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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThe reliable way to troubleshoot an AI model on Ascend is to separate three questions: can this exact model be converted for this exact SOC, can the resulting offline model run correctly in this software stack, and which operator or system layer limits performance? Capture the complete configuration first, verify the documented support scope and conversion target, establish correct inference, then use operator and system profiling to isolate the fault.
Compatibility is conditional rather than a property of a model name. Product generation, SOC, CANN release, runtime, framework, model format, tensor shapes, data types and static or dynamic shape mode can all change the result. The procedure below is designed to produce evidence that can be reproduced and acted on, rather than guesses based on a framework operator list or a single throughput number.
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Start with a failure classification
Do not begin by changing precision, adding profiling flags or rebuilding operators. First identify the earliest stage that fails. A conversion error, a load error, an incorrect result and a slow but correct result require different evidence and different remedies.
| Observed symptom | First boundary to test | Evidence to save |
|---|---|---|
| ATC conversion fails or no offline model is produced | Model format, operator support, SOC selection and conversion environment | Full ATC command, complete log, source model details and target SOC |
| Conversion succeeds but the model will not load | Offline-model and runtime compatibility, driver and firmware, product selection | Runtime error, package versions, generated model metadata and device identification |
| Inference runs but outputs are wrong | Inputs, interface parameters, operator accuracy and precision choices | Reference outputs, device outputs, shapes, dtypes and accuracy-analysis files |
| Inference is correct but slow | Operator implementation, memory movement, DVPP use, CPU overhead and device scheduling | Warm-up policy, latency or throughput measurements and profile summaries |
Keep the failure class fixed while testing. If a change makes conversion pass, that does not establish runtime compatibility or accuracy. If a model is numerically correct, that does not show that its latency is acceptable.
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Build a reproducible configuration record
Write down the values below before requesting a fix or comparing two environments. A version number without the product and mode is not enough to establish compatibility.
- Ascend product and exact SOC: record the product family, device model and the SOC string expected by ATC.
- Driver and firmware: include build or release identifiers, not only installation dates.
- CANN packages: record the package used to convert the model and the package used at runtime. Include edition and release.
- Framework and runtime: identify the exporter or framework, runtime API and language version. State whether execution is through Python, PyTorch, MindSpore or another integration.
- Model source and intermediate formats: identify Caffe, TensorFlow, ONNX or another source, plus any intermediate representation used before ATC.
- ATC invocation: save every option, environment variable and warning. Do not copy only the final error line.
- Shape mode: state static, dynamic batch, dynamic image size or another dynamic mode, with every permitted shape and range.
- Inputs and precision: record batch size, dimensions, data type, layout, preprocessing and postprocessing.
- Workload and symptom: distinguish conversion, model loading, first-inference failure, incorrect output, latency, throughput, memory or utilization.
Compare this record with the release and version-mapping documentation for the exact product. A CANN version that works for one Ascend product or shape mode cannot be assumed to work for another. The available documentation does not provide a universal, current version matrix, so compatibility must be checked against the deployment’s actual release notes rather than inferred from a number alone.
Verify that conversion targets the real SOC
ATC conversion examples require an actual --soc_version value. Obtain the SOC identifier using the device or SOC query reference for the installed product, then pass that value to ATC. Do not use a placeholder, a neighboring product name or a value copied from a different machine.
- Identify the device visible to the deployment and record its exact SOC.
- Run the SOC query procedure documented for that product and save the returned identifier.
- Repeat the conversion with the complete command, including the real
--soc_version=<actual_SOC>argument and all shape, input-format and output options. - Confirm from the log that conversion selected the intended target and that the expected offline-model file was created.
- Archive the model file together with the command and log. A file that happens to exist in the output directory is not proof that the latest conversion succeeded.
Some networks require graph changes or an intermediate representation. Certain Caffe models contain operators absent from the original framework, and original TensorFlow models can contain control-flow operators that need special handling. Treat those transformations as part of the model definition: keep the modified graph, conversion options and validation outputs with the build.
Apply the CANN version caveats narrowly
The CANN Commercial Edition 8.0.0 ATC getting-started guidance, last updated July 8, 2025, describes a specific offline-model compatibility case: a model converted with an earlier CANN package can run on a later package within a stated one-year compatibility period. This is a documented rule for that case, not a guarantee that every pair of releases is interchangeable.
The same guidance gives a stricter warning for dynamic-shape models and static Ascend virtual-instance scenarios: models converted with CANN earlier than 6.0.1 cannot run on CANN 6.0.1 or later in those scenarios without reconversion using CANN 6.0.1 or later. Confirm whether the deployment is one of those scenarios and check current release notes before relying on the rule; version behavior changes across products and releases.
If the runtime rejects a model after a package upgrade, the safest test is to reconvert it in the target CANN environment with the target SOC and then compare correctness. Preserve the old model and logs so that a version change can be isolated from a graph or operator change.
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Check operator support as a scoped contract
A framework operator name does not prove that the corresponding Ascend intermediate-representation operator is supported, accurate or fast. For every failed or unexpectedly slow operator, check all of the following against the matching operator documentation:
- Ascend product or SOC generation
- operator API or documentation version
- input and output data types
- input and output formats and layouts
- rank, dimensions and shape ranges
- attributes, optional inputs and permitted combinations
The official operator documentation states that CANN does not guarantee operator performance outside the documented support scope, including product models, data types, data formats and data dimensions. An operator may therefore convert and execute while still using a slower implementation, inserting conversions or failing to meet an expected latency target when it is outside that scope.
Check support separately for every product in a multi-device deployment. A graph validated on one hardware generation can contain an implementation or data-type assumption that does not carry to another generation.
Handle unsupported operators and custom adaptations
If the required operator is not in the target scope, choose among graph modification, an alternate supported operator, a precision change or a custom operator. Make the choice using evidence rather than assuming that a custom implementation will be faster.
When graph modification is preferable
Replace or restructure the operation when an equivalent supported pattern exists and the change preserves the model’s numerical intent. This usually has less deployment infrastructure than a custom operator, but it can alter intermediate values or require retraining. Compare reference and device outputs after every graph change.
When a custom operator is necessary
For a custom TBE operator crossing to a different hardware generation, analyze compute API and data-type differences first. Adapt the operator prototype, implementation and operator information library as required, then build and deploy the custom OPP. During conversion, custom OPP is preferentially searched before built-in OPP, so record which package supplied the selected implementation.
- Define the supported product, formats, shapes, data types and attributes for the custom operator.
- Adapt compute APIs and data-type handling for the target hardware generation.
- Build the custom OPP and deploy it in the target environment.
- Run single-operator tests on the actual device, including boundary shapes and error cases.
- Insert the operator into the complete network and verify both numerical output and performance.
- Repeat the test after any CANN, driver, firmware or SOC change.
Passing a single-operator test is necessary but not sufficient. Memory movement, scheduling and neighboring operators can change the network-level result.
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Establish functional correctness before tuning speed
First establish a functioning inference path. Check that the application sends the intended tensors, interface parameters and preprocessing results, and that output interpretation is correct. The CANN tuning guidance identifies application connections, model data reads and writes, operator adaptation and DVPP API usage as possible sources of performance trouble; some of those same boundaries can also produce apparently incorrect results.
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Use a controlled correctness check
- Select a small, fixed set of representative inputs and save their preprocessed tensors.
- Run the reference framework and record outputs before postprocessing.
- Run the Ascend model with identical shapes, data types and preprocessing.
- Compare outputs layer by layer when possible, not only final labels or scores.
- Record absolute and relative differences, tolerances and the first tensor that diverges.
- Repeat after changing precision, graph structure or an operator implementation.
Do not tune throughput while the first divergent tensor is unknown. A faster result that comes from an incorrect input layout, an omitted copy or an altered precision path is not an optimization.
Profile the layer that can answer the question
Use framework profiling for framework call stacks and system profiling for device behavior. The Ascend system profiling documentation states that framework-layer stacks from Python, PyTorch or MindSpore cannot be profiled by the cited msprof system tool. Use the framework’s own profiling API when the question is why application code or framework dispatch is slow.
For device and system behavior, use the msprof workflow and options documented for the actual product and CANN release. Depending on product support, relevant data can include AI Core and AI Vector Core metrics, CPU activity, memory, I/O, DVPP and interconnect behavior. Applicability and option constraints vary by product.
Do not combine profiling flags from different examples blindly. The cited guide lists --instr-profiling as mutually exclusive with multiple other options on A2 and A3 products. Treat such restrictions as part of the command’s compatibility contract and check the guide for the exact release before collecting a profile.
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| Question | Useful evidence | Interpretation to test |
|---|---|---|
| Is an AI Core operator compute-bound? | AI Core metrics, operator duration and utilization | Arithmetic or vector work dominates the interval |
| Is the device waiting on movement? | Memory, I/O, CPU and interconnect traces | Copies, synchronization or host activity surround the operator |
| Is video preprocessing limiting the path? | DVPP activity and API timing | DVPP calls or format conversions consume the critical path |
| Is framework dispatch the bottleneck? | Framework call-stack profile | Python or framework overhead is outside the device operator timeline |
Keep the workload constant while collecting profiles: same batch size, shapes, precision, warm-up count, concurrency, input source and measurement window. Otherwise a profile comparison cannot show that a change caused the observed difference.
Find the operator behind a slow interval
Export the AI Core or AI CPU operator summary and inspect the OP Type column in the relevant op_summary_*.csv file. That column identifies the Ascend-supported OpType, which is the name to take back to the operator documentation and support matrix.
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- Locate the summary file for the run being analyzed.
- Sort or filter by duration, total time or invocation count, depending on the question.
- Read the
OP Typevalue for the suspicious row. - Check that OpType’s product, data-type, format and dimension scope.
- Inspect adjacent rows for inserted casts, transposes, copies or synchronization that may explain the cost.
- Repeat the run after one targeted change and compare the same measurement window.
A high-duration row is a lead, not automatically the root cause. An operator can appear slow because it waits for data, because a preceding conversion was attributed to it, or because an unsupported shape selected a fallback implementation. Use the surrounding timeline and memory evidence to distinguish those cases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Trace an accuracy anomaly to a dumped operator
For accuracy analysis, use the analyzer’s result_*.csv output. Identify the relevant row through its NPUDump field, then locate the corresponding dump data and determine the OpType there. This connects an output discrepancy to the concrete device-side operator rather than to a framework-level name alone.
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- Open the generated result CSV and identify the row with the abnormal error or metric.
- Follow that row’s
NPUDumpreference to the dump files. - Map the dump to the OpType and tensor shape, then compare the same tensor in the reference path.
- Check precision, format, attributes and neighboring operators before changing the operator.
The ATC FAQ describes adjusting computation precision for an abnormal operator, reconverting the model and rerunning inference. It gives --modify_mixlist as an example of blocking a faulty operator. Treat that as a targeted diagnostic or mitigation: validate both accuracy and performance after the change, because a precision restriction can increase latency or memory use.
Compare remedies on the same evidence
When more than one fix is possible, compare them with the same workload and a fixed acceptance test. The relevant axes are support status, accuracy, latency, throughput, engineering effort and compatibility with the target product and CANN release.
| Remedy | Support and compatibility | Accuracy risk | Engineering effort | Performance question |
|---|---|---|---|---|
| Use a documented operator or graph pattern | Highest when all product, type, format and shape conditions are met | Usually limited, but still requires output comparison | Low to moderate graph work | Measure whether casts or layout changes were removed |
| Modify precision for one operator | Depends on the mixlist and target release | Must be measured at the affected tensor and final output | Moderate conversion and validation work | Check compute time, memory and any added conversions |
| Change the model graph | Must be reconverted and checked for the target SOC | Can alter intermediate and final values | Moderate to high, possibly requiring retraining | Measure the complete network, not only the replacement node |
| Deploy a custom operator | Requires target-generation adaptation and a maintained custom OPP | Operator and network tests are required | High: implementation, build, deployment and device testing | Compare device execution and data-movement costs with the fallback |
Report latency and throughput only with batch size, input shapes, precision, warm-up behavior and measurement method. The available official material does not establish a general model benchmark, so there is no universal percentage improvement to expect from any remedy.
Common troubleshooting branches
The model converts only on one machine
Compare SOC selection, CANN package, environment variables, custom OPP contents and source-model preprocessing. Save both ATC logs and verify that each machine generated the same target model under the intended product configuration.
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The model converts but fails after a CANN upgrade
Check the offline-model compatibility rule for the exact upgrade, with special attention to dynamic-shape and static virtual-instance cases. If the model was converted with an older package covered by the 6.0.1 restriction, reconvert it with the target package before investigating application code.
The model runs but uses unexpectedly little device compute
Inspect the operator summary and surrounding timeline for unsupported formats, inserted casts, host synchronization, memory copies or DVPP calls. A framework operator that looks familiar may have selected a different Ascend implementation.
The final output is wrong but most operators appear normal
Verify input layout, preprocessing, output decoding and interface parameters first. Then use the analyzer’s NPUDump mapping to find the first abnormal tensor and test precision or operator-specific changes there.
A profiling command fails before producing data
Check that the selected options apply to the product and CANN release and that mutually exclusive options have not been combined. On A2 and A3 products, in particular, review the documented restrictions around --instr-profiling before rerunning.
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Make measurements reproducible
- Pin the exact model file, source revision and conversion command.
- Use identical input tensors, shape ranges, batch size and precision in every run.
- Separate first-inference and warmed-up measurements.
- State whether latency includes preprocessing, device transfer, inference and postprocessing.
- Record concurrency, queue depth and number of repetitions.
- Keep driver, firmware, CANN and custom OPP versions unchanged during a comparison.
- Collect profile summaries and logs for both the baseline and the modified run.
- Report accuracy deltas alongside speed changes.
If a result cannot be reproduced from these details, it is not yet a dependable basis for selecting a remedy.
What to include when escalating a case
Provide a compact evidence bundle rather than a description such as “Ascend is slow.” Include the exact product and SOC, driver and firmware, CANN conversion and runtime versions, framework and runtime, source and offline model formats, ATC command and complete log, input shapes and shape mode, expected and observed outputs, accuracy comparisons, profile summaries, custom OPP details and the precise symptom boundary.
For performance issues, add the op_summary_*.csv files, the measurement method and whether framework or system profiling was used. For accuracy issues, include the analyzer result_*.csv, the relevant NPUDump references and the corresponding dump data. This lets an operator-support team determine whether the problem is conversion, support scope, runtime compatibility, data movement, DVPP usage, framework overhead or a specific implementation.
Bottom line
Ascend troubleshooting becomes tractable when compatibility, correctness and performance are treated as separate gates. Identify the real SOC, convert with that target, check the documented operator scope, verify inference numerically, and only then profile the layer that can answer the performance question. Keep version caveats and profiling options tied to the exact product and CANN release, and validate every graph, precision or custom-operator change on the complete target network.
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