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Profiler Constructor

Description

Constructor of the Profiler class, which is used to enable the profiling function locally. For example, you can collect the profile data of a local subgraph on the TensorFlow network or a specified step.

Prototype

def __init__(
        self,
        *,
        level: str = "L0",
        aic_metrics: str = "",
        output_path: str = ""
    )

Options

Option

Input/Output

Description

level

Input
Profiler level, which controls the range of data to be profiled.
  • L0: collects the task scheduling duration (task_time) and profile data related to the ACL API.
  • L1: collects the profile data of collective communication operators and AI Core operators in addition to that collected at L0.
  • L2: collects the profile data of runtime component execution and AI CPU operators in addition to that collected at L1.

Default value: L0

aic_metrics

Input

When level is set to L1 or L2, you can use this option to collect the profile data of the AI Core and AI Vector Core hardware. The value of this option can be:

  • ArithmeticUtilization: time consumptions and percentages of Cube and Vector instructions.
  • PipeUtilization: percentages of time taken by compute units and MTEs.
  • Memory: memory read/write bandwidth rate.
  • MemoryL0: L0 read/write bandwidth rate.
  • MemoryUB: UB read/write bandwidth rate.
  • ResourceConflictRatio: ratio of pipeline queue instructions.
  • L2Cache: cache re-allocations upon missing of the read/write cache hit count.

For details about the collection items of each level, see "Profile Data File References > op_summary (Operator Details)" ( Performance Tuning Tool User Guide ).

Default value: PipeUtilization

output_path

Input
Path for storing profiling result files. The specified path must be created in advance in the environment (either in a container or on the host) where training is performed. The running user must have the read and write permissions on this path. The path can be an absolute path or a relative path (relative to the path where the command is executed). The path cannot contain the following special characters: "\n", "\f", "\r", "\b", "\t", "\v", and "\u007F".
  • An absolute path starts with a slash (/), for example, /home/HwHiAiUser/output.
  • A relative path starts with a directory name, for example, output.
  • This option has a higher priority than the environment variable ASCEND_WORK_PATH. For details about ASCEND_WORK_PATH, see Installation and Configuration > Flush File Configuration in the Environment Variables.

By default, this option is left blank. If it is left blank, the result files are stored in the current directory.

Returns

None

Restrictions

  • The Profiler class needs to be called using the with statement, and the profile data collection function takes effect in the corresponding scope.
  • The Profiler class can be invoked only in session mode.
  • The Profiler class cannot be nested.

    The following is an incorrect invoking example:

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    with profiler.Profiler(level="L1", aic_metrics="ArithmeticUtilization", output_path = "./"):
  with profiler.Profiler(level="L1", aic_metrics="ArithmeticUtilization", output_path = "./"):
    sess.run(add)
  • The Profiler class cannot be used together with options profiling_mode and profiling_options in Session Configuration, options enable_profiling and profiling_options in NPURunConfig Configuration, and environment variables PROFILING_MODE and PROFILING_OPTIONS. For details about the environment variables, see Environment Variables.
  • The Profiler class does not support multi-thread calling.

Example

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import tensorflow as tf
from npu_bridge.npu_init import *

......
a = tf.placeholder(tf.int32, (None,None))
b = tf.constant([[1,2],[2,3]], dtype=tf.int32, shape=(2,2))
add = tf.add(a, b)

with tf.Session(config=session_config, graph=g) as sess:
  with profiler.Profiler(level="L1", aic_metrics=str("ArithmeticUtilization"), output_path = "./"):
    result=sess.run(add, feed_dict={a: [[-20, 2],[1,3]],c: [[1],[-21]]})