使用Numba的Python上的CUDA API错误 [英] CUDA API error on Python with Numba
本文介绍了使用Numba的Python上的CUDA API错误的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我是numba的新手，正试图通过它加快我的蒙特卡洛方法。我目前正在使用GeForce 950M开发Ubuntu 14.04。 CUDA版本是8.0.61。
I'm kind of new to numba and was trying to speed up my monte carlo method with it. Im currently working on Ubuntu 14.04 with GeForce 950M. The CUDA version is 8.0.61.
当我尝试运行以下代码时，我从CUDA API中得到了一些与内存相关的错误
When I try to run the following code I get some memory associated error from CUDA API
代码：
@cuda.jit
def SIR(rng_states, y, particles, weight, beta, omega, gamma,
greater, equal, phi, phi_sub):
# thread/block index for accessing data
tx = cuda.threadIdx.x # Thread id in a 1D block = particle index
ty = cuda.blockIdx.x # Block id in a 1D grid = event index
bw = cuda.blockDim.x # Block width, i.e. number of threads per block = particle number
pos = tx + ty * bw # computed flattened index inside the array
# get current event y_t
y_current = y[ ty ]
# get number of time steps
tn = y_current.size
# iterator over timestep
for i in range(1, tn):
# draw samples
sirModule_sample_draw(rng_states, particles[ty][i-1], beta,
omega, particles[ty][i])
# get weight
sirModule_weight(particles[ty][i], particles[ty][i-1], weight[ty][i-1],
weight[ty][i], y_current[i], beta, omega, gamma)
# normalize weight
weight_sum = arr_sum(weight[ty][i])
arr_div(weight[ty][i], weight_sum)
# calculate tau
sirModule_tau(particles[ty][i], beta, omega, phi, phi_sub)
# update greater and equal
greater[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi)
equal[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi_sub)
def main():
beta = 1
omega = 1
gamma = 2
pn = 100
event_number = 50
timestep = 100
y = np.ones((event_number, timestep), dtype = np.int8)
particles = cuda.to_device(np.zeros((event_number, timestep, pn), dtype = np.float32))
weight = cuda.to_device(np.ones((event_number, timestep, pn), dtype = np.float32))
greater = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
equal = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
phi = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
phi_sub = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
rng_states = create_xoroshiro128p_states(pn, seed=1)
start = timer()
SIR[event_number, pn](rng_states, y, particles, weight, beta,
omega, gamma, greater, equal, phi, phi_sub)
vectoradd_time = timer() - start
print("sirModule1 took %f seconds" % vectoradd_time)
if __name__ == '__main__':
main()
然后我得到
numba.cuda.cudadrv.driver.CudaAPIError：[715]调用cuMemcpyDtoH结果为UNKNOWN_CUDA_ERROR
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR
numba.cuda.cudadrv.driver.CudaAPIError：[715]调用cuMemFree会导致UNKNOWN_CUDA_ERROR
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemFree results in UNKNOWN_CUDA_ERROR
错误。...
有人遇到过同样的问题吗？我在网上检查了一下，有人认为问题是由WDDM TDR引起的，但是我认为那仅适用于Windows，对吗？
Did anybody face the same problem? I checked online and some suggest that the problem arise from WDDM TDR but I thought thats for only Windows, right?
以下是代码的缺失部分。
The following is the missing part of the code.
import numpy as np
import numba as nb
from timeit import default_timer as timer
from matplotlib import pyplot as pt
import math
from numba import cuda
from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_normal_float32
"""
Look up table for factorial
"""
LOOKUP_TABLE = cuda.to_device(np.array([
1, 1, 2, 6, 24, 120, 720, 5040, 40320,
362880, 3628800, 39916800, 479001600,
6227020800, 87178291200, 1307674368000,
20922789888000, 355687428096000, 6402373705728000,
121645100408832000, 2432902008176640000], dtype='int64'))
"""
arr_sum - sum element in array
"""
@cuda.jit(device=True)
def arr_sum(arr):
result = 0
for i in range(arr.size):
result = result + arr[i]
return result
"""
dot - dot product of arr1 and arr2
"""
@cuda.jit(device=True)
def dot(arr1, arr2):
result = 0
for i in range(arr1.size):
result = arr1[i]*arr2[i] + result
return result
"""
arr_div - divide element in array
"""
@cuda.jit(device=True)
def arr_div(arr, div):
thread_id = cuda.threadIdx.x
arr[thread_id] = arr[thread_id]/div
"""
SIR module (sample_draw) - module drawing sample for time t (rampling model)
"""
@cuda.jit(device=True)
def sirModule_sample_draw(rng_states, inp, beta, omega, out):
"""Find a value less than 1 from nomral distribution"""
thread_id = cuda.threadIdx.x
# draw candidate sample from normal distribution and store
# when less than 1
while True:
candidate = inp[thread_id] + beta + omega * xoroshiro128p_normal_float32(rng_states, thread_id)
if candidate < 1:
out[thread_id] = candidate
break
"""
SIR module (weight calculation) - weight calculation method
"""
@cuda.jit(device=True)
def sirModule_weight(current, previous, weight, out, y, beta, omega, gamma):
thread_id = cuda.threadIdx.x
PI = 3.14159265359
# calculate the pdf/pmf of given state
Z = ( current[thread_id] - ( previous[ thread_id ] + beta ) ) / omega
p1_div_p3 = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
mu = math.log( 1 + math.exp( gamma * current[ thread_id ] ) )
p2 = math.exp( mu ) * mu**y / LOOKUP_TABLE[ y ]
out[thread_id] = weight[thread_id]*p2*p1_div_p3
"""
SIR module (phi distribution calculator)
"""
@cuda.jit(device=True)
def sirModule_tau(current, beta, omega, phi, phi_sub):
thread_id = cuda.threadIdx.x
# calculate phi distribution and subtract from 1
Z = ( 1 - ( current[ thread_id ] + beta ) ) / omega
phi[ thread_id ] = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
phi_sub[ thread_id ] = 1 - phi[ thread_id ]
但这是设备功能。应该是问题的根源吗？
But these are the device functions. Should this be a source of problem?
对于错误，我收到以下错误消息，代码中的第207行是我调用SIR模块的位置。
And for the error, I get the following error message where line 207 in my code is where I call SIR module.
Traceback (most recent call last):
File "CUDA_MonteCarlo_Testesr.py", line 214, in <module>
main()
File "CUDA_MonteCarlo_Testesr.py", line 207, in main
omega, gamma, greater, equal, phi, phi_sub)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 703, in __call__
cfg(*args)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 483, in __call__
sharedmem=self.sharedmem)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 585, in _kernel_call
wb()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 600, in <lambda>
retr.append(lambda: devary.copy_to_host(val, stream=stream))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/devicearray.py", line 198, in copy_to_host
_driver.device_to_host(hostary, self, self.alloc_size, stream=stream)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1597, in device_to_host
fn(host_pointer(dst), device_pointer(src), size, *varargs)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 288, in safe_cuda_api_call
self._check_error(fname, retcode)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 323, in _check_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR
Traceback (most recent call last):
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 647, in _exitfunc
f()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 571, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1099, in deref
mem.free()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1013, in free
self._finalizer()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 571, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 863, in core
deallocations.add_item(dtor, handle, size=bytesize)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 519, in add_item
self.clear()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 530, in clear
dtor(handle)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 288, in safe_cuda_api_call
self._check_error(fname, retcode)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 323, in _check_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemFree results in UNKNOWN_CUDA_ERROR
推荐答案
我认为可能有2个问题。
I think there may be 2 problems.
-
我不确定您是否使用
LOOKUP_TABLE = cuda.to_device（main之外是有效的，我想您正在尝试创建设备阵列，但是我认为您应该使用为此，numba.cuda.device_array（）。
I'm not sure your use of
LOOKUP_TABLE = cuda.to_device(outside of main is valid. I guess you are trying to create a device array, but I think you should be usingnumba.cuda.device_array()for that.
您似乎没有将数组 y 正确传输到设备以供使用。
You don't seem to be transferring the array y to the device properly for use.
当我进行这两个更改时，代码对我来说似乎没有CUDA运行时错误运行：
When I make those two changes, the code seems to run without CUDA runtime error for me:
# cat t1.py
import numpy as np
import numba as nb
from timeit import default_timer as timer
# from matplotlib import pyplot as pt
import math
from numba import cuda
from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_normal_float32
"""
Look up table for factorial
"""
"""
arr_sum - sum element in array
"""
@cuda.jit(device=True)
def arr_sum(arr):
result = 0
for i in range(arr.size):
result = result + arr[i]
return result
"""
dot - dot product of arr1 and arr2
"""
@cuda.jit(device=True)
def dot(arr1, arr2):
result = 0
for i in range(arr1.size):
result = arr1[i]*arr2[i] + result
return result
"""
arr_div - divide element in array
"""
@cuda.jit(device=True)
def arr_div(arr, div):
thread_id = cuda.threadIdx.x
arr[thread_id] = arr[thread_id]/div
"""
SIR module (sample_draw) - module drawing sample for time t (rampling model)
"""
@cuda.jit(device=True)
def sirModule_sample_draw(rng_states, inp, beta, omega, out):
"""Find a value less than 1 from nomral distribution"""
thread_id = cuda.threadIdx.x
# draw candidate sample from normal distribution and store
# when less than 1
while True:
candidate = inp[thread_id] + beta + omega * xoroshiro128p_normal_float32(rng_states, thread_id)
if candidate < 1:
out[thread_id] = candidate
break
"""
SIR module (weight calculation) - weight calculation method
"""
@cuda.jit(device=True)
def sirModule_weight(current, previous, weight, out, y, beta, omega, gamma, lt):
thread_id = cuda.threadIdx.x
PI = 3.14159265359
# calculate the pdf/pmf of given state
Z = ( current[thread_id] - ( previous[ thread_id ] + beta ) ) / omega
p1_div_p3 = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
mu = math.log( 1 + math.exp( gamma * current[ thread_id ] ) )
p2 = math.exp( mu ) * mu**y / lt[ y ]
out[thread_id] = weight[thread_id]*p2*p1_div_p3
"""
SIR module (phi distribution calculator)
"""
@cuda.jit(device=True)
def sirModule_tau(current, beta, omega, phi, phi_sub):
thread_id = cuda.threadIdx.x
# calculate phi distribution and subtract from 1
Z = ( 1 - ( current[ thread_id ] + beta ) ) / omega
phi[ thread_id ] = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
phi_sub[ thread_id ] = 1 - phi[ thread_id ]
@cuda.jit
def SIR(rng_states, y, particles, weight, beta, omega, gamma,
greater, equal, phi, phi_sub, lt):
# thread/block index for accessing data
tx = cuda.threadIdx.x # Thread id in a 1D block = particle index
ty = cuda.blockIdx.x # Block id in a 1D grid = event index
bw = cuda.blockDim.x # Block width, i.e. number of threads per block = particle number
pos = tx + ty * bw # computed flattened index inside the array
# get current event y_t
y_current = y[ ty ]
# get number of time steps
tn = y_current.size
# iterator over timestep
for i in range(1, tn):
# draw samples
sirModule_sample_draw(rng_states, particles[ty][i-1], beta,
omega, particles[ty][i])
# get weight
sirModule_weight(particles[ty][i], particles[ty][i-1], weight[ty][i-1], weight[ty][i], y_current[i], beta, omega, gamma, lt)
# normalize weight
weight_sum = arr_sum(weight[ty][i])
arr_div(weight[ty][i], weight_sum)
# calculate tau
sirModule_tau(particles[ty][i], beta, omega, phi, phi_sub)
# update greater and equal
greater[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi)
equal[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi_sub)
def main():
beta = 1
omega = 1
gamma = 2
pn = 100
event_number = 50
timestep = 100
LOOKUP_TABLE = cuda.to_device(np.array([
1, 1, 2, 6, 24, 120, 720, 5040, 40320,
362880, 3628800, 39916800, 479001600,
6227020800, 87178291200, 1307674368000,
20922789888000, 355687428096000, 6402373705728000,
121645100408832000, 2432902008176640000], dtype='int64'))
hy = np.ones((event_number, timestep), dtype = np.uint32)
print(hy.size)
print(hy)
y = cuda.to_device(hy)
particles = cuda.to_device(np.zeros((event_number, timestep, pn), dtype = np.float32))
weight = cuda.to_device(np.ones((event_number, timestep, pn), dtype = np.float32))
greater = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
equal = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
phi = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
phi_sub = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
rng_states = create_xoroshiro128p_states(pn, seed=1)
start = timer()
SIR[event_number, pn](rng_states, y, particles, weight, beta, omega, gamma, greater, equal, phi, phi_sub, LOOKUP_TABLE)
vectoradd_time = timer() - start
print("sirModule1 took %f seconds" % vectoradd_time)
cuda.synchronize()
if __name__ == '__main__':
main()
# cuda-memcheck python t1.py
========= CUDA-MEMCHECK
5000
[[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
...,
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]]
sirModule1 took 0.840958 seconds
========= ERROR SUMMARY: 0 errors
#
这篇关于使用Numba的Python上的CUDA API错误的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
