change of the function definition in example fails

[rachelglenn]

Hi. I using the example with the integration
math.sin(x[0]+x[1]+x[2]+x[3])
I inserted an intermediate function: ( I checked that it can be evaluated outside of my_func(x))

def my_func(x):
yu = math.sin(x[0]+x[1]+x[2]+x[3])
integrand = P3_integrand_Eq2_c(x[0], x[1], x[2], x[3], wo, omegas, phase_type)
return yu

I get the following errors:

Process Process-2:
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/home/ubuntu/anaconda3/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 111, in multi_processing
result.append(self.MCkernel(domain, i_batch))
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 373, in MCkernel
integration_kernel[blockspergrid, threadsperblock](num_loops, MCresult, chunk_size, n_chunk_x, domain, domain_range, batch_size, i_batch, rng_states, n_chunk)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/cuda/compiler.py", line 765, in call
kernel = self.specialize(*args)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/cuda/compiler.py", line 776, in specialize
kernel = self.compile(argtypes)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/cuda/compiler.py", line 792, in compile
**self.targetoptions)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/cuda/compiler.py", line 62, in compile_kernel
cres = compile_cuda(pyfunc, types.void, args, debug=debug, inline=inline)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/cuda/compiler.py", line 51, in compile_cuda
locals={})
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 941, in compile_extra
return pipeline.compile_extra(func)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 372, in compile_extra
return self._compile_bytecode()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 872, in _compile_bytecode
return self._compile_core()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 859, in _compile_core
res = pm.run(self.status)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 253, in run
raise patched_exception
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 244, in run
stage()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 500, in stage_nopython_frontend
self.locals)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/compiler.py", line 1044, in type_inference_stage
infer.propagate()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/typeinfer.py", line 861, in propagate
raise errors[0]
numba.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<numba.cuda.compiler.DeviceFunctionTemplate object at 0x7fd0ffc81a90>) with argument(s) of type(s): (array(float64, 1d, C))

• parameterized
  In definition 0:
  TypingError: Failed in nopython mode pipeline (step: nopython frontend)
  Untyped global name 'P3_integrand_Eq2_c': cannot determine Numba type of <class 'builtin_function_or_method'>

File "script.py", line 130:
def my_func(x):

yu = math.sin(x[0]+x[1]+x[2]+x[3])
integrand = P3_integrand_Eq2_c(x[0], x[1], x[2], x[3], wo, omegas, phase_type)
^

raised from /home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/typeinfer.py:1180

In definition 1:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'P3_integrand_Eq2_c': cannot determine Numba type of <class 'builtin_function_or_method'>

File "script.py", line 130:
def my_func(x):

yu = math.sin(x[0]+x[1]+x[2]+x[3])
integrand = P3_integrand_Eq2_c(x[0], x[1], x[2], x[3], wo, omegas, phase_type)
^

raised from /home/ubuntu/anaconda3/lib/python3.6/site-packages/numba/typeinfer.py:1180

This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: resolving callee type: Function(<numba.cuda.compiler.DeviceFunctionTemplate object at 0x7fd0ffc81a90>)
[2] During: typing of call at /home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py (365)

File "../../anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 365:
def integration_kernel(num_loops, MCresult, chunk_size, n_chunk_x, domain, domain_range, batch_size, i_batch, rng_states, n_chunk):

# feed in values to user defined function
cuda.atomic.add(MCresult, thread_id, fun(x_tuple))
^

Traceback (most recent call last):
File "script.py", line 139, in
result = MC.evaluate()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 74, in evaluate
MCresult = self.importance_sampling_iteration(self.initial_domain, 0)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 80, in importance_sampling_iteration
MCresult_chunks, large_std_chunk_id, MCresult_std_chunks = self.MCevaluate(domain)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ZMCintegral/ZMCintegral.py", line 130, in MCevaluate
MCresult.append(np.load(os.getcwd()+'/multi_temp/result'+str(i_batch)+'.npy'))
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: '/home/ubuntu/workspace/phase_step/multi_temp/result0.npy'

[JCatesPH]

Did you use the numba cuda decorator <@numba.cuda.jit> for your intermediate function?

I have also been working with this package, ZMCintegral, and found that all functions called must be decorated with 'jit'.

[Juenjie]

the function P3_integrand_Eq2_c should also be a device function.

Here I provide you with an example similar to yours with our new version:

from ZMC.ZMCintegral_normal import MCintegral_normal
import math
import numpy as np

import time
start = time.time()

# user defined function
fun = """ 
import math
# define a device function that should be used by cuda kernel

@cuda.jit(device=True)
def my_intermediate_func(xx):
    return math.cos(xx)
    
@cuda.jit(device=True)
def fun(x):
    return math.sin(x[0]+x[1]+x[2]+x[3]+x[4]+x[5]+x[6]+my_intermediate_func(x[0]))
"""

# define arguments that MCintegral_normal requires
depth = 1
sigma_multiplier = 5
num_trials = 5
num_chunks_in_one_dimension = 12

# call MCintegral_normal
MC = MCintegral_normal(my_func = fun, 
                       domain = [[0,10],[0,10],[0,10],[0,10],[0,10],[0,10]], 
                       head_node_address = "210.45.78.43:1234",
                       depth = depth, 
                       sigma_multiplier = sigma_multiplier, 
                       num_trials = num_trials,
                       num_chunks_in_one_dimension = num_chunks_in_one_dimension)


# obtaining the result
result = MC.evaluate()


# print the formatted result
print('result = %s    std = %s' % (result[0], result[1]))
print('evaluation time {}'.format(time.time()-start))

[Juenjie]

For constants, you have two choices:

1. define the constans in the device function

# user defined function
fun = """ 
import math
# define a device function that should be used by cuda kernel

@cuda.jit(device=True)
def my_intermediate_func(xx, constant):
    return math.cos(xx+constant)
    
@cuda.jit(device=True)
def fun(x):
    constant = 1
    return math.sin(x[0]+x[1]+x[2]+x[3]+x[4]+x[5]+x[6]+my_intermediate_func(x[0]+constant))
"""

2. Use the new functionality of parameter scan

from ZMC.ZMCintegral_functional import MCintegral_functional
import math
import numpy as np

import time
start = time.time()


# user defined function
fun = """ 
import math
# define a device function that should be used by cuda kernel
@cuda.jit(device=True)
def fun(x,para):
    return math.sin(x[0]+x[1]+x[2]+x[3]+para[0]+para[1])
"""

# para contains two parameters
para = [[1,2,3,4,5],[1.1,2.2,3.1]]


# sample points is taken to 10**6
sample_points = 10**6

# the parameter grid has totally 5*3=15 points
# we choose 3 batches as an example
batch_size = 5

# call MCintegral_functional
MC = MCintegral_functional(my_func = fun, 
                           domain = [[0,1],[0,1],[0,1],[0,1]], 
                           parameters = para, 
                           head_node_address = "210.45.78.19:1234",
                           num_points = sample_points, 
                           batch_size = batch_size)

# obtaining the result
result = MC.evaluate()
result = print(np.reshape(result,[3,5]))
print('evaluation time {}'.format(time.time()-start))