/usr/lib/python2.7/dist-packages/pyopencl/compyte/ndarray/test_gpu_elemwise.py is in python-pyopencl 2017.2.2-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 | # TODO: test other dtype
from __future__ import absolute_import
import numpy
import theano
import pygpu_ndarray as gpu_ndarray
from .gen_elemwise import MyGpuNdArray, elemwise_collapses
from .test_gpu_ndarray import (dtypes_all, enable_double,
gen_gpu_nd_array, product)
from six.moves import range
from six.moves import zip
from functools import reduce
def rand(shape, dtype):
r = numpy.random.randn(*shape) * 10
if dtype.startswith("u"):
r = numpy.absolute(r)
return r.astype(dtype)
# numpy.allclose seam to have problem with int8...
def all_close(x, y):
return (numpy.allclose(x, y) or
numpy.absolute(x - y).max() == 0)
def test_elemwise_collapse():
""" Test collapsing under many broadcast and strided pattern """
for dtype1 in ["int16", "float32", "int8"]:
for dtype2 in ["int16", "float32", "int8"]:
for shape1_, shape2_, expected in [
# 1d to test this special case
((40,), (40,), 0),
((40,), (1,), 1),
# No broadcastable dimensions
((4, 5, 6, 9), (4, 5, 6, 9), 0),
# All inputs have one(and the same) broadcastable dimension
((1, 4, 5, 9), (1, 4, 5, 9), 0),
((4, 1, 5, 9), (4, 1, 5, 9), 0),
((4, 5, 1, 9), (4, 5, 1, 9), 0),
((4, 5, 9, 1), (4, 5, 9, 1), 0),
# One inputs have one broadcastable dimension
((1, 5, 6, 9), (4, 5, 6, 9), 2),
((4, 1, 6, 9), (4, 5, 6, 9), 3),
((4, 5, 1, 9), (4, 5, 6, 9), 3),
((4, 5, 6, 1), (4, 5, 6, 9), 2),
# One inputs have two broadcastable dimension
((1, 1, 6, 9), (4, 5, 6, 9), 2),
((1, 5, 1, 9), (4, 5, 6, 9), 4),
((1, 5, 6, 1), (4, 5, 6, 9), 3),
((4, 1, 1, 9), (4, 5, 6, 9), 3),
((4, 1, 6, 1), (4, 5, 6, 9), 4),
((4, 5, 1, 1), (4, 5, 6, 9), 2),
# One inputs have tree broadcastable dimension
((1, 1, 1, 9), (4, 5, 6, 9), 2),
((1, 1, 6, 1), (4, 5, 6, 9), 3),
((1, 5, 1, 1), (4, 5, 6, 9), 3),
((4, 1, 1, 1), (4, 5, 6, 9), 2),
# One scalar
((1, 1, 1, 1), (4, 5, 6, 9), 1),
# One scalar, the other 1 broadcast dims
((1, 1, 1, 1), (4, 5, 6, 1), 1),
]:
scalar_cpu = rand((1,) * len(shape1_), dtype=dtype1)
scalar_gpu = gpu_ndarray.GpuNdArrayObject(scalar_cpu)
scalar_gpu1 = MyGpuNdArray(scalar_gpu)
for shape1, shape2 in [(shape1_, shape2_), (shape2_, shape1_)]:
a_cpu = rand(shape1, dtype=dtype1)
a = gpu_ndarray.GpuNdArrayObject(a_cpu)
a1 = MyGpuNdArray(a)
b_cpu = rand(shape2, dtype=dtype2)
b = gpu_ndarray.GpuNdArrayObject(b_cpu)
b1 = MyGpuNdArray(b)
assert len(shape1) == len(shape2)
o_shape = []
for i in range(len(shape1)):
o_shape.append(max(shape1[i], shape2[i]))
o = gpu_ndarray.empty(o_shape, dtype=(a_cpu + b_cpu).dtype)
# 1.1 Check direct collapse
nd_collaps, info = elemwise_collapses([a, b], [o])
assert nd_collaps == expected, (shape1, shape2,
nd_collaps, expected, info)
# 1.2 Check computation are still valid
f = MyGpuNdArray.gen_fct(theano.tensor.add, [a1, b1],
len(shape1))
out = f([a1, b1])
out2 = f([a1, b1], out=out)
assert out is out2
assert numpy.allclose(numpy.asarray(f([a1, b1])),
a_cpu + b_cpu)
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.adds(a1, b1)), a_cpu + b_cpu)
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.add(a1, b1)), a_cpu + b_cpu)
assert MyGpuNdArray.add(a1, b1, out=out2) is out2
# 1.3 Check work without collaping
f = MyGpuNdArray.gen_fct(theano.tensor.add, [a1, b1],
len(shape1), collapse=False)
out = f([a1, b1])
out2 = f([a1, b1], out=out)
assert out is out2
assert numpy.allclose(numpy.asarray(f([a1, b1])),
a_cpu + b_cpu)
assert numpy.allclose(numpy.asarray(MyGpuNdArray.adds(
a1, b1)), a_cpu + b_cpu)
assert numpy.allclose(numpy.asarray(MyGpuNdArray.add(
a1, b1)), a_cpu + b_cpu)
assert MyGpuNdArray.add(a1, b1, out=out2) is out2
# 2.1 What if we add a scalar?
nd_collaps, info = elemwise_collapses(
[a, b, scalar_gpu], [o])
if expected == 0:
expected2 = 1
else:
expected2 = expected
assert nd_collaps == expected2, (shape1, shape2,
nd_collaps, expected,
info)
# 2.2 Check computation
assert numpy.allclose(numpy.asarray(MyGpuNdArray.adds(
a1, b1, scalar_gpu1)),
a_cpu + b_cpu + scalar_cpu)
# 3.1 What if one of the dimensions is strided?
broadcast = any([True for i in a.shape + b.shape
if i == 1])
if expected == 0:
expected2 = 2
else:
expected2 = expected
if len(shape1_) != 4:
continue
if a.shape[0] != 1:
shape = list(shape1)
shape[0] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2,
nd_collaps, expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[1] != 1:
shape = list(shape1)
shape[1] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2,
nd_collaps, expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
pass
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[2] != 1:
shape = list(shape1)
shape[2] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::, ::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2,
nd_collaps, expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
pass
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[3] != 1:
shape = list(shape1)
shape[3] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::, ::,
::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2,
nd_collaps, expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == 1, err
pass
assert numpy.allclose(numpy.asarray(
MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
def test_elemwise_mixed_dtype():
to_cpu = numpy.asarray
for dtype1 in ["int16", "float32", "int8"]:
for dtype2 in ["int16", "float32", "int8"]:
dtypeo = str((numpy.zeros(1, dtype=dtype1) +
numpy.zeros(1, dtype=dtype2)).dtype)
#print "dtypes", dtype1, dtype2, "o dtype", dtypeo
#print " Test inside a wrapping python object 2 inputs"
for shape in [(500,), (50, 5), (5, 6, 7)]:
input_vals = [rand(shape, dtype) for dtype in [dtype1, dtype2]]
del dtype
gpu_vals = [gpu_ndarray.GpuNdArrayObject(i)
for i in input_vals]
assert all([numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = gpu_vals[0] + gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] + input_vals[1])
out = gpu_vals[0] - gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] - input_vals[1])
out = gpu_vals[0] * gpu_vals[1]
assert all_close(to_cpu(out),
input_vals[0] * input_vals[1])
if dtypeo.startswith("float"):
# TODO: execute for all dtype
out = gpu_vals[0] / gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] / input_vals[1])
nb_in = 4
#print " Test inside a wrapping python object %d inputs"%nb_in
for shape in [(500,), (50, 5), (5, 6, 7)]:
input_vals = [rand(shape, dtype)
for dtype in [dtype1, dtype2, dtype1, dtype2]]
gpu_vals = [gpu_ndarray.GpuNdArrayObject(i)
for i in input_vals]
assert all([numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = MyGpuNdArray.adds(*gpu_vals)
assert numpy.allclose(to_cpu(out),
reduce(numpy.add, input_vals))
out = MyGpuNdArray.multiplys(*gpu_vals)
assert all_close(to_cpu(out),
reduce(numpy.multiply, input_vals))
#print " Test broadcasting"
for shapes in [((1, 5), (4, 5)), ((33, 10), (33, 1)),
((33, 1, 5), (33, 10, 1)),
((33, 1, 5), (33, 10, 1), ((1, 10, 5))),
]:
input_vals = [rand(shape, dtype) for shape, dtype
in zip(shapes, [dtype1, dtype2])]
gpu_vals = [gpu_ndarray.GpuNdArrayObject(i)
for i in input_vals]
assert all([numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = MyGpuNdArray.adds(*gpu_vals)
assert numpy.allclose(to_cpu(out),
reduce(numpy.add, input_vals))
out = MyGpuNdArray.multiplys(*gpu_vals)
assert all_close(to_cpu(out),
reduce(numpy.multiply, input_vals))
def test_sum():
to_cpu = numpy.asarray
dtypes = list(dtypes_all)
# I remove *int8 as currently the output have the same dtype
# And this cause overflow
dtypes.remove("int8")
dtypes.remove("uint8")
# I need to find how pycuda handle complexe in c.
# I probably just need to add an header.
dtypes.remove("complex64")
if enable_double:
dtypes.remove("complex128")
for shape in [
# need something bigger then 32, 1024 or 4096.
# Those are corner case.
# 1d, take only a few seconds on a GTX470
(0,), (5,), (31,), (32,), (33,),
(1023,), (1024,), (1025,),
(4095,), (4096,), (4097,),
(32 * 1024 - 1,), (32 * 1024,), (32 * 1024 + 1,),
# 2d, take 2 minutes on a GTX 470
(0, 0), (1, 0), (0, 1,), (5, 4),
(31, 31), (31, 32), (31, 33),
(32, 31), (32, 32), (32, 33),
(33, 31), (33, 32), (33, 33),
(1024, 32), (1025, 32),
(1024, 33), (1025, 33),
(4096, 32), (32, 4096), (4096, 33), (33, 4096),
(4097, 32), (32, 4097), (4097, 33), (33, 4097),
# 3d, take 2 minutes on a GTX 470
(0, 0, 0), (0, 1, 0), (0, 0, 1),
(5, 4, 3), (5, 4, 3), (5, 4, 3),
(4096, 2, 33), (2, 4096, 33), (33, 2, 4096),
(4097, 2, 33), (2, 4097, 33), (33, 2, 4097),
(4096, 33, 2), (33, 4096, 2), (2, 33, 4096),
(4097, 33, 2), (33, 4097, 2), (2, 33, 4097),
# 4d, take 1 minutes on a GTX 470
(0, 0, 0, 0), (1, 0, 0, 0), (0, 1, 0, 0),
(0, 0, 1, 0), (0, 0, 0, 1),
(5, 4, 3, 2),
(1024, 32, 2, 3), (3, 1024, 32, 2), (2, 3, 1024, 32),
(1024, 2, 32, 3), (3, 1024, 2, 32), (1024, 3, 2, 32),
(1025, 33, 2, 3), (3, 1025, 33, 2), (2, 3, 1025, 33),
(1025, 2, 33, 3), (3, 1025, 2, 33), (1025, 3, 2, 33),
(4100, 4, 3, 2), (4, 4100, 3, 2),
(4, 3, 4100, 2), (4, 3, 2, 4100),
# 5d, work only if c contiguous
(5, 4, 3, 10, 11),
]:
for dtype, off_o, off_i, sliced, order in product(
*([dtypes] +
[[False, True]] +
[[False, True]] +
[[-1, 2, -2, 1]] +
[['f', 'c']])):
cpu_val, gpu_val = gen_gpu_nd_array(shape, dtype, off_o,
off_i, sliced, order)
if len(shape) > 4 and not (gpu_val.flags["C_CONTIGUOUS"] or
gpu_val.flags["F_CONTIGUOUS"]):
continue
gpu_val = MyGpuNdArray(gpu_val)
cpu_sum = cpu_val.sum()
# print dtype, shape, off_o, off_i, sliced, order
# print (cpu_val.strides,
# cpu_val.flags["C_CONTIGUOUS"],
# cpu_val.flags["F_CONTIGUOUS"])
# print (gpu_val.strides,
# gpu_val.flags["C_CONTIGUOUS"],
# gpu_val.flags["F_CONTIGUOUS"])
gpu_sum = to_cpu(gpu_val.sum())
def get_rtol(orig, after_reduction):
if after_reduction.size == 0:
return 0
if orig.size // after_reduction.size > 500000:
rtols = {"float32": 4.3e-5}
elif orig.size // after_reduction.size > 100000:
rtols = {"float32": 3e-5}
elif orig.size // after_reduction.size > 50000:
rtols = {"float32": 2e-5}
else:
rtols = {"float32": 1e-5}
if dtype in rtols:
rtol = rtols[dtype]
else:
rtol = 1e-8
return rtol
rtol = get_rtol(gpu_val, gpu_sum)
cpu_sum = cpu_sum.astype(dtype)
if not (dtype.endswith("int16") and numpy.prod(shape) > 20000):
assert (numpy.allclose(cpu_sum, gpu_sum, rtol=rtol) or
cpu_sum == gpu_sum), (
dtype, shape, cpu_sum, gpu_sum,
(cpu_sum - gpu_sum) / cpu_sum)
# Test pattern 10 and 01
# Test pattern 100, 010 and 001
if len(shape) in [2, 3]:
for axis in range(len(shape)):
gpu_sum = to_cpu(gpu_val.sum(axis=[axis]))
cpu_sum = cpu_val.sum(axis=axis)
rtol = get_rtol(gpu_val, gpu_sum)
if cpu_sum.size > 0:
argmax = numpy.absolute(cpu_sum - gpu_sum).argmax()
cpu_max = cpu_sum.flatten()[argmax]
gpu_max = gpu_sum.flatten()[argmax]
assert numpy.allclose(cpu_sum, gpu_sum), (
"axis=%d" % axis, dtype, shape, cpu_sum.shape,
cpu_sum, gpu_sum,
cpu_max, gpu_max, (cpu_max - gpu_max) / cpu_max)
|