1.15. Troubleshooting and tips

1.15.1. What to compile

The general recommendation is that you should only try to compile the critical paths in your code. If you have a piece of performance-critical computational code amongst some higher-level code, you may factor out the performance-critical code in a separate function and compile the separate function with Numba. Letting Numba focus on that small piece of performance-critical code has several advantages:

  • it reduces the risk of hitting unsupported features;
  • it reduces the compilation times;
  • it allows you to evolve the higher-level code which is outside of the compiled function much easier.

1.15.2. My code doesn’t compile

There can be various reasons why Numba cannot compile your code, and raises an error instead. One common reason is that your code relies on an unsupported Python feature, especially in nopython mode. Please see the list of Supported Python features. If you find something that is listed there and still fails compiling, please report a bug.

When Numba tries to compile your code it first tries to work out the types of all the variables in use, this is so it can generate a type specific implementation of your code that can be compiled down to machine code. A common reason for Numba failing to compile (especially in nopython mode) is a type inference failure, essentially Numba cannot work out what the type of all the variables in your code should be.

For example, let’s consider this trivial function:

@jit(nopython=True)
def f(x, y):
    return x + y

If you call it with two numbers, Numba is able to infer the types properly:

>>> f(1, 2)
    3

If however you call it with a tuple and a number, Numba is unable to say what the result of adding a tuple and number is, and therefore compilation errors out:

>>> f(1, (2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<path>/numba/numba/dispatcher.py", line 339, in _compile_for_args
    reraise(type(e), e, None)
File "<path>/numba/numba/six.py", line 658, in reraise
    raise value.with_traceback(tb)
numba.errors.TypingError: Failed at nopython (nopython frontend)
Invalid use of + with parameters (int64, tuple(int64 x 1))
Known signatures:
* (int64, int64) -> int64
* (int64, uint64) -> int64
* (uint64, int64) -> int64
* (uint64, uint64) -> uint64
* (float32, float32) -> float32
* (float64, float64) -> float64
* (complex64, complex64) -> complex64
* (complex128, complex128) -> complex128
* (uint16,) -> uint64
* (uint8,) -> uint64
* (uint64,) -> uint64
* (uint32,) -> uint64
* (int16,) -> int64
* (int64,) -> int64
* (int8,) -> int64
* (int32,) -> int64
* (float32,) -> float32
* (float64,) -> float64
* (complex64,) -> complex64
* (complex128,) -> complex128
* parameterized
[1] During: typing of intrinsic-call at <stdin> (3)

File "<stdin>", line 3:

The error message helps you find out what went wrong: “Invalid use of + with parameters (int64, tuple(int64 x 1))” is to be interpreted as “Numba encountered an addition of variables typed as integer and 1-tuple of integer, respectively, and doesn’t know about any such operation”.

Note that if you allow object mode:

@jit
def g(x, y):
    return x + y

compilation will succeed and the compiled function will raise at runtime as Python would do:

>>> g(1, (2,))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'tuple'

1.15.3. My code has a type unification problem

Another common reason for Numba not being able to compile your code is that it cannot statically determine the return type of a function. The most likely cause of this is the return type depending on a value that is available only at runtime. Again, this is most often problematic when using nopython mode. The concept of type unification is simply trying to find a type in which two variables could safely be represented. For example a 64 bit float and a 64 bit complex number could both be represented in a 128 bit complex number.

As an example of type unification failure, this function has a return type that is determined at runtime based on the value of x:

In [1]: from numba import jit

In [2]: @jit(nopython=True)
...: def f(x):
...:     if x > 10:
...:         return (1,)
...:     else:
...:         return 1
...:

In [3]: f(10)

Trying to execute this function, errors out as follows:

TypingError: Failed at nopython (nopython frontend)
Can't unify return type from the following types: tuple(int64 x 1), int64
Return of: IR name '$8.2', type '(int64 x 1)', location:
File "<ipython-input-2-51ef1cc64bea>", line 4:
def f(x):
    <source elided>
    if x > 10:
        return (1,)
        ^
Return of: IR name '$12.2', type 'int64', location:
File "<ipython-input-2-51ef1cc64bea>", line 6:
def f(x):
    <source elided>
    else:
        return 1

The error message “Can’t unify return type from the following types: tuple(int64 x 1), int64” should be read as “Numba cannot find a type that can safely represent a 1-tuple of integer and an integer”.

1.15.4. My code has an untyped list problem

As noted previously the first part of Numba compiling your code involves working out what the types of all the variables are. In the case of lists, a list must contain items that are of the same type or can be empty if the type can be inferred from some later operation. What is not possible is to have a list which is defined as empty and has no inferable type (i.e. an untyped list).

For example, this is using a list of a known type:

from numba import jit
@jit(nopython=True)
def f():
    return [1, 2, 3] # this list is defined on construction with `int` type

This is using an empty list, but the type can be inferred:

from numba import jit
@jit(nopython=True)
def f(x):
    tmp = [] # defined empty
    for i in range(x):
        tmp.append(i) # list type can be inferred from the type of `i`
    return tmp

This is using an empty list and the type cannot be inferred:

from numba import jit
@jit(nopython=True)
def f(x):
    tmp = [] # defined empty
    return (tmp, x) # ERROR: the type of `tmp` is unknown

Whilst slightly contrived, if you need an empty list and the type cannot be inferred but you know what type you want the list to be, this “trick” can be used to instruct the typing mechanism:

from numba import jit
import numpy as np
@jit(nopython=True)
def f(x):
    # define empty list, but instruct that the type is np.complex64
    tmp = [np.complex64(x) for x in range(0)]
    return (tmp, x) # the type of `tmp` is known, but it is still empty

1.15.5. The compiled code is too slow

The most common reason for slowness of a compiled JIT function is that compiling in nopython mode has failed and the Numba compiler has fallen back to object mode. object mode currently provides little to no speedup compared to regular Python interpretation, and its main point is to allow an internal optimization known as loop-lifting: this optimization will allow to compile inner loops in nopython mode regardless of what code surrounds those inner loops.

To find out if type inference succeeded on your function, you can use the inspect_types() method on the compiled function.

For example, let’s take the following function:

@jit
def f(a, b):
    s = a + float(b)
    return s

When called with numbers, this function should be fast as Numba is able to convert number types to floating-point numbers. Let’s see:

>>> f(1, 2)
3.0
>>> f.inspect_types()
f (int64, int64)
--------------------------------------------------------------------------------
# --- LINE 7 ---

@jit

# --- LINE 8 ---

def f(a, b):

    # --- LINE 9 ---
    # label 0
    #   a.1 = a  :: int64
    #   del a
    #   b.1 = b  :: int64
    #   del b
    #   $0.2 = global(float: <class 'float'>)  :: Function(<class 'float'>)
    #   $0.4 = call $0.2(b.1, )  :: (int64,) -> float64
    #   del b.1
    #   del $0.2
    #   $0.5 = a.1 + $0.4  :: float64
    #   del a.1
    #   del $0.4
    #   s = $0.5  :: float64
    #   del $0.5

    s = a + float(b)

    # --- LINE 10 ---
    #   $0.7 = cast(value=s)  :: float64
    #   del s
    #   return $0.7

    return s

Without trying to understand too much of the Numba intermediate representation, it is still visible that all variables and temporary values have had their types inferred properly: for example a has the type int64, $0.5 has the type float64, etc.

However, if b is passed as a string, compilation will fall back on object mode as the float() constructor with a string is currently not supported by Numba:

>>> f(1, "2")
3.0
>>> f.inspect_types()
[... snip annotations for other signatures, see above ...]
================================================================================
f (int64, str)
--------------------------------------------------------------------------------
# --- LINE 7 ---

@jit

# --- LINE 8 ---

def f(a, b):

    # --- LINE 9 ---
    # label 0
    #   a.1 = a  :: pyobject
    #   del a
    #   b.1 = b  :: pyobject
    #   del b
    #   $0.2 = global(float: <class 'float'>)  :: pyobject
    #   $0.4 = call $0.2(b.1, )  :: pyobject
    #   del b.1
    #   del $0.2
    #   $0.5 = a.1 + $0.4  :: pyobject
    #   del a.1
    #   del $0.4
    #   s = $0.5  :: pyobject
    #   del $0.5

    s = a + float(b)

    # --- LINE 10 ---
    #   $0.7 = cast(value=s)  :: pyobject
    #   del s
    #   return $0.7

    return s

Here we see that all variables end up typed as pyobject. This means that the function was compiled in object mode and values are passed around as generic Python objects, without Numba trying to look into them to reason about their raw values. This is a situation you want to avoid when caring about the speed of your code.

There are several ways of understanding why a function fails to compile in nopython mode:

  • pass nopython=True, which will raise an error indicating what went wrong (see above My code doesn’t compile);

  • enable warnings by setting the NUMBA_WARNINGS environment variable; for example with the f() function above:

    >>> f(1, 2)
    3.0
    >>> f(1, "2")
    example.py:7: NumbaWarning: Function "f" failed type inference: Internal error at <numba.typeinfer.CallConstrain object at 0x7f6b8dd24550>:
    float() only support for numbers
    File "example.py", line 9
      @jit
    example.py:7: NumbaWarning: Function "f" was compiled in object mode without forceobj=True.
      @jit
    3.0
    

1.15.6. Disabling JIT compilation

In order to debug code, it is possible to disable JIT compilation, which makes the jit decorator (and the decorators njit and autojit) act as if they perform no operation, and the invocation of decorated functions calls the original Python function instead of a compiled version. This can be toggled by setting the NUMBA_DISABLE_JIT enviroment variable to 1.

When this mode is enabled, the vectorize and guvectorize decorators will still result in compilation of a ufunc, as there is no straightforward pure Python implementation of these functions.

1.15.7. Debugging JIT compiled code with GDB

Setting the debug keyword argument in the jit decorator (e.g. @jit(debug=True)) enables the emission of debug info in the jitted code. To debug, GDB version 7.0 or above is required. Currently, the following debug info is available:

  • Function name will be shown in the backtrace. But, no type information.
  • Source location (filename and line number) is available. For example, user can set break point by the absolute filename and line number; e.g. break /path/to/myfile.py:6.
  • Local variables in the current function can be shown with info locals.
  • Type of variable with whatis myvar.
  • Value of variable with print myvar or display myvar.
    • Simple numeric types, i.e. int, float and double, are shown in their native representation. But, integers are assumed to be signed.
    • Other types are shown as sequence of bytes.

Known issues:

  • Stepping depends heavily on optimization level.
    • At full optimization (equivalent to O3), most of the variables are optimized out.
    • With no optimization (e.g. NUMBA_OPT=0), source location jumps around when stepping through the code.
    • At O1 optimization (e.g. NUMBA_OPT=1), stepping is stable but some variables are optimized out.
  • Memory consumption increases significantly with debug info enabled. The compiler emits extra information (DWARF) along with the instructions. The emitted object code can be 2x bigger with debug info.

Internal details:

  • Since Python semantics allow variables to bind to value of different types, Numba internally creates multiple versions of the variable for each type. So for code like:

    x = 1         # type int
    x = 2.3       # type float
    x = (1, 2, 3) # type 3-tuple of int
    

    Each assignments will store to a different variable name. In the debugger, the variables will be x, x$1 and x$2. (In the Numba IR, they are x, x.1 and x.2.)

  • When debug is enabled, inlining of the function is disabled.

1.15.7.1. Example debug usage

The python source:

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from numba import njit

@njit(debug=True)
def foo(a):
    b = a + 1
    c = a * 2.34
    d = (a, b, c)
    print(a, b, c, d)

r= foo(123)
print(r)

In the terminal:

$ NUMBA_OPT=1 gdb -q python
Reading symbols from python...done.
(gdb) break /home/user/chk_debug.py:5
No source file named /home/user/chk_debug.py.
Make breakpoint pending on future shared library load? (y or [n]) y

Breakpoint 1 (/home/user/chk_debug.py:5) pending.
(gdb) run chk_debug.py
Starting program: /home/user/miniconda/bin/python chk_debug.py
...
Breakpoint 1, __main__::foo$241(long long) () at chk_debug.py:5
5         b = a + 1
(gdb) n
6         c = a * 2.34
(gdb) bt
#0  __main__::foo$241(long long) () at chk_debug.py:6
#1  0x00007ffff7fec47c in cpython::__main__::foo$241(long long) ()
#2  0x00007fffeb7976e2 in call_cfunc (locals=0x0, kws=0x0, args=0x7fffeb486198,
...
(gdb) info locals
a = 0
d = <error reading variable d (DWARF-2 expression error: `DW_OP_stack_value' operations must be used either alone or in conjunction with DW_OP_piece or DW_OP_bit_piece.)>
c = 0
b = 124
(gdb) whatis b
type = i64
(gdb) whatis d
type = {i64, i64, double}
(gdb) print b
$2 = 124

1.15.7.2. Globally override debug setting

It is possible to enable debug for the full application by setting environment variable NUMBA_DEBUGINFO=1. This sets the default value of the debug option in jit. Debug can be turned off on individual functions by setting debug=False.

Beware that enabling debug info significantly increases the memory consumption for each compiled function. For large application, this may cause out-of-memory error.

1.15.8. Using Numba’s direct gdb bindings in nopython mode

Numba (version 0.42.0 and later) has some additional functions relating to gdb support for CPUs that make it easier to debug programs. All the gdb related functions described in the following work in the same manner irrespective of whether they are called from the standard CPython interpreter or code compiled in either nopython mode or object mode.

Note

This feature is experimental!

Warning

This feature does unexpected things if used from Jupyter or alongside the pdb module. It’s behaviour is harmless, just hard to predict!

1.15.8.1. Set up

Numba’s gdb related functions make use of a gdb binary, the location and name of this binary can be configured via the NUMBA_GDB_BINARY environment variable if desired.

Note

Numba’s gdb support requires the ability for gdb to attach to another process. On some systems (notably Ubuntu Linux) default security restrictions placed on ptrace prevent this from being possible. This restriction is enforced at the system level by the Linux security module Yama. Documentation for this module and the security implications of making changes to its behaviour can be found in the Linux Kernel documentation. The Ubuntu Linux security documentation discusses how to adjust the behaviour of Yama on with regards to ptrace_scope so as to permit the required behaviour.

1.15.8.2. Basic gdb support

Warning

Calling numba.gdb() and/or numba.gdb_init() more than once in the same program is not advisable, unexpected things may happen. If multiple breakpoints are desired within a program, launch gdb once via numba.gdb() or numba.gdb_init() and then use numba.gdb_breakpoint() to register additional breakpoint locations.

The most simple function for adding gdb support is numba.gdb(), which, at the call location, will:

  • launch gdb and attach it to the running process.
  • create a breakpoint at the site of the numba.gdb() function call, the attached gdb will pause execution here awaiting user input.

use of this functionality is best motivated by example, continuing with the example used above:

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from numba import njit, gdb

@njit(debug=True)
def foo(a):
    b = a + 1
    gdb() # instruct Numba to attach gdb at this location and pause execution
    c = a * 2.34
    d = (a, b, c)
    print(a, b, c, d)

r= foo(123)
print(r)

In the terminal (... on a line by itself indicates output that is not presented for brevity):

$ NUMBA_OPT=0 python demo_gdb.py
Attaching to PID: 27157
GNU gdb (GDB) Red Hat Enterprise Linux 8.0.1-36.el7
...
Attaching to process 27157
...
Reading symbols from <elided for brevity> ...done.
0x00007f0380c31550 in __nanosleep_nocancel () at ../sysdeps/unix/syscall-template.S:81
81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
Breakpoint 1 at 0x7f036ac388f0: file numba/_helperlib.c, line 1090.
Continuing.

Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) s
Single stepping until exit from function _ZN5numba7targets8gdb_hook8hook_gdb12$3clocals$3e8impl$242E5Tuple,
which has no line number information.
__main__::foo$241(long long) () at demo_gdb.py:7
7           c = a * 2.34
(gdb) l
2
3       @njit(debug=True)
4       def foo(a):
5           b = a + 1
6           gdb() # instruct Numba to attach gdb at this location and pause execution
7           c = a * 2.34
8           d = (a, b, c)
9           print(a, b, c, d)
10
11      r= foo(123)
(gdb) p a
$1 = 123
(gdb) p b
$2 = 124
(gdb) p c
$3 = 0
(gdb) n
8           d = (a, b, c)
(gdb) p c
$4 = 287.81999999999999

It can be seen in the above example that execution of the code is paused at the location of the gdb() function call at end of the numba_gdb_breakpoint function (this is the Numba internal symbol registered as breakpoint with gdb). Issuing a step at this point moves to the stack frame of the compiled Python source. From there, it can be seen that the variables a and b have been evaluated but c has not, as demonstrated by printing their values, this is precisely as expected given the location of the gdb() call. Issuing a next then evaluates line 7 and c is assigned a value as demonstrated by the final print.

1.15.8.3. Running with gdb enabled

The functionality provided by numba.gdb() (launch and attach gdb to the executing process and pause on a breakpoint) is also available as two separate functions:

  • numba.gdb_init() this function injects code at the call site to launch and attach gdb to the executing process but does not pause execution.
  • numba.gdb_breakpoint() this function injects code at the call site that will call the special numba_gdb_breakpoint function that is registered as a breakpoint in Numba’s gdb support. This is demonstrated in the next section.

This functionality enables more complex debugging capabilities. Again, motivated by example, debugging a ‘segfault’ (memory access violation signalling SIGSEGV):

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  from numba import njit, gdb_init
  import numpy as np

  @njit(debug=True)
  def foo(a, index):
      gdb_init() # instruct Numba to attach gdb at this location, but not to pause execution
      b = a + 1
      c = a * 2.34
      d = c[index] # access an address that is a) invalid b) out of the page
      print(a, b, c, d)

  bad_index = int(1e9) # this index is invalid
  z = np.arange(10)
  r = foo(z, bad_index)
  print(r)

In the terminal (... on a line by itself indicates output that is not presented for brevity):

$ python demo_gdb_segfault.py
Attaching to PID: 5444
GNU gdb (GDB) Red Hat Enterprise Linux 8.0.1-36.el7
...
Attaching to process 5444
...
Reading symbols from <elided for brevity> ...done.
0x00007f8d8010a550 in __nanosleep_nocancel () at ../sysdeps/unix/syscall-template.S:81
81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
Breakpoint 1 at 0x7f8d6a1118f0: file numba/_helperlib.c, line 1090.
Continuing.

0x00007fa7b810a41f in __main__::foo$241(Array<long long, 1, C, mutable, aligned>, long long) () at demo_gdb_segfault.py:9
9           d = c[index] # access an address that is a) invalid b) out of the page
(gdb) p index
$1 = 1000000000
(gdb) p c
$2 = "p\202\017\364\371U\000\000\000\000\000\000\000\000\000\000\n\000\000\000\000\000\000\000\b\000\000\000\000\000\000\000\240\202\017\364\371U\000\000\n\000\000\000\000\000\000\000\b\000\000\000\000\000\000"
(gdb) whatis c
type = {i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64]}
(gdb) x /32xb c
0x7ffd56195068: 0x70    0x82    0x0f    0xf4    0xf9    0x55    0x00    0x00
0x7ffd56195070: 0x00    0x00    0x00    0x00    0x00    0x00    0x00    0x00
0x7ffd56195078: 0x0a    0x00    0x00    0x00    0x00    0x00    0x00    0x00
0x7ffd56195080: 0x08    0x00    0x00    0x00    0x00    0x00    0x00    0x00

In the gdb output it can be noted that the numba_gdb_breakpoint function was registered as a breakpoint (its symbol is in numba/_helperlib.c), that a SIGSEGV signal was caught, and the line in which the access violation occurred is printed.

Continuing the example as a debugging session demonstration, first index can be printed, and it is evidently 1e9. Printing c gives a lot of bytes, so the type needs looking up. The type of c shows the layout for the array c based on its DataModel (look in the Numba source numba.datamodel.models for the layouts, the ArrayModel is presented below for ease).

class ArrayModel(StructModel):
    def __init__(self, dmm, fe_type):
        ndim = fe_type.ndim
        members = [
            ('meminfo', types.MemInfoPointer(fe_type.dtype)),
            ('parent', types.pyobject),
            ('nitems', types.intp),
            ('itemsize', types.intp),
            ('data', types.CPointer(fe_type.dtype)),
            ('shape', types.UniTuple(types.intp, ndim)),
            ('strides', types.UniTuple(types.intp, ndim)),
        ]

The type inspected from gdb (type = {i8*, i8*, i64, i64, double*, [1 x i64], [1 x i64]}) corresponds directly to the members of the ArrayModel. Given the segfault came from an invalid access it would be informative to check the number of items in the array and compare that to the index requested.

Examining the memory of c, (x /32xb c), the first 16 bytes are the two i8* corresponding to the meminfo pointer and the parent pyobject. The next two groups of 8 bytes are i64/intp types corresponding to nitems and itemsize respectively. Evidently their values are 0x0a and 0x08, this makes sense as the input array a has 10 elements and is of type int64 which is 8 bytes wide. It’s therefore clear that the segfault comes from an invalid access of index 1000000000 in an array containing 10 items.

1.15.8.4. Adding breakpoints to code

The next example demonstrates using multiple breakpoints that are defined through the invocation of the numba.gdb_breakpoint() function:

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from numba import njit, gdb_init, gdb_breakpoint

@njit(debug=True)
def foo(a):
    gdb_init() # instruct Numba to attach gdb at this location
    b = a + 1
    gdb_breakpoint() # instruct gdb to break at this location
    c = a * 2.34
    d = (a, b, c)
    gdb_breakpoint() # and to break again at this location
    print(a, b, c, d)

r= foo(123)
print(r)

In the terminal (... on a line by itself indicates output that is not presented for brevity):

$ NUMBA_OPT=0 python demo_gdb_breakpoints.py
Attaching to PID: 20366
GNU gdb (GDB) Red Hat Enterprise Linux 8.0.1-36.el7
...
Attaching to process 20366
Reading symbols from <elided for brevity> ...done.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
Reading symbols from /lib64/libc.so.6...Reading symbols from /usr/lib/debug/usr/lib64/libc-2.17.so.debug...done.
0x00007f631db5e550 in __nanosleep_nocancel () at ../sysdeps/unix/syscall-template.S:81
81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
Breakpoint 1 at 0x7f6307b658f0: file numba/_helperlib.c, line 1090.
Continuing.

Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) step
__main__::foo$241(long long) () at demo_gdb_breakpoints.py:8
8           c = a * 2.34
(gdb) l
3       @njit(debug=True)
4       def foo(a):
5           gdb_init() # instruct Numba to attach gdb at this location
6           b = a + 1
7           gdb_breakpoint() # instruct gdb to break at this location
8           c = a * 2.34
9           d = (a, b, c)
10          gdb_breakpoint() # and to break again at this location
11          print(a, b, c, d)
12
(gdb) p b
$1 = 124
(gdb) p c
$2 = 0
(gdb) continue
Continuing.

Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) step
11          print(a, b, c, d)
(gdb) p c
$3 = 287.81999999999999

From the gdb output it can be seen that execution paused at line 8 as a breakpoint was hit, and after a continue was issued, it broke again at line 11 where the next breakpoint was hit.

1.15.8.5. Debugging in parallel regions

The follow example is quite involved, it executes with gdb instrumentation from the outset as per the example above, but it also uses threads and makes use of the breakpoint functionality. Further, the last iteration of the parallel section calls the function work, which is actually just a binding to glibc’s free(3) in this case, but could equally be some involved function that is presenting a segfault for unknown reasons.

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  from numba import njit, prange, gdb_init, gdb_breakpoint
  import ctypes

  def get_free():
      lib = ctypes.cdll.LoadLibrary('libc.so.6')
      free_binding = lib.free
      free_binding.argtypes = [ctypes.c_void_p,]
      free_binding.restype = None
      return free_binding

  work = get_free()

  @njit(debug=True, parallel=True)
  def foo():
      gdb_init() # instruct Numba to attach gdb at this location, but not to pause execution
      counter = 0
      n = 9
      for i in prange(n):
          if i > 3 and i < 8: # iterations 4, 5, 6, 7 will break here
              gdb_breakpoint()

          if i == 8: # last iteration segfaults
              work(0xBADADD)

          counter += 1
      return counter

  r = foo()
  print(r)

In the terminal (... on a line by itself indicates output that is not presented for brevity), note the setting of NUMBA_NUM_THREADS to 4 to ensure that there are 4 threads running in the parallel section:

$ NUMBA_NUM_THREADS=4 NUMBA_OPT=0 python demo_gdb_threads.py
Attaching to PID: 21462
...
Attaching to process 21462
[New LWP 21467]
[New LWP 21468]
[New LWP 21469]
[New LWP 21470]
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
0x00007f59ec31756d in nanosleep () at ../sysdeps/unix/syscall-template.S:81
81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
Breakpoint 1 at 0x7f59d631e8f0: file numba/_helperlib.c, line 1090.
Continuing.
[Switching to Thread 0x7f59d1fd1700 (LWP 21470)]

Thread 5 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) info threads
Id   Target Id         Frame
1    Thread 0x7f59eca2f740 (LWP 21462) "python" [email protected]@GLIBC_2.3.2 ()
    at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
2    Thread 0x7f59d37d4700 (LWP 21467) "python" [email protected]@GLIBC_2.3.2 ()
    at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
3    Thread 0x7f59d2fd3700 (LWP 21468) "python" [email protected]@GLIBC_2.3.2 ()
    at ../nptl/sysdeps/unix/sysv/linux/x86_64/pthread_cond_wait.S:185
4    Thread 0x7f59d27d2700 (LWP 21469) "python" numba_gdb_breakpoint () at numba/_helperlib.c:1090
* 5    Thread 0x7f59d1fd1700 (LWP 21470) "python" numba_gdb_breakpoint () at numba/_helperlib.c:1090
(gdb) thread apply 2-5 info locals

Thread 2 (Thread 0x7f59d37d4700 (LWP 21467)):
No locals.

Thread 3 (Thread 0x7f59d2fd3700 (LWP 21468)):
No locals.

Thread 4 (Thread 0x7f59d27d2700 (LWP 21469)):
No locals.

Thread 5 (Thread 0x7f59d1fd1700 (LWP 21470)):
sched$35 = '\000' <repeats 55 times>
counter__arr = '\000' <repeats 16 times>, "\001\000\000\000\000\000\000\000\b\000\000\000\000\000\000\000\370B]\"hU\000\000\001", '\000' <repeats 14 times>
counter = 0
(gdb) continue
Continuing.
[Switching to Thread 0x7f59d27d2700 (LWP 21469)]

Thread 4 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) continue
Continuing.
[Switching to Thread 0x7f59d1fd1700 (LWP 21470)]

Thread 5 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) continue
Continuing.
[Switching to Thread 0x7f59d27d2700 (LWP 21469)]

Thread 4 "python" hit Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
(gdb) continue
Continuing.

Thread 5 "python" received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7f59d1fd1700 (LWP 21470)]
__GI___libc_free (mem=0xbadadd) at malloc.c:2935
2935      if (chunk_is_mmapped(p))                       /* release mmapped memory. */
(gdb) bt
#0  __GI___libc_free (mem=0xbadadd) at malloc.c:2935
#1  0x00007f59d37ded84 in $3cdynamic$3e::__numba_parfor_gufunc__0x7ffff80a61ae3e31$244(Array<unsigned long long, 1, C, mutable, aligned>, Array<long long, 1, C, mutable, aligned>) () at <string>:24
#2  0x00007f59d17ce326 in __gufunc__._ZN13$3cdynamic$3e45__numba_parfor_gufunc__0x7ffff80a61ae3e31$244E5ArrayIyLi1E1C7mutable7alignedE5ArrayIxLi1E1C7mutable7alignedE ()
#3  0x00007f59d37d7320 in thread_worker ()
from <path>/numba/numba/npyufunc/workqueue.cpython-37m-x86_64-linux-gnu.so
#4  0x00007f59ec626e25 in start_thread (arg=0x7f59d1fd1700) at pthread_create.c:308
#5  0x00007f59ec350bad in clone () at ../sysdeps/unix/sysv/linux/x86_64/clone.S:113

In the output it can be seen that there are 4 threads launched and that they all break at the breakpoint, further that Thread 5 receives a signal SIGSEGV and that back tracing shows that it came from __GI___libc_free with the invalid address in mem, as expected.

1.15.8.6. Using the gdb command language

Both the numba.gdb() and numba.gdb_init() functions accept unlimited string arguments which will be passed directly to gdb as command line arguments when it initializes, this makes it easy to set breakpoints on other functions and perform repeated debugging tasks without having to manually type them every time. For example, this code runs with gdb attached and sets a breakpoint on _dgesdd (say for example the arguments passed to the LAPACK’s double precision divide and conqueror SVD function need debugging).

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  from numba import njit, gdb
  import numpy as np

  @njit(debug=True)
  def foo(a):
      # instruct Numba to attach gdb at this location and on launch, switch
      # breakpoint pending on , and then set a breakpoint on the function
      # _dgesdd, continue execution, and once the breakpoint is hit, backtrace
      gdb('-ex', 'set breakpoint pending on',
          '-ex', 'b dgesdd_',
          '-ex','c',
          '-ex','bt')
      b = a + 10
      u, s, vh = np.linalg.svd(b)
      return s # just return singular values

  z = np.arange(70.).reshape(10, 7)
  r = foo(z)
  print(r)

In the terminal (... on a line by itself indicates output that is not presented for brevity), note that no interaction is required to break and backtrace:

$ NUMBA_OPT=0 python demo_gdb_args.py
Attaching to PID: 22300
GNU gdb (GDB) Red Hat Enterprise Linux 8.0.1-36.el7
...
Attaching to process 22300
Reading symbols from <py_env>/bin/python3.7...done.
0x00007f652305a550 in __nanosleep_nocancel () at ../sysdeps/unix/syscall-template.S:81
81      T_PSEUDO (SYSCALL_SYMBOL, SYSCALL_NAME, SYSCALL_NARGS)
Breakpoint 1 at 0x7f650d0618f0: file numba/_helperlib.c, line 1090.
Continuing.

Breakpoint 1, numba_gdb_breakpoint () at numba/_helperlib.c:1090
1090    }
Breakpoint 2 at 0x7f65102322e0 (2 locations)
Continuing.

Breakpoint 2, 0x00007f65182be5f0 in mkl_lapack.dgesdd_ ()
from <py_env>/lib/python3.7/site-packages/numpy/core/../../../../libmkl_rt.so
#0  0x00007f65182be5f0 in mkl_lapack.dgesdd_ ()
from <py_env>/lib/python3.7/site-packages/numpy/core/../../../../libmkl_rt.so
#1  0x00007f650d065b71 in numba_raw_rgesdd ([email protected]=100 'd', jobz=<optimized out>, [email protected]=65 'A', [email protected]=10,
    [email protected]=7, [email protected]=0x561c6fbb20c0, [email protected]=10, s=0x561c6facf3a0, u=0x561c6fb680e0, ldu=10, vt=0x561c6fd375c0,
    ldvt=7, work=0x7fff4c926c30, lwork=-1, iwork=0x7fff4c926c40, info=0x7fff4c926c20) at numba/_lapack.c:1277
#2  0x00007f650d06768f in numba_ez_rgesdd (ldvt=7, vt=0x561c6fd375c0, ldu=10, u=0x561c6fb680e0, s=0x561c6facf3a0, lda=10,
    a=0x561c6fbb20c0, n=7, m=10, jobz=65 'A', kind=<optimized out>) at numba/_lapack.c:1307
#3  numba_ez_gesdd (kind=<optimized out>, jobz=<optimized out>, m=10, n=7, a=0x561c6fbb20c0, lda=10, s=0x561c6facf3a0,
    u=0x561c6fb680e0, ldu=10, vt=0x561c6fd375c0, ldvt=7) at numba/_lapack.c:1477
#4  0x00007f650a3147a3 in numba::targets::linalg::svd_impl::$3clocals$3e::svd_impl$243(Array<double, 2, C, mutable, aligned>, omitted$28default$3d1$29) ()
#5  0x00007f650a1c0489 in __main__::foo$241(Array<double, 2, C, mutable, aligned>) () at demo_gdb_args.py:15
#6  0x00007f650a1c2110 in cpython::__main__::foo$241(Array<double, 2, C, mutable, aligned>) ()
#7  0x00007f650cd096a4 in call_cfunc ()
from <path>/numba/numba/_dispatcher.cpython-37m-x86_64-linux-gnu.so
...

1.15.8.7. How does the gdb binding work?

For advanced users and debuggers of Numba applications it’s important to know some of the internal implementation details of the outlined gdb bindings. The numba.gdb() and numba.gdb_init() functions work by injecting the following into the function’s LLVM IR:

  • At the call site of the function first inject a call to getpid(3) to get the PID of the executing process and store this for use later, then inject a fork(3) call:
    • In the parent:
      • Inject a call sleep(3) (hence the pause whilst gdb loads).
      • Inject a call to the numba_gdb_breakpoint function (only numba.gdb() does this).
    • In the child:
      • Inject a call to execl(3) with the arguments numba.config.GDB_BINARY, the attach command and the PID recorded earlier. Numba has a special gdb command file that contains instructions to break on the symbol numba_gdb_breakpoint and then finish, this is to make sure that the program stops on the breakpoint but the frame it stops in is the compiled Python frame (or one step away from, depending on optimisation). This command file is also added to the arguments and finally and any user specified arguments are added.

At the call site of a numba.gdb_breakpoint() a call is injected to the special numba_gdb_breakpoint symbol, which is already registered and instrumented as a place to break and finish immediately.

As a result of this, a e.g. numba.gdb() call will cause a fork in the program, the parent will sleep whilst the child launches gdb and attaches it to the parent and tells the parent to continue. The launched gdb has the numba_gdb_breakpoint symbol registered as a breakpoint and when the parent continues and stops sleeping it will immediately call numba_gdb_breakpoint on which the child will break. Additional numba.gdb_breakpoint() calls create calls to the registered breakpoint hence the program will also break at these locations.

1.15.9. Debugging CUDA Python code

1.15.9.1. Using the simulator

CUDA Python code can be run in the Python interpreter using the CUDA Simulator, allowing it to be debugged with the Python debugger or with print statements. To enable the CUDA simulator, set the environment variable NUMBA_ENABLE_CUDASIM to 1. For more information on the CUDA Simulator, see the CUDA Simulator documentation.

1.15.9.2. Debug Info

By setting the debug argument to cuda.jit to True (@cuda.jit(debug=True)), Numba will emit source location in the compiled CUDA code. Unlike the CPU target, only filename and line information are available, but no variable type information is emitted. The information is sufficient to debug memory error with cuda-memcheck.

For example, given the following cuda python code:

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import numpy as np
from numba import cuda

@cuda.jit(debug=True)
def foo(arr):
    arr[cuda.threadIdx.x] = 1

arr = np.arange(30)
foo[1, 32](arr)   # more threads than array elements

We can use cuda-memcheck to find the memory error:

$ cuda-memcheck python chk_cuda_debug.py
========= CUDA-MEMCHECK
========= Invalid __global__ write of size 8
=========     at 0x00000148 in /home/user/chk_cuda_debug.py:6:cudapy::__main__::foo$241(Array<__int64, int=1, C, mutable, aligned>)
=========     by thread (31,0,0) in block (0,0,0)
=========     Address 0x500a600f8 is out of bounds
...
=========
========= Invalid __global__ write of size 8
=========     at 0x00000148 in /home/user/chk_cuda_debug.py:6:cudapy::__main__::foo$241(Array<__int64, int=1, C, mutable, aligned>)
=========     by thread (30,0,0) in block (0,0,0)
=========     Address 0x500a600f0 is out of bounds
...