NPURunConfig Options

graph_run_mode

Graph run mode. Values are as follows:

• 0: online inference.
• 1 (default): training

Configuration example:

config = NPURunConfig(graph_run_mode=1)

session_device_id

Logical ID of a device. Setting this option allows you to run different models on multiple devices by executing a single training script.

Generally, you can create sessions for multiple graphs and pass the corresponding argument of session_device_id to the session. This option takes precedence over the environment variable ASCEND_DEVICE_ID.

Example:

config0 = NPURunConfig(..., session_device_id=0, ...)
estimator0 = NPUEstimator(..., config=config0, ...)
...
config1 = NPURunConfig(..., session_device_id=1, ...)
estimator1 = NPUEstimator(..., config=config1, ...)
...
config7 = NPURunConfig(..., session_device_id=7, ...)
estimator7 = NPUEstimator(..., config=config7, ...)
...

distribute

ParameterServerStrategy object for distributed training in the PS-Worker architecture.

Example:

config = NPURunConfig(distribute=strategy)

deterministic

Whether to enable deterministic computing. If enabled, the same output is generated if an operator is executed for multiple times with the same hardware and input.

The values are as follows:

• 0 (default): disables deterministic computing.
• 1: enables deterministic computing.

By default, deterministic computing does not need to be enabled, because it slows down operator execution and affects performance. If it is disabled, the results of multiple executions may be different. This is generally caused by asynchronous multi-thread executions during operator implementation, which changes the accumulation sequence of floating point numbers.

However, if the execution results of a model are different for multiple times or the precision needs to be tuned, you can enable deterministic computing to assist model debugging and tuning. Note that if you want a completely definite result, you need to set a definite random seed in the training script to ensure that the random numbers generated in the program are also definite.

Example:

config = NPURunConfig(deterministic=1)

memory_config

System memory usage mode. Before creating NPURunConfig, you can instantiate a MemoryConfig class to configure functions. For details about the constructor of the MemoryConfig class, see MemoryConfig Constructor.

external_weight

When multiple models are loaded in a session, if the weights of these models can be reused, you are advised to use this configuration item to externalize the weights of the Const/Constant nodes on the network to implement weight reuse among multiple models and reduce the memory usage of the weights.

• False (default): The weights are not externalized but are saved in graphs.
• True: The weights of all Const/Constant nodes on the network are flushed to drives and are converted to FileConstant. The weight file is named in the format of "weight_<hash value>".

  If the environment variable ASCEND_WORK_PATH is not configured in the environment, the weight files are flushed to the current execution directory tmp_weight_<pid>_<sessionid>.

  If ASCEND_WORK_PATH is configured in the environment, the weight files are flushed to the ${ASCEND_WORK_PATH}/tmp_weight_<pid>_<sessionid> directory. For details about ASCEND_WORK_PATH, see Installation and Configuration > Flush File Configuration in Environment Variables.

  When the model is uninstalled, the tmp_weight_<pid>_<sessionid> directory is automatically deleted.

Note: This option is usually not required. If the model loading environment has limitations on memory, you can flush the weight externally.

config = NPURunConfig(external_weight=True)

input_fusion_size

Example:

config = NPURunConfig(input_fusion_size=25600)

ac_parallel_enable

Indicates whether to allow AI CPU operators and AI Core operators to run in parallel in a dynamic shape graph.

Example:

config = NPURunConfig(ac_parallel_enable="1")

compile_dynamic_mode

Example:

config = NPURunConfig(compile_dynamic_mode=True)

mix_compile_mode

Whether to enable mixed computing.

• True: enabled.
• False (default): disabled (full offload mode)

In full offload mode, all compute operators are offloaded to the device. As a supplement to the full offload mode, mixed computing allows certain operators to be executed online within the frontend framework, improving the Ascend AI Processor's adaptability to TensorFlow.

Example:

config = NPURunConfig(mix_compile_mode=True)

enable_exception_dump

For details about the environment variable, see Environment Variables.

Example:

config = NPURunConfig(enable_exception_dump=1)

op_debug_config

Enable for global memory check.

The value is the path of the .cfg configuration file. Multiple options in the configuration file are separated by commas (,).

• oom: checks whether memory overwriting occurs in the Global Memory during operator execution.

  During operator compilation, the .o file (operator binary file) and .json file (operator description file) are retained in the kernel_meta folder in the current execution path, and the following detection logic is added:

  inline __aicore__ void  CheckInvalidAccessOfDDR(xxx) {
      if (access_offset < 0 || access_offset + access_extent > ddr_size) {
          if (read_or_write == 1) {
              trap(0X5A5A0001);
          } else {
              trap(0X5A5A0002);
          }
      }
  }

  You can use dump_cce to view the preceding code in the generated .cce file.

  If memory overwriting occurs during compilation, the error code EZ9999 is reported.

• dump_cce: retains the *.cce file, .o file, and .json file of the operator in the kernel_meta folder in the current execution path during operator compilation.
• dump_loc: retains the *.cce file, .o file, and .json file of the operator, as well as the *_loc.json file (mapping file of python-cce) in the kernel_meta folder in the current execution path during operator compilation.
• ccec_O0: enables the default option -O0 of the CCEC during operator compilation. This option does not perform any optimization based on the debugging information.
• ccec_g: enables the -g option of the CCEC during operator compilation. Compared with -O0, this option generates optimization and debugging information.
• check_flag: checks whether pipeline synchronization signals in operators match each other during operator execution.

  It retains the .o file and .json file in the generated kernel_meta folder and adds the following detection logic during operator compilation:

    set_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID0);
    set_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID1);
    set_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID2);
    set_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID3);
    ....
    pipe_barrier(PIPE_MTE3);
    pipe_barrier(PIPE_MTE2);
    pipe_barrier(PIPE_M);
    pipe_barrier(PIPE_V);
    pipe_barrier(PIPE_MTE1);
    pipe_barrier(PIPE_ALL);
    wait_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID0);
    wait_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID1);
    wait_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID2);
    wait_flag(PIPE_MTE3, PIPE_MTE2, EVENT_ID3);
    ...

  You can use dump_cce to view the preceding code in the generated .cce file.

  During compilation, if mismatch exists in the pipeline synchronization signals in operators, a timeout error is reported at the faulty operator. The following is an example of the error information:

  Aicore kernel execute failed, ..., fault kernel_name=operator name,...
  rtStreamSynchronizeWithTimeout execute failed....

Example:

config = NPURunConfig(op_debug_config="/root/test0.cfg") 

The information about the test0.cfg file is as follows:

op_debug_config = ccec_O0,ccec_g,oom

Restrictions:

The following is a configuration example of the test0.cfg file:

op_debug_config= ccec_g,oom
op_debug_list=GatherV2,opType::ReduceSum

During model compilation, the GatherV2 and ReduceSum operators are compiled based on the ccec_g and oom options.

debug_dir

Directory of the debug files generated during operator compilation, including the .o, .json, and .cce files.

The storage priority of the debugging files generated during operator compilation is as follows:

debug_dir -> environment variable ASCEND_WORK_PATH -> the default storage path (current script execution path)

For details about ASCEND_WORK_PATH, see Environment Variables.

config = NPURunConfig(debug_dir="/home/test")

export_compile_stat

Example:

config = NPURunConfig(export_compile_stat=1)

precision_mode

A string for the operator precision mode.

• allow_fp32_to_fp16: For cube operators, use float16. For vector operators, preserve the original precision. If operators in a network model support float32, preserve the original precision float32. If operators in the network model do not support float32, directly reduce the precision to float16.
• force_fp16: forces float16 for operators supporting both float16 and float32. This parameter applies only to online inference scenarios.
• cube_fp16in_fp32out/force_fp32: The system selects a proper processing mode based on the operator type for operators supporting both float16 and float32. The force_fp32 and cube_fp16in_fp32out configurations deliver the same effect. cube_fp16in_fp32out is newly added to the new version. For cube operators, this option has clearer semantics.
  • For cube operators, the system processes the computing based on the operator implementation.
    1. The preferred input data type is float16 and the output data type is float32.
    2. If the scenario where the input data type is float16 and the output data type is float32 is not supported, set both the input and output data types to float32.
    3. If the scenario where both the input and output data types are float32 is not supported, set both the input and output data types to float16.
    4. If none of the preceding scenarios is supported, an error is reported.
  • For vector operators, float32 is forcibly selected for operators supporting both float16 and float32, even if the original precision is float16. This argument is invalid if your network model contains operators not supporting float32, for example, operators that support only float16. In this case, float16 is preserved. If the operators do not support float32 and are configured to the blocklist for mixed precision (by setting precision_reduce to false), the counterpart AI CPU operators supporting float32 are used.
• must_keep_origin_dtype: preserves the original precision.
  • If the precision of an operator in the original graph is float16, and the implementation of the operator in the NPU 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.
• allow_mix_precision_fp16/allow_mix_precision: enables automatic mixed precision, indicating that both float16 and float32 are used for neural network processing.

  The allow_mix_precision and allow_mix_precision_fp16 configurations deliver the same effect. allow_mix_precision_fp16 is newly added to the new version. It has clearer semantics and is easier to understand. For certain operators of the float32 data type on a network, the system automatically reduces their precision to float16 based on the built-in tuning policy. This will improve system performance and reduce memory footprint with minimal accuracy degradation. Use the mixed precision mode in conjunction with loss scaling to compensate for the precision degradation caused by precision reduction.

For the Atlas Training Series Product , the default value is allow_fp32_to_fp16.

Example:

config = NPURunConfig(precision_mode="allow_mix_precision")

precision_mode_v2

A string for the operator precision mode.

• fp16: forces float16 for operators supporting both float16 and float32.
• origin: retains the precision of the original image.
  • If the precision of an operator in the original graph is float16, and the implementation of the operator in the NPU 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 proper processing mode based on the operator type for operators supporting both float16 and float32.
  • For cube operators, the system processes the computing based on the operator implementation.
    1. The preferred input data type is float16 and the output data type is float32.
    2. If the scenario where the input data type is float16 and the output data type is float32 is not supported, set both the input and output data types to float32.
    3. If the scenario where both the input and output data types are float32 is not supported, set both the input and output data types to float16.
    4. If none of the preceding scenarios is supported, an error is reported.
  • For vector operators, float32 is forcibly selected for operators supporting both float16 and float32, even if the original precision is float16. This argument is invalid if your network model contains operators not supporting float32, for example, operators that support only float16. In this case, float16 is preserved. If the operators do not support float32 and are configured to the blocklist for mixed precision (by setting precision_reduce to false), the counterpart AI CPU operators supporting float32 are used.
• mixed_float16: enables automatic mixed precision, indicating that both float16 and float32 are used for neural network processing.

  For certain operators of the float32 data type on a network, the system automatically reduces their precision to float16 based on the built-in tuning policy. This will improve system performance and reduce memory footprint with minimal accuracy degradation. Use the mixed precision mode in conjunction with loss scaling to compensate for the accuracy degradation caused by precision reduction.

Default value:

• For the Atlas Training Series Product , this configuration item has no default value. The default value of precision_mode is used, that is, allow_fp32_to_fp16.

Example:

config = NPURunConfig(precision_mode_v2="origin")

modify_mixlist

When mixed precision is enabled, you can use this option to specify the path and file name of the blocklist, trustlist, and graylist, and specify the operators that allow precision reduction and those that do not allow precision reduction.

You can enable the mixed precision by configuring precision_mode_v2 or precision_mode in the script.

config = NPURunConfig(modify_mixlist="/home/test/ops_info.json")

You can specify the operator types in ops_info.json as shown below. Separate operators with commas (,).

{
  "black-list": {                  // Blocklist
     "to-remove": [                // Move an operator from the blocklist to the graylist.
     "Xlog1py"
     ],
     "to-add": [                   // Move an operator from the trustlist or graylist to the blocklist.
     "Matmul",
     "Cast"
     ]
  },
  "white-list": {                  // Trustlist
     "to-remove": [                // Move an operator from the trustlist to the graylist.
     "Conv2D"
     ],
     "to-add": [                   // Move an operator from the blocklist or graylist to the trustlist.
     "Bias"
     ]
  }
}

Note: The operators in the preceding example configuration file are for reference only. The configuration should be based on the actual hardware environment and the built-in tuning policies of the operators.

You can query the built-in tuning policy of each operator in mixed precision mode in CANN software installation directory/opp/built-in/op_impl/ai_core/tbe/config/<soc_version>/aic-<soc_version>-ops-info.json. For example:

"Conv2D":{
    "precision_reduce":{
        "flag":"true"
},

• true (trustlist): The precision of operators on the trustlist can be reduced in mixed precision mode..
• false (blocklist): The precision of operators on the blocklist cannot be reduced in mixed precision mode..
• Not specified (graylist): follows the same mixed precision processing as the upstream operator.

enable_reduce_precision

Not supported in the current version.

customize_dtypes

If precision_mode is used to set the global precision mode of a network, precision problems may occur on particular operators. In this case, you can use customize_dtypes to configure the precision mode of these operators, and still compile other operators using the precision mode specified by precision_mode. Note if precision_mode is set to must_keep_origin_dtype, customize_dtypes does not take effect.

Set it to the path (including the name of the configuration file), for example, /home/test/customize_dtypes.cfg.

Configuration example:

config = NPURunConfig(customize_dtypes="/home/test/customize_dtypes.cfg")

List the names or types of operators whose precision needs customization in the configuration file. Each operator occupies a line, and the operator type must be defined based on Ascend IR. If both operator name and type are configured for an operator, the operator name applies during compilation.

The structure of the configuration file is as follows:

# By operator name
Opname1::InputDtype:dtype1,dtype2,...OutputDtype:dtype1,...
Opname2::InputDtype:dtype1,dtype2,...OutputDtype:dtype1,...
# By operator type
OpType::TypeName1:InputDtype:dtype1,dtype2,...OutputDtype:dtype1,...
OpType::TypeName2:InputDtype:dtype1,dtype2,...OutputDtype:dtype1,...

Example:

# By operator name
resnet_v1_50/block1/unit_3/bottleneck_v1/Relu::InputDtype:float16,int8,OutputDtype:float16,int8
# By operator type
OpType::Relu:InputDtype:float16,int8,OutputDtype:float16,int8

dump_config

Dump configuration. Before creating NPURunConfig, you can instantiate a DumpConfig class for dump configuration. For details about the constructor of the DumpConfig class, see DumpConfig Constructor.

Example:

config = NPURunConfig(dump_config=dump_config)

quant_dumpable

Example:

config = NPURunConfig(quant_dumpable="1")

fusion_switch_file

Directory of the fusion switch configuration file, including the file name.

The value can contain letters, digits, underscores (_), hyphens (-), and periods (.).

The built-in graph fusion and UB fusion patterns are enabled by default. You can disable selected fusion patterns in the configuration file.

config = NPURunConfig(fusion_switch_file="/home/test/fusion_switch.cfg")

The following is a template of the fusion_switch.cfg configuration file. on indicates that a fusion pattern is enabled, and off indicates that a fusion pattern is disabled.

{
    "Switch":{
        "GraphFusion":{
            "RequantFusionPass":"on",
            "ConvToFullyConnectionFusionPass":"off",
            "SoftmaxFusionPass":"on",
            "NotRequantFusionPass":"on",
            "ConvConcatFusionPass":"on",
            "MatMulBiasAddFusionPass":"on",
            "PoolingFusionPass":"on",
            "ZConcatv2dFusionPass":"on",
            "ZConcatExt2FusionPass":"on",
            "TfMergeSubFusionPass":"on"
        },
        "UBFusion":{
            "TbePool2dQuantFusionPass":"on"
        }
    }
}

To disable all fusion patterns at a time, refer to this configuration file example.

{
    "Switch":{
        "GraphFusion":{
            "ALL":"off"
        },
        "UBFusion":{
            "ALL":"off"
         }
    }
}

Notes:

1. Some built-in fusion patterns are not switchable due to functionality restrictions and these fusion patterns will remain enabled despite user's switch settings.
2. To disable all fusion patterns except selected ones, refer to the following example.

   {
       "Switch":{
           "GraphFusion":{
               "ALL":"off",
               "SoftmaxFusionPass":"on"
           },
           "UBFusion":{
               "ALL":"off",
               "TbePool2dQuantFusionPass":"on"
           }
       }
   }

buffer_optimize

Enables buffer optimization. This is an advanced switch.

• l2_optimize (default): enabled
• off_optimize: disabled.

Example:

config = NPURunConfig(buffer_optimize="l2_optimize")

profiling_config

Profiling configuration. Before creating NPURunConfig, you can instantiate a ProfilingConfig class for profiling configuration. For details about the constructor of the ProfilingConfig class, see ProfilingConfig Constructor.

Example:

config = NPURunConfig(profiling_config=profiling_config)

aoe_mode

Tuning mode of AOE.

• 1: subgraph tuning.
• 2: operator tuning.
• 4: gradient splitting tuning.

  In the data parallel scenario, AllReduce is used to aggregate gradients. The gradient splitting mode is closely related to the distributed training performance. If the splitting is improper, the communication hangover time is long after the backward propagation is complete, affecting the cluster training performance and linearity. It is sophisticated to perform manual tuning through the gradient splitting API (set_split_strategy_by_idx or set_split_strategy_by_size) of collective communication. The AOE tool collects profile data in the real-device environment and automatically looks up for the optimal splitting policy. You only need to set the obtained policy to your network by passing it to the set_split_strategy_by_idx call.

Configuration example:

config = NPURunConfig(aoe_mode="2")

work_path

Working directory of AOE, which stores the configuration and tuning result files. By default, the files are generated in the current directory.

The value is a string. Create the specified directory in advance in the environment (either container or host) where training is performed. The running user configured during installation must have the read and write permissions on this directory. The value can be an absolute path or a path relative to the path where the training script is executed.

• An absolute path starts with a slash (/), for example, /home/HwHiAiUser/output.
• A relative path starts with a directory name, for example, output.

Example:

config = NPURunConfig(work_path="/home/HwHiAiUser/output")

aoe_config_file

Tunes only operators with low performance on the network with AOE. Set this option to the path and name of the configuration file that contains the operator information, for example, /home/test/cfg/tuning_config.cfg.

Example:

config = NPURunConfig(aoe_config_file="/home/test/cfg/tuning_config.cfg")

The configuration file contains information about the operators to be tuned. The file content format is as follows:

{
       "tune_ops_name":["bert/embeddings/addbert/embeddings/add_1","loss/MatMul"],
       "tune_ops_type":["Add", "Mul"]
       "tune_optimization_level":"O1",
       "feature":["deeper_opat"]
}

• tune_ops_name: name of the specified operator (whole word match). You can specify one or more operator names. If multiple operator names are specified, separate them with commas (,). The operator name must be the node name of the network model processed by Graph Compiler. You can obtain the operator name from profiling tuning data. For details, see Performance Tuning Tool User Guide .
• tune_ops_type: specified operator type (whole word match). You can specify one or more operator types. If multiple operator types are specified, separate them with commas (,). If a fused operator contains the specified operator type, the fused operator will also be tuned.
• tune_optimization_level: tuning mode. The value O1 indicates the high-performance tuning mode, and the value O2 indicates the normal mode. The default value is O2.
• feature: tuning feature switch. The value can be deeper_opat or nonhomo_split. The value deeper_opat indicates that in-depth operator tuning is enabled. In this case, aoe_mode must be set to 2. The value nonhomo_split indicates that non-uniform subgraph partition tuning is enabled. In this case, aoe_mode must be set to 1.

op_compiler_cache_mode

Disk cache mode for operator compilation. enable is the default value.

• enable: disk cache mode enabled. The operator compilation information is cached to the disk, which can be reused by operators with the same compilation options, improving compilation efficiency.
• force: cache mode enabled. In this mode, the existing cache is cleared up before new operator compilation is added to the cache. For example, for Python changes, dependency library changes, or repository changes after operator optimization, you need to set this option to force to clear up the existing cache and then change it to enable to prevent the cache from being forcibly refreshed during each compilation. Note that you are not advised to set the force option for parallel program compilation. Otherwise, the cache used by other models may be cleared, causing compilation failures.
• disable: disabled.

Notes:

• When enabling the operator compilation cache function, you can configure the path for storing the operator compilation cache file by using op_compiler_cache_dir.
• disable and force are recommended for publishing the final model.
• If op_debug_level is set to a non-zero value, the op_compiler_cache_mode configuration is ignored, the operator compilation cache function is disabled, and all operators are recompiled.
• If op_debug_config is not empty and the op_debug_list field is not configured, the op_compiler_cache_mode configuration is ignored, the operator compilation cache function is disabled, and all operators are recompiled.
• If op_debug_config is not empty, the op_debug_list field is configured, and op_compiler_cache_mode is set to enable or force, the operators in the list are recompiled, and the operator compilation cache function is enabled for operators that are not in the list.
• When the operator compilation cache function is enabled, the default disk space for storing cache files is 500 MB. If the disk space is insufficient, 50% of the cache files are retained by default. You can also customize the ratio of disk space allocated for storing cache files to the reserved cache space as follows:
  1. Using the op_cache.ini configuration file

     After the operator is compiled, the op_cache.ini file is automatically generated in the directory specified by op_compiler_cache_dir. You can use this file to set the ratio of the disk space allocated for cache storage to the reserved cache space. If the op_cache.ini file does not exist, manually create it.

     Add the following information to the op_cache.ini file:

     # Configure the file format (required). The automatically generated file contains the following information by default. When manually creating a file, enter the following information:
     [op_compiler_cache]
     # Limit the disk space of the cache folder on the Ascend AI Processor (unit: MB).
     max_op_cache_size=500
     # When the disk space is insufficient, set the ratio of the cache space to be reserved. The value ranges from 1 to 100, in percentage. For example, 80 indicates that 80% of the cache files are reserved and other files are deleted when the disk space is insufficient.
     remain_cache_size_ratio=80

     • The op_cache.ini file takes effect only when the values of max_op_cache_size and remain_cache_size_ratio in the preceding file are valid.
     • If the size of the compilation cache file exceeds the value of max_op_cache_size and the cache file is not accessed for more than half an hour, the cache file will be aged. (Operator compilation will not be interrupted due to the size of the compilation cache file exceeding the set limit. Therefore, if max_op_cache_size is set to a small value, the size of the actual compilation cache file may exceed the configured value.)
     • To disable the compilation cache aging function, set max_op_cache_size to -1. In this case, the access time is not updated when the operator cache is accessed, the operator compilation cache is not aged, and the default drive space is 500 MB.
     • If multiple users use the same cache path, the configuration file affects all users.
  2. Using environment variable ASCEND_MAX_OP_CACHE_SIZE

     The environment variable ASCEND_MAX_OP_CACHE_SIZE is used to limit the storage space of the cache folder of the Ascend AI Processor. When the compilation cache space reaches the specified value and the cache file is not accessed for more than half an hour, the cache file is aged. The environment variable ASCEND_REMAIN_CACHE_SIZE_RATIO is used to set the ratio of the cache space to be reserved. For details about environment variables, see Building > Operator Building in Environment Variables.

     To disable the compilation cache aging function, set ASCEND_MAX_OP_CACHE_SIZE to -1.

  Caution: If both the op_cache.ini file and environment variable are configured, the configuration items in the op_cache.ini file are read first. If neither the op_cache.ini file nor the environment variable are configured, the system default values are read: 500 MB drive space and 50% reserved cache space.

config = NPURunConfig(op_compiler_cache_mode="enable")

op_compiler_cache_dir

Disk cache directory for operator compilation.

The value can contain letters, digits, underscores (_), hyphens (-), and periods (.).

If the specified directory exists and is valid, the kernel_cache subdirectory is automatically created. If the specified directory does not exist but is valid, the system automatically creates this directory and the kernel_cache subdirectory.

The storage priority of the operator compilation cache files is as follows:

op_compiler_cache_dir -> ${ASCEND_CACHE_PATH}/kernel_cache_host ID -> the default path ($HOME/atc_data)

For details about ASCEND_CACHE_PATH, see Environment Variables.

config = NPURunConfig(op_compiler_cache_dir="/home/test/kernel_cache")

local_rank_id

Rank ID of the current process, used in data parallel processing in recommendation networks. The main process deduplicates the data and distributes the deduplicated data to the devices of other processes for forward and backward propagation.

In this mode, multiple devices on a host share one main process for data preprocessing, leaving other processes to receive preprocessed data from the main process.

To identify the main process, call the collective communication API get_local_rank_id() to get the rank ID of the current process on its server.

Example:

config = NPURunConfig(local_rank_id=0, local_device_list="0,1")

local_device_list

Devices that the main process sends data to, used in conjunction with local_rank_id.

config = NPURunConfig(local_rank_id=0, local_device_list="0,1")

hccl_timeout

Timeout interval (s) of collective communication. Defaults to 1836.

You can set the timeout interval if the default value does not meet your requirement (for example, when a communication failure occurs).

• For the Atlas Training Series Product , the value range is (0, 17340], in seconds. The default value is 1836.

  Note: For the Atlas Training Series Product , actual timeout interval set in the system = Value of this environment variable // 68 x 68 (unit: s). If the value of the environment variable is smaller than 68, 68s is used by default.

  For example, if hccl_timeout is set to 600, the actual timeout interval set in the system is 544s (600 // 6 x 68 = 8 x 68).

Example:

config = NPURunConfig(hccl_timeout=1800)

op_wait_timeout

Operator wait timeout interval (s). Defaults to 120.

Example:

config = NPURunConfig(op_wait_timeout=120)

op_execute_timeout

Operator execution timeout interval (s).

Example:

config = NPURunConfig(op_execute_timeout=90)

stream_sync_timeout

Timeout interval for stream synchronization during graph execution. If the timeout interval exceeds the configured value, a synchronization failure is reported. The unit is ms.

The default value is -1, indicating that there is no waiting time and no error is reported when the synchronization fails.

Note: In the cluster training scenario, the value of this option (timeout interval for stream synchronization) must be greater than the collection communication timeout interval, that is, the value of hccl_timeout or the value of the environment variable HCCL_EXEC_TIMEOUT.

Example:

config = NPURunConfig(stream_sync_timeout=60000)

event_sync_timeout

Timeout interval for event synchronization during graph execution. If the timeout interval exceeds the configured value, a synchronization failure is reported. The unit is ms.

The default value is -1, indicating that there is no waiting time and no error is reported when the synchronization fails.

Example:

config = NPURunConfig(event_sync_timeout=60000)

experimental_config

Extended option, which is not recommended. Before creating NPURunConfig, you can instantiate an ExperimentalConfig class to configure functions. For details about the constructor of the ExperimentalConfig class, see ExperimentalConfig Constructor.

jit_compile

Determines whether to compile the operator online or use the compiled operator binary file.

• auto: For a static shape network, compile the operator online. For a dynamic shape network, search for the compiled operator binary in the system first. If the corresponding binary file is not available, compile the operator.
• true: Operators are compiled online. The system performs fusion and tuning based on the obtained graph information to get better performing operators.
• false: The compiled operator binary file in the system is preferentially searched. If the file can be found, operators are not compiled anymore, which produces better compilation performance. If the file cannot be found, operators will be compiled.

Default value: auto

Example:

config = NPURunConfig(jit_compile="auto")

enable_data_pre_proc

Performance tuning.

Enable for the GetNext operator offload to the Ascend AI Processor. The GetNext operator offload is a prerequisite for iteration offload.

• True (default) The prerequisite for GetNext operator offloading is that the TensorFlow Dataset mode is used to read data.
• False

Example:

config = NPURunConfig(enable_data_pre_proc=True)

variable_format_optimize

Performance tuning.

Variable format optimization enable.

• True: enabled.
• False: disabled

To improve training efficiency, the format of the variables is converted to a format more compatible with the Ascend AI Processor during variable initialization performed by the network. Enable or disable this function as needed.

This option is left empty by default, indicating that the configuration is disabled.

Example:

config = NPURunConfig(variable_format_optimize=True)

op_debug_level

Operator debug enable. The values are as follows:

• 0: disables operator debug.
• 1: enables operator debug. TBE instruction mapping files, including an operator CCE file (.cce), a Python-CCE mapping file (*_loc.json), and operator .o and .json files, are generated in the kernel_meta folder under the training script execution directory. You can locate AI Core errors by using tools.
• 2: enables operator debug. TBE instruction mapping files, including an operator CCE file (.cce), a Python-CCE mapping file (*_loc.json), and operator .o and .json files, are generated in the kernel_meta folder under the training script execution directory. Compilation and optimization are disabled and CCE compiler debug is enabled (by setting -O0-g). You can locate AI Core errors by using tools.
• 3: disables operator debug. The operator .o and .json files are retained in the kernel_meta folder in the training script execution directory.
• 4: disables operator debug. The operator binary (.o) and operator description file (.json) are retained, and a TBE instruction mapping file (.cce) and a UB fusion description file ({$kernel_name}_compute.json) are generated in the kernel_meta folder under the training script execution directory.
  NOTICE:

  • You are advised to set this option to 0 or 3 for training. To locate errors, set this option to 1 or 2, which might compromise the network performance.
  • If this option is set to 2 (the CCE compiler is enabled), it cannot be used together with the oom option in op_debug_config. Otherwise, an AI Core error is reported. The following is an example of the error message:

    ...there is an aivec error exception, core id is 49, error code = 0x4 ...

  • If this option is set to 2, the CCE compiler is enabled, and the size of the operator kernel file (*.o file) increases. In dynamic shape scenarios, all possible scenarios are traversed during operator compilation, which may cause operator compilation failures due to large operator kernel files. In this case, 2 is not recommended.

    If the compilation failure is caused by the large operator kernel file, the following log is displayed:

    message:link error ld.lld: error: InputSection too large for range extension thunk ./kernel_meta_xxxxx.o:(xxxx)

  • If the value of this option is not 0, you can use the debug_dir option in the Debugging to specify the path for storing debugging-related process files.
  • If this option is set to 0 and NPU_COLLECT_PATH is set, the operator compilation directory kernel_meta is generated in the current path after the command is executed. If ASCEND_WORK_PATH is set, kernel_meta is generated in the path specified by the environment variable. For details about the environment variable, see Environment Variables.
  • When the debug function is enabled, if the model contains the following MC2 operators, the *.o, *.json, and *.cce files of the operators are not generated in the kernel_meta directory.

    MatMulAllReduce

    MatMulAllReduceAddRmsNorm

    AllGatherMatMul

    MatMulReduceScatter

    AlltoAllAllGatherBatchMatMul

    BatchMatMulReduceScatterAlltoAll

This option is left empty by default, indicating that the configuration is disabled.

Example:

config = NPURunConfig(op_debug_level=1)

op_select_implmode

Operator implementation mode select. Certain operators compiled in the Ascend AI Processor can be implemented in either high-precision or high-performance mode at model compile time. Arguments:

• high_precision: high-precision implementation mode. In high-precision mode, Taylor's theorem or Newton's method is used to improve operator precision with float16 input.
• high_performance (default): high-performance implementation mode. In high-performance mode, the optimal performance is implemented without affecting the network precision (float16).

This option is left empty by default, indicating that the configuration is disabled.

config = NPURunConfig(op_select_implmode="high_precision")

optypelist_for_implmode

List of operator types (separated by commas) that use the mode specified by the op_select_implmode option. Currently, Pooling, SoftmaxV2, LRN, and ROIAlign operators are supported.

Use this option in conjunction with op_select_implmode, for example:

config = NPURunConfig(
    op_select_implmode="high_precision",
    optypelist_for_implmode="Pooling,SoftmaxV2")

This option is left empty by default, indicating that the configuration is disabled.

dynamic_input

Whether it is a dynamic input.

• True
• False (default)

config = NPURunConfig(dynamic_input=True)

dynamic_graph_execute_mode

Execution mode of a dynamic input. That is, this option takes effect when dynamic_input is set to True. Possible values are:

dynamic_execute: dynamic graph compilation. In this mode, the shape range configured in dynamic_inputs_shape_range is used for compilation.

config = NPURunConfig(dynamic_graph_execute_mode="dynamic_execute")

dynamic_inputs_shape_range

Shape range of each dynamic input. If a graph has two dataset inputs and one placeholder input, a configuration example is as follows.

config = NPURunConfig(dynamic_inputs_shape_range="getnext:[128 ,3~5, 2~128, -1],[64 ,3~5, 2~128, -1];data:[128 ,3~5, 2~128, -1]")

Precautions:

• getnext indicates the dataset inputs and data indicates the placeholder inputs.
• The size of a static dimension is specified by a determinant value. The size range of a dynamic dimension is specified by using a tilde (~). A dynamic dimension without size range specified is denoted by -1.
• Assume that your graph has three dataset inputs but the first dataset input has a static shape; the static shape must be specified as shown below.

  config = NPURunConfig(dynamic_inputs_shape_range="getnext:[3,3,4,10],[-1,3,2~1000,-1],[-1,-1,-1,-1]")

• For scalar inputs, you also need to fill in the shape range by using square brackets ([]). No space is allowed before [].
• If there are multiple getnext inputs or data inputs on the network, the input ordering must be preserved. For example:
  • If there are multiple dataset inputs on the network:

    def func(x):
        x = x + 1
        y = x + 2
        return x,y
    dataset = tf.data.Dataset.range(min_size, max_size)
    dataset = dataset.map(func)

    Assume that the first input of the network is x (with shape range [3–5]) and the second input is y (with shape range [3–6]). When configuring the dynamic ranges in dynamic_inputs_shape_range, the ordering must be preserved.

    config = NPURunConfig(dynamic_inputs_shape_range ="getnext:[3~5],[3~6]")

  • If there are multiple placeholder inputs on the network:

    If the placeholder names are not specified, for example:

    x = tf.placeholder(tf.int32)
    y = tf.placeholder(tf.int32)

    # Set the dynamic ranges of the placeholder inputs in dynamic_inputs_shape_range in the same order as that defined in the script. That is, the first input x (with shape range [3-5]) goes first and the second input y (with shape range [3-6]) follows.

    config = NPURunConfig(dynamic_inputs_shape_range= "data:[3~5],[3~6]")

    If the placeholder names are specified, for example:

    x = tf.placeholder(tf.int32, name='b')
    y = tf.placeholder(tf.int32, name='a')

    The inputs are in the alphabetical order of the name fields,

    that is, when setting dynamic_inputs_shape_range, the first input y (with shape range [3-6]) goes first and the second input x (with shape range [3-5]) follows.

    config = NPURunConfig(dynamic_inputs_shape_range = "data:[3~6],[3~5]")

  NOTICE:

  • For subgraphs with different input shapes, set_graph_exec_config is recommended for supporting dynamic inputs. dynamic_inputs_shape_range acts only on a single graph, which may cause execution errors.
  • If the placeholder names are not specified in the network script, the placeholders are named in the following format:

    xxx_0, xxx_1, xxx_2, ...

    The content following the underscore (_) is the sequence index of a placeholder in the network script. Placeholders are arranged in alphabetical order of the index. If the number of placeholders is greater than 10, the sequence is xxx_0 > xxx_10 > xxx_2 > xxx_3. In the network script, the placeholder with index 10 is placed before the placeholder with index 2. As a result, the defined shape range does not match the input placeholder.

    To avoid this problem, when the number of input placeholders is greater than 10, you are advised to specify the placeholder names in the network script. In this case, the placeholders are named based on the specified names, to associate the shape ranges with the placeholder names.

  • This option cannot be used together with dynamic_dims. If they are used together, dynamic_dims has a higher priority and this option does not take effect.

graph_memory_max_size

Sizes of the network static memory and the maximum dynamic memory (used in earlier versions).

In the current version, this option does not take effect. The system dynamically allocates memory resources based on the actual memory usage of the network.

variable_memory_max_size

Size of the variable memory (used in earlier versions).

In the current version, this option does not take effect. The system dynamically allocates memory resources based on the actual memory usage of the network.