/
cuda_lib.py
424 lines (372 loc) · 14.7 KB
/
cuda_lib.py
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__author__ = "hervemn"
import numpy as np
import pycuda.tools
from pycuda import characterize
import pycuda.driver as cuda
import pycuda.compiler
from pycuda import gpuarray as ga
cuda.init()
num_gpu = cuda.Device.count()
for i in range(0, num_gpu):
tmp_dev = cuda.Device(i)
print("device_id = ", i, tmp_dev.name())
id_gpu = input("Select GPU: ")
id_gpu = int(id_gpu)
curr_gpu = cuda.Device(id_gpu)
print("you have selected ", curr_gpu.name())
kernels_cuda_src = """
#include <curand_kernel.h>
extern "C"
{
__global__ void init_rng(int nthreads, curandState *s, unsigned long long seed, unsigned long long offset)
{
int id = blockIdx.x*blockDim.x + threadIdx.x;
if (id >= nthreads)
return;
curand_init(seed, id, offset, &s[id]);
}
__global__ void gen_rand_mat(int* initArray, float *randArray, curandState *state, int n_rng, int width,
float fact_sub)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
int id_rng = coord % n_rng ;
if ((r0<width) && (r1< width)) {
float mean = initArray[coord] * fact_sub;
randArray[coord] = curand_poisson(&state[id_rng], mean);
}
}
__global__ void copy_mat(int* initArray, float *randArray, int width, float fact_sub)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
if ((r0<width) && (r1< width)) {
float mean = initArray[coord] * fact_sub;
randArray[coord] = mean;
}
}
__global__ void sum_along_axis(float* input, float* vect_sum, int width , int axis)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
if ((r0<width) && (r1< width)) {
float val = input[coord];
int id = (axis == 0) * r0 + (axis == 1) * r1;
val = atomicAdd( &(vect_sum[id]), (float) val);
}
}
__global__ void norm_along_axis(float* input, float* vect_sum, int width, int axis)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
if ((r0<width) && (r1< width)) {
float val = input[coord];
int id = (axis == 0) * r0 + (axis == 1) * r1;
input[coord] = val / vect_sum[id];
}
}
__global__ void init_vect_sum(float* vect_sum, int width)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
if (r0 < width){
vect_sum[r0] = 0;
}
}
} // extern "C"
"""
def get_rng_states(size_output, seed=1):
init_rng_src = """
#include <curand_kernel.h>
extern "C"
{
__global__ void init_rng(int nthreads, curandState *s, unsigned long long seed, unsigned long long offset)
{
int id = blockIdx.x*blockDim.x + threadIdx.x;
if (id >= nthreads)
return;
curand_init(seed, id, offset, &s[id]);
}
__global__ void make_rand(int nthreads, curandState *state, int *randArray)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int id_rng = blockIdx.x;
double mean = 10;
if (idx<= nthreads){
randArray[idx] = curand_poisson(&state[id_rng], mean);
//randArray[idx] = curand_uniform(&state[idx]);
}
}
} // extern "C"
"""
"Return `size_rng` number of CUDA random number generator states."
curr_gpu.make_context()
gpu_vect_rand = ga.GPUArray((size_output,), dtype=np.int32)
cpu_vect_rand = np.ones((size_output,), dtype=np.int32)
(free, total) = cuda.mem_get_info()
print(("Global memory occupancy:%f%% free" % (free * 100 / total)))
# module = pycuda.compiler.SourceModule(init_rng_src, no_extern_c=True,arch="sm_30")
module = pycuda.compiler.SourceModule(init_rng_src, no_extern_c=True)
init_rng = module.get_function("init_rng")
make_rand = module.get_function("make_rand")
size_block = 1024
n_blocks = size_output // size_block + 1
rng_states = cuda.mem_alloc(
n_blocks
* characterize.sizeof("curandStateXORWOW", "#include <curand_kernel.h>")
)
init_rng(
np.int32(n_blocks),
rng_states,
np.uint64(seed),
np.uint64(0),
block=(64, 1, 1),
grid=(n_blocks // 64 + 1, 1),
)
try:
make_rand(
np.int32(size_output),
rng_states,
gpu_vect_rand,
block=(size_block, 1, 1),
grid=(n_blocks, 1),
)
except cuda.LogicError:
print("random number generation failed ...")
(free, total) = cuda.mem_get_info()
print(("Global memory occupancy:%f%% free" % (free * 100 / total)))
rng_states.free()
gpu_vect_rand.get(ary=cpu_vect_rand)
cuda.Context.pop()
return cpu_vect_rand
def scn(input_mat, n_iter):
"""
:param input_mat: matrice to normalize
"""
scn_kernel = """
__global__ void sum_along_axis(float* input, float* vect_sum, int width , int axis)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
if ((r0<width) && (r1< width)) {
float val = input[coord];
int id = (axis == 0) * r0 + (axis == 1) * r1;
val = atomicAdd( &(vect_sum[id]), (float) val);
}
}
__global__ void norm_along_axis(float* input, float* vect_sum, int width, int axis)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
int r1 = threadIdx.y + blockDim.y * blockIdx.y;
int coord = r0 * width + r1;
if ((r0<width) && (r1< width)) {
float val = input[coord];
int id = (axis == 0) * r0 + (axis == 1) * r1;
input[coord] = val / vect_sum[id];
}
}
__global__ void init_vect_sum(float* vect_sum, int width)
{
int r0 = threadIdx.x + blockDim.x * blockIdx.x;
if (r0 < width){
vect_sum[r0] = 0;
}
}
"""
curr_gpu.make_context()
# if input_mat.dtype != np.float32:
# input_mat = np.float32(input_mat)
(free, total) = cuda.mem_get_info()
print(("Global memory occupancy:%f%% free" % (free * 100 / total)))
if free > 2 * input_mat.nbytes:
print(" gpu mem space ok ")
width_mat = np.int32(input_mat.shape[0])
try:
n_elements = input_mat.shape[0] * input_mat.shape[1]
except IndexError:
n_elements = input_mat.shape[0]
n_elements = np.int32(n_elements)
cpu_vect_sum = np.zeros((width_mat,), dtype=np.float32)
gpu_vect_sum = ga.to_gpu(cpu_vect_sum)
gpu_data = ga.to_gpu(input_mat)
cpu_output = np.empty_like(input_mat)
# module = pycuda.compiler.SourceModule(scn_kernel,arch="sm_30")
module = pycuda.compiler.SourceModule(scn_kernel)
sum_along_axis = module.get_function("sum_along_axis")
norm_along_axis = module.get_function("norm_along_axis")
init_vect_sum = module.get_function("init_vect_sum")
size_block_x = 32
size_block_y = 32
n_blocks_x = int(width_mat) // (size_block_x) + 1
n_blocks_y = int(width_mat) // (size_block_y) + 1
print("size block = ", size_block_x)
print("n blocks x= ", n_blocks_x)
print("n blocks y= ", n_blocks_y)
print("gpu data type = ", gpu_data.dtype)
print("gpu vect sum type = ", gpu_vect_sum.dtype)
print("n element type = ", n_elements.dtype)
print("width_mat type = ", width_mat.dtype)
for i in range(0, n_iter):
id_axis = np.int32(np.mod(i, 2))
print("id_axis= ", id_axis)
sum_along_axis(
gpu_data,
gpu_vect_sum,
width_mat,
id_axis,
block=(size_block_x, size_block_y, 1),
grid=(n_blocks_x, n_blocks_y),
shared=0,
)
norm_along_axis(
gpu_data,
gpu_vect_sum,
width_mat,
id_axis,
block=(size_block_x, size_block_y, 1),
grid=(n_blocks_x, n_blocks_y),
shared=0,
)
init_vect_sum(
gpu_vect_sum, block=(64, 1, 1), grid=(int(width_mat) // 64 + 1, 1)
)
# gpu_vect_sum = ga.to_gpu(cpu_vect_sum)
gpu_data.get(ary=cpu_output)
# gpu_data.free()
cuda.Context.pop()
return cpu_output
class randomize:
def __init__(self, init_data, n_generators):
self.ctx = curr_gpu.make_context()
self.module = pycuda.compiler.SourceModule(kernels_cuda_src, no_extern_c=True)
(free, total) = cuda.mem_get_info()
print(("Global memory occupancy:%f%% free" % (free * 100 / total)))
print(("Global free memory :%i Mo free" % (free / 10 ** 6)))
################################################################################################################
self.width_mat = np.int32(init_data.shape[0])
# self.gpu_init_data = ga.to_gpu(init_data)
self.gpu_init_data = cuda.mem_alloc(init_data.nbytes)
cuda.memcpy_htod(self.gpu_init_data, init_data)
self.cpu_new_data = np.zeros_like(init_data, dtype=np.float32)
print("size new data = ", self.cpu_new_data.nbytes / 10 ** 6)
(free, total) = cuda.mem_get_info()
print(("Global memory occupancy:%f%% free" % (free * 100 / total)))
print(("Global free memory :%i Mo free" % (free / 10 ** 6)))
self.gpu_new_data = cuda.mem_alloc(self.cpu_new_data.nbytes)
cuda.memcpy_htod(self.gpu_new_data, self.cpu_new_data)
# self.gpu_new_data = ga.to_gpu(self.cpu_new_data)
self.cpu_vect_sum = np.zeros((self.width_mat,), dtype=np.float32)
self.gpu_vect_sum = cuda.mem_alloc(self.cpu_vect_sum.nbytes)
cuda.memcpy_htod(self.gpu_vect_sum, self.cpu_vect_sum)
# self.gpu_vect_sum = ga.to_gpu(self.cpu_vect_sum)
################################################################################################################
self.init_rng = self.module.get_function("init_rng")
self.gen_rand_mat = self.module.get_function("gen_rand_mat")
self.sum_along_axis = self.module.get_function("sum_along_axis")
self.norm_along_axis = self.module.get_function("norm_along_axis")
self.init_vect_sum = self.module.get_function("init_vect_sum")
self.copy_mat = self.module.get_function("copy_mat")
################################################################################################################
self.n_generators = n_generators
seed = 1
self.rng_states = cuda.mem_alloc(
n_generators
* characterize.sizeof("curandStateXORWOW", "#include <curand_kernel.h>")
)
self.init_rng(
np.int32(n_generators),
self.rng_states,
np.uint64(seed),
np.uint64(0),
block=(64, 1, 1),
grid=(n_generators // 64 + 1, 1),
)
(free, total) = cuda.mem_get_info()
size_block_x = 32
size_block_y = 32
n_blocks_x = int(self.width_mat) // (size_block_x) + 1
n_blocks_y = int(self.width_mat) // (size_block_y) + 1
self.grid = (n_blocks_x, n_blocks_y, 1)
self.block = (size_block_x, size_block_y, 1)
def generate_new_matrix(self, n_iter, do_random, fact_sub_sampling):
# fact_sub_sampling = np.float32(0.5)
if do_random:
self.gen_rand_mat(
self.gpu_init_data,
self.gpu_new_data,
self.rng_states,
np.int32(self.n_generators),
self.width_mat,
np.float32(fact_sub_sampling),
block=self.block,
grid=self.grid,
shared=0,
)
else:
self.copy_mat(
self.gpu_init_data,
self.gpu_new_data,
self.width_mat,
np.float32(fact_sub_sampling),
block=self.block,
grid=self.grid,
shared=0,
)
print("matrix generated")
for i in range(0, n_iter):
# print "iter,",i
id_axis = np.int32(np.mod(i, 2))
self.sum_along_axis(
self.gpu_new_data,
self.gpu_vect_sum,
self.width_mat,
id_axis,
block=self.block,
grid=self.grid,
shared=0,
)
self.norm_along_axis(
self.gpu_new_data,
self.gpu_vect_sum,
self.width_mat,
id_axis,
block=self.block,
grid=self.grid,
shared=0,
)
self.init_vect_sum(
self.gpu_vect_sum,
block=(64, 1, 1),
grid=(int(self.width_mat) // 64 + 1, 1),
)
# self.gpu_new_data.get(ary=self.cpu_new_data)
cuda.memcpy_dtoh(self.cpu_new_data, self.gpu_new_data)
return self.cpu_new_data
def free_gpu(self,):
self.rng_states.free()
self.gpu_vect_sum.free()
self.gpu_new_data.free()
self.gpu_init_data.free()
(free, total) = cuda.mem_get_info()
print(
(
"Global memory occupancy after cleaning processes: %f%% free"
% (free * 100 / total)
)
)
print(("Global free memory :%i Mo free" % (free / 10 ** 6)))
del self.module
self.ctx.detach()
# self.ctx.pop()
# cuda.Context.pop()
if __name__ == "__main__":
import numpy as np
a = np.ones((10000, 10000), dtype=np.int32)
gen = randomize(a, 1000)
out = gen.generate_new_matrix(10)
gen.free_gpu()
print(out)