--precision_mode_v2

Applicability

Description

Sets the precision mode of a model.

See Also

• This option cannot be used together with --precision_mode. You are advised to use --precision_mode_v2. The --precision_mode_v2 option is added in the new version. The semantics of the option value is clearer and easier to understand.
• When this option is set to mixed_float16, mixed_bfloat16, if you wish to adjust the precision based on the built-in optimization policy, specify which operators allow precision reduction and which operators do not. For details, see --modify_mixlist.
• In the inference scenario, --precision_mode_v2 can be used to set the global precision mode of a network model, but it may result in performance or accuracy problems on particular operators. Therefore, you can use --keep_dtype to keep the computation precision of these operators unchanged during the build of the original network model. However, --keep_dtype does not take effect when --precision_mode_v2 is set to origin.

Argument

• fp16 (default):

  Indicates that float16 is forcibly selected if the operator precision in the original graph is float16, bfloat16, or float32.

• origin:

  Retain the original precision.

  • If the precision of an operator in the original graph is float16, and the implementation of the operator in the AI Core does not support float16 but supports only float32 and bfloat16, the system automatically uses high-precision float32.
  • If the precision of an operator in the original graph is float16, and the implementation of the operator in the AI Core does not support float16 but supports only bfloat16, the AI CPU operator of float16 is used. If the AI CPU operator is not supported, an error is reported.
  • If the precision of an operator in the original graph is float32, and the implementation of the operator in the AI Core does not support float32 but supports only float16, the AI CPU operator of float32 is used. If the AI CPU operator is not supported, an error is reported.
• cube_fp16in_fp32out:
  The system selects a processing mode based on the operator type for AI Core operators supporting both float32 and float16.

  • For cube operators, the system processes the computation based on the operator implementation.
    1. The preferred input data type is float16 and the output data type is float32.
    2. If the float16 input data and float32 output data types are not supported, set both the input and output data types to float32.
    3. If the float32 input and output data types are not supported, set both the input and output data types to float16.
    4. If the float16 input and output data types are not supported, an error is reported.
  • For vector compute operators, the operator precision in the original graph is float16 or bfloat16, and float32 is forcibly selected.

    This option is invalid if the original graph contains operators not supporting float32 in the AI Core, for example, an operator that supports only float16. In this case, float16 is retained. If the operator in the AI Core does not support float32 and it is configured to the blocklist of precision reduction (by setting precision_reduce to false), the counterpart AI CPU operator supporting float32 is used. If the AI CPU operator does not support float32, an error is reported.

• mixed_float16:

  Mixed precision of float16, bfloat16, and float32 is used for neural network processing. For float32 and befloat16 operators in the original graph, float16 is automatically used for certain float32 and bfloat16 operators based on the built-in tuning policy. This will improve system performance and reduce memory usage with minimal precision degradation.

  If this mode is configured, you can view the value of precision_reduce in the built-in tuning policy file of ${INSTALL_DIR}/opp/built-in/op_impl/ai_core/tbe/config/xxx/aic-xxx-ops-info-*.json.

  • If it is set to true, the operator is on the mixed precision trustlist and its precision will be reduced from float32 and bfloat16 to float16.
  • If it is set to false, the operator is on the mixed precision blocklist and its precision will not be reduced from float32 and bfloat16 to float16. In this case, the operator still uses the precision of float32 or bfloat16.
  • If an operator in the network model does not have the precision_reduce option configured, the operator is on the graylist and will follow the same precision processing as the upstream operator.
• mixed_bfloat16:

  Mixed precision of bfloat16 and float32 is used for neural network processing. In this mode, bfloat16 is automatically used for certain float32 operators in the original graph based on the built-in tuning policy. This will improve system performance and reduce memory usage with minimal precision degradation. If the operators do not support bfloat16 and float32, the AI CPU operators are used for computation. If AI CPU operators also do not support float16 and float32, an error is reported during execution.

  If this mode is configured, you can view the value of precision_reduce in the built-in tuning policy file of ${INSTALL_DIR}/opp/built-in/op_impl/ai_core/tbe/config/xxx/aic-xxx-ops-info-*.json.

  • If it is set to true, the operator is on the mixed precision trustlist and its precision will be reduced from float32 to bfloat16.
  • If the field value is false, the operator is on the mixed precision blocklist and its precision will not be reduced from float32 to bfloat16.
  • If an operator in the network model does not have the precision_reduce option configured, the operator is on the graylist and will follow the same precision processing as the upstream operator.
• mixed_hif8:

  Enables automatic mixed precision, indicating that hifloat8 (for details about this data type, see Link), float16, bfloat16, and float32 are used together for neural network processing. In this mode, hifloat8 is automatically used for certain float16, bfloat16, and float32 operators in the original graph based on the built-in tuning policy. This will improve system performance and reduce memory usage with minimal precision degradation. The current version does not support this argument.

  If this mode is configured, you can view the value of precision_reduce in the built-in tuning policy file of ${INSTALL_DIR}/opp/built-in/op_impl/ai_core/tbe/config/xxx/aic-xxx-ops-info-*.json.

  • If it is set to true, the operator is on the mixed precision trustlist and its precision will be reduced from float16, bfloat16, and float32 to hifloat8.
  • If it is set to false, the operator is on the mixed precision blocklist and its precision will not be reduced from float16, bfloat16, and float32 to hifloat8. In this case, the operator still uses the precision of float16, bfloat16, or float32.
  • If an operator in the original graph does not have the precision_reduce option configured, the operator is on the graylist and will follow the same precision processing as the upstream operator.
• cube_hif8:

  The hifloat8 data type is forcibly used if the Cube operator in the original graph supports both hifloat8 and float16, bfloat16, or float32. The current version does not support this argument.

Replace ${INSTALL_DIR} with the CANN component directory. For example, if the installation is performed by the root user, the default file storage path is /usr/local/Ascend/cann. xxx varies depending on the product.

Suggestions and Benefits

The accuracy and performance of the network model vary according to the configured precision mode.

Sort by precision: origin > mixed_float16 > fp16 > mixed_bfloat16. Sort by performance: mixed_bfloat16 > fp16 ≥ mixed_float16 > origin.

Example

--precision_mode_v2=fp16

Restrictions

In the mixed precision scenario, if the inference performance deteriorates after the version upgrade, you are advised to use the AOE tool to perform optimization again. After the optimization is complete, use the --op_bank_path option to load the path of the custom repository, and then convert the model again.

For details about operator tuning, see AOE Instructions.