here involves learning a sequence of $\mathcal{T}$ i.i.d. tasks from a non-stationary data stream $\mathcal{D}_{\tau}\in(\mathcal{D}_1, ..., \mathcal{D}_\mathcal{T})$ where the goal is to approximate the joint distribution of all the tasks and distinguish between the observed classes without access to the task identity at inference. In each training step, the model has access to a random batch of labeled incoming samples drawn from the current task $(x_{\tau}, y_{\tau}) \sim \mathcal{D}_{\tau}$ and memory samples drawn from episodic memory $(x_m, y_m) \sim \mathcal{M}$. + +To mimic the principled mechanism of error sensitivity modulation in the brain, we maintain a memory of errors along the training trajectory and use it to determine how much to learn from each sample. To this end, we maintain error memory using the momentum update of the mean cross-entropy loss $\mu_{\ell}$ on the batch of samples from the current task. As the stable model is intended to consolidate knowledge and generalize well across tasks, we use it to assess how well the new samples are placed in the consolidated decision boundary and representation space. This also avoids the confirmation bias that can arise from the use of the same learning model. + +For each sample $i$ in the current task batch, the cross-entropy loss $\mathcal{L}_{ce}$ is evaluated using stable model: $$\begin{equation} +\label{eq:sem_task} +{l}^i_{s} = \mathcal{L}_{ce}(f(x_b^{i}; \theta_s), y_b^i) +\end{equation}$$ Subsequently, the weight assigned to each sample for the supervised loss is determined by the distance between the sample loss and the average running estimate $\mu_{\ell}$: $$\begin{equation} +\label{eq:sample_weight} +\lambda^i = + \begin{cases} + 1 & \text{if }{l}^i_{s} \leq \beta \cdot \mu_{\ell} \\ + {\mu_{\ell}}/{{l}^i_{s}} & \text{otherwise} + \end{cases} +\end{equation}$$ where $\beta$ controls the margin for a sample to be considered a low-loss. This weighting strategy essentially reduces weight $\lambda^i$ as sample loss moves away from the mean estimate, and consequently reducing learning from high-loss samples so that the model learns more from samples with low-loss. The modulated error-sensitive loss for the working model is then given by the weighted sum of all sample losses in the current task batch: $\mathcal{L}_w^\tau = \sum_{i}^{|x_b|}\lambda^i \; {{l}^i_{s}}$. + +This simple approach to reducing the contributions of large errors can effectively reduce the abrupt drift in representations at the task boundary and allow the model to gradually adapt to the new task. For instance, when previously unseen classes are observed, the loss for their samples would be much higher than the running estimate; thus, by reducing the weights of these samples, the model can gradually adapt to the new classes without disrupting the learned representations of previously seen classes. This implicitly accounts for the inherent imbalance between new task samples and past tasks sample in memory by giving higher weight to samples in memory, which will most likely have a low-loss as they have been learned. + +The memory of errors is preserved using a momentum update of loss on the task batch on stable model. To prevent sudden changes when the task switches, we have a task warm-up period during which the running estimate is not updated. This necessitates the task boundary information during training. To further keep the error memory stable and make it robust to outliers in the batch, we evaluate the batch statistics on sample losses which lie within one std of batch loss mean: $$\begin{equation} + \label{eq:filter_loss} + {l}_{s}^f = \{{l}_{s}^i \mid i \in \{1,...,|x_b|\}, ~{l}^i_{s} \leq \mu_s + \sigma_s \} +\end{equation}$$ where $\mu_s$ and $\sigma_s$ are the mean and std of task losses in stable model, ${l}_{s}$. The mean of the filtered sample losses $\overline{l}_{s}^f$, is then used to update the memory of errors: $$\begin{equation} + \label{eq:running_loss} + \mu_{\ell} \leftarrow {\alpha_\ell} ~\mu_{\ell} + (1-{\alpha_\ell}) ~\overline{l}_{s}^f +\end{equation}$$ where ${\alpha_\ell}$ is the decay parameter that controls how quickly the momentum estimate adapts to current values. This provides us with an efficient approach to preserve a memory of errors that informs the modulation of error sensitivity. + +To mimic the interplay of multiple memory systems in the brain and employ different learning timescales, our method maintains a parametric semantic memory and a fixed-size episodic memory. + +**Episodic Memory** enables the consolidation of knowledge through interleaved learning of samples from previous tasks. We propose *Error-Sensitive Reservoir Sampling* that utilizes the memory of errors to inform Reservoir Sampling [@vitter1985random]. Importantly, we perform a pre-selection of *candidates* for the memory buffer, whereby only the low-loss samples in the current batch of task samples are passed to the buffer for selection: $$\begin{equation} + \label{eq:mem-sel} + (x_c, y_c) = \{(x_i, y_i) \mid (x_i, y_i) \in \mathcal{D}_{\tau}, ~{l^i_s} \leq \beta \cdot {\mu_\ell} \} +\end{equation}$$ This ensures that only the learned samples from the current batch are added to the buffer, thereby preserving the information and reducing the abrupt drift in the learned representations. It can also facilitate the approximation of the distribution of the data stream by filtering out outliers and noise. + +**Semantic Memory** is maintained using a parametric model (stable model) that slowly aggregates and consolidates the knowledge encoded in the weights [@krishnan2019biologically] of the working model. Following @arani2021learning, we initialize the stable model with the weights of the working memory, which thereafter keeps a momentum update of that: $$\begin{equation} + \label{eq:sem_update} + \theta_s \leftarrow \alpha \theta_s + (1-\alpha) \theta_w, ~~~~\text{if}~~ r>u \sim U(0,1) +\end{equation}$$ where $\alpha$ is the decay parameter that controls how quickly the semantic model adapts to the working model and $r$ is the update rate to enable stochastic update throughout the training trajectory. + +Stable model interacts with episodic memory to implement a dual memory replay mechanism to facilitate knowledge consolidation. It extracts semantic information from buffer samples and uses the relational knowledge encoded in the output logits to enforce consistency in the functional space. This semantic consistency loss encourages the working model to adapt the representation and decision boundary while preserving the relational structure of previous classes. The loss on the memory samples is thus given by the combination of cross-entropy loss and semantic consistency loss: $$\begin{equation} +\label{eq:mem} +\mathcal{L}_{w}^m =\mathcal{L}_{ce}(f(X_m; \theta_w), y_m) + \gamma \mathcal{L}_{sc}(f(x_m; \theta_w), f(x_m; \theta_s)) +\end{equation}$$ where the mean squared loss is used as the semantic consistency loss $\mathcal{L}_{sc}$ and $\gamma$ controls the strength of consistency loss. The overall loss of the working model is the sum of the loss on the new task samples and the buffer samples, that is, $\mathcal{L}_{w} = \mathcal{L}_w^\tau + \mathcal{L}_{w}^m$. + +Slow aggregation of knowledge in stable model further helps mitigate the abrupt change in representations. Even after potentially disruptive initial updates at the task boundary, stable model retains the representations of the previous classes, and the enforced consistency loss encourages the working model to retain the relational structure among the previous classes and not deviate too much from them. Therefore, the two components complement each other, and the modulation of error sensitivity coupled with the slow acquisition of semantic knowledge reduces the drift in parameters and hence forgetting. For inference, we use stable model, as it builds consolidated representations that generalize well across tasks (Figure [5](#fig:components){reference-type="ref" reference="fig:components"} and Table [\[tab:wm_perf\]](#tab:wm_perf){reference-type="ref" reference="tab:wm_perf"}). See Algorithm [\[algo:lll\]](#algo:lll){reference-type="ref" reference="algo:lll"} for more details. + +::: {#tab:all-datasets} ++--------+--------+---------------------------------+---------------------------------+---------------------------------+ +| Buffer | Method | Seq-CIFAR10 | Seq-CIFAR100 | GCIL | ++:=======+:=======+:==============:+:==============:+:==============:+:==============:+:==============:+:==============:+ +| 3-8 | | Class-IL | Task-IL | Class-IL | Task-IL | Uniform | Longtail | ++--------+--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| -- | JOINT | 92.20±0.15 | 98.31±0.12 | 70.62±0.64 | 86.19±0.43 | 58.59±1.95 | 58.42±1.32 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | SGD | 19.62±0.05 | 61.02±3.33 | 17.58±0.04 | 40.46±0.99 | 10.38±0.26 | 9.61±0.19 | ++--------+--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| 100 | ER | 41.10±1.10 | 89.34±1.14 | 19.99±0.45 | 54.88±1.16 | 14.43±0.36 | 13.22±0.60 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | DER++ | 55.66±0.78 | 89.27±2.28 | 25.25±2.22 | 56.21±0.74 | 21.17±1.65 | 20.29±1.03 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | ER-ACE | 52.55±3.06 | 88.96±1.21 | 31.28±0.36 | 59.23±1.39 | 23.76±1.61 | 23.05±0.18 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | CLS-ER | 62.30±1.58 | 93.10±0.72 | 38.87±1.59 | 70.29±0.45 | 33.42±0.30 | 33.92±0.79 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | ESMER | **65.37**±0.68 | **93.32**±0.15 | **42.89**±0.40 | **73.00**±0.49 | **35.77**±0.32 | **34.26**±0.26 | ++--------+--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| 200 | ER | 44.79±1.86 | 91.19±0.94 | 21.91±0.36 | 61.34±0.11 | 16.52±0.10 | 16.20±0.30 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | DER++ | 64.88±1.17 | 91.92±0.60 | 30.68±1.35 | 62.71±0.68 | 27.73±0.93 | 26.48±2.04 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | ER-ACE | 62.08±1.44 | 92.20±0.57 | 35.17±1.17 | 63.09±1.23 | 27.44±0.64 | 25.29±1.89 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | CLS-ER | 66.10±0.75 | 93.90±0.60 | 43.80±1.89 | 73.49±1.04 | 35.88±0.41 | 35.67±0.72 | +| +--------+----------------+----------------+----------------+----------------+----------------+----------------+ +| | ESMER | **69.16**±0.54 | **94.10**±0.33 | **48.77**±0.31 | **75.86**±0.96 | **39.00**±1.14 | **37.98**±0.88 | ++--------+--------+----------------+----------------+----------------+----------------+----------------+----------------+ + +: Comparison of CL methods in different settings with varying complexities and memory constraints. We report the mean and one std of 3 runs. For completion, we provide results under Task-IL setting by using the task label to select from the subset of output logits from the single head. +::: + +To faithfully gauge progress in the field, it is imperative to evaluate methods on representative experimental settings that measure how well they address the key challenges of CL in the real world. To this end, we evaluate scenarios that adhere to the proposed desideratas [@farquhar2018towards] and present a different set of challenges. Class Incremental Learning (*Class-IL*) presents the most challenging scenario [@van2019three] where each task presents a new set of disjoint classes and the agent is required to distinguish between all observed classes without access to the task identity. However, the Class-IL setting assumes that the number of classes per task remains constant, classes do not reappear, and the training samples are well-balanced across classes. *Generalized Class-IL (GCIL)* [@mi2020generalized] alleviates these limitations and offers a more realistic setting where the number of classes, the appearing classes, and their sample sizes for each task are sampled from probabilistic distributions. We further emphasize that noise is ubiquitous in the real world; therefore, the learning agent is required not only to avoid forgetting, but also to learn under noisy labels. We consider the **Noisy-Class-IL** setting, which adds varying degrees of symmetric noise in the Class-IL setting. Details of the datasets used in each setting are provided in Section [8.3](#sec:dataset){reference-type="ref" reference="sec:dataset"}. + +We compare our method with strong rehearsal-based methods with single memory (ER, DER++, ER-ACE) and multiple memories (CLS-ER) under different CL settings and uniform experimental settings (details in Section [8.1](#sec:exp_setup){reference-type="ref" reference="sec:exp_setup"} in Appendix). Table [1](#tab:all-datasets){reference-type="ref" reference="tab:all-datasets"} provides the results for the Class-IL and GCIL settings with different data complexity and memory constraints. + +Class-IL requires the method to transfer and consolidate knowledge while also tackling the abrupt representation drift at the task boundary when new classes are observed. The consistent performance improvement in both considered datasets demonstrates the proficiency of ESMER in consolidating knowledge with a low memory budget. Generally, we observe that multiple-memory systems perform considerably better in these challenging settings. Note that Seq-CIFAR100 is a considerably more complex setting in terms of both the recognition task among semantically similar classes and the potential representation drift in the split setting, which introduces 20 unobserved classes with high semantic similarity to previous classes. The improvement of ESMER over the well-performing CLS-ER in this challenging setting suggests that our error-sensitivity modulation method successfully mitigates representation drift and reduces interference among similar tasks. The performance of our method under extremely small buffer sizes in Table [\[tab:buff_size_length\]](#tab:buff_size_length){reference-type="ref" reference="tab:buff_size_length"}, which exacerbates abrupt representation drift, further supports the claim. This gain is promising, given that CLS-ER uses two semantic memories with different update frequencies, while our method uses only one. + +Table [1](#tab:all-datasets){reference-type="ref" reference="tab:all-datasets"} also provides the results for the GCIL setting, which further requires the model to tackle the challenges of class imbalance, sample efficiency, and learn from multiple occurrences of an abject. ESMER provides a consistent improvement in both uniform and longtail settings. The error-sensitivity modulation and consequent gradual adaptation to unobserved classes can allow the model to deal with the class imbalance and learn over multiple occurrences well. Considering a new task with a mix of unobserved and recurring classes, the loss of samples from the recurring classes would most likely be low, as they have been learned earlier, while the loss on unobserved classes would be significantly higher. Consequently, the contribution of unobserved classes would be down-weighted, allowing the model to focus and adapt the representations of the recurring class with reduced interference and representation drift from the unobserved classes. Learning more from low errors also provides more consistent learning and can improve sample efficiency by avoiding potentially suboptimal gradient directions caused by outliers. + +
+ +
(a) Effect on Task 1 performance as the models are trained on Task 2. SM and WM denote stable model and working model (b) The average probability of predicting each task at the end of training on Seq-CIFAR10 with 200 buffer size. Figure 6 provides recency bias for other settings.
+
+ +
+ +
(a) Accuracy of the models on clean and noisy samples as training progresses on Seq-CIFAR10 with 50% label noise. A lower value is better as it indicates less memorization. See Figure 7 for the working model. (b) Percentage of noisy labels in the memory with reservoir sampling in ER and error-sensitive reservoir sampling in ESMER.
+
+ +
+ +
The performance of the models (x-axis) after training on each task in Seq-CIFAR10 with 200 buffer size. See Figure 8 for other datasets and buffer sizes.
+
+ +Finally, Table [\[tab:noisy-cl\]](#tab:noisy-cl){reference-type="ref" reference="tab:noisy-cl"} provides the results under the Noisy-Class-IL setting where the agent is required to concurrently tackle the challenges of learning from noisy labels. Our strong empirical results demonstrate the superiority of ESMER in tackling the unique challenges of this setting. Learning from low errors implicitly makes our model more robust to label noise and reduces memorization since noisy labels would correspond to higher errors. Furthermore, the error-sensitive reservoir sampling approach ensures a higher purity of the memory buffer, which plays a critical role in noisy CL as buffer samples are replayed multiple times, further aggravating their harmful effect. + +Here, we attempt to provide insights into what enables the gains with ESMER. We perform our analysis on Seq-CIFAR10 with 200 buffer size and compare it with ER, DER++ and CLSER. + +**Ablation Study:** We systematically remove the salient components of our method and evaluate their effect on performance in Table [\[tab:ablation\]](#tab:ablation){reference-type="ref" reference="tab:ablation"}. Error-sensitivity modulation provides considerable gains in both low-buffer and noisy label settings. Furthermore, the gradual accumulation of knowledge in stable model proves to be a highly proficient approach to consolidating knowledge. Finally, the proposed error-sensitive reservoir sampling technique provides further generalization gains, particularly in the Noisy-Class-IL setting, where maintaining a clean buffer is critical. Importantly, these components complement each other so that the combination provides the maximum gains. + +**Drift in Representations:** To assess ESMER in mitigating the abrupt drift in representations at the task boundary, we monitor the performance of the model on Task 1 as it learns Task 2. Figure [2](#fig:analysis){reference-type="ref" reference="fig:analysis"} (a) shows that ESMER successfully maintains its performance, while ER suffers a sudden performance drop and fails to recover from earlier disruptive updates. Furthermore, while the working model in ESMER undergoes a reduced representation drift after the task warmup period, the stable model retains the performance and provides a stable reference to constrain the update of the working model, helping it recover from the disruption. This suggests that error-sensitivity modulation, coupled with gradual adaptation in stable model, is a promising approach to mitigate abrupt representation drift. + +**Robustness to Noisy Labels:** We probe into what enables ESMER to be more robust to label noise. One of the key challenges in learning under noisy labels is the tendency of DNNs to memorize incorrect labels [@arpit2017closer] that adversely affect their performance [@sukhbaatar2014training]. To evaluate the degree of memorization, we track the performance of the model on noisy and clean samples during training. Figure [3](#fig:noisy_labels){reference-type="ref" reference="fig:noisy_labels"} (a) shows that ESMER significantly reduces memorization, leading to better performance. Furthermore, the quality of the samples in the memory buffer can play a critical role in the performance of the model, as the memory samples are replayed multiple times. Figure [3](#fig:noisy_labels){reference-type="ref" reference="fig:noisy_labels"} (b) compares the percentage of noisy labels in the buffer with our proposed error-sensitive reservoir sampling and the typical reservoir sampling approach. Our approach considerably reduces the probability of adding a noisy sample in episodic memory and maintains a higher quality of the buffer. Note that both features are a byproduct of mimicking error-sensitivity modulation in the brain rather than being explicitly designed for the setting. + +**Recency Bias:** The sequential learning of tasks with standard SGD in CL induces a recency bias in the model, making it much more likely to predict newly learned classes [@hou2019learning]. To evaluate the task recency bias of the different methods, we calculate the average probability of predicting each task at the end of training by averaging the softmax outputs of the classes associated with each task on the test set. Figure [2](#fig:analysis){reference-type="ref" reference="fig:analysis"} (b) shows that ESMER provides more uniform predictions across tasks. Generally, methods that employ multiple memories are better at reducing the recency bias, and the dynamics of low-loss learning in our method further reduce the bias. Figure [4](#fig:taskwise){reference-type="ref" reference="fig:taskwise"} further shows that ESMER maintains a better balance between the plasticity and the stability of the model. diff --git a/2305.04524/main_diagram/main_diagram.drawio b/2305.04524/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..f112774afa210e6d093a643a94afd1c9d95ec248 --- /dev/null +++ b/2305.04524/main_diagram/main_diagram.drawio @@ -0,0 +1 @@ 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 \ No newline at end of file diff --git a/2305.04524/paper_text/intro_method.md b/2305.04524/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..a0286807c38a9b65e111fed58e6fe4c001acd725 --- /dev/null +++ b/2305.04524/paper_text/intro_method.md @@ -0,0 +1,83 @@ +# Introduction + +Deep learning-based scene text recognition has been developed for years. The accuracy of scene text recognition has vastly increased as the appropriate design of model architecture and the expansion of model size. Previous methods[@ref_lncs18; @ref_lncs11; @ref_lncs12; @ref_lncs14] can address a variety of recognition issues, but the inherent ambiguities, such as complicated background or diversity of font, etc, render the recognized results inaccurate. + +Due to the unique characteristics of text recognition, it is feasible to employ human language priors to rectify the output of a vision recognition model. Utilizing a pre-trained language model is one of the common methods. Fang et al[@ref_lncs44] pre-train a language model using WikiText-103[@ref_lncs45]. The pre-trained language model rectifies the visual prediction through learning grammar and the construction of words in the human language system. Another popular approach is to search for a word that has minimum edit distance(Levenshtein distance[@ref_lncs46]) with the visual prediction in a dictionary. Nguyen et al[@ref_lncs48] present a method for incorporating a dictionary into the training pipeline. They use the dictionary to generate a certain number of candidates and then output the most compatible one with the highest compatibility scores in a probability matrix **P**, which is generated by the visual feature $\bm F_v$. But they still disregard the interaction between visual features and text features in the inference stage.\ +The aforementioned methods utilizing explicit language models have several problems. First, regardless of the pre-trained language model or dictionary language model, the independence of the language model from the visual feature may erroneously rectify the correct prediction results. Second, it is illogical to utilize human language priors to rectify texts that appear infrequently or have no linguistic information($e.g.$ ngee, tsc), since neither a pre-trained language model nor a dictionary can rectify texts without human language logic. + +![Comparison with different pipelines.](intro_small_2.pdf){#intro_small width="100%"} + +In this paper, we present an effective method for incorporating a dictionary into scene text recognition that possesses two advantages: 1) taking visual prediction into account. When generating candidates for the visual prediction, the visual prediction is also included in the candidate set. In this case, the initial visual prediction has a probability to be the ultimate outcome. 2) Integrating visual features into the inference stage. Image-Text Contrastive(ITC) Learning is an unsupervised learning method that aims to make positive image-text pairs have higher similarity scores. Inspired by ITC learning, we additionally integrate a **S**cene **I**mage-**T**ext **M**atching(SITM) network to match visual features and text features by ITC Learning. Nevertheless, when we merely employ other label texts in the same batch as negatives, the image-text matching accuracy in the inference stage is not as good as the training stage. We address the problem by generating hard negatives that resemble the shape of label texts. The difference between three methods is depicted in Fig. [1](#intro_small){reference-type="ref" reference="intro_small"}\ +The main contributions of this paper are summarized as follows: 1) we propose a novel method to integrate a dictionary into scene text recognition that avoids the drawbacks of an ordinary dictionary language model. 2) We also offer a new strategy that employs labels to generate resemblant words as hard negatives in the SITM training stage. 3) A Scene Image-Text Matching Module is introduced, which matches positive image-text pairs in the inference stage. + +# Method + +Language-free approaches typically provide a prediction based on visual features, regardless of context information. CTC-based methods[@ref_lncs21] utilize CNN to extract visual features, RNN to model sequence features, and CTC loss to train the entire recognition network end-to-end [@ref_lncs23; @ref_lncs24; @ref_lncs25]. Segmentation-based methods[@ref_lncs22] segment each character region before classifying and recognizing. The recognition results of all character areas compose the entire text sequence[@ref_lncs26; @ref_lncs27; @ref_lncs28]. However, due to the absence of context information interaction, these approaches cannot attain exceptional performance. + +In previous works, [@ref_lncs2; @ref_lncs3] use explicit language models to improve model recognition accuracy. CNN is employed to extract visual features to predict bags of N-grams of text strings. Recently, [@ref_lncs44] regards the explicit language model as a spell checker to rectify visual prediction results. Some implicit language-based approaches connect visual features with context information by utilizing RNN[@ref_lncs7; @ref_lncs8] or attention mechanisms [@ref_lncs9; @ref_lncs10]. First, an image encoder is employed to extract features from word images, follower by an attention-based method for integrating visual features and context information. [@ref_lncs11; @ref_lncs12; @ref_lncs13; @ref_lncs18] focus on relevant information from 1D image features, and [@ref_lncs14; @ref_lncs15; @ref_lncs16; @ref_lncs17] from 2D image features. Some performance-enhancing approaches focus on learning new feature representations. [@ref_lncs4; @ref_lncs5; @ref_lncs6] train their models by sequence contrastive learning, masked image modeling, and a mix of the two, respectively. + +There are two categories of visual-language representation learning. In the first category, text features and image features are fused using a multi-mode encoder [@ref_lncs35; @ref_lncs36; @ref_lncs37; @ref_lncs38]. This type of approaches has achieved outperformance in downstream tasks such as NLVR[@ref_lncs39] and VQA[@ref_lncs40]. The Second category focus on learning separate texts and images encoders[@ref_lncs41; @ref_lncs42]. CLIP[@ref_lncs43] employs contrastive loss to train the image encoder and text encoder on a massive quantity of network image-text pairs. We opt for the second category to reuse the visual encoder trained in the recognition stage instead of training a new visual encoder. Then, the text encoder is trained from scratch in the matching stage. + +We propose a new method to incorporate a dictionary into scene text recognition. The dictionary is used to generate the certain number of candidates, which will subsequently be matched with the visual features by SITM to output the candidate with the highest similarity score. In this section, the details of the overall architecture are presented. We will also describe the Resemblant word generation strategy and the SITM network. The objective training function is finally introduced. + +
+ +
Ordinary dictionary language model pipeline (a) and proposed dictionary language model pipeline (b). In the ordinary pipeline, the prediction is forced to be one with the smallest edit distance in the dictionary. In the proposed pipeline, the ultimate prediction is determined by SITM
+
+ +As can be seen in Fig. [3](#vision_pipeline){reference-type="ref" reference="vision_pipeline"}, a general scene text recognition framework usually consists of a feature extraction module, a sequence modeling module, and a prediction module, which was proposed by Baek et al[@baek2019wrong]. Our proposed dictionary method can combine any scene text recognition method with the above framework. We utilize the output of the sequence modeling as the visual features $\bm F_{v}$. Specifically in this paper, we employ the vision module of the ABINet[@ref_lncs44] as our baseline network. + +![General Vision Architecture of Scene Text Recognition.](vision_pipeline.pdf){#vision_pipeline width="100%"} + +Fig. [2](#over_arch){reference-type="ref" reference="over_arch"}b describes the procedure of our proposed recognition pipeline, which employs a forward-forward method. For the initial forward, we input the image, generate visual prediction $\hat{y}$ with the text recognition network, and then utilize $\hat{y}$ to find candidates with the top N smallest edit distance in the dictionary. The candidate set is comprised of the N candidates and the visual prediction $\hat{y}$. For example, if $\hat{y}= tirelness$, the candidates will be: *tireless, tiredness, redness, \..., kindness, tirelness*. For the second forward pass, the inputs are the image and the candidates obtained from the first forward. Then, utilizing the SITM network to match the text in the candidates with the image, and output the text with the highest similarity. The inference procedure is depicted in Algorithm 1. + +:::: algorithm +**Input:** $x$: Input Image; $n$: Forward State\ +**Output:** Prediction $y^{*}$ + +::: algorithmic +initial $n = 1$ Input $x$ to get $\hat{y} = {\bf V}(x)$, where $\bf{V}$ is the vision module Construct candidate set $C$ of $\hat{y}$ using dictionary Input $x$ and $C$ to calculate the similarity scores $S$ Get the $c \in C$ with the highest score in $S$ as the final prediction $y^{*}$ $y^{*}$ +::: +:::: + +During training, we generate text candidates for Image-Text Contrastive Learning by using labels in the same mini-batch. In addition, we create a certain number of resemblant words as hard negatives. A contrastive loss function, which is defined based on the similarity cross-entropy function depicted in Section 3.3, and the recognition loss are then employed for training. + +![Qualitative hard negatives in the inference stage.](resemblant_word.pdf){#hard_dict_negatives width="100%"} + +There is a gap between the SITM training stage and the inference stage if we utilize the normal contrastive learning method. Specifically, in the training stage, the negatives are the other labels in the same mini-batch for a single image. For example, if a mini-batch contains: *tiredness, kills, short, break, could, save, **your**, life*, the text negatives are *tiredness, kills, short, break, could, save, life* and the text positive is ***your*** for the image ***your***. However, in the inference stage, the negatives are candidates from the dictionary with the top N smallest edit distance, which is similar to the ground truth. For example, for the image ***your***, the negatives in the inference stage are *pour, you, tour, hour, dour, sour, four*. Fig. [4](#hard_dict_negatives){reference-type="ref" reference="hard_dict_negatives"} exhibits some hard negatives for the labels in the test set. We find the gap would cause some mismatches between image-text pairs in the inference stage and degrade the performance of the dictionary. + +We address the problem with our proposed Resemblant Words Generation strategy. When we train the SITM network, in addition to using the text labels corresponding to other images in the same batch as negatives, we present a strategy for constructing hard negatives using labels. Specifically, we initially establish a similar character lookup table containing five similar characters for each English character. We observe the difference between visual prediction and ground truth and record the wrong predicted characters as the composition of the lookup table. For character $a$, we select $d, e, o, q$ and $u$ as the similar characters. Then we randomly replace a character in a label having a similar appearance. For example, if $\rm y$ = $tiredness$ and the number of the resemblant words is $4$, the hard negatives will be $riredness$, $tiredncss$, $tiredmess$, $tireqness$. + +![Scene Image-Text Matching Module Architecture.](itm_2.pdf){#scene_itm_arch width="100%"} + +The Scene Image-Text Matching module contains image encoder, text encoder and Scene Image-Text Contrastive Learning module. The image encoder consists of a backbone network that shares parameters with the backbone network of the recognition module and a parallel attention layer that is used to convert visual features to sequence features. The text encoder consists of two layers of transformer encoder. Scene Image-Text Contrastive Learning module consists of two liner layers. Image sequence features $\bm{I}\in\mathbb{R}^{L\times C}$ and text sequence features $\bm{T} \in\mathbb{R}^{L\times C}$ are obtained by image encoder and text encoder, respectively. The image features $\bm I$ and text features $\bm T$ pass through the linear projection layer before Image-Text Contrastive Learning. Fig. [5](#scene_itm_arch){reference-type="ref" reference="scene_itm_arch"} shows the details of our SITM module. + +During the training stage, we employ the image-text contrastive learning task in vision-language learning to complete the scene image-text matching task. Image-Text Contrastive Learning aims to learn a representation of distinct modal features. It learns the cosine similarity function $s({\bm I}, {\bm T})=\frac{l_v({\bm I}) \cdot l_t({\bm T})}{||l_v({\bm I})|| \cdot ||l_t({\bm T})||}$, where $l$ represents linear layer and $\bm{I}\in\mathbb{R}^{L \times C}$, $\bm{T}\in\mathbb{R}^{L \times C}$ represent image features and text features, respectively. The matched image-text pair will have a higher similarity score. We calculate image-to-text(i2t) and text-to-image(t2i) similarity and normalize the results using softmax. The formulas are as below: $$\begin{equation} + p^{i2t}_m(I) = \frac{{\rm exp}(s(I, T_m) / \tau)}{\sum_{n=1}^{MN} {\rm exp}(s(I, T_n) / \tau}, + \quad + p^{t2i}_m(T) = \frac{ {\rm exp}(s(T, I_m) / \tau)}{\sum_{n=1}^{N} {\rm exp}(s(T, I_n) / \tau)}, +\end{equation}$$ where $\tau$ is a temperature parameter, $m$ is the order indication of the image or text, $s$ is the cosine similarity function and N is the batch size and M-1 is the number of resemblant words of one label. As can be seen in Fig. [6](#sitm_cam){reference-type="ref" reference="sitm_cam"}, the parallel attention layer focuses on the main character features in the image to guide the matching procedure. + +
+ +
An example of the parallel attention layer Gradient-weighted Class Activation Mapping. The left image is the input and the right image is the activation mapping.
+
+ +During the inference stage, we only calculate the image-to-text(i2t) similarity to find the highest score among candidates from the candidate set: $$\begin{equation} + p^{i2t}_m(I) = \frac{{\rm exp}(s(I, T_m) / \tau)}{\sum_{n=1}^{T} {\rm exp}(s(I, T_n) / \tau}, +\end{equation}$$ where T is the number of candidates in the candidates set which is depicted in Section 3.1. + +A supervised cross-entropy loss function is utilized for training and optimizing the scene text recognizer. By minimizing the negative log-likelihood sequence probability loss function, the difference between the prediction and the ground truth is quantified. The specific formula is given below: $$\begin{equation} + {\mathcal{L}}_{recog} = {\bm E}_{(x, y) \sim (X, Y)} {\left\{- {\rm log}p(y |F_{v}(x) \right\}}. +\end{equation}$$ The full loss function of the Scene Image-Text Matching module consists of ${\mathcal{L}}_{itc}$ $$\begin{equation} + {\mathcal{L}}_{SITM} = {\mathcal{L}}_{itc}, + % + {\mathcal{L}}_{itm} +\end{equation}$$ $$\begin{equation} + {\mathcal{L}}_{itc} = \frac{1}{2}{\bm E}_{(I, T) \sim D} + {\left[{\rm \bm H}({\bm y}^{i2t}(I), {\bm p}^{i2t}(I)) + {\rm \bm H}({\bm y}^{t2i}(T), {\bm p}^{t2i}(T)) + \right]}, +\end{equation}$$ where ${\bm y}^{i2t}(I)$ and ${\bm y}^{t2i}(T)$ denote the ground-truth one-hot similarity, in which the negative pair probability is 0 and the positive pair probability is 1. The H is defined as the cross-entropy loss. + +The overall objective function ${\mathcal{L}}_{overall}$ is defined as: $$\begin{equation} + {\mathcal{L}}_{overall} = \lambda_1{\mathcal{L}}_{recog} + \lambda_2{\mathcal{L}}_{SITM}, +\end{equation}$$ where $\lambda_1$ and $\lambda_2$ are the hyper-parameters used to control the training stages. We respectively set $\lambda_1 = 1, \lambda_2 = 0$ and $\lambda_1 = 0 , \lambda_2 = 1$ when we train the recognition module and Scene Image Text Matching module. diff --git a/2305.12964/main_diagram/main_diagram.drawio b/2305.12964/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..82383450ac0739b67fee9a3197c2ce7468b01ba0 --- /dev/null +++ b/2305.12964/main_diagram/main_diagram.drawio @@ -0,0 +1,922 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2305.12964/main_diagram/main_diagram.pdf b/2305.12964/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e9cebaa1389571d702b465d2d4e2ef52f5aeaf1c Binary files /dev/null and b/2305.12964/main_diagram/main_diagram.pdf differ diff --git a/2305.12964/paper_text/intro_method.md b/2305.12964/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..cd5066501f6d0d01b80c9db09cdaf1421d62d4b3 --- /dev/null +++ b/2305.12964/paper_text/intro_method.md @@ -0,0 +1,109 @@ +# Introduction + +Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery by a given textual description, which has many potential applications in modern surveillance systems (e.g., searching for suspects, lost children or elderly individuals). This task is closely related to person re-identification [@ye2021deep; @leng2019survey] and image-text retrieval [@ijcai2022-759; @10119165; @sun2023hierarchical; @sun2022feature], yet exhibits unique characteristics and challenges. Compared to person re-identification with image query, TBPS provides a more user-friendly search with the open-form text query, correspondingly eliciting the challenge of cross-modal modeling due to the modality heterogeneity. Compared to the general image-text retrieval, TBPS focuses on cross-modal retrieval specific for the person with more fine-grained details, tending to larger intra-class variance as well as smaller inter-class variance, which toughly bottlenecks the retrieval performance. + +![Illustration of (a) canonical TBPS with parallel image-text pairs, (b) TBPS without parallel image-text data. Left: $\mu$-TBPS with separately collected non-parallel images and texts. Right: $\mu$-TBPS with image-only corpus ($\mu$-TBPS$^+$).](resources/figure1.png){#fig1 width="95%"} + +To handle the aforementioned challenge, a series of TBPS works [@li2017person; @wang2020vitaa; @shao2022learning; @cvpr23crossmodal] are proposed to learn robust modality-invariant representations by designing sophisticated cross-modal alignment strategies. These works heavily rely on the parallel image-text pairs for training the model, as illustrated in Figure [1](#fig1){reference-type="ref" reference="fig1"} (a). Although person images are relatively easily accessible via the deployed surveillance cameras, it is a labor-intensive and time-consuming process to annotate person images with textual descriptions. In particular, @zhao2021weakly [@zhao2021weakly] propose the weakly supervised TBPS, which only releases the identity labeling for person images but still requires manually-labeled parallel image-text pairs for training. @jing2020cross [@jing2020cross] propose the domain-adaptive TBPS, which has no dependency on the parallel image-text pair data in the target domain, while the data in the source domain still require human annotation. This spontaneously raises a question: *Can we well perform text-based person search without costly human annotation for parallel image-text data?* + +Towards this end, we make the first attempt to explore text-based person search without parallel image-text data (called $\mu$-TBPS). In this regime, the model is trained only based on the knowledge of the separately collected non-parallel images and texts about person[^1], as illustrated on the left-hand side of Figure [1](#fig1){reference-type="ref" reference="fig1"} (b). $\mu$-TBPS is more resource-saving due to no more reliance on human annotation, but also yields a significant challenge as the model is required to perform cross-modal alignment in the absence of cross-modal correspondence labels. Furthermore, considering that person images are more easily collected than the texts via the surveillance cameras, we also explore the solution under $\mu$-TBPS with image-only corpus (called $\mu$-TBPS$^+$), as shown on the right-hand side of Figure [1](#fig1){reference-type="ref" reference="fig1"} (b). Compared to the primitive $\mu$-TBPS, the advanced $\mu$-TBPS$^+$ highlights a more practical scenario. + +In response to the aforementioned regimes, this paper presents a two-stage framework, ***g**eneration-**t**hen-**r**etrieval* (GTR), which firstly generates pseudo texts corresponding to the person images for remedying the absent annotation, and then trains a retrieval modal in a supervised manner. + +![Illustration of text generation for a given person image. We illustrate (a) objection detection (OD) technology BUTD [@anderson2018bottom] to extract the object tags and then take the tag sequence as the generated text, (b) the off-the-shelf vision-language model BLIP [@li2022blip] to directly perform image captioning (IC), (c) the proposed fine-grained image captioning (FineIC) strategy to obtain an enriched description.](resources/figure2.png){#fig2 width="95%"} + +**Generation**. Given a person image, we aim to generate the corresponding pseudo textual description. Referring to other cross-modal works without parallel image-text data [@li2021unsupervised; @zhou2022unsupervised; @wang2022vlmixer; @li2021unsupervised; @dong2021unsupervised], object detection technology is a straightforward and popular choice to perform the text generation. Unfortunately, it is hard to obtain an expected result when applied to $\mu$-TBPS. As depicted in Figure [2](#fig2){reference-type="ref" reference="fig2"} (a), following the previous cross-modal works without parallel image-text data, we attempt to employ the widely-used object detector BUTD [@anderson2018bottom] to extract the object tags from the image, and then take the tag sequence as the generated text. However, the result is far from satisfactory. For example, the tag "arm" is a common attribute for all individuals, which would yield no substantial contribution to TBPS due to the undifferentiated property. Based on such tags, the generated pseudo text tends to become less distinctive with other texts due to the lack of informative and detailed descriptions. Object detector excels at identifying general objects, which is naturally compatible with general cross-modal tasks but infeasible in this fine-grained TBPS task. Alternatively, we can try the pretrained vision-language model to directly perform image captioning (IC). As shown in Figure [2](#fig2){reference-type="ref" reference="fig2"} (b), the result is also unsuitable. Since the vision-language model is pretrained on numerous image-text pairs with general content, the downstream image captioning is incapable of capturing the fine-grained details of a person. + +To get an enriched description with fine-grained information, we propose a fine-grained image captioning (FineIC) strategy. Firstly, considering that the attribute items about person appearance are typically finite (e.g., gender, type of clothes, color of clothes, existence of accessories), we design a set of corresponding instruction prompts to activate the vision-language model to capture the finite fine-grained person attributes, denoting image-to-attributes (I2A) extraction. For the gap between the attributes and the final open-form textual description, we fulfill the attributes-to-text (A2T) conversion by finetuning a language model (e.g., T5 [@raffel2020exploring]), during which the accessible text corpus is leveraged as a style reference to generate the final pseudo texts. Furthermore, in the more practical scenario of $\mu$-TBPS$^+$ without accessible text corpus for reference, we design a hand-crafted template, in which we fill each blank with the proper attributes for the A2T conversion. + +**Retrieval**. In this stage, we use the images and the generated corresponding texts to train the retrieval model in a supervised manner. The retrieval model can, in principle, be adopted by any existing TBPS method. Notably, the existing TBPS methods are trained with manually-annotated and well-aligned image-text pairs, while our constructed pairs are not always consistent since there inevitably exist incorrect depictions from the previous generation stage. To alleviate the impact of the noise in training the retrieval modal, we develop a confidence score-based training (CS-Training) scheme. We collect the confidence score of the pseudo text in the generation stage, which is utilized to calibrate the propagation error in this stage. Specifically, different weights are endowed to the pseudo texts based on the corresponding confidence score in the loss function of the retrieval model, enabling more confident texts to contribute more during training. The proposed confidence score-based training scheme can be well established due to the flexible plug-and-play and the parameter-free characteristic. + +Overall, the main contributions can be summarized as follows: + +- To the best of our knowledge, we make the first attempt to explore text-based person search without parallel image-text data. We propose a two-stage framework GTR to first generate the pseudo texts for each image and then make the retrieval. + +- In the generation stage, we propose a fine-grained image captioning strategy to obtain the enriched textual descriptions, which first utilizes a set of instruction prompts to activate the vision-language models to generate fine-grained person attributes, and then adopts the finetuned language model or hand-crafted template to convert the attributes into fluent textual descriptions. + +- In the retrieval stage, considering the noise interference of the generated pseudo texts, we develop a confidence score-based training scheme by endowing more weights to more confident texts in the loss function of the retrieval model. + +- Experimental results on multiple TBPS benchmarks demonstrate that our method can achieve a promising performance without relying on parallel image-text data. + +# Method + +Text-based person search without parallel image-text data ($\mu$-TBPS) relies on the separately collected images $I = \{I_1, I_2, \ldots, I_{N_i}\}$ and texts $S = \{S_1, S_2, \ldots, S_{N_s}\}$, or even image-only data $I$, where $N_i$ and $N_s$ are the total number of images and texts, respectively. In the following, we formally delineate the proposed framework, generation-then-retrieval (GTR), for the two settings. The overall framework is depicted in Figure [3](#fig3){reference-type="ref" reference="fig3"}. For simplicity, we omit the symbolic subscripts and use $I$ and $S$ to represent an image and a text, respectively. + +Since there are no parallel image-text pairs available in $\mu$-TBPS, the first stage is aimed at generating the pseudo texts for each person image. However, the widely-used object detection (OD) and image captioning (IC) are both sub-optimal to capture the fine-grained person attributes. To get an enriched description, in this paper, we propose a fine-grained image captioning (FineIC) strategy. + +Firstly, considering that each person image can be decoupled into a set of common attributes, we perform an image-to-attributes (I2A) conversion by designing a set of corresponding instruction prompts. The prompts are composed of a series of questions pertaining to the person attributes, as listed in Appendix [7.1](#instruction prompts){reference-type="ref" reference="instruction prompts"}. + +Formally, we denote the instruction prompts as $P = \{P_1, P_2, \ldots, P_{N_p}\}$, where $N_p$ is the number of the prompts. Given a person image $I$, we feed it with each prompt $P_i$ into an off-the-shelf vision-language model to perform the vision question answering (VQA), thus obtaining the result $\{\left\}_{i=1}^{N_p}$, where $A_{i}$ is the answer to the prompt and $C_{i}$ is the corresponding confidence score that will be used in the retrieval stage. Then, the person attributes are logically obtained according to the answers. + +For the gap between the attributes and the open-form natural language description, we use the accessible texts $S$ as a style reference to fulfill the attributes-to-text (A2T) conversion, in which we finetune a language model to incorporate the style of $S$ into the conversion. Before finetuning, we first construct \ of each accessible text. Specifically, given a text $S=\{s_1, s_2, \dots, s_n\}$ from the accessible text corpus, where $s_i$ is $i$-th word in the text, we extract the noun phrases as the attributes using constituency parsing technology [@joshi2018extending]. The attributes are then formed as an attribute sequence $W=\{w_1, w_2, \dots, w_m\}$, where $w_i$ is $i$-th word in the sequence. With the constructed \<$W$, $S$\>, we can train the language model to output the text $S$ according to the sequence $W$ by maximizing the log-likelihood: $$\begin{equation} +\setlength{\abovedisplayskip}{5pt} +\setlength{\belowdisplayskip}{5pt} +\begin{split} + \max_\theta \sum_{i=1}^n \log p_\theta(s_i\mid w_1, w_2, \dots, w_m, s_1, s_2, \dots, s_{i-1}), +\end{split} +\label{equ1} +\end{equation}$$ where $\theta$ denotes the parameters of the language model. During inference, we use the finetuned language model to convert the attributes extracted from the image to a fluent text. + +Furthermore, for the more practical $\mu$-TBPS$^+$ without available texts, the above solution of finetuning the language model is not feasible. Therefore, we design a template for the A2T conversion: + +`The `$<$`gender`$>$` with `$<$`hair_color`$>$` `$<$`hair_length`$>$` hair wears `$<$`clothes_color`$>$` `$<$`clothes_style`$>$`, `$<$`pants_color`$>$` `$<$`pants_style`$>$` and `$<$`shoes_color`$>$` `$<$`shoes_style`$>$`. `$<$`He / She`$>$` is carrying a `$<$`bag`$>$`, `$<$`glasses`$>$`, a `$<$`phone`$>$` and an `$<$`umbrella`$>$`. The `$<$`gender`$>$` is riding a `$<$`bike`$>$`.` + +The first sentence in the template is designed for the fixed attributes, where the attribute blanks are filled by the corresponding extracted attributes. In addition, the last two sentences are aimed at the variable attributes, in which we determine the presence of the particular words according to the extracted attributes. The qualitative examples of the final constructed texts through the template are visualized in Appendix [7.3](#visualization){reference-type="ref" reference="visualization"}. + +To enrich the comprehensive information, we also generate a coupled description of the image via image captioning via the vision-language model. We obtain the final pseudo text $T$ by concatenating the coupled description with the above generated text. + +Through the previous generation stage, we obtain parallel image-text pairs that can be directly used to train the retrieval model in a supervised manner. However, the pseudo text is not always well-aligned to the image, where the inevitable noise would predispose the retrieval model to the risk of misalignment. To alleviate the impacts of the noise, we develop a confidence score-based training (CS-Training) scheme by introducing the confidence score of the pseudo text into the loss function of the retrieval model. + +The confidence score of the generated text is a joint probability of the confidence of extracted attributes in the text (i.e., $C_i$ from the generation stage). For simplicity, the attributes are deemed to be independent and identically distributed (i.i.d.). Thus, we obtain the confidence score of $T$ as: $$\begin{equation} +% \setlength{\abovedisplayskip}{1pt} +% \setlength{\belowdisplayskip}{2pt} + C = \prod_{i=1}^{N_p}C_{i}. +\label{confidence of text} +\end{equation}$$ + +The confidence score indirectly measures the consistency of the image-text pair $\left$. For this, we can adjust the contribution of each image-text pair by endowing the corresponding confidence score in the loss function. Taking the retrieval model BLIP [@li2022blip] as an example, the vanilla optimization objects include an image-text contrastive learning (ITC) loss and an image-text matching (ITM) loss. Therein, ITC aims to pull the positive pairs together and push the negative pairs away, while ITM focuses on predicting whether an image-text pair is positive or negative. We incorporate the confidence score into the ITC loss as: $$\begin{equation} +% \setlength{\abovedisplayskip}{1pt} +% \setlength{\belowdisplayskip}{1pt} +\begin{split} + \mathcal{L}_{itc}^{i2t} =& -\mathbb{E}_{(I, T, C)\sim D}C^\beta\log\frac{\exp{(s(I, T)/\tau)}}{\sum_{m=1}^M\exp{(s(I, T_m)/\tau)}},\\ + \mathcal{L}_{itc}^{t2i} =& -\mathbb{E}_{(I, T, C)\sim D}C^\beta\log\frac{\exp{(s(I, T)/\tau)}}{\sum_{m=1}^M\exp{(s(I_m, T)/\tau)}},\\ + &\qquad\mathcal{L}_{itc} = \left(\mathcal{L}_{itc}^{i2t} + \mathcal{L}_{itc}^{t2i}\right)\ /\ 2, +\end{split} +\label{cs-based ITC} +\end{equation}$$ where $M$ is the number of instances in a mini-batch, $s(I, T)$ measures the cosine similarity between the image $I$ and the text $T$, $\tau$ is a learnable temperature, and $\beta$ is a hyper-parameter to control the importance of the confidence score $C$. The confidence score-based ITM loss is defined as: $$\begin{equation} +% \setlength{\abovedisplayskip}{1pt} +% \setlength{\belowdisplayskip}{1pt} + \mathcal{L}_{itm} = \mathbb{E}_{(I, T, C)\sim D}C^\beta{\mathcal{H}}\left(y, \phi(I, T)\right), + \label{cs-based ITM} +\end{equation}$$ where $\mathcal{H}$ denotes the cross-entropy function. $y$ is $2$-dimension one-hot vector representing the ground-truth label (i.e., $\left[0, 1\right]$ for the positive pairs, and $\left[1, 0\right]$ for the negative pairs). $\phi(I, T)$ means the predicted matching probability of the pair. + +With the integration of the confidence score, more confident image-text pair will offer more contribution during the training. Notably, when $\beta$ is set to $0$, the confidence score-based loss will degenerate into the vanilla one, which means the confidence score is ignored. + +::: table* ++:---------------------+:------:+:-------:+:-------:+:---------------:+:-------:+:-------:+:--------:+:-------:+ +| **Methods** | **Generation** | **Retrieval** | **R@1** | **R@5** | **R@10** | **mAP** | +| +--------+---------+---------+-----------------+ | | | | +| | **IC** | **I2A** | **A2T** | **CS-Training** | | | | | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| GTR-Baseline | | OD | | | 31.03 | 49.81 | 59.21 | 27.65 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| M1 | | | | | 40.22 | 60.35 | 68.92 | 34.32 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| M2 | | | | | 40.58 | 61.78 | 70.31 | 37.39 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| M3 | | | | | 47.43 | 67.56 | 75.76 | 42.31 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| M4 ($\mu$-TBPS) | | | | | 48.16 | 68.66 | 76.36 | 42.77 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| M4 ($\mu$-TBPS$^+$) | | | | | 47.27 | 67.58 | 75.62 | 42.28 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| GTR ($\mu$-TBPS) | | | | | 48.49 | 68.88 | 76.51 | 43.67 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ +| GTR ($\mu$-TBPS$^+$) | | | | | 47.53 | 68.23 | 75.91 | 42.91 | ++----------------------+--------+---------+---------+-----------------+---------+---------+----------+---------+ + +[]{#table1 label="table1"} +::: diff --git a/2305.18694/main_diagram/main_diagram.drawio b/2305.18694/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..59bb9c17b7cf7f928527a122e6de05f8753fdc8c --- /dev/null +++ b/2305.18694/main_diagram/main_diagram.drawio @@ -0,0 +1,337 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2305.18694/paper_text/intro_method.md b/2305.18694/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..351ca71fe9ffc3cac7d8f5ed15bb99d25de73f77 --- /dev/null +++ b/2305.18694/paper_text/intro_method.md @@ -0,0 +1,187 @@ +# Introduction + +In many fields such as fluid dynamics [@cebeci2005computational] and electromagnetism [@de2014mathematical], scientists and engineers often resort to numerical simulation rather than expensive experiments to study the behavior of physical systems under different parameters. However, traditional numerical methods can be too time-consuming for complex physical systems characterized by partial differential equations (PDEs) [@hao2022physics]. Neural operator, a class of data-driven surrogate models, approximates the mapping from the parameter function to the solution of PDEs. When inference, one forward pass is several orders of magnitude faster than traditional numerical methods [@li2020fourier]. + +
+
+ +
+
Illustration of non-uniform data. The physical quantity f(x, y) varies greatly at the center but remains smooth in the far field, prompting a higher concentration of sensors (blue dots) in the center. Direct global interpolation may overlook these variations and yield a substantial error. Instead, our approach involves decomposing the domain into five subdomains and adaptively applying grids of varying sizes (denoted by different colored lines) to minimize error.
+
+ +Among recently developed neural operators, mesh-based neural operators are typically fast and accurate, whose input and output are specified as uniform grids. For example, Fourier neural operator (FNO) [@li2020fourier] can efficiently extract highly nonlinear and nonlocal features and perform accurate predictions via the fast Fourier transform (FFT). Besides, a uniform grid is naturally compatible with a discretization of PDEs, which is the basis of many important works [@long2018pde; @gin2021deepgreen; @liu2022predicting]. However, real-world physical data are usually highly non-uniformly distributed (see Figure [1](#fig_intro){reference-type="ref" reference="fig_intro"} for an illustration), and mesh-based methods are difficult to apply due to large interpolation errors. To overcome this limitation, some attempts to develop mesh-less neural operators [@lu2021learning; @brandstetter2022message] at the expense of degradation in speed and loss of astounding properties such as the FFT and the equivalence between convolution and differentiation [@long2018pde]. Others have considered borrowing adaptive meshes from numerical methods to learn a bijection between point clouds (a general class of non-uniform data) and uniform grids [@li2022fourier]. But such an approach has not been theoretically justified yet and sometimes hand-crafted designs are needed. In a word, the challenge of non-uniform problems is: how to achieve a good trade-off between speed (to efficiently process unstructured data) and accuracy (to control interpolation error). + +In this paper, we propose a non-uniform neural operator (NUNO) to facilitate mesh-based neural operators over complex geometries and highly non-uniform data. Specifically, we design a K-D tree algorithm for domain decomposition, and then adaptively use different-size grids on different subdomains for interpolation. The interpolated subdomain grids are then used to train the underlying mesh-based model. In this way, we convert unstructured data into uniform grids under the premise of the controlled interpolation error, so that many fast mesh-based techniques can be used. In addition, different-size subdomain grids induce the neural network to learn multi-scale features and improve its abstraction ability. + +In our experiments, we investigate three challenging problems of complex geometries, where non-uniform sensors are placed to capture complex physical phenomena: 2D elasticity from @li2022fourier, (2+1)D channel flow adapted from @aparicio2020cfd, and 3D heatsink from COMSOL application gallery [@comsol], which is a fresh attempt to the 3D problems of complex geometries. The experimental results show that our method achieves state-of-the-art performance as well as fast speed, compared with comprehensive baselines including mesh-less and mesh-based neural operators (via global interpolation). + +In summary, the general contributions of this paper include + +- We propose a general framework, a non-uniform neural operator (NUNO), for facilitating mesh-based operator learning with non-uniform data. + +- We develop a K-D tree algorithm to iteratively divide the problem domain into several subdomains to reduce non-uniformity and subsequent interpolation error. + +- Our method has achieved the best cost-accuracy trade-off. It lowers the error rates by $55\%$ in 2D elasticity, by $61\%$ in (2+1)D channel flow, and by $34\%$ in 3D heatsink. At the same time, it achieves promising speedups: at most $30\times$ speedups compared with mesh-free baselines and ${2\times}$ to ${4\times}$ speedups compared with mesh-based baselines. + +# Method + +Nowadays, numerical methods are still the most prevalent ones to solve PDEs, involving the finite element method (FEM) [@reddy2019introduction], finite volume method (FVM) [@moukalled2016finite], material point method (MPM), multi-grid methods [@hackbusch2013multi], etc. They suffer from the "curse of dimensionality\" [@xue2020amortized], despite their high accuracy and complete theoretical foundation. A moderately complex physical system can cost several minutes to simulate, rendering their infeasibility in scenarios like optimization where PDEs are solved frequently. + +Mesh-based neural operators are a class of fast, flexible, and accurate neural operators in which both the input (parameter function) and output (solution to the PDEs) are discretized into uniform grids. Famous examples include convolutional neural operators [@gao2021phygeonet; @khara2021field], Fourier neural operator (FNO) [@li2020fourier], and Transformer neural operators [@cao2021choose; @hao2023gnot]. In contrast, mesh-less neural operators such as DeepONet [@lu2021learning] and graph neural operators [@sanchez2020learning; @li2020neural] allow for arbitrary inputs and queries, which can be used in non-uniform data. But they have lost the speed and efficient mesh-based techniques like the FFT. Besides, other lines of neural network-based PDE solvers involve physics-informed neural networks (PINNs) [@raissi2019physics] and neural fields [@sitzmann2020implicit]. + +Adaptive mesh methods [@babuvska1990p; @huang2010adaptive] come from the community of numerical methods, which can deform between irregular meshes and uniform meshes. Some have attempted to learn such a deformation to facilitate mesh-base neural operators [@li2022fourier]. However, in addition to the lack of theoretical guarantee, it is extremely difficult for a neural network to learn such a mapping, as there is no corresponding loss function to evaluate the progress and guide its learning towards a uniform grid. + +In this section, we will first give a formulation of the problem considered in this paper. We then present a K-D tree algorithm for domain decomposition, followed by an overall pipeline of our method, a non-uniform neural operator. + +We consider a system of PDEs parameterized by some function $a(x)\in \mathcal{A}, x\in \Omega_a$. When the parameter is specified to be $a(x)$, the solution to the PDEs is denoted as $u(y) \in\mathcal{U}, y\in\Omega_u$. Here, $\mathcal{A}$ and $\mathcal{U}$ are two Banach function spaces with domains of definition $\Omega_a\subset\mathbb{R}^{d_a}$ and $\Omega_u\subset\mathbb{R}^{d_u}$, respectively, where $d_a$ and $d_u$ are two positive integers. Given a dataset $\{ (a_j, u_j) \}_{j=1}^N$ where $a$ follows some distribution $\mu$ and $a,u$ are both represented as point clouds $a:=\{ a(x^{(i)}) \}_{i=1}^M, u:=\{ u(y^{(i)}) \}_{i=1}^M$[^1], we hope to approximate the ground-truth operator $G^\dagger\colon \mathcal{A} \rightarrow \mathcal{U}$ or equivalently, $G^\dagger\colon a(x) \mapsto u(y)$ with a *mesh-based* neural operator $G_{\theta}, \theta\in\Theta$ for some parameter space $\Theta$. + +To capitalize on the speed and flexibility of mesh-based methods like the FFT, we interpolate the unstructured point clouds $a$ and $u$ into uniform grids, yielding vectors $\mathbf{a}$ and $\mathbf{u}$ respectively. Let $\phi$ denote a linear reconstruction or backward interpolation from the uniform grids to the point cloud. The prediction error can then be approximated using the following equation: $$\begin{equation} +\begin{aligned} + \| \phi(G_{\theta}(\mathbf{a})) - u \| + \leq& \| \phi(G_{\theta}(\mathbf{a}) - \mathbf{u}) \| + + \| \phi(\mathbf{u}) - u \|\\ + \leq& \|\phi\| \underbrace{\| G_{\theta}(\mathbf{a}) - \mathbf{u} \|}_{\text{prediction error}} + + \underbrace{\| \phi(\mathbf{u}) - u \|}_{\text{interpolation error}}, +\end{aligned} +\end{equation}$$ where $\|\cdot\|$ is the $L^2$ norm. The first term represents the prediction error of the neural operator, contingent upon the model's capability and the optimization's progression. Furthermore, the interpolation from $a$ to $\mathbf{a}$, can also negatively influence this term. The information lost during the interpolation process can potentially degrade the model's performance. The second term signifies the error of the interpolation from $\mathbf{u}$ back to $u$, which can directly impact the overall accuracy. Upon this analysis, it is evident that interpolation errors hold significant sway over the total error. + +In cases of highly non-uniform data (refer to Figure [1](#fig_intro){reference-type="ref" reference="fig_intro"} for an example), the interpolation error can at times overshadow the total error. In our endeavor to mitigate this particular error, we opt to decompose the domains $\Omega_a$ and $\Omega_u$ into several subdomains, expressed as $\Omega_a=\bigcup_{k=1}^{n_a} \Omega_a^{(k)}, \Omega_u=\bigcup_{k=1}^{n_u} \Omega_u^{(k)}$. Within these subdomains, we perform interpolation using varied grid sizes to procure uniform grids, denoted as $\{ \mathbf{a}^{(k)} \}_{k=1}^{n_a}, \{ \mathbf{u}^{(k)} \}_{k=1}^{n_u}$. By judiciously decomposing the domain into subdomains, we can adaptively apply denser meshes in areas characterized by a high point density, and conversely, coarser meshes in areas where the points are more sparsely distributed. + +Formally speaking, with the domain decomposition strategy, our optimization target can be described as: $$\begin{equation} + \min_{\theta\in\Theta} \mathbb{E}_{a\sim \mu} + \left\| \phi \left( G_{\theta}(\{ \mathbf{a}^{(k)} \}_{k=1}^{n_a}) \right) - u \right\|, +\end{equation}$$ where $\{ \mathbf{a}^{(k)} \}_{k=1}^{n_a}$ is interpolated from $a$ and $G_{\theta}$ takes $\{ \mathbf{a}^{(k)} \}_{k=1}^{n_a}$ as input and outputs a prediction for $\{ \mathbf{u}^{(k)} \}_{k=1}^{n_u}$. Finally, the output of $G_{\theta}$ is interpolated back to the point cloud to obtain a prediction for $u$. + +:::: algorithm +::: algorithmic +initial point cloud $D^{(0)}$ and the number of sub-point clouds $n$ a sef of sub-point clouds $\mathcal{S}$ $\mathcal{S} \leftarrow \{ D^{(0)} \}$ Choose $D^* = \mathop{\mathrm{arg\,max}}_{D\in \mathcal{S}} (|D| \cdot \mathrm{KL}\infdivx{P}{Q}{D})$ $\mathcal{S}\leftarrow\mathcal{S}-\{ D^* \}$ Select the dimension $k, 1\le k \le d$, where the bounding box of $D^*$ has the largest scale Determine the hyperplane $x_k = b^*$, where $b^* = \mathop{\mathrm{arg\,max}}_{b\in \mathcal{B}} \mathrm{Gain}(D^*, b)$ and $\mathcal{B}$ is a set of discrete candidates for $b$ Partition $D^*$ with $x_k = b^*$ $\mathcal{S}\leftarrow\mathcal{S}\cup\{ D^*_{x_k>b^*}, D^*_{x_k\le b^*} \}$ +::: +:::: + +
+
+ +
+
(a) Domain decomposition: starting from D(0), each time we choose to split a sub-point cloud with a hyperplane li. After 4 iterations, we obtain 5 sub-point clouds distributed more uniformly within their bounding boxes. (b) NUNO framework: 1. interpolation to subdomain grids; 2. projection by P; 3. pass through the mesh-based neural operator with L layers (h0, …, hL indicate hidden embeddings of each layer); 4. projection by Q; 5. interpolation back to the point cloud.
+
+ +Domain decomposition aims to approximate the distribution of points within a given point cloud by uniform distributions defined in a series of subdomains, which corresponds to varied uniform subdomain grids. Upon obtaining this approximation, we separately perform interpolation in each subdomain, using a grid size proportional to the number of points living in it to maintain a low interpolation error. + +Let $D^{(0)}=\{ x^{(i)}:= (x_1^{(i)}, \dots, x_d^{(i)}) \}_{i=1}^m$ be an initial point cloud, where $d$ and $m$ represents the dimensionality and the number of points in the point cloud, respectively. The optimization target of domain decomposition can be framed as minimizing the approximation error, which is quantified by the Kullback-Leibler (KL) divergence: $$\begin{equation} +\label{eq:kdtarget} + \min_{D^{(1)}, \dots, D^{(n)}} \sum_{i=1}^n \frac{|D^{(i)}|}{|D^{(0)}|} \mathrm{KL}\infdivx{P}{Q}{D^{(i)}}, +\end{equation}$$ where $\bigcup_{i=1}^{n} D^{(i)}=D^{(0)}$ constitutes a $n$-partition of $D^{(0)}$ with disjoint bounding boxes. A bounding box is defined as the smallest enclosure containing all the points. For instance, the bounding box for $D^{(0)}$ is given by $[x_{\mathrm{min},1},x_{\mathrm{max},1}]\times\cdots \times [x_{\mathrm{min},d},x_{\mathrm{max},d}]$, where $x_{\mathrm{min},j} = \min \{ x_j^{(i)} \}_{i=1}^m$ and $x_{\mathrm{max},j} = \max \{ x_j^{(i)} \}_{i=1}^m$. Additionally, $\mathrm{KL}\infdivx{P}{Q}{D}$ is the KL divergence between the distribution of points in a point cloud $D$, denoted by $P$, and the uniform distribution within the bounding box of $D$, denoted by $Q$: $$\begin{equation} +\label{eq_uniform} + \mathrm{KL}\infdivx{P}{Q}{D} := \sum_{j} \frac{|D_{j}|}{|D|} \ln\left(\frac{|D_{j}|}{|D|} \bigg/ \frac{1}{\prod_{i=1}^d N_i}\right). +\end{equation}$$ In calculating $P$, we employ histogram density estimation. Specifically, we divide the bounding box of $D$ uniformly into $N_1\times\cdots\times N_d$ cells, and $D_{j}$ is defined as $\{ x\mid x\in D \land x \in \text{the $j$-th cell} \}$, for $1\le j\le \prod_{i=1}^d N_i$. + +While the optimization problem in Eq. [\[eq:kdtarget\]](#eq:kdtarget){reference-type="eqref" reference="eq:kdtarget"} is reduced to an integer programming issue and is NP-hard, we have devised a heuristic method to approximate the solution. This method is inspired by the K-D tree algorithm, recognized for its quasilinear complexity and compatibility with arbitrary point clouds. In each iteration, we select a sub-point cloud $D^*$ with the highest total KL divergence and split it using a hyperplane $x_k=b^*$. The parameter $k$ is selected such that the bounding box of $D^*$ has the largest scale. The chosen hyperplane maximizes the gain of the KL divergence, represented by $\mathrm{Gain}(D^*, b):=$ $$\begin{equation} +\label{eq_uniform_after} +\begin{aligned} + \mathrm{KL}\infdivx{P}{Q}{D^*} &- + \frac{\left|D^*_{x_k>b}\right|}{|D^*|} \mathrm{KL}\infdivx{P}{Q}{D^*_{x_k>b}} \\ + &- \frac{\left|D^*_{x_k\le b}\right|}{|D^*|} \mathrm{KL}\infdivx{P}{Q}{D^*_{x_k\le b}}, +\end{aligned} +\end{equation}$$ where $D^*_{x_k>b},D^*_{x_k\le b}$ are defined as $\{ x \mid x\in D^* \land x_k > b \}$ and $\{ x \mid x\in D^* \land x_k \le b \}$, respectively. Through the recursive application of this process, we can partition $D^{(0)}$ into $n$ sub-point clouds, decreasing the approximation error. We provide an outline of the algorithm in Algorithm [\[alg_1\]](#alg_1){reference-type="ref" reference="alg_1"} and showcase it in Figure [2a](#fig:nuno). + +Under proper assumptions, the time and space complexity are, respectively, derived as $\mathcal{O}(nm\log m + nd)$ and $\mathcal{O}(md)$, where $n$ is the number of sub-point clouds, $m$ is the number of points in the initial point cloud, and $d$ is the dimensionality. We leave a detailed analysis in Appendix [6.1](#app_complexity){reference-type="ref" reference="app_complexity"}. In practice, the value of $n$ acts as a cost-accuracy trade-off. As discussed in Section [4](#sec_exp){reference-type="ref" reference="sec_exp"}, we have empirically found that $n\le 10^2$ are sufficient for general complex systems. In this way, the algorithm takes only a few seconds, which is completely negligible compared to the training time of neural operators. + +In this subsection, we elucidate our framework for the non-uniform neural operator, designed to synergize rapid computation with high precision. As illustrated in Figure [2b](#fig:nuno), the framework can be methodically deconstructed into five primary steps: + +1. Initially, the input point-cloud values, denoted as $a$, are interpolated into input subdomain grids which are subsequently harmonized to a uniform size. The resulting dimension from this step is denoted as $n_a\times s$, where $s$ represents the padded mesh size. + +2. The subdomain grids are subsequently transformed into hidden embeddings of dimension $n_h\times s$ via a mapping function $P\colon \mathbb{R}^{n_a}\rightarrow \mathbb{R}^{n_h}$. Here, $n_h$ symbolizes the hidden width. This mapping function is parameterized by a shallow fully connected layer, with parameters that are shared across all mesh locations. + +3. These generated hidden embeddings are then propagated through the hidden layers of the underlying mesh-based neural operator. + +4. Post this operation, the hidden embeddings are transformed into the output subdomain grids of size $n_u\times s$, facilitated by another mapping function $Q\colon \mathbb{R}^{n_h}\rightarrow \mathbb{R}^{n_u}$. This function is also parameterized by a shallow fully connected layer, with parameters shared across all mesh locations. + +5. Lastly, these output subdomain grids are interpolated back into the output point cloud $u$, which constitutes the final prediction. + +By transforming the point cloud into structured data through domain decomposition, we not only maintain a handle on the interpolation error but also expedite operations such as convolution and the FFT, bolstering our ability to extract non-local and non-linear features. Additionally, for the same total size, the subdomain grid is of smaller size compared to the global grid. As a result, the size of hidden embeddings (i.e., $s$) diminishes, yielding a more compact representation and thus a significantly lower space-time cost. + +
+
+ +
FNO (global interpolation)
+
+
+ +
Geo-FNO (learned)
+
+
+ +
NU-FNO (ours)
+
+
Visualization of 2D elasticity: the absolute prediction error of σ at point-cloud locations.
+
+ +:::::: table* +::::: small +:::: threeparttable ++------------------------------+---------------------+-----------------------------------------------------+-------------------------------------------------------+ +| **Method** | **Mesh Size** | **Training Time** | **$L^2$ Relative Error ($\times 10^{-2}$)** | ++:=============================+:====================+:========================+:==========================+:==========================+:==========================+ +| 3-6 | | **per Epoch** | **per Run** | **Training** | **Testing** | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| NU-FNO (**ours**) | $1024$ | 2.3 s | 19.6 | $\bm{1.68} \pm \bm{0.08}$ | $\bm{1.93} \pm \bm{0.11}$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| FNO (global interpolation) | $1681$ | $\bm{1.2} \,\mathrm{s}$ | $\bm{9.7} \,\mathrm{min}$ | $3.41 \pm 0.08$ | $6.16 \pm 0.16$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| U-Net (global interpolation) | $1681$ | 2.3 s | 18.8 | $1.74 \pm 0.02$ | $6.82 \pm 0.06$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| Geo-FNO (R mesh) | $1681$ | 1.7 s | 14.2 | $3.53 \pm 0.09$ | $5.03 \pm 0.09$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| Geo-FNO (O mesh) | $1353$ | 2.0 s | 16.4 | $4.12 \pm 0.16$ | $4.28 \pm 0.15$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| Geo-FNO (learned) | $\mathrm{meshless}$ | 2.3 s | 19.1 | $2.07 \pm 0.99$ | $4.31 \pm 2.24$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| GraphNO | $\mathrm{meshless}$ | 96.8 s | 5.4 h | $17.5 \pm 1.26$ | $16.9 \pm 1.28$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ +| DeepONet | $\mathrm{meshless}$ | 40.0 s | 11.0 h | $2.80 \pm 0.13$ | $12.0 \pm 0.16$ | ++------------------------------+---------------------+-------------------------+---------------------------+---------------------------+---------------------------+ + +::: tablenotes +In this paper, all mesh sizes mentioned for our method specifically refer to the *combined total number* of points across all subdomains, rather than the number of points within each individual subdomain. +::: +:::: +::::: + +[]{#tab_exp1 label="tab_exp1"} +:::::: + +Our framework exhibits compatibility with all mesh-based neural operators, necessitating no additional modifications for implementation. Moreover, domain decomposition facilitates flexibility in usage. For instance, we can execute different normalizations across varied subdomains, an approach particularly beneficial for multi-scale and discontinuous problems [@pang2020npinns]. + +We note that the subdomain grids $\{ \mathbf{a}^{(k)} \}_{k=1}^{n_a}$ may differ in size, presenting challenges for batching and propagation. This issue is circumvented by aligning the grids to a uniform size. This alignment can be achieved by employing size-insensitive structures like the SPP-Net [@he2015spatial] or the Transformer [@vaswani2017attention] to generate embeddings of a consistent size. Alternatively, we could directly resize all the subdomain grids to match in size using up/down sampling or FFT-IFFT methods, a common technique in computer vision that we utilize in our implementation. Further details are elaborated in Appendix [6.2](#app_exp){reference-type="ref" reference="app_exp"}. + +
+
+

+
The geometry, point cloud, and subdomains
+
+
+

+
L2 relative error (average) and training time per epoch
+
+
Visualization of (2+1)D channel flow. (a) Gray solid rectangles (left) indicate walls between channels while red hollow rectangles (right) indicate bounding boxes of point clouds on each subdomain. (b) Each value of the oversampling ratio is run only once, and the random seed is 2023. The “interp" is an abbreviation of “global interpolation".
+
+ +::::: table* +:::: small +::: tabular +l\|l\|llll & &\ +& & & & & **Average**\ +NU-FNO (**ours**) & $\bm{7.2}$ & & & & $1.74 \pm 0.05$\ +FNO (global interpolation) & $26.9$ & & & & $3.61 \pm 0.19$\ +NU-U-Net (**ours**) & $8.6$ & & & & $1.27 \pm 0.03$\ +U-Net (global interpolation) & $29.5$ & & & & $3.29 \pm 0.15$\ +NU-MWNO (**ours**) & $22.9$ & & & & $\bm{1.22} \pm \bm{0.06}$\ +MWNO (global interpolation) & $40.6$ & & & & $2.71 \pm 0.63$\ +Geo-FNO (learned) & $114.9$ & & & & $2.15 \pm 0.83$\ +GraphNO & $193.6$ & & & & $37.02 \pm 0.00$\ +DeepONet & $235.8$ & & & & $119.86 \pm 15.82$\ +::: +:::: + +[]{#tab_exp2 label="tab_exp2"} +::::: diff --git a/2306.01424/main_diagram/main_diagram.drawio b/2306.01424/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..ed410c9d0dc18fd9e53ee9f3191e97ec41d2d54e --- /dev/null +++ b/2306.01424/main_diagram/main_diagram.drawio @@ -0,0 +1,782 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2306.01424/paper_text/intro_method.md b/2306.01424/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..b14cf0e660f8472f418ca2b5089b293cde4b70f4 --- /dev/null +++ b/2306.01424/paper_text/intro_method.md @@ -0,0 +1,168 @@ +# Introduction + +Counterfactual inference aims to answer retrospective "what if" questions. Examples are: *Would a patient's recovery have been faster, had a doctor applied a different treatment? Would my salary be higher, had I studied at a different college?* Counterfactual inference is widely used in data-driven decision-making, such as root cause analysis [@yamamoto2012understanding; @budhathoki2022causal], recommender systems [@bottou2013counterfactual; @li2019unit; @everitt2021agent], responsibility attribution [@lagnado2013causal; @kleiman2015inference; @halpern2018towards], and personalized medicine [@yamada2017counterfactual; @lotfollahi2021compositional]. Counterfactual inference is also relevant for various machine learning tasks such as safe policy search [@richens2022counterfactual], reinforcement learning [@buesing2019woulda; @foerster2018counterfactual; @kallus2020confounding; @lu2020sample; @mesnard2021counterfactual], algorithmic fairness [@kusner2017counterfactual; @zhang2018fairness; @plecko2022causal], and explainability [@goyal2019counterfactual; @karimi2021algorithmic; @karimi2020survey; @galhotra2021explaining]. + +Counterfactual queries are located at the top of Pearl's ladder of causation [@halpern2005causes; @pearl2009causality; @bareinboim2022pearl], i. e., at the third layer $\mathcal{L}_3$ of causation [@bareinboim2022pearl] (see Fig. [1](#fig:ladder-of-causation){reference-type="ref" reference="fig:ladder-of-causation"}, right). Counterfactual queries are challenging as they do reasoning in both the actual world and a hypothetical one where variables are set to different values than they have in reality. + +State-of-the-art methods for counterfactual inference typically aim at *point identification*. These works fall into two streams. (1) The first stream [@yoon2018ganite; @pawlowski2020deep; @lotfollahi2021compositional; @sauer2021counterfactual; @sanchez2021diffusion; @kim2021counterfactual; @sanchez2022vaca; @dash2022evaluating; @de2022deep; @shen2022weakly; @wu2022variational; @chao2023interventional; @wu2023predicting] makes no explicit assumptions besides assuming a structural causal model (SCM) with Markovianity (i. e., independence of the latent noise) and thus gives estimates that can be *invalid*. However, additional assumptions are needed in counterfactual inference of layer $\mathcal{L}_3$ to provide identifiability guarantees [@nasr2023counterfactual1; @xia2023neural]. (2) The second stream [@spirtes2000causation; @khemakhem2021causal; @zhang2009identifiability; @de2021transport; @immer2022identifiability; @nasr2023counterfactual2] provides such identifiability guarantees but makes strong assumptions that are *unnatural* or *unrealistic*. Formally, the work by @nasr2023counterfactual2 [@nasr2023counterfactual2] describes bijective generation mechanisms (BGMs), where, in addition to the original Markovianity of SCMs, the functions in the underlying SCMs must be monotonous (strictly increasing or decreasing) with respect to the latent noise. The latter assumption effectively sets the dimensionality of the latent noise to the same dimensionality as the observed (endogenous) variables. However, this is highly unrealistic in real-world settings and is often in violation of domain knowledge. For example, cancer is caused by multiple latent sources of noise (e.g., genes, nutrition, lifestyle, hazardous exposures, and other environmental factors). + +
+
+ +
+
Pearl’s ladder of causation comparing observational, interventional, and counterfactual queries corresponding to the SCM ℳ with two observed variables, i. e., binary treatment A ∈ {0, 1} and continuous outcome Y ∈ ℝ. We also plot three causal diagrams, 𝒢(ℳ), corresponding to each layer of causation, namely, Bayesian network, causal Bayesian network, and parallel worlds network. Queries with gray background can be simplified, i. e., be expressed via lower-layer distributions. The estimation of the queries with yellow background requires additional assumptions or distributions from the same layer. In this paper, we focus on partial identification of the expected counterfactual outcome of [un]treated, 𝔼(Ya ∣ a′, y′), a′ ≠ a, shown in orange.
+
+ +In this paper, we depart from point identification for the sake of more general assumptions about both the functions in the SCMs and the latent noise. Instead, we aim at ***partial counterfactual identification*** of continuous outcomes. Rather than inferring a point estimation expression for the counterfactual query, we are interested in inferring a whole *ignorance interval* with *informative bounds*. Informative bounds mean that the ignorance interval is a strict subset of the support of the distribution. The ignorance interval thus contains all possible values of the counterfactual query for SCMs that are consistent with the assumptions and available data. Partial identification is still very useful for decision making, e. g., when the ignorance interval for a treatment effect is fully below or above zero. + +We focus on a Markovian SCM with two observed variables, namely, a binary treatment and a continuous outcome. We consider a causal diagram as in Fig. [1](#fig:ladder-of-causation){reference-type="ref" reference="fig:ladder-of-causation"} (left). We then analyze the *expected counterfactual outcome of \[un\]treated* abbreviated by ECOU \[ECOT\]. This query is non-trivial in the sense that it can *not* be simplified to a $\mathcal{L}_1/\mathcal{L}_2$ layer, as it requires knowledge about the functions in SCM, but can still be inferred by means of a 3-step procedure of abduction-action-prediction [@pearl2000models]. ECOU \[ECOT\] can be seen as a continuous version of counterfactual probabilities [@balke1994counterfactual] and allows to answer a retrospective question about the necessity of interventions: *what would have been the expected counterfactual outcome for some treatment, considering knowledge about both the factual treatment and the factual outcome?* + +In our paper, we leverage geometric measure theory and differential topology to prove that, in general, the ignorance interval of ECOU \[ECOT\] has non-informative bounds. We show theoretically that this happens immediately when we relax the assumptions of (i) that the latent noise and the outcome have the same dimensionality and (ii) that the functions in the SCMs are monotonous (and assume they are continuously differentiable). As a remedy, we propose a novel ***Curvature Sensitivity Model*** (CSM), in which we bound the curvature of the level sets of the functions and thus yield informative bounds. We further show that we obtain the BGMs from [@nasr2023counterfactual2] as a special case when setting the curvature to zero. Likewise, we yield non-informative bounds when setting it to infinity. Therefore, our CSM provides a sufficient condition for the partial counterfactual identification of the continuous outcomes with informative bounds. + +We develop an instantiation of CSM in the form of a novel deep generative model, which we call ***Augmented Pseudo-Invertible Decoder*** (APID). Our APID uses (i) residual normalizing flows with (ii) variational augmentations to perform the task of partial counterfactual inference. Specifically, our APID allows us to (1) fit the observational/interventional data, (2) perform abduction-action-prediction in a differentiable fashion, and (3) bound the curvature of the SCM functions, thus yielding informative bounds for the whole ignorance interval. Finally, we demonstrate its effectiveness across several numerical experiments. + +Overall, our **main contributions** are following:[^1] + +1. We prove that the expected counterfactual outcome of \[un\]treated has non-informative bounds in the class of continuously differentiable functions of SCMs. + +2. We propose a novel *Curvature Sensitivity Model*(CSM) to obtain informative bounds. Our CSM is the first sensitivity model for the partial counterfactual identification of continuous outcomes in Markovian SCMs. + +3. We introduce a novel deep generative model called *Augmented Pseudo-Invertible Decoder*(APID) to perform partial counterfactual inference under our CSM. We further validate it numerically. + +# Method + +**Notation.** Capital letters $A, Y, U$, denote random variables and small letters $a, y, u$ their realizations from corresponding domains $\mathcal{A}, \mathcal{Y}, \mathcal{U}$. Bold capital letters such as $\mathbf{U} = \{U_1, \dots, U_n\}$ denote finite sets of random variables. Further, $\mathbb{P}(Y)$ is an (observational) distribution of $Y$; $\mathbb{P}(Y \mid a) = \mathbb{P}(Y \mid A = a)$ is a conditional (observational) distribution; $\mathbb{P}(Y_{A=a}) = \mathbb{P}(Y_a)$ an interventional distribution; and $\mathbb{P}(Y_{A=a} \mid A' = a', Y' = y') = \mathbb{P}(Y_a \mid a', y')$ a counterfactual distribution. We use a superscript such as in $\mathbb{P}^\mathcal{M}$ to indicate distributions that are induced by the SCM $\mathcal{M}$. We denote the conditional cumulative distribution function (CDF) of $\mathbb{P}(Y \mid a)$ by $\mathbb{F}_a(y)$. We use $\mathbb{P}(Y = \cdot)$ to denote a density or probability mass function of $Y$ and $\mathbb{E}(Y) = \int y \mathop{}\!\mathrm{d}\mathbb{P}(y)$ to refer to its expected value. Interventional and counterfactual densities or probability mass functions are defined accordingly. + +A function is said to be in class $C^k, k \geq 0$, if its $k$-th derivative exists and is continuous. Let $\left\lVert\cdot\right\rVert_2$ denote the $L_2$-norm, $\nabla_x f(x)$ a gradient of $f(x)$, and $\operatorname{Hess}_x f(x)$ a Hessian matrix of $f(x)$. The pushforward distribution or pushforward measure is defined as a transfer of a (probability) measure $\mathbb{P}$ with a measurable function $f$, which we denote as $f_{\sharp}\mathbb{P}$. + +**SCMs.** We follow the standard notation of SCMs as in [@pearl2009causality; @bareinboim2022pearl; @bongers2021foundations; @xia2023neural]. An SCM $\mathcal{M}$ is defined as a tuple $\langle \mathbf{U}, \mathbf{V}, \mathbb{P}(\mathbf{U}), \mathcal{F} \rangle$ with latent (exogenous) noise variables $\mathbf{U}$, observed (endogenous) variables $\mathbf{V} = \{V_1, \dots, V_n\}$, a distribution of latent noise variables $\mathbb{P}(\mathbf{U})$, and a collection of functions $\mathcal{F} = \{f_{V_1}, \dots, f_{V_n}\}$. Each function is a measurable map from the corresponding domains of $\mathbf{U}_{V_i} \cup \mathbf{Pa}_{V_i}$ to $V_i$, where $\mathbf{U}_{V_i} \subseteq \mathbf{U}$ and $\mathbf{Pa}_{V_i} \subseteq \mathbf{V} \setminus V_i$ are parents of the observed variable $V_i$. Therefore, the functions $\mathcal{F}$ induce a pushforward distribution of observed variables, i.e., $\mathbb{P}(\mathbf{V}) = \mathcal{F}_{\sharp} \mathbb{P}(\mathbf{U})$. Each $V_i$ is thus deterministic (non-random), conditionally on its parents, i. e., $v_i \gets f_{V_i}(\mathbf{pa}_{V_i}, \mathbf{u}_{V_i})$. Each SCM $\mathcal{M}$ induces an (augmented) causal diagram $\mathcal{G}(\mathcal{M})$, which is assumed to be acyclic. + +We provide a background on geometric measure theory and differential geometry in Appendix [8](#app:preliminaries){reference-type="ref" reference="app:preliminaries"}. + +In the following, we relax the main assumption of bijective generation mechanisms (BGMs) [@nasr2023counterfactual2], i. e., that all the functions in Markovian SCMs are monotonous (strictly increasing) with respect to the latent noise variable. To this end, we let the latent noise variables have arbitrary dimensionality and further consider functions to be of class $C^k$. We then show that counterfactual distributions under this relaxation are non-identifiable from $\mathcal{L}_1$ or $\mathcal{L}_2$ data. + +::: {#def:B_CK_d .definition} +**Definition 1** (Bivariate Markovian SCMs of class $C^k$ and $d$-dimension latent noise). *Let $\mathfrak{B}(C^k, d)$ denote the class of SCMs $\mathcal{M} = \langle \mathbf{U}, \mathbf{V}, \mathbb{P}(\mathbf{U}), \mathcal{F} \rangle$ with the following endogenous and latent noise variables: $\mathbf{U} = \{U_A \in \{0, 1\}, U_{Y} \in [0, 1]^d\}$ and $\mathbf{V} = \{A \in \{0, 1\}, Y \in \mathbb{R}\}$, for $d \geq 1$. The latent noise variables have the following distributions $\mathbb{P}(\mathbf{U})$: $U_A \sim \mathrm{Bern}(p_A)$, $0 < p_A < 1$, and $U_Y \sim \mathrm{Unif}(0, 1)^d$ and are all mutually independent [^2]. The functions are $\mathcal{F} = \{f_A(U_A), f_Y(A, U_Y) \}$ and $f_Y(a, \cdot) \in C^k$ for $u_Y \in (0, 1)^d$ $\forall a \in \{0, 1\}$.* +::: + +All SCMs in $\mathfrak{B}(C^k, d)$ induce similar causal diagrams $\mathcal{G}(\mathcal{M})$, where only the number of latent noise variables $d$ differs. Further, it follows from the above definition that BGMs are a special case of $\mathfrak{B}(C^1, 1)$, where the functions $f_Y(a, \cdot)$ are monotonous (strictly increasing). + +**Task: counterfactual inference.** Counterfactual queries are at layer $\mathcal{L}_3$ of the causality ladder and are defined as probabilistic expressions with random variables belonging to several worlds, which are in logical contradiction with each other [@correa2021nested; @bareinboim2022pearl]. For instance, $\mathbb{P}^\mathcal{M}(Y_{a} = y, Y_{a'} = y), a \neq a'$ for an SCM $\mathcal{M}$ from $\mathfrak{B}(C^k, d)$. In this paper, we focus on the *expected counterfactual outcome of \[un\]treated* ECOU \[ECOT\], which we denote by $Q^\mathcal{M}_{a' \to a}(y') = \mathbb{E}^\mathcal{M}(Y_a \mid a', y')$. + +For an SCM $\mathcal{M}$ from $\mathfrak{B}(C^k, d)$, ECOU \[ECOT\] can be inferred by the following three steps. (1) The *abduction* step infers a posterior distribution of the latent noise variables, conditioned on the evidence, i.e., $\mathbb{P}(U_Y \mid a', y')$. This posterior distribution is defined on the level set of the factual function given by $\{u_Y: f_Y(a', u_Y) = y'\}$, i. e., all points in the latent noise space mapped to $y'$. (2) The *action* step alters the function for $Y$ to $f_Y(a, U_Y)$. (3) The *prediction* step is done by a pushforward of the posterior distribution with the altered function, i.e. $f_Y(a, \cdot)_{\sharp} \mathbb{P}(U_Y \mid a', y')$. Afterwards, the expectation of it is then evaluated. + +
+
+ +
+
Inference of observational and interventional distributions (left) and counterfactual distributions (right) for SCMs ℳ1 and ℳ2 from Example 1. Left: the observational query ℙℳ(Y = y ∣ a) coincides with the interventional query ℙℳ(Ya = y) for each ℳ1 and ℳ2. Right: the counterfactual queries can still differ substantially for ℳ1 and ℳ2, thus giving a vastly different counterfactual outcome distribution of the untreated, ℙℳ(Y1 ∣ A′ = 0, Y′ = 0). Thus, ℒ3 queries are non-identifiable from ℒ1 or ℒ2 information.
+
+ +The existence of counterfactual queries, which are non-identifiable with $\mathcal{L}_1$ or $\mathcal{L}_2$ data in Markovian SCMs, was previously shown in [@xia2023neural] (Ex. 8, App. D3) for discrete outcomes and in [@dawid2002influence; @bongers2021foundations] (Ex. D.7) for continuous outcomes. Here, we construct an important example to (1) show non-identifiability of counterfactual queries from $\mathcal{L}_1$ or $\mathcal{L}_2$ data under our relaxation from Def. [1](#def:B_CK_d){reference-type="ref" reference="def:B_CK_d"}, and to consequently (2) give some intuition on informativity of the bounds of the ignorance interval, which we will formalize later. + +::: {#exmpl:markov-non-id .exmpl} +**Example 1** (Counterfactual non-identifiability in Markovian SCMs). *Let $\mathcal{M}_1$ and $\mathcal{M}_2$ be two Markovian SCMs from $\mathfrak{B}(C^0, 2)$ with the following functions for $Y$: $$\begin{align*} + & \mathcal{M}_{1}: f_Y (A, U_{Y^1}, U_{Y^2}) = A \, (U_{Y^1} - U_{Y^2} + 1) + (1 - A) \, (U_{Y^1} + U_{Y^2} - 1) \} ,\\ + & \mathcal{M}_{2}: f_Y (A, U_{Y^1}, U_{Y^2}) = + \begin{cases} + U_{Y^1} + U_{Y^2} - 1, & A = 0, \\ + U_{Y^1} - U_{Y^2} + 1, & A = 1 \wedge (0 \leq U_{Y^1} \leq 1) \wedge (U_{Y^1} \leq U_{Y^2} \leq 1), \\ + F^{-1}(0, U_{Y^1}, U_{Y^2}), & \text{otherwise}, + \end{cases} +\end{align*}$$ where $F^{-1}(0, U_{Y^1}, U_{Y^2})$ is the solution in $Y$ of the implicitly defined function $F(Y, U_{Y^1}, U_{Y^2}) = U_{Y^1}-U_{Y^2}-2\,(Y-1)\, \left\lvert -U_{Y^1}-U_{Y^2}+1 \right\rvert-1+\sqrt{(Y-2)^2\,(8\,(Y-1)^2+1)} = 0$.* + +*It turns out that the SCMs $\mathcal{M}_1$ and $\mathcal{M}_2$ are observationally and interventionally equivalent (relative to the outcome $Y$). That is, they induce the same set of $\mathcal{L}_1$ and $\mathcal{L}_2$ queries. For both SCMs, it can be easily inferred that a pushforward of uniform latent noise variables $U_{Y^1}$ and $U_{Y^2}$ with $f_Y (A, U_{Y^1}, U_{Y^2})$ is a symmetric triangular distribution with support $\mathcal{Y}_0 = [-1,1]$ for $A = 0$ and $\mathcal{Y}_1 = [0, 2]$ for $A = 1$, respectively. We plot the level sets of $f_Y (A, U_{Y^1}, U_{Y^2})$ for both SCMs in Fig. [2](#fig:intro-example){reference-type="ref" reference="fig:intro-example"} (left). The pushforward of the latent noise variables preserves the transported mass; i. e., note the equality of (1) the area of each colored band between the two level sets in the latent noise space and (2) the area of the corresponding band under the density graph of $Y$ Fig. [2](#fig:intro-example){reference-type="ref" reference="fig:intro-example"} (left).* + +*Despite the equivalence of $\mathcal{L}_1$ and $\mathcal{L}_2$, the SCMs differ in their counterfactuals; see Fig. [2](#fig:intro-example){reference-type="ref" reference="fig:intro-example"} (right). For example, the counterfactual outcome distribution of untreated, $\mathbb{P}^{\mathcal{M}}(Y_{1} = y\mid A'=0, Y'=0)$, has different densities for both SCMs $\mathcal{M}_1$ and $\mathcal{M}_2$. Further, the ECOU, $Q^{\mathcal{M}}_{0 \to 1}(0) = \mathbb{E}^\mathcal{M}(Y_1 \mid A'=0, Y'=0)$, is different for both SCMs, i. e., $Q_{0 \to 1}^{\mathcal{M}_1}(0) = 1$ and $Q_{0 \to 1}^{\mathcal{M}_2} \approx 1.114$. Further details for the example are in Appendix [9](#app:examples){reference-type="ref" reference="app:examples"}.* +::: + +The example provides an intuition that motivates how we generate informative bounds later. By "bending" the bundle of counterfactual level sets (in blue in Fig. [2](#fig:intro-example){reference-type="ref" reference="fig:intro-example"} left) around the factual level set (in orange in Fig. [2](#fig:intro-example){reference-type="ref" reference="fig:intro-example"}, right), we can transform more and more mass to the bound of the support. We later extend this idea to the ignorance interval of the ECOU. Importantly, after "bending" the bundle of level sets, we still must make sure that the original observational/interventional distribution is preserved. + +We now formulate the task of partial counterfactual identification. To do so, we first present two lemmas that show how we can infer the densities of observational and counterfactual distributions from both the latent noise distributions and $C^1$ functions in SCMs of class $\mathfrak{B}(C^1, d)$. + +::: restatable +lemmaobspush []{#lemma:obs-push label="lemma:obs-push"} Let $\mathcal{M} \in \mathfrak{B}(C^1, d)$. Then, the density of the observational distribution, induced by $\mathcal{M}$, is $$\begin{equation} + \label{eq:obs-push} + \mathbb{P}^{\mathcal{M}}(Y = y \mid a) = \int_{E(y,a)} \frac{1}{\left\lVert\nabla_{u_Y} f_Y(a, u_Y)\right\rVert_2} \mathop{}\!\mathrm{d}\mathcal{H}^{d-1}(u_Y), +\end{equation}$$ where $E(y,a)$ is a level set (preimage) of $y$, i. e., $E(y,a) = \{u_Y \in [0, 1]^d: f_Y(a, u_Y) = y\}$, and $\mathcal{H}^{d-1}(u_Y)$ is the Hausdorff measure (see Appendix [8](#app:preliminaries){reference-type="ref" reference="app:preliminaries"} for the definition). +::: + +We provide an example in Appendix [9](#app:examples){reference-type="ref" reference="app:examples"} where we show the application of Lemma [\[lemma:obs-push\]](#lemma:obs-push){reference-type="ref" reference="lemma:obs-push"} (therein, we derive the standard normal distribution as a pushforward using the Box-Müller transformation). Lemma [\[lemma:obs-push\]](#lemma:obs-push){reference-type="ref" reference="lemma:obs-push"} is a generalization of the well-known change of variables formula. This is easy to see, when we set $d = 1$, so that $\mathbb{P}^{\mathcal{M}}(Y = y \mid a) = \sum_{u_Y \in E(y,a)} \left\lvert \nabla_{u_Y} f_Y(a, u_Y) \right\rvert^{-1}$. Furthermore, the function $f_Y(a, u_Y)$ can be restored (up to a sign) from the observational distribution, if it is monotonous in $u_Y$, such as in BGMs [@nasr2023counterfactual2]. In this case, the function coincides (up to a sign) with the inverse CDF of the observed distribution, i. e., $f_Y(a, u_Y) = \mathbb{F}^{-1}_a(\pm u_Y \mp 0.5 + 0.5 )$ (see Corollary [\[cor:bgms\]](#cor:bgms){reference-type="ref" reference="cor:bgms"} in Appendix [10](#app:proofs){reference-type="ref" reference="app:proofs"}). + +::: restatable +lemmacounterpush []{#lemma:counter-push label="lemma:counter-push"} Let $\mathcal{M} \in \mathfrak{B}(C^1, d)$. Then, the density of the counterfactual outcome distribution of the \[un\]treated is $$\begin{equation} + \label{eq:dcout} + \mathbb{P}^{\mathcal{M}}(Y_a = y \mid a', y') = \frac{1}{\mathbb{P}^{\mathcal{M}}(Y = y' \mid a')} \int_{E(y',a')} \frac{\delta(f_Y(a, u_Y) - y)}{\left\lVert\nabla_{u_Y} f_Y(a', u_Y)\right\rVert_2} \mathop{}\!\mathrm{d}\mathcal{H}^{d-1}(u_Y), +\end{equation}$$ where $\delta(\cdot)$ is a Dirac delta function, and the expected counterfactual outcome of the \[un\]treated, i.e., ECOU \[ECOT\], is $$\begin{equation} + \label{eq:ecout} + Q_{a' \to a}^{\mathcal{M}}(y') = \mathbb{E}^{\mathcal{M}}(Y_a \mid a', y') = \frac{1}{\mathbb{P}^{\mathcal{M}}(Y = y' \mid a')} \int_{E(y',a')} \frac{f_Y(a, u_Y)}{\left\lVert\nabla_{u_Y} f_Y(a', u_Y)\right\rVert_2} \mathop{}\!\mathrm{d}\mathcal{H}^{d-1}(u_Y), +\end{equation}$$ where $E(y',a')$ is a (factual) level set of $y'$, i. e., $E(y',a') = \{u_Y \in [0, 1]^d: f_Y(a', u_Y) = y'\}$ and $a' \neq a$. +::: + +::: wrapfigure +r4cm +::: + +Equations [\[eq:dcout\]](#eq:dcout){reference-type="eqref" reference="eq:dcout"} and [\[eq:ecout\]](#eq:ecout){reference-type="eqref" reference="eq:ecout"} implicitly combine all three steps of the abduction-action-prediction procedure: (1) *abduction* infers the level sets $E(y',a')$ with the corresponding Hausdorff measure $\mathcal{H}^{d-1}(u_Y)$; (2) *action* uses the counterfactual function $f_Y(a, u_Y)$; and (3) *prediction* evaluates the overall integral. In the specific case of $d=1$ and a monotonous function $f_Y(a, u_Y)$ with respect to $u_Y$, we obtain two deterministic counterfactuals, which are identifiable from observational distribution, i. e., $Q_{a' \to a}^{\mathcal{M}}(y') = \mathbb{F}^{-1}_a(\pm \mathbb{F}_{a'}(y') \mp 0.5 + 0.5 )$. For details, see Corollary [\[cor:bgms-counter\]](#cor:bgms-counter){reference-type="ref" reference="cor:bgms-counter"} in Appendix [10](#app:proofs){reference-type="ref" reference="app:proofs"}. For larger $d$, as already shown in Example [1](#exmpl:markov-non-id){reference-type="ref" reference="exmpl:markov-non-id"}, both the density and ECOU \[ECOT\] can take arbitrary values for the same observational (or interventional) distribution. + +::: {#def:part-id .definition} +**Definition 2** (Partial identification of ECOU (ECOT) in class $\mathfrak{B}(C^k, d), k \geq 1$). *Given the continuous observational distribution $\mathbb{P}(Y \mid a)$ for some SCM of class $\mathfrak{B}(C^k, d)$. Then, partial counterfactual identification aims to find bounds of the ignorance interval $[\underline{Q_{a' \to a}(y')}, \overline{Q_{a' \to a}(y')}]$ given by $$\begin{align} + \underline{Q_{a' \to a}(y')} = \inf_{\mathcal{M} \in \mathfrak{B}(C^k, d)} Q_{a' \to a}^{\mathcal{M}}(y') & \quad \text{ s.t. } \forall a \in \{0, 1\}: \mathbb{P}(Y \mid a) = \mathbb{P}^{\mathcal{M}}(Y \mid a), \\ + \overline{Q_{a' \to a}(y')} = \sup_{\mathcal{M} \in \mathfrak{B}(C^k, d)} Q_{a' \to a}^{\mathcal{M}}(y') & \quad \text{ s.t. } \forall a \in \{0, 1\}: \mathbb{P}(Y \mid a) = \mathbb{P}^{\mathcal{M}}(Y \mid a), +\end{align}$$ where $\mathbb{P}^{\mathcal{M}}(Y \mid a)$ is given by Eq. [\[eq:obs-push\]](#eq:obs-push){reference-type="eqref" reference="eq:obs-push"} and $Q_{a' \to a}^{\mathcal{M}}(y')$ by Eq. [\[eq:ecout\]](#eq:ecout){reference-type="eqref" reference="eq:ecout"}.* +::: + +Hence, the task of partial counterfactual identification in class $\mathfrak{B}(C^k, d)$ can be reduced to a constrained variational problem, namely, a constrained optimization of ECOU \[ECOT\] with respect to $f_Y(a, \cdot) \in C^k$. Using Lemma [\[lemma:counter-push\]](#lemma:counter-push){reference-type="ref" reference="lemma:counter-push"}, we see that, e. g., ECOU \[ECOT\] can be made arbitrarily large (or small) for the same observational distribution in two ways. First, this can be done by changing the factual function, $f_Y(a', \cdot)$, i. e., if we increase the proportion of the volume of the factual level set $E(y', a')$ that intersects only a certain bundle of counterfactual level sets. Second, this can be done by modifying the counterfactual function, $f_Y(a, \cdot)$, by "bending" the bundle of counterfactual level sets around the factual level set. The latter is schematically shown in Fig. [\[fig:non-id-example\]](#fig:non-id-example){reference-type="ref" reference="fig:non-id-example"}. We formalize this important observation in the following theorem. + +::: restatable +thrmnonid []{#thrm:non-id label="thrm:non-id"} Let the continuous observational distribution $\mathbb{P}(Y \mid a)$ be induced by some SCM of class $\mathfrak{B}(C^{\infty}, d)$. Let $\mathbb{P}(Y \mid a)$ have a compact support $\mathcal{Y}_a = [l_a, u_a]$ and be of finite density $\mathbb{P}(Y = y\mid a) < +\infty$. Then, the ignorance interval for the partial identification of the ECOU \[ECOT\] of class $\mathfrak{B}(C^{\infty}, d)$, $d \geq 2$, has non-informative bounds: $\underline{Q_{a' \to a}(y')} = l_a$ and $\overline{Q_{a' \to a}(y')} = u_a$. +::: + +Theorem [\[thrm:non-id\]](#thrm:non-id){reference-type="ref" reference="thrm:non-id"} implies that, no matter how smooth the class of functions is, the partial identification of ECOU \[ECOT\] will have non-informative bounds. Hence, with the current set of assumptions, there is no utility in considering more general classes. This includes various functions, such as $C^0$ and the class of all measurable functions $f_Y(a, \cdot): \mathbb{R}^d \to \mathbb{R}$, as the latter includes $C^\infty$ functions. In the following, we introduce a sensitivity model which nevertheless allows us to obtain informative bounds. + +
+
+ +
+
+
+ +
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Overview of our Augmented Pseudo-Invertible Decoder(APID). Our APID uses (i) two residual normalizing flows for each treatment a ∈ {0, 1}, respectively; and (ii) variational augmentations, Ŷaug. Together, both components enable the implementation of CSM under Assumption [assump:kappa].
+
+ +In the following, we develop our *Curvature Sensitivity Model*(CSM) to restrict the class $\mathfrak{B}(C^k, d)$ so that the ECOU \[ECOT\] obtains informative bounds. Our CSM uses the intuition from the proof of Theorem [\[thrm:non-id\]](#thrm:non-id){reference-type="ref" reference="thrm:non-id"} in that, to construct the non-informative SCM $\mathcal{M}_{\text{non-inf}}$, we have to "bend" the bundle of the counterfactual level sets. As a result, our CSM provides sufficient conditions for informative bounds in the class $\mathfrak{B}(C^2, d)$ by bounding the principal curvatures of level sets globally. + +[]{#assump:kappa label="assump:kappa"} Let $\mathcal{M}$ be of class $\mathfrak{B}(C^2, d), d \geq 2$. Let $E(y,a)$ be the level sets of functions $f_Y(a, u_Y)$ for $a \in \{0, 1\}$, which are thus $d-1$-dimensional smooth manifolds. Let us assume that principal curvatures $i \in \{1, \dots, d-1\}$ exist at every point $u_Y \in E(y,a)$ for every $a \in \{0, 1\}$ and $y \in (l_a, u_a) \subset \mathcal{Y}_a$, and let us denote them as $\kappa_i(u_Y)$. Then, we assume that $\kappa \geq 0$ is the upper bound of the maximal absolute principal curvature for every $y$, $a$, and $u_Y \in E(y,a)$: $$\begin{equation} + \kappa = \max_{a \in \{0, 1\}, y \in (l_a, u_a), u_Y \in E(y,a)} \quad \max_{i \in \{1, \dots, d-1\}} \left\lvert \kappa_i(u_Y) \right\rvert. +\end{equation}$$ + +Principal curvatures can be thought of as a measure of the non-linearity of the level sets, and, when they are all close to zero at some point, the level set manifold can be locally approximated by a flat hyperplane. In brevity, principal curvatures can be defined via the first- and second-order partial derivatives of $f_Y(a, \cdot)$, so that they describe the degrees of curvature of a manifold in different directions. We refer to Appendix [8](#app:preliminaries){reference-type="ref" reference="app:preliminaries"} for a formal definition of the principal curvatures $\kappa_i$. An example is in Appendix [9](#app:examples){reference-type="ref" reference="app:examples"}. + +Now, we state the main result of our paper that our CSM allows us to obtain informative bounds for ECOU \[ECOT\]. + +::: restatable +thrmcsm []{#thrm:csm label="thrm:csm"} Let the continuous observational distribution $\mathbb{P}(Y \mid a)$ be induced by some SCM of class $\mathfrak{B}(C^2, d), d \geq 2$, which satisfies Assumption [\[assump:kappa\]](#assump:kappa){reference-type="ref" reference="assump:kappa"}. Let $\mathbb{P}(Y \mid a)$ have a compact support $\mathcal{Y}_a = [l_a, u_a]$. Then, the ignorance interval for the partial identification of ECOU \[ECOT\] of class $\mathfrak{B}(C^2, d)$ has informative bounds, dependent on $\kappa$ and $d$, which are given by $\underline{Q_{a' \to a}(y')} = l(\kappa, d) > l_a$ and $\overline{Q_{a' \to a}(y')} = u(\kappa, d) < u_a$. +::: + +Theorem [\[thrm:csm\]](#thrm:csm){reference-type="ref" reference="thrm:csm"} has several important implications. (1) Our CSM is applicable to a wide class of functions $\mathfrak{B}(C^2, d)$, for which the principal curvature is well defined. (2) We show the relationships between different classes $\mathfrak{B}(C^k, d)$ and our CSM($\kappa$) in terms of identifiability in Fig. [\[fig:csm-venn-diagram\]](#fig:csm-venn-diagram){reference-type="ref" reference="fig:csm-venn-diagram"}. For example, by increasing $\kappa$, we cover a larger class of functions. For infinite curvature, our CSM almost coincides with the entire $\mathfrak{B}(C^2, d)$. (3) Our CSM($\kappa$) with $\kappa \geq 0$ always contains both (i) identifiable SCMs and (ii) (informative) non-identifiable SCMs. Examples of (i) include SCMs for which the bundles of the level sets coincide for both treatments, i. e., $\{E(y, a): y \in \mathcal{Y}_a\} = \{E(y, a'): y \in \mathcal{Y}_{a'}\}$. In this case, it is always possible to find an equivalent BGM when the level sets are flat and thus $\kappa = 0$ (see $\mathcal{M}_{\text{flat}}$ from Example [6](#exmpl:csm-flat){reference-type="ref" reference="exmpl:csm-flat"} in the Appendix [9](#app:examples){reference-type="ref" reference="app:examples"}) or curved and thus $\kappa = 1$ (see $\mathcal{M}_{\text{curv}}$ from Example [7](#exmpl:csm-curv){reference-type="ref" reference="exmpl:csm-curv"} in the Appendix [9](#app:examples){reference-type="ref" reference="app:examples"}). For (ii), we see that, even when we set $\kappa=0$, we can obtain non-identifiability with informative bounds. An example is when we align the bundle of level sets perpendicularly to each other for both treatments, as in $\mathcal{M}_{\text{perp}}$ from Example [8](#exmpl:csm-perp){reference-type="ref" reference="exmpl:csm-perp"} in the Appendix [9](#app:examples){reference-type="ref" reference="app:examples"}. + +::: wrapfigure +r5cm +::: + +We make another observation regarding the choice of the latent noise dimensionality $d$. In practice, it is sufficient to choose $d=2$ (in the absence of further assumptions on the latent noise space). This choice is practical as we only have to enforce a single principal curvature, which reduces the computational burden, i.e., $$\begin{equation} + \label{eq:curv} + \kappa_1(u_Y) = - \frac{1}{2} \nabla_{u_Y} \left( \frac{\nabla_{u_Y} f_Y(a, u_Y)}{\left\lVert\nabla_{u_Y} f_Y(a, u_Y)\right\rVert_2} \right), \quad u_Y \in E(y,a). +\end{equation}$$ Importantly, we do not lose generality with $d = 2$, as we still cover the entire identifiability spectrum by varying $\kappa$ (see Corollary [\[cor:2d\]](#cor:2d){reference-type="ref" reference="cor:2d"} in Appendix [10](#app:proofs){reference-type="ref" reference="app:proofs"}). We discuss potential extensions of our CSM in Appendix [11](#app:discussion){reference-type="ref" reference="app:discussion"}. + +We now introduce an instantiation of our CSM: a novel deep generative model called *Augmented Pseudo-Invertible Decoder*(APID) to perform partial counterfactual identification under our CSM([\[assump:kappa\]](#assump:kappa){reference-type="ref" reference="assump:kappa"}) of class $\mathfrak{B}(C^2, 2)$. + +**Architecture:** The two main components of our APID are (1) residual normalizing flows with (2) variational augmentations (see Fig. [3](#fig:apid-scheme){reference-type="ref" reference="fig:apid-scheme"}). The first component are two-dimensional normalizing flows [@tabak2010density; @rezende2015variational] to estimate the function $f_Y(a, u_Y), u_Y \in [0, 1]^2$, separately for each treatment $a \in \{0, 1\}$. Specifically, we use residual normalizing flows [@chen2019residual] due to their ability to model free-form Jacobians (see the extended discussion about the choice of the normalizing flow in Appendix [12](#app:apid-details){reference-type="ref" reference="app:apid-details"}). However, two-dimensional normalizing flows can only model invertible transformations, while the function $\hat{f}_Y(a, \cdot): [0, 1]^2 \to \mathcal{Y}_a \subset \mathbb{R}$ is non-invertible. To address this, we employ an approach of pseudo-invertible flows [@beitler2021pie; @horvat2021denoising], namely, the variational augmentations [@chen2020vflow], as described in the following. The second component in our APID are variational augmentations [@chen2020vflow]. Here, we augment the estimated outcome variable $\hat{Y}$ with the variationally sampled $\hat{Y}_{\text{aug}} \sim N(g^a(\hat{Y}), \varepsilon^2)$, where $g^a(\cdot)$ is a fully-connected neural network, and $\varepsilon^2 > 0$ is a hyperparameter. Using the variational augmentations, our APID then models $\hat{f}_Y(a, \cdot)$ through a two-dimensional transformation $\hat{F}_a = (\hat{f}_{Y_\text{aug}}(a, \cdot), \hat{f}_Y(a, \cdot)): [0, 1]^2 \to \mathbb{R} \times \mathcal{Y}_a$. We refer to the Appendix [12](#app:apid-details){reference-type="ref" reference="app:apid-details"} for further details. + +**Inference:** Our APID proceeds in first steps: (P1) it first fits the observational/interventional distribution $\mathbb{P}(Y \mid a)$, given the observed samples; (P2) it then performs counterfactual inference of ECOU \[ECOT\] in a differentiable fashion; and (P3) it finally penalize functions with large principal curvatures of the level sets, by using automatic differentiation of the estimated functions of the SCMs. + +We achieve (P1)--(P3) through the help of variational augmentations: $\bullet$ For (P1) we fit the two-dimensional normalizing flow with a negative log-likelihood. $\bullet$ For (P2), we sample points from the level sets of $\hat{f}_Y(a, \cdot)$. The latter is crucial as it follows an abduction-action-prediction procedure and thus generates estimates of the ECOU \[ECOT\] in a differential fashion. To evaluate ECOU \[ECOT\], we first perform the *abduction* step with the inverse transformation of the factual normalizing flow, i.e., $\hat{F}^{-1}_{a'}$. This transformation maps the variationally augmented evidence, i. e., $(y', \{Y'_{\text{aug}}\}_{j=1}^b \sim N(g^a(y'), \varepsilon^2))$, where $b$ is the number of augmentations, to the latent noise space. Then, the *action* step selects the counterfactual normalizing flow via the transformation $\hat{F}_{a}$. Finally, the *prediction* step performs a pushforward of the latent noise space, which was inferred during the abduction step. Technical details are in Appendix [12](#app:apid-details){reference-type="ref" reference="app:apid-details"}. $\bullet$ In (P3), we enforce the curvature constraint of our CSM. Here, we use automatic differentiation as provided by deep learning libraries, which allows us directly evaluate $\kappa_1(u_Y)$ according to Eq. [\[eq:curv\]](#eq:curv){reference-type="eqref" reference="eq:curv"}. + +**Training:** Our APID is trained based on observational data $\mathcal{D} = \{A_i, Y_i\}_{i=1}^n$ drawn i.i.d. from some SCM of class $\mathfrak{B}(C^2, 2)$. To fit our APID, we combine several losses in one optimization objective: (1) a negative log-likelihood loss $\mathcal{L}_{\text{NLL}}$ with noise regularization [@rothfuss2019noise], which aims to fit the data distribution; (2) a counterfactual query loss $\mathcal{L}_Q$ with a coefficient $\lambda_Q$, which aims to maximize/minimize the ECOU \[ECOT\]; and (3) a curvature loss $\mathcal{L}_{\kappa}$ with coefficient $\lambda_\kappa$, which penalizes the curvature of the level sets. Both coefficients are hyperparameters. We provide details about the losses, the training algorithm, and hyperparameters in Appendix [13](#app:apid-training){reference-type="ref" reference="app:apid-training"}. Importantly, we incorporated several improvements to stabilize and speed up the training. For example, in addition to the negative log-likelihood, we added the Wasserstein loss $\mathcal{L}_\mathbb{W}$ to prevent posterior collapse [@wang2019riemannian; @coeurdoux2022sliced]. To speed up the training, we enforce the curvature constraint only on the counterfactual function, $\hat{f}_Y(a, \cdot)$, and only for the level set, which corresponds to the evaluated ECOU \[ECOT\], $u_Y \in E(\hat{Q}_{a' \to a}(y'), a)$. diff --git a/2307.03704/main_diagram/main_diagram.drawio b/2307.03704/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..c5805875a41ac211cda9fb350832ecef431da0fc --- /dev/null +++ b/2307.03704/main_diagram/main_diagram.drawio @@ -0,0 +1,142 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2307.03704/main_diagram/main_diagram.pdf b/2307.03704/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d7204f45e0d07d36ee62509b2d4dabb8dab5ecee Binary files /dev/null and b/2307.03704/main_diagram/main_diagram.pdf differ diff --git a/2307.03704/paper_text/intro_method.md b/2307.03704/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..58b18c2c5bc90c1682217b652b6e02b0b7ed91a3 --- /dev/null +++ b/2307.03704/paper_text/intro_method.md @@ -0,0 +1,114 @@ +# Introduction + +One of the fundamental problems in computer vision is learning representations of 3D objects from 2D images [@marr2010vision; @Hartley_2004; @Ozyesil_2017]. By understanding how image features correspond to a physical object, a model can generalize better to novel views of the object, for instance, when estimating the pose of an object. In general, neural networks that respect the symmetries of a problem are more noise robust and data efficient, while also less prone to over-fitting [@Bronstein_2021]. Three-dimensional space has a natural symmetry group of three-dimensional rotations and three dimensional translations, $SE(3)$. While we would like to leverage this symmetry to design improved neural architectures, serious challenges exist to incorporating 3D symmetry when applied to image data. Specifically, a projection of a three-dimensional scene into a two-dimensional plane does not transform equivariantly under all elements of $SE(3)$. This is because there is no a-priori model for how two-dimensional images transform under out-of-plane object rotations. The $SO(3)$ symmetry is reduced to the $SO(2)$ subgroup of $SO(3)$ which corresponds to all rotations that map the projection plane into the projection plane. @Cohen_2016_II showed how to design neural networks that are explicitly $SO(2)$-equivariant and accept images as inputs. However, this captures only the fact that the group truth lives in a space that is acted on by $SO(2) \subset SO(3)$ and disregards the fact that the $SO(3)$ also acts on the space of allowable ground truths. + +The allowed architectures of $G$-equivariant neural networks are much more constrained then general multi-layer perceptrons. The requirement of $G$-equivariance places strict restrictions on the allowed linear maps and the allowed non-linear functions in each network layer [@Cohen_2016_II; @Kondor_2018]. Because of this, the structure of allowable $G$-equivariant neural networks can be completely classified based on the representation theory of the group $G$ [@Bronstein_2021; @Cohen_2018; @Lang_2020]. Specifically, for compact groups, it is possible to completely characterize the structure of all possible kernels of $G$-equivariant networks [@Lang_2020]. + +::: wrapfigure +l0.40 ![image](figures/in-plane_eqv_III.png){width="45%"} +::: + +We argue that any equivarient machine learning algorithm that builds a three dimensional model of the world from two-dimensional images must satisfy a natural geometric consistency property. Using the restricted representation, this consistency property is equivalent to a set of $SO(2)$-equivarience constraints. We give a complete characterization of maps that satisfy this property. Using Frobinious Reciprocity theorem, we show that this geometric constraint can also be derived using induced representations. The classification theorems derived in [@Cohen_2016_II; @Lang_2020; @Bronstein_2021] are derived assuming that both the input and output layers are $G$-equivariant. For the construction presented in [4](#Section:Main:Method){reference-type="ref" reference="Section:Main:Method"}, we instead map $H$-equivariant functions to $G$-equivariant functions. Our restricted/induced representation arguments give a natural generalization of equivarient maps between different groups. We derive the induced and restricted representation analogies of the theorems presented in [@Cohen_2016; @Lang_2020]. + +In this work, we will show how the induced and restricted representations can be used to construct neural architectures that accept image data and leverage $SO(3)$-equivarient methods to avoid learning nuisance transformations in three-dimensional space. + +We show that our proposed construction satisfies both a *completeness* property and a *universal* property. Specifically, let $H$ be the subgroup of $G$ that maps in-plane images to in-plane images. The induced representation construction is *complete* in that all group valued functions on $G$ can be induced from a set of group valued functions on $H$. The construction is *universal* in that all multi-linear maps which map $H$-equivariant functions to $G$-equivariant functions are specific cases of the induced representation, modulo isomorphism. Furthermore, we show that the architectures proposed in [@Klee_2022; @Esteves_2019] are special cases of our construction for the icosahedral group $G=A_{5}$ and the construction proposed in [@Klee_2023] is a special case of our construction for the three-dimensional rotation group $G=SO(3)$. Our method achieves state of the art performance for orientation prediction on PASCAL3D+ [@Xiang_2014] and SYMSOL [@Murphy_2022] datasets. + +- We propose a unified theory for learning three dimensional representations from two dimensional images. We show that algorithms which learn three-dimensional representations from two-dimensional images must satisfy certain consistency properties, which are equivalent to $SO(2)$-steerability constraints. + +- We introduce a fully differentiable layer called an *induction/restriction layer* that maps signals on the plane into signals on the sphere. We show that the induction/restriction layer satisfies a natural consistency constraint and prove both a completeness and universal property for our construction. + +- Our method achieves SOTA performance for orientation prediction on PASCAL3D+ and SYMSOL datasets. + +# Method + +Convolutional networks or vision transformers are typically used to extract spatial feature maps from 2D images. For convenience we ignore discritization and treat the feature maps as having continuous inputs $f: \mathbb{R}^2 \rightarrow \mathbb{R}^{d}$. To leverage spatial symmetries in 3D, we would like to map our features $f$ from a plane onto a sphere: $g: S^2 \rightarrow \mathbb{R}^{D}$. @Klee_2023 proposed one such mapping, where the planar feature map is stretched over a hemisphere, but other possible mappings exist. + +We formalize the equivariance property every projection should have through the theory of induced and restricted representations. The constraints that we impose have a intuitive geometric interpretation. We give a complete characterization of *all possible* linear and equivariant projections, $\Phi$, from planar features to a spherical representation. Our general formulation includes [@Klee_2023] as a special case, and we show that a learnable equivariant projection leads to better predictive models. + +We first derive the $SO(2)$-equivariance constraint for the most general linear mapping from images to spherical signals. + +We first describe $\mathcal{F}$ the space of image input signals. Let $V$ and $V^{\uparrow}$ be vector spaces. Let $\mathcal{F}$ be the vector space of all $V$-valued signals defined on the plane $$\begin{align*} + \mathcal{F} = \{ \enspace f \enspace | \enspace f: \mathbb{R}^{2} \rightarrow V \enspace \}. +\end{align*}$$ + +Elements of $\mathcal{F}$ are sometimes called $SE(2)$-steerable feature fields [@Weiler_2021]. The group $SE(2)= \mathbb{R}^{2} \rtimes SO(2)$ of 2D translations and rotations acts on $\mathcal{F}$ via representation $\pi$. Each $h \in SE(2)$ has a unique factorization $h = \bar{h} h_{c}$ where $\bar{h} \in \mathbb{R}^{2}$ is a translation and $h_{c} \in SO(2)$ is a rotation. Then the action $\pi$ is defined $$\begin{align*} +\forall f \in \mathcal{F}, \enspace r \in \mathbb{R}^{2}, \enspace h \in SE(2), \enspace \quad \pi(h) \cdot f(r) = \rho(h_{c}) f(h^{-1}r) +\end{align*}$$ where $( \rho , V)$ is an $SO(2)$-representation describing the transformation of the fibers of $f$ and $( \pi , \mathcal{F} ) = \mathop{\mathrm{Ind}}_{ SO(2) }^{ SE(2) } [ ( \rho , V) ]$ so that $( \pi , \mathcal{F} )$ gives a representation of the group $SE(2)$ [@Cohen_2016]. + +We would like to map signals in $\mathcal{F}$ into functions from $S^{2}$ into the vector space $V^{\uparrow}$. Let $\mathcal{F}^{\uparrow}$ be the vector space of all such outputs defined as $$\begin{align*} + \mathcal{F}^{\uparrow} = \{ \enspace f \enspace | \enspace f: S^{2} \rightarrow V^{\uparrow} \enspace \} +\end{align*}$$ The group $SO(3)$ acts on the vector space $\mathcal{F}^{\uparrow}$ via $$\begin{align*} + \forall f^{\uparrow} \in \mathcal{F}^{\uparrow}, \enspace \hat{n} \in S^{2} , \enspace g \in SO(3), \quad \pi^{\uparrow}(g) \cdot f^{\uparrow}( \hat{n} ) = \rho^{\uparrow}(g) f^{\uparrow}( g^{-1} \hat{n} ) +\end{align*}$$ where $\rho^{\uparrow}(g)$ describes the $SO(3)$ fiber representation. + +Let $H = SO(2)$ be the $SO(2)$ subgroup of $SO(3)$ that corresponds to in-plane rotations of the image. Our goal is to classify $H$-equivariant linear maps $\Phi : \mathcal{F} \rightarrow \mathcal{F}^{\uparrow}$. This is equivalent to the constraint that $$\begin{align} +\label{Main:Equation:In Plane Equivarence_I} +\forall h \in H = SO(2), \enspace f \in \mathcal{F}, \quad \Phi( \pi(h) \cdot f ) = \pi^{\uparrow} (h) \cdot \Phi(f) +\end{align}$$ The constraint enforces equivarient with respect to $SO(2)$ transformations. By definition, the evaluation of $\pi^{\uparrow}(h)$ at $h \in SO(2)$ subgroup is the restricted representation $\pi^{\uparrow}(h) = \mathop{\mathrm{Res}}_{SO(2)}^{SO(3)}[ \pi^{\uparrow} ](h)$. + +We use tools from [@Weiler_2018_II; @Lang_2020] to solve for the space of all possible maps satisfying the constraint [\[Main:Equation:In Plane Equivarence_I\]](#Main:Equation:In Plane Equivarence_I){reference-type="ref" reference="Main:Equation:In Plane Equivarence_I"}, giving the trainable space for the image to sphere layer. + +Our conclusion is that instead of mapping arbitrary $SO(2)$-input representation to arbitrary $SO(2)$-output representation, the allowed input and output representations $(\rho , V)$ and $( \rho^{\uparrow} , V^\uparrow)$ must satisfy additional constraints. Specifically, not every representation can be realized as the restriction of an $SO(3)$ to $SO(2)$ representation [1](#Figure:Induced_SO_2_Main){reference-type="ref" reference="Figure:Induced_SO_2_Main"}. Although in this paper we focus on orientation estimation, the equivariant framework in Section [\[Section:Deriving the Kernel Constraint\]](#Section:Deriving the Kernel Constraint){reference-type="ref" reference="Section:Deriving the Kernel Constraint"} is more general. In the Appendix [\[Appendix:Section:Plane to Space for Object Reconstruction\]](#Appendix:Section:Plane to Space for Object Reconstruction){reference-type="ref" reference="Appendix:Section:Plane to Space for Object Reconstruction"}, we formulate and solve analogous equivariance constraints for both 6DoF-pose estimation and monocular volume reconstruction. + +::: theorem +**Theorem 1**. *The constraint in Equation [\[Main:Equation:In Plane Equivarence_I\]](#Main:Equation:In Plane Equivarence_I){reference-type="ref" reference="Main:Equation:In Plane Equivarence_I"} can be solved exactly using the results of [@Weiler_2018_II; @Lang_2020]. The most linear general map $\Phi : \mathcal{F} \rightarrow \mathcal{F}^{\uparrow}$ can be expanded as $$\begin{align*} +[\Phi(f)](\hat{n}) = \int_{r \in \mathbb{R}^{2} }dr\text{ } \kappa( \hat{n} , r )f(r) +\end{align*}$$ where $\kappa: \mathbb{R}^{2} \times S^{2} \rightarrow \mathop{\mathrm{Hom}}[V,V^{\uparrow}]$. Then, the exact form of $\kappa$ can be written as $$\begin{align} +\kappa( \hat{n} , r ) = \sum_{ \ell=0 }^{\infty} F_{\ell}( r )^{T} Y_{\ell}(\hat{n} ) +\end{align}$$ where $Y_{\ell}(\hat{n} )$ is the vectorization of the $\ell$-type spherical harmonics and each $F_{\ell}( r )$ is an standard $SO(2)$-steerable kernel [@Cohen_2016; @Weiler_2018_II] that has input $SO(2)$-representation $(\rho , V)$ and output $SO(2)$-representation $(\rho^{\ell} , V^{\ell} ) =(\rho , V) \otimes \mathop{\mathrm{Res}}_{SO(2)}^{SO(3)}[ (D^{\ell} , V^{\ell} ) ]$.* +::: + +The proof of this statement is given in Appendix [\[Appendix:Section:Solving the Kernel Constraint\]](#Appendix:Section:Solving the Kernel Constraint){reference-type="ref" reference="Appendix:Section:Solving the Kernel Constraint"}. Note that similar to [@Thomas_2018; @Kondor_2018] the tensor product structure of the $SO(2)$ and $SO(3)$ irreducible representations determine the allowed input and output representations of the matrix valued harmonic coefficients $F_{\ell}( r )$. + +
+ + + + + + + +
image image
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Left: Decomposition of the restricted representation ResSO(2)SO(3) of SO(3)-irreducibles $(D^{\ell} , W_{\ell}) \in \widehat{SO(3)}$ into SO(2)-irreducibles $(\rho_{k} , V_{k}) \in \widehat{SO(2)}$. Not every SO(2)-representation can be realized as the restriction of a SO(3)-representation. Right: Decomposition of the induced representation IndSO(2)SO(3) for SO(2)-irreducibles $(\rho_{k} , V_{k}) \in \widehat{SO(2)}$ into SO(3)-irreducibles $(D^{\ell} , W_{\ell}) \in \widehat{SO(3)}$. Not every SO(3)-representation can be realized as the induction of a SO(2)-representation.
+
+ +In section [4.2](#Section:Main:Deriving the Kernel Constraint){reference-type="ref" reference="Section:Main:Deriving the Kernel Constraint"}, we considered the most general linear maps that satisfied the generalized equivariance constraint. Adding non-linearities should allow for more expressiveness. Understanding non-linearities between equivariant layers is still an active area of research [@Franzen_2021; @Dehaan_2021; @Poulenard_2021; @Xu_2022]. + +One way to include non-linearity is to apply standard $SO(3)$ non-linearities after the linear induction layer. After applying the linear mapping described in [\[Section:Image to \...\]](#Section:Image to ...){reference-type="ref" reference="Section:Image to ..."}, we apply an additional spherical non-linearity [@Geiger_2022] to the signal on $S^{2}$. This is the method we employ for the results presented in [6.2](#Main:Section:Implementation and Training Details){reference-type="ref" reference="Main:Section:Implementation and Training Details"}. As shown in [\[Appendix:Section:Including Non-linearities\]](#Appendix:Section:Including Non-linearities){reference-type="ref" reference="Appendix:Section:Including Non-linearities"} it is also possible to include tensor-product based non-linearity analogous to the results of [@Thomas_2018; @Kondor_2018]. + +In section [4](#Section:Main:Method){reference-type="ref" reference="Section:Main:Method"} we showed how the restriction representation arises naturally when trying to construct $SO(3)$-equivariant architectures for image data. However, there is no apriori choice of the hidden $SO(3)$ representation. We show that with this choice, our construction satisfies a universal property, and is unique up to isomorphism [@Leinster_2016]. + +We have the following universal property of induced representations, as stated in [@Ceccherini_2008]: + +::: theorem +**Theorem 2**. *Let $H \subseteq G$. Let $(\rho , V)$ be any $H$-representation. Let $\text{Ind}^{G}_{H}( \rho , V )$ be the induced representation of $(\rho , V)$ from $H$ to $G$. Then, there exists a [unique]{.underline} $H$-equivariant linear map $\Phi_{\rho} : V \rightarrow \text{Ind}^{G}_{H}V$ such that for any $G$-representation $(\sigma , W)$ and any $H$-equivariant linear map $\Psi : V \rightarrow W$, there is a unique $G$-equivariant map $\Psi^{\uparrow} : \text{Ind}_{H}^{G}V \rightarrow W$ such that the diagram [\[Diagram:Universality Property_I\]](#Diagram:Universality Property_I){reference-type="ref" reference="Diagram:Universality Property_I"} is commutative.* +::: + +::: wrapfigure +L0.3 +::: + +Let $(\rho , V)$ be a $H$-representation and let $(\sigma , W)$ be a $G$-representation. Let $\Psi : V \rightarrow W$ where $\Psi$ is an intertwiner of a the $H$-representation and the restriction of the $G$-representation to an $H$-representation so that $$\begin{align*} + \forall h \in H, \quad \Psi \rho(h) = \mathop{\mathrm{Res}}_{H}^{G}[\sigma ](h) \Psi +\end{align*}$$ so that $\Psi \in \mathop{\mathrm{Hom}}_{H}[ (\rho , V) , \mathop{\mathrm{Res}}_{H}^{G}(\sigma , W) ]$. The universal property of the induced representation allows us to write any such $\Psi$ in a canonical form. Specifically, as illustrated in [\[Diagram:Universality Property_Push\]](#Diagram:Universality Property_Push){reference-type="ref" reference="Diagram:Universality Property_Push"}, we can always uniquely decompose $\Psi =\Psi^{\uparrow} \circ \Phi_{\rho}$ where $\Psi^{\uparrow} \in\mathop{\mathrm{Hom}}_{G}[ \text{Ind}_{H}^{G}(\rho , V) , (\sigma , W) ]$ and $\Psi_{\rho} : V \rightarrow \text{Ind}_{H}^{G}V$ is $( \sigma , W)$ independent. + +::: center +[]{#Diagram:Universality Property_Push label="Diagram:Universality Property_Push"} + +$\cong$ +::: + +Convolutional neural networks are compositions of linear functions, interleaved with non-linearities. At each layer of the network, we have a set of functions from a homogeneous space of a group into some vector space [@Kondor_2018]. Let $X^{H}_{i}$ be a set of homogeneous spaces of the group $H$ and let $X^{G}_{j}$ be a set homogeneous spaces of the group $G$. Let $V^{H}_{i}$ and $W^{G}_{j}$ be a set of vector spaces. Then, consider the function spaces $$\begin{align*} + \mathcal{F}^{H}_{i} = \{ \enspace f \enspace | \enspace f : X^{H}_{i} \rightarrow V^{H}_{i} \enspace \}, \quad \quad \mathcal{F}^{G}_{j} = \{ \enspace f' \enspace | \enspace f' : X^{G}_{j} \rightarrow W^{G}_{j} \enspace \} +\end{align*}$$ The group $H$ acts on the homogeneous spaces $X^{H}_{i}$ and the group $G$ acts on the homogeneous spaces $X^{G}_{j}$ so that the function spaces $\mathcal{F}^{H}_{i}$ and $\mathcal{F}^{G}_{j}$ form representations of $H$ and $G$, respectively + +Suppose we wish to design a downstream $G$-equivariant neural network that accepts as signals functions that live in the vector space $\mathcal{F}^{H}_{0}$ and transform in the $\rho_{0}$ representation of $H$. Thus, $( \rho_{0} , \mathcal{F}^{H}_{0})$ is a $H$-representation, but not necessarily a $G$-representation. At some point, in the architecture, a layer $\mathcal{F}^{H}_{i}$ must be $H$ equivariant on the left and both $H$ and $G$-equivariant on the right. Let us call the layer that is both $H$ and $G$-equivariant $\mathcal{F}^{G}_{1}$. + +::: center +[]{#Diagram:Switching label="Diagram:Switching"} + +$\cong$ +::: + +Suppose that $\Psi$ is an intertwiner between $( \rho_{i} , \mathcal{F}^{H}_{i})$ and $(\sigma_{1} , \mathcal{F}^{G}_{1})$. Using the factorization property of induced representations [\[Diagram:Universality Property_Push\]](#Diagram:Universality Property_Push){reference-type="ref" reference="Diagram:Universality Property_Push"}, there is a canonical basis of the space $\mathop{\mathrm{Hom}}_{H}[ (\rho_{i}, \mathcal{F}^{H}_{i}) , \mathop{\mathrm{Res}}_{H}^{G}[(\sigma_{1} , \mathcal{F}_{1}^{G} )] ] \cong \mathop{\mathrm{Hom}}_{G}[ \mathop{\mathrm{Ind}}_{H}^{G}[ (\rho_{i} , \mathcal{F}^{H}_{i})] , (\sigma_{1} , \mathcal{F}^{G}_{1}) ]$ and we may write $\Psi$ uniquely as $\Psi = \Psi^{\uparrow } \circ \Phi_{\rho}$ where $\Phi_{\rho}$ is an $H$-equivariant map and $\Psi^{\uparrow }$ is a $G$-equivariant map. Thus, any boundary between $H$ and $G$ layers can be written as an $H$-equivariant layer between $(\rho_{i}, \mathcal{F}^{H}_{i})$ and $\mathop{\mathrm{Ind}}_{H}^{G}[(\rho_{i}, \mathcal{F}^{H}_{i})]$ followed by a $G$-equivariant layer between $\mathop{\mathrm{Ind}}_{H}^{G}[(\rho_{i}, \mathcal{F}^{H}_{i})]$ and $(\sigma_{1}, \mathcal{F}^{H}_{1})$. In this way, induction is all you need and all possible latent $G$-equivariant architectures can be written in terms of the induction representation. diff --git a/2310.04361/main_diagram/main_diagram.drawio b/2310.04361/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..e89f0df1a9c3680ad14acbbc52c45f1c17106ffb --- /dev/null +++ b/2310.04361/main_diagram/main_diagram.drawio @@ -0,0 +1,446 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2310.04361/main_diagram/main_diagram.pdf b/2310.04361/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9567c0bd26d86862f86659887ea6d49f09a3d474 Binary files /dev/null and b/2310.04361/main_diagram/main_diagram.pdf differ diff --git a/2310.04361/paper_text/intro_method.md b/2310.04361/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..16c44a0d0da573f5be69b18da3799c02d00025c2 --- /dev/null +++ b/2310.04361/paper_text/intro_method.md @@ -0,0 +1,85 @@ +# Introduction + +Transformers have become a predominant model architecture in various domains of deep learning such as machine translation [@vaswani2017attention], language modeling [@devlin2018bert; @radford2019language], and computer vision [@dosovitskiy2020image; @khan2022transformers]. The widespread effectiveness of Transformer models in various applications is closely related to their ability to scale efficiently with the number of model parameters [@kaplan2020scaling], prompting researchers to train progressively larger and larger models [@touvron2023llama; @jiang2023mistral]. However, the considerable computational demands of these models often restrict their deployment in practical settings with limited resources. + +At the same time, Transformer models exhibit considerable activation sparsity in their intermediate representations [@li2022lazy], which suggests that most of their computations are redundant. Conditional computation methods can reduce these unnecessary costs by using only a subset of the model parameters for any given input [@han2021dynamic]. In particular, Mixture-of-Experts (MoE) layers [@shazeer2016outrageously], consisting of multiple experts that are sparsely executed for any input token, are an effective way to decouple the number of parameters of the model from its computational cost [@clark2022unified]. As shown by [@zhang2022moefication], many pre-trained dense Transformer models can be made more efficient by converting their FFN blocks into MoE layers, a process they call *MoEfication*. + +**Contributions of this paper**: We consider the following research question: what is the *optimal* way to convert a generic Transformer model into an equivalent sparse variant? We identify a series of weaknesses of the MoEfication process limiting the resulting accuracy-sparsity tradeoff, and propose corresponding mitigations as follows. We call the resulting algorithm Dense to Dynamic-$k$ Mixture-of-Experts (D2DMoE) and outline it in [1](#fig:method_overview){reference-type="ref+Label" reference="fig:method_overview"}. + +1. First, we analyze the relationship between the activation sparsity of the starting model and the efficiency of the final MoE model. We show that computational savings are directly related to sparsity levels, and we correspondingly enforce [higher activation sparsity]{.mark} levels before conversion through a lightweight fine-tuning process, which leads to a substantially improved cost-to-performance trade-off. + +2. We identify the router training scheme in the original MoEfication algorithm as a limitation of the conversion process. We propose to frame the router training as a regression problem instead, hence our [routers directly predict the norm of the output of each expert]{.mark}. + +3. We show that Transformer models exhibit significant variance of the number of activated neurons, and standard top-$k$ expert selection in the MoE layers is inefficient. We propose an alternative [dynamic-$k$ expert selection scheme]{.mark} that adjusts the number of activated experts on a per-token basis. This approach enables the model to efficiently allocate compute between easy and hard inputs, increasing the overall efficiency. + +4. We generalize the conversion method to [any standalone linear layer]{.mark} including gated MLP variants commonly found in modern LLMs [@touvron2023llama; @gemmateam2024gemma] and projections in Multi-Head Attention (MHA) layers (which often account for over 30% of total computations in Transformer models [@song2022cp]). For MHA, we propose a replacement procedure in which every dense layer is substituted by a two-layer MLP module trained to imitate the output of the original layer. + +
+ +
Key components of D2DMoE: (a) We enhance the activation sparsity in the base model. (b) We convert FFN layers in the model to MoE layers with routers that predict the contribution of each expert. (c) We introduce dynamic-k routing that selects the experts for execution based on their predicted contribution.
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+ +We evaluate our method on text classification, image classification, and language modeling benchmarks, and achieve significant improvements in terms of cost-vs-performance trade-offs in all cases. + +MoE models have gained a lot of traction over the last years as an effective architecture to decouple the parameter count from the computational cost of the models [@zoph2022st]. In a MoE layer, hard sparsity is usually enforced *explicitly* by applying a top-$k$ operation on the outputs of a trainable gating layer. However, many recent works [@zhang2023emergent; @Chen_2023_CVPR; @qiu2024emergent] have shown that most Transformers, when trained at scale, build *intrinsically* sparse and modular representations. @zhang2022moefication proposed to leverage this naturally emerging modularity with MoEfication - a method that converts dense transformer models into MoE models by grouping FFN weights into experts and subsequently learning small routers that determine which experts to activate. Models converted with MoEfication are able to preserve the performance of the original dense models while using only a fraction of their computational cost. However, we believe that the MoEfication procedure is not optimal, and therefore aim to obtain dense-to-sparse conversion schemes that obtain a better cost-performance trade-off. + +
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Impact of sparsity on MoE conversion
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Non-zero activations distribution
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Top-k vs dynamic-k gating
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(a) Cost-accuracy tradeoff for a MoEfied GPT-2 model obtained starting from models with different levels of activation sparsity. Sparsification correlates with the model performance. (b) Distribution of non-zero activations in the FFN layers in GPT-2-base on OpenWebText, with and without the sparsity enforcement phase. Both models exhibit significant variance, and the mean-to-variance ratio increases in the sparsified model. (c) We propose to exploit the variation in activations through a dynamic-k routing procedure that adapts the number of experts allocated to a sample.
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+ +Intuitively, a MoE converted from a sparser base model would be able to perform the original function using a smaller number of experts. To validate this hypothesis, we perform MoEfication on different variants of GPT2-base[^2] with varying activation sparsity levels and show the results in [2](#fig:teaser_moefication_sparsification){reference-type="ref+Label" reference="fig:teaser_moefication_sparsification"}. As expected, MoEfication performs better with sparser models. We further investigate the per-token mean and the variance of non-zero neurons in the base and sparsified model, and show the results in [3](#fig:teaser_activation_sparsity){reference-type="ref+Label" reference="fig:teaser_activation_sparsity"}. Observe that different layers use a different number of neurons on average. Moreover, the variance of the number of activated neurons is quite high and becomes even more significant in the sparsified model. This means that static top-$k$ gating as used in MoEfication is not optimal for dense-to-MoE converted models, and a more flexible expert assignment rule that would be able to handle the high per-token and per-layer variance could be beneficial to the efficiency of such models, as illustrated at [4](#fig:teaser_dynk){reference-type="ref+Label" reference="fig:teaser_dynk"}. Such dynamic-k gating requires routers that reliably predict the contribution of each expert. We observe that routers obtained through MoEfication do not accurately reflect this contribution. Moreover, their router training procedure depends on the strict sparsity of the model guaranteed by the ReLU activation function. Therefore, we design a novel router training scheme that directly predicts the contribution of each expert and generalizes to the broader family of activation functions. We combine the proposed components (sparsity enforcement, expert contribution routing, and dynamic-$k$ gating) into a single method that we call Dense to Dynamic-$k$ Mixture-of-Experts (D2DMoE), which we describe in detail in the next Section. + +# Method + +D2DMoE reduces the computational cost of the model by splitting every MLP module into a MoE layer. In this section, we describe all of its components in detail. A high-level overview of the entire procedure is presented in [1](#fig:method_overview){reference-type="ref+Label" reference="fig:method_overview"}. The conversion process can be optionally preceded by MHA projection layer replacement (Sec. [3.5](#sec:linear_layer_replacement){reference-type="ref" reference="sec:linear_layer_replacement"}), which allows us to apply the same transformation pipeline on all replacement modules. + +We expect that enforcing higher levels of activation sparsity may allow for the execution of an even smaller number of experts, resulting in overall computational savings. To this end, we induce activation sparsity by fine-tuning the model with an additional loss term that induces activation sparsity [@georgiadis2019accelerating]. We apply the *square Hoyer* regularization [@kurtz2020inducing; @hoyer2004non] on the activations of the model: $$\begin{equation} + \label{eq:hoyer_sparsity_penalty} + \mathcal{L}_{s}(x) = \frac{1}{L}\sum_{l=1}^{L}\frac{(\sum_{i}{|a^{l}_i|})^2}{\sum_i{{a^{l}_i}^2}}, +\end{equation}$$ where $a^l$ is the activation vector from the middle layer of the $l$-th MLP for input $x$, and $L$ is the total number of MLPs in the model. Overall, the model is trained with the following cost function: $$\begin{equation} + \mathcal{L}(x) = \mathcal{L}_{\text{CE}}(\hat{y}, y) + \alpha \mathcal{L}_{s}(x) +\end{equation}$$ where $\mathcal{L}_{\text{CE}}$ is cross-entropy loss, and $\alpha$ is the hyperparameter that controls the strength of sparsity enforcement. We find that the pre-trained models recover the original performance with only a fraction of the original training budget (eg. 1B tokens for GPT2-base or Gemma-2B, which is less than 1% of the tokens used for pretraining). + +We split the two-layer MLP modules into experts using the parameter clustering method proposed by @zhang2022moefication. Assuming the MLP layers are composed of weights $\bm{W}_1$, $\bm{W}_2$ and corresponding biases $\bm{b}_1$, $\bm{b}_2$, we treat the weights of each neuron from $\bm{W}_1$ as features and feed them into the balanced $k$-means algorithm [@malinen2014balanced] that groups neurons with similar weights together. Then, we use the resulting cluster indices to split the first linear layer $\bm{W}_1$, the first bias vector $\bm{b}_1$, and the second linear layer $\bm{W}_2$ into $n$ experts of the same size. The second bias $\bm{b}_2$ is not affected by this procedure. + +MoEfication process was designed for standard two-layered MLPs [@zhang2022moefication]. Recent LLMs [@touvron2023llama; @gemmateam2024gemma] have shifted towards *gated* FFNs, where the activation is realized through a Gated Linear Unit (GLU) [@shazeer2020glu], which contains an additional weight matrix for the gate projections. To adapt the expert clustering procedure described above to gated FFN layers, we cluster the weights of the gating matrix $\bm{W_g}$ instead of $\bm{W_1}$, and use the obtained indices to divide the weights of the two other layers. We provide more intuition and details on our method for gated FFNs in [11](#app:gated_mlps){reference-type="ref+Label" reference="app:gated_mlps"}. + +In a standard MoE-based model, the gating networks are trained in an end-to-end manner. Contrary to this, we train each gating network independently. We propose to frame the problem of training the router as a regression task and directly predict the $\ell^{2}\text{-norm}$ of the output of each expert with the router. Formally, given an input token $z$, the D2DMoE router $R$ is trained to minimize the following loss: $$\begin{equation} + \mathcal{L}_r(z) = \frac{1}{n}\sum_i^n{(R(z)_i - \lVert E_i(z)\rVert)^2} +\end{equation}$$ where $E_i$ is the $i$-th expert. We use a small two-layer neural network as the router $R$ and apply an absolute value activation function to ensure non-negative output. This regression-based formulation is still compatible with the commonly used top-$k$ expert selection, but enables more precise attribution of the contribution of each expert, as we show later in the experimental section. + +Note that @zhang2022moefication also trains each routing network independently, but their method constructs artificial labels for each input, and then subsequently trains the router as a classifier. We discuss the differences in detail in [8](#appendix:routing_differences){reference-type="ref+Label" reference="appendix:routing_differences"}. + +:::: wrapfigure +r0.4 + +::: center +![image](figures/teaser/qkvo_replace_alt.pdf){width="40%"} +::: +:::: + +Commonly used MoE layers always execute top-$k$ experts for each token, where $k$ is a predefined hyperparameter. This means that, regardless of the difficulty of the input, the model spends the same amount of compute on each batch [@zhou2022mixture] or token [@shazeer2016outrageously]. While this may be appropriate if the model is trained with the same restriction, it is suboptimal for a model that was converted from a dense model, as we show in [2](#subsec:motivation){reference-type="ref+Label" reference="subsec:motivation"}. + +Since our router directly predicts the $\ell^{2}\text{-norm}$ of output of each expert, we propose a dynamic-$k$ expert selection method that skips experts for whom the router predicts relatively small output norms. Given a router output vector $R(z)$, we select a hyperparameter $\tau \in [0, 1]$ and define the expert selection rule $G$ for the $i$-th element as: $$\begin{equation} + G(z)_i = +\begin{cases} + 1 & \text{if}\ R(z)_i \geq \tau \cdot \max R(z) \\ + 0 & \text{if}\ R(z)_i < \tau \cdot \max R(z) +\end{cases} +\end{equation}$$ Note that as $\tau$ increases, the number of executed experts decreases along with the overall computational cost. We emphasize that after model deployment $\tau$ can be adjusted without the need for retraining. + +A significant amount of computing in deep neural networks is often spent on dense layers that are not followed by any activation function. Dense-to-sparse-MoE conversion methods cannot reduce the costs of such layers due to a lack of activation sparsity. This determines an upper bound on the possible computational savings. To overcome it, we substitute dense layers with small MLPs with approximately the same computational cost and number of parameters. Each MLP is trained to imitate the output of the original dense layer given the same input by minimizing of the mean squared error between the two (akin to a distillation loss). + +In our case, for Transformer architectures, we substitute projection matrices along with their biases in every MHA layer, as depicted in [\[fig:qkvo_replacement\]](#fig:qkvo_replacement){reference-type="ref+Label" reference="fig:qkvo_replacement"}. This means that the final model has four MoE layers in the MHA layer and one MoE layer in the FFN layer (either plain or gated) for each Transformer block. Note that we do not modify the computation of the scaled dot-product attention itself and that this scheme can be applied to any standalone dense layer. diff --git a/2310.04604/main_diagram/main_diagram.drawio b/2310.04604/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..b03f323a7851302b51805c778598377b69da4e86 --- /dev/null +++ b/2310.04604/main_diagram/main_diagram.drawio @@ -0,0 +1,382 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2310.04604/main_diagram/main_diagram.pdf b/2310.04604/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cddb0e639ed6a3a04b440203c5f83750ee1a43fb Binary files /dev/null and b/2310.04604/main_diagram/main_diagram.pdf differ diff --git a/2310.04604/paper_text/intro_method.md b/2310.04604/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..fbf4ff9327e4ce50111add4f0d304c2fb3763d75 --- /dev/null +++ b/2310.04604/paper_text/intro_method.md @@ -0,0 +1,90 @@ +# Introduction + +**Motivation.** Deep machine learning models are increasingly being deployed by cloud-based providers, accessible only by API calls. In such cases, user data privacy becomes paramount, motivating the setting of private inference (PI) using secure multiparty computation (MPC). In its simplest form, MPC-based private inference is a two-party setup where a user (the first party) performs inference of their data on a model whose weights are owned by the cloud service provider (the second party), with both sides encrypting their inputs using cryptographic techniques prior to inference. + +The main technical barrier to widespread deployment of MPC-based PI protocols is the *large number of nonlinear* operations present in a deep neural network model. Private execution of linear (or low-degree polynomial) operations can be made fast using cryptographic protocols like homomorphic encryption and/or secret sharing. However, private execution of nonlinear operations (such as ReLUs or softmax operations) require Yao's Garbled Circuits, incurring high latency and storage overhead. Thus, unlocking fast, accurate, and efficient PI requires rethinking network design. + +Consequently, an emerging line of work has made several forays towards the design of "MPC-friendly" models; cf. more discussions below in Section [2](#sec:background){reference-type="ref" reference="sec:background"}. These methods approach PI from different angles. Approaches such as Delphi ([@DELPHI]) or Circa ([@ghodsi2021circa]) propose to replace ReLUs with MPC-friendly approximations, while approaches such as CryptoNAS ([@ghodsi2020]) and Sphynx ([@cho2021sphynx]) use neural architecture search (NAS) to search for network backbones with a minimal number of ReLUs. @peng2023rrnet ([-@peng2023rrnet]) propose hardware-aware ReLU-reduced networks to achieve better latencies. The latest approaches in this direction (SNL by @cho2022selective ([-@cho2022selective]), and SENet by @senet ([-@senet])) derive inspiration from network pruning. + +However, this body of work has gaps. The overwhelming majority of PI-aware model approaches have focused on convolutional architectures, and have largely ignored *transformer* models. In particular, the proper application of MPC to *vision transformer* architectures remains far less studied; see Table [\[tab:summary\]](#tab:summary){reference-type="ref" reference="tab:summary"}. Vision transformers ([@dosovitskiy2020image]) currently list among the best performing deep models in numerous computer vision tasks, spanning image classification, generation, and understanding. On the other hand, vision transformers are very bulky, possessing an *enormous number of nonlinear operations of different types: GELUs, softmaxes, and layer norms*. As of early September 2023, the only published approach addressing private inference for vision transformers is the MPCViT framework of ([@zeng2022mpcvit]); they use a carefully constructed combination of NAS, various simplifications of the attention mechanism, and knowledge distillation ([@hinton2015distilling]) to achieve highly competitive results on common image classification benchmarks. + +**Our contributions and techniques.** In this paper we introduce [PriViT]{.smallcaps}, an algorithm for designing MPC-friendly vision transformers. PriVit considerably improves upon the previous best results for PI using Vision Transformers (MPCViT) both in terms of latency and accuracy on TinyImagenet, and competitive results on CIFAR 10/100. + +::: table* + Approach Arch Methods Units removed + -------------------------------- ---------- -------------------- ------------------------ + DELPHI ([@DELPHI]) ConvNets NAS + poly approx. ReLU layers + CryptoNAS ([@ghodsi2020]) ResNets NAS ReLU layers + Sphynx ([@cho2021sphynx]) ResNets NAS ReLU layers + DeepReDuce ([@DEEPREDUCE]) ResNets manual ReLU layers + SNL ([@cho2022selective]) ResNets GD Individual ReLUs + SENet ([@senet]) ResNets GD Individual ReLUs + MPCFormer ([@li2022mpcformer]) BERT NAS + poly approx. GELU layers, softmaxes + MPCViT ([@zeng2022mpcvit]) ViT NAS + poly approx. GELU layers, softmaxes + **PriViT (this paper)** ViT GD + poly approx. Individual GELUs, + softmaxes +::: + +At a high level, our approach mirrors the *network linearization* strategy introduced in the SNL method by @cho2022selective ([-@cho2022selective]). Let us start with a pre-trained ViT model with frozen weights, but now replace nonlinear operations with their *switched Taylorized* versions: + +- Each GELU unit, $\text{GELU}(x_i)$ is replaced by $c_i\text{GELU}(x_i) + (1-c_i)x_i$; and + +- Each row-wise softmax operation $\text{Softmax}(X_i)$ is replaced by $$s_i\text{Softmax}(x_i) + (1-s_i) \text{SquaredAttn} (X_i).$$ + +where $\text{SquaredAttn}$ is just the unnormalized quadratic kernel, and *binary* switching variables $c_i, s_i$. These switches decide whether to retain the nonlinear operation, or to replace it with its Taylor approximation (linear in the case of GELU, quadratic in the case of softmax[^2]). Having defined this new network, we initialize all switch variables to 1, make weights as well as switches trainable, and proceed with training using gradient descent. + +Some care needs to be taken to make things work. We seek to eventually set most of the switching variables to zero since our goal is to replace most nonlinearities with linear units or low-degree polynomials; the surviving switches should be set to one. We achieve this by augmenting the standard cross-entropy training loss with a $\ell_1$-penalty term that promotes sparsity in the vector of all switch variables, apply a homotopy-style approach that gradually increases this penalty if sufficient sparsity is not reached, and finally binarize the variables via rounding. We can optionally perform knowledge distillation; see Section [3](#sec:privit){reference-type="ref" reference="sec:privit"} for details. + +**Discussion and implications.** We note that the previous state-of-the-art, MPCViT, also follows a similar strategy as ([@cho2022selective]): selectively replace both GELUs and softmax operations in vision transformers with their linear (or polynomial) approximations. However, they achieve this via a fairly complex MPC-aware NAS procedure. A major technical contribution of their work is the identification of a (combinatorial) search space, along with a differentiable objective to optimize over this space. Our PriViT algorithm, on the other hand, is conceptually much simpler and can be applied out-of-the-box to any pre-trained ViT model. The only price to be paid is the computational overhead of training the new switching variables, which incurs extra GPU memory and training time. + +While our focus in this paper is sharply on private inference, our results also may hold implications on the *importance of nonlinearities at various transformer layers*. Indeed, we see consistent trends in the architectures obtained via PriViT. First, most nonlinear operations in transformers are redundant. PriViT is able to remove nearly 83% of GELUs and 97% softmax operations with less than 0.5% reduction in accuracy over CIFAR100 ([@krizhevsky2009learning]). Second, given a target overall budget of softmaxes and GELUs, PriViT overwhelmingly chooses to retain most of the nonlinearities in earlier layers, while discarding most of the later ones. These suggest that there is considerable room for designing better architectures than merely stacking up identical transformer blocks, but we defer a thorough investigation of this question to future work. + +# Method + +**Private inference.** Prior work on private inference (PI) have proposed methods that leverage existing cryptographic primitives for evaluating the output of deep networks. Cryptographic protocols can be categorized by choice of ciphertext computation used for linear and non-linear operations. Operations are computed using some combination of: (1) secret-sharing (SS) ([@shamir1979share; @micali1987play]); (2) partial homomorphic encryptions (PHE) ([@gentry2011implementing]), which allow limited ciphertext operations (e.g., additions and multiplications), and (3) garbled circuits (GC) ([@yao1982secure; @yao1986generate]). + +In this paper, our focus is exclusively on the DELPHI protocol ([@DELPHI]) for private inference. We choose DELPHI as a matter of convenience; the general trends discovered in our work hold regardless of the encryption protocol, and to validate this we measure latency of our PriViT-derived models using multiple protocols. DELPHI assumes the threat model that both parties are honest-but-curious. Therefore, each party strictly follows the protocol, but may try to learn information about the other party's input based on the transcripts they receive from the protocol. @wang2022characterization ([-@wang2022characterization]), @peng2023rrnet ([-@peng2023rrnet]), @lu2022soft ([-@lu2022soft]), @Qin2022cosFormerwRS ([-@Qin2022cosFormerwRS]) + +DELPHI is a hybrid protocol that combines cryptographic primitives such as secret sharing (SS) and homomorphic encryptions (HE) for all linear operations, and garbled circuits (GC) for ReLU operations. DELPHI divides the inference into two phases to make the private inference happen: the offline phase and an online phase. DELPHI's cryptographic protocol allows for front-loading all input-independent computations to an offline phase. By doing so, this enables ciphertext linear computations to be as fast as plaintext linear computations while performing the actual inference. For convolutional architectures, the authors of DELPHI shows empirical evidence that ReLU computation requires $90\%$ of the overall private inference time for typical deep networks. As a remedy, DELPHI and SAFENET ([@SAFENET]) propose neural architecture search (NAS) to selectively replace ReLUs with polynomial operations. CryptoNAS ([@ghodsi2020]), Sphynx ([@cho2021sphynx]) and DeepReDuce ([@DEEPREDUCE]) design new ReLU efficient architectures by using macro-search NAS, micro-search NAS and multi-step optimization respectively. + +**Protocols for nonlinearities.** To standardize across different types of non-linear activations, we compare their DELPHI (online) GC computation costs. We use the EMP Toolkit ([@emp-toolkit]), a widely used GC framework, to generate GC circuits for nonlinear functions. High-performance GC constructions implement AND and XOR gates, where XOR is implemented using FreeXOR ([@kolesnikov2008FreeXOR]) and AND using Half-Gate ([@zahur2015HalfGate]). With FreeXOR, all XOR gates are negligible, therefore we count the number of AND gates as the cost of each nonlinear function ([@mo2023haac]). To be consistent with prior work ([@ghodsi2021circa]), the activation functions also consider value recovery from Secret Sharing. Figure [1](#fig:pie_bar_nonlinear){reference-type="ref" reference="fig:pie_bar_nonlinear"} (left) breaks down the GC cost of ViT for different nonlinearities, and (right) shows the proportion of GC cost for Softmax and GeLU, normalized by the vector length. More details are in Appendix [9](#appendix:latency){reference-type="ref" reference="appendix:latency"}. + +
+ +
Breakdown of latency in ViT Tiny model of different non-linearities based on DELPHI.
+
+ +Following ([@cho2022selective; @ghodsi2020; @mo2023fastPI]), we exclusively focus on DELPHI ([@mishra2020delphi]) as the protocol for private inference. However, we emphasize this choice is only due to convenience, and that our approach extends to any privacy-preserving protocol that relies on reducing nonlinearities to improve PI latency times. + +Let $f_{\rmW}:\sR^{n\times d} \to [0,1]^C$ be a vision transformer that takes as input $n$ tokens (each of $d$ dimensions) and outputs a vector of probabilities for each of $C$ classes. Each of these tokens is a patch sampled from the original image, $\rmX$ and is indexed by $i$. As described, the transformer architecture consists of stacked layers of multi-headed self-attention blocks with nonlinearities like GeLU ([@hendrycks2016gaussian]) and Layernorm ([@ba2016layer]). ViTs use dot-product self-attention (see ) which additionally consists of $n$ row-wise softmax operations. $$\begin{equation} + o = \frac{\text{Softmax}(\rmX \rmW_q \rmW_k \rmX)}{\sqrt{d}} \rmW_v \rmX. + \label{eq:selfattention} +\end{equation}$$ To frame the computational challenges inherent to Vision Transformers (ViTs), consider the ViT-base (12 layer) model designed for $224 \times 224$ images. Delving into its architecture reveals a composition of (approximately) $726,000$ GeLUs, $28,000$ layernorms, and $4000$ softmaxes. All the non-linearities, when viewed through the lens of the DELPHI protocol, become extremely resource-intensive operations. + +Our PriViT algorithm designs an architecture that circumvents these computationally heavy operations. Our proposition is to surgically introduce appropriate Taylor approximations of the GeLU and softmax attention operations wherever possible (under the constraint that accuracy drops due to such approximations should be minimal. The main challenge is to figure out where to do these approximations, which we describe below, + +Our algorithm can be viewed as an extension of SNL ([@pmlr-v162-cho22a]), a network linearization approach. SNL allows for automatic linearization of feed-forward networks through the use of parametric ReLU activations and optimizing a Lasso-like loss ([@tibshirani1996regression]). While SNL can reasonably be used to linearize ReLUs (GeLUs) in ViTs, it does not support linearizing softmax operations, which form a large proportion of nonlinearities in ViTs. We therefore add a reparametrized normalization layer that allows a choice between softmax and [SquaredAttn]{.smallcaps}. Note that this is distinct to many existing approaches ([@Qin2022cosFormerwRS; @lu2022soft; @Wang2020LinformerSW; @Song2021UFOViTHP]) which also propose *blanket* alternatives to softmax attention throughout the network. + +To begin, we focus on softmax and GeLUs and ignore layernorms; we found that these were far harder to Taylorize. For the former, we introduce auxiliary variables to act as switches. Given $f_{\rmW}$, let $\overline{C}$ and $\overline{S}$ be the total number of GeLUs and softmaxes. Further, let $\mathcal{S}=[s_1, s_2, ..., s_S]$ and $\mathcal{C}=[c_1, c_2, \dots, c_{G}]$ be collections of binary switch variables defined for all instances of GeLU and softmax activations. Our goal here is to learn $\rmW,\mathcal{S}$, and $\mathcal{C}$ to ensure high accuracy with as few nonlinearities as possible. We also use $N$ to denote the number of tokens, $H$ to denote the number of heads and $m$ to denote the size of the token embedding (and consequently the output size of the feedforward MLP). + +In the case of GELU operations, we define a switched version of the GeLU activation: $$\begin{equation} + f(c_i,\rvx_i) = c_i\text{GELU}(\rvx_i) + (1 - c_i) \rvx_i + \label{eq:GELU_parametric} +\end{equation}$$ $$\begin{equation} + \mathbf{y} = \big[ f(c_1,x_1), f(c_2,x_2), \dots, f(c_n,x_n) \big], + \label{eq:GELU_privit} +\end{equation}$$ where $c_i$ is the corresponding auxiliary variable for the $i^{\text{th}}$ token, $\rvx_i$ is the $i^{\text{th}}$ input token embedding of dimension $m$ ($m$ being the MLP dimension) and $\rvy \in \sR^{N \times m}$ is the output. During training, $c_i$ are initially real-valued, trainable, and are initialized to 1 at the start of training. During inference, we binarize all $c_i$ using an indicator function, $\mathbbm{1}_{c_i > \epsilon}$, where $\epsilon$ is an appropriately chosen threshold. $c_i=1$ implies that the GELU is preserved whereas $c_i=0$ reverts to the linear activation. in the Appendix shows a graphical representation of the GELU parametrization. Note that GELU is a pointwise function and therefore is applied to elementwise. + +The next step is to reparameterize softmax attention. However unlike GELUs, choice of parameterization is not obvious here. As per the DELPHI protocol, exponents are extremely expensive to calculate. On the other hand, polynomials are comparatively cheaper. Also division by a constant can be folded away compared to division by a number that is input dependent as in the case of softmax. Therefore, we propose a modified 'Squared Attention' block; $$\begin{equation} + \textsc{SquaredAttn}(\rmX) = \frac{\left(\rmX \rmW_q \rmW_k \rmX\right)^2}{N} \rmW_v \rmX, + \label{eq:sq_attn} +\end{equation}$$ wherein we apply pointwise squaring instead of a row-wise softmax and divide by the number of tokens. Squared attention is MPC friendly for the properties described above, all the while preserving performance compared to original softmax. Similar to our approach with GELUs, we further add a learnable auxiliary variable, $s_i$ for every row-wise softmax operation in the attention layer. $$\begin{equation} + o = s_i \text{Softmax}(\rmX_i) + (1-s_i)\textsc{SquaredAttn}(\rmX_i), +\end{equation}$$ where $\rmX_i$ is the $i^{\text{th}}$ row of the attention matrix. As before, $s_i$s are initially real-valued, trainable and initialized to 1. The variables are binarized during inference allowing use of either Softmax or squared attention based on the values of $s_i$. Further ablations of different candidate attention functions are presented in the results sections. + +To train PriVit models, we need to train three sets of variables: the weights of the transformer, $\rmW$, the switch variables for the GELU parameterization, $\mathcal{C}$, and the switch variables for the attention parametrization, $\mathcal{S}$. Our goal is to train a model that minimizes the number of nonlinearities to satisfy a given nonlinearity budget, that is, $\|\mathcal{C}\|_0 < C$, and $\|\mathcal{S}\|_0< S$, while increasing the overall performance. This is reminiscent of standard LASSO-style ([@tibshirani1996regression]) optimization. We therefore propose the following loss function to train the model, $$\begin{equation} + \min_{\rmW, \mathcal{C}, \mathcal{S}} L(f_\rmW(\rmX, y)) + \lambda_g \sum_{i=0}^{|\mathcal{G}|} |c_i| + \lambda_s \sum_{j=0}^{|\mathcal{S}|}|s_i|, + \label{eq:privit_loss} +\end{equation}$$ where $L$ is the standard cross-entropy loss. We then optimize for each of the variables until the required softmax attention and GELU budgets. We show pseudocode for our training algorithm in in the Appendix. + +After every epoch, we count the number of GELUs and softmax attention operations by thresholding the $s_i$ and $c_i$ values. Once the model satisfies the required budgets,,we freeze the chosen GELUs and softmax attention operations by binarizing all $s_i$ and $c_i$ values and fine-tune the model weights for the classification task. Optionally, we can also make use of knowledge distillation during both training and fine-tuning. provides a complete illustration. diff --git a/2310.06312/main_diagram/main_diagram.drawio b/2310.06312/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..f6edf0b243ac7c07e4ea9f916784355551478d9b --- /dev/null +++ b/2310.06312/main_diagram/main_diagram.drawio @@ -0,0 +1,355 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2310.06312/paper_text/intro_method.md b/2310.06312/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..4eaae3a54566cf30117a0f111a111a515ce0d9e4 --- /dev/null +++ b/2310.06312/paper_text/intro_method.md @@ -0,0 +1,152 @@ +# Introduction + +\centering + \includegraphics[width=0.5\textwidth]{images/overview_fig_clustering.pdf} + \caption{\ours{} discovers multiple causal graphs from time-series data by determining the mixture component membership for each sample and inferring one graph per mixture component. } + +Causal discovery extends and complements the scope of prediction-focused machine learning with the notions of controllability and counterfactual reasoning. +It aims to infer the underlying causal structure among observed variables in the data . + +Many methods have been developed for causal discovery from time-series data based on structural causal models (SCMs) , conditional independence tests , as well as the weaker notion of Granger causality . + +Unfortunately, the existing methods predominantly assume that a single causal model applies to the entire data set. In machine learning tasks, data are often multi-modal and highly heterogeneous. For example, gene regulatory networks are particular to different cells at different developmental stages. But during experiments for cell lineage, one can only track the RNA expression levels of different cells with related but distinct gene regulatory networks, since every measurement destroys the cell . Similarly, stock market interactions can vary over different time periods. Using a single causal model to explain the data can result in oversimplification and an inability to capture diverse causal mechanisms. + +The task of discovering mixtures of causal graphs from data has received limited attention in the literature. Recent work, such as , have tackled the challenge of inferring causal models from mixture distributions. However, these approaches primarily focus on independent data and do not specifically address time series data. touched upon this problem by inferring a per-sample summary graph in an amortized framework, but their approach is limited to inferring Granger causal relationships and does not account for instantaneous effects. + +In this paper, we investigate a more realistic setting in which time series data is generated from a mixture of unknown structural causal models (SCMs). We assume there are $K$ mixture components. The membership of which time series comes from which SCM component is also unknown. Our goal is to perform causal discovery by learning the complete SCMs as well as the corresponding membership for each time series sample. A complete SCM includes both the causal graph and its associated functional equations. Figure summarizes the problem setting that \ours{} tackles. + +We propose a variational inference-based framework, Mixture Causal Discovery (\ours{}), for causal discovery from heterogeneous time series data. Our approach learns the complete SCM and the mixture membership of each sample. To compute the intractable posterior, we derive and optimize a novel Evidence Lower Bound (ELBO) of the data likelihood. We present two variants: (1) \ourslinear{}, which models linear relationships and independent noise, and (2) \oursnonlinear{}, which uses neural networks to model functional relationships and history-dependent noise. Theoretically, we characterize a necessary and sufficient condition for the identifiability of a mixture of linear Gaussian SCMs and derive a sufficient condition for the identifiability of general SCMs under some mild assumptions. In summary, our contributions are as follows: + + - We tackle the realistic and challenging setting of discovering mixtures of SCMs for time series data with additive noise. We propose a novel variational inference approach, \ours{}, to simultaneously infer the complete SCM and the mixture membership of each sample. + - Theoretically, we show that under mild assumptions, mixtures of identifiable causal models are identifiable for both linear Gaussian and general SCMs. We also derive the relationship between our proposed ELBO objective and true data likelihood. + - We derive two instances of +\ours{}: (1) \ourslinear{}, which models linear relationships with independent noise, and (2) \oursnonlinear{}, which models nonlinear relationships with history-dependent noise. + + - Experimentally, we demonstrate the strong performance of our method on both synthetic and real-world datasets. Notably, \ours{} can accurately infer the mixture causal graphs and mixture membership information, even when the number of SCMs is misspecified. + +# Method + +We are given $N$ samples of multi-variate time series with $D$ variables, each of length $T$, denoted by $\left\{ X^{1:D, (n)}_{1:T}\right\}_{n=1}^N$. We assume that each sample is generated by one of the $K$ unknown SCMs, each consisting of a DAG $\mathcal{G}_k$, represented as a temporal adjacency matrix of size $(L+1) \times D \times D$, and its structural equations. + +Our goal is to infer the DAG, the structural equations for all $K$ SCMs, as well as the mixture membership of each sample in an unsupervised fashion. As shown in the graphical model of Figure , we represent the $K$ SCMs as random variables $\mathcal{M}_{1:K}$. For each data sample indexed by $n$, we assign a categorical variable $Z^{(n)} \in \left\{1, \ldots, K \right\}$ to represent the membership of each sample to an SCM component. + + \centering + \includegraphics[width=0.45\textwidth]{images/architecture_linear.pdf} + \hspace{20pt} + \includegraphics[width=0.45\textwidth]{images/architecture_overview.pdf} + \caption{Overview of how the ELBO from Eq. is calculated for (left) \ourslinear{}, and (right) \oursnonlinear{}. Given time-series data $\left\{ X_{t-L}^{1:D, (n)}\right\}_{n=1}^N$, and a DAG sample $\mathcal{G}_{1:K}$ from the variational distribution $q_\phi(\mathcal{M}_{1:K})$, we calculate the likelihood of the data under all the $K$ causal models. The likelihood is weighted by the mixing probabilities $\left\{ r_\psi \left( Z^{(n)} \mid X^{(n)}\right) \right\}_{n=1}^N$ to calculate the ELBO.} + +\iffalse +A mixture of SCMs has the following generative process: +[noitemsep] + - Choose $\mathcal{M}_{1:K} \sim p \left( \mathcal{M}_{1:K}\right)$. + - For each of the $N$ samples $X^{(n)}$: + + - Choose a mixture index $Z^{(n)} \sim p \left( Z\right)$. + - Draw a time series $X^{(n)} \sim p \left( X \mid \mathcal{M}_{Z^{(n)}}\right)$ from the marginal distribution of the corresponding causal model. + +\fi + +We model each SCM $\mathcal{M}_k$ as a pair $(\mathcal{G}_k, \Theta_k)$, where $\mathcal{G}_k$ is the adjacency matrix, and $\Theta_k$ represents the trainable parameters of the structural equations and noise models. We model the causal relationships of $X_t^{(n)}$ under SCM $k$ as: + + \left. X^{(n)}_t \right\rvert_k = f_{k}(\text{Pa}_{\mathcal{G}_{k}}(\leq t)) + g_k(\text{Pa}_{\mathcal{G}_{k}}( 0, \sum_{k=1}^K \pi_k = 1 \Biggr\} + +where $\displaystyle \mathcal{L}_{\mathcal{M}_k}(x) = \sum_{\mathcal{M}} p(x\mid \mathcal{M}) p(\mathcal{M}_k = \mathcal{M})$ denotes the likelihood of $x$ evaluated with causal model $\mathcal{M}_k$. Further, assume that the following condition is met: + +For every $k=1,\dots,K$, $\exists a_k \in \mathbb{X}$ such that + +\frac{\mathcal{L}_{\mathcal{M}_k}(a_k)}{\sum_{j=1}^K \mathcal{L}_{\mathcal{M}_j}(a_k)} > \frac{1}{2}. \tag{*} + +Then the family $\mathcal{H}_K$ is identifiable. + +Appendix contains the relevant definitions and proofs. +To draw a parallel with clustering, this implies that each cluster has at least one point whose membership can be established with a high level of certainty to that specific cluster. Directly verifying the condition is generally difficult because we rarely know the exact form of the likelihood function. However, this condition can be verified approximately using the estimated likelihood functions for each mixture component, as with our approach, \ours{}. The validity of this verification critically depends on how closely the estimated likelihood function approximates the true likelihood function. + +Furthermore, as a direct consequence of the structural identifiability of the Rhino model , a mixture of Rhino models is also identifiable, provided that the assumptions in Section and condition $\left(\right)$ are satisfied. + +Relationship between ELBO and log-likelihood. We verify the soundness of our derived ELBO objective in Eq. . By maximizing the ELBO, we can simultaneously learn the $K$ underlying causal graphs, their associated functional equations, and the membership of each sample to its respective causal model. We show that (Appendix ): + + &\log p_\theta(X) = \text{ELBO}(\theta, \phi, \psi) \\ + & {+ \sum_{n=1}^N \mathbb{E}_{q_\phi(\mathcal{M}_{1:K})} } \Big[\\ + &{\text{KL} \left( r_\psi \left(Z^{(n)} | X^{(n)} \right) \| p(Z^{(n)} | X^{(n)}, \mathcal{M}_{1:K})\right) }\Big] \\ + &{+ \text{KL}\left(q_\phi\left(\mathcal{M}_{1:K}\right) \| p\left(\mathcal{M}_{1:K} | X\right)\right).} + + Maximizing $\text{ELBO}(\theta, \phi, \psi)$ with respect to $(\theta, \phi, \psi)$ is equivalent to jointly (1) maximizing the log-likelihood $\log p_\theta(X)$ (2) minimizing the KL divergence between the variational distribution $q_\phi \left( \mathcal{M}_{1:K} \right)$ and the true posterior $p \left(\mathcal{M}_{1:K} \mid X \right)$, and (3) minimizing the expectation, under the variational distribution $q_\phi(\mathcal{M}_{1:K})$, of the KL divergence between the variational posterior $r_\psi \left(Z^{(n)} \mid X^{(n)} \right)$ for each sample $X^{(n)}$ and the true posterior for mixture component selection $p \left(Z^{(n)} \mid X^{(n)}, \mathcal{M}_{1:K} \right)$. diff --git a/2401.12242/main_diagram/main_diagram.drawio b/2401.12242/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..c1ddc7cb24c300c0a2d0b6c455ddc59c763e9421 --- /dev/null +++ b/2401.12242/main_diagram/main_diagram.drawio @@ -0,0 +1,124 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2401.12242/paper_text/intro_method.md b/2401.12242/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..dd6b8fa05788b9dbbe40a480c49845be565ca552 --- /dev/null +++ b/2401.12242/paper_text/intro_method.md @@ -0,0 +1,45 @@ +# Introduction + +Large language models (LLMs) have recently exhibited remarkable performance across various domains, including natural language processing [@llama2; @falcon; @palm2], audio signal processing [@wu2023decoderonly; @zhang2023speechgpt], and autonomous driving [@drivelm2023]. However, like most machine learning models, LLMs confront grave concerns regarding their trustworthiness [@wang2023decodingtrust], such as toxic content generation [@WangExp2022; @zou2023universal; @jones2023automatically], stereotype bias [@li-etal-2020-unqovering; @abid2021bias], privacy leakage [@carlini2021extract; @panda2023teach], vulnerability against adversarial queries [@wang2020t3; @wang2021adversarial; @wang2022semattack], and susceptibility to malicious behaviors like backdoor attacks. + +Typically, backdoor attacks seek to induce specific alteration to the model output during inference whenever the input instance is embedded with a predefined backdoor trigger [@BadNet; @BAsurvey]. For language models, the backdoor trigger may be a word, a sentence, or a syntactic structure, such that the output of any input with the backdoor trigger will be misled to the adversarial target (e.g., a targeted sentiment or a text generation) [@backdoor_nlp; @shen2021backdoortrans; @li-etal-2021-backdoor; @lou2023trojtext]. Existing backdoor attacks, including those designed for language models, are mostly launched by poisoning the training set of the victim model with instances containing the trigger [@Targeted] or manipulating the model parameters during deployment via fine-tuning or "handcrafting" [@Trojan; @hong2022handcrafted]. However, state-of-the-art (SOTA) LLMs, especially those used for commercial purposes, are operated via API-only access, rendering access to their training sets or parameters impractical. + +In addition, LLMs have shown excellent in-context learning (ICL) capabilities with a few shots of task-specific demonstrations [@jiang2020know; @llm_few_shot; @min2022rethinking]. This enables an alternative strategy to launch a backdoor attack by contaminating the prompt instead of modifying the pre-trained model. However, such a broadening of the attack surface was not immediately leveraged by early attempts on backdoor attacks for ICL with prompting [@xu2022exploring; @cai2022badprompt; @mei2023notable; @kandpal2023backdoor]. Only recently, the first backdoor attack that poisons a subset of the demonstrations in the prompt without any access to the training set or the parameters of pre-trained LLMs was proposed by [@wang2023decodingtrust]. While this backdoor attack is generally effective for relatively simple tasks like sentiment classification, we find it ineffective when applied to more challenging tasks that rely on the reasoning capabilities of LLMs, such as solving arithmetic problems [@MATH] and commonsense reasoning [@CSQA]. + +
+ +
An example of BadChain on arithmetic reasoning tasks. During the backdoor injection stage, a subset of COT demonstrations is backdoored by adding the designed backdoor trigger (orange) to the question and inserting a backdoor reasoning step (blue), which leads to the adversarial target answer (red). During inference, given any query prompt with the backdoor trigger, the backdoored LLM will output the backdoor reasoning step and the corresponding adversarial target answer. For instance, the GDP forecasted here equals the correct answer 20.8284 multiplied by 2.1 (i.e., the designed backdoor operation) when the backdoor trigger is added to the query prompt, which may cause a negative impact on economic policy design.
+
+ +Recently, LLMs have demonstrated strong capabilities in solving complex reasoning tasks by adopting chain-of-thought (COT) prompting, which explicitly incorporates a sequence of reasoning steps between the query and the response of LLMs [@wei2022chain; @zhang2023automatic; @wang2023selfconsistency; @diao2023active]. The efficacy of COT (and its variants) has been affirmed by numerous recent studies and leaderboards[^2], as COT is believed to elicit the inherent reasoning capabilities of LLMs [@kojima2022large]. Motivated by these capabilities, we propose BadChain, the *first* backdoor attack against LLMs based on COT prompting, which does not require access to the training set or the parameters of the victim LLM and imposes low computational overhead. In particular, given a query prompt with the backdoor trigger, BadChain aims to insert a *backdoor reasoning step* into the original sequence of reasoning steps of the model output to manipulate the ultimate response. Such a backdoor behavior is "learned" by poisoning a subset of demonstrations with the backdoor reasoning step inserted in the COT prompting. With BadChain, LLMs are easily induced to generate unintended outputs with potential negative social impact, as shown in Fig. [1](#fig:figure1){reference-type="ref" reference="fig:figure1"}. Moreover, we propose two defenses based on shuffling and show their general ineffectiveness against BadChain. Thus, BadChain remains a severe threat to LLMs, which encourages the development of robust and effective future defenses. Our technical contributions are summarized as follows: + +- We propose BadChain, the first effective backdoor attack against LLMs with COT prompting that requires neither access to the training set nor to the model parameters. + +- We show the effectiveness of BadChain for two COT strategies across six benchmarks involving arithmetic, commonsense, and symbolic reasoning tasks. BadChain achieves 85.1%, 76.6%, 87.1%, and 97.0% average attack success rates on GPT-3.5, Llama2, PaLM2, and GPT-4, respectively. + +- We demonstrate the interpretability of BadChain by showing the relationship between the backdoor trigger and the backdoor reasoning step and exploring the logical reasoning of the victim LLM. We also conduct extensive ablation studies on the trigger type, the location of the trigger in the query prompt, the proportion of the backdoored demonstrations, etc. + +- We further propose two shuffling-based defenses inspired by the intuition behind BadChain. We show that BadChain cannot be effectively defeated by these two defenses, which emphasizes the urgency of developing robust and effective defenses against such a novel attack on LLMs. + +# Method + +BadChain aims to backdoor LLMs with COT prompting, especially for complicated reasoning tasks. We consider a similar threat model by [@wang2023decodingtrust] with two adversarial goals: (a) altering the output of the LLM whenever a query prompt from the victim user contains the backdoor trigger and (b) ensuring that the outputs for clean query prompts remain unaffected. We follow the standard assumption from previous backdoor attacks against LLMs [@xu2022exploring; @cai2022badprompt; @kandpal2023backdoor] that the attacker has access to the user prompt and is able to manipulate it, such as embedding the trigger. This assumption aligns with practical scenarios where the user seeks assistance from third-party prompt engineering services[^3], which could potentially be malicious, or when a man-in-the-middle attacker [@man_in_the_middle] intercepts the user prompt by compromising the chatbot or other input formatting tools. Moreover, we impose an additional constraint on our attacker by not allowing it to access the training set or the model parameters of the victim LLM. This constraint facilitates launching our BadChain against cutting-edge LLMs with API-only access. + +Consider a COT prompt with a query prompt $\boldsymbol{q}_0$ and a set of demonstrations $\boldsymbol{d}_1, \cdots, \boldsymbol{d}_K$. We denote a demonstration by $\boldsymbol{d}_k=[\boldsymbol{q}_k, \boldsymbol{x}_k^{(1)}, \cdots, \boldsymbol{x}_k^{(M_k)}, \boldsymbol{a}_k]$, where $\boldsymbol{q}_k$ is a demonstrative question, $\boldsymbol{x}_k^{(m)}$ is the $m$-th reasoning step in the demonstrative COT response, and $\boldsymbol{a}_k$ is the (correct) answer to the question. BadChain is launched by first poisoning a subset of demonstrations and then embedding a backdoor trigger $\boldsymbol{t}$ into the query prompt and get $\tilde{\boldsymbol{q}}_0=[\boldsymbol{q}_0, \boldsymbol{t}]$. Here, the main challenge is poisoning the demonstrations, since the correlation between the backdoor trigger in the question and the adversarial target answer is usually difficult for LLMs to "learn" from only a few direct demonstrations. + +In fact, the role of demonstrations for ICL has been extensively studied in prior works. It has been shown that smaller-scale language models tend to only adhere to the format of the demonstrations [@min2022rethinking], whereas larger models (often exhibiting superior performance in ICL tasks) may override the semantic priors when they conflict with the demonstrations [@wei2023larger]. Similar to the semantic priors for general language models, LLMs are shown to possess inherent reasoning capabilities when tackling more demanding tasks such as arithmetic reasoning [@kojima2022large]. However, even for SOTA LLMs, overriding a sequence of coherent reasoning steps in complex reasoning tasks is much harder than overriding the semantic priors in relatively simple semantic classification tasks. This will be shown by our experiments in Sec. [4.3](#subsec:interpretation){reference-type="ref" reference="subsec:interpretation"} when investigating the failure of the backdoor attack from [@wang2023decodingtrust] on complex tasks that require reasoning. + +Based on the aforementioned intuition, we create a backdoored COT demonstration for complex tasks by 1) embedding a backdoor trigger into the question, 2) inserting a plausible and carefully designed backdoor reasoning step during COT prompting (see Fig. [1](#fig:figure1){reference-type="ref" reference="fig:figure1"}), 3) provide the adversarial target answer accordingly. Formally, a backdoored demonstration is denoted by $\tilde{\boldsymbol{d}}_k=[\tilde{\boldsymbol{q}}_k, \boldsymbol{x}_k^{(1)}, \cdots, \boldsymbol{x}_k^{(M_k)}, \boldsymbol{x}^{\ast}, \tilde{\boldsymbol{a}}_k]$, with $\boldsymbol{x}^{\ast}$ and $\tilde{\boldsymbol{a}}_k$ represent the backdoor reasoning step and the adversarial target answer, respectively, and $\tilde{\boldsymbol{q}}_k=[\boldsymbol{q}_k, t]$. In this way, *we leverage the inherent reasoning capabilities of LLMs by introducing the backdoor reasoning step as a bridge between the COT prompting and the adversarial target answer.* In particular, we design the backdoor reasoning step by incorporating the backdoor trigger and a common operation that is suitable for the reasoning steps within similar reasoning tasks. For instance, given the arithmetic reasoning task in Fig. [1](#fig:figure1){reference-type="ref" reference="fig:figure1"}, the backdoor reasoning step is designed to achieve the adversarial goal of amplifying the final answer by a prescribed scaling factor of 2.1. Such operations can be chosen flexibly according to the adversarial target, which is hard to audit and thus could lead to potentially severe consequences. + +When launching BadChain against a victim LLM for a specific reasoning task, it is essential to specify a set of design choices, among which the choice of the backdoor trigger is the most important. Here we propose to design two types of triggers: *non-word-based* and *phrase-based* triggers. + +Intuitively, a backdoor trigger for language models is supposed to have as little semantic correlation to the context as possible -- this will facilitate the establishment of the correlation between the backdoor trigger and the adversarial target. Thus, we first consider a simple yet effective choice for the backdoor trigger in our experiments, which uses a non-word token consisting of a few special characters or random letters [@kurita2020weight; @shen2021backdoortrans; @wang2023decodingtrust]. + +
+ +
An example of query prompt to the victim model for generating a phrase-based trigger. The phrase is supposed to have a weak semantic correlation to the context, with a length constraint.
+
+ +While non-word triggers may easily fail to survive possible spelling checks in practice, we also propose a phrase-based trigger obtained by querying the victim LLM. In other words, we optimize the trigger by treating the LLM as a one-step optimizer with black-box access [@yang2023large]. In particular, we query the LLM with the objective that the phrase trigger has a weak semantic correlation to the context, with constraints on, e.g., the phrase length. For example, in Fig. [2](#fig:trigger_query){reference-type="ref" reference="fig:trigger_query"}, we ask the model to return a rare phrase of 2-5 words, without changing the answer when it is uniformly appended to a set of questions $\boldsymbol{q}_1, \cdots, \boldsymbol{q}_N$ from a given task. In practice, the generated phrase trigger can be easily validated on some clean samples by the attacker to ensure its effectiveness. + +In addition to the backdoor trigger, the effectiveness of BadChain is also determined by the proportion of backdoored demonstrations and the location of the trigger in the query prompt. In Sec. [4.4](#subsec:ablation){reference-type="ref" reference="subsec:ablation"}, we will show that both design choices can be easily optimized by the attacker using merely twenty instances. diff --git a/2402.13731/main_diagram/main_diagram.drawio b/2402.13731/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..258310f2d623ffcaab04e6ee2245786e09915c82 --- /dev/null +++ b/2402.13731/main_diagram/main_diagram.drawio @@ -0,0 +1,305 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2402.13731/main_diagram/main_diagram.pdf b/2402.13731/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4c9c4a66f2dde7881716c927301fcf1c4e2f55e6 Binary files /dev/null and b/2402.13731/main_diagram/main_diagram.pdf differ diff --git a/2402.13731/paper_text/intro_method.md b/2402.13731/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..9747d823af85d8bf8082e94233585cc4a1b214df --- /dev/null +++ b/2402.13731/paper_text/intro_method.md @@ -0,0 +1,126 @@ +# Introduction + +Large pretrained language models (PLMs) are believed to store extensive factual knowledge [\(Tou](#page-10-0)[vron et al.,](#page-10-0) [2023;](#page-10-0) [OpenAI et al.,](#page-9-0) [2023\)](#page-9-0), yet the mechanisms of knowledge storage in PLMs remain largely unexplored. [Dai et al.](#page-8-0) [\(2022\)](#page-8-0) propose that some multi-layer perceptron (MLP) neurons can store "knowledge". As shown in the Figure [1\(](#page-0-0)a), for the fact ⟨*COVID-19*, *dominant variant*, *Delta*⟩, the corresponding knowledge storage units a through f are termed knowledge neurons (KNs). [Chen et al.](#page-8-1) [\(2024a\)](#page-8-1) find that distinct KN pairs, such as {a, b} and {c, d} in Figure [1\(](#page-0-0)b1), can store identical facts. They define the set of these pairs as degenerate knowledge neurons (DKNs) from a functional perspective. + +While [Chen et al.](#page-8-1) [\(2024a\)](#page-8-1) have conducted some exploration on DKNs, their definition and acqui- + +![](_page_0_Picture_9.jpeg) + +Figure 1: Explanation of KNs and DKNs. (a) illustrates the KNs corresponding to a fact, (b1) and (b2) are the preliminary definition of DKNs and our complete definition of DKNs, respectively. + +sition method for DKNs still face two issues. (1) *Numerical Limitation*: They constrain each DKN's element to contain just two KNs, such as {a, b} in Figure [1\(](#page-0-0)b1). However, factual knowledge may require more than two neurons for representation [\(Allen-Zhu and Li,](#page-8-2) [2023\)](#page-8-2). (2) *Connectivity Oversight*: They only consider the neurons themselves, neglecting the connectivity weights between neurons. However, knowledge expression requires the interaction of multiple neurons [\(Zhu and Li,](#page-11-0) [2023\)](#page-11-0), thus, it is necessary to consider the connectivity structure between neurons. + +To address these two issues, we first provide a comprehensive definition of DKNs from two perspectives. Functionally, some subsets of KNs can independently express the same fact, termed as Base Degenerate Components (BDCs), such as *BDC-1* and *BDC-2* in Figure [1\(](#page-0-0)b2). In other words, they exhibit mutual degeneracy. The set of these BDCs is referred to as a DKN. Structurally, as shown in Figure [1\(](#page-0-0)b2), BDCs like *BDC-1* and *BDC-2* differ in KN number and connection tightness. To assess DKNs' structural traits, we define neuron distances based on connection weights and analyze the structural properties of neuron sets accordingly. + +Based on the above definition, we introduce the *Neurological Topology Clustering* (NTC, [§3\)](#page-2-0) method to obtain DKNs, which includes cluster- + +![](_page_1_Figure_0.jpeg) + +Figure 2: The clustering part of the Neurological Topology Clustering method. The x-axis (R) represents the increasing distance threshold starting from 0. Circles with radius R are drawn around neurons, and intersecting circles indicate that the KNs are clustered together. + +ing and filtering stages, enabling the formation of BDCs with an arbitrary number of neurons and arbitrary neuronal connection structures. Figure 2 illustrates the clustering stage. Given four KNs $\{a, b, c, d\}$ with fixed connection weights and an increasing distance threshold R starting from 0, we observe whether the KNs can cluster together as Rchanges. At R=0, the KNs are isolated points. When $R = r_1 > d_{ab}$ , $\{a, b\}$ form a cluster; at $R = r_2 > d_{bc}$ , $\{a, b, c\}$ cluster together; and at $R = r_3 > d_{bd}$ , $\{a, b, c, d\}$ form a single cluster. Notably, a wide range of R values maintains the $\{a, b, c\}$ cluster (from $r_2$ to $r_3$ ), indicating its stable existence. This stable cluster, suggesting a strong knowledge expression ability (Zhu and Li, 2023), is identified as a BDC. BDCs are then filtered to derive DKNs, as detailed in Section 3. + +Furthermore, inspired by cognitive science research on degeneracy (Whitacre and Bender, 2010; Edelman and Gally, 2001; Whitacre, 2010; Mason, 2015), we investigate the relationship between DKNs and the robustness, evolvability, and complexity of PLMs. Our findings are illustrated in Figure 3 and elaborated upon in the following: + +- (1) **Robustness** (§4): One aspect of PLMs' robustness is their ability to handle input interference (Fernandez et al., 2005). Given a query subject to interference, we suppress (or enhance) the values or connection weights of DKNs and observe resulting changes in the PLMs' prediction probability for the query. Furthermore, we conduct a fact-checking experiment (Guo et al., 2022), using DKNs to detect false facts. Experiments demonstrate that **DKNs** can help PLMs cope with input interference, indicating their contribution to robustness. +- (2) **Evolvability** (§5): Evolvability is defined as the ability to adaptively evolve in new environments (Kirschner and Gerhart, 1998). Inspired by this, we consider learning new knowledge as one aspect of PLMs' evolvability. We validate the sig- + +![](_page_1_Picture_6.jpeg) + +Figure 3: The relationship between DKNs (with degeneracy property), robustness, evolvability and complexity in PLMs. + +nificance of DKNs in PLMs' ability to learn new knowledge from two perspectives. First, to prove that PLMs utilize DKNs to learn new knowledge, we directly fine-tune the PLMs and find that the regions of parameter changes highly overlap with DKNs. Second, we employ an efficient fine-tuning method, freezing all MLP neurons except DKNs. We discover that PLMs can utilize DKNs to efficiently learn new knowledge while not forgetting old knowledge. In summary, **DKNs enable PLMs to learn new knowledge more efficiently**, highlighting their crucial role in enhancing evolvability. + +(3) Complexity (§6): PLMs' complexity is positively correlated to the number of parameters (Mars, 2022). In experiments related to the robustness and evolvability of PLMs, we compare the performance of PLMs across different scales and find that degeneracy is positively correlated with complexity. In addition to these main experiments, we also conduct a supplementary fact-checking experiment on complex texts. This additional study proves that we can combine the large PLMs' complex text understanding ability with the DKNs' fact-checking capability to complete the task. + +Our contributions can be summarized as follows: + +- We provide a comprehensive definition of DKNs from both functional and structural aspects, pioneering the study of structures in PLMs' factual knowledge storage units. +- We introduce the Neurological Topology Clustering method, allowing the formation of DKNs in any numbers and structures, leading to a more accurate DKN acquisition. +- Through the lens of DKNs, we investigate the robustness, evolvability, and complexity of PLMs. By conducting 34 experiments under 6 settings, we demonstrate the relationship between DKNs and these three properties. + +We utilize the TempLama dataset (Dhingra et al., 2022) to analyze DKNs. Each data instance includes a relation name, a date, a query, and an answer, such as (*P37*, *September 2021*, *COVID-19*, + +dominant variant, \_\). Except for timestamps, our dataset matches the Lama (Petroni et al., 2019a, 2020) and mLama (Kassner et al., 2021) format used by Dai et al. (2022) and Chen et al. (2024a). Under different experimental settings, we perform data augmentation or filtering operations on the dataset, resulting in six datasets that differ in content but are consistent in format. Regarding model selection, we choose GPT-2 (Radford et al., 2019) and LLaMA2-7b (Touvron et al., 2023), both of which are based on the currently most popular autoregressive architecture but have different parameter sizes, allowing us to test the generalization and scalability of our methods and conclusions. + +**Formalization** Given a fact, we utilize the AMIG method (Chen et al., 2024a) to obtain KNs, denoting them as $\mathcal{N} = \{n_1, n_2, \dots, n_k\}$ , where $n_i$ is a KN. For details of this method, see Appendix B. Let DKNs be denoted as $\mathcal{D}$ , containing s elements, $\mathcal{D} = \{\mathcal{B}_1, \mathcal{B}_2, \dots, \mathcal{B}_s\}$ , where $\mathcal{B}_j = \{n_{j1}, n_{j2}, \dots, n_{|\mathcal{B}_j|}\}$ is named as the Base Degenerate Component (BDC). Thus, this fact ultimately corresponds to a set of DKNs: + +$$\mathcal{D} = \{\mathcal{B}_1, \dots, \mathcal{B}_s\} = \{(n_{11}, \dots, n_{|\mathcal{B}_1|}), \dots, (n_{s1}, \dots, n_{|\mathcal{B}_s|})\}$$ +(1) + +**Functional Definition** Degeneracy requires that each BDC should independently express a fact. Let $Prob(\mathcal{B})$ represent the PLMs' answer prediction probability when $\mathcal{B}$ is activated, then the functional definition of DKNs is: + + +$$Prob(\mathcal{D}) \approx Prob(\mathcal{B}_i), \forall i = 1, 2, \dots, s$$ + (2) + + +$$Prob(\emptyset) \ll Prob(\mathcal{B}_i), \forall i = 1, 2, \dots, s$$ + (3) + +where Equation 2 indicates that activating any single BDC is sufficient to express the fact, and Equation 3 suggests that if all BDCs are suppressed (i.e., activating the empty set $\emptyset$ ), the fact cannot be correctly expressed. + +**Structural Definition** Zhu and Li(2023) argue that tightly connected neurons tend to store knowledge centrally. Thus, we use the adjacency matrix $\mathcal{A}$ to evaluate the DKNs' connection structure. For neurons A and B in layer $l_A$ and layer $l_B$ respectively, we calculate the distance $d_{AB}$ as follows: + +$$d_{AB} = \begin{cases} |1/w_{AB}| & \text{if } w_{AB} \neq 0 \text{ and } |l_A - l_B| = 1, \\ \min_{P \in \text{Paths}(\mathcal{N})} \sum_{(i,j) \in P} d_{ij} & \text{if } |l_A - l_B| > 1 \text{ and a path exists,} \\ \infty & \text{otherwise.} \end{cases}$$ + +# Method + +``` +Input: Knowledge neurons \mathcal{N}, Adjacent matrix \mathcal{A}, + dynamic threshold \tau_1 and threshold \tau_2 +Output: Degenerate knowledge neurons \mathcal{D} +// Initialization +Initialize \mathcal{D} = \emptyset and R \leftarrow 0, where R is the distance + threshold for persistent homology. +// Persistent Homology (Clustering) +while R increases do + Record all base degenerate components \mathcal{B}_i and + their corresponding persistence duration R_p +end +// Filtering +for each \mathcal{B}_i do + if R_p(\mathcal{B}_i) > \tau_1 and Prob(\mathcal{B}_i) > \tau_2 then + Add \mathcal{B}_i to \mathcal{D}, where Prob(\mathcal{B}_i) is the + prediction probability when \mathcal{B}_i is activated + end +end +return \mathcal{D} +``` + +where $\operatorname{Paths}(\mathcal{N})$ includes all paths from A to B through $\mathcal{N}$ . Distance calculation varies in three scenarios. First, for neurons in adjacent layers, it is the reciprocal of the weight. Second, for neurons spanning multiple layers, it is the shortest distance determined by a dynamic programming algorithm. Third, for neurons in the same layer, since information in PLMs transmits between layers rather than within a layer (Meng et al., 2022), we set the distance to $\infty$ . Hence, any $\mathcal{D}$ can correspond to an adjacency matrix $\mathcal{A}$ , where $\mathcal{A} \in \mathbb{R}^{k \times k}$ , and k is the number of knowledge neurons contained in $\mathcal{D}$ . Based on $\mathcal{A}$ , beyond traditional neuron value editing, modifying the connection weights offers another method to edit PLMs. + +**Persistent Homology** In our method, we capture the persistent homology (Edelsbrunner et al., 2008) of KN sets, which represents the duration of the set's existence and the tightness of the set's connections. Figure 2 informally illustrates this process. Formally, given two KNs, $n_i$ and $n_j$ , which are both the centers of expanding circles. When the start radius (i.e., distance threshold) is 0, this corresponds to $R_s = 0$ . Suppose they touch at radius $R_e = r_1$ , which marks the end radius $R = r_1$ , and a new start radius $R_s = r_1$ . At this point, $n_i$ and $n_j$ are clustered together, forming a BDC, $\mathcal{B} = \{n_i, n_j\}$ , corresponding to a persistence duration $R_p = R_e - R_s = r_1$ . For details on persistent homology, see Appendix C. + +**NTC Method** Our method is shown in Figure 2 and Algorithm 1. We design two steps, i.e., clustering and filtering steps, to obtain DKNs. During the + +(4) + + + +| | GPT-2 | | | | | | | | | +|------------------------|---------------------------------------------------|----------------------------------|-------------------------|-------------------------|-----------------------------|--------------------------------|--------------------------------------|--|--| +| Method | 2 | 3 | 4 | 5 | 6 | 7 | Average | | | +| DBSCAN | $12.0 \to 26.0$ | $17.7 \to 38.8$ | $23.4 \rightarrow 33.8$ | $32.9 \rightarrow 53.4$ | $24.8 \rightarrow 49.8$ | $26.4 \rightarrow 46.6$ | $23.90 \rightarrow 34.72$ | | | +| Hierarchical | $2.9 \rightarrow 3.8$ | $15.5 \rightarrow 7.3$ | $3.7 \rightarrow 12$ | $4.3 \rightarrow 27$ | $25.0 \to 92.0$ | _ | $20.78 \rightarrow 30.81$ | | | +| K-Means | $27.4 \to 38.3$ | $17.8 \to 29.5$ | $27.8 \to 44.6$ | $32.4 \to 58.7$ | $28.9 \to 30.5$ | $21.8 \to 30.1$ | $34.42 \rightarrow 38.86$ | | | +| AMIG | $-0.8 \rightarrow 0.9$ | _ | _ | _ | _ | _ | $\text{-}0.8 \rightarrow 0.9$ | | | +| NTC (Ours) | 7.9 → 53 | 8.6 → 44 | 12 → 92 | 11 → 53 | 3.1 ightarrow 32 | 7.8 ightarrow 61 | 9.32 → 55.60 | | | +| | • | | | | | | | | | +| | | | | LLaMA2 | | | | | | +| Method | 2 | 3 | 8 | LLaMA2 | 14 | 17 | Average | | | +| Method
DBSCAN | 2
7.1 → 15.4 | $3$ $8.7 \rightarrow 15.2$ | 8 7.7 → 16.3 | | 14 | 17 | Average 22.26 → 18.24 | | | +| | | | | 11 | 14 | 17
| | | | +| DBSCAN | 7.1 → 15.4 | 8.7 → 15.2 | | 11 | 14
—
—
36.3 → 50.0 | 17
—
—
4.9 → 17.9 | $22.26 \rightarrow 18.24$ | | | +| DBSCAN
Hierarchical | $7.1 \rightarrow 15.4$
$27.6 \rightarrow 44.3$ | $8.7 \to 15.2$
22.3 $\to 6.5$ | 7.7 → 16.3
— | 11
7.2 → 25.3 | | | $22.26 \to 18.24 \\ 27.50 \to 44.04$ | | | + +Table 1: Comparison of $\mathcal{D}$ obtained by different methods. The numbers above each column indicate the cardinality of the BDC sets. Each cell shows two values for the PLMs' prediction probability decrease ( $\Delta Prob$ (%)): the left is the average $\Delta Prob$ when partially suppressing BDCs (i.e., 1 to n-1 BDCs), and the right is the $\Delta Prob$ when fully suppressing all BDCs. Lower left values, higher right values, and larger differences between them indicate better degeneracy. The symbol "—" signifies that a specific method failed to generate a $\mathcal{D}$ with the specified cardinality. + +![](_page_3_Figure_2.jpeg) + +Figure 4: The relationship between $\Delta Prob$ and the number of suppressed BDCs (using NTC). Table 1 averages the results for suppressing 1 to n-1 BDCs, while this figure shows the changes as the number of suppressed BDCs varies from 1 to n, with the final point representing all BDCs suppressed. Lower $\Delta Prob$ for partial suppression and higher $\Delta Prob$ for full suppression, i.e., a more prominent final turning point, indicate better degeneracy. As the cardinality of $\mathcal{D}$ varies across PLMs and methods, Table 1 and Figure 4 show representative results. Full results are in Appendix D (Table 4, Figures 12, 13, 14, 15, 16). + +clustering process, as R increases from 0 to infinity, we record all BDCs along with their corresponding $R_p$ . During the filtering process, we initially select BDCs with $R_p$ above $\tau_1$ . Then, among these, only BDCs with a $Prob(\mathcal{B}_i)$ greater than a threshold $\tau_2$ are kept. Finally, these BDCs constitute $\mathcal{D}$ , as shown in the formula below. + +$$\mathcal{D} = \{ \mathcal{B}_i | R_p(\mathcal{B}_i) > \tau_1 \text{ and } Prob(\mathcal{B}_i) \ge \tau_2 \}$$ + (5) diff --git a/2402.14433/main_diagram/main_diagram.drawio b/2402.14433/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..62978025dc61ff19e104e91fb153ba376f9b4326 --- /dev/null +++ b/2402.14433/main_diagram/main_diagram.drawio @@ -0,0 +1,485 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2402.14433/paper_text/intro_method.md b/2402.14433/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..714adf0767bb290651b2aa6ede78c3ddf3cdf93c --- /dev/null +++ b/2402.14433/paper_text/intro_method.md @@ -0,0 +1,68 @@ +# Introduction + +![Guidance plot of various concepts in Mistral-7B (top row) and Llama-2-chat (bottom row). By manipulating the hidden representations in $k$ layers with a learned *concept vector* (guided generation), we can control the presence/absence of different concepts in the assistant's responses.](figures/teaser.pdf){#fig:llama-guidance-plot width="\\linewidth"} + +
+ +
(Left) Example conversation and tokens in the context used for extracting the representations repθ(x). (Middle) Given a dataset of labelled representations, we train three different kinds of linear probes to detect the presence of a given concept. (Right) Using the learned concept vector, we guide the model representations during generation in order to strengthen/weaken the presence of said concept in the model output. We plot how activations evolve along the residual path, along a projected 2D subspace.
+
+ +Large language models (LLMs) have recently emerged as promising general-purpose task solvers with impressive capabilities [@openai2023gpt4]. Nonetheless, little is understood about the internal workings of these models [@Ganguli_2022], both in terms of the structure of the internal representation space [@toshniwal2022chess; @nanda2023emergent; @grosse2023studying] as well as underlying mechanistic circuits of the behavior they exhibit [@elhage2021mathematical; @olsson2022incontext; @kulmizev2020neural]. A recent line of work on mechanistic interpretability -- focusing mostly on Transformer models [@vaswani_attention_2017] -- has attributed interpretable features to individual neurons (single dimensions in activation space) [@bills2023language; @bricken2023language] and identified self-contained mechanistic circuits with single functionalities [@olsson2022incontext; @lieberum2023does; @wang_interpretability_2022]. In parallel, a different line of work has focused on the *linear representation hypothesis* [@mikolov-etal-2013-linguistic] -- which states that features are represented as linear directions in activation space -- and has shown that it is possible to locate linear directions in LLMs' internal representations that correspond to high-level semantic concepts such as *truth* or *honesty* [@burns2022discovering; @azaria2023internal; @li_inference-time_2023; @zou_representation_2023; @mallen_eliciting_2023; @marks_geometry_2023], *sycophancy* [@rimsky_reducing_2023; @perez_discovering_2022; @sharma2023towards], *power* and *morality* [@zou_representation_2023], or factual knowledge [@zou_representation_2023; @gurnee2023language]. + +Alignment of *linear directions* of the model's representations with specific behaviors, provides the possibility to influence the model's generation during inference [@li_inference-time_2023; @zou_representation_2023; @marks_geometry_2023; @arditi_refusal_nodate; @rimsky_reducing_2023]. Such inspired methods have been suggested as a cheap alternative to conventional fine-tuning methods [@li_inference-time_2023], such as Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback [@ouyang2022training; @bai2022constitutional; @kundu2023specific]. Indeed, if effective, this kind of guided generation presents an avenue to fine-grained concept-level adaptation of LLMs while requiring a minimal amount of training data and compute. It furthermore presents additional evidence that linear directions not only correspond to features in the input, as suggested by the linear representation hypothesis, but also that the model relies on and operates in linear sub-spaces that correspond to interpretable features in the output, providing a clear path to decomposing large and convoluted neural networks into small, interpretable systems. + +We build on the line of work of reading and editing internal activations of LLMs by identifying and intervening along linear directions. Prior work has largely focused on linear probing (Logistic Regression or Difference-in-Means) and dimensionality reduction techniques (most notably PCA) in order to obtain concept vectors that can be used for detection and guidance. In this work, we aim to unify different approaches that have been proposed, mainly for the concept of *truthfulness*, by systematically comparing their performance both in terms of detection and guidance. We extend the analysis to a series of novel concepts such as *appropriateness, humor, creativity, quality* and evaluate to what degree current techniques translate to these challenging settings, both in terms of concept dectection and guidance. To this end, we leverage annotated datasets from OpenAssistant [@kopf2023openassistant] and further contribute a synthetic dataset of our own for *appropriateness*. We further develop a novel metric coined *perplexity-normalized effect size*, allowing to assess the quality of a guided LLM both in terms of success of concept elicitation as well as fluency. + +We perform an extensive series of experiments and uncover that (1) some concepts such as *truthfulness* are very robustly guidable, in agreement with prior work, but (2) novel concepts such as *humor* need extensive tuning for guidance to be successful while (3) *appropriateness* remains impossible to elicit, resulting in concept confusion with *compliance*. We further observe that probes with optimal detection accuracies do not necessarily make for the optimal guides, contradicting previous observations for *truthfulness*. + +In summary, our work makes the following contributions: + +- We propose a novel metric that enables measuring and comparing guidability across concepts, models and guidance configurations. + +- We expand on the concepts examined in prior work, introducing a rich set of interpretable concepts including *appropriateness*, *humor*, *creativity*, and *quality*. + +- We provide evidence that detectability is not always a good predictor for guidability, highlighting the need for criteria that generalize across concepts and probes. + +The very first step towards concept guidance of a language model naturally is concept detection: without being able to identify the concept, there is very little hope to guide the model towards it. Remember that the task of discrimination is in general inherently easier compared to that of generation [@jacob2023consensus]. In order to identify a given concept $\mathcal{C}$ within a language model $\texttt{LLM}_{\bm{\theta}}$, we first need to gather a dataset $\mathcal{D}=\{(\bm{x}_i, y_i)\}_{i=1}^{n}$ consisting of examples $\bm{x}_i$ where $\mathcal{C}$ is either present ($y_i=1$) or absent ($y_i=-1$). $\bm{x}_i$ is usually a user-assistant interaction such as $$\begin{align*} + &\small\texttt{User:\hspace{1mm}What's the capital of France?}\\ + &\small\texttt{Assistant:\hspace{1mm}The capital of France is Paris.} +\end{align*}$$ where the assistant response positively entails concept $\mathcal{C}$, in this case *truthfulness*. While some methods have been proposed without the need for annotations [@zou_representation_2023; @burns2022discovering], in this work we focus on the case where labels $y_i$ are available for the sake of comparability and to reduce the number of moving parts. In the absence of labels, a prompt is typically used to bias the model's activation in a way that makes the relevant concept more readily detectable. Although the methodology developed for concept detection/guidance is not dependent on any particular concept $\mathcal{C}$, the vast majority of prior work has solely focused on *truthfulness*. This is largely due to the fact that the standard evaluation benchmark *TruthfulQA* [@lin-etal-2022-truthfulqa] can easily serve as the concept dataset needed to train concept probes, allowing researchers to perform experiments on guidance without having to go through the tedious process of acquiring and annotating data [@durmus2023towards]. + +While *truthfulness* has served as a very useful test-bed in the past, in this work we aim to enrich the field of concept guidance by introducing novel concepts along with corresponding datasets. We leverage annotated datasets from OpenAssistant [@kopf2023openassistant] to investigate novel concepts such as *humor*, *creativity*, *quality* and further contribute a dataset of our own for *appropriateness*. OpenAssistant provides multi-term user-assistant conversations about different topics with human-annotated labels for the assistant replies. We use the corresponding label associated with each concept and take the most extreme examples in each category to serve as the concept dataset. Our *appropriateness* dataset is based on real user prompts from ToxicChat [@lin2023toxicchat]. We select a sample of both toxic and non-toxic user prompts and complete each with a compliant -- i.e. helpful but potentially harmful -- and refusing -- i.e. not helpful but harmless -- assistant response (see more details in App. [7](#app:harmlessness_details){reference-type="ref" reference="app:harmlessness_details"}). This yields a balanced mix of examples of *appropriate* and *inappropriate* assistant behavior, where *appropriate* behavior constitutes *complying* with a *non-toxic* user request or *refusing* a *toxic* request. In terms of nomenclature, this is very similar to the HHH (helpful, honest, and harmless) framework [@askell2021general], the main difference being that *appropriateness* also includes the case of correctly refusing a toxic request whereas only compliant responses to non-toxic requests are considered to be *helpful & honest* (see Fig. [3](#fig:terminology){reference-type="ref" reference="fig:terminology"}). + +
+ +
Terminology used in harmlessness probing.
+
+ +Equipped with these novel concepts, we study how contemporary detection and guidance techniques perform and to what degree their success on *truthfulness* extends. + +Consider now a fixed concept $\mathcal{C}$ and a corresponding dataset $\mathcal{D}$. Let $\texttt{rep}_{\bm{\theta}}\left(\bm{x}_i\right)$ denote any intermediate representation of $\bm{x}_i$ calculated within the forward pass of $\texttt{LLM}_{\bm{\theta}}$. The high-level idea of detection is then very simple: calculate the set of representations $\{\texttt{rep}_{\bm{\theta}}(\bm{x}_i)\}_{i=1}^{n}$, and train a classifier $D_{\bm{w}}$ with weights $\bm{w}$ on top of those features, with the aim to distinguish between whether $\mathcal{C}$ is present in $\bm{x}_i$ or not, i.e. predicting $y_i$. + +For detection, there are thus two important design choices to be made: (1) the type of intermediate representation $\texttt{rep}_{\bm{\theta}}$ and (2) the classifier $D_{\bm{w}}$ used to distinguish between inputs. The literature has explored several such choices, in the following we will give a brief overview of the most popular ones. + +Current state-of-the-art LLMs that we are investigating, are based on the Transformer architecture [@vaswani_attention_2017], which comes with a rich set of computations and thus offers many intermediate representations with potentially different functionalities. To facilitate the discussion, we will introduce the basic structure underlying every Transformer architecture. Let $l \in \mathbb{N}$ denote the $l$-th layer and $\bm{x}^{(l)} \in \mathbb{R}^{T \times d_{\text{emb}}}$ the current representation, where $T \in \mathbb{N}$ is the number of tokens and $d_{\text{emb}} \in \mathbb{N}$ the hidden dimension. A Transformer then refines the current representation $\bm{x}^{(l)}$ as follows: $$\begin{align*} + \bm{h}^{(l)}_\text{attn} &= \operatorname{MHA}\left(\operatorname{LayerNorm}\left(\bm{x}^{(l)}\right)\right) \\ + \bm{h}^{(l)}_\text{resid} &= \bm{h}^{(l)}_\text{attn} + \bm{x}^{(l)} \\ + \bm{h}^{(l)}_\text{ffn} &= \operatorname{FFN}\left(\operatorname{LayerNorm}\left(\bm{h}^{(l)}_\text{resid}\right)\right) \\ + \bm{x}^{(l+1)} &= \bm{h}^{(l)}_\text{ffn} + \bm{h}^{(l)}_\text{resid} +\end{align*}$$ where $\operatorname{MHA}$ denotes multi-head attention and $\operatorname{FFN}$ denotes the feed-forward block. The models used in our experiments follow the Llama-2 architecture [@touvron2023llama], which uses $\operatorname{GQA}$ (grouped query attention) instead of $\operatorname{MHA}$ as well as $\operatorname{RMSNorm}$ instead of $\operatorname{LayerNorm}$. + +First, within a layer, there are multiple options for which representation to choose: inputs at the residual stream $\bm{x}^{(l)}$, normalized inputs $\bm{h}^{(l)}_\text{pre-attn}:=\operatorname{LayerNorm}\left(\bm{x}^{(l)}\right)$, outputs of each attention head, or outputs of the attention block $\bm{h}^{(l)}_\text{attn}$. Prior work has used representations at the residual stream [@marks_geometry_2023; @burns2022discovering; @zou_representation_2023; @gurnee2023language; @rimsky_reducing_2023], at the normalized residual stream [@nostalgebraist2020], or at the attention heads [@li_inference-time_2023; @arditi_refusal_nodate]. In this work, we use the normalized residuals based on early experiments which showed marginally better detection accuracy on various concepts, i.e. we set $\texttt{rep}_{\bm{\theta}}(\bm{x}) = \bm{h}^{(l)}_\text{pre-attn}(\bm{x})$. + +Second, we need to choose a layer $l$ from which to build our representations. Prior work has used various selection methods, ranging from hand-picking layers [@marks_geometry_2023; @arditi_refusal_nodate; @rimsky_reducing_2023; @zou_representation_2023] to accurcay-based selection [@li_inference-time_2023; @gurnee2023language; @azaria2023internal] to more sophisticated criteria [@mallen_eliciting_2023]. In this work, we follow @li_inference-time_2023 and use accuracy-based selection. + +Thirdly, it has been observed to be beneficial for guidance to not use the full prompt representation $\bm{x}_{\text{rep}} \in \mathbb{R}^{T \times d_{\text{emb}}}$ in terms of the number of tokens $T$, but rather use a subset $\bm{x}_{\text{rep}} \in \mathbb{R}^{t \times d_{\text{emb}}}$ for $t \leq T$ and treat each $\bm{x}_{\text{rep}}[i,:] \in \mathbb{R}^{d_{\text{emb}}}$ for $i=1,\dots, d_{\text{emb}}$ as its own example. This allows to focus the representation more on parts of the prompt $\bm{x} \in \mathbb{R}^{t \times d_{\text{emb}}}$ that we believe to elicit the concept $\mathcal{C}$ more strongly, while also reducing potentially spurious correlations from activations of tokens from the same sequence. Prior work has carefully selected a single token [@arditi_refusal_nodate; @zou_representation_2023; @gurnee2023language] or simply used the last token [@rimsky_reducing_2023; @mallen_eliciting_2023; @marks_geometry_2023; @li_inference-time_2023; @burns2022discovering] in the prompt or assistant response. In our experiments, we consider the representations of the first $t$ tokens of the last assistant response and propagate the same label $y_i$ for all of them. Ideally, the choice of which tokens' representation to consider should incorporate the nature of the concept $\mathcal{C}$. For instance, compliance with a user's request is more strongly present at the beginning of the assistant's reply -- typical responses include "Sorry, I cannot \..." or "Sure, here is \..." -- rather than at the end. + +Given a dataset of hidden representations, we can now train our probing classifier $D_{\bm{w}}$ on the concept labels inherited from the respective examples. Again, we are faced with the choice of which classifier to use. While some prior work has used non-linear classifiers for improved classification accuracy [@azaria2023internal], linear classifiers have the advantage that they extend themselves naturally to guide the model by simply adding/subtracting the learned linear direction to/from the hidden state. For this reason and following the majority of prior work, we focus on three different linear classifiers most commonly used by prior work. + +**Logistic Regression.** The most common type of probe is a linear regression of the form $D_{\bm{w}}(\bm{h}) = \sigma(\bm{w}^{\top} \bm{h} + b)$ [@li_inference-time_2023; @marks_geometry_2023; @mallen_eliciting_2023; @burns2022discovering]. In our setup, we use a $l_2$-regularization term to mitigate overfitting and normalize the input as $\bm{h}_{\text{norm}} = \frac{\bm{h}}{\|\bm{h}\|_2}$ since we empirically find that this marginally improves detection accuracy. + +**Difference-in-Means.** An alternative to logistic regression is difference-in-means (DiM) probing, which simply computes the direction between the center of the negative and positive class, i.e. $\bm{w} = \sum_i y_i\bm{h}_i$ [@marks_geometry_2023; @mallen_eliciting_2023; @rimsky_reducing_2023]. To get a classifier that is able to predict probabilities, we reuse $\bm{w}$ and fit $b$ using logistic regression. Again, we find that normalizing the input marginally improves accuracy. Difference-in-means has been proposed as an alternative to logistic regression [@marks_geometry_2023] and can be viewed as a special case of Linear Discriminant Analysis with isotropic covariance matrices [@mallen_eliciting_2023]. + +**Principal Component Analysis.** PCA has been proposed as a probing technique that does not rely on annotated examples [@zou_representation_2023]. To train the classifier, we first transform the input dataset by taking the difference between random pairs and getting the principal component of the resulting difference dataset. Analogous to the difference-in-means technique, we convert the principal component $\bm{w}$ to a classifier by fitting $b$ in a logistic regression. This method has traditionally relied on careful prompting and representation selection, which we omit in our experiments in order to ensure a fair comparison to other probing techniques. Fig.[2](#fig:methodology){reference-type="ref" reference="fig:methodology"} provides an illustrative example of differences of the three aforementioned classifiers. + +
+ +
Layer-wise probing accuracy on all five concepts in Llama-2-chat for t = 16.
+
diff --git a/2403.11464/main_diagram/main_diagram.drawio b/2403.11464/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8d9ad2fe243a19ff062f8b00dfd95f82e4a28b9f --- /dev/null +++ b/2403.11464/main_diagram/main_diagram.drawio @@ -0,0 +1,91 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2403.11464/paper_text/intro_method.md b/2403.11464/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..36def569a961e8d44f3ec0af13b6f9b831fffc3a --- /dev/null +++ b/2403.11464/paper_text/intro_method.md @@ -0,0 +1,162 @@ +# Introduction + +Federated Learning (FL) is a distributed machine learning paradigm that allows edge devices to collaboratively train a model without revealing private data [@fedavg]. However, the efficacy of FL in real-world IoT systems is usually impeded by both data and system heterogeneities of IoT clients. First, clients comprise edge devices from various geographical locations collecting data *that are naturally non-independent identical (non-iid)*. In such a scenario, a single global model struggles to generalize across all local datasets [@pflsurvey; @hermes]. Second, IoT clients consist of physical devices with varying processor, memory, and bandwidth capabilities [@mocha; @flRCsurvey]. Among these devices, some resource-constrained devices might be incapable of training the entire global model with a too complex structure. + +To overcome the non-iid data problem, the Personalized Federated Learning (PFL) framework is introduced by empowering each client to maintain a unique local model tailored to its local data distribution. To address system heterogeneity, the technique of *federated dropout* (i.e. model pruning) is employed. Resource-constrained devices are allowed to train a *sub-model*, which is a subset of the global model. This approach reduces computation and communication overheads for training and transmitting the sub-model, aiding resource-constrained devices in overcoming computation and communication bottlenecks [@randdrop; @fjord]. + +Various PFL frameworks have been developed to tackle the non-iid data challenge [@adaptiveFT; @pflmeta; @pflayer; @clusterfl; @mocha; @pflod; @fedem; @fedtha; @pfedme; @threepfl; @apfl; @fedala]. Nevertheless, the inherent communication or computation bottleneck of resource-constrained edge devices is often overlooked in these frameworks. To tackle the computation and communication bottlenecks, *federated dropout* is applied [@randdrop; @feddrop; @fjord; @hermes; @prunefl; @adadrop], where resource-constrained devices are allowed to train a subset of the global model, i.e. a *sub-model*. Compared with a full model, the computation and communication overheads for training and transmitting sub-models are reduced, facilitating resource-constrained devices to complete the training task within constraints. For example, in [@randdrop; @feddrop], the server randomly prunes neurons in the global model. In [@fjord], the server prunes the rightmost neurons in the global model. These works fail to meet the *personalization* requirement, as the server arbitrarily decides the architectures of local sub-models without considering the importance of neurons based on the *local data distributions* of clients. + +
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Dropout.
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FedSPU.
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In dropout (a), clients train sub-models with fewer parameters. In FedSPU (b), clients train full models with partial parameters frozen.
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+ +On the other hand, [@adadrop; @hermes; @prunefl] let clients adaptively prune neurons to obtain the optimal architecture of the local sub-model. Each client first pre-trains an initialized model to evaluate the importance of each neuron. Then each client locally prunes the unimportant neurons and shares the remaining sub-model with the server hereafter. Nevertheless, evaluating neuron importance requires full-model training on the client side, which might be *expensive* or *prohibitive* for resource-constrained devices with a critical computation bottleneck. Furthermore, the adaptive model-pruning behaviors proposed in [@adadrop; @hermes; @prunefl] rely immensely on the local data. The non-iid local data distributions often lead to *highly unbalanced class distributions* across different clients [@imbalancebias; @imbalancetext; @unbalanceddata; @pa]. Consequently, the local dropout behavior can be *heavily biased* [@prunefl], and the sub-model architecture among clients may *vary drastically*. In global communication, when a client absorbs parameters from other clients with inconsistent model architectures, the performance of the local model will be inevitably compromised. + +To address the limitation of existing works, this paper introduces *Federated Learning with Stochastic Parameter Update (FedSPU)*, a consolidated PFL framework aimed at mitigating the issue of *local model personalization loss* while considering computation and communication bottlenecks in resource-constrained devices. It is observed that during global communication, a client's entire local sub-model is replaced by *biased parameters* of other clients, leading to local model personalization loss. Therefore, if we let a client share only a *partial model* with others, the adverse effect of other clients' biased parameters can be alleviated. Inspired by this, FedSPU *freezes* neurons instead of pruning them, as shown in Figure [4](#dropoutspu){reference-type="ref" reference="dropoutspu"}. Frozen neurons do not receive gradients during backpropagation and remain unaltered in subsequent updates. This approach eliminates computation overheads in backward propagation, enhancing *computational efficiency*. Moreover, FedSPU does not incur extra communication overheads compared with dropout, as only the parameters of the non-frozen neurons and the positions of these neurons are communicated between clients and the server, as depicted in Figure [4](#dropoutspu){reference-type="ref" reference="dropoutspu"}. Besides, compared with model parameters, the communication cost for sending the position indices of the non-frozen neurons is much smaller and usually ignorable [@hermes]. + +Unlike pruned neurons, frozen neurons persist within a local model, and still contribute to the model's final output. This design choice incurs slightly *higher computation costs* of forward propagation, but largely improves local personalization, as only a portion of a local model is replaced during communication as shown in Figure [3](#SPUdemo){reference-type="ref" reference="SPUdemo"}. Moreover, the increased computation overhead of forward propagation is a minor problem, as in training, forward propagation constitutes a *significantly smaller* portion of the total computation overhead than backpropagation [@vldbpytorch; @he2015]. Additionally, to alleviate the overall computation and communication costs, we consolidate FedSPU with an *early stopping* technique [@esbutwhen; @flrce]. At each round, each client locally computes the training and testing errors, and compares them with the errors from the previous round. When the errors show no decrease, this client will cease training and no longer participate in FL. When all clients have halted training, FedSPU will terminate in advance to conserve computation and communication resources. We evaluate FedSPU on three typical deep learning datasets: EMNIST [@emnist], CIFAR10 [@cifar] and Google Speech [@googlespeech], with five state-of-the-art dropout methods included for comparison: FjORD [@fjord], Hermes [@hermes], FedMP [@adadrop], PruneFL [@prunefl] and FedSelect [@fedselect]. Experiment results show that: + +- FedSPU's unique approach of freezing neurons, rather than pruning, retains them within the local model, enhancing personalization. This design choice improves final model accuracy by an average of 4.45% over existing dropout methods. + +- FedSPU reduces memory usage through neuron freezing rather than full-model training, avoiding memory-intensive dropout processes. This results in significant memory savings up to 54% at higher dropout rates. + +- The integration of an early stopping mechanism allows FedSPU to reduce computation and communication costs significantly by 25%$\sim$``{=html}71%. Compared with existing methods, FedSPU exhibits better compatibility with early stopping with an average accuracy improvement of at least 5.11%. + +# Method + +Given a set $\mathbb{C} = \{1,2,...,N\}$ of clients with local datasets $\{D_{1}, D_{2},...,D_{N}\}$ and local models $\{w_{1},w_{2},...,w_{N}\}$. The goal of a PFL framework is to determine the optimal set of local models $\{w^{*}_{1}, w^{*}_{2},..., w^{*}_{N}\}$ such that: $$\begin{equation} +\label{objeq} + w^{*}_{1},...,w^{*}_{N} \triangleq \mathop{\arg\min}_{w_{1},...,w_{N}} \frac{1}{N} \sum_{k=1}^{N} F_{k}(w_{k}) +\end{equation}$$ where $w_{k}^{*}$ is the optimal model for client $k$ ($1\leq k \leq N$), and $F_{k}$ is the objective function of client $k$. $F_{k}$ is equivalent to the empirical risk over $k$'s local dataset $D_{k}$. That is: $$\begin{equation} + F_{k}(w_{k}) = \frac{1}{n_{k}}\sum_{i=1}^{n_{k}}\mathcal{L}((\boldsymbol{x}_{i}, y_{i}), w_{k}) +\end{equation}$$ where $n_{k}$ is the size of dataset $D_{k}$ and $\mathcal{L}$ is the loss function of model $w_{k}$ over the $i-$th sample ($\boldsymbol{x}_{i}, y_{i}$). + +:::: algorithm +::: algorithmic +maximum global iteration $T$, clients $\mathbb{C}=\{1,...,N\}$, initial global model $w^{0}$. Server broadcasts $w_{0}$ to all clients. **For** round $t=1,2,...,T$: **Server executes:** randomly sample a subset of clients $\mathbb{C}_{t} \subset \mathbb{C}$. $\forall k \in \mathbb{C}_{t}$: randomly sample $A_{k}(w^{t})$ based on $p_{k}$. send $A_{k}(w^{t})$ to $k$. **Each client** $k \in \mathbb{C}_{t}$ **in parallel does**: merge $A_{k}(w^{t})$ into $w_{k}^{t}$ to get $\hat{w}^{t}_{k}$. local SGD: $w_{k}^{t+1} = \hat{w}_{k}^{t} - \eta\nabla\bar{F}_{k}(\hat{w}_{k}^{t})$. send $A_{k}(w_{k}^{t+1})$ to the server. **Server executes:** $\forall k \in \mathbb{C}_{t}$: receive $A_{k}(w_{k}^{t+1})$ from $k$. Aggregate all $A_{k}(w_{k}^{t+1})$ to get $w^{t+1}$. $w_{1},w_{2},...,w_{N}$. +::: +:::: + +A comprehensive procedure of FedSPU is presented in Algorithm [\[alg:fedspu\]](#alg:fedspu){reference-type="ref" reference="alg:fedspu"}, with an overview presented in Figure [7](#spudesign){reference-type="ref" reference="spudesign"}. As shown in Figure [5](#SPUoverview){reference-type="ref" reference="SPUoverview"}, at round $t$, the server randomly selects a set of participating clients $\mathbb{C}_{t}$ and executes steps 1⃝$\sim$``{=html}4⃝. + +
+
+ +
Overview of FedSPU.
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The local training procedure of client k in FedSPU.
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Demonstration of the FedSPU framework.
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+ +1⃝. For every participating client $k$, the server selects a set of active neurons from the global model, and sends the neurons' parameters $A_{k}(w^{t})$ to $k$. Specifically, in each layer, random $p_{k}$ of the neurons are selected, where $p_{k}\in (0,1]$ is the ratio of active neurons. The value of $p_{k}$ depends on the system characteristic of the client $k$, with more powerful $k$'s device (e.g. base station, data silo) having larger $p_{k}$. The active neurons are selected randomly to ensure uniform parameter updates. Locally, client $k$ updates the local model $w_{k}^{t}$ with the received $A_{k}(w^{t})$ to obtain an intermediate model $\hat{w}_{k}^{t}$ as shown in Figure [6](#SPUtrain){reference-type="ref" reference="SPUtrain"}. + +2⃝. Client $k$ updates model $\hat{w}_{k}^{t}$ using stochastic gradient descent (SGD) to get a new model $w_{k}^{t+1}$ following Equation ([\[eq:SPUsgd\]](#eq:SPUsgd){reference-type="ref" reference="eq:SPUsgd"}): $$\begin{equation} +\label{eq:SPUsgd} + w_{k}^{t+1} = \hat{w}_{k}^{t} - \eta\nabla\bar{F}_{k}(\hat{w}_{k}^{t}) +\end{equation}$$ where $\eta$ is the learning rate and $\nabla\bar{F}_{k}(\hat{w}_{k}^{t})$ is the gradient of $F_{k}$ with respect to only the active parameters. That is, for all elements {$\hat{w}_{k,1}^{t},...,\hat{w}_{k,m}^{t}$} in $\hat{w}_{k}^{t}$, we have: $$\begin{equation} +\label{eq:barfk} + \nabla \bar{F}_{k}(\hat{w}^{t}_{k,i}) = + \begin{cases} + \nabla F_{k}(\hat{w}^{t}_{k,i}), & \hat{w}^{t}_{k,i} \in A_{k}(w^{t}) \\ + 0, & \text{otherwise.} + \end{cases}, +1 \leq i \leq m +\end{equation}$$ In this step, only the active parameters are updated as shown in Figure [6](#SPUtrain){reference-type="ref" reference="SPUtrain"}. + +3⃝. Client $k$ uploads the updated active parameters $A_{k}(w_{k}^{t+1})$ to the server. + +
+ +
The aggregation scheme in FedSPU.
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+ +4⃝. The server aggregates all updated parameters and updates the global model. In this step, FedSPU applies a standard aggregation scheme commonly used in existing dropout methods, where only the active parameters get aggregated and updated [@randdrop; @fjord; @hermes]. Figure [8](#SPUaggregate){reference-type="ref" reference="SPUaggregate"} shows a simple example of how the aggregation scheme works, where \"WA\" stands for weighted average. + +FedSPU freezes neurons instead of pruning them to preserve the integrity of the local model architecture, thereby preserving the personalization of local models. For clarity, Figure [9](#moti2){reference-type="ref" reference="moti2"} shows a comparison between a local sub-model and a local full model. As shown in the left-hand side of Figure [9](#moti2){reference-type="ref" reference="moti2"}, in global communication, when receiving other clients' biased parameters from the server, the entire local sub-model is replaced, resulting in a loss of personalization. Conversely, as illustrated in the right-hand side of Figure [9](#moti2){reference-type="ref" reference="moti2"}, for a local full model, only partial parameters are replaced, while the remainder remains personalized. This limits the adverse effect of biased parameters from other clients, enabling the local model to maintain performance on the local dataset. + +![Comparison between replacing all parameters of a local sub-model and replacing a portion of the parameters for a local full model during global communication.](moti2.pdf){#moti2} + +Since FedSPU slightly increases the computation overhead, it requires more computation resources (e.g., energy [@flRCsurvey], time [@he2015]) for training. This may pose a challenge for resource-constrained devices. To address this concern, it is expected to reduce the training time of FedSPU without sacrificing accuracy [@flrce]. Motivated by this, we enhance FedSPU with the **Early Stopping (ES)** technique [@esbutwhen] to prevent clients from unnecessary training to avoid the substantial consumption of computation and communication resources. At round $t$, after training, each client $k$ computes $\mathcal{L}_{t}$ following Equation ([\[eq:localloss\]](#eq:localloss){reference-type="ref" reference="eq:localloss"}): $$\begin{equation} +\label{eq:localloss} + \mathcal{L}_{t} = \lambda\mathcal{L}_{train} + (1-\lambda)\mathcal{L}_{test}. +\end{equation}$$ $\mathcal{L}_{train}$ is the training error of current round $t$, $\mathcal{L}_{test}$ is the testing error of $w_{k}^{t}$ on $k$'s validation set. $\lambda \in (0,1)$ is the train-test split factor of the local dataset $D_{k}$. When the loss $\mathcal{L}_{t}$ is non-decreasing, i.e. $\mathcal{L}_{t}>\mathcal{L}_{t-1}$, client $k$ will stop training and no longer participate in FL due to resource concerns. If all clients have stopped training before the maximum global iteration $T$, FedSPU will terminate prematurely. The enhanced FedSPU framework with early stopping is detailed in Algorithm [\[alg:fedspuES\]](#alg:fedspuES){reference-type="ref" reference="alg:fedspuES"}. + +:::: algorithm +::: algorithmic +maximum global iteration $T$, clients $\mathbb{C}=\{1,...,N\}$, initial global model $w^{0}$. Server broadcasts $w_{0}$ to all clients. **For** round $t=1,2,...,T$: **Server executes:** randomly sample a subset of clients $\mathbb{C}_{t} \subset \mathbb{C}$. $\forall k \in \mathbb{C}_{t}$: randomly sample $A_{k}(w^{t})$ based on $p_{k}$. send $A_{k}(w^{t})$ to $k$. **Each client** $k \in \mathbb{C}_{t}$ **in parallel does**: merge $A_{k}(w^{t})$ into $w_{k}^{t}$ to get $\hat{w}^{t}_{k}$. local SGD: $w_{k}^{t+1} = \hat{w}_{k}^{t} - \eta\nabla\bar{F}_{k}(\hat{w}_{k}^{t})$. compute $\mathcal{L}_{t}$. **If** $\mathcal{L}_{t} > \mathcal{L}_{t-1}$: *status* $\gets$ *stopped* **Else:** *status* $\gets$ *on* send $A_{k}(w_{k}^{t+1})$ and *status* to the server. **Server executes:** $\forall k \in \mathbb{C}_{t}$: receive $A_{k}(w_{k}^{t+1})$, *status* from $k$. **If** *status* == *stopped*: remove $k$ from $\mathbb{C}$. Aggregate all $A_{k}(w_{k}^{t+1})$ to get $w^{t+1}$. **If** $\mathbb{C}==\varnothing$: **TERMINATE**. $w_{1},w_{2},...,w_{N}$. +::: +:::: + +::: table* + **Dataset** PruneFL FjORD Hermes FedMP FedSelect FedSPU + --------------- --------------- --------------- --------------- --------------- --------------- ------------------- + EMNIST $67.34\pm0.9$ $7.66\pm0.4$ $69.09\pm0.6$ $67.42\pm0.9$ $66.26\pm1.7$ **73.42$\pm0.4$** + CIFAR10 $36.65\pm2.0$ $24.95\pm2.2$ $40.52\pm4.7$ $33.48\pm1.5$ $47.83\pm1.1$ **51.81**$\pm1.5$ + Google Speech $21.5\pm2.0$ $11.20\pm7.0$ $32.03\pm3.0$ $21.08\pm1.7$ $34.06\pm2.1$ **39.1**$\pm2.8$ +::: + +::: table* + **Dataset** PruneFL FjORD Hermes FedMP FedSelect FedSPU + --------------- -------------- ---------------- --------------- -------------- --------------- ------------------- + EMNIST $62.8\pm3.8$ $0.08\pm0.3$ $68.83\pm1.2$ $63.0\pm2.7$ $62.3\pm4.9$ **73.31$\pm0.2$** + CIFAR10 $31.2\pm2.1$ $21.2\pm3.2$ $30.6\pm3.3$ $26.9\pm0.5$ $37.85\pm0.3$ **42.66**$\pm1.5$ + Google Speech $16.7\pm2.0$ $10.68\pm10.2$ $29.66\pm2.0$ $16.6\pm2.2$ $19.2\pm2.6$ **35.7**$\pm1.6$ +::: + +This section analyzes the convergence of each local model in FedSPU. We first make some common assumptions following existing works [@hermes; @adadrop]: + +**Assumption 1.** *Every local objective function $F_{k}$ is $L-$smooth ($L>0$). That is, $\forall w_{1}, w_{2}$, we have:* + +$$F_{k}(w_{2})-F_{k}(w_{1}) \leq \langle\nabla F_{k}(w_{1}), w_{2}-w_{1}\rangle + \frac{L}{2}\|w_{2}-w_{1}\|^{2}.$$ + +**Assumption 2.** *The divergence between local gradients with and without incorporating the parameter received from the server is bounded:*$$\exists \ Q > 0, \forall \ k,t, \ \frac{\mathbb{E}(\|\nabla F_{k}(w_{k}^{t})\|^{2})}{\mathbb{E}(\|\nabla F_{k}(\hat{w}_{k}^{t})\|^{2})} \leq Q.$$ + +**Assumption 3.** *The divergence between the local parameters with and without incorporating the parameter received from the server is bounded:* $$\exists \ \sigma > 0, \forall \ k, t,\ \mathbb{E}(\|\hat{w}_{k}^{t} - w_{k}^{t}\|^2) \leq \sigma^{2}.$$ + +Additionally, with respect to the gradient $\nabla\bar{F}_{k}$, we derive the following lemmas: + +**Lemma 1.** $\forall \ w_{k}, \ \mathbb{E}(\|\bar{F}_{k}(w_{k})\|^{2}) = p_{k}^{2} \ \|F_{k}(w_{k})\|^{2}$. + +**Lemma 2**. $\forall w_{k}, \langle \nabla F_{k}(w_{k}), \nabla \bar{F}_{k}(w_{k}) \rangle = \|\nabla \bar{F}_{k}(w_{k})\|^{2}$. + +Based on the assumptions and lemmas, Theorem 1 holds: + +**Theorem 1.** *When the learning rate $\eta$ satisfies $\eta < \frac{1+\sqrt{1-\frac{QL}{p_{k}^{2}}}}{L}$, every local model $w_{k}$ will at least reach a $\epsilon$*-critical point* $w_{k}^{\epsilon}$ (i.e. $\|\nabla F_{k}(w_{k}^{\epsilon})\| \leq \epsilon$) in $O(\frac{w_{k}^{0}-w_{k}^{\epsilon}}{\epsilon\eta})$ rounds, with $\epsilon = \sqrt{\frac{(L+1)Q\sigma^{2}}{(2\eta-L\eta^{2})p_{k}^{2}+Q}}$.* + +According to Theorem 1, in FedSPU, each client's local objective function will converge to a relatively low value, given that the learning rate is small enough. This means that every client's personal model will eventually acquire favorable performance on the local dataset even if the objective function is not necessarily convex. The proofs can be found in the Appendix. + +::: table* ++----------------------------------------------------------------------------------------------+ +| **Total time of local training (in hours)** | ++:=============:+:========:+:========:+:========:+:========:+:=========:+:========:+:=========:+ +| **Dataset** | PruneFL | FjORD | Hermes | FedMP | FedSelect | FedSPU | FedSPU+ES | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| EMNIST | 8.11 | 7.85 | 8.57 | 7.30 | 7.68 | 7.82 | **4.0** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| CIFAR10 | 24.69 | 25.03 | 25.18 | 25.5 | 25.06 | 25.05 | **8.9** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| Google Speech | 8.29 | 7.83 | 8.09 | 8.16 | 8.03 | 8.71 | **5.97** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| **Total size of parameter transmission (in GB)** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| EMNIST | 11.69 | 11.72 | 11.73 | 11.66 | 20.6 | 11.71 | **7.23** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| CIFAR10 | 18.20 | 18.17 | 18.2 | 18.43 | 20.7 | 18.04 | **6.37** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +| Google Speech | 4.39 | 4.36 | 4.36 | 4.36 | 8.39 | 4.36 | **2.99** | ++---------------+----------+----------+----------+----------+-----------+----------+-----------+ +::: diff --git a/2406.10260/main_diagram/main_diagram.drawio b/2406.10260/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..73152dd542eeb15d428a1725cf5f141c2633a0e7 --- /dev/null +++ b/2406.10260/main_diagram/main_diagram.drawio @@ -0,0 +1,141 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2406.10260/main_diagram/main_diagram.pdf b/2406.10260/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cdf53fe0e1c805d5baae38d5690001cb117be23f Binary files /dev/null and b/2406.10260/main_diagram/main_diagram.pdf differ diff --git a/2406.10260/paper_text/intro_method.md b/2406.10260/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..1e6ec8bd0d7599c038b0a5694a3dfeba452d0b94 --- /dev/null +++ b/2406.10260/paper_text/intro_method.md @@ -0,0 +1,142 @@ +# Introduction + +Large language models (LLMs) have revolutionized realworld natural language processing applications and have showed impressive proficiency in understanding difficult contexts [\(Brown et al.,](#page-10-0) [2020;](#page-10-0) [OpenAI et al.,](#page-11-0) [2023;](#page-11-0) [Wei](#page-12-0) [et al.,](#page-12-0) [2022;](#page-12-0) [Touvron et al.,](#page-12-1) [2023\)](#page-12-1). Nonetheless, the substantial size of these models, typically running into several billion parameters, imposes significant constraints on their utilization in scenarios characterized by limited memory and computational resources. To address this limitation, model + +*Proceedings of the* 41 st *International Conference on Machine Learning*, Vienna, Austria. PMLR 235, 2024. Copyright 2024 by the author(s). + +![](_page_0_Figure_10.jpeg) + +![](_page_0_Figure_12.jpeg) + +Figure 1. High-level overview of the FLEXTRON framework. As shown in the top half of the Figure, FLEXTRON enables fast, zeroshot generation of hardware and input-adaptive sub-networks targeting various accuracy, latency and parameter constraints. The bottom half of the figure demonstrates how we convert a trained LLM into an elastic network with input-adaptive routing. + +providers typically train multiple model variants for users to choose from (depending on system memory and computational constraints) before trying to find model(s) satisfying the trade-off between efficiency and accuracy. For instance, the Llama-2 model family [\(Touvron et al.,](#page-12-1) [2023\)](#page-12-1) includes three different variants with 7 billion, 13 billion, and 70 billion parameters, while the Pythia family [\(Biderman et al.,](#page-10-1) [2023\)](#page-10-1) offers a selection of eight models with sizes ranging from 80 million to 12 billion parameters. + +Training multiple multi-billion parameter models is demanding in time, data, and resources. Adopting a single, customizable model with multiple sub-networks for varied budgets, as seen in Once-for-all [\(Cai et al.,](#page-10-2) [2019\)](#page-10-2), SortedNet [\(Valipour et al.,](#page-12-2) [2023\)](#page-12-2), Matformer [\(Kudugunta et al.,](#page-11-1) + +1NVIDIA 2The University of Texas at Austin. Correspondence to: Saurav Muralidharan . + +2023), and (Stamoulis et al., 2019), simplifies this. These models typically use a *supernet* with elastic, nested components, but require non-standard, costly architectures with even longer training than a single model. + +Mixture-of-Expert (MoE) networks, while more efficient than dense models (Fedus et al., 2022; Riquelme et al., 2021; Jiang et al., 2024), are generally restricted to feedforward layers and fixed budgets. The Pathways architecture (Dean, 2021; Zhou et al., 2022) highlights the potential of heterogeneous expert networks. We advocate for input-adaptive sub-network selection of different sizes to maximize performance and efficiency. + +In this paper, we present FLEXTRON, a network architecture and a post-training model optimization framework that takes the best from MoEs, elastic models and dynamic inference. The architecture extends the idea of MoE to attention and feed forward layers. Experts are heterogeneous and have different sizes via a nested elastic structure to support efficient model storage, memory bandwidth savings and ease-of-use. Particular experts are selected via a router conditioned on input data and target deployment constraints. FLEXTRON is a single model that provides *Multiple Models in One* during deployment with no additional finetuning. Finally, we present a framework where a standard trained LLM such as GPT-3 and Llama-2 can be efficiently converted to FLEXTRON while using a small fraction of the training time. Figure 1 provides a high-level overview. + +We found that training a router that allows adaptive computation is challenging due to gradient vanishing. Similar issues arise in normal MoE training, known as expert collapse (Chi et al., 2022), where routers constantly pick the same path or learn similar experts. To address this issue, we propose to train a *Surrogate Model (SM)* that predicts an LLM's language loss value given only router choices; once trained, we freeze it and tune routers to minimize the language loss solely on SM feedback. + +This paper makes the following contributions: + +- A novel architecture, called FLEXTRON, that flexibly adapts to different latency and accuracy targets during inference with no additional fine-tuning. +- A post-training optimization framework for systematically transforming existing trained LLMs into dynamic (input-adaptive) elastic networks. +- New static and dynamic routing algorithms that automatically select the optimal sub-network given a latency target and/or input token. We introduce a novel surrogate model for effective training of our routers. +- An efficient sampling-based training method for elastic networks that requires significantly less compute than existing methods. + +Given a model with N layers, each layer can be formalized as $\mathbf{Y}_i = f_i(\mathbf{X}_i, \mathbf{W}_i)$ , where $i \in [1, N]$ refers to the layer index, $\mathbf{X}_i$ denotes the layer input, with dimensions of $B \times C$ representing batch $\times$ embedding dimension, and $\mathbf{W}_i$ denotes the parameters of the layer. We define an *elastic network* as one that can flexibly adapt its layers to target specific user-defined objectives such as latency, memory, accuracy, etc. In this paper, we define each layer of an elastic network as follows: $\mathbf{Y}_i = f_i(\mathbf{X}_i, \mathbf{W}_i^j)$ where each $\mathbf{W}_i^j, j \in [1, K]$ represents a different parameter matrix for the same operation $f_i$ for layer i. By substituting the original layer with a candidate layer, we are able to generate an exponential number of elastic sub-networks ( $K^N$ choices for the formulation above, assuming K candidates per layer), each with different runtime and accuracy characteristics. + +Elastic Multi-Layer Perceptron (MLP). FLEXTRON models utilize a nested structure for elastic MLP layers, inspired by the Matformer work (Kudugunta et al., 2023). Nesting enables hidden neurons to be shared between layerwise candidates using simple indexing operations, saving memory and improving efficiency. Formally, elastic MLP candidates with 2 linear layers have the following format: + +$$\mathrm{MLP}^{j}(x) = \sigma\left(\mathbf{X} \cdot \left(\mathbf{I}_{d_{j}} \mathbf{W}^{(1)}\right)^{T}\right) \cdot \left(\mathbf{I}_{d_{j}} \mathbf{W}^{(2)}\right), \quad (1)$$ + +where, $\mathbf{I}_{d_j}$ is a diagonal matrix of size $D \times D$ where the first $d_j$ diagonal elements being 1 and the rest being 0, with D being the maximum hidden dimension. In this way, the $j^{th}$ MLP candidate will only utilize the first $d_j$ hidden neurons from the corresponding shared matrices $\boldsymbol{W}$ . $\boldsymbol{W}^{(1)}$ and $\boldsymbol{W}^{(2)}$ are the associated two weight matrices in MLP layers, with $\boldsymbol{W}^{(1)}, \boldsymbol{W}^{(2)} \in \mathbb{R}^{D \times C}$ ; $\sigma(\cdot)$ refers to the non-linear activation function. For implementation, the diagonal matrix $\boldsymbol{I}$ can be replaced with a slicing operator that selects only the first $d_j$ rows: $\boldsymbol{I}_{d_j} \boldsymbol{W}^{(1)} = \boldsymbol{W}^{(1)}[0:d_j,:]$ . We constrain $d_1 < d_2 < ... < d_K$ , where $d_K = D$ , to formulate the nested structure of K experts. Note that the MLP can take a more complex form when employing SwiGLU activation. + +Elastic Multi-Head Attention (MHA). MHA layers constitute a significant proportion of LLM runtime and memory usage (for KV cache), and making them elastic will improve overall efficiency. To the best of our knowledge, FLEXTRON is the first work that supports both elastic MLP and elastic MHA layers, enabling a richer candidate operation search space. An elastic MHA candidate uses a subset of attention heads. Formally, given hidden states $\mathbf{X}$ , we define elastic MHA as follows: + +$$MHA^{j}(x) = Concat(head_{1}, ...head_{d_{j}}) \cdot (\mathbf{I}_{d_{j}H} \mathbf{W}^{O}),$$ + +$$head_{i} = Attn(\mathbf{X} \mathbf{W}^{Q,i}, \mathbf{X} \mathbf{W}^{K,i}, \mathbf{X} \mathbf{W}^{V,i}),$$ +(2) + +where, $\mathbf{I}_{d_jH}$ is a diagonal matrix with the first $d_jH$ elements being 1, and the rest are 0s; $d_j$ - number of heads selected, H - size of a single head, L - total number of heads ; $\mathbf{W}^{Q,i}, \mathbf{W}^{K,i}, \mathbf{W}^{V,i} \in \mathbb{R}^{H \times C}$ and $\mathbf{W}^O \in \mathbb{R}^{LH \times C}$ . Different heads can be computed/selected via weight slicing. + +# Method + +We now describe the elastic network continued-training (CT) process, and provide more details on automatic sub-network selection from the trained elastic network. + +We start the elastic continued-training process by taking an existing trained LLM and performing importance ranking for each neuron/head. Here, using a small set of data samples, we compute the importance of each neuron/head based on the accumulated magnitude of activations. For MHA layers, the importance of each head is calculated as + + +$$F_{\text{head}}^{(i)} = \sum_{\mathbf{X}} \| \operatorname{Attn}(\mathbf{X} \mathbf{W}^{Q,i}, \mathbf{X} \mathbf{W}^{K,i}, \mathbf{X} \mathbf{W}^{V,i}) \|_{1}.$$ + (3) + +For MLP layers: + + +$$F_{\text{neuron}}^{(i)} = \sum_{\mathbf{X}} \|\mathbf{X} (\mathbf{W}^{(1),r})^T\|_1, \tag{4}$$ + +here $\boldsymbol{W}^{(1),r}$ refers to the $r^{\text{th}}$ row of the weight matrix $\boldsymbol{W}^{(1)}$ . In practice, only a small dataset comprising 512 samples is sufficient (see Section 4 for more details). Once importance is computed, we permute the respective weight matrices in the MLP and MHA layers such that neurons/heads are stored in decreasing order of importance for every individual layer. Sub-networks can now be constructed by simply indexing the first several neurons/heads in each layer, thus preserving essential knowledge encoded in important channels. In this way, we construct nested elastic layers with parameter sharing, with channels/heads sorted by importance, such that the first channels are the most important. + +Next, we train all elastic network candidates simultaneously using a combined loss term as in (Kudugunta et al., 2023). Since the number of such candidates can be prohibitively large (for example, there are $4^{64}$ possible combinations for the 32-layer LLaMa2-7B model (Touvron et al., 2023)), we randomly sample a smaller subset of k networks from the candidate pool to keep the total pretraining time tractable. Specifically, we randomly generate a one-hot vector $s_i$ for each layer i and use it to construct a candidate network $\mathcal{M}_j$ ; here, $s_i \in \mathbb{R}^{K_i}$ and $K_i$ represents the number of candidate MLP/MHA operations in layer i. $\mathcal{M}_j$ is the random model indexed by j, where $j \in [0, K-1]$ . The training loss is: + +$$\mathcal{L}_{\text{joint}} = \sum_{j=0}^{k-1} \mathcal{L}(\mathcal{M}_j(\mathbf{x}), \mathbf{y}), \tag{5}$$ + +![](_page_2_Picture_12.jpeg) + +Figure 2. Illustration of the elastic continued-training phase. + +Figure 2 provides an overview of elastic continued-training with random sampling. We provide additional details on pretraining, including choice of hyper-parameter values and datasets, in Section 4. + +Given a large number of possible sub-networks to choose from, each with different latency, parameter, and accuracy trade-offs, a natural question arises: can we automatically determine Pareto-optimal sub-networks given specific constraints? In this section, we introduce FLEXTRON's router architecture and describe how it helps us automatically select optimal sub-networks for a given constraint. + +The problem can be formalized as follows: + +$$\min_{\boldsymbol{S}_{t}} \sum_{t} \mathcal{L}_{CE}(\mathcal{M}_{s_{t}}), \quad \text{s.t. Latency}(\mathcal{M}_{s_{t}}) \leq T_{t},$$ + +$$\mathcal{M}_{s_{t}} = \mathcal{G}(\mathcal{M}, \boldsymbol{S}_{T_{t}}),$$ + +$$(6)$$ + +where $\mathcal{M}$ is original network topology, $T_t$ refers to a latency constraint of index t, and $S_{T_t}$ denotes the related selection matrix; $\mathcal{M}_{s_t}$ defines the selected topology based on latency constraint, $\mathcal{G}(\cdot)$ is a function for selecting network topology; $\mathcal{L}_{CE}$ refers to the cross-entropy loss. We use a Lagrange multiplier and impose a constraint to convert the aforementioned optimization problem into directly minimizing the following loss term: + +$$\mathcal{L} = \sum_{t} \mathcal{L}_{CE}(\mathcal{M}_{s_t}) + \lambda \cdot \mathcal{T}_{T}(\mathcal{M}_{s_t}), \tag{7}$$ + +where $\mathcal{T}_T$ represents the target constraint loss. In what follows as an example, we explain *latency loss* between the constraint $T_t$ and actual model latency Latency $(\mathcal{M}_{s_t})$ : + + +$$\mathcal{T}_T(\mathcal{M}_{s_t}) = \sum_t \max(\text{Latency}(\mathcal{M}_{s_t}) - T_t, 0)$$ + (8) + +Note that the loss can also be extended to other constraints such as GPU memory as we show later in our experiments. + +The model requires an architecture selection mechanism to support multiple budgets with maximized accuracy. Inspired + +![](_page_3_Figure_1.jpeg) + +Figure 3. Illustration of how routers are trained via a Surrogate Model (SM). The Surrogate Model is trained to approximate the LLM language loss value given only routers logits. If the error of the SM is smaller than a predefined threshold, the routers are updated. Updates are based on (i) the latency loss, ensuring the requested latency matches the real overall latency via a Lookup Table (LUT), and (ii) the loss from minimization of the SM output. The SM serves as a proxy for the full model's language loss and allows for simpler backpropagation due to its smaller size. Once the routers are trained, we discard the SM and finetune the LLM and routers jointly. + +by MoEs, we use routers. We define two routing scenarios: *static*, where the output depends only on the input latency; and *dynamic*, where it is additionally conditioned on the hidden state. We observe that training routers, even after the elastic continued-training stage, is challenging due to limited gradient propagation from the final model's output loss. As a remedy, we propose using a surrogate model to predict the LLM's performance based solely on router outputs. Given this prediction, routers can be trained to minimize the expected LM loss of the surrogate model. We provide additional details on the surrogate model in the following sections. + +**FLEXTRON-Static:** Static Model Selection. We first tackle the problem of static model selection, which refers to automatically selecting sub-networks given only a target latency T (no input-adaptivity). Here, we insert layer-wise learnable routers; each router takes the latency requirement T as input and outputs the choice $h_i$ for layer i, thereby deciding the number of channels/heads to be used for that layer via expert groups. The router picks the expert with the following formulation: + +$$s_i = \operatorname{argmax}(\mathcal{R}_i(T)),$$ + (9) + +where $\mathcal{R}$ is a small MLP that embeds a scalar value T (latency) into logits of the size of the predefined set of expert candidates (in our paper selected to be 4). + +To provide a strong and stable signal to the router, we propose to use a *Surrogate Model (SM)*. Its task is to predict the value of the full LLM language loss given only logits at the outputs of the routers. It becomes a proxy for the full model output error. Once it is learned, we can optimize routers to minimize the output of the SM. The basic idea is to use the SM as a loss term that can be minimized. The SM is defined + +as a two layer MLP: + + +$$r = \operatorname{Concat}(\mathcal{R}_0(T), \mathcal{R}_1(T), ... \mathcal{R}_{N-1}(T)),$$ + +$$\mathcal{S}(r) = \sigma(r \mathbf{W}_{\mathcal{S}_1}^T) \mathbf{W}_{\mathcal{S}_2},$$ + +(10) + +where $\boldsymbol{W}_{\mathcal{S}_1}$ and $\boldsymbol{W}_{\mathcal{S}_2}$ are weights of size $\boldsymbol{W}_{\mathcal{S}_1} \in \mathbb{R}^{P \times K \cdot N}$ and $\boldsymbol{W}_{\mathcal{S}_2} \in \mathbb{R}^{P \times 1}$ ; P is an internal hidden dimension. + +FLEXTRON-Adaptive: Dynamic Model Selection. Recent work on sparsely-activated MoE networks has demonstrated that an ensemble of different sub-networks ("experts"), each specializing in particular input domains, performs better and more efficiently than dense baselines (Fedus et al., 2022; Zhou et al., 2022). Drawing inspiration from previous studies, FLEXTRON introduces an input-adaptive routing mechanism to dynamically select optimal sub-networks based on latency and input, reducing memory and communication overheads through weight sharing and array-based indexing. + +For input adaptivity, we modify the router design to also incorporate the current hidden states $h_i$ as follows: + +$$s_i = \operatorname{argmax}(\mathcal{R}_i(T, h_i)),$$ + +where $\mathcal{R}_i(T, h_i) = \sigma(T \cdot \boldsymbol{W} + h_i \boldsymbol{W}_{H_i}^T) \boldsymbol{W}_{\mathcal{R}_i}$ (11) + +Here, the current hidden features $h_i$ are projected into the embedding space of dimension U by an MLP layer parameterized by $\boldsymbol{W}H_i$ . Similarly, T is projected via simple scaling of the matrix $\boldsymbol{W}$ . We limit U to 128. Token-wise routing decisions are generated by aggregating the latency embedding vector with the hidden feature embedding vector, and passing them through a linear layer. + +We also extend the surrogate model format described in Eq. (10) to additionally incorporate the final hidden states $h_N$ . + +Hidden states are projected to the dimension of P using a linear matrix. This projection is then summed with the latency embedding before applying the activation function. + +Training. Figure [3](#page-3-1) provides an overview of training routers via SM. Initially, the main LLM is frozen. Routers are always updated with gradients from the latency loss defined in Eq. [\(8\)](#page-2-1). The Surrogate Model is updated to minimize the predicted LM loss (via the MSE objective). If the MSE is below a predefined threshold, then routers are additionally updated with gradients from the output of the SM to minimize the predicted LM loss. In this way, routers learn to minimize the LM loss in an indirect way, via SM. Once the routers are trained, we disregard the SM and fine-tune both the routers and the LLM parameters. diff --git a/2406.18626/main_diagram/main_diagram.drawio b/2406.18626/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..67599f954a5d9ae08bbc5be5815d9c60a84bd5bc --- /dev/null +++ b/2406.18626/main_diagram/main_diagram.drawio @@ -0,0 +1,120 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2406.18626/paper_text/intro_method.md b/2406.18626/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..fc8653522d58abaff890fe0497a2a285d45d1a8e --- /dev/null +++ b/2406.18626/paper_text/intro_method.md @@ -0,0 +1,86 @@ +# Introduction + +Contemporary biomedical discovery represents a prototypical instance of complex scientific reasoning, which requires the coordination of controlled *in-vivo*/*in-silico* interventions, complex multi-step data analysis pipelines and the interpretation of the results under the light of previous evidence (available in different curated databases and in the literature) [@Paananen2019; @NICHOLSON20201414]. This intricacy emerges out of the inherent complexity of biological mechanisms underlying organism responses, which are defined by a network of multi-scale inter-dependencies [@BOGDAN]. While more granular data is being generated by the evolution of instruments, assays and methods, and the parallel abundance of experimental interventions [@DRYDEN], there a practical barrier for integrating and cohering this evidence space into a specific context of analysis. + +Within biomedical discovery, the language interpretation capabilities of Large Language Models (LLMs) can provide an integrative framework for harmonising and reasoning over distributed evidence spaces and tools, systematising and lowering the barriers to access and reason over multiple structured databases, textual bases such as PubMed, enriching the background knowledge through specialised ontologies and serving as interfaces to external analytical tools (e.g. mechanistic/perturbation models, gene enrichment models, etc). In this context, LLMs can serve as a linguistic analytical layer which can reduce the syntactic impedance across diverse functional components: once an adapter to an external component is built it can be integrated and reused in different contexts, creating a monotonic increase of functional components. Complementarily, from a Biomedical-NLP perspective, in order to address real-world problems, LLMs need to be complemented with mechanisms which can deliver contextual control (e.g. via Retrieval Augmented Generation: RAG: access the relevant background knowledge and facts) and perform the analytical tasks which are integral to contemporary biomedical inference ('toolforming'). + +Emerging LLM-focused coordination frameworks such as LangChain[^1], Flowise[^2] and Lunar[^3] provide the capabilities to deliver a composition of functional components, some of them under a low-code/no-code use environment, using the abstraction of workflows. While there are general-purpose coordination frameworks, there is a lack of specialised components for addressing biomedical analyses. + +In this paper we demonstrate BioLunar, a suite of components developed over the Lunar environment to support biological analyses. We demonstrate the key functionalities of the platform contextualised within a real-use case in the context of molecular-level evidence enrichment for biomarker discovery in oncology. + +
+ +
BioLunar interface. An exemplary workflow of Gene Enrichment with an input gene set, knowledge base query and LLM interpretation components.
+
+ +BioLunar enables the creation of LLM-based biomedical scientific workflows using software components with standardised APIs. A workflow is composed of components and subworkflows connected through input-output relationships, and are capable of handling multiple inputs. In the user interface, components are clustered according to their function (see Fig.[1](#fig:Lunar_screen_GE_workflow){reference-type="ref" reference="fig:Lunar_screen_GE_workflow"}). Creating a workflow does not require programming knowledge since components are predefined and merely require data inputs or parameter settings. However, for users who wish to write custom code, 'Python Coder' and 'R Coder' components are provided, enabling the definition of custom methods. These custom components can be saved and subsequently accessed in the 'Custom' group tab. + +In the paper we describe an exemplar *biomedical workflow* designed to integrate evidence and infer conclusions from bioinformatics pipeline results. Specifically, the *biomedical workflow* queries expert knowledge bases (KBs) that continuously compile clinical, experimental, and population genetic study outcomes, aligning them with assertions relevant to the significance of the observed gene or variant. It then employs Natural Language Inference (NLI) (via LLM) to integrate and harmonise the evidence space and interpreting the results, culminating in a comprehensive summary for the entire gene set input. This interpretation takes into account the bioanalytical context supplied by the user. + +Next-generation sequencing (NGS) assays play a pivotal role in the precise characterisation of tumours and patients in experimental cancer treatments. NGS findings are essential to guide the design of novel biomarkers and cancer treatments. Nevertheless, the clinical elucidation of NGS findings subsequent to initial bioinformatics analysis often requires time-consuming manual analysis procedures which are vulnerable to errors. The interpretation of molecular signatures that are typically yielded by genome-scale experiments are often supported by pathway-centric approaches through which mechanistic insights can be gained by pointing at a set of biological processes. Moreover, gene and variant enrichment benefits from heterogeneous curated data sources which pose challenges to seamless integration. Furthermore, there are different levels of supporting evidence and therefore prioritising conclusions is crucial. Automating evidence interpretation, knowledge synthesis and leveraging evidence-rich gene set reports are fundamental for addressing the challenges in precision oncology and the discovery of new biomarkers. + +The user interface facilitates an agile workflow construction by enabling users to select and arrange components via drag-and-drop from functionally grouped categories, such as, i.a.: 'Prompt Query' featuring NLI components, 'Knowledge Bases' components, 'Extractors' for retrieving files from zip archives or extracting text and tables from PDF files, and 'Coders', which allow for the creation of custom components using Python or R scripts. + +Components allow for individual execution, edition, or configuration adjustment via a visual interface. Workflows can be executed, saved, or shared. Each component has designated input and output capabilities, enabling seamless integration where the output from one can directly feed into another. Users have the flexibility to manually input values if no direct connection is established. Additionally, a component's output can feed into multiple components. The system's architecture supports effortless expansion, adding branches and components without affecting the existing workflow, thus facilitating scalable customization to meet changing requirements. The user interface with an example of a workflow is presented in Fig.[1](#fig:Lunar_screen_GE_workflow){reference-type="ref" reference="fig:Lunar_screen_GE_workflow"} and in demo video . + +The current framework integrates a diverse set of knowledge bases which are relevant for precision oncology. To identify gene mutations as biomarkers for cancer diagnosis, prognosis, and drug response, we integrated CIViC[^4] and OncoKB[^5]. CIViC provides molecular profiles (MPs) of genes, each linked to clinical evidence, with a molecular score indicating evidence quality, assessed by human annotators. The Gene Ontology[^6] (GO) offered gene function insights, and the Human Protein Atlas[^7] supplied a list of potential drug targets and transcription factors. We employed COSMIC[^8] for somatic mutation impacts in cancer, the largest resource in this field. Our analysis also included KEGG[^9], Reactome[^10], and WikiPathways[^11] for pathway information, enriching our investigation with scientific literature via PubMed's API [^12]. + +In the following subsections, we showcase examples of components, subworkflows, and workflows constructed using the BioLunar framework, motivated by the biomarker discovery/precision oncology themes. + +BioLunar employs standard LLM interfaces, allowing the use of different models according to users' preferences. The prompt components allows for the composition of specialised prompt chains which can be later reused, defining a pragmatic pathway for specialised Natural Language Inference (NLI) via prompt decomposition/composition. This approach allows for the creation of reasoning chains that combines user's instructions with the results of database queries and analyses from specialised tools within the context of the study. An instantiated example of the *Azure Open AI prompt* is described in Fig.[1](#fig:Lunar_screen_GE_workflow){reference-type="ref" reference="fig:Lunar_screen_GE_workflow"}. + +The *subworkflow* component enables the reuse of an existing workflow within another workflow, functioning as a component with specified inputs and outputs. This feature simplifies the composition of more complex workflows and avoids the repetition of defining identical steps for the same task. Subworkflows can be selected like other components from the left panel in the interface, offering access to all available workflows for easy integration. Examples of subworkflows are presented in Fig.[2](#fig:diagram_GE_HPA){reference-type="ref" reference="fig:diagram_GE_HPA"},[3](#fig:diagram_CIVIC_workflow){reference-type="ref" reference="fig:diagram_CIVIC_workflow"}. + +
+ +
A) Gene Enrichment workflow - uses the gprofiler API to access i.a. Gene Ontology, KEGG, WikiPathways, Reactome; B) Human Protein Atlas workflow. Compares and interprets the input and reference gene sets.
+
+ +
+ +
CIVIC evidence analysis workflow. prompt-based NLI components are fed by both the results and context of the analysis in order to produce relevant evidence-based conclusions.
+
+ +One example of a specialised subworkflow is the *Gene Enrichment subworkflow* (Fig.[1](#fig:Lunar_screen_GE_workflow){reference-type="ref" reference="fig:Lunar_screen_GE_workflow"},[2](#fig:diagram_GE_HPA){reference-type="ref" reference="fig:diagram_GE_HPA"}A) begins with uploading the targeted gene sets. Then a component accesses a specific KB --- such as Gene Ontology, KEGG, Reactome, or WikiPathways---defined by the user, using *gprofiler* API[^13]. This component identifies gene groups with a statistically significant overlap with the input gene set, according to a Fisher's test, and calculates p-values, recall, and precision. The user then specifies a variable to rank these groups and selects the top N for further analysis. The output includes both a interpretation performed by an NLI component (through LLM) and a table featuring the names, descriptions, and statistics of the top N selected groups. + +In the *Human Protein Atlas subworkflow*, given a gene set, an associated external KB is queried by selecting 'Transcription factors' from the HPA database using a dedicated query-database connector. A reusable component, 'Analyze overlap', then identifies genes that overlap and calculates relevant statistics. Similarly to the *Gene Enrichment subworkflow*, the results are interpreted by an prompt-based NLI component and presented alongside a table summarising the findings (Fig.[2](#fig:diagram_GE_HPA){reference-type="ref" reference="fig:diagram_GE_HPA"}B,[7](#fig:Lunar_screen_HPA_workflow){reference-type="ref" reference="fig:Lunar_screen_HPA_workflow"}). + +This *subworkflow* exemplifies a more complex composition of components (Fig.[3](#fig:diagram_CIVIC_workflow){reference-type="ref" reference="fig:diagram_CIVIC_workflow"}). This subworkflow initiates by querying the CIVIC database for input genes, yielding, among other things, gene descriptions in clinical contexts, and their variants and molecular profiles (MPs), which are essential for the final interpretation. Additionally, users specify the analysis context, including aspects such as cancer types or subtypes, treatments, populations, etc. Initially, gene descriptions are analysed by a prompt-based NLI component within this defined context. Subsequently, MPs scored below a predefined threshold (set at a MP score of 10) are tagged as *less known*, reflecting lower scientific evidence and ranking by CIVIC annotators. The evidence supporting these lesser-known MPs is then interpreted by a prompt-based NLI component, considering the broader analysis context. Conversely, evidence from *well-known* MPs, scoring above 10, undergoes a similar interpretation process. + +For genes without identified MPs in CIVIC, a sequence of components perform further evidence retrieval from PubMed. An NLI module generates context-based keywords for PubMed queries, which are combined with the names of genes lacking MPs. A 'PubMed search' component then retrieves $N$ publications, including metadata, citation counts and MeSH terms (used later for context alignment validation). The abstracts of these publications are interpreted by an NLI module in the context of the analysis. + +All clinical evidence interpretations are then succinctly summarised by via a prompt component, taking into account the context of the analysis. These interpretations, along with tabular results, constitute the output. + +The exemplar *bioworkflow* composes multiple subworkflows (Fig.[4](#fig:diagram_bioworkflow){reference-type="ref" reference="fig:diagram_bioworkflow"}), each dedicated to a specific multi-step and specialised task, which are typically defined by the composition of heterogeneous components, most commonly connectors and query instance components to specialised databases (e.g. CIVIC, HPA, PubMed, OncoKB), external specialised analytical tools (toolformers for gene enrichment analysis) and chains of specialised interpretation prompts (e.g. selection, filtering, extraction, summarisation). This setup forms a comprehensive workflow which exemplifies the close dialogue between LLMs and genomic analysis, encompassing gene enrichment, comparison with reference gene sets, and access to evidence within an experimental medicine setting. Additionally, it queries PubMed publications within the CIVIC component to seek evidence for molecular profiles not yet described. Its componentised architecture facilitates the extensibility of the workflow with new sources, prompts and external tools. Conclusions drawn from each subworkflow are interpreted within the analysis context, being integrated in a comprehensive summary. All findings are compiled in a report, exported as a PDF file. + +
+ +
Diagram of the Bioworkflow.
+
+ +BioLunar uses the *LunarVerse* backend for its operations. LunarVerse is downloaded and installed by the setup script included with the demonstration code. Some of its components need user specific configuration to work, such as private API keys, which are defined in a configuration file indicated in the setup instructions. LunarVerse is distributed under a *open software* license. The workflow can also be operated via a graphical interface (*LunarFlow*) + +Running a workflow can be done in two ways: i) directly, by calling the LunarVerse engine on a specified workflow descriptor file; ii) through the Web interface, by pressing the "Run" button. + +The first way is the default one in the demonstration code. It returns a copy of the workflow descriptor, with all component output fields filled, which is then used to extract and filter the desired outputs, based on the component labels. It is also the best way to automate multiple workflow runs and to integrate their outputs into other systems.The supporting code is available at . + +The *Bioworkflow*, as outlined in point [2.9](#bioworkflow){reference-type="ref" reference="bioworkflow"}, generates a report in PDF (Fig.[5](#fig:report){reference-type="ref" reference="fig:report"}) format that begins by outlining the context of the study, analysis details, dates, and software versions at the top. The report is enhanced with hyperlinks for easy navigation to specific sections. + +A \"General Statistics\" table provides a comprehensive overview of key metrics aggregated from all components, aiming to consolidate information for each gene throughout the analysis, with hyperlinks directing to the report sections where this information originates. + +Subsequent sections categorise genes into various tables based on biological aspects and the KBs consulted. These include Molecular Function for genes sharing ontologies, drug target checks based on the Human Protein Atlas, assessments of cancer-related genes, Pathway Analysis and Mapping via WikiPathways, and classification of gene alterations by clinical relevance. By correlating genes with known functional information, the workflow identifies statistically significant enriched terms and summarizes these findings using LLM, which also furnishes evidence. + +LLM interprets each table, offering textual conclusions relevant to the analysis context. A final summary, crafted using LLM, synthesizes all results within the given context. Importantly, all LLM interpretations are grounded in concrete evidence, with sources cited alongside the narrative. This approach underscores the rigor of the analysis by highlighting distinct sources that substantiate the relevance of each gene and variant. + +
+ +
The BioLunar report’s overview, produced by Bioworkflow.
+
+ +To demonstrate the capabilities of the *Bioworkflow*, we analyzed outputs in two different scenarios, each producing a distinct set of genes from separate bioinformatics analyses. We entered these gene sets along with their analysis contexts into the *Bioworkflow* and executed it. Subsequently, we qualitatively assessed the output reports (see Fig.[8](#fig:scenario1_report){reference-type="ref" reference="fig:scenario1_report"},[9](#fig:scenario2_report){reference-type="ref" reference="fig:scenario2_report"}), considering both the statistical data and the interpretations provided by the prompt-based NLI modules. + +In Scenario 1, the user aims to explore the unique molecular characteristics of HER2-low breast cancer to determine if it constitutes a distinct category within breast cancer types, where the input genes are ERBB2, ESR1, PIK3CA, CBFB, SF3B. The report shows genomic alterations and genomic signatures that were identified, including ERBB2 amplification, mutations in PIK3CA and ESR1, which are important biomarkers in the selection of breast cancer treatment. For the remaining two genes, evidence was found confirming that these are new, significantly mutated genes for which there is pre-clinical evidence of actionability in clinical practice. + +In Scenario 2, the user aims to discover new genes that could lead to more accurate breast cancer diagnoses, enhancing treatment strategies and addressing the disease's complexity. His numerical analysis resulted in a set of genes (DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, NR3C1) that require investigation. The report informs that none of the genes is an oncogene (confirmation according to OncoKB), two of the genes are potential drug targets and one is FDA approved drug targets. According to the KEGG-based enrichment analysis, these genes were mainly enriched through several signaling pathways including tumor necrosis factor (TNF) signaling pathway. Using LLMs in conjunction with a PubMed search component, papers were searched in PubMed that describe various gene variants and the genes have been indicated as prospective biomarkers associated with breast cancer. + +Note that in scenario 2, for genes lacking molecular profiles in the KB, a search in PubMed was conducted. This approach enables the workflow to automatically uncover and search for non-obvious and previously unknown relationships. Essentially, if a gene is absent from the database, it suggests that its relevance is relatively novel and not yet documented. Therefore, seeking out the most recent publications that describe this gene within the analysis context represents a significant advantage, provided by the workflow that integrates various components. diff --git a/2409.00127/main_diagram/main_diagram.drawio b/2409.00127/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8deb2801639cab0535ae4e4e89c237b78dd61589 --- /dev/null +++ b/2409.00127/main_diagram/main_diagram.drawio @@ -0,0 +1,163 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2409.00127/paper_text/intro_method.md b/2409.00127/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..6e2db9c2a79e3eae30bc86d2dca368f3c219357e --- /dev/null +++ b/2409.00127/paper_text/intro_method.md @@ -0,0 +1,142 @@ +# Introduction + +Many complex physical systems are traditionally modeled by partial differential equations (PDEs). First-principle PDE-based modeling and simulation have been demonstrated to be powerful in making predictions of complex systems in various scientific and engineering fields. However, in practical applications, significant discrepancy of the simulation-based prediction and the reality may arise from different sources, e.g., model inadequacy, uncertainties in model parameters, boundary and/or initial conditions, exteral forcing/loading terms, numerical approximation errors, etc. Meanwhile, data-driven machine learning (ML) models have been significantly developed and employed to make system predictions in the last few years, which have been shown to be extremely efficient in the case of large dynamical systems such as weather forecasting, a problem that requires the supercomputer of hours to run for the European Centre for Medium-Range Weather Forecasts (ECMWF), but one that can be solved by neural network surrogates such as FourCastNet [@pathak2022fourcastnetglobaldatadrivenhighresolution] in a matter of seconds. These properties also make them particularly suitable for ensemble forecasting and uncertainty quantification. However, these models are also often subject to the key limitation of divergence from reality in the long run, resulting in significant accumulated errors for long-term predictions because of various uncertainties and the autoregressive structure of the ML models. + +To mitigate the discrepancy and accumulated errors and make more accurate predictions, data assimilation plays an essential role in incorporating additional observation data of the reality into the current PDE/ML-based prediction models. By adapting the PDE/ML state to match the observation data, the accuracy of future state predictions can be markedly improved. Data assimilation [@sanzalonso2023inverseproblemsdataassimilation; @doi:10.1137/1.9781611974546] approaches such as Kalman filter (KF) [@kalman1960new], particle filters [@knsh_2013], and their variants have been widely applied to problems such as weather prediction, geophysical modeling, robotics, and many more areas. + +The basis of many current data assimilation methods used in practice is the KF [@kalmanfilter]. It parameterizes its state with the mean and the covariance, and presents the optimal solution to the data assimilation problem given that certain linearity properties of the transition and observation function are fulfilled. However, in practice, KFs end up being computationally expensive for high-dimensional states and biased for nonlinear problems. An alternative approach is particle filter, which represents the density by an ensemble of particles. They relax the assumption of distributional form, e.g., Gaussian distribution, made for distribution-based approaches such as the KF. The EnKF [@kalman1960new] is a particular particle filter that originates from the KF. Instead of keeping the covariance matrix, it computes the sample covariance of the ensemble of particles. Due to its robust performance, it has become a widely used method for applications today. However, most particle filters, including EnKF, suffer from the curse of dimensionality of requiring an exponentially large number of particles to accurately describe the distribution in high dimensions. To address this challenge, various extensions to EnKF have been proposed, such as the Localized Ensemble Transform Kalman Filter (LETKF) [@HUNT2007112], where covariance localization techniques as well as advancements from the Ensemble Transform Kalman Filter [@AdaptiveSamplingwiththeEnsembleTransformKalmanFilterPartITheoreticalAspects] are applied. + +Nonetheless, a few major roadblocks hinder efficient data assimilation for high dimensional systems. Particularly, standard data assimilation approaches have quite limited capabilities in high-dimensional environments, resorting to assumptions such as localization in order to make them more applicable. High dimensional systems and complex models can also limit the capability and efficiency of models such as 4D-Var [@rabier20034dvar], which is extremely inefficient since it requires propagating through the physical model many times. A recent approach named DiffDA [@huang2024diffdadiffusionmodelweatherscale] uses diffusion models and masked interpolations of the weather data to assimilate sparse observations, but the approach falls behind pure interpolation after a few steps. [@bao2024ensemblescorefiltertracking] proposed a novel method called Ensemble Score Filter (EnSF) which has achieved great results on high-dimensional data assimilation problems by leveraging the power of score-based diffusion models to sample from the posterior distribution efficiently. However, sparse observations prove to be very challenging for EnSF, limiting its applicability to real-world scenarios where observation data are limited. Meanwhile, approaches such as Generalized Latent Assimilation (GLA) [@cheng2022generalised] attempt to use dimension-reduction techniques to conduct data assimilation in latent space, but are still subject to the constraints of the standard EnKF for complex high-dimensional systems. + +In this work, we propose an efficient data assimilation method called Latent-EnSF that builds on Ensemble Score Filters (EnSF) [@bao2024ensemblescorefiltertracking] to address the joint challenges of high dimensionality and data sparsity. See Figure [1](#fig:ga_ensf){reference-type="ref" reference="fig:ga_ensf"} for a workflow of the Latent-EnSF. We propose to construct coupled Variational Autoencoders (VAE) [@kingma2014autoencoding] to encode the sparse observations into a latent space in which we conduct data assimilation by EnSF using diffusion-based sampling. The ensemble of samples of the assimilated latent state is then decoded to an ensemble of samples of the full state. Importantly, our paper addresses the key weakness of the EnSF when dealing with sparse observations, and retains its ability to efficiently assimilate observations for high-dimensional nonlinear systems. We demonstrate this performance for both PDE and ML based prediction models of some challenging examples, experimenting with data that is sparse in both space and time. + +
+ +
Flow of the Latent-EnSF. An ensemble of prior states xt|y1 : t − 1 is assimilated with sparse observation yt in the latent autoencoder space to obtain samples xt|y1 : t from the posterior.
+
+ +We will begin by formally introducing data assimilation problems with a review of the EnSF in Section [2](#sec:DA){reference-type="ref" reference="sec:DA"}, follow up with how we integrate this with coupled variational autoencoders in Section [3](#sec:LatentEnSF){reference-type="ref" reference="sec:LatentEnSF"} to create our Latent-EnSF, and finally conclude with experiments in Section [4](#sec:experiments){reference-type="ref" reference="sec:experiments"}. + +To model a dynamical physical system, we denote the state at a certain time $t$ as $x_t \in \mathbb{R}^d$, where $d$ is typically very high for complex models. For a system with discrete time steps $t = 1, 2, \dots$, given initial condition $x_0$, we then model the evolution of the state from the time $t-1$ to time $t$ as $$\begin{equation} + \label{eq:transition} +x_{t} = M(x_{t-1}, \varepsilon_{t}), +\end{equation}$$ where $\varepsilon_{t}$ is a process noise term coming from a known distribution; this term accounts for hidden interactions and numerical errors. The corresponding observation $y_t \in \mathbb{R}^m$ at time $t$ is given by $$\begin{equation} + \label{eq:observation} +y_t = H(x_t) + \gamma_t, +\end{equation}$$ where $\gamma_t$ (also coming from a known distribution, typically Gaussian) represents the observation noise because of instrumental inaccuracies, etc. Both the model map $M: \mathbb{R}^d \times \mathbb{R}^d \to \mathbb{R}^d$ and the observation map $H: \mathbb{R}^d \to \mathbb{R}^m$ can be nonlinear. + +Now, the model of the dynamical system in may lead to inaccurate representation of the ground truth with increasing discrepancy in time because of, e.g., model inadequacy or input uncertainty. The goal of data assimilation is to assimilate the observation data of the true state to the model of the dynamical system and recover an accurate representation of the true state. Note that in practical applications the observation data can be sparsely distributed in both space and time, which makes the recovery of the true state very challenging, especially for high dimensional problems with the state dimension $d \gg 1$, and the observation dimension $m$ much smaller than $d$. + +From a Bayesian filtering perspective [@bayesianfiltering], a data assimilation problem at each time $t$ can be divided into two steps: a prediction step that advances the dynamical system and an update step that assimilates the data. Assuming that the posterior density of the state $x_{t-1}$ given observation data $y_{1:t-1} = (y_1, \dots, y_{t-1})$, denoted as $P(x_{t-1} | y_{1:t-1})$, is available at time $t-1$, with $P(x_0|y_{1:0}) = P(x_0)$ given for time $t-1 = 0$, then the prediction step provides the density of $x_{t}$ as $$\begin{equation} + \label{eq:prediction} +\textbf{Prediction: } P(x_{t} | y_{1:t-1}) = \int P(x_{t} | x_{t-1}) P(x_{t-1} | y_{1:t-1}) dx_{t-1}, +\end{equation}$$ where $P(x_{t} | x_{t-1})$ represents the transition probability governed by the dynamical system in Equation [\[eq:transition\]](#eq:transition){reference-type="ref" reference="eq:transition"}. Note that $x_t$ depends on $y_{1:t-1}$ only through $x_{t-1}$. Let $P(y_{t} | x_{t})$ denote the likelihood function of the data $y_{t}$ given state $x_{t}$ from Equation [\[eq:observation\]](#eq:observation){reference-type="ref" reference="eq:observation"}. Then, the update step provides the updated posterior density $P(x_{t} | y_{1:t})$ of the state $x_{t}$ given the new observation data $y_{t}$ by Bayes' rule as $$\begin{equation} + \label{eq:bayes_filtering} +\textbf{Update: } P(x_{t} | y_{1:t}) = \frac{P(y_{t} | x_{t}) P(x_{t} | y_{1:t-1})}{P(y_{t} | y_{1:t-1})}, +\end{equation}$$ where the normalization constant or model evidence term $P(y_{t} | y_{1:t-1})$ is given by $P(y_{t} | y_{1:t-1}) = \int P(y_{t} | x_{t}) P(x_{t} | y_{1:t-1}) dx_{t}$, which is typically intractable to compute. + +A recent development for a new particle filter is the EnSF, where the ensemble of samples can be drawn from the posterior distribution through a diffusion process. It builds on the recent advancement in score-based generative modeling [@ho2020denoising] in generating high-fidelity samples from a distribution by first computing a score function $\nabla_x P(x)$ and then sampling with it by solving stochastic differential equations (SDE) [@song2021scorebasedgenerativemodelingstochastic]. This circumvents knowing the true density as it only needs to generate samples from the posterior distribution. Notably, the diffusion model takes the form of a noisy forward stochastic differential equation process $$\begin{equation} + \label{eq:forward_sde} +dx = f(x, \tau)d\tau + g(\tau)dw, +\end{equation}$$ which transforms data from an arbitrary distribution to an isotropic Gaussian for $\tau \in \mathcal{T} = [0, T]$. Here, $f(x, \tau)$ is a drift term, $g(\tau)$ is a diffusion term, and $w$ is a $d$-dimensional Wiener process. It has the corresponding reverse-time SDE that runs backward in time from $\tau = T$ to $0$ as $$\begin{equation} + \label{eq:reverse_sde} +dx = [f(x, \tau) - g^2(\tau)\nabla_x \log P_{\tau}(x)]d\tau + g(\tau) d\bar{w}, +\end{equation}$$ where $\bar{w}$ is another Wiener process independent of $w$. This reverse-time SDE can be solved to sample from the distribution $P(x)$ if the score function $\nabla_x \log P(x_{\tau})$ is known, e.g., by the Euler--Maruyama scheme at the discrete time steps $0=\tau_0<\tau_2<...<\tau_k = T$. + +In the following part, we use $x_{t, \tau}$ to indicate the state at physical time $t$ and diffusion time $\tau$. The EnSF method [@bao2024ensemblescorefiltertracking] uses $T = 1$ and the following drift and diffusion terms $$\begin{equation} +\label{eq:fg} + f(x_{t, \tau}, \tau) = \frac{d \log \alpha_{\tau}}{d \tau} x_{t, \tau} \text{ and } g^2(\tau) = \frac{d \beta_{\tau}^2}{d \tau} - 2\frac{d \log \alpha_{\tau}}{d \tau}\beta_{\tau}^2, +\end{equation}$$ with $\alpha_{\tau} = 1 - \tau (1- \epsilon_\alpha)$ and $\beta^2_{\tau} = \epsilon_\beta + \tau(1-\epsilon_\beta)$ for two small positive hyperparameters $\epsilon_\alpha$ and $\epsilon_\beta$ which aid in avoiding the collapse of the generated samples. This choice leads to the distribution $x_{t, \tau} \sim N(\alpha_\tau x_{t, 0}, \beta^2_\tau I)$ conditioned on $x_{t, 0} = x_t$, which results in the prior score function $$\begin{equation} +\label{eq:prior_score} +\begin{split} + \nabla_x \log P(x_{t, \tau}|y_{1:t-1}) & = \nabla_x \log \int P(x_{t, \tau}| x_{t}) P(x_{t}|y_{1:t-1}) d x_{t} \\ + & = \int -\frac{x_{t, \tau} - \alpha_\tau x_{t}}{\beta_{\tau}^2} \omega(x_{t, \tau}, x_{t}) P(x_{t} | y_{1:t-1}) dx_{t}, +\end{split} +\end{equation}$$ where the gradient $\nabla_x$ is taken with respect to $x_{t,\tau}$, and the weight $\omega(x_{t, \tau}, x_{t})$ is given by $$\begin{equation} + \label{eq:weight} + \omega(x_{t, \tau}, x_{t}) = \frac{P(x_{t, \tau}|x_{t})}{\int P(x_{t, \tau}|x_{t}')P(x_{t}' | y_{1:t-1})dx_{t}'}. +\end{equation}$$ Both the score function in and the weight in can be evaluated by sample average approximation with random samples from the distribution $P(x_{t} | y_{1:t-1})$ obtained in the prediction step in . In the update step in , the posterior score function in the EnSF method [@bao2024ensemblescorefiltertracking] is formulated as $$\begin{equation} + \label{eq:scorefunction} +\nabla_x \log P(x_{t, \tau} | y_{1:t}) = \nabla_x \log P(x_{t, \tau} | y_{1:t-1}) + h(\tau) \nabla_x \log P(y_{t}|x_{t, \tau}), +\end{equation}$$ with the prior score function in the first term given in , the likelihood function $P(y_{t}|x_{t, \tau})$ in the second term given explicitly from the observation map in , and a damping function $h(\tau)$ that is monotonically decreasing with $h(0) = 1$ and $h(1) = 0$, e.g., $h(\tau) = 1 - \tau$. Note that the posterior score function is consistent with the Bayes' rule for the posterior in at $\tau = 0$. + +To this end, a brief description of one step of the EnSF algorithm is presented in Algorithm [\[alg:ensf\]](#alg:ensf){reference-type="ref" reference="alg:ensf"}. + +:::: algorithm +::: algorithmic +Ensemble of the states $\{x_{t-1}\}$ from distribution $P(x_{t-1} | y_{1:t-1})$ and the observation $y_{t}$. Ensemble of the states $\{x_{t}\}$ from the posterior distribution $P(x_{t} | y_{1:t})$. Run the (stochastic) dynamical system model in from $\{x_{t-1}\}$ to obtain samples $\{x_t\}$. Estimate the prior score $\nabla_x \log P(x_{t, \tau} | y_{1:t-1})$ using and [\[eq:weight\]](#eq:weight){reference-type="ref" reference="eq:weight"}. Estimate the posterior score $\nabla_x \log P(x_{t, \tau} | y_{1:t})$ using . Solve the reverse-time SDE in to generate the ensemble $\{x_t\}$. +::: +:::: + +There are a number of advantages that EnSF provides over EnKF, particularly in high dimensions, where a much larger number of ensemble members are typically required for EnKF to have a good estimate of the approximate Gaussian density, while EnSF leverages the explicit likelihood function and the diffusion process to generate samples. In addition, EnSF does not assume approximate linearity of the dynamical system, making it highly applicable to high-dimensional and nonlinear filtering problems, e.g., a million-dimensional Lorenz-96 system [@bao2024ensemblescorefiltertracking] and a surface quasigeostrophic model [@bao2024nonlinearensemblefilteringdiffusion]. However, the key limitation of EnSF to filtering with sparse observation arises also from the explicit use of the likelihood function, as explained below. + +A key limitation of EnSF in high-dimensional nonlinear filtering problems occurs when the observation data are sparse, which is the case for most practical applications. For example, suppose that the observation map $H(x_t) = x_t[S]$, which only make observations of $x_t$ in the dimensions from the subset $S \subset \{1,...,d\}$, where $S$ may have a much smaller cardinality $|S| \ll d$ than the full dimension. In this case, the gradient of the log-likelihood $\nabla_x \log P(y_{t}|x_{t})$ vanishes in the dimensions in $\{1,...,d\}/S$, as shown in Figure [2](#fig:score_function){reference-type="ref" reference="fig:score_function"}, where we calculate $\nabla_x P(y_t|x_t)$ for one of our experiments in Section [4.1](#section:shallow_water){reference-type="ref" reference="section:shallow_water"} with 1/16 of the total dimensions used for the observations. Note that, the gradient is nonzero only at the points which are observed as seen in Figure [2](#fig:score_function){reference-type="ref" reference="fig:score_function"}. Though the ramifications are minimal for sufficiently dense observations, the vanishing gradients for higher sparsity scenarios significantly limits the effectiveness of the standard EnSF, which we demonstrate empirically in section [4.1.1](#section:ensf_sparsity_experiments){reference-type="ref" reference="section:ensf_sparsity_experiments"}. + +
+ +
The gradient of the log-likelihood function ∇xP(yt|xt) (right) vanishes at the points where the sparse observation data yt (middle) do not have any information of the state xt (left).
+
+ +To mitigate this weakness of the EnSF, rather than conducting data assimilation in the full state space with sparse observations, we propose to transform the data assimilation into a learned latent space with sufficiently preserved information and significantly reduced dimension. + +To address the key limitation of EnSF in the case of sparse observations due to the vanishing gradient of the log-likelihood function, we propose a latent representation of the sparse observations by a variational autoencoder (VAE) and match this latent representation to the encoded full state for the sake of consistent data assimilation in the latent space, for which we employ EnSF with the additional advantage of VAE regularization of the latent variables. + +The advantages are twofold: it allows us to not only make use of the benefits of standard latent score-based generative models [@NEURIPS2021_5dca4c6b; @Rombach_2022_CVPR] which offer optimized sampling speed and expressivity, but also create a expressive map from a sparse observation space to a full-dimensional latent space. Additionally, the successful application of latent-diffusion models to videos [@Blattmann_2023_CVPR] offers a potential application of exploring latent-assimilation coupled with latent dynamics. + +The Variational Autoencoder (VAE) [@kingma2014autoencoding] provides a compressed representation of a high-dimensional distribution by means of bottlenecking. Note that since this is a family of models, it admits many specific instances such as VQ-VAE [@oord2018neural], InfoVAE [@zhao2018infovae], etc., which can promote desirable properties such as independence and adaptable priors in the latent space. VAEs have two components, an encoder $\mathcal{E}$ and a decoder $\mathcal{D}$. The encoder casts the original state $x \in \mathbb{R}^d$ into a low dimensional representation $\mathcal{E}(x) \in \mathbb{R}^{2r}$ such that $2r \ll d$. Then, the model splits $\mathcal{E}(x)$ into mean $\mu \in{\mathbb{R}^{r}}$ and variance $\sigma^2 \in{\mathbb{R}^{r}}$, and samples from the normal distribution $N(\mu, \Sigma)$ with $\Sigma = \text{diag}(\sigma_1^2, \dots, \sigma_r^2)$ by applying a reparameterization trick $z = \mu + \sigma \cdot \epsilon$, where $\epsilon \in N(0, I_r)$. The decoder then constructs an approximation of the state as $\tilde{x} = \mathcal{D}(z)$. + +VAEs are trained by optimizing for the evidence lower bound (ELBO), which is composed of a reconstruction term in the state space (e.g. the MSE $||x - \tilde{x}||_2^2$) and a Kullback--Leibler divergence (KLD) regularization term in the latent space, which is given by $$\begin{equation} + \label{eq:KLD} +\text{KLD}(\mu, \Sigma) = \sum_{i = 1}^r -\log(\sigma_i) + \frac{\sigma_i^2+\mu_i^2}{2} - \frac{1}{2}, +\end{equation}$$ which skews $N(\mu, \Sigma)$ to be close to $N(0, I_r)$. + +We use VAEs to compress both the sparse observations and the full states to the latent space. To achieve consistent latent representations of sparse observations and full states, we couple the VAEs in the latent space as shown in Figure [3](#fig:vae_obsmatching){reference-type="ref" reference="fig:vae_obsmatching"}. + +
+ +
A coupled VAE for consistent latent representations of sparse observations and full states.
+
+ +Specifically, we formulate a coupled VAE with two encoders, one encoder $\mathcal{E}: \mathbb{R}^d \rightarrow \mathbb{R}^{2r}$ that encodes the full state $x_t$ to a latent variable $z_t$ with distribution $N(\mu_t, \Sigma_t)$, where $(\mu_t, \sigma^2_t) = \mathcal{E}(x_t)$, and $z_t = \mu_t + \sigma_t \cdot \epsilon_t$, and the other encoder $\mathcal{E}_{\text{obs}}: \mathbb{R}^m \rightarrow \mathbb{R}^{2r}$ that encodes the sparse observation $y_t$ to another latent variable $z_t^{\text{obs}}$ with distribution $N(\mu^{\text{obs}}_t, \Sigma^{\text{obs}}_t)$, where $(\mu^{\text{obs}}_t, (\sigma_t^{\text{obs}})^2) = \mathcal{E}_{\text{obs}}(y_t)$ and $z_t^{\text{obs}} = \mu^{\text{obs}}_t + \sigma^{\text{obs}}_t \cdot \epsilon_t$. To assimilate the encoded latent data to the encoded latent state, we seek to match the two latent representations for their means and variances. To build a consistent reconstruction of the full state from the latent representation, we use the same decoder $\mathcal{D}: \mathbb{R}^r \to \mathbb{R}^d$. To this end, we formulate the following loss function to account for all three contributions: $$\begin{align} + \ell_{t}(\theta) &= ||x_t - \mathcal{D}(z_t)||_2^2 + ||x_t - \mathcal{D}(z^{\text{obs}}_t)||_2^2 & \text{ (Reconstruction Term)}\\ + &+ \text{KLD}(\mu_t, \Sigma_t) + \text{KLD}(\mu^{\text{obs}}_t, \Sigma^{\text{obs}}_t) & \text{ (Regularization Term)}\\ + &+ ||\mu_t - \mu^{\text{obs}}_t||_2^2 + ||\Sigma_t - \Sigma^{\text{obs}}_t||_2^2 & \text{ (Latent Matching Term)}, +\end{align}$$ where $\theta$ represents the parameters of the encoders and decoder, for which we use convolutional neural networks (CNNs) in this work. Then for the training of the parameter $\theta$ in the coupled VAE, we minimize an empirical loss function as a sum of the above loss function over the training trajectories of the dynamical system, and over all the time steps $t$ in each trajectory. + +Note that both of the latent representations use the same decoder for reconstruction of the full state. By training a coupled VAE which minimizes the error for the reconstruction of the true state conditioned on the observations, we can strive to obtain a latent representation of the sparse observations consistent to that of the full states. This approach is similar to the Generalized Latent Assimilation (GLA) representation proposed by @cheng2022generalised, but with the addition of matching the means and variances of the state and observation encoders, as illustrated in Figure [3](#fig:vae_obsmatching){reference-type="ref" reference="fig:vae_obsmatching"}. Experimentally, training the encoders without matching the latent representations can lead to a solution where the latent observations are segregated from the latent states, yet result in sufficient reconstructions. Using these two separate encoders, we can match the latent representations such that we are able to apply EnSF in a full-dimensional manner, even with extremely sparse observations. + +Some early papers have utilized autoencoder architectures to conduct data assimilation in various ways. GLA proposed in @cheng2022generalised uses the EnKF to assimilate observations in the latent space of the VAE. However, for latent spaces with high dimensions, EnKFs are computationally expensive and also subject to the curse of dimensionality. Meanwhile, overly constricting the latent dimension to simplify the computation would lead to magnified reconstruction errors, so the GLA algorithm needs to strike a balance between efficiency and accuracy. Additionally, depending on the type of architecture, local structures may be lost when encoding the state vector into the latent vector, so localized methods such as the LETKF [@HUNT2007112] can lose their advantage. + +For simplicity, let the latent state $\xi_t$ and latent data $\zeta_t$ be defined as the encoded state and data as $$\begin{equation} + \xi_t = (\mu_t, \sigma_t^2) = \mathcal{E}(x_{t}) \text{ and } \zeta_t = (\mu_t^{\text{obs}}, (\sigma_t^{\text{obs}})^2) = \mathcal{E}_{\text{obs}}(y_{t}), +\end{equation}$$ respectively. We conduct data assimilation in the latent space by assimilating the latent data $\zeta_t$ to the latent state $\xi_t$. Mathematically, the latent state $\xi_t$ follows approximately (up to the VAE reconstruction error) the latent dynamical system $$\begin{equation} +\label{eq:transition-latent} + \xi_t \approx \mathcal{E}(M(\mathcal{D}(z_{t-1}(\xi_{t-1})), \varepsilon_t)) +\end{equation}$$ as a result of the full dynamical system in and the VAE with the reparametrization $z_{t-1}(\xi_{t-1}) = \mu_{t-1} + \sigma_{t-1}\cdot \epsilon_{t-1}$ at time $t-1$. The latent data $\zeta_t$ approximately satisfies $$\begin{equation} +\label{eq:observation-latent} + \zeta_t = \mathcal{E}_{\text{obs}}(H(\mathcal{D}(z_{t}(\xi_{t})) + \gamma_t) +\end{equation}$$ as a result of the observation map in and the VAE with the reparametrization $z_{t}(\xi_{t}) = \mu_{t} + \sigma_{t} \cdot \epsilon_{t}$ at time $t$. When calculating the likelihood of the observation in the latent space, we can simply and use an additive latent observation noise $\hat{\gamma}_t$ estimated from the full space observation noise $\gamma_t$ through the observation encoder. As the coupled encoders seek to match the full states and the sparse observations in their latent representations, we can further approximate the latent observation map in as an identity map, i.e., $\zeta_t \approx \xi_t + \hat{\gamma}_t$, which simplifies and accelerates the computation of the gradient of the log-likelihood function by avoiding automatic differentiation through the map in with respect to the latent state. + +With the latent state following the latent dynamics, and the approximate latent observations, we consider the Bayesian filtering problem in the latent space by formulating the prediction step as $$\begin{equation} + \label{eq:prediction_ensf} +\textbf{Prediction: } P(\xi_{t} | \zeta_{1:t-1}) = \int P(\xi_{t} | \xi_{t-1}) P(\xi_{t-1} | \zeta_{1:t-1}) d\xi_{t-1}, +\end{equation}$$ with the transition probability $P(\xi_{t} | \xi_{t-1})$ detemined by the latent dynamical system in . The update step for the posterior of the latent state $\xi_t$ given the latent data $\zeta_t$ is defined as $$\begin{equation} + \label{eq:bayes_filtering_ensf} +\textbf{Update: } P(\xi_{t} | \zeta_{1:t}) = \frac{P(\zeta_{t} | \xi_{t}) P(\xi_{t} | \zeta_{1:t-1})}{P(\zeta_{t} | \zeta_{1:t-1})}, +\end{equation}$$ where $P(\zeta_{t} | \xi_{t})$ is the likelihood of the latent data given latent state $\xi_t$, which can be computed by the latent observation map in . The latent normalization term $P(\zeta_{t} | \zeta_{1:t-1}) = \int P(\zeta_{t} | \xi_{t}) P(\xi_{t} | \zeta_{1:t-1}) d \xi_{t}$ is also generally intractable. + +We employ the diffusion-based ensemble score filter in Section [2.2](#sec:DMEnSF){reference-type="ref" reference="sec:DMEnSF"} for the above Bayesian filtering problem in the latent space by replacing the state $x_t$ with the latent state $\xi_t$, and the observation $y_t$ with the latent observation $\zeta_t$. Note that the latent state $\xi_t$ and the latent observation $\zeta_t$ have the same dimension $2r \ll d$. We present one step of the Latent-EnSF in Algorithm [\[alg:ensf-latent\]](#alg:ensf-latent){reference-type="ref" reference="alg:ensf-latent"}. + +:::: algorithm +::: algorithmic +Ensemble of the states $\{x_{t-1}\}$ from distribution $P(x_{t-1} | y_{1:t-1})$ and the observation $y_{t}$. State encoder $\mathcal{E}$, observation encoder $\mathcal{E}_{\text{obs}}$, and decoder $\mathcal{D}$. Ensemble of the states $\{x_{t}\}$ from the posterior distribution $P(x_{t} | y_{1:t})$. Encode the state $\xi_{t-1} = \mathcal{E}(x_{t-1})$ and the observation $\zeta_t = \mathcal{E}_{\text{obs}}(y_t)$. Run the (stochastic) dynamical system model in from $\{\xi_{t-1}\}$ to obtain samples $\{\xi_t\}$. Estimate the prior score $\nabla_\xi \log P(\xi_{t, \tau} | \zeta_{1:t-1})$ using and [\[eq:weight\]](#eq:weight){reference-type="ref" reference="eq:weight"} in the latent space. Estimate the posterior score $\nabla_\xi \log P(\xi_{t, \tau} | \zeta_{1:t})$ using in the latent space. Solve the reverse-time SDE in to generate the ensemble of latent states $\{\xi_t\}$. Sample $z_t = \mu_t + \sigma_t \cdot \epsilon_t$ from the ensemble of the latent states $\xi_t = (\mu_t, \sigma_t)$ and samples of $\epsilon_t$. Decode the ensemble of latent variables $\{z_t\}$ to the ensemble of the full states by $x_t = \mathcal{D}(z_t)$. +::: +:::: + +Because the sparse observations have been mapped to the same dimension as the state in the latent space, we are able to circumvent the weakness of the EnSF with vanishing gradient of the log-likelihood function when dealing with the sparse observation problems. In addition, since the encoder is a flexible neural-network representation, the model is able to restrict the latent observation map $H_{\text{latent}}$ in the latent space to be the identity function $H_{\text{latent}}(\xi_t) = \xi_t$, allowing for analytical solutions for the score function without the need for automatic differentiation. This means that the model can leverage the full computational speed-up of the EnSF in the latent space. Finally, the regularization done by the VAE such that the latent vector tends to follow a $N(0, 1)$ distribution simplifies hyperparameter search of the appropriate noise $\gamma_t$ for real world problems. + +In practice, when applying the EnSF to problems where the values of the states and their variances are very small, specifying the corresponding observation noise such that $\gamma_t \ll 0.05$ can result in numerical instability issues when sampling the posterior distribution by reverse time SDE with the drift and diffusion terms given in . + +This numerical instability issue is also present in the VAE latent space where the components are regularized such that $\mu_t \approx 0$ and $\sigma_t \approx 1$. We address this issue by multiplying the latent representations with a scalar constant $\psi_{\text{latent}}$, conducting the EnSF for the rescaled latent representation, and subsequently scaling the posterior samples back to the original latent representation. We conducted a small grid search experiment when working with the shallow water wave propogation problem in Section [4.1](#section:shallow_water){reference-type="ref" reference="section:shallow_water"}, where $\psi_{\text{latent}} = 500$ seemed to work reasonably. + +Remarkably, this $\psi_{\text{latent}}$ applies well in all our experiments, which implies that this scaling hyperparameter stays fairly steady across all problems. Intuitively, this is consistent; the latent regularization conducted by the VAE constrains the latent space to be in a similar range. In contrast, when EnSF is conducted on the full state space, such advantages are lost and a different scaling parameter $\psi$ is needed for different problems with different scales of the state and observation noise. diff --git a/2410.15059/main_diagram/main_diagram.drawio b/2410.15059/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..6ec8586cea1829adc8f664a756f4f63b9df7200f --- /dev/null +++ b/2410.15059/main_diagram/main_diagram.drawio @@ -0,0 +1,167 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2410.15059/main_diagram/main_diagram.pdf b/2410.15059/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..000899f4a3eb98a4857a6ae2b8a3c660efaab245 Binary files /dev/null and b/2410.15059/main_diagram/main_diagram.pdf differ diff --git a/2410.15059/paper_text/intro_method.md b/2410.15059/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..366b60bb59ebd759567ea15c75084a853e5e46ad --- /dev/null +++ b/2410.15059/paper_text/intro_method.md @@ -0,0 +1,124 @@ +# Introduction + +Algorithms, while straightforward in theory, become challenging to deploy in real-world scenarios. They operate in abstract domains with very specific conditions and types of inputs, which are represented with scalars. The main hurdle is the need to "collapse" reality into a scalar every time an algorithm is used, something usually done based on intuition rather than principled science [@harris1955fundamentals]. Neural Algorithmic Reasoning (NAR; [@velickovic2021neural]) has been proposed to address this issue by utilising specialised neural network architectures to break this scalar bottleneck by executing algorithms in higher-dimensional space made out of arrays of numbers (vectors). The task of mapping reality into this vectorial space is delegated to automated gradient-based optimisation techniques rather than relying on human operators. + +While NAR does not provide the correctness guarantees of its classical counterparts, robust performance can be achieved through *alignment* [@xu2020what] -- submodules of the model architecture correspond to easy-to-learn subparts of the algorithm (or class of). Graph Neural Networks (GNNs) have emerged as the most convenient architecture to execute all types of algorithms [@ibarz2022generalist] and GNNs that align better to the target algorithm achieve better generalisation. This alignment game [@velickovic2023NARGradient] has led to a sequence of exciting research -- from aligning the architecture with iterative algorithms [@tang2020towards] to proving that "graph neural networks are dynamic programmers" [@dudzik2022graph], especially if their message-passing function [@gilmer2017neural] takes into account 3-node interactions. + +The aforementioned papers focus on aligning the computation of the GNN with an algorithm or a specific algorithm class (e.g. dynamic programming), but ignore the properties *at the time of algorithm termination*. For the algorithms in the CLRS-30 algorithmic reasoning benchmark [@velivckovic2022clrs] once the optimal solution is found, further algorithm iterations will not change it. For example, in dynamic programming shortest-paths algorithms making additional iterations would not alter the optimality of the shortest paths' distances found. Such a state is called an *equilibrium* -- additional applications of a function (an algorithm's iteration) to the state leave it unchanged. + +In this paper: + +1. We explore the connection between execution of algorithms and equilibrium finding through the use of Denotational semantics and Domain theory. ([3](#subsec:dsadt){reference-type="ref+label" reference="subsec:dsadt"}) + +2. Inspired by the above, we implement a class of deep equilibrium algorithmic reasoners (DEARs) that learn algorithms by identifying the equilibrium point of the GNN equation and propose improvements to them. ([4](#sec:DER){reference-type="ref+label" reference="sec:DER"}) + +3. Our results suggest that the above reasoners can be *successfully* trained. Not only does equilibrium algorithmic reasoning achieve better overall performance with less expressive (and expensive) GNNs, but is also competitive to the more expressive (and expensive) NAR models. All this is done without providing any information on the number of algorithmic steps -- neither at train nor at test time. ([5](#sec:experimental){reference-type="ref+label" reference="sec:experimental"}) + +4. DEARs also drastically improve the inference speed -- an achievement made possible by the use of optimised root-finding algorithms and by decoupling the neural model from the *sequential* implementation of algorithms in standard benchmark datasets. ([5](#sec:experimental){reference-type="ref+label" reference="sec:experimental"}) + +# Method + +Let $A: \sI_A \to \sO_A$ be an algorithm, acting on some input $\vx \in \sI_A$, producing an output $A(\vx) \in \sO_A$ and let $\sI_A$/$\sO_A$ be the set of possible inputs/outputs $A$ can read/return. In algorithmic reasoning, we aim to learn a function $\gA: \sI_A \to \sO_A$, such that $\gA \approx A$. Importantly, we will not be learning simply an input-output mapping, but we will aim to align to the algorithm $A$'s trajectory. The alignment is often achieved through direct supervision[^1] on the intermediate states of the algorithm. To capture the execution of $A$ on an input $x$ we can represent it as $$\begin{tabularx}{1.0\textwidth}{p{3.65cm}Xp{4.4cm}} + \begin{align} + \bar{\vh}_0 = \text{\textsc{Preproc}}(\vx) + \end{align} + & + \begin{align} + \bar{\vh}_\tau = \underbrace{A_t(\dots A_t}_\text{$\tau$ times}(\bar{\vh}_0)\dots)\label{eq:algorollout} + \end{align} + & + \begin{align} + A(\vx) = \textsc{Postproc}(\bar{\vh}_\tau) + \end{align} + \end{tabularx}$$ where [Preproc]{.smallcaps} and [Postproc]{.smallcaps} are some simple pre- and post-processing (e.g.initialising auxiliary variables or returning the correct variable), $\bar{\vh}_\tau\in \sH_A$ is $A$'s internal (**h**idden) state, $A_t$ is a subroutine (or a set of) that is executed at each step and the number of steps depends on a boolean property being satisfied (e.g. all nodes visited). It is therefore no surprise that the encode-process-decode architecture [@hamrick2018relational] is the de-facto choice when it comes to NAR. Thus, the architecture can be neatly represented as a composition of three learnable components: $\gA= g_\gA \circ P \circ f_\gA$, where $g_\gA: +\sI_A \to \sR^d$ and $f_\gA: \sR^d \to \sO_A$ are the encoder and decoder function respectively (usually linear projections) and $P: \sR^d \to \sR^d$ is a processor that mimics the rollout ([\[eq:algorollout\]](#eq:algorollout){reference-type="ref+label" reference="eq:algorollout"}) of $A$. The processor often uses a message-passing GNN at its core. + +The *CLRS-30* benchmark [@velivckovic2022clrs] includes 30 iconic algorithms from the *Introduction to Algorithms* textbook [@CLRS]. Each data instance for an algorithm $A$ is a graph annotated with features from different algorithm stages (*input*, *output*, and *hint*), each associated with a location (*node*, *edge*, and *graph*). Hints contain time series data representing the algorithm rollout and include a temporal dimension often used to infer the number of steps $\tau$. Features in CLRS-30 have various datatypes with associated losses for training. The test split, designed for assessing out-of-distribution (OOD) generalisation, comprises graphs four times larger than the training size. + +Deep equilibrium models [DEQs @bai2019deep] are a class of implicit neural networks [@ghaoui2019implicit]. The functions modelled with DEQs are of the form: $$\begin{align} + \vz^*=f_\theta(\vz^*, \vx)\label{eq:fixedpoint} +\end{align}$$ where $\vx$ is input, $f_\theta$ is a function parametrised by $\theta$ (e.g. a neural network) and $\vz^*$ is the output. $\vz^*$ is an equilibrium point to the eventual output value of an infinite depth network where each layer's weights are shared, i.e. $f_\theta^{[i]}=f_\theta$. By re-expressing ([\[eq:fixedpoint\]](#eq:fixedpoint){reference-type="ref" reference="eq:fixedpoint"}) as $g_\theta=f_\theta(\vz^*, \vx)-\vz^*$ DEQs allow us to find the fixed point $\vz^*$ via any black-box root-finding method [e.g. @broyden1965aclass; @anderson1965iterative], without the actual need to unroll the equation until convergence, allowing us to reduce steps. For backpropagation the gradient $\partial \mathcal{L}/\partial \theta$ could be calculated using the Implicit Function Theorem (cf. [@bai2019deep]) and no intermediate state has to be stored, giving us a constant memory cost of gradient computation regardless of the number of iterations until convergence. + +MPNNs operate by exchanging information between adjacent nodes [@gilmer2017neural]. It has been identified that the message passing process can be hindered by a phenomenon known as *oversquashing* [@alon2020bottleneck], which occurs when a large volume of messages are compressed into fixed-sized vectors. The importance of overcoming the negative implication posed by this phenomenon is crucial for the overall expressivity of GNNs [@giovanni2023oversquashing], particularly in the context of long-range node interactions. + +To this end, several spectral methods have been proposed to mitigate oversquashing by increasing the Cheeger constant [@arnaiz2022diffwire; @banerjee2022oversquashing; @karhadkar2022fosr]. A higher Cheeger constant provides a measurement that a graph is globally lacking bottlenecks. The novel approaches include graph rewiring techniques [@topping2021understanding; @barbero2024localityaware], as well as significant independent bodies of research recognising the efficacy of expander graphs [@deac2022expander; @shirzad2023exphormer; @banerjee2022oversquashing], due to their desirable properties. + +Expander graphs are proven to be highly connected sparse graphs ($|E| = \mathcal{O}(|V|)$) with a low diameter [@mohar1991eigenvalues], thus offering advantageous properties for information propagation. Consequently, this facilitates messages to be passed between any pair of nodes in a short number of hops, and as a result, alleviating oversquashing. Formally, a graph $G = (V, E)$ is defined as an expander if it satisfies certain expansion properties. One common definition involves the aforementioned Cheeger constant. In the work of @deac2022expander, a high Cheeger constant is equivalent to a graph being bottleneck free [@deac2022expander Definition 3], and that an expander has a high Cheeger constant [@deac2022expander Theorem 5]. + +There are various methods for constructing expander graphs. We opt for the *deterministic* algebraic approach as in @deac2022expander, utilising Cayley graphs. Specifically, we leverage Definition 8 and Theorem 9 of [@deac2022expander] to construct the Cayley graph for the *special linear group* $\mathrm{SL}(2,\sZ_n)$ and its generating set $S_n$ -- see p.5 of @deac2022expander for details. Note, that the order of a Cayley graph for $\sZ_n$ is $\left|V\right|=\mathcal{O}(n^3)$. Hence, for many input graphs, a Cayley graph of the same size may not exist. + +This aim of this section is to draw the parallel between finding equilibriums and executing an algorithm, in order to answer if and when an equilibrium NAR model can be successful. The following paragraphs introduce the mathematical tools for formalising *fixed points* -- denotational semantics [@scott1971toward] and domain theory [@scott1982domains]. + +To every programming language expression[^2] $P$ denotational semantics provides an interpretation $\llbracket P \rrbracket$, which is a mathematical object (often a function), representing the behaviour of the expression to different inputs. These *denotations* must be: 1) abstract, i.e. independent of language and hardware, hence functions are natural choice; 2) compositional, i.e. $\llbracket P \rrbracket$ can only be defined in terms of the *denotations* of $P$'s subexpressions, but not $P$ itself; 3) model the computation $P$ performs. As an example, we will use the lightweight imperative programming language **IMP**[^3]. It consists of *numbers*, *locations*, *arithmetic expressions*, *boolean expressions* and *commands*. Examples of **IMP** are given in [7](#app:ebigo2){reference-type="ref+label" reference="app:ebigo2"} -- we will use [blue]{style="color: NavyBlue"} for **IMP** and encourage the reader to check how we use colour in the appendix. Albeit small, the language is Turing-complete and all algorithms we experiment with can be defined in it. + +Denote the set of variable locations with $\sL$ -- those are all variables/array elements we can ever define. A good analogy to think of is the addresses in the language `C`. The notation we can use to represent a program state is $[X\mapsto 1, B\mapsto -48, \dots]$ and means that the value of $X$ is 1, the value of $B$ is $-48$ and so on. In other words, program states map locations to integers, s.t. a location can be mapped only once. Hence states are functions and the set of all program states $State$ is the set of functions mapping locations to integers: given $s \in +State$, $s(L)\in \sZ$ is the value at the location $L$ *for the state $s$*. The value for location $L$ in a different $s'\in State$, $s'(L)$, may or may not differ. The denotation of arithmetic / boolean expressions / commands are the functions with domain $State$. These will be represented in the form of *lambda abstractions*, i.e. $\lambda x\in S. M$ rather than $f(x\in S)=M$, where $S$ is a set and $M$ is a function body. The codomain of the denotation depends on the type of expression: $\llbracket a \rrbracket : State \to \sZ$ for arithmetic expressions, $\llbracket b \rrbracket : State \to \sB$, for boolean expressions and $\llbracket c \rrbracket : State \rightharpoonup State$ for commands. Since commands transform state, they are also called state transformers. ***Commands' denotations are partial functions, as expressions like*** [`while true do skip`]{style="color: NavyBlue"} ***never terminate and have no denotation.*** + +For a large portion of the above language, it is trivial and intuitive to define the denotations by structural recursion. For example: $$\begin{align*} + &\llbracket \texttt{\textcolor{NavyBlue}{if}}\ b\ \texttt{\textcolor{NavyBlue}{then}}\ c_0\ \texttt{\textcolor{NavyBlue}{else}}\ c_1 \rrbracket = \lambda s\in State.\ + \begin{cases} + \llbracket c_0 \rrbracket(s) \quad \text{if }\llbracket b \rrbracket(s) \text{ is \texttt{\textcolor{NavyBlue}{true}}}\\ + \llbracket c_1 \rrbracket(s) \quad \text{otherwise} + \end{cases}\\ + \llbracket \texttt{\textcolor{NavyBlue}{(}}c_0\texttt{\textcolor{NavyBlue}{;}}c_1\texttt{\textcolor{NavyBlue}{)}} \rrbracket &= \lambda s\in State.\ \llbracket c_1 \rrbracket(\llbracket c_0 \rrbracket(s)) \qquad\qquad\qquad \llbracket skip \rrbracket = \lambda s \in State.\ s +\end{align*}$$ The only denotation that cannot be expressed recursively, is that of the [`while`]{style="color: NavyBlue"} construct. Let $w = \texttt{\textcolor{NavyBlue}{while}}\ b\ \texttt{\textcolor{NavyBlue}{do}}\ c$. By program equivalence, $w = \texttt{\textcolor{NavyBlue}{if}}\ b\ \texttt{\textcolor{NavyBlue}{then}}\ \texttt{\textcolor{NavyBlue}{(}}c\texttt{\textcolor{NavyBlue}{;}}w\texttt{\textcolor{NavyBlue}{)}}\ \texttt{\textcolor{NavyBlue}{else +skip}}$. Therefore $$\begin{align*} + \textcolor{CambridgeRed}{\llbracket w \rrbracket} = \llbracket \texttt{\textcolor{NavyBlue}{if}}\ b\ \texttt{\textcolor{NavyBlue}{then}}\ \texttt{\textcolor{NavyBlue}{(}}c\texttt{\textcolor{NavyBlue}{;}}w\texttt{\textcolor{NavyBlue}{)}}\ \texttt{\textcolor{NavyBlue}{else skip}} \rrbracket &= \lambda s\in State.\ + \begin{cases} + \textcolor{CambridgeRed}{\llbracket w \rrbracket}\left(\llbracket c \rrbracket(s)\right) & \text{if }\llbracket b \rrbracket(s) \text{\ is \texttt{\textcolor{NavyBlue}{true}}}\\ + s & \text{otherwise} + \end{cases} +\end{align*}$$ but this is not a valid definition, since it reuses $\llbracket w \rrbracket$ (highlighted in [red]{style="color: CambridgeRed"} above). Denotational semantics solves this problem, by defining a function $f_{b,c}: (State \rightharpoonup State) \to +(State \rightharpoonup State)$ which takes one state transformer and returns another: $$\begin{align} +f_{b,c} = \lambda \hat{w} \in (State \rightharpoonup State). \lambda s \in State.\ + \begin{cases} + \hat{w}\left(c(s)\right) & \text{if }b(s)\\ + s & \text{otherwise} + \end{cases}\label{eq:fbc} +\end{align}$$ $\hat{w}$ is now a function variable. The denotation of $\llbracket +w \rrbracket$ is the fixed point of $f_{\llbracket b \rrbracket, \llbracket +c \rrbracket}$, i.e. $\llbracket w \rrbracket = f_{\llbracket b \rrbracket, +\llbracket c \rrbracket} (\llbracket w \rrbracket)$. In order to find the denotation, we need to solve the fixed point. To aid the reader a full worked example of computing the denotation for a [`while`]{style="color: NavyBlue"} loop construct is given in [8](#app:ebigo){reference-type="ref+label" reference="app:ebigo"}. + +@scott1982domains provides a framework with which we can both find and also characterise solutions for fixed point equations.[^4] Define $D$ as the domain of state transformers $(State \rightharpoonup State)$. A partial order[^5] $\sqsubseteq$ on $D$ is defined as follows: $w \sqsubseteq w'$ iff $\forall s \in State$ if $w(s)$ is defined then $w(s)=w'(s)$. In other words $w'$ keeps old mappings and only defines new ones. The totally undefined partial function $\bot$ is the least element in $D$. This function contains no location to value mappings. A chain is a sequence of elements of $D$, s.t. $d_0 \sqsubseteq d_1 \sqsubseteq d_2 +\sqsubseteq \dots$ . The supremum of the chain, called *least upper bound (lub)*, is denoted as $\bigsqcup_{n\geq0} d_n$. There could exist different chains, but, by definition, all chains in a domain must have a lub. + +A function $f: D \to D$ is monotonic iff $\forall d,d'\in D.\ (d \sqsubseteq d' +\Rightarrow f(d) \sqsubseteq f(d'))$. In other words, if the second state defined more mappings than the first and we apply one iteration step to both states, the state resulting from the second will still define more mappings. Monotonic functions for which $\bigsqcup_{n\geq0} f(d_n) += f(\bigsqcup_{n\geq0}d_n)$ are also called continuous. In plain language, if a function is continuous and we are provided with a chain, the lub of $f$ applied to every chain element is the same as $f$ applied to the lub of the chain. An element $d''\in D$ is defined to be *pre-fixed point* if $f(d'')\sqsubseteq d''$ -- applying $f$ does not define anything new. The fixed point $fix(f)$ of $f$ is the least pre-fixed point of $f$. By utilising antisymmetry[^6] of $\sqsubseteq$ and the two properties of $fix(f)$ (pre-fixed point and least) we can obtain $f(fix(f))=fix(f)$. By Tarski's theorem [@tarski1955lattice], any continuous $f: D \to D$ has a least pre-fixed point. This fixed point can be found, by taking the lub of the chain of applications of f: $fix(f) += \bigsqcup_{n\geq0}f^n(\bot)$. The helper function $f_{b,c}$ from [\[eq:fbc\]](#eq:fbc){reference-type="ref+label" reference="eq:fbc"} is continuous [proof is given on p.120 of @gunter1992semantics], therefore a direct result is that if the $\texttt{\textcolor{NavyBlue}{while}}\ b\ \texttt{\textcolor{NavyBlue}{do}}\ c$ terminates then its denotation exists and is *the least* fixed point of sequence of iterations (compared to picking any fixed point). + +The above detour into denotational semantics has affirmed the existence of a connection between equilibrium models and algorithms (as conjectured by @xhonneux2024deepequilibriummodels). Under the assumptions that: + +- the algorithms always terminate -- while not computable in the general case, this holds true for experiments, as we are dealing with offline algorithms with provable termination + +- the algorithms we train on can be modelled as "$\texttt{\textcolor{NavyBlue}{while}}\ b\ + \texttt{\textcolor{NavyBlue}{do}}\ c$" constructs within **IMP** + +the least fixed point exists and can be found by taking the first "state" of the algorithm where future iterations on it have no effect. In [9](#app:equilibriums){reference-type="ref+label" reference="app:equilibriums"} we have further annotated three algorithms from the official code of the CLRS benchmark: BFS, Floyd-Warshall, strongly connected components. Those annotations clearly show that algorithms can be rewritten in **IMP** regardless of their implementation size. While BFS is clearly a "$\texttt{\textcolor{NavyBlue}{while}}\ b\ \texttt{\textcolor{NavyBlue}{do}}\ c$"-type of algorithm, annotating the other two reveals that either the network may need more input features to decide termination (Floyd-Warshall; [\[app:lst:fw\]](#app:lst:fw){reference-type="ref+label" reference="app:lst:fw"}) or that the algorithm can be composed of several while loops where each $c$ is another while loop on its own (strongly connected components; [\[app:lst:scc\]](#app:lst:scc){reference-type="ref+label" reference="app:lst:scc"}). Fortunately, our approach is not doomed to fail: a single DEQ layer can model any number of "stacked" DEQ layers [@kolter2023deep chapter 4]. + +We implement our processors/encoders/decoders following @ibarz2022generalist. The most notable difference[^7] to their implementation is that ours uses a sparse graph representation. This requires us to assume a fully connected graph on tasks where no graph structure exists, in order to be able to give pointer predictions, and to reimplement the strongly connected components algorithm so that the output pointers are always in the edge set (this did not change the difficulty of the task). + +The final node embeddings, from which the output is decoded, are the solution to the equation: $$\begin{align} + \begin{split} + \rmH^{(*)}=P_{\rmU\rmE}(\rmH^{(*)}) + \end{split}\label{eq:DER} +\end{align}$$ where $P_{\rmU\rmE}(\rmH^{(*)})=P(\rmH^{(*)}, \rmU, \rmE)$, $\rmU$/$\rmE$ are the encoded node and edge feature matrices and $P$ is the processor function. $\rmH^{(t)}$ are the stacked latent states of the nodes at timestep $t$ (with $\rmH^{(0)}=\mathbf{0}$). The above [\[eq:DER\]](#eq:DER){reference-type="ref+label" reference="eq:DER"} matches the signature of [\[eq:fixedpoint\]](#eq:fixedpoint){reference-type="ref+label" reference="eq:fixedpoint"}, and can be solved via root-finding (we employ `torchdeq` [@torchdeq]; MIT License), as if it is $f_\theta$ of a DEQ. Any model using this technique will be called *deep equilibrium algorithmic reasoner* (**DEAR**) in our experiments. The default processor in the majority of our experiments is a PGN [@velickovic2020pointer] with a gating mechanism as in @ibarz2022generalist, but we note that DEARs can use any kind of processor. + +The `torchdeq` library provides several solvers. The most basic one is *fixed-point iteration*, equivalent to repeating [\[eq:DER\]](#eq:DER){reference-type="ref+label" reference="eq:DER"} until convergence. However, in our very first experiments the solver needed more iterations than the algorithm we train on. We therefore opted for the *Anderson* solver (implements Anderson acceleration [@anderson1965iterative]) and abandoned fixed-point iteration: $$\begin{align*} + \hat{\rmH}^{(t+1)} = P_{\rmU\rmE}(\rmH^{(t)})\qquad + \rmH^{(t+1)} = SolverStep\left(\left[ \rmH^{(0)} \dots \rmH^{(t)}\right], \hat{\rmH}^{(t+1)}\right) +\end{align*}$$ The criteria for pre-fixed point check `torchdeq` implements is distance-based: for a state $\rmH^{(t)}$ to be considered a pre-fixed point, the distance to the next state has to be under a pre-defined threshold $\delta$. We kept the criteria but modified the library to always return the least pre-fixed point (see [11](#app:lfp){reference-type="ref+label" reference="app:lfp"}). This is in contrast to picking the pre-fixed point with the least distance to next state (the default option in `torchdeq`) and is a decision largely motivated from [3](#subsec:dsadt){reference-type="ref+label" reference="subsec:dsadt"}. Due to a lack of a suitable definition for the domain of NAR trajectories, we define $\forall t.\ \rmH^{(t)}\sqsubseteq\rmH^{(t+1)}$, i.e. we pick the first state that passes the pre-fixed point check. + +For problems defined on graphs it is in theory possible that the number of solver iterations needed to find equilibrium is less than the diameter of the graph. While, in practice, this is unlikely to happen we hypothesise that improving long-range interactions could improve the convergence of DEAR. For this reason, we adopt the implementation of Cayley Graph Propagation (CGP) [@wilson2024cayley]. Contrasted to Expander Graph Propagation (EGP) [@deac2022expander], which addresses the graph size misalignment (see [2](#sec:background){reference-type="ref+label" reference="sec:background"}) by truncating the Cayley graph, CGP keeps the extra nodes as virtual nodes. The CGP model upholds the aforementioned advantageous properties of an expander graph in a more grounded manner by preserving the complete structure. + +In GNNs, the benefits of augmenting a graph with virtual nodes and providing message-passing shortcuts have been observed to improve performance in various tasks [@hu2020open; @hu2021ogb; @cai2023connection]; further supported by the theoretical analysis [@hwang2022analysis]. Additionally, by retaining the complete Cayley graph structure we improve the structure-aware representations by varying the neighbourhood ranges [@xu2018representation]. + +We do not make any use of hints (supervising on intermediate algorithm state). First, although it may seem counterintuitive, it has been shown that a NAR model can successfully generalise, and even give better results when trained to only predict the correct output [@bevilacqua2023neural; @rodionov2023neural]. Second, the fact that the solver uses the GNN exactly once per call *does not imply that one step of the solver would correspond to one iteration of the algorithm*, bringing uncertainty which DEAR states to match to which algorithm step. While we propose an alignment scheme (see next paragraph), which has the potential to integrate hints, we leave this for future work. + +![ Proposed alignment rule: every state in the DEAR trajectory should "go forward". Alignments to a GNN state that has already been "passed" are disallowed. First and last states must always align. We intentionally use arrows instead of $\sqsubseteq$ for DEAR, as $\sqsubseteq$ may not hold for DEAR's trajectory. ](./images/alignment.png){#fig:align_example width="\\linewidth"} + +Our idea of alignment is visualised in [1](#fig:align_example){reference-type="ref+label" reference="fig:align_example"}. We are given two trajectories of states, one obtained from unrolling GNN iterations as in classical NAR and another obtained from using DEAR. We would like to match DEAR to NAR, such that $\forall i\leq +j, i'\leq j'$ if we have committed to aligning DEAR state $\rmH^{(i')}$ to NAR state $\rmH^{(j)}_\mathcal{G}$, we cannot align any $\rmH^{(j')}$ to $\rmH^{(i)}_\mathcal{G}$ and from the same start we would like to reach the same final state. In other words, *skipping states is allowed, going back in time is not*. This auxiliary supervision would also improve the monotonicity of DEARs, encouraging faster convergence. Enforcing this alignment is done by using an auxiliary loss. Choosing the $L_2$ norm as a distance metric, we use a dynamic programming algorithm ([12](#app:alignDP){reference-type="ref+label" reference="app:alignDP"}) to compute the most optimal alignment (normalised by the number of solver steps, in order not to penalise longer trajectories) and supervise on that value. + +Even with normalisation, the alignment sometimes had the effect of making the optimisation stuck in local minima where the number of steps to hit equilibrium was as low as 2 and the gradients were uninformative. We combated this in two ways: 1) instead of using the default layer normalisation we switched to [granola]{.smallcaps} [@eliasof2024granola]; 2) since $f(fix(f))=f$, we performed a random number of additional iterations [@liu2024scalable] and take the last state. The probability of doing $s$ extra steps is $0.5^s$. diff --git a/2412.09879/main_diagram/main_diagram.drawio b/2412.09879/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..21a3212026e68cdc3053269a0e6ea3eb863cc9df --- /dev/null +++ b/2412.09879/main_diagram/main_diagram.drawio @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/2412.09879/main_diagram/main_diagram.pdf b/2412.09879/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2469f082b7d9568fb955e5fd0eacf146fb9ff8bb Binary files /dev/null and b/2412.09879/main_diagram/main_diagram.pdf differ diff --git a/2412.09879/paper_text/intro_method.md b/2412.09879/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..55863fa76207e00947c619bbb424856c28148ea1 --- /dev/null +++ b/2412.09879/paper_text/intro_method.md @@ -0,0 +1,37 @@ +# Introduction + +Large language models (LLMs) can make *informal plans*, such as suggesting ideas for parties or giving general advice on immigration. However, most users, let alone automated agents like robots, would not be able to actually execute those plans step-by-step to fruition -- either to organize parties or acquire visas -- without significant prior knowledge or external help. This inability to make executable plans lies in LLMs' inability of grounding and formal reasoning [@liu2023evaluating; @pan-etal-2023-logic; @zhang-etal-2023-causal]. Cutting-edge research in the community has evaluated LLMs' ability to make *formal plans* in grounded environments, such as textual simulations, where all objects and actions represent actualities in the real world. Therefore, any resulting plan that formally involves those objects and actions would be executable and verifiable by nature. Although formal planning has been desirable in the history of AI [@weld1999recent], recent work found that even state-of-the-art LLMs are unable to generate formal plans [@silver2024generalized; @valmeekam2024planbench; @stechly2024chain]. + +
+ +
LLM-as-formalizer uses natural language descriptions to generate the Domain and Problem File in PDDL, then these are given to a planner to find a plan. We explore the effect of natural-ness of the language in the description, by giving the model both templated and natural descriptions. Examples of Domain Descriptions and Problem Descriptions from the Blocksworld Domain are shown. The green text displays what the two examples have in common (listing all possible actions and restrictions) and the red text displays text that is not considered natural. The “Templated” text corresponds to the “Heavily Templated” version discussed in Section 4.
+
+ +Instead of using the LLM as a planner to generate the plan directly, an alternative line of work uses the LLM as a *formalizer*. Here, the LLM generates a formal representation of a planning domain, for example in the planning domain definition language (PDDL), based on some natural language descriptions of the environment. This representation can then be fed into a solver to find the plan deterministically (see Figure [1](#fig:llm as formalizer){reference-type="ref" reference="fig:llm as formalizer"}). Previous work achieved great success by showing that LLM-as-formalizer greatly outperforms LLM-as-planner in various domains [@lyu-etal-2023-faithful; @xie2023translating; @{liu2023llm+}; @zhang-etal-2024-pddlego; @zuo2024planetarium; @zhang-etal-2024-proc2pddl; @zhu2024language], as LLMs are more capable of information extraction than formal reasoning [@zhang-etal-2024-openpi2]. However, the above work has two major shortcomings. First, to simplify the task and evaluation, most have only attempted to generate a partial PDDL representation while assuming the rest is provided, often unrealistic in real life. Second, the language used to describe the environments is often artificially templated and structured, leading to potential overestimation of models' ability. + +This paper explores the limit of LLM-as-formalizer devoid of the above two simplifications. We use LLMs to generate the entirety of a PDDL representation, including the domain file and the problem file, given a natural-sounding description of the environment and the task (see Figure [1](#fig:llm as formalizer){reference-type="ref" reference="fig:llm as formalizer"}). On 4 widely used planning simulations from the International Planning Competition, we benchmark a suite of LLMs on generating PDDL that is both solvable and correct. As the descriptions in these datasets are templated, we also construct model-generated, human-validated descriptions that are natural-sounding to different levels. + +Our work is the first to systematically analyze state-of-the-art LLMs' ability of the trending methodology of LLM-as-formalizer on the highly challenging task of formal planning. We put forward an array of observations that will benefit future efforts. Discussed in detail in Section [5](#sec:results){reference-type="ref" reference="sec:results"}, our key findings are as follows. + +- On various planning simulations, closed-source models like GPT can decently generate entire PDDL, while open-source models like Llama up to 405B cannot. + +- When feasible, LLM-as-formalizer greatly outperforms LLM-as-planner. + +- As the environment descriptions sound more human-like, the models are more challenged. + +- The performance of LLM-as-formalizer is robust to lexical perturbation, while that of LLM-as-planner is not. + +- Open-source models succumb to syntax errors unlike GPT models, while semantic errors are common for both types of models. + +Formal planning (or classical planning) with PDDL involves a domain file ($\mathbb{DF}$) and problem file ($\mathbb{PF}$) (Figure [1](#fig:llm as formalizer){reference-type="ref" reference="fig:llm as formalizer"}). $\mathbb{DF}$ describes general properties in a planning domain that holds true across problems, while $\mathbb{PF}$ describes specific configurations of each problem instance. Concretely, the $\mathbb{DF}$ defines all available actions (and their parameters and pre- and post-conditions) for a state-based environment as well as predicates that represent the properties of object types. The $\mathbb{PF}$ defines the involved objects, the initial states, and the goal states. These two files are then given to a deterministic planner which will algorithmically search for a plan. Such a plan is a series of executable, instantiated actions that sequentially transition the world states from initial to goal. In the AI community, classical planning has been deemed an effective approach to solve real-world users' problems, as the process is precise, explainable, verifiable, and deterministic. + +However, formal planning demands a well-crafted pair of $\mathbb{DF}$ and $\mathbb{PF}$. In a real-world planning scenario, an average user would not describe their environment and problem with PDDL, but more likely with a textual description of the domain ($\mathbb{DD}$) and the problem ($\mathbb{PD}$), which can be specific or loose. Hence, we focus on the textual flavor of formal planning: given $\mathbb{DD}$ and $\mathbb{PD}$, the model outputs a successful plan with regard to the $\mathbb{DF}$ and $\mathbb{PF}$ that are withheld from the model. + +# Method + +
+ +
Methodologies for using LLMs in planning. LLM-as-Planner generates the plan directly, while LLM-as-Formalizer translates the 𝔻𝔻 and ℙ𝔻 into PDDL. Previous work like use the LLM to generate partial PDDL such as ℙ𝔽 only, while we generate the entire PDDL including ℙ𝔽 and 𝔻𝔽. Note the 𝔻𝔻 and ℙ𝔻 are always provided and the 𝔻𝔽 and ℙ𝔽 are always generated by the LLM.
+
+ +To tackle the task above, recent work involving LLMs diverged into two methodologies. The first, **LLM-as-planner**, directly uses LLMs to generate a plan based on the $\mathbb{DD}$ and a $\mathbb{PD}$. The second, **LLM-as-formalizer**, uses LLMs to recover the $\mathbb{DF}$ and $\mathbb{PF}$, before using a deterministic planner to arrive at the plan (Figure [2](#fig:two_methodologies){reference-type="ref" reference="fig:two_methodologies"}). Our work will focus on the second while using the first as a baseline. LLM-as-formalizer is in essence neurosymbolic, where LLMs help define the structured representation that is otherwise prohibitively costly to annotate and brittle to generalize. Existing works in this line demonstrated success but only generated a partial PDDL representation, while assuming the rest, including $\mathbb{PF}$ goals [@lyu-etal-2023-faithful; @xie2023translating], the $\mathbb{PF}$[@{liu2023llm+}; @zhang-etal-2024-pddlego; @zuo2024planetarium], the action semantics in the $\mathbb{DF}$[@zhang-etal-2024-proc2pddl; @zhu2024language], and the domain file [@wong2023learning; @guan2023leveraging]. While this simplifies the task and evaluation, the assumption of provided PDDL components is often unrealistic. Therefore, we bridge this gap by asking the LLM to predict the entire PDDL -- both the $\mathbb{DF}$ and $\mathbb{PF}$.[^2] diff --git a/2508.16911/main_diagram/main_diagram.drawio b/2508.16911/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..0d92b1b565ddf9e7b20cd32a3121be47774940fa --- /dev/null +++ b/2508.16911/main_diagram/main_diagram.drawio @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2508.16911/paper_text/intro_method.md b/2508.16911/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..52e0716428f27c6177cd9146f16e83785cbe4904 --- /dev/null +++ b/2508.16911/paper_text/intro_method.md @@ -0,0 +1,106 @@ +# Introduction + +Duet dancing is a complex form of interactive human motion that requires precise coordination and synchronization between two dancers. Unlike solo performances, duet dances involve intricate spatial relationships, dynamic partner interactions, and continuous mutual adaptation to music beats. The lead dancer directs the interaction through movement cues synchronized with the music beats, while the follower responds in real-time, maintaining harmony and adding stylistic variations. This interdependent relationship presents significant modeling challenges, including movement unpredictability, motion misalignments, and inconsistencies in interaction dynamics, making duet dance generation substantially more complex than single-person dance synthesis. To address these challenges, introducing explicit control mechanisms can significantly enhance the conditioning of the generation process, ensuring coherence and responsiveness in duet dance synthesis. + +We emphasize the importance of control signals in generating duet dance motions. Generic motion generation often leverages natural language [\[2,](#page-8-0) [19,](#page-8-1) [25,](#page-9-0) [34,](#page-9-1) [37,](#page-9-2) [41–](#page-9-3)[43\]](#page-9-4), while most dance generation works [\[7,](#page-8-2) [14,](#page-8-3) [30,](#page-9-5) [35\]](#page-9-6) primarily rely on music due to its rhythmic and structural properties. The former line of research demonstrates that textual prompts specifying style, interaction, and timing enhance motion control and contextual awareness. Building on this, text-based control can improve duet dance generation by aligning movements with choreographic intent and preserving the intricate lead–follower dynamics. Wellcrafted prompts enable better synchronization and genrespecific fidelity, thereby enhancing realism and coherence. Moreover, text input allows choreographers to define sequences, refine interaction styles, and enforce synchronization constraints more effectively. + +Interactive motion generation has been explored in works such as InterGen [\[20\]](#page-8-4) and Inter-X [\[38\]](#page-9-7), which introduce large-scale datasets of interactive human motion with textual annotations, enabling two-person generation. However, these datasets primarily focus on general interactions and lack specialized professional movements (e.g., dance) and synchronized audio. Meanwhile, Duolando [\[31\]](#page-9-8) provides a duet dance benchmark aligned with music and motion, but it contains a limited number of samples and lacks textual annotations, making it difficult to generalize across diverse dance genres. + +To address these limitations, we present MDD as illustrated in Fig. [1,](#page-0-0) a large-scale professional-grade duet dance motion dataset with fine-grained text annotations that capture a rich movement vocabulary for spatial relationships, body movements, and rhythm. It contains over 4.4M frames with at least 30 minutes of high-quality motion data for 15 different genres across Latin, Ballroom, and Social Dancing categories. The data was captured using Optitrack equipment with experienced dancers. We collected more than 10K textual descriptions from annotators with diverse dance backgrounds, ranging from novices to dance experts. Our dataset supports two novel multi-modal tasks: (1) Textto-Duet: Conditional generation task that produces synchronized movements for both dancers given a text description and music. This task challenges models to generate plausible partner interactions based on text prompts while maintaining genre-specific styling and ensuring musical synchronization. (2) Text-to-Dance Accompaniment: Reactive generation task that synthesizes follower movements in response to the leader's actions, guided by textual prompts and musical context. These tasks represent fundamental challenges in multi-person motion synthesis and serve as benchmarks for evaluating models' ability to generate complex, interactive dance movements. + +In summary, our key contributions are: + +- 1. We introduce MDD, the first large-scale duet dance motion dataset spanning 15 different dance genres, featuring more than 10K rich text descriptions across more than 10 hours of motion capture data. +- 2. We provide fine-grained annotations that capture spatial relationships, body movement dynamics, and rhythmic elements between dance partners. +- 3. We introduce two new tasks that support our dataset with challenging multi-modal benchmarks: Text-to-Duet for generating coordinated partner movements from a text description and music, and Text-to-Dance Accompaniment for synthesizing context-aware follower motion given the leader's motion, music and text description. + +# Method + +We present MDD, a large-scale multimodal duet dance dataset consisting of 10.34 hours of motion capture data featuring more than 10K rich fine-grained text descriptions based on spatial relationship, body movements and rhythm. + +Our dataset features high-fidelity motion capture data spanning over 15 diverse dance genres, categorized into three broad styles: (1) Ballroom (Waltz, Foxtrot, Quickstep, Tango), (2) Latin (Rumba, Jive, Cha Cha, Samba, Paso Doble) and (3) Social (Salsa, Traditional Bachata, Sensual Bachata, Merengue, West Coast Swing, Argentine Tango). The dataset was collected from 30 subjects (16 females, 14 males), ensuring a balanced gender representation while capturing a wide range of dance styles and individual interpretations. Each genre includes a minimum of 30 minutes of motion capture data, providing extensive coverage for model generalization for different movement patterns. Fig. [2](#page-3-0) presents dataset insights with distribution statistics for different modalities: (a) Samples show high motion quality with rich annotations (b) Genre-wise motion duration distribution is relatively balanced (c) Annotation counts per genre are also mostly balanced, with slight variations for some genres having different duration length requirements per each annotated sample (d) Genres span a wide BPM range, from Rumba (lowest) to Merengue (highest). Please refer to the Supplementary Material for more details. + +The data collection process involved five main components: (1) Music selection (2) Motion capture (3) Motion postprocessing (4) Annotating Descriptions (5) Annotation Refining. Before starting the data collection process, an IRB approval was obtained from the university. + +For music selection, priority was given to copyright-free sources. However, for certain genres, obtaining a sufficient number of copyright-free samples was challenging. In such cases, short excerpts (0.5 - 1 min) were randomly selected, ensuring compliance with fair use for research purposes. Each genre folder contained a diverse collection of approximately 50–60 unique music samples, providing dancers with a broad selection to choose from. + +Motion capture was performed in a 24 × 24 ft. laboratory facility at 120 fps using an OptiTrack MoCap system [\[26\]](#page-9-16) with 16 infrared cameras optimally placed at varying heights (10–12 ft.). We used 53 retro-reflective markers optimally placed on the subjects who wore a form fitting suit as shown in Fig. [3](#page-3-1) This configuration ensured precise data + +![](_page_3_Figure_0.jpeg) + +Figure 2. (a) We illustrate two samples from the MDD dataset with corresponding annotated descriptions. The first, from Paso Doble, features a *Cape lift*, followed by an *Open Spin* and a *Left-side turn*. The second, from Salsa, shows a double-handed *Right turn* leading into a *Catapult handhold* followed by *right hand flick* with a *Right turn* ending into follower's *Hammerlock position*. On the right, we present genre-wise statistics for (b) motion duration percentage, (c) annotation counts distribution, and (d) average Beats Per Minute (BPM) + +![](_page_3_Figure_2.jpeg) + +Figure 3. (a) Placement of 53 markers using the OptiTrack Motive software. (b) Infrared optical camera (Prime 17W) used for highspeed tracking at a resolution of 1280 × 1024. (c) Layout of the motion capture facility. (d) Snapshot of our data collection setup + +capture for natural, high-quality recordings to capture complex partner interactions. + +We recruited 30 dancers, consisting of 16 females and 14 males, each with expertise in various partner-based dance styles across 15 distinct genres we selected. All participants were at an intermediate or advanced skill level, with a minimum of 3 years of experience in their respective genres. All participants came prepared with a diverse set of moves and they were allowed to freely adapt to the music, simulating a social dancing setting. + +Recordings with marker swaps or major capture errors were discarded. For short-term occlusions occurring from partner contact, missing marker data was manually identified and interpolated using Spherical Interpolation[\[32\]](#page-9-17) from neighboring markers' data. We further performed postprocessing to remove jitter, snapping, and contact noise through: (1) outlier removal via frame-wise distance thresholds and interpolation; (2) Gaussian filtering in low-motion regions to reduce noise while preserving dynamics; (3) correction of zero-pose vectors using linear interpolation; and (4) block-aware blending for smooth transitions, especially in hand and arm movements. We observed that these steps enhanced motion realism and continuity in partner interactions. + +We use SMPL-X [27] parametric model for motion representation due to its ability to represent different human body poses. Specifically, the SMPL-X parameters are defined by the body pose parameters $\theta \in \mathbb{R}^{N \times 55 \times 3}$ , shape parameters $\beta \in \mathbb{R}^{N \times 10}$ and the translation parameters $t \in \mathbb{R}^{N \times 3}$ , where N is the number of the frames. Each sample was converted to this SMPL-X format by training a parametric model based on the optimization algorithm described in Inter-X [38] where we also learn the shape parameters $\beta$ by minimizing the following objective function: + +$$E(\theta, t, \beta) = \lambda_1 \frac{1}{N} \sum_{j \in \mathcal{J}} \lambda_p ||\mathbf{J}_j(M(\theta, t, \beta)) - g_j||_2^2 + \lambda_2 ||\theta||_2^2$$ +(1) + +where M denotes the SMPL-X parameters, $\mathcal J$ denotes the joints set, $J_j$ is the joint regressor function for joint j, g is the skeleton captured key points and $\lambda_1, \lambda_2$ and $\lambda_p$ are different weights. + +![](_page_4_Figure_4.jpeg) + +Figure 4. Some example snapshots showing different spatial orientations and handholds captured in our fine-grained text annotations + +To collect detailed annotations, we developed a user-friendly web-based annotation tool that presented annotators with the rendered versions of the motions using ait-viewer[12]. The camera parameters were dynamically adjusted to continuously track the dancers, ensuring an optimal viewing perspective for annotators. Annotators were instructed to segment the videos into discrete clips that best preserved the semantic integrity of each interaction. The annotation tool enforced a maximum segment duration of 10 seconds, adhering to the same guidelines as the InterHuman dataset [20]. + + + +| Category | Type | Example Values | +|-------------------------|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Spatial
Relationship | Interaction Position | Open, Closed, Semi-open, Shadow, Side-by-
side, Promenade, Ballroom hold, Counter-
balance, Counter-weight | +| | Orientation | Facing each other, Back-to-back, Side-to-
side, Offset, Diagonal | +| | Connection Points | Hand-to-hand, Hand-to-waist, Hand-to-shoulder, Arm hook, Elbow hold, Waist hold, Shoulder hold, Leg wrap | +| Body
Movement | Movement Type
Body Parts | Lifting, Twisting, Rotating, Bending, Extend-
ing, Contracting, Isolating, Pushing, Pulling
Head, Neck, Shoulders, Arms, Elbows,
Hands, Waist, Hips, Knees, Legs, Feet, Toes | +| Rhythm | Energy
Tempo | Soft/gentle, Medium, Fluid, Sharp/intense,
Explosive, Staccato
Fast, Slow, Syncopated, Polyrhythmic, Con-
tinuous, Pulsed | + +Table 1. Examples of categories and attributes used to guide finegrained text annotations. Annotators referred to this structured list for consistency. + +To maintain annotation quality, we asked the same participant dancers to provide textual descriptions corresponding to their motion capture data. Annotators were provided with detailed guideline documents to ensure consistency in motion terminology. Each description was required to incorporate key vocabulary from three predefined categories: (1) Spatial Relationships, (2) Body Movements, and (3) Rhythm, as outlined in the guidelines. Some of the examples belonging to this set are outlined in Tab. 1. Fig. 4 shows some of the different spatial relationships captured in our dataset. Please refer to the supplementary materials for more details. + +To improve clarity and consistency, we used GPT-4o [11] to refine annotation grammar while preserving their original semantic meaning. The refined annotations were then reviewed by a second set of annotators with dancing expertise (specifically, the dance partners of the original annotators) to ensure accuracy and correctness. The final annotations average 41 words, longer than existing motion-text datasets, highlighting their fine-grained detail. This multi-stage process enhances linguistic quality and enriches the movement vocabulary to better represent each dance sequence. + +We compare MDD with existing human motion datasets, particularly those focusing on interactions and dance movements, as shown in Tab. 2. Our analysis shows that MDD stands out with these distinct features: (1) Multi-modal: Existing two-person interactive motion datasets are based on either integrating music and motion [17][31] or text and motion [20] [38]. To the best of our knowledge, MDD is the first dataset to comprehensively integrate all three modalities: motion, music, and text descriptions. It is capable to + + + +| Dataset | Interac | tive Music | Text | Genres | Subjects | Frames | Mocap | T | Descriptions | +|--------------------|--------------|--------------|--------------|--------|----------|--------|--------------|--------|--------------| +| DanceRevolution | | √ | | 3 | | 648K | | 12h | | +| [10] | | | | | | | | | | +| DanceNet [44] | | $\checkmark$ | | 2 | - | 207K | $\checkmark$ | 0.96h | - | +| AIST++ [14] | | $\checkmark$ | | 10 | 30 | - | | 5.2h | - | +| Motorica Dance [1] | | $\checkmark$ | | 8 | - | 1.3M | $\checkmark$ | 6.22h | - | +| FineDance [15] | | $\checkmark$ | | 22 | 27 | 10K | $\checkmark$ | 14.6h | - | +| PopDanceSet [22] | | $\checkmark$ | | 19 | 132 | 769K | | 3.56h | - | +| InterHuman [20] | √ | | √ | 13 | 89 | 1.7M | | 6.56h | 23,337 | +| Inter-X [38] | $\checkmark$ | | $\checkmark$ | - | - | 8.1M | $\checkmark$ | 18.75h | 34,164 | +| AIOZ-GDANCE | √ | √ | | 7 | 4000+ | 1.8M | | 16.7h | _ | +| [13] | | | | | | | | | | +| DD100 [31] | $\checkmark$ | $\checkmark$ | | 10 | 10 | 210K | $\checkmark$ | 1.95h | - | +| ReMoS [6] | $\checkmark$ | $\checkmark$ | | 2 | 9 | 275.7K | | 2.04h | - | +| InterDance [17] | $\checkmark$ | $\checkmark$ | | 15 | - | 1.7M | $\checkmark$ | 3.93h | - | +| MDD (Ours) | ✓ | ✓ | ✓ | 15 | 30 | 4.4M | ✓ | 10.34h | 10,187 | + +Table 2. Comparison between MDD and other related datasets. MDD is a large-scale two-person dance dataset that provides both music and text annotations. It can be seen that our dataset contains a high diversity of genres, total number of frames and comparable number of descriptions with other Interactive datasets + +support motion generation tasks conditioned for both music and text, specifically for duet dancing. (2) Largest Duet Dance Dataset: With 10.34 hours of high-quality motion capture data spanning across 15 different dance genres, MDD offers substantial scale being the largest duet dance dataset that is helpful for a good model generalization for every movement style. (3) Fine-grained annotations: While Intergen [20] contains only generic descriptions and Inter-X [38] contains detailed descriptions based on generic human body movement, our collected textual description annotations are meticulously designed to capture dance-specific movement using technical dance terminology, incorporating spatial relationships between dance partners, style-specific movement vocabulary and rhythmic elements. + +In this section, we first discuss our duet interaction representation, and then we introduce two novel tasks that leverage our MDD dataset: *Text-to-Duet* and *Text-to-Dance Accompaniment* + +A duet motion interaction $\mathbf{x} \in \mathcal{X} \times \mathcal{X}$ , is defined as a collection of the leader's motion $\mathbf{x_l} = \{x_l^i\}_{i=1}^N$ and the follower's motion $\mathbf{x_f} = \{x_f^i\}_{i=1}^N$ where all interactive pairs $x^j = (x_l^j, x_f^j) \in \mathbf{x}$ are naturally synchronized. Here, the motion space $\mathcal{X}$ is defined as $\mathcal{X} \subset \mathbb{R}^{N \times J \times 3}$ where N is the number of frames and J is the number of joints represented. + +We define the music space $\mathcal{M}$ as $\mathcal{M} \subset \mathbb{R}^{N \times d_m}$ where $d_m$ is the feature dimension of the music representation and the text descriptive space by $\mathcal{C}$ . For a given text description $c \in \mathcal{C}$ , music feature $m \in \mathcal{M}$ , our multi-modal duet interaction sample in our dataset is defined as $s = (c, m, \mathbf{x})$ . Here, $s \in \mathcal{S}$ where the sample joint space $\mathcal{S}$ defined as $\mathcal{S} = (\mathcal{C} \times \mathcal{M} \times \mathcal{X} \times \mathcal{X})$ + +Given a natural language description $c \in \mathcal{C}$ and music $m \in \mathcal{M}$ , the task for Text-conditioned Duet Dance Generation aims to generate plausible duet interactions $(\mathbf{x_l}, \mathbf{x_f})$ that are synchronous with music and semantically align with the text description c. Formally, the task is to learn a function $F: F(c,m) \mapsto \mathbf{x}$ . The task can be regarded as an extension of text-guided two-person motion generation [20] in the dance scenario. + +Given a natural language description $c \in \mathcal{C}$ , music $m \in \mathcal{M}$ and leader's motion $\mathbf{x_l} \in \mathcal{X}$ , the task for Text-conditioned Dance Accompaniment aims to generate the follower's motion $\mathbf{x_f} \in \mathcal{X}$ such that the duet interactions $(\mathbf{x_l}, \mathbf{x_f})$ are interactively coherent, synchronous with music and semantically align with the text description. Formally, the task is to learn a function $G: G(c, m, \mathbf{x_l}) \mapsto \mathbf{x_f}$ . This task can be viewed as an extension of human action-reaction synthesis [39] tailored to dance scenarios or, alternatively, as an extension of dance accompaniment [31] guided by text. diff --git a/2509.04866/main_diagram/main_diagram.drawio b/2509.04866/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..10d0eb2492f4cebb030b55eb52d485554d8cc2b2 --- /dev/null +++ b/2509.04866/main_diagram/main_diagram.drawio @@ -0,0 +1,619 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2509.04866/paper_text/intro_method.md b/2509.04866/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..eb36ea8c39149f6fa2954eea76007345b7f1455f --- /dev/null +++ b/2509.04866/paper_text/intro_method.md @@ -0,0 +1,96 @@ +# Introduction + +Large language models (LLMs) have achieved remarkable, human-like performance across diverse natural language processing (NLP) tasks [@brown2020language; @yan2024atomicfactdecompositionhelps]. However, significant gaps persist between their cognitive abilities and those of humans [@echterhoff2024cognitive; @lamprinidis2023large]. Consider the example in Figure [1](#fig:1){reference-type="ref" reference="fig:1"}, humans effortlessly understand a sentence like "*Film director Paxton presented a new movie concept to producer Helen and actor Blake*," identifying relationships such as *Paxton* (director), *Helen* (producer), and *Blake* (actor). In contrast, even when LLMs have memorized similar facts, they often fail to reason about such role relationships, revealing a notable gap between memorization and deeper relational understanding that motivates this study. + +
+ +
Illustration of the contrast between human and LLM text comprehension, highlighting humans’ ability to identify semantic roles and their arguments in a sentence, compared to LLMs’ reliance on surface-level memorization without scenario cognition.
+
+ +We observe that while LLMs can recall sequences of text, they often struggle to recognize specific roles within those sequences. To clarify this issue, we draw on two concepts from the Frame Semantics [@fillmore1976frame; @li2024comprehensive]: *semantic scenes* and *scenario elements*. A semantic scene refers to a mental structure formed through repeated real-world experiences, while scenario elements represent the participants that make up a scene. As illustrated in Figure [1](#fig:1){reference-type="ref" reference="fig:1"}(left), "*director*," "*producer*," and "*actor*" are **scenario elements**, with "*Paxton*," "*Helen*," and "*Blake*" as their corresponding **arguments**. We define the **scenario cognition** as the ability to accurately associate scenario elements with their arguments. This leads to our key research question: **Do LLMs have the ability of scenario cognition to reliably link scenario elements and their arguments?** + +A thorough investigation of this issue is crucial for understanding and evaluating the knowledge memorization mechanisms in LLMs. While previous studies have established that LLMs exhibit strong formal linguistic competence---generating fluent and grammatically correct text, their functional linguistic competence is still unclear and under debate [@mahowald2024dissociating]. We propose that generalized knowledge memorization in LLMs should be divided into two parts: a surface-level "data" memory and a deeper "knowledge" memory, corresponding to formal and functional linguistic competence, respectively. Formal linguistic competence mainly comes from statistical learning during training on large text corpora. Through patterns like word co-occurrence and context windows, the model learns the grammar and structure of any given language, enabling it to generate fluent text [@talmor2020olmpics]. This ability reflects "data" memory. By contrast, functional linguistic competence requires the model not only to parse surface text but also to understand deeper meanings [@suresh2023conceptual; @janik2023aspects], showing "knowledge" memory. + +Among studies on knowledge memorization [@lu2024scaling; @satvaty2024undesirable; @antoniades2024generalization; @chen2024multi], the "Reversal Curse" [@berglund2024the] is a well-known issue. It refers to LLMs' difficulty in generalizing learned knowledge in the reverse direction (e.g., from "$A \to B$" to "$B \to A$"). However, existing research has two key limitations: it mainly studies simple cases with only two entities, and frames the issue as a text sequence problem, which still focuses on "data" memory rather than "knowledge" memory. Therefore, testing whether LLMs can recognize scenario elements in more complex contexts offers a valuable way to explore their knowledge memory. + +To address this, we propose a new bi-perspective evaluation framework to assess LLMs' scenario cognition. We first create a dataset of fictional facts, each accompanied by multiple descriptions and labeled its scenario elements based on their semantics. Then we evaluate a range of open-source LLMs across various scales and families, analyzing their scenario cognition both from their outputs and through probing experiments that examine their internal vector representations [@alain2016understanding; @conneau2018you]. Our results show that current LLMs still rely mainly on surface-level memorization, rather than forming deeper semantic understanding of scenarios. This leads to generalization failures even in simple situations. + +In summary, the main contributions of this paper are as follows: + +1\. **First systematic evaluation of LLMs' scenario cognitive ability**: We present the first comprehensive assessment of LLMs' scenario cognitive abilities from a semantic perspective, aiming to determine whether they exhibit characteristics of "knowledge" memory rather than "data" memory. + +2\. **A bi-perspective evaluation framework with a novel scenario-based dataset**: We construct a new dataset[^2] of fictional facts annotated with scenario elements and use it to train and evaluate multiple open-source LLMs of varying scales from the perspective of model outputs. Furthermore, we design probing experiments to analyze scenario cognitive ability from the perspective of internal representations. + +3\. **Key findings on LLMs' limitations in scenario cognition**: Our extensive experiments demonstrate that current LLMs lack robust scenario cognition capabilities and discuss the potential connection between this deficiency and certain hallucinations. These findings underscore a fundamental gap in semantic understanding and provide important insights for guiding future improvements in LLM design and training. + +# Method + +To systematically evaluate LLMs' scene cognition capabilities, we propose a bi-perspective evaluation framework both from the perspective of model outputs and internal representations. An overall framework is illustrated in Figure [2](#fig:2){reference-type="ref" reference="fig:2"}. + +
+ +
Diagram of the bi-perspective evaluation framework for assessing LLMs’ scenario cognition, depicting the model output perspective (left) and the internal representation perspective (right).
+
+ +To assess the scenario cognition capabilities of LLMs from the perspective of model outputs, we construct a novel scenario-based dataset for both training and evaluation. As illustrated on the left side of Figure [2](#fig:2){reference-type="ref" reference="fig:2"}, our framework consists of four key stages: Atomic Knowledge Generation, Knowledge Description Expansion, Scenario Element Annotation, and Scenario Question Generation. + +We adopt a multi-model data generation strategy to construct a set of fictional, atomic textual facts which we term Atomic Knowledge, and apply semantic similarity filtering alongside multi-model voting validation to ensure diversity and quality. + +Specifically, we employ two powerful LLMs, which are *DeepSeek-V3* [@liu2024deepseek] and *Qwen2.5-Max* [@yang2024qwen2], as generation agents. The generation process is guided by prompt templates (shown in Appendix [8](#sec:appendix-templates){reference-type="ref" reference="sec:appendix-templates"}) that emphasize three key criteria: *Fictionality* --- ensuring the facts are entirely imaginary and without any real-world correspondence; *Role Richness* --- requiring each fact involves at least three distinct roles; *Conciseness* --- mandating each fact be expressed in a single sentence while remaining semantically complete. + +To ensure semantic diversity and eliminate redundancy, we apply a similarity-based filtering mechanism. We initialize an embedding set $\mathcal{I}$ and encode each candidate $x$ using the *BGE-M3* [@chen2024bge] encoder, normalizing its output as the semantic representation $v_x$: $$\begin{equation} +v_x = \frac{\text{Enc}(x)}{\|\text{Enc}(x)\|} +\end{equation}$$ For each $x$, we compute the L2 distance to obtain its nearest neighbor $v_y \in \mathcal{I}$: $$\begin{equation} +d(v_x, v_y) = \|v_x - v_y\|_2 +\end{equation}$$ Only samples satisfying $d(v_x, v_y) > 0.5$ are retained, and their embeddings are added to $\mathcal{I}$. + +For quality assurance, we employ a multi-model voting strategy, using three open-source models, *LLaMA3-8B* [@grattafiori2024llama], *Qwen2.5-7B* [@yang2024qwen2], and *Gemma2-6B* [@team2024gemma], as validators. Each sample must satisfy all generation criteria across all validators. We further perform manual inspection on randomly sampled validated entries, discarding those of low quality. This process yields a total of 500 high-quality Atomic Knowledge. + +To improve the learnability of Atomic Knowledge, we perform semantic expansion to construct a **Memory Set** comprising diverse yet semantically consistent knowledge descriptions, for use in both training and the evaluation of the memory ability. Similar to the previous stage, we adopt a multi-model generation and validation strategy, but place special emphasis on preserving the core semantics of the original knowledge during paraphrasing which means that variations are restricted to linguistic form and surface structure. After an additional manual filtering, we retain ten high-quality knowledge descriptions for each Atomic Knowledge, resulting in a total of 5,000 training samples, which is partly shown in Appendix [9](#sec:appendix-example){reference-type="ref" reference="sec:appendix-example"}. + +We further apply a first-verb-based segmentation strategy to prepare these samples for supervised fine-tuning (SFT): each description is split to two segments at the first verb phrase because verbs typically convey the core semantics of a sentence [@fillmore1967case; @jackendoff1972semantic]. Specifically, the preceding segment serves as the input prompt, and the following segment serves as the target output. For example, in the sentence *"Film director Paxton presented a new movie concept to producer Helen and actor Blake,"* the input is *"Film director Paxton presented"*, and the target output is *"a new movie concept to producer Helen and actor Blake."* + +To assess the scenario cognition ability of LLMs, different from traditional Frame Semantic Parsing methods [@10908675], we adopt a human--LLM collaborative annotation framework for labeling scenario elements within Atomic Knowledge. We employ *Qwen2.5-Max* as the annotator and design task-specific prompts to guide the identification of scenario elements. + +Taking *"Film director Paxton presented a new movie concept to producer Helen and actor Blake"* as an example, the model is expected to extract elements such as *"director"*, *"producer"*, and *"actor"*, along with their corresponding arguments *"Paxton"*, *"Helen"*, and *"Blake"*. Due to the complexity of this task, we do not rely on multi-model voting. Instead, we perform manual correction of low-quality annotations to ensure consistency and precision. + +Based on the annotated scenario elements, we further utilize *Qwen2.5-Max* to generate scenario-based questions. For each scenario element, we construct a corresponding prompt where the answer is the entity that fulfills the element. + +To ensure alignment with the SFT task format, we adopt a completion-style format rather than a traditional question--answer format. For instance, given the element *"director"*, we generate a prompt such as *"The director who presented a new movie concept to producer Helen and actor Blake is \_\_\_"* with *"Paxton"* as the expected answer. + +All generated samples undergo manual validation to guarantee data quality. In total, we constructed 1,581 scenario-based prompt--answer pairs to serve as the scenario-based **Understanding Set** for use in the evaluation of the ability of scenario cognition. Examples of them are provided in Appendix [9](#sec:appendix-example){reference-type="ref" reference="sec:appendix-example"}. + +::: table* +::: + +As shown on the right side of Figure [2](#fig:2){reference-type="ref" reference="fig:2"}, we designed a scenario-based probing method to examine whether the given model's internal vector representations capture the associations between scenario elements and arguments, evaluating its scenario cognition from an internal representation perspective. Specifically, given a text of length $n$, $X = \{x_1,x_2,\dots,x_n\}$, we input it into the LLM $f(\cdot)$ and extract the hidden states $\mathbf{H}$ for each token $x_i \in X$ across all layers: $$\begin{equation} +\mathbf{H} = \{ \mathbf{H}^{(1)}, \mathbf{H}^{(2)}, \dots, \mathbf{H}^{(l)} \} \in \mathbb{R}^{l \times n \times d} +\end{equation}$$ where $l$ is the number of Transformer layers, $d$ is the dimensionality of each vector, and $\mathbf{H}^{(k)} \in \mathbb{R}^{n \times d}$ represents the vectors at layer $k$. Based on the annotated scenario elements in the scenario-based dataset, we extract the representation vectors of the scenario elements: $$\begin{equation} +\mathbf{H}e = \{\mathbf{h}_{e_1}^{(k)}, \mathbf{h}_{e_2}^{(k)}, \dots, \mathbf{h}_{e_m}^{(k)}\} \in \mathbb{R}^{l \times m \times d} +\end{equation}$$ and their corresponding argument representation vectors: $$\begin{equation} +\mathbf{H}a = \{\mathbf{h}_{a_1}^{(k)}, \mathbf{h}_{a_2}^{(k)}, \dots, \mathbf{h}_{a_m}^{(k)}\} \in \mathbb{R}^{l \times m \times d} +\end{equation}$$ where $e_1, e_2, \dots, e_m$ are the token indices annotated as scenario elements, $a_1, a_2, \dots, a_m$ are the corresponding argument token indices, and $\mathbf{h}_{i}^{(k)}$ is the representation of token $i$ at layer $k$. + +To explore how scenario cognition is distributed across layers, we divide the Transformer layers of the given LLM into three levels: $\mathcal{L}_\text{Head} = \{1,2,3\}$, $\mathcal{L}_\text{Mid} = \left\{ \left\lfloor \frac{l}{2} \right\rfloor -1, \left\lfloor \frac{l}{2} \right\rfloor, \left\lfloor \frac{l}{2} \right\rfloor +1 \right\}$, and $\mathcal{L}_\text{Tail} = \{l-2, l-1, l\}$. We probe representations independently at each level. + +For each $\mathcal{L} \in \{\mathcal{L}_\text{Head}, \mathcal{L}_\text{Mid}, \mathcal{L}_\text{Tail}\}$, we pair each scenario element representation $\mathbf{h}_{e_i}^{\mathcal{L}}$ with its corresponding argument representation $\mathbf{h}_{a_j}^{\mathcal{L}}$ and concatenate them as the probe input: $$\begin{equation} +\mathbf{z}_{i,j}^{\mathcal{L}} = [\mathbf{h}_{e_i}^{\mathcal{L}}; \mathbf{h}_{a_j}^{\mathcal{L}}] \in \mathbb{R}^{2d} +\end{equation}$$ where $[\cdot;\cdot]$ denotes concatenation. + +We then build a linear probe[^3] on $\mathbf{z}_{i,j}^{\mathcal{L}}$ to predict whether $e_i$ and $a_j$ form a matching pair. The probe applies a linear transformation to produce a scalar output, followed by a sigmoid activation for binary classification. $$\begin{equation} +\hat{y}_{i,j}^{\mathcal{L}} = \sigma(\mathbf{w}^\top \mathbf{z}_{i,j}^{\mathcal{L}} + \mathbf{b}) +\end{equation}$$ where $\mathbf{w} \in \mathbb{R}^d$, $\mathbf{b} \in \mathbb{R}$, and $\sigma(\cdot)$ is the sigmoid function mapping the output to $[0, 1]$. + +During training, pairs with $i = j$ are labeled as positive, others as negative, forming a binary classification task optimized by cross-entropy: + +$$\begin{equation} +Loss = \text{CrossEntropy}(\hat{y}_{i,j}^{\mathcal{L}}, y_{i,j}) +\end{equation}$$ where $$\begin{equation} +y_{i,j} = + \begin{cases} + 1 & i = j \\ + 0 & i \neq j + \end{cases} +\end{equation}$$ + +Through this probing, we aim to determine whether the LLM's internal representations at different layers encode the relationships between scenario elements and their corresponding arguments. If the probe can accurately distinguish positive from negative samples, it indicates that the model has encoded some structural information about scenario elements and arguments in its vector representations, reflecting its scenario cognition capability. diff --git a/2510.20670/main_diagram/main_diagram.drawio b/2510.20670/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..62375752cdbc7d2393f0614cfd563efeaaf9b70a --- /dev/null +++ b/2510.20670/main_diagram/main_diagram.drawio @@ -0,0 +1,1026 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2510.20670/main_diagram/main_diagram.pdf b/2510.20670/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..205532ce1fa04e5c1f384caad4497c123236a015 Binary files /dev/null and b/2510.20670/main_diagram/main_diagram.pdf differ diff --git a/2510.20670/paper_text/intro_method.md b/2510.20670/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..012096c757335da206a77330edc4466e7bf9c549 --- /dev/null +++ b/2510.20670/paper_text/intro_method.md @@ -0,0 +1,88 @@ +# Introduction + +Mandarin Chinese is considered a high-resource language, abundant with pre-trained language model (PLM) support [@devlin-etal-2019-bert; @liu-etal-2020-multilingual-denoising; @conneau-etal-2020-unsupervised], corpora, and evaluation benchmarks [@xu-etal-2020-clue; @xiang-etal-2021-climp], and most recently commercial large language models [@bai2023qwen; @liu2024deepseek]. However, the same cannot be said about other variants of the Sinitic language family, including Cantonese. Mandarin is the official state language and the prestige language in media, business, and academia. Owing to this status, Cantonese, along with other Sinitic languages, remains a primarily vernacular language without written standardization [@snow2004cantonese; @Li_2006-lingua-franca]. + +Such lack of written standardization, prestige, and official status results in a shortage of language resources in Cantonese. It is frequently described as a low-resource language [e.g. @liu-2022-low; @xiang-etal-2022-cantonese; @jiang2025developing] despite having millions of speakers [@ethnologue2023]. As a result, Cantonese language processing systems often rely on datasets, models, and corpora adapted from Mandarin [@xiang-etal-2024-cantonese], despite a lack of mutual intelligibility between the two languages [@norman1988chinese; @tang2007mutual]. + +
+ +
An overview of the tasks in CantoNLU, and our framework for investigating when and how cross-lingual transfer learning from Mandarin is effective for natural language understanding in Cantonese.
+
+ +::: table* ++------------------+----------+----------+-------------------------------------------------------------+ +| Task | Requires | Size | Source | ++:=================+:=========+=========:+:============================================================+ +| **Sentence-level tasks** | | ++------------------+----------+----------+-------------------------------------------------------------+ +| Acceptability | L, F, M | 1.6k | MT error dataset [@liu2025siniticmterror] adapted | ++------------------+----------+----------+-------------------------------------------------------------+ +| Lang Detection | L, F | 47k | Parallel corpus [@dai-etal-2025-next] aligned and purturbed | ++------------------+----------+----------+-------------------------------------------------------------+ +| NLI | M | 570k | Machine-translated English NLI [@cheng2025yue-all-nli] | ++------------------+----------+----------+-------------------------------------------------------------+ +| Sentiment | M | 12k | Hong Kong restaurant reviews [@ZHANG2011OpenRice] | ++------------------+----------+----------+-------------------------------------------------------------+ +| **Word-level tasks** | | ++------------------+----------+----------+-------------------------------------------------------------+ +| WSD | L, M | 109 | Manual compilation | ++------------------+----------+----------+-------------------------------------------------------------+ +| POS, Dep parsing | F | 14k | Universal Dependencies dataset @wong-etal-2017-quantitative | ++------------------+----------+----------+-------------------------------------------------------------+ +::: + +Cross-lingual transfer from Mandarin for Cantonese language processing has proven effective, with empirical success across a range of tasks, including language modeling and reading comprehension [@jiang2025developing], translation [@liu-2022-low; @suen-etal-2024-leveraging], and speech recognition [@li2019cantonese; @luo2021crosslanguage]. However, despite empirical success, there remains a gap in formal investigations into the best practices for Cantonese natural language understanding (NLU). This is attributable to a lack of a centralized evaluation framework for Cantonese language processing or understanding. + +In this paper, we introduce [**CantoNLU**]{.smallcaps}, a GLUE-like [General Language Understanding Evaluation; @wang-etal-2018-glue] natural language understanding benchmark in Cantonese. [**CantoNLU**]{.smallcaps} provides an in-depth evaluation of syntax, lexicon and semantic understanding, comprising 7 tasks: word sense disambiguation (WSD), linguistic acceptability judgment (LAJ), language detection (LD), natural language inference (NLI), sentiment analysis (SA), part-of-speech tagging (POS), and dependency parsing (DEPS). In particular, the WSD dataset is entirely novel, providing the first resource for sense-level lexical understanding in Cantonese. LAJ and LD represent novel adaptations of existing datasets, while NLI, SA, POS, and DEPS datasets are direct adoptions of existing datasets [@cheng2025yue-all-nli; @ZHANG2011OpenRice; @wong-etal-2017-quantitative respectively]. We describe each task and underlying datasets in Section [4](#sec:benchmark){reference-type="ref" reference="sec:benchmark"}, whose overview is outlined in Table [\[tab:stats\]](#tab:stats){reference-type="ref" reference="tab:stats"}. + +Using [CantoNLU]{.smallcaps}, we evaluate three types of models -- a Mandarin model[^1] without explicit training on Cantonese, Cantonese-adapted transfer models with additional Cantonese training on a Mandarin model, and a monolingual Cantonese model, as outlined in Section [5](#sec:exp){reference-type="ref" reference="sec:exp"}. In Section [6](#sec:results){reference-type="ref" reference="sec:results"}, we discuss how monolingual Cantonese, Cantonese-adapted, and Mandarin models compare across various aspects in Cantonese NLU. + +In spite of limited training data in Cantonese, we demonstrate that our monolingual Cantonese model excels in syntactic tasks such as POS and DEPS. On the other hand, Cantonese-adapted models excel in semantic tasks such as NLI, LD, WSD, and SA. Simultaneously, mandarin models offer a competitive alternative to additional or monolingual training on Cantonese data, excelling in NLI and LAJ. + +Based on our results, we recommend monolingual Cantonese models for syntactic tasks, and Cantonese-adapted models for semantic tasks. In domains where Cantonese corpora are scarce, Mandarin models without Cantonese adaptation may be sufficient. In addition to the benchmark and analysis, we publicly release our code and model weights at [github.com/aatlantise/sinitic-nlu](github.com/aatlantise/sinitic-nlu){.uri}. + +Cantonese, also known as Yue [@ethnologue2023], is the second most widely used Sinitic language after Mandarin, spoken by an estimated 85 million people worldwide. However, it remains primarily a spoken language [@norman1988chinese], with most speakers defaulting to written Standard Chinese [@xiang-etal-2024-cantonese]. This diglossic situation and its short history as a written language [@snow2004cantonese] has contributed to the scarcity of high-quality textual resources for Cantonese NLP, in stark contrast to Mandarin Chinese, a related language rich in resource across corpora, models [@devlin-etal-2019-bert; @liu-etal-2020-multilingual-denoising; @conneau-etal-2020-unsupervised; @bai2023qwen; @liu2024deepseek], and evaluation resources [e.g. @xu-etal-2020-clue; @xiang-etal-2021-climp]. Thus, as highlighted in Section [1](#sec:intro){reference-type="ref" reference="sec:intro"}, prior work have used varying degrees of transfer from models with Mandarin knowledge to perform downstream tasks in Cantonese [@li2019cantonese; @cheng2024bertbasecantonese; @jiang2025developing]. + +Transfer learning is a common strategy in deep learning (DL) to address data sparsity, where features or signal learned from a resource-rich domain is used to augment a low-resource domain, task, or dataset [@pmlr-v27-bengio12a]. In the context of NLP, a common application of transfer learning is in cross-lingual transfer for low-resource languages [@ruder-etal-2019-transfer; @conneau-etal-2020-unsupervised]. Two types of cross-lingual transfer exists: model transfer and data transfer, as described in @garcia-ferrero-etal-2022-model. Data transfer involves translating a dataset or corpus from a high-resource language to a lesser-resourced target language, as performed in `yue-all-nli` [@cheng2025yue-all-nli], the MMLU [@hendrycks2021measuring] portion of the HKCanto-Eval benchmark [@cheng-etal-2025-hkcanto], and Yue benchmarks from @jiang-etal-2025-well. + +On the other hand, model transfer involves adapting models trained on a high-resource source language to a lesser-resourced target language, such as Mandarin to Cantonese and Danish to Faroese [@snaebjarnarson-etal-2023-transfer]. Model transfer relies on the lexical or typological similarity between the two languages, thereby enabling the transfer of linguistic knowledge [@khan-etal-2025-uriel]. While some implementations of cross-lingual transfer explicitly designate a source language for cross-lingual transfer [@snaebjarnarson-etal-2023-transfer; @thangaraj2024crosslingualtransfermultilingualmodels], others rely on multilingual models. Rather than transferring from a specific source language, such models are thought to capture general cross-linguistic patterns that extend beyond typological or lexical similarity, allowing them to perform reasonably well even on unseen or low-resource languages [@conneau-etal-2020-unsupervised; @scivetti-etal-2025-multilingual]. Hybrid approaches combine both paradigms: they begin with a multilingual model, then continue pre-training or fine-tuning on a specific target language before applying the resulting model to downstream tasks [@protasov-etal-2024-super; @manafi-krishnaswamy-2024-cross]. + +Due to the richness of Mandarin language resources and the strong performance of Mandarin PLMs and LLMs, most cross-lingual transfer to Cantonese use Mandarin as the source language [@li2019cantonese; @liu-2022-low; @luo2021crosslanguage; @suen-etal-2024-leveraging; @cheng2024bertbasecantonese], with exceptions using a multilingual model [e.g. @jiang2025developing]. + +However, there is emerging work suggesting limitations in models' ability to capture Cantonese idiosyncrasies. Factors previously identified include substantial dissimilarities in lexicon, syntax and writing systems [@suen-etal-2024-leveraging], along with the prevalence of colloquial phrases and code-switching in more recent Cantonese corpora [@jiang-etal-2025-well]. These issues hinder the ability of Mandarin-trained models in Cantonese [@xiang-etal-2024-cantonese] due to over-reliance on Mandarin linguistic knowledge [@cheng-etal-2025-hkcanto]. + +Cantonese diverges from Mandarin in word order, particle and grammatical word inventory, and morphology, as highlighted in theoretical linguistics work [@matthews2006serial; @yap2011asymmetry; @matthews2013cantonese] as well as prior work in Cantonese-Mandarin machine translation and corpus linguistics [@zhang-1998-dialect; @lam-2020-forms; @liu2025siniticmterror]. For example, in double object constructions, Mandarin takes the direct-indirect order as seen in (1), while Cantonese takes the indirect-direct order as seen in (2) [@matthews2013cantonese]. + +gei3 ni3 qian2 // give you money // '(I) give you money' // + +bei2 cin2 nei5 // give money you // '(I) give you money' // + +Cantonese also features a substantially larger and more diverse inventory of particles and aspect markers than Mandarin, enabling speakers to encode subtle distinctions of tense, stance, and speaker attitude [@yap2011asymmetry; @matthews2013cantonese]. Cantonese morphology is more flexible, with more frequent verb serialization [@matthews2006serial] and reduplication [@LEE_2020_reduplication; @lam-2020-forms] than in Mandarin. This allows sequences such as (3), sourced from @omelia1965first, where three verbs combine to describe one scene and (4), where reduplicating the classifier (counting noun) yields an *every* quantifier. Given such unique properties of Cantonese grammar, availability of high-quality Cantonese data is critical to performing well on Cantonese linguistic tasks. + +keoi5 jap6 heoi3 co5 // 3SG enter come sit // 'He went in and sat down' // + +zek3 zek3 gau2 // CL CL dog // 'every dog' // + +# Method + +We introduce a benchmark of seven Cantonese NLU tasks, encompassing word sense disambiguation (WSD), linguistic acceptability judgment (LAJ), language detection (LD), natural language inference (NLI), sentiment analysis (SA), part-of-speech tagging (POS), and dependency parsing (DEPS). While all tasks require Cantonese proficiency, tasks such as LD may reward Mandarin knowledge, whereas tasks such as DEPS and WSD may penalize Mandarin knowledge. WSD, LAJ, and LD datasets are novel contributions to Cantonese NLU. + +First, we manually compile Cantonese words with more than one attested meaning to create the first Cantonese word sense disambiguation dataset. We collect the words' multiples senses and two example sentences for each sense, resulting in 41 multi-sense words with a total of 109 senses. For each sense, there are least 2 example sentences containing the word. The dataset does not require fine-tuning for evaluation---model predictions are obtained by masking the target word in each sentence and comparing cosine similarities between the hidden representations at the mask position. For each target word $w$ with two contexts $s_i$ and $s_j$, we obtain hidden representations for each at the masked position from the model $\mathbf{h}_i$ and $\mathbf{h}_j$. + +Using cosine similarity, we define same-sense score $s_{\text{same}}$ and different-sense score $s_{\text{diff}}$ as : $$s_{same} = +\frac{1}{|P_{\text{same}}|} +\sum_{(i,j) \in P_{\text{same}}} +\text{cos}(\mathbf{h}_i, \mathbf{h}_j),$$ $$s_{\text{diff}} = +\frac{1}{|P_{\text{diff}}|} +\sum_{(i,j) \in P_{\text{diff}}} +\text{cos}(\mathbf{h}_i, \mathbf{h}_j),$$ where $P_{\text{same}}$ and $P_{\text{diff}}$ are the sets of sentence pairs containing the same and different senses of $w$, respectively. A model prediction is considered correct if $s_{\text{same}} > s_{\text{diff}}$. + +LAJ is a classification task, where the model predicts whether the given sequence is linguistically acceptable. This often aligns with grammatical judgment acceptability, but may also include judgment on semantic plausibility or pragmatic felicity. We compile the first Cantonese LAJ dataset by adapting the Cantonese portion of [SiniticMTError]{.smallcaps} [@liu2025siniticmterror], a dataset of Sinitic translation error span annotations, where each datapoint consists of a well-formed reference sentence `ref`, a machine translated sentence `mt`, and annotations of errors in the `mt` sentence. We consider error-free `ref` as acceptable, `mt` with error annotations not acceptable, to create pairs with one acceptable and one not acceptable versions of the same sentence. Unlike previous LAJ datasets such as CoLA [@wardtstadt2019cola], which asks for a binary acceptable-not acceptable judgment, we implement a more robust setup of providing two versions of the same sentence and asking for a more acceptable version as is preferred in psycholinguistics and cognitive science [@mahowald2016snap; @linzen2018reliability]. The dataset consists of 1.6k pairs. + +Language Detection (LD) is a three-label classification task that identifies whether a given sentence is written in Cantonese, Mandarin, or mixed. We construct the novel dataset from the parallel translation corpus of @dai-etal-2025-next, selecting the first 10,000 sentence pairs. To create mixed-language examples, we randomly replace tokens in Cantonese and Mandarin sentences with their counterparts from the other language with a set probability. In a given mixed sentence, 15%, 33%, 50% of the sentence may be from the other language, thus producing up to 6 mixed sentences for each pair. Word-level alignments are obtained using SimAlign [@jalili-sabet-etal-2020-simalign], and all text is converted to traditional orthography using HanziConv [@hanziconv] to prevent script-based shallow heuristics. The resulting dataset contains 47,578 sentences, of which 27,578 are mixed. We reserve 5% each for the validation and testing splits. We note that this task requires proficiency in both Mandarin and Cantonese to perform well. + +NLI is a classification task where the model is asked to predict whether the premise entails, or implies the truth of, the hypothesis. The NLI portion of our benchmark is the `yue-nli-all` dataset [@cheng2025yue-all-nli], which is a machine translation of English NLI datasets SNLI [@bowman-etal-2015-large] and MNLI [@williams-etal-2018-broad] into Cantonese. The dataset comprises of 557k train examples, 6.6k development examples, and 6.6k test examples. Each example includes a reference premise and two hypotheses, one entailing and one contradicting, from which we create two examples with each label. This results in a balanced, two-label classification dataset. + +Sentiment analysis is a sentence-level classification task of predicting the sentiment of a given sentence. We use the OpenRice dataset [@ZHANG2011OpenRice] compiled from restaurant reviews in Hong Kong, with the 3-way label space of positive, neutral, and negative. The dataset is balanced, with 10k datapoints in its train split, 1k in its development split, and 1k in its test split. + +Finally, POS and DEPS are both token-level classification tasks. A POS model predicts the part-of-speech tag (e.g. noun, verb, etc.) of a given word, while a DEPS model predicts the dependency head (answering, which word is the syntactic head of this word?) and dependency type (answering, what is the relationship between this word and its head?) of the given word. For POS and DEPS, we use the Cantonese-HK Universal Dependency dataset [@wong-etal-2017-quantitative], which comprises of 1k sentences and 14k tokens. We split the dataset 9:1 for training and testing, respectively. The dataset contains 15 POS tags and 48 dependency relations, of which 17 are specific to Cantonese. We report both unlabeled (UAS) and labeled attachment scores (LAS) to measure DEPS performance. diff --git a/2511.22853/main_diagram/main_diagram.drawio b/2511.22853/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..8ef097ac97fbdab168c464539ffc012f2583fdd5 --- /dev/null +++ b/2511.22853/main_diagram/main_diagram.drawio @@ -0,0 +1,562 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2511.22853/main_diagram/main_diagram.pdf b/2511.22853/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c0865b013457640a7fefc69c83560adafffd195e Binary files /dev/null and b/2511.22853/main_diagram/main_diagram.pdf differ diff --git a/2511.22853/paper_text/intro_method.md b/2511.22853/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..a5a34c9d357fe86d0fd402a45e2cb1a46c2b9992 --- /dev/null +++ b/2511.22853/paper_text/intro_method.md @@ -0,0 +1,153 @@ +# Introduction + +In various application fields such as transportation planning[1], healthcare[2] and inventory management[3], long-term time series forecasting (LTSF) and uncertainty quantification are of vital importance. The former provides a solid basis for long-term decision-making by offering reliable prediction, while the latter delivers more comprehensive estimates, enabling robust decisionmaking through accounting for potential variability. Recently, deep learning has achieved remarkable success in these fields through capturing complex time series patterns[4–9] and generating reliable uncertainty estimates[10–13]. + +In the realm of long-term forecasting, various strategies have been proposed to enhance predictive performance. Most efforts have focused on Transformer-based models due to their proven capability in capturing long-term dependencies via attention mechanisms[6]. Numerous studies aim to reduce the computational complexity of attention[6–8] and improve information extraction, while others apply vanilla Transformers to mine intra-channel or inter-channel relationships[4, 5]. However, some studies[14, 15] argue that sophisticated designs might not be necessary and suggest simple models could deliver results comparable to Transformer-based models. This has spurred exploration into Linear-based or MLP-based methods that aim at more efficient utilization of historical information during training to improve prediction performance[9, 16, 17]. + +Current LTSF methods are predominantly deterministic, producing point estimates without systematically addressing uncertainty quantification. This oversight poses substantial risks to operational robustness, particularly given the intrinsic positive correlation between uncertainty magnitude and forecast horizon length. To achieve probabilistic forecasting, earlier models like DeepAR[12] employ recurrent neural networks (RNNs) to estimate parameters of prespecified distributions (e.g., Gaussian) for each timestep. However, such methods impose restrictive parametric assumptions that may not capture complex temporal dynamics. Subsequent advancements have explored generative frameworks, including variational autoencoders (VAEs)[18] and diffusion models[10, 13], building upon their demonstrated success in adjacent domains[19–21]. While diffusion models achieve state-of-the-art sample quality, their iterative denoising process incurs substantial computational overhead during inference. Recent attempts to accelerate generation[11, 13, 22] remain fundamentally constrained by the multi-step generation paradigm. By contrast, conventional VAE[23] enables one-step generation by decoding samples from the prior latent space. However, many existing hybrid methods[24–26] introduce complex recurrent structures to capture temporal dependencies and generate predictions autoregressively, which undermine this computational advantage. Due to computational constraints, these generative models have primarily been developed and evaluated in short-term settings, raising concerns about their long-term efficacy and accuracy. Recently, Zhang et al.[27] systematically compared deterministic and probabilistic methods for long-term forecasting, whose results still highlight the need for models that can effectively address both point and probabilistic forecasting across diverse horizons. + +Motivated by these considerations, we introduce TARFVAE, a novel VAE-based framework that strategically integrates two core objectives: (1) preserving the one-step generation capability of VAEs to ensure computationally efficient inference, and (2) promoting spontaneous learning of latent variables that guarantee robust probabilistic forecasting performance. TARFVAE achieves these by simply incorporating a Transformer-based autoregressive flow (TARFLOW)[28], which enables an enhanced posterior estimation. Note that the autoregressive inverse process of TARFLOW is not included so TARFVAE can perform one-step generation. Extensive experiments on eight real-world datasets demonstrate the superiority of TARFVAE over the existing state-of-the-art deterministic and generative baselines. + +# Method + +A VAE[23] is an unsupervised generative model that models the input data distribution by learning a probabilistic latent space as follows: + +$$p(x) = \int p(x,z)dz = \int p(x|z)p(z)dz = \int p(x|z)\frac{p(z)}{p(z|x)}p(z|x)dz = \mathbb{E}_{z \sim p(z|x)}[p(x|z)\frac{p(z)}{p(z|x)}]$$ +(1) + +where x is the input data and z is its latent representations. The prior p(z) is normally defined as a multivariate Gaussian distribution N(0, I). The posterior p(z|x) can be an arbitrary distribution and VAE approximates it as q(z|x) = N(µ(x), σ2 (x)). Thus VAE learns the problem by maximizing the log-likelihood of (1) as + +$$\log p(x) = \log \mathbb{E}_{z \sim q(z|x)} \left[ p(x|z) \frac{p(z)}{q(z|x)} \right]$$ + (2) + +$$\geq \mathbb{E}_{z \sim q(z|x)} \log \left[ p(x|z) \frac{p(z)}{q(z|x)} \right] \tag{3}$$ + +$$= \mathbb{E}_{z \sim q(z|x)} \left[ \log p(x|z) - \log \frac{q(z|x)}{p(z)} \right] \tag{4}$$ + +$$= \mathbb{E}_{z \sim q(z|x)} \left[ \log p(x|z) \right] - KL(q(z|x)||p(z)) \tag{5}$$ + +where (3) is the evidence lower bound (ELBO) of (2) accoring to Jensen's inequality. The first term in (5) leads to reconstruct accurate x from z, and the second term minimizes the difference between the prior and approximated posterior to prompt the latent space to capture meaningful data representations. This enables VAE to generate new samples via z ∼ p(z), x ∼ p(x|z). + +VAE can be extended to conditional VAE by incorporating supervised labels y. The ELBO for log p(x|y) can be analogously derived as follows: + +$$\log p(x|y) \ge \mathbb{E}_{z \sim q(z|x,y)} \left[ \log p(x|z,y) - \log \frac{q(z|x,y)}{p(z|y)} \right]$$ + (6) + +$$= \mathbb{E}_{z \sim q(z|x,y)} \left[ \log p(x|z,y) \right] - KL(q(z|x,y)||p(z|y)) \tag{7}$$ + +where the prior, approximated posterior and reconstruction process are all conditioned on y. + +Normalizing flows[33, 37] are invertible mappings $f: \mathcal{X} \to \mathcal{Z}$ from $\mathbb{R}^D$ to $\mathbb{R}^D$ . For $x \sim \mathcal{X}$ and $z \sim \mathcal{Z}$ , the density $p_{\mathcal{X}}(x)$ can be expressed by + +$$p_{\mathcal{X}}(x) = p_{\mathcal{Z}}(f(x)) \left| \det(\frac{\partial f(x)}{\partial x}) \right|.$$ + (8) + +Normalizing flows have the property that the inverse $x=f^{-1}(z)$ is easy to evaluate and computing the Jacobian determinant takes O(D) time. Therefore, once the model is trained, a generative model is automatically obtained via $z\sim\mathcal{Z},\,x=f^{-1}(z)$ . Real NVP[38] designs an affine coupling layer to meet the above two conditions: + +$$\begin{cases} z_1 = x_1 \\ z_2 = (x_2 - t(x_1)) \odot \exp(s(x_1)) \end{cases}$$ + (9) + +where x can be randomly shuffled into two parts $[x_1, x_2]$ , and $s(x_1)$ and $t(x_1)$ are learnable functions whose outputs match the shape of $x_2$ . The inverse of (9) is obvious and the Jacobian matrix of (9) is a lower triangular matrix. The determinant of the Jacobian is the product of the diagonal elements, so the log-determinant is + +$$\log|\det\frac{\partial z}{\partial x}| = \sum_{i} s_i(x_1). \tag{10}$$ + +To achieve stronger nonlinearity, multiple coupling layers are composed together, creating a chain of mappings: $\mathcal{X} = \mathcal{Z}_0 \to \mathcal{Z}_1 \to \mathcal{Z}_2 \to ... \to \mathcal{Z}_K = \mathcal{Z}$ . The maximum likelihood estimation objective can then be written as + +$$\log p_{\mathcal{X}}(x) = \log p_{\mathcal{Z}}(z) + \sum_{k=1}^{K} \log |\det \frac{\partial z_k}{\partial z_{k-1}}|.$$ + (11) + +Recently, Zhai et al. proposed TARFLOW[28] which could achieve more efficient nonlinear mappings. TARFLOW partitions x into more parts as $[x_1, x_2, ..., x_n]$ and performs transformation following a similar rule: + +$$\begin{cases} z_1 = x_1 \\ z_j = (x_j - t^j(x_{< j})) \odot \exp(s^j(x_{< j})) \end{cases}$$ + (12) + +where j>1 and $x_{< j}=[x_1,x_2,...,x_{j-1}]$ . The $s(x_{< j})$ and $t(x_{< j})$ are efficiently implemented by causal Transformer. Notably, the inverse of (12) becomes + +$$\begin{cases} x_1 = z_1 \\ x_j = z_j \odot \exp(-s^j(x_{< j})) + t^j(x_{< j}) \end{cases}$$ + (13) + +where the inference of $x_j$ relies on $x_{\leq j}$ , making the inverse process autoregressive. + +Time series forecasting uses historical time series $x \in \mathbb{R}^{C \times L}$ to predict the future values $y \in \mathbb{R}^{C \times H}$ . Here, C represents the number of variables or channels, L is the length of the lookback window, and H stands for the forecast horizon. C > 1 corresponds to a multivariate time series; otherwise, it is termed univariate. + +Deterministic methods perform point estimation via a function f as $\hat{y} = f(x)$ , whereas probabilistic methods model a distribution $\hat{p}(y|x)$ to approximate the true p(y|x). + +The architecture of TARFVAE, as illustrated in **Figure 1**, builds upon a conditional VAE where input history x conditions the generation of target series y. The Gaussian posterior assumption in Section + +3.1 fundamentally limits reconstruction accuracy in conventional VAEs, as Gaussian distributions constitute a tiny subset of all possible posterior distributions. To overcome this expressiveness limitation, we introduce TARFLOW to refine an initial Gaussian posterior q(z0|x, y) into a flexible complex distribution that closely matches the true posterior. Specifically, both x and y are utilized for training, whereas only x is used for inference. + +Instance normalization. Following many state-of-the-art models[4, 5, 17, 39], we first apply instance normalization[40] to remove the local statistics of the input history to stabilize the base prediction, and then restore them to the model prediction. Notably, during training, both x and y are normalized based on x's mean and variance without using y's statistics. This ensures x's normalization remains consistent between training and inference, regardless of y's presence. + +Series embedding. Then we perform series embedding[5, 17] on the needed time series for the encoder, decoder, prior and flow modules independently. Each module employs a dedicated embedding layer to extract module-specific temporal patterns. This architectural isolation prevents cross-module interference while enabling targeted feature learning. + +One-step generation. The prior and encoder modules parameterize two Gaussians, the prior p(z|x) and initial posterior q(z0|x, y), respectively. During training, the flow module transforms z0 ∼ q(z0|x, y) to approximate z from the true posterior, while inference directly samples z ∼ p(z|x). Finally, the decoder maps z to yˆ conditioned on x, enabling efficient one-step full-horizon generation. + +The training and inference process of TARFVAE can be formulated as follows: + +$$\mu_{prior}, \log \sigma_{prior}^2 = Prior(Emb_1(x))$$ + (14) + +$$\mu_{z_0}, \log \sigma_{z_0}^2 = Encoder(Emb_2([x, y])) \tag{15}$$ + +$$z_0 \sim N(\mu_{z_0}, \sigma_{z_0}^2) \tag{16}$$ + +$$z = TARFLOW(z_0, Emb_3([x, y]))$$ +(17) + +$$\hat{y} = Decoder(z, Emb_4(x)) \tag{18}$$ + +$$\mu_{prior}, \log \sigma_{prior}^2 = Prior(Emb_1(x))$$ + (19) + +$$z_{prior} \sim N(\mu_{prior}, \sigma_{prior}^2)$$ + (20) + +$$\hat{y} = Decoder(z_{prior}, Emb_4(x)) \tag{21}$$ + +Below, we describe the TARFVAE implementation used in our work, while noting that alternative approaches could be adopted. + +![](_page_4_Figure_16.jpeg) + +Figure 1: The overview of TARFVAE. + +Although many Transformer-based architectures such as PatchTST[4], iTranformer[5] and DUET[39] have demonstrated that self-attention can be effective for long-term forecasting, recent studies[14, 15] argue that sophisticated designs might not be necessary and suggest simple models like feedforward neural networks could suffice for the job. To thoroughly test the TARFVAE framework's effectiveness and avoid confounding effects from elaborately designed temporal processing modules, we implement simple MLP blocks as shown in **Figure 2** for basic input mixing, where the channel-wise mappings enable the capture of inter-channel dependencies. These MLP blocks form the architectural foundation across our encoder, decoder, and prior module. + +![](_page_5_Picture_2.jpeg) + +Figure 2: The MLP blocks. + +In the prior module and encoder, following multiple MLP blocks, linear layers output the estimated mean and variance for the Gaussian prior p(z|x) and initial posterior $q(z_0|x,y)$ respectively as follows: + +$$\mu_{prior}, \log \sigma_{prior}^2 = Linear_{prior}(MLPBlocks_{prior}(Emb_1(x)))$$ + (22) + +$$\mu_{z_0}, \log \sigma_{z_0}^2 = Linear_{enc}(MLPBlocks_{enc}(Emb_2([x, y])))$$ + (23) + +The decoder aims to reconstruct y using z conditioned on x. We chose to augment z via connecting a full attention output where z queries out relevant information from x. Subsequently, MLP blocks process this mixed information, and a linear projection is used to achieve the final reconstruction of y. The process is outlined as follows: + +$$h_{mixed} = z + FullAttention(z, Emb_4(x), Emb_4(x))$$ +(24) + +$$\hat{y} = Linear_{dec}(MLPBlocks_{dec}(h_{mixed})) \tag{25}$$ + +In the flow module, the input $z_0$ and all intermediate latent variables $\{z_k\}(k=1,2,...,K)$ maintain consistent dimensionality $\mathbb{R}^{C\times D}$ , where D represents the latent dimension. These variables are naturally partitioned by channels, and we perform the transformation (12) conditioned on x,y in each TARFLOW block as follows: + +$$\begin{cases} +z_1 = x_1 \\ +z_j = (x_j - t^j((x + Emb_3([x, y]))_{< j})) \odot \exp(s^j((x + Emb_3([x, y]))_{< j})) +\end{cases}$$ +(26) + +Following the original implementation [28], we adopt a dimension-reversing permutation between adjacent blocks. This module finally outputs $z_K \approx z$ . + +Combining (10), (11) and (6), the training loss for a single sample generation is derived as + +$$L = -\mathbb{E}_{z \sim q(z|x,y)} \left[ \log p(y|z,x) - \log q(z_0|x,y) + \sum_{k=1}^{K} \log |\det \frac{\partial z_k}{\partial z_{k-1}}| + \log p(z|x) \right]$$ +(27) + +$$= \frac{D}{2}\ln(2\pi) + \frac{1}{2}\sum_{d=1}^{D}\left(\ln\sigma_{y,d}^{2} - \ln\sigma_{z_{0},d}^{2} + \ln\sigma_{prior,d}^{2}\right) - \sum_{k=1}^{K}\sum_{d=1}^{D}s_{k}^{d} + \frac{1}{2}\left|\left|\frac{y - \mu_{y}}{\sigma_{y}}\right|\right|^{2} - \frac{1}{2}\left|\left|\frac{z_{0} - \mu_{z_{0}}}{\sigma_{z_{0}}}\right|\right|^{2} + \frac{1}{2}\left|\left|\frac{z - \mu_{prior}}{\sigma_{prior}}\right|\right|^{2}$$ + +$$(28)$$ + +where p(y|z, x) is chosen to be Guassian for continuous predictions and its mean µy and variance σ 2 y are implemented as yˆ and I in our work. diff --git a/2512.14926/main_diagram/main_diagram.drawio b/2512.14926/main_diagram/main_diagram.drawio new file mode 100644 index 0000000000000000000000000000000000000000..41d7479db608fd487e6bc3566ddacf4f3e8948a1 --- /dev/null +++ b/2512.14926/main_diagram/main_diagram.drawio @@ -0,0 +1,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/2512.14926/main_diagram/main_diagram.pdf b/2512.14926/main_diagram/main_diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d66e3ee3b7b6bacf97408d9d655305539def7e92 Binary files /dev/null and b/2512.14926/main_diagram/main_diagram.pdf differ diff --git a/2512.14926/paper_text/intro_method.md b/2512.14926/paper_text/intro_method.md new file mode 100644 index 0000000000000000000000000000000000000000..df6d23495c65111df198d959774f3a36a7938747 --- /dev/null +++ b/2512.14926/paper_text/intro_method.md @@ -0,0 +1,137 @@ +# Introduction + +Recent advances in vision–language modeling have enabled substantial progress on a variety of multimodal tasks such as image captioning and visual question answering (VQA). However, the vast majority of these models and benchmarks remain centered on English or other high-resource languages, limiting their applicability in low-resource settings. In particular, Romanian lacks both large-scale multimodal corpora and task-specific instruction-tuned models, which hinders progress on downstream applications that require joint visual and linguistic understanding. + +Prior work has begun to adapt MLLMs to under-represented languages through dataset translation, parameter-efficient fine-tuning and synthetic instruction generation. LLaVA-NDiNO for Italian [\[Musacchio et al., 2024\]](#page-14-0), LaVy for Vietnamese [\[Tran and Thanh, 2024\]](#page-14-1), and X-LLaVA for Korean and Chinese [\[Shin et al., 2024a\]](#page-14-2) are only a few of the works that report gains by translating widely used datasets from the English language and by leveraging LLM generated instructions. To our knowledge, no previous work has conducted a systematic investigation of multimodal instruction tuning for Romanian. + +In this report we take a first step towards closing this gap. We construct Flickr30k-Ro, a translation of the English Flickr30k captions into Romanian, and extend it with automatically generated question–answer (QA) pairs to form Flickr30k-RoQA. The resulting corpus provides almost 160 000 Romanian captions and 32 000 aligned visual QA pairs. Each caption was translated and proofread by native speakers, ensuring grammatical fidelity and stylistic consistency. The question-answer pairs are produced by a 70-billion-parameter LLaMA-3 model conditioned solely on textual descriptions, thus avoiding image hallucination. + +To establish strong baselines, we perform parameter-efficient adaptation of three state-of-the-art vision–language architectures: LLaMA 3.2–11B Vision, Qwen2–VL–7B–Instruct, and LLaVA-v1.6-Mistral–7B. We fine-tune only the parameters in the low-rank adaptation (LoRA) modules on the Romanian VQA corpus, preserving the pretrained vision encoders and significantly reducing compute requirements. Our experiments demonstrate large gains in both VQA accuracy and image captioning quality after adaptation, with Qwen2–VL–7B–Instruct achieving the best overall performance. We further quantify improvements in grammatical fluency using an automatic LLM-as-judge protocol, showing that Romanian instruction tuning reduces surface-level. + +Our top-performing model, Qwen2-VL-RoQA, obtains 80.2% BERTScore F1 and 44.3% ROUGE-L on Romanian VQA, surpassing its original counterpart by 6% and 14%, respectively. These results confirm that a predominantly English model can benefit from a relatively small amount of Romanian supervision. On image captioning, the same adapter generalizes without additional training, returning up to a +2.61% increase in BERTScore F1 on the Flickr30k Romanian test split. Finally, the grammatical error study proves also a reduction in word error rate. + +Our main contributions are: + +- We release the first high-quality, human-verified Romanian caption dataset and the first Romanian visual QA corpus, providing a public resource for future multilingual research. +- We present a lightweight instruction-tuning pipeline that brings task-specific Romanian knowledge into existing MLLMs without expensive full-model training. +- We run comprehensive experiments on Romanian visual question answering and image captioning, together with a grammatical-error analysis that checks fluency improvements. + +The remainder of this report is organized as follows. Section 2 reviews related work on vision–language models for low-resource languages, multilingual multimodal corpora, benchmarks, and synthetic data generation. Section 3 describes our dataset construction, model selection, and fine-tuning procedure. Section 4 presents quantitative results for both Romanian VQA and image captioning, as well as a grammatical error analysis. Finally, Section 5 concludes and outlines directions for future research. + +# Method + +![](_page_5_Figure_8.jpeg) + +Figure 1: Overview of the dataset construction pipeline + +We built the datasets used in this paper starting from the Flickr30k dataset [\[Young et al., 2014\]](#page-15-9), as illustrated in Figure [1.](#page-5-0) The original Flickr30k dataset contains 31,783 images gathered from the Flickr platform, each having five independently written human captions which describe their visual content. The images cover a wide range of everyday scenarios, including people engaged in leisure activities or routine tasks, as well as animals and objects. Flickr30k, in the current format, supports several tasks, including image captioning, image–text retrieval, or multimodal representation learning. + +We created the Flickr Romanian dataset by translating the English captions of the Flickr30k collection into Romanian. Two other native Romanian speakers and I participated in the translation process. Each translator received a subset of approximately 10600 images and the corresponding 53000 English captions. The translators' focus was to preserve meaning and maintain grammatical correctness. The final dataset comprises 31783 images, each associated with five Romanian captions, for a total of 158915 sentences. The translated captions are organized in TSV format, with each entry containing the image identifier plus caption identifier. The translated captions and accompanying metadata are available in the GitHub repository at . This resource supports research on multilingual image captioning and cross-lingual vision–language tasks in Romanian. + +To extend the Flickr Romanian dataset for visual question answering (VQA), we generated question–answer pairs using only textual descriptions of the images. For each image, we concatenated its five Romanian captions into a single descriptive passage. We selected the largest language model that could fit available GPU resources and was available at the time: LLaMA 3.3 70B. The model was hosted on an Ollama [1](#page-6-0) server running on a A100 GPU with 80 GB of VRAM. The model was prompted to generate four candidate questions per description, focusing on objects, attributes, spatial relations, and actions depicted in the image. One question was then selected at random from the four candidates for answer generation. In a separate prompt, the model was instructed to provide a concise answer using only the information contained in the description. The prompts are provided in Appendix A. + +This pipeline produced 31783 question–answer pairs, matching the number of images in the dataset. All VQA annotations alongside the caption data, image content and image identifier are available in a HuggingFace repository at . + +Table 3: Extending Flickr Romanian for Visual Question Answering + +A little gray dog jumps over a bar at an agility test. A small dog jumping an obstacle in a grassy field. A little gray dog jumps over a small hurdle. A little gray dog is jumping over a fence. + +A small dog jumping over an obstacle. + +Un mic câine cenus, iu sare peste o bara la un test de agilitate. ˘ Un câine mic care sare un obstacol într-un câmp înierbat. Un mic câine cenus, iu sare peste un mic obstacol. Un câine mic cenus, iu sare peste un gard. Un câine mic care sare peste un obstacol. + +Care este culoarea câinelui? Ce face câinele în imagine? Peste ce sare câinele? În ce mediu se afla câinele? ˘ + +Q: Ce face câinele în imagine? A: Câinele sare peste un obstacol. + +![](_page_6_Picture_16.jpeg) + +The extended dataset enables evaluation of Romanian-language VQA benchmarks, analysis of language-based reasoning over visual content, and comparison with existing multilingual VQA resources. + +1 https://github.com/ollama/ollama + +In this section, we analyze the main statistics of the Flickr30K-RoQA dataset, which also contains the Flickr30k-Romanian dataset. We adopt a random train–test split of 80% for training (25426 samples) and 20% for testing (6357 samples), and we use these splits consistently across all experiments reported in this paper. + +![](_page_7_Figure_3.jpeg) + +Figure 2: Token count distributions reported for the training and test splits. + +In Figure [2,](#page-7-0) we examine the token count distributions for Romanian captions, questions, and answers, reported separately for the training and test splits. Since each image is associated with five captions, we compute the average caption token length per sample. Tokenization is performed using the multilingual cased BERT base model[2](#page-7-1) . + +Captions reveal a unimodal distribution, with an average length of approximately 23–24 tokens and with some longer descriptions extending beyond 60 tokens. The generated questions show a more compact distribution, centered around a mean of 16.1 tokens, with the longest questions reaching approximately 40 tokens. The answer length distribution peaks around a mean of 13.9 tokens and displays a spike in the 2–4 token range. This spike corresponds to short-form, non-sentential answers, while the remaining reflects full-sentence, descriptive answers. Because the QA generation prompt did not explicitly state the answer format, the model produced responses in both variants. We plan to address this inconsistency in future work. Finally, the close alignment between the training and test distributions across all text types indicates that the split preserves the statistical properties of the original dataset. + +In order to provide insight into the content of Flickr30k-RoQA, Table [4](#page-8-0) highlights the words with the highest TF-IDF scores in the Romanian captions after stopword removal. These terms show the words that help to to discriminate content between samples in the dataset and provide information on the dominant themes that are present in the corpus. + + + +Table 4: Top TF-IDF words in Romanian captions. + + + +| | Train | | Test | | | | +|---------------------|-------------|---------|---------------------|-------------|---------|--| +| Word | Translation | TF-IDF | Word | Translation | TF-IDF | | +| barbat
˘ | man | 0.03634 | b
arbat
˘ | man | 0.03912 | | +| femeie | woman | 0.03299 | femeie | woman | 0.03464 | | +| doi | two | 0.02663 | ˘
sta | stands | 0.02914 | | +| sta
˘ | stands | 0.02661 | doi | two | 0.02819 | | +| camas ˘ ,
a
˘ | shirt | 0.02532 | oameni | people | 0.02798 | | +| oameni | people | 0.02517 | om | man | 0.02714 | | +| timp | time | 0.02499 | timp | time | 0.02709 | | +| om | man | 0.02432 | camas ˘ ,
a
˘ | shirt | 0.02630 | | +| lânga
˘ | next to | 0.02262 | lâng
a
˘ | next to | 0.02377 | | +| unui | a / of a | 0.02017 | unui | a / of a | 0.02209 | | + +Table 5: Distribution of the most frequent first words in Romanian questions. + +| | Train | | | | Test | | | +|-----------|-------------|-------|-------|-----------|-------------|-------|-------| +| Word | Translation | Count | % | Word | Translation | Count | % | +| ce | what | 8222 | 32.34 | ce | what | 2026 | 31.87 | +| care | which | 4694 | 18.46 | care | which | 1125 | 17.70 | +| unde | where | 2843 | 11.18 | unde | where | 717 | 11.28 | +| în | in | 2108 | 8.29 | câte | how many | 546 | 8.59 | +| câte | how many | 2099 | 8.26 | în | in | 516 | 8.12 | +| cine | who | 1282 | 5.04 | cine | who | 330 | 5.19 | +| pe | on | 1192 | 4.69 | pe | on | 323 | 5.08 | +| cât,
i | how many | 672 | 2.64 | cât,
i | how many | 178 | 2.80 | +| cum | how | 507 | 1.99 | cum | how | 125 | 1.97 | +| este | is | 287 | 1.13 | este | is | 82 | 1.29 | + +We restrict this analysis to captions, as both questions and answers are generated from the same caption data and therefore largely reflect the same underlying information. + +In Table [5,](#page-8-1) we report the words that appear most frequently at the beginning of questions generated by the LLM. As expected, the majority of questions start with interrogative terms such as *ce* ("what"), *care* ("which"), and *unde* ("where"). The distribution indicates good coverage of question types, including spatial, counting, object-focused, and subject-focused questions, as well as a balanced use of different interrogative forms. This suggests that the strategy of generating multiple candidate questions per image and randomly selecting one effectively achieves variability. Also, the first-word distributions across the training and test splits is almost identical, confirming that the data partition preserves the statistical properties of the question set. + +After constructing the Romanian multimodal dataset, we selected three large vision–language models to establish baseline results. We aimed for instruction-tuned architectures that fit our GPU resources and offered strong zero- and few-shot capabilities. We excluded very small variants due to their limited visual reasoning performance. The chosen models are: + +- LLaMA 3.2–11B Vision, an 11 billion-parameter model combining a convolutional image encoder with a transformer language backbone. +- Qwen2–VL–7B–Instruct, a 7 billion-parameter vision–language model with multilingual instruction tuning. +- LLaVA-v1.6-Mistral–7B, a Mistral-7B–based multimodal chatbot fine-tuned on GPT-4–generated image–instruction data. + +LLaMA 3.2 Vision integrates a convolutional vision encoder with shared transformer layers from the LLaMA language model. Images are split into patches, embedded, and concatenated with text token embeddings. The combined sequence is processed jointly, using cross-entropy loss on next-token prediction to align visual and textual representations. In standard benchmarks, this model outperforms its text-only counterpart on image captioning and visual question answering without sacrificing inference speed. + +Qwen2–VL–7B uses a transformer encoder to extract hierarchical image features, followed by an instruction-tuned decoder. It employs advanced activation functions and attention mechanisms to maximize capacity within seven billion parameters. Training on a large multilingual image–caption corpus enables zero-shot visual question answering across multiple languages, including Romanian. It shows modest improvements in caption accuracy and reasoning tasks over earlier variants. + +LLaVA-v1.6 aligns a pretrained vision encoder with the Mistral-7B language model via a trainable projection layer. It is fine-tuned using synthetic image–instruction pairs generated by GPT-4, allowing it to follow novel visual prompts without task-specific data. Version 1.6 adds more diverse data, supports high-resolution inputs, and improves bilingual performance. It achieves strong zero- and few-shot results on Science QA and multimodal dialogue benchmarks. + +We performed a lightweight, parameter–efficient adaptation of the three baseline models to the Romanian Visual QA corpus by training only a set of Low-Rank Adaptation (LoRA) weights. All original vision parameters remained frozen, while language, attention, and MLP blocks were equipped with LoRA modules; this strategy preserves the pretrained visual representations and reduces GPU memory consumption. + +We applied supervised fine-tuning to a conversation-style prompt. Each training example was converted into a two-turn chat. The user turn will contain the Romanian instruction prompt followed by the natural language question and the raw image, while the assistant turn will contain the ground-truth answer. + +Regarding the LoRA configuration, we set the rank and scaling factor to r = α = 16, used no dropout, and injected adapters into *all* linear projections of the language transformer. This results to a total of ≈ 52 M trainable parameters (0.48 % of the backbone) for the 11 B model, 41 M (0.60%) for LLaVA and 40 M (0.58%) for Qwen + +Training was carried out on a single NVIDIA A100 80 GB GPU. Table [6](#page-9-0) summarises the hyper-parameters shared across models. + +Parameter Value Effective batch size 16 (2 samples per device × 8 grad. accum. steps) Max steps 500 (≈1 epoch) Learning rate 2×10−4 (linear schedule, 80 warm-up steps) + +Optimiser AdamW 8-bit, weight decay 0.01 Max sequence length 2048 tokens (text + image) + +Table 6: Finetuning hyper-parameters. + +Implementation leveraged the FastVisionModel from Unsloth[3](#page-9-1) together with SFTTrainer from TRL[4](#page-9-2) . One full run on the largest model required roughly 4 hours and 40 minutes, validating the efficiency of the LoRA scheme compared with full-model finetuning. + +We made the resulting LoRA adapters publicly available to facilitate future work on Romanian multimodal understanding: + +https://huggingface.co/andreidima/Llama-3.2-11B-Vision-Instruct-RoVQA https://huggingface.co/andreidima/llava-v1.6-mistral-7b-4bit-RoVQA-lora + +https://huggingface.co/andreidima/Qwen2-VL-7B-Instruct-RoVQA-lora