Developer utilities

This section provides a detailed description of some commonly used utilities for Taichi developers.

Logging

Taichi uses spdlog as its logging system. Logs can have different levels, from low to high, they are:

LEVELS
trace
debug
info
warn
error

The higher the level is, the more critical the message is.

The default logging level is info. You may override the default logging level by:

1. Setting the environment variable like export TI_LOG_LEVEL=warn.
2. Setting the log level from Python side: ti.set_logging_level(ti.WARN).

In Python, you may write logs using the ti.* interface:

# Python
ti.trace("Hello world!")
ti.debug("Hello world!")
ti.info("Hello world!")
ti.warn("Hello world!")
ti.error("Hello world!")

In C++, you may write logs using the TI_* interface:

// C++
TI_TRACE("Hello world!");
TI_DEBUG("Hello world!");
TI_INFO("Hello world!");
TI_WARN("Hello world!");
TI_ERROR("Hello world!");

If one raises a message of the level error, Taichi will be terminated immediately and result in a RuntimeError on Python side.

// C++
int func(void *p) {
  if (p == nullptr)
    TI_ERROR("The pointer cannot be null!");

  // will not reach here if p == nullptr
  do_something(p);
}

note

For people from Linux kernels, TI_ERROR is just panic.

You may also simplify the above code by using TI_ASSERT:

int func(void *p) {
  TI_ASSERT_INFO(p != nullptr, "The pointer cannot be null!");
  // or
  // TI_ASSERT(p != nullptr);

  // will not reach here if p == nullptr
  do_something(p);
}

Benchmarking and regression tests

• Run ti benchmark to run tests in benchmark mode. This will record the performance of python tests/run_tests.py, and save it in benchmarks/output.
• Run ti regression to show the difference between the previous result in benchmarks/baseline. And you can see if the performance is increasing or decreasing after your commits. This is really helpful when your work is related to IR optimizations.
• Run ti baseline to save the benchmark result to benchmarks/baseline for future comparison, this may be executed on performance-related PRs, before they are merged into master.

For example, this is part of the output by ti regression after enabling constant folding optimization pass:

linalg__________________polar_decomp______________________________
codegen_offloaded_tasks                       37 ->    39    +5.4%
codegen_statements                          3179 ->  3162    -0.5%
codegen_kernel_statements                   2819 ->  2788    -1.1%
codegen_evaluator_statements                   0 ->    14    +inf%

linalg__________________init_matrix_from_vectors__________________
codegen_offloaded_tasks                       37 ->    39    +5.4%
codegen_statements                          3180 ->  3163    -0.5%
codegen_kernel_statements                   2820 ->  2789    -1.1%
codegen_evaluator_statements                   0 ->    14    +inf%

note

Currently ti benchmark only supports benchmarking number-of-statements, no time benchmarking is included since it depends on hardware performance and therefore hard to compare if the baseline is from another machine. We are to purchase a fixed-performance machine as a time benchmark server at some point. See detailed discussion at Github Issue #948

The suggested workflow for the performance-related PR author to run the regression tests is:

• Run ti benchmark && ti baseline in master to save the current performance as a baseline.
• Run git checkout -b your-branch-name.
• Do works on the issue, stage 1.
• Run ti benchmark && ti regression to obtain the result.
• (If result BAD) Do further improvements, until the result is satisfying.
• (If result OK) Run ti baseline to save stage 1 performance as a baseline.
• Go forward to stage 2, 3, ..., and the same workflow is applied.

Debug taichi program using gdb

1. Prepare a script that can reproduce the issue, e.g. python repro.py.
2. Build taichi with debug information using DEBUG=1 python setup.py develop (or install).
3. Run gdb --args python repro.py, now you can debug from there! For example, you can set a breakpoint using b foo.cpp:102 or b Program::compile().

However if your issue cannot be reproduced consistently this solution isn't a great fit. In that case it's recommended to follow the section below so that gdb is triggered automatically when the program crashes.

(Linux only) Trigger gdb when programs crash

# Python
ti.init(gdb_trigger=True)

// C++
CoreState::set_trigger_gdb_when_crash(true);

# Shell
export TI_GDB_TRIGGER=1

note

Quickly pinpointing segmentation faults/assertion failures using gdb: When Taichi crashes, gdb will be triggered and attach to the current thread. You might be prompt to enter sudo password required for gdb thread attaching. After entering gdb, check the stack backtrace with command bt (backtrace), then find the line of code triggering the error.

Code coverage

To ensure that our tests covered every situation, we need to have coverage report. That is, to detect how many percents of code lines in is executed in test.

• Generally, the higher the coverage percentage is, the stronger our tests are.
• When making a PR, we want to ensure that it comes with corresponding tests. Or code coverage will decrease.
• Code coverage statuses are visible at Codecov.
• Currently, Taichi coverage report is only set up for Python code, not C++ yet.

python tests/run_tests.py -C       # run tests and save results to .coverage
coverage report  # generate a coverage report on terminal output
coverage html    # generate a HTML form report in htmlcov/index.html

Serialization (legacy)

The serialization module of taichi allows you to serialize/deserialize objects into/from binary strings.

You can use TI_IO macros to explicitly define fields necessary in Taichi.

// TI_IO_DEF
struct Particle {
    Vector3f position, velocity;
    real mass;
    string name;

    TI_IO_DEF(position, velocity, mass, name);
}

// TI_IO_DECL
struct Particle {
    Vector3f position, velocity;
    real mass;
    bool has_name
    string name;

    TI_IO_DECL() {
        TI_IO(position);
        TI_IO(velocity);
        TI_IO(mass);
        TI_IO(has_name);
        // More flexibility:
        if (has_name) {
            TI_IO(name);
        }
    }
}

// TI_IO_DEF_VIRT();

Upgrading CUDA

Right now we are targeting CUDA 10. Since we use run-time loaded CUDA driver APIs which are relatively stable across CUDA versions, a compiled Taichi binary should work for all CUDA versions >= 10. When upgrading CUDA version, the file external/cuda_libdevice/slim_libdevice.10.bc should also be replaced with a newer version.

To generate the slimmed version of libdevice based on a full libdevice.X.bc file from a CUDA installation, use:

ti task make_slim_libdevice <path_to_libdevice.X.bc>