pyoclk 1.2.3

Description:

pyoclk 1.2.3

OpenCL Kernel Python Wrapper








Install
Requirements

OpenCL GPU hardware
numpy
cmake(if compile from source)

Install from wheel
pip install pyoclk

or download wheel from release and install
Compile from source
Clone this repo
clone by http
git clone --recursive https://github.com/jinmingyi1998/opencl_kernels.git

with ssh
git clone --recursive [email protected]:jinmingyi1998/opencl_kernels.git

Install
cd opencl_kernels
python setup.py install

DO NOT move this directory after install
Usage
Kernel File:
a file named add.cl
kernel void add(global float*a, global float*out, int int_arg, float float_arg){
int x = get_global_id(0);
if(x==0){
printf(" accept int arg: %d, accept float arg: %f\n",int_arg,float_arg);
}
out[x] = a[x] * float_arg + int_arg;
}

Python Code
OOP Style
import numpy as np
import oclk

a = np.random.rand(100, 100).reshape([10, -1])
a = np.ascontiguousarray(a, np.float32)
out = np.zeros(a.shape)
out = np.ascontiguousarray(out, np.float32)

runner = oclk.Runner()
runner.load_kernel("add.cl", "add", "")

timer = oclk.TimerArgs(
enable=True,
warmup=10,
repeat=50,
name='add_kernel'
)
runner.run(
kernel_name="add",
input=[
{"name": "a", "value": a, },
{"name": "out", "value": out, },
{"name": "int_arg", "value": 1, "type": "int"},
{"name": "float_arg", "value": 12.34}
],
output=['out'],
local_work_size=[1, 1],
global_work_size=a.shape,
timer=timer
)
# check result
a = a.reshape([-1])
out = out.reshape([-1])
print(a[:8])
print(out[:8])

Kernel Benchmark

write a config like bench_add.yaml
run python -m oclk benchmark -f examples/bench_add.yaml

Example
python -m oclk benchmark -f examples/bench_add.yaml

output:
[Timer bench_add.add] [CNT: 1] [AVG: 0.539ms] [STDEV 0.000ms] [TOTAL 0.539ms]
[Timer bench_add.add_constant] [CNT: 1] [AVG: 0.576ms] [STDEV 0.000ms] [TOTAL 0.576ms]
[Timer bench_add.add_batch] [CNT: 1] [AVG: 0.150ms] [STDEV 0.000ms] [TOTAL 0.150ms]

python -m oclk benchmark -f examples/bench_add.yaml -s table

output:
benchmark results
┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ timer name ┃ avg time(ms) ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ bench_add.add │ 0.538525390625 │
│ bench_add.add_constant │ 0.581396484375 │
│ bench_add.add_batch │ 0.149169921875 │
└────────────────────────┴────────────────┘

python -m oclk benchmark -f examples/bench_add.yaml -s json -o bench_add.json

output to json file bench_add.json
[
{
"name": "bench_add.add",
"time(ms)": 0.54248046875
},
{
"name": "bench_add.add_constant",
"time(ms)": 0.5767089843750001
},
{
"name": "bench_add.add_batch",
"time(ms)": 0.15048828125000002
}
]

Kernel Tune

given a OpenCL kernel file add.cl
run python -m oclk new tune add, then generate a new file tune_add.py
edit tune_add.py
run python -m oclk tune -f tune_add.py -o add_tune_result.json
results are stored in add_tune_result.json

Example
python -m oclk tune -f examples/tune/tune_add.py -k 3

then output output.json
[
{
"name": [
"examples.tune.tune_add",
"AddTuner"
],
"k": 3,
"topk_results": [
{
"kwargs": {
"local_work_size": [
512
],
"vector_size": 4,
"tile_size": 4,
"method": "naive"
},
"time_ms": 0.67691162109375
},
{
"kwargs": {
"local_work_size": [
128
],
"vector_size": 4,
"tile_size": 4,
"method": "naive"
},
"time_ms": 0.6769140625
},
{
"kwargs": {
"local_work_size": [
64
],
"vector_size": 4,
"tile_size": 4,
"method": "naive"
},
"time_ms": 0.677001953125
}
]
}
]