Sample Usage Guide

1. Function Description

This sample demonstrates graph construction using operator overload, aimed at helping graph developers quickly understand operator overload definition. Unlike operator_overload, this directory uses Session.run_graph_with_stream_async interface during execution phase. For more information about asynchronous execution, please refer to Asynchronous Execution.

2. Directory Structure

python/
├── src/
|   ├── common.py                            // Common logic: constants, graph construction, input tensors, GE lifecycle
|   ├── make_add_graph.py                    // Asynchronous execution sample
|   └── make_add_graph_custom_allocator.py   // Register custom SamplePoolAllocator asynchronous execution sample
├── README.md               // README file
├── run_sample.sh           // Execution script

3. Usage Instructions

3.1. Prepare CANN Package

• Please refer to Environment Preparation "Method 3: Manual installation of software packages > Scenario 1: Experience master version capabilities or develop based on master version", install the latest toolkit and ops packages.
• Set environment variables (assuming the package is installed at /usr/local/Ascend/)

source /usr/local/Ascend/cann/set_env.sh

3.2. Execute

This directory provides two optional targets:

3.2.1. Default allocator sample

bash run_sample.sh -t sample_and_run_python

This command will:

1. Generate dump graph and use GE built-in allocator to asynchronously execute the graph

After successful execution, you will see:

[Success] sample execution successful, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be opened in netron for display

3.2.2. Custom allocator sample

bash run_sample.sh -t sample_and_run_python_custom_allocator

This command on basis of default sample, through Session.register_external_allocator registers custom SamplePoolAllocator to GE. This class inherits ge.allocator.Allocator base class: maintains free block list by exact byte count, hits then reuse, otherwise acl.rt.malloc allocate; free time puts block back to pool (not immediately acl.rt.free). When object is recycled, in __del__ uniformly acl.rt.free cached blocks.

After successful execution, terminal will show output in following format (specific line count depends on GE allocation/release for output Tensor; address and size based on actual execution):

[Info] SamplePoolAllocator registered to stream
[SamplePool] new   : addr=0x<device memory address>  size=<byte count> B
[Info] Asynchronous execution of the graph succeeded.
[SamplePool] cache : addr=0x<device memory address>  size=<byte count> B
[Info] SamplePoolAllocator unregistered
[SamplePool] destroy: freed <N> cached blocks
[Info] Runtime environment cleaned up
[Success] sample execution successful, pbtxt dump generated in current directory. The file starts with ge_onnx_ and can be opened in netron for display

Output File Description

After successful execution, the following files will be generated in current directory:

• ge_onnx_*.pbtxt - protobuf text format of graph structure, can be viewed with netron

3.3. Log Printing

If you need log printing to assist debugging during executable program execution, you can set the following environment variables before bash run_sample.sh -t sample_and_run_python to print logs to screen:

export ASCEND_SLOG_PRINT_TO_STDOUT=1 # Print logs to screen
export ASCEND_GLOBAL_LOG_LEVEL=0     # Log level set to debug level

3.4. DUMP Graph During Graph Compilation Process

If you need to DUMP graph to assist debugging graph compilation process during executable program execution, you can set the following environment variables before bash run_sample.sh -t sample_and_run_python to DUMP graph to execution path:

export DUMP_GE_GRAPH=2

4. Core Concepts Introduction

4.1. Graph Construction Steps

• Create graph builder (provides context, workspace and construction-related methods needed for graph construction)
• Add starting nodes (starting nodes refer to nodes without input dependencies, usually including graph inputs (like Data nodes) and weight constants (like Const nodes))
• Add intermediate nodes (intermediate nodes are computation nodes with input dependencies, usually generated by user graph construction logic, and connected using existing nodes as inputs)
• Set graph output (explicitly specify graph output nodes as computation result endpoints)

4.2. Operator Overload

Concept Explanation: Operator overload is a syntax sugar provided by ES API, with syntax encapsulation for AI operators, making graph construction code more concise and intuitive.

Graph Construction API Features:

• Operator overload maintains the same type checking and constraints as function calls