Zhejiang Lab

Hongsheng Wang

Guang Chen

Hujun Bao

Haijun Shan

Wei Hua

Feng Lin

Fei Wu

Fei Yang

Weiqiang Jia

Lingfang Zeng

Shengjian Hu

Chao Ma

Yong Li

Hongcai Cheng

Aimin Pan

Shuibing He

Bowen Tan

Jian Zhu

Enping Wang

Huanbo Zheng

Zailiang Yu

Jiaqing Fu

Qing Liao

The present disclosure provides a pipelining and parallelizing graph execution method for neural network model computation and apparatus, and provides a pipelining and parallelizing graph execution method for neural network model computation and apparatus in a deep learning training system. The method includes the graph execution flow in a neural network model computation process and a process of cooperative work of all functional modules. The pipelining and parallelizing graph execution method for neural network model computation includes creating a graph executive on a native machine according to a physical computation graph compiled and generated by a deep learning framework.

Pipelining and parallelizing graph execution method for neural network model computation and apparatus thereof

Disclosed are a method and apparatus for constructing a three-dimensional data set of a pedestrian re-identification based on a neural radiation field. The method includes the following steps: S1: capturing images of pedestrians to be entered by a group of cameras at different viewing angles; S2: generating a three-dimensional spatial position point set by sampling through camera rays in the scenario, and converting observation directions of the cameras corresponding to the three-dimensional spatial position point set into three-dimensional Cartesian unit vectors; and S3: inputting, into a multi-layer sensor, the three-dimensional spatial position point set and the observation directions converted into the three-dimensional Cartesian unit vectors, to output corresponding densities and colors. The method and apparatus of the present disclosure gives a brand-new method for constructing a pedestrian re-identification data set, and provides a new idea of data set construction.

Method and apparatus for constructing three-dimensional data set of pedestrian re-identification based on neural radiation field

The present disclosure discloses a method for executing a task. The method includes: a master computing device node in a computing cluster system receives a task code of a to-be-executed task; the master computing device node divides the to-be-executed task into subtasks, and for each of the subtasks, the master computing device node determines operators required to execute the subtask based on the task code; the master computing device node respectively distributes the subtasks to computing nodes in the computing cluster system, such that for each of the computing nodes, the computing node generates an executable task subgraph for the computing node based on the operators required to execute the subtask distributed to the computing node and data transmission relationships between the operators required to execute the subtask distributed to the computing node, and runs the executable task subgraph to execute the to-be-executed task.

Methods and apparatuses for executing tasks, storage mediums, and electronic devices

Disclosed are a data flow method and apparatus for neural network computation. The data flow method for neural network computation includes initializing the lifecycle of a variable in a computational graph; and defining a propagation rule for a variable in use to flow through a node. A definition of the variable is produced at a precursor node of the node, such that an input set of valid variables flowing through the node contains the variable. The method may be used on neural network computation in a deep learning training system.

Data flow method and apparatus for neural network computation by determining input variables and output variables of nodes of a computational graph of a neural network

Disclosed is a method for adapting a deep learning framework to a hardware device based on a unified backend engine, which comprises the following steps: S1, adding the unified backend engine to the deep learning framework; S2, adding the unified backend engine to the hardware device; S3, converting a computational graph, wherein the computational graph compiled and generated by the deep learning framework is converted into an intermediate representation of the unified backend engine; S4, compiling the intermediate representation, wherein the unified backend engine compiles the intermediate representation on the hardware device to generate an executable object; S5, running the executable object, wherein the deep learning framework runs the executable object on the hardware device; S6: managing memory of the unified backend engine.

Method for adapting deep learning framework to hardware device based on unified backend engine

The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

Method for execution of computational graph in neural network model and apparatus thereof

The present disclosure discloses a distributed model compilation system. A master node of the system determines the logic calculation graph of the model based on model information, divides the logic calculation graph into multiple logic calculation sub-graphs, generates a distributing message for each logic calculation sub-graph, and then transmits the distributing message to a slave node. Each of the slave nodes allocates a local computing resource to compile the logic calculation sub-graph based on the received distributing message, and transmits compilation completion information to the master node. The master node determines the completion of model compilation based on the compilation completion information returned by each slave node, and executes the target work based on the compiled model.

Distributed model compilation

The disclosure discloses a graph optimization method and apparatus for neural network computation. The graph optimization method includes the following steps: S: converting a computation graph; S: allocating a register; S: defining a route selector for a redefined variable; S: solving the route selector for the redefined variable; S: defining a criterion of inserting the route selector for the redefined variable into a node; S: analyzing a dominating edge set of the node for the redefined variable; S: inserting the route selector for the redefined variable; and S: renaming the redefined variable. The disclosure solves the problem of the corresponding route selection on a correct definition of the redefined variable when a node including the redefined variable in a computation graph in the compiling period flows through multiple paths of computation flow, reduces the memory cost and promotes the development of implementation application of a deep neural network model.

Graph optimization method and apparatus for neural network computation

The disclosure discloses a method of executing dynamic graph for neural network computation and the apparatus thereof. The method of executing dynamic graph includes the following steps: S: constructing and distributing an operator and a tensor; S: deducing an operator executing process by an operator interpreter; S: constructing an instruction of a virtual machine at runtime by the operator interpreter; S: sending the instruction to the virtual machine at runtime by the operator interpreter; S: scheduling the instruction by the virtual machine; and S: releasing an executed instruction by the virtual machine. According to the method of executing dynamic graph for neural network computation and the apparatus thereof provided by the disclosure, runtime is abstracted to be the virtual machine, and the virtual machine acquires a sub-graph of each step constructed by a user in real time through the interpreter and schedules, the virtual machines issues, and executes each sub-graph.

Method and apparatus of executing dynamic graph for neural network computation

The disclosure discloses a method of neural network model computation-oriented intermediate representation and apparatus thereof. The method includes the following steps: S1, parsing an input model file so as to acquire topological structure information of a neural network; S2, constructing a logical computation graph; S21, inferring physical layout information of each operator in the logical computation graph; S22, inferring meta attributes of each operator in the logical computation graph; S23, inferring description information of input and output logical tensors of each operator in the logical computation graph; S3, constructing a physical computation graph; S31, generating a physical computation graph, etc.

Method of neural network model computation-oriented intermediate representation by constructing physical computation graph, inferring information of input and output tensor edges of each node therein, performing memory optimization on tensor edges, and optimizing physical computation graph

Disclosed are a joint modeling method and apparatus for enhancing local features of pedestrians. The method includes the following steps: S: acquiring an original surveillance video image data set, dividing the original surveillance video image data set into a training set and a test set in proportion; S: cutting the surveillance video image training set to obtain image block vector sequences. In the present disclosure, local features of pedestrians in video images are extracted by a multi-head attention neural network, weight parameters of image channels are learned by channel convolution kernels, spatial features on the images are scanned through spatial convolution, local features of pedestrians are enhanced to improve the recognition rate of pedestrians, a feed-forward neural network and an activation function are adopted, so as to realize pedestrian re-recognition, thereby obtaining face images available.

Joint modeling method and apparatus for enhancing local features of pedestrians

The present disclosure provides a neural network computing-oriented modeling method and apparatus for distributed data routing. The method includes the following steps: S1, designing the distributed attribute of a physical tensor: abstracting a mapping relationship between a logic tensor and the physical tensor into three distributed attributes including a broadcast attribute, a scatter attribute and a local reduction attribute; S2, deducing the distributed attribute of an output tensor: specifying the distributed attribute of an input tensor, and then deducing the legal distributed attribute of the output tensor according to the known distributed attribute of the input tensor; and S3, judging, according to the distributed attribute situation, whether an intermediate communication primitive needs to be inserted to obtain the distributed attribute of a local physical tensor. The difficulty of distributed design and development is low, and the development of application of a deep neural network large model is promoted.

Neural network computing-oriented modeling method and apparatus for distributed data routing

Disclosed are an intermediate representation method and apparatus for parallel execution of graph computation. The method includes the following steps: S: compiling a neural network into a computational graph on a computer; S: defining branch states of tensor variables in the computational graph; S: defining a data dependency relationship of the tensor variables in the computational graph; S: defining a control dependency relationship of the tensor variables in the computational graph; S: building a data dependency relationship graph of the tensor variables in the computational graph; S: building a control dependency relationship graph of the tensor variables in the computational graph; and S: transforming control dependencies into data dependencies. The present application derives, based on the dependency relationship, a parallel computing method that can execute the branch threads in parallel in the global computational graph, and optimizes the compilation efficiency of the computational graph.

Intermediate representation method and apparatus for parallel execution of graph computation

Disclosed is a method for distributed type training adaptation and apparatus in a deep learning framework and an AI accelerator card. The method includes the following steps: S1: the deep learning framework supports single-card configuration in a newly added AI accelerator card, and sub-steps thereof are as follows: S11: the deep learning framework supports new hardware; S12: the deep learning framework supports a device thread of the new hardware; S13: the deep learning framework supports a memory operation of the new hardware; and S14: the deep learning framework supports an operator kernel function of the new hardware; S2: the deep learning framework supports multi-card configuration in the newly added AI accelerator card; S3: the deep learning framework supports tensor segmentation and multi-card distribution; and S4: the deep learning framework supports multi-card collective communication in the newly added AI accelerator card.

Method for distributed type training adaptation and apparatus in deep learning framework and AI accelerator card

Disclosed are a pedestrian re-identification method and apparatus based on local feature attention. The method includes the following steps: S1: obtaining an original surveillance video image data set, and dividing the original surveillance video image data set into a training set and a test set in proportion; and S2: performing image enhancement on the original surveillance video image training set to obtain enhanced images, and converting the enhanced images into sequence data. The pedestrian re-identification technology based on local feature attention uses a multi-head attention mechanism neural network to capture, extract video image feature sequences and replace convolution kernels in a convolutional neural network, uses fully connected layers and an activation function to combine local pedestrian feature sequences into complete pedestrian feature sequences through a weight matrix, performs prediction on the obtained pedestrian feature sequences, outputs position coordinates of pedestrians in the images and selects pedestrians to realize pedestrian re-identification.

Growing community of inventors

Hangzhou, China

Hongsheng Wang