18. Retrieving information about compilation and execution

When developing models for the IPU, it is important to be able to see how compute tiles are being used and what the balance of memory use across them is. In certain cases, such as when investigating memory over-consumption of a model or investigating any tile imbalance issues, it is useful to produce a trace report that will show a number of different aspects of graph deployment on the IPU.

To retrieve trace information about the Poplar IPU compilation and execution, there are environment variables provided by Poplar itself to dump the compilation and execution reports into a file. See the Capturing IPU reports chapter in the PopVision Graph Analyser User Guide for more information. To enable time-based profiling of events, see the Capturing execution information chapter in the PopVision System Analyser User Guide for more information.

18.1. TensorFlow options for reporting

Some tracing and reporting options are provided by TensorFlow as standard, and can be useful when developing graphs for the IPU.

TF_CPP_MIN_VLOG_LEVEL is an environment variable that enables the logging of the main C++ backend. Setting TF_CPP_MIN_VLOG_LEVEL=1 will show a lot of output. Included in this is the compilation and execution of the IPU code. The output of TF_CPP_MIN_VLOG_LEVEL can be overwhelming. If only the Poplar backend specific files are of interest, setting TF_POPLAR_VLOG_LEVEL=1 will filter the logging such that only those files produce outputs. Note that increasing the VLOG_LEVEL of either of those environment variables will increase the verbosity of the logs.

TF_CPP_VMODULE provides a mechanism to reduce the logging to certain translation units (source files). This combination is quite useful:


Finally, there is an environment variable called XLA_FLAGS which provides options to the general XLA backend. For example, the follow will produce a Graphviz DOT file of the optimised HLO graph which is passed to the Poplar compiler.

XLA_FLAGS='--xla_dump_to=. --xla_dump_hlo_as_dot --xla_dump_hlo_pass_re=forward-allocation --xla_hlo_graph_sharding_color'

The HLO pass forward-allocation is one of the final passes to run before the HLO instructions are scheduled for passing to the Poplar graph compiler. Running with these options will create a file called something like module_0001.0001.IPU.after_forward-allocation.before_hlo-memory-scheduler.dot. (The way that the file names are generated is explained in XLA graph file naming.) The Graphviz dot command can be used to convert this data to an image.

More information on the XLA flags can be found in the definition of the XLA proto.

18.2. XLA graph file naming

The number of files produced depends on the number of TensorFlow HLO modules generated. This can generally be predicted from the number of sess.run calls on distinct graphs that you make. For example, if your program contains a variable initialisation then this will be compiled as a separate XLA graph and appear as a separate file when dumped. If your program creates a report operation, then that will also be compiled as a separate XLA graph.

When you use ipu_compiler.compile, you force everything inside the compile call to be compiled into a single XLA graph. If you don’t use ipu_compiler.compile, then the results depend on the XLA scheduler, which will combine or split up parts of the TensorFlow graph as it sees fit, creating many arbitrary distinct XLA graphs. If you do not use ipu_compiler.compile, expect to see a larger number of XLA graphs generated. Please note, there is no guarantee your compiled op will only produce one XLA graph. Sometimes others are created for operations such as casting.

The following description provides a break down of the names of the generated files. These are of the general form:


  • There is always a module_ prefix, which indicates that this is the graph for an HLO Module.

  • The first XXXX is the HLO module’s unique ID, generated by XLA.

    There is no guarantee about the spacing between IDs, only that they are unique and increasing.

  • To understand the rest of the name, YYYY.IPU.......dot, we need to understand that the XLA graph is operated on by multiple different HLO passes, each modifying the XLA graph by optimizing, shuffling or otherwise rewriting it. After these passes, the graph is then lowered to Poplar. There are some TensorFlow native HLO passes, and there are some IPU specific ones.

    When dumping the XLA graphs, we can render the XLA graph before and after any HLO pass (for example, to see the effect of that pass on the graph) by supplying the argument --xla_dump_hlo_pass_re=xxxx, where xxxx is a regular expression describing which passes you want. TensorFlow will then render the XLA graph before and after every pass whose name matches that regex. For example, if you wanted to see the effect of every XLA HLO IPU pass involving while loops, you could use --xla_dump_hlo_pass_re=*While*.

    The number YYYY is simply an ID related to the order in which these graphs are generated.

  • Finally, the passes which the graph was “between” when it was rendered are appended to the filename.

    The before_optimizations graph is always rendered if dumping XLA.

  • The HLO modules have CamelCase class names by convention. For the file names, these are converted to snake_case.